From c5f5613007d58bb21936babdee111d1e1fb561c9 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 18:53:25 +0200 Subject: [PATCH 001/297] Improved performance of analytical integral drastically (so it's now on a level with the Sandia code) --- mala/targets/calculation_helpers.py | 39 ++++++++++++----------------- mala/targets/dos.py | 31 ++++------------------- mala/targets/ldos.py | 24 +++++------------- 3 files changed, 27 insertions(+), 67 deletions(-) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index 170b9ff09..ea7ff808a 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -3,7 +3,7 @@ from ase.units import kB from scipy import integrate import mpmath as mp - +import time def integrate_values_on_spacing(values, spacing, method, axis=0): """ @@ -343,20 +343,21 @@ def analytical_integration(D, I0, I1, fermi_energy_ev, energy_grid, energy_grid_edges[0] = energy_grid[0] - spacing energy_grid_edges[-1] = energy_grid[-1] + spacing - if len(D.shape) > 1: - real_space_grid = D.shape[0] - integral_value = np.zeros(real_space_grid, dtype=np.float64) - else: - real_space_grid = 1 - integral_value = 0 - # Calculate the weights. + # It is not really possible to express this as a vector operation, + # since mp.polylog (which is called in function_mappings) does not support + # that. + beta = 1 / (kB * temperature_k) for i in range(0, gridsize): - # Calculate beta and x - beta = 1 / (kB * temperature_k) - x = beta*(energy_grid_edges[i]-fermi_energy_ev) - x_plus = beta*(energy_grid_edges[i+1]-fermi_energy_ev) - x_minus = beta*(energy_grid_edges[i-1]-fermi_energy_ev) + # Some aliases for readibility + ei = energy_grid_edges[i+1] + ei_plus = energy_grid_edges[i+2] + ei_minus = energy_grid_edges[i] + + # Calculate x + x = beta*(ei-fermi_energy_ev) + x_plus = beta*(ei_plus-fermi_energy_ev) + x_minus = beta*(ei_minus-fermi_energy_ev) # Calculate the I0 value i0 = function_mappings[I0](x, beta) @@ -368,19 +369,11 @@ def analytical_integration(D, I0, I1, fermi_energy_ev, energy_grid, i1_plus = function_mappings[I1](x_plus, beta) i1_minus = function_mappings[I1](x_minus, beta) - # Some aliases for readibility - ei = energy_grid_edges[i] - ei_plus = energy_grid_edges[i+1] - ei_minus = energy_grid_edges[i-1] - weights_vector[i] = (i0_plus-i0)*(1 + ((ei-fermi_energy_ev)/(ei_plus-ei)))\ + (i0-i0_minus)*(1-((ei-fermi_energy_ev)/(ei-ei_minus))) - \ ((i1_plus-i1) / (ei_plus-ei)) + ((i1 - i1_minus) / (ei - ei_minus)) - if real_space_grid == 1: - integral_value += weights_vector[i] * D[i] - else: - for j in range(0, real_space_grid): - integral_value[j] += weights_vector[i] * D[j, i] + + integral_value = np.dot(D, weights_vector) return integral_value diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 5fc2e220c..3a8b4999e 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -125,16 +125,10 @@ def read_from_qe_dos_txt(self, file_name, directory): return np.array(return_dos_values) - def get_energy_grid(self, shift_energy_grid=False): + def get_energy_grid(self): """ Get energy grid. - Parameters - ---------- - shift_energy_grid : bool - If True, the entire energy grid will be shifted by - ldos_gridoffset_ev from the parameters. - Returns ------- e_grid : numpy.array @@ -146,9 +140,6 @@ def get_energy_grid(self, shift_energy_grid=False): self.parameters.ldos_gridsize * \ self.parameters.ldos_gridspacing_ev grid_size = self.parameters.ldos_gridsize - if shift_energy_grid is True: - emin += self.parameters.ldos_gridspacing_ev - emax += self.parameters.ldos_gridspacing_ev linspace_array = (np.linspace(emin, emax, grid_size, endpoint=False)) return linspace_array @@ -191,10 +182,7 @@ def get_band_energy(self, dos_data, fermi_energy_eV=None, if temperature_K is None: temperature_K = self.temperature_K - if shift_energy_grid and integration_method == "analytical": - energy_grid = self.get_energy_grid(shift_energy_grid=True) - else: - energy_grid = self.get_energy_grid() + energy_grid = self.get_energy_grid() return self.__band_energy_from_dos(dos_data, energy_grid, fermi_energy_eV, temperature_K, integration_method) @@ -238,10 +226,7 @@ def get_number_of_electrons(self, dos_data, fermi_energy_eV=None, fermi_energy_eV = self.fermi_energy_eV if temperature_K is None: temperature_K = self.temperature_K - if shift_energy_grid and integration_method == "analytical": - energy_grid = self.get_energy_grid(shift_energy_grid=True) - else: - energy_grid = self.get_energy_grid() + energy_grid = self.get_energy_grid() return self.__number_of_electrons_from_dos(dos_data, energy_grid, fermi_energy_eV, temperature_K, @@ -286,10 +271,7 @@ def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, if temperature_K is None: temperature_K = self.temperature_K - if shift_energy_grid and integration_method == "analytical": - energy_grid = self.get_energy_grid(shift_energy_grid=True) - else: - energy_grid = self.get_energy_grid() + energy_grid = self.get_energy_grid() return self.\ __entropy_contribution_from_dos(dos_data, energy_grid, fermi_energy_eV, temperature_K, @@ -334,10 +316,7 @@ def get_self_consistent_fermi_energy_ev(self, dos_data, # Parse the parameters. if temperature_K is None: temperature_K = self.temperature_K - if shift_energy_grid and integration_method == "analytical": - energy_grid = self.get_energy_grid(shift_energy_grid=True) - else: - energy_grid = self.get_energy_grid() + energy_grid = self.get_energy_grid() fermi_energy_sc = toms748(lambda fermi_sc: (self. __number_of_electrons_from_dos diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 10ef67b60..ad4d2f76a 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -147,16 +147,10 @@ def read_from_cube(self, file_name_scheme, directory, units="1/eV"): ldos_data[:, :, :, i-1] = data[:, :, :] return ldos_data - def get_energy_grid(self, shift_energy_grid=False): + def get_energy_grid(self): """ Get energy grid. - Parameters - ---------- - shift_energy_grid : bool - If True, the entire energy grid will be shifted by - ldos_gridoffset_ev from the parameters. - Returns ------- e_grid : numpy.array @@ -168,9 +162,6 @@ def get_energy_grid(self, shift_energy_grid=False): self.parameters.ldos_gridsize * \ self.parameters.ldos_gridspacing_ev grid_size = self.parameters.ldos_gridsize - if shift_energy_grid is True: - emin += self.parameters.ldos_gridspacing_ev - emax += self.parameters.ldos_gridspacing_ev linspace_array = (np.linspace(emin, emax, grid_size, endpoint=False)) return linspace_array @@ -595,24 +586,21 @@ def get_density(self, ldos_data, fermi_energy_ev=None, temperature_K=None, energy_grid_spacing = self.parameters.ldos_gridspacing_ev # Build the energy grid and calculate the fermi function. - if shift_energy_grid and integration_method == "analytical": - emin += energy_grid_spacing - emax += energy_grid_spacing - energy_vals = np.arange(emin, emax, energy_grid_spacing) - fermi_values = fermi_function(energy_vals, fermi_energy_ev, + energy_grid = self.get_energy_grid() + fermi_values = fermi_function(energy_grid, fermi_energy_ev, temperature_K, energy_units="eV") # Calculate the number of electrons. if integration_method == "trapz": density_values = integrate.trapz(ldos_data_used * fermi_values, - energy_vals, axis=-1) + energy_grid, axis=-1) elif integration_method == "simps": density_values = integrate.simps(ldos_data_used * fermi_values, - energy_vals, axis=-1) + energy_grid, axis=-1) elif integration_method == "analytical": density_values = analytical_integration(ldos_data_used, "F0", "F1", fermi_energy_ev, - energy_vals, + energy_grid, temperature_K) else: raise Exception("Unknown integration method.") From ecbf99aadf4e4742b3b1cfb425235ada1f295ea5 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 20:39:51 +0200 Subject: [PATCH 002/297] Added functionality to cache DOS --- mala/targets/ldos.py | 144 +++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 138 insertions(+), 6 deletions(-) diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index ad4d2f76a..0a7b44623 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -24,6 +24,11 @@ def __init__(self, params): """ super(LDOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize + self.cached_dos_exists = False + self.cached_dos = [] + self.cached_density_exists = False + self.cached_density = [] + @staticmethod def convert_units(array, in_units="1/eV"): @@ -557,6 +562,9 @@ def get_density(self, ldos_data, fermi_energy_ev=None, temperature_K=None, dimensions. """ + if self.cached_density_exists: + return self.cached_density + if fermi_energy_ev is None: fermi_energy_ev = self.fermi_energy_eV if temperature_K is None: @@ -579,12 +587,6 @@ def get_density(self, ldos_data, fermi_energy_ev=None, temperature_K=None, else: raise Exception("Invalid LDOS array shape.") - # Parse the information about the energy grid. - emin = self.parameters.ldos_gridoffset_ev - emax = self.parameters.ldos_gridsize * self.parameters.\ - ldos_gridspacing_ev + self.parameters.ldos_gridoffset_ev - energy_grid_spacing = self.parameters.ldos_gridspacing_ev - # Build the energy grid and calculate the fermi function. energy_grid = self.get_energy_grid() fermi_values = fermi_function(energy_grid, fermi_energy_ev, @@ -634,7 +636,15 @@ def get_density_of_states(self, ldos_data, grid_spacing_bohr=None, - "trapz" for trapezoid method - "simps" for Simpson method. - "summation" for summation and scaling of the values (recommended) + + Returns + ------- + dos_values : np.array + The DOS. """ + if self.cached_dos_exists: + return self.cached_dos + if grid_spacing_bohr is None: grid_spacing_bohr = self.grid_spacing_Bohr @@ -700,3 +710,125 @@ def get_density_of_states(self, ldos_data, grid_spacing_bohr=None, (grid_spacing_bohr ** 3) return dos_values + + def get_and_cache_density_of_states(self, ldos_data, + grid_spacing_bohr=None, + integration_method="summation"): + """ + Calculate a DOS from LDOS data and keep it in memory. + + For all subsequent calculations involving the DOS, this cached + DOS will be used. Usage of this function is advised for time-critical + calculations. + + Parameters + ---------- + ldos_data : numpy.array + LDOS data, either as [gridsize, energygrid] or + [gridx,gridy,gridz,energygrid]. + + grid_spacing_bohr : float + Grid spacing (in Bohr) used to construct this grid. As of now, + only equidistant grids are supported. + + integration_method : str + Integration method used to integrate LDOS on the grid. + Currently supported: + + - "trapz" for trapezoid method + - "simps" for Simpson method. + - "summation" for summation and scaling of the values (recommended) + + Returns + ------- + dos_values : np.array + The DOS. + + """ + self.uncache_density_of_states() + self.cached_dos = self.\ + get_density_of_states(ldos_data, + grid_spacing_bohr=grid_spacing_bohr, + integration_method=integration_method) + self.cached_dos_exists = True + return self.cached_dos + + def uncache_density_of_states(self): + """ + Uncache a DOS. In subsequent calculations, a new DOS is calculated. + """ + self.cached_dos_exists = False + + def get_and_cache_density_cached(self, ldos_data, + fermi_energy_ev=None, + temperature_K=None, + conserve_dimensions=False, + integration_method="analytical", + shift_energy_grid=True): + """ + Calculate an electronic density from LDOS data and keep it in memory. + + For all subsequent calculations involving the electronic density, this + cached density will be used. Usage of this function is advised for + time-critical calculations. + + Parameters + ---------- + conserve_dimensions : bool + If True, the density is returned in the same dimensions as + the LDOS was entered. If False, the density is always given + as [gridsize]. + + fermi_energy_ev : float + Fermi energy level in eV. + + temperature_K : float + Temperature in K. + + integration_method : string + Integration method to be used. Currently supported: + + - "trapz" for trapezoid method + - "simps" for Simpson method. + - "analytical" for analytical integration. Recommended. + + shift_energy_grid : bool + When using the analytical integration, one has to shift the energy + grid by setting this parameter to True. Elsewise keep on False. + + ldos_data : numpy.array + LDOS data, either as [gridsize, energygrid] or + [gridx,gridy,gridz,energygrid]. + + integration_method : string + Integration method to integrate LDOS on energygrid. + Currently supported: + + - "trapz" for trapezoid method + - "simps" for Simpson method. + - "analytical" for analytical integration. Recommended. + + Returns + ------- + density_data : numpy.array + Density data, dimensions depend on conserve_dimensions and LDOS + dimensions. + """ + self.uncache_density() + self.cached_density = self.\ + get_density(ldos_data, + fermi_energy_ev=fermi_energy_ev, + temperature_K=temperature_K, + conserve_dimensions=conserve_dimensions, + integration_method=integration_method, + shift_energy_grid=shift_energy_grid) + + self.cached_density_exists = True + return self.cached_density + + def uncache_density(self): + """ + Uncache a density. In subsequent calculations, a new one is calculated. + """ + self.cached_density_exists = False + From d825ff7aeb734f119952a4a8d1fd8f35017136f8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 20:41:11 +0200 Subject: [PATCH 003/297] Removed unnecessary import --- mala/targets/calculation_helpers.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index ea7ff808a..e976ab677 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -3,7 +3,6 @@ from ase.units import kB from scipy import integrate import mpmath as mp -import time def integrate_values_on_spacing(values, spacing, method, axis=0): """ From 017a62fe78ada086b75a108e9843d9ce496efcb8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 20:41:36 +0200 Subject: [PATCH 004/297] Fixed formattinh --- mala/targets/calculation_helpers.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index e976ab677..fd456e4f1 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -4,6 +4,7 @@ from scipy import integrate import mpmath as mp + def integrate_values_on_spacing(values, spacing, method, axis=0): """ Integrate values assuming a uniform grid with a provided spacing. From 9af52aa4e31deef1ae15712d1ed397f509077762 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 20:58:10 +0200 Subject: [PATCH 005/297] Fixed style violation in docstring --- mala/targets/ldos.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 0a7b44623..3119539f3 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -754,9 +754,7 @@ def get_and_cache_density_of_states(self, ldos_data, return self.cached_dos def uncache_density_of_states(self): - """ - Uncache a DOS. In subsequent calculations, a new DOS is calculated. - """ + """Uncache a DOS, to calculate a new one in following steps.""" self.cached_dos_exists = False def get_and_cache_density_cached(self, ldos_data, @@ -827,8 +825,6 @@ def get_and_cache_density_cached(self, ldos_data, return self.cached_density def uncache_density(self): - """ - Uncache a density. In subsequent calculations, a new one is calculated. - """ + """Uncache a density, to calculate a new one in following steps.""" self.cached_density_exists = False From 8365c05d4564c8f2063ae432b6a28cf27423b814 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 21:04:58 +0200 Subject: [PATCH 006/297] Made integration test a little bit more robust --- test/mala_integration.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/mala_integration.py b/test/mala_integration.py index 873d3565e..a21b674f1 100644 --- a/test/mala_integration.py +++ b/test/mala_integration.py @@ -138,7 +138,10 @@ def qe_ldos_to_density(accuracy): np.save(path_to_ldos_npy, ldos_dft) # Calculate the quantities we want to compare. - density_mala = ldos_calculator.get_density(ldos_dft) + self_consistent_fermi_energy = ldos_calculator.\ + get_self_consistent_fermi_energy_ev(ldos_dft) + density_mala = ldos_calculator.\ + get_density(ldos_dft, fermi_energy_ev=self_consistent_fermi_energy) density_mala_sum = density_mala.sum() density_dft_sum = density_dft.sum() From c88231b483f6df6c99bd9a8f7fccd0f47676de03 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 31 May 2021 17:22:24 +0200 Subject: [PATCH 007/297] Added a way to restrict target data to physical meaningful values --- mala/common/parameters.py | 13 +++++++++++++ mala/network/tester.py | 4 ++++ mala/targets/target_base.py | 30 ++++++++++++++++++++++++++++++ 3 files changed, 47 insertions(+) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 26b6cefd0..4754fe62e 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -139,6 +139,19 @@ def __init__(self): self.ldos_gridsize = 0 self.ldos_gridspacing_ev = 0 self.ldos_gridoffset_ev = 0 + self.restrict_targets = "zero_out_negative" + + @property + def restrict_targets(self): + """Controls if and how targets are restricted to physical values..""" + return self._restrict_targets + + @restrict_targets.setter + def restrict_targets(self, value): + if value != "zero_out_negative" and value != "absolute_values": + self._restrict_targets = None + else: + self._restrict_targets = value class ParametersData(ParametersBase): diff --git a/mala/network/tester.py b/mala/network/tester.py index 69cb6ced2..aed6d98cd 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -91,6 +91,10 @@ def test_snapshot(self, snapshot_number): inverse_transform(self.network(inputs). to('cpu'), as_numpy=True) + # Restricting the actual quantities to physical meaningful values, + # i.e. restricting the (L)DOS to positive values. + predicted_outputs = self.data.target_calculator.\ + restrict_data(predicted_outputs) return actual_outputs, predicted_outputs def __prepare_to_test(self): diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 016476342..5bb3f523a 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -280,3 +280,33 @@ def backconvert_units(array, out_units): """ raise Exception("No unit back conversion method implemented " "for this target type.") + + def restrict_data(self, array): + """ + Restrict target data to only contain physically meaningful values. + + For the LDOS this e.g. implies non-negative values. The type + of data restriction is specified by the parameters. + + Parameters + ---------- + array : numpy.array + Numpy array, for which the restrictions are to be applied. + + Returns + ------- + array : numpy.array + The same array, with restrictions enforced. + """ + if self.parameters.restrict_targets == "zero_out_negative": + array[array < 0] = 0 + return array + elif self.parameters.restrict_targets == "absolute_values": + array[array < 0] *= -1 + return array + elif self.parameters.restrict_targets is None: + return array + else: + raise Exception("Wrong data restriction.") + + From 5daa4aa2db5ecc71b1af2166e0aa35c5b338282f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 31 May 2021 17:46:28 +0200 Subject: [PATCH 008/297] Fixed docstring --- mala/common/parameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 4754fe62e..7b5371398 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -143,7 +143,7 @@ def __init__(self): @property def restrict_targets(self): - """Controls if and how targets are restricted to physical values..""" + """Control if and how targets are restricted to physical values..""" return self._restrict_targets @restrict_targets.setter From 5178f3ebb4f0dd08afb3b31c74183c46db6eb993 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 14:52:46 +0200 Subject: [PATCH 009/297] Added early stopping and learning rate scheduling patience to optuna --- mala/network/objective_base.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 8a012349f..2ce3f0c14 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -117,6 +117,14 @@ def parse_trial_optuna(self, trial: Trial): elif "mini_batch_size" in par.name: self.params.running.mini_batch_size = par.get_parameter(trial) + elif "early_stopping_epochs" in par.name: + self.params.running.early_stopping_epochs = par.\ + get_parameter(trial) + + elif "learning_rate_patience" in par.name: + self.params.running.learning_rate_patience = par.\ + get_parameter(trial) + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From f7e3a68c5869c0360bb1648b40f510d28a5c615c Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 15:00:58 +0200 Subject: [PATCH 010/297] Also added learning rate decay --- mala/network/objective_base.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 2ce3f0c14..19623512c 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -125,6 +125,10 @@ def parse_trial_optuna(self, trial: Trial): self.params.running.learning_rate_patience = par.\ get_parameter(trial) + elif "learning_rate_decay" in par.name: + self.params.running.learning_rate_decay = par.\ + get_parameter(trial) + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From 527ab0b2d150b7dbe7983b5483d03916ee56c0da Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 22:37:51 +0200 Subject: [PATCH 011/297] Updated stopping of optuna optimization based on heartbeat --- mala/common/parameters.py | 26 ++++++++++++++++++++++++++ mala/network/hyper_opt_optuna.py | 22 ++++++++++++++++++++-- 2 files changed, 46 insertions(+), 2 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index e8270c194..36e83d461 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -386,6 +386,32 @@ class ParametersHyperparameterOptimization(ParametersBase): Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. Currently. this only works with optuna. + + study_name : string + Name used for this study (in optuna#s storage). Necessary + when operating with a RDB storage. + + rdb_storage : string + Adress of the RDB storage to be used by optuna. + + rdb_storage_heartbeat : int + Heartbeat interval for optuna (in seconds). Default is None. + If not None and above 0, optuna will record the heartbeat of intervals. + If no action on a RUNNING trial is recognized for longer then this + interval, then this trial will be moved to FAILED. In distributed + training, setting a heartbeat is currently the only way to achieve + a precise number of trials: + + https://github.com/optuna/optuna/issues/1883 + + For optuna versions below 2.8.0, larger heartbeat intervals are + detrimental to performance and should be avoided: + + https://github.com/optuna/optuna/issues/2685 + + For MALA, no evidence for decreased performance using smaller + heartbeat values could be found. So if this is used, 1s is a reasonable + value. """ def __init__(self): diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 4b38085a6..0e155dd90 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -213,8 +213,26 @@ def load_from_file(cls, params, file_path, data): return loaded_hyperopt def __check_max_number_trials(self, study, trial): - number_of_completed_trials = len([t for t in study.trials if t.state == - optuna.trial.TrialState.COMPLETE]) + """Check if this trial was already the maximum number of trials.""" + # How to check for this depends on whether or not a heartbeat was + # used. If one was used, then both COMPLETE and RUNNING trials + # Can be taken into account, as it can be expected that RUNNING + # trials will actually finish. If no heartbeat is used, + # then RUNNING trials might be Zombie trials. + # See + # https://github.com/optuna/optuna/issues/1883#issuecomment-841844834 + # https://github.com/optuna/optuna/issues/1883#issuecomment-842106950 + + if self.params.hyperparameters.rdb_storage_heartbeat is None: + number_of_completed_trials = len([t for t in study.trials if + t.state == optuna.trial. + TrialState.COMPLETE]) + else: + number_of_completed_trials = len([t for t in study.trials if + t.state == optuna.trial. + TrialState.COMPLETE or + t.state == optuna.trial. + TrialState.RUNNING]) if number_of_completed_trials >= self.params.hyperparameters.n_trials: self.study.stop() From 2fac7d993ad01d98117ebe28e3a254ee13b69d11 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 14 Jun 2021 02:39:18 +0200 Subject: [PATCH 012/297] added range analysis functionality to oat --- examples/ex04_hyperparameter_optimization.py | 1 - mala/network/hyper_opt_oat.py | 79 +++++++++++++++----- mala/network/objective_base.py | 8 ++ 3 files changed, 68 insertions(+), 20 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 1263fbe1c..ef069aff3 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -126,4 +126,3 @@ def run_example04(desired_loss_improvement_factor=1): "was off. If you haven't changed any parameters in " "the example, there might be a problem with " "your installation.") - diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index b342796ae..211c3c767 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -3,6 +3,7 @@ from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase import numpy as np +import itertools from mala.common.parameters import printout @@ -24,11 +25,12 @@ def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] - self.best_trial = None + self.optimal_params = None self.n_factors = None - self.n_levels = None + self.factor_levels = None self.strength = None self.N_runs = None + self.OA = None def perform_study(self): """ @@ -37,9 +39,16 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ + self.n_factors = len(self.params.hyperparameters.hlist) + + self.factor_levels = [par.num_choices for par in self.params. + hyperparameters.hlist] + self.strength = 3 + self.N_runs = self.number_of_runs() + self.OA = self.get_orthogonal_array() number_of_trial = 0 self.objective = ObjectiveBase(self.params, self.data_handler) - for row in self.orthogonal_arr: + for row in self.OA: printout("Trial number", number_of_trial) self.trial_losses.append(self.objective(row)) number_of_trial += 1 @@ -49,20 +58,36 @@ def perform_study(self): def set_optimal_parameters(self): """ + Find the optimal set of hyperparameters by doing range analysis. + This is done using loss instead of accuracy as done in the paper. + Set the optimal parameters found in the present study. The parameters will be written to the parameter object with which the hyperparameter optimizer was created. """ - # Getting the best trial based on the test errors - idx = self.trial_losses.index(min(self.trial_losses)) - self.best_trial = self.orthogonal_arr[idx] - self.objective.parse_trial_oat(self.best_trial) - - @property - def orthogonal_arr(self): - """Orthogonal array used for optimal hyperparameter sampling.""" - arrayclass = oa.arraydata_t(self.n_levels, self.N_runs, self.strength, + + def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] + + R = [[self.trial_losses[indices(i, l)] for l in range(levels)] + for (i, levels) in enumerate(self.factor_levels)] + + A = [[i/len(j) for i in j] for j in R] + + self.optimal_params = np.array([i.index(max(i)) for i in A]) + importance = np.argsort([max(i)-min(i) for i in A]) + + print("Order of Importance: ") + printout( + [self.params.hyperparameters.hlist[idx].name for idx in self.optimal_params], " > ") + + print("Optimal Hyperparameters:") + self.objective.parse_trial_oat(self.optimal_params) + + def get_orthogonal_array(self): + """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + + arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) arraylist = [arrayclass.create_root()] @@ -110,12 +135,28 @@ def add_hyperparameter(self, opttype="float", name="", low=0, high=0, super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, low=low, high=high, choices=choices) - self.n_factors = len(self.params.hyperparameters.hlist) - # if self.n_factors>4: - # raise Exception("Sorry only upto 3 factors are supported") + def number_of_runs(self): + """ + Calculate the minimum number of runs required for an Orthogonal array - self.n_levels = min([par.num_choices for par in self.params. - hyperparameters.hlist]) - self.strength = 3 - self.N_runs = pow(self.n_levels, self.n_factors) + Based on the factor levels and the strength of the array requested + + Parameters + ---------- + factor_levels : list + A list of number of choices of each hyperparameter + + strength : int + A design parameter for Orthogonal arrays + strength 2 models all 2 factor interactions + strength 3 models all 3 factor interactions + + This is function is taken from the example notebook of OApackage + """ + + runs = [np.prod(tt) for tt in itertools.combinations( + self.factor_levels, self.strength)] + + N = np.lcm.reduce(runs) + return N diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index fc82828c5..d9ab739e3 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -122,6 +122,10 @@ def parse_trial_oat(self, trial): trial : numpy.array Row in an orthogonal array which respresents current trial. """ + if self.optimize_layer_list: + self.params.network.layer_sizes = \ + [self.data_handler.get_input_dimension()] + if self.optimize_activation_list: self.params.network.layer_activations = [] @@ -130,6 +134,10 @@ def parse_trial_oat(self, trial): if "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial, factor_idx)) + elif "ff_neurons_layer" in par.name: + if self.params.network.nn_type == "feed-forward": + self.params.network.layer_sizes.\ + append(par.get_parameter(trial, factor_idx)) elif "trainingtype" in par.name: self.params.running.trainingtype = par.\ get_parameter(trial, factor_idx) From ace8e795a90863345875cb858f02473611c6e26d Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 14 Jun 2021 16:42:59 +0200 Subject: [PATCH 013/297] fixed a few bugs --- mala/network/hyper_opt_oat.py | 116 ++++++++++++++++++---------------- 1 file changed, 60 insertions(+), 56 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 211c3c767..338ef75ba 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -32,6 +32,38 @@ def __init__(self, params, data): self.N_runs = None self.OA = None + def add_hyperparameter(self, opttype="categorical", name="", low=0, high=0, + choices=None): + """ + Add a hyperparameter to the current investigation. + + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + Currently supported are: + + - categorical (list) + + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. + + low : float or int + Currently unsupported: Lower bound for numerical parameter. + + high : float or int + Currently unsupported: Higher bound for numerical parameter. + + choices : + List of possible choices (for categorical parameter). + """ + super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, + low=low, high=high, + choices=choices) + def perform_study(self): """ Perform the study, i.e. the optimization. @@ -69,72 +101,24 @@ def set_optimal_parameters(self): def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - R = [[self.trial_losses[indices(i, l)] for l in range(levels)] - for (i, levels) in enumerate(self.factor_levels)] + R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + for (idx, levels) in enumerate(self.factor_levels)] A = [[i/len(j) for i in j] for j in R] - self.optimal_params = np.array([i.index(max(i)) for i in A]) + # Taking loss as objective to minimise + self.optimal_params = np.array([i.index(min(i)) for i in A]) importance = np.argsort([max(i)-min(i) for i in A]) print("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in self.optimal_params], " > ") + [self.params.hyperparameters.hlist[idx].name for idx in importance], " > ") print("Optimal Hyperparameters:") - self.objective.parse_trial_oat(self.optimal_params) - - def get_orthogonal_array(self): - """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" - - arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, - self.n_factors) - arraylist = [arrayclass.create_root()] - - # extending the orthogonal array - options = oa.OAextend() - options.setAlgorithmAuto(arrayclass) - - for _ in range(self.strength + 1, self.n_factors + 1): - arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, - options) - dd = np.array([a.Defficiency() for a in arraylist_extensions]) - idxs = np.argsort(dd) - arraylist = [arraylist_extensions[ii] for ii in idxs] - - return np.unique(np.array(arraylist[0]), axis=0) + for (idx, par) in enumerate(self.params.hyperparameters.hlist): + printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') - def add_hyperparameter(self, opttype="float", name="", low=0, high=0, - choices=None): - """ - Add a hyperparameter to the current investigation. - - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - Currently supported are: - - - categorical (list) - - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. - - low : float or int - Currently unsupported: Lower bound for numerical parameter. - - high : float or int - Currently unsupported: Higher bound for numerical parameter. - - choices : - List of possible choices (for categorical parameter). - """ - super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, - low=low, high=high, - choices=choices) + self.objective.parse_trial_oat(self.optimal_params) def number_of_runs(self): """ @@ -160,3 +144,23 @@ def number_of_runs(self): N = np.lcm.reduce(runs) return N + + def get_orthogonal_array(self): + """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + + arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, + self.n_factors) + arraylist = [arrayclass.create_root()] + + # extending the orthogonal array + options = oa.OAextend() + options.setAlgorithmAuto(arrayclass) + + for _ in range(self.strength + 1, self.n_factors + 1): + arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, + options) + dd = np.array([a.Defficiency() for a in arraylist_extensions]) + idxs = np.argsort(dd) + arraylist = [arraylist_extensions[ii] for ii in idxs] + + return np.unique(np.array(arraylist[0]), axis=0) From 0fdb75866c40a512ace02fc7cd1ecde4587f2922 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 15 Jun 2021 09:41:21 +0200 Subject: [PATCH 014/297] Checkpoints can be continued from different RDB provider --- mala/network/hyper_opt_optuna.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 0e155dd90..7ad288673 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -129,7 +129,7 @@ def checkpoint_exists(cls, checkpoint_name): param_name])) @classmethod - def resume_checkpoint(cls, checkpoint_name): + def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): """ Prepare resumption of hyperparameter optimization from a checkpoint. @@ -139,7 +139,13 @@ def resume_checkpoint(cls, checkpoint_name): Parameters ---------- checkpoint_name : string - Name of the checkpoint from which + Name of the checkpoint from which the checkpoint is loaded. + + alternative_storage_path: string + Alternative storage string to load the study from. + For applications on an HPC cluster it might be necessary to + slightly modify the storage path between runs, since the SQL + server might be running on different nodes each time. Returns ------- @@ -163,6 +169,9 @@ def resume_checkpoint(cls, checkpoint_name): loaded_params = Parameters.load_from_file(param_name) loaded_iscaler = DataScaler.load_from_file(iscaler_name) loaded_oscaler = DataScaler.load_from_file(oscaler_name) + if alternative_storage_path is not None: + loaded_params.hyperparameters.rdb_storage = \ + alternative_storage_path printout("Preparing data used for last checkpoint.") # Create a new data handler and prepare the data. From f033504b9f928f7253fd3fc2beeda3ddaf940290 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 24 Jun 2021 21:11:32 +0200 Subject: [PATCH 015/297] Moved one notebook to mala_notebooks and deleted another --- ml-dft-sandia/notebooks/mass-density-Al.ipynb | 152 ------ ml-dft-sandia/notebooks/postprocess.ipynb | 508 ------------------ 2 files changed, 660 deletions(-) delete mode 100644 ml-dft-sandia/notebooks/mass-density-Al.ipynb delete mode 100644 ml-dft-sandia/notebooks/postprocess.ipynb diff --git a/ml-dft-sandia/notebooks/mass-density-Al.ipynb b/ml-dft-sandia/notebooks/mass-density-Al.ipynb deleted file mode 100644 index e3a3c7436..000000000 --- a/ml-dft-sandia/notebooks/mass-density-Al.ipynb +++ /dev/null @@ -1,152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "source": [ - "# Load packages\n", - "import math\n", - "import scipy as sp\n", - "import numpy as np\n", - "from scipy import optimize\n", - "#from scipy import interpolate\n", - "#from scipy.interpolate import InterpolatedUnivariateSpline\n", - "#from scipy import integrate\n", - "%pylab inline\n", - "#from scipy.optimize import fmin, curve_fit\n", - "#import pandas as pd\n", - "#import mpmath as mp\n", - "#from mpmath import polylog\n", - "import seaborn as sns\n", - "from IPython.display import HTML" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Determine mass density" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Lattice constant:', 16.914007594384106)\n", - "('Volume of supercell [Angstrom^3]:', 4838.821077828514)\n", - "('Volume of supercell [Bohr^3]:', 32654.184089542396)\n", - "('Mass density [g/cc]:', 1.0000000000000007)\n" - ] - } - ], - "source": [ - "# Mass density of Aluminum under ambient conditions: 2.7 g/cc\n", - "amu = 1.660539040E-27 # atomic mass unit in kg\n", - "Angstrom2m = 1.0E-10 # Bohr radius in m\n", - "Angstrom2Bohr=1.88973 # Conversion from Angstrom to Bohr\n", - "Al_amu = 26.981539 # molar mass of Aluminum\n", - "#lattice = 16.914007594384106 # Angstrom\n", - "## lattice constants in Angstrom\n", - "a = lattice #8.582861487 \n", - "b = a\n", - "c = a #8.091999410\n", - "print(\"Lattice constant:\", a)\n", - "alpha = math.radians(90.0) # in radians\n", - "beta = alpha\n", - "gamma = alpha\n", - "## Volume of parallelepiped\n", - "V_Al = a*b*c*math.sqrt(1.0+2.0*math.cos(alpha)*math.cos(beta)*math.cos(gamma)\\\n", - " -math.cos(alpha)**2-math.cos(beta)**2-math.cos(gamma)**2)\n", - "print(\"Volume of supercell [Angstrom^3]:\", V_Al)\n", - "print(\"Volume of supercell [Bohr^3]:\", V_Al*Angstrom2Bohr**3)\n", - "## Compute mass density in g/cm^3\n", - "N_Al = 108\n", - "# number of atoms in the super cell\n", - "rho_Al = N_Al*Al_amu*amu*1000/(V_Al*Angstrom2m**3*100**3)\n", - "print(\"Mass density [g/cc]:\", rho_Al)\n", - "\n", - "# Compare this with the volume in VASP OUTCAR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Determine lattice constant of cubic lattice for a given mass density" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "def lattice_par(rho_Al):\n", - " # Input : Mass density in g/cc\n", - " # Output: lattice constant in Angstrom\n", - " amu = 1.660539040E-27 # atomic mass unit in kg\n", - " Angstrom2m = 1.0E-10 # Bohr radius in m\n", - " Al_amu = 26.981539 # molar mass of Aluminum\n", - " N_Al = 108\n", - " result = (N_Al*Al_amu*amu*1000/(rho_Al*Angstrom2m**3*100**3))**(1./3.)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "16.914007594384106" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lattice_par(1.0)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml-dft-sandia/notebooks/postprocess.ipynb b/ml-dft-sandia/notebooks/postprocess.ipynb deleted file mode 100644 index 97b5d3756..000000000 --- a/ml-dft-sandia/notebooks/postprocess.ipynb +++ /dev/null @@ -1,508 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "source": [ - "# Load packages\n", - "import math\n", - "import scipy as sp\n", - "import numpy as np\n", - "from scipy import optimize\n", - "#from scipy import interpolate\n", - "#from scipy.interpolate import InterpolatedUnivariateSpline\n", - "#from scipy import integrate\n", - "%pylab inline\n", - "#from scipy.optimize import fmin, curve_fit\n", - "import pandas as pd\n", - "#import mpmath as mp\n", - "#from mpmath import polylog\n", - "import seaborn as sns\n", - "from IPython.display import HTML" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparison of forces" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data files\n", - "filepath='/Users/acangi/WORK/proj/MLMM_LDRD/mlmm-ldrd-data/aluminum/code-comparison/'\n", - "\n", - "## vasp\n", - "raForces_vasp = np.loadtxt(filepath+'forces_vasp.csv', delimiter=',', usecols={0,1,2}, dtype=np.float)\n", - "\n", - "## qe\n", - "raForces_qe = np.loadtxt(filepath+'forces_qe.csv', delimiter=',', usecols={0,1,2}, dtype=np.float)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert units\n", - "## Conversion factors\n", - "Rydberg2eV = 13.6056980659\n", - "Bohr2Ang = 0.529177\n", - "\n", - "## Convert qe forces from Ry/Bohr to eV/Angstrom\n", - "raForces_qe_cnvt = raForces_qe*Rydberg2eV/Bohr2Ang\n", - "\n", - "## Difference in forces\n", - "tabDeltaForces = raForces_vasp - raForces_qe_cnvt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.054608880128638404" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.max(np.abs(tabDeltaForces))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute mean and standard deviation\n", - "AD, STD = np.mean(tabDeltaForces), np.std(tabDeltaForces)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute absolute mean and absolute standard deviation\n", - "AAD, ASTD = np.mean(abs(tabDeltaForces)), np.std(abs(tabDeltaForces))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-4.946491356861077e-07, 0.01891805923542814)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AD, STD" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.01546489428387067, 0.010896330128467564)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#\n", - "AAD, ASTD" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.191582366095191" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Mean absoulte percentage deviation\n", - "AAD/np.max(abs(raForces_qe_cnvt))*100" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " x y z\n", - "0 -0.022537 -0.022397 0.017390\n", - "1 0.019059 0.023620 -0.016686\n", - "2 0.021634 0.019195 -0.000053\n", - "3 0.023948 -0.020076 0.043107\n", - "4 0.026041 -0.010650 0.009122\n", - "5 0.020240 0.013751 0.023454\n", - "6 -0.003563 -0.002417 -0.003920\n", - "7 -0.005582 0.003436 0.019703\n", - "8 0.008100 -0.000550 -0.001811\n", - "9 -0.025523 0.024057 -0.030199\n", - "10 0.012439 -0.021227 -0.021183\n", - "11 0.030189 -0.004612 -0.008447\n", - "12 -0.035746 0.043295 0.019007\n", - "13 0.006569 -0.000585 -0.011529\n", - "14 0.007962 0.008863 0.008664\n", - "15 -0.013066 -0.014063 0.010798\n", - "16 0.015031 -0.022828 -0.012386\n", - "17 -0.030767 -0.005241 -0.004074\n", - "18 -0.015550 0.000575 0.018040\n", - "19 -0.021410 0.017525 -0.019255\n", - "20 -0.008709 -0.025105 -0.037323\n", - "21 -0.019928 -0.021155 0.027445\n", - "22 0.006975 -0.054609 0.051178\n", - "23 0.026862 0.029882 -0.001727\n", - "24 0.015763 -0.017278 -0.017399\n", - "25 0.002982 0.002079 -0.017018\n", - "26 -0.018463 0.006366 -0.004049\n", - "27 0.019955 0.038022 0.010014\n", - "28 -0.022257 0.017349 -0.009230\n", - "29 0.013994 -0.003917 -0.013153\n", - "30 -0.011239 0.015926 -0.008056\n", - "31 -0.008248 -0.017354 -0.017320\n", - "32 -0.005396 -0.005860 -0.010104\n", - "33 0.000010 -0.005750 0.004888\n", - "34 0.009031 0.007135 -0.007747\n", - "35 -0.018805 0.004598 0.009809" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Difference in forces from vasp and qe [eV/Ang]\n", - "pd.DataFrame(tabDeltaForces, columns=[\"x\", \"y\", \"z\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 9af4a0f26520e7382162f125bd12c6e4d9d8ff31 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 24 Jun 2021 21:49:00 +0200 Subject: [PATCH 016/297] Deleted two notebooks --- .../networks/analysis/LDRD_Continuation.ipynb | 903 -------- .../networks/analysis/MLMM_Paper.ipynb | 1976 ----------------- 2 files changed, 2879 deletions(-) delete mode 100644 ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb delete mode 100644 ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb diff --git a/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb b/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb deleted file mode 100644 index 9cf7c68ca..000000000 --- a/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb +++ /dev/null @@ -1,903 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "import torch.multiprocessing as mp" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "ldos1 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 4.2863e-20, 1.4137e-17, 2.8122e-15, 3.4376e-13, 2.5890e-11,\n", - " 1.2055e-09, 3.4866e-08, 6.3044e-07, 7.1944e-06, 5.2556e-05,\n", - " 2.5118e-04, 8.1233e-04, 1.8697e-03, 3.2849e-03, 4.7808e-03,\n", - " 6.1845e-03, 7.3970e-03, 8.3634e-03, 9.1797e-03, 1.0015e-02,\n", - " 1.0954e-02, 1.1993e-02, 1.3073e-02, 1.4078e-02, 1.4940e-02,\n", - " 1.5734e-02, 1.6631e-02, 1.7725e-02, 1.8942e-02, 2.0081e-02,\n", - " 2.1010e-02, 2.1832e-02, 2.2678e-02, 2.3535e-02, 2.4364e-02,\n", - " 2.5091e-02, 2.5650e-02, 2.6091e-02, 2.6430e-02, 2.6678e-02,\n", - " 2.6956e-02, 2.7326e-02, 2.7748e-02, 2.8150e-02, 2.8430e-02,\n", - " 2.8605e-02, 2.8961e-02, 2.9697e-02, 3.0664e-02, 3.1628e-02,\n", - " 3.2484e-02, 3.3168e-02, 3.3573e-02, 3.3811e-02, 3.4068e-02,\n", - " 3.4251e-02, 3.4359e-02, 3.4573e-02, 3.4912e-02, 3.5276e-02,\n", - " 3.5587e-02, 3.5915e-02, 3.6400e-02, 3.6897e-02, 3.7237e-02,\n", - " 3.7635e-02, 3.8187e-02, 3.8633e-02, 3.8930e-02, 3.9345e-02,\n", - " 3.9964e-02, 4.0532e-02, 4.0845e-02, 4.1136e-02, 4.1708e-02,\n", - " 4.2345e-02, 4.2678e-02, 4.2825e-02, 4.3083e-02, 4.3414e-02,\n", - " 4.3778e-02, 4.4349e-02, 4.5068e-02, 4.5733e-02, 4.6377e-02,\n", - " 4.7008e-02, 4.7606e-02, 4.8404e-02, 4.9363e-02, 5.0141e-02,\n", - " 5.0802e-02, 5.1549e-02, 5.2171e-02, 5.2565e-02, 5.3014e-02,\n", - " 5.3452e-02, 5.3470e-02, 5.3135e-02, 5.2714e-02, 5.2108e-02,\n", - " 5.1356e-02, 5.0661e-02, 4.9838e-02, 4.8698e-02, 4.7533e-02,\n", - " 4.6771e-02, 4.6539e-02, 4.6769e-02, 4.7425e-02, 4.8363e-02,\n", - " 4.9215e-02, 4.9655e-02, 4.9897e-02, 5.0485e-02, 5.1396e-02,\n", - " 5.1955e-02, 5.1835e-02, 5.1571e-02, 5.1796e-02, 5.2395e-02,\n", - " 5.2821e-02, 5.2973e-02, 5.3249e-02, 5.3773e-02, 5.4275e-02,\n", - " 5.4583e-02, 5.4791e-02, 5.5120e-02, 5.5466e-02, 5.5497e-02,\n", - " 5.5585e-02, 5.6265e-02, 5.6960e-02, 5.6868e-02, 5.6360e-02,\n", - " 5.6439e-02, 5.7312e-02, 5.8339e-02, 5.9041e-02, 5.9364e-02,\n", - " 5.9409e-02, 5.9495e-02, 6.0037e-02, 6.0831e-02, 6.1194e-02,\n", - " 6.1146e-02, 6.1493e-02, 6.2416e-02, 6.3285e-02, 6.3832e-02,\n", - " 6.4235e-02, 6.4601e-02, 6.5036e-02, 6.5250e-02, 6.3608e-02,\n", - " 5.7445e-02, 4.5166e-02, 2.8865e-02, 1.4088e-02, 5.0001e-03,\n", - " 1.2429e-03, 2.0983e-04, 2.3451e-05, 1.7005e-06, 7.8811e-08,\n", - " 2.3106e-09, 4.2564e-11, 4.9071e-13, 3.5333e-15, 1.5863e-17,\n", - " 3.9707e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])\n", - "\n", - "ldos2 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 4.4031e-20, 1.4570e-17, 2.9047e-15, 3.5605e-13, 2.6909e-11,\n", - " 1.2586e-09, 3.6615e-08, 6.6716e-07, 7.6907e-06, 5.6929e-05,\n", - " 2.7672e-04, 9.1333e-04, 2.1474e-03, 3.8297e-03, 5.5646e-03,\n", - " 7.0337e-03, 8.0747e-03, 8.5989e-03, 8.6677e-03, 8.5503e-03,\n", - " 8.5693e-03, 8.8747e-03, 9.3478e-03, 9.8050e-03, 1.0232e-02,\n", - " 1.0714e-02, 1.1289e-02, 1.1946e-02, 1.2682e-02, 1.3432e-02,\n", - " 1.3983e-02, 1.4230e-02, 1.4374e-02, 1.4660e-02, 1.5096e-02,\n", - " 1.5556e-02, 1.6036e-02, 1.6584e-02, 1.7075e-02, 1.7375e-02,\n", - " 1.7545e-02, 1.7723e-02, 1.7977e-02, 1.8295e-02, 1.8629e-02,\n", - " 1.8954e-02, 1.9297e-02, 1.9667e-02, 1.9994e-02, 2.0295e-02,\n", - " 2.0670e-02, 2.1030e-02, 2.1234e-02, 2.1433e-02, 2.1763e-02,\n", - " 2.2096e-02, 2.2356e-02, 2.2619e-02, 2.2956e-02, 2.3270e-02,\n", - " 2.3409e-02, 2.3501e-02, 2.3780e-02, 2.4121e-02, 2.4331e-02,\n", - " 2.4528e-02, 2.4795e-02, 2.4989e-02, 2.5100e-02, 2.5265e-02,\n", - " 2.5524e-02, 2.5805e-02, 2.6023e-02, 2.6222e-02, 2.6590e-02,\n", - " 2.7127e-02, 2.7607e-02, 2.7913e-02, 2.8014e-02, 2.7854e-02,\n", - " 2.7565e-02, 2.7320e-02, 2.7089e-02, 2.6936e-02, 2.6944e-02,\n", - " 2.6873e-02, 2.6539e-02, 2.6155e-02, 2.5857e-02, 2.5561e-02,\n", - " 2.5309e-02, 2.5138e-02, 2.4852e-02, 2.4337e-02, 2.3768e-02,\n", - " 2.3259e-02, 2.2664e-02, 2.1968e-02, 2.1374e-02, 2.0850e-02,\n", - " 2.0271e-02, 1.9722e-02, 1.9242e-02, 1.8718e-02, 1.8204e-02,\n", - " 1.7969e-02, 1.8247e-02, 1.9174e-02, 2.0802e-02, 2.2958e-02,\n", - " 2.5230e-02, 2.7298e-02, 2.9229e-02, 3.1129e-02, 3.2579e-02,\n", - " 3.2924e-02, 3.2167e-02, 3.1211e-02, 3.0957e-02, 3.1263e-02,\n", - " 3.1468e-02, 3.1641e-02, 3.2186e-02, 3.2771e-02, 3.2901e-02,\n", - " 3.2872e-02, 3.3164e-02, 3.3614e-02, 3.3693e-02, 3.3362e-02,\n", - " 3.3284e-02, 3.3847e-02, 3.4494e-02, 3.4476e-02, 3.3920e-02,\n", - " 3.3649e-02, 3.4062e-02, 3.4764e-02, 3.5070e-02, 3.4624e-02,\n", - " 3.3857e-02, 3.3628e-02, 3.4217e-02, 3.4942e-02, 3.4929e-02,\n", - " 3.4141e-02, 3.3450e-02, 3.3512e-02, 3.4124e-02, 3.4786e-02,\n", - " 3.5090e-02, 3.4906e-02, 3.4631e-02, 3.4489e-02, 3.3711e-02,\n", - " 3.0844e-02, 2.4790e-02, 1.6309e-02, 8.2395e-03, 3.0345e-03,\n", - " 7.8056e-04, 1.3539e-04, 1.5405e-05, 1.1265e-06, 5.2191e-08,\n", - " 1.5170e-09, 2.7486e-11, 3.0919e-13, 2.1542e-15, 9.2805e-18,\n", - " 2.1218e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])\n", - "\n", - "ldos3 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 5.1944e-20, 1.7156e-17, 3.4159e-15, 4.1805e-13, 3.1533e-11,\n", - " 1.4711e-09, 4.2653e-08, 7.7378e-07, 8.8683e-06, 6.5146e-05,\n", - " 3.1353e-04, 1.0221e-03, 2.3694e-03, 4.1713e-03, 6.0213e-03,\n", - " 7.6604e-03, 9.0615e-03, 1.0369e-02, 1.1836e-02, 1.3505e-02,\n", - " 1.5023e-02, 1.6090e-02, 1.6783e-02, 1.7236e-02, 1.7495e-02,\n", - " 1.7693e-02, 1.8069e-02, 1.8806e-02, 1.9856e-02, 2.0964e-02,\n", - " 2.1834e-02, 2.2374e-02, 2.2712e-02, 2.3056e-02, 2.3566e-02,\n", - " 2.4139e-02, 2.4576e-02, 2.4904e-02, 2.5302e-02, 2.5833e-02,\n", - " 2.6322e-02, 2.6583e-02, 2.6725e-02, 2.6945e-02, 2.7256e-02,\n", - " 2.7583e-02, 2.7909e-02, 2.8217e-02, 2.8610e-02, 2.9271e-02,\n", - " 3.0065e-02, 3.0646e-02, 3.0900e-02, 3.1086e-02, 3.1373e-02,\n", - " 3.1669e-02, 3.2055e-02, 3.2530e-02, 3.2911e-02, 3.3237e-02,\n", - " 3.3623e-02, 3.4061e-02, 3.4491e-02, 3.4837e-02, 3.5061e-02,\n", - " 3.5295e-02, 3.5616e-02, 3.5933e-02, 3.6232e-02, 3.6601e-02,\n", - " 3.7050e-02, 3.7417e-02, 3.7578e-02, 3.7712e-02, 3.8066e-02,\n", - " 3.8577e-02, 3.9056e-02, 3.9512e-02, 3.9902e-02, 4.0012e-02,\n", - " 3.9988e-02, 4.0162e-02, 4.0429e-02, 4.0643e-02, 4.0906e-02,\n", - " 4.1163e-02, 4.1329e-02, 4.1582e-02, 4.1891e-02, 4.2013e-02,\n", - " 4.2028e-02, 4.2103e-02, 4.2225e-02, 4.2496e-02, 4.2971e-02,\n", - " 4.3267e-02, 4.3047e-02, 4.2649e-02, 4.2448e-02, 4.2262e-02,\n", - " 4.1968e-02, 4.1685e-02, 4.1314e-02, 4.0663e-02, 3.9955e-02,\n", - " 3.9623e-02, 3.9730e-02, 4.0168e-02, 4.0882e-02, 4.1651e-02,\n", - " 4.2308e-02, 4.2950e-02, 4.3711e-02, 4.4580e-02, 4.5294e-02,\n", - " 4.5380e-02, 4.4678e-02, 4.3837e-02, 4.3663e-02, 4.4060e-02,\n", - " 4.4520e-02, 4.5103e-02, 4.6137e-02, 4.7578e-02, 4.8987e-02,\n", - " 4.9865e-02, 5.0162e-02, 5.0285e-02, 5.0334e-02, 5.0151e-02,\n", - " 5.0113e-02, 5.0683e-02, 5.1484e-02, 5.1904e-02, 5.2038e-02,\n", - " 5.2379e-02, 5.3093e-02, 5.3961e-02, 5.4575e-02, 5.4629e-02,\n", - " 5.4445e-02, 5.4777e-02, 5.5862e-02, 5.6974e-02, 5.7175e-02,\n", - " 5.6487e-02, 5.5976e-02, 5.6289e-02, 5.7025e-02, 5.7643e-02,\n", - " 5.7789e-02, 5.7386e-02, 5.7039e-02, 5.7019e-02, 5.5835e-02,\n", - " 5.0889e-02, 4.0526e-02, 2.6302e-02, 1.3039e-02, 4.6908e-03,\n", - " 1.1779e-03, 2.0027e-04, 2.2503e-05, 1.6390e-06, 7.6241e-08,\n", - " 2.2396e-09, 4.1207e-11, 4.7199e-13, 3.3510e-15, 1.4689e-17,\n", - " 3.5537e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250,)\n", - "(250,)\n", - "(250,)\n", - "(250,)\n" - ] - } - ], - "source": [ - "egrid = np.linspace(-10,14.9,250)\n", - "\n", - "print(egrid.shape)\n", - "print(ldos1.shape)\n", - "print(ldos2.shape)\n", - "print(ldos3.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size' : 17}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.plot(egrid, ldos1, \"-r\")\n", - "ax0.plot(egrid, ldos2, \"-b\")\n", - "ax0.plot(egrid, ldos3, \"-g\")\n", - "ax0.legend([\"LDOS 1\", \"LDOS 2\", \"LDOS 3\"])\n", - "ax0.set_ylabel(\"LDOS\")\n", - "ax0.set_xlabel(\"Energy (eV)\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./representative_ldos.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "xgrid = np.linspace(0.0,75*.1656,75)\n", - "\n", - "water = np.load(\"./water_densities/H2O_ldos_75x75x75grid_1elvls_snapshot15.npy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/site-packages/matplotlib/tight_layout.py:176: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", - " warnings.warn('Tight layout not applied. The left and right margins '\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size' : 36}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, ax = plt.subplots(1,1)\n", - "#fig.set_figheight(15)\n", - "#fig.set_figwidth(15)\n", - "\n", - "cbar_max = 1.4\n", - "\n", - "z = 70\n", - "\n", - "im = ax.contourf(xgrid, xgrid, water[:,:,z,0], cmap=\"seismic\")\n", - "ax.set_title(\"H2O Density Z-Slice at %2.1f (e/A^3)\" % (z*.1656))\n", - "\n", - "cbar = fig.colorbar(im, ax=ax, boundaries=np.linspace(0,1.4,8))\n", - "#cbar.set_ticks(np.linspace(0,1.4,8))\n", - "cbar.set_clim(0.0, cbar_max)\n", - "#cbar.ax.set_zlim([0,1.4])\n", - "plt.tight_layout()\n", - "\n", - "#plt.savefig(\"./water_density_z%d.eps\" % z, format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "t_loss = np.zeros(28093)\n", - "\n", - "loss_fname = \"./fp_ldos_synapse_Al_2gpus_298K_2.699gcc_94fp_250ldos_200nxyz_1555628922.log\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "28093\n" - ] - } - ], - "source": [ - "count = 0\n", - "with open(loss_fname) as f:\n", - " for idx, line in enumerate(f):\n", - " find_str = 'Loss: '\n", - "# find_str = 'stdout'\n", - "# if (idx < 250):\n", - " #print(line)\n", - "# continue\n", - "# elif (idx < 300 and find_str in line):\n", - "# print(\"\\nFound: \", line)\n", - "# before, after = line.split(find_str, 1)\n", - "# print(\"After: \", after)\n", - " if (find_str in line):\n", - " before, after = line.split(find_str, 1)\n", - " t_loss[count] = float(after)\n", - " count += 1\n", - " else:\n", - " continue\n", - "\n", - " print(idx)\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1171647" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_loss[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.00256788]\n", - " [0.00513576]\n", - " [0.00770364]]\n", - "[[8800.]\n", - " [8800.]\n", - " [8800.]]\n" - ] - } - ], - "source": [ - "train_loss = t_loss[t_loss > 0]\n", - "\n", - "epochs = 25\n", - "\n", - "#print(train_loss[-1])\n", - "\n", - "train_loss.size\n", - "\n", - "batch_idx = np.linspace(0, epochs - 1, train_loss.size)\n", - "\n", - "lr_epochs = np.zeros([3, 1]);\n", - "lr_epochss = np.zeros([3, 1]);\n", - "\n", - "for i in range(3):\n", - " lr_epochs[i, :] = np.array([22]) * 400\n", - " lr_epochss[i, :] = batch_idx[np.array([22]) * 400]\n", - " \n", - " \n", - " \n", - "lr_slice = np.zeros([3, 1])\n", - "\n", - "for i in range(1):\n", - " lr_slice[0, i] = train_loss[int(lr_epochs[0,i])] * .50\n", - " lr_slice[1, i] = train_loss[int(lr_epochs[0,i])] * 1.0\n", - " lr_slice[2, i] = train_loss[int(lr_epochs[0,i])] * 1.5 \n", - "\n", - "\n", - "print(lr_slice)\n", - "print(lr_epochs)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 17}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.semilogy(lr_epochss[:,0], lr_slice[:,0], '-r')\n", - "#ax0.semilogy(lr_epochss[:,1], lr_slice[:,1], '-r')\n", - "#ax0.semilogy(lr_epochss[:,2], lr_slice[:,2], '-r')\n", - "#ax0.semilogy(lr_epochss[:,3], lr_slice[:,3], '-r')\n", - "ax0.semilogy(batch_idx, train_loss, \"-b\")\n", - "ax0.legend(['LR Reduction'])\n", - "ax0.set_ylabel(\"Training Loss\")\n", - "ax0.set_xlabel(\"Epoch\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./loss_curve_lstm.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "clusters = [4, 6, 8, 10, 12, 15, 20, 50, 100, 150, 200, 400, 800, 1600, 3200]\n", - "centroid_distances = [1.94e6, 1.51e6, 1.14e6, 1.01e6, 9.03e5, 8.24e5, 7.20e5, 4.85e5, 3.67e5, 3.2e5, 2.95e5, 2.48e5, 2.19e5, 1.95e5, 1.75e5]\n", - "\n", - "#clusters = [50, 100, 150, 200, 400]\n", - "#centroid_distances = [1.53e7, 8.58e6, 7.51e6, 7.19e6, 5.51e6]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 15}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.semilogy(clusters, centroid_distances, '-b')\n", - "#ax0.plot(clusters, centroid_distances, '-b')\n", - "#ax0.set_ylim([1e6, 5e7])\n", - "#ax0.set_xlim([40, 450])\n", - "#ax0.set_xticks([0,5,10,15,20,25])\n", - "ax0.legend(['298K Snapshot Clustering'])\n", - "ax0.set_ylabel(\"Sum of Squared Centroid Distances\")\n", - "ax0.set_xlabel(\"Num Clusters\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./clustering_error.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "400.0" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "8000000 / 2 / 1000 / 10" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "not 1" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.3334572 , 0.41337177, 0.46787082, 0.46534866])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#snapshots = np.array([2,4,6,8,10])\n", - "#train_time = np.array([418.0638, 1032.7823, 1641.2181, 2240.6008, 2708.6924]) / 2\n", - "\n", - "snapshots = np.array([4,6,8,10]) - 2\n", - "train_time = np.array([1656.3196, 3506.6325, 5206.3768, 6890.7759]) / 5\n", - "\n", - "snapshots_cluster = np.array([4,6,8,10]) - 2 \n", - "train_time_cluster = np.array([552.3117, 1449.5429, 2435.9118, 3206.6133]) / 5\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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kXES2i0i9OOX1nbJwEenpr3iNMRnXP//8Q69evShevDgjRoygadOmhIWFMX36dEsa8fDnRE5TgfpXlS0DyqtqRWAH0AtARMoCLYFyzj7jRCSTiGQCPgQaAGWBZ5y6xhhzw44dO0bv3r0JDAxk2LBhNGrUiLCwMGbMmEHp0qXdDi/V8tutKlX9WUQCryr7Ls7qKuBJZ7kxMEdVLwC7RSQcqOZsC1fVXQAiMsepG4YxxiTR8ePHGTVqFKNHj+bff//lqaeeol+/fpQrV87t0NIEN9s42gFzneUieBLJJRFOGcC+q8rvi+9gItIR6AhkuD7VxhjvnDhxgtGjRzN69GhOnjxJ8+bNCQkJoUKFCm6Hlqa4kjhEpDcQDcxMrmOq6kRgIkBwcLCNZ22MuezUqVOMGTOGkSNHcuLECZo2bUpISAiVKlVyO7Q0KcUTh4g8DzQE6ur/JizYD8R9Vr+oU0YC5cYYk6B///2X999/n/fee4/jx4/TuHFj+vfvT+XKld0OLU3zZ+P4NUSkPtAdaKSqZ+Ns+gpoKSK3iEhxoCSwGlgDlBSR4iJyM54G9K9SMmZjTNpz+vRphg4dSmBgIH369KFGjRqsXbuWL7/80pJGMvDbFYeIzAZqA/lEJAIIwdOL6hZgmTOZ0ipVfUlVt4rIPDyN3tFAJ1WNcY7zKrAUyARMUdWt/orZGJO2nTlzhg8//JDhw4dz9OhRHn/8cfr378+9997rdmjpiqTH6S2Dg4M1o8/bbUxGcvbsWcaPH8+wYcM4cuQI9erVY8CAAdx3X7x9acx1iMg6VQ1OrF6K3qoyxpjkdO7cOUaNGsVdd93FW2+9ReXKlfn1119ZsmSJJQ0/siFHjDFpzrlz55g4cSKhoaEcPHiQunXr8vnnn1OjRg23Q8sQLHEYY9KM8+fPM2nSJIYMGUJkZCS1a9dm7ty5PPTQQ26HlqFY4jDGpHoXLlxg8uTJDBkyhP379/PQQw8xa9Ysateu7XZoGZIlDmNMqnXx4kU++eQTBg8ezL59+6hRowbTpk2jTp06OD0zjQuscdwYk+pERUXx8ccfU6pUKV566SWKFi3Kd999x3//+1/q1q1rScNlljiMMalGVFQUkydPplSpUnTs2JFChQqxZMkSfvnlFx599FFLGKmEJQ5jjOuio6OZOnUqZcqUoUOHDuTLl49vv/2W3377jXr16lnCSGUscRhjXBMdHc306dMJCgqibdu25M6dm6+//prVq1fz+OOPW8JIpSxxGGNSXExMDDNnzqRcuXK0adOGW2+9lS+//JK1a9fSsGFDSxipnCUOY0yKiYmJYfbs2ZQvX57/+7//I2vWrHzxxResX7+exo0bW8JIIxLsjuuMSPs48CBwO3AO2AJ8q6rb/R+eMSY9iI2N5bPPPmPgwIGEhYVRvnx55s+fT9OmTbnpJvv7Na257r+YiPQFfgceBjYC0/AMaZ4ZGC0iS0SkfIpEaYxJk2JjY5k/fz6VKlWiZcuWAMydO5eNGzfSvHlzSxppVEJXHJtU9Z3rbHtXRApz5SRLxhgDeBLGl19+yYABA9i0aRNlypRh9uzZtGjRgkyZMrkdnrlB1033qrrw6jLxyOFsj1TV1f4MzhiTtqgqCxcupGrVqjRv3pzz588zY8YMtmzZQsuWLS1ppBOJXieKyHQRySUi2YHNQLiIvOn/0IwxaYWq8vXXXxMcHEyTJk04ffo006dPZ+vWrTz33HOWMNIZb24wVlTVU0ATYBlwJ/C8P4MyxqQNqsq3335LtWrVaNSoESdOnGDq1Kn8+eeftGrVisyZbTi89MibxJFFRDIDjYGFqnoRiPVvWMaY1ExVWbJkCdWrV6dhw4YcPXqUyZMns23bNtq0aWMJI53zJnFMAvYCAcBPInIHcNqvURljUiVVZfny5dSsWZMGDRpw8OBBPv74Y3bs2EG7du3IkiWL2yGaFJBo4lDVUap6u6o+pp4JyiOAOv4PzRiTmvz888/Url2bRx55hL179zJ+/Hh27txJhw4dLGFkMAk9x9FS4nmMU1VjVfWiiASKyAP+Dc8Y47Zff/2VRx55hFq1arFz507Gjh1LeHg4L730EjfffLPb4RkXJHTFUQTYICITReRFEWkmIs+KSD8R+QEYDfxzvZ1FZIqIHBaRLXHK8ojIMhHZ6bwHOOUiIu+LSLiIbBKRKnH2aePU3ykibW78IxtjvLF69WoaNGhAjRo12Lx5M6NGjeKvv/7i1Vdf5ZZbbnE7POOihJ7jeA+oCizA86DfE8ADeJJFe1VtksiwI1OB+leV9QSWq2pJYLmzDtAAKOm8OgLjwZNogBDgPqAaEHIp2Rhj/OOPP/7gP//5D/fddx9r1qzh3XffZdeuXbz++utky5bN7fBMKpBg1wdVjQYWOy+fqOrPIhJ4VXFjoLazPA1YAfRwyqc7bSirRCS382R6bWCZqh4DEJFleJLRbF/jMcYkbPPmzYSEhLBgwQICAgIYPHgwnTt3JmfOnG6HZlKZlO4zV1BVI53lg0BBZ7kIsC9OvQin7HrlxphkEhYWxoABA5g3bx65cuWif//+vP7669x2221uh2ZSKdc6W6uqiogm1/FEpCOe21zccccdyXVYY9KtHTt2MHDgQGbNmkWOHDno06cPb775JgEBdjfYJCylh6Y85NyCwnk/7JTv58oBE4s6Zdcrv4aqTlTVYFUNzp8/f7IHbkx6sWvXLtq2bUtQUBALFiyge/fu7N69m3feeceShvGKN2NV5ReRCSLyjbNeVkSeT+L5vgIu9YxqAyyMU97a6V1VHTjp3NJaCjwmIgFOo/hjTpkxxkd79uzhhRdeoHTp0syZM4fXX3+dXbt2ERoaSr58+dwOz6Qh3tyqmgrMxNOIDbATmOuUX5eIzMbTuJ1PRCLw9I4KBeaJSHtgD/CUU30RngmjwoGzQFsAVT0mIu8Aa5x6Ay81lBtjvBMREcGQIUOYNGkSIsLLL79Mz549uf32290OzaRR3iSOAqo6S0S6AahqlIgkOlaVqj5znU1146mrQKfrHGcKMMWLOI0xcURGRhIaGsqECROIjY2lffv2vP322xQrZtPomBvjTeI44zxPoQAici9wyq9RGWOS7PDhwwwbNoxx48YRFRVF27Zt6d27N4GBgW6HZtIJbxLHW8DXwF0i8hOe7rBP+jUqY4zP/vnnH4YPH87YsWM5f/48rVq1om/fvtx9991uh2bSmUQTh6quFZGHgSBAgDBnaHVjTCpw/PhxRo4cyejRozlz5gzPPPMM/fr1o3Tp0m6HZtKpRBOHiNwEPAIEOvUfEhFU9X0/x2aMScDJkycZM2YMI0eO5OTJk7Ro0YKQkBDKlSvndmgmnfPmVtVCPO0bm7EJnIxx3enTpxk7dizDhw/n+PHjNGnShAEDBlCxYkW3QzMZhDeJI1BVK/g9EmNMgs6ePcuHH37Iu+++y9GjR2nYsCH9+/enatWqbodmMhhvnhxfKiI2cZMxLjl37hyjR4/mrrvuonv37lStWpVVq1bx9ddfW9IwrvDmiuO/wNfOuFIX8TSQq6rm8WtkxmRwFy5cYNKkSQwZMoQDBw5Qp04d5s+fT82aNd0OzWRw3iSO0cCDWBuHMSni4sWLTJ06lUGDBrFv3z4efPBBZs6cSe3atd0OzRjAu1tVEcAfqhqlqjGXXv4OzJiMJjo6mk8++YTSpUvz4osvUqRIEZYtW8ZPP/1kScOkKt5ccYQDP4jIIuDCpULrjmtM8oiJiWHWrFkMHDiQ8PBwgoODGTduHPXr10dE3A7PmGt4kzginFcuP8diTIYSGxvLvHnz6N+/P9u3b6dSpUosXLiQ//znP5YwTKrmzZPjfVMiEGMyitjYWBYsWEBISAhbt26lXLlyzJ8/n6ZNm3LTTSk9RY4xvrtu4hCR91S1q4gswBngMC5VbebXyIxJZ1SVr776ipCQEDZu3EiZMmWYM2cOLVq0sIRh0pSErjjmOu8fpEQgxqRXqsrixYvp168f69ato0SJEnz66ac888wzZMqUye3wjPFZQonjRWC1qi5PqWCMSU9Ule+//55+/fqxatUqAgMDmTJlCq1atSJzZm+aF41JnRK6Pr4nxaIwJp1ZsWIFtWrV4rHHHmP//v1MmDCB7du307ZtW0saJs1L6Dc4u4hUwPOk+DVUdZN/QjIm7frll1/o27cvP/74I7fffjsffPABHTp04JZbbnE7NGOSTUKJowjwIfEnDgUe8ktExqRBv//+O/369eO7776jYMGCjB49mo4dO5ItWza3QzMm2SWUOMJV1ZKDMQlYt24dISEhfPvtt+TLl4/hw4fzyiuvkD17drdDM8Zv7GarMUmwceNG+vfvz5dffklAQABDhgyhc+fO3HrrrW6HZozfJZQ43k6xKIxJI7Zu3Ur//v2ZP38+t912GwMHDqRLly7kymUDK5iM47q9qlR1sb9OKiJviMhWEdkiIrNFJKuIFBeR30UkXETmisjNTt1bnPVwZ3ugv+Iy5nq2b9/Os88+S4UKFVi6dCl9+/Zl9+7d9O3b15KGyXBS/HFVESkCvAYEq2p5IBPQEhgGjFLVEsBxoL2zS3vguFM+yqlnTIoIDw+nTZs2lC1bloULF9KjRw92797NwIEDCQgIcDs8Y1zh1jgHmYFsIpIZyA5EAnWA+c72aUATZ7mxs46zva7YCHDGz/7++286dOhAmTJlmDdvHm+88Qa7d+9m6NCh5M2b1+3wjHFVoo3jIlICeBMIjFtfVR9LyglVdb+IjAD2AueA74B1wAlVjXaqReDpDozzvs/ZN1pETgJ5gaNXxdkR6Ahwxx13JCU0Y9i3bx+DBw9m8uTJZMqUiU6dOtGzZ08KFy7sdmjGpBre9KqaD0wGZgA3PIGTiATguYooDpwAPgPq3+hxVXUiMBEgODj4mkEZjUnIgQMHGDp0KBMnTkRV6dixI7169aJo0aJuh2ZMquNN4ohV1bHJeM5HgN2qegRARL4AagC5RSSzc9VRFNjv1N8PFAMinFtbtwH/JGM8JgM7dOgQw4YNY/z48URHR9O2bVt69+7NnXfe6XZoxqRa123jEJFcIpILWCgiHUUk/6Uypzyp9gLVRSS701ZRFwgDfgSedOq0ARY6y1856zjbf1BVu6IwN+To0aP06NGDu+66izFjxtCyZUu2b9/OxIkTLWkYk4iErji24hla5FJDdNwJnRRIUkOCqv4uIvOB9UA08AeeW0zfAnNEZJBTNtnZZTLwqYiEA8fw9MAyJkmOHTvGyJEjGTNmDGfOnOHZZ5+lX79+lCpVyu3QjEkzJD3+8R4cHKxr1651OwyTipw8eZJRo0YxatQoTp06xVNPPUVISAhly5Z1OzRjUg0RWaeqwYnVS7Q7roi8JCK546wHOD2YjEn1/v33XwYPHkxgYCADBgzgkUceYdOmTcydO9eShjFJ5M1zHC+p6olLK6p6HHjZfyEZc+POnDnDu+++S/HixenTpw8PPvgg69ev5/PPP6dChQpuh2dMmuZNr6or5rYUkZuALP4Jx5gbc+7cOT766CNCQ0M5fPgw9evXZ8CAAVSrVs3t0IxJN7y54ljmjCdVS0RqATOB7/0clzE+OX/+PGPHjuXuu+/mzTffpGLFivzyyy8sXrzYkoYxycybK45uwCvAG876MmCC3yIyxgcXL15kypQpDB48mIiICB566CFmz55NrVq13A7NmHQr0cShqjEiMh7PVYbimeApOpHdjPGrqKgopk+fzjvvvMOePXu4//77mTp1KnXq1MGGMjPGv7zpVfUgEA5MAqYAO0Skhr8DMyY+0dHRTJ8+naCgIDp06ECBAgVYvHgxv/zyC3Xr1rWkYUwK8OZW1SjgcVUNAxCRIOBTING+vsYkl5iYGObOncuAAQPYsWMH99xzD19//TVPPPGEJQtjUpg3jeM3X0oaAKr6J3Cz/0Iy5n9UlQULFlCxYkWee+45brnlFr744gvWrVtHw4YNLWkY4wJvEsd6EflIRGo6r/F4hgQxxq9+//13HnzwQZo1a0ZsbCxz585lw4YNNG3a1BKGMS7y6gFAYBfQ3XntAl70Z1AmY9u9ezctW7akevXqhIeHM2HCBDZv3sxTTz3FTTe5NfeYMeYSb3pVnReRUXh96k9nAAAW2klEQVQGIYwFdlqvKuMPx48fZ/DgwYwdO5ZMmTLRt29funXrRs6cOd0OzRgThzczANbHM3rtXjwj5RYVkRdU9Tt/B2cyhosXLzJu3DgGDhzIiRMnaNu2LQMHDqRIkSKJ72yMSXHe9KoaDTyiqjsARKQUnrkygvwZmEn/VJXPP/+cnj178tdff/Hoo48yfPhwKlWq5HZoxpgEeHPD+PSlpAHgLJ/xX0gmI/jtt9+oUaMGLVq0IFu2bCxevJilS5da0jAmDfDmimO1iHwFzMPz5HgL4HcRaQSgql/5MT6Tzvz111/06tWLzz77jEKFCjFp0iSef/55MmXKlPjOxphUwZvEkRM4CdRz1v8FcuFJIIpnaldjEnTs2DEGDRrEBx98QJYsWejfvz9du3bl1ltvdTs0Y4yPvOlV1SolAjHp04ULF/jggw8YNGgQp06dol27dgwcOJDChQu7HZoxJomu28YhIrPjLA+5attifwZl0j5VZe7cuQQFBfHWW29RvXp1NmzYwMcff2xJw5g0LqHG8TJxlutfta2QH2Ix6cTKlSu5//77admyJTlz5mTp0qUsXrzYZt4zJp1IKHFoEreZDGrnzp00b96cBx98kH379jFlyhTWr1/PY4895nZoxphklFDiyC4iFUSkEpDNWa54af1GTioiuUVkvohsE5E/ReR+EckjIstEZKfzHuDUFRF5X0TCRWSTiFS5kXOb5Hf06FG6dOlC2bJlWbp0Ke+88w47duygbdu21lvKmHQoocbxI8A4Z/lonOVL6zdiDLBEVZ8UkZuB7MDbwHJVDRWRnkBPoAfQACjpvO4DxjvvxmWXpmsdPHgw//77Ly+88AL9+/enUCG7k2lMenbdxKGqD/rjhCJyG/AQ8LxznovARRFpDNR2qk0DVuBJHI2B6aqqwCrnaqWwqkb6Iz6TuEsj1fbq1Ys9e/bwxBNPMGzYMMqVK+d2aMaYFODGUKPF8VzNfCIif4jIJBHJARSMkwwOAgWd5SLAvjj7RzhlxgU///wz1atX59lnnyUgIIDvv/+eb775xpKGMRmIG4kjM1AFGK+q9+AZvqRn3ArO1YVPDfAi0lFE1orI2iNHjiRbsMZjx44dNGnShFq1ahEZGcm0adNYt24ddevWdTs0Y0wKcyNxRAARqvq7sz4fTyI5JCKFAZz3w872/UCxOPsXdcquoKoTVTVYVYPz58/vt+AzmiNHjtC5c2fKlSvH8uXLGTx4MNu3b6d169Y2N4YxGZQ3w6pXjKf4JLBPVWN9PaGqHhSRfSJSWlW3A3WBMOfVBgh13hc6u3wFvCoic/A0ip+09g3/O3fuHGPGjGHo0KGcOXOGjh07EhISQsGCBRPf2RiTrnkzVtVkoDKwFc98HEF4vuRzikhHVV2ehPN2BmY6Pap2AW3xXP3ME5H2wB7gKafuIuBxIBw469Q1fhIbG8usWbN4++232bdvH40aNWLYsGGUKVMm8Z2NMRmCN4njb6C9qm4CEJEKQF883Wfn40kqPlHVDUBwPJuuuWHutHd08vUcxncrVqyga9eurF+/nqpVqzJ9+nRq167tdljGmFTGm5vUQZeSBoCqbgbKqmq4/8IyKenPP/+kUaNGPPzwwxw5coQZM2awevVqSxrGmHh5c8WxTUTGAnOc9aedslsAm3s8DTt8+DD9+/dn4sSJ5MiRg9DQUF577TWyZbuhgQGMMemcN4mjNZ42iUtdZn8BeuFJGtYXMw06e/Yso0ePJjQ0lHPnzvHyyy/Tr18/rDeaMcYb3szHcRYY5ryudjLZIzJ+Exsby4wZM+jduzcRERE0adKE0NBQSpcu7XZoxpg0JNE2DhGpLiKLRSRMRHZceqVEcCb5LF++nKpVq9KmTRsKFy7MTz/9xIIFCyxpGGN85s2tqk+A7sA6IMa/4ZjktnXrVrp3786iRYu48847mTVrFk8//bQ9vGeMSTJvEscpVf3a75GYZHXw4EFCQkKYNGkSOXPmZPjw4bz66qtkzZrV7dCMMWmcN4njBxEZCnwBXLhUGLeLrkk9zpw5w8iRIxk2bBgXLlygc+fO9O3bl7x587odmjEmnfAmcdS86h08AxA+lPzhmKSKiYlh+vTp9OnThwMHDtC8eXOGDh1KyZIl3Q7NGJPOeNOryi/zcpjks2zZMt566y02bdrEfffdx7x586hRo4bbYRlj0qnrJg4ReUZVZ4vIa/FtV9X3/ReW8caWLVvo1q0bS5YsoXjx4sydO5cWLVogIm6HZoxJxxK64ghw3u2psFQmMjKSfv36MWXKFHLlysV7771Hp06duOWWW9wOzRiTASQ0dew4571vyoVjEnL69Gnee+893n33XaKioujSpQt9+vQhT548bodmjMlAvJmPIx/QDgiMW19VO/ovLBNXTEwMU6dOpW/fvkRGRtKiRQuGDh3K3Xff7XZoxpgMyJteVQuBVcBK7AHAFLd06VLeeusttmzZwgMPPMDnn3/O/fff73ZYxpgMzJvEkUNVu/o9EnOFjRs30q1bN5YtW8bdd9/N/PnzadasmTV8G2Nc5824E4tF5DG/R2IA2L9/P+3ateOee+5h3bp1jB49mrCwMJo3b25JwxiTKnhzxfES0ENEzgIX8Uwfq6pqLbLJ6N9//2X48OGMGDGCmJgYunbtyttvv01AQEDiOxtjTAryJnHk83sUGVh0dDRTpkyhX79+HDp0iJYtWzJkyBCKFy/udmjGGBOvhB4ALKmqO4Fy16liY1XdAFVl8eLFdOvWjbCwMGrWrMnChQu577773A7NGGMSlNAVR0+gPfBhPNtsrKobsGHDBt566y2WL19OiRIl+OKLL2jSpIm1YRhj0oSEHgBs77z7ZawqEckErAX2q2pDESmOZ17zvHjm/milqheduc2nA1WBf4CnVfVvf8TkbxEREfTp04fp06eTJ08e3n//fV588UVuvvlmt0MzxhiveTWbj4iUEZFmIvLspVcynLsL8Gec9WHAKFUtARzHc7WD837cKR9F/FPYpmqnTp2iT58+lCxZkjlz5tC9e3fCw8Pp3LmzJQ1jTJrjzdSxfYCJwEdAA2A08OSNnFREigJPAJOcdQHqAPOdKtOAJs5yY2cdZ3tdSSP3dKKjo/noo48oWbIkgwcPpnnz5mzfvp3Q0FBy587tdnjGGJMk3lxxPA08DESqaiugEpDjBs87Gs90tLHOel7ghKpGO+sRQBFnuQiwD8DZftKpn2qpKt988w0VKlTg5ZdfJigoiDVr1jBjxgzuvPNOt8Mzxpgb4k3iOKeqMUC0iOQEDgJJ/vYTkYbAYVVdl9RjXOe4HUVkrYisPXLkSHIe2ifr1q2jTp06/Oc//0FVWbhwIT/++CPBwcGuxWSMMcnJm8Txh4jkBqbgacxe7bySqgbQSET+xtMYXgcYA+QWkUuN9UWB/c7yfqAYgLP9NjyN5FdQ1YmqGqyqwfnzp/xI8Hv37qVVq1YEBwezZcsWPvzwQzZv3kyjRo2st5QxJl1JMHE4bQn9VfWEqn6Ip13iRVVtndQTqmovVS2qqoFAS+AHVX0O+JH/tZ20wTO4IsBXzjrO9h9UVZN6/uR28uRJevXqRalSpZg/fz69evUiPDycV155hSxZsrgdnjHGJLsEnxxXVRWRZUB5Zz3cj7H0AOaIyCDgD2CyUz4Z+FREwoFjeJKN66Kiopg4cSL9+/fn6NGjtG7dmkGDBlGsWDG3QzPGGL/yZsiRDSJyj6r+kdwnV9UVwApneRdQLZ4654EWyX3upFJVvvrqK7p3786OHTt4+OGHGTFiBFWqVHE7NGOMSRHXvVUVp73hHmCNiGwXkfUi8oeIrE+Z8FKXNWvWULt2bZo0aUKmTJn45ptvWL58uSUNY0yGktAVx2qgCtAohWJJtf7++2969+7NrFmzKFCgAB999BHt27cnc2ZvLtiMMSZ9SeibTwBU9a8UiiXVOXHiBEOHDmXMmDHcdNNN9OnTh+7du5MzZ063QzPGGNcklDjyi8ib19uoqiP9EE+qcPHiRT766CMGDhzIsWPHaNOmDe+88w5FixZ1OzRjjHFdQokjE3ArzpVHRqCqLFiwgB49ehAeHk7dunUZMWIElStXdjs0Y4xJNRJKHJGqOjDFIkkFdu7cSYsWLQgKCmLRokXUr1/fHt4zxpirJNrGkZGUKlWKH374gRo1aljDtzHGXEdC3451UyyKVKRWrVpuh2CMManadZ/jUNVjKRmIMcaYtMGriZyMMcaYSyxxGGOM8YklDmOMMT6xxGGMMcYnljiMMcb4xBKHMcYYn1jiMMYY4xNLHMYYY3xiicMYY4xPLHEYY4zxiSUOY4wxPrHEYYwxxieWOIwxxvgkxROHiBQTkR9FJExEtopIF6c8j4gsE5GdznuAUy4i8r6IhIvIJhGpktIxG2OM+R83rjiiga6qWhaoDnQSkbJAT2C5qpYEljvrAA2Aks6rIzA+5UM2xhhzSYonDlWNVNX1zvK/wJ9AEaAxMM2pNg1o4iw3Bqarxyogt4gUTuGwjTHGOFxt4xCRQOAe4HegoKpGOpsOAgWd5SLAvji7RThlVx+ro4isFZG1R44c8VvMxhiT0bmWOETkVuBz4HVVPRV3m6oqoL4cT1Unqmqwqgbnz58/GSM1xhgTlyuJQ0Sy4EkaM1X1C6f40KVbUM77Yad8P1Aszu5FnTJjjDEucKNXlQCTgT9VdWScTV8BbZzlNsDCOOWtnd5V1YGTcW5pGWOMSWGZXThnDaAVsFlENjhlbwOhwDwRaQ/sAZ5yti0CHgfCgbNA25QN1xhjTFwpnjhUdSUg19lcN576CnTya1DGGGO8Zk+OG2OM8YklDmOMMT6xxGGMMcYnljiMMcb4xBKHMcYYn1jiMMYY4xNLHMYYY3xiicMYY4xPLHEYY4zxiSUOY4wxPrHEYYwxxieWOIwxxvjEEocxxhifWOIwxhjjE0scxhhjfGKJwxhjjE8scRhjjPGJJQ5jjDE+scRhjDHGJ5Y4jDHG+MQShzHGGJ+kmcQhIvVFZLuIhItIT7fjMcaYjCpNJA4RyQR8CDQAygLPiEhZd6MyxpiMKU0kDqAaEK6qu1T1IjAHaOxyTMYYkyGllcRRBNgXZz3CKTPGGJPCMrsdQHIRkY5AR2f1tIhsv4HD5QOO3nhUrksvnwPss6RW6eWzpJfPATf2We70plJaSRz7gWJx1os6ZZep6kRgYnKcTETWqmpwchzLTenlc4B9ltQqvXyW9PI5IGU+S1q5VbUGKCkixUXkZqAl8JXLMRljTIaUJq44VDVaRF4FlgKZgCmqutXlsIwxJkNKE4kDQFUXAYtS6HTJcssrFUgvnwPss6RW6eWzpJfPASnwWURV/X0OY4wx6UhaaeMwxhiTSljicIhIMRH5UUTCRGSriHRxO6akEpGsIrJaRDY6n2WA2zHdCBHJJCJ/iMg3bsdyI0TkbxHZLCIbRGSt2/HcCBHJLSLzRWSbiPwpIve7HVNSiEhp59/j0uuUiLzudlxJJSJvOP/nt4jIbBHJ6pfz2K0qDxEpDBRW1fUikhNYBzRR1TCXQ/OZiAiQQ1VPi0gWYCXQRVVXuRxakojIm0AwkEtVG7odT1KJyN9AsKqm+ecFRGQa8F9VneT0dMyuqifcjutGOEMb7QfuU9U9bsfjKxEpguf/ellVPSci84BFqjo1uc9lVxwOVY1U1fXO8r/An6TRp9PV47SzmsV5pcm/EESkKPAEMMntWIyHiNwGPARMBlDVi2k9aTjqAn+lxaQRR2Ygm4hkBrIDB/xxEksc8RCRQOAe4Hd3I0k65/bOBuAwsExV0+pnGQ10B2LdDiQZKPCdiKxzRjpIq4oDR4BPnFuIk0Qkh9tBJYOWwGy3g0gqVd0PjAD2ApHASVX9zh/nssRxFRG5FfgceF1VT7kdT1KpaoyqVsbzlH01ESnvdky+EpGGwGFVXed2LMmkpqpWwTPKcycRecjtgJIoM1AFGK+q9wBngDQ91YFzu60R8JnbsSSViATgGfy1OHA7kENE/s8f57LEEYfTHvA5MFNVv3A7nuTg3EL4EajvdixJUANo5LQNzAHqiMgMd0NKOucvQlT1MLAAz6jPaVEEEBHnKnY+nkSSljUA1qvqIbcDuQGPALtV9YiqRgFfAA/440SWOBxOg/Jk4E9VHel2PDdCRPKLSG5nORvwKLDN3ah8p6q9VLWoqgbiuY3wg6r65S8ofxORHE6nC5zbOo8BW9yNKmlU9SCwT0RKO0V1gTTXieQqz5CGb1M59gLVRSS7831WF09bbbJLM0+Op4AaQCtgs9M2APC288R6WlMYmOb0ErkJmKeqaborazpQEFjg+f9MZmCWqi5xN6Qb0hmY6dzi2QW0dTmeJHMS+aPAi27HciNU9XcRmQ+sB6KBP/DTU+TWHdcYY4xP7FaVMcYYn1jiMMYY4xNLHMYYY3xiicMYY4xPLHEYY4zxiSUOk+aISN44o5keFJH9cdZv9vIYn8R5DuF6dTqJyHPJFHNjJ76NzgjMHZLjuF6cd4aINPGh/l0i0tKfMZm0z57jMGmOqv4DVAYQkf7AaVUdEbeO8wCUqGq8Y1ypaqLPHajqhzceLYjILcB4PCPjHnDW70yOY/vBXXgetpzjdiAm9bIrDpNuiEgJ56/5mcBWoLCITBSRtc4cBf3i1F0pIpVFJLOInBCRUOdq4DcRKeDUGXRpbganfqgzz8l2EXnAKc8hIp87553vnKvyVaHdBghwDEBVL6jqDmf/GSIyRkR+FZFdItLUKc8lIj+IyHoR2eSM23XpM24VkTnimQdjnjM6ACIy3Iljk4gMi3P+h+M5/k0iMlI88zZsFpEnnbqhTv0NIvKaiFQQkTXO+iYRuSv5/sVMWmWJw6Q3ZYBRqlrWGRuqp6oGA5WAR0WkbDz73Ab8pKqVgN+Adtc5tqhqNaAbcCkJdQYOqmpZ4B08oypfwRmbaimwR0RmicgzIhL3/14BPCMXNAGGOmXn8MwHUwXPGESj4tQvC4xW1SDgPPCiiBQEHgfKqWrFOMe53vFbAEGXfi7AKCdh9gR+VNXKqvo+8Aowwhkw8178NEy3SVsscZj05i9VjTu73jMish7PMAxBeL50r3ZOVRc7y+uAwOsc+4t46tTEua2jqhvxXOlcQ1Wfx/MFvRbPl3PcoSC+dOZQ2cT/5oARIFRENgHfAcVEJJ+zbXecSblmODEcwzP0/MfOVcWZRI5fE5jtjKJ8EM8EQMHxhP4r0EdEugPFVPV8vD8Zk6FY4jDpzeUvTBEpCXQB6jh/hS8B4ptK82Kc5Riu3/Z3wYs616Wqm5wBNOsBzeM5LngSBkBrPFdCVZy/9o/yv9ivHidIndFQg4Ev8VxZfJvI8b2N+VOgqXOMJZJ2h4I3ycgSh0nPcgH/AqfEMzVwPT+c4xfgKQARqUA8VzROe0XcL9zKQGKzzN2GZy6SaBF5lCtnoywuIvc6y88CK52Rd3M5g1m+QTy3zK7yX6Cl09ZREM+trLV4fl4548R+l6qGq+oY4BugYiLHNRmA9aoy6dl6PMN9b8PzRf2LH84xFpguImHOucKAk1fVEaCXiHyMp+3iNNdvR7nkU+BrEdkMrAZ2xtn2J/Cm0wi/Gc9tr7zAF06PrZuANxM5/nygOrAJzxXMm6p6WEROAJlEZCOeaQZyicgzQBSe9o3+iRzXZAA2Oq4xN0A8cztnVtXzzq2x74CSqhrtp/OVAOY7t6+McYVdcRhzY24FljsJRIAX/ZU0jEkt7IrDGGOMT6xx3BhjjE8scRhjjPGJJQ5jjDE+scRhjDHGJ5Y4jDHG+MQShzHGGJ/8P7OlY38mNSIjAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(snapshots, train_time, 'k-')\n", - "#plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(0)\n", - "plt.ylabel(\"Training Epoch Time (s)\")\n", - "plt.xlabel(\"Training Snapshots\")\n", - "plt.legend([\"Big Data Case (2 V100s)\", \"Big Cluster Case\"])\n", - "\n", - "plt.savefig(\"./big_data_training_times.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(snapshots, train_time, 'b-')\n", - "plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(0, 1800)\n", - "plt.ylabel(\"Training Epoch Time (s)\")\n", - "plt.xlabel(\"Training Snapshots\")\n", - "plt.legend([\"Big Data Case (2 V100s)\", \"Big Cluster Case (2 V100s)\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./big_cluster_training_times.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "csr = np.array([.25, .5, 1.0])\n", - "val_err = np.array([5.45775e-2, 5.29625e-2, 5.100578e-2])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(csr, val_err, 'g-')\n", - "#plt.plot(snapshots, train_time, 'b-')\n", - "#plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(.05, .06)\n", - "plt.ylabel(\"Final Validation Loss\")\n", - "plt.xlabel(\"Sampling Ratio\")\n", - "plt.legend([\"Big Cluster Case\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./big_cluster_sampling_error.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "mp_complete = mp.Value('i', False)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mp_complete.value = True\n", - "mp_complete.value == False" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2193.25" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "17546 / 8" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "sil_stand = np.array([0.447642,\n", - "0.227938,\n", - "0.0761155,\n", - "-0.0883255,\n", - "-0.174398,\n", - "-0.215808,\n", - "-0.234282,\n", - "-0.17385,\n", - "-0.137261])\n", - "\n", - "sil_norm = np.array([0.433647,\n", - "0.226572,\n", - "0.0813862,\n", - "-0.071797,\n", - "-0.177958,\n", - "-0.205834,\n", - "-0.225432,\n", - "-0.165705,\n", - "-0.132394,\n", - "-0.121013])\n", - "\n", - "iner_stand = np.array([7.16E+07,\n", - "7.03E+07,\n", - "6.90E+07,\n", - "6.69E+07,\n", - "6.21E+07,\n", - "5.44E+07,\n", - "4.40E+07,\n", - "3.79E+07,\n", - "3.50E+07])\n", - "\n", - "iner_norm = np.array([1.37E+06,\n", - "1.35E+06,\n", - "1.32E+06,\n", - "1.28E+06,\n", - "1.19E+06,\n", - "1.04E+06,\n", - "839276,\n", - "726160,\n", - "671359,\n", - "635393])\n", - "\n", - "clusters = np.array([4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(clusters[:-1], sil_stand, 'b-')\n", - "#plt.plot(clusters, sil_norm, 'g-')\n", - "#plt.ylim(0, 1800)\n", - "plt.ylabel(\"Silhouette Score\")\n", - "plt.xlabel(\"Number of Clusters\")\n", - "plt.legend([\"Standardized FP\", \"Normalized FP\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./cluster_silho.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.semilogy(clusters[:-1], iner_stand, 'b-')\n", - "#plt.semilogy(clusters, iner_norm, 'g-')\n", - "#plt.ylim(2e7, 8e7)\n", - "#plt.xlim(0, 2100)\n", - "plt.ylabel(\"Inertia\")\n", - "plt.xlabel(\"Number of Clusters\")\n", - "plt.legend([\"Standardized FP\", \"Normalized FP\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./cluster_inertia.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb b/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb deleted file mode 100644 index 8fbca4478..000000000 --- a/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb +++ /dev/null @@ -1,1976 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "run1 = [[2524.7672468464098,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2524.284297288213,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2540.5346296203934,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2538.192478718474,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2537.7505319554557,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2528.1831923262403,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2531.1879539456913,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2528.6472414223667,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2531.146822509088,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2524.265726593582,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2525.130644423422,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2524.9720542683363,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2525.875747479948,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n", - "\n", - "\n", - "\n", - "run2 = [[2529.400883838115,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2529.0800950112975,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2545.192170218334,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2543.3688692048613,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2542.702495425762,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2532.891425190408,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2535.451981420631,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2533.840190810313,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2536.2361696338758,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2528.221130661553,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2530.13889287563,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2528.907989518418,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2530.1443926152356,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "run3 = [[2521.8308895694972,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2521.8850606562005,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2532.7997776476477,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2531.3470642505627,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2531.316218173628,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2526.2936656319825,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.833866461784,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2527.260749618935,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2529.5233302856964,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.444354073414,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2523.2371151566604,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2522.126651196736,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2523.954851817506,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "model3 = np.array(run3)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13\n", - "[[2524.76724685 2521.01508856 2522.16279024]\n", - " [2524.28429729 2525.04190127 2526.18636597]\n", - " [2540.53462962 2568.85406654 2570.00780166]\n", - " [2538.19247872 2560.91764762 2562.07124992]\n", - " [2537.75053196 2561.82551903 2562.98011185]\n", - " [2528.18319233 2530.8366533 2531.98598868]\n", - " [2531.18795395 2528.63210775 2529.77568218]\n", - " [2528.64724142 2528.16835882 2529.30777035]\n", - " [2531.14682251 2533.28377499 2534.43861391]\n", - " [2524.26572659 2519.88264663 2521.02839901]\n", - " [2525.13064442 2520.96725194 2522.11667815]\n", - " [2524.97205427 2521.70485347 2522.84663038]\n", - " [2525.87574748 2523.88330861 2525.03502324]]\n", - "[[2524.76724685 2521.01508856 2522.16279024]\n", - " [2524.28429729 2525.04190127 2526.18636597]\n", - " [2528.18319233 2530.8366533 2531.98598868]\n", - " [2531.18795395 2528.63210775 2529.77568218]\n", - " [2528.64724142 2528.16835882 2529.30777035]\n", - " [2531.14682251 2533.28377499 2534.43861391]\n", - " [2524.26572659 2519.88264663 2521.02839901]\n", - " [2525.13064442 2520.96725194 2522.11667815]\n", - " [2524.97205427 2521.70485347 2522.84663038]\n", - " [2525.87574748 2523.88330861 2525.03502324]\n", - " [2540.53462962 2568.85406654 2570.00780166]\n", - " [2538.19247872 2560.91764762 2562.07124992]\n", - " [2537.75053196 2561.82551903 2562.98011185]]\n" - ] - } - ], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "model3 = np.array(run3)\n", - "\n", - "snapshots = model1.shape[0]\n", - "\n", - "print(snapshots)\n", - "print(model1)\n", - "\n", - "neworder = [10,11,12,2,3,4,5,6,7,8,9]\n", - "oldorder = [2,3,4,5,6,7,8,9,10,11,12]\n", - "\n", - "model1[neworder] = model1[oldorder]\n", - "model2[neworder] = model2[oldorder]\n", - "model3[neworder] = model3[oldorder]\n", - "\n", - "#temp1 = np.zeros([3,3])\n", - "\n", - "#temp1 = model1[2:5,:]\n", - "#temp2 = model2[2:5,:]\n", - "#temp3 = model3[2:5,:]\n", - "\n", - "#print(temp1)\n", - "\n", - "#model1[2:10, :] = model1[5:, :]\n", - "#model1[10:, :] = temp1\n", - "\n", - "print(model1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs([-1, 1, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Summed Squared Error\n", - "84.21139569798906\n", - "422.98330325707366\n", - "54.049557249920795\n", - "\n", - "Model MEAN meV/Atom\n", - "10.211334627756052\n", - "23.78797119067304\n", - "6.918106837911786\n", - "\n", - "Model Max meV/Atom\n", - "17.12140611919999\n", - "35.826722422344304\n", - "17.74604557573589\n", - "\n", - "Model Max Error Snapshots\n", - "6\n", - "7\n", - "2\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "model1_rmse = np.sum(((model1[:10,0] - model1[:10,1]) ** 2))\n", - "model2_rmse = np.sum(((model2[:10,0] - model2[:10,1]) ** 2))\n", - "model3_rmse = np.sum(((model3[:10,0] - model3[:10,1]) ** 2))\n", - "\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(model1_rmse)\n", - "print(model2_rmse)\n", - "print(model3_rmse)\n", - "\n", - "\n", - "model1_mean = np.mean((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_mean = np.mean((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_mean = np.mean((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(model1_mean * mev_atoms)\n", - "print(model2_mean * mev_atoms)\n", - "print(model3_mean * mev_atoms)\n", - "\n", - "\n", - "\n", - "model1_max = np.max((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_max = np.max((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_max = np.max((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel Max meV/Atom\")\n", - "print(model1_max * mev_atoms)\n", - "print(model2_max * mev_atoms)\n", - "print(model3_max * mev_atoms)\n", - "\n", - "\n", - "model1_argmax = np.argmax((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_argmax = np.argmax((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_argmax = np.argmax((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel Max Error Snapshots\")\n", - "print(model1_argmax)\n", - "print(model2_argmax)\n", - "print(model3_argmax)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), model1[:,0], 'bo')\n", - "plt.plot(range(snapshots), model1[:,1], 'go')\n", - "plt.plot(range(snapshots), model1[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 1')\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_model1_inference.png\", format='png')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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chx56iKCgICZMmMDjjz9OqVKluPXWW93HJ0yYwNChQ6lcuTIVK1Zk9+7d55Vxxx138PLLLzN+/HgGDx5MZGQk06dPp3Pnzn5fX3h4OC+++CLDhw/nzJkz1KlThw8//JBq1ar5XYYxBZEt4uYjs0Xctm3bRu3atQNcI2MKjov6M5T2zNrq1flZi0uSLeJmjDHmomRBxxhjTMBY0DHGGBMwFnSMMcYEjAUdY4wxAWNBxxhjTMBY0DHGGBMwFnSMMcYEjAUdY4wxAWNBxxhjTMBY0Cng+vbti4gwYMCA8449/vjjiAi33XabOy295Z+zKjuz7WIQERHB1KlT87saxhgCGHRE5AkR2Sgif4jIYRFZJCJ1ffIkiIj6bBt88kSIyNsi8ouIpIjINyLSyydPSVeeY67tbREJD8R1ZiVxSyJVJ1el0KhCVJ1clcQtiXl+zsqVKzN37lxOnDjhTjt79ixvvfXWBS3kNmXKFA4cOODeQkJCmDx5sldaTnkudW2MKTgC2dJpC0wHWgA3AmeB5SLiO0/9cqC8x3arz/G3gNpAF6Cua/9tEWntkecdoBHQ0bU1At7OxWvJkcQticQuiiX5WDKKknwsmdhFsXkeeOrXr0/NmjWZO3euO23JkiUEBwfTNm2SwxwoUaIEERER7k1EzksDWLRoES1btiQ8PJyrrrqKW2+9lZ07d7rL+f777xER5s2bR5s2bQgODubNN98EYMaMGVSuXJmQkBC6du3KlClTCA4O9qrHhx9+SMOGDQkODqZ69eqMHDnSHbSaNWvGwYMHeeSRRxAR92t//fVXevbsSZkyZQgODqZGjRq88sorOf5bGGP8E7Cgo6o3q+osVf1OVbcA9wFlgJY+WU+p6i8e228+x1sA01T1S1X9SVUnAHuBpgAiUhsn0MSq6npVXQ88ANwmItfk5TVmJW5FHClnUrzSUs6kELciLs/PPWDAAGbOnOnenzlzJv369QvILbCUlBQee+wxkpKSWLFiBUFBQXTp0oWzZ8965RsxYgRDhgxh27Zt3HrrraxevZqBAwcyZMgQNm/eTIcOHc5b8XThwoX079+fIUOGsHXrVuLj43n77bcZNWoUAEuXLqVMmTKMHTuWAwcOkJyc7D7Xzp07+fjjj/n++++Jj4+nXLlyef63MOZyl5/r6YThBL2jPumtROQQ8DuwBohT1UMex9cB3UVkoeu1nXGC13LX8ebAceALj9d8DpzACVjbc/k6/Lbn2J5speemnj17Mnz4cHbu3ElYWBjLli3j5Zdf5plnnsnzc/fo0cNrf9asWZQqVYrNmzcTHf33bOhDhw71WtH0n//8J507d2bo0KEAREVFsWHDBq8W2/PPP09cXBy9e/cGoHr16owZM4aBAwfy/PPPU6pUKQoVKkRYWJi75QWQnJxMdHS0+/xVq1bN9es2xpwvPwcSTAE2A+s90pYBvYF2wDCc1stKEfFc5rI7oMAR4BSQCPxDVdOWuowADqvHQkGu3w+5jp1HRGJFJElEkg4fPpwb15auKiXS7z/JKD03lSxZkq5duzJz5kzefPNN2rZte0H9OdmxY8cOevToQfXq1SlevDiVK1cGYM8e72DrGYDAue3WtGlTr7Trr7/e/buq8vXXX/PMM88QGhrq3vr378/Ro0c5etT3/zN/++c//0lCQgINGzbkX//6F+vWrbvQyzTG+CFfWjoiMhFoBbRSVfeC9ar6nke2LSKyCUgGOgFpawU/D5QG2uMEnjuAt0Sktap+k5P6qGo8EA/OIm45KcMfY9qNIXZRrNcttpDCIYxpNyavTumlf//+9OnTh9DQ0PNuU+UVVaVjx47UqlWL119/nfLlywNw7bXXcvr0aa+8xYoVy3bZqampPP/883Tp0uW848WLF8/wtV26dCE5OZmlS5eyYsUKbr75Znr37m39OsbksYAHHRGZBNwDxKjqT5nlVdX9IvIzUNP12quBR4AGHgHmGxG5wZV+P/ALUEZEJK21I07HRVnXsXzTq54zyC5uRRx7ju2hSokqjGk3xp2e19q1a0dQUBBHjhzxuo2Vl/bv38+uXbtITEykefPmAHzxxRf4s2JtrVq12Lhxo1faV1995f69UKFCNGjQgB07dlCjRo0MywkKCuLcuXPnpZctW5a+ffvSt29f2rdvz/3338+0adMoVMieJDAmrwQ06IjIFKAHTsD53o/8pYGKQNrY2xDXT99vkHP8fatwPRCK07eT1q/THCiGdz9PvuhVr1fAgowvEeHbb79FVSlSpEiG+U6ePMnmzZu90kJCQoiKisr2OcuWLUt4eDgzZsygTJky7Nmzx/18UFYGDRpE+/btmTx5Mp06dWLlypUsWbLE67XPPvssd911FxUrVuSuu+6iUKFCbNmyhc2bNzN27FjA6a9Zs2YNd999N8HBwVx11VU8+eSTNGvWjDp16nDq1Ck++ugjrrnmGgs4xuSxQD6nMw3oB/QEjrqet4kQkVDX8VARGS8izUWkqoi0BRbh9MXMdxXzPfADMF1EmorI1SIyDOiQlkdVt+H0Dc1wldUcmAEsVtV8G0RwsQgLC8v0thPAjz/+SMOGDb22nj175uh8hQsX5r333uOrr76ibt26DB48mBdffNGvL/e2bdsydepUxo8fz3XXXceyZcsYPny415Dp22+/nQULFrBs2TKio6Np1qwZ48ePJzIy0p1nzJgx7Nixg+rVq1OxYkV3vR5//HHq169P69atOXfuHB9++OF5dTDG5C7x5zZHrpxIJKMTjVLVkSJSFPgIaAiE47RuVgFPq+pej3JqAi/g9AmF4gShiar6pkeeksDLwO2upIXAw6r6e1b1jI6O1qSkpHSPbdu2jdq1a2dVhMlDDz30EElJSefddjOXhov6M5T2zNrq1flZi0uSiGxS1eiscwbw9pqqZno/RVX/Am72o5ydwF1Z5DkK3JutCpqL0osvvkjHjh0pVqwYy5YtY+bMmUyePDm/q2WMyaH8fE7HmCytX7+eCRMm8Oeff1K9enUmTpzIgw8+mN/VMsbkkAUdc1H76KOP8rsKxphclK2BBCJSSERCss5pjDHGnC/LoCMit4jIWyKSDJwG/hSREyLymYjEiUiFvK+mMcaYgiDDoCMiXUVkBzATOAOMBbridPb3wxlZ1h74SUReFZEyAaivMcaYS1hmfTojgKHAUlVNTef4XAARqQgMwpkzbUKu19AYY0yBkWHQUdXrMzrmk28f8K9cq5ExxpgCK9M+HRF5XkSqBqYqxhhjCrqsBhL0A34QkeUico+IBAWiUubyUrVqVcaPH5/f1ciRvn37ctttt+V3NYy5ZGQVdCrjLB3wB86y0AdEZIqI1Mvzmplc0bdvX0QEEaFw4cKULVuWmJgYpk2b5l7SOU3btm3deT23zZs3p5vuuY0cOTLL83tuzZo1c+fZuHEjAwcOzMs/gzHmIpFp0FHVVFVdrKp3ApVw5jzrAGwWka9ci5+FBaKiBcG4ceNYtWqVV9qqVasYN25cnp63ffv2HDhwgN27d/Ppp5/SuXNnnn32WW644QZOnDjhlbdfv34cOHDAa6tXr57X/jPPPEOlSpW80oYPH57l+T23pUuXuo+XKVOGkBB7/MtcGvLrc1xQ+P1wqKoeUtWXVLUOcAPwLTAe2J9XlStomjRpQvfu3d1v2FWrVtG9e3eaNGmSp+ctUqQIERERVKxYkQYNGjB06FBWr17Nf//73/M+KCEhIURERHhtV1xxhdd+WFjYeWmhoaFZnt9zK1WqlPu47+21HTt20KZNG4KDg7nmmmtYunQpoaGhJCQkuPPs27ePe+65h5IlS1KyZEk6derEzp073cdHjhxJ3bp1ee+997j66qsJCwvjjjvu4MiRIwB8+umnBAUF8euvv3rV9cknn6R+/foA/Prrr/zjH/+gUqVKFC1alGuvvZZZs2Zl+rdu27YtDz/8sFea7y04VWXcuHFcffXVFC1alHr16jF79myv14wePZrIyEj33y5tOW6T//Lrc1xQZHtpA9eCaMVxZoIuAvyV25UqqGJiYpg7dy7du3fnmWeeoXv37sydO5eYmJiA16Vu3bp07NiRDz74IODnzkxqaipdu3blyiuvZMOGDSQkJDBq1ChOnTrlzpOSkkJMTAzBwcGsWbOG9evXU758edq3b09Kyt+rsu7evZs5c+Ywf/58Pv30U77++mvi4uIAZ0G70qVLM2/ePHd+VeWdd97h3nuduWJPnjxJo0aNWLx4Mf/73/8YNGgQDzzwACtWrLiga3zqqad44403mDZtGlu3buWJJ57ggQceYMmSJQB88MEHjB8/nunTp7Nz504WL1583rLdJv/ExMQQ+0Is7Tu3R9oI7Tu3J/aF2Hz5HF+K/A46IlJdRJ7HWT56Mc6yAvfiLLJm/BQTE8NDDz3Ec889x0MPPZSvb9Q6derw00/ei7fGx8cTGhrq3nJjcs1ly5Z5lRkaGsrjjz+ebt7//Oc/bN++nbfeeosGDRrQvHlzJk2axNmzZ9153nvvPVSVWbNmUb9+fWrVqsWMGTM4fvw4ixcvduc7e/YsCQkJ1K9fn+bNmxMbG+sOGFdccQX33HMPiYmJ7vyff/45e/fuda8dVLFiRR577DEaNGhA9erViY2N5c477+Tdd9/N8d/ixIkTTJw4kddff52OHTtSrVo1evbsyf/93/8xbdo0AJKTkylfvjw33XQTVapUITo6+rzWk8k/iVsSmXxwMqmNU2EtpDZOZfLBySRuScz6xSbzCT9FJBjoBvTHuaX2M/AGMEtV9+R99QqeVatW8corr/D000/zyiuvEBMTk2+BR1XPW8GzR48ePPvss+79rBZ880fr1q2Jj4/3SgsPD0837/fff0+FChXci62BczvDc9G3TZs2sWvXLsLCvLsTU1JS+PHHH937kZGRlChRwr1foUIFDh065N6/9957mTx5MsnJyURGRpKYmEibNm2oVKkSAOfOneOFF15gzpw57Nu3j1OnTnH69Gnapq27kgNbt27l5MmTdOzY0etvf+bMGapWrQpAt27dmDJlCtWqVePmm2+mY8eO3H777Zmu9moCJ25FHCk7UiAJaA0kQUq1FOJWxOXbqsCXkqxmmT4IBOGs4NkJ+FQDtepbAZR27zftllpMTEy+3mLbunUr1atX90orUaIENWrUyNXzhISE5GqZqampNGjQgPfee++8Y559RYULF/Y6JiKkpv49uUajRo2oVasW77zzDsOHD2fevHlefVzjx49nwoQJTJkyhXr16hEaGsqTTz7pFbh8FSpUCN+PiOcowbTzL1q0iCpVqnjlS6tv5cqV2b59OytWrGD58uUMGzaMUaNG8eWXX1KsWLEMz20CI3lzMszD+e94Ndc2D5JJzt+KXSKyCjojgbdV9UgA6lLgbdy40SvApPXxbNy4MeBB57vvvmPZsmU89dRTAT1vVmrVqsX+/fvZv38/FSo4c8kmJSWdFyzeffddSpcunWGLyV/33nsviYmJ1K1blxMnTnD33Xe7j61bt47OnTtz3333AU7LcMeOHZmes0yZMhw4cMAr7ZtvvnG3YurUqUORIkVITk7mxhtvzLCc4OBgOnXqRKdOnRgxYgQRERF8/vnn3HTTTRdwtSY3hP8azu/dfneCDTg/uznpJmuZBh1VnZT2u+tW223A1cAMVf1dRK4Gjqrqb3lbzYLhX/86f7agQNxeO3XqFL/88gupqakcPnyYFStWMHbsWBo3bpzpUOfcPr+nK664gjJlzp8jtkOHDlxzzTX06dOH8ePH89dffzF06FCuvPJK9+2oXr16MX78eLp06cLo0aOpUqUKe/fuZcGCBTz44IPUrFnT77r16tWLp556iqeffprOnTt73U6Miopizpw5rFu3jtKlS/Pyyy+za9cuGjZsmGF5N954I4MHD2bhwoVcc801zJgxg71797qDTlhYGMOHD2f48OGoKq1bt+b48eNs2LCBQoUKERsbS0JCAmfPnuX6668nNDSUOXPmULhw4Wxdl8k7U8dOJXZRLCln/h60EhIVwtTOU/OxVpcOvwYSiEgN4HvgVWAMkHYP4yHABqdf5JYvX0758uWpUqUK7dq1Y+HChYwcOZK1a9cG5HZN2vk9t4y+uAsVKsRI/OGIAAAgAElEQVT8+fM5deoUTZs2pU+fPsTFxSEiBAcHA87turVr11K9enW6detGrVq16NOnD0ePHqVkyZLZqltkZCStWrXim2++cY9aS/PUU0/RtGlTbrnlFlq3bk2xYsXo1Svze/b9+/d3by1btiQsLIyuXbt65XnuuecYOXIk48eP59prr6VDhw588MEHVKvm/Nc5PDycN954gxtuuIG6devywQcf8OGHH7qPm/zVq14v4jvHE1kiEkGILBFJfOd468/xk/jTRSMii3Gex3kI+B24TlV/EpHWOIMKrs7bagZOdHS0JiUlpXts27Zt1K5dO8A1Mt988w0NGjQgKSmJxo0b53d1zAW4qD9DaQNEVq/Oz1pckkRkk6pG+5PX3+WqWwDNVPWcz2inPYAt4mZy1fz58ylWrBg1a9Zk9+7dDB06lOuuu45GjRrld9WMMRfI36ADUDidtCrAsVyqizEA/Pnnnzz++OPs3buXkiVL0rZtWyZNmnTe8G5jzKXH36DzKc6CbgNc+yoixYFRwJK8qJi5fPXu3dumfTGmgPJ3RoKhQCsR2Q4EA3OA3UAEzgqjxhhjLjH5MXmpX0FHVfcDDYAXgRk4z+L+C2ikqofzrHbGGGPyTH5MXpqdWab/UtWZqvqwqg5U1ddV1Sb7NMaYPBCIVkh+TEKcYdARkVb+FiIiobawmzHG5J5AtUL2l97PmUZneO655zjT6Az7S+ftajWZtXReF5EVIvIP16CB84hIfREZB/wAXJcnNTTGmMtQIJZQSNySyIApAzi27hi0hmPrjjFgyoA8nTE7s6BTF/gQeBb4TUS2i8gqEflYRDaIyFHgS5ylDW5U1dmZlIWIPCEiG0XkDxE5LCKLRKSuT54EEVGfbUM6ZTUVkf+IyHER+VNEvhCR0h7HS4rI2yJyzLW9LSI2MZIx5pIRiCUUhr06jFPvnnImL70R6Aan3j3FsFeH5do5fGUYdFT1rKpOU9VaQDPgFWAzzno6y4H7gYqq2ktVt/pxrrbAdJwHTW8EzgLLRaSUT77lQHmP7VbPgyJyPc4Q7tWuejXGWcH0jEe2d4BGQEfX1gh42486XnZ8V+3MLSLC+++/n+HxI0eOICKsDsDT3751uZjqltd2796NiJDRLBvm4pXuEgo7nCUUcsvBHQf/ni0b3JOXHtxxMNfO4cuv53RUNQnn0nNMVW/23BeR+3AeLG2Js3RCmlOq6j07pLdJwDRVHeORtsOj3No4gaaVqq53pT0AfCYi16jq9gu5jktN3759OXLkiNfiZp42btyYJ/OvHThwINvzoKW9LjIykgMHDhAeHs748eNJSEggOTmZIkWKcPXVV9O7d28effTRgNfN0+HDh3n22WdZunSpu65169ZlxIgRdOjQ4YLKvhi1bduWunXrMnWqTWoZKIFYQiGyUyTJx3zKqwaRDSJz7Ry+sjMjQW4Lw2lpHfVJbyUih3DmeFsDxKnqIQARKQs0B94RkXVAFLAdGKmqaWsINweOA194lPk5cAKnlXVZBZ2spDfTc26IiIjI0esWLlxIixYtuOqqq3jmmWeYPn06U6dOpWnTppw4cYKvv/6a5OQL+9DltG6e7rrrLlJSUnjjjTeoUaMGhw4dYs2aNfz6668XXLYxEJglFMa0G3P+jNmFQxjTbkwmr7owfg+ZzgNTcG7XrfdIWwb0BtoBw4CmwEoRSVsyMW3FsVHATOBm4DPgExFJG8gQARz2XGzO9fsh17HziEisiCSJSNLhw5fXY0e+t9d++OEH2rZtS3BwMNdccw2LFy8mNDSUhIQEIOPbNVndwtq4cSONGzcmODiYhg0b8uWXX6ZbnwULFtClSxfACUAPPvgg99xzD9WrV6devXr07t2bp59+2p0/NTWV5557jsqVK1OkSBHq1avHggULMr3mnNYtze+//85nn33GCy+8QLt27YiMjKRJkyYMHz6ce+65x52vatWqPP/88zzwwAMUL16cSpUq8dJLL3mVNXHiROrXr0+xYsWoWLEi999/P7///rv7eEJCAqGhoSxatIioqCiCg4OJiYnxWmZ87969dOnShVKlShESEkKtWrXOW+AuOTmZDh06EBISQp06dfjPf/7jdXzt2rVcf/31BAcHU65cOYYMGcLp06cBp7W8Zs0apk2bhoggIuzevZszZ87w6KOPUqFCBYoUKULlypUZMcKeFc8tU8dOJSQqxCstJCqEqWNzr7WZHzNm50tLR0QmAq1wboGdS0tXVc9PyhYR2YTTh9QJZ1BDWpCcoaozXb9/LSIxwIM4s2Bnm6rGA/HgzDLt9wsHD4bNm3Nyypxr0AAmT86TolNTU+natSslS5Zk/fr1pKSkMGjQIE6dOnVB5R4/fpxOnTrRpk0b3nzzTfbt28fgwYPPy/fnn3+ycuVK9y2ciIgIVq9ezcGDBylXrly6ZU+ZMoWXXnqJV199lejoaGbPns2dd97Jpk2baNCgQa7VzVNoaCihoaEsXLiQVq1auZdcSM+kSZMYNWoUjz32GB9//DGPPvoorVq1onnz5oCzlMPkyZOpXr06ycnJPPLIIzzyyCO8/fbfXZCnTp1i1KhRzJo1i5CQEAYNGsSdd97J119/jYgwcOBATp48yapVqyhevDjbt5/fmI+Li+Oll15i+vTpPP/889xzzz0kJycTGhrKvn37uOWWW7jvvvtISEjgxx9/5P7776dQoULulVN37NhBrVq1GDt2LOC0kCdPnsz8+fN57733qFq1Kj///HO65zY5k/bFH7cijj3H9lClRBXGtBuT6wGhV71eAV2WIeAtHRGZBPwDZ8TbT5nldc2E8DOQtnpV2pKMvgMXtuJMPgrwC1BGPGaHdP1e1nXMZGD58uVs3bqV2bNn07BhQ1q2bMnkyZM5e/bsBZX7zjvvcPr0aWbNmkXdunW5+eabiYs7vzN02bJlREVFuZfQnjhxIr/99hvly5fn2muv5f777+fDDz/0Wg56/PjxDB8+nJ49exIVFcXo0aO54YYb/B4c4W/dPF155ZUkJCQwe/ZswsPDad68OcOHD0+3hXTTTTfx8MMPU6NGDR555BFq1KjBihUr3McHDx7MjTfeSNWqVWnTpg3jxo1j7ty5Xiulnj17lilTptCyZUsaNmzI22+/zZYtW9zlJCcn06pVK6677jqqVatGx44d6dixo1c9hgwZQufOnalZsyZjx47lt99+Y7PrP0zTp0+nQoUKTJ8+ndq1a3PbbbfxwgsvMHXqVFJSUihRogRBQUGEhIQQERFBREQEV1xxBcnJyURFRXHDDTdQpUoVWrRoQb9+/fz6uxv/9KrXi92Dd5P6bCq7B+8uEGv2+NXSEZEyuTHdjYhMAXoAMar6vR/5S+MMyU4LNrtx1vW5xidrFLDF9ft6IBSnbyetX6c5UAzvfp4Ll0ctjvyybds2KlasSJUqVdxp119/PYUKXdj/TbZt20b9+vUJDQ11p6X9T9+T5601cJZ2/u6779i0aRPr1q1j7dq1dO/enZtuuonFixdz/Phx9u/fT8uWLb3KadWqFUuXLs3Vuvm666676NSpE5999hnr169n2bJlTJgwgTFjxvDkk0+689WvX9/rdRUqVODQoUPu/ZUrV/Lvf/+bbdu2cezYMc6dO8fp06f55Zdf3Mt1FypUiKZNm7pfExkZSYUKFdi6dSvt27dn0KBBPPjggyxbtox27drRtWvX89Yd8qxHWrlp9di2bRvNmjXz+ndu1aoVp0+f5ocffjjvGtL07duXDh06EBUVxU033cStt97KLbfccsHvF1Ow+fvu2Cci74vILZ4tiOwQkWlAP6AncFREIlxbqOt4qIiMF5HmIlJVRNrijGo7BMwHd9/MS8CjItJNRGqIyJM4Q6dnuPJsw+kbmuEqq7nr2OLLbeRaXkj7QvFsbZw5cyaj7H47e/YsS5Ys8Qo6aedr0qQJQ4YMYf78+SQkJPDxxx+zdu3aTMsLxDIIwcHBdOjQgWeeeYYvvviCAQMGMHLkSHdfCEDhwt4rgoiIuxWTnJxMp06dqF27NvPmzWPTpk3MnOncNfYsI+11GRkwYAC7du2iX79+7NixgxYtWjBy5EivPJ71SCvLszWVkczO26hRI3bv3s2///1vUlNT6dOnDx06dPCrXHP58jfodAJOAx8Ae0TkORHJ7mqhA3FGrK3AabmkbcNdx88B9YAFOEOg38QZadZcVf9MK0RVJwNjgQnAN8AdwC2q+o3HuXq6jn3i2r4B7stmfS87tWvXZt++fezdu9ed9tVXX3l9iaSNdjtw4IA7bXMW/Vq1a9dmy5YtnDhxwp22YYP3M79r1qwhNDSU6OjMFx+sU6cO4PTFFC9enAoVKvD555975Vm3bp07X1b8qZu/6tSpw9mzZzl58qRf+ZOSkjh9+jSTJk2iefPmREVFsX//+VOQpKam8tVXX7n39+zZw/79+71W4KxUqRKxsbHMnTuX0aNHEx8f73e9a9euzYYNG7z+ndetW0dQUBBXX+18zIOCgjh37tx5rw0LC+Puu+/mlVdeYcmSJaxcuZIffvjB73Oby4+/z+n8B/iP66n+XjgtlidFZA3wBvCBqmb6SVPVTP/r6Zo89ObM8njkfRFnxuuMjh8F7s3o+OXmjz/+OC8whIeHU7VqVa+09u3bU6tWLXr37s2kSZP466+/GDJkCFde+ffbpGjRojRr1owXX3yRq6++mmPHjvHEE09kev6ePXsSFxdH//79eeaZZ9i/fz9jxngPyVywYAG33367V9rdd99Ny5YtadGiBREREezatYsnnniCcuXK0aJFCwAee+wxnnnmGWrWrEnjxo2ZPXs2n332Gf/973/9+tv4Uzdfv/76K926daN///7Ur1+fsLAwkpKSGDduHO3ataN48XRnjTpPzZo1SU1NZfLkydx5551s2LCByencsr3yyisZPHgwU6ZMoWjRogwZMoRrr72W9u3bAzBo0CBuueUWoqKi+OOPP1i2bJnfQRdg4MCBTJ48mYEDBzJo0CB++uknRowYwcMPP0xIiDN6qmrVqnz11Vfs3r2b0NBQSpUqxeTJkylfvjwNGjSgcOHCvPPOO+5ResZkSFVztAH/BE4CqcBvwAtAaE7Lu1i2xo0ba0a2bt2a4bGLVZ8+fRQ4b7vrrrtUVTUyMlJfeukld/7t27dr69atNSgoSGvUqKELFizQYsWK6axZs9x5tm7dqi1atNCiRYtq3bp1de3atQrovHnz3Hl89zds2KANGzbUoKAgrV+/vi5cuFABXbVqlaqqVqlSRT/55BOvusfHx2u7du20bNmyGhQUpJUqVdIePXrod999585z7tw5HT16tFaqVEkLFy6sdevW1fnz53uVc6F183Xy5El94oknNDo6WsPDw7Vo0aJao0YNHTJkiP7666/ufL5/W1XVNm3a6D//+U/3/pQpU7RChQoaHBysN954o86ZM0cB3bVrl6qqzpo1S4sVK6YfffSR1qhRQ4OCgrR169a6c+dOdxkPP/yw1qhRQ4sUKaKlS5fWHj166M8//6yqqrt27VJAN27cmOnfZM2aNdq0aVMNCgrSsmXL6uDBg/XkyZPu49u3b9dmzZpp0aJF3fWLj4/Xhg0bamhoqIaFhWnr1q31888/T/dvluai/gy1aeNsJtuAJPXzO1ZU/R8hLCLlgT5AX6AS8D5OS6cC8ARwRFXbX0AMzHfR0dGa0ZQh27Zt87qlcbkIDQ1l6tSp9O3bN0/K//rrr4mJieHw4cPn9YFc7hISEnj44Yc5fvx4flclV1zUn6G2bZ2fBWD6o0ATkU2qmvm9cRd/R6/dCfQHbgK+A/4fkKiqxzzybASyHJFmjK8zZ87w8ssvW8Ax5jLg78Ohs4B3cTr1N2WQ5wCQd3MnmAKradOmXkOCjTEFl79Bp7yqpmSWQZ2BAKMuvErmYlNQbu1civr27ZtntzWNyQ/+Bp3SGYzXV+Ck5sKDo8YYYwo+f4PObpwAky4R+QPnFty/VPXC5ky5yKlqQB48NKagyc6gJVNw+Rt0/gGMA17FWS0U4HogFhgJhANPAX/irDRaIF1xxRWcOXOGoKCg/K6KMZecv/76ywaLGL+DzkPAEFX90CNtpYhsBwapahvXGjijKMBBJzw8nIMHD1KxYkWbX8oYP6kqf/31F/v27ctwtnBz+fA36FzP3xNqevoOaOL6fT3OszsFVunSpW36dmNyoHDhwpQrV87v2RpMweVv0EnGuZX2mE/6/wF7XL+XwZmZoMAqVKiQ1wzMxhhjssffoDMM+EBEbgU2utKigauBu1z7TYC5uVs9Y4wxBYm/E34uEZGaOH07tVzJC4FXVXWPK8/0vKmiMcaYgiLLoCMihXFmGpimqk9mld8YY4zJSJZDsFT1DM5aOPZwijHGmAvi77jfT4Ab87IixhhjCj5/BxKsAMaKSH1gE3DC86DP8zvGGGNMuvwNOlNdPx9N55gCV+ROdYwxxhRk/o5es8fvjTHGXDALJsYYk02JiVC1KhQq5PxMTMzvGl06/Ao64hgoIv8TkRQRqe5KHyEi3fO2isYYc/FITITYWEhOBlXnZ2ysBR5/+dvSGYQzi3Q83kOn9wEP53aljDHmYhUXByk+S1qmpDjpJmv+Bp0Hgf9T1SmA53o5/wWuzfVaGWPMRWrPnuylG2/+Bp1InBmlfZ0BiuZedYwx5uKW0Zy/eTEXcEHsO/I36PwENEon/VZga+5VxxhjLm5jxkBIiHdaSIiTnpsC1XcU6MDmb9AZD0wVkV44fTrNReRZnDnZXsqryhljzMWmVy+Ij4fISBBxfsbHO+m5KRB9R/kxKEL8XbdcRP4PZzBBZVfSfuBZVX0jj+qWL6KjozUpKSm/q2GMCbS2bZ2fq1fnZy3cChVyAoEvEUhNzZ1zVK3qBBpfkZGwe7f/5YjIJlWN9iev38/pqOprqhoJlAUiVLVSQQs4xhhzsQhE31F+DIrI9sOhqnpEVQ/lRWWMMcY4AtF3FMhBEWn8fTi0lIi8IiI7ROR3EfnDc/OzjCdEZKPrNYdFZJGI1PXJkyAi6rNtyKA8EZGPXXnu9jlWUkTeFpFjru1tEQn3p57GGHMxCETfUaAGRXjyd8LPN4CGOA+H7seZ5DO72gLTcZa7FmA0sFxE6qjqbx75lgP3eeyfzqC8YUBGdzbfAaoAHV37rwNvA51zUG9jjMkXvXrl/gAF3/LBGZywZ4/TwhkzJm/P6W/QaQd0UNUvc3oiVb3Zc19E7gOOAS2BRR6HTqnqL5mVJSJNcGZJaAwc9DlWGyfYtFLV9a60B4DPROQaVd2e02swxpiCJq8Dmy9/+3QOAcdz+dxhrvMf9UlvJSKHXLfyXhORsp4HRSQMpyUTm0HfUnNXXb/wSPscZw2gFulVRERiRSRJRJIOHz6cw8sxxhiTFX+DThwwWkRCc/HcU4DNwHqPtGVAb5yW1TCgKbBSRIp45HkVWKaqH2dQbgRwWD3Ggrt+P+Q6dh5VjVfVaFWNLlOmTE6vxxhjTBb8vb32FFAVOCQiyTjT37ipav3snFREJgKtcG6BnfMo5z2PbFtEZBOQDHQCPnTdkrsO8Gs8uDHGmIuLv0Hn/dw6oYhMAu4BYlT1p8zyqup+EfkZqOlKagfUAY6LeE52zRwRWa+qrYBfgDIiImmtHXEyl3UdM8YYk0/8XTl0VG6cTESmAD1wAs73fuQvDVQEDriS4nCm5PG0BRgOLHDtrwdCcfp20vp1mgPF8O7nMcYYE2CZ9umIyE0icqXHfpjP8WAR6e/PiURkGtAP6AkcFZEI1xbqOh4qIuNFpLmIVBWRtjij2g4B8wFUdZ+qfue5uYrfm9ZqUtVtOH1DM1xlNQdmAItt5JrJbQVxFmBj8lJWAwk+Bkp57O9LWzXUpQTwmp/nGogzYm0FTsslbRvuOn4OqIfTYtkBvAlsB5qr6p9+niNNT+Ab4BPX9g3ez/6YfFRQvqhtBUljsi+r22uSxb7fVDXT16rqX8DNmeXxt1xVPQrcm92yTN5L+6JOmz037YsaAvusQG7IbBbgS+1ajAmUbM+9ZsyFKEhL/SYnjwNW+aSucqUbY9JjQccEVKBmtQ3ELbxy5ZoA3fk78KwCurvSjTHp8Sfo1BeRRiLSCOf22rUe+9flbfVMQROIWW0D1dcyYUIMV0TFQtH20FqgaHuuiIplwoSY3D2RMQWIP0HnEyDJtYXgdPSn7S/Lu6qZ/JDXLYRAzGobsFt49RO5otdkaJIKa4Emqc5+fRtJYExGshpIUC0gtTAXhUB08gdiVttA3cKLWxHH6Z9SnP9+tQaS4HS1FOJWxNGrno0kMCY9fi9Xfbm4nJerLllyHL//3gTwvD20ivDwjRw9+q/8qla25dYSvFmRvgLzgG44/z3bhXtfE+xzdcm5yJarvpTkyXLVpuBzAs75HeNO+qUjUAtThf8a/nfAwfWzmyvdGJMuCzrGLTIyBqp5d4xTLdZJv4QEYsVFgKljpxIS5R3dQqJCmDp2au6eyJgCxN8JP81l4NbHE3ll32T4zNUx3joVbpjMrRXrAJdWH0UgFqZK67eJWxHHnmN7qFKiCmPajbH+HGMyYS0d47b0VBz87N0xzs8pTnouGTduHKtWeT9QuWrVKsaNuzQfqOxVrxe7B+8m9dlUdg/efckGnIIyNREUrGspiCzo5IKC8iZP3pz8d8f4ja6f81zpuaRJkyZ0797dHXhWrVpF9+7dadIk9/qNClpgy2sFaQ65gnQtBZaqprvh9CKv9GfLqIxLcWvcuLFmx+zZqiEhqs5b3NlCQpz03DR7tmpkpKqI8zO3y1dVDb8tXOmDMtJj64OG3xaeq+d58vUntVCxQkprtFCxQvrk60/mavkrV67U0qVL68qVK9PdN94iI73fv2lbZGTunyuv38cXdC1t2jibyTYgSf38js0s6LzssU0HjuHM+vyWa/velTbN35NdClt2g04gPrABC2zfztaQMSFeQSdkTIjO/jb3TuQ+R2sUUFrn/jlU8z6wBVJef1GLpP8edpZBzD2BeB9f0LVY0MmxXAk6XplgEvD/cD3X45E+GZji78kuhS27QScQH9iA/k/029kaOSlSZaRo5KTIXA8GkZMindZUiBNwCHFaU5GTInPtHIEKbIEQiC/qQL2/AnEea+nkj7wIOr8CUemkRwG/+XuyS2G7GFs6gfqfaCC4A06fv2/fpe3nlkAEtkApSC3pQLyPL+haLOjkWHaCjr8DCQRngTVf6aVdVgLxIGIgJskMlEA8UBmIARGBEogpfQL1XFMg3seBuhaTc/4GnZnA6yIyQkTaurYROKuGzsq76l38AvEmb958HEWKeI/GKlJkFc2bX3qjsQLxQGVBmikgUP/h6NXLmSIoNdX5mRdf0oGaKSIQ12Jyzt+g8y/g38Aj/D1q7RHgBdexy1pev8ljY5sQFNSdcuVWIQLlyq0iKKg7sbGX1vQ04DzXEt85nsgSkQhCZIlI4jvH5+rzLQVppoBAfVEHgrVCDOBfn47nBhQHimf3dZfKlt0+nUBJG/b79NNP2/BfP+T1gIhACsRweaPWp3MByEafTranwVHVP3I78Jms7S+9nzONzvDcc89R4qYS7C+9P7+rdFHrVa/XJTs7gK9ATOlTUIwbN44mTZoQE/P3fIGrVq1i48aN/Otfl/1NmYuCX7fXRKSUiLwiIjtE5HcR+cNzy+tKXu4StyQyYMoAjq07Bq3h2LpjDJgygMQt9pi1MZ4CMeOFuTD+tnTeABoC8cB+wBYLCaBhrw7j1Lun/u4crwan3j3FsCLD6DXN/gtsTJqYmBhiX4ilfef2pDZOpdCmQoyYMsKr5WPyl79Bpx3QQVW/zMvKmPQd3HEw3dFYB3cczMdaGXPxSdySyOSDk0lt7MyUnto6lckHJ1NnS50Cc7v1Uufv6LVDwPG8rIjJWGSnyPMXDq/mSjfmEhGIiVjjVsSRssN7pvSUHc4S4ubi4G/QiQNGi0hoXlbmUhSID9KYdmMIKewzBLhwCGPaXYLjZs1FJ1Czcgeiv6UgPRhcUPkbdJ4CbgIOicg2EfnWc8vD+l30AvFBCsSzLebyFajOd8/+FmkjtO/cntgXYnO1v6UgPRhcUPnbp/N+ntbiEhaojsuCNATY+C8QQ4AD9R4ORH/L1LFTiV0US8qZFHdaSFQIUztfeg8GF1j+PtBzuWzZXk+nAM1obC4+gVgbKFDv4UBNxJrjB4Pt4dAcI7dnmc6NDXgC2Aj8ARwGFgF1ffIkgOuN//e2weN4KZz1fb4H/gL2Aq8AV/mUUxJ4G2e9n2Ou38P9qWe2Z5kuQDMam4tTXs9GEaj3cCBmGL8gFnRyLDtBx6/bayIShDOY4B9AFaCwT2vpCj+KaYuzGNxGnFmrRwPLRaSOqv7mkW85cJ/H/mmP3ysAFXHme9vq+n068C5On1Oad1z17Ojafx0n8HT2o57Z4tVx6XqGhnmQjHVcmtyR17NRBOo9HP5rOL93+936Wy5z/g4keA7oA0wAUoHHgGk46+wM9KcAVb1ZVWep6nequgUnsJQBWvpkPaWqv3hsv3mU8Z2q3qmqC1X1B1Vd46pLexEpDiAitXGCTayqrlfV9cADwG0ico2f1+s367g0eSkQs1EE6j1ckCZiNTnnb9DpDjyoqjOAc8ACVX0UeBbokMNzh7nOf9QnvZWIHHJNufOaiJTNopziwCkgreewOc4zRV945PkcOAG0SK8AEYkVkSQRSTp8+HC2LsI+SCYvec1G4RoCfOrdUwx7dViunSNQ72EbhWnA/9Fr5XBuZ4HzhZ72X6BlwIs5PPcUYDOw3iNtGfAhsAuoCjwPrBSRxqp6yrcAEQnHaYW9pqpnXckRwGHXfUYAVFVF5JDr2HlUNR5nih+io6OzNcVP2gcmbkUce47toUqJKoxpN8Y+SCZXBGI2ikC+h20UpvE36OzB6U/ZA/wA3AxswmlV/JXdk4rIRCQY4hkAABEtSURBVKAV0EpVz6Wlq+p7Htm2iMgmIBnohBOMPMsIxRmMsI98XtPHPkgmr0R2iiT5mE/fSjWIbJC7s1HYe9gEir+31+bjzL8GTgtllIjswhlt9np2Tigik3AGJNyoqj9llldV9wM/AzV9yggFlrp2b1PVkx6HfwHKiIh45BegrOuYMZcMm43CFDR+tXRU9QmP398XkZ9x+kd2qOpif08mIlOAHkCMqn7vR/7SOCPUDnikhQEf44yA66iqvnPCrQdCcVphaf06zYFiePfzGHPRs9u3pqARj66PvD2RyDScEWt38Hf/EMBxVT3uar2MBD7ACTJVcZbIrgzUVtU/XQHnU5zBA3cAf3qU85uqnnad62OgEhDrOhYP7FbVLIdMR0dHa1JSUk4v0xhzqWrb1vm5enV+1uKSJCKbVDXan7z+PqdTJK0jX0Qq4nyZhwALVfUzP+uVNrR6hU/6KJxgcw6oB/TGGahwAFgFdFfVtODSGGjm+n2HTzkxwGrX7z1xHiL9xLW/EHjYz3oaY4zJI5kGHddzLR8CtVwTe/YC/oPT0kgFhojI3ar6UVYnUlXJ4vhfOAMUMsuzGue2WlbnOgrcm1U+Y4wxgZXVQILxOC2O24HvcDrvlwElcKaamQGMyMsKGmOMKTiyur3WDGfF0M0ishZnHrPpqpoKICIvAxvyuI7GGGMKiKxaOlcB+wFc/Son8J5B4CjOzALGGGNMlvx5Tsd3eFtghrsZY4wpcPwZvTZbRNKmoAkGXhORtHnOiuRNtYwxxhREWQWdN332Z6eT561cqosxxpgCLtOgo6r9AlURY4wxBZ+/c68ZY4wxF8yCjjHGmICxoGOMMSZgLOgYY4wJGAs6xhhjAsaCjjHGmICxoGOMMSZgLOgYY4wJGAs6xhhjAsaCjjHG/P/27j7erunO4/jnm5SgQqdSpBTTF4NKNDRoiIdGI+2Eqal0VD00SrWjWoy8TNW82sx4mFaJpB5aUR2ho2g7QaqUeOh0SD1kECREK/EYkijRyAPJ/OaPta5u282999x77J1zfd+v137de/ZaZ+/fOvee/Ttr73X2sso46ZiZWWWcdMzMrDJOOmZmVhknHTMzq4yTjpmZVcZJx8zMKuOkY2ZmlXHSMTOzyjjpmJlZZZx0zMysMk46ZmZWmcqSjqTTJN0n6VVJiyRNkzSoVOdySVFafl+q00/SBZIWS3pN0g2StizV2Spv/7Vc7weS1q2inWZmtmZV9nT2Ay4G9gRGAKuA6ZLeX6o3HRhYWP62VD4ROAQ4DNgb2Aj4laS+APnnjUD/XH4YMAY4r+ktMjOzhrynqh1FxKjiY0lHAkuAvYBphaKVEfFCe9uQtDFwDHB0RNxa2M5TwCeB3wAHADsBW0fEM7nOqcCPJZ0eEa82tWFmZtZldV7T6Z/3/3Jp/XBJCyXNlXSppE0LZR8D1gFuaVuRE8scUg8KYBgwpy3hZL8B+uXnm5lZTepMOpOAB4EZhXU3A0cB+wOnALsDt0vql8s3B1YDi0vbejGXtdV5sVS+OD9vc9oh6ThJ90u6f9GiRd1rjZmZdaqy02tFkiYAw4HhEbG6bX1EXF2o9rCkmaRTZ6OB/3qn4omIycBkgKFDh8Y7tR8zs3e7yns6ks4nXdwfERFPdlQ3Ip4HngW2y6teAPoCA0pVN8tlbXU2K5UPyM9r91qRmZlVo9KkI2kSf0k4j3Wh/gBgC2BBXjUTeAMYWaizJbAjcHdeNQPYsTSMeiSwMj/fzMxqUtnpNUkXAUcCBwMvS2q7vrI0IpZK2hAYD/ySlGS2Af4dWAhMBYiIJZIuA86RtBB4CZgAzCINtYY0yOBR4ApJpwCbAN8HLvXINTOzelXZ0zmeNGLtNlJSaVvG5fLVwGDgemAuMAV4HBgWEX8ubOckUhK6BrgLWAoc1HZtKP8cDSzL5deQEtk4zMysVlV+T0edlC8HRnVUJ9dbCXw9L2uq8zRwYKMxmpnZO8v3XjMzs8o46ZiZWWWcdMzMrDK1fDnUzGytM2RI3RG8KzjpmJkBTJxYdwTvCj69ZmZmlXHSMTOzyjjpmJlZZZx0zMysMk46ZmZWGScdMzOrjJOOmZlVxknHzMwqowjPzlwkaRFpiuzuGAAsbmI4deotbekt7QC3ZW3UW9oBPWvL1hHxga5UdNJpIkn3R8TQuuNoht7Slt7SDnBb1ka9pR1QXVt8es3MzCrjpGNmZpVx0mmuyXUH0ES9pS29pR3gtqyNeks7oKK2+JqOmZlVxj0dMzOrjJOOmZlVxknHzMwq46TTBJKOlzRP0gpJMyXtXXdMjZJ0mqT7JL0qaZGkaZIG1R1XT+V2haQL646luyQNlDQl/11WSJotad+642qEpL6Szii8T+ZJOlPSWj97saR9JN0g6bn8vzS2VC5J4yU9L2m5pDsl7VRTuB3qqC2S1pH0PUmzJL0maYGkqyRt1cwYnHR6SNKhwCTgbGAX4G7gpmb/oSqwH3AxsCcwAlgFTJf0/jqD6glJHweOA2bVHUt3SXofcBcgYDSwI/B1YGGdcXXDPwNfA74B7ACcmB+fVmdQXbQh8Agp5uXtlJ8KnEL6u+xG+tvcKql/ZRF2XUdt2QDYFTgr//wM8CHg5mZ+OPDotR6SdA8wKyK+XFj3BPCLiGiFN1S7JG0ILAEOjohpdcfTKEkbA/8LHAt8B3gkIk6oN6rGSTob2Dci9qo7lp6Q9CvgpYj4YmHdFGCTiDiwvsgaI2kpcEJEXJ4fC3geuDAizsrr1iclnnERcUldsXam3JY11PkI8Ciwc0Q83Iz9uqfTA5LWBT4G3FIquoXUY2hl/Un/Hy/XHUg3TSYl/jvqDqSHDgbukXSNpIWSHpR0Qj7YtZL/AT4haQd482A2Avh1rVH13F8Dm1M4BkTEcuC/af1jAMBG+WfTjgNr/fnUtdwAoC/wYmn9i8Anqw+nqSYBDwIz6g6kUZK+DGwLHFF3LE3wYeB44Hzgu8AQ4IJc1krXqb5H+iAzW9Jq0rHnrIi4uN6wemzz/LO9Y8AWFcfSVPlD9XnAtIh4tlnbddKxt5E0ARgODI+I1XXH0whJ25Ourw2PiDfqjqcJ+gD3F07VPiBpO9L1kFZKOocCRwFfIJ2uGQJMkjQvIi6rNTJ7m3wN56fA+4C/a+a2fXqtZxYDq4HNSus3A16oPpyek3Q+cBgwIiKerDuebhhG6oE+KmmVpFXAvsDx+XG/esNr2AJgdmndHKDVBqp8Hzg3Iq6OiIcj4kpgAq0xkKAjbe/z3nQMeA/wM2BnYP+IeKmZ23fS6YGIeB2YCYwsFY0kjWJrKZIm8ZeE81jd8XTTdcBg0ifptuV+4Or8++v1hdYtdwHbl9b9Dd2f86kuG5A+oBWtpvWPQfNIyeXNY4Ck9YC9ac1jwDrANaSE84mIaHri9Om1npsAXCnpXtIB4qvAB4Ef1RpVgyRdBBxJunD9sqS2c9VLI2JpfZE1JiJeAV4prpP0GvCniHiknqh65Hzgbkmnkw4Gu5CGHX+r1qgaNw34pqR5pNNruwD/BFxRa1RdkEdybpsf9gG2kjSE9D/1tKSJwLckPQbMBf4FWApcVUvAHeioLaRReD8nDfs+CIjCcWBJHiDRcxHhpYcL6ULvfGAlqeezT90xdaMNsYZlfN2xNaFtd5KGtNYeSzfjHw08BKwgHdS+Qf66Q6sspEEEE0k9tOXAk6Rrb+vVHVsXYt9vDe+Ny3O5gPGkU6ErgN8Cg+qOu9G2ANt0cBwY26wY/D0dMzOrTKufTzUzsxbipGNmZpVx0jEzs8o46ZiZWWWcdMzMrDJOOmZmVhknHevV8kRVY+qO490gT17WSveDsxo46VhLk7SrpNWS7urm89eR9G1Jf8wzWj4k6VOlOl/Lsym+mpcZkkaX6pwh6bE84+LLkm6TtGepznxJ40rr/k3SMkkHdRDjsZIekLRU0pIcy5ndae/aTtLYPM+L9VJOOtbqjiXNeDpI0o7deP6ZwD+SvuX/EdLti6ZK2qVQ51nSzJe7AkOB24HrJO1cqPM46c7Pg0l36J5HmnGxfCNIACT1kXRx3u+oWMNEeZK+BPwgxzUE+DhwBuleZmatp+7bMnjx0t0FWJ90n7XBwGWkuxiX6wQwpoNtPA+cWFr3S+Cnnez7T8BXOijfKO97VGHdfGAcsC7pBqQLgI92sp/ruhDLeNIUxJ8H/gj8OT9vQKHObqSJxhYDr5ImVRvWzmt1AnAjsIx0y5ojSnW+ndevJN3o8opC2Z2kDwBn5/0sBM4F+hTq/BUwhTQp2HJgOrBTLtuPXngbJi9vXdzTsVY2Bngq0jS6VwJH5bvkNqIf6X5ZRctJvZW3kdRX0udJc823exfhPPnVcaSD+4Ol4veSbn65G2nOn4c6ie8FYHdJH+6k3jakOWv+HjiAdEPNswrl/Umv0d7A7jmuX0vapLSdfwVuIPWqJgNXSBqa23UIKWkeD2wHHAjcW3r+4cAq0qyZJwAn5bjaXA7sAXwmx7GM1CNcn/R6npTXDczLuZ2021pN3VnPi5fuLqRP1uPy7yL1JMaU6nTW07mKND/N9qTTzSNJB72VpXqDSXcOXkXqXY1uZ1sH5jr/BzwH7F4qn0/qIbwCfLCLbRxImr01gCdIE2sdBaxTqDOelDg3Lqw7HfhDB9sVqad1RGFdAJeW6k0n97RId4V+vLjvdv4eM0rrbgV+nH/fLu9jn0L5xsAS4Nj8eCzpzua1/395eWcW93SsJUnaltQbuQog0hHrP4FjGtzUiaQD6WzSXDsXAv9BShxFj5M+/e8B/BCYImlQqc4duc6ewM3AtZIGlurcQjq99h1J6iy4iFgQEcNISW8iKVlcAtwrqXhd56mIWFJ4/DywadsDSZtKukTSXElLSKfgNuXtk8GVpyefQbrWBem29+sB8yRdJulz7UyKN6v0uBjHjqTX9c195JgfLuzDejknHWtVxwJ9gacLM4R+EzhA0oe6upGIWBQRB5NOe20N7EDqrTxZqvd6RPwhImZGmjr6QeDkUp3Xcp3fR8QxwBs5zqLfkqYqOBy4tCuJJ2/7kYi4KCIOJ/XGhgD/UKhSnpo7eOv7ewrplN7JpKQ4hDRAYt2u7D/H8AypR/gV0qnD84CZkt7bQBxr3HxX47DW5qRjLSdPp/tF0lTHxRlCP0r6pH10o9uMiBUR8RxpYsNDgOs7eUof0vWghutExB3Ap0nXOn4iqdH3Ydv01Rs28JzhwAURcWNEPErq6ZR7YZBGx5Ufz2l7kF+nGyPiZFIS2wnYq4sxzCG9JsPaVkjaiNSLa2vT66QPE9ZLeeZQa0WjgQGk6w9vmb9d0tXAVyWdkU+5dUjSHsAWpJ7LFqTrI32Acwp1vksa0fUM6YL8F0gjrUbn8o2AU0kDBBYAHyANn94SuLa9/UbE7ySNAm4C+ko6OiLK0zkj6YekU1S3k3omA0kzUy4jnarrqrnAEZLuIfXqzqH9qbs/K+k+0vWZMcD+pFOKSBpLOmbcQ+oNHkrq2TzRlQAi4glJ1wOXSDqOdG3rLFKvqW2WzfnAepJGAg8AyyJiWQPttLWcezrWio4B7ignnOznpJFcI9spa896pO/qzAamkgYADI807XWbzUkX8B8HbiN9wv90RNyUy1eRPvFPJR2ApwGbkC6Yl69xvCki7s5xHkSa8ry9T/i3kg7615ISx9S8fmREzO1iGwG+ROoZzSQN1/4J6QBfNp7U05tF+v7S0RFxXy57hfTa/440RPsQ4LMRMa+BOI4mjXi7If/cAPhU5KmQ82vyI+BnwCJSMrdexDOHmhmQbhkEfC4iflF3LNZ7uadjZmaVcdIxM7PK+PSamZlVxj0dMzOrjJOOmZlVxknHzMwq46RjZmaVcdIxM7PK/D9Odktkxtck2wAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(snapshots), model2[:,0], 'bo')\n", - "plt.plot(range(snapshots), model2[:,1], 'go')\n", - "plt.plot(range(snapshots), model2[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 2')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(snapshots), model3[:,0], 'bo')\n", - "plt.plot(range(snapshots), model3[:,1], 'go')\n", - "plt.plot(range(snapshots), model3[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 3')\n", - "\n", - "plt.savefig(\"./mlmm_933K_model3_inference.png\", format='png')\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 13)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(13)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "54.6875" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "\n", - "\n", - "14 / 256 * 1000\n" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "run1 = [[2595.595905758535,\n", - "2594.2049589268686,\n", - "2595.3470902759054],\n", - "\n", - "[2598.052971247566,\n", - "2596.9779771297976,\n", - "2598.1203692340027],\n", - "\n", - "[2595.9637761890644,\n", - "2594.734865188264,\n", - "2595.876315137265],\n", - "\n", - "[2597.553173168981,\n", - "2595.437704293007,\n", - "2596.580145546497],\n", - "\n", - "[2596.9032110335843,\n", - "2594.812768615752,\n", - "2595.9576941828914],\n", - "\n", - "[2597.8899978230506,\n", - "2596.7844567694615,\n", - "2597.926529438901],\n", - "\n", - "[2597.2149758691526,\n", - "2596.1136735263253,\n", - "2597.2555939402955],\n", - "\n", - "[2597.102376603127,\n", - "2596.2552500670186,\n", - "2597.3974589366044],\n", - "\n", - "[2596.644038473075,\n", - "2595.6202455973435,\n", - "2596.7634174928216],\n", - "\n", - "[2596.724604651852,\n", - "2595.6639045552283,\n", - "2596.80694152332]]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "run2 = [[2595.1155760249526,\n", - "2594.2049589268686,\n", - "2595.3470902759054],\n", - "\n", - "[2596.8842014114193,\n", - "2596.9779771297976,\n", - "2598.1203692340027],\n", - "\n", - "[2595.2954535258427,\n", - "2594.734865188264,\n", - "2595.876315137265],\n", - "\n", - "[2596.6438913614766,\n", - "2595.437704293007,\n", - "2596.580145546497],\n", - "\n", - "[2596.0234692157082,\n", - "2594.812768615752,\n", - "2595.9576941828914],\n", - "\n", - "[2596.8655709592113,\n", - "2596.7844567694615,\n", - "2597.926529438901],\n", - "\n", - "[2596.023085934723,\n", - "2596.1136735263253,\n", - "2597.2555939402955],\n", - "\n", - "[2596.2236963398723,\n", - "2596.2552500670186,\n", - "2597.3974589366044],\n", - "\n", - "[2595.7497067817535,\n", - "2595.6202455973435,\n", - "2596.7634174928216],\n", - "\n", - "[2595.85595531512,\n", - "2595.6639045552283,\n", - "2596.80694152332]]" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "\n", - "snapshots = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Summed Squared Error\n", - "18.77167354418078\n", - "4.142384809659819\n", - "\n", - "Model MEAN meV/Atom\n", - "5.093447714422794\n", - "1.7604047950262824\n", - "\n", - "Model Max meV/Atom\n", - "8.263550296774724\n", - "4.7292992185798965\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "model1_rmse = np.sum(((model1[:10,0] - model1[:10,1]) ** 2))\n", - "model2_rmse = np.sum(((model2[:10,0] - model2[:10,1]) ** 2))\n", - "\n", - "\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(model1_rmse)\n", - "print(model2_rmse)\n", - "\n", - "\n", - "\n", - "model1_mean = np.mean((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_mean = np.mean((np.abs(model2[:10,0] - model2[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(model1_mean * mev_atoms)\n", - "print(model2_mean * mev_atoms)\n", - "\n", - "\n", - "\n", - "\n", - "model1_max = np.max((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_max = np.max((np.abs(model2[:10,0] - model2[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel Max meV/Atom\")\n", - "print(model1_max * mev_atoms)\n", - "print(model2_max * mev_atoms)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), model1[:,0] * mev_atoms, 'bo')\n", - "plt.plot(range(snapshots), model1[:,1] * mev_atoms, 'go')\n", - "plt.plot(range(snapshots), model1[:,2] * mev_atoms, 'kx')\n", - "#plt.plot([9.5,9.5], [np.min(model1)*.99, np.max(model1)*1.01], 'r-')\n", - "plt.xlabel('Al 298K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "#plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues'])\n", - "#plt.ylim([2590, 2600])\n", - "plt.ylim([10125, 10155])\n", - "plt.title('Model 1')\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(range(snapshots), model2[:,2] * mev_atoms, 'c*')\n", - "plt.plot(range(snapshots), model2[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), model2[:,1] * mev_atoms, 'ko', mfc='none')\n", - "#plt.errorbar(range(snapshots), model2[:,1] * mev_atoms, yerr=5, fmt='ko', ecolor='r', capsize=3)\n", - "#plt.plot([9.5,9.5], [np.min(model1)*.99, np.max(model1)*1.01], 'r-')\n", - "plt.xlabel('Al 298K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "#plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.legend(['DFT Eigenvalues', 'ML Predictions', 'ML Targets'])\n", - "plt.ylim([10120, 10170])\n", - "#plt.title('Model 2')\n", - "plt.tight_layout()\n", - "plt.savefig(\"./mlmm_298K_model2_inference.eps\", format='eps')\n", - "\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mldos1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./933K/Al_ldos_200x200x200grid_250elvls_snapshot0.npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mldos2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mldos1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.001\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 446\u001b[0m return format.read_array(fid, allow_pickle=allow_pickle,\n\u001b[0;32m--> 447\u001b[0;31m pickle_kwargs=pickle_kwargs)\n\u001b[0m\u001b[1;32m 448\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[0;31m# Try a pickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0;31m# We can use the fast fromfile() function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m \u001b[0;31m# This is not a real file. We have to read it the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "#ldos1 = np.load(\"./933K/Al_ldos_200x200x200grid_250elvls_snapshot0.npy\")\n", - "#ldos2 = ldos1 * 1.001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#ldos1 = np.reshape(ldos1, [200**3, 250])\n", - "#ldos2 = np.reshape(ldos2, [200**3, 250])\n", - "\n", - "#dens1 = np.sum(ldos1, axis=1)\n", - "#dens2 = np.sum(ldos2, axis=1)\n", - "\n", - "#dens1.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#xs = dens2[:vals]/dens1[:vals] #* (1 - dens2[:100] / dens1[:100])\n", - "#ys = dens2[:vals]/dens1[:vals] #* (1 + dens2[:100] / dens1[:100])\n", - "\n", - "#vals = 10000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(range(vals), (.5+np.random.rand(vals))*dens2[:vals], 'r.')\n", - "#plt.plot(dens1[:vals], dens2[:vals], 'r.')\n", - "#plt.plot(dens1[:vals], dens1[:vals], 'k-')\n", - "#plt.xlim([0,10])\n", - "#plt.ylim([0,10])\n", - "\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "liquid = [[2521.8308895694972,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2521.8850606562005,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2526.2936656319825,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.833866461784,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2527.260749618935,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2529.5233302856964,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.444354073414,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2523.2371151566604,\n", - 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- "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "solid = [[2547.575728787133,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2548.1962671881156,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2552.4417450901065,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2555.94411593651,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2554.147350170825,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2558.2741317887476,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2546.4076867624854,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2548.22534731669,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2546.025313525691,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2549.4435230800555,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2566.043629326736,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2562.1968937857278,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2563.341062741356,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2564.251867895487,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.1119598426894,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2567.5599195526966,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2564.7642859454118,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2562.5438655120643,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2565.280995619667,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2563.578013682739,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "hybrid = [[2522.960411449796,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2520.3572945491037,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2525.2498148260256,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.6146817945337,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2526.0101497844194,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2527.2948307215715,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.0130879673156,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2522.045909249362,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2521.0722131535085,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2522.0769612460326,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2563.900224948418,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2560.992381267455,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2560.9967273595557,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2562.2416591636847,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2560.912119353269,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2564.7785899256237,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2562.2408245441275,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2559.405012532466,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2562.239119309403,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2561.1779808982187,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "liquid = np.array(liquid)\n", - "solid = np.array(solid)\n", - "hybrid = np.array(hybrid)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10493.59389833247\n", - "Solid: 6483.851548699937\n", - "Hybrid: 193.75928559519446\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 66.37409103449308\n", - "Solid: 52.92982433674975\n", - "Hybrid: 9.41850013657497\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "liquid_rmse = np.sum(((liquid[:20,0] - liquid[:20,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[:20,0] - solid[:20,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[:20,0] - hybrid[:20,1]) ** 2))\n", - "\n", - "print(\"All Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[:20,0] - liquid[:20,1])))\n", - "solid_mean = np.mean((np.abs(solid[:20,0] - solid[:20,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[:20,0] - hybrid[:20,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[:20,0] - liquid[:20,1])))\n", - "solid_max = np.max((np.abs(solid[:20,0] - solid[:20,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[:20,0] - hybrid[:20,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Liquid Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 54.049557249920795\n", - "Solid: 6445.134836632674\n", - "Hybrid: 103.57273255435054\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 6.918106837911786\n", - "Solid: 98.93174387461166\n", - "Hybrid: 9.77712253056282\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 17.74604557573589\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "liquid_rmse = np.sum(((liquid[:10,0] - liquid[:10,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[:10,0] - solid[:10,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[:10,0] - hybrid[:10,1]) ** 2))\n", - "\n", - "print(\"Liquid Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[:10,0] - liquid[:10,1])))\n", - "solid_mean = np.mean((np.abs(solid[:10,0] - solid[:10,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[:10,0] - hybrid[:10,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[:10,0] - liquid[:10,1])))\n", - "solid_max = np.max((np.abs(solid[:10,0] - solid[:10,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[:10,0] - hybrid[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solid Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10439.54434108255\n", - "Solid: 38.716712067261305\n", - "Hybrid: 90.18655304084393\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 125.83007523107437\n", - "Solid: 6.927904798887852\n", - "Hybrid: 9.059877742587119\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 12.614455251767964\n", - "Hybrid: 19.350943710090718\n" - ] - } - ], - "source": [ - "liquid_rmse = np.sum(((liquid[10:20,0] - liquid[10:20,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[10:20,0] - solid[10:20,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[10:20,0] - hybrid[10:20,1]) ** 2))\n", - "\n", - "print(\"Solid Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[10:20,0] - liquid[10:20,1])))\n", - "solid_mean = np.mean((np.abs(solid[10:20,0] - solid[10:20,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[10:20,0] - hybrid[10:20,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[10:20,0] - liquid[10:20,1])))\n", - "solid_max = np.max((np.abs(solid[10:20,0] - solid[10:20,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[10:20,0] - hybrid[10:20,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 9 10 11 12 13 14 15 16 17 18 19]\n", - "[ 0 1 2 3 4 5 6 7 8 9 19]\n", - "[ 0 1 2 3 4 5 15 16 17 18 19]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10439.549459512444\n", - "Solid: 6451.102575526406\n", - "Hybrid: 127.09295260525487\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 114.41638344977032\n", - "Solid: 90.80545451532838\n", - "Hybrid: 10.409332534062056\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "#lt = np.arange(9,10)\n", - "#st = np.arange(0,10)\n", - "#ht = np.arange(0,6)\n", - "\n", - "#Solid\n", - "#lt = np.arange(10,20)\n", - "#st = np.arange(19,20)\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "lt = np.arange(9,20)\n", - "st = np.arange(0,10)\n", - "st = np.append(st, [19])\n", - "ht = np.arange(0,6)\n", - "ht = np.append(ht, [15,16,17,18,19])\n", - "\n", - "\n", - "print(lt)\n", - "print(st)\n", - "print(ht)\n", - "\n", - "liquid_rmse = np.sum(((liquid[lt,0] - liquid[lt,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[st,0] - solid[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[ht,0] - hybrid[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[lt,0] - liquid[lt,1])))\n", - "solid_mean = np.mean((np.abs(solid[st,0] - solid[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[lt,0] - liquid[lt,1])))\n", - "solid_max = np.max((np.abs(solid[st,0] - solid[st,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", - " 13, 14, 15, 16, 17, 18, 19, 111, 131])" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = np.arange(0,20)\n", - "\n", - "np.append(t, [111, 131])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), liquid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), liquid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), liquid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Liquid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_liquid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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WdiUNo6SJjY0lJiaG6dOnk5KSwvTp04mJiSmSINS/f3/mzp3rfD137lwefvjhIukmO3v2LE8//TTx8fGsXbsWHx8funfvzsWLF93yjRw5kmHDhpGQkMCtt97K+vXrGTRoEMOGDWPnzp1069Yty06uS5cupV+/fgwbNow9e/Ywe/Zs3nvvPcaOHQvA8uXLqVKlCi+99BLJyckkJiY677V//36++OILfvzxR2bPnk21apnHGBklkqrm68BaTsc/v9ddKUfz5s3VuLzt2bOnwGVERERoXFycW1pcXJxGREQUuOyc9OnTR6Ojo/X3339XPz8/3bdvnyYnJ6uPj48mJiY6z2cYPXr0JdfH399f33777Tzz/f777wrotm3bVFU1ISFBAX399dfd8t15553avXt3t7SHHnpIfX19na9btGihkyZNcssTGxurFSpUcL6uVq2aTp8+3S1Pt27ddODAgbbel+EZuf0MYQ3msvX71G4XHI6H/4eBv4C/RSRRRAYVRlA0jJIuISGBdu3auaW1a9eOhISEQr93hQoVuOuuu5g7dy7vvPMOHTt2LLL5afv27eO+++6jbt26lCtXjlq1agFw+PBht3xRUe7bwfz444+0bNnSLe2GG25wfq+q7Nixg+eff56AgADn0a9fP/744w/++OOPHOv0+OOPM2/ePJo1a8YzzzzDpk2bcsxrlCy2JqKKyHPAs1ijxDL+dW8EJohIOVWdUEj1M4wSKTw8nE2bNtGpUydn2qZNm4pso7t+/frRp08fAgICsnRlFRZV5eabb6Zhw4b873//o3r16gBERESQmprqltff3z/fZaenp/Piiy/SvXv3LOfLlct5umH37t1JTExk+fLlrF27ln/961/07t3bPAe6DORnKZ4Bqurawb1WRPYDLwEmABlXlZiYGPr378+cOXNo164dmzZton///owfP75I7t+lSxd8fHw4ceIEd955Z5HcMykpiYMHD/LBBx/QunVrAL7++uuMrvlcNWzYkG3btrmlbd261fm9l5cXTZs2Zd++fYSFheVYjo+PD2lpaVnSq1atSt++fenbty9du3blkUceYcaMGXh52e7kMYqB3QBUFdiWTfpWsq4oYBhXvJ49ewIwZMgQ5/bE48ePd6YXNhHh+++/R1Xx9fXNMV9KSgo7d+50Sytbtiz169fP9z2rVq1K+fLlmTVrFlWqVOHw4cPO+Ud5GTp0KF27dmXq1KlER0cTFxfHsmXL3K4dPXo099xzDzVq1OCee+7By8uLH374gZ07d/LSSy8BUKdOHTZs2MC9996Ln58flSpV4rnnnqNVq1Y0atSI8+fP8+mnn9KgQQMTfC4Ddv+F9mFtiZDZA1jzcQzjqtOzZ0927dpFWloau3btKrLgkyEwMDDXrimAn3/+mWbNmrkdDzyQ3Y9y3ry9vfnwww/ZunUrkZGRPPHEE0ycONHWL/qOHTvy+uuvM3nyZK677jpWrFjB8OHD3YZh33HHHSxZsoQVK1YQFRVFq1atmDx5MiEhIc4848ePZ9++fdStW5caNWo46zVixAiaNGlC+/btSUtL4+OPP76k92gULbHTfBaRu4GFwHqsyZ0AbYEOQA9V/bSwKljSREVFaXx8fHFXwyiAjBaLUbwee+wx4uPjs3TNGSVfbj9DIrJdVaOyPZmJrS44Vf3YsWHbMP7Z+iABaKmqO+yUYRjG1W3ixIncfPPN+Pv7s2LFCubOncvUqVPzvtC4Ytl9BoSqbgceLMS6GIZxBdu8eTNTpkzh1KlT1K1bl1deeYWBAwcWd7WMYmR3GHYaUF1Vj2VKrwQcU9VShVE5wzCuHJ9+etX01Bs22R2EkNMwF18gNYdzhmEYhpGjXFtALitdKzBQRE67nC6FNRn1x0Kqm2EYhnEFy6sLbojjqwCPAK4zwFKx9uExnbiGYRhGvuUagFQ1FEBE1gF3q2rOCzIZhmEYRj7Y3Y6hU3bBR0RCReRFz1fLMAzDuNLle60KEfERkZ4isgb4Cejl+WoZhmEYV7r8bMfQWEReA34D3gd+BlpndNMZhmHUqVOHyZMnF3c1Lknfvn257bbb8s5oeEyuAUhEAkXkURHZBnwNlAPuA9KBaaq6NbfrDcPwnL59+yIiiAje3t5UrVqVTp06MWPGDOe21Rk6duzozOt67Ny5M9t012PMmDF53t/1aNWqlTPPtm3bGDTIbBNm2JPXKLgjwGZgGvCxqp4FimTrX8MwsuratSvvvfceaWlpHD9+nLi4OEaPHs17773H2rVr3fbhefjhh52rSGeoUqUKycnJztdvvPEGc+fOdVuPLSAgIM/7u/Lx8XEr3zDsyqsL7jxwjePwyyOvYVx1ks+fp8OOHfx2/nyR3M/X15egoCBq1KhB06ZNefLJJ1m/fj3ffvstkyZNcstbtmxZgoKC3I5SpUq5vQ4MDMySllsAyri/61GxYkXn+cxdcPv27aNDhw74+fnRoEEDli9fTkBAAPPmzXPmOXLkCPfffz8VKlSgQoUKREdHs3//fuf5MWPGEBkZyYcffsi1115LYGAgd955JydOnABg1apV+Pj4cPLkSbe6PvfcczRp0gSAkydP0rNnT2rWrEmZMmWIiIjg7bffzvWz7tixI4MHD3ZLy9xNp6pMmjSJa6+9ljJlytC4cWPef/99t2vGjRtHSEiI87Pr3bt3rve9muQVgIKBKcDtwBERWSoiPbAmphrGVe+FQ4fY9NdfjDt0qNjqEBkZyc0338zixYuLrQ7ZSU9P56677qJ06dJs2bKFefPmMXbsWM67BOuzZ8/SqVMn/Pz82LBhA5s3b6Z69ep07dqVs2fPOvMdOnSIBQsW8Mknn7Bq1Sp27NhBTEwMYG3OV7lyZRYtWuTMr6rMnz+fBx+0lq9MSUnh+uuv5/PPP2f37t0MHTqURx99lLVr1xboPY4aNYo5c+YwY8YM9uzZw7PPPsujjz7KsmXLAFi8eDGTJ09m5syZ7N+/n88//zzL1uRXs7zmAV0AFgGLRCQE6Au87LhurIjMA1Y58hnGVaPMxo2kpKc7X7+RnMwbycn4eXlxrn37Iq9Po0aNWLNmjVva7Nmz3VoaDz74IG+++WaB7rNixYosLaTHH3+ciRMnZsm7evVq9u7dy6pVq5x797z66qu0bdvWmefDDz9EVXn77bedXfuzZs2iatWqfP755/z73/8G4OLFi8ybN49rrrkGgAEDBjhbMKVKleL+++/ngw8+cC5u+tVXX/HLL7849z6qUaMGTz/9tPO+AwYMIC4ujtjYWLp06XJJn8WZM2d45ZVXWLVqFTfeeCMAoaGhbN26lRkzZhAdHU1iYiLVq1fnpptuwtvbm9q1axMVZWungqtCflbDTsQKOuOAm4D+wGIgBShfONUzjJLpwA03MPznn/n0xAnOpqdT1suLuypXZvK11xZLfVQ1y7PZ++67j9GjRztf57V5nR3t27dn9uzZbmnly2f/4//jjz8SHBzsDD4ALVq0cNvAbvv27Rw8eJDAwEC3a8+ePcvPP//sfB0SEuIMPgDBwcEcO/bP2sgPPvggU6dOJTExkZCQED744AM6dOhAzZo1AUhLS2PChAksWLCAI0eOcP78eVJTU+nYsWP+PwSHPXv2kJKSws033+z22V+4cIE6deoA0KNHD6ZNm0ZoaCj/+te/uPnmm7njjjty3cX2apLXWnAVMk9AVWsHu5XAShGpQj62aBCR9sBwoDlW997DqjrP5bwAo4EBQAXgG+BxVd3tOF8H+C/QCagOJAMLgHGqes6lnNrADKAzcA6YDwxX1VSXPB2AV4AIIAmYpKoF+/PQuGpU9/WlXKlSpKSn4+flRUp6OuVKlSKomH6x7Nmzh7p167qlXXPNNYSFhXn0PmXLlvVomenp6TRt2pQPP/wwyznXZ0ve3t5u50SEdJcW6PXXX0/Dhg2ZP38+w4cPZ9GiRW7PxCZPnsyUKVOYNm0ajRs3JiAggOeee84tiGXm5eVF5g07XUcbZtz/s88+o3bt2m75Mupbq1Yt9u7dy9q1a1mzZg1PPfUUY8eO5ZtvvnEbMHK1yqsF9JuIfAUsAZao6iHXk6p6HHg1H/cLAHYB7zqOzJ4BnsLq6tsLPA+sFpEGqnoKaIi1COpjwH4gHJgNVMIKWohIKWAZcBJrsdRKwDtY69kNceQJBZYDc7ECaDtgpogcV9WS1ZFulFhHL1xgYHAwA4KDmZ2URHJq8SwMv2vXLlasWMGoUaOK5f45adiwIUlJSSQlJREcHAxAfHx8lsARGxtL5cqVc2xJ2fXggw/ywQcfEBkZyZkzZ7j33nud5zZt2sTtt9/OQw89BFgtxn379uV6z8wjBgG+++47Z+umUaNG+Pr6kpiYSOfOnXMsx8/Pj+joaKKjoxk5ciRBQUF89dVX3HTTTQV4t1eGvAJQCHAH0B2YICJ7+ScYfZvfm6nqcqxf/DieHzk5Wj9PABMygoCI9AGOAQ8As1R1BbDC5bIDIjIeeAFHAMLqHowAQlT1F0c5zwD/E5EYVf0bawHVJFXNWGw1wbHj63CsbkXDyNPHkZHO72fUr18k9zx//jy//fYb6enpHD9+nLVr1/LSSy/RvHlzhg8fXmT3d1WqVKlsh19369aNBg0a0KdPHyZPnsy5c+d48sknKV26tLPLqlevXkyePJnu3bszbtw4ateuzS+//MKSJUsYOHAg9erVs123Xr16MWrUKP773/9y++23u3U51q9fnwULFrBp0yYqV67M9OnTOXjwIM2aNcuxvM6dO/PEE0+wdOlSGjRowKxZs/jll1+cASgwMJDhw4czfPhwVJX27dtz+vRptmzZgpeXFwMGDGDevHlcvHiRG264gYCAABYsWIC3t3e+3teVLNdRcKr6m6rOVtVooArwIlAXWCMiiSIyXUS6OlodBRUKBAGrXO5/DtgItMnlunKAazdhayAhI/g4rMTau6i5S55VuFsJRImIN4ZRQq1Zs4bq1atTu3ZtunTpwtKlSxkzZgwbN24ski6djPu7Hjn9Evfy8uKTTz7h/PnztGzZkj59+hATE4OI4OdnzeooW7YsGzdupG7duvTo0YOGDRvSp08f/vjjDypUqJCvuoWEhNCuXTu+++475+i3DKNGjaJly5bccssttG/fHn9/f3r1yn0VsX79+jmPtm3bEhgYyF133eWW54UXXmDMmDFMnjyZiIgIunXrxuLFiwkNtRaIKV++PHPmzOHGG28kMjKSxYsX8/HHHzvPX/VUNd8HVjdYF6wJqoeA34Fe+SzjNNDX5XUbrOHdtTPlmwuszKGMEOAE8KRL2mwgLlM+AS4CPR2v9wHPZ8rT3nH/6tncZwAQD8TXrl1bjcvbnj17irsKV62dO3cqoPHx8cVdFaMAcvsZAuLVZhzIaxBCmKr+lE3QSgPWOo6hItIUKNKWg4hUw+qOW03+nkPlm6rOxgpsREVFmTlQhmHTJ598gr+/P/Xq1ePQoUM8+eSTXHfddVx//fXFXTWjBMhrIuo+EVkvIr1EJMeVEFR1p6puy+m8TRkdy9UypVdzOQeAiAQB67AGNDzkiLqu5WQuozJWq+23XPJUw2olnbiUyhuGkdWpU6cYPHgwjRo1olevXoSHh7Ny5UqznJcB5B2AooHjWN1gSSLyuojk/NSuYA5iBYZuGQmOoHcj1kKoGWnVgfVAAlaX2sVM5WwGwkWkpktaN6xlhba75OmW6bpuWE1HM6nWMDykd+/e7Nu3j3PnzpGUlMT8+fOpVi3z337G1SqvQQhfqGoPoAbWAIQOQLyIfCsij4nINbldn5mIBIhIU0eXnRdQ2/G6tqMVMxUYISJ3i0gkMA/rWdF8x/XBwAasQPUEUFlEghxHxkCIVcBu4F0RaSYiXbFWb3hLrRFwAG8CNURkqoiEi8gjWEO/L8915A3DMC5DdndEPaGqr6hqY6zBAvHABKxWUXbzeXISBexwHGWAsY7vxznOT8J6njPDcY/qwE1qzQECa4h1PaxAeBhrImrGUctR1zSslttZ4CusiaqLsYZYZ7yfg8CtWAMPdgIxwH/UzAEyDMMoMuL++MTmRVYH7r+BmUB5VfXEMOzLQlRUlMbHxxd3NYwCSEhIIDw8vLirYRiXrdx+hkRku6raWvBnidIgAAAgAElEQVTO9lpwjoLDgH5Ab6yH9quA/+WnDMMwDMMAGwFIRMpgtXb6YS1ZcwjrGcrbqnqkUGtnGIZhXLHymgf0Flbw8QE+xXoeU7ANNAzDMAyDvAchtMRafTpYVXua4GMYJVfm3Ug9RUT46KOPcjx/4sQJRIT169d7/N551aUk1a2wHTp0CBHhSnoGndcw7OtU9TV12ZJBRKqJiK3Rc4ZheE7m7aAz27ZtG4MGDfL4fZOTk7n99tsv6bqMrbLT0tKYOHEi4eHhlC1blgoVKhAVFcVrr71WLHVzdfz4cQYNGkSdOnXw9fWlWrVqdOnShdWrVxeo3JIqu63Gi4utQQiOBTrHY22DUAaoj7US9UQgUVVnFl4VDcOwI7sVqT0hKCjokq5bunQpbdq0oVKlSjz//PPMnDmT119/nZYtW3LmzBl27NhBYmJisdTN1T333MPZs2eZM2cOYWFhHDt2jA0bNnDy5MkCl23kzm5LZjRwO9beOedd0rdiTeA0DKOYZe6C++mnn+jYsSN+fn40aNCAzz//nICAAOc23Tl16eTVzbVt2zaaN2+On58fzZo145tvvsm2PkuWLKF79+6AFYwGDhzI/fffT926dWncuDG9e/fmv//9rzN/eno6L7zwArVq1cLX15fGjRuzZMmSXN/zpdYtw59//smXX37JhAkT6NKlCyEhIbRo0YLhw4dz//33O/PVqVOHF198kUcffZRy5cpRs2ZNXn75ZbeyXnnlFZo0aYK/vz81atTgkUce4c8//3SenzdvHgEBAXz22WfUr18fPz8/OnXqxIEDB5x5fvnlF7p3707FihUpW7YsDRs2zLJZX2JiIt26daNs2bI0atQoS0tt48aN3HDDDfj5+VGtWjWGDRtGqmOvqr59+7JhwwZmzJiBiCAiHDp0iAsXLvCf//yH4OBgfH19qVWrFiNHjsz1s/MEuwGoJzBQVZcA6S7pu7BaQ4ZhlCDp6encddddpKens3nzZubOncuYMWM4f/583hfn4vTp00RHR1O3bl3i4+OZMGFCtvsQnTp1iri4OGcACgoKYv369Rw9ejTHsqdNm8bLL7/MxIkT+eGHH7jrrru4++672blzp0fr5iogIICAgACWLl1KSkpKrnlfffVVGjduzLfffsuIESN45pln2Lx5s/O8l5cXU6dOZffu3cyfP5+tW7cyZMgQtzLOnz/P2LFjefvtt9m8eTNpaWncfffdzp1XBw0axNmzZ1m3bh27d+9m6tSpWTbNi4mJ4T//+Q/fffcdLVq04P777+f06dMAHDlyhFtuuYVmzZqxY8cO5syZQ2xsLM8++6zzM27dujUPP/wwycnJJCcnU6tWLV577TU++eQTPvzwQ/bv38+CBQto0KCBrc+9QOwsmY21rXUdx/engLqO7yOA03aX3r4SjubNm6txect2KfmhQ1U7dCjaY+jQfNW7T58+Gh0dneP5kJAQffnll1VVdeXKlerl5aWJiYnO819++aUC+vbbb6uq6sGDBxXQbdu2uZUD6KJFi7J9PWvWLL3mmmv01KlTzvPvvfeeArpu3Tpn2sKFC7Vx48bO17t379bw8HAVEW3UqJH2799fFy9erOnp6c48wcHBOnbsWLe6dOjQQXv16uXRumX20UcfaYUKFdTX11dbtWqlTz31lG7ZssUtT0hIiN5///1uaWFhYfrCCy/kWO4XX3yhPj4+mpaWpqqqb7/9tgK6adMmZ55Dhw6pl5eXrl69WlVVGzdurGPGjMm2vIx/rzfffNOZ9uuvvyqgX375paqqPvfccxoWFua8Z8Z9fXx89MyZM6pqfaaPP/64W9lDhgzRzp07u/175MZT2zHYbQHtxlq2JrN/888Cn4ZhlBAJCQnUqFGD2rVrO9NuuOEGvLwKNn4oISGBJk2aEBAQ4Exr3bp1lnyu3W9gbV+9a9cuvvnmGx555BFOnjzJv//9b6Kjo0lPT+fvv/8mKSmJtm3bupXTrl079uzZ49G6ZXbPPfeQlJTEZ599xi233MLXX39Nq1ateOmll9zyNWnSxO11cHAwx44dc76Oi4ujW7du1KxZk8DAQO6++25SU1PddpD18vKiZcuWztchISEEBwc73+PQoUN58cUXad26NaNGjWL79qy/Xl3rkbHVeUY9EhISaNWqldu/c7t27UhNTeWnn7LsrOPUt29fdu7cSf369Xn88cdZtmyZ29bphcXuSghjgfdFpBbWtgY9RKQh1lbZ0YVVOcMoMlOnFncNilzGLyl1WY7rwoWCLwZ/8eJFli1bluXZhJeXFy1atKBFixYMGzaM999/n4ceeoiNGzfmuj9QUWzd4OfnR7du3ejWrRvPP/88jzzyCGPGjGH48OH4+PgA4O3tvuWZiDh/SScmJhIdHc3//d//MW7cOCpVqsS3335Lz549nc9f7Lyf/v37869//Yvly5ezZs0a2rRpw7PPPsuYMWOceVzrkVGWnWCR232vv/56Dh06xMqVK1m7di19+vThuuuuY/Xq1QX+oyU3dhcj/QyrtXMT1jOg0ViLgt6uqmsKrXaGYVyS8PBwjhw5wi+//LMz/datW91+UWWMmktOTnam5fW8JTw8nB9++IEzZ84407Zs2eKWZ8OGDQQEBBAVlftyYI0aNQKsZzflypUjODiYr776yi3Ppk2bnPnyYqdudjVq1IiLFy/m+VwoQ3x8PKmpqbz66qu0bt2a+vXrk5SUlCVfeno6W7dudb4+fPgwSUlJbuuq1axZkwEDBrBw4ULGjRvH7Nmzbdc7PDycLVu2uP07b9q0CR8fH6699loAfHx8SEtLy3JtYGAg9957L2+88QbLli0jLi4u11aTJ9heC05VVwIrC7EuhmHk4e+//84SJMqXL0+dOnXc0rp27UrDhg3p3bs3r776KufOnWPYsGGULv3Pj3yZMmVo1aoVEydO5Nprr+Wvv/5yPqzOyQMPPEBMTAz9+vXj+eefJykpifHjx7vlWbJkCXfccYdb2r333kvbtm1p06YNQUFBHDx4kGeffZZq1arRpk0bAJ5++mmef/556tWrR/PmzXn//ff58ssv+fbbb219NnbqltnJkyfp0aMH/fr1o0mTJgQGBhIfH8+kSZPo0qUL5cqVs3XvevXqkZ6eztSpU7n77rvZsmULU7NpVZcuXZonnniCadOmUaZMGYYNG0ZERARdu3YFrC64W265hfr16/P333+zYsUK2wEYrEEMU6dOZdCgQQwdOpQDBw4wcuRIBg8eTNmyZQFrRN/WrVs5dOgQAQEBVKxYkalTp1K9enWaNm2Kt7c38+fPd472K1R2HxZlHIAfUNb1yG8Zl/NhBiFc/nJ7gFqS9enTR4Esxz233KKq7oMQVFX37t2r7du3Vx8fHw0LC9MlS5aov7+/cxCCqvVZtGnTRsuUKaORkZG6cePGXB/0q6pu2bJFmzVrpj4+PtqkSRNdunSp24P+2rVr68qVK93qPnv2bO3SpYtWrVpVfXx8tGbNmnrffffprl27nHnS0tJ03LhxWrNmTfX29tbIyEj95JNP3MopaN0yS0lJ0WeffVajoqK0fPnyWqZMGQ0LC9Nhw4bpyZMnnfkyf7aqWR/mT5s2TYODg9XPz087d+6sCxYsUEAPHjyoqtZgAH9/f/300081LCxMfXx8tH379rp//35nGYMHD9awsDD19fXVypUr63333ae//vqrqtofNLJhwwZt2bKl+vj4aNWqVfWJJ57QlJQU5/m9e/dqq1attEyZMs76zZ49W5s1a6YBAQEaGBio7du316+++irbz0zVc4MQbG3HICIhwGtAJ8A/myBmtmMwLhtX1HYMe/daX20OmQ0ICOD111+nb9++hVKdHTt20KlTJ44fP57lmcnVbt68eQwePNg5ZPpyVtTbMbyP1fIZAhzF+svLMAzDzYULF5g+fboJPoYtdgNQM6CFqiYUZmUMw7i8tWzZ0m2YsWHkxu74uu+AwlloyjCMInP69OlC634zcte3b98rovvNk+y2gAYAr4nIa1jL77hNFlDVw56umGEYhnFlsxuAvLC24P4E9+c/4nh91QxCMAzDMDzDbgB6BzgGjMAMQjCuAKpaJDPsDeNKY2fktF12A1BDoKmq7vPYnQ2jmHh7e3Pu3DnnxDzDMOy7cOGC24TmgrA7CGErEOqROxpGMatatSpHjhzh7NmzHv1rzjCudOnp6Rw9epRrrrnGI+XZDWNvAFNFZArwA1kHIdhaK0NE2gPDgeZAMPCwqs5zOS9Y68wNACoA3wCPq+pulzwxwK1AU6xVGLL0o4hIbWAG0BlrK4n5wHBVTXXJ0wF4BWtLiSRgkqq+aed9GJe3jOVVkpKSPLL4ZrHKWGm5CFYuNgwAf39/Kleu7JGy7AagWMfX7FbFy88ghACsUXTvOo7MngGewtpldS/wPLBaRBqo6ilHHl/gY2A98FzmAkSkFLAMOAncCFTCeoYlWBNpEZFQYDkwF2uX13bATBE5rqqLbb4X4zJWrlw52+t8lWiPPWZ9Xb++WKthGJfCbgDySPebqi7H+sWPiMxzPedo/TwBTMgIAiLSB2vwwwPALEcZzzvO3ZvDbW7CatWEqOovjrzPAP8TkRhV/RsYCCSpasZ2hQkicgNW68wEIMMwjCJgKwCpamJhVwQryAUBq1zue05ENgJtcAQgG1oDCRnBx2ElVsupObDOkWdVputWAn1ExFtVL/N+GcMwjJIvx0EIItLObiEiEiAijQtYlyDH18ybxh91OWe3nMxlnADSXMrJLs9RrIDsmc5NwzAMI1e5jYL7n4isFZGeIpJtZ7mINBGRScBPwHWFUsMSQEQGiEi8iMQfP368uKtjGIZxRcgtAEViPewfDfwuIntFZJ2IfCEiW0TkD6xRajWAzqr6fgHrkrFxerVM6dVcztktJ3MZlbEGSvyWS55qwEWs1pIbVZ2tqlGqGpWxi6RhGIZRMDkGIFW9qKozVLUh0AprKPZOIBFYAzwC1FDVXqq6xwN1OYgVGLplJIiIH9ZItq/zUc5mIFxEXLfy6wacB7a75OmW6bpuWBspmec/hmEYRcDuIIR4oMC7sIlIABDmeOkF1BaRpsDvqnpYRKYCz4nIj8A+YBRwGmseT0YZtYGKQB3H66aOUz+p6mmswQW7gXdF5CmsYdgvA285RsABvAkMdtxvFtAWa+h3z4K+R8MwDMMez6ynYF8U1ii0DGMdxztYAWASUAZrEmnGRNSbXOYAAYwD+ri83uH42glYr6ppIhINzAS+wpqI+gHwdMYFqnpQRG4FXgUew5qI+h8zB8gwDKPo2NqS2/iH2ZLbKFE6drS+momoRgmRny257a4FZxiGYRgeZQKQYRiGUSxsBSARMWOPDcMwDI+y2wI6IiIficgtYnbxMgzDMDzAbgCKBlKxFuo8LCIviMi1hVctwzAM40pnKwCp6mpVfQBrD58JwC3APhGJE5FejgmjhmEYhmFbvgYhqOqfjtURooD/YK1S/R6QJCITHBNNDcMwDCNP+ZqIKiLVsSaB9gVqAh8Cc7BaRs9iTTTt6tkqGoZhGFciWwFIRO4G+mFt9rYLeA34QFX/csmzDfixMCppGIZhXHnstoDextqWu7Wqbs8hTzIw3iO1MgzDMK54dgNQdVU9m1sGVT2Hta6bYRiGYeTJbgCqnMP0HwVSVNXs0mYYhmHki91RcIew9uvJfBwCfhORP0TkFREp6tW1DcPIRfL583TYsYPfzp8v7qoYRhZ2A1BP4Fes/Xm6OY5RwGGswQljgIeA/3q+ioZhXIrY2FgiJk5k4x9/0GjiRGJjY4u7Sobhxm6L5TFgmKp+7JIWJyJ7gaGq2kFEjmE9Axrt6UoahpE/PuvWcaF6daheHYA/2rfnAaDPunWkdupUvJUzDAe7LaAbgB+ySd8FtHB8vxlrbpBhGMUsNCaGLqqU9bJ+xMt6edFFldDnnivmmtlXXN2HsT/E0mB2G+TTaTSY1ZrYH/LfcjRdn/bYDUCJwIBs0v8PqxsOoArwuycqZRhGwfz0zTdcW706Kenp+Hl5kZKeTlj16vy0dWtxV822Fw4dYtNffzHu0KEiu2fsD7EMfXkoSUldoFxjkpK7MvTlofkKQqbr0z5bO6I6trheDPwMbHMkRwHXAveo6nIRGQSEqeqThVXZksDsiGqUKDnsiBoZGcnFCc9zLPVH/jz4HuVDH6KqT0NKjxzHrl27irya+VFm40ZS0tOzpPt5eXGufXvb5SSfP8/9e/awoFEjgnx9bV0ja1ZBaZ+sJy6mol1vyvN6n3XruJDNiGFv1SLt+ryU9+4pHt8RVVWXAfWAJUA5x7EUaKCqyx15Zl7pwccwLhedHurEz317MSolgNOPf8eolAB+7tuLTg+V/Oc/U5KT8d+8GV/HH8e+qvhv3swrycm2y7jkVsjjPemUnu7Wddk5PR0G3W/r8pLQ9Tlk4hBqjnuOjX/8QY1xzzFk4pAiu3d+5dkCEhFvrBUOZqhqYpHUqgQzLSCjRMmpBTQzkjsv3Mmnb31KQkIC4eHh3Pl/d/Kp96fsGlSyW0CRkZH4jh/Jt+WCIf0CeHlz/d9HOB8z0VbrrSCtEPESores4YuzpfDx8iI1PZ1by6bxeauuaHrevUWlSpWiw8ZPWZfq76x7Z58zrG9/J2lpaXleX1Cl49aS5lUqa73S07jYuUuh3x883AJS1QvAIMBsRGcYl4mEEwmMHjyaXbt2kZaWxq5duxg9eDQJJxKKu2p52pOwh4T03+geKGyLuoHugUJC+lH2JOyxdX1oTAwRp5KR9FQAJD2ViFPJtlohNevWZM2+7dweoHx5XRNuD1BW79tOzbr2xlcFhwbz9cEEt7p/dTCB4NBgW9cXVKnBD9Hs7O9uLbDrz/5OqccftHV97A+xRM6MpNS4UkTOjLykARj5YXcQwkqgc2FWxDAMzwmvHM6mw5vc0jYd3kR45fAiuX9BRpL5VPPhxXNefBrViahryvNpVCdePOeFT7Vsns1kY/83W0hMSQAvb/xEwMubxJQ97N/6TZ7XTnphEuVGTua79/vR8pWKfPd+P8qNnMykFybZq3x7qPjcVIaeguvK+jP0lPUa+4+uCiT1x2Sur9PAbfDJ9XUakrr3tzyvjf0hlpi4GKbfMp2UmBSm3zKdmLiYwg1CqprngdUC+g2YijXh9G7Xw04ZV8rRvHlzNYwSo0MH68hk/vfzNXRqqMYdiNPUi6kadyBOQ6eG6vzv59sqdv7387X+rNbKJ1O1/putbF/neu87tsWp17p1ese2/N1b7hUNDQ3VuLg4TU1N1bi4OA0NDVW5V2xd71vdVxsv/1gH7d2rO0+d0kF792rj5R+rb3Vfe/WfP18jIiLUy8tLIyIidP58++/da6yXvvv+u27Xv/v+u+o11st2GfO/n68RMyLUa6yXRsyIyNdnX5D3HjEjQuMOxGlSSoq2//ZbTU5J0bgDcRoxI8L2/VVVgXi1+fvUbgBKz+VIs3uzK+EwAcgoUXIIQKqX/ouswAFk7Spl3bosh6xdZev6iBkRGjM1xu2XeMzUGNu/CAsawAoi45e4q/z8Ei/oHw6DJwzW0pVK65QPpuiZc2d0ygdTtHSl0jp4wuA8r/Ua66WpF1P1sR9/VK916/SxH3/U1Iup+QqeqoUQgDx1YDVElwJHsBYy7ZvpvGAt65MEnAPWAxGZ8lTA2oX1L8fxHlA+U57GwAZHGUeA53EMuHDJcw+wBzjv+HqXnfdgApBRouQSgC5VQQMIvSup/0svqW9cnLJunfrGxan/Sy8pvSvaur6gv4QLGsAKwhN1L2grZPCEwepb3VcR1Le6r63go1rwf/cM+QlARb14aADW6gnvOo7MngGewtpxdS9W4FgtIg1U9ZQjz3ygNnCz4/X/sILQ7QAiUg5YDWzEWqWhIdZ+RmeAKY48rYEFWMsGfYzVlbhIRNqqat4dxYZxBdN3euLf8CkutmrFeRF8VSm9ZQtnfpwMnU/meb3v6tM0+XcY34jg5xhJ1rxxGNunn7F1/56NewIw5IshJJxIILxyOOM7j3em5yXmxhhi4mKY89kc2tVux6bDm+i/tD/jbyz87coKWveEEwm0q92OoT8dcE7CnRbWLl+DR6aPmM70EdPzXfe+O1cx77Qfpdt15IJXKbzT07i4aR19A1Kgc7d8l2eH3YmogrUe3ONAKBCpqgdEZCRwQFUX5vvGIqeBwao6z+UeScDrqjrekVYGOAYMV9VZIhKO1Vppp6pfOfK0A74EGqrqXhF5DJgIVFNrjyJEZJSj/jVVVUVkAVBRVbu51GcNcFxVc/2fYoZhGyVKDsOwC8Iv2I/r33qPb/yrOIcitzp9nO0DHiIlKSXP671KeeH30SRuqtWcUfWa8eL+Haz6ZTsp9z5DelrWCaaFIfaHWMZ/Od4ZBGJujLEdBIqTV9xq1Ms7S7qkXyC9kIJAhoIOf8/g8YmowFCs1a9n4z4c+wgw2HbNchcKBAGrMhIcAWQj0MaR1Bo4DXztct1XWK0b1zxfZgQfh5VAMFDHJc8q3K10KcMoIYp6WKgBqUdT2Zma6DYUeceFRFKPptq6vlF4I578/U9+2jqEG6ZV4qetQ3jy9z9pFN6okGv+j56Ne7Jr0C7Snk9j16Bdl0XwAasVIhvX4p1uzRnyTk9DNq6h786VhX7vhIQEaoU2YVCNmuy8oS2DatSkVuh1JCQU3tB9uwFoIPB/qjoNuOiS/i0Q4aG6BDm+Hs2UftTlXBBWK8XZbHN8fyxTnuzKwEaeILIhIgNEJF5E4o8fN3vvFZViGRZqFDiAxMTEMH/8fKaHTyfl2RSmh09n/vj5xMTEFHLNL39b535Bs+saWBNp085zQYRm1zVg69wVhX7v8PBwhp48yYz69bkuIIAZ9esz9ORJwsMLcei+nQdFWA/zQxzfnwLqOr6vD5y1+8ApU5mncRmEgNX6UKB2pnxzgZWO75/D6vLLXNYB4FnH96uAuZnO13aU3drxOhXonSlPb+B8XvU2gxCKTkFHFF0VCmEQwvz587MdRZaf4cgFGcp8NfPy8tLu333nNoy6+3ffqZdX/kaiXQpP/LurFs4ghAPA9VirYru6FeuZjCdkzJSqxj8rbGe8/s0lTxUREccbzXh2VDVTnmqZyq7mci63PHnP1jKKTMYDWVftaufvgayRfz17Oh6kDxniXMZn/PjxznS7ZeQnv2HJaIV0ciwZNKN+fdatW8dPhdkKcfDEv3u+2YlSwMNYz3t6YbVcemGNIDsD3Gc32mUqM3MLSIBk4DmXND/gb+BRx+twrJZMG5c8GS2nBo7Xjzmu8XPJ85yj/hmDLhYAqzLVZxUQm1e9r7YWUEEmxRWUaQHZUAgtIKP4eKoVUpwojHlAWHv/JPLPBNRfgf52r3eUEQA0dRxnsYZZN8XR7QaMwJrbczcQCXyINTIu0KWML7A2x2vtOH4APnM5fw1WS+ZDRxl3OwLSUy552mA9yxqJNUz7WeACcENe7+FqCkAFndNwud//smAC0BXncu++LJQApP/88q4MVM3vdY5rOzpaK5mPefpPK2iMoyWUgjWZNDJTGRWA9x1B5W/H99lNRN3oKCPZ0VrLPBH1XuBHrOdBCdhcUuhqCkCeaIEUtAVVnC2wy4IJQEYJk58AZGsekPGPq2keUKlxpUiJScG71D/zEi6kXcBvvB9pz+e9tHzGKLY5d2SaEJiPiXlGHgphHpBhFITH5wGJSEUReUNE9onInyLyt+tRsOoahe1S59IUdEXl8V+OZ84dc+gU2gnvUt50Cu3EnDvmMP7Lwp+RbhhGyWd3FNwcoBnWRNQkrG4z4zKQUysEyLMVEnNjDP2X9s+2BWOHGcVmGEZu7AagLkA3NeukXXZcWyGAsxUy5IsheQaggq5rldGCyrg3FO2eNIZhlGx2A9AxrGHTxmWmoK2Qno17XvLzmoK2oAzDuLLZXYonBhgnIgGFWRnD84pzZ8yejXsyvvN4hnwxBL/xfgz5YogZgJAPZh0840pntwU0Cmshz2Mikog1Z8ZJVZt4uF6GhxR3K6QgLairWUGe3RnG5cJuAPqoUGth5KogS8sX9DnO1aw4l/QvyLM7w7hs2J0wZI5Ln4hakMmUZjWA4lHcn3vG9siust0e2UxENUoYPLUSAnATUNrldWCm835AP7s3uxKO/AYgT23R68qsh2bfpQb/4v7cbd/fBCCjhPFkAErDZdkdrKVv6rq8rgak2b3ZlXDkNwAV9BeZ7b+EjSwKEvw98bkXScvXBCCjhMlPAMrrGZDk8drIQ0GHQZu5NJeuIM9RCvq5F3QQgXl2Z1wVcotOWKteu7aAnJvRqWkB2VLQFlBxP4u4nBWkFXPZdJ2aFpBRwlAIG9IZl6igw6DNX8KXriCtmIJ+7mYZIsPIm50A1EREfnd8L0CEiJR3vK5cONW6cngigJi5NJfGE8H/Uj9303VqGHmzE4BW4v7sZ0mm82Zh0jyYAFI8irP1WNwTgA3jcpBXAAotkloYRiEpruBvuk4NI2+5BiBVTSyqihjGlca0fA0jd3YXIzUMwzAMjzIByDAMwygWJgAZhmEYxcIEIMMwDKNYmABkGIZhFIscR8GJyDpszvFR1c4eq5FhGIZxVcitBbQL2O04fgSaAzWAXx1HsCPNo2uLiEigiEwVkUQROSciX4tIC5fz1URknogkichZEVkhIvUyleErItNF5ISInBGRpSJSM1Oe2iLymeP8CRF5TUR8PPleDMMwjJzlGIBUdUjGAZwH3gEaqmpvx9EQeBu46OE6/Q/4F9AHaAysAtaISA0REeBToB5wJ9AMSHSc93cpYypwD9ATuBEoB3wuIqUAHF+XAYGO8z2Be4EpHn4vhgcknz9Phx07+O38+eKuimEYHmT3GVBv4HXHSqeuZgIPeaoyIlIGK3CMVNX1qvqTqo4BfgIewwo8rYBBqrpVVfc60stgBRFE5BqgP3PV/nMAABVlSURBVPC0qq5W1W8ddWwCdHXc6iYgAnhIVb9V1dXAM8D/iUg5T70fwzNeOHSITX/9xbhDh4q7KoZheJDdACRYrZHMsksriNJAKSAlU/o5oB3g63jtPK+q6VgttIylh5sD3lgtp4w8v2B1FbZxJLUGEhzpGVY6ym/uiTdiFFyZjRuR9et5IzmZdOCN5GRk/XrKbNxY3FUzDMMD7AagucD/RGSkiHR0HCOBt7C64TxCVU8Bm4FRji63UiLyIFbAqI71LOow8JKIVBQRHxEZAdR0nAcIwtrJ9USm4o86zmXkOZrp/AnHdUEYJcKU5GT8N2/G19Hw9lXFf/NmXklOLuaaGYbhCXYD0DPA/wOGAHGOYwgwwXHOkx7C2gjvV6yWzX+AWCBdVS8AdwPXAieBs0An4AvHNYVCRAaISLyIxB8/frywblOoLsfnKDNfeIHOrVpxQQQ/Ly8uiNClVStmvPBCcVfNMAwPsBWAVDVdVSepag2gPFBeVWs40tI8WSFV/VlVOwABQC1VbYnVpXbAcX67qjZ11KO6qt4MVMo4D/yG1Y2Xea+iao5zGXmqZTpf2XHdb5nSUdXZqhqlqlFVqlQp6FsscrGxsURMnMjGP/6g0cSJxMbGFneVbElISMCrUiUGBgez5frrGRgcjFSqREKC2dTNMK4E+Z6Iqqp/q+rfhVGZTPc5o6rJIlIBa1Tckkzn/1LV444h2FEu57cDF/j/7d15mFxVmcfx769jFjDAMISlCaBhWKbTEYkJWyYEeDCCExgdiUqQ1QZEMAIjoNAuQWwXQIYxLEKMQoAEEUYgMjgsKUQgCSaCMSYkhJ1Os4clZKXz+sd7O5RFp/t2VXff6ur38zz3SdW9p+4991al3j7nvnUOjG0pm6Rg1wCPJKtmAzUFqdlj8RbX/K44n6z0y+U4prqaFWPGQFUVK8aM4ZjqavrlcllXrV01NTWc+frrXLnHHnx84ECu3GMPznz9dWpqYlK3ECpBqgCU3G+5WtJSSW9Kejt/6cwKSTpM0qclDZE0Fsjh935+lWz/vKRDJO0q6TPAvcDtZnYPeGACpgIXS/qkpOHADcAC4L7kMPfgv2+aJmm4pE8ClwBTuiO4dqch9fUcasbmVf5Wb15VxaFmDLnggtT7KLX7rtjX19fXU1dXRy6XY/369eRyOerq6qivry+qHiGE8pJmRlTwL/ThwLXAcrp2FtSt8PtNOwFvALcB9cn9H/Bkg8vwLrQmYBpQeFPgLPz3Sb/GU7TvB45v6S40s2ZJ4/A08ofxLLubgHO77rSysWzuXA6urib30ksMqKpizYYN7FZdTe7RR1O9fsaMGZzx5JOsGD2aoT/5CVfuvjsTJnRsjpv8NOqr9twz9etajjNx4kQWL15MTU0NDQ0NHT5+CKE86YM/7WmlkLdyxprZ3K6vUnkbOXKkzZs3L+tqpDZs2DC2njyZvQYP5tQdd+Ta5ctZ0NjIiokTWbhwYZuv7ZfLsV76wPq+Zqw75JB2j73Zgw+yZsMHc0MGVFWxesyY9CcRNu3gg/3fBx7IshYhbCRpvpmNTFM27T2gV4CVxVcpZKW+vp7GujrGNzYytH9/xjc20piyG6vU7rtIow4htCVtAKoHvi9pYFdWJnS+CRMm0NDQwMSJExkwYAATJ05M3Y21bO5c/qW6mjUbNvxD992ylN13kUYdQmhL2ntA3wY+Crwi6Tk8y2wjM9urk+sVCjStXcvRixbx66FD2aF///ZfkGfChAlF3TepqalhUVMTpxV036XNQlu8eDG7bbMNpw0YsPH1jWvWRBp1CAFI3wK6FbgU+AlwM54YkL+ELpTV73hK6b6DSKPuDD3xB8QhpJWqBWRmF3Z1RULr+uVyrK+uhmofaWjFmDEcA5yQy6VKBChFqVloLWnUU6dOZfTo0Tz00EPU1dXR0NDQldWuKMVmEIbQE6TKggvv6+4suD1HjWLnhgZm9+nDqg0b2LyqigOam3nhggtYMnt2t9WjWDNmzKChoWFjAKuvr4806hRSZxBGFlwoM52eBZcM+nlh8kPUNZKa85fSqhvaUmoiQNYmTJjAwoULaW5uZuHChb0u+BTbhRYZhKE3SHsP6CJ8grif4oN+ngtciQ8IenrXVK2yFPtFtDERIG88tL81NcV9lG5S6j2YYucyigzC0BukDUBfAE4zs2vwKQvuMLOvA98jb8y1sGnFfhGVmggQSlPs+1bqXEYxEGvoDdKmYW8PLEoer8RHogb4PZ4ZFzahsC//6qYmrm5qSj0aQAxHU7piUthLfd9+2tTEeU8/zXv7789aif5mfGjOHC7ZdddUx2/JIDwkSTS5co89yOVyLIuWb6ggaVtAzwM7Jo+X4aNTg08Ut7qzK1VJOqMvv7ffRylFsSnspb5vpXahxUCsoTdIOxbcj4CVZtYgaTw+QdyLwGDgEjPrNf8rOpoFN2zYMHadPJm7JPpVVbFuwwaOMOOpFGOxhdKUMpZdqe9bnz59OPKxxxhc8CPcmcOH09ycLm8nVQZhZMGFMtORLLi0vwM6P+/xrZJeBEYBS83sd8VVs3eI0QCyM6S+fpMp7LSTwl7q+9YZXWjFjmARQk/R4QnpAMxsjpldFsGnfTEaQHZKSWEv9X2LLrQQUjCzdhegf97jwcCF+ARuB6Z5fSUtI0aMsI6YPn26DRkyxGbNmmXr1q2zWbNm2ZAhQ2z69Okd2k/ouNraWhs9a5advmSJPf7OO3b6kiU2etYsq62tbfe1nfG+TZ8+3Wpra62qqspqa2u75j0/6CBfQigTwDxL+X3aXuDZE585tBl4DBgKNALvAG/hk759Nu3BKmHpaAAy66YvovABpQaRHvG+RQAKZaYjAajNJARJM/EZRf8bOBo4EJ9d9JSkyGRghJnt34mNsrLW0yak6+0qfiigSEIIZaYzkxD2x2dCfVzSg3ir5yoz25AcaDIwp6TahtCF4kZ+COWrvSSEbYDlAGb2DvAusCJv+wpgi66pWgghhEqWJguusI8uhs8OIYRQsjS/A7pRUstIjAOAKZJWJc87NjVnCCGEkGgvAF1f8PzGVspM66S6hBBC6EXaDEBmdlJ3VSSEEELvUtRICCGEEEKpyi4ASdpC0uWSnpO0WtIjkvbJ2z5Q0mRJLybbl0g6u2Af/ZMyr0l6V9KdknYqKLOLpJnJ9tck/UxSv+46zxBC6O3KLgABv8CnezgB+BhwD3CfpMHJ9suAccBxQA3QAPxY0nF5+7gcOAqYgP94dkvgd5L6ACT/3oWnkB+YlBuPz/gaQgihG5RVAJK0GR44vmVmD5jZMjObhM9B9NWk2CjgBjPLmdmzZjYN/zHsfsk+tgLqgHPN7F4z+zMerPYCPpns41NALXCcmf3ZzO4FzgNOkbRlt5xsCCH0cmUVgPCkiD7AmoL1q4HRyeOHgCMl7QwgaRSwNz47K8AIoC/ecgLAzF4AFuPBC3wivcXJ+hb/j6eVj+iskwkhhLBpZRWAktEWZgPfljRYUh9Jx+IBozop9nXgL8DzktYDfwC+ae9PDbEDPnjqawW7fznZ1lLm5YLtryWv26FgPZJOlTRP0rxXX321pHMMIYTgyioAJY4DNuAzrq7FA86MZB3ARLwl8x94a+Vs4FJJh3dVhczsWjMbaWYjt9122646TAgh9CplF4DM7CkzOwgYCOxsZvviXWpPJ/eIfgScZ2YzzWyBmV0B3Ayck+ziJbwbb1DBrrdPtrWU2b5g+6DkdS8RQgihy5VdAGphZu+aWZOkrfGsuDvwQNQX7yrL18z75zIfWA+MbdmYpGDXAI8kq2YDNQWp2WPxFtf8Tj6VEEIIrUgzFly3knQYHkyeAHbDZ159AviVma2X9Ac87Xol8BxwEHA8nsWGmb0laSpwsaRXgNfx1O0FwH3JYe7BJ9qbJukb+KjflwBTzOzt7jnTEELo3couAAFb4d1sOwFvALcB9Wa2Ptl+dLL9JuCf8SD0HeCKvH2chc/W+mt8Qr37gePNrBnAzJoljQOuAh7Gs+xuAs7t0jMLIYSwUdkFIDO7Bbilje0vAW2OUWdma/FkhYltlHkeOKLIaoYQQihR2d4DCiGEUNkiAIUQQshEBKAQQgiZiAAUQgghExGAQgghZEJmlnUdehRJr+Kp38UYxAfHqAvpxLUrXly74sR1K85HzCzVmGURgLqRpHlmNjLrevREce2KF9euOHHdul50wYUQQshEBKAQQgiZiADUva7NugI9WFy74sW1K05cty4W94BCCCFkIlpAIYQQMhEBKIQQQiYiAHUTSadLekbSGknzJR2YdZ3KnaRJkqxgiRlrC0gaI+lOSY3JNTqxYLuSa7lc0mpJD0iqzai6ZSXFtbuulc/gnIyqW3EiAHUDSV8E/gf4ITAcn5n1bkm7ZFqxnmEJUJ23fCzb6pSlgcBC4Ex8bqtC5wHfwKcn2Qd4BbhX0hbdVsPy1d61A5/IMv8z+O/dU7XKF0kI3UDSXGCBmZ2St+5J4FYzOz+7mpU3SZOA8WY2LOu69BTJTMFfM7PrkucClgNXmFlDsm4zPAidY2bXZFXXclN47ZJ11wGDzCzmDusC0QLqYpL6ASPwacDz3QOM6v4a9Ti7Jl1Hz0i6WdKuWVeohxkC7EDe58/MVgMPEp+/tEZLekXSUklTJG2XdYUqRQSgrjcI6AO8XLD+ZfyLIWzaXOBE4HDgFPx6PSJpmywr1cO0fMbi81ec3wPHA4fi3Zj7ArMk9c+0VhWi7KbkDqGFmd2d/zy5+fs0cAJwWSaVCr2Kmd2c9/SvkubjgxGPA/43m1pVjmgBdb3XgGZg+4L12wOR0dUBZrYS+Buwe9Z16UFaPmPx+esEZrYceJH4DHaKCEBdzMzWAfOBsQWbxuLZcCElSQOAfwWasq5LD/IMHmg2fv6S63gg8fnrMEmDgMHEZ7BTRBdc97gMuEHSo8DDwGnAjsDPM61VmZN0KTATeB7YDvgO8GHg+izrVW4kDQR2S55WAbtI2ht4w8yel3Q5cIGkJ4ClwLeBlcD0TCpcRtq6dskyCbgNDzgfBX6EZxD+trvrWokiDbubSDod/z1GNf67g7PN7MFsa1XeJN0MjMETOV4F5gDfMbNFmVaszEg6GMi1sul6MzsxScX+HvAVYGs8ueMMM1vYfbUsT21dO+CrwO34b/f+CQ9COfwz+EJ31bGSRQAKIYSQibgHFEIIIRMRgEIIIWQiAlAIIYRMRAAKIYSQiQhAIYQQMhEBKIQQQiYiAIVeKZlYbHzW9egNkgnwrsi6HqH8RAAKFUnSJyQ1S3q4yNf3lfRdSU8ls9j+RdLhBWXOkLRA0tvJMlvSuIIyF0l6QtK7klZIul/SqIIyz0o6p2Dd9yWtknRkG3U8WdJjklZKeiupyw+KOd9yJ+nEZL6eUEEiAIVKdTJwFTBMUk0Rr/8B/kv4rwND8WGTfitpeF6ZF4FvAp8ARgKzgNsl7ZVXZglwBj6T62h8bLbfSyocHBQASVWSrkqOe5iZzdxEuS8DP0vqtTewP3ARsHkR5xpCNswsllgqagE2A97Ev/SnApe2Usbw2VY3tY/lwJkF624Dbmzn2G8AX2lj+5bJsQ/LW/cscA7QD7gZH/Ll4+0c5/YUdZmED/t0NPAU8E7yukF5ZfbBJ6t7DXgbeAg4oJVr9TXgLmAVPh3BsQVlvpusX4sPfjotb9sD+B8DP0yO8wpwKVCVV2ZrfPibFfjU2PcBtcm2g5M65C+Tsv6cxVL6Ei2gUInGA8+Z2V+BG4DjJfXt4D76A2sK1q3GWzEfIKmPpKOBgWxilOlkdtxT8S/6xws2fxgfeHUfYLSZ/aWd+r0E7JtihtiPAl8E/hP4FD6uWUPe9i3wa3QgPtna48D/tTLp34XAnXhr61pgmqSRyXkdhQfQ0/FpCo4AHi14/ZeA9/BZWL8GnJXUq8V1wH7AZ5J6rMJbipvh1/OsZF11slzaznmHniDrCBhLLJ294H9xn5M8Ft7CGF9Qpr0W0HRgMbAn3lU9Fv8CXFtQ7mP4yNLv4a2uca3s64ikzAagEdi3YPuzeMvhTWDHlOdYDcxOzuNJ4EZ85s6+eWUm4UF0q7x19cCyNvYrvAV2bN46A6YUlLuPpAUG/Bfe1dh3E/t8AJhdsO5e4BfJ492TY4zJ274V8BZwcvL8RGBl1p+tWDp3iRZQqCiSdsNbKdMBzL+9bgLqOrirM/Ev1UXAOuAK4Fd4EMm3BG8V7AdcDVwvaVhBmVxSZhQ+xfMtkqoLytyDd8F9Lxm9uk1m1mRmB+AB8HI8cFwDPCop/z7Qc2b2Vt7z5fjUFgBI2k7SNZKWSnoL76bbDtil4JCzW3k+NHn8G2AA8IykqZI+38qU1QsKnufXowa/rhuPkdT5r3nHCBUoAlCoNCcDfYDnJb0n6T3gW8CnJO2cdidm9qqZfRbvGvsIPhHeSnxK8Pxy68xsmZnNN7Pz8S6sswvKvJuUmWNmdcD6pJ75/oBP8/wlYEqaIJTse6GZXWlmX8JbaXsDX8grsr7wJfzj//vr8W6/s/EAuTeeXNEvzfGTOryAtxS/gncv/hSYL+nDHajHJnefth6h54kAFCqGpA8BJwDn41+kLcvH8b/AT+roPs1sjZk14pM3HgXc0c5LqvD7Rx0uY2Y54NP4vZFfSuro/8+WeZIGduA1o4HJZnaXmf0NbwEVts7As+wKny9ueZJcp7vM7Gw8oNUC/5ayDovxa3JAywpJW+Ktu5ZzWof/YREqSMyIGirJOHzyuilm9nr+hmRyu9MkXZR0y7VJ0n741MuPJ/9Owr8kL84r82M8M+wF/Gb+MXjG1rhk+5b4JIQz8fsq2+Ip2TsBt7R2XDP7o6TDgLuBPpJOMrPmVup3Nd6NNQtvsVTjM52uwrvz0loKHCtpLt7auxj/si/0OUl/wu/njAcOxbsdkXQi/l0yF28lfhFv8TyZpgJm9qSkO4BrJJ2K3wtrwFtTLbO2PgsMkDQWeAxYZWarOnCeoQxFCyhUkjogVxh8Er/BM8LGptzXAPy3QIvw6Zcb8ey0N/PK7IDf/F8C3I//5f9pM7s72f4e3hL4Lf5lPBPYBr/ZXnhPZCMzeySp55H4VO6t/eV/Lx4AbsGDSMsU0WPNbGnKcwT4Mt5imo+ngP8S/7IvNAlvAS7Afx91kpn9Kdn2Jn7t/4infR8FfM7MnulAPU7CM+fuTP7dHDjczFbDxmvyc2AGPjvueR3YdyhTMSNqCKFNkgz4vJndmnVdQmWJFlAIIYRMRAAKIYSQieiCCyGEkIloAYUQQshEBKAQQgiZiAAUQgghExGAQgghZCICUAghhExEAAohhJCJvwNwduY+jdOYTgAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), solid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), solid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), solid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Solid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_solid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), hybrid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), hybrid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), hybrid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Hybrid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_hybrid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "liq4 = [[2524.23514216074,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2525.6347157315986,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2531.4687232522165,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2531.832410675329,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2531.208538690001,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2533.483755344074,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2525.4552490099536,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2526.57492642237,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2525.1307673043702,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2526.905758130126,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2538.219303762208,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2535.601334478212,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2535.611246973794,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2536.5593466841938,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2535.548948343239,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2539.381754920726,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2537.0398432433617,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2533.9415342719476,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2537.5028172493517,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2536.334095559646,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "sol4 = [[2552.918876986915,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2552.6678956679702,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2557.3896372227778,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2560.8382029603194,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2558.3085941472054,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2561.620752700051,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2552.550564634455,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2554.1610496637845,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2551.29241698577,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2554.2033509307184,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2567.855699355657,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.862055843559,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2564.552729641682,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2565.8148044177,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.541567864262,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2568.4582317202203,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2565.7773925052693,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2563.7487684577154,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2566.518701070757,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2564.854235490092,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [], - "source": [ - "liq4 = np.array(liq4)\n", - "sol4 = np.array(sol4)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5 6 7 8 9]\n", - "[0 1 2 3 4 5 6 7 8 9]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 83.41198931685739\n", - "Solid: 9198.508853675552\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 13.928609825732607\n", - "Solid: 118.17788928617698\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 21.904978464297287\n", - "Solid: 129.66327237607265\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "lt = np.arange(5,10)\n", - "st = np.arange(0,10)\n", - "\n", - "#Solid\n", - "#lt = np.arange(10,20)\n", - "#st = np.arange(15,20)\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "#lt = np.arange(5,20)\n", - "#st = np.arange(0,10)\n", - "#st = np.append(st, [15, 16, 17, 18, 19])\n", - "\n", - "print(lt)\n", - "print(st)\n", - "\n", - "liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "#hybrid_rmse = np.sum(((hybrid[ht,0] - hybrid[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "#print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "#hybrid_mean = np.mean((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "#print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "#hybrid_max = np.max((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "#print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [], - "source": [ - "hyb2 = [[2530.1636910482493,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2528.3441435359878,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2534.770878300083,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2537.7022737740676,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2534.8139649652912,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2536.357871334713,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2528.4355011583275,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2529.6269715881513,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2528.296386608331,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2529.49845054759,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2567.7293194572267,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.511184145917,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2564.408985678869,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2565.397314568127,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.1735220454584,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2568.7459807814384,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2565.5528382498314,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2563.7149491554924,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2566.3428527763367,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2564.9303824189697,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [], - "source": [ - "hyb2 = np.array(hyb2)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0 1 2 3 4 5 6 7 13 14 15 16 17 18 19]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Hybrid: 438.1843466265312\n", - "\n", - "Model MEAN meV/Atom\n", - "Hybrid: 17.524357093706772\n", - "\n", - "Model MAX meV/Atom\n", - "Hybrid: 35.73672847902287\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "#ht = np.arange(0,8)\n", - "\n", - "#Solid\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "ht = np.arange(0,8)\n", - "ht = np.append(ht, [13, 14, 15, 16, 17, 18, 19])\n", - "\n", - "print(ht)\n", - "\n", - "\n", - "#liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "#solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hyb2[ht,0] - hyb2[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "#print(\"Liquid: \", liquid_rmse)\n", - "#print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "#liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hyb2[ht,0] - hyb2[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "#print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "#print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "#liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_max = np.max((np.abs(hyb2[ht,0] - hyb2[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "#print(\"Liquid: \", liquid_max * mev_atoms)\n", - "#print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "hyb8 = [[2529.2420773099448,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2527.663233710719,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2532.7841534807176,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2534.965077874318,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2532.578262585185,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2534.16070863972,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2526.9522792785956,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2529.128561811317,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2527.95711135918,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2528.5952169597854,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2565.925022333284,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.587286915419,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2563.7078509088924,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2564.343968755005,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2563.4518821361544,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2567.3211912054185,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2564.8371692912433,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2562.5468929043986,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2564.650134486075,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2563.70946291344,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [], - "source": [ - "hyb8 = np.array(hyb8)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 1]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Hybrid: 74.55472770527697\n", - "\n", - "Model MEAN meV/Atom\n", - "Hybrid: 21.188127333750195\n", - "\n", - "Model MAX meV/Atom\n", - "Hybrid: 32.13667481377058\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "ht = np.arange(0,2)\n", - "\n", - "#Solid\n", - "#ht = np.arange(19,20)\n", - "\n", - "# Hybrid\n", - "#ht = np.arange(0,2)\n", - "#ht = np.append(ht, [19])\n", - "\n", - "print(ht)\n", - "\n", - "\n", - "#liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "#solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hyb8[ht,0] - hyb8[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "#print(\"Liquid: \", liquid_rmse)\n", - "#print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "#liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hyb8[ht,0] - hyb8[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "#print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "#print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "#liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_max = np.max((np.abs(hyb8[ht,0] - hyb8[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "#print(\"Liquid: \", liquid_max * mev_atoms)\n", - "#print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From fd5fb2ef14fa5d06cd64dcebfcecc99986b58046 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 11:33:08 +0200 Subject: [PATCH 017/297] Implemented DOS calculation from QE output in DOS class, code taken from Normand Modines Jupyter notebook and modified slightly Co-authored-by: Normand Modine --- mala/targets/calculation_helpers.py | 33 +++++++++++++++++++++ mala/targets/dos.py | 46 +++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index 170b9ff09..85dceff71 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -384,3 +384,36 @@ def analytical_integration(D, I0, I1, fermi_energy_ev, energy_grid, for j in range(0, real_space_grid): integral_value[j] += weights_vector[i] * D[j, i] return integral_value + + +# Define Gaussian +def gaussians(grid, centers, sigma): + """ + Calculate multiple gaussians on the same grid, but with different centers. + + + Gaussian functions are used as approximations to the delta in the + Brillouin zone integration. Note that this defines Gaussians without the + factor of 1/sqrt(2). All the Gaussians will have the same sigmas + Parameters + ---------- + grid : np.array + Grid on which this Gaussian is defined. + + centers : np.array + Array of centers for the Gaussians + + sigma : float + Sigma value for the Gaussian. + + Returns + ------- + multiple_gaussians : np.array + multiple gaussians on the same grid, but with different centers. + + + """ + multiple_gaussians = 1.0/np.sqrt(np.pi*sigma**2) * \ + np.exp(-1.0*((grid[np.newaxis] - + centers[..., np.newaxis])/sigma)**2) + return multiple_gaussians \ No newline at end of file diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 5fc2e220c..4676f6679 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -4,6 +4,7 @@ from scipy import integrate, interpolate from scipy.optimize import toms748 from ase.units import Rydberg +import ase.io from mala.common.parameters import printout @@ -125,6 +126,51 @@ def read_from_qe_dos_txt(self, file_name, directory): return np.array(return_dos_values) + def read_from_qe_out(self, path_to_file=None, smearing_factor=2): + """ + Calculate the DOS from a Quantum Espresso DFT output file. + + The DOS will be read calculated via the eigenvalues and the equation + + D(E) = sum_i sum_k w_k delta(E-epsilon_{ik}) + + Parameters + ---------- + path_to_file : string + Path to the QE out file. If None, the QE output that was loaded + via read_additional_calculation_data will be used. + + smearing_factor : int + Smearing factor relative to the energy grid spacing. Default is 2. + + Returns + ------- + dos_data: + DOS data in 1/eV. + """ + # dos_per_band = delta_f(e_grid,dft.eigs) + if path_to_file is None: + atoms_object = self.atoms + else: + atoms_object = ase.io.read(path_to_file, format="espresso-out") + kweights = atoms_object.get_calculator().get_k_point_weights() + if kweights is None: + raise Exception("QE output file does not contain band information." + "Rerun calculation with verbosity set to 'high'.") + + # Get the gaussians for all energy values and calculate the DOS per + # band. + dos_per_band = gaussians(self.get_energy_grid(), + atoms_object.get_calculator(). + band_structure().energies[0, :, :], + smearing_factor*self.parameters. + ldos_gridspacing_ev) + dos_per_band = kweights[:, np.newaxis, np.newaxis]*dos_per_band + + # QE gives the band energies in eV, so no conversion necessary here. + dos_data = np.sum(dos_per_band, axis=(0, 1)) + return dos_data + def get_energy_grid(self, shift_energy_grid=False): """ Get energy grid. From 18e860f7e28758345a061ff57c4117a1ddb143c6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 13:58:53 +0200 Subject: [PATCH 018/297] Implemented calculation of number of electrons from QE eigenvalues code taken from Normand Modines Jupyter notebook and modified slightly Co-authored-by: Normand Modine --- mala/targets/calculation_helpers.py | 4 ++-- mala/targets/density.py | 3 ++- mala/targets/dos.py | 2 ++ mala/targets/target_base.py | 6 ++++++ 4 files changed, 12 insertions(+), 3 deletions(-) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index 85dceff71..196b2e3f0 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -414,6 +414,6 @@ def gaussians(grid, centers, sigma): """ multiple_gaussians = 1.0/np.sqrt(np.pi*sigma**2) * \ - np.exp(-1.0*((grid[np.newaxis] - - centers[..., np.newaxis])/sigma)**2) + np.exp(-1.0*((grid[np.newaxis] - centers[..., np.newaxis])/sigma)**2) + return multiple_gaussians \ No newline at end of file diff --git a/mala/targets/density.py b/mala/targets/density.py index 136c33ac4..3222358e6 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -356,5 +356,6 @@ def from_ldos(cls, ldos_object): return_density_object.total_energy_dft_calculation = \ ldos_object.total_energy_dft_calculation return_density_object.kpoints = ldos_object.kpoints - + return_density_object.number_of_electrons_from_eigenvals = \ + ldos_object.number_of_electrons_from_eigenvals return return_density_object diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 4676f6679..f12cdae33 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -429,6 +429,8 @@ def from_ldos(cls, ldos_object): return_dos_object.total_energy_dft_calculation = \ ldos_object.total_energy_dft_calculation return_dos_object.kpoints = ldos_object.kpoints + return_dos_object.number_of_electrons_from_eigenvals = \ + ldos_object.number_of_electrons_from_eigenvals return return_dos_object diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 016476342..4322754d8 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -37,6 +37,7 @@ def __init__(self, params): self.temperature_K = None self.grid_spacing_Bohr = None self.number_of_electrons = None + self.number_of_electrons_from_eigenvals = None self.band_energy_dft_calculation = None self.total_energy_dft_calculation = None self.grid_dimensions = [0, 0, 0] @@ -222,6 +223,11 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): self.temperature_K) eband_per_band = kweights[np.newaxis, :] * eband_per_band self.band_energy_dft_calculation = np.sum(eband_per_band) + enum_per_band = fermi_function(eigs, + self.fermi_energy_eV, + self.temperature_K) + enum_per_band = kweights[np.newaxis, :] * enum_per_band + self.number_of_electrons_from_eigenvals = np.sum(enum_per_band) else: raise Exception("Unsupported auxiliary file type.") From 521e0814a429bff2d8283fbcaeefac507480bb89 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 14:20:10 +0200 Subject: [PATCH 019/297] Added a test for the eigenvalue summation --- test/mala_integration.py | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/test/mala_integration.py b/test/mala_integration.py index 873d3565e..86c86f3d9 100644 --- a/test/mala_integration.py +++ b/test/mala_integration.py @@ -11,7 +11,7 @@ # In order to test the integration capabilities of MALA we need a # QuantumEspresso # calculation containing the following: -# 1. Outfile from the run +# 1. Outfile from the run (containing the full band output!) # 2. LDOS cube files. # 3. Density cube file. # 4. A DOS file. @@ -185,6 +185,34 @@ def qe_ldos_to_dos(accuracy): else: return False +# Check whether we get the same result when calculating the DOS via the +# DFT eigenvalues and via the DOS calculated by pp.x. +def pwevaldos_vs_ppdos(accuracy): + dos_calculator = DOS(test_parameters) + dos_calculator.read_additional_calculation_data("qe.out", path_to_out) + + # Read the DOS data. + if numpy_arrays: + dos_from_pp = np.load(path_to_dos_npy) + else: + # DOS is in 1/eV so no conversion necessary. + dos_from_pp = dos_calculator.read_from_qe_dos_txt(path_to_dos_qe[1], + path_to_dos_qe[0]) + + # Calculate the quantities we want to compare. + dos_from_dft = dos_calculator.read_from_qe_out() + dos_pp_sum = dos_from_pp.sum() + dos_dft_sum = dos_from_dft.sum() + rel_error = np.abs(dos_dft_sum-dos_pp_sum) / dos_pp_sum + + printout("Relative error for sum of DOS: ", rel_error) + + # Check against the constraints we put upon ourselves. + if rel_error < accuracy: + return True + else: + return False + if __name__ == "__main__": @@ -204,3 +232,7 @@ def qe_ldos_to_dos(accuracy): test1 = qe_ldos_to_dos(0.0000001) printout("Integrate QE LDOS over spatial grid and get correct DOS " "compared to QE - success:", test1) + + test1 = pwevaldos_vs_ppdos(0.0001) + printout("Reading DOS from pp.x compared to calculating DOS from DFT " + "eigenvalues - success:", test1) From a0ae7d0b717ea42b785ebbc5398c9e1a16019a87 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 16:26:22 +0200 Subject: [PATCH 020/297] Removed ipython notebooks --- .../notebooks/dos2eband/dos2eband_02.ipynb | 4 +-- .../notebooks/dos2eband/dos2eband_nm04.ipynb | 28 ++++--------------- .../notebooks/dos2eband/dos2eband_nm11.ipynb | 4 +-- .../notebooks/dos2eband/dos2eband_nm15.ipynb | 4 +-- .../notebooks/dos2eband/dos2eband_nm17.ipynb | 16 ++++++++--- 5 files changed, 23 insertions(+), 33 deletions(-) diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb index 27916b42f..fbd7a16fe 100644 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb +++ b/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb @@ -781,9 +781,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.8.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb index f432f2712..53c163299 100644 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb +++ b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb @@ -735,13 +735,7 @@ "91 -0.0006601058353226108 -1.1789325071731582e-10 -0.00011014266829079133 4.547473508864641e-13\n", "92 0.00132096517336322 -1.1789325071731582e-10 0.007852551183987089 4.547473508864641e-13\n", "93 0.00020571833454141597 -1.1789325071731582e-10 -0.006892176095334435 4.547473508864641e-13\n", - "94 7.425794251503248e-06 -1.1789325071731582e-10 -0.0031314102870965144 -4.547473508864641e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "94 7.425794251503248e-06 -1.1789325071731582e-10 -0.0031314102870965144 -4.547473508864641e-13\n", "95 -0.0011109472937960163 -1.1789325071731582e-10 0.00441628471844524 4.547473508864641e-13\n", "96 0.00016933372432959004 -1.1789325071731582e-10 -0.0005063980133854784 4.547473508864641e-13\n", "97 2.85445787540084e-06 -1.1789325071731582e-10 -0.002920579854162497 4.547473508864641e-13\n", @@ -992,13 +986,7 @@ "105 -1.0401012680990789 -0.7118252047044962 -8.781078122656254 -5.665074190522773\n", "106 -2.05905352628929 -2.0013761623009714 -16.85901112572128 -15.867329573942243\n", "107 -0.34752300371189904 -0.1738756204301808 -3.6346764077966327 -1.7789036202780153\n", - "108 1.2752463712151894 1.71755219511806 9.074459488210323 13.03086496842434\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "108 1.2752463712151894 1.71755219511806 9.074459488210323 13.03086496842434\n", "109 0.29219512948805004 0.7502252984742199 1.6191282202021284 5.693631240757895\n", "110 -1.7293844850763662 -1.5842363392348489 -14.145657927382672 -12.506592987315798\n", "111 -1.2633904726218361 -1.213897331353337 -10.658278606282238 -9.794505900155855\n", @@ -1268,13 +1256,7 @@ "104 -0.4816473260644898 -0.06950899385810771 -4.873432946977118 -1.1240183844565763\n", "105 -0.4917050348005887 -0.15656098695274068 -4.93165823369327 -1.7782994981776028\n", "106 -0.49146320364741314 -0.34993380650348627 -4.911621505276344 -3.2764727910653164\n", - "107 -0.47106407385149396 -0.29906395613454606 -4.721560350727032 -2.879040362084197\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "107 -0.47106407385149396 -0.29906395613454606 -4.721560350727032 -2.879040362084197\n", "108 -0.44286042031683337 -0.1037300245809547 -4.500521893400219 -1.3516501266130945\n", "109 -0.4439179699379565 -0.051027761327418375 -4.481829759047741 -0.9216857833230279\n", "110 -0.4543494800495864 -0.24935489044241876 -4.545376784458313 -2.4532639539438605\n", @@ -2158,9 +2140,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.8.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb index 5c849709d..03cc338ff 100644 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb +++ b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb @@ -2398,9 +2398,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.8.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb index f818d884a..672d62813 100644 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb +++ b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb @@ -2634,9 +2634,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.8.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb index 5520b5f83..7295a987f 100644 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb +++ b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb @@ -2762,9 +2762,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "188.49839105\n" + ] + } + ], "source": [ "eband_qe = 737.82754675+4.77073244-554.09988814\n", "print(eband_qe)" @@ -3330,9 +3338,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.8.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 66ff8c17182f156498a24d0a4b3019f0b275a35a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 16:30:32 +0200 Subject: [PATCH 021/297] Fixed docstring --- mala/targets/calculation_helpers.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index 196b2e3f0..2353f4b1b 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -391,7 +391,6 @@ def gaussians(grid, centers, sigma): """ Calculate multiple gaussians on the same grid, but with different centers. - Gaussian functions are used as approximations to the delta in the Brillouin zone integration. Note that this defines Gaussians without the factor of 1/sqrt(2). All the Gaussians will have the same sigmas From d80b5d18c12fe6052fe15a2b3357ad19edfb0201 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 16:44:49 +0200 Subject: [PATCH 022/297] I had forgotten to actually delete the notebooks --- .../dos2eband_02-checkpoint.ipynb | 789 ---- .../933K/2.699gcc/snapshot0/Al.scf.pw | 296 -- .../933K/2.699gcc/snapshot1/Al.scf.pw | 296 -- .../933K/2.699gcc/snapshot10/Al.scf.pw | 296 -- .../933K/2.699gcc/snapshot11/Al.scf.pw | 296 -- .../933K/2.699gcc/snapshot12/Al.scf.pw | 296 -- .../933K/2.699gcc/snapshot2/Al.scf.pw | 296 -- .../notebooks/dos2eband/dos2eband_02.ipynb | 789 ---- .../notebooks/dos2eband/dos2eband_nm04.ipynb | 2148 ----------- .../notebooks/dos2eband/dos2eband_nm11.ipynb | 2406 ------------ .../notebooks/dos2eband/dos2eband_nm15.ipynb | 2642 ------------- .../notebooks/dos2eband/dos2eband_nm17.ipynb | 3346 ----------------- .../notebooks/dos2eband/snap_0/Al.dos | 140 - ml-dft-sandia/notebooks/dos2eband/snap_0/EIGS | 461 --- .../notebooks/dos2eband/snap_0/eigvals_01 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_02 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_03 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_04 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_05 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_06 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_07 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_08 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_09 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_10 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_11 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_12 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_13 | 461 --- .../notebooks/dos2eband/snap_0/eigvals_14 | 461 --- .../notebooks/dos2eband/snap_0/k_weights | 1 - .../dos2eband/snap_0_666/200327132835.err | 1 - .../notebooks/dos2eband/snap_0_666/Al.scf.pw | 299 -- .../notebooks/dos2eband/snap_0_666/pbs.job | 49 - .../dos2eband/snap_0_888/200330132037.err | 2 - .../notebooks/dos2eband/snap_0_888/Al.dos_2g | 252 -- .../dos2eband/snap_0_888/Al.dos_2g.old | 202 - .../notebooks/dos2eband/snap_0_888/Al.dos_g | 252 -- .../dos2eband/snap_0_888/Al.dos_g.old | 202 - .../notebooks/dos2eband/snap_0_888/Al.scf.pw | 299 -- .../notebooks/dos2eband/snap_0_888/pbs.job | 52 - .../notebooks/dos2eband/snap_1/Al.dos | 140 - ml-dft-sandia/notebooks/dos2eband/snap_1/EIGS | 461 --- .../notebooks/dos2eband/snap_1/eigvals_01 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_02 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_03 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_04 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_05 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_06 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_07 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_08 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_09 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_10 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_11 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_12 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_13 | 461 --- .../notebooks/dos2eband/snap_1/eigvals_14 | 461 --- .../notebooks/dos2eband/snap_1/k_weights | 1 - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Updated 200220_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Fermi Level\n", - "# The Fermi energy printed out by QE (in the DOS output file) is 7.770. \n", - "# The output is truncated and it turns this is too inaccurate to obtain the correct electron number. \n", - "# We determined the Fermi energy to higher accuracy (by imposing the correct electron number = 3*256 = 768).\n", - "# There are 3 electrons per atom due to the choice of pseudopotential. \n", - "# CAUTION: Need to define an accurate Fermi energy for each snapshot. \n", - "fermi_energy = 7.770345\n", - "\n", - "# Boltzmann's constant\n", - "k = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load eigenvalues and DOS from QE output for two snapshots" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# filepath: blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333\n", - "## Snapshot 0: Eigenvalues (from PW std output file, slurm-1006575.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe_00 = np.loadtxt('snap_0/EIGS', delimiter=',')\n", - "k_weights_qe_00 = np.loadtxt('snap_0/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe_00 = np.loadtxt('snap_0/Al.dos', skiprows=1)\n", - "## Snapshot 1: Eigenvalues (from PW std output file, slurm-1006846.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe_01 = np.loadtxt('snap_1/EIGS', delimiter=',')\n", - "k_weights_qe_01 = np.loadtxt('snap_1/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe_01 = np.loadtxt('snap_1/Al.dos', skiprows=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energy, eF, t): \n", - " return 1.0 / (1.0 + np.exp((energy - eF) / (k * t)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(en, eF, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((en-eF)/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define functions which compute reference values of the electron number and band energy = sum of eigenvalues.\n", - "Recall the some definitions:\n", - "\n", - "The electron number is defined as\n", - "$$ N = \\sum_{k,i} w_{k,i}\\, f(\\epsilon(k,i) - \\mu)\\,,$$\n", - "the band energy is defined as\n", - "$$ E_b = \\sum_{k,i} w_{k,i}\\, \\epsilon(k,i) f(\\epsilon(k,i) - \\mu)\\,,$$ \n", - "where $i$ denotes the band, $k$ the k point, $w_{k,i}$ the weight of the k point, $\\epsilon(k,i)$ the eigenvalue for a given k point and band, $\\mu$ the chemical potential (which equals the Fermi energy at zero temperature), and $f$ the Fermi-Dirac distribution function (which truncates the sum at the chemical potential). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating electron number from sum of eigenvalues\n", - "def gen_ENUM(k_weights, array_eigs):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " # output:\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=fermi_energy, t=temp)\n", - " enum = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " enum += k_weights[idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating sum of eigenvalues = band energy\n", - "def gen_EBAND(k_weights, array_eigs):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " # output:\n", - " ## eband\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=fermi_energy, t=temp)\n", - " eband = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " eband += k_weights[idx_k]*array_eigs[idx_bnd , :][idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return eband" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the reference values for the electron number and the band energy by directly summing over the eigenvalues.\n", - "It turns out the value of the band energy obtained from QE below is not correct." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNAPSHOT 0\n", - "Electron number: 768.3800699587922\n", - "Band energy [eV]: 2598.726246188354\n", - " \n", - "SNAPSHOT 1\n", - "Electron number: 768.0000014009643\n", - "Band energy [eV]: 2598.579547828817\n" - ] - } - ], - "source": [ - "# Reference values of electron number and band energy\n", - "## Snapshot 0\n", - "enum_ref_00 = gen_ENUM(k_weights_qe_00, eigs_qe_00)\n", - "eband_ref_00 = gen_EBAND(k_weights_qe_00, eigs_qe_00)\n", - "print(\"SNAPSHOT 0\")\n", - "print(\"Electron number:\", enum_ref_00)\n", - "print(\"Band energy [eV]:\", eband_ref_00)\n", - "print(\" \")\n", - "\n", - "## Snapshot 1 #Need to determine more accurate Fermi energy for snaphot2\n", - "#fermi_energy = 7.7733618\n", - "enum_ref_01 = gen_ENUM(k_weights_qe_01, eigs_qe_01)\n", - "eband_ref_01 = gen_EBAND(k_weights_qe_01, eigs_qe_01)\n", - "print(\"SNAPSHOT 1\")\n", - "print(\"Electron number:\", enum_ref_01)\n", - "print(\"Band energy [eV]:\", eband_ref_01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, the electron number and band energy can be by aid of the density of states. This is useful, because, eventually, we will predict the local DOS/DOS from ML.\n", - "\n", - "In terms of the DOS, they are defined as \n", - "\n", - "$$N = \\int_{-\\infty}^{\\infty} d\\epsilon\\ D(\\epsilon)\\, f(\\epsilon)$$\n", - "and\n", - "$$E_b = \\int_{-\\infty}^{\\infty} d\\epsilon\\ D(\\epsilon)\\, f(\\epsilon)\\, \\epsilon\\,,$$\n", - "\n", - "\n", - "where $\\epsilon$ denotes the energy as a continuous variable, $D(\\epsilon)$ the DOS, $f(\\epsilon)$ the Fermi-Dirac distribution function.\n", - "\n", - "The explicit defintion of the DOS is given as a sum of $\\delta$-functions over the spectrum of eigenvalues.\n", - "$$ D(\\epsilon) = \\sum_i \\sum_k w_k\\, \\delta(\\epsilon-\\epsilon(i,k))$$\n", - "\n", - "Commonly (in particular, for the purposes of visualization), the $\\delta$-functions are represented by particular choice of functions (for example, Gaussians)\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "where $\\sigma$ denotes the width." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS sum over eigenvalues\n", - "def gen_DOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " ra_dos_ik[idx_bnd].append(gaussian(ra_en, array_eigs[idx_bnd , :][idx_k], sigma))\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating electron number from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enumFromDOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## enum\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights, array_en, array_eigs, sigma)\n", - " enum = sp.integrate.trapz(ra_dos*ra_fd, array_en)\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_ebandFromDOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## eband [Ry]\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights, array_en, array_eigs, sigma)\n", - " eband = sp.integrate.simps(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS and compare with QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en_00 = dos_qe_00[: , 0]\n", - "ra_dos_00 = gen_DOS(k_weights_qe_00, ra_en_00, eigs_qe_00, sigma=sigma_qe*Ry2eV )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_00[: , 0], dos_qe_00[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe_00[: , 0] , ra_dos_00, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We recover the QE-DOS result by using the same parameters as in the input for computing the DOS.\n", - "Now we can go ahead and investigate different energy grids and smearing values in order to improve upon the band energy and achieve better agreement with the band-energy output of QE." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**CAUTION: This is not correct. The direct sum over eigenvalues (see analysis above) does not yield the same value for the band energy.** \n", - "The QE output of snapshot 0 is given at ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```. \n", - "QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution. \n", - "\n", - "**However, this does not agree with the reference value of the band energy we obtain above by explicitly summing over the eigenvalues (which we know is the correct band energy.)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(\"Band energy from QE output [Ry]:\", eband_qe)\n", - "print(\"Reference value of band energy (correct) [Ry]:\", eband_ref_00/Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we analyze the behavior of the predicted electron number and band energy with respect to the choice of energy bin and smearing. One important question to answer is whether we can recover an accurate band energy (error < 10 meV) for a relatively smooth DOS which will be more suited for ML." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Snapshot 0:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define energy bin\n", - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe_00[: , 0][1]-dos_qe_00[: , 0][0])*0.5\n", - "print(ewidth)\n", - "# Define smearing\n", - "ra_sigma_00 = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum_00 = np.zeros(len(ra_sigma_00))\n", - "ra_eband_00 = np.zeros(len(ra_sigma_00))\n", - "ra_en = np.linspace(dos_qe_00[0, 0], dos_qe_00[-1, 0], int((dos_qe_00[-1, 0]-dos_qe_00[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma_00)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum_00[i] = gen_enumFromDOS(k_weights_qe_00, ra_en, eigs_qe_00, sigma=ra_sigma_00[i])\n", - " # Generate band energy\n", - " ra_eband_00[i] = gen_ebandFromDOS(k_weights_qe_00, ra_en , eigs_qe_00, sigma=ra_sigma_00[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum_00)\n", - "print(ra_eband_00)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref_00, ra_sigma_00[0], ra_sigma_00[-1], linewidth=3)\n", - "ax.plot(ra_sigma_00, ra_enum_00, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref_00/Ry2eV, ra_sigma_00[0], ra_sigma_00[-1], linewidth=3)\n", - "ax.scatter(ra_sigma_00, ra_eband_00, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(\"Error in band energy [Ry/atom]\", np.min(abs(ra_eband_00-eband_ref_00/Ry2eV))/256)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate the corresponding DOS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos_00 = gen_DOS(k_weights_qe_00, ra_en, eigs_qe_00, sigma=ra_sigma_00[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos_00, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe_00[: , 0], dos_qe_00[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Snapshot 1:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define energy bin\n", - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0])*0.5\n", - "print(ewidth)\n", - "# Define smearing\n", - "ra_sigma_01 = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "ra_en_01 = np.linspace(dos_qe_01[0, 0], dos_qe_01[-1, 0], int((dos_qe_01[-1, 0]-dos_qe_01[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma_01)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enumFromDOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_ebandFromDOS(k_weights_qe_01, ra_en_01 , eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_en_01[1]-ra_en_01[0])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot0/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot0/Al.scf.pw deleted file mode 100644 index 830c9337c..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot0/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.57477164 0.63046187 0.21656476 -Al 0.85248017 0.44389598 0.67239040 -Al 0.03994941 0.64118785 0.79236078 -Al 0.25274132 0.88644851 0.87652872 -Al 0.31882745 0.66279089 0.04567437 -Al 0.44155915 0.11244582 0.02249422 -Al 0.51446954 0.62305476 0.50278527 -Al 0.15152081 0.74260921 0.28712404 -Al 0.11776733 0.84363764 0.59709204 -Al 0.84642794 0.08546095 0.23319279 -Al 0.81380679 0.05564631 0.91033719 -Al 0.32176613 0.29129213 0.84724466 -Al 0.05601237 0.32653343 0.16438889 -Al 0.71827413 0.46810126 0.80967370 -Al 0.51739646 0.08152952 0.31785518 -Al 0.50878305 0.46737826 0.19365655 -Al 0.14952101 0.96434756 0.44292303 -Al 0.65162797 0.93927828 0.50954606 -Al 0.82634918 0.55013184 0.51358700 -Al 0.07930174 0.41263684 0.42643924 -Al 0.99006669 0.94441834 0.49791326 -Al 0.65637720 0.05893206 0.23389922 -Al 0.31356810 0.26785295 0.39587215 -Al 0.33053887 0.49114649 0.12280239 -Al 0.63215913 0.24989962 0.06263221 -Al 0.04905748 0.60972745 0.24290518 -Al 0.46379650 0.82787777 0.35694231 -Al 0.85876047 0.68664759 0.62039542 -Al 0.34465316 0.19108192 0.71703178 -Al 0.11834227 0.69302326 0.50416952 -Al 0.17838347 0.92586121 0.73359803 -Al 0.68167192 0.66839852 0.47622151 -Al 0.53030268 0.44532334 0.50351956 -Al 0.04111442 0.76347762 0.88627482 -Al 0.04005202 0.09693783 0.47036815 -Al 0.37513829 0.86005516 0.51627308 -Al 0.92460929 0.99460031 0.71067025 -Al 0.23061726 0.92307000 0.29958371 -Al 0.42000948 0.60620748 0.17988676 -Al 0.79491900 0.46700714 0.34964560 -Al 0.20631468 0.17877136 0.79668803 -Al 0.94637106 0.93368940 0.23448380 -Al 0.57486570 0.34475004 0.88809522 -Al 0.10558037 0.36031436 0.00280131 -Al 0.57390087 0.56995109 0.64729707 -Al 0.49723943 0.84933765 0.61363830 -Al 0.56376837 0.54502414 0.35990151 -Al 0.47505059 0.71602232 0.92436958 -Al 0.85316356 0.59988063 0.75216097 -Al 0.09251854 0.80060933 0.09235451 -Al 0.42737112 0.41873013 0.83309613 -Al 0.75869758 0.27337016 0.62274950 -Al 0.68028835 0.50764119 0.21145512 -Al 0.16606015 0.35761965 0.28880424 -Al 0.78446744 0.29752156 0.07131244 -Al 0.56567174 0.79115725 0.47561601 -Al 0.79736467 0.39908799 0.20967568 -Al 0.25163000 0.62587285 0.39263331 -Al 0.38784515 0.80860018 0.02163971 -Al 0.78388035 0.94598426 0.31127474 -Al 0.35089955 0.83324121 0.72653423 -Al 0.85680578 0.06046498 0.50904651 -Al 0.25258043 0.44825100 0.58037136 -Al 0.67683022 0.74664960 0.09633878 -Al 0.91583468 0.66197358 0.89481724 -Al 0.34698132 0.97754843 0.95291736 -Al 0.95704028 0.77428193 0.50365437 -Al 0.13576755 0.64027295 0.12348984 -Al 0.92115170 0.02785762 0.36321250 -Al 0.24794140 0.46017602 0.40606404 -Al 0.82832438 0.41388068 0.94758371 -Al 0.67495001 0.99577311 0.95515150 -Al 0.56418361 0.82323880 0.19664745 -Al 0.85243943 0.14579451 0.08195693 -Al 0.20375538 0.51227089 0.01288841 -Al 0.48452378 0.26562796 0.77776134 -Al 0.13725499 0.18735720 0.60582032 -Al 0.85251564 0.80181864 0.15221907 -Al 0.34580983 0.94525589 0.13406721 -Al 0.18984827 0.32408808 0.49049804 -Al 0.69531010 0.43372872 0.63808864 -Al 0.36588610 0.38026297 0.49869006 -Al 0.68115429 0.41546501 0.00707650 -Al 0.03458838 0.25908053 0.51043115 -Al 0.62403475 0.73051286 0.61591876 -Al 0.21478391 0.36476340 0.12271602 -Al 0.79403931 0.50020230 0.08278029 -Al 0.22566272 0.09502195 0.49604431 -Al 0.74166866 0.16589241 0.80873795 -Al 0.45394228 0.26112631 0.96350354 -Al 0.31019964 0.65836171 0.71688422 -Al 0.33429809 0.12208071 0.87836067 -Al 0.96002590 0.67036782 0.11632246 -Al 0.59212977 0.17069976 0.89939107 -Al 0.91512733 0.53096635 0.19839054 -Al 0.38729330 0.05069505 0.58216582 -Al 0.56385928 0.04790556 0.60919640 -Al 0.10453300 0.36554379 0.62321778 -Al 0.30088270 0.23512435 0.55899494 -Al 0.25326962 0.60216496 0.56426269 -Al 0.65468898 0.39080680 0.38702457 -Al 0.56567643 0.33214719 0.62481022 -Al 0.60576373 0.06248460 0.08126760 -Al 0.79858968 0.86611816 0.99357432 -Al 0.93825536 0.34367553 0.38713272 -Al 0.07969002 0.63608811 0.98056435 -Al 0.50210284 0.93808560 0.73766799 -Al 0.22892307 0.90292697 0.02965359 -Al 0.34848137 0.36350975 0.21896478 -Al 0.09610446 0.01033057 0.61319428 -Al 0.19208453 0.75868136 0.96019784 -Al 0.07198867 0.03197409 0.30770502 -Al 0.28521992 0.09468288 0.04164588 -Al 0.98907478 0.09422530 0.16352079 -Al 0.79161402 0.90311614 0.79617426 -Al 0.92889007 0.27793636 0.66411451 -Al 0.12496822 0.17096487 0.08626692 -Al 0.53795382 0.74763911 0.76071518 -Al 0.63810914 0.83309434 0.84302156 -Al 0.91319573 0.76074678 0.77070111 -Al 0.15216765 0.47687341 0.73970523 -Al 0.98914627 0.53992374 0.66422157 -Al 0.04190898 0.21873614 0.74849454 -Al 0.92011594 0.24176890 0.21063159 -Al 0.06820983 0.04809178 0.77844102 -Al 0.02282025 0.73049101 0.63797392 -Al 0.41188163 0.53284316 0.59491923 -Al 0.33950050 0.37088705 0.98845088 -Al 0.96712101 0.39316968 0.55348956 -Al 0.99246003 0.33166675 0.89520231 -Al 0.37904167 0.53113108 0.43832993 -Al 0.57387900 0.19384208 0.18801655 -Al 0.40010152 0.77852532 0.19169822 -Al 0.90624905 0.85718742 0.64316828 -Al 0.51882395 0.02056504 0.90659939 -Al 0.75649193 0.82178980 0.56871194 -Al 0.79990448 0.14287460 0.38692067 -Al 0.46289384 0.19863650 0.61071091 -Al 0.32680942 0.72970852 0.86542329 -Al 0.46787130 0.23934032 0.30459802 -Al 0.40322483 0.96226153 0.29442843 -Al 0.49317970 0.97752238 0.48552292 -Al 0.38849268 0.12869386 0.44586290 -Al 0.61424586 0.91993618 0.31159921 -Al 0.64488923 0.61123791 0.78755290 -Al 0.71645472 0.14100602 0.00895767 -Al 0.83480854 0.90797286 0.48113649 -Al 0.43545585 0.68297895 0.64851932 -Al 0.51100487 0.38187483 0.03733030 -Al 0.14792083 0.62570049 0.68063492 -Al 0.65205720 0.26988686 0.50380153 -Al 0.26860611 0.77081156 0.59895433 -Al 0.79766166 0.01096405 0.09068293 -Al 0.72381713 0.77788315 0.25375002 -Al 0.73645324 0.18886282 0.17858114 -Al 0.07844142 0.91301161 0.95242993 -Al 0.50658003 0.70758450 0.09747765 -Al 0.88440572 0.84348175 0.35270295 -Al 0.01544908 0.39187033 0.76117522 -Al 0.15025824 0.31719674 0.82911584 -Al 0.40362847 0.10683642 0.19282651 -Al 0.75042789 0.75396697 0.71739746 -Al 0.64490698 0.36321380 0.15781509 -Al 0.62441637 0.23286516 0.34841356 -Al 0.24890645 0.80974074 0.15902473 -Al 0.33157553 0.99253644 0.79946028 -Al 0.77248738 0.74608928 0.87878425 -Al 0.69777509 0.91014208 0.16083279 -Al 0.78644455 0.96741980 0.63556087 -Al 0.79375981 0.29674135 0.34248058 -Al 0.86267370 0.24113317 0.94533714 -Al 0.11216486 0.19716697 0.92735820 -Al 0.67644664 0.50532373 0.48856786 -Al 0.36600658 0.70540228 0.47528118 -Al 0.09969146 0.20522191 0.24361755 -Al 0.10218608 0.52021205 0.55960698 -Al 0.89125039 0.68865405 0.26215014 -Al 0.00993457 0.42710892 0.28317024 -Al 0.42180334 0.25920806 0.10998547 -Al 0.30405551 0.77899960 0.31635055 -Al 0.23911474 0.32629455 0.69621831 -Al 0.21993414 0.60131706 0.85606277 -Al 0.24265884 0.95084729 0.57794296 -Al 0.29798133 0.09815019 0.32138813 -Al 0.77870517 0.64379785 0.16855570 -Al 0.15603381 0.20080671 0.38897358 -Al 0.50941849 0.97824886 0.18106830 -Al 0.64160541 0.02725315 0.77289387 -Al 0.26503471 0.23472643 0.00216269 -Al 0.25990925 0.63982250 0.22405250 -Al 0.91979693 0.39301929 0.09735002 -Al 0.82037667 0.13992412 0.67933857 -Al 0.91929726 0.54492381 0.36314152 -Al 0.42652713 0.65182358 0.33402965 -Al 0.65965991 0.30717155 0.75270435 -Al 0.91297938 0.12885055 0.81947808 -Al 0.33112911 0.50651628 0.28910964 -Al 0.84943967 0.31123643 0.80234733 -Al 0.96006486 0.93418825 0.06819968 -Al 0.46004333 0.86513309 0.88422059 -Al 0.71365538 0.82537224 0.39996589 -Al 0.21640511 0.04071447 0.19095136 -Al 0.56101875 0.45350906 0.74869482 -Al 0.27147639 0.22498283 0.23917605 -Al 0.83227982 0.69662660 0.42650862 -Al 0.39776163 0.36100502 0.64864653 -Al 0.72467409 0.62276093 0.33650332 -Al 0.09057410 0.56700069 0.39052666 -Al 0.70007290 0.13235864 0.56393670 -Al 0.02249321 0.70221147 0.35745918 -Al 0.93506140 0.94944439 0.86528222 -Al 0.47575339 0.93450507 0.03993958 -Al 0.96505251 0.17402962 0.34921094 -Al 0.08439368 0.50313326 0.90196749 -Al 0.58072297 0.52001932 0.89810992 -Al 0.80398364 0.37480349 0.50862377 -Al 0.61140405 0.16284712 0.70590063 -Al 0.60534317 0.85375105 0.00797941 -Al 0.45883895 0.42324755 0.35062021 -Al 0.46171080 0.55530870 0.03526843 -Al 0.05909061 0.84493683 0.39319878 -Al 0.63464478 0.68710957 0.96255084 -Al 0.90965645 0.48133760 0.83469803 -Al 0.97791299 0.08543923 0.97769198 -Al 0.81196413 0.68793715 0.02401677 -Al 0.92779797 0.78669985 0.98307004 -Al 0.88912523 0.22311764 0.51941204 -Al 0.98675091 0.11832256 0.62427098 -Al 0.32793154 0.50381155 0.72140048 -Al 0.66104307 0.04557555 0.39388302 -Al 0.16695435 0.50052819 0.21358574 -Al 0.48792149 0.28892388 0.46614692 -Al 0.18465104 0.03622256 0.89037000 -Al 0.32858795 0.97321493 0.42648167 -Al 0.59622194 0.73632289 0.32901421 -Al 0.63077579 0.55381811 0.05615386 -Al 0.46184678 0.58770006 0.78886099 -Al 0.97862865 0.23819162 0.06653690 -Al 0.36855172 0.55515890 0.92537911 -Al 0.97867034 0.60915028 0.51936234 -Al 0.20407144 0.80838614 0.45051253 -Al 0.25551299 0.43070882 0.86139082 -Al 0.55475571 0.14345541 0.47190679 -Al 0.76981765 0.57042268 0.92746538 -Al 0.71723781 0.29339095 0.89804963 -Al 0.10421405 0.91959816 0.18230576 -Al 0.93541500 0.53055606 0.98469651 -Al 0.02783327 0.87507401 0.75544200 -Al 0.17238518 0.75312988 0.77720857 -Al 0.47043815 0.10077689 0.75912776 -Al 0.01709523 0.78643129 0.22208659 -Al 0.64077734 0.88664330 0.68764304 -Al 0.72353341 0.61148059 0.64968068 -Al 0.24784782 0.06758699 0.67191406 -Al 0.04259226 0.50316477 0.10478923 -Al 0.10496891 0.02784405 0.05259849 diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot1/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot1/Al.scf.pw deleted file mode 100644 index 73eeb903f..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot1/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.62108581 0.64054929 0.21771043 -Al 0.83291078 0.44541570 0.66671615 -Al 0.02631386 0.61955137 0.78724707 -Al 0.27217058 0.88824020 0.88897111 -Al 0.34215281 0.64502695 0.03923128 -Al 0.46776104 0.12527433 0.07375344 -Al 0.54389706 0.64185463 0.46557682 -Al 0.16353054 0.76477892 0.23643026 -Al 0.12764435 0.87098716 0.57697039 -Al 0.88332670 0.10489691 0.26906817 -Al 0.84968378 0.98328601 0.94631548 -Al 0.33783950 0.30774924 0.84222685 -Al 0.10433379 0.35347123 0.16866283 -Al 0.71283370 0.45953383 0.78284027 -Al 0.46721816 0.06700196 0.33920024 -Al 0.50468454 0.45747278 0.17292427 -Al 0.11178981 0.96569966 0.44760401 -Al 0.68304449 0.97206547 0.46365167 -Al 0.82413381 0.53298289 0.52393630 -Al 0.07353224 0.34860860 0.41369435 -Al 0.98430228 0.95071125 0.60134460 -Al 0.73027445 0.04996070 0.16915048 -Al 0.32989219 0.27050096 0.37085113 -Al 0.35435035 0.49969171 0.12329664 -Al 0.61474621 0.22222238 0.06674680 -Al 0.05229603 0.59951027 0.24098251 -Al 0.47393874 0.80465902 0.39436573 -Al 0.84335088 0.67254999 0.62033416 -Al 0.37035797 0.17093772 0.73467519 -Al 0.14885556 0.69702020 0.50962572 -Al 0.18757527 0.93785112 0.76121441 -Al 0.70948197 0.66569975 0.49119417 -Al 0.52083894 0.42725531 0.50902587 -Al 0.05983025 0.79215367 0.85971163 -Al 0.01197121 0.09071888 0.47348885 -Al 0.34929195 0.84249674 0.50181846 -Al 0.90753173 0.03421916 0.73474865 -Al 0.25543497 0.92379574 0.28321770 -Al 0.47190534 0.61447019 0.16420777 -Al 0.77406881 0.44011630 0.35828740 -Al 0.20996399 0.20401427 0.81000389 -Al 0.94699717 0.95041329 0.21273335 -Al 0.58591604 0.37644514 0.91171571 -Al 0.05970940 0.36493387 0.00701442 -Al 0.54542817 0.54995238 0.59814255 -Al 0.47400902 0.84208232 0.59629371 -Al 0.53444494 0.51974005 0.32300725 -Al 0.48532211 0.67887486 0.88988820 -Al 0.83214198 0.58576721 0.75057875 -Al 0.08536208 0.82443726 0.04666791 -Al 0.44627532 0.44800916 0.84506562 -Al 0.74542801 0.28544169 0.64327078 -Al 0.67981199 0.48377363 0.20260465 -Al 0.20507384 0.36126801 0.33150372 -Al 0.78197092 0.28204411 0.08274986 -Al 0.59562130 0.80828940 0.49362247 -Al 0.81047617 0.36890542 0.22580180 -Al 0.18909018 0.64254856 0.34320030 -Al 0.35417481 0.82115820 0.01602270 -Al 0.78285070 0.94791713 0.31739830 -Al 0.35074287 0.83974540 0.72970552 -Al 0.83142401 0.03304571 0.52922412 -Al 0.24578633 0.45492951 0.59769877 -Al 0.65285019 0.74642030 0.10254922 -Al 0.93251953 0.63717668 0.91105943 -Al 0.37673334 0.98265122 0.96829478 -Al 0.00894253 0.80915403 0.48972751 -Al 0.11180212 0.68315701 0.11532893 -Al 0.93471066 0.97192956 0.39217134 -Al 0.22211061 0.49687765 0.42270738 -Al 0.75012143 0.41664461 0.93860648 -Al 0.68447103 0.98710860 0.92863003 -Al 0.53764527 0.77970294 0.21613632 -Al 0.86237836 0.13453489 0.10927819 -Al 0.20185618 0.57099749 0.04989245 -Al 0.48363787 0.26610135 0.81044003 -Al 0.15186446 0.18379008 0.60927833 -Al 0.84201233 0.79915884 0.12889553 -Al 0.34756681 0.01176107 0.17682145 -Al 0.20914305 0.30737013 0.49085788 -Al 0.67238933 0.43963097 0.59559968 -Al 0.35515725 0.37336073 0.51505990 -Al 0.62361772 0.40440904 0.07619316 -Al 0.00661597 0.25483175 0.52867065 -Al 0.59286121 0.71697868 0.61693698 -Al 0.24045197 0.39185008 0.08088622 -Al 0.78486887 0.49041038 0.07504823 -Al 0.22045665 0.10520203 0.48488285 -Al 0.73638490 0.11089319 0.83058605 -Al 0.47780703 0.26379665 0.97971244 -Al 0.34509515 0.64427726 0.73973158 -Al 0.39642584 0.14286614 0.89444349 -Al 0.95722566 0.67806959 0.06516717 -Al 0.58292489 0.16179910 0.90273659 -Al 0.89518657 0.51572245 0.21567843 -Al 0.40298402 0.01339664 0.59515333 -Al 0.56975639 0.01756603 0.59748122 -Al 0.09871082 0.34656382 0.59327940 -Al 0.32760968 0.22892564 0.59144442 -Al 0.26843823 0.60324546 0.55698987 -Al 0.59964510 0.38267912 0.38152437 -Al 0.57825439 0.29371542 0.64752101 -Al 0.60070159 0.04253796 0.06390030 -Al 0.74762777 0.84441796 0.98659913 -Al 0.91821039 0.35020570 0.37850700 -Al 0.08219912 0.66313462 0.96069564 -Al 0.48167849 0.93858924 0.73167282 -Al 0.24058679 0.96411388 0.03558089 -Al 0.34319364 0.35944277 0.22240570 -Al 0.12923950 0.02565323 0.60779287 -Al 0.20576023 0.75638458 0.95056796 -Al 0.07497843 0.04637210 0.31543248 -Al 0.30502832 0.12753752 0.03350405 -Al 0.01492240 0.08787266 0.14870321 -Al 0.80682819 0.89173462 0.80733484 -Al 0.91597906 0.28726329 0.67839686 -Al 0.14223667 0.21450574 0.08581469 -Al 0.50130681 0.76091878 0.75109153 -Al 0.62960468 0.85353535 0.83983330 -Al 0.93586034 0.74855810 0.77554407 -Al 0.14004230 0.49321540 0.75149219 -Al 0.95884008 0.54953030 0.67642352 -Al 0.05124769 0.23896242 0.76040441 -Al 0.91154887 0.24120648 0.21697956 -Al 0.08681299 0.07483685 0.77530806 -Al 0.06043411 0.72608530 0.63681672 -Al 0.39202400 0.49708287 0.60632040 -Al 0.41329826 0.40448743 0.01888096 -Al 0.96746934 0.43983702 0.51643000 -Al 0.93927153 0.33254998 0.89050645 -Al 0.39213106 0.54738539 0.45161588 -Al 0.56770597 0.15345694 0.21426193 -Al 0.39631546 0.79042534 0.17195863 -Al 0.86500793 0.84191625 0.67083573 -Al 0.51931762 0.01465624 0.92230576 -Al 0.74086530 0.80882362 0.56629875 -Al 0.76329834 0.10386745 0.39640256 -Al 0.49746047 0.17753970 0.60715492 -Al 0.33487403 0.72685812 0.87344259 -Al 0.48535201 0.28604982 0.30399438 -Al 0.43173421 0.91103684 0.28976779 -Al 0.51520089 0.93626805 0.46150447 -Al 0.38306187 0.14074309 0.46452298 -Al 0.60824538 0.87964575 0.33153387 -Al 0.61914856 0.66465746 0.79715259 -Al 0.71706044 0.12501043 0.98758864 -Al 0.86701725 0.88279877 0.50225596 -Al 0.40753992 0.70185386 0.59135321 -Al 0.49515300 0.28646290 0.15044068 -Al 0.17343401 0.63877765 0.70612133 -Al 0.66234521 0.27762483 0.50338706 -Al 0.24334701 0.75396338 0.61899310 -Al 0.83415645 0.94659398 0.10624057 -Al 0.72456858 0.78590195 0.25423966 -Al 0.74270810 0.18993641 0.22983375 -Al 0.12189429 0.93473560 0.93029157 -Al 0.50230340 0.71700250 0.04005277 -Al 0.90069673 0.81686147 0.32798932 -Al 0.01439921 0.40385680 0.72507715 -Al 0.14726886 0.36313798 0.85439998 -Al 0.39281495 0.18444201 0.23289209 -Al 0.73882543 0.75165513 0.72369933 -Al 0.64352216 0.31101485 0.21345625 -Al 0.61375903 0.19483124 0.36964126 -Al 0.23395303 0.81475404 0.11327353 -Al 0.35028507 0.00296589 0.78987603 -Al 0.78911242 0.73475872 0.86066437 -Al 0.65192858 0.89803728 0.14203202 -Al 0.75051263 0.99522843 0.65386191 -Al 0.76022681 0.28281816 0.36806117 -Al 0.84657953 0.20647752 0.95416932 -Al 0.08601102 0.20866154 0.90856527 -Al 0.66467567 0.51388873 0.44757924 -Al 0.31273250 0.70198866 0.45334566 -Al 0.07886093 0.20840252 0.23707782 -Al 0.10994505 0.52203555 0.55636925 -Al 0.89400507 0.65704805 0.26968233 -Al 0.02322961 0.43907581 0.27747353 -Al 0.33784952 0.26112874 0.09493391 -Al 0.31878910 0.78819615 0.31782494 -Al 0.22743011 0.31345241 0.71435082 -Al 0.20497997 0.60102316 0.87044594 -Al 0.25628518 0.94228232 0.59453422 -Al 0.28101473 0.09361818 0.33300714 -Al 0.77574934 0.63917399 0.16442398 -Al 0.14452694 0.20474115 0.39161117 -Al 0.53234308 0.99926071 0.20208819 -Al 0.63378491 0.99903361 0.77586072 -Al 0.23550670 0.28836264 0.96073115 -Al 0.29369618 0.66519510 0.20811408 -Al 0.94272333 0.39507321 0.13707073 -Al 0.81896691 0.13715820 0.67504494 -Al 0.90757578 0.54416971 0.38833380 -Al 0.42365839 0.66576657 0.32648295 -Al 0.63906596 0.29587480 0.79165633 -Al 0.92445285 0.17432372 0.81665280 -Al 0.32445922 0.52053883 0.30147675 -Al 0.80242064 0.30556526 0.79765086 -Al 0.98352329 0.95397209 0.02992284 -Al 0.46168605 0.85457732 0.89338350 -Al 0.74750146 0.81973142 0.40951112 -Al 0.18680816 0.08649793 0.16117706 -Al 0.54306534 0.43687518 0.71205279 -Al 0.24477140 0.22984340 0.24393662 -Al 0.85368492 0.69561215 0.43472736 -Al 0.39258827 0.34960603 0.68389374 -Al 0.73221268 0.60467499 0.33884472 -Al 0.06616104 0.54858473 0.40171580 -Al 0.70368449 0.11973518 0.55734597 -Al 0.03844527 0.71744260 0.35284579 -Al 0.97012372 0.94077687 0.84673214 -Al 0.47151588 0.94024276 0.08249191 -Al 0.97473010 0.18583872 0.36372816 -Al 0.06857245 0.50859058 0.92758425 -Al 0.59790018 0.53174843 0.88691581 -Al 0.80745861 0.37806373 0.50424768 -Al 0.64220558 0.14539471 0.70949592 -Al 0.58777596 0.86835463 0.00105926 -Al 0.41653639 0.41799513 0.37155293 -Al 0.49739037 0.54631390 0.00190262 -Al 0.06604427 0.86929638 0.32488679 -Al 0.64284842 0.71117348 0.94505155 -Al 0.91344810 0.48448815 0.83538811 -Al 0.97440029 0.09542063 0.94315710 -Al 0.81429142 0.69038477 0.01080191 -Al 0.91069891 0.82049739 0.93548689 -Al 0.84839205 0.20277490 0.53899341 -Al 0.99145195 0.13425309 0.63944509 -Al 0.30048932 0.48616708 0.75459260 -Al 0.64043008 0.04407971 0.31923162 -Al 0.18913884 0.50901438 0.20370389 -Al 0.47797093 0.27077048 0.47333010 -Al 0.22648964 0.06909235 0.88501336 -Al 0.33923478 0.98060433 0.43153221 -Al 0.60442924 0.71556781 0.35427854 -Al 0.66460179 0.58292982 0.04251515 -Al 0.48874852 0.58288300 0.75034825 -Al 0.97270806 0.23825617 0.03988598 -Al 0.37221880 0.57368795 0.89702246 -Al 0.99064983 0.62524755 0.51357068 -Al 0.19558560 0.81094938 0.39352152 -Al 0.27006344 0.45471270 0.91188303 -Al 0.55821711 0.09725552 0.47100498 -Al 0.76377880 0.58071576 0.89375485 -Al 0.71252063 0.27369364 0.93685153 -Al 0.12050162 0.91889284 0.17249126 -Al 0.91489163 0.46147608 0.98941428 -Al 0.02718933 0.87312592 0.72057604 -Al 0.20671734 0.76885833 0.77970135 -Al 0.49366876 0.10341861 0.76371770 -Al 0.00600166 0.78443680 0.18002335 -Al 0.62224981 0.86429787 0.67252083 -Al 0.69585498 0.59298076 0.66237448 -Al 0.26711828 0.08893515 0.66650479 -Al 0.05242168 0.52093345 0.09240899 -Al 0.11795383 0.06836059 0.01163851 diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot10/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot10/Al.scf.pw deleted file mode 100644 index b313879f5..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot10/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.00518829 0.00356501 0.98791897 -Al 0.00222191 0.13453940 0.11763345 -Al 0.12110347 0.99364921 0.12494853 -Al 0.11818861 0.12588069 0.99073258 -Al 0.00845727 0.00205675 0.25908128 -Al 0.03211246 0.12003628 0.36844451 -Al 0.12686519 0.98943532 0.36074737 -Al 0.14291592 0.12438468 0.23972361 -Al 0.00223606 0.98994139 0.50673488 -Al 0.99677299 0.13407831 0.62773526 -Al 0.11611898 0.99373014 0.63843963 -Al 0.12852260 0.13550884 0.50441864 -Al 0.00228543 0.99623742 0.75524920 -Al 0.97645393 0.12987079 0.87426509 -Al 0.12794806 0.01294677 0.87049605 -Al 0.12171736 0.12628441 0.74555146 -Al 0.99435455 0.23727612 0.99099955 -Al 0.01177951 0.36446420 0.11012020 -Al 0.13094126 0.23063292 0.11619068 -Al 0.13264832 0.35959784 0.98560643 -Al 0.01947177 0.24042304 0.24484141 -Al 0.99582073 0.37421412 0.36593777 -Al 0.13140224 0.24949750 0.37985388 -Al 0.12685522 0.36873851 0.23472870 -Al 0.00601142 0.25793001 0.51809184 -Al 0.00473613 0.38447453 0.62727243 -Al 0.12827519 0.25091026 0.63552511 -Al 0.11886290 0.38534167 0.49782246 -Al 0.99938144 0.25371900 0.75535161 -Al 0.99476132 0.36540992 0.86831817 -Al 0.12586684 0.23790194 0.87561347 -Al 0.11939885 0.36792742 0.75142379 -Al 0.99976850 0.49873944 0.98581816 -Al 0.98696743 0.63309127 0.11912346 -Al 0.11773085 0.49504140 0.11233206 -Al 0.12212522 0.62669157 0.98913158 -Al 0.99654304 0.48977098 0.23547843 -Al 0.98703335 0.63256726 0.37480575 -Al 0.10439504 0.50224306 0.37798465 -Al 0.12461175 0.61474723 0.24535347 -Al 0.98528942 0.50551753 0.49926364 -Al 0.00592043 0.60975040 0.62800791 -Al 0.13733296 0.48418419 0.62105260 -Al 0.11669745 0.62690148 0.49729174 -Al 0.99887305 0.50855326 0.76314694 -Al 0.01540105 0.63263269 0.87462249 -Al 0.13630515 0.49084700 0.89517306 -Al 0.12197448 0.63003525 0.75108286 -Al 0.99054590 0.75082041 0.99764465 -Al 0.98367196 0.87147752 0.11361634 -Al 0.11526503 0.74334806 0.11959483 -Al 0.12216203 0.87677470 0.00828756 -Al 0.00610748 0.74356303 0.24779213 -Al 0.99858631 0.87019306 0.38682102 -Al 0.10916246 0.73487762 0.38316910 -Al 0.13460407 0.85444406 0.25203331 -Al 0.99413431 0.74583300 0.50404073 -Al 0.98441209 0.87117792 0.62176477 -Al 0.10056616 0.74916145 0.62606709 -Al 0.12780142 0.88141278 0.49115094 -Al 0.97763798 0.74174636 0.74299466 -Al 0.99834243 0.88873388 0.89168128 -Al 0.14236315 0.77016294 0.88557120 -Al 0.10198680 0.86911057 0.74124561 -Al 0.25412647 0.99364625 0.00700858 -Al 0.25005094 0.11341867 0.13189214 -Al 0.38224266 0.00280935 0.12256209 -Al 0.38861826 0.11857598 0.99269867 -Al 0.25249830 0.00026697 0.26702078 -Al 0.24554726 0.11025190 0.38482362 -Al 0.38422919 0.01072749 0.37157606 -Al 0.37620179 0.13635528 0.25337487 -Al 0.25271010 0.99930598 0.50041773 -Al 0.25071085 0.12963214 0.62003056 -Al 0.37550421 0.01092590 0.61489455 -Al 0.37363617 0.14091220 0.50453049 -Al 0.25901142 0.99956673 0.74847157 -Al 0.26039063 0.11403604 0.87808276 -Al 0.38512240 0.98453169 0.87572573 -Al 0.36013003 0.11635579 0.77463764 -Al 0.26154579 0.22818761 0.01214907 -Al 0.23510920 0.37273360 0.12282410 -Al 0.39075780 0.24889600 0.12882660 -Al 0.38672315 0.36283989 0.00387710 -Al 0.25185041 0.25512451 0.25588275 -Al 0.24602127 0.38415989 0.35125722 -Al 0.37444317 0.26156650 0.36932558 -Al 0.37918793 0.36482048 0.23694650 -Al 0.25038777 0.25712797 0.49289437 -Al 0.25927125 0.36028126 0.64700610 -Al 0.36010540 0.24629383 0.62448929 -Al 0.36760419 0.37804499 0.50837011 -Al 0.24386491 0.25414291 0.76403944 -Al 0.25694999 0.37239108 0.88647054 -Al 0.38803805 0.25819558 0.86885061 -Al 0.39875931 0.37657452 0.76023057 -Al 0.26236759 0.49678644 0.01263916 -Al 0.24296175 0.63257392 0.12702601 -Al 0.37985377 0.49944601 0.12561102 -Al 0.36620870 0.61948153 0.00919132 -Al 0.26151482 0.50790380 0.24077530 -Al 0.25027249 0.61340420 0.36478022 -Al 0.37887910 0.47991548 0.35522089 -Al 0.38928607 0.61064503 0.24901158 -Al 0.26196637 0.49980316 0.50541479 -Al 0.24220702 0.62546741 0.61312369 -Al 0.36882949 0.49848132 0.65307592 -Al 0.35549552 0.61687072 0.50303060 -Al 0.25804498 0.47180014 0.76489766 -Al 0.24449073 0.61928638 0.87576275 -Al 0.38262823 0.50651159 0.87573934 -Al 0.35864801 0.64705572 0.74361030 -Al 0.25183481 0.75219373 0.00128730 -Al 0.24417962 0.88721153 0.12340070 -Al 0.39359309 0.75669881 0.13328541 -Al 0.37901687 0.87201637 0.02031994 -Al 0.27229927 0.75284596 0.25265201 -Al 0.25879781 0.85876946 0.38570484 -Al 0.38899532 0.75458685 0.37681566 -Al 0.37776265 0.89104110 0.25287808 -Al 0.23924190 0.73593885 0.49744398 -Al 0.24890469 0.86809897 0.60609107 -Al 0.36098645 0.75601087 0.61527487 -Al 0.38031625 0.87403201 0.48513761 -Al 0.23905734 0.76431565 0.74743325 -Al 0.24459003 0.88295081 0.87267617 -Al 0.35899675 0.75828211 0.87990673 -Al 0.36496814 0.87423404 0.74022344 -Al 0.50646206 0.01326787 0.99902447 -Al 0.50919701 0.13246851 0.11985017 -Al 0.63718857 0.00496138 0.11217757 -Al 0.62755815 0.12266421 0.97788932 -Al 0.50100880 0.00946745 0.24271122 -Al 0.50912531 0.12269642 0.38642596 -Al 0.62691462 0.00294036 0.37688534 -Al 0.64122728 0.11313385 0.25155305 -Al 0.50493980 0.00476837 0.50152382 -Al 0.50666445 0.12715326 0.62369881 -Al 0.63950995 0.99831468 0.61956463 -Al 0.63171800 0.13181365 0.49787812 -Al 0.48711068 0.99745626 0.74674203 -Al 0.51551971 0.11529596 0.86247870 -Al 0.63926115 0.99948041 0.83249027 -Al 0.65355259 0.12857630 0.72329032 -Al 0.50353759 0.25395184 0.99970510 -Al 0.51239178 0.36270782 0.13159800 -Al 0.62853335 0.24426968 0.14009652 -Al 0.63549813 0.35806940 0.99337420 -Al 0.52230425 0.23543148 0.26174737 -Al 0.50170223 0.37243976 0.35654180 -Al 0.64051205 0.25628921 0.37658009 -Al 0.63219784 0.37391496 0.25652977 -Al 0.49778324 0.25330358 0.49919555 -Al 0.49726361 0.37147631 0.62349847 -Al 0.62454774 0.26022474 0.63254804 -Al 0.61848909 0.36887689 0.49727756 -Al 0.49157160 0.22452425 0.74397457 -Al 0.48780242 0.37291044 0.89286602 -Al 0.62753123 0.24958369 0.87617470 -Al 0.60551364 0.37487263 0.76231912 -Al 0.50265201 0.50772668 0.99888090 -Al 0.51785974 0.62680863 0.13766941 -Al 0.63531903 0.50271490 0.13719287 -Al 0.63676918 0.63791571 0.99375411 -Al 0.50031197 0.48873671 0.23622600 -Al 0.49005905 0.61871216 0.37660480 -Al 0.61792792 0.48141513 0.38003717 -Al 0.63151472 0.61483726 0.26204703 -Al 0.48053458 0.48757111 0.48399634 -Al 0.46912258 0.63601034 0.62403736 -Al 0.59490169 0.50344733 0.62638005 -Al 0.60631265 0.62006920 0.50016155 -Al 0.50274258 0.50727498 0.75495920 -Al 0.48843114 0.64839979 0.87688759 -Al 0.61068632 0.50105027 0.89376588 -Al 0.59350719 0.64201122 0.74400251 -Al 0.48571827 0.74015950 0.99883917 -Al 0.50825429 0.89190105 0.11493698 -Al 0.62291624 0.76740233 0.12222527 -Al 0.63055817 0.87012409 0.00245697 -Al 0.51715517 0.73918487 0.25833222 -Al 0.50089424 0.88830769 0.36248813 -Al 0.61900857 0.73850743 0.38419534 -Al 0.61597026 0.88002727 0.24637279 -Al 0.48994594 0.76888379 0.51150148 -Al 0.50263673 0.90334148 0.62590374 -Al 0.60491824 0.75335763 0.62759893 -Al 0.61363884 0.87005923 0.50597812 -Al 0.48354677 0.77955609 0.73455386 -Al 0.51587930 0.89268698 0.89376110 -Al 0.62783566 0.74689666 0.86725500 -Al 0.61031700 0.87714623 0.75589810 -Al 0.75363315 0.99821040 0.99742924 -Al 0.75511863 0.13081842 0.13289225 -Al 0.88763858 0.01192720 0.13221398 -Al 0.86182498 0.13480650 0.00130590 -Al 0.74869069 0.00173981 0.25987278 -Al 0.75479489 0.13289727 0.40002656 -Al 0.87100412 0.00391153 0.38978780 -Al 0.89408860 0.14291965 0.26614553 -Al 0.74902004 0.99895888 0.50510598 -Al 0.75736463 0.13065713 0.60552045 -Al 0.88617425 0.99256774 0.62512125 -Al 0.89069061 0.13165506 0.49588748 -Al 0.76807193 0.98758409 0.73323091 -Al 0.75121839 0.12516642 0.85081313 -Al 0.86129148 0.99329109 0.86965708 -Al 0.86679940 0.12572173 0.74279637 -Al 0.74399052 0.23464508 0.97992181 -Al 0.75097870 0.35395981 0.15206695 -Al 0.87880005 0.24794231 0.12565196 -Al 0.87972692 0.36963332 0.99470781 -Al 0.76816855 0.24427025 0.25322878 -Al 0.76436655 0.37508066 0.37866786 -Al 0.88326190 0.26687911 0.40100156 -Al 0.87683158 0.37609954 0.25526195 -Al 0.74449706 0.27356236 0.50803174 -Al 0.74914496 0.39874807 0.64120002 -Al 0.86983032 0.24354768 0.63031318 -Al 0.87386513 0.38976290 0.51941270 -Al 0.75500396 0.25812057 0.73690540 -Al 0.74653008 0.36639993 0.87144555 -Al 0.87530036 0.25978447 0.86781981 -Al 0.88381855 0.38226607 0.74618072 -Al 0.74789025 0.50109044 0.00066145 -Al 0.76152969 0.63133799 0.11689554 -Al 0.88593821 0.49175395 0.12778569 -Al 0.86409783 0.61844808 0.99333327 -Al 0.74978900 0.50080603 0.24565012 -Al 0.76382742 0.62256071 0.37174165 -Al 0.87121536 0.49749286 0.38186693 -Al 0.88657518 0.62247084 0.25448128 -Al 0.74434534 0.50502028 0.50701912 -Al 0.72929111 0.62292713 0.63136663 -Al 0.86773891 0.51735998 0.64800040 -Al 0.85494407 0.61897276 0.50946640 -Al 0.73582686 0.50159348 0.76010049 -Al 0.74086465 0.62424129 0.87322336 -Al 0.87037120 0.49065063 0.86753762 -Al 0.86031396 0.63244518 0.76916385 -Al 0.74872142 0.75807691 0.98789469 -Al 0.74579462 0.87545768 0.14311744 -Al 0.87255945 0.76965125 0.11316542 -Al 0.85374546 0.87231249 0.00121863 -Al 0.76054771 0.73845794 0.23760014 -Al 0.75059396 0.85783054 0.37700315 -Al 0.88350866 0.75558997 0.36329071 -Al 0.88014712 0.88813051 0.23506911 -Al 0.72977947 0.74121761 0.50357392 -Al 0.75531111 0.88095013 0.61051136 -Al 0.85515073 0.74322600 0.61394923 -Al 0.86454255 0.86961613 0.50275624 -Al 0.74679467 0.75264780 0.73763491 -Al 0.75412808 0.87006055 0.85982851 -Al 0.87671455 0.75534015 0.87914969 -Al 0.87133699 0.86706346 0.74561647 diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot11/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot11/Al.scf.pw deleted file mode 100644 index 34675c1b4..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot11/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.00858289 0.01093749 0.99854894 -Al 0.00650019 0.10646378 0.14261096 -Al 0.11414445 0.99120282 0.13071859 -Al 0.13613163 0.11418453 0.96893017 -Al 0.99188790 0.00290036 0.28155398 -Al 0.01071068 0.12986677 0.38369003 -Al 0.14084471 0.99236998 0.37096713 -Al 0.12161004 0.11119674 0.26652391 -Al 0.01781931 0.99367868 0.48970131 -Al 0.99458495 0.11525955 0.63182212 -Al 0.11556474 0.99458500 0.60825805 -Al 0.13958498 0.12065153 0.49389065 -Al 0.00720192 0.99903777 0.74800024 -Al 0.98924305 0.13490549 0.89024407 -Al 0.13349779 0.99354044 0.84717213 -Al 0.12865504 0.13077759 0.75989174 -Al 0.99643586 0.23944491 0.00512210 -Al 0.00389022 0.39610574 0.11733760 -Al 0.12386092 0.23646580 0.11749215 -Al 0.12569339 0.37838641 0.01508017 -Al 0.00227652 0.24752433 0.25287832 -Al 0.02235238 0.37705521 0.38998160 -Al 0.13150792 0.26278574 0.37005527 -Al 0.13084340 0.36210508 0.24691288 -Al 0.99429545 0.24346983 0.52577720 -Al 0.98994458 0.37286007 0.63688652 -Al 0.13583066 0.25727161 0.61363231 -Al 0.12889933 0.36777235 0.49684355 -Al 0.99712003 0.24133391 0.76229279 -Al 0.00472385 0.36946518 0.87158490 -Al 0.12916835 0.24849073 0.87709119 -Al 0.11691349 0.36586636 0.75506487 -Al 0.00584253 0.49702625 0.00649414 -Al 0.98286828 0.62471811 0.11074647 -Al 0.13295936 0.50479973 0.11622544 -Al 0.12336840 0.62103060 0.00361731 -Al 0.99616524 0.48925375 0.25362513 -Al 0.98117131 0.62644935 0.37136093 -Al 0.12894634 0.50379571 0.37242499 -Al 0.11281094 0.61791522 0.23653922 -Al 0.01808736 0.50293326 0.50861626 -Al 0.00904743 0.61591806 0.62103423 -Al 0.13848768 0.50133681 0.62704074 -Al 0.14125752 0.61723661 0.49711754 -Al 0.99447209 0.50092890 0.73587422 -Al 0.00233139 0.61987218 0.88485758 -Al 0.11423935 0.49978838 0.87062184 -Al 0.13290899 0.61725594 0.74270006 -Al 0.99086456 0.75364660 0.99968861 -Al 0.00398990 0.87229282 0.12787519 -Al 0.12239428 0.75017273 0.12784381 -Al 0.12555114 0.90816791 0.98258663 -Al 0.01049989 0.74713048 0.24369317 -Al 0.01452501 0.86242344 0.36343987 -Al 0.14068372 0.71919916 0.37983031 -Al 0.12732315 0.86545353 0.25547295 -Al 0.00324572 0.73973536 0.49213570 -Al 0.00415696 0.87955716 0.61086809 -Al 0.12238405 0.73452563 0.63076634 -Al 0.12409440 0.87390067 0.49943718 -Al 0.98777456 0.73322840 0.75407881 -Al 0.99646019 0.86583180 0.85498692 -Al 0.13659795 0.76454256 0.86532624 -Al 0.10978263 0.87092221 0.73606175 -Al 0.25915597 0.00401851 0.00187250 -Al 0.24571288 0.13490653 0.12313283 -Al 0.38461535 0.01321989 0.12024588 -Al 0.38254938 0.10881029 0.99932284 -Al 0.23630941 0.00365269 0.23682128 -Al 0.23932729 0.13748136 0.37767376 -Al 0.35175036 0.00299014 0.35462908 -Al 0.38160570 0.11768793 0.24656927 -Al 0.25022772 0.00247378 0.50223552 -Al 0.25290592 0.12804105 0.64129006 -Al 0.35833252 0.00892378 0.61879582 -Al 0.36587948 0.13756262 0.48839728 -Al 0.26255835 0.99810339 0.75801599 -Al 0.25478784 0.11463255 0.86633148 -Al 0.38643670 0.99579132 0.87259852 -Al 0.37406867 0.13033491 0.75869549 -Al 0.25590653 0.22641386 0.99306590 -Al 0.24480256 0.37198323 0.12480935 -Al 0.36307446 0.24728100 0.12855749 -Al 0.36611837 0.37770560 0.98956894 -Al 0.25732328 0.24036596 0.24963263 -Al 0.26178391 0.37436523 0.37213467 -Al 0.39540356 0.25277326 0.36554549 -Al 0.37211418 0.36624633 0.24368251 -Al 0.24998185 0.25956939 0.50866947 -Al 0.25933098 0.38042074 0.63280852 -Al 0.36977365 0.24938293 0.63194413 -Al 0.37611510 0.37176966 0.52143167 -Al 0.25125820 0.26625872 0.75123849 -Al 0.24572735 0.37921961 0.87722562 -Al 0.38001966 0.26265017 0.87915180 -Al 0.38797703 0.38734783 0.75318925 -Al 0.25337644 0.48311489 0.98893756 -Al 0.25082130 0.63030047 0.12522591 -Al 0.36812638 0.50542986 0.11192537 -Al 0.37147702 0.64352610 0.99748713 -Al 0.25482885 0.49279128 0.24426320 -Al 0.26255859 0.61980747 0.35381809 -Al 0.39833472 0.47411286 0.37543666 -Al 0.38601867 0.62823543 0.22622947 -Al 0.28657218 0.49549567 0.52595028 -Al 0.24380946 0.62661418 0.62404858 -Al 0.39373417 0.50706466 0.64301044 -Al 0.37095206 0.62533078 0.48733605 -Al 0.24736526 0.49933268 0.74125274 -Al 0.24761888 0.63431750 0.88196157 -Al 0.37149023 0.51800057 0.87696325 -Al 0.37794096 0.63369203 0.75816843 -Al 0.23836561 0.76056427 0.99477818 -Al 0.24131568 0.89210906 0.11549309 -Al 0.37048120 0.76124854 0.12049514 -Al 0.38519915 0.86810347 0.00567275 -Al 0.25016305 0.75138659 0.23978858 -Al 0.25572040 0.86443513 0.38218184 -Al 0.37122386 0.75221266 0.36822337 -Al 0.37304725 0.88660401 0.24725417 -Al 0.24853807 0.74934332 0.49937981 -Al 0.24578430 0.86783388 0.61230958 -Al 0.36545620 0.75806076 0.62410880 -Al 0.38269084 0.86964335 0.49019401 -Al 0.24754642 0.75355619 0.74245691 -Al 0.25394675 0.88863666 0.88148363 -Al 0.37863264 0.75636537 0.87606615 -Al 0.35761682 0.86440202 0.74521648 -Al 0.51499715 0.00319261 0.00477126 -Al 0.51481261 0.12996220 0.13474526 -Al 0.62158434 0.00614594 0.12215865 -Al 0.65237837 0.12549271 0.00390157 -Al 0.49754806 0.00528446 0.26262522 -Al 0.50287427 0.12470574 0.37188463 -Al 0.61946372 0.98982600 0.38218034 -Al 0.64935763 0.10729665 0.24758591 -Al 0.47551685 0.99956968 0.49901800 -Al 0.50892011 0.13889582 0.61668574 -Al 0.60076090 0.01007511 0.63385797 -Al 0.63091576 0.13976607 0.49362185 -Al 0.49421776 0.01312506 0.75862686 -Al 0.49621059 0.12291397 0.86890150 -Al 0.63934845 0.99916982 0.85049437 -Al 0.61681448 0.14508342 0.73986697 -Al 0.48769740 0.25124783 0.00207689 -Al 0.47463706 0.36641884 0.12184645 -Al 0.62560511 0.23877120 0.14181062 -Al 0.59987624 0.35913576 0.00109698 -Al 0.50793621 0.24051529 0.24970049 -Al 0.50335967 0.35603215 0.38725303 -Al 0.63660326 0.25582457 0.38160068 -Al 0.60783240 0.36222884 0.24609293 -Al 0.51154027 0.25629003 0.51497816 -Al 0.52090502 0.39798657 0.61938646 -Al 0.64120602 0.25487225 0.63557789 -Al 0.63985186 0.37922251 0.51756772 -Al 0.48558201 0.25005398 0.74979241 -Al 0.48305174 0.38493797 0.89300468 -Al 0.60493260 0.24647258 0.86952639 -Al 0.61161918 0.37481564 0.75721499 -Al 0.49465181 0.51702544 0.99192035 -Al 0.49914787 0.63113208 0.11509975 -Al 0.62302427 0.47390439 0.11172027 -Al 0.63016191 0.62362099 0.00625799 -Al 0.49412118 0.49545462 0.22574940 -Al 0.47747692 0.63009549 0.35695734 -Al 0.60810916 0.50761338 0.35978059 -Al 0.59868251 0.62462031 0.24140141 -Al 0.49397117 0.49436373 0.50461690 -Al 0.49527334 0.63575966 0.61335415 -Al 0.62202937 0.52376172 0.61754791 -Al 0.61067026 0.62615954 0.48975529 -Al 0.50378713 0.52486314 0.76214834 -Al 0.50342176 0.64218225 0.89639227 -Al 0.63460995 0.49635123 0.89215000 -Al 0.62306277 0.62243114 0.77040359 -Al 0.49813316 0.75553856 0.01249464 -Al 0.49821260 0.89797411 0.13401262 -Al 0.62674909 0.76314352 0.11415762 -Al 0.64633386 0.88142948 0.99393902 -Al 0.50262788 0.75466934 0.22817995 -Al 0.49695095 0.88053472 0.36735805 -Al 0.60144892 0.74827175 0.35927704 -Al 0.62697231 0.87481939 0.25321199 -Al 0.50976097 0.75616883 0.50449068 -Al 0.47924483 0.88507555 0.63166182 -Al 0.61072067 0.73626637 0.64143886 -Al 0.62405649 0.86250950 0.51229166 -Al 0.49627788 0.75770414 0.74912230 -Al 0.50931543 0.88500510 0.89103730 -Al 0.62093952 0.75937091 0.86356121 -Al 0.61079532 0.88542366 0.75706146 -Al 0.76942928 0.01275478 0.98896792 -Al 0.76900380 0.13833165 0.12755103 -Al 0.88843162 0.00002330 0.11723526 -Al 0.87904013 0.14493043 0.01566621 -Al 0.76299977 0.98949876 0.25726062 -Al 0.75660628 0.13497699 0.40157499 -Al 0.87337804 0.99298996 0.39733808 -Al 0.86509532 0.14084476 0.24611173 -Al 0.72996921 0.01379975 0.51259482 -Al 0.75608860 0.12392179 0.62199785 -Al 0.88836023 0.99588670 0.61441490 -Al 0.87436399 0.11623832 0.50237334 -Al 0.74398167 0.97164587 0.72782530 -Al 0.77313058 0.12980295 0.87514246 -Al 0.86187816 0.97468338 0.84696398 -Al 0.86126118 0.11153114 0.74510311 -Al 0.75396696 0.25339185 0.00296242 -Al 0.74015671 0.36400648 0.13693966 -Al 0.87351879 0.25698453 0.12214002 -Al 0.86846946 0.37844467 0.99520824 -Al 0.75292998 0.26282060 0.25201310 -Al 0.74094593 0.37281295 0.38427625 -Al 0.88374473 0.25018272 0.39052697 -Al 0.87964520 0.37266828 0.25636568 -Al 0.76129696 0.26752007 0.50952263 -Al 0.75056265 0.38534230 0.64503133 -Al 0.86909435 0.24681380 0.65092018 -Al 0.87561027 0.37153049 0.49836062 -Al 0.75238644 0.24593310 0.76155881 -Al 0.74153230 0.37239140 0.87651124 -Al 0.87743734 0.24764967 0.87830838 -Al 0.87763737 0.37125664 0.76989004 -Al 0.74584829 0.49283393 0.00898408 -Al 0.71809766 0.62079947 0.12517126 -Al 0.85685040 0.50082651 0.12954336 -Al 0.86086108 0.62132596 0.00015819 -Al 0.73061951 0.47970375 0.26100073 -Al 0.74614824 0.61097569 0.34777627 -Al 0.86786155 0.49865301 0.37183224 -Al 0.87376143 0.65193653 0.23354491 -Al 0.77182689 0.49756351 0.51125741 -Al 0.75218156 0.61968730 0.63092796 -Al 0.88241459 0.51069354 0.63527469 -Al 0.87228183 0.62094869 0.51073240 -Al 0.74780567 0.49613700 0.76263882 -Al 0.74787743 0.61833493 0.87864454 -Al 0.88339699 0.49583713 0.88714142 -Al 0.86136746 0.62650713 0.77123128 -Al 0.74945256 0.74756882 0.99327091 -Al 0.74817869 0.88539443 0.13689326 -Al 0.85856705 0.75790909 0.10780622 -Al 0.87931271 0.88451540 0.99870784 -Al 0.73274428 0.74316657 0.24890149 -Al 0.77570677 0.86918008 0.37857483 -Al 0.88402575 0.75230304 0.36329815 -Al 0.89399754 0.86267000 0.23918041 -Al 0.73639190 0.72941077 0.47788508 -Al 0.76226014 0.84168480 0.61434570 -Al 0.87216378 0.74040872 0.62510747 -Al 0.86559942 0.86095482 0.49903510 -Al 0.74108688 0.74090179 0.75678531 -Al 0.74478553 0.87153100 0.87185596 -Al 0.86380129 0.75015417 0.86619297 -Al 0.87277279 0.87628994 0.73369200 diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot12/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot12/Al.scf.pw deleted file mode 100644 index 27d5a4dd0..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot12/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.99356356 0.01003234 0.00089136 -Al 0.98575032 0.10784255 0.14311819 -Al 0.12807012 0.99852230 0.13484251 -Al 0.10989125 0.12653909 0.00754698 -Al 0.99878356 0.98912360 0.25217628 -Al 0.99525099 0.12291132 0.40579143 -Al 0.10611383 0.99632605 0.37614334 -Al 0.10664056 0.12882222 0.26294333 -Al 0.99591937 0.98413624 0.51004513 -Al 0.99873239 0.11964307 0.63516025 -Al 0.12971843 0.98524407 0.61614047 -Al 0.13018385 0.12041564 0.52884396 -Al 0.00208971 0.99522038 0.74327318 -Al 0.01253943 0.14023697 0.89875777 -Al 0.12106366 0.01531784 0.86781823 -Al 0.12956652 0.11909856 0.73876577 -Al 0.02979535 0.24871571 0.01054554 -Al 0.01464289 0.38201263 0.13229357 -Al 0.12182155 0.25149981 0.14253406 -Al 0.12590822 0.38872776 0.99830530 -Al 0.99093004 0.25894721 0.27317879 -Al 0.99602139 0.39049519 0.39439336 -Al 0.12671012 0.23775179 0.41706343 -Al 0.10739525 0.37802063 0.27792375 -Al 0.99999498 0.26184062 0.50700042 -Al 0.01095485 0.37103636 0.62538640 -Al 0.12370907 0.25512713 0.63086101 -Al 0.13261428 0.39036405 0.51287211 -Al 0.00432402 0.25285010 0.75523883 -Al 0.00659139 0.37128221 0.88548254 -Al 0.12972864 0.26206274 0.86786229 -Al 0.13430250 0.38027196 0.76033850 -Al 0.00258158 0.52051824 0.99403345 -Al 0.00530641 0.61791722 0.12303543 -Al 0.12448527 0.48735464 0.11630500 -Al 0.13464893 0.61836395 0.99415827 -Al 0.00225155 0.50050483 0.23070247 -Al 0.01053709 0.60194789 0.37342236 -Al 0.13869118 0.49572983 0.37383649 -Al 0.12651329 0.62381920 0.23766416 -Al 0.00762097 0.49942317 0.50285866 -Al 0.99980680 0.63046327 0.61529426 -Al 0.11974711 0.51537222 0.64109264 -Al 0.13244798 0.61664118 0.49576361 -Al 0.00736615 0.51490744 0.74392904 -Al 0.99940384 0.61284340 0.86526765 -Al 0.12169270 0.50044314 0.89618547 -Al 0.11489764 0.64028970 0.75141705 -Al 0.01586274 0.73555320 0.98553893 -Al 0.00572588 0.85797060 0.11259533 -Al 0.11854537 0.74277112 0.12396578 -Al 0.12702961 0.88501137 0.99969620 -Al 0.00924495 0.75172075 0.24043823 -Al 0.97978514 0.86779037 0.37399871 -Al 0.13199369 0.74460697 0.34998344 -Al 0.13374326 0.86313364 0.23418417 -Al 0.99961163 0.72979918 0.48927139 -Al 0.01766119 0.86531167 0.62828693 -Al 0.12352606 0.74884077 0.61307360 -Al 0.12210593 0.85898068 0.48396704 -Al 0.98478303 0.75002992 0.74060546 -Al 0.98635845 0.86548458 0.87219614 -Al 0.12385645 0.75495956 0.87511538 -Al 0.12794609 0.88350612 0.74281708 -Al 0.23281891 0.00683942 0.99845315 -Al 0.24260286 0.12738938 0.12290146 -Al 0.37319926 0.01604871 0.11133828 -Al 0.37473305 0.12460102 0.99720594 -Al 0.24957219 0.00950823 0.25267825 -Al 0.26124499 0.13695771 0.38311453 -Al 0.37195119 0.00445083 0.35773736 -Al 0.38972988 0.11263582 0.25781414 -Al 0.25141209 0.99457904 0.47453026 -Al 0.27883910 0.12291896 0.61413592 -Al 0.36879991 0.98963524 0.60746848 -Al 0.38667358 0.12478710 0.48190908 -Al 0.25305982 0.00921240 0.74336945 -Al 0.24766462 0.12997246 0.86286458 -Al 0.36915206 0.00779588 0.87171951 -Al 0.37284756 0.13919954 0.74851621 -Al 0.23065113 0.24321274 0.02171583 -Al 0.24503152 0.35412918 0.14217556 -Al 0.37642074 0.23243096 0.12604506 -Al 0.35211490 0.34697485 0.02143041 -Al 0.23439098 0.24562997 0.26084078 -Al 0.25683578 0.37534243 0.39489128 -Al 0.37913489 0.24801094 0.38742430 -Al 0.36911931 0.38424552 0.26137548 -Al 0.24756600 0.26698802 0.52545655 -Al 0.24197568 0.38837382 0.63539973 -Al 0.37413785 0.24821818 0.63050193 -Al 0.39304087 0.38288625 0.51289106 -Al 0.25453562 0.25266592 0.76308536 -Al 0.25666988 0.38418381 0.87516202 -Al 0.36189232 0.24441308 0.87851881 -Al 0.37645368 0.37064658 0.74714361 -Al 0.24095624 0.49707451 0.98612493 -Al 0.25558784 0.61819079 0.10547977 -Al 0.36037732 0.48043934 0.10902784 -Al 0.36469897 0.62467287 0.00040859 -Al 0.25076300 0.50086934 0.23483277 -Al 0.27105230 0.61707349 0.35892034 -Al 0.38488185 0.51051738 0.37533992 -Al 0.36488260 0.62551518 0.23799240 -Al 0.27029579 0.50307561 0.49210801 -Al 0.26295349 0.62334039 0.62588465 -Al 0.36903317 0.51652512 0.62682347 -Al 0.37853376 0.62927934 0.50127062 -Al 0.24372608 0.49811390 0.75521477 -Al 0.23967853 0.63040389 0.86170476 -Al 0.36649781 0.51369507 0.86400692 -Al 0.36227678 0.63416902 0.74670007 -Al 0.24986000 0.74573080 0.99761359 -Al 0.24121059 0.88237354 0.11861331 -Al 0.36388068 0.75204259 0.10948028 -Al 0.34602111 0.87067128 0.97480433 -Al 0.25928845 0.75271950 0.23418736 -Al 0.24493557 0.86546187 0.36675790 -Al 0.37091151 0.74415446 0.36708527 -Al 0.36343262 0.89161499 0.23301950 -Al 0.25603880 0.74242507 0.49913233 -Al 0.25404950 0.86959241 0.63426666 -Al 0.37309762 0.75767445 0.61889837 -Al 0.38158614 0.87119960 0.49199500 -Al 0.24206879 0.73566518 0.73749330 -Al 0.24073126 0.87157190 0.86717599 -Al 0.38304113 0.74107688 0.87729227 -Al 0.36408259 0.85799339 0.75252114 -Al 0.50513062 0.99641307 0.98400984 -Al 0.50296346 0.11448407 0.12668005 -Al 0.62334617 0.00081674 0.10975498 -Al 0.61750462 0.12749031 0.00009284 -Al 0.51024423 0.99838161 0.25179943 -Al 0.51489566 0.11485434 0.37313705 -Al 0.64582982 0.00199386 0.37353296 -Al 0.61796938 0.12867744 0.24490015 -Al 0.51716374 0.99973136 0.48138001 -Al 0.50610684 0.12531688 0.63067543 -Al 0.62036274 0.00178667 0.62155819 -Al 0.62084175 0.12791261 0.50681745 -Al 0.49832577 0.99575068 0.73836487 -Al 0.50070010 0.14207927 0.87351497 -Al 0.63908772 0.00569409 0.88160058 -Al 0.63355509 0.12222953 0.75987675 -Al 0.50038998 0.25762406 0.01340511 -Al 0.48193974 0.37834507 0.13306162 -Al 0.61140817 0.23758186 0.12714498 -Al 0.63563834 0.37082311 0.99664333 -Al 0.47615289 0.26037558 0.26170828 -Al 0.50244274 0.38657000 0.38397936 -Al 0.61447512 0.26576159 0.36920027 -Al 0.62654667 0.36545813 0.23416209 -Al 0.50543096 0.24073779 0.51048729 -Al 0.50459825 0.38682566 0.61881449 -Al 0.62855761 0.26430993 0.63207037 -Al 0.63568155 0.38123575 0.50712325 -Al 0.50967113 0.25120456 0.74106710 -Al 0.48421989 0.36729941 0.88177216 -Al 0.62384526 0.26301694 0.87186622 -Al 0.63073225 0.36866811 0.75201037 -Al 0.48097910 0.48390417 0.99821036 -Al 0.50722141 0.64728210 0.11287231 -Al 0.61881676 0.51277894 0.13060944 -Al 0.63477511 0.61919051 0.99455981 -Al 0.48687654 0.50626543 0.24284525 -Al 0.47859383 0.62869280 0.35594998 -Al 0.61413153 0.50701952 0.38002146 -Al 0.62280048 0.62696908 0.27497080 -Al 0.50294549 0.52214771 0.50004990 -Al 0.50146342 0.62822278 0.62300088 -Al 0.63162580 0.51446293 0.61988389 -Al 0.61141308 0.63472036 0.48349964 -Al 0.51260085 0.46918515 0.76028414 -Al 0.51104040 0.60857060 0.89485797 -Al 0.65011959 0.49446663 0.87902306 -Al 0.61362423 0.62093954 0.75813041 -Al 0.48710437 0.75745600 0.99501901 -Al 0.48973515 0.86789951 0.09837923 -Al 0.63616173 0.74976861 0.12205086 -Al 0.61393755 0.88107241 0.00121918 -Al 0.49251403 0.76169421 0.23530339 -Al 0.48843469 0.86577219 0.36627813 -Al 0.60980726 0.75511509 0.38136663 -Al 0.63006496 0.89021637 0.25609292 -Al 0.50317495 0.75413157 0.50360256 -Al 0.50186666 0.86110304 0.61274668 -Al 0.62591280 0.73642028 0.63902310 -Al 0.62635617 0.86957077 0.50039669 -Al 0.48848703 0.74126400 0.75365852 -Al 0.48830430 0.89929262 0.87274161 -Al 0.60419105 0.75863399 0.88220727 -Al 0.60897796 0.87202443 0.75776707 -Al 0.75198199 0.99514896 0.99067194 -Al 0.74437405 0.11725698 0.11920301 -Al 0.88952245 0.98274769 0.11531456 -Al 0.87028913 0.12588560 0.00787712 -Al 0.75365490 0.00061326 0.23774267 -Al 0.74578279 0.14348118 0.37030803 -Al 0.87559753 0.00289877 0.37717284 -Al 0.86859827 0.11175353 0.25779592 -Al 0.76189478 0.99350554 0.50477659 -Al 0.74128373 0.12545870 0.63043970 -Al 0.88529468 0.00275624 0.61235046 -Al 0.85535532 0.13345023 0.50518137 -Al 0.76889044 0.02237831 0.76412046 -Al 0.74708388 0.13464176 0.88235626 -Al 0.88898039 0.97942093 0.86774633 -Al 0.90108592 0.12092111 0.77053068 -Al 0.75532731 0.25389389 0.01334853 -Al 0.74021578 0.38131483 0.11953630 -Al 0.88138249 0.23845556 0.12745773 -Al 0.89094944 0.36508644 0.00523307 -Al 0.75431519 0.24787375 0.22378998 -Al 0.75943440 0.38120771 0.36654359 -Al 0.87415826 0.24436743 0.38648693 -Al 0.87703009 0.36167569 0.26034223 -Al 0.75796453 0.26249101 0.50398517 -Al 0.76349973 0.38560940 0.63662388 -Al 0.88315901 0.25170637 0.62818998 -Al 0.87181258 0.36382161 0.50864451 -Al 0.77820221 0.24752534 0.75132653 -Al 0.76996909 0.36273668 0.88154811 -Al 0.87893323 0.24322421 0.88438692 -Al 0.87131771 0.40206408 0.74936811 -Al 0.75595322 0.50634839 0.00512157 -Al 0.75419889 0.63612856 0.15389514 -Al 0.87950252 0.50493572 0.13697031 -Al 0.86711156 0.64367810 0.00025671 -Al 0.73854042 0.50230799 0.26121321 -Al 0.76718935 0.59600012 0.37447511 -Al 0.89096036 0.49190123 0.35505283 -Al 0.87160994 0.61211988 0.25373954 -Al 0.74202255 0.49273495 0.50505658 -Al 0.74787670 0.61804328 0.61136827 -Al 0.88110310 0.50937986 0.62923862 -Al 0.87509207 0.61073232 0.48931031 -Al 0.74702002 0.50347998 0.74961012 -Al 0.73619298 0.62628413 0.87284646 -Al 0.88140637 0.51382436 0.88158995 -Al 0.85403888 0.63099412 0.74884340 -Al 0.74123069 0.77509948 0.00205987 -Al 0.75265569 0.89651831 0.10672897 -Al 0.87799168 0.74945915 0.12091763 -Al 0.88885237 0.85893169 0.99713137 -Al 0.74546051 0.76042638 0.25358915 -Al 0.76622972 0.86281674 0.37754999 -Al 0.88245870 0.72775273 0.37174090 -Al 0.87867874 0.86315492 0.25152519 -Al 0.74554072 0.77004286 0.51338619 -Al 0.73128958 0.88528307 0.63568263 -Al 0.87388715 0.72935706 0.60818655 -Al 0.87314642 0.85904168 0.51145345 -Al 0.75249519 0.75486973 0.74441073 -Al 0.73301069 0.87728214 0.87216128 -Al 0.86527550 0.73970698 0.87581861 -Al 0.87501572 0.86803272 0.72360716 diff --git a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot2/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot2/Al.scf.pw deleted file mode 100644 index 181d0b614..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/933K/2.699gcc/snapshot2/Al.scf.pw +++ /dev/null @@ -1,296 +0,0 @@ -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.false., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.00590928377 - nbnd=576 -/ -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000000000 0.00000000000000 0.00000000000000 -0.00000000000000 16.19800000000000 0.00000000000000 -0.00000000000000 0.00000000000000 16.19800000000000 - -ATOMIC_POSITIONS crystal -Al 0.60575303 0.65152903 0.25701091 -Al 0.85404731 0.46357614 0.67433872 -Al 0.99877772 0.60914911 0.82390070 -Al 0.27859413 0.89969962 0.89125424 -Al 0.30361768 0.66392310 0.01769301 -Al 0.45909444 0.11041865 0.04158860 -Al 0.51344602 0.66594214 0.49290494 -Al 0.11668300 0.77201108 0.24587013 -Al 0.13530006 0.81932385 0.59349137 -Al 0.90351025 0.12815182 0.25840625 -Al 0.84038666 0.99784379 0.91619911 -Al 0.33487752 0.27498441 0.82918654 -Al 0.07299958 0.34882994 0.16877466 -Al 0.71791859 0.50242523 0.77044836 -Al 0.47841715 0.09506908 0.31515214 -Al 0.49660987 0.43020678 0.18806053 -Al 0.12450417 0.96724343 0.47450171 -Al 0.72333297 0.98318325 0.48682347 -Al 0.83966188 0.56122424 0.52319773 -Al 0.09102734 0.43111722 0.43724920 -Al 0.98924304 0.96842994 0.58600029 -Al 0.76078454 0.04874999 0.17920286 -Al 0.36679411 0.22930742 0.35843401 -Al 0.31831677 0.52075466 0.11108633 -Al 0.63539181 0.23634137 0.02619844 -Al 0.03355046 0.63075531 0.22423275 -Al 0.45024345 0.81675038 0.42315535 -Al 0.87293176 0.69783453 0.61431133 -Al 0.39033617 0.17759780 0.69667792 -Al 0.17649947 0.68154954 0.46579851 -Al 0.18426206 0.94174442 0.74348653 -Al 0.68099671 0.70810844 0.52213904 -Al 0.53876639 0.40915266 0.49815122 -Al 0.02693556 0.76157904 0.90856401 -Al 0.05916912 0.10215678 0.50228288 -Al 0.27550096 0.82766390 0.48058623 -Al 0.93426446 0.02357777 0.73836719 -Al 0.19476741 0.95062519 0.31447977 -Al 0.48632386 0.58472326 0.19500125 -Al 0.78348425 0.48413805 0.33214649 -Al 0.24224007 0.13523114 0.76319141 -Al 0.89883576 0.94937607 0.21968713 -Al 0.60597007 0.35588071 0.90524082 -Al 0.09911145 0.35154851 0.99980366 -Al 0.55260123 0.53856538 0.60958928 -Al 0.42927683 0.79394568 0.58958516 -Al 0.54009471 0.53378352 0.35324718 -Al 0.45537526 0.66343491 0.89127739 -Al 0.84934146 0.60974455 0.75658591 -Al 0.07913572 0.78491549 0.08110364 -Al 0.44829382 0.38966873 0.83349141 -Al 0.77396185 0.29960716 0.63240968 -Al 0.65079274 0.49234018 0.23641034 -Al 0.26446145 0.39048346 0.33250067 -Al 0.81262978 0.26217759 0.05296408 -Al 0.57703112 0.83594868 0.52428150 -Al 0.78398215 0.37381555 0.20171047 -Al 0.22917357 0.66297862 0.30292850 -Al 0.35381317 0.81909122 0.01501819 -Al 0.78507039 0.97664676 0.33911735 -Al 0.32016285 0.83130659 0.75206696 -Al 0.88449864 0.09528742 0.51153741 -Al 0.25119871 0.45909167 0.60155061 -Al 0.64362575 0.73857168 0.12463268 -Al 0.89318594 0.66367051 0.93502635 -Al 0.36876695 0.99862495 0.99965200 -Al 0.97270251 0.82083602 0.50017917 -Al 0.18303659 0.65016600 0.13349029 -Al 0.92487837 0.01931785 0.36704216 -Al 0.24017859 0.53863193 0.41692874 -Al 0.76351266 0.35979086 0.92798958 -Al 0.69261513 0.96531906 0.92574822 -Al 0.54435528 0.79814819 0.22920632 -Al 0.88169857 0.07921779 0.07201909 -Al 0.19037486 0.50578691 0.98494926 -Al 0.52922613 0.25110530 0.77593027 -Al 0.15539472 0.21097437 0.61960001 -Al 0.81382418 0.78895868 0.14437238 -Al 0.32365096 0.00583642 0.18758443 -Al 0.21010421 0.32461183 0.47448313 -Al 0.67931177 0.43063387 0.62478492 -Al 0.38344826 0.36620567 0.49844866 -Al 0.62756477 0.39253491 0.05427839 -Al 0.04682937 0.28097788 0.50446976 -Al 0.56786163 0.71331755 0.63707233 -Al 0.20499961 0.40406683 0.11178370 -Al 0.77903451 0.47441750 0.07174782 -Al 0.22846884 0.14259648 0.45574527 -Al 0.77482681 0.08054184 0.80552625 -Al 0.47923856 0.24010639 0.93365490 -Al 0.29990335 0.60411420 0.72048002 -Al 0.38280909 0.10841593 0.87730851 -Al 0.94793317 0.65472788 0.09620920 -Al 0.59933166 0.10935208 0.92404396 -Al 0.89442099 0.50241381 0.19983297 -Al 0.44926320 0.00833731 0.62232778 -Al 0.59695865 0.02133443 0.59458453 -Al 0.08655773 0.39355389 0.61631447 -Al 0.34115760 0.23177981 0.54128643 -Al 0.25916501 0.59690511 0.56619443 -Al 0.64351181 0.39513499 0.37563901 -Al 0.59353298 0.30235916 0.62681368 -Al 0.62691439 0.99158161 0.07129740 -Al 0.76622660 0.82221380 0.93801245 -Al 0.92729066 0.38258702 0.37765979 -Al 0.07650070 0.62917548 0.99482990 -Al 0.47817196 0.90578786 0.74200364 -Al 0.22480218 0.92469426 0.03924198 -Al 0.34823255 0.35930170 0.19419389 -Al 0.13636063 0.03351556 0.62896613 -Al 0.17602567 0.76468162 0.96634007 -Al 0.09760671 0.08909331 0.34825974 -Al 0.27721080 0.11723323 0.04428822 -Al 0.01786215 0.05815666 0.17369783 -Al 0.75981280 0.90938277 0.78308219 -Al 0.94258367 0.28384175 0.66654037 -Al 0.14218452 0.21042290 0.08924696 -Al 0.44046393 0.72432858 0.74510634 -Al 0.59621620 0.79905290 0.82780970 -Al 0.85756155 0.77744494 0.79610813 -Al 0.15233203 0.50405207 0.73704253 -Al 0.99647060 0.57634925 0.66519171 -Al 0.09405667 0.29738578 0.74163973 -Al 0.92912407 0.28945567 0.23243480 -Al 0.07945465 0.06249319 0.78565280 -Al 0.99789740 0.73017028 0.71705143 -Al 0.40169242 0.46113196 0.64790027 -Al 0.45044267 0.41037427 0.01883480 -Al 0.97070557 0.44634947 0.51674565 -Al 0.94682928 0.34384609 0.91774951 -Al 0.41358077 0.54206941 0.47677968 -Al 0.59047497 0.13244991 0.17673347 -Al 0.38139496 0.84107462 0.19171900 -Al 0.89044259 0.85951746 0.66473633 -Al 0.51085164 0.97589896 0.90281085 -Al 0.75483645 0.84004773 0.58447879 -Al 0.77655170 0.14718522 0.40190238 -Al 0.51342028 0.15597548 0.59328953 -Al 0.30856351 0.71638344 0.86536442 -Al 0.50467659 0.26656937 0.27062658 -Al 0.40950050 0.93478141 0.30704980 -Al 0.49632654 0.96808837 0.47871400 -Al 0.40418357 0.10985918 0.47401821 -Al 0.58263174 0.91403908 0.34832193 -Al 0.59696883 0.62241142 0.77593821 -Al 0.73991348 0.10302885 0.02793209 -Al 0.86155405 0.93600436 0.49469915 -Al 0.42141007 0.62518513 0.61389495 -Al 0.49715974 0.27258546 0.10353424 -Al 0.13115310 0.66411053 0.67922921 -Al 0.69541286 0.28124963 0.48771501 -Al 0.28443482 0.73854518 0.63466638 -Al 0.78897333 0.91943947 0.05570799 -Al 0.73481538 0.78532083 0.26855645 -Al 0.74649227 0.20675779 0.21551230 -Al 0.09848211 0.93034960 0.90790256 -Al 0.47655445 0.72588041 0.08904022 -Al 0.87531324 0.84029999 0.34980348 -Al 0.99279039 0.40406803 0.76307686 -Al 0.18198345 0.37950459 0.84522858 -Al 0.40178622 0.17276487 0.19543897 -Al 0.71078380 0.73365080 0.72057766 -Al 0.62942309 0.32366623 0.20641029 -Al 0.63686154 0.22946259 0.34123840 -Al 0.24013316 0.81026002 0.15791607 -Al 0.35630389 0.01695099 0.75698602 -Al 0.74100807 0.67195587 0.86435892 -Al 0.68130390 0.91853308 0.20358444 -Al 0.77962187 0.01122033 0.65414509 -Al 0.80594434 0.29814741 0.35727759 -Al 0.86719566 0.17063689 0.91377250 -Al 0.09617445 0.19417816 0.88057037 -Al 0.65830987 0.52803190 0.46225234 -Al 0.36330294 0.69203800 0.47978740 -Al 0.05561280 0.19005294 0.23739241 -Al 0.10262102 0.54047468 0.55857653 -Al 0.85293507 0.66532676 0.27457398 -Al 0.03116483 0.45385623 0.27613007 -Al 0.34357680 0.26351094 0.06026797 -Al 0.30236968 0.80077588 0.31964535 -Al 0.25435070 0.31352025 0.67989784 -Al 0.17836992 0.60659356 0.86175773 -Al 0.29483216 0.92671019 0.60422234 -Al 0.30028865 0.09575216 0.33463906 -Al 0.75617106 0.61550222 0.17541513 -Al 0.09268865 0.30757655 0.33375746 -Al 0.52398220 0.98165168 0.19439287 -Al 0.63971205 0.00787398 0.77775910 -Al 0.22177991 0.24899445 0.94011739 -Al 0.34557390 0.68988019 0.17837390 -Al 0.94091897 0.38962752 0.10653767 -Al 0.84567655 0.16455278 0.70114561 -Al 0.92843787 0.57487185 0.37553427 -Al 0.45982769 0.67769308 0.32351874 -Al 0.68094911 0.33089169 0.76635291 -Al 0.96855302 0.17934132 0.79734378 -Al 0.34456289 0.54422723 0.27667147 -Al 0.83636652 0.30880295 0.79439045 -Al 0.98808212 0.95738151 0.04642294 -Al 0.44348916 0.83387989 0.87367433 -Al 0.73870250 0.84315554 0.42904688 -Al 0.18038021 0.08273780 0.18717403 -Al 0.57131991 0.43398227 0.74465081 -Al 0.21598675 0.23797312 0.24892073 -Al 0.84537907 0.71808566 0.44831914 -Al 0.43695274 0.31554804 0.66222382 -Al 0.72039467 0.64690944 0.37327103 -Al 0.09497748 0.58350950 0.36078199 -Al 0.72629010 0.14052787 0.56119854 -Al 0.99503255 0.73702352 0.35211987 -Al 0.94501761 0.90294973 0.84286789 -Al 0.48249488 0.89864129 0.08240329 -Al 0.95500406 0.20842505 0.38776407 -Al 0.05418622 0.47213303 0.89961162 -Al 0.56391593 0.51635959 0.90478074 -Al 0.79354948 0.39581885 0.49269573 -Al 0.65825223 0.16927147 0.70039422 -Al 0.59517577 0.82824228 0.00902820 -Al 0.42734755 0.39985828 0.34840924 -Al 0.46096611 0.57666934 0.03829465 -Al 0.03598724 0.91497138 0.30402213 -Al 0.60224247 0.68381609 0.96974136 -Al 0.87866679 0.47820858 0.84293085 -Al 0.98801737 0.05116872 0.90077649 -Al 0.74532926 0.65189942 0.02370924 -Al 0.91461552 0.81309523 0.01487106 -Al 0.87135192 0.26692808 0.52123400 -Al 0.99693287 0.13753877 0.63816845 -Al 0.29602995 0.43550655 0.77581617 -Al 0.64839415 0.05967065 0.30852787 -Al 0.18991769 0.52913218 0.22927879 -Al 0.49814734 0.25850215 0.45307479 -Al 0.20451406 0.06743441 0.90276964 -Al 0.30901662 0.96992467 0.44238835 -Al 0.60461483 0.76131782 0.38082532 -Al 0.61278211 0.56504988 0.09421874 -Al 0.43915140 0.54644328 0.76611022 -Al 0.98734185 0.20458698 0.02194457 -Al 0.34278239 0.53720103 0.90390850 -Al 0.99384272 0.65970963 0.50713589 -Al 0.11906814 0.81496274 0.41657465 -Al 0.29503749 0.39353297 0.95550697 -Al 0.58730818 0.10631869 0.44904168 -Al 0.73959367 0.51816043 0.92591593 -Al 0.71316569 0.20708547 0.87551824 -Al 0.10855245 0.91696956 0.17233559 -Al 0.92188162 0.51501383 0.00866568 -Al 0.04338507 0.88971714 0.70834859 -Al 0.16282443 0.77993128 0.78410004 -Al 0.50189598 0.08363789 0.76231698 -Al 0.95992165 0.79921291 0.18436796 -Al 0.62743852 0.87590224 0.68348598 -Al 0.71935603 0.59681697 0.62990780 -Al 0.29482637 0.09349561 0.60385284 -Al 0.06399989 0.50471951 0.10357905 -Al 0.11637871 0.06533322 0.03330068 diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb deleted file mode 100644 index fbd7a16fe..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_02.ipynb +++ /dev/null @@ -1,789 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Updated 200220_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Fermi Level\n", - "# The Fermi energy printed out by QE (in the DOS output file) is 7.770. \n", - "# The output is truncated and it turns this is too inaccurate to obtain the correct electron number. \n", - "# We determined the Fermi energy to higher accuracy (by imposing the correct electron number = 3*256 = 768).\n", - "# There are 3 electrons per atom due to the choice of pseudopotential. \n", - "# CAUTION: Need to define an accurate Fermi energy for each snapshot. \n", - "fermi_energy = 7.770345\n", - "\n", - "# Boltzmann's constant\n", - "k = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load eigenvalues and DOS from QE output for two snapshots" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# filepath: blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333\n", - "## Snapshot 0: Eigenvalues (from PW std output file, slurm-1006575.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe_00 = np.loadtxt('snap_0/EIGS', delimiter=',')\n", - "k_weights_qe_00 = np.loadtxt('snap_0/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe_00 = np.loadtxt('snap_0/Al.dos', skiprows=1)\n", - "## Snapshot 1: Eigenvalues (from PW std output file, slurm-1006846.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe_01 = np.loadtxt('snap_1/EIGS', delimiter=',')\n", - "k_weights_qe_01 = np.loadtxt('snap_1/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe_01 = np.loadtxt('snap_1/Al.dos', skiprows=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energy, eF, t): \n", - " return 1.0 / (1.0 + np.exp((energy - eF) / (k * t)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(en, eF, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((en-eF)/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define functions which compute reference values of the electron number and band energy = sum of eigenvalues.\n", - "Recall the some definitions:\n", - "\n", - "The electron number is defined as\n", - "$$ N = \\sum_{k,i} w_{k,i}\\, f(\\epsilon(k,i) - \\mu)\\,,$$\n", - "the band energy is defined as\n", - "$$ E_b = \\sum_{k,i} w_{k,i}\\, \\epsilon(k,i) f(\\epsilon(k,i) - \\mu)\\,,$$ \n", - "where $i$ denotes the band, $k$ the k point, $w_{k,i}$ the weight of the k point, $\\epsilon(k,i)$ the eigenvalue for a given k point and band, $\\mu$ the chemical potential (which equals the Fermi energy at zero temperature), and $f$ the Fermi-Dirac distribution function (which truncates the sum at the chemical potential). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating electron number from sum of eigenvalues\n", - "def gen_ENUM(k_weights, array_eigs):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " # output:\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=fermi_energy, t=temp)\n", - " enum = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " enum += k_weights[idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating sum of eigenvalues = band energy\n", - "def gen_EBAND(k_weights, array_eigs):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " # output:\n", - " ## eband\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=fermi_energy, t=temp)\n", - " eband = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " eband += k_weights[idx_k]*array_eigs[idx_bnd , :][idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return eband" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the reference values for the electron number and the band energy by directly summing over the eigenvalues.\n", - "It turns out the value of the band energy obtained from QE below is not correct." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNAPSHOT 0\n", - "Electron number: 768.3800699587922\n", - "Band energy [eV]: 2598.726246188354\n", - " \n", - "SNAPSHOT 1\n", - "Electron number: 768.0000014009643\n", - "Band energy [eV]: 2598.579547828817\n" - ] - } - ], - "source": [ - "# Reference values of electron number and band energy\n", - "## Snapshot 0\n", - "enum_ref_00 = gen_ENUM(k_weights_qe_00, eigs_qe_00)\n", - "eband_ref_00 = gen_EBAND(k_weights_qe_00, eigs_qe_00)\n", - "print(\"SNAPSHOT 0\")\n", - "print(\"Electron number:\", enum_ref_00)\n", - "print(\"Band energy [eV]:\", eband_ref_00)\n", - "print(\" \")\n", - "\n", - "## Snapshot 1 #Need to determine more accurate Fermi energy for snaphot2\n", - "#fermi_energy = 7.7733618\n", - "enum_ref_01 = gen_ENUM(k_weights_qe_01, eigs_qe_01)\n", - "eband_ref_01 = gen_EBAND(k_weights_qe_01, eigs_qe_01)\n", - "print(\"SNAPSHOT 1\")\n", - "print(\"Electron number:\", enum_ref_01)\n", - "print(\"Band energy [eV]:\", eband_ref_01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, the electron number and band energy can be by aid of the density of states. This is useful, because, eventually, we will predict the local DOS/DOS from ML.\n", - "\n", - "In terms of the DOS, they are defined as \n", - "\n", - "$$N = \\int_{-\\infty}^{\\infty} d\\epsilon\\ D(\\epsilon)\\, f(\\epsilon)$$\n", - "and\n", - "$$E_b = \\int_{-\\infty}^{\\infty} d\\epsilon\\ D(\\epsilon)\\, f(\\epsilon)\\, \\epsilon\\,,$$\n", - "\n", - "\n", - "where $\\epsilon$ denotes the energy as a continuous variable, $D(\\epsilon)$ the DOS, $f(\\epsilon)$ the Fermi-Dirac distribution function.\n", - "\n", - "The explicit defintion of the DOS is given as a sum of $\\delta$-functions over the spectrum of eigenvalues.\n", - "$$ D(\\epsilon) = \\sum_i \\sum_k w_k\\, \\delta(\\epsilon-\\epsilon(i,k))$$\n", - "\n", - "Commonly (in particular, for the purposes of visualization), the $\\delta$-functions are represented by particular choice of functions (for example, Gaussians)\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "where $\\sigma$ denotes the width." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS sum over eigenvalues\n", - "def gen_DOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " ra_dos_ik[idx_bnd].append(gaussian(ra_en, array_eigs[idx_bnd , :][idx_k], sigma))\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating electron number from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enumFromDOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## enum\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights, array_en, array_eigs, sigma)\n", - " enum = sp.integrate.trapz(ra_dos*ra_fd, array_en)\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_ebandFromDOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## eband [Ry]\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights, array_en, array_eigs, sigma)\n", - " eband = sp.integrate.simps(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS and compare with QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en_00 = dos_qe_00[: , 0]\n", - "ra_dos_00 = gen_DOS(k_weights_qe_00, ra_en_00, eigs_qe_00, sigma=sigma_qe*Ry2eV )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_00[: , 0], dos_qe_00[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe_00[: , 0] , ra_dos_00, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We recover the QE-DOS result by using the same parameters as in the input for computing the DOS.\n", - "Now we can go ahead and investigate different energy grids and smearing values in order to improve upon the band energy and achieve better agreement with the band-energy output of QE." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**CAUTION: This is not correct. The direct sum over eigenvalues (see analysis above) does not yield the same value for the band energy.** \n", - "The QE output of snapshot 0 is given at ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```. \n", - "QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution. \n", - "\n", - "**However, this does not agree with the reference value of the band energy we obtain above by explicitly summing over the eigenvalues (which we know is the correct band energy.)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(\"Band energy from QE output [Ry]:\", eband_qe)\n", - "print(\"Reference value of band energy (correct) [Ry]:\", eband_ref_00/Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we analyze the behavior of the predicted electron number and band energy with respect to the choice of energy bin and smearing. One important question to answer is whether we can recover an accurate band energy (error < 10 meV) for a relatively smooth DOS which will be more suited for ML." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Snapshot 0:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define energy bin\n", - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe_00[: , 0][1]-dos_qe_00[: , 0][0])*0.5\n", - "print(ewidth)\n", - "# Define smearing\n", - "ra_sigma_00 = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum_00 = np.zeros(len(ra_sigma_00))\n", - "ra_eband_00 = np.zeros(len(ra_sigma_00))\n", - "ra_en = np.linspace(dos_qe_00[0, 0], dos_qe_00[-1, 0], int((dos_qe_00[-1, 0]-dos_qe_00[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma_00)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum_00[i] = gen_enumFromDOS(k_weights_qe_00, ra_en, eigs_qe_00, sigma=ra_sigma_00[i])\n", - " # Generate band energy\n", - " ra_eband_00[i] = gen_ebandFromDOS(k_weights_qe_00, ra_en , eigs_qe_00, sigma=ra_sigma_00[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum_00)\n", - "print(ra_eband_00)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref_00, ra_sigma_00[0], ra_sigma_00[-1], linewidth=3)\n", - "ax.plot(ra_sigma_00, ra_enum_00, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref_00/Ry2eV, ra_sigma_00[0], ra_sigma_00[-1], linewidth=3)\n", - "ax.scatter(ra_sigma_00, ra_eband_00, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(\"Error in band energy [Ry/atom]\", np.min(abs(ra_eband_00-eband_ref_00/Ry2eV))/256)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate the corresponding DOS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos_00 = gen_DOS(k_weights_qe_00, ra_en, eigs_qe_00, sigma=ra_sigma_00[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos_00, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe_00[: , 0], dos_qe_00[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Snapshot 1:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define energy bin\n", - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0])*0.5\n", - "print(ewidth)\n", - "# Define smearing\n", - "ra_sigma_01 = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "ra_en_01 = np.linspace(dos_qe_01[0, 0], dos_qe_01[-1, 0], int((dos_qe_01[-1, 0]-dos_qe_01[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma_01)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enumFromDOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_ebandFromDOS(k_weights_qe_01, ra_en_01 , eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_en_01[1]-ra_en_01[0])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb deleted file mode 100644 index 53c163299..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm04.ipynb +++ /dev/null @@ -1,2148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Attila Cangi, 14.02.2020" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Fermi Level\n", - "fermi_energy = 7.770345\n", - "\n", - "# Boltzmann's constant\n", - "k = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load eigenvalues and DOS from QE output" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# filepath: blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333\n", - "## Snapshot 0: Eigenvalues (from PW std output file, slurm-1006575.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe = np.loadtxt('snap_0/EIGS', delimiter=',')\n", - "k_weights_qe = np.loadtxt('snap_0/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe = np.loadtxt('snap_0/Al.dos', skiprows=1)\n", - "## Snapshot 1: Eigenvalues (from PW std output file, slurm-1006846.out)\n", - "### rows: band index, row i: eigs[i , :]\n", - "### cols: k points, col j: eigs[: , j]\n", - "eigs_qe_01 = np.loadtxt('snap_1/EIGS', delimiter=',')\n", - "k_weights_qe_01 = np.loadtxt('snap_1/k_weights', delimiter=',')\n", - "## DOS\n", - "dos_qe_01 = np.loadtxt('snap_1/Al.dos', skiprows=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energy, eF, t): \n", - " return 1.0 / (1.0 + np.exp((energy - eF) / (k * t)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(en, eF, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((en-eF)/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a discretized delta function that maintains 0th and 1st moments\n", - "def delta_M1(en, eF):\n", - " de = en-eF\n", - " de_above = np.min(de,initial=np.max(de),where=np.greater(de,0.0))\n", - " de_below = np.max(de,initial=np.min(de),where=np.less_equal(de,0.0))\n", - " result = 1.0 - np.abs(de)/(de_above-de_below)\n", - " result = result*np.greater_equal(result,0.0)*np.less_equal(result,1.0)\n", - " result = result/(de_above-de_below)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS(k_weights, array_en, array_eigs, sigma):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " ra_dos_ik[idx_bnd].append(gaussian(ra_en, array_eigs[idx_bnd , :][idx_k], sigma))\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_M1(k_weights, array_en, array_eigs):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " ra_dos_ik[idx_bnd].append(delta_M1(ra_en, array_eigs[idx_bnd , :][idx_k]))\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from eigenvalues\n", - "def gen_EIG(k_weights, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## eig: band energy\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=E_fermi, t=temperature)\n", - " eig = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " eig += k_weights[idx_k]*array_eigs[idx_bnd , :][idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return eig" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating number of electrons from eigenvalues\n", - "def gen_NUM(k_weights, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## num: number of electrons\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_fd = fd_function(array_eigs, eF=E_fermi, t=temperature)\n", - " num = 0.0\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " num += k_weights[idx_k]*ra_fd[idx_bnd , :][idx_k] \n", - " return num" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.7730052436877\n" - ] - } - ], - "source": [ - "eband_ref=gen_EIG(k_weights_qe, eigs_qe)\n", - "print(eband_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0000498916445\n" - ] - } - ], - "source": [ - "enum_ref=gen_NUM(k_weights_qe, eigs_qe)\n", - "print(enum_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband(k_weights, array_en, array_eigs, sigma, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " #eband = sp.integrate.simps(ra_dos*array_en*ra_fd, array_en)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_M1(k_weights, array_en, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " ra_dos = gen_DOS_M1(k_weights_qe, array_en, eigs_qe)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enum(k_weights, array_en, array_eigs, sigma, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " enum = sp.integrate.trapz(ra_dos*ra_fd, array_en)\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enum_M1(k_weights, array_en, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " ra_dos = gen_DOS_M1(k_weights_qe, array_en, eigs_qe)\n", - " enum = sp.integrate.trapz(ra_dos*ra_fd, array_en)\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_int(k_weights, array_en, array_eigs, sigma, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " f_dos = sp.interpolate.interp1d(array_en,ra_dos)\n", - " eband, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*e*fd_function(e, eF=E_fermi, t=temperature),\n", - " array_en[0],array_en[-1],limit=500,points=(E_fermi))\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_int_M1(k_weights, array_en, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " ra_dos = gen_DOS_M1(k_weights_qe, array_en, eigs_qe)\n", - " f_dos = sp.interpolate.interp1d(array_en,ra_dos)\n", - " eband, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*e*fd_function(e, eF=E_fermi, t=temperature),\n", - " array_en[0],array_en[-1],limit=500,points=(E_fermi))\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enum_int(k_weights, array_en, array_eigs, sigma, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " f_dos = sp.interpolate.interp1d(array_en,ra_dos)\n", - " enum, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*fd_function(e, eF=E_fermi, t=temperature),\n", - " array_en[0],array_en[-1],limit=500,points=(E_fermi))\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def gen_enum_int_M1(k_weights, array_en, array_eigs, E_fermi = fermi_energy, temperature = temp):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## E_fermi: fermi_energy used in generating the occupations\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=E_fermi, t=temperature)\n", - " ra_dos = gen_DOS_M1(k_weights_qe, array_en, eigs_qe)\n", - " f_dos = sp.interpolate.interp1d(array_en,ra_dos)\n", - " enum, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*fd_function(e, eF=E_fermi, t=temperature),\n", - " array_en[0],array_en[-1],limit=500,points=(E_fermi))\n", - " return enum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the DOS from its naive definition\n", - "$$ D(E) = \\sum_i \\sum_k w_k\\, \\delta(\\epsilon-\\epsilon_{ik})$$\n", - "where $i$ labels the band and $k$ the k point. We represent the $\\delta$-functions as a Gaussian\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "with a width $\\sigma$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV )\n", - "ra_en_M1 = ra_en[::1]\n", - "ra_dos_M1 = gen_DOS_M1(k_weights_qe, ra_en_M1, eigs_qe )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "ax.plot(ra_en_M1, ra_dos_M1, linestyle='--', linewidth=3, color='blue', label='computed M1')\n", - "\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We recover the QE-DOS result by using the same parameters as in the input for computing the DOS.\n", - "Now we can go ahead and investigate different energy grids and smearing values in order to improve upon the band energy and achieve better agreement with the band-energy output of QE." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy\" (or better, sum of eigenvalues) is defined as\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} dE\\ D(E)\\, f(E)\\, E$$\n", - "\n", - "where $E$ denotes the energy, $D(E)$ the DOS, $f(E)$ the Fermi-Dirac distribution function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy\" is also called the \"single-particle energy\" or the \"one-electron energy\" and is obtained from summing the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\epsilon_i\\ .$$\n", - "\n", - "For look at the particular values for a given snapshot (data from ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```). QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "737.82754675" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "737.82754675" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correct band energy from QE output is" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "188.49839105\n" - ] - } - ], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(eband_qe)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 -5.234489245958457 -1.1743850336642936e-10 -45.807938886497595 1.8189894035458565e-12\n", - "11 -3.0085239583360135 -1.184616849059239e-10 -26.794918722090188 -5.002220859751105e-12\n", - "12 -1.6938272863526436 -1.1732481652870774e-10 -15.31026468328173 2.7284841053187847e-12\n", - "13 -0.9298947758965141 -1.1789325071731582e-10 -8.515658528185668 -4.547473508864641e-13\n", - "14 -0.49824413626174646 -1.1789325071731582e-10 -4.599498588736424 9.094947017729282e-13\n", - "15 -0.26069453180468827 -1.1800693755503744e-10 -2.4242788304977694 -4.547473508864641e-13\n", - "16 -0.13643360852029218 -1.1743850336642936e-10 -1.2616944080477879 3.183231456205249e-12\n", - "17 -0.06895619423528387 -1.1743850336642936e-10 -0.6568335573683726 9.094947017729282e-13\n", - "18 -0.03418726555844387 -1.1857537174364552e-10 -0.32203098547824993 -2.7284841053187847e-12\n", - "19 -0.011282068307764348 -1.1789325071731582e-10 -0.12597016910467573 4.547473508864641e-13\n", - "20 -0.007201335063200531 -1.177795638795942e-10 -0.06170976398198036 1.8189894035458565e-12\n", - "21 -0.002444702199454696 -1.1800693755503744e-10 -0.024341267848740245 -4.547473508864641e-13\n", - "22 -0.0012694421254764165 -1.1800693755503744e-10 -0.009928782347287779 4.547473508864641e-13\n", - "23 0.0005336689835075958 -1.176658770418726e-10 -0.00406024975791297 9.094947017729282e-13\n", - "24 -0.0027406659099824537 -1.1800693755503744e-10 -0.012838814472615923 -2.2737367544323206e-12\n", - "25 0.0004738793097658345 -1.1800693755503744e-10 0.0045840101593057625 -1.3642420526593924e-12\n", - "26 -0.0003504365429307654 -1.1789325071731582e-10 -0.010819808310316148 -4.547473508864641e-13\n", - "27 -3.8703179598087445e-05 -1.1789325071731582e-10 0.0017257026747756754 4.547473508864641e-13\n", - "28 0.001842684864527655 -1.177795638795942e-10 0.013415315645033843 4.547473508864641e-13\n", - "29 -0.000985194552640678 -1.1823431123048067e-10 -0.0037054365066069295 -1.3642420526593924e-12\n", - "30 -0.0008896181742557019 -1.1800693755503744e-10 -0.008360815092146368 -4.547473508864641e-13\n", - "31 4.840791962124058e-05 -1.1800693755503744e-10 0.0014842382961433032 4.547473508864641e-13\n", - "32 0.0032736276467630887 -1.1789325071731582e-10 0.01575374872300017 -4.547473508864641e-13\n", - "33 -0.002582423709213799 -1.1812062439275905e-10 -0.008453579465822258 -4.547473508864641e-13\n", - "34 -0.00212382576478376 -1.1789325071731582e-10 -0.014720101219609205 4.547473508864641e-13\n", - "35 0.00035434004757917137 -1.177795638795942e-10 0.004589871522057365 9.094947017729282e-13\n", - "36 0.004566362448031214 -1.1789325071731582e-10 0.010334915335988626 4.547473508864641e-13\n", - "37 -0.004377595713094706 -1.1789325071731582e-10 -0.011156207361182169 4.547473508864641e-13\n", - "38 0.0035675146625635534 -1.177795638795942e-10 0.02104273133454626 4.547473508864641e-13\n", - "39 -0.002355550988113464 -1.1789325071731582e-10 -0.008779958306149638 4.547473508864641e-13\n", - "40 -0.00012235896292622783 -1.1789325071731582e-10 -0.008012976144982531 -4.547473508864641e-13\n", - "41 0.0004999652636570318 -1.176658770418726e-10 -0.00230668753010832 9.094947017729282e-13\n", - "42 -0.0021075651889077562 -1.177795638795942e-10 -0.008575368438414444 4.547473508864641e-13\n", - "43 0.0030319030553300763 -1.1789325071731582e-10 0.016562732218972087 4.547473508864641e-13\n", - "44 0.0005433604214886145 -1.1812062439275905e-10 0.011731928565041017 -2.2737367544323206e-12\n", - "45 -0.0010088273838846362 -1.176658770418726e-10 -0.008708943430974614 4.547473508864641e-13\n", - "46 -0.001603367676466405 -1.1812062439275905e-10 -0.013802711930566147 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4.547473508864641e-13\n", - "135 -0.0003735190744009742 -1.176658770418726e-10 0.0027690808747138362 4.547473508864641e-13\n", - "136 0.0008443424435427005 -1.1800693755503744e-10 9.370140787723358e-05 -4.547473508864641e-13\n", - "137 -0.0006366657436274181 -1.1800693755503744e-10 -0.004140103426834685 -4.547473508864641e-13\n", - "138 0.0004790252793327454 -1.1789325071731582e-10 0.0027747441185965727 -4.547473508864641e-13\n" - ] - } - ], - "source": [ - "emin = dos_qe[: , 0][0] - 1.0\n", - "emax = dos_qe[: , 0][-1] + 1.0\n", - "n_egrid_max = int(len(dos_qe[:,0]))\n", - "#n_egrids = np.int32(np.arange(1,11)/10.0*n_egrid_max)\n", - "n_egrids = np.arange(10,n_egrid_max)\n", - "ra_enum = []\n", - "ra_eband = []\n", - "ra_enum_M1 = []\n", - "ra_eband_M1 = []\n", - "for n_egrid in n_egrids:\n", - " ra_en = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = ra_en[1] - ra_en[0]\n", - " # Generate electron number \n", - " ra_enum = np.append(ra_enum,gen_enum(k_weights_qe, ra_en, eigs_qe, sigma_mod, E_fermi = 15.0) - gen_NUM(k_weights_qe, eigs_qe, E_fermi = 15.0))\n", - " # Generate band energy\n", - " ra_eband = np.append(ra_eband,gen_eband(k_weights_qe, ra_en, eigs_qe, sigma_mod, E_fermi = 15.0)*Ry2eV - gen_EIG(k_weights_qe, eigs_qe, E_fermi = 15.0))\n", - " # Generate electron number \n", - " ra_enum_M1 = np.append(ra_enum_M1,gen_enum_M1(k_weights_qe, ra_en, eigs_qe, E_fermi = 15.0) - gen_NUM(k_weights_qe, eigs_qe, E_fermi = 15.0))\n", - " # Generate band energy\n", - " ra_eband_M1 = np.append(ra_eband_M1,gen_eband_M1(k_weights_qe, ra_en , eigs_qe, E_fermi = 15.0)*Ry2eV - gen_EIG(k_weights_qe, eigs_qe, E_fermi = 15.0))\n", - " print(\"{0} {1} {2} {3} {4}\".format(n_egrid,ra_enum[-1],ra_enum_M1[-1],ra_eband[-1],ra_eband_M1[-1]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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DpgM/AqtNll992kuNZ/TsCdu2wb59/u6JRpPlqFSpEt26dQNg6NCh3Lp1y+NjXLx4EYDw8HC7befPn7dK87SkdOnSAOzfv99u2/Xr11NLvbtD+fLlGTNmDAA7duxw2jYwMJD+/fvToEEDpJTs3LnT5fGdvceVK1dmucE1OTmZ4cOHc+PGDSpVqkTnzp1TtxUuXJjhw4cDyi1lvDczvvvuO5YtW0bu3LkZNWqU1TZXlqwTJ06k/m181xpzPBEfv6Usa1OW35ws63zbTY1HBAWpgNMPPvB3TzSaLMm0adOIiIjg999/p3Xr1mzfvt203a5du0xjGqKiogD4/PPPrawYiYmJDBw4kEuXLpker3Xr1gDMnTvXyvpw/fp1hg0bZjV4GWzbto1vvvmG69ev22374YcfAKwCUKdNm2Zq2Thy5EhqHIKzgFXb9zh9+nQrd9Dhw4cZOnSoy/0zk61bt3LfffexYMECChQowLx588iVK5dVm9dee43atWtz/PhxWrZsaSfApJTMnj2bnj17AjB+/Hiq28yXVb9+fWbMmGH6/R49epTHHnsMgAYNGrj1Gedk3A44lVK2yMB+aHzNsGFQpQq8+SY4qean0eREQkNDWb9+Pd27dycuLo7atWtToUIFqlWrRnBwMFeuXGHfvn2pg3irVq2sBpMBAwbw3nvvsXXrViIjI2nSpAlSSn777TcCAwMZOHAgn376qd15mzRpQseOHVm6dCl16tShadOm5M6dm82bNxMQEMCAAQNS4wYMjh8/Ts+ePcmfPz916tQhPDycW7dusW3bNo4cOUJwcLBVHMfHH3/ME088QWRkJNWrV6dgwYKcOXOGuLg4bt26Rc+ePU0rttry3//+l59//pkZM2awevVqateuzcWLF1m7di0NGzakRIkSrF+/3tuvwCuWLFmSmvKclJTEpUuX2LlzJ//88w8A1atX57PPPkuN67EkKCiIVatW8fDDD7Nq1Spq1qxJnTp1qFixYurEcqdOnSJPnjxMnjyZkSNH2h3j4MGDDB06lCeffJLo6GjKlSuHEIK///6bjRs3kpycTHh4uGm2lMYGRwVA9JINi4zZ0qePlBMnZv55NZpsxI8//ij79u0rK1SoIAsWLCjz5MkjQ0NDZYMGDeQzzzzjcF6Xs2fPyiFDhshy5crJwMBAWaZMGfnYY4/JU6dOmRYLM7h+/bocPXq0jIiIkHny5JElS5aUAwcOlKdPnzbd7/Tp03LChAmyffv2MiIiQubLl08WLlxYVq9eXY4cOdJugrMffvhBPv7447JWrVoyNDRUBgYGyvDwcNm2bVs5f/58eefOHav2zoqMbd++XXbo0EEWL15cBgUFySpVqsjXXntN3rhxI7WY2OrVq632cbTewFWhNWf7WC5BQUEyLCxM1q9fXw4bNkwuX77c7UJf3333nXz44YdlmTJlZN68eWVwcLCsVq2afPrpp+WhQ4cc7rdz5075zjvvyA4dOsjKlSvLQoUKydy5c8vQ0FDZrFkzOWnSJJmQkOD2+7rbwUmRMSG9TLcTQgQCIcBNKaVjB5oGgNjYWJlRRXUcsmkTdO8Ohw6BjQlSo9FoNJqMRAixRUoZa7bN41woIURfIcQm4CrwDzDZYltXIcQ8IUQ5r3ur8R1160KxYvDjj/7uiUaj0Wg0qXgkPoQQc4DZQAxwHZX5YskBoCfQzRed0/iAESNUyXWNRqPRaLIIbosPIUQ/oC+wA4gFCtu2kVLuBf4G7CcV0PiH7t1h61ZwMvukRqPRaDSZiSeWj8FAItBJSrlVOg4W2QVEpLdjGh8RFASDBsG0af7uiUaj0Wg0gGfiowbwp5TS1ZR9l4AS3ndJ43OGDlWTzTmoqqjRaDQaTWbiifjIA7gzeoUBrovnazKPsmWhaVP48kt/90Sj0Wg0Go/ExwlczGorhMgFVAMOp6dTmgxg+HCYPj3HzmSq0Wg0mqyDJ+JjOVBBCNHbSZvHgZLAsnT1SuN72rSBxERIma1Ro9FoNBp/4Yn4eBsVcPqpEGK8EMKoXxskhKgihHgFeBeIBz70cT816SUgQM33MmOGv3ui0Wg0mhyORxVOhRAtgYWYpNmian4kAP+RUq71TffuHvxS4dSWc+egUiU4ehTuuce/fdFoNBrNXY3PKpxKKVcDVVFVTfegCo3dQsV4fAhU18IjCxMWBvffrzJfNBqNRqPxEx6XV5dSnpFSviCljJZSFpRS5pNSVpJSPiWl/CcjOqnxIUOHKteLDjzVaDQajZ/wWHxosjnNmkFyMsTF+bsnGo1Go8mh5PZmJyFEaaAZUCZl1UngN235yAYIoQJPP/pI1f7QaDQajSaT8XRiuWJCiK+BY8AXwFspy1zgmBBivhAizOe91PiWvn3VTLcXLvi7JxqNX4iIiEAI4XRZsmRJavtffvkFIQRt2rTxY6/do0mTJqnvoXdvZ5URYPTo0altK1SokEk9vPspU6aM1bUUEBBAoUKFCA8Pp23btrz44ovs3r3brWPdvHmTadOm0bZtW0qUKEFgYCDFihWjadOmvP322yQmJjrdPz4+nldeeYWYmBgKFSpEYGAgJUuWpHbt2gwePJjPPvuM5ORkX7xtj3Db8iGEKAqsAyoCycB6lAgBNZdLA9RstjWFEA2llBd92tMsgBCiAvAc6r1WB/ZLKZ0WXsuSFC0KnTvDnDnw3HP+7o1G4zfuu+8+SpQwnw3i3nvvzeTeKGbNmsXgwYMZNGgQs2bNStexFi9eTEJCAoUKFbLbdufOHebq4HNTXnrpJd58803GjRvHSy+95PVx7r//fsLC1PP4tWvXOH/+PBs3buSXX35hwoQJdOnShRkzZqS2sWXnzp106dKFo0ePEhgYSKNGjShZsiTx8fH8/vvvxMXF8c4777BgwQKaNGlit//u3btp06YNZ8+eJTg4mLp161K8eHGuXLnCzp07mTVrFrNmzaJHjx4EBQV5/T69Qkrp1gJ8gBIdK4HyJtsjUYXI7gDvu3vc7LQA/0HN2rsA2AnsdnffmJgYmaVYv17KChWkvHPH3z3RaDKdsmXLSkCuXr3arfZXr16V+/btkydOnMjYjkkpZ86cKQE5aNAgr/Zv3LixBGRsbKwE5MyZM03bLVu2TAKybt26EpDly5dPT7fvKsaMGSMBOW7cOK/2L126tATkunXr7LbduXNHLlq0SEZEREhARkVFyYsXL9q1O3DggCxcuLAEZI8ePeT58+etticmJsoRI0ZIQObNm1du2LDB7hjR0dESkH369JEJCQl22/fs2SNHjhwpb9686dX7dAWwWToYEz1xu3QBzgNdpJR25dOllEeAB4ELQFcPjpud+EFKGS6l7AZs9Xdn0kWDBpA/P6xe7e+eaDRZnvz58xMVFUV4eLi/u+I2ffv2JSAggDlz5phuN9b3798/0/qkgYCAALp27crmzZspV64c+/fvZ9SoUXbtevfuzeXLl3nooYf46quvCA0NtdpesGBBPvzwQ0aMGMHNmzd55JFHuH37dur2/fv3s3PnTgIDA/n4448JDg62O0fVqlWZPHkygYGBvn+jLvBEfIQBa6WUVx01SNm2FiiW3o5lRaSUme8YyygsA081Go1THMV8HDp0KDVeIikpiUmTJhEdHU2BAgWsBov9+/fTt29fypYtS2BgIMHBwZQrV44HH3yQxYsXp7YrU6YMgwcPBuCTTz6xiht47LHHPOpzeHg4rVu35vfff+evv/6y2nbx4kW+//57oqOjqVOnjoMjKI4dO8bQoUOJjIwkb9683HPPPbRq1Yqvv/7atP1LL72EEII33niDEydO0LdvX0qUKEH+/PmJiYlh0aJFqW3XrVtH+/btCQkJoUCBArRu3ZotW7Y47MuFCxd48cUXqVGjBgUKFKBAgQLExsby/vvvk5RkP59p7969EULwxRdfcPDgQXr16kVYWBh58+alSpUqTJ482Sre4fbt2wghePPNNwF4+eWXrb6DN954w+ln5QkhISH873//A+Dzzz/n/PnzqdtWrlzJpk2bCAwMZOrUqQghHB5n4sSJFC1alMOHD1t9J+fOnQMgODg4810qbuCJ+DgJuCOPAoFT3nRGCFFZCPGUEOILIcR+IUSyEEIKIbq5se8jQoh1QojLQogrQojNQognhBA6ndgRvXvDL7/AmTP+7olGk61JTk6ma9euvPLKK5QoUYLOnTtTtWpVALZv305sbCxz586lQIECdOrUKTXW5KeffuKTTz5JPU737t1p1KgRABUrVqRfv36pS+PGjT3u14ABAwDsrB9fffUVN2/eTN3uiPXr11OrVi1mzJhBQEAADz74ILGxscTFxdGrVy8GDhzocN8jR44QExPD+vXradGiBbVr12br1q1069aNBQsW8O2339KqVSv+/fdf2rRpQ3h4OL/++istWrTg8GH7uUl37NhBdHQ0EyZM4PLly7Rq1YrmzZtz5MgRnn76aTp27GgqQAC2bNlCTEwMmzdvplWrVjRq1IhDhw4xatQoRo4cmdouICCAfv36ER0dDUDt2rWtvoOaNWs6/bw8pVOnThQuXJikpCTWrFmTuv77778HVMxI8eLFnR4jf/78PPzww1b7QVrMUnx8PF988YVP++0THPljbBdUVksCUMJJmxIpbSa5e1yb/d8DpMnSzcV+U1PaXQeWAotT+iGBRUCAN/1xcc45ZOeYD4PBg6V8801/90KjyVQ8jflYuXKlBGTr1q2t1v/111+p96mIiAh5+PBhu3379OkjATlp0iS7bQkJCfKPP/6wWuermI/FixfL69evy8KFC8vw8HB5xyK+KyYmRubJk0eeO3dO/vHHH6YxH1evXk2NXRg5cqTV/tu3b5ehoaESkLNmzbLaz4iXMNvvgw8+kIAMDw+XRYoUkYsWLUrddvv2bfnQQw9JQA4ZMsTqmFeuXEn9ziZNmiRv376duu3ChQuyVatWpjEajz76aGpfxo0bJ5OTk1O3rVq1SgohZK5cueTJkydN30NGxHzY0qJFCwnIsWPHpq5r2LChBOSbbt6bZ82aJQEZGRlptf7+++9Pff/169eXL730klyyZIn8559/PHo/3oKPYj5eR5VUXy2EuN92oxCiPbAqpc1YD45ryW7UBHY9gAooF45ThBAPAcOBM0C0lLKjlLIrKitnHyr+5EmT/QoLIaLcWPJ7+V6yB48/Dh9/rAqPaTQ5jJYtW5qm2XoTBzFx4kQiIyPt1p89exZQT7G2BAcH06BBA4/P5S5BQUH07NmTv//+m1WrVgGwa9cutmzZQocOHShWzLGH/JtvvuHkyZOUL1+et956i4CAtOGiZs2ajB2rbvOTJ0823T8yMpIJEyZY7Tds2DCKFCnC33//TadOnejaNS08MFeuXLzwwgsArLaJRfv00085fvw4jzzyCKNGjSJXrlyp20JCQvjss8/InTs3U6ZMMe1LgwYNUt1BBq1ataJNmzbcuXPHyuqQ2Rjuufj4+NR1hgvGldXDwGhn6boBmDdvHt27d0cIwYYNG3jjjTfo0qULZcqUISoqirfffpsbN2744m14jEPxIYT41XJBWRTuAJWBpUKIeCHElpQlHlgGRKW0WepNZ6SUs6SUz0sp50uToFYH/Dfl9QUpZapjU0p5FhiW8u9oE/dLV5Q4cbXU8+a9ZBtiYqBIEfDjj0/jH1zVuciKi6+57777rMzqxmKWtuiMgIAAunTpYrqtXj11CxkyZAi//PILt27dSne/PcEQUobrxd1A07Vr1bPfo48+Su7c9lUZDJfN/v37UwWWJa1btyZPnjxW63Lnzk3ZsmUBaN++vd0+FStWBODUKWvP/Y8//giQ6l6wpUyZMkRGRnL27FmOHDlit71Dhw6m+0VFRZmeLzMxYk4sRZqnKCODPUWKFOGbb77hwIEDTJo0iS5duqQGTR84cIDnn3+eRo0acfnyZa/P7S3O6ny0cLJNAPekLLY0Qpl5MhwhRBkgBjW53be226WUa4UQJ4HSqNoc6y22zUG5TjQDBsDs2dCqlb97oslEHN2wchKjR4+mRYsW6T6OUfzJ0Tni4uJYs2YNbdu2JW/evNSuXZvmzZvTu3dvqlfP2FJBDRo0ICoqisWLF6f6/8PCwhwOyAYnT54EoFy5cqbbC0k91gAAACAASURBVBQoQPHixTl79iwnT560e0ovU6aM6X4FCxZ0uN3YZvs0bggKS0uJI86fP29ngXJUs8Wof+Kvp39QQbQARYsWTV0XGhrKoUOHTEWdGUZwqSNLVsWKFa0yavbv38+UKVOYNm0a27Zt45VXXuH999/39i14hTPx0TLTeuE9tVNe90gprztoswklPmpjIT40Fjz6KIwdC5cvQ+HC/u6NRpPtyJcvn8NtBQoUYPXq1fzxxx8sX76c33//nT/++IM///yTSZMmMW7cOMaMGZOh/RswYAAvvPAC/fv359y5czz77LOm1gwzvLU4uXqS9+RJ/86dOwB07NiRkJAQp20tB3FvzpWZJCcns337dgBq1KiRuj4mJoY///yTP//8063jbNy4MXU/d4iKimLKlClIKZk2bRpLlizJOuJDSuky3iILYEjy407anLBp6zUp8R8PpPxbFihkkYmzSUp53Kb9EGAI+K9aoluEhkLr1jB/PqSk+Wk0Gt/SsGFDGjZsCMCtW7eYO3cujz/+OK+88go9evTI0PLmffr04cUXX2TpUuURdyempXTp0gCmbgyAq1evpj6ZG20zivDwcA4fPsyIESO47777MvRcmcn3339PQkICgYGBNG/ePHV9586dmTp1KsuXL+fs2bNOYz+uXbvGggULAJU94wnt2rVj2rRpdrEimUHWlIPuUzDl1WHtEeBKyqt9hRXPCUO5d75FuaXCLf63sxRJKT+WUsZKKWOdBXZlCQzXi0ajyXACAwMZNGgQdevWJTk5mV27dlltA6wKRqWXkiVL8uCDDxISEkLz5s2tnrIdYQyG8+bNS7U8WGLEjkRFRbkdGOktRrDut9/aedczhIz4DmyJj4/n2WefBZRlytKi065dO2JiYrh16xZPPPGEUxfpCy+8wMWLFylXrhw9e/ZMXe+OW/XECfVs7shFlpFkd/GRqUgpj0kphYNljr/7ly7at4ejR2H/fn/3RKO5q5g6dSoHDx60W3/o0CH27dsHkBqECWlWBGObr5g/fz4XLlxwO7OjR48elCpVikOHDjFmzBirYly7du3itddeA+C5TJgfaujQoZQuXZpPP/2U119/nevX7b3sR44c4csvv/TJ+TLqOwDlalmyZAl169bl6NGjVKtWjYkTJ9q1++KLLwgODmbhwoU88sgjqbEhBleuXOH//u//mDJlCoGBgcybN88qwHfr1q20adOGpUuXmoqoNWvWMG7cOEB915mN2xPLAQgh7kGltbYESgGOyqZJKWX5dPbNHQyrRgEnbQzriPOp/3I6uXNDnz5qsrm33vJ3bzSau4bp06czYsQIypcvT/Xq1SlQoABnzpwhLi6OW7du0bt3b6sqo40bNyYsLIyNGzdSt25dqlWrRu7cuWnatCn9+vXLtH7nz5+f+fPn06FDByZOnMjChQuJjY0lPj6eNWvWkJSUxIABAxg0aFCG96VQoUIsW7aMjh07MnbsWD744ANq1KhBqVKlSExMZO/evRw+fJjGjRvz6KOPpvt87du3JygoiPnz53PmzBnKly+fWhbdVaCuJePHj0+dNO769eucP3+erVu3pmaXPPTQQ0yfPp3CJrF2UVFRrF27lq5du/L111+zePFiq4nl4uLiuHr1KsWKFePbb7+1S9mWUrJq1SpWrVpFcHAwderUoWTJkly7do2DBw+yP+VBs02bNrz44oveflRe48mstkbdjRKobBdnZFYY/bGU17JO2hiTMRxz0kYDyvXSpg288YYSIxqNJt2MHz+eZcuWsWHDBn7//XcSEhIoUaIELVu2ZPDgwXYZHEFBQfz888+MGTOGDRs2sHXr1lSrQ2aKD1BCaNu2bbz11lssX76cRYsWkS9fPho1asTjjz9Or169Mq0vNWvWZNeuXUybNo3vvvuOrVu3sn79esLCwrj33nvp3bs33bq5LIbtFqVLl2bp0qWMGzeObdu2sW7dOqSUREREeCQ+fvrpJ0AF7RYoUIAiRYoQGxtL/fr1eeSRR6hWrZrT/WvXrs3+/fuZNWsWixcvZteuXcTFxVGoUCFq1apFp06dGD58uOm8LbVq1WL16tWsXLmSuLg4Tpw4wcaNG0lOTiYsLIzOnTvTq1cvevTokSFp7K4Q7qbbCSG+AzoB64D/AX+RZnmwwzb40qvOCbEGaA48LKVcYLI9HBVQegsoYpbxIoT4GygDNJFS/p7ePnlLbGys3Lx5s79O7z4NGsArr8ADD7huq9FoNBqNA4QQW6SUsWbbPHm8bYGyHrSVUmZulRwHSCn/FkJsBeoADwOfW24XQjRHCY8zwB+Z38NsiBF4qsWHRqPRaDIITwJOJbAxqwgPCyakvE5McQ0BIIQIA6al/PuWvJtmpM1IevaElSvBotSvRqPRaDS+xBPxsR0V75FhCCHqCCH+NBaURQNgvM36VFLcMdNT+rZLCPGDEGIRyi1UFVgCmBf819hTuDB06ADz5vm7JxqNRqO5S/FEfEwGmgghGmVUZ4BCQH2LxYiiqWiz3gop5XDgUWArKkbkPuAQMAJ4SEppn6SucYyu+aHRaDSaDMTtmA8p5VIhxDPAMiHEFGA58A9g6s6QUp4wW+/iHGtwnUnjaN95gH5c9wWtWim3y44dULOmv3uj0Wg0mrsMT4uMbQPOAi+i0m4PA0dNFvN6vJrsQUAA9OunrR8ajUajyRA8qfPRAvgZMKZujMdJqq0mm9O/v0q7nTQJHMzWqdFoNBqNN3iSajsOJTwmobJHLmVMlzRZgshIqFgR1qyBdu383RuNRqPR3EV44napBWyRUo7WwiOH0KIFxMX5uxcajUajucvwRHxcR6WvanIKTZrAunX+7oVGo9Fo7jI8ER/rAOeF6DV3F40awaZNcCur1ZXTaDQaTXbGE/HxMlBeCPFURnVGk8UoXBgqVICtW/3dE41Go9HcRXgScBoLzAbeFUJ0w3Wdj8/N1muyGU2bqrgPm+maNRqNRqPxFk/ExxzU/C4CaAy4qnSqxcfdQJMmqtT6c8/5uycajUajuUvwRHx8jhIfmpxEkybwxBOQnKyKj2k0Go1Gk048Ka/ePwP7ocmqlC4NhQrB/v1Qtaq/e6PRaDSauwD9KKtxjRH3odFoNBqND9DiQ+MaXe9Do9FoND7Ek7ld+npyYJ3tchfRtCm8+aa/e6HRaDSauwRvsl1cIVLaafFxt1C5Mly9Cn//DeHh/u6NRqPRaLI5vsh2CQDKAnWAAsAS4HL6u6bJMgihXC8rVsCgQf7ujUaj0WiyOT7LdhFChKEESgVc1wDRZDeeeQZ694aePaFAAX/3RqPRaDTZGJ8FnEopzwGPAKWBV311XE0WoVkzaNwYJk70d080Go1Gk83xabaLlPIisAl4yJfH1WQRJk2CadPg6FF/90Sj0Wg02ZiMSLW9BZTMgONq/E14ODz9tC61rtFoNJp04VPxIYQogZr35bwvj6vJQjz3HGzbBqtW+bsnGo1Go8mmeFLno5mTzQWBKOAJoAjwVTr7pcmqBAXBf/8LM2dC69b+7o1Go9FosiGepNquwXWdDwFsA17ytkOabEDr1vDqqyClSsPVaDQajcYDPBEfv+FYfNwCTgKrgPlSyqT0dkyThSlXTs1we/gwVKjg795oNBqNJpvhSZ2PFhnYD012QgiVevvbb1p8aLIeSUlw65auR6PRZGH0xHIa7zDEh0aTlbhwQc1F9OST/u6JRqNxghYfGu+4W8THc89BixYwbBh88IGav0aTPTl2TBXCK1sW/vjD373RaDROcOh2cZHd4hIp5V0wMmkcEhUFiYnZf7K5X36BIUPg9m3YuVPN3vvZZ9C+vb97pvGEXbvg/vvhhRdg6FAoUgQSEqBQIX/3TKPRmOAs5mMN7s1ia4Z0cWxNdseI+1i3Dh55xN+98Z6TJ+Ghh6B4cfX/unVq/prHH4eXXlKBtZqsz/PPw+jRMGKE+r9mTdi6VVm1NBpNlsPZnXWvF8t1VLqtzr/MCWR318vNm3D5MhQrlrauaVPYvBlWrlQCJKeQnAxbtrhut2AB7NiR8f3xhEuX4PffoV+/tHWxsbBpk//6pNFonOJQfEgpq0spa7izAD2BI0C+lN3/yYzOa/xMVhMfv/6qnnxv3XKv/ZkzyuJha90oWRJ++kkJkF9+8X0/syJ79igrQZKLLPmZM+GddzKlS26zbJnqe3Bw2rq6dZWI1Gg0WZJ02ZSFEOFCiNnAdqAjcAl4Hqjkg75psjrR0XD6NJw75++eKLZsgU8/hc6d4coV1+1PnYLSpc23FSyoJtEbOhSuX/e8Ly+9BF9+6fl+3nLlCrRtq2JXvOHMGXUMVwP2kSOwaBFcverdeTKCRYvgwQet19Wtqy0f2YUjR+Dff/3dC00m45X4EEKECCHeBQ4A/YCbwFtApJRyspTypg/7qMmq5Mqlsgvi4ly3ff99NdhnJJcuqYDDMmWgVSs472KKoZMnoVQpx9sfeECZ78eN86wfP/wA48dn7vw3c+cqK83Zs97tf+aMel292nGbO3fgxAmoVw+++86786SXI0dUZV2Da9fU++7UybpdpUoQH68WTdZm1CgYPtzfvdBkMh6JDyFEfiHEy8Bh4ClUUOlHQAUp5Rgp5eUM6KMmK9OsGaxd67rdO+9kvIvm0iUIDVWugXbtoEED53EMp045Fx8A770Hs2apTBh3OHUKBg+GN96AAwfc73t6kBKmTFFFtf7x0uN55owasH/91XGbf/5R8TGPPQZffOHdedJLrVpKaBn8/LMSQyEh1u0CAqBOHe16yQ4cOgSLF6sJKzU5BrfEhxAitxBiBEp0vAoEA/OBKlLK4VLKMxnXRU2Wpl079RScnOy4zZUrKiX34MGM7culSyrFUgg1+E+YoFJmP/zQ+mnZwJnbxaBECXj9dWVRcUVysgp6HDYMBg5Mn/hYs8b9miO//qoG27Ztva9TcvYsdO8OGzaoQFwzjhyByEj4z39g/XrvrSzecu2aWkaNSrPUmLlcDGJjtfjI6kippml4+WU1YaUmx+BSfAghHgX2A+8DxYGVQKyUspeU8nAG9y/LIIR4UAgRJ4S4IIS4IYQ4LISYLIQo7O+++ZVatdRT54oVjtvs369eM0N8FLb4Orp3V8Wm5syBPn3s27tj+QA1qLsjJN57D27cgDFjVCBrUpL3Zv+33nI/22bKFBVoGx6efstHlSrw55/mbQzxUaCAiqv5+mvvzuUt588rsThwYFpg8bJl0KWLeXsd95H1OXdOzZQ9ahT89Zdzt5/mrsKh+BBCPCCE2A58DkQCm4DWUsr2UsqcaB8rippcbwjQHiXGBgLf+rNTWYLHH4ePP3a8fd8+VXUysywfllSooNIwFy5UT82WuIr5MAgLc+8p/8svlWjInVtZXypX9t76ceGCclP9+KPzdsePq9okvXurWJf0iI8SJVSsjCPXiyE+QJ0vs10v586p72LsWNi9WwmQqlVVdpIZWnxkfQ4fhvLlITBQxVaNHm1updTcdTizfCwFagDXgAnAcOCyEKKOO0tmdD4zkVLOklK+KKVcJKVcI6X8ABgNtBVCuDGC3cX06qWeWBwFlO7bp55ODxxw/8aSmKhcOq5SPy0xEx+gnqwiI9WNzhJ3LR8FC6p+O8uguX1bWXhq1Upbl17x8dZb8Oyzzj+DadOgb19ljfCF+GjZ0vHTp6X4aNVKncuwamUGhvgIClJZTbNmOXa5AEREKBdSRgc6+5pOnVQcRE7AEB+givvdvAlLlrje77//VVZGTbbFnZiP/KhBdpMHy8aM6GwW5ELKa6Bfe+FvgoOVi2P2bPPt+/aprJg8edxPyz19WtXZ8CSrwpH4AKhY0d7y4k7MBygrRvHizvt+4IAa/C1nUnVHfCQkmK+/cEGJiogImDrVvM3162oQfuIJ9X94uPcxH4b4aNJEVQa1tRIBHD2aJj5y51ai85NPvDufN5w7l1YQrlEjmDfPurCYLUJkv7iP27dVEK2j39LdhqX4CAhQqe0//OB8n0uXlDDfsME3fcjs2CUN4Fx8nEjH4vEdUAhRWQjxlBDiCyHEfiFEshBCCiG6ubHvI0KIdUKIy0KIK0KIzUKIJ4QQPq+NLYTIJYQIEkLEAK8A30spj/n6PNmOIUNUlsmdO/bb9u1TsQSVKrnvevn3X3UzcjTwmuFMfFSqpHzKBleuKItCYTdDdooXd36T2r5dlfS2xJX42LhRuQxsa3Ncv676FhwM//ufmm/GLG140yZ14zZu3t5aPpKSVKXXkBAlnmrXVq4qWywtHwDPPKPET2ZNxmdYPgx69lTZTc7IbuLj77+VsJs713kQ992CpfgA9ffRo8732btXva5bl/7zb9+ufjc5xdKUhXBW4TRCSlnO28WLvgwD3gMeBSrjZol2IcRU4EsgFliHCoitBEwBFmSAAIlHlZHfDJwGsvHEJj4kJkYNBCtXWq+/dUvNNlqxomfi4+JFaN5cDd579rhuf+uWMtlaWh4ssbV8nD6tXC7CzZkAXMV97NjhmfiQUg3e164pK4cl8fHqCV8IJdq6dYPp0+2PsXu3KvRmUKqUel9mAtAZhkXBqPTasqV93EdioiosZsyBA8rSMnw4vPiiZ+fzlvPnrcWHO2RU3MeZM9C/v5oVeeZMJSR9waFDKkU8JCRnBF/aio/ISPfER1iYeer+L7+4d78AJboHDlQPLDo2KNPJSrNm7QbeBnoAFQCXxSOEEA+hYlHOANFSyo5Syq5ARWAf0BV40mS/wkKIKDeW/CanbQE0Bh4HqgE/CCFyefWO7zaGDIEZM6zX/fUX3Hsv5M2rxIe7MRD//qtuMIMHq7gGV1y+nJZma0bFitaWD3eDTQ1cuV3MxEfFispaYFZ19NtvlfCoUsX+uBcuWD/Rt2lj/vS+ezdUr572f968cM899iJp3TpzS4aB4XIxMAs6PXoUypWz/3yff14VU3NkXbhwQYlIW4EFqk+vvaZqwHz0kWthamv5cIfoaPcHI3e5fFmlcBcqpETbH3+o/81qwWzb5llczOHDKki6Xz81u7I7HDoEAwa4f46shK34uPdeJaCdxTnt3ave759/2v+2Ro1SgtAdJk9W398zz7g3r5HGp2QZ8ZES0Pm8lHK+Bym8RmL4C1LK1JFFSnkWZUkBGG1i/eiKEieulnom/dwupVwvpfw45TgtU141vXqpgcgyjsFwuYCyBHjidilaVAmar75yHBthYIgPR9haXdyN9zBw5XYxEx/58qlB/dgx6/U3bqi6If/7n3K72B7XVnzUrGk+mduePVCtmvU6s3TbV16Bjh2VqFizxj7o11Z8NGigbvCXLqWts3W5GAQHKwExcqR5MPGTT6ob+zffWK+XEgYNUtaMkyeVGHvrLfv9LbGM+XCXsmWVJcnV9eMuN26oOidNmqiqvS+8oFxP3bqpz9aW1183zwRLTjZ/wj90SA3GjzwC33+vLE6umDlTTfiX1bJELlxQn48jS1xioloss5Xy5FHXojNX3p49agLIiAjrwmTHjqlrads214Jz/34lemfMSP88QO4GvkqprpGePWHSJO/Pd5eQZcSHpwghygAxwC1M0l2llGuBk0AJoIHNtjlSSuHGssZFN7YDyShLjSY4WAWWLl+ets5SfHjqdrnnHiUQWreGzz933t5ZvAeoG9zVq0qkgPuZLgbO3C5nz6ontTJl7LeZuV7ee09lxbRoYW5RsRUfkZHq87Cc/0JKe8sH2Md9SAm7dqmn8r59lZn5/fet9zEm2DMIClKDq2V5eEfiA9Qx//3XPjh48WIVvPr55/bf3x9/qNcPP4R331UplsePmx/fwBvLR0CA+g5cWR/WrHEdeHjtmhLYJUrABx9YW4GaN7d3A9y5o45rZhFZu1Zlc9liWD7CwlT14EWLnPfp9m0VH3Lrlutg7ilT0l9F9J9/1HHuu09NvuiM995TAnviRPPthw+ra8rWmlaunHPXy969KsW6aVPrz3zJElV/ZvhwdW5HJCcr4fvqq0rA1KmjrlPbGJtjx5xfk9evq2DvggVV9o1lcb7ERNWHZ5+Fp55S7apUUenhuXM7t0TmELKt+ABqp7zukVI6mvlrk01bX9MQ9RkeyaDjZz86dVJPbAaW4qNCBTWIuROTYFg+QP1gp051nurqSnwIYe168VR8OLN8GFYPM5ePrfg4f16Ze40nn7Aw1+IjIABq1LAexM6cUettB2Nb8WH0uUwZFaPw5pv2N76zZ60tH6DcCJaDizPxkSuXEjSDBysxceeOsjY88UTaRH8nTlh/Dp9+qkSL8ZlFRNhbiGzxJuYD1PW3b5/j7YblIibGfqqApCT1OfTpo4RwUJByh9jOhGzM8Gxpfdi+XT3J79plf84tW5SVw9ayYemGcMf1sny5su7Uru06aPKnn9IXR9K9u7rON26E+vXVgOvI2pKQoFxpK1aoQdjMsmDrcjFwJj4SEpQQL1vWflbtxYtVSv+wYcoS5Ghup1On1H3AmE8mJET93mwfjJ58Ul07L79sP5Hi7t2qrH98vBJDBw6o62f1amUJjIxU2TilS6v3ExWlUsN37VIZPWZuyBxGdhYfRlCrs8elEzZtvUYIsVwI8bwQooMQorUQYiSwANgJmCamCyGGpGTebD7vapKzu4VOndRNzvDFWoqPfPnUIO7qCReU+LjnHvV3s2YqayE8XA1wZil2rsQHWAed+jLmwyzTxcBWfMyYoW6QFSuq/80sKufP22dx1KypzmNguFxsBU+ZMtYma8M6YrQzs8TYul0A7r9fpXwag4sz8QEqSDUuTt30GzdWQqdHD/V37tzKjWDMyXLliir6Zll11nAXORKmUnrndgH1lGxkSNgyc6YqWrZ+vUob7tlTuX/i4tTgVLq0Kn5Vr56ynnz1lYqtsSU8XAU7W362v/4KDz+sBIztd2xYICyFiVFq3BiQO3ZUgtPZ72XOHPVZV6jgWnwkJHif1bFxowrKPH1aWbFee02td1QEb8YMVRm4RQtlJXr0UfsB3JH4iIxU15sZe/eqgTwgQFk+4uKUxeL8efUQ0KaNukYcBWlDWjyZpYCMjbWO+7h+XQnRDRtUP6tUgaefTrumW7RQrsavvlIW3YULVeB1v37q+/r9d7Vt5Ei135NPKmuiEOq37Svxcfq071KOM5nsLD4Kprw6m9vbeFQO9sH5NgK9ga9RYqM/alK9plLKW2Y7SCk/llLGSilji3lz08yOhIer5Y8/1E3h4EF1szBwN+jUcLuA+sHOnatuPOXLq5uybQlwd8SHZbqtpzEfztwuZvEeBpaDfVKSehp80iIG2h23Cyg3jWXch5nLBexjPmzbVaqkBiBLE7OZ+KhYUVWdNHznrsSH8V5Xr1YWjevXlZXFoE8fVRE1OVkJlKZNrX39QUHqCfT0afNjJySoNkFBzvtgRtWq5paPGTPUHECrV6vP5b771AD788/q6blMGXWdrV+vvjNL15QZtk/iv/6qXIbR0fbWj+3b1YBn+Z2eOaMETKFC6v+8eaFrV8dFt+LjVXZZz57q+3IlLBITvRcfH3ygLJCBKSWNhFDTCIwbZ2/9uHFDuVtGj1b/9+ypxNuoUdbtvLF87N2bFudUqpS6R+zdq2qDtGuXdn08/bQSH2bxGJYPNga2Kdlr1qjfXI0aqp7MV1+p+0WXLkqc7tunRJ8h6oVQAvvECWVJq1TJvP/gW/Hx0kv2n6srNm7MEoX3srP4yFSklC9LKaOllMEpSw0p5atSSh9Fst1FdO6sXC/Hj6sfuXEzBfeDTi3dLgYlS6obWtu29tVKPbV8ZITbxQzLeIMlS9QAbtnWTNSYiQ/boFNH4sPW7bJ7t7qBGhQsqD7XEyfS1tnGfIC6mRqul+Rk5RKJiDB/j5YEBKgg4V9+gfwWyWI1a6rrYN06dXM2y85w5nrx1uoB6qnV1vJx86bKivj1V2U1MChTRg08u3apJ1lXgssSyxmek5LU02/z5vYus2vX1ODaq5f1d2oEm1rSvr0SQ2bMmwcdOqhaNe5aPmx/N+5w+rSaQ2fgQOv1Dz6oYqhsM6M+/1y5gSyv8ylTVPyK5ffgrfioWjXtf0PwGS4Xg2rVlHj46iv7Y5iJj5gYa/GxbJn6bA0aN1aDfK9eSjin52HynnvU52aWBecJ8fHK4rJ1q/vHklI9CLRu7XfXT3YWH4ZVw0FhByDNOuJGyLjGZ3TqpJ5ELF0uBu4GnVpaPmwxEwK2k8qZYVg+pFTiw9GcIGbcc48yG9+yMXLduKFuopY3REtKl1ZPnJcvq3iIJ20yv921fNSooT5PIwVxzx7H4sPS7bJrl327qChr65OZ5QPSXC+nTinBkt8s89xNhFA3vVdfVee2vLEblC3rXHx4E+8BaoA7eVJZYww2bVLC0Gzw8xZDfEiZVgAuJMTe8rFrl/oObC0fRrCpJa1bKxFj9gQ/e7Z6+gb3xEdiovp8ba9hV3z0kRp0bX+PuXIpgfbGG2nrbt9W8Uy2M9QWLqzEy6xZaet8JT5+/FF97rbXVJcu5oGdZuKjTh1ljbpzR31/S5eaX6O+IFcu9aBkGUDuDbNmqfcYHu5+Ovm+fep30KWL+n27k02VQWRn8XEs5bWskzbhNm01mUGdOuqi/u47+0HZXfFhdoMwcCQ+3LV8/PuvMmk7KkhmRkCAetqxFQp796rjmsUBGPtVqqRSSY8csZ+B1V3LhzF3izE/jlmaLSixc+qUslYkJ5u3s437MAs4BRXHsXGjemr3xALgiEcfVU+pvXunme8tcWb58DbYFFTQZ/ny1tfdb7+pgcuXVKiQZiX69VeV2gz2lo9t25RlIDpaWaYMF5jZYFykiNrftprnjh3qOjHOUaFCmrB2RGKi+gzdibkyuHlTuadGjDDf3quXOt64cSoeq0YNJSKbNLFv+9hjyn1644YSQKdOqba2lCihrDS2MSJgfz03a6asFA0b2j98hIaqhxhbzO4V99yjznvggPpN/wtgyQAAIABJREFUS2n++/IVoaGOA2Ld4fZtVf/oySdV8K+7cR+LFimL1fjxytrzn//4bY4cr8RHSonxMCHEvY4WX3fUBCNnrJoQIp+DNnVt2moyg4AAFZfx2Wfmlg93Yj7M3C4G3oqPkBD1BL5jh2fxHgZmQsGZy8WgcmXlHx86VA2Etsc8d8560DATH5AW93HihEprNhNnQUHqczh3Ls3tZfu5WLqCrl1TA4yZ1ahgQeWrnzXLN+KjVCnlozayDGyJiHA8MKbH8gH2rpffflMuEV8iRJobwFJ8VK+unjgN0/i2beq7LFJEXZOGK8TM7QLmrpePPlKuq1wp9Q2LFlXnj48379utW+qpvnp1z1wv33yjRJIjy17u3Kpexv796nfw5ZfWqfaWREYq0bVokfqeS5c2F6EBAeZCNDFRDdiW7r9y5dR11dWk1FLRoubiw9GDjeF6MVwu7lY/9oZixezdHsnJKk7FrM+2/PCD+vxiYtRv1N0KuwsXKvEhhMogLFZMCcj0uoC8wCPxIYSoL4RYjnJjnAaOOlgyPPVUSvk3sBU1qdvDJn1tDpRBVT/9I6P7o7Ghc2c1qNmKj7Jl1Q3EbOIygxs31I/BkZnfW/EhhBI/a9Z4Fu/h7LzOMl0MoqJU/wYPtt+WP78SJEYRLCnVTSkkxL6tEffhyOViYLhebOM9DCwtH4bVw9GN9v77lQXLF+IDVJaEI1dHRsV8gHXGy+3bKojU7Ok8vTRrptJLN25UsQGgRFzJkmluEcPyAdaxPGZuF1CBsJYD+qVL8PXX8PjjaeuEcO56SUxUgtUd94wlH3yg6lQ4o2tXJTpGjFBWz9y5HbcdMkQVXXPkcjEoV84+42X/fnXtGoIL1PuePVsFe9riqfgwgk5t4z0yArOg07Nn4f/+T1lcvvrKuRXL0oXrruXjyBFlbWrcWP2fK5eyRF27pr6XTC5S57b4EEI0BtYAbYEg4BI+nFjOSyakvE4UQqT+aoUQYYBRk/stKWUOmKEpi9GqlfqB2Zouc+VSA5mzG6Bh9XA0IHorPkC5SNau9V582Lpd3LF8NG2qbiqOsiUsj3vlihIj+UyMecZAtXu3c5OwEXRqFu8B1uLDLNjUkvbt1ROZr8SHMzIq5gOsM162bVPnMhN46aVZM2UtqF7dOtDaiPu4fVuJR+OasRQfjiwfMTHqezICiefMUd+L7TXsa/Fx+7YS1+3bu9feHTp3ViLip59ciw/buA9HrsZ27aw/awNH4sPRvSI2VhXW27YtzWqVUZiJj9On1XWzeDFMmKCEv1lRuN271Wf40EPq/xo1lLBwFb9hBOVairfAQGWJ2rdPBdRmogDxxPLxGpAXmAmUkFKG+HJiOSFEHSHEn8YC1EnZNN5mfSpSygXAdFQV011CiB+EEIuAv4CqqJTYKZ72ReMD8uVTN0sz90GlSo7rLoDzeA9In/ioVEmlT/rC7SKle+KjdWt4+233juvI5QLW4sOZ5cNIt3XU7t571U35yhXHwaYG1aqp45k9kfuasmWVxcZsNldful0yIt7DoGpV5cKyHbyio1Xcx/796toLTsn+N77TixfVYG9m3cmVS2V4LV+uPpupU81jMJwJi4QENUBXqOC+2+XyZfVeLAer9BIYqIJkp0/3XHzYBpu6IiTE3A3l6P5Su7Y6R7Nm5uLfl5iJDyMIvkEDVXPk/vuVWGvXTlnTvvtOudpatlT1QwyXVWCguo5czU9juFxsKVBAWXuWL8/Usu+eiI96wD4p5eNSShd1fL2iEFDfYjFqc1S0WW+FlHI4aibcrUBz4D7gEDACeEhK6eEUnxqf4SgIs1cvlTJ75oz5dmeZLqAGofPnrQcpTywfN2/6xu3y998qxiI9gyJYVzl1Jj7KlFHZLr/+6trt4kx8BASkBd86CjY1EEK5KBo2dP/9eEu+fOo7NLsu0hNwCkp0HjmiPr+1a30f72FgpBrb3uRr1FCWD0uXC6SJD8MN4cjaZ7heli9XwqVRI/s2zmp9eGP5cCeDzBsee0x9DxktPvLlUw8IlllO4Fh8FC6srpOMdrmAY8uHkYGXJ49ydx0+rO6Xzz+vXGB16ijX0MiR1vu6ivs4dUoJ35YtzbcXLaquLVcPUj7EiXPODoGq5pkhpMyj4lWEj5RyHjDPpx3SZBzdu6sfQseOaiCwzTpxFmwKSukXLKjaGaZzVxPLGRjFf7wVH5ZVRnfsUIGD6cVS1Fy44Di2QQh1c1i92vlNuEwZlfv/11/2MTcGhuvFleXDOF5mYcR92H4/6bV8BAUpi8/Bg6oqpu3sy75kwgT7dYblwwi6NDDm7dmyxflg3K6dmifk8mXl6zcTKRUqOJ4BOiFBiQ9jyvo7d1xbNNwV9J5SoYKqsVLPbt7ONDxxuzhCiDTXi6Wl05lldcYMNcBnNKGh9pNFnj5tf90HBiprh6tZi+vXV8X7HLFkiRJVZgG+BqVKeXdf9BJPLB+7UO4NjSb9vPyyehrs1cu+pLYrywdYD9i3b6unm4IFne8DaWXNvfmR2bpd3HG5uHtcdywfoM4XEeH8vRqFsu6917H52Mh4cRXzkdk4ivtIb8ApKCE2f776fD2p8eILypdX1866ddaC1Zi3Z9Ei566tUqXU97pli6oYaoarmI9ChdT1EBqq6p64IqPEByg3pDNRa5RYN2IQEhPVtVrOQ4++WdyHs/fVooV5/IivMUu19bT2kCW2lo/Ll1Wp9x491Ou775q7XPyIJ+LjfaCpEMIHj3qaHI8QKur92jX7eAhXMR9gLT4M37Q7qXGFCinrhzvVOp2dE9zLdHH3uO6Kj9hY19aW8HB1HGeuGU8sH5mJWbptcrIaQJx9Lu5QtapKG84ol4szcuVS59+82dryAWnWLFcFz3r0UGXDHQnKYsVUSq1Z8SrD7QLuu17ctSZmBEWKqKwZI2bjww/hgQecZ9KYUbSofdyHO/eXjMYs1dbM8uEukZHqAcyo8dOvnxJuXbsqQTV6dOa4kzzA7W9SSvmNEKIqsFII8QqwTEp5wtV+Go1D8uRR6YJff2293pXbBdSAbcQGePqE5k6dEUfntMx22bEDXn/du2NZEhamLBXgWnz07KkKAznDMDE7Ex9RUao+Q968WU98WLq2QAmPwoU9H3hsqVpV3ZwzKtjUFTVqKIuDraUpOlpZ71wF9b74onOBbaTbHj6sRKolRsApKJFz6JDrjI6MtHy4g+F6uXVLXaubNrnex5aQEGvLx82bztP4MwtnAafeIESa9cOwaH7zjeO4uyyA279mIYSlbXwKMEU4/iFIKWU67xSaHMG999o/6V686PpGbGmFyKybZGioeoq6cyftKcPZBFLuYmv5cOZzzpXLtXspXz510zWr8WFglJoPCcl64sN2IrX0xnsYGPEv/hIf0dHm8wMZ1jNXlg93LHuGVcNWfNhaPtzJeMmogFN3McTHlCmqRo436d62bhfD6pGRBcTcwVXAqTfUrw/vvafEx6ZNWVp4gOcBpxnRVpOTKVvWepIzcN/ykdniI08edTOOj0+bZju9T+PgfqqtJ3TrplL2HFGokFpOnMj6MR++iPcAFaz45JPmJb0zgwEDVOqkLdHRKhbJm/RvW4wy67YkJKQJuAoV7K2NZmQFy8c336gZsr21VtqKD3+/J4PgYGWFuXFDBUPfuaOu8/Q8CNSrpyyxy5cr12sWx+2YDyllgCdLRnZacxcRFqZujJYVTz2N+cjMJzTjvL7KdAHPAk7d5aOPXGepVK6srCiezHGT0Rhi1LLYka8sH/nyqXRFf1G4sPnTe8GCKgvHF/U0HMVzGAGnztrY4u+BOjJSBeK++Waa1cZTHFk+/I0QaZZUUL/7woWdZ6O4om1bNZFemza+6WMGo0WCxr8EBNjPxOpptktm3iQNF4mvMl1AvdcrV9STkK/EhztUrpy1XC6ghFBwsLV7wlfiIyfgqNaHpdulfHlluXNVzdKfAaeg3I8tW6rgSW+xDTjNKuIDrF0v6Qk2Ncid27m1M4uhxYfG/9i6XrKq2wXSXCS+ynSBtBlzz5/PXPERFZX1xAfYz/GS3gJjOQlHVg3LgNNChVTApVn8iSX+tnw0aKAK6gWkY5iyDTjNSuLD+M1D+oJNsykeO6yFEHmAbkALwHBSnkTN+7JASpnkq85pcgi2QadZ3fJx+rSqHhod7dvjnjmj3rsr4eUrWrb0y2yWLjHiPoynuHPnnAfPatIoUUINsDdvWgccWlo+IE2kOBOf/g449QVZNeYDfG/5yGZ4JD6EEDHAt0BZ7INKHwPeEEI8LKXc6qP+aXIClpYPKd2P+TCmor90Ka14WEZTvLgKgAsJ8e1NLCxM+f0LFlSBrZlBzZqZWk7ZbWxrfWi3i/tYxhJYDmZGhVMDI+PF2cy+WWmg9pasGvMB1uIjB1o+PJnVtgywHIhAzVo7ARicskxIWVcOWC6E8EHYtibHYGn5uHpVBV25ShMLClLLpUuZ73ZZtcr3g3bx4qp8dGa5XLIytm4XLT48w6x6pmXAKZhPWW/L3SI+skvMhxYfDhkNFAU+ACpKKcdIKT9JWcYAFVBVUENS2mo07mFp+XDH5WJQooRyvWRmYFzx4uqm7KtMF4OwMDVxlhYfKiAyLk59r6BjPjzFrIaErdslJMS8Eqol/9/encfLUZX5H/98s5FACDEkAc2OIIorkEAGZQ3I4sqIG+iAI+MMizCOIOCMI66IjijDpigYlGFc+DkqOg6yLwOjBFB2ZV/CDpKFJGa5z++PU53bdHqrvr3d7u/79apX3Vt1qvrpSqX7ueecOqfTHU6boVyfj255T6U1H33W7JIn+dgPeAD4RLl+HRGxFvhkVuaA5oRnfaG45qOezqYFhX4f7e7zAc2v+Zg61TUfBfvskwYC2223NDuvaz7yKTd0d3GHU0gJfrXkY2Bgw4RlONp448FBASF9VrjmoyvkST6mAb+LqPx8VkQMAL8D+iuFs6GZMSMNO71uXb6aj04kH4UvwVY0u9x/v5MPSONdnHkmHHJImjp+2bLu+Wt1OCit+VizJi3Fc8JMnJj+31SydGnqf9SMsUc6qTCzbSHR6qZml+Ik0R1Oq1pJanapZVJW1qw+Y8emD4Snnsr34dCJ5OPlL08TNeWdXbOWqVPTX5vNGMmzF0jwqU+lxPScc4b2uGW/Ke3zsWxZSiSKhxSfOLF6zUcv9PcoKPT7eMUruiv5KCSJAwPdN8FjG+T5H30bsIekV1cqIGlb0iO4tw0xLus3s2alppdub3bZaKM0A2mzvwwLzTmu+XipD34Qrr2201EML6U1H6WdTSF9AVer+ei15KPQ76Ob3lchSXzuudS81eVzsTRbnk/Q84AxwJWS/lbS+nFgJY2W9BHgCmA08J3mhmk9b+bM1Ok0b7PL44+nodlrTbbW7QrNOU4+bKjKJR+lfTdqNbv0QmfTguJOp91U87H55unfqQ87m0KOZpeI+IGk/YAPkpKLb0t6AghSH48RpLE/LoqI/2hFsNbDCjUfeTqEbbFFGhtjwoThXy3v5MOapbTDaWlnU6jd4bQXBhgrKNR8rFuXHuUvvRadMm5cGtPnT3/qu86mkHN49Yg4BDgaeAgYCUwHZmQ/PwgcHREfanKM1g8KNR95m13uuac3PiTHjEl/aTr5sKGqp+Zj441TJ9TVq8ufo5uaJ4aqkHy88EL3/aEyeTLcdltf1nzk/leIiLMj4pWkpGN+tsyIiK0j4uxmB2h9olDzkbfZ5bnneudDctdd0wBbZkNRrsNpafIhVW966bXk47nnuvM9TZ4Mt9/umo9qJB0j6fDC7xGxOCJ+ly2LWxOe9Y1Gaz6g+z5QGvWLX/TlX0DWZIW+BIVREco1u0B/JR/PP99d/T0KpkxJNR9OPqo6DXhXqwKxPtdIh9ONN04dTXvlQ9KsGQp9CZYvT79XGiysWr+PXuxw2o3Jx+TJ8OCDfflHR57k4xlgWasCsT43aVJqg3744XwfEFts0TsfkmbNUtzptHRSuYJaNR+90JcKurvmo9DHyzUfVV0PzGtVINbnpFT78eyz+aaUd/JhtqHifh/lxvmA6gON9VqzSzf3+QDXfNTweWC6pM9JxUPlmTXJrFlpnecDwsmH2YaKn3ip1uziPh+d1cc1H3mGV98e+AHwL8BBkn4OPEyFodQj4vtDD8/6ysyZ6S+0PPNJzJrVd8MSm9VUnHy4w2l3Jx8TJ6YpJvpMnuRjIWlAMQGvASoOs55x8mH5zJqVr8kF4NRTh//kV2bNVm/NR/F088V6qcPp+PGpP9mTTw7WrnaLKVP6stYD8iUf3yclH2atMXNm/r9MxoypXcas39Tb4fSBB8of30sdTgsz2z7wAOy3X6ejean58+F73+t0FB2RZ3j1w1oYhxnMmwf77tvpKMyGv8IjnFC5w2mlR20jUs1HryQfkJKP++/vvmaXMWNg5507HUVHdNE4s9b3tt0WTjml01GYDX/1NLtU6vOxfHnqgzB6dGtjbKdJk9IEbt2WfPSxPCOcPiDp1DrKnSLp/qGFZWZmDau3w2m5mo9e6mxasPnmad1r72sYy1PzMRuYUke5yVlZMzPrhKE8attrTS4w2JHdNR9doxXNLuOAtS04r5mZ1WPKlDTI2Nq1sGoVbLLJhmUqNbv0Ys1HIfnotfc1jDU1+ZC0GfBm4MlmntfMzHIo1GosXZoeNS03LmQh+YiShxh7NfkYP763+rEMc1WfdpFU+hzWQZL2qHKuLbL1eUMPrftIOgwo91zUWRFxdJvDMTMrb9So1HTy8MPlm1wgfRFvtBG8+GL6Yi7o1eSj197TMFfrUdvZRT8HMD5bKlkN/Aw4YWhhdb39gCVFv7umx8y6y+TJaWyLcp1NCwqP2xYnH700wFjB5pu7v0eXqZV8zMnWAh4ALgaOr1B2NfBMRPRDf4+bI+LZTgdhZlZRYayPSjUfMNj0MmPG4LZeGmCsYNq0vpy8rZtVTT4i4uHCz5IuAK4r3mZmZl1qypRU81Er+Sh93PaFF9KxvWSXXeDnP+90FFak7g6nEfGRiDi/lcFI2lbSsZIulHSPpAFJIemgOo49WNJ1kpZIWi5pkaSjJLXiiZ47JK2T9KCkz0rKM0y9mVnr1dvsUvrESy/2+ZBS/xbrGnV/aUqaAewJ/DYi/lihzLbAzsCVEfFYA/EcARyb9yBJZwFHAquAK4A1wALgTGCBpIMiYqCBeEo9AXwW+B2wDtgf+AypeeqwJpzfzKw5Jk+G665L84dUUqnmo9eSD+s6ef5iPwb4J2C7GuUWAqcCJzUQzx3A14BFwM2kp2Z2r3aApPeQEo8ngd0i4t5s+xbAVcCBwMeB00uO2wyoZzrBRyJiBUBEXApcWrTvMklLgJMlfSEiPLKrmXWHyZPhoYdgn30qlylX89GLHU6t6+RpkngrcGelWg+AbN+dQEOzg0XEdyPiUxHx4xxf5IUk54RC4pGd6ylSTQrAiWWaXw4E7q5j2anG6/84W+9QZ7xmZq03eTKsXl292aXcQGO92OHUuk6e5GMGcF8d5e4DZjYWTj6SpgM7kp60+Unp/oi4BlgMbAnML9m3MCJUx3J1G96KmVlzFTqNVutwWm5mWze7WBvkST7Gkr7ka1kNlBnLtyW2z9Z3RsTKCmVuKinbbB8gjYFyc4vOb2aW3+TJad1IzYeTD2uxPH0+FpNqGWrZgfYNulUYh6Ta47+PlJRtmKRLgStJfVMGSB1OjwTOi4jS0WDNzDqnkHzkedQ2ws0u1hZ5aj6uArbKhhgvS9KhwCtJX9DtUBiW78UqZZZn6yr/A+t2N/C3pH4ePwP2Io3m+g/lCkv6WPbI76JnnnmmCS9vZlanepKP0g6nq1bByJEwdmxrY7O+lyf5OI30COu5kr4kaavCDklzJH0JODcrc1pzw+wOEfGPEbFtRGwSERtFxOsi4usRsa5C+XMjYm5EzJ3Sa4P2mFl3mzAhzd9Sq9mluObDtR7WJnkGGbsH+Fj264nAvZL+IukvpE6mJ2bn+/uIuLPpkZZXqNWo1sekUDuyrMWxmJl1DynVfuSp+Vi8GKZObX1s1vdyjf4ZEd8HdgEuAVYCo7NlZbZtl4hY2OQYq3koW8+qUqYwacFDVcqYmfWeD3wA5lTp7lba4fTaa+HNb259XNb3cg8LHhGLgHdn42ZMJj3p8VyTRhDN69Zs/VpJ4yo88TKvpKyZWX84rUYL+KabwooVsHYtjBoFV14Jhx7antisrzU870lEDETE0xHxTIcSDyLiUeAWYAzw3tL9knYHppOevrmxvdGZmXU5KfXxWLIkJSDXXw977NHpqKwPNJR8SHqtpMMlnSTpnUXbR0ga07zw6nJKtj5V0tZFsUwFzs5+/UqnEiQzs65W6HR6yy0wY0bvzWhrXSlXs4ukmaS5W4rnW7kA+EX28+HAOZLeGhFX5A1G0g4MJgwwOI/MlyUdV9gYEfOLfr5Y0jmkodRvl3Q5gxPLTSA9Entm3ljMzPpCodPplVfCnnt2OhrrE3lmtZ0MXEsaOv124DrSAFvFfgKcBbyLNLtsXhNIs+KW2qbaQRFxpKTrgaNIidFI4B7gfOAc13qYmVVQqPm46io44oja5c2aIE/Nx0mkxONU4NMREZJeknxExJ8l3Qa8pZFgsnlU1OCxFwEXNXKsmVnfetnL4Omn4YYb4Ic/7HQ01ify9Pl4B/AgWeJRpdwDwCuGFJWZmbXHxInwm9/ANtukRMSsDfLOantLjcQDYC3gO9jMbDh42cvgkkvc38PaKk/ysRKoZ6rD2cALtQqZmVkXKPT52GuvTkdifSRP8nEHsKOkigP/S5oGvJE09oaZmXW7iRPTZHK77trpSKyP5Ek+LiLVfHy73Fge2Yin/w5sBFzYnPDMzKylJk2CHXesPgGdWZPledrlu8AhwPuAeZJ+lW1/naRTgXeTHom9Gj91YmY2PLztbfCmN3U6CuszdScfEbFW0gHAd0gJyNHZrrnZAmlAr0Pr6JRqZmbdYNNN4dWv7nQU1mdyjXAaEcuAD0j6HLA/sBVpQK9HgV9HhCdvMzMzs6pyz2oLEBF3A3c3ORYzMzPrAw3PamtmZmbWCCcfZmZm1lYVm10krRvCeSMiGmrSMTMzs95WLUFoaIK3JhxrZmZmPaxi8hERbpIxMzOzpnOCYWZmZm3l5MPMzMzaqmLyIWk3Sa/Ke0JJe0s6ZmhhmZmZWa+qVvNxNXBCuR2Snpd0RoXjDgG+McS4zMzMrEfVanap9NTKRGCTJsdiZmZmfcB9PszMzKytnHyYmZlZWzn5MDMzs7Zy8mFmZmZt5eTDzMzM2qrW5G9bStot574thxiTmZmZ9bBayce+2VIqquwzMzMzq6ha8vEIKckwMzMza5pqs9rObmMcZmZm1ifc4dTMzMzaysmHmZmZtZWTDzMzM2srJx9mZmbWVk4+zMzMrK2cfJiZmVlbOfkwMzOztnLyUSdJV0uKCsuJnY7PzMxsuKg1vLoNOhKYULLtw9n2/25/OGZmZsNTQ8mHpJHA5sDYSmUi4pFGg+pGEXFX6TZJ/w7cHhG3dSAkMzOzYSlX8iFpZ+DzwK7ARlWKRt5zDzeStgHmASd0OhYzM7PhpO4EQdKbgcsZTDr+DCxtViCStgX2I32hzwVeBQh4b0RcXOPYg4EjgDcAI4F7gO8B50TEQLNiLPEhYAC4qEXnNzMz60l5aic+R0o8vgN8JiKebnIsRwDH5j1I0lmkfhergCuANcAC4ExggaSDWpSAHAJcExGPteDcZmZmPStP8rETcHdE/H2LYrkD+BqwCLgZOA/YvdoBkt5DSjyeBHaLiHuz7VsAVwEHAh8HTi85bjPg5XXE9EhErCjzuvOBVwJfruMcZmZmViRP8iGgZR0rI+K7L3kxqZ7DTsrWJxQSj+xcT0k6ArgaOFHSGSW1HweSmmVq2TM7R6kPkWpaqjYHmZmZ2YbyjPNxO7BlqwLJS9J0YEdgNfCT0v0RcQ2wmBTz/JJ9CyNCdSxXl3ndUcD7gUsioml9XszMzPpFnuTjdGBXSW9qVTA5bZ+t74yIlRXK3FRSthn2BSYDFzbxnGZmZn2j7uQjIn4EfAm4TNIRkma2Lqy6zMnWD1cpUxhrZE6VMnl9CHgO+HWtgpI+JmmRpEXPPPNME0MwMzMbvvI8aruu6NczgTOr9MuIiGj1OB/js/WLVcosz9abNuMFJY0H3glcEBFrapWPiHOBcwHmzp0bzYjBzMxsuMvb4bQVZYeNiFgObNLpOMzMzIazupOPiOi2SegKtRrVkoFC7ciyFsdiZmZmdeq2hCKPh7L1rCplZpSUNTMzsw4bzsnHrdn6tZLGVSgzr6SsmZmZddiwTT4i4lHgFmAM8N7S/ZJ2B6aTRj+9sb3RmZmZWSUV+3xIeoA0O+3eEfFg9nu9IiJeOeToajuFNMDYqZJuiIj7ACRNBc7OynylhZPLmZmZWU7VOpzOJiUfo4t+r1fux0ol7cBgwgCwXbb+sqTj1p84Yn7RzxdLOoc0Kd3tki5ncGK5CcDPSI8Fm5mZWZeolnwUBuZaXPJ7q0wAdi6zfZtqB0XEkZKuB44iTUQ3ErgHOB84x7UeZmZm3aVi8hERD1f7vdmyeVQaGh8kIi4CLmpqQGZmZtYSw7bDqZmZmQ1PTj7MzMysrZx8mJmZWVs5+TAzM7O2cvJhZmZmbeXkw8zMzNrKyYeZmZm1Vd3Jh6SfSjq7dkkzMzOzyvLUfLwN2LxVgZiZmVl/yJN8LGZwnhczMzOzhuRJPn4J7Cpp41YFY2ZmZr0vT/JxMrAUuFjSjNaEY2ZmZr2u2qy2pf4NuBN4O3CvpFuAh4GVZcpGRHy0CfGZmZlZj8mTfBwGRPbzGGB+tpQTgJMPMzMz20Ce5OMjLYvCzMzM+kbdyUdEXNDKQMzMzKw/eIRTMzMza6s8zS7rSRoD7AhMyzYtBm6OiNXNCszMzMx6U67kQ9Jo0iO3RwFWTuzJAAAZl0lEQVSbluxeLukM4HMRsaY54ZmZmVmvqTv5kDSSNNDY3oCAJ4AHst1bAS8HTgLmSTogItY1OVYzMzPrAXn6fHwM2Ae4F9g/IqZFxK7ZMg3YH/gTKTn5u+aHamZmZr0gT/LxN8CLwIKIuLR0Z7Ztb2AFcGhzwjMzM7Nekyf52A64KiIWVyqQ7bsqK2tmZma2gTzJx2hSrUYtK/Dst2ZmZlZBnuTjYdKstmMqFcj27ZqVNTMzM9tAnuTjF6QnWi6QNLF0p6TNgPOBLYGfNyc8MzMz6zV5xvn4KvBB4H3A/pIuAR4kTSK3FfAO0tgfj2VlzczMzDaQZ26X5yTtBVwEzAUOYXCWW2Xrm4CDI+L5pkZpZmZmPSPXCKcRcR+wk6S3ALvz0uHVr4mI65scn5mZmfWYPCOcTgAiIpZlSYYTDTMzM8stT4fTF4DLWxWImZmZ9Yc8yccy0tDqZmZmZg3Lk3zcDUxvVSBmZmbWH/IkH98B3iJpx1YFY2ZmZr2v7uQjIs4DzgYuk3SCpFdJ2qh1oZmZmVkvqjv5kLQOOArYDPgyqRlmhaR1ZZa1LYq3oyTtJ+kmSaskPSHpNEnjOh2XmZnZcJKn2UU5ljznHRYk7Qn8CvgT8C7gVODvSUPKm5mZWZ3yjHDacwlFTp8Bfh8Rh2S/XypJwGmSTo2I33cwNjMzs2Gj3xOKPHYCflOyrfD7O9oci5mZ2bCVp8/H85KubVUgkraVdKykCyXdI2lAUkg6qI5jD5Z0naQlkpZLWiTpKEnNTK4GgL+UbFudrbdr4uuYmZn1tDxzu4wBHm1VIMARwLF5D5J0FnAksAq4AlgDLADOBBZIOigiBpoQ359ItR/FCr9PasL5zczM+kKe5OM+YHKrAgHuAL4GLAJuBs4jTV5XkaT3kBKPJ4HdIuLebPsWwFXAgcDHgdNLjtsMeHkdMT0SESuyn88GzpN0LPAD4NWkp37WkWpFzMzMrA55ko8LgS9ImhMRDzY7kIj4bvHvqS9nTSdl6xMKiUd2rqckHQFcDZwo6YyS2o8Dge/Vcf49s3MALAReD/wb8E1Sk8vJwCeAJ+oJ1szMzPJ1OP0GcClwpaT3d3qAMUnTgR1JScBPSvdHxDXAYmBLYH7JvoURoTqWq4uOGYiIT5Bqf94ITAW+C0wBbmzR2zQzM+s5eWo+7iWN4TELuAhA0tPAyjJlIyJeOfTwqto+W98ZEeViALgJmJaVvaEZLxoRS4DbACR9njTb7wbJj5mZmZWXJ/mYXfRzoU1kiwplo6Fo8pmTrR+uUuaRkrINk7QTqRnmVmAs8E7gI8AhEfFChWM+BnwMYObMmUMNwczMrCfkST6G/AXeZOOz9YtVyizP1ps24fVWk/qK/AupuepmYP+IKB37Y72IOBc4F2Du3LntSMjMzMy6Xp4RTqvVMPS8bATT+TULmpmZWVXDeYTTQq3GJlXKFGpHlrU4FjMzM6tTxeRD0m6SXpX3hJL2lnTM0MKqy0PZelaVMjNKypqZmVmHVav5uBo4odyObKj1MyocdwjpsdxWuzVbv7bKtPbzSsqamZlZh9Vqdqk00tdEqjd3tFxEPArcQhr2/b2l+yXtDkwnjX7qcTjMzMy6xHDu8wFwSrY+VdLWhY2SppKGQwf4SpPmdjEzM7MmyPOobUtJ2oHBhAEGZ4r9sqTjChsjYn7RzxdLOoc0Kd3tki5ncGK5CcDPSBPMmZmZWZfomuSDlCzsXGb7NtUOiogjJV0PHEWaiG4kcA9wPnCOaz3MzMy6S9ckH9k8KnXNJlfm2IvIhnw3MzOz7jbc+3yYmZnZMFOr5mNLSbvl3LflEGMyMzOzHlYr+dg3W0pFlX1mZmZmFVVLPh6hPbPTmpmZWR+pmHxExOw2xmFmZmZ9wh1OzczMrK2cfJiZmVlbOfkwMzOztnLyYWZmZm3l5MPMzMzaysmHmZmZtZWTDzMzM2srJx9mZmbWVk4+zMzMrK1qze1iXejpp5/ma1/7GitXrmTVqlWsXr2aMWPGMG7cOMaNG4ckBgYGGBgYICLW/1y8LSIYMWIEI0aMYOTIket/lsSqVavWnxtg5MiRjBw5klGjRq3/ecSIlLdGDI7AX/i53LbSnyuR1PC2vGVrxVfP+8j7XiVVXZoVVycU4i9+L7XWBUO9d/LcD42c36wfbL/99hx++OFteS0nH8PQqFGjmDp1KmPHjmXcuHGMGTOGv/zlL6xYsYKVK1cCrE8mipOK4t+BlyQk69atW//z2LFj2XjjjRk7diySWLt2LevWrWPdunXrfx4YGCj7RVJtW+nPpcp9AdS7Le/x9cZXT7l6z1VI+qotzYyrnYqTh9JEotq6GfdO3vuh266dWbeYPn16217LyccwNGnSJI4//vhOh2FmZtYQ9/kwMzOztnLyYWZmZm3l5MPMzMzaysmHmZmZtZWTDzMzM2srJx9mZmbWVk4+zMzMrK2cfJiZmVlbOfkwMzOztnLyYWZmZm3l5MPMzMzaysmHmZmZtZWTDzMzM2srVZqO2ppL0jPAw52Oo4tMBp7tdBDDlK9d43zthsbXr3H9eO1mRcSUcjucfFhHSFoUEXM7Hcdw5GvXOF+7ofH1a5yv3Uu52cXMzMzaysmHmZmZtZWTD+uUczsdwDDma9c4X7uh8fVrnK9dEff5MDMzs7ZyzYeZmZm1lZMPawpJoyUtkPR1SYskLZW0WtJiSRdL2qPCcQslRZXlnja/lY5o9DpIGiHpqOyaL5e0RNJ1kj7Y7vfQCZL2qHHdipeZRcf1zX0naVtJx0q6UNI9kgay93hQHccenN1PS7L7a1F2v1X97pC0n6TfSHpe0gpJd0j6Z0kbNe+dtV7ea9fo52B2bN/ckwCjOh2A9Yzdgcuyn58ErgVeBLYD3gO8R9IXIuJfKxz/v8B9ZbY/0exAu1zd10HSSOCnwDuBpcBvgI2ABcBFkuZHxLEtjLUbPAlcUGX/TsBrgPuBR8vs74f77ggg930g6SzgSGAVcAWwhnRvnQkskHRQRAyUOe5TwKnAOuBq4M+kz4cvAm+XtCAiVjT2Vtou77Ub6ucg9Mc9CRHhxcuQF2Av4GJg1zL73g+sBQLYs2Tfwmz7YZ1+Dx2+frmvA/DJ7Jg7gS2Ktm9D+uAL4F2dfm8dvq53Zdfh00O93sN1AQ4Hvgq8D3glKSEI4KAqx7wnK/MEsE3R9i2KrumxZY6bCwyQvnB3Lto+HrgmO+4bnb4mrbp2jX4O9ts9GRFudrHmiIgrI+KgiLiuzL4fkf5jAXyorYH1qKzW41PZr0dExFOFfRFxL3BC9us/tzu2biHpr0i1HusYvP/6TkR8NyI+FRE/joj76zzspGx9QnY/Fc71FKk2AODEMs0vJwICTo2I3xYdtxz4CCkxOVLSxEbeS7vlvXb+HKyfkw9rl1uz9fSORtE7/gqYCjwWEdeW2f8TUjX5PEnT2hpZ9/jbbP0/EfF4RyMZRiRNB3YEVpPuo5eIiGuAxcCWwPyi48YA+2e//keZ4x4AbgTGAAc0PfDhwZ+DGff5sHbZJltXarfcU9IbSNWzTwHXA5dFmTblHlfvddg+W99U7iQRsULSncCbsmVxi+LtSpI2JlVzA5xXpajvuw0V7q07I2JlhTI3AdOysjdk27YFNgaer1JLcBPw5uy4i5oT7rBS63MQ+uSedPJhLSdpS+Cw7Nf/V6HY35TZdpekD0TE7S0JrDvVex3mZOtqkxU+Qko85lQp06veC2wKPA38sko533cbqvfeKi5b/PMjVFbuuL5Q5+cg9Mk96WYXaylJo4ALgc2AKyLikpIivweOIfUGHw+8Ang78Ids2+V90myQ9zqMz9YvVjnn8my9aXNDHRYKTS7fj4g1Zfb7vqus0XvL92QFdXwOQp/dk675sFb7FunxvEcp08kqIr5ZsulF4FeSLiP1jp9P6vx2dIvj7Chfh+aRtDWwW/br+eXK+Hpbm1X9HIT+uydd82EtI+l04KOkxz4XRMST9R4bEauBU7Jf+7VzWrXrUPgLcpMqhxf+El3W7Li6XKHW48aIuDvPgb7vgMbvLd+TZQzlcxB695508mEtIenrpCrEZ0j/4e6tcUg5hRH9eqaqsUHlrsND2XpWleNmlJTtedkjyIU282odTavp9/vuoWyd994q/DyTyvrqnmzS5yD04D3p5MOaTtJXgX8CngP2joi7GjzV5tl6edVSva/cdbglW88rd0D2tMfrsl9vLVemR+1L+oBeDvyowXP0+31XuF9eK2lchTLzSspC+oJcCUyS9MoKx+1U5rie1MTPQejBe9LJhzWVpK8Ax5OGVN4nIm4bwunel63LPk7aR8pdhxtJf01Nl7TbhofwXmA0cFNE9NNjth/N1j/OBrZqRF/fdxHxKCm5HUO6j15C0u6kcSqeJN2HheNWA7/Ofj2kzHFbkcanWQ38qumBd5Emfw5CL96TnR5i1UvvLKS5G4L0H27HOsq/idSbe2TJ9lGkocPXZefbt9PvrcXXraHrABzH4PDqU4u2b0MaR6CvhlcHJpO+2ALYpdnXu1cW6hte/SAGh1ffumj71Ox+qzS8+jwGh1ffqWj7+KLXHTbDqzd47XJ9DmbH9N09qewNmg2JpHcCP89+XUT6gCrnnoj4SnbMu4H/Ap4n/aX1NKl68fWkx8wGgBMj4mstDL3jGr0OWf+G/wLeQZpY7gpSbcfewFjgjIg4pk1vo+MkfQI4jXSPvaZKub667yTtAJxdtGk70qOu95KuAQARMb/kuLNJQ6mvAi5ncGK5CcDPSF/A68q8XvHEclcCL5AmXJsK/BbYK4bJxHJ5r10jn4PZcX11TwKu+fDSnIU0eE7UsVxddMwc4JukERIXkz7kVpL+Y59PnX81DPdlKNeB1HR6NHAz6a/NpaQREQ/u9PvqwHW8LbvHjm/V9R6OC7BHPf83Kxx7MGmW1aXZ/XUzcBQwosZr7kea3fXP2bW9kzTP0Eadvh6tvHaNfA724z0Z4ZoPMzMzazN3ODUzM7O2cvJhZmZmbeXkw8zMzNrKyYeZmZm1lZMPMzMzaysnH2ZmZtZWTj7MzMysrZx8mDWJpFdL+pakP0paIWmlpEck3SDp65L2KXPMQ5JC0uz2R9wakg6XdLOkF7P3FpIm1jhmj6Ky1ZaH2vQ2eoqk+ZLOlnSHpD9LWiPpeUk3STpD0l5DOPfC7N/msJzHnZwdd3Kjr23D16hOB2DWCyS9H/g+aTKuxaQ5IP4MTAF2IE2otTtp1MeeJentwHdIIzRexuAQ1KvrPMWLwMVV9j/beHT9R9KmwHkMThD3GGkE3KXARNLw3UcDR0u6PiJ27Uig1necfJgNkaQtSUMgjwE+QZpTZV3R/hHAW7Kl1ALSfCy9MvNs4UvumIj4TgPHPxsRhzUxnr4laSNSArgzcBdwZERcU6bcG0lTv+/f4EudBHyFNAmdWV2cfJgN3duBjYEbI+KbpTsjYgC4NltK993f+vDaaka2vrejURjA50iJx32kWX6XlCsUEX8ADpW0UyMvEhFP4MTDcnKfD7Ohm5qtn857YLk+H0Vt4bn6PkjaXNIXJd0uaXnW5+IWSZ+QNLqB2EZLOlrSbyUtzfqw3C3pK5I2Lym7UFIAe2abriqK9eS8r50jxvX9BiRtIenbkh6T9BdJD2axjq1y/M6Sfpgds1rSM5J+IalcLRWF95T9/NGia/OSfi2S5ki6UNLT2XW7U9JxkkaW/ptLGi9piaS1kqZXifXm7LgD6rgum5EmgAP4RKXEo1hE/K7MedbHKundkq7K+oyEpDdlZSr2+cjuoeMk3SVplaQnJf1A0qxa8Vhvc82H2dA9kq0XSHpdRNwxxPP9Hrigwr43AW8kTVe+nqTXA/9Dmn77MVKfkxGkv3xPA94m6YCIqKvvRfaF/WvSrJ4rgKuy9a7ACcAHJO0VEQ9kh1yfrfcDtgAuBZ4sej+tNoM046pIM4NOIDVznUCaBv2dpQdI+iRQmKL8FuBGYDrwNtL1+odKTUeSzgCOJM34+kvgVaTZSpH0OuAaYBLp3riS1L/ii8AGtQsRsVzS94BjgY8B/1rm9eaT+g49QPp3rmVPYDypz81/11G+lk+S+ob8jnRfzCBN815R1tz4U1LN4CrSdVhGamrcH/hVE+Ky4arT0+p68TLcF2BTUp+NANaQPlQ/BewNbFbj2Iey42bX8TrbAS+QEo93F20fR/pSCuBEYFTRvkmkdv8ATs7xnr6aHXM3MK3ktS7O9t1Y5rirs3175LyGe2THPZTzuJMZnKb8O8CYon2vIX3ZBfDmkuP2z7YvBnYu2fdmYAmpk+yrSvYVXusFYKcy8Qi4NStzHjC6aN+2wONF55hdtG8b0pf548XHFO3/fnbMcXVely9k5S8f4r1duD/XAG+rUGZhVuawku0fz7Y/BmxdtH1s0T2U67700jtLxwPw4qUXluyL7qaiD9TCso701/H7KxxXV/IBbFlU9piSfUdk239U4dhXZF+kzwCq472MK/rS3qfM/slVvtSHmnzUWr5ZctzJ2fZHgHFlznt2tv9fS7b/Ntu+f4V4jsv2f71keyGOT1c4brds//PApmX2H1F0jtkl+/472/6+Mtd7FbASmFTn9TwnO9d/Vti/Q5Y0lC5zK9yf51Z5rYWUTz7uL7c92zeVVJPm5KNPFze7mDVBRNwNzJO0C6nafmfSB/zLgF2AXSTtHw08ySFpE1LV/izg9Ij495IihT4AP6kQ2+OS7iXVnGwD/KnGS+5IqrJ/PCI2eDQ4Ip6VdAnwQVLS8L91vpV61HrUdoN+CZkrI2Jlme33ZOtXFDZImkxq/lgK/KbC+QpPhfxVhf0/rbB992z9y4hYVmb/RaSEqJwzSDUyRwI/Ltr+UWAjYGFEPF/uwAbMBA4ts/2XwKIy2yu937KyvitbkWpzLirdHxFPS/oN8K4857Xe4eTDrIki4gZSn4NCm/d84LPAW0lPFPwqIsomCeVIGgn8Jykh+BnpkchSW2Xrn0iqdcop1E4+pmXrB6uUKfT1mFalTCMafdT2kQrbl2br4k6nc7L1BGBtjWs2pcL2hytsn1Ztf0QskbQE2KzM7v8hPSW0u6TtIuKu7B76h2z/WdUCLVEYD6Vs/BHxM1ITEQCSrmYwcSqn0vutpNBx9vGo3M/ooZzntB7i5MOsRSI9YntD9nTC70g1Ie+mQg1FBd8E3kFqJjg4O2epkdn6V9QehOu5HK8dOcp2WtXOjyUK12sJKaGrpuz1rFDL8pIiVfaVjTUiQtKZwOmk2o+jSbVas4GbIqJcjUQlt2TrHSSNqHDf5FHr/Zrl4uTDrMUiYp2kK0nJR6W/pDcg6Z9IX0APAO+s8oX3KKkz4zkR0YwnCAoDns2pUqZQ2zIcB0d7NFuvabCWpZrHs3XZR0klTSA1xVWyEPgS8GFJJ5KSEMhX6wHp6aQXs9c6gNSc0k6F++IVksZUqP2Y3cZ4rMt4nA+zIVIdbR2kNnZIPf/rOedfkx4DfR44ICKqjSHy62z93ipl8rgZWA5Mk7SgTGybk2pjIHUwHVYiYjFwOzBZ0h5NPn1hILm3SxpfZv8Ha8S2lPSY9QTSI7f7kmqrfpQniIh4gcG+Jadl4360TUQ8Smq2GwF8oHS/pCnABnMdWf9w8mE2dEdK+p7KjBApaZSkvwMOyjbV/BLJxnS4kPR447sj4o81DjmX9Nf8odlgWxuXOeccSR+q9dqwvknhW9mvp0t6edF5xpKepBgP/F9ENLOzaTt9JltfKOmtpTuzwcD2yv4t8riGlNhMIn3pr69dlrQNZcbwKONMUrPN8aTP6PMjYlXOOMheaxGpk/ENksr26ZA0h6IOuU1U6Bj9RUmFmrLCsO9nkUYFtj7lZhezoRsNHAYcJulJ0qBaz5O+gN7A4Af7VyPi0jrO9yXS4673AR+V9NEyZZ6NiONg/SBVbyNVrX8W+Lik20hNAJuSHgPemtRv5MI639NngLmkp1nuzZqNVpIGGXs5qYPnIXWeK4/JkhbWKHNkRKwYyotExM+zQca+Clwq6U/AH0k1PlsC25MGBjsC+L8c5w1JHybVCP0d8FZJN2bn2pP0bzSPVBNWtiNmRNwj6TJSJ+UBUrLXyHtcldVcLQQOBK6W9Bjp/nyBdI9tQ5pcTqR+In9o5LUqOIP0HvYH7szuoeWkwd/GksYu+Zsmvp4NI04+zIbuPFLP/b1Jj3C+njSOwRpSM8sFwHcj4vpKJyhR6BC5dbaU8zBpLAoAIuJ2SW8g9RF4F6l/yS6ksT0eJT0xU+0R1pfIvrjeSnrS4sOkL87RpPf5A1Iilafzar02ofwjoMX+kTRGxJBExGmSriANhrUHqRlgLWmekmuBS8j5iGl23j9Imgt8nvTleyCpCeJk4BukJ3AGGJzxt5xC8vHriKj21FGtWJYCf509An4oKXnclXSdl2VxfZvUCfqqiGhaJ+Osr9O7SE9oHUb6/7EEuAL4NLX/na2HqYn3mpmZVSFpV1Jic0dEvL5KuVtJQ+kfEBG/rlTObLhynw8zsybKJop7TZntryH1z4HUFFLp+ANJicfd1DePi9mw42YXM7Pm2hK4KxtV9l5SP4fZpIHiRpImWHvJKLXZE0SnkvoJFUasPb6ZzSBm3cTNLmZmTZSN5fFZYC/S7K+bkcbcuBP4IfCtiFhTcsxsUv+LtaSOxqdExPfbF7VZezn5MDMzs7Zynw8zMzNrKycfZmZm1lZOPszMzKytnHyYmZlZWzn5MDMzs7Zy8mFmZmZt9f8BlyuZV91qx1gAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_enum), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_enum_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_num_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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MV9nhw4fdur+6UqJECQC2bt3qVXb27NmkIfqDoVy5cowcORKA33//PWDdiIgI+vXrR4MGDRAR1q9fn+L+A53j999/n+ka4YSEBB544AHOnTtHxYoV6dSpU1JZvnz5eOCBBwANhznn5ovPP/+cr776imzZsjF8+HC3spQ8Y/v27Uv637nWFt8EEiBdgC+B/caYscaYChlkU2ZgbeLfKsaYnH7q1POoe+USEaETEA4fDrZPv8WSxKRJkyhdujTLly+nZcuWrFu3zme9DRs2+My5qFSpEgAzZsxw84qcPHmSAQMGcOzYMZ/7a9myJQAzZ85082acPXuW+++/362Rc1i7di2ffPIJZ896O6+//PJLALdE2kmTJvn0lOzatSspTyJQ4q3nOU6ePNktDLVz504GDRqU4vYZyZo1a2jbti2zZ88mKiqKDz/8kKxZs7rVefbZZ6lVqxZ79+6lefPmXkJNRJg+fTo9e/YEYOzYsVStWtWtTv369ZkyZYrP67t7927uvvtuABo0aBDUd3wtEyhxdhI6PkoxYAQwwhizHHgXmCUi/rPArnBEZL8xZg1QG+gOzHAtN8Y0RcdSOQj8nPEWpgPdu8Mrr8BHH8Gdd4bbGoslUxAdHc2KFSu4/fbbWbZsGbVq1aJ8+fJUqVKFPHnycOrUKbZs2ZLU2Ldo0cKt0enfvz+vvfYaa9asoWzZsjRp0gQR4aeffiIiIoIBAwbw7rvveh23SZMmdOjQgfnz51O7dm1uvvlmsmXLxqpVq8iSJQv9+/dPymtw2Lt3Lz179iRXrlzUrl2bmJgYLly4wNq1a9m1axd58uRxyzN5++23GTx4MGXLlqVq1arkzp2bgwcPsmzZMi5cuEDPnj19jqDryRNPPMG3337LlClTWLx4MbVq1eLo0aP8+OOPNGzYkKJFi7JixYrUXoJUMW/evKSu4PHx8Rw7doz169fz559/AlC1alXef//9pLwjVyIjI1m0aBHdu3dn0aJF1KhRg9q1a1OhQoWkCQb/+usvsmfPzvjx4xk2bJjXPrZt28agQYN48MEHqV69OmXKlMEYw/79+1m5ciUJCQnExMT47B1m8cDfACqJ7qoItJH+Bh2ozRm07QQwFWgcaPvMupDCYG6Jdbol1vkbKO+yvjCwKbFsaFraFfbJyX76SaRUKZGzZ8Nrh8WSCfn666+lT58+Ur58ecmdO7dkz55doqOjpUGDBvLwww/7nbcnNjZW7r33XilTpoxERERIyZIl5e6775a//vrL56BsDmfPnpXHH39cSpcuLdmzZ5dixYrJgAED5O+///a53d9//y0vvviitGvXTkqXLi05c+aUfPnySdWqVWXYsGFeE919+eWXct9990nNmjUlOjpaIiIiJCYmRlq3bi2zZs2SS5cuudUPNJjbunXrpH379lKkSBGJjIyUypUry7PPPivnzp1LGrRt8eLFbtv4W++Q0oB2gbZxXSIjI6Vw4cJSv359uf/++2XBggVBD6j2+eefS/fu3aVkyZKSI0cOyZMnj1SpUkUeeugh2bFjh9/t1q9fL6+88oq0b99ebrjhBsmbN69ky5ZNoqOj5ZZbbpFx48bJiRMngj6vqx0CDOZmJMiscmNMcaAv0AedVZjEm2A76l2ZISIHQxVJGYExpjbqGXK4Ee06vB1ICjqKx3guxphJ6BD454CFQDzQEsgLzENFzqW0srNu3bqSXgMXBU3nztCkCTz6aHjtsFgsFss1gTFmtYjU9VkWrEjx2GFDYADqZcmLipVLwLeoYPkyLRvvy8UY0wxIcUhHEfFKzzbG9AIGA9WArMBW9Bwni0iaJnBkCpGydSvcfLMm0gaYXMtisVgslrQgzUWKy45zomGRfkAzl6LDIlI01Tu+RskUIgVg0CCIitIcFYvFYrFY0pFAIuWyuheLyFkRmSkiLYF26Nw9BrBjrF/JPPMMvPce7N4dbkssFovFcg1zWSLFGJPbGDPQGLMUDfU44mR/gM0smZ2iReE//4HEsRUsFovFYgkHqRIpxpjmxpgZaM+Xt9EZki8As1CPSpkAm1uuBIYNgyVLIHHoaIvFYrFYMppA46S4YYwpjeae9AWuR8M6AOvQRNL/icg/aWueJWzkzg2jRqk35dtvw22NxWKxWK5BAnpSjDG5jDF9jTGLgR3AU+hQ8f8AE4DaIlJbRCZYgXIVcvfdsG2belQsFovFYslg/HpSjDHvoj13olCvSQLwPeo1mScioU9kYbmyiIiAMWPgiSdgxQoIYQIti8VisVgul0CelH7oTL+7gaeB0iLSTkRmWYFyDXHHHXD6NCTO/WGxWCwWS0YRSKR8ALQQkfIi8ryI/JlRRlkyEVmywAsvaG7KpUwzPp/FYrFYrgH8ihQR6SMiSzLQFktmpUMHyJNHJx+0WCwWiyWDCLkLsjGmvDHmZWPMMmPMH8aYcS5l9Y0x9xpj8qetmZawYgy8+CI8/TRcsJE+i8VisWQMIYkUY8xAYCMwDGgElAeiXarkAiYDXdLKQEsmoWlTuOEGeOedcFtisVgslmuEoEWKMaYxMAWdEXg4UJ/ksVIcfgSOA53SykBLJmLsWM1POX063JZYLBaL5RogFE/KY+hsx7eKyCsi8ptnhcRZgdcCldPIPktmolYtnSH5jTfCbYnFkiGULl0aY0zAZd68eUn1Fy5ciDGGVq1ahdHq4GjSpEnSOfTu3Ttg3ccffzypbvny5TPIwqufkiVLut1LWbJkIW/evMTExNC6dWuefPJJNm7cGNS+zp8/z6RJk2jdujVFixYlIiKCQoUKcfPNN/Pyyy9z8uTJgNvHxcXx9NNPU6dOHfLmzUtERATFihWjVq1a3HPPPbz//vskJCSkxWmHRNAjzgINgZUi8nMK9Q4CPmcztFwFjBkDjRvrTMnXXRduayyWDKFt27YULep7Yvfrr78+g61Rpk6dyj333MPAgQOZOnXqZe1r7ty5nDhxgrx583qVXbp0iZkzZ17W/q9WRo0axQsvvMCYMWMYNWpUqvdz6623UrhwYQDOnDnD4cOHWblyJQsXLuTFF1+kc+fOTJkyJamOJ+vXr6dz587s3r2biIgIGjVqRLFixYiLi2P58uUsW7aMV155hdmzZ9OkSROv7Tdu3EirVq2IjY0lT5481KtXjyJFinDq1CnWr1/P1KlTmTp1Kj169CAyMjLV55kaQhEp+YBguiHnDnG/liuJihWhc2cYP15DPxbLNcDjjz9Os2bNUqzXqFEjtmzZQlRUVPoblUbUrVuXVatWMWvWLO6++26v8gULFvDXX39Rr149fvvNy4FuSQOefPJJL/GQkJDA559/ziOPPMK8efPYunUrK1as4DqPl8Nt27Zxyy23cPz4cXr06MGECROIjk5OFT116hRPPPEEEyZMoFWrVvz000/cdNNNbvu48847iY2N5a677mLixInkyZPHrXzz5s28++67ZMlyWXMSp4pQjniI4CYOvAE4kDpzLFcETz4Jb70Fx46F2xKLJVORK1cuKlWqRExMTLhNCZo+ffqQJUsW3nvvPZ/lzvp+/fplmE0WyJIlC126dGHVqlWUKVOGrVu3Mnz4cK96vXv35vjx43Tt2pWPPvrITaAA5M6dmzfffJMhQ4Zw/vx5evXqxcWLF5PKt27dyvr164mIiODtt9/2EigAN954I+PHjyciIiLtTzQFQhEpy4Haxhi/oRxjTGugIrDkMu2yZGbKlIGOHWHChHBbYrFkKvzlpOzYsSMpnyM+Pp5x48ZRvXp1oqKi3BqVrVu30qdPH0qVKkVERAR58uShTJky/Pvf/2bu3LlJ9UqWLMk999wDwLRp09zyGnx5QwIRExNDy5YtWb58Odu3b3crO3r0KF988QXVq1endu3aAfezZ88eBg0aRNmyZcmRIwfXXXcdLVq04OOPP/ZZf9SoURhjeP7559m3bx99+vShaNGi5MqVizp16vDZZ58l1V26dCnt2rWjYMGCREVF0bJlS1YHmKH9yJEjPPnkk1SrVo2oqCiioqKoW7cur7/+OvHx8V71e/fujTGGDz74gG3btnHHHXdQuHBhcuTIQeXKlRk/frxbPsbFixcxxvBCojf5qaeecrsGzz//fMDvKhQKFizIq6++CsCMGTM4fPhwUtn333/Pb7/9RkREBBMnTsQEmLrkpZdeokCBAuzcudPtmhw6dAiAPHnyZHgoJxhCESmvor15PjPGtDHGuG1rjLkFndfnIvBm2ployZQ88YQm0J46FW5LLJYrhoSEBLp06cLTTz9N0aJF6dSpEzfeeCMA69ato27dusycOZOoqCg6duyYlAvzzTffMG3atKT93H777TRq1AiAChUq0Ldv36SlcePGIdvVv39/AC9vykcffcT58+eTyv2xYsUKatasyZQpU8iSJQv//ve/qVu3LsuWLeOOO+5gwIABfrfdtWsXderUYcWKFTRr1oxatWqxZs0aunXrxuzZs/n0009p0aIF//zzD61atSImJoYffviBZs2asXPnTq/9/f7771SvXp0XX3yR48eP06JFC5o2bcquXbt46KGH6NChg0+hArB69Wrq1KnDqlWraNGiBY0aNWLHjh0MHz6cYcOGJdXLkiULffv2pXr16gDUqlXL7RrUqFEj4PcVKh07diRfvnzEx8ezxGXC1y+++ALQnJYiRYoE3EeuXLno3r2723aQnFMVFxfHBx98kKZ2pwkiEvSCjo+SAFxCZ0K+BBwFYhP/TwAeCmWfdkle6tSpI1cUPXqIvPxyuK2wWNKNUqVKCSCLFy8Oqv73338vgLRs2dJt/fbt2wXtHSmlS5eWnTt3em171113CSDjxo3zKjtx4oT8/PPPbuveeecdAWTgwIHBn5ALjRs3FkDmzp0rZ8+elXz58klMTIxcunQpqU6dOnUke/bscujQIfn5558FkHLlyrnt5/Tp01KiRAkBZNiwYW7br1u3TqKjowWQqVOnum03cuTIpO/Ec7s33nhDAImJiZH8+fPLZ599llR28eJF6dq1qwBy7733uu3z1KlTSdds3LhxcvHixaSyI0eOSIsWLQSQMWPGuG135513JtkyZswYSUhISCpbtGiRGGMka9ascuDAAZ/n4Lm/YHG+t6VLl6ZYt1mzZgLI6NGjk9Y1bNhQAHnhhReCOt7UqVMFkLJly7qtv/XWW5POv379+jJq1CiZN2+e/PnnnyGdT2oBVomfdjGkLBgReQVoD6xCE2kNkB8ohA7y1llEXgtln5YrmCefhFdegbNnw22JxZKuNG/e3Gf349Tkabz00kuULVvWa31sbCygb8We5MmThwYNGoR8rGCJjIykZ8+e7N+/n0WLFgGwYcMGVq9eTfv27SlUqJDfbT/55BMOHDhAuXLl+O9//+uWXFmjRg1Gjx4NwPjx431uX7ZsWV588UW37e6//37y58/P/v376dixI126JI8PmjVrVkaMGAHA4sWL3fb17rvvsnfvXnr16sXw4cPJmjVrUlnBggV5//33yZYtGxP8hKobNGiQFIZyaNGiBa1ateLSpUtuXoyMxgkLxsXFJa1zQj8peVEcnHquISOADz/8kNtvvx1jDL/++ivPP/88nTt3pmTJklSqVImXX36Zc+fOpcVphEzIqboi8o2I1EeFyU1o1+SSIlJDRL4IvLXlqqJ6dahfH1zc0Jari5TGCMmMS3rQtm1bN3e+s/jqzhmILFmy0LlzZ59lTo+Le++9l4ULF3Ihg6egcASXE/IJNmH2xx9/BLSHSLZs3h07nVDR1q1bk4SYKy1btiR79uxu67Jly0apUqUAaNeundc2FSpUAOCvv/5yW//1118DJIU1PClZsiRly5YlNjaWXbt2eZW3b9/e53aVKlXyebyMxMmJuZweNuq08CZ//vx88skn/PHHH4wbN47OnTsnJX//8ccfPPbYYzRq1Ijjx4+n+tipJdVdhUUkDohLsaLl6mbYMB0zZfBgnePHclXh76F2rRFsF+SUcAbZ8neMZcuWsWTJElq3bk2OHDmoVasWTZs2pXfv3lStWvWyjx+IBg0aUKlSJebOnZuUn1C4cGG/DbfDgQPambNMGd+dP6OioihSpAixsbEcOHDA662/ZMmSPrfLnTu333KnzPPt3hEerp4Xf81hVT0AACAASURBVBw+fNjLo+VvzBtn/JhweRNAk4EBChQokLQuOjqaHTt2+BR/vnCSZP15xipUqODWg2jr1q1MmDCBSZMmsXbtWp5++mlef/311J5CqrDjmVgujyZNNNyzdi2kkP1vsVzr5MyZ029ZVFQUixcv5ueff2bBggUsX76cn3/+mV9++YVx48YxZswYRo4cma729e/fnxEjRtCvXz8OHTrEI4884tM74ovUerFS8gyE4jm4dOkSAB06dKBgwYIB67o29qk5VkaSkJDAunXrAKhWrVrS+jp16vDLL7/wyy+/BLWflStXJm0XDJUqVWLChAmICJMmTWLevHmZR6QYY8YCL4lIqv07xph8wAgReTK1+7BkcoyBPn1gxgwrUiyWNKBhw4Y0bNgQgAsXLjBz5kzuu+8+nn76aXr06JGuw9LfddddPPnkk8yfPx8IbmyUEiVKAPgMnwCcPn066U3fqZtexMTEsHPnToYMGULbtm3T9VgZyRdffMGJEyeIiIigadOmSes7derExIkTWbBgAbGxsQFzU86cOcPs2bMB7S0UCm3atGHSpEleuSwZQSDZOALYZYwZbYwJadxnY8z1xphngF3onD+Wq5m77oKPPgI/3fosFkvqiIiIYODAgdSrV4+EhAQ2bNjgVga4Dcx1uRQrVox///vfFCxYkKZNm7q9tfvDaTQ//PDDJE+GK05uS6VKlYJO8EwtTtLxp59+mq7HcUiPa+BJXFwcjzzyCKCeLlcPUZs2bahTpw4XLlxg8ODBAcOzI0aM4OjRo5QpU4aePXsmrQ8mpLtv3z7Af2guPQkkUhoDO4HRqFhZaIx5whjTzBhTxBiTDcAYky3xc3NjzJPGmB9QcfI0sB1olN4nYQkz5cpBhQqwYEG4LbFYrlgmTpzItm3bvNbv2LGDLVu2ACQlk0KyV8IpSytmzZrFkSNHgu7J0qNHD4oXL86OHTsYOXKk26BnGzZs4NlnnwXg0UcfTVM7fTFo0CBKlCjBu+++y3PPPcdZHz0Pd+3axf/+9780OV56XQPQEM+8efOoV68eu3fvpkqVKrz00kte9T744APy5MnDnDlz6NWrV1LuisOpU6f4z3/+w4QJE4iIiODDDz90S1Res2YNrVq1Yv78+T7F1pIlSxgzZgyg1zqj8RvuEZFfgJuMMb2Ah4AWQHPXOsaY80AO11WJf38BXheRT9LWXEumxQn5dOgQbkssliuSyZMnM2TIEMqVK0fVqlWJiori4MGDLFu2jAsXLtC7d2+3UV8bN25M4cKFWblyJfXq1aNKlSpky5aNm2++mb59+2aY3bly5WLWrFm0b9+el156iTlz5lC3bl3i4uJYsmQJ8fHx9O/fn4EDB6a7LXnz5uWrr76iQ4cOjB49mjfeeINq1apRvHhxTp48yebNm9m5cyeNGzfmzjvvvOzjtWvXjsjISGbNmsXBgwcpV65c0nD2KSUcuzJ27NikyQPPnj3L4cOHWbNmTVJvmq5duzJ58mTy5cvntW2lSpX48ccf6dKlCx9//DFz5851m2Bw2bJlnD59mkKFCvHpp596dWUXERYtWsSiRYvIkycPtWvXplixYpw5c4Zt27axdetWAFq1asWTT2Z85kaKGVEi8iHwYeJw+J2BZkBNIBfgjKF7GlgDLAbmici6dLHWknnp3h0eewz++cfOjmyxpIKxY8fy1Vdf8euvv7J8+XJOnDhB0aJFad68Offcc49Xj5XIyEi+/fZbRo4cya+//sqaNWuSvBgZKVJABdPatWv573//y4IFC/jss8/ImTMnjRo14r777uOOO+7IMFtq1KjBhg0bmDRpEp9//jlr1qxhxYoVFC5cmOuvv57evXvTrVu3NDlWiRIlmD9/PmPGjGHt2rUsXboUEaF06dIhiZRvvvkG0OTjqKgo8ufPT926dalfvz69evWiSpUqAbevVasWW7duZerUqcydO5cNGzawbNky8ubNS82aNenYsSMPPPCAz3l5atasyeLFi/n+++9ZtmwZ+/btY+XKlSQkJFC4cGE6derEHXfcQY8ePdKti38gTGq7GBpjcqEDuh0TETuaVxpQt25dWbVqVbjNSD233w6tWsG994bbEovFYrFcIRhjVouIz3kBU93fSkTOiMjfVqBYkujTRwd2s2NrWCwWiyUNyJydwi1XJrfeCseOQeIIlBaLxWKxXA5WpFjSjqxZdXbkNJym3GKxWCzXLlakWNKWO++EHTsgyBEQLRaLxWLxhxUplrQle3YYMQJeeCHcllgsFovlCseKFEva078/rFmj8/lYLBaLxZJKrEixpD2RkTo78tix4bbEYrFYLFcwVqRY0of77oMffoDEOR8sFovFYgmVoEWKMaZyehpiucqIitLB3WbMCLclFovFYrlCCcWTsjFxksEuxhjrgbGkTP/+8N57dnA3i8VisaSKUMTGIXSSwdnAXmPMKGNM+s67bbmyqVcPIiJg2bJwW2KxWCyWK5BQREoM0AtYDpQAnkXFykfGmCbpYZzlCscY9aZMnx5uSywWi8VyBRK0SBGRiyLysYjcAlQH3gYuAD2AH40x640x9xljotLJVsuVSO/eMHcunDoVbkssFovFcoWRqtwSEdkoIvejHpX/AFuBqsAk4IAx5nVjTKW0M9NyxVKsGDRuDHPmhNsSi8VisVxhXFYCrIicFJEJqEB5CTBAXuBBYJMxZr4xpurlm3nlYIzpboyZZ4zZb4w5nehhuv+aTja2IR+LxWKxpILLajiNMfmNMY8AfwCPJa7+A5gIHAb+BawyxrS9LCuvLIYB54HhQAdgHvAGKuKuTTp2hE2bYNeucFtiySguXgy3BRaL5SogVSLFGFPXGPMucAB4GSgLfA20E5HKIvIgmmg7DMgGXEvT4nYUkR6J+TuLReRp4E1giDEmR7iNCwsREXDHHfD+++G2JO1Zvdp2sfZkzRqoWzfcVlgslquAUAZzy2GM6WeM+RX4FeiHJs6+BlQUkY4i8p1TX0TiReRVYAFQJW3NzryIyGEfq9cCkUCBDDYn89C/v4qUhIRwW5J2rFihjbGd8dmdDRvg99/hzz/DbYnFYrnCCcWTcgCYBtQDtgAPACVEZJiIBPLj/w2kmQfBGHODMWaoMeYDY8xWY0yCMUaMMd2C2LaXMWapMea4MeaUMWaVMWZwBuSL3AwcRceauTapVQvy54clS8JtSdogAo89BjfdBO+8k/r9nDyZdjYFw2+/we7d6XuM7dv17/ffp+9xLhfb48xiyfSE0jhfB3wBtBKRqiLyloicCWK7ceggcGnF/aj35k7gBjRZN0WMMROB/wF1gaXA90BFYAIwO72EijGmLtAfeFVELqXHMa4Y+vULfwKtCDz1FJzxcesmJAQfuvniCzhxAubN0y7WJ06EbsuSJVCoEHz9dejbgoaaevUKvv6pU3Dbbel/DbZvh+bN4bvvUq4bLhISICYGzp0LtyUWiyUAoTTMZUWki4j8EMoBRGSbiPwYol2B2IjmwfQAygMp7tsY0xX1/BwEqotIBxHpAlRAvUJd0B5JntvlM8ZUCmLJ5ee4RYE5wEqu5cRZhzvvhC+/TF2D7o8jR+DCheDrHzoEzz8Pr73mvj4hAVq2DC5v5uJFePxx+O9/tYt1y5bw4Yeh2S0CTzwBQ4aoeAskVH791fdEjT/9BLNnw+nTwR3zxRfh0iXYti00W105fFj3EYjt22HwYFi40Du898UXcP586o+fVpw4AceOwd9/h9sSi8USgFAGc9ubnoYEi4hMFZHHRGSWiOwMcrMnEv+OEJHtLvuKRT0zAI/78KZ0QUVMSstNngc0xuQDvgHOAJ1EJD5IW69eChWCFi1g1izf5b/8AuPGhbbPAQNg2rTg62/fDqVKwf/9H8TGJq+fNk0TPmfPTnkf06dD0aJw6636+Z57Qg/5zJ+v4mLcOG24/QmVHTugdWuYMsW7bO1aFUw//ZTy8XbvhrfegsmTk8MxqaFXr8AeEhG1uUULiI5WGx22bYPOneHzz1N//GB4910910AcO6Z/DxxIX1ssacOuXdC1a7itsISBq37sDmNMSaAOmuT7qWd5opfnAFAUaOBR9p6ImCCWJR7HjERDY4XRHk9x6XN2VyD9+umkg76YOhXGjvXtGfHlSQBYvz60sML27XDLLXDXXfDss7ru0CEYOVLFwk8/+Q4FicDevfDxx/DMMyouTGKksXVriItTkRMMCQl6vOefhyxZoEGDZKHy0UfJ9c6fhx49oGlTzSXxZN06bfSDyf0YPhwefljDMNu3p75H0p49gb0Phw5pb67rrtPvxdW2V1+FSpXcz/FycISGJytW+L/HPLf966+0scWSvmzYAJ99pgL4aiZYr+g1RCi9ey4FuZwzxvyZOKBZ5/Q0PkhqJf7dJCJn/dT5zaNuqjHGZANmoVMH3JpZPFCZhltv1QeNZ8ghIQG++goKFvRudLdvh7Jl4fhx9/WnTmmDuWRJ8ONybNsGFSvCqFHw6aewdSsMGwZ9+6oYqF0bFi923+aXX6BECahfHz75BF54QSdPdMiSBQYODN6b8sknkCuXjh/j0KCBhkcef1yFmgg8+iiULq3ibdUqd2Fx7px+L0OHpixSlizR7YcNU/EQEaFiIlRE1PNw2FcHtkS2b4cKFfT/Nm2SBeSRI3re8+bBDz94C4wff9TvsHdv6N49ZZEhAlWrwubN3mX79sHKlYHttCLlysJ5SfHnhb0amD5dw8f794fbkkxFKJ4UE+QSARQHOgFzjDHhHmq0TOLfQGLBeU0vE6BOsEwEOgJjgVzGmAYuS17PysaYexN7Ga06HOiherWQPbs2RJ7Jm6tWaQM6dKiKB1feflvzINavd1+/eTPceCNcf71uHwxOI1qwIIwYoS7kpUvVOwLQvr2GYlx58UXNH/n7b02S7dfPe7/9+2sjvDOFCGR8PDz9tAoR45HzXb06/PyzhpxatdLwz7RpUKQI5M7t/ha5caOeR+PG2tX34EH/xxw7Vr02OXPq5woVUhfy+ecfOHs2eJHSrJmKhdOnNcz073+rQGzZUt+KHS5c0O+0fHlo21avwSOPBM5d2rxZBZOvN+t9+9Rj8803/rd3BG9mCPeIwJYt4bYic7NvH/zrX2kjUkTcQ72ZgTVrtLdg9+5w//127CUXQslJyQKMB06hPXZqoj1+8gE10MTQk8D/AdcDfdFRZ/sYY0LogpDm5E78G8iP5vRFzJMGx3NG1x0H/Oyx1PasLCJvi0hdEalbqFChNDj8FcCAAZqg6ur9mD9fPQtdu7onV54/r3VbtdLwhiubNkGVKlrm6U1YvBhGj/Y+9rZtyY3okCHqBZk4EaIS58Xs0EE9Os5DYvduWL5cbfYUFa6ULAn/+Y+KhnLl4L77fI8TMn++5rO08NPhrXhxDTmVL68P5Pz5df1NN7mHfNat027d2bJpCGfhQt/7E9FeQK1aJa9LrUhxGvQjR/zXcRUpuXPrODILFuh3/Mgjuv6OO9xDPtOnq3h54gkNw/Xrp/YG6oXknO+ePe7rRbRBu+8+vY7+OHZMBbMvT8qECWpzRrFzp3rSfDVMR46kXYO1Y4eGJTMbM2akLNL27dP75tAh+OOPyzveihX6m0kPfP024uM1b612bfXWtm+voeZ//tHyuDh97k2erMvevfrCYwFCC/f0Bx4C2ojI4yKyXkSOJ87fs0FEngDaoBMOthWRmUBn1LvSLx1sz5SISOlgc1euWW68UZNXv/02ed2XX6pIKVFChYcjOubOVQ9Dt27+RYpn7gNoSOaDD9zXJSRog+A0opGR6p1p3z65TqVK2nht2KCfJ03SRjMqiMm9n3lGvS1O9+Q33/Su8/332g04ELlza6JsnTrJ6+rVcxcpa9eqSAHf5+9w8KAKsSJFktf5EilnzqjdgXruHDig+wrWk+LYNnSonsuNN+q6Dh30XA4e1LDV88/DmDHu+3n4YXj9df/2LFyo34mnSImLU49R9+4aaor3k69+7JgKI1+elE8+gT59Utegi6i4CuTZ8uTvv/V+8ZV31bp12k3O2aePiunu3VXApdRLKzVs2eLuJXPF13d94oS+LHTvrl46f+zbB2XKaL1gvSnffus7X+2nn1QIpLW34vPP9Xc2fHjyC1h8vIqrv/5Sj/Bzz8GgQXo+5curV7VXL32+deumodipU/X+z4yCMgyEEu4ZDCwVEb/Da4rIr+gYJA8kfv4FHW31snM9LgPHSxKolXG8LRk8stY1zMCByb1y/vxT47ANEvOWb789+UE0ZQrcey/UrOlfpNx8s7pLncG5Nm/WcMihQ8lvK6APirx5IY+Lw8zTO2KMNqLz52vDPX06PPBA8OdljNr04IO+38YXLdJwR6j4Eik1a+r/jkjx9dDdsAGqVXM/zwoVvHOCvvhCG4DatdVuX/v680+44YbQREqbNrrdsGHJ63LmVEE6a5bm8dSsqZ4iVxo2hMKF1S5P4uO1oRkwwFuk7N2r4b/ixbVR+/ln33YeO6bXyZcnZfduvace9BqVIDDx8SpoR49WT1mweT9OPUcYO5w7p/exr95r589752gFc5wff9T7b8QIFYdpzVNPeXvKRDQHrFQp73Dohx/q/VuliuZj+cO5rrffHryXYfZsfcnwZNky/W37S7xODb/9BnffrSHa9ev1vj9wAHr21Ov42WfqVWzaVO/9adN0mwMHND/txReT91W/vibMP/xw2gmpdevSR5RmBCIS1II29v8Lot7/gFMunz8Fzgd7nFAXYAkgQDc/5Z0Sy9cE2MdniXWGpJedwSx16tSRa4bjx0Xy5RM5eFBk8mSR3r2Tyw4cEMmfX+T330WKFBE5f17k9GmRnDlFLlxIrhcTI7Jjh/7frJnI/Pn6/wMPiDz1lEiTJiILFybXX7RI5OabU7ZtwQKRRo1E3nlHpEOH1J1ffLzIddeJ/P138rr9+0UKFhS5dCn0/R07JhIVpfu9eFEkd26Rf/7RsoQEkVKlRDZt8t5u/HiRBx90X7d6tUj16u7rhgwReeklkTlzRCpWFPn3v7339cwzInfcIVK6tG8bExLUxuPHk9ddvCjy/vta5srXX4vUqiVSrJjImjW+9/fxx76v1/LlIjVriqxapX9d+ewzkU6d9P9Ro0Qee8z3vh96SM8nKsrdtnPnRCIiRE6cEKlQQfcXDCdOiLRuLdKxo96ro0eLVK0qcuhQyttOnCgCImPHuq9fvVqkfHmRAgVE9u1zL+vfX6Rfv+Bsc8ibV+ToUf1/yxaRwoVFzp4NbR+B+PNPved//ll/t198od/tQw/pdRo8WGTgwOT6CQm6fsECtSsmRuTbb733e+6cSPbsei9duiRSsqTIxo3J5f7OoXlzPef4+OR1ly7ps6VQIZENG4I/t/Pn1Q5f7N6t9/G8efr54kWRJ58UyZFD7wd/2wXi5EmROnX0d/rBB+7n4IuLF0VGjhS56y7vsqNHRbJmFbntNt1vJgRYJX7axVA8KefRPJSUqJlY1yGC8HoonIEaqhhjcvqpU8+jriW9yZsXunTReLQT6nEoXlxDPE5+QkSEvm2UKqW9cUDfIuPi9G0Z9G1s4UJd/9FH6lKtU0fzMRy2b1cXf0o0bapvsOPGhf427ZAtm75Nu7qbFy3SdVlS0fM/Xz4dIXXTJs0tKFQoOV/FGP8hn40b1ZPiSoUKug/Xt7Tly6FJE01u3bhRXeWeQ/b/+aeGmPzlpPz9t4bF8rrkh2fNqmEGT49Vq1bq8m7UKDls5UnXruopcb2GoNe5ZUvt+eTpSdm3T+8T0DCev7yUY8c09GGMe4Lu3r26Pk8e9aINHpyy211Ez6dsWX1jzpVLvSm33ZZ8noGIjVWbPT0p69apd7FHD/dBBnfu1K7wK1YE3q8r58+r98C5ZypV0t9HWnUHB/WK3XGH2vzll+otve029Wb98IOGOubO1e8YNNn9xAn9jq67Ts9xwABvT92BA/pMyJpVfztOyGfvXs09ypfPd47V7t26jev9s2mTjt9Ts2bwc0uJ6O+ia1dvz8aJE3qfjRiRHMbNmlXDzU4SfI5UzAqTO7d6Wl58Ub/X8uX1N+qL2Fj13KxYod+v5+/2++/1uVOwoHoIr7A5tUJ5Wi4DKhljnvZXwRgzCqiMhnwcyqDz94QFEdkPrEHFUnfPcmNMU6AkOhqtH9+wJV0YOFDjtEuXaq8OV7p3V7fp3Xcnr3MN+WzeDJUrJzf4TvLs++9rg128uG+R4hqK8EeOHNoIZsninnAaKm3buod8UhvqcXBCPq6hHgd/IsUJ97iSJ48uTqjj5ElNRnRyYLJn1+Rfz54zBw5o43bhgu/h5IP9fp1jvPUWvPyy/zrZsmm+wiuvuK9fuFCvS4ECGvt3ddvv26dhAdDv69Ch5EbRlePHtcEuUcI95LNrl4oN0CToXr30Xgw0fkVsrAqHyZPVZlDxM2aMCoyaNbXr/ezZvscAio3V6+dLpNSsqQ339OnJo/c+/7yGzw4eDD5vIS5OGylXsTh0qI68nBYhhfh4/S07odF69VRI5M2r9+V11+n1uvdeeClx8O0pUzSh1PkNN2+uLyuTJ7vv2/Wagn6nr7+uocmCBTU0uGmTtz1//aXXzzWpfNkybahLlAi+Z9e0abqvffu8w1j33KP3ydCh3tvVqqUvWKnFGO3RtGSJJp536eL9G1+wQMNIjRppmfPXlW++gU6dNNfFEZHB9obMDPhzsXguaA+eM8AldJTVZ9A5afoBo4FNiWVngJqJ21wPJAATgj1OqAsphHsS63RLrPM3UN5lfeFEuwUYml42BrtcU+EeEXX3Vqwo0qKFd9mRIyKvvea+7r//FXnkEf3/nXdE+vRJLrt4UV3NJUqILF2q6zZtEilXLrlOx44azgiGn38W+e674M/FF3v2qFv50iU912LFRLZvT/3+3nxT5J57NITx3HPuZUeOqGv7/PnkdRcviuTKpaEIT5o0EVm8WP///nuRxo3dy7t0EfnkE/d11appaKZ4ce/wg4hek1BDEClx7JjI9ddr6EBE3dVRUSKnTunnqlVF1q1Lrt+tm7vdd92l4RRPmjfXUGDz5nr+DpMm6XfscPGiSN++Irfc4vt7FNEw4i23+D+H06dFZszQ0MaoUd7lXbqIzJwpEhnpfv1uvlltS0jQ7/6HH/T+KVhQQ30tW4p89ZX/47qybp1+V64kJIhUrqz7dRg/3neoLyVmzRJp2jTleocO6e9082YNuxw86F7+0Ufex3//fZE773S3+733RA4f1s+PPKKhSld27tQQ6Pz5Ggp26NVLZNo0vQ7PPpuyvbt2iURHa2ho5UoNYzkhvLfe0nDMmTMp7yctWLpUnydz54qsXy/Stq2GA11DZK+/rqFAh0uXRIoWTQ6Li2gIMzo6+GdhQoLIkiVpcw5+IC3CPSLyO9ABiEUn9nsKmIrOjPw06kE5hA4B72Q4ngVaAy967TCVGGNqG2N+cRaSu/WO9VjvavtsYDI6quwGY8yXxpjPgO3AjcA8dKJBS0bivG36egspWNB7vasnxUmadciaVd/EoqP1zQY0yTM2Njl5NthwD+jbRuvWoZ2PJ6VK6dvj2rUapnI8FKnF8aQ43Y9dKVhQvRyuLuFduzT51DVR2MG1h8/y5cnfmWu5L09KyZIaavKVPBuKJyVY8uXT5Mp77lE39dKl6vFxelt5hnw837rbtPE98/axY+pJKV7c3ZOye3dyCBH0vnr3Xb1v2rXzPXaLM16PP3Ll0tDlY4/57j7rhHtKlUouF1FPYo0a+jsZOFDtGDNGQ5D58+s9+ovffgzuHD6s180VY/Q39vrr+nncOHjjDf2+QvWuTJwYXIJ5oUI6plC7dvr7cu11Bur127jRfZ2TNOtqd9+++lsH373Vdu3S63jLLfqbcUaRXrZMw5olSqQc9khI0HDziBE6cGC9enodhw6F33/XZOBZs5LHH0pvmjTRMOygQeqR/de/9Dno6oVu316Tdx2v2++/6+/f9bnTpYt6YIYO1fnHUrrW+/bpmEdvvJHmpxQMIQXHRScXLIeOgTId+C5xeQ/1qpQTkYUu9Q+LyCIRScsRk/IC9V0W5wlcwWO9p+0PoDMnrwGaouOZ7ACGAF3lWp+hOFzcfru6IoPBESki3iIFtBGYMCHZpZ01qz7k16zRsMDu3ZcnElKDE/JZtEhDFIHGWkmJGjW0EVu1yjvcA/rgd+3W7SvU4+Daw8efSHF98J89q72noqMDi5Ty5UM7p2Bo3Fgb5jvv1O/SNQRXpkxgkVKypO/uwI5I8Qz3eIoU0HDElCnaUA0a5L2vlESKqy2+QgyxsdpYV62a3EDv2aN5CY6wuPNO7en01VfJ4r1hw+BFypEjyY26K3fdpdd/6FDNfVi+XH9foQwsuWmT3ktdugRX/9FHNQx3333eZRUrak8/1+7IrnlGvqhY0bu32u7dGrbLk0cF/bJlup+zZ/XeDhTuuXhRhc2QIfpdPPxwctmzz+oAhW3a6DQPN9wQ3DmnFbVr6z2yfbuOy+QZTipXTkNrTpj7m2/0ueBrP7/8oiKrRw/3XpCeHDyov4n//td3b7t0JuQMPhE5KyIzReRuEbk1cRkoIu+LiI9JT9IWEVkiQcyn42fbD0WksYjkFZEoEakjIhNFJMFXfUsmo0gRzRfZv9+3SKlfX982XHHyUvbtU69CRr31OLRtq8mzl5uPAjquy403aqNZooTvY3mKlKpVfe+rYkV90F28qA+rRo3cyz1FyoEDekxjtOH0lTybHp4Uh8cf13yPCRPcv0dXT8r583D0qA6W51C4sO8G19WT4tpYueakuJIli9rwo49J14MVKf7e3h2RUq1acl6Kk4/iEB2tYn748OTk1/r1tcH0nGnaF748KaBennvv1bfvJUtUSFWunJyg7rkPX8yYod6R7NlTtgN06PcdO3z/HrJn13vIdboDT+HpSSBPCuhxFi1KTg43Rs/T17UYrmEX7QAAIABJREFUPly/6wED9H778EN92XHIlUvPd/BgHTk7HERHq4fRH86AlKAixZkI1ZMSJfQ7KVZM7zVf9zbo/Vmlik5pMXCgdyJ7OhPKYG5HjTFBTLdqsaQjNWvqw/TEicAPLgdHpIQS6klLmjbV4y9e7H+U2VCoV0/fDH15ZG66SQWc4xnw1bPHwXmwb9igDbXnW7bng//PP5OFUXS0d4PlDJSXHp4U0Ibigw90QkXXeZNKl9a3ZlcbXXtP+fL6iOj9kzdvcJ4U12OdP+89tkqwIqV4ce0B5SoqzpzRJM+8eQOLFNCk8BEjkj87Xq1ghtT3J1JAvQO//558fStX9t5nQoJ6DZzv2pX169WrEwq+RLZD1aruScQpiZQSJVR0uvZqcTwpoJ63hQuTQz3ONp6eFBFN5l63To//xhsqZjxp1EgHYcusOOM8HTum59Ksmf+6OXNquO+ttzSpdsYM7zqOiL7pJk2Ovu221M39lUpC8aREAHbmI0t4qVkT/vc/9549gXBEiutw+BlJVJS+8ZYs6f6Gn1p69NAuvb7Ilk0fyE6350DhnvLl9W1z6VLvUA/o29WpU8k5GE4+Cvhu+A8c0Lc7X/kvaUWxYtpDxvWN3dWT4qsxK1BAH9auUzCcOqVeqezZ3T0px49r7xtfYRFQYVi3rvub5OHDKjKCubaRkfoduT7gDx1Sb48xKYsUX/d7sHkpgURKtmzqIXCoVMlbpOzYoSEBz3wRUK9LpUop2xAsrt+DJM4+HhPjv36WLHo/u+ZQuXpS6tfXsq++ShYp0dEqalx7qR04oCG20qXT7lzCQaNGev4zZuj5BuM9vvVW7Wnn6ol1OHgwOXeoSxcNEWXgFC6hiJQdgJ9fr8WSQdSsqW9FnqEef1SqpG8Cv/0WHpEC2oW1a9e02VezZoHdzE5eytmz+nD35z3KlUuTbT/5xLdIMcb9we/qSfElUtIz1BOIlERK1qwao3ftquuEesA9cdbxogTKG6pb17375pYt6kUJNtfIM8zgvKWCvvkfPqzC0JdI8UWwIsVfToovfHlS1qzRv56zTp85o41YWjbsrsmzcXEa4s3rNTerO555Ka6elOzZtdvxoUPJCedZsniH+tJabIWL7Nk1Z+aZZ/yHenzh6VV0iI11F+GNGl1ebl2IhCJSPgBuMcakxUzBFkvqqFlTXc/BihQneXbevPCEe0ATBJ99NmOO1batjpOwaZOKjEDjNFSooANA+RIpTrkT8knJkxIukXLddXo/HDvmPyzgaa+rSClWTBvZhAT/+SiueIqUYEM9Dp7Js4cOJYuUrFl1X0uXqtciJVsg+OTZQJ4UT/yJlLJlvUXKtm16nznjw6QFrp6UlJJmHVzv1ZMnVTwVLpxc3qqVCjpXL5xnyGfLlqtDpID28vnnn9BEiqdoc3AV0mEgFJHyKrAA+MEY08MYk4ph9CyWy6RcOQ2hBCtSQEM+J0+Gz5OSkZQoocv06f5DPQ4VKujbtT/x5ilSXD0pnomz4RIpxiR7U/yJFM/kWVeRkiOHhmAOHw6cj+LgiBSn22aoIsUzedazAahWTXNvqlULLpxZrZrandI8PqGIlFKl9Po6c2GBhrh69/YWL+nhfYiJ0cHz4uJSzkdxcPWk+PKI3XOPduF2xfNaXC2eFFCR0rdvaL/JYsXUk+LZJfkKEinb0QHdSgEfAmeMMX8bY3b5WHYG3pXFkkqcIadDSdSrU0cf+Ck1QFcL7dqpSPHXs8ehYsXArltXkfLnn8meFF+Jszt2hE8EpiRSPD0px4+7945wQj7BiBSnh9P+xPS8y/Wk+BIp8+YFF+oB9QzUru0++aQvQgn3ZM2q19J1zJY1a7Qb9JYt7o1YejTsxiQnzwYrUly71PvyiEVFeYekPK/F1q3qRboaKFAA3nsvtG3y5NFr7zkW0MGDaZNPl0pCESmlUYECYBKXIonrfS0WS/owdGjym3AwNGqkD/LLGaL6SqJtW81JScmTcvfd8Oab/svLl/fvScks4R4ITqS4Jqu6elIgORbvmsfgDyd51gn5pIUnxTUsUa2aJnMGK1Ig5byUhATtmh2sSAH3kM+ePdrIV6yoDdl+l/4T6eV9cEI+ngO5+cPpUg/uSbOB8Az3XE2elNTiObghXFGelDIhLEEEUy2WDKJ8+ZTfNK8mmjRRT0H16oHr5c+f8vgTO3Zoz5jYWHUHg76lHT+e3GPGyedIr+7HKeF0Q963z3cvkEA5KZAciw+2cXNEyj//aEjEVzdVfwTKSYFkYRmKSGnY0H1+Gk/++Ud7rQQ7jgm4i5TVq5PndbrxRve8lPTK43CSZ4P1pBQqpPdjXFxwYhPck5iPH9cllGt5NeIpUs6e1W73gcZlSWdCGRZ/byhLehptsVgCkCOHNrjBJBwGomhRfUht26Zv4U4j5/SYOXpUP+/frz2FXLuxZiSlS2tDmiOH7y7QKYkU5416z57geqnUq6ciZcsWbcxD6ekQqHcP6P93352yF8yVf/1LGxZ/Mz6HEupxcB3Qbc0a9USCihRHvFy6pN6L9Bh11fGkBJs4a0yyNyU1npQ//tDzSM0M5VcTniLF8fRlYG8eT67xK2KxXKUUKHD5+3C6IS9Z4j34lmteSjhDPaDCYuVK/2/cgRJnQR/Ma9aowMmdO+Xj1amjImXTptBCPZAc7nHyOny50t95R8dUCZYcOXRAroce0rdeT0JJmnVwHSvFU6Q4npR9+/Q+COY7CxVnioBgwz2QnJcSrCfFNfRmQz2KL5ESxnwUSIVIMcbkM8YMNsZ8YIxZYIx5zKWsojGmjTEmg8cet1gs6UKFCjparqcb3NU7EW6RUqaMDqjmrzHz5UnxTJxdvjy4hg30oR0VpZ6LUEVK3rzqiXJ643jmpKSWW2/VRvbVV73LUiNSKlbUxj4+3j3cU7lyskhJz4a9QAEVjXFxwTeSTg+f3buD84gVL67f/6VLVqQ4+BIpYcxHgRBFijGmHbALeAPoBbQCXK/sDcA3QJAzxlkslkxN+fI6p4enJyUziZT8+bXxDyRSXBNnjx/3Dvf8809ovb/q1k2dSHGO9+efKgBOnNBQWVrw6qswfrz3WBepCfdERqqdS5aoqHLykRxPikj6jytSrZqKY9e5cwJRoQL89JPeC8F4dyIiNGx56NDVNUbK5XAlixRjTFXgM3TW4UlAD7SHjyvfAmeA29LKQIvFEkYqVFAxkpk9Kc5YKaF4UjzDPRC6SLl4MXVdVp3k2cOHVTykVR5E+fI6cKDr/D6QOk8KaKP9v/9pqMfJSShUSAduO3gw/b0P1aoFH+oB9aT8/HNo19ERjFdT9+PLwVOkhLn7MYTmSXkSyAF0E5EHReRTzwoiEg+sRcdTsVgsVzqO+PCVk+IM6BZukQKa9OjPhoIF1VNy6ZJ+9hQphQrp23qoIiVnztQlJzvJs+nxljpiBHz2mXppHFIrUipX1n05oR4HJ3k2vRv2m2/WeXeCpUIFFY7Bhu1Ar8WePRoiClfvtMyE56izV5InBWgGrBWRL1KodwAolmqLLBZL5sFp+P15Ui5e1Id8uXIZbpobM2fq7Ky+yJZNRYnTG8lTpDjhjFAatyZNYNy44EMRrji9StIqH8UVJ+zlDMQGqQv3gAqQkyeTk2Zd12/enP6elE6d9DsOlvz59b4M1ZPy0096f4eSrHy1UqyYztQdKLE7gwlFpBREJxlMiQjAJs5aLFcDhQtrw+dPpOzbp3XC/YDPkSNw2MQ15OOZOAvw1FPatThYoqJgyJDQ7YRkT4rnGClpRY0a8PvvyZ8vx5MC3iLFmV/o/PmwhwK8qFAhNLFZooSOMWPzUZScOfXedibkdJ0BOUyEIlL+AYIZ6aYcEJs6cywWS6bCGO3d4xlKcRr9zBDqCQYneVbEe1h8gHvvTXmm3bTC1ZOSHg1A9eppI1KqVNFZtz3zQm68Eb7+Whv2MI6f4ZNnn9VxY4KlZEmbj+KJa17KFdYFeSVQz/x/e3ce51R97nH884ggylaQYVNRtGARF0A2cQNx34oV6wbu11ZUXFutrb0qet2u1ta1KorUXXvVqkWsWnAtisgiCi4IyCYiiqzK8tw/fidDJpNkkkwyk5l8369XXoec8zsnT84ckie/81vMUn4imVlvYHfgreoGJiJFIr7hZExsnJS6lKR8/XUYnK5Bg9qt+SlkmxSoXJOydGluSUrz5iFBTfzb77JLGGm3GL/YDzxwU0+kTMTaWqkmZZPEJKUO1aTcCTQEnjazSkMMmtmOwAOAA3fnJzwRKUqxmZDrWpKS2B6lNsQnKflukwIhSZk2bdPzWC+ifGnfPtRE1YcvdiUplcWSlCIYEh+yGxZ/HHA7sBvwkZlNIyQkB5rZRGAm0A34k7u/WYhgRaRIxJKUTz6pG0lKbNTZYkhSWreGVavCaKqF+JW67bZhksIlS8LruId2BvkSm6U4lzFiik2srZWSlE1iSUqsFqWWb+ll1UHf3S8AhhPanOxKGCdlW6A3sBy40N0vzXeQIlJkttgi3DJ5//26kaTE16TU8i9DzMIXwZQphUlSzDbd8ond6sn3F83TT8MRR+T3mLWheXMYOzZ/A+rVB7FZwYvgVg/kMCy+u99DSEx6Ab8ETgT2Adq7+1/yG56IFK3WrUMvgGx6U9SWWMPZxNFma8s224TuvYX6EoglKfm+1RPTrl1u3a+L0aGH1nYExSWxJqWWbZ7LTu6+EZgcPUSkFJWVhVsJW2xR25FUrZjapMCm2wy5NGjNxB57hEav3boV7jWkfoolKUXQ/Rg0C7KI5KqsrG7c6oHiS1K22SZMotewYWGOv/vuofFsrt2PpXTFRp0tgu7HkENNipltAwwEOgCp+vG5u4+sTmAiUuTKymCrrWo7iswUU8NZCDUphfyV2q1baNS8cGFhbvdI/dWuXbg1unBhUTSOzjhJMTMDbiM0nI3VwCS2xvJonQNKUkTqs733DrUBdcHWW4dh8ZctK46YC52kbLllGB7+jTegf//CvY7UPw0bhv8j06fDwIG1HU1WNSm/Ac4HNhJmO54JfF+IoESkDjjrrNqOIHMNG0KzZmEiuWJo6Hv44YXv9rrHHvD886nnNBJJJdb7rI7d7jkdWAcM0jgoIlLnlJWFweeOPba2Iwm3yXbdtbCvsfvu8Pjjut0j2StkF/ksZdNwthPwhhIUEamTysrgs8+Ko01KTdhjj7BUw1nJVocOYVnHkpTvgCWFCkREpKDKymD1aiUpIlXp0AEaNar9gQ/JLkl5jTCyrIhI3RObJ6cIPnhrRIcOoXFz7FexSKY6dAjtUYpglutskpQrgTIzu7JQwYiIFEysRqFUalLM4M03w9DvItno0KEobvVAdg1n9wYeBK4ys8OBscA8Qm+fStx9TPXDExHJk1JLUkRydcABRTOfUTZJymg2jYPSF+hTRXklKSJSPMrKQlfkLbes7UhEiluTJkUzvk42ScoYQpIiIlL3lJWFWpQiuM8uIpnJOElx99MKGIeISGF16KAxQ0TqGE0wKCKlYZdd4LXXajsKEcmCkhQRKQ1mRTHMt4hkLmWSYmavmdlvU2wbYWYHpth2p5l9k68ARUREpDSlq0kZAKSaAes24KQU27YC1MdPREREqkW3e0RERKQoKUkRERGRoqQkJc/M7DQz8ySPO2o7NhERkbokm8HcJDuHAsvjni+urUBERETqIiUphfO+uy+t7SBERETqKt3uERERkaJUVU3KoWaWaojGVNu6VjOmtMxsZ8KtlN5AL6ALYdLD49z96Sr2PQk4B9gdaADMJMzsfLe7J53NuRo+NLMywkzRo4Hr3H19nl9DRESk3qoqSWkXPbLdVsiJCM8BLsh2JzO7ExgOrAVeBdYBg4A7gEFmNiRPicoi4L+Bd4ENwGHAlUAn4LQ8HF9ERKQkpEtSTq+xKLLzIXAzMAl4HxgF7J9uBzM7lpCgLAb2c/dPo/VtgX8DxwDnA39O2K8F0D6DmOa5+2oAdx8HjIvb9i8zWw5cZWYj3f3zDI4nIiJS8lImKe7+UE0Gkil3vz/+uWU27frvouVlsQQlOtZXZnYOMB643MxuT6hNOYZwO6gqA6NjpPIkcBXQE1CSIiIikoF633DWzLYF9gR+BJ5K3O7uE4AFhFtX/RK2jXZ3y+AxvgbeioiISEmp90kK0CNaznD3NSnKvJdQNt9OILTTeb9AxxcREal3SmGclE7Rcm6aMvMSyubMzMYBrxHazmwkNJwdDoxy99nVPb6IiEipKIUkpWm0XJWmzMpo2SwPr/cxcAawLeH8fgpcRpg5uhIzOxs4G6Bjx455eHkREZH6oRSSlBrl7hcCF2ZR/l7gXoBevXoVsuu2iIhInVIKbVJitSRN0pSJ1basKHAsIiIikqFSSFLmRMvt05TZLqGsiIiI1LJSSFI+iJbdzGzLFGV6J5QVERGRWlbvkxR3/xKYDDQCjkvcbmb7Exq5LgbeqdnoREREJJWUDWfN7I/VOK67+8hq7J9v1xMGcrvRzN52988AzKwNcFdU5oYCTDIoIiIiOTL35B1KzGwjYQCyxHHnq+qBYoQkpUH1w0saV082JRYAuxC6Dn8KLIutdPd+CfvdRZiccC3wCpsmGGwOPAsMcfcNhYg5U7169fJJkybVZggiIiI1yszed/deybal64J8dZJ1nYBTgDXAy2xqaLoDcBCwJfAQhW2A2hzom2R953Q7uftwM3sTOJcwIWEDYCbwAHC3alFERESKS7oJBiskKWbWkTCs+9PAue7+dcL21oQajiOBpBlRPkTz5GQ0q2CSfR8FHs1rQCIiIlIQ2TScvRb4ARiamKAAuPtSYGhU5rr8hCciIiKlKpsk5SDgDXf/MVWBaNubwIHVDUxERERKWzZJyk/IbG6bpkCL3MIRERERCbJJUmYDA80s5UzB0bYDorIiIiIiOcsmSXmQ0HtngpkNM7PyRrdmtrmZDQX+DWwBjM5rlCIiIlJyspkF+TZC190jCEnIA2a2MNrWgZDwGPBP4NY8xigiIiIlKOOaFHdfDxwNXEAYB6UBYWK+7aJ/zwEuAo6u7UHRREREpO7LpiYFD8PT3g7cbmbbEOa8AVjg7vPzHZyIiIiUrqySlHjuvgBYkMdYRERERMrV+1mQRUREpG7KuibFzPYiTMzXAWicopi7+5nVCUxERERKW8ZJipltATwBHBVblaa4A0pSREREJGfZ1KRcRejdsxL4G2EG4e8LEJOIiIhIVknK8cAqoLe7zypQPCIiIiJAdg1nOwBvKkERERGRmpBNkvI1ur0jIiIiNSSbJOWfQP/4OXtERERECiWbJOXKaHlH1NNHREREpGCyqRX5NTAO+C/gUDN7DZgHbExS1t19ZB7iExERkRKVbRdkJ4yP0hE4LUmZ2HYHlKSIiIhIzrJJUq4uWBQiIiIiCTJOUtxdSYqIiIjUGE0wKCIiIkVJSYqIiIgUpVxmQW4MDAS6AM1JPtGgeveIiIhItWSVpJjZscA9QKt0xVDvHhEREammjJMUM+sLPE4YF+UxYFdgN+AG4KfAQUALYBQwP++RioiISEnJpiblUkIblsHu/qKZPQjs5u6/BzCz1sCDwOFAz7xHKiIiIiUlm4az/YEP3f3FZBvdfSlwErAFGlNFREREqimbJKU1MCvu+XoAM9sytsLdVwCvA4flJToREREpWdkkKd8SaklivouW2yaUc6BNdYISERERySZJ+ZIwZ0/Mh4SePEfGVphZE2AfYEFeohMREZGSlU3D2fHABWZW5u5fAy8Aq4HrzawdoUfPKYTbQv+X70BFRESktGSTpDwFdAd6AC+7+zdmdglwF6HnD4SalS+BK/MapYiIiJScbCYYfJcwFkr8ur+a2fvAsYQB3mYCD7r7d0kOISIiIpKxrIfFT+Tuk4BJeYhFREREpJwmGBQREZGiVK2aFDMz4FCgK7ASeMnd5+UjMBERESltaZMUM2sE/AoYADQEPgLudve5ZtYGeAnYI26XdWY2wt3vLVC8IiIiUiJSJilRgvJvoB+h1w6EMVHOMrN+wB2E3j5LgTnADoTux3ea2Xvu/kHhwhYREZH6Ll2blHOBvYBvgOuA84GHgZbAnwk9fW4E2rp7H6AtcDPQICorIiIikrN0t3uOB9YB/d39s2jdnWb2GXAVYfC2P7i7A7i7m9kVhEkG9y1cyCIiIlIK0tWk/Ax4Oy5BiXkoWk519w3xG6LnU4Bt8heiiIiIlKJ0SUozwuixieZHy69T7LeUihMRlhwzG2xm75rZCjNbbGZ/N7POtR2XiIhIXZIuSTFgfeLKxNoTqcjMBhHmLpoJ/ILQPudnwCtm1rw2YxMREalLqj3irFRyIjAXODXWXsfM5gITgb2BsbUYm4iISJ1RVZLSzsz2y3Jbu2rGVNc1BFbEEpRIbC4jS1JeREREkqgqSTkkeiTyNNsKysx2Joxy2xvoBXQhfPkf5+5PV7HvScA5wO6ErtIzgQcJA9RtzFOIo4GTzOx84G/AT4D/BT4GXs3Ta4iIiNR76ZKUeYRkpNicA1yQ7U5mdicwHFhLSBbWAYMIg9INMrMh+UhU3P3fZvYL4BHgL9HqD4GD3P2H6h5fRESkVKRMUtx9hxqMIxsfEgaNmwS8D4wC9k+3g5kdS0hQFgP7ufun0fq2hFF1jyE0cP1zwn4tgPYZxDTP3VdH+/QHxkRx/QPYGrgS+IeZ7ePuazJ7myIiIqWtzjWcdff745+HOQ6r9LtoeVksQYmO9ZWZnQOMBy43s9sTalOOIdwOqsrA6BgQak/+7e4XxcX4H0LN1DBA8xqJiIhkIF0X5HrBzLYF9gR+BJ5K3O7uE4AFhAa//RK2jXZ3y+AxPm63XQgD2sUfZz5h/Jid8vrmRERE6rF6n6QAPaLljDS3Wt5LKFsdcwlJUTkz254w+eKcPBxfRESkJJRCktIpWs5NU2ZeQtnquBM42sxuN7MDzex44AVgCfBkYmEzO9vMJpnZpK+/TjWIr4iISOmpc21SctA0Wq5KU2ZltGyWh9e7k3BraThwOrAC+A/wS3f/JrGwu99L1E6lV69exdibSkREpFaUQpJSo6JB3MoTDxEREclNKdzuidWSNElTJlbbsqLAsYiIiEiGSiFJmRMtt09TZruEsiIiIlLLSiFJ+SBadjOzLVOU6Z1QVkRERGpZTm1SzKwBYSTVxqnKuPu8VNtqkrt/aWaTgZ7AcYTRYMuZ2f7AtoTRaN+p+QhFREQkmaxqUsysr5mNI7TdWAR8keIxO89xVtf10fJGM/tpbKWZtQHuip7ekMdJBkVERKSaMq5JMbO9gVeALaJV3wLfFyKoKuLoyabEAsIIrwD/Y2aXxla6e7+4fz9tZncTJiecbmavsGmCwebAs4SJBkVERKRIZHO752pCgnIfcKW7LylMSFVqDvRNsr5zup3cfbiZvQmcS5iQsAEwE3gAuFu1KCIiIsUlmySlD/Cxu/+qUMFkIponJ6NZBZPs+yjwaF4DEhERkYLIpk2KAdMKFYiIiIhIvGySlOmEmYJFRERECi6bJOXPwL5m1r1QwYiIiIjEZJykuPsTwHXAv8zsHDPrWLiwREREpNRl0wV5Q9zTO4A7zFK2X3V31+SFIiIikrNsEolsetTk1PtGREREJCbjJMXdS2GeHxERESkSSjxERESkKClJERERkaKkJEVERESKUso2KWY2G3DgQHf/InqeKXf3naodnYiIiJSsdA1ndyAkKQ3jnmfKc4xHREREBEifpHSKlgsSnouIiIgUXMokxd3npnsuIiIiUkhqOCsiIiJFSUmKiIiIFCUlKSIiIlKUlKSIiIhIUVKSIiIiIkVJSYqIiIgUJSUpIiIiUpQyTlLM7P/M7K5CBiMiIiISk01NyhHA1oUKRERERCReNknKAjbN4yMiIiJSUNkkKS8A+5rZVoUKRkRERCQmmyTlKuB74Gkz264w4YiIiIgE6WZBTvS/wAzgSOBTM5sMzAXWJCnr7n5mHuITERGREpVNknIa4NG/GwH9okcyDihJERERkZxlk6ScXrAoRERERBJknKS4+0OFDEREREQknkacFRERkaKUze2ecmbWCNgT2CZatQB4391/zFdgIiIiUtqySlLMrCGhK/K5QLOEzSvN7Hbgandfl5/wREREpFRlnKSYWQPCgG4HAgYsAmZHm3cE2gO/A3qb2eHuviHPsYqIiEgJyaZNytnAQcCnwGHuvo277xs9tgEOAz4hJDH/lf9QRUREpJRkk6ScAqwCBrn7uMSN0boDgdXAqfkJT0REREpVNknKLsC/3X1BqgLRtn9HZUVERERylk2S0pBQS1KV1Wi2ZBEREammbJKUuYRZkBulKhBt2zcqKyIiIpKzbJKUfxB68DxkZj9J3GhmLYAHgHbAc/kJT0REREpVNuOk3AScCPwSOMzMnge+IEwmuCNwFGHslPlRWREREZGcZTN3zzdmdgDwKNALOJlNsyJbtHwPOMndl+U1ShERESk5WY046+6fAX3MbB9gfyoOiz/B3d/Mc3wiIiJSorIZcbY54O6+IkpGSiohMbOfApcC/YBdgZnuvmu2ZURERCQz2TSc/Q54pVCB1AHdgCOAz4CPqlFGREREMpBNkrKCMCR+qXre3bdz9yHA5GqUERERkQxkk6R8DGxbqECKnbtvzEcZERERyUw2Scp9wD5mtmchAjGznc3sAjN72MxmmtlGM3MzG5LBvieZ2RtmttzMVprZJDM718yyeX8iIiJSRLLpgjzKzPYA/mVmNwLPAHPd/Yc8xXIOcEG2O5nZncBwYC3wKrAOGATcAQwysyGq4RAREal7sundsyHu6f9ED8wsWXF396y6NwMfAjcDk4D3gVGEbs7pYjqWkKAsBvZz90+j9W0JEx0eA5wP/DlhvxaE0XOrMs/dM5mvSERERPIsm0QiaTaSh7IAuPv9FQ6QPPlJ9LtoeVksQYmO9ZWZnQOMBy43s9sTalNIOMADAAAgAElEQVSOAR7M4PgDo2OIiIhIDcu4zYa7b5bNo5BBA5jZtsCewI/AU0ninUAYZK4dYdyS+G2j3d0yeIwv9PsQERGR5Opyw9Ie0XKGu69JUea9hLIiIiJSR2TTJmUZ8KG771fAeLLRKVrOTVNmXkLZnJnZVsDh0dPtgeZxPY/ec/e5mZRJctyzgbMBOnbsWN0wRURE6o1s2qQ0Ar4sVCA5aBotV6UpszJaNsvD67Wh8m2l2PPTgdEZlqnA3e8F7gXo1auXJ24XEREpVdkkKZ8BrQsVSLFz9zlU0SA4kzIiIiKSmWzapDwM7Gdm1b51kiexWpImacrEaltWFDgWERERybNskpQ/AeOA18zseDPbokAxZWpOtNw+TZntEsqKiIhIHZHN7Z5PCbcytgceBTCzJUCynjXu7jtVP7y0PoiW3cxsyxQ9fHonlC1Zr7/+Os2aNaNHD3V0EhGRuiGbJGWHuH/H2l20TVG24A1A3f1LM5sM9ASOA8bEbzez/QkTIi4G3il0PMXuN7/5DStXrmTq1Klsvnm2gwGLiIjUvGxu93TK4rFjfsNM6fpoeaOZ/TS20szaAHdFT28o9bl7Zs+ezezZs2nTpg333XdfbYcjIiKSEXMvjl6vZtaTTYkFwC6ErsOfAstiK929X8J+dxEmJ1wLvMKmCQabA88CQ9w9ft6hotWrVy+fNGlS3o97ww03MGfOHIYPH85BBx3ErFmz+MlPfpL31xEREcmWmb3v7r2SbSumEWebA33jHrGxTTonrK/A3YcDJwOTCRMSHkLoLn0ecGxdSVAK6YknnuCEE05g99135+c//zkjR47M+hgTJ07ku+++K0B0IiIiyaWsSTGz/YDF7v5JVgc0OxDYxd3/kof4SkohalJmzZrFwIED+fLLL2nQoAFfffUV3bp145133qFz584ZH6d3794MGzaMESNG5DU+EREpbbnWpIwHLktxwGVmdnuK/U4mdFeWIvDEE08wZMgQGjRoAEDbtm258MILufnmmzM+xoYNG5gxYwYvv/xypW2PPPIIw4cPz1u8IiIiMVXd7kk1eupPSD+ImhSJ2K2eeIcccgjvvvtuxsf4/PPPadKkCa+//jo//PBDhW2PPvoo48ePz0eorF69ms8//zwvx6qLpk2blnT9vHnz+Pbbb2s4Gkm0evVq5syZk/P+S5cuZeHChfkLqJ6YO3cuP/74Y22HIUWqmNqkSJ59+OGHrFixgn79KrQ1ZrfdduOTTz6plHCkMn36dPbaay+6du3K22+/Xb5+xYoVvPHGG8yZM4fVq1dXO96bbrqJ/v37s3Tp0mofq65Zs2YNe+65J598Uvnu6kUXXcQdd9xRC1FJvF/96lecfPLJOe9/00038fvf/z6PEdUPxx13HLffnqpiXkqdkpR67IknnuCXv/wlm21W8c/cuHFjdtppJz788MOMjjN9+nR23313Dj744Aq3fMaOHcvee+9N165dk9YCfPbZZxnHunHjRh566CH23HNPzjvvvIz3qy8mT57M+vXrk9ZwTZw4kYkTJ9ZCVBLz7LPP8tZbbzF16lTWrVuX0zH0d6xs7dq1TJkyhdGjR1MsPU2luChJqcfWrl3LSSedlHRbz549+eCDzAbinTZtGrvtthuHHHII48aNK1//7LPPMnjwYHr27MnkyZMr7DN//nx23nnnjG9TTJgwgebNm/P3v/+dKVOm8NRTiZNJ128TJ05kyy23rPQltmDBApYtW8bEiRP1IV5Lli5dyvDhwxkzZgydOnVKeVsunfXr1zN58mTmzJnD8uXLCxBl3TRlyhR22WUXVq9eXekzRASUpNRrN998Mz179ky6rUePHhknKdOnT2e33Xajb9++zJ49myVLlvDjjz8yduxYjj766KTHmjBhAhs3buSNN97I6DVGjx7NaaedxpZbbslDDz3E+eefz5IlSzLatz6YOHEiQ4cOrZSkTJw4kYEDB9KoUSO++OKLWoqutJ177rmceOKJ7LPPPvTt2zen2pAZM2awzTbb0KtXL957770CRFk3TZw4kX79+nHqqafy4IMP1nY4UoSUpJSoHj16ZPTLZdWqVSxYsIAuXbrQsGFDBg4cyCuvvML48ePp2rUr7du3T5qkjB8/no4dO2bUqHbFihU899xz5ff7+/bty+mnn551d+e3334746So2EycOJFzzjmHGTNmsHbt2grr+/btS58+fXSroBY8+uijTJ06lWuvvRYg5ySlpv6Oa9asqVPtO2Ln5ZRTTuHxxx/PuJ2clI6qkpR2ZrZf4qOKbe0KHLPkQffu3Zk+fTobNqQf627GjBnsvPPO5fP9HHzwwYwbN45nnnmGwYMHA7D77rvz0UcfVbhXP378eK644oqMkpSnnnqKAQMG0KZNm/J1V155JWPHjs2qV8vIkSO55pprMi5fLL766iuWL1/OHnvsQZcuXZgyZUr5ttiHeK5fjpK7d999lwsuuIDHHnuMLbfcEiDnJKOm/o4vvfQSI0aMYPbs2QV7jXyKnZcddtiBPfbYg+eff762Q5Ji4+5JH8BGYEOuj1TH1SP1Y8899/SatNNOO/lHH32Utsz999/vw4YNK3/++eefe7t27bx9+/Y+a9as8vVdu3b1KVOmuLv7/Pnzfeutt/a1a9d606ZNfdmyZWlfY9999/Vnnnmm0vrBgwf7mDFjMnovy5cv96ZNm3qzZs2qfL1i89xzz/nBBx/s7u6/+tWv/LbbbnN39/Xr15efv9dee8379etXm2GWlDlz5nj79u39ueeeq7B+3bp1GV3Tibp16+aTJk3yuXPneps2bXzjxo35DLfcKaec4i1atPBbb721IMfPpyVLlnjz5s19w4YN7u4+ZswYP+KII2o5KqkNwCRP8b2YriZlXjUfUuQyueUT69kTs+OOO9KkSRNatmxJly5dKhwrdstnwoQJ7LfffmyxxRb069ePN998M+XxP/vsM2bOnMnhhx9eadvgwYN59tlnM3ov//znP9l3330ZOHAgL774Ykb7FIt3332Xvn3DjA/xv7Q/+ugjOnToQMuWLenVqxfTpk3TeBI14Pvvv+eoo47i0ksv5eijj66wbfPNN6dnz55kMzL0ihUr+OKLL9h9993Zbrvt2GyzzZg3L/8fkevWreOFF17ghhtu4Jlnnsn78fPtvffeo3fv3uW9D3/xi1/w9ttvs2jRolqOTIpJyiTF3Xdw9065PmryTUhuEtuSLFq0iEGDBrFy5crydbGePfEGDx5caYC4+GONHz+eAQMGADBgwIC0t3zuu+8+Tj75ZBo1alRp25FHHskrr7zCmjVrqnwvzz77LMccc0xWiU06d999NzfccEO1jwNw//33p50vKVblDRWTlPj1zZo1Y8cdd8ypZ0l9tGHDBo466ihmzJiRttwLL7zAueeem9Wxhw8fzl577cVFF12UdHu2t2wmTZpE9+7dadiwIWaWl1s+Dz74IFdffXWFdW+88QY77rgjp512GlOnTq2xhucjR45k1KhRWe83ceJE+vTpU/68SZMm/OIXv+CBBx7I+Bjr16/niCOO4OOPP6607ZZbbqlT7XMkhVRVLHrU/9s9Y8eO9QMOOKD8+YUXXuiNGzf2G2+80d3dN27c6FtvvbUvWLCgwn7r16+vVF396quv+j777OPu7p07d/apU6e6u/sbb7zhPXv2TPr6S5cu9VatWvmcOXNSxjhw4MBKVe6J1q5d6y1atPBFixb5119/7c2bN/fVq1en3Sed77//3svKyrxly5b+1Vdf5Xwcd/eVK1d627ZtvUWLFr5w4cJK2zds2OAtWrTwJUuWlD9v3ry5L1myxM866yy//fbby8ueeeaZfscdd1QrnvrikUce8caNG/txxx2XssyGDRt8l1128a222qr8eqzKhx9+6G3atPEVK1akLPP000/7kUcemXGs119/vV944YXlz//nf/7HL7744oz3T7Rq1Spv166dt2jRwufPn1++/vzzz/frrrvO3d2HDBnio0aNyvk1MrVgwQJv0aKFt23b1letWpXVvocccog/++yzFdZ99NFHXlZWlvb8xxszZow3btzYTzjhhArrly5d6i1btvTWrVv7d999l1VcUvPI8XaP1HOx2g93Z/HixTz00EP84x//4NZbb2XVqlUsXrwYd6d9+/YV9mvQoAFmFWdM6N69O1OnTmX+/Pl888037LrrrkCYmHDWrFlJZ1C+9dZbOfbYY9l+++1TxphJzchrr71Gt27daNeuHa1bt6ZHjx688sormZ6GSu644w4OOuggTjjhBG655ZacjwNwzz33sO+++3Laaadx0003Vdo+a9YsWrVqRVlZGQCbbbYZvXv35r333qtQkwK59yypbzZs2MDIkSN57LHHmDBhQsralKeffppmzZpxzTXXZNygeuTIkVx88cU0bdo0ZZnY3yF8tlYt33/He++9l7322oszzzyTG2+8EQg/NmPjFkH4f1MTt3xuuukmzjjjDPbee2/uueeejPdz9wq3OWO6du3KoEGDuPPOO6s8xoYNG7j22mt5/PHHee211yrUptx6660MGTKEww8/nL/8RXPd1mmpshc96n9Nirt7+/bt/YsvvvCLL77YR4wY4e7uxx13nN98880+btw4HzBgQMbH2n777f3qq6/2wYMHV1g/aNAgf/755yusi9WifPHFF2mPOWfOHG/durWvW7cuZZmzzz7bb7755vLnf/rTn/yMM87IOO54sVqUjz/+2OfNm+etWrUqr+XIVuwX77Rp03zhwoXesmVLX7RoUYUyDz74YKVfgVdccYVfcsklvtVWW/kPP/xQvn7q1KnepUuXnGKpTx599FHv37+/b9y40W+88UY//vjjK5XZsGGDd+vWzceOHVtemzVt2rS0x50xY0bGv+I7dOjgs2fPrrLcxo0bvX379hXKLl++3Js0aeI//vhjlfsnWr16tbdv394/+OADX7Rokbds2dIXLFjgkyZN8s6dO5fXcH777bferFmzjGskchF/TU+ZMsXbtWuXcW3KrFmzvGPHjkm3zZgxo8raLHf3hx9+2PfZZx/fuHGjX3/99X7SSSe5e8XPlk8++US1KXUAaWpSav2LWY/aTVIOP/xwv+eee8o/7Nzdp02b5u3atfNrrrnGzz///IyPNXjwYG/btm1575SYa665xi+55JIK637/+9/7WWedldFxe/bs6ePHj0+6bcOGDd62bVv/9NNPy9d98cUXXlZW5uvXr8849pjrr7/eTzzxxPLnv/71r/2yyy7L+jju7rfccosfe+yx5c9HjBjhF110UYUyv/71r/1Pf/pThXXPPfect2rVqlJvnvjePqVq/fr13rVrVx83bpy7u69YscLbtGnjM2bMqFDuySef9D59+pR/ad98880+ZMiQtMc+4YQT/Prrr88ojmOOOcYfe+yxKsvNmzfPy8rKKt0e7datm7///vsZvVa82267rcKPgIsuushHjBjhf/jDH/y3v/1thbIHHXSQP/3001m/RqYuvPDCCrexjjnmmIx7FY0ZMybtrbrjjz++/LZzMuvXr/edd97ZX3nlFXev+OMi8bNl2LBhPnLkyIziktqhJKWOPGojSfnDH/7gLVq08PPOO6/C+mOPPdabNm3q9957b8bHuuaaaxwo74oc8/rrr3v8e/vmm2+8VatWGf0SjR03/sMw3ltvveW77rprpfXdu3f3CRMmZBy7e/jCKysrq9Ate+7cud6qVSv/+uuvszpWrBYlvi3EggULvGXLlr548eLydT169PC33367wr6LFi1ywC+44IJKx91vv/38pZdeyiqW+uSxxx7zfv36VfjSv/766yvURsVqUV588cXydbHalOnTpyc9bqwtxPfff59RHIntTFJ56qmnkrZfOf300/2uu+7K6LViYrUokydPLl8Xq03ZYYcdKl1Hd911l5988slZvUamYrUo8e2sPvjgA2/fvn1G7cHOPfdc/9///d+U26tqG/TII4/43nvvXeE6uO666/zII4+s9Nkya9Ysb926tS9fvjyTtya1IF2Ssnmt3muSWtejRw/WrFnD5ZdfXmH9H//4R/7+979X6H6cybFatmxZqTdQnz59mDlzJpdffjlmxrRp0zjmmGPo1CmzTmCDBw/m4IMPpnHjxpW2/ec//ym/D5+4z5VXXkn//v0zjv/jjz9m0KBBdO3atXxdx44dOe644xg2bBjdu3fP+FiffPIJ/fv3r3D+OnTowNChQxk2bBh77rkn7s7MmTMrHbddu3Z07NixQs+HmL59+3LzzTdnNEheffTkk09y1113VWgTde6557LTTjtx6aWX0rBhQxYvXsxWW23FYYcdVl6mSZMmXHLJJZx11lkMHDiw0nFff/11LrroIpo1a5ZRHH379uXMM89Mek3Ge+uttzjooIOS7n/fffdl1RX5s88+o0+fPvTo0aN8Xbt27Tj11FN57LHHKrXvOProo7niiiv43e9+l/FrZGrSpEmccsopFdqrde/enb59+zJs2DA6d+6cdv8XXniBv/3tbym3d+vWjQEDBjBs2DB+9rOfVdr+xBNPcO+991a4Ds477zxuueWWSp8tXbp04dBDD2Xo0KF069Ytm7cpKVxxxRUZ/1+pLgtJjBSDXr16eTbjL+TD6tWreeeddxg0aFClbS+99BIHHnhg+WizmRxr/PjxScc8eeKJJ8pHwdxss804/fTTK4wwm4678+CDD/LVV19V2rbZZptxxhlnlDc8jfnmm28YNWpUlSPqxjMzTjnlFDp06FBh/dKlS3nggQeyPtbQoUPZdtttK6xftmwZo0aNYv369QBst912DB06tNL+L7/8Mv369aN58+YV1n/++ec8+eSTGcdR35SVlXHmmWdWarj9+uuv89Zbb5U/Hzx4cIVkE8KQ8X/961+Tdmlv3LgxZ599Nk2aNMkojh9++IE77rgjo3FrTj75ZDp27Fhh3dKlSxk1ahQbN27M6PVihg4dynbbbVdh3bfffsv06dPZb7/9KpV/7LHHmDNnTlavkYnNN9+cs846i5YtW1ZY/+WXX/LII49Q1fdKo0aNOP/885MOPRCzcOFCxowZk/RYbdq04Ywzzqh0Hbz55pt06dKl0mfL4sWLNdNyHo0YMSLj/yuZMLP33b1X0m36oxWP2khSREREalO6JEVdkEVERKQoKUkRERGRoqQkRURERIqSkhQREREpSkpSREREpCgpSREREZGipCRFREREipKSFBERESlKSlJERESkKClJERERkaKkJEVERESKkpIUERERKUpKUkRERKQoaRbkImJmXwNzazuOItIaWFrbQdRhOn+507nLnc5d7kr13G3v7mXJNihJkaJlZpNSTd8tVdP5y53OXe507nKnc1eZbveIiIhIUVKSIiIiIkVJSYoUs3trO4A6Tucvdzp3udO5y53OXQK1SREREZGipJoUERERKUpKUqTGmFlDMxtkZreY2SQz+97MfjSzBWb2tJkNSLHfaDPzNI+ZNfxWakWu58HMNjOzc6NzvtLMlpvZG2Z2Yk2/h9piZgOqOHfxj45x+5XEtWdmO5vZBWb2sJnNNLON0fsbksG+J0XX0/Lo+poUXW9pv1/M7FAze9nMlpnZajP70Mx+b2Zb5O+d1Yxsz1+un4XRviVxTcZsXtsBSEnZH/hX9O/FwOvAKmAX4FjgWDMb6e5/TLH/W8BnSdYvynegRS7j82BmDYD/A44GvgdeBrYABgGPmlk/d7+ggLEWi8XAQ2m29wG6Ap8DXybZXt+vvXOArK8DM7sTGA6sBV4F1hGurTuAQWY2xN03Jtnvt8CNwAZgPPAt4fPhWuBIMxvk7qtzeyu1ItvzV93PQqj/12Tg7nroUSMP4ADgaWDfJNuOB9YDDgxM2DY6Wn9abb+HWj5/WZ8H4JJonxlA27j1nQkfjg78vLbfW20/gI+ic3FFdc95XXwAZwE3Ab8EdiIkDg4MSbPPsVGZRUDnuPVt487nBUn26wVsJHwp941b3xSYEO33p9o+J4U8f7l+FpbSNRl76HaP1Bh3f83dh7j7G0m2PUH4zwcwtEYDq6eiWpTfRk/PcfevYtvc/VPgsujp72s6tmJiZnsRalE2sOkaLCnufr+7/9bdn3T3zzPc7XfR8rLoeood6ytCzQLA5Ulu+1wOGHCju0+M228lcDohgRluZj/J5b3UhmzPnz4LM6ckRYrJB9Fy21qNov7YC2gDzHf315Nsf4pQPd/bzLap0ciKyxnR8iV3X1irkdQRZrYtsCfwI+E6qsDdJwALgHZAv7j9GgGHRU8fSbLfbOAdoBFweN4Drzv0WRhRmxQpJp2jZap7qgPNbHdCtfBXwJvAvzzJPe96LtPz0CNavpfsIO6+2sxmAN2jx4ICxVu0zGwrQvU6wKg0RXXtVRS7tma4+5oUZd4DtonKvh2t2xnYCliWpsbhPWDvaL9H8xNunVPVZyGUyDWpJEWKgpm1A06Lnv49RbFTkqz7yMxOcPfpBQmsOGV6HjpFy3STVs4jJCid0pSpz44DmgFLgBfSlNO1V1Gm11Z82fh/zyO1ZPuVjAw/C6FErknd7pFaZ2abAw8DLYBX3f35hCJTgBGElu9NgQ7AkcDUaN0rJXK7Itvz0DRarkpzzJXRsll+Q60zYrd6xrj7uiTbde0ll+u1pWsyjQw+C6HErknVpEgxuIfQbfFLkjQUc/fbElatAl40s38RegP0IzTiO6/AcdYqnYf8MrOfAvtFTx9IVkbnXGpY2s9CKL1rUjUpUqvM7M/AmYTusIPcfXGm+7r7j8D10dOSbWSX5jzEfpE2SbN77JftinzHVQfEalHecfePs9lR117O15auyRSq81kI9feaVJIitcbMbiFUW35N+E/5aRW7JBMbXbHeVG/mKNl5mBMtt0+z33YJZUtC1D07dk8/XYPZdEr52psTLbO9tmL/7khqJXdN5umzEOrhNakkRWqFmd0EXAx8Axzo7h/leKito+XKtKXqv2TnYXK07J1sh6hny67R0w+SlanHDiF8kK8EnsjxGKV87cWul25mtmWKMr0TykL4El0DtDKznVLs1yfJfvVWHj8LoR5ek0pSpMaZ2Q3AbwhDYR/k7tOqcbhfRsuk3WxLSLLz8A7hl9m2ZrZf5V04DmgIvOfupdb9+Mxo+WQ0iFguSvbac/cvCUlwI8J1VIGZ7U8Y42Mx4TqM7fcjMDZ6enKS/XYkjO/zI/Bi3gMvMnn+LIT6eE3W9pC3epTWgzA3hxP+U+6ZQfnuhJbrDRLWb04Y8n1DdLxDavu9Ffi85XQegEvZNCx+m7j1nQljMJTcsPhAa8KXoAP9833O68ODzIbFH8KmYfF/Gre+TXS9pRoWvzebhsXvE7e+adzr1qlh8XM8f1l9Fkb7lNw1adEbFCk4MzsaeC56OonwQZbMTHe/IdpnMPAMsIzwy20JoUpzN0LXu43A5e5+cwFDr3W5noeo7cUzwFGECQZfJdSeHAg0Bm539xE19DaKgpldBNxKuM66pilXMteemfUE7opbtQuhC/CnhPcPgLv3S9jvLsIQ+GuBV9g0wWBz4FnCl/SGJK8XP8Hga8B3hEn32gATgQO8Dk0wmO35y+WzMNqvZK7JcrWdJelROg/CAEWewWN83D6dgNsII1YuIHwYriH853+ADH+B1PVHdc4D4bbuecD7hF+v3xNGpzyptt9XLZ3LadF19ptCnfO69gAGZPJ/M8W+JxFm5P0+ur7eB84FNqviNQ8lzAT8bXReZxDmkdqits9Hoc9fLp+FpXZNxh6qSREREZGipIazIiIiUpSUpIiIiEhRUpIiIiIiRUlJioiIiBQlJSkiIiJSlJSkiIiISFFSkiIiIiJFSUmKSA0ys5+Z2T1mNsvMVpvZGjObZ2Zvm9ktZnZQkn3mmJmb2Q41H3FhmNlZZva+ma2K3pub2U+q2GdAXNl0jzk19DbqFTPrZ2Z3mdmHZvatma0zs2Vm9p6Z3W5mB1Tj2KOjv81pWe53VbTfVbm+ttRtm9d2ACKlwsyOB8YQJmVbQJjf41ugDOhJmFhtf8IonPWWmR0J3EcYLfNfbBo2/McMD7EKeDrN9qW5R1d6zKwZMIpNEwXOJ4xI/D3wE8KQ6+cB55nZm+6+b60EKiVJSYpIDTCzdoRhqxsBFxHmzNkQt30zYJ/okWgQYb6d+jJTcezLcIS735fD/kvd/bQ8xlOyzGwLQqLYF/gIGO7uE5KU2wO4GDgsx5f6HXADYTJCkYwpSRGpGUcCWwHvuPttiRvdfSPwevRI3PZ54cOrUdtFy09rNQoBuJqQoHxGmBF6ebJC7j4VONXM+uTyIu6+CCUokgO1SRGpGW2i5ZJsd0zWJiXuXn1WbTPMbGszu9bMppvZyqhNyGQzu8jMGuYQW0MzO8/MJprZ91Ebm4/N7AYz2zqh7Ggzc2BgtOrfcbFele1rZxFjebsGM2trZn81s/lm9oOZfRHF2jjN/n3N7PFonx/N7Gsz+4eZJav1Ivaeon+fGXduKrS7MbNOZvawmS2JztsMM7vUzBok/s3NrKmZLTez9Wa2bZpY34/2OzyD89KCMBEgwEWpEpR47v5ukuOUx2pmg83s31GbFjez7lGZlG1SomvoUjP7yMzWmtliM/ubmW1fVTxS/6kmRaRmzIuWg8xsV3f/sJrHmwI8lGJbd2APYEP8SjPbDXiJMKX7fEKbmM0Iv6RvBY4ws8PdPaO2IdEX+1jCDLCrgX9Hy32By4ATzOwAd58d7fJmtDwUaAuMAxbHvZ9C244wQ68RZpFtTri9dhmwC3B04g5mdgkQm/Z+MvAOsC1wBOF8/TrVLSszux0YTpgh+AWgC2FmW8xsV2AC0IpwbbxGaP9xLVCptsLdV5rZg8AFwNnAH5O8Xj9C26bZhL9zVQYCTQltgv6ZQfmqXEJou/Iu4brYDtiYbofoNuf/EWoa1xLOwwrCLc7DgBfzEHk6GzcAAAfWSURBVJfUZbU9DbMeepTCA2hGaFPiwDrCh+9vgQOBFlXsOyfab4cMXmcX4DtCgjI4bv2WhC8vBy4HNo/b1orQLsGBq7J4TzdF+3wMbJPwWk9H295Jst/4aNuALM/hgGi/OVnud1W0nxMa7DaK29aV8KXowN4J+x0WrV8A9E3YtjewnNDYt0vCtthrfQf0SRKPAR9EZUYBDeO27QwsjDvGDnHbOhO+9BfG7xO3fUy0z6UZnpeRUflXqnltx67PdcARKcqMjsqclrD+/Gj9fOCncesbx11DWV2XetSvh273iNQAd19BSEgmEWowDwduJOrdYmZvRb1/chY1zv0n0IJQff9s3ObTgE7Ak+5+g7uvj4ttGXAq4UvmXDOzDF5rS+Cc6OkIdy9v1Ovua4BfAyuBfma2d3XeVxLbV3Gbq1Kbn8iXUazlNUXu/jHwt+jpoITyV0XLs9x9YvwGd3+L8CXfEPhVite7yZPcHiHUNHUn9Oy60N3XxR13VnTcStz9U0INSXvgmPhtZtYa+CWhNuKBFPEkah0tv0620cx6RrdpEh+9UhzvQXfPtubjwmj5B3f/LLbS3dcSaqHWZHk8qWd0u0ekhkRfiL3NrD/hdkFfQvV8S6A/0N/MDvMceq6YWRPCLYXtgT+7+18SisTaKDyVIraFZvYpoSamM/BJFS+5J+FWwUJ3r9Rl2t2XmtnzwImEGpC3MnwrmaiqC3KyxADgtSiBSjQzWnaIrYi+9PsQuuG+nOJ4sV4we6XY/n8p1u8fLV+IktdEjwJ3pdj3dkINz3Dgybj1ZwJbAKOjpDMfOhKS10QvEJLtRKneb1JR25odCbVDjyZud/clZvYy8PNsjiv1i5IUkRrm7m8T2kTE7sn3A/4bOJjQg+JFd0+aTCRjZg2AxwiJw7OErqKJdoyWT2VQUVJG1UnKNtHyizRlYm1RtklTJhe5dkGel2L999EyvvFsp2jZHFhfxTkrS7F+bor126Tb7u7LzWw5oUYs0UuEXlH7m9ku7v5RdA39Otp+Z7pAE8TGk0kaf1QTV/7GzWw8mxKsZFK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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_eband), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_eband_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_band_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 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0.4188600435529679 0.8774775175215836 3.1374262213407746\n", - "137 -0.7781071269176891 -0.6833999624237777 -6.511932174692902 -5.444925346104355\n", - "138 -0.6904581518492705 -0.6448701757365143 -5.900149444024464 -5.222298774437149\n" - ] - } - ], - "source": [ - "emin = dos_qe[: , 0][0] - 1.0\n", - "emax = dos_qe[: , 0][-1] + 1.0\n", - "n_egrid_max = int(len(dos_qe[:,0]))\n", - "#n_egrids = np.int32(np.arange(1,11)/10.0*n_egrid_max)\n", - "n_egrids = np.arange(10,n_egrid_max)\n", - "ra_enum = []\n", - "ra_eband = []\n", - "ra_enum_M1 = []\n", - "ra_eband_M1 = []\n", - "for n_egrid in n_egrids:\n", - " ra_en = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = ra_en[1] - ra_en[0]\n", - " # Generate electron number \n", - " ra_enum = np.append(ra_enum,gen_enum(k_weights_qe, ra_en, eigs_qe, sigma_mod) - enum_ref)\n", - " # Generate band energy\n", - " ra_eband = np.append(ra_eband,gen_eband(k_weights_qe, ra_en, eigs_qe, sigma_mod)*Ry2eV - eband_ref)\n", - " # Generate electron number \n", - " ra_enum_M1 = np.append(ra_enum_M1,gen_enum_M1(k_weights_qe, ra_en, eigs_qe) - enum_ref)\n", - " # Generate band energy\n", - " ra_eband_M1 = np.append(ra_eband_M1,gen_eband_M1(k_weights_qe, ra_en , eigs_qe)*Ry2eV - eband_ref)\n", - " print(\"{0} {1} {2} {3} {4}\".format(n_egrid,ra_enum[-1],ra_enum_M1[-1],ra_eband[-1],ra_eband_M1[-1]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_enum), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_enum_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_num_vs_N_grid_RT_Fermi_function.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_eband), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_eband_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_band_vs_N_grid_RT_Fermi_function.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":17: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n", - ":15: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n", - ":16: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " eband, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 -13.279457382996725 -1.5943287158244175 -204.99901992889727 -73.64132975782468\n", - "11 -10.584905418379662 -3.3596062347261295 -170.35528764235596 -76.26173069497872\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":18: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " eband, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12 -7.622572447322909 -3.2910656504813005 -135.78485770069528 -67.63305431379604\n", - "13 -4.958764023423441 -2.3245302207935765 -105.08001968634562 -53.79662825543892\n", - "14 -3.338969396864286 -0.8014179529504872 -83.82751336060619 -36.63252201057048\n", - "15 -3.0658370744960166 -0.5376671044202794 -74.48984024576794 -30.75794666310685\n", - "16 -2.7000378202089905 -0.7014599707579237 -65.73139962042387 -29.078775773406505\n", - "17 -2.0273009942399085 -1.1582635551835665 -55.54811007212311 -29.840695007916565\n", - "18 -1.3925325945957638 -0.9418433490864118 -46.312441692213724 -25.32366446955257\n", - "19 -1.201152508004725 -1.4583033871500675 -41.13879844898838 -27.25127105671436\n", - "20 -1.3217698372741324 -1.3978542702327559 -38.93848348297706 -25.66185703090696\n", - "21 -1.257249997465692 -1.1708179852660123 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-2.954403181319776 -1.1411467524885666\n", - "133 -0.2970644535420206 -0.17071466740776486 -2.950219918032417 -1.6392737564474373\n", - "134 -0.28748691053112907 -0.13242073731032633 -2.9244179162378714 -1.3861573755284553\n", - "135 -0.2679364721085449 -0.05429060497726823 -2.7572455783219993 -0.7578980948169374\n", - "136 -0.25939896241914084 -0.05255782778954199 -2.696800979237196 -0.7524658988008923\n", - "137 -0.2706360223538695 -0.12470273770406948 -2.7208878252190516 -1.3090674675413538\n", - "138 -0.2706336612926634 -0.1379835044530182 -2.771722780447817 -1.4066602749298909\n" - ] - } - ], - "source": [ - "emin = dos_qe[: , 0][0] - 1.0\n", - "emax = dos_qe[: , 0][-1] + 1.0\n", - "n_egrid_max = int(len(dos_qe[:,0]))\n", - "#n_egrids = np.int32(np.arange(1,11)/10.0*n_egrid_max)\n", - "n_egrids = np.arange(10,n_egrid_max)\n", - "ra_enum = []\n", - "ra_eband = []\n", - "ra_enum_M1 = []\n", - "ra_eband_M1 = []\n", - "for n_egrid in n_egrids:\n", - " ra_en = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = ra_en[1] - ra_en[0]\n", - " # Generate electron number \n", - " ra_enum = np.append(ra_enum,gen_enum_int(k_weights_qe, ra_en, eigs_qe, sigma_mod) - enum_ref)\n", - " # Generate band energy\n", - " ra_eband = np.append(ra_eband,gen_eband_int(k_weights_qe, ra_en, eigs_qe, sigma_mod)*Ry2eV - eband_ref)\n", - " # Generate electron number \n", - " ra_enum_M1 = np.append(ra_enum_M1,gen_enum_int_M1(k_weights_qe, ra_en, eigs_qe) - enum_ref)\n", - " # Generate band energy\n", - " ra_eband_M1 = np.append(ra_eband_M1,gen_eband_int_M1(k_weights_qe, ra_en , eigs_qe)*Ry2eV - eband_ref)\n", - " print(\"{0} {1} {2} {3} {4}\".format(n_egrid,ra_enum[-1],ra_enum_M1[-1],ra_eband[-1],ra_eband_M1[-1]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_enum), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_enum_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_num_vs_N_grid_RT_Fermi_function_integrated.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the two DOS definitions \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(n_egrids, np.abs(ra_eband), linestyle='-', linewidth=1, color='red', label='Gaussian DOS')\n", - "ax.plot(n_egrids, np.abs(ra_eband_M1), linestyle='-', linewidth=1, color='black', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_band_vs_N_grid_RT_Fermi_function_integrated.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*0.79255\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth = dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "print(ewidth)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "10e-3/Ry2eV" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth*Ry2eV" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dos_qe[: , 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe[: , 0][1]-dos_qe[: , 0][0])*0.5\n", - "print(ewidth)\n", - "ra_sigma = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum = np.zeros(len(ra_sigma))\n", - "ra_eband = np.zeros(len(ra_sigma))\n", - "ra_en = np.linspace(dos_qe[0, 0], dos_qe[-1, 0], int((dos_qe[-1, 0]-dos_qe[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum[i] = gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[i])\n", - " # Generate band energy\n", - " ra_eband[i] = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=ra_sigma[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum)\n", - "print(ra_eband)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.plot(ra_sigma, ra_enum, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref/Ry2eV, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.scatter(ra_sigma, ra_eband, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(np.min(abs(ra_eband-eband_ref/Ry2eV))/256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(4*ra_sigma[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate electron number \n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*0.79255\n", - "gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Error in band energy compared to QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By adjusting the width of the Gaussian smearing we achieved the desired accuracy in the band energy, here < 1 meV.\n", - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can also increase the smearing width and also obtain an accurate band energy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]\n", - "emax = dos_qe[: , 0][-1]\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*29.177\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also generate a different energy grid. However, it the integral quickly converges with respect to the number of grid points. We can increase the range of the enegy grid to make sure the DOS decays to zero at the limits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*29.908\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check for another snapshot.\n", - "The band energy from the QE output of snapshot 1 is:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe_01 = 739.08601067+4.18224128-553.98606038\n", - "print(eband_qe_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth_01=dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0]\n", - "print(ewidth_01)\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "ra_en_01 = np.linspace(emin, emax, int(len(dos_qe_01[: , 0])))\n", - "ra_sigma_01 = np.linspace(20*ewidth_01, ewidth_01, 101)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "for i in range(len(ra_sigma)):\n", - " ra_en_01 = dos_qe[: , 0]\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enum(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_eband(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(768, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_enum_01, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(eband_qe_01, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_eband_01, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, 2*int(len(dos_qe_01[: , 0])))\n", - "sigma_mod = sigma_qe*22.90#22.882\n", - "eband_01 = gen_eband(k_weights_qe_01, ra_en , eigs_qe_01, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband_01))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error_01 = eband_01-eband_qe_01\n", - "print(\"Error in Rydberg\", eband_error_01)\n", - "print(\"Error in eV\", eband_error_01*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_01[: , 0], dos_qe_01[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Perform search over smearing width using sp.optimize library. But this requires adapting the definitions of the functions above.\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb deleted file mode 100644 index 03cc338ff..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm11.ipynb +++ /dev/null @@ -1,2406 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Attila Cangi, 14.02.2020 \\\n", - "Normand Modine, 01.04.2020" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "from scipy.optimize import root_scalar\n", - "from scipy.optimize import bisect\n", - "from scipy.optimize import toms748\n", - "from scipy.special import spence\n", - "import mpmath as mp\n", - "import matplotlib.pyplot as plt\n", - "from ase import Atoms\n", - "from ase.io import read\n", - "from functools import partial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Boltzmann's constant\n", - "kB = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Conversion factor from Bohr to Angstroms\n", - "Br2AA = 0.52917721\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032\n", - "\n", - "# Read and analyze QE calculated LDOS for snapshot 2\n", - "read_QE_LDOS = True\n", - "\n", - "# Read and analyze Machine Learned LDOS for snapshot 2\n", - "read_ML_LDOS = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the relevant results of each Density-Functional-Theory calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the relevant results of a DFT calculation read from a file\n", - "class DFT_results:\n", - " def __init__(self, out_file):\n", - " # input:\n", - " ## out_file: path to the output file from a DFT run using a code that ase.io.read can read\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## out_file: the DFT output file used to construct the object\n", - " ## eigs: the Kohn-Sham eigenvalues\n", - " ### eigs rows: band index, row i: eigs[i , :]\n", - " ### eigs cols: k points, col j: eigs[: , j]\n", - " ## kpoints: the kpoints used in the calculation\n", - " ## kweights: the kweights for the kpoints used in the calculation\n", - " ## fermi_energy: the Fermi energy printed in the output file\n", - " ### note that the Fermi energy is not printed to enought digits to give an accurate number of electrons\n", - " \n", - " atoms = read(filename=out_file)\n", - " self.out_file = out_file\n", - " self.eigs = np.transpose(atoms.get_calculator().band_structure().energies[0,:,:])\n", - " self.kpoints = atoms.get_calculator().get_ibz_k_points()\n", - " self.kweights = atoms.get_calculator().get_k_point_weights()\n", - " self.fermi_energy = atoms.get_calculator().get_fermi_level()\n", - " self.volume = atoms.get_volume()\n", - " # I'd rather not do the following \"grep\" type search, but I can't find a ASE command to get n_electrons\n", - " with open(out_file) as out:\n", - " for line in out:\n", - " if \"number of electrons =\" in line:\n", - " self.n_electrons = np.float64(line.split('=')[1])\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the results of a Density-of-State calculation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the DOS on a grid of E values using its naive definition\n", - "$$ D(E) = \\sum_i \\sum_k w_k\\, \\delta(E-\\epsilon_{ik})$$\n", - "where $i$ labels the band and $k$ the k point. The $\\delta$-functions appearing in this definition are represented on a grid by the function parameter delta_f." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the results of a Density-of-States calculation\n", - "class DOS:\n", - " def __init__(self, dft, e_grid, dos):\n", - " # You probably should not call this constructer directly.\n", - " # Instead you should call one of the factory methods:\n", - " ## DOS.from_calculation(dft, e_grid, delta_f)\n", - " ## DOS.from_dos_file(dft, file)\n", - " ## DOS.from_ldos_file(dft,file)\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## dft: the DFT_results instance used to generate the DOS\n", - " ## e_grid: the array of energy values at which the DOS is evauated\n", - " ## dos: the DOS evaluated at the energies in e_grid\n", - " \n", - " self.dft = dft\n", - " self.e_grid = e_grid\n", - " self.dos = dos\n", - " \n", - " @classmethod\n", - " def from_calculation(cls, dft, e_grid, delta_f):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which to evaluate the DOS\n", - " ## delta_f: a function that represents a delta function on a grid\n", - " \n", - " dos_per_band = delta_f(e_grid,dft.eigs)\n", - " dos_per_band = dft.kweights[np.newaxis,:,np.newaxis]*dos_per_band\n", - " dos = np.sum(dos_per_band,axis=(0,1))\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_dos_file(cls, dft, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## file: a file containing an energy grid and a dos as columns\n", - " ## The first line of this file is considered a comment and skipped.\n", - " \n", - " data = np.loadtxt(file, skiprows=1)\n", - " e_grid = data[:,0]\n", - " dos = data[:,1]\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_ldos_file(cls, dft, e_grid, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which the LDOS has been evaluated\n", - " ## file: a file containing LDOS results in numpy format\n", - " \n", - " ldos = np.load(file)\n", - " ldos = ldos / Ry2eV # Quantum Espresso calculates LDOS per Ry. We use per eV units.\n", - " if ldos.shape[3] != e_grid.shape[0]:\n", - " raise ValueError('Size of e_grid does not match length of 4th axis of LDOS')\n", - " cell_volume = dft.volume/(ldos.shape[0]*ldos.shape[1]*ldos.shape[2]*Br2AA**3)\n", - " dos = np.sum(ldos, axis=(0,1,2))*cell_volume\n", - " return cls(dft, e_grid, dos) " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Load results of DFT runs snapshots 0 and 1 using various k-point grids" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dft_0_444 = DFT_results('snap_0/slurm-1006575.out')\n", - "dft_0_555 = DFT_results('snap_0_555/slurm-1001448.out')\n", - "dft_0_666 = DFT_results('snap_0_666/200327132835.out')\n", - "dft_0_888 = DFT_results('snap_0_888/200409131941.out')\n", - "dft_1_444 = DFT_results('snap_1/slurm-1006846.out')\n", - "dft_1_888 = DFT_results('snap_1_888/200409131805.out')\n", - "dft_2_888 = DFT_results('snap_2_888/200413195430.out')\n", - "\n", - "eigs_qe = dft_0_888.eigs\n", - "kpoints_qe = dft_0_888.kpoints\n", - "k_weights_qe = dft_0_888.kweights\n", - "fermi_energy = dft_0_888.fermi_energy\n", - "\n", - "eigs_qe_01 = dft_1_444.eigs\n", - "k_weights_qe_01 = dft_1_444.kweights" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Load some DOS values calculated by QE's postprocessor for comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dos_qe_0_888_g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_g')\n", - "dos_qe_0_888_2g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_2g')\n", - "dos_qe_1_888_2g = DOS.from_dos_file(dft_1_888, 'snap_1_888/Al.dos_2g')\n", - "dos_qe_2_888_2g = DOS.from_dos_file(dft_2_888, 'snap_2_888/Al.dos_2g')\n", - "\n", - "if read_QE_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " dos_from_ldos_2_888_2g = DOS.from_ldos_file(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/ldos_200x200x200grid_250elvls_snapshot2.npy')\n", - "\n", - "if read_ML_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " dos_ml_2_888_2g = DOS.from_ldos_file(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/FeedForward_fp_ldos_predictions_may5.npy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions\n", - "\n", - "The Fermi-Dirac distribution function is needed to evaluate the occupations of the Kohn-Sham states as a function of energy" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energies, e_fermi, temperature): \n", - " return 1.0 / (1.0 + np.exp((energies - e_fermi) / (kB * temperature)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Gaussian is one possible representation of the $\\delta$-functions occuring in DOS definition\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "with a width $\\sigma$." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function\n", - "def fermi_integral_0(energies, e_fermi, temperature):\n", - " x = (energies - e_fermi) / (kB * temperature)\n", - " return kB * temperature * (0.5*x - np.log(2.0*np.cosh(x/2.0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy)\n", - "def fermi_integral_1(energies, e_fermi, temperature):\n", - " #x = (energies - e_fermi) / (kB * temperature)\n", - " #return (kB * temperature)**2 * (-x*np.log(2.0*np.cosh(x/2.0)) - spence(1.0 + np.exp(x)))\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**2 * \n", - " (-x*mp.log(2.0*mp.cosh(x/2.0)) - mp.polylog(2,-mp.exp(x))) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy) squared\n", - "def fermi_integral_2(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**3 *\n", - " ( - x**3/6.0 - x**2*mp.log(2.0*mp.cosh(x/2.0))\n", - " - 2.0*x*mp.polylog(2,-mp.exp(x))\n", - " + 2.0*mp.polylog(3,-mp.exp(x)) ) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1696824228988496\n", - "0.16968242289884924\n", - "3.608224830031759e-16\n", - "-0.013419715723285694\n", - "-0.01341971572328568\n", - "-1.3877787807814457e-17\n", - "0.0016322822031528132\n", - "0.0016322822031528126\n", - "6.505213034913027e-19\n" - ] - } - ], - "source": [ - "# Test the Fermi integral functions\n", - "energies = np.array([6.83,7.11])\n", - "e_fermi = 7.0\n", - "fi_0 = fermi_integral_0(energies, e_fermi, temp)\n", - "aint_0 = fi_0[1] - fi_0[0]\n", - "qint_0, abserr = sp.integrate.quad(\n", - " lambda e: fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_0)\n", - "print(qint_0)\n", - "print(aint_0 - qint_0)\n", - "fi_1 = fermi_integral_1(energies, e_fermi, temp)\n", - "aint_1 = fi_1[1] - fi_1[0]\n", - "qint_1, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_1)\n", - "print(qint_1)\n", - "print(aint_1 - qint_1)\n", - "fi_2 = fermi_integral_2(energies, e_fermi, temp)\n", - "aint_2 = fi_2[1] - fi_2[0]\n", - "qint_2, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)**2*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_2)\n", - "print(qint_2)\n", - "print(aint_2 - qint_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos\n", - "def analytic_enum(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept + (fi_1[1:] - fi_1[:-1])*slope)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos times the energy\n", - "def analytic_eband(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_2 = fermi_integral_2(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept*e_fermi\n", - " + (fi_1[1:] - fi_1[:-1])*(fermi_intercept + slope*e_fermi)\n", - " + (fi_2[1:] - fi_2[:-1])*slope)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(e_grid, centers, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((e_grid[np.newaxis]-centers[...,np.newaxis])/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a discretized delta function that maintains 0th and 1st moments\n", - "def delta_M1(e_grid, centers):\n", - " de = e_grid[np.newaxis]-centers[...,np.newaxis]\n", - " de_above = np.min(de,axis=-1,initial=np.max(de),where=np.greater(de,0.0))\n", - " de_below = np.max(de,axis=-1,initial=np.min(de),where=np.less_equal(de,0.0))\n", - " e_spacing = de_above - de_below\n", - " result = 1.0 - np.abs(de)/e_spacing[...,np.newaxis]\n", - " result = result*np.greater_equal(result,0.0)*np.less_equal(result,1.0)\n", - " result = result/e_spacing[...,np.newaxis]\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The total number of electrons is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ by summing the occupations over all of the states\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik})\\ ,$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$. The weights $w_k$ arise from an implicit integral over the Brillouin zone, which is evaluated as a simple sum over a regular grid of k points. This simple approach to evaluating the integral often gives reasonable results since $\\epsilon_{ik}$ is periodic in $k$, but it becomes less accurate when the Fermi level lies within a band due to the non-analytic behavior of the Fermi-Dirac function at the Fermi level. The sum over a regular grid of k-points is additionally reduced by symmetry, which results in different $w_k$ values for different $k$ values." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating the number of electrons from DFT results\n", - "def dft_2_enum(dft, e_fermi = None, temperature = temp):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## enum: number of electrons\n", - "\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " enum_per_band = fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " enum_per_band = dft.kweights[np.newaxis,:]*enum_per_band\n", - " enum = np.sum(enum_per_band)\n", - " return enum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy,\" which is also called the \"single-particle energy\" or the \"one-electron energy,\" is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ ,$$\n", - "\n", - "where the various terms are defined above." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DFT results\n", - "def dft_2_eband(dft, e_fermi = None, temperature = temp):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## eband: band energy\n", - "\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = toms748(lambda e_fermi: dft_2_enum(dft, e_fermi, temperature) - dft.n_electrons, a = np.min(dft.eigs), b = np.max(dft.eigs))\n", - " eband_per_band = dft.eigs*fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " eband_per_band = dft.kweights[np.newaxis,:]*eband_per_band\n", - " eband = np.sum(eband_per_band)\n", - " return eband" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3654808767733\n" - ] - } - ], - "source": [ - "eband_0_888_ref=dft_2_eband(dft_0_888)\n", - "print(eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3470902759054\n" - ] - } - ], - "source": [ - "eband_0_888_sc_ref=dft_2_eband(dft_0_888, e_fermi = 'sc')\n", - "print(eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.146096688316\n" - ] - } - ], - "source": [ - "eband_1_888_ref=dft_2_eband(dft_1_888)\n", - "print(eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.1203692340027\n" - ] - } - ], - "source": [ - "eband_1_888_sc_ref=dft_2_eband(dft_1_888, e_fermi = 'sc')\n", - "print(eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.8711214367395\n" - ] - } - ], - "source": [ - "eband_2_888_ref=dft_2_eband(dft_2_888)\n", - "print(eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.876315137265\n" - ] - } - ], - "source": [ - "eband_2_888_sc_ref=dft_2_eband(dft_2_888, e_fermi = 'sc')\n", - "print(eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0023586003111\n" - ] - } - ], - "source": [ - "enum_0_888_ref=dft_2_enum(dft_0_888)\n", - "print(enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0032983385398\n" - ] - } - ], - "source": [ - "enum_1_888_ref=dft_2_enum(dft_1_888)\n", - "print(enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9993338972209\n" - ] - } - ], - "source": [ - "enum_2_888_ref=dft_2_enum(dft_2_888)\n", - "print(enum_2_888_ref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, the total number of electrons can be obtained from the DOS\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} dE\\ D(E)\\, f(E)\\,$$\n", - "\n", - "where $E$ denotes the energy, $D(E)$ the DOS, and $f(E)$ the Fermi-Dirac distribution function." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def dos_2_enum(dos, e_fermi = None, temperature = temp, integration = 'trapz'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " # output:\n", - " ## enum: number of electrons\n", - " \n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.trapz(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.simps(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " enum, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " enum = analytic_enum(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return enum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, the band energy can be obtained from the DOS\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} dE\\ D(E)\\, f(E)\\, E .$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate E*DOS*FD to obtain band energy\n", - "def dos_2_eband(dos, e_fermi = None, temperature = temp, integration = 'trapz'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " # output:\n", - " ## eband: calculated band energy in eV\n", - " \n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = toms748(lambda e_fermi: dos_2_enum(dos, e_fermi, temperature, integration) - dos.dft.n_electrons,\n", - " a = dos.e_grid[0], b = dos.e_grid[-1])\n", - " #print(dos_2_enum(dos, e_fermi, temperature, integration))\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.trapz(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.simps(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " eband, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*e*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " eband = analytic_eband(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return eband" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Use the same grid as used by the Quantum Espresso postprocessor\n", - "e_grid = dos_qe_0_888_g.e_grid\n", - "#e_grid = np.arange(-5.0,15.0,0.1)\n", - "\n", - "# Determine the smearing for the Gaussian representation of the delta function\n", - "sigma = e_grid[1] - e_grid[0]\n", - "narrow_gaussian = partial(gaussian, sigma = sigma)\n", - "wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - "# Compute the DOS for snap_0 with 888 k-points using each representation of the delta function\n", - "dos_0_888_g = DOS.from_calculation(dft_0_888, e_grid, narrow_gaussian )\n", - "dos_0_888_2g = DOS.from_calculation(dft_0_888, e_grid, wide_gaussian )\n", - "dos_0_888_M1 = DOS.from_calculation(dft_0_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_1 with 888 k-points using each representation of the delta function\n", - "dos_1_888_g = DOS.from_calculation(dft_1_888, e_grid, narrow_gaussian )\n", - "dos_1_888_2g = DOS.from_calculation(dft_1_888, e_grid, wide_gaussian )\n", - "dos_1_888_M1 = DOS.from_calculation(dft_1_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_2 with 888 k-points using each representation of the delta function\n", - "dos_2_888_g = DOS.from_calculation(dft_2_888, e_grid, narrow_gaussian )\n", - "dos_2_888_2g = DOS.from_calculation(dft_2_888, e_grid, wide_gaussian )\n", - "dos_2_888_M1 = DOS.from_calculation(dft_2_888, e_grid, delta_M1)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_0_888_g.e_grid, dos_qe_0_888_g.dos, linestyle='--', linewidth=3, color='yellow', label='QE-DOS G')\n", - "ax.plot(dos_qe_0_888_2g.e_grid, dos_qe_0_888_2g.dos, linestyle='--', linewidth=3, color='black', label='QE-DOS 2G')\n", - "ax.plot(dos_0_888_2g.e_grid, dos_0_888_2g.dos, linestyle='-', linewidth=3, color='blue', label='wide Gaussian')\n", - "#ax.plot(dos_0_888_g.e_grid, dos_0_888_g.dos, linestyle='-', linewidth=3, color='green', label='narrow Gaussian')\n", - "#ax.plot(dos_0_888_M1.e_grid, dos_0_888_M1.dos, linestyle='-', linewidth=3, color='red', label='M1 delta representation')\n", - "\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(0.55, 1.0))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can recover the QE-DOS result by using the same parameters as in the input for computing the DOS. In this case, the QE-DOS is evaluated using 4x4x4 k-point sampling, while the calculated DOS is evaluated using 8x8x8 k-point sampling, so they will not match exactly.\n", - "\n", - "Now we can go ahead and investigate different energy grids and smearing values in order to improve upon the band energy and achieve better agreement with the band-energy output of QE." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":25: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28304728577404603\n", - "0.10647239198749503\n", - "0.04538864728692715\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'quad') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28310624238531545\n", - "0.10668622714081266\n", - "0.04539697333041204\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'analytic') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2832576592146552\n", - "0.10654533739250382\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_0_888_2g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_qe_0_888_g, integration = 'analytic') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":25: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28533298020636266\n", - "0.10744953331345641\n", - "0.052445261101411234\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'quad') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2856521550982052\n", - "0.10731624971276688\n", - "0.052275393387503755\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'analytic') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28996261895235875\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_1_888_2g, integration = 'analytic') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":25: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24194209412007694\n", - "0.07849218150795423\n", - "0.027242952126243836\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'quad') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.242208420970087\n", - "0.07857100610192447\n", - "0.027246274673075277\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'analytic') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24870929593282654\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_2_888_2g, integration = 'analytic') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":29: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " eband, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0630292256505527\n", - "0.4771164300154851\n", - "0.17639036704667888\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'quad') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'quad') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'quad') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0640137830100684\n", - "0.47773027019366054\n", - "0.17632259172978593\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'analytic') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0764587860849133\n", - "0.5076685364392688\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_0_888_2g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_qe_0_888_g, integration = 'analytic') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1429938325936746\n", - "-0.3540806182554661\n", - "-0.1776480946614356\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_0_888_g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_0_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1317269716214469\n", - "-0.32304349480136807\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_qe_0_888_g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0844960764575262\n", - "0.4831471591801346\n", - "0.22979756721179\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_1_888_2g, integration = 'analytic') - eband_1_888_ref)\n", - "print(dos_2_eband(dos_1_888_g, integration = 'analytic') - eband_1_888_ref)\n", - "print(dos_2_eband(dos_1_888_M1, integration = 'analytic') - eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.079419705624332\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_1_888_2g, integration = 'analytic') - eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1431815692749296\n", - "-0.35388643114720253\n", - "-0.17795321962739763\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)\n", - "print(dos_2_eband(dos_1_888_g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)\n", - "print(dos_2_eband(dos_1_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1818663040244246\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7459952235367382\n", - "0.2597264144997098\n", - "0.034636043579666875\n", - "0.7456178884153815\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_2_888_2g, integration = 'analytic') - eband_2_888_ref)\n", - "print(dos_2_eband(dos_2_888_g, integration = 'analytic') - eband_2_888_ref)\n", - "print(dos_2_eband(dos_2_888_M1, integration = 'analytic') - eband_2_888_ref)\n", - "if read_QE_LDOS:\n", - " print(dos_2_eband(dos_from_ldos_2_888_2g, integration = 'analytic') - eband_2_888_ref)\n", - "if read_ML_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(dos_2_eband(dos_ml_2_888_2g, integration = 'simps'),\n", - " dos_2_eband(dos_ml_2_888_2g, integration = 'simps') - dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(\"Changing energy grid!\")\n", - " dos_ml_2_888_2g.e_grid = np.linspace(-10.0,15.0,250)\n", - " print(dos_2_eband(dos_ml_2_888_2g, integration = 'simps'))\n", - " print(dos_2_eband(dos_ml_2_888_2g, e_fermi = 7.7967, integration = 'simps'))\n", - " cell_volume = dos_ml_2_888_2g.dft.volume/(200.0*Br2AA)**3\n", - " print(cell_volume, cell_volume**(1.0/3.0))\n", - " print(dos_2_eband(dos_ml_2_888_2g, e_fermi = 7.7967, integration = 'simps')*0.153049**3/cell_volume)\n", - " print(\"Resetting energy grid!\")\n", - " dos_ml_2_888_2g.e_grid = np.linspace(-10.0,14.9,250)\n", - " print(dos_2_eband(dos_ml_2_888_2g, integration = 'analytic'),\n", - " dos_2_eband(dos_ml_2_888_2g, integration = 'analytic') - eband_2_888_ref)\n", - " print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic'))\n", - " print(dos_2_eband(dos_ml_2_888_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_ml_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print (eband_2_888_sc_ref)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8088733359650178\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_2_888_2g, integration = 'analytic') - eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.142285266507315\n", - "-0.35289876827846456\n", - "-0.17781404765173647\n", - "-1.1414499490047092\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - "print(dos_2_eband(dos_2_888_g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - "print(dos_2_eband(dos_2_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - "if read_QE_LDOS:\n", - " print(dos_2_eband(dos_from_ldos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1300799679156626\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "241\n", - "242\n", - "243\n", - "244\n", - "245\n", - "246\n", - "247\n", - "248\n", - "249\n", - "250\n", - "251\n" - ] - } - ], - "source": [ - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_g = []\n", - "dosn_0_888_2g = []\n", - "dosn_0_888_M1 = []\n", - "\n", - "for n_egrid in n_egrids:\n", - " \n", - " print(n_egrid)\n", - " \n", - " e_grid_n = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = e_grid_n[1] - e_grid_n[0]\n", - " narrow_gaussian = partial(gaussian, sigma = sigma)\n", - " wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - " dosn_0_888_g.append(DOS.from_calculation(dft_0_888, e_grid_n, narrow_gaussian ))\n", - " dosn_0_888_2g.append(DOS.from_calculation(dft_0_888, e_grid_n, wide_gaussian ))\n", - " dosn_0_888_M1.append(DOS.from_calculation(dft_0_888, e_grid_n, delta_M1))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_g = []\n", - "eband_error_0_888_g = []\n", - "for dos in dosn_0_888_g:\n", - " enum_error_0_888_g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_g)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_g)\n", - "\n", - "enum_error_0_888_2g = []\n", - "eband_error_0_888_2g = []\n", - "for dos in dosn_0_888_2g:\n", - " enum_error_0_888_2g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_2g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_2g = np.array(enum_error_0_888_2g)\n", - "eband_error_0_888_2g = np.array(eband_error_0_888_2g)\n", - "\n", - "enum_error_0_888_M1 = []\n", - "eband_error_0_888_M1 = []\n", - "for dos in dosn_0_888_M1:\n", - " enum_error_0_888_M1.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_M1.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_M1 = np.array(enum_error_0_888_M1)\n", - "eband_error_0_888_M1 = np.array(eband_error_0_888_M1)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_band_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5\n", - "0.6\n", - "0.7\n", - "0.8\n", - "0.9\n", - "1.0\n", - "1.1\n", - "1.2\n", - "1.5\n", - "1.7\n", - "2.0\n", - "2.5\n", - "3.0\n", - "3.5\n", - "4.0\n" - ] - } - ], - "source": [ - "n_egrid = 250\n", - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_vg = []\n", - "\n", - "sigma = e_grid[1] - e_grid[0]\n", - "sigma_multipliers = np.array([0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.5,1.7,2.0,2.5,3.0,3.5,4.0])\n", - "\n", - "for sigma_multiplier in sigma_multipliers:\n", - " \n", - " print(sigma_multiplier)\n", - " delta_f = partial(gaussian, sigma = sigma*sigma_multiplier)\n", - " dosn_0_888_vg.append(DOS.from_calculation(dft_0_888, e_grid, delta_f ))" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_vg = []\n", - "eband_error_0_888_vg = []\n", - "for dos in dosn_0_888_vg:\n", - " enum_error_0_888_vg.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_vg.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_vg)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_vg)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, enum_error_0_888_vg, linestyle='-', linewidth=1, color='blue', label='Gaussian DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, eband_error_0_888_vg, linestyle='-', linewidth=1, color='blue', label='Gaussian DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_band_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cells below include various older calculations and ideas. Most of them would have to be updated to work with the current code structure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe[: , 0][1]-dos_qe[: , 0][0])*0.5\n", - "print(ewidth)\n", - "ra_sigma = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum = np.zeros(len(ra_sigma))\n", - "ra_eband = np.zeros(len(ra_sigma))\n", - "ra_en = np.linspace(dos_qe[0, 0], dos_qe[-1, 0], int((dos_qe[-1, 0]-dos_qe[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum[i] = gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[i])\n", - " # Generate band energy\n", - " ra_eband[i] = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=ra_sigma[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum)\n", - "print(ra_eband)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.plot(ra_sigma, ra_enum, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref/Ry2eV, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.scatter(ra_sigma, ra_eband, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(np.min(abs(ra_eband-eband_ref/Ry2eV))/256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(4*ra_sigma[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate electron number \n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*0.79255\n", - "gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy\" is also called the \"single-particle energy\" or the \"one-electron energy\" and is obtained from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ .$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$.\n", - "\n", - "For look at the particular values for a given snapshot (data from ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```). QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "737.82754675" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correct band energy from QE output is" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(eband_qe)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Error in band energy compared to QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By adjusting the width of the Gaussian smearing we achieved the desired accuracy in the band energy, here < 1 meV.\n", - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can also increase the smearing width and also obtain an accurate band energy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]\n", - "emax = dos_qe[: , 0][-1]\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*29.177\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also generate a different energy grid. However, it the integral quickly converges with respect to the number of grid points. We can increase the range of the enegy grid to make sure the DOS decays to zero at the limits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*29.908\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check for another snapshot.\n", - "The band energy from the QE output of snapshot 1 is:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe_01 = 739.08601067+4.18224128-553.98606038\n", - "print(eband_qe_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth_01=dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0]\n", - "print(ewidth_01)\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "ra_en_01 = np.linspace(emin, emax, int(len(dos_qe_01[: , 0])))\n", - "ra_sigma_01 = np.linspace(20*ewidth_01, ewidth_01, 101)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "for i in range(len(ra_sigma)):\n", - " ra_en_01 = dos_qe[: , 0]\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enum(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_eband(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(768, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_enum_01, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(eband_qe_01, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_eband_01, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, 2*int(len(dos_qe_01[: , 0])))\n", - "sigma_mod = sigma_qe*22.90#22.882\n", - "eband_01 = gen_eband(k_weights_qe_01, ra_en , eigs_qe_01, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband_01))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error_01 = eband_01-eband_qe_01\n", - "print(\"Error in Rydberg\", eband_error_01)\n", - "print(\"Error in eV\", eband_error_01*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_01[: , 0], dos_qe_01[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Perform search over smearing width using sp.optimize library. But this requires adapting the definitions of the functions above.\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb deleted file mode 100644 index 672d62813..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm15.ipynb +++ /dev/null @@ -1,2642 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Attila Cangi, 14.02.2020 \\\n", - "Normand Modine, 01.04.2020" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "from scipy.optimize import root_scalar\n", - "from scipy.optimize import bisect\n", - "from scipy.optimize import toms748\n", - "from scipy.special import spence\n", - "import mpmath as mp\n", - "import matplotlib.pyplot as plt\n", - "from ase import Atoms\n", - "from ase.io import read\n", - "from functools import partial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Boltzmann's constant\n", - "kB = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Conversion factor from Bohr to Angstroms\n", - "Br2AA = 0.52917721\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032\n", - "\n", - "# Read and analyze QE calculated LDOS for snapshot 2\n", - "read_QE_LDOS = False\n", - "\n", - "# Read and analyze Machine Learned LDOS for snapshot 2\n", - "read_ML_LDOS = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the relevant results of each Density-Functional-Theory calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the relevant results of a DFT calculation read from a file\n", - "class DFT_results:\n", - " def __init__(self, out_file):\n", - " # input:\n", - " ## out_file: path to the output file from a DFT run using a code that ase.io.read can read\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## out_file: the DFT output file used to construct the object\n", - " ## eigs: the Kohn-Sham eigenvalues\n", - " ### eigs rows: band index, row i: eigs[i , :]\n", - " ### eigs cols: k points, col j: eigs[: , j]\n", - " ## kpoints: the kpoints used in the calculation\n", - " ## kweights: the kweights for the kpoints used in the calculation\n", - " ## fermi_energy: the Fermi energy printed in the output file\n", - " ### note that the Fermi energy is not printed to enought digits to give an accurate number of electrons\n", - " \n", - " atoms = read(filename=out_file)\n", - " self.out_file = out_file\n", - " self.eigs = np.transpose(atoms.get_calculator().band_structure().energies[0,:,:])\n", - " self.kpoints = atoms.get_calculator().get_ibz_k_points()\n", - " self.kweights = atoms.get_calculator().get_k_point_weights()\n", - " self.fermi_energy = atoms.get_calculator().get_fermi_level()\n", - " self.volume = atoms.get_volume()\n", - " # I'd rather not do the following \"grep\" type search, but I can't find a ASE command to get n_electrons\n", - " with open(out_file) as out:\n", - " for line in out:\n", - " if \"number of electrons =\" in line:\n", - " self.n_electrons = np.float64(line.split('=')[1])\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the results of a Density-of-State calculation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the DOS on a grid of E values using its naive definition\n", - "$$ D(E) = \\sum_i \\sum_k w_k\\, \\delta(E-\\epsilon_{ik})$$\n", - "where $i$ labels the band and $k$ the k point. The $\\delta$-functions appearing in this definition are represented on a grid by the function parameter delta_f." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the results of a Density-of-States calculation\n", - "class DOS:\n", - " def __init__(self, dft, e_grid, dos):\n", - " # You probably should not call this constructer directly.\n", - " # Instead you should call one of the factory methods:\n", - " ## DOS.from_calculation(dft, e_grid, delta_f)\n", - " ## DOS.from_dos_file(dft, file)\n", - " ## DOS.from_ldos_data(dft,e_grid,ldos_data)\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## dft: the DFT_results instance used to generate the DOS\n", - " ## e_grid: the array of energy values at which the DOS is evauated\n", - " ## dos: the DOS evaluated at the energies in e_grid\n", - " \n", - " self.dft = dft\n", - " self.e_grid = e_grid\n", - " self.dos = dos\n", - " \n", - " @classmethod\n", - " def from_calculation(cls, dft, e_grid, delta_f):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which to evaluate the DOS\n", - " ## delta_f: a function that represents a delta function on a grid\n", - " \n", - " dos_per_band = delta_f(e_grid,dft.eigs)\n", - " dos_per_band = dft.kweights[np.newaxis,:,np.newaxis]*dos_per_band\n", - " dos = np.sum(dos_per_band,axis=(0,1))\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_dos_file(cls, dft, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## file: a file containing an energy grid and a dos as columns\n", - " ## The first line of this file is considered a comment and skipped.\n", - " \n", - " data = np.loadtxt(file, skiprows=1)\n", - " e_grid = data[:,0]\n", - " dos = data[:,1]\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_ldos_data(cls, dft, e_grid, ldos_data):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which the LDOS has been evaluated\n", - " ## ldos_data: a 4-dimensional Numpy array containing LDOS results\n", - " \n", - " if ldos_data.shape[3] != e_grid.shape[0]:\n", - " raise ValueError('Size of e_grid does not match length of 4th axis of ldos_data')\n", - " cell_volume = dft.volume/(ldos_data.shape[0]*ldos_data.shape[1]*ldos_data.shape[2]*Br2AA**3)\n", - " dos = np.sum(ldos_data, axis=(0,1,2))*cell_volume\n", - " return cls(dft, e_grid, dos) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the results of a Local-Density-of-States calculation\n", - "class LDOS:\n", - " def __init__(self, dft, e_grid, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which the LDOS has been evaluated\n", - " ## file: a file containing LDOS results in numpy format\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## dft: the DFT_results instance used to generate the DOS\n", - " ## e_grid: the array of energy values at which the DOS is evauated\n", - " ## ldos: the LDOS read from the file\n", - " ## dos: the DOS evaluated from the LDOS\n", - " \n", - " self.dft = dft\n", - " self.e_grid = e_grid\n", - " self.ldos = np.load(file)\n", - " self.ldos = self.ldos / Ry2eV # Quantum Espresso calculates LDOS per Ry. We use per eV units.\n", - " self.cell_volume = dft.volume/(self.ldos.shape[0]*self.ldos.shape[1]*self.ldos.shape[2]*Br2AA**3)\n", - " self.dos = DOS.from_ldos_data(dft,e_grid,self.ldos)\n", - " self.e_fermi = dos_2_efermi(self.dos)\n", - " self.eband = dos_2_eband(self.dos, e_fermi = self.e_fermi)\n", - " dw = get_density_weights(self.e_grid, self.e_fermi, temp)\n", - " self.density = np.sum(self.ldos*dw[np.newaxis,np.newaxis,np.newaxis,:], axis=(3))" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Load results of DFT runs snapshots 0 and 1 using various k-point grids" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dft_0_444 = DFT_results('snap_0/slurm-1006575.out')\n", - "dft_0_555 = DFT_results('snap_0_555/slurm-1001448.out')\n", - "dft_0_666 = DFT_results('snap_0_666/200327132835.out')\n", - "dft_0_888 = DFT_results('snap_0_888/200409131941.out')\n", - "dft_1_444 = DFT_results('snap_1/slurm-1006846.out')\n", - "dft_1_888 = DFT_results('snap_1_888/200409131805.out')\n", - "dft_2_888 = DFT_results('snap_2_888/200413195430.out')\n", - "\n", - "eigs_qe = dft_0_888.eigs\n", - "kpoints_qe = dft_0_888.kpoints\n", - "k_weights_qe = dft_0_888.kweights\n", - "fermi_energy = dft_0_888.fermi_energy\n", - "\n", - "eigs_qe_01 = dft_1_444.eigs\n", - "k_weights_qe_01 = dft_1_444.kweights" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Load some DOS values calculated by QE's postprocessor for comparison" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions\n", - "\n", - "The Fermi-Dirac distribution function is needed to evaluate the occupations of the Kohn-Sham states as a function of energy" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "dos_qe_0_888_g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_g')\n", - "dos_qe_0_888_2g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_2g')\n", - "dos_qe_1_888_2g = DOS.from_dos_file(dft_1_888, 'snap_1_888/Al.dos_2g')\n", - "dos_qe_2_888_2g = DOS.from_dos_file(dft_2_888, 'snap_2_888/Al.dos_2g')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energies, e_fermi, temperature): \n", - " return 1.0 / (1.0 + np.exp((energies - e_fermi) / (kB * temperature)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Gaussian is one possible representation of the $\\delta$-functions occuring in DOS definition\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "with a width $\\sigma$." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function\n", - "## Note that this could be written as an array operation in Numpy using ln(exp(2*cosh(x/2))),\n", - "## but I am using the mpmath polylog() function for consistency and to avoid over/underflow\n", - "def fermi_integral_0(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64(kB * temperature * (x + mp.polylog(1,-mp.exp(x)))))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy)\n", - "## Note that this could be written as an array operation in Numpy using ln(exp(2*cosh(x/2))) and np.spence()\n", - "## but I am using the mpmath polylog() function for consistency and to avoid over/underflow\n", - "def fermi_integral_1(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**2 *\n", - " (x**2/2.0 + x*mp.polylog(1,-mp.exp(x)) - mp.polylog(2,-mp.exp(x))) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy) squared\n", - "## As far as I can tell, there is no polylog(3,x) function for Numpy so I am using mpmath\n", - "## This also helps avoid over/underflow.\n", - "def fermi_integral_2(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**3 *\n", - " ( x**3/3.0 + x**2 * mp.polylog(1,-mp.exp(x))\n", - " - 2.0*x*mp.polylog(2,-mp.exp(x))\n", - " + 2.0*mp.polylog(3,-mp.exp(x)) ) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1696824228988496\n", - "0.16968242289884924\n", - "3.608224830031759e-16\n", - "-0.013419715723285694\n", - "-0.01341971572328568\n", - "-1.3877787807814457e-17\n", - "0.0016322822031528137\n", - "0.0016322822031528126\n", - "1.0842021724855044e-18\n" - ] - } - ], - "source": [ - "# Test the Fermi integral functions against quadrature\n", - "energies = np.array([6.83,7.11])\n", - "e_fermi = 7.0\n", - "fi_0 = fermi_integral_0(energies, e_fermi, temp)\n", - "aint_0 = fi_0[1] - fi_0[0]\n", - "qint_0, abserr = sp.integrate.quad(\n", - " lambda e: fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_0)\n", - "print(qint_0)\n", - "print(aint_0 - qint_0)\n", - "fi_1 = fermi_integral_1(energies, e_fermi, temp)\n", - "aint_1 = fi_1[1] - fi_1[0]\n", - "qint_1, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_1)\n", - "print(qint_1)\n", - "print(aint_1 - qint_1)\n", - "fi_2 = fermi_integral_2(energies, e_fermi, temp)\n", - "aint_2 = fi_2[1] - fi_2[0]\n", - "qint_2, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)**2*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_2)\n", - "print(qint_2)\n", - "print(aint_2 - qint_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weights that will compute the analytic integral of the Fermi function\n", - "# times an arbitrary linearly interpolated function\n", - "def get_density_weights(energies, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_0 = fi_0[1:] - fi_0[:-1]\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_1 = fi_1[1:] - fi_1[:-1]\n", - " weights = np.zeros(energies.size)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " weights[1:] = weights[1:] + fi_1/delta_e\n", - " weights[1:] = weights[1:] + fi_0 * (1.0 + (e_fermi - energies[1:])/delta_e)\n", - " weights[:-1] = weights[:-1] - fi_1/delta_e\n", - " weights[:-1] = weights[:-1] + fi_0 * (1.0 - (e_fermi - energies[:-1])/delta_e)\n", - " return weights" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weights that will compute the analytic integral of the Fermi function\n", - "# times the energy times an arbitrary linearly interpolated function\n", - "def get_energy_weights(energies, e_fermi, temperature):\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_1 = fi_1[1:] - fi_1[:-1]\n", - " fi_2 = fermi_integral_2(energies, e_fermi, temperature)\n", - " fi_2 = fi_2[1:] - fi_2[:-1]\n", - " weights = np.zeros(energies.size)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " weights[1:] = weights[1:] + fi_2/delta_e\n", - " weights[1:] = weights[1:] + fi_1 * (1.0 + (e_fermi - energies[1:])/delta_e)\n", - " weights[:-1] = weights[:-1] - fi_2/delta_e\n", - " weights[:-1] = weights[:-1] + fi_1 * (1.0 - (e_fermi - energies[:-1])/delta_e)\n", - " weights = weights + e_fermi*get_density_weights(energies, e_fermi, temperature)\n", - " return weights" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos\n", - "def analytic_enum(energies, dos, e_fermi, temperature):\n", - " return np.sum(dos*get_density_weights(energies, e_fermi, temperature))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos\n", - "def analytic_enum2(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept + (fi_1[1:] - fi_1[:-1])*slope)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos times the energy\n", - "def analytic_eband(energies, dos, e_fermi, temperature):\n", - " return np.sum(dos*get_energy_weights(energies, e_fermi, temperature))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos times the energy\n", - "def analytic_eband2(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_2 = fermi_integral_2(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept*e_fermi\n", - " + (fi_1[1:] - fi_1[:-1])*(fermi_intercept + slope*e_fermi)\n", - " + (fi_2[1:] - fi_2[:-1])*slope)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(e_grid, centers, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((e_grid[np.newaxis]-centers[...,np.newaxis])/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a discretized delta function that maintains 0th and 1st moments\n", - "def delta_M1(e_grid, centers):\n", - " de = e_grid[np.newaxis]-centers[...,np.newaxis]\n", - " de_above = np.min(de,axis=-1,initial=np.max(de),where=np.greater(de,0.0))\n", - " de_below = np.max(de,axis=-1,initial=np.min(de),where=np.less_equal(de,0.0))\n", - " e_spacing = de_above - de_below\n", - " result = 1.0 - np.abs(de)/e_spacing[...,np.newaxis]\n", - " result = result*np.greater_equal(result,0.0)*np.less_equal(result,1.0)\n", - " result = result/e_spacing[...,np.newaxis]\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The total number of electrons is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ by summing the occupations over all of the states\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik})\\ ,$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$. The weights $w_k$ arise from an implicit integral over the Brillouin zone, which is evaluated as a simple sum over a regular grid of k points. This simple approach to evaluating the integral often gives reasonable results since $\\epsilon_{ik}$ is periodic in $k$, but it becomes less accurate when the Fermi level lies within a band due to the non-analytic behavior of the Fermi-Dirac function at the Fermi level. The sum over a regular grid of k-points is additionally reduced by symmetry, which results in different $w_k$ values for different $k$ values." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating the number of electrons from DFT results\n", - "def dft_2_enum(dft, e_fermi = None, temperature = temp):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## enum: number of electrons\n", - "\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " enum_per_band = fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " enum_per_band = dft.kweights[np.newaxis,:]*enum_per_band\n", - " enum = np.sum(enum_per_band)\n", - " return enum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy,\" which is also called the \"single-particle energy\" or the \"one-electron energy,\" is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ ,$$\n", - "\n", - "where the various terms are defined above." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DFT results\n", - "def dft_2_eband(dft, e_fermi = None, temperature = temp):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## eband: band energy\n", - "\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = toms748(lambda e_fermi: dft_2_enum(dft, e_fermi, temperature) - dft.n_electrons, a = np.min(dft.eigs), b = np.max(dft.eigs))\n", - " eband_per_band = dft.eigs*fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " eband_per_band = dft.kweights[np.newaxis,:]*eband_per_band\n", - " eband = np.sum(eband_per_band)\n", - " return eband" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3654808767733\n" - ] - } - ], - "source": [ - "eband_0_888_ref=dft_2_eband(dft_0_888)\n", - "print(eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3470902759054\n" - ] - } - ], - "source": [ - "eband_0_888_sc_ref=dft_2_eband(dft_0_888, e_fermi = 'sc')\n", - "print(eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.146096688316\n" - ] - } - ], - "source": [ - "eband_1_888_ref=dft_2_eband(dft_1_888)\n", - "print(eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.1203692340027\n" - ] - } - ], - "source": [ - "eband_1_888_sc_ref=dft_2_eband(dft_1_888, e_fermi = 'sc')\n", - "print(eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.8711214367395\n" - ] - } - ], - "source": [ - "eband_2_888_ref=dft_2_eband(dft_2_888)\n", - "print(eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.876315137265\n" - ] - } - ], - "source": [ - "eband_2_888_sc_ref=dft_2_eband(dft_2_888, e_fermi = 'sc')\n", - "print(eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0023586003111\n" - ] - } - ], - "source": [ - "enum_0_888_ref=dft_2_enum(dft_0_888)\n", - "print(enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0032983385398\n" - ] - } - ], - "source": [ - "enum_1_888_ref=dft_2_enum(dft_1_888)\n", - "print(enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9993338972209\n" - ] - } - ], - "source": [ - "enum_2_888_ref=dft_2_enum(dft_2_888)\n", - "print(enum_2_888_ref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, the total number of electrons can be obtained from the DOS\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} dE\\ D(E)\\, f(E)\\,$$\n", - "\n", - "where $E$ denotes the energy, $D(E)$ the DOS, and $f(E)$ the Fermi-Dirac distribution function." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def dos_2_enum(dos, e_fermi = None, temperature = temp, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the linearly interpolated dos\n", - " # output:\n", - " ## enum: number of electrons\n", - " \n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.trapz(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.simps(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " enum, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " enum = analytic_enum(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the self-consistent Fermi energy such that dos_2_enum(...) = dos.dft.n_electrons\n", - "def dos_2_efermi(dos, temperature = temp, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the linearly interpolated dos\n", - " # output:\n", - " ## e_fermi: the self-consistent Fermi energy\n", - " \n", - " e_fermi = toms748(lambda e_fermi: dos_2_enum(dos, e_fermi, temperature, integration) - dos.dft.n_electrons,\n", - " a = dos.e_grid[0], b = dos.e_grid[-1])\n", - " return e_fermi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, the band energy can be obtained from the DOS\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} dE\\ D(E)\\, f(E)\\, E .$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate E*DOS*FD to obtain band energy\n", - "def dos_2_eband(dos, e_fermi = None, temperature = temp, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the energy times the linearly interpolated dos\n", - " # output:\n", - " ## eband: calculated band energy in eV\n", - " \n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = dos_2_efermi(dos, temperature, integration)\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.trapz(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.simps(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " eband, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*e*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " eband = analytic_eband(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return eband" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Use the same grid as used by the Quantum Espresso postprocessor\n", - "e_grid = dos_qe_0_888_g.e_grid\n", - "#e_grid = np.arange(-5.0,15.0,0.1)\n", - "\n", - "# Determine the smearing for the Gaussian representation of the delta function\n", - "sigma = e_grid[1] - e_grid[0]\n", - "narrow_gaussian = partial(gaussian, sigma = sigma)\n", - "wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - "# Compute the DOS for snap_0 with 888 k-points using each representation of the delta function\n", - "dos_0_888_g = DOS.from_calculation(dft_0_888, e_grid, narrow_gaussian )\n", - "dos_0_888_2g = DOS.from_calculation(dft_0_888, e_grid, wide_gaussian )\n", - "dos_0_888_M1 = DOS.from_calculation(dft_0_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_1 with 888 k-points using each representation of the delta function\n", - "dos_1_888_g = DOS.from_calculation(dft_1_888, e_grid, narrow_gaussian )\n", - "dos_1_888_2g = DOS.from_calculation(dft_1_888, e_grid, wide_gaussian )\n", - "dos_1_888_M1 = DOS.from_calculation(dft_1_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_2 with 888 k-points using each representation of the delta function\n", - "dos_2_888_g = DOS.from_calculation(dft_2_888, e_grid, narrow_gaussian )\n", - "dos_2_888_2g = DOS.from_calculation(dft_2_888, e_grid, wide_gaussian )\n", - "dos_2_888_M1 = DOS.from_calculation(dft_2_888, e_grid, delta_M1)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_0_888_g.e_grid, dos_qe_0_888_g.dos, linestyle='--', linewidth=3, color='yellow', label='QE-DOS G')\n", - "ax.plot(dos_qe_0_888_2g.e_grid, dos_qe_0_888_2g.dos, linestyle='--', linewidth=3, color='black', label='QE-DOS 2G')\n", - "ax.plot(dos_0_888_2g.e_grid, dos_0_888_2g.dos, linestyle='-', linewidth=3, color='blue', label='wide Gaussian')\n", - "#ax.plot(dos_0_888_g.e_grid, dos_0_888_g.dos, linestyle='-', linewidth=3, color='green', label='narrow Gaussian')\n", - "#ax.plot(dos_0_888_M1.e_grid, dos_0_888_M1.dos, linestyle='-', linewidth=3, color='red', label='M1 delta representation')\n", - "\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(0.55, 1.0))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can recover the QE-DOS result by using the same parameters as in the input for computing the DOS.\n", - "\n", - "Now we can go ahead and investigate different energy grids and smearing values in order to improve upon the band energy and achieve better agreement with the band-energy output of QE." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":26: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28304728577404603\n", - "0.10647239198749503\n", - "0.04538864728692715\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'quad') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28310624238542914\n", - "0.10668622714081266\n", - "0.04539697333029835\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'analytic') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28325765921397306\n", - "0.1065453373927312\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_0_888_2g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_qe_0_888_g, integration = 'analytic') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":26: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28533298020636266\n", - "0.10744953331345641\n", - "0.052445261101411234\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'quad') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2856521550979778\n", - "0.10731624971231213\n", - "0.052275393386821634\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'analytic') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28996261895224507\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_1_888_2g, integration = 'analytic') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":26: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24194209412007694\n", - "0.07849218150795423\n", - "0.027242952126243836\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'quad') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24220842096963224\n", - "0.07857100610215184\n", - "0.027246274673416337\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'analytic') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2487092959330539\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_qe_2_888_2g, integration = 'analytic') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":28: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " eband, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0630292256505527\n", - "0.4771164300154851\n", - "0.17639036704667888\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'quad') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'quad') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'quad') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.064013783009159\n", - "0.47773027019775327\n", - "0.17632259174706633\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'analytic') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7286718881982779\n", - "0.06695301014724464\n", - "-0.27639639720928244\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'trapz') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'trapz') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'trapz') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.276224426794215\n", - "0.44716557464926154\n", - "0.16444612119903468\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, integration = 'simps') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_g, integration = 'simps') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_0_888_M1, integration = 'simps') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.076458786079911\n", - "0.50766853644609\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_0_888_2g, integration = 'analytic') - eband_0_888_ref)\n", - "print(dos_2_eband(dos_qe_0_888_g, integration = 'analytic') - eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.142993832596403\n", - "-0.35408061825773984\n", - "-0.17764809464233622\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_0_888_g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_0_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1317269716278133\n", - "-0.32304349481273675\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "print(dos_2_eband(dos_qe_0_888_g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0844960764566167\n", - "0.4831471591865011\n", - "0.22979756722861566\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_1_888_2g, integration = 'analytic') - eband_1_888_ref)\n", - "print(dos_2_eband(dos_1_888_g, integration = 'analytic') - eband_1_888_ref)\n", - "print(dos_2_eband(dos_1_888_M1, integration = 'analytic') - eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0794197056247867\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_1_888_2g, integration = 'analytic') - eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1431815692762939\n", - "-0.35388643115402374\n", - "-0.1779532195914726\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)\n", - "print(dos_2_eband(dos_1_888_g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)\n", - "print(dos_2_eband(dos_1_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.181866304027153\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.745995223531736\n", - "0.2597264144933433\n", - "0.03463604357057193\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_2_888_2g, integration = 'analytic') - eband_2_888_ref)\n", - "print(dos_2_eband(dos_2_888_g, integration = 'analytic') - eband_2_888_ref)\n", - "print(dos_2_eband(dos_2_888_M1, integration = 'analytic') - eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8088733359654725\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_2_888_2g, integration = 'analytic') - eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1422852665114078\n", - "-0.3528987682757361\n", - "-0.17781404766901687\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - "print(dos_2_eband(dos_2_888_g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - "print(dos_2_eband(dos_2_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.1300799679197553\n" - ] - } - ], - "source": [ - "print(dos_2_eband(dos_qe_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can go also investigate how quantities evaluated from the LDOS calculated either by Quantum Espresso\n", - "or by machine learning compare with the results evaluated from the DOS that we calculate from eigenvalues." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "if read_QE_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " ldos_2_888_2g = LDOS(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/ldos_200x200x200grid_250elvls_snapshot2.npy')\n", - "\n", - "if read_ML_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " ldos_ml_2_888_2g = LDOS(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/FeedForward_fp_ldos_predictions_may5.npy')" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# This checks how well the eband and electron density computed in various ways from the LDOS agree\n", - "if read_QE_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(dos_2_eband(ldos_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(ldos_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(ldos_2_888_2g.eband, ldos_2_888_2g.eband - eband_2_888_sc_ref)\n", - " print(np.sum(ldos_2_888_2g.density)*ldos_2_888_2g.cell_volume,\n", - " np.sum(ldos_2_888_2g.density)*ldos_2_888_2g.cell_volume - ldos_2_888_2g.dft.n_electrons)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "# This shows that I can get agreement with Austin's band energy for the ML predicted LDOS by altering parameters\n", - "if read_ML_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps'),\n", - " dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps') - dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(\"Changing energy grid!\")\n", - " ldos_ml_2_888_2g.dos.e_grid = np.linspace(-10.0,15.0,250)\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps'))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 7.7967, integration = 'simps'))\n", - " cell_volume = ldos_ml_2_888_2g.dft.volume/(200.0*Br2AA)**3\n", - " print(cell_volume, cell_volume**(1.0/3.0))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 7.7967, integration = 'simps')*0.153049**3/cell_volume)\n", - " print(\"Resetting energy grid!\")\n", - " ldos_ml_2_888_2g.dos.e_grid = np.linspace(-10.0,14.9,250)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# This checks how well the eband and electron density computed in various ways from the LDOS agree\n", - "if read_ML_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(ldos_ml_2_888_2g.eband, ldos_ml_2_888_2g.eband - eband_2_888_sc_ref)\n", - " print(np.sum(ldos_ml_2_888_2g.density)*ldos_ml_2_888_2g.cell_volume,\n", - " np.sum(ldos_ml_2_888_2g.density)*ldos_ml_2_888_2g.cell_volume - ldos_ml_2_888_2g.dft.n_electrons)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will investigate how quantities calculated from various definitions of the DOS change as the energy grid is varied. This both gives us an idea of the error associated with various definitions of the DOS and how much the error varies with details of the calculation (e.g., grid spacing)." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "241\n", - "242\n", - "243\n", - "244\n", - "245\n", - "246\n", - "247\n", - "248\n", - "249\n", - "250\n", - "251\n" - ] - } - ], - "source": [ - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_g = []\n", - "dosn_0_888_2g = []\n", - "dosn_0_888_M1 = []\n", - "\n", - "for n_egrid in n_egrids:\n", - " \n", - " print(n_egrid)\n", - " \n", - " e_grid_n = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = e_grid_n[1] - e_grid_n[0]\n", - " narrow_gaussian = partial(gaussian, sigma = sigma)\n", - " wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - " dosn_0_888_g.append(DOS.from_calculation(dft_0_888, e_grid_n, narrow_gaussian ))\n", - " dosn_0_888_2g.append(DOS.from_calculation(dft_0_888, e_grid_n, wide_gaussian ))\n", - " dosn_0_888_M1.append(DOS.from_calculation(dft_0_888, e_grid_n, delta_M1))" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_g = []\n", - "eband_error_0_888_g = []\n", - "for dos in dosn_0_888_g:\n", - " enum_error_0_888_g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_g)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_g)\n", - "\n", - "enum_error_0_888_2g = []\n", - "eband_error_0_888_2g = []\n", - "for dos in dosn_0_888_2g:\n", - " enum_error_0_888_2g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_2g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_2g = np.array(enum_error_0_888_2g)\n", - "eband_error_0_888_2g = np.array(eband_error_0_888_2g)\n", - "\n", - "enum_error_0_888_M1 = []\n", - "eband_error_0_888_M1 = []\n", - "for dos in dosn_0_888_M1:\n", - " enum_error_0_888_M1.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_M1.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_M1 = np.array(enum_error_0_888_M1)\n", - "eband_error_0_888_M1 = np.array(eband_error_0_888_M1)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_band_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5\n", - "0.6\n", - "0.7\n", - "0.8\n", - "0.9\n", - "1.0\n", - "1.1\n", - "1.2\n", - "1.5\n", - "1.7\n", - "2.0\n", - "2.5\n", - "3.0\n", - "3.5\n", - "4.0\n" - ] - } - ], - "source": [ - "n_egrid = 250\n", - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_vg = []\n", - "\n", - "sigma = e_grid[1] - e_grid[0]\n", - "sigma_multipliers = np.array([0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.5,1.7,2.0,2.5,3.0,3.5,4.0])\n", - "\n", - "for sigma_multiplier in sigma_multipliers:\n", - " \n", - " print(sigma_multiplier)\n", - " delta_f = partial(gaussian, sigma = sigma*sigma_multiplier)\n", - " dosn_0_888_vg.append(DOS.from_calculation(dft_0_888, e_grid, delta_f ))" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_vg = []\n", - "eband_error_0_888_vg = []\n", - "for dos in dosn_0_888_vg:\n", - " enum_error_0_888_vg.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_vg.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_vg)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_vg)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, enum_error_0_888_vg, linestyle='-', linewidth=1, color='blue', label='Gaussian DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, eband_error_0_888_vg, linestyle='-', linewidth=1, color='blue', label='Gaussian DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_band_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cells below include various older calculations and ideas. Most of them would have to be updated to work with the current code structure." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dos_qe' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdos_qe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdos_qe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mra_sigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mra_enum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mra_sigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'dos_qe' is not defined" - ] - } - ], - "source": [ - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe[: , 0][1]-dos_qe[: , 0][0])*0.5\n", - "print(ewidth)\n", - "ra_sigma = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum = np.zeros(len(ra_sigma))\n", - "ra_eband = np.zeros(len(ra_sigma))\n", - "ra_en = np.linspace(dos_qe[0, 0], dos_qe[-1, 0], int((dos_qe[-1, 0]-dos_qe[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum[i] = gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[i])\n", - " # Generate band energy\n", - " ra_eband[i] = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=ra_sigma[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum)\n", - "print(ra_eband)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.plot(ra_sigma, ra_enum, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref/Ry2eV, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.scatter(ra_sigma, ra_eband, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(np.min(abs(ra_eband-eband_ref/Ry2eV))/256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(4*ra_sigma[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate electron number \n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*0.79255\n", - "gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy\" is also called the \"single-particle energy\" or the \"one-electron energy\" and is obtained from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ .$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$.\n", - "\n", - "For look at the particular values for a given snapshot (data from ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```). QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "737.82754675" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correct band energy from QE output is" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(eband_qe)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Error in band energy compared to QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By adjusting the width of the Gaussian smearing we achieved the desired accuracy in the band energy, here < 1 meV.\n", - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can also increase the smearing width and also obtain an accurate band energy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]\n", - "emax = dos_qe[: , 0][-1]\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*29.177\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also generate a different energy grid. However, it the integral quickly converges with respect to the number of grid points. We can increase the range of the enegy grid to make sure the DOS decays to zero at the limits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*29.908\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check for another snapshot.\n", - "The band energy from the QE output of snapshot 1 is:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe_01 = 739.08601067+4.18224128-553.98606038\n", - "print(eband_qe_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth_01=dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0]\n", - "print(ewidth_01)\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "ra_en_01 = np.linspace(emin, emax, int(len(dos_qe_01[: , 0])))\n", - "ra_sigma_01 = np.linspace(20*ewidth_01, ewidth_01, 101)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "for i in range(len(ra_sigma)):\n", - " ra_en_01 = dos_qe[: , 0]\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enum(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_eband(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(768, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_enum_01, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(eband_qe_01, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_eband_01, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, 2*int(len(dos_qe_01[: , 0])))\n", - "sigma_mod = sigma_qe*22.90#22.882\n", - "eband_01 = gen_eband(k_weights_qe_01, ra_en , eigs_qe_01, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband_01))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error_01 = eband_01-eband_qe_01\n", - "print(\"Error in Rydberg\", eband_error_01)\n", - "print(\"Error in eV\", eband_error_01*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_01[: , 0], dos_qe_01[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Perform search over smearing width using sp.optimize library. But this requires adapting the definitions of the functions above.\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb b/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb deleted file mode 100644 index 7295a987f..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/dos2eband_nm17.ipynb +++ /dev/null @@ -1,3346 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Attila Cangi, 14.02.2020 \\\n", - "Normand Modine, 01.04.2020" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import integrate\n", - "from scipy import interpolate\n", - "from scipy.optimize import minimize\n", - "from scipy.optimize import root_scalar\n", - "from scipy.optimize import bisect\n", - "from scipy.optimize import toms748\n", - "from scipy.special import spence\n", - "import mpmath as mp\n", - "import matplotlib.pyplot as plt\n", - "from ase import Atoms\n", - "from ase.io import read\n", - "from functools import partial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Temperature\n", - "temp = 298\n", - "\n", - "# Boltzmann's constant\n", - "kB = 8.617333262145e-5\n", - "\n", - "# Conversion factor from Rydberg to eV\n", - "Ry2eV = 13.6056980659\n", - "\n", - "# Conversion factor from Bohr to Angstroms\n", - "Br2AA = 0.52917721\n", - "\n", - "# Gaussian smearing in QE-DOS calculations\n", - "# taken from QE-DOS input file\n", - "sigma_qe = 0.032\n", - "\n", - "# Read and analyze QE calculated LDOS for snapshot 2\n", - "read_QE_LDOS = False\n", - "\n", - "# Read and analyze QE calculated LDOS for 933K snapshot 0\n", - "read_QE_933K_LDOS = False\n", - "\n", - "# Read and analyze Machine Learned LDOS for snapshot 2\n", - "read_ML_LDOS = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the relevant results of each Density-Functional-Theory calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the relevant results of a DFT calculation read from a file\n", - "class DFT_results:\n", - " def __init__(self, out_file):\n", - " # input:\n", - " ## out_file: path to the output file from a DFT run using a code that ase.io.read can read\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## out_file: the DFT output file used to construct the object\n", - " ## eigs: the Kohn-Sham eigenvalues\n", - " ### eigs rows: band index, row i: eigs[i , :]\n", - " ### eigs cols: k points, col j: eigs[: , j]\n", - " ## kpoints: the kpoints used in the calculation\n", - " ## kweights: the kweights for the kpoints used in the calculation\n", - " ## fermi_energy: the Fermi energy printed in the output file\n", - " ### note that the Fermi energy is not printed to enought digits to give an accurate number of electrons\n", - " \n", - " atoms = read(filename=out_file)\n", - " self.out_file = out_file\n", - " self.eigs = np.transpose(atoms.get_calculator().band_structure().energies[0,:,:])\n", - " self.kpoints = atoms.get_calculator().get_ibz_k_points()\n", - " self.kweights = atoms.get_calculator().get_k_point_weights()\n", - " self.fermi_energy = atoms.get_calculator().get_fermi_level()\n", - " self.volume = atoms.get_volume()\n", - " # I'd rather not do the following \"grep\" type search, but I can't find a ASE command to get n_electrons\n", - " with open(out_file) as out:\n", - " for line in out:\n", - " if \"number of electrons =\" in line:\n", - " self.n_electrons = np.float64(line.split('=')[1])\n", - " if \"Fermi-Dirac smearing, width (Ry)=\" in line:\n", - " self.electronic_temperature = np.float64(line.split('=')[2])*Ry2eV/kB\n", - " # And this is a real kludge to account for the temperature only being printed to 2 significant figures\n", - " if (self.electronic_temperature > 295.0) and (self.electronic_temperature < 301.0):\n", - " self.electronic_temperature = 298.0\n", - " if (self.electronic_temperature > 923.0) and (self.electronic_temperature < 943.0):\n", - " self.electronic_temperature = 933.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a structure to hold the results of a Density-of-State calculation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the DOS on a grid of E values using its naive definition\n", - "$$ D(E) = \\sum_i \\sum_k w_k\\, \\delta(E-\\epsilon_{ik})$$\n", - "where $i$ labels the band and $k$ the k point. The $\\delta$-functions appearing in this definition are represented on a grid by the function parameter delta_f." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the results of a Density-of-States calculation\n", - "class DOS:\n", - " def __init__(self, dft, e_grid, dos):\n", - " # You probably should not call this constructer directly.\n", - " # Instead you should call one of the factory methods:\n", - " ## DOS.from_calculation(dft, e_grid, delta_f)\n", - " ## DOS.from_dos_file(dft, file)\n", - " ## DOS.from_ldos_data(dft,e_grid,ldos_data)\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## dft: the DFT_results instance used to generate the DOS\n", - " ## e_grid: the array of energy values at which the DOS is evauated\n", - " ## dos: the DOS evaluated at the energies in e_grid\n", - " \n", - " self.dft = dft\n", - " self.e_grid = e_grid\n", - " self.dos = dos\n", - " \n", - " @classmethod\n", - " def from_calculation(cls, dft, e_grid, delta_f):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which to evaluate the DOS\n", - " ## delta_f: a function that represents a delta function on a grid\n", - " \n", - " dos_per_band = delta_f(e_grid,dft.eigs)\n", - " dos_per_band = dft.kweights[np.newaxis,:,np.newaxis]*dos_per_band\n", - " dos = np.sum(dos_per_band,axis=(0,1))\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_dos_file(cls, dft, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## file: a file containing an energy grid and a dos as columns\n", - " ## The first line of this file is considered a comment and skipped.\n", - " \n", - " data = np.loadtxt(file, skiprows=1)\n", - " e_grid = data[:,0]\n", - " dos = data[:,1]\n", - " return cls(dft, e_grid, dos)\n", - " \n", - " @classmethod\n", - " def from_ldos_data(cls, dft, e_grid, ldos_data):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which the LDOS has been evaluated\n", - " ## ldos_data: a 4-dimensional Numpy array containing LDOS results\n", - " \n", - " if ldos_data.shape[3] != e_grid.shape[0]:\n", - " raise ValueError('Size of e_grid does not match length of 4th axis of ldos_data')\n", - " cell_volume = dft.volume/(ldos_data.shape[0]*ldos_data.shape[1]*ldos_data.shape[2]*Br2AA**3)\n", - " dos = np.sum(ldos_data, axis=(0,1,2))*cell_volume\n", - " return cls(dft, e_grid, dos) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Class that encapsulates the results of a Local-Density-of-States calculation\n", - "class LDOS:\n", - " def __init__(self, dft, e_grid, file):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_grid: energy grid [eV] on which the LDOS has been evaluated\n", - " ## file: a file containing LDOS results in numpy format\n", - " # attributes (you probably should consider all of these to be read only):\n", - " ## dft: the DFT_results instance used to generate the DOS\n", - " ## e_grid: the array of energy values at which the DOS is evauated\n", - " ## ldos: the LDOS read from the file\n", - " ## dos: the DOS evaluated from the LDOS\n", - " \n", - " self.dft = dft\n", - " self.e_grid = e_grid\n", - " self.ldos = np.load(file)\n", - " self.ldos = self.ldos / Ry2eV # Quantum Espresso calculates LDOS per Ry. We use per eV units.\n", - " self.cell_volume = dft.volume/(self.ldos.shape[0]*self.ldos.shape[1]*self.ldos.shape[2]*Br2AA**3)\n", - " self.dos = DOS.from_ldos_data(dft,e_grid,self.ldos)\n", - " self.e_fermi = dos_2_efermi(self.dos)\n", - " self.eband = dos_2_eband(self.dos, e_fermi = self.e_fermi)\n", - " dw = get_density_weights(self.e_grid, self.e_fermi, dft.electronic_temperature)\n", - " self.density = np.sum(self.ldos*dw[np.newaxis,np.newaxis,np.newaxis,:], axis=(3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load results of DFT runs for various snapshots using various k-point grids" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "298.0\n", - "933.0\n" - ] - } - ], - "source": [ - "dft_0_444 = DFT_results('snap_0/slurm-1006575.out')\n", - "dft_0_555 = DFT_results('snap_0_555/slurm-1001448.out')\n", - "dft_0_666 = DFT_results('snap_0_666/200327132835.out')\n", - "dft_0_888 = DFT_results('snap_0_888/200409131941.out')\n", - "dft_1_444 = DFT_results('snap_1/slurm-1006846.out')\n", - "dft_1_888 = DFT_results('snap_1_888/200409131805.out')\n", - "dft_2_888 = DFT_results('snap_2_888/200413195430.out')\n", - "dft_3_888 = DFT_results('snap_3_888/slurm-1011259.out')\n", - "dft_4_888 = DFT_results('snap_4_888/200510234842.out')\n", - "dft_5_888 = DFT_results('snap_5_888/200511213031.out')\n", - "dft_933K_0 = DFT_results('933K/2.699gcc/snapshot0/200520201637.out')\n", - "dft_933K_1 = DFT_results('933K/2.699gcc/snapshot1/200522121822.out')\n", - "dft_933K_2 = DFT_results('933K/2.699gcc/snapshot2/200523094828.out')\n", - "dft_933K_10 = DFT_results('933K/2.699gcc/snapshot10/200530100350.out')\n", - "dft_933K_11 = DFT_results('933K/2.699gcc/snapshot11/200530100638.out')\n", - "dft_933K_12 = DFT_results('933K/2.699gcc/snapshot12/200530100937.out')\n", - "print(dft_0_888.electronic_temperature)\n", - "print(dft_933K_0.electronic_temperature)\n", - "\n", - "eigs_qe = dft_0_888.eigs\n", - "kpoints_qe = dft_0_888.kpoints\n", - "k_weights_qe = dft_0_888.kweights\n", - "fermi_energy = dft_0_888.fermi_energy\n", - "\n", - "eigs_qe_01 = dft_1_444.eigs\n", - "k_weights_qe_01 = dft_1_444.kweights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load some DOS values calculated by QE's postprocessor for comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "dos_qe_0_888_g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_g')\n", - "dos_qe_0_888_2g = DOS.from_dos_file(dft_0_888, 'snap_0_888/Al.dos_2g')\n", - "dos_qe_1_888_2g = DOS.from_dos_file(dft_1_888, 'snap_1_888/Al.dos_2g')\n", - "dos_qe_2_888_2g = DOS.from_dos_file(dft_2_888, 'snap_2_888/Al.dos_2g')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define functions\n", - "\n", - "In order to evaluate the occupations of the Kohn-Sham states as a function of the energy, we need the Fermi-Dirac distribution function\n", - "\\begin{equation}\n", - "f(E) = \\frac{1}{1 + \\exp(\\beta\\, E)},\n", - "\\end{equation}\n", - "where $\\beta = \\frac{1}{k_B T}$, $k_B$ is the Boltmann constant, and $T$ is the electronic temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Fermi-Dirac distribution function\n", - "def fd_function(energies, e_fermi, temperature): \n", - " return 1.0 / (1.0 + np.exp((energies - e_fermi) / (kB * temperature)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to calculate quantities of interest, we need to integrate various moments of $F(E)$ times $D(E)$. If we assume that $D(E)$ is a linear function between the grid of energy points where it evaluated, then we can perform these integrals analytically. This makes sense because $f(E)$ changes rapidly compared to $D(E)$ for many systems of interest. In particular, $f(E)$ changes from one to zero over a few $k_BT$, while the grid spacing that we use to tabulate $D(E)$ is several times $k_BT$ at room temperature. In order to evaluate these integrals analytically, we can use the representation of $f(E)$ in terms of the polylogarithm\n", - "\\begin{equation}\n", - "f(x) = 1 + \\operatorname{Li_0}(-\\exp(x))\n", - "\\end{equation}\n", - "A useful relationship for the polylogarithm function is\n", - "\\begin{equation}\n", - "\\frac{d\\operatorname{Li_n}(x)}{dx} = \\frac{1}{x}\\operatorname{Li_{n-1}}(x)\n", - "\\end{equation}\n", - "Thus,\n", - "\\begin{equation}\n", - "\\frac{d\\operatorname{Li_n}(-\\exp(x))}{dx} = \\operatorname{Li_{n-1}}((-\\exp(x)).\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function\n", - "## Note that this could be written as an array operation in Numpy using ln(exp(2*cosh(x/2))),\n", - "## but I am using the mpmath polylog() function for consistency and to avoid over/underflow\n", - "def fermi_integral_0(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64(kB * temperature * (x + mp.polylog(1,-mp.exp(x)))))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy)\n", - "## Note that this could be written as an array operation in Numpy using ln(exp(2*cosh(x/2))) and np.spence()\n", - "## but I am using the mpmath polylog() function for consistency and to avoid over/underflow\n", - "def fermi_integral_1(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**2 *\n", - " (x**2/2.0 + x*mp.polylog(1,-mp.exp(x)) - mp.polylog(2,-mp.exp(x))) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the integral of the Fermi Function times the energy (relative to the Fermi energy) squared\n", - "## As far as I can tell, there is no polylog(3,x) function for Numpy so I am using mpmath\n", - "## This also helps avoid over/underflow.\n", - "def fermi_integral_2(energies, e_fermi, temperature):\n", - " xa = (energies - e_fermi) / (kB * temperature)\n", - " results = np.array([])\n", - " for x in xa:\n", - " results = np.append(results, np.float64((kB * temperature)**3 *\n", - " ( x**3/3.0 + x**2 * mp.polylog(1,-mp.exp(x))\n", - " - 2.0*x*mp.polylog(2,-mp.exp(x))\n", - " + 2.0*mp.polylog(3,-mp.exp(x)) ) ))\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1696824228988496\n", - "0.16968242289884924\n", - "3.608224830031759e-16\n", - "-0.013419715723285694\n", - "-0.01341971572328568\n", - "-1.3877787807814457e-17\n", - "0.0016322822031528137\n", - "0.0016322822031528126\n", - "1.0842021724855044e-18\n" - ] - } - ], - "source": [ - "# Test the Fermi integral functions against quadrature\n", - "energies = np.array([6.83,7.11])\n", - "e_fermi = 7.0\n", - "fi_0 = fermi_integral_0(energies, e_fermi, temp)\n", - "aint_0 = fi_0[1] - fi_0[0]\n", - "qint_0, abserr = sp.integrate.quad(\n", - " lambda e: fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_0)\n", - "print(qint_0)\n", - "print(aint_0 - qint_0)\n", - "fi_1 = fermi_integral_1(energies, e_fermi, temp)\n", - "aint_1 = fi_1[1] - fi_1[0]\n", - "qint_1, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_1)\n", - "print(qint_1)\n", - "print(aint_1 - qint_1)\n", - "fi_2 = fermi_integral_2(energies, e_fermi, temp)\n", - "aint_2 = fi_2[1] - fi_2[0]\n", - "qint_2, abserr = sp.integrate.quad(\n", - " lambda e: (e - e_fermi)**2*fd_function(e, e_fermi, temp),\n", - " energies[0],energies[-1])\n", - "print(aint_2)\n", - "print(qint_2)\n", - "print(aint_2 - qint_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weights that will compute the analytic integral of the Fermi function\n", - "# times an arbitrary linearly interpolated function\n", - "def get_density_weights(energies, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_0 = fi_0[1:] - fi_0[:-1]\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_1 = fi_1[1:] - fi_1[:-1]\n", - " weights = np.zeros(energies.size)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " weights[1:] = weights[1:] + fi_1/delta_e\n", - " weights[1:] = weights[1:] + fi_0 * (1.0 + (e_fermi - energies[1:])/delta_e)\n", - " weights[:-1] = weights[:-1] - fi_1/delta_e\n", - " weights[:-1] = weights[:-1] + fi_0 * (1.0 - (e_fermi - energies[:-1])/delta_e)\n", - " return weights" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weights that will compute the analytic integral of the Fermi function\n", - "# times the energy times an arbitrary linearly interpolated function\n", - "def get_energy_weights(energies, e_fermi, temperature):\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_1 = fi_1[1:] - fi_1[:-1]\n", - " fi_2 = fermi_integral_2(energies, e_fermi, temperature)\n", - " fi_2 = fi_2[1:] - fi_2[:-1]\n", - " weights = np.zeros(energies.size)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " weights[1:] = weights[1:] + fi_2/delta_e\n", - " weights[1:] = weights[1:] + fi_1 * (1.0 + (e_fermi - energies[1:])/delta_e)\n", - " weights[:-1] = weights[:-1] - fi_2/delta_e\n", - " weights[:-1] = weights[:-1] + fi_1 * (1.0 - (e_fermi - energies[:-1])/delta_e)\n", - " weights = weights + e_fermi*get_density_weights(energies, e_fermi, temperature)\n", - " return weights" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos\n", - "def analytic_enum(energies, dos, e_fermi, temperature):\n", - " return np.sum(dos*get_density_weights(energies, e_fermi, temperature))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos\n", - "def analytic_enum2(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept + (fi_1[1:] - fi_1[:-1])*slope)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos times the energy\n", - "def analytic_eband(energies, dos, e_fermi, temperature):\n", - " return np.sum(dos*get_energy_weights(energies, e_fermi, temperature))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the analytic integral of the Fermi function times the linearly interpolated dos times the energy\n", - "def analytic_eband2(energies, dos, e_fermi, temperature):\n", - " fi_0 = fermi_integral_0(energies, e_fermi, temperature)\n", - " fi_1 = fermi_integral_1(energies, e_fermi, temperature)\n", - " fi_2 = fermi_integral_2(energies, e_fermi, temperature)\n", - " delta_e = energies[1:] - energies[:-1]\n", - " delta_dos = dos[1:] - dos[:-1]\n", - " slope = delta_dos / delta_e\n", - " fermi_intercept = (energies[1:]*dos[:-1] - energies[:-1]*dos[1:])/delta_e + slope*e_fermi\n", - " return np.sum((fi_0[1:] - fi_0[:-1])*fermi_intercept*e_fermi\n", - " + (fi_1[1:] - fi_1[:-1])*(fermi_intercept + slope*e_fermi)\n", - " + (fi_2[1:] - fi_2[:-1])*slope)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another quantity that we will need to evaluate the \"total-energy\" as defined by Quantum Espresso is the electronic entropy, which techically makes this a \"total free energy\" rather than a \"total energy\". In terms of the DOS, the electronic entropy is given by\n", - "\\begin{equation}\n", - "S = \\int D(E) s(E - \\mu) dE,\n", - "\\end{equation}\n", - "where we have defined\n", - "\\begin{equation}\n", - "s(E) = -[f(E)\\log(f(E)) + (1 - f(E))\\log(1 - f(E))],\n", - "\\end{equation}\n", - "For future use, note that\n", - "\\begin{align}\n", - "\\frac{ds}{dE} &= -[\\log(f(E)) - \\log(1 - f(E))]\\frac{df}{dE}\\\\\n", - " &= \\log((1 - f(E))/f(E))\\frac{df}{dE}\\\\\n", - " &= \\beta E \\frac{df}{dE}.\n", - "\\end{align}\n", - "Rather than directly calculating $S$, we will calculate the \"single particle energy\"\n", - "\\begin{equation}\n", - "E_{sp} = E_{band} - \\beta^{-1} S - \\mu \\int \\rho d^3r.\n", - "\\end{equation}\n", - "In terms of $E_{sp}$, the total energy is given by\n", - "\\begin{equation}\n", - "E_{TOT} = E_{sp}(P) - \\int V \\rho d^3r + E_{HXC}[\\rho] + \\mu \\int \\rho d^3r.\n", - "\\end{equation}\n", - "Using the definitions of the various terms in $E_{sp}$ in terms of $D(E)$, we get\n", - "\\begin{align}\n", - "E_{sp} &= \\int D(E) [(E - \\mu)f(E - \\mu) - \\beta^{-1} s(E - \\mu)] dE\\\\\n", - " &= \\int D(E) g(E - \\mu) dE,\n", - "\\end{align}\n", - "where we have defined the new quantity\n", - "\\begin{equation}\n", - "g(E) = E f(E) - \\beta^{-1} s(E).\n", - "\\end{equation}\n", - "Note that\n", - "\\begin{align}\n", - "\\frac{dg}{dE} &= f(E) + E \\frac{df}{dE} - E \\frac{df}{dE}\\\\\n", - " &= f(E).\n", - "\\end{align}\n", - "Thus, in order to evaluate $E_{sp}$ from $D(E)$ using analytic integration, we need to evaluate the integral of the linearly interpolated DOS times $g(E - \\mu)$, where\n", - "\\begin{equation}\n", - "g(E) = \\int f(E) dE.\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Gaussian is one possible representation of the $\\delta$-functions occuring in DOS definition\n", - "$$\\delta(\\epsilon-\\epsilon_{ik}) = \\frac{1}{\\sqrt{\\pi\\sigma^2}}\\exp{\\left[-\\left(\\frac{\\epsilon-\\epsilon_{ik}}{\\sigma}\\right)^2\\right]}$$ \n", - "with a width $\\sigma$." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Gaussian\n", - "## Note: Gaussian without factor of 1/sqrt(2)\n", - "def gaussian(e_grid, centers, sigma):\n", - " result = 1.0/np.sqrt(np.pi*sigma**2)*np.exp(-1.0*((e_grid[np.newaxis]-centers[...,np.newaxis])/sigma)**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a discretized delta function that maintains 0th and 1st moments\n", - "def delta_M1(e_grid, centers):\n", - " de = e_grid[np.newaxis]-centers[...,np.newaxis]\n", - " de_above = np.min(de,axis=-1,initial=np.max(de),where=np.greater(de,0.0))\n", - " de_below = np.max(de,axis=-1,initial=np.min(de),where=np.less_equal(de,0.0))\n", - " e_spacing = de_above - de_below\n", - " result = 1.0 - np.abs(de)/e_spacing[...,np.newaxis]\n", - " result = result*np.greater_equal(result,0.0)*np.less_equal(result,1.0)\n", - " result = result/e_spacing[...,np.newaxis]\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The total number of electrons is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ by summing the occupations over all of the states\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik})\\ ,$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$. The weights $w_k$ arise from an implicit integral over the Brillouin zone, which is evaluated as a simple sum over a regular grid of k points. This simple approach to evaluating the integral often gives reasonable results since $\\epsilon_{ik}$ is periodic in $k$, but it becomes less accurate when the Fermi level lies within a band due to the non-analytic behavior of the Fermi-Dirac function at the Fermi level. The sum over a regular grid of k-points is additionally reduced by symmetry, which results in different $w_k$ values for different $k$ values." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating the number of electrons from DFT results\n", - "def dft_2_enum(dft, e_fermi = None, temperature = None):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## enum: number of electrons\n", - "\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " if temperature is None:\n", - " temperature = dft.electronic_temperature\n", - " enum_per_band = fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " enum_per_band = dft.kweights[np.newaxis,:]*enum_per_band\n", - " enum = np.sum(enum_per_band)\n", - " return enum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy,\" which is also called the \"single-particle energy\" or the \"one-electron energy,\" is obtained directly from the Kohn-Sham eigenvalues $\\epsilon_{ik}$ from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ ,$$\n", - "\n", - "where the various terms are defined above." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DFT results\n", - "def dft_2_eband(dft, e_fermi = None, temperature = None):\n", - " # input:\n", - " ## dft: a DFT_results instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " # output:\n", - " ## eband: band energy\n", - "\n", - " if temperature is None:\n", - " temperature = dft.electronic_temperature\n", - " if e_fermi is None:\n", - " e_fermi = dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = toms748(lambda e_fermi: dft_2_enum(dft, e_fermi, temperature) - dft.n_electrons, a = np.min(dft.eigs), b = np.max(dft.eigs))\n", - " eband_per_band = dft.eigs*fd_function(dft.eigs, e_fermi=e_fermi, temperature=temperature)\n", - " eband_per_band = dft.kweights[np.newaxis,:]*eband_per_band\n", - " eband = np.sum(eband_per_band)\n", - " return eband" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3654808767733\n" - ] - } - ], - "source": [ - "eband_0_888_ref=dft_2_eband(dft_0_888)\n", - "print(eband_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.3470902759054\n" - ] - } - ], - "source": [ - "eband_0_888_sc_ref=dft_2_eband(dft_0_888, e_fermi = 'sc')\n", - "print(eband_0_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.146096688316\n" - ] - } - ], - "source": [ - "eband_1_888_ref=dft_2_eband(dft_1_888)\n", - "print(eband_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2598.1203692340027\n" - ] - } - ], - "source": [ - "eband_1_888_sc_ref=dft_2_eband(dft_1_888, e_fermi = 'sc')\n", - "print(eband_1_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.8711214367395\n" - ] - } - ], - "source": [ - "eband_2_888_ref=dft_2_eband(dft_2_888)\n", - "print(eband_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.876315137265\n" - ] - } - ], - "source": [ - "eband_2_888_sc_ref=dft_2_eband(dft_2_888, e_fermi = 'sc')\n", - "print(eband_2_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2596.5716167851165\n" - ] - } - ], - "source": [ - "eband_3_888_ref=dft_2_eband(dft_3_888)\n", - "print(eband_3_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2596.580145546497\n" - ] - } - ], - "source": [ - "eband_3_888_sc_ref=dft_2_eband(dft_3_888, e_fermi = 'sc')\n", - "print(eband_3_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.920755644534\n" - ] - } - ], - "source": [ - "eband_4_888_ref=dft_2_eband(dft_4_888)\n", - "print(eband_4_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2595.9576941828914\n" - ] - } - ], - "source": [ - "eband_4_888_sc_ref=dft_2_eband(dft_4_888, e_fermi = 'sc')\n", - "print(eband_4_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2597.926529438901\n" - ] - } - ], - "source": [ - "eband_5_888_sc_ref=dft_2_eband(dft_5_888, e_fermi = 'sc')\n", - "print(eband_5_888_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2597.9345413595865\n" - ] - } - ], - "source": [ - "eband_5_888_ref=dft_2_eband(dft_5_888)\n", - "print(eband_5_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2522.1627902370897\n" - ] - } - ], - "source": [ - "eband_933K_0_sc_ref=dft_2_eband(dft_933K_0, e_fermi = 'sc')\n", - "print(eband_933K_0_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2526.186365968908\n" - ] - } - ], - "source": [ - "eband_933K_1_sc_ref=dft_2_eband(dft_933K_1, e_fermi = 'sc')\n", - "print(eband_933K_1_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2531.9859886821237\n" - ] - } - ], - "source": [ - "eband_933K_2_sc_ref=dft_2_eband(dft_933K_2, e_fermi = 'sc')\n", - "print(eband_933K_2_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2570.0078016632747\n" - ] - } - ], - "source": [ - "eband_933K_10_sc_ref=dft_2_eband(dft_933K_10, e_fermi = 'sc')\n", - "print(eband_933K_10_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2562.0712499151396\n" - ] - } - ], - "source": [ - "eband_933K_11_sc_ref=dft_2_eband(dft_933K_11, e_fermi = 'sc')\n", - "print(eband_933K_11_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2562.9801118510595\n" - ] - } - ], - "source": [ - "eband_933K_12_sc_ref=dft_2_eband(dft_933K_12, e_fermi = 'sc')\n", - "print(eband_933K_12_sc_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0023586003111\n" - ] - } - ], - "source": [ - "enum_0_888_ref=dft_2_enum(dft_0_888)\n", - "print(enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0032983385398\n" - ] - } - ], - "source": [ - "enum_1_888_ref=dft_2_enum(dft_1_888)\n", - "print(enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9993338972209\n" - ] - } - ], - "source": [ - "enum_2_888_ref=dft_2_enum(dft_2_888)\n", - "print(enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9989062624795\n" - ] - } - ], - "source": [ - "enum_3_888_ref=dft_2_enum(dft_3_888)\n", - "print(enum_3_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9952628516897\n" - ] - } - ], - "source": [ - "enum_4_888_ref=dft_2_enum(dft_4_888)\n", - "print(enum_4_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.001027091929\n" - ] - } - ], - "source": [ - "enum_5_888_ref=dft_2_enum(dft_5_888)\n", - "print(enum_5_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0001451617269\n" - ] - } - ], - "source": [ - "enum_933K_0_ref=dft_2_enum(dft_933K_0)\n", - "print(enum_933K_0_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0015998160123\n" - ] - } - ], - "source": [ - "enum_933K_1_ref=dft_2_enum(dft_933K_1)\n", - "print(enum_933K_1_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9953637109259\n" - ] - } - ], - "source": [ - "enum_933K_2_ref=dft_2_enum(dft_933K_2)\n", - "print(enum_933K_2_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.9996917469026\n" - ] - } - ], - "source": [ - "enum_933K_10_ref=dft_2_enum(dft_933K_10)\n", - "print(enum_933K_10_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "768.0004204822181\n" - ] - } - ], - "source": [ - "enum_933K_11_ref=dft_2_enum(dft_933K_11)\n", - "print(enum_933K_11_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "767.995471771574\n" - ] - } - ], - "source": [ - "enum_933K_12_ref=dft_2_enum(dft_933K_12)\n", - "print(enum_933K_12_ref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, the total number of electrons can be obtained from the DOS\n", - "\n", - "$$N_e = \\int_{-\\infty}^{\\infty} D(E)\\, f(E)\\ dE\\,$$\n", - "\n", - "where $E$ denotes the energy, $D(E)$ the DOS, and $f(E)$ the Fermi-Dirac distribution function." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating integrated density (electron number) from DOS\n", - "## Integrate DOS*FD to obtain band energy\n", - "def dos_2_enum(dos, e_fermi = None, temperature = None, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the linearly interpolated dos\n", - " # output:\n", - " ## enum: number of electrons\n", - " \n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " if temperature is None:\n", - " temperature = dos.dft.electronic_temperature\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.trapz(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " enum = sp.integrate.simps(dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " enum, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " enum = analytic_enum(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return enum" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the self-consistent Fermi energy such that dos_2_enum(...) = dos.dft.n_electrons\n", - "def dos_2_efermi(dos, temperature = None, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the linearly interpolated dos\n", - " # output:\n", - " ## e_fermi: the self-consistent Fermi energy\n", - " \n", - " if temperature is None:\n", - " temperature = dos.dft.electronic_temperature\n", - " e_fermi = toms748(lambda e_fermi: dos_2_enum(dos, e_fermi, temperature, integration) - dos.dft.n_electrons,\n", - " a = dos.e_grid[0], b = dos.e_grid[-1])\n", - " return e_fermi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, the band energy can be obtained from the DOS\n", - "\n", - "$$E_{band} = \\int_{-\\infty}^{\\infty} E\\, D(E)\\, f(E)\\ dE .$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate E*DOS*FD to obtain band energy\n", - "def dos_2_eband(dos, e_fermi = None, temperature = None, integration = 'analytic'):\n", - " # input:\n", - " ## dos: a DOS instance\n", - " ## e_fermi: Fermi energy used in generating the occupations, defaults to Fermi energy from dft\n", - " ## temperature: temperature used in generating the occupations\n", - " ## integration: method of integration, which can be one of the following strings:\n", - " ### 'trapz': sp.integrate.trapz\n", - " ### 'simps': sp.integrate.simps\n", - " ### 'quad': sp.integrate.quad with linear interpolation of dos using sp.interpolate.interp1d\n", - " ### 'analytic': analytic integration of the Fermi function times the energy times the linearly interpolated dos\n", - " # output:\n", - " ## eband: calculated band energy in eV\n", - " \n", - " if temperature is None:\n", - " temperature = dos.dft.electronic_temperature\n", - " if e_fermi is None:\n", - " e_fermi = dos.dft.fermi_energy\n", - " elif e_fermi == \"self-consistent\" or e_fermi == \"sc\":\n", - " e_fermi = dos_2_efermi(dos, temperature, integration)\n", - " if integration == 'trapz':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.trapz(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'simps':\n", - " occupations = fd_function(dos.e_grid, e_fermi, temperature)\n", - " eband = sp.integrate.simps(dos.e_grid * dos.dos * occupations, dos.e_grid)\n", - " elif integration == 'quad':\n", - " f_dos = sp.interpolate.interp1d(dos.e_grid,dos.dos)\n", - " eband, abserr = sp.integrate.quad(\n", - " lambda e: f_dos(e)*e*fd_function(e, e_fermi, temperature),\n", - " dos.e_grid[0], dos.e_grid[-1], limit=500, points=(e_fermi))\n", - " elif integration == 'analytic':\n", - " eband = analytic_eband(dos.e_grid, dos.dos, e_fermi, temperature)\n", - " else:\n", - " raise ValueError('argument \"integration\" does not match an implemented method')\n", - " return eband" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute total DOS" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Use the same grid as used by the Quantum Espresso postprocessor\n", - "e_grid = dos_qe_0_888_g.e_grid\n", - "#e_grid = np.arange(-5.0,15.0,0.1)\n", - "\n", - "# Determine the smearing for the Gaussian representation of the delta function\n", - "sigma = e_grid[1] - e_grid[0]\n", - "narrow_gaussian = partial(gaussian, sigma = sigma)\n", - "wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - "# Compute the DOS for snap_0 with 888 k-points using each representation of the delta function\n", - "dos_0_888_g = DOS.from_calculation(dft_0_888, e_grid, narrow_gaussian )\n", - "dos_0_888_2g = DOS.from_calculation(dft_0_888, e_grid, wide_gaussian )\n", - "dos_0_888_M1 = DOS.from_calculation(dft_0_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_1 with 888 k-points using each representation of the delta function\n", - "dos_1_888_g = DOS.from_calculation(dft_1_888, e_grid, narrow_gaussian )\n", - "dos_1_888_2g = DOS.from_calculation(dft_1_888, e_grid, wide_gaussian )\n", - "dos_1_888_M1 = DOS.from_calculation(dft_1_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_2 with 888 k-points using each representation of the delta function\n", - "dos_2_888_g = DOS.from_calculation(dft_2_888, e_grid, narrow_gaussian )\n", - "dos_2_888_2g = DOS.from_calculation(dft_2_888, e_grid, wide_gaussian )\n", - "dos_2_888_M1 = DOS.from_calculation(dft_2_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_3 with 888 k-points using each representation of the delta function\n", - "dos_3_888_g = DOS.from_calculation(dft_3_888, e_grid, narrow_gaussian )\n", - "dos_3_888_2g = DOS.from_calculation(dft_3_888, e_grid, wide_gaussian )\n", - "dos_3_888_M1 = DOS.from_calculation(dft_3_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_4 with 888 k-points using each representation of the delta function\n", - "dos_4_888_g = DOS.from_calculation(dft_4_888, e_grid, narrow_gaussian )\n", - "dos_4_888_2g = DOS.from_calculation(dft_4_888, e_grid, wide_gaussian )\n", - "dos_4_888_M1 = DOS.from_calculation(dft_4_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for snap_4 with 888 k-points using each representation of the delta function\n", - "dos_5_888_g = DOS.from_calculation(dft_5_888, e_grid, narrow_gaussian )\n", - "dos_5_888_2g = DOS.from_calculation(dft_5_888, e_grid, wide_gaussian )\n", - "dos_5_888_M1 = DOS.from_calculation(dft_5_888, e_grid, delta_M1)\n", - "\n", - "# Compute the DOS for the 933K snapshots using the wide Gaussian delta function\n", - "dos_933K_0_2g = DOS.from_calculation(dft_933K_0, e_grid, wide_gaussian )\n", - "dos_933K_1_2g = DOS.from_calculation(dft_933K_1, e_grid, wide_gaussian )\n", - "dos_933K_2_2g = DOS.from_calculation(dft_933K_2, e_grid, wide_gaussian )\n", - "dos_933K_10_2g = DOS.from_calculation(dft_933K_10, e_grid, wide_gaussian )\n", - "dos_933K_11_2g = DOS.from_calculation(dft_933K_11, e_grid, wide_gaussian )\n", - "dos_933K_12_2g = DOS.from_calculation(dft_933K_12, e_grid, wide_gaussian )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_0_888_g.e_grid, dos_qe_0_888_g.dos, linestyle='--', linewidth=3, color='yellow', label='QE-DOS G')\n", - "ax.plot(dos_qe_0_888_2g.e_grid, dos_qe_0_888_2g.dos, linestyle='--', linewidth=3, color='black', label='QE-DOS 2G')\n", - "ax.plot(dos_0_888_2g.e_grid, dos_0_888_2g.dos, linestyle='-', linewidth=3, color='blue', label='wide Gaussian')\n", - "#ax.plot(dos_0_888_g.e_grid, dos_0_888_g.dos, linestyle='-', linewidth=3, color='green', label='narrow Gaussian')\n", - "#ax.plot(dos_0_888_M1.e_grid, dos_0_888_M1.dos, linestyle='-', linewidth=3, color='red', label='M1 delta representation')\n", - "\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(0.55, 1.0))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.xlim(-4.0,10.0)\n", - "\n", - "ax.plot(dos_0_888_M1.e_grid, dos_0_888_M1.dos, linestyle='-', linewidth=2, color='red', label='M1 Delta Function')\n", - "ax.plot(dos_1_888_g.e_grid, dos_0_888_g.dos, linestyle='-', linewidth=2, color='green', label='1G Gaussian')\n", - "ax.plot(dos_0_888_2g.e_grid, dos_0_888_2g.dos, linestyle='-', linewidth=2, color='blue', label='2G Gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(0.55, 1.0))\n", - "plt.savefig('DOS_w_different_deltas.png')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To a visual level of detail, we can recover the QE-DOS result by using the same parameters as in the input for computing the DOS. Since the QE-DOS is only printed to 4 significant figures by Quantum Espresso, we can avoid these round off errors by using the DOS that we evaluate from the eigenvalues.\n", - "\n", - "Now we can go ahead and investigate different integration approaches to calculate the integrated electron number and band energy. Since we do not have the true Quantum Espresso band energy, we will use the band energy that we evaluate directly from the eigenvalues as a reference.\n", - "\n", - "First, let's convince ourselves that the 'analytical' integration gives essentially the same answers as 'quad' integration, but without all the warnings." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":28: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enum using numerical quadrature: 768.2854 768.2886 768.2413 768.2851 768.2743 768.2967\n", - "Enum using analytical integration: 768.2855 768.2890 768.2415 768.2852 768.2745 768.2967\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Enum using numerical quadrature: {0:12.4f}{1:12.4f}{2:12.4f}{3:12.4f}{4:12.4f}{5:12.4f}\".format(\n", - "dos_2_enum(dos_0_888_2g, integration = 'quad'),\n", - "dos_2_enum(dos_1_888_2g, integration = 'quad'),\n", - "dos_2_enum(dos_2_888_2g, integration = 'quad'),\n", - "dos_2_enum(dos_3_888_2g, integration = 'quad'),\n", - "dos_2_enum(dos_4_888_2g, integration = 'quad'),\n", - "dos_2_enum(dos_5_888_2g, integration = 'quad')))\n", - "print(\"Enum using analytical integration: {0:12.4f}{1:12.4f}{2:12.4f}{3:12.4f}{4:12.4f}{5:12.4f}\".format(\n", - "dos_2_enum(dos_0_888_2g, integration = 'analytic'),\n", - "dos_2_enum(dos_1_888_2g, integration = 'analytic'),\n", - "dos_2_enum(dos_2_888_2g, integration = 'analytic'),\n", - "dos_2_enum(dos_3_888_2g, integration = 'analytic'),\n", - "dos_2_enum(dos_4_888_2g, integration = 'analytic'),\n", - "dos_2_enum(dos_5_888_2g, integration = 'analytic')))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 'analytical' and 'quad' integration techniques for electron number give the same answers up to a few 10,000ths of an electron. This is probably reasonable agreement given the warnings from the numerical quadrature routine. Notice above that the agreement is much better when we only integrate over one energy interval." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":30: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " eband, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eband using numerical quadrature: 2596.4285 2599.2285 2596.6170 2597.6608 2596.9518 2599.0965\n", - "Eband using analytical integration: 2596.4295 2599.2306 2596.6171 2597.6608 2596.9520 2599.0979\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Eband using numerical quadrature: {0:12.4f}{1:12.4f}{2:12.4f}{3:12.4f}{4:12.4f}{5:12.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, integration = 'quad'),\n", - "dos_2_eband(dos_1_888_2g, integration = 'quad'),\n", - "dos_2_eband(dos_2_888_2g, integration = 'quad'),\n", - "dos_2_eband(dos_3_888_2g, integration = 'quad'),\n", - "dos_2_eband(dos_4_888_2g, integration = 'quad'),\n", - "dos_2_eband(dos_5_888_2g, integration = 'quad')))\n", - "print(\"Eband using analytical integration: {0:12.4f}{1:12.4f}{2:12.4f}{3:12.4f}{4:12.4f}{5:12.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, integration = 'analytic'),\n", - "dos_2_eband(dos_1_888_2g, integration = 'analytic'),\n", - "dos_2_eband(dos_2_888_2g, integration = 'analytic'),\n", - "dos_2_eband(dos_3_888_2g, integration = 'analytic'),\n", - "dos_2_eband(dos_4_888_2g, integration = 'analytic'),\n", - "dos_2_eband(dos_5_888_2g, integration = 'analytic')))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 'analytical' and 'quad' integration techniques for band energy give the same answers up to a few meV with the agreement often being better. This is probably reasonable agreement given the warnings from the numerical quadrature routine. Notice above that the agreement is much better when we only integrate over one energy interval. From now on, we will ignore the distinction between 'quad' and 'analytic' integration, and just use 'analytic' when we want to evaluate an accurate integral of the linearly interpolated DOS times the Fermi function (times the energy in the case of eband)." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":28: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28304728577404603\n", - "0.10647239198749503\n", - "0.04538864728692715\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'quad') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'quad') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28310624238542914\n", - "0.10668622714081266\n", - "0.04539697333029835\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_0_888_2g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_g, integration = 'analytic') - enum_0_888_ref)\n", - "print(dos_2_enum(dos_0_888_M1, integration = 'analytic') - enum_0_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":28: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.28533298020636266\n", - "0.10744953331345641\n", - "0.052445261101411234\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'quad') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'quad') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2856521550979778\n", - "0.10731624971231213\n", - "0.052275393386821634\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_1_888_2g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_g, integration = 'analytic') - enum_1_888_ref)\n", - "print(dos_2_enum(dos_1_888_M1, integration = 'analytic') - enum_1_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":28: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", - " the requested tolerance from being achieved. The error may be \n", - " underestimated.\n", - " enum, abserr = sp.integrate.quad(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24194209412007694\n", - "0.07849218150795423\n", - "0.027242952126243836\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'quad') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'quad') - enum_2_888_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24220842096963224\n", - "0.07857100610215184\n", - "0.027246274673416337\n" - ] - } - ], - "source": [ - "print(dos_2_enum(dos_2_888_2g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_g, integration = 'analytic') - enum_2_888_ref)\n", - "print(dos_2_enum(dos_2_888_M1, integration = 'analytic') - enum_2_888_ref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When using analytic integration with a self-consistent Fermi energy, the errors are very consistent between different room temperature snapshots. Gaussian smearing with a width equal to the grid spacing reduces the errors by more than a factor of 3 relative to Gaussian smearing with a width equal to twice the grid spacing. The \"1st moment\" method of smearing reduces the errors by another factor of 2. However, as we saw above, the latter two methods produce a fairly noisy DOS even when applied to DFT calculations with 8x8x8 k-point sampling." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n", - "Eband errors using 2g Gaussian smearing: -1.1430 -1.1432 -1.1423 -1.1432 -1.1458 -1.1429\n", - "Eband errors using 1g Gaussian smearing: -0.3541 -0.3539 -0.3529 -0.3541 -0.3552 -0.3531\n", - "Eband errors using 1st Moment smearing: -0.1776 -0.1780 -0.1778 -0.1781 -0.1790 -0.1781\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Eband errors using 2g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_g, e_fermi = 'sc', integration = 'analytic') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_g, e_fermi = 'sc', integration = 'analytic') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_g, e_fermi = 'sc', integration = 'analytic') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1st Moment smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_M1, e_fermi = 'sc', integration = 'analytic') - eband_5_888_sc_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Without a self-consistent Fermi energy, the errors are much less consistent between different room temperature snapshots even when comparing to a reference energy calculated directly from the eigenvalues using the same (non-self-consistent) Fermi energy from the Quantum Espresso output file. From now on, we will only consider calculations with a self-consistent Fermi energy." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n", - "Eband errors using 2g Gaussian smearing: 1.0640 1.0845 0.7460 1.0891 1.0313 1.1633\n", - "Eband errors using 1g Gaussian smearing: 0.4777 0.4831 0.2597 0.5117 0.4885 0.4647\n", - "Eband errors using 1st Moment smearing: 0.1763 0.2298 0.0346 0.1630 0.2098 0.1390\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Eband errors using 2g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, integration = 'analytic') - eband_0_888_ref,\n", - "dos_2_eband(dos_1_888_2g, integration = 'analytic') - eband_1_888_ref,\n", - "dos_2_eband(dos_2_888_2g, integration = 'analytic') - eband_2_888_ref,\n", - "dos_2_eband(dos_3_888_2g, integration = 'analytic') - eband_3_888_ref,\n", - "dos_2_eband(dos_4_888_2g, integration = 'analytic') - eband_4_888_ref,\n", - "dos_2_eband(dos_5_888_2g, integration = 'analytic') - eband_5_888_ref))\n", - "print(\"Eband errors using 1g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_g, integration = 'analytic') - eband_0_888_ref,\n", - "dos_2_eband(dos_1_888_g, integration = 'analytic') - eband_1_888_ref,\n", - "dos_2_eband(dos_2_888_g, integration = 'analytic') - eband_2_888_ref,\n", - "dos_2_eband(dos_3_888_g, integration = 'analytic') - eband_3_888_ref,\n", - "dos_2_eband(dos_4_888_g, integration = 'analytic') - eband_4_888_ref,\n", - "dos_2_eband(dos_5_888_g, integration = 'analytic') - eband_5_888_ref))\n", - "print(\"Eband errors using 1st Moment smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_M1, integration = 'analytic') - eband_0_888_ref,\n", - "dos_2_eband(dos_1_888_M1, integration = 'analytic') - eband_1_888_ref,\n", - "dos_2_eband(dos_2_888_M1, integration = 'analytic') - eband_2_888_ref,\n", - "dos_2_eband(dos_3_888_M1, integration = 'analytic') - eband_3_888_ref,\n", - "dos_2_eband(dos_4_888_M1, integration = 'analytic') - eband_4_888_ref,\n", - "dos_2_eband(dos_5_888_M1, integration = 'analytic') - eband_5_888_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simpson's rule integration with a self-consistent Fermi energy gives a reasonably consistent band energy error with the wider Gaussian smearing, but it becomes much less consistent for the narrower Gaussian and \"first-moment\" smearing. It also produces relatively large errors in the first-moment case. This suggests that Simpson's method is fairly sensitive to noise in the data that it is trying to integrate." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n", - "Eband errors using 2g Gaussian smearing: -1.1122 -1.1124 -1.1115 -1.1126 -1.1152 -1.1122\n", - "Eband errors using 1g Gaussian smearing: 0.2135 0.2004 0.1684 0.0431 0.0414 0.1158\n", - "Eband errors using 1st Moment smearing: 2.4966 2.6293 2.4520 2.0538 1.9102 1.7458\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":3: RuntimeWarning: overflow encountered in exp\n", - " return 1.0 / (1.0 + np.exp((energies - e_fermi) / (kB * temperature)))\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Eband errors using 2g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'simps') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'simps') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'simps') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_2g, e_fermi = 'sc', integration = 'simps') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_2g, e_fermi = 'sc', integration = 'simps') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_2g, e_fermi = 'sc', integration = 'simps') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_g, e_fermi = 'sc', integration = 'simps') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_g, e_fermi = 'sc', integration = 'simps') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_g, e_fermi = 'sc', integration = 'simps') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_g, e_fermi = 'sc', integration = 'simps') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_g, e_fermi = 'sc', integration = 'simps') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_g, e_fermi = 'sc', integration = 'simps') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1st Moment smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_M1, e_fermi = 'sc', integration = 'simps') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_M1, e_fermi = 'sc', integration = 'simps') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_M1, e_fermi = 'sc', integration = 'simps') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_M1, e_fermi = 'sc', integration = 'simps') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_M1, e_fermi = 'sc', integration = 'simps') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_M1, e_fermi = 'sc', integration = 'simps') - eband_5_888_sc_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule integration with a self-consistent Fermi energy gives remarkably good results. The errors for each smearing method are as consistent as analytic integration across all five snapshots, and the absolute errors are actually somewhat smaller. I'm not sure why the Fermi surface, which is essentially a 50 meV wide step being integrated on a 100 meV wide grid, doesn't cause more trouble. You would thing that the answer would be significantly wrong whenever the Fermi energy isn't half way between two energy grid points. Maybe the self-consistent solution for the Fermi energy cancels out a large part of the error at the Fermi surface? I don't understand it, so I don't trust it." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " snap0 snap1 snap2 snap3 snap4 snap5\n", - "Eband errors using 2g Gaussian smearing: -1.0392 -1.0388 -1.0384 -1.0393 -1.0418 -1.0384\n", - "Eband errors using 1g Gaussian smearing: -0.2488 -0.2482 -0.2476 -0.2486 -0.2495 -0.2475\n", - "Eband errors using 1st Moment smearing: -0.0711 -0.0709 -0.0713 -0.0711 -0.0717 -0.0715\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":3: RuntimeWarning: overflow encountered in exp\n", - " return 1.0 / (1.0 + np.exp((energies - e_fermi) / (kB * temperature)))\n" - ] - } - ], - "source": [ - "print(\" snap0 snap1 snap2 snap3 snap4 snap5\")\n", - "print(\"Eband errors using 2g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_2g, e_fermi = 'sc', integration = 'trapz') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_g, e_fermi = 'sc', integration = 'trapz') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_g, e_fermi = 'sc', integration = 'trapz') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_g, e_fermi = 'sc', integration = 'trapz') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_g, e_fermi = 'sc', integration = 'trapz') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_g, e_fermi = 'sc', integration = 'trapz') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_g, e_fermi = 'sc', integration = 'trapz') - eband_5_888_sc_ref))\n", - "print(\"Eband errors using 1st Moment smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_0_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_0_888_sc_ref,\n", - "dos_2_eband(dos_1_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_1_888_sc_ref,\n", - "dos_2_eband(dos_2_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_2_888_sc_ref,\n", - "dos_2_eband(dos_3_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_3_888_sc_ref,\n", - "dos_2_eband(dos_4_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_4_888_sc_ref,\n", - "dos_2_eband(dos_5_888_M1, e_fermi = 'sc', integration = 'trapz') - eband_5_888_sc_ref))" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 933K: snap0 snap1 snap2 snap10 snap11 snap12\n", - "Eband errors using 2g Gaussian smearing: -1.1485 -1.1452 -1.1502 -1.1546 -1.1545 -1.1555\n" - ] - } - ], - "source": [ - "print(\" 933K: snap0 snap1 snap2 snap10 snap11 snap12\")\n", - "print(\"Eband errors using 2g Gaussian smearing: {0:8.4f}{1:8.4f}{2:8.4f}{3:8.4f}{4:8.4f}{5:8.4f}\".format(\n", - "dos_2_eband(dos_933K_0_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_0_sc_ref,\n", - "dos_2_eband(dos_933K_1_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_1_sc_ref,\n", - "dos_2_eband(dos_933K_2_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_2_sc_ref,\n", - "dos_2_eband(dos_933K_10_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_10_sc_ref,\n", - "dos_2_eband(dos_933K_11_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_11_sc_ref,\n", - "dos_2_eband(dos_933K_12_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_12_sc_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also investigate how quantities evaluated from the LDOS calculated either by Quantum Espresso\n", - "or by machine learning compare with the results evaluated from the DOS that we calculate from eigenvalues." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "if read_QE_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " ldos_2_888_2g = LDOS(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/ldos_200x200x200grid_250elvls_snapshot2.npy')\n", - " \n", - "if read_QE_933K_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " ldos_933K_0_2g = LDOS(dft_933K_0, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/Al_ldos_200x200x200grid_250elvls_snapshot0.npy')\n", - "\n", - "if read_ML_LDOS:\n", - " ldos_e_grid = np.linspace(-10.0,14.9,250)\n", - " ldos_ml_2_888_2g = LDOS(dft_2_888, ldos_e_grid,\n", - " '/Users/namodin/Documents/Machine Learning/ldos_data/FeedForward_fp_ldos_predictions_may5.npy')" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# This checks how well the eband and electron density computed in various ways from the LDOS agree\n", - "if read_QE_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(dos_2_eband(ldos_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(ldos_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(ldos_2_888_2g.eband, ldos_2_888_2g.eband - eband_2_888_sc_ref)\n", - " print(np.sum(ldos_2_888_2g.density)*ldos_2_888_2g.cell_volume,\n", - " np.sum(ldos_2_888_2g.density)*ldos_2_888_2g.cell_volume - ldos_2_888_2g.dft.n_electrons)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "# This checks how well the eband and electron density computed in various ways from the LDOS agree\n", - "if read_QE_933K_LDOS:\n", - " print(dos_2_eband(dos_933K_0_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_933K_0_2g, e_fermi = 'sc', integration = 'analytic') - eband_933K_0_sc_ref)\n", - " print(dos_2_eband(ldos_933K_0_2g.dos, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(ldos_933K_0_2g.dos, e_fermi = 'sc', integration = 'analytic') - eband_933K_0_sc_ref)\n", - " print(ldos_933K_0_2g.eband, ldos_933K_0_2g.eband - eband_933K_0_sc_ref)\n", - " print(np.sum(ldos_933K_0_2g.density)*ldos_933K_0_2g.cell_volume,\n", - " np.sum(ldos_933K_0_2g.density)*ldos_933K_0_2g.cell_volume - ldos_933K_0_2g.dft.n_electrons)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "# This shows that I can get agreement with Austin's band energy for the ML predicted LDOS by altering parameters\n", - "if read_ML_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps'),\n", - " dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps') - dos_2_eband(dos_2_888_2g, integration = 'simps'))\n", - " print(\"Changing energy grid!\")\n", - " ldos_ml_2_888_2g.dos.e_grid = np.linspace(-10.0,15.0,250)\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, integration = 'simps'))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 7.7967, integration = 'simps'))\n", - " cell_volume = ldos_ml_2_888_2g.dft.volume/(200.0*Br2AA)**3\n", - " print(cell_volume, cell_volume**(1.0/3.0))\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 7.7967, integration = 'simps')*0.153049**3/cell_volume)\n", - " print(\"Resetting energy grid!\")\n", - " ldos_ml_2_888_2g.dos.e_grid = np.linspace(-10.0,14.9,250)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "# This checks how well the eband and electron density computed in various ways from the LDOS agree\n", - "if read_ML_LDOS:\n", - " print(dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic'),\n", - " dos_2_eband(ldos_ml_2_888_2g.dos, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)\n", - " print(ldos_ml_2_888_2g.eband, ldos_ml_2_888_2g.eband - eband_2_888_sc_ref)\n", - " print(np.sum(ldos_ml_2_888_2g.density)*ldos_ml_2_888_2g.cell_volume,\n", - " np.sum(ldos_ml_2_888_2g.density)*ldos_ml_2_888_2g.cell_volume - ldos_ml_2_888_2g.dft.n_electrons)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we will investigate how quantities evaluated from the DOS calculated by Quantum Espresso and saved to\n", - "a file compare with the results evaluated from the DOS that we calculate from eigenvalues. First, let's look at the integrated number of electrons:" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enum errors using our DOS: 0.2831 0.2857 0.2422\n", - "Enum errors using Q-E DOS: 0.2833 0.2900 0.2487\n" - ] - } - ], - "source": [ - "print(\"Enum errors using our DOS: {0:8.4f}{1:8.4f}{2:8.4f}\".format(\n", - " dos_2_enum(dos_0_888_2g, integration = 'analytic') - enum_0_888_ref,\n", - " dos_2_enum(dos_1_888_2g, integration = 'analytic') - enum_1_888_ref,\n", - " dos_2_enum(dos_2_888_2g, integration = 'analytic') - enum_2_888_ref))\n", - "print(\"Enum errors using Q-E DOS: {0:8.4f}{1:8.4f}{2:8.4f}\".format(\n", - " dos_2_enum(dos_qe_0_888_2g, integration = 'analytic') - enum_0_888_ref,\n", - " dos_2_enum(dos_qe_1_888_2g, integration = 'analytic') - enum_1_888_ref,\n", - " dos_2_enum(dos_qe_2_888_2g, integration = 'analytic') - enum_2_888_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The integrated number of electrons from the QE DOS isn't great, but it isn't terrible either. This is presumably due to round off errors when Q-E prints the numbers to the file with only 4 digits of accuracy. Now, we can check the energies: " - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eband errors using our DOS: -1.1430 -1.1432 -1.1423\n", - "Eband errors using Q-E DOS: -1.1317 -1.1819 -1.1301\n" - ] - } - ], - "source": [ - "print(\"Eband errors using our DOS: {0:8.4f}{1:8.4f}{2:8.4f}\".format(\n", - " dos_2_eband(dos_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref,\n", - " dos_2_eband(dos_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref,\n", - " dos_2_eband(dos_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref))\n", - "print(\"Eband errors using Q-E DOS: {0:8.4f}{1:8.4f}{2:8.4f}\".format(\n", - " dos_2_eband(dos_qe_0_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref,\n", - " dos_2_eband(dos_qe_1_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref,\n", - " dos_2_eband(dos_qe_2_888_2g, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The band energy errors using the Q-E DOS are much less consistent than the errors that we get with the DOS that we calculate from eigenvalues. This shows the importance of controlling round off errors when doing DFT-like calculations.\n", - "\n", - "Now we will investigate how quantities calculated from various definitions of the DOS change as the energy grid is varied. This both gives us an idea of the error associated with various definitions of the DOS and how much the error varies with details of the calculation (e.g., grid spacing)." - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "241\n", - "242\n", - "243\n", - "244\n", - "245\n", - "246\n", - "247\n", - "248\n", - "249\n", - "250\n", - "251\n" - ] - } - ], - "source": [ - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_g = []\n", - "dosn_0_888_2g = []\n", - "dosn_0_888_M1 = []\n", - "\n", - "for n_egrid in n_egrids:\n", - " \n", - " print(n_egrid)\n", - " \n", - " e_grid_n = np.linspace(emin, emax, n_egrid)\n", - " sigma_mod = e_grid_n[1] - e_grid_n[0]\n", - " narrow_gaussian = partial(gaussian, sigma = sigma)\n", - " wide_gaussian = partial(gaussian, sigma = 2.0*sigma)\n", - "\n", - " dosn_0_888_g.append(DOS.from_calculation(dft_0_888, e_grid_n, narrow_gaussian ))\n", - " dosn_0_888_2g.append(DOS.from_calculation(dft_0_888, e_grid_n, wide_gaussian ))\n", - " dosn_0_888_M1.append(DOS.from_calculation(dft_0_888, e_grid_n, delta_M1))" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_g = []\n", - "eband_error_0_888_g = []\n", - "for dos in dosn_0_888_g:\n", - " enum_error_0_888_g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_g)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_g)\n", - "\n", - "enum_error_0_888_2g = []\n", - "eband_error_0_888_2g = []\n", - "for dos in dosn_0_888_2g:\n", - " enum_error_0_888_2g.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_2g.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_2g = np.array(enum_error_0_888_2g)\n", - "eband_error_0_888_2g = np.array(eband_error_0_888_2g)\n", - "\n", - "enum_error_0_888_M1 = []\n", - "eband_error_0_888_M1 = []\n", - "for dos in dosn_0_888_M1:\n", - " enum_error_0_888_M1.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_M1.append(dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)\n", - "enum_error_0_888_M1 = np.array(enum_error_0_888_M1)\n", - "eband_error_0_888_M1 = np.array(eband_error_0_888_M1)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(enum_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot band energy errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Size of Energy Grid')\n", - "ax.set_ylabel(r'Error in Band Energy (eV)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.yscale(\"log\")\n", - "\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_2g), linestyle='-', linewidth=1, color='blue', label='Wide Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_g), linestyle='-', linewidth=1, color='green', label='Narrow Gaussian DOS')\n", - "ax.plot(np.array(n_egrids), np.abs(eband_error_0_888_M1), linestyle='-', linewidth=1, color='red', label='First Moment DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_band_vs_N_grid_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5\n", - "0.6\n", - "0.7\n", - "0.8\n", - "0.9\n", - "1.0\n", - "1.1\n", - "1.2\n", - "1.5\n", - "1.7\n", - "2.0\n", - "2.5\n", - "3.0\n", - "3.5\n", - "4.0\n" - ] - } - ], - "source": [ - "n_egrid = 250\n", - "emin = e_grid[0]\n", - "emax = e_grid[-1]\n", - "n_egrid_max = len(e_grid)\n", - "n_egrids = np.arange(n_egrid_max - 10, n_egrid_max + 1)\n", - "\n", - "dosn_0_888_vg = []\n", - "dosn_1_888_vg = []\n", - "dosn_2_888_vg = []\n", - "\n", - "sigma = e_grid[1] - e_grid[0]\n", - "sigma_multipliers = np.array([0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.5,1.7,2.0,2.5,3.0,3.5,4.0])\n", - "\n", - "for sigma_multiplier in sigma_multipliers:\n", - " \n", - " print(sigma_multiplier)\n", - " delta_f = partial(gaussian, sigma = sigma*sigma_multiplier)\n", - " dosn_0_888_vg.append(DOS.from_calculation(dft_0_888, e_grid, delta_f ))\n", - " dosn_1_888_vg.append(DOS.from_calculation(dft_1_888, e_grid, delta_f ))\n", - " dosn_2_888_vg.append(DOS.from_calculation(dft_2_888, e_grid, delta_f ))" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_0_888_vg = []\n", - "eband_error_0_888_vg = []\n", - "for dos in dosn_0_888_vg:\n", - " enum_error_0_888_vg.append(dos_2_enum(dos, integration = 'analytic') - enum_0_888_ref)\n", - " eband_error_0_888_vg.append((dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_0_888_sc_ref)/256.0*1000.0)\n", - "enum_error_0_888_g = np.array(enum_error_0_888_vg)\n", - "eband_error_0_888_g = np.array(eband_error_0_888_vg)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_1_888_vg = []\n", - "eband_error_1_888_vg = []\n", - "for dos in dosn_1_888_vg:\n", - " enum_error_1_888_vg.append(dos_2_enum(dos, integration = 'analytic') - enum_1_888_ref)\n", - " eband_error_1_888_vg.append((dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_1_888_sc_ref)/256.0*1000.0)\n", - "enum_error_1_888_g = np.array(enum_error_1_888_vg)\n", - "eband_error_1_888_g = np.array(eband_error_1_888_vg)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "enum_error_2_888_vg = []\n", - "eband_error_2_888_vg = []\n", - "for dos in dosn_2_888_vg:\n", - " enum_error_2_888_vg.append(dos_2_enum(dos, integration = 'analytic') - enum_2_888_ref)\n", - " eband_error_2_888_vg.append((dos_2_eband(dos, e_fermi = 'sc', integration = 'analytic') - eband_2_888_sc_ref)/256.0*1000.0)\n", - "enum_error_2_888_g = np.array(enum_error_2_888_vg)\n", - "eband_error_2_888_g = np.array(eband_error_2_888_vg)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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MmAHZy5Omunszy+5DPtvTln79NQIDoVIl44S389ROxkeOp6p1VRa8UfDKSQ+LzAEQQgghhBBPhkuXYPp0mDkTrl0DIKVRS6Zbf8gX+1sy+H8ax34CIyyvD0DUiSjGR45nfbxh07QKlhWY0X4G5SzKGSegfEgCIIQQQgghSr7z52HqVJg1yzDRF7jWpA2TLcYy/5/mDB0K8b+AjY1xwtuSuIXxkePZmLARABtzGwY1HsRQz6ElqvMPT3ACoGlaV6AfUB8wAeKAhcBXSin9fZRnAvQBugJ1gbLABWAfME8p9VsxhS6EEEIIIW47c8YwsferrwxLewJXPDvwkW4sixM8CQqCY32hbNlHH5pSis3HNzM+cjyRiZEAlLMox5AmQwhsGohdGbtHH1QhPJEJgKZps4D+QBqwAcMmb62BL4HWmqZ1KkoSoGlaReB3oBGQDOwAbgDOQBvgHCAJgBBCCCFEcTl1CiZNgvnzIS0NgEverzMu4wN+PdWIUaPgWABYWj760JRSbEjYwPjI8Ww7sQ0wDPUJbBLIkKZDqGBppPFHhfTEJQCapnXE0Pk/CzRXSv2bfdwBw47PbwKDgM8LWZ4O+BVD5/9zYJRSKu2O8zaASzE2QQghhBCi9EpMNGze9c03kJ4OwPlmbzEm9QM2nXmB0aNhWg8wN3/0oSmlWHdsHeMjx7Pj1A4AbC1tGeY5jEGNB1HesvyjD+o+PHEJADA6+zv4ducfQCl1TtO0fsBmYJSmaTML+RagD+AFrFJKBd59Uil1HTjw4GELIYQQQpRi8fHw6acQHg6ZmShN40zzzoy8EkL0+ecICYE5XcHUCL1XpRS/H/2dCZET2JW0C4CKZSoy3HM4AxoPKHFj/P/LE5UAaJrmBDQE0oGf7j6vlIrUNC0JcASaAtsLUezA7O9pxRWnEEIIIYTIduQIfPIJfPcdZGWhdDpONX+XoRdDiLv0DB98ABG+YGLy6ENTSrHqyCombJlAzGnDMu6VrSoT5BVE/0b9sTa3fvRBFYMnKgEAXsj+/lspdbOAa6IxJAAv8B8JgKZpVYF6QBawQ9O02kBnwAnDXIBI4A8lmykIIYQQQhTN4cPw8ceweDHo9SgTE4636MmgM2M4ff0pxn4Eb7wBOt2jD02v9Pz6z69MiJxA7NlYAOzL2jPSayTve7xPWXMjzDguRk9aAlAz+zvxHtecuOvae3ku+/sShhWFJpH7NxsFbNc07U2l1PmiBCqEEEIIUSodOAAffQQ//QRKoUxNOdo8gP4nRnM9zZWx0+CVV0DTHn1oeqVnxeEVTNgygf3n9gNQxboKwd7B9G3YFyszq0cf1EPwpCUAt9/D3LjHNSnZ34VZJdbuju9pwGJgInAK8ABmYZgf8BPQoqBCNE3rC/QFqF69eiGqFUIIIYR4wsTGGjr+P/8MgDI3J86rF+/FB0NWDcbOgTZtjNPxz9JnsfzwciZumcjB8wcBqGZTjVHeo+jt3psyZmUefVAP0ZOWABS32y+dTIFtSqmud5zbpGnay8ARoLmmaa2UUpvyK0QpNQ+YB+Dh4SHDhYQQQghRJHFxccyYMYNNmzZx8uRJlFJUrlwZJycnPD09ad++PW3btjV2mPmLjoaJE+E3w4rpytKSA0370PffkVibODFxEbQo8DFqblp2dlBco6+z9Fks/XspE7dM5PDFwwA4lXNidLPRBLwQgKXp/a8xevLkSVatWkVMTAzR0dEcOnSIrKwsJk+eTFBQULHEf7+etATg9tP9ew3Muv2W4Hohyrvzmvl3n1RKndI0bTXQCWiFYZlRIYQQQohis2TJEnr06EF6ejqOjo60bNkSW1tbLly4wN69e9mxYweRkZElLwHYscPQ8f/9dwBUmTLENulH77ggqlpVZfpP4OlpnNAy9Zn8ePBHPtryEf9c+geA6uWrM6bZGHo26ImFqQXh4eH4+/vj5+dHeHh4ketYvnw5Q4cOLebIi8eTlgAcz/6ucY9rnO+69l4SCvh3ftdUKUR5QgghhBCFdvbsWQICAkhPT2f69OkMGjQIkzuWw9Hr9Wzbto1t27YZMcq7bNli6Pj/+ScAqmxZdjcaSK+/h/FUBXvmr4KGDY0TWqY+k+/2f8fHWz/maPJRAFwquBDyYgg9nu+BuUnxbS5Qs2ZNhgwZQsOGDfHw8ODTTz/l22+/LbbyH8STlgDEZn/X1TStTAErATW669p7+QfDfIKyQMUCrqmU/Z1SwHkhhBBCiPuyatUqUlNT8fT0JDAwz3ZE6HQ6mjdvTvPmzY0Q3R2Ugk2bYMIEiIw0HLKxIcp9ML0PBvK8fSV++Bzq1zdOeBlZGSz6axGfbPuE+MvxALjZuhHyYgjd6nfDzMSs2Ot84403eOONN3L+1hljOaMClJxIioFS6iSwFzAHfO8+r2laCwxLeJ4FdhSivAxgVfafrfMpzwy4/f+4mPuLWgghhBAif+fPGxYZtLe3L/K9mqbljJlfsmQJnp6eWFtbY2NjQ+vWrQt8a7Br1y5GjBiBh4cHDg4OmJubU61aNTp16sTOnTtzX6wU/PEHoTVqoLVuTWhkJMdsbGhj/xxmKZa02DqFzAotaNx4Ks8+m5mnrqysLObMmYOXlxfly5fH3NwcBwcH3N3dGT58OBcuXCiwfYVpU3pWOvP2zKP2l7Xp/W1v4r+Nx3SmKaYfm3Jp3CUWDVvET0vybB2Fi4sL/v7+AEREROT8lpqm0bNnz3v97I+FJyoByPZp9neYpmm1bh/UNM0emJ3952d37gKsadpATdPiNE1bVEB5eqCvpmnt7rjHBAgD3IAkYEXxNkMIIYQQpd3t1QM3bNjAwYMH76uMDz/8kK5du2Jubo6Pjw9OTk5s3LiR1q1bs2NH3uehISEhTJ8+nYyMDBo3bszrr79OxYoVWb58Oc2aNeOn7OU7Wb0amjaF9u3h5EkAttm/QO0UU3akXKJdh1a8/HIrkpLiCQoKwtfXF71en6uuXr160a9fP/bt20eTJk3o1KkTzz//PFevXmXatGkcO3bsvtp0K/MWX0V/Ra0vavHeqvc4fuA4urk6iAHncs50fKsjjRo1IioqinfffZcePXrkmljcqVMnvL29AXBzc8PPzy/n06xZs/v671CiKKWeuA+Gjr4CbgK/AT8DV7OPrQBM7ro+NPvc5gLKG4QhCdADO4FlwLHse64AnoWNrWHDhkoIIYQQojCuXbumqlWrpgBlamqqXnnlFRUWFqbWr1+vrly5cs97s/spys7OTsXExOQcz8rKUn369FGAatOmTZ77fv/9d3X27Nk8x3/99VdlZmam7Kyt1Y0GDZQypAEqs2Jl1cW5TU59HTp0VDdv3sy578iRI8rR0VEBatasWTnHjx8/rgDl7Oycb32xsbHq3LlzRW7T042fVo5THRWhKEJRdWbUURWrVFSACgwMVJmZmTn3HThwQNnb2ytAzZkzJ1ddCxcuVIDy8/O7x69ceH5+fgpQkydPLpby/gsQowrojz6JbwBQSvUH3sUwHKgF0A44CgwEOiqlsopY3kzgJWANUAt4HcP8iXlAA6XUfw4nEkIIIcR90rTH81MMbGxs+PPPP/Hw8CAzM5M1a9YQHBxM27ZtsbOzw9vbmyVLltyzjPHjx9Pwjlm3Op2OiRMnArB161YyMjJyXd++fXscHBxyF6LX81paGr5ly5KcksKmffvIqlyF5V5TqZGVQEI1w9NyKysrwsNnY2n5/8tnPvXUUzn1TZ8+Pef47eFN7u7ueesDGjRoUODQp7vbdCvrFtX/Z3hb8s+ef0i6kkQ9+3os7bSUUbajuHT2Ei4uLkyaNCnXJOp69eoxfvx4AKZMmZJvXU+iJzIBAFBK/aCU8lZKlVNKlVVKNVRKzVJ3DP2549pQpZSmlGp5j/I2K6VeVUpVUkqZK6VqKKXeU0odf5jtEEIIIUTp9swzzxAdHU1UVBRjxoyhdevW2Nraotfr2b59O126dLnnuPRXX301zzEHBwdsbW25desWly5dynP+4sWLhIeHEzRsGL1btaKnnR09336bg1euAPBjjTdxzohnd7Nh7IkrS/v2hvvatm2bb6e9a9eu6HQ6jh49SlJSEgB16tTBxsaG1atX88knn5CYmFjo3+R2m1IzUpm+YzquX7gyNnosWAJZ8HWbr/nr/b/wrevL1i1bc2IwM8s72bdnz55ompYrtifdk7YKkBBCCCGeNMW06dPjzsvLCy8vL8Cw/OfOnTsZP34869atIyIiAh8fH3x986yBkjOP4G7lypXj8uXLpKWl5To+d+5chg0bRmpqaoGxnKjxPAd+LkPFu9ZIrFmzZr7XW1hYULVqVZKSkjh16hSOjo7Y2NiwYMECAgICCAkJISQkBEdHRzw9PfHx8aFLly653iTcqWKVikzZPoXJ2ydz/kb2m4Sq7py0O8mF0xdoXb01Os3wnPt2p76g2CwtLalWrRpJSUkkJSXh6OhYYLufFE/sGwAhhBBCiCeVTqfDy8uLNWvW4O7uDsDKlSsLvLawonfsoF+/fmSkpjIZiAOSq9bgq4bzqFrxFi++OBqAVq1Uns7//ejUqRMnTpwgPDycgIAArK2tWbZsGf7+/tSpU4eT2ZOL7+Y605UR60dw/sZ5PKp58Ns7vxHTJwYrM6sC69KKaVjWk0ASACGEEEKIx5SJiQkvvfQSwD2XzPxPt27B3Lksb98epRSDgYHVn+IP93Cey/qXq759+CfBnCpVjt6zmOPHj+d7PD09nTNnzgDkecJeoUIF/Pz8+Oabb4iLi+Po0aO0atWKxMREgoODAbh26xofb/k4556LqRdp4tiENV3XsLv3bl6t/WqBHfzb9cXHx+d7Pi0tjdOnT+cb25NKEgAhhBBCiBJKFWL404kTJwBwcnK6v0oiIqBWLXj/fZKvXQPgX8du1M44RFY3P/6JNyM4GNLSLrB+/fp7FrVu3TouXryY5/jixYvR6/W4ubn9Z5xubm6EhIQAELsvlgmRE6gxowYfbPog55o/uv3Bjl476PBUh/98st+iRYucGDIz8+5FEBERgVKKWrVq5UoAzM0NuwLnd8/jThIAIYQQQogSavbs2fj7+7N79+485zIzM5k/fz7Lli0DoHPnzoUv+NIluHrV8O/QUDh1ihs163GySncAYnWH2Ls/jaFDoWxZuH79OgEBAVzJngRckNTUVAYMGMCtW7dyjh07doyxY8cCMGTIkJzjsbGxLFmyhJs3b+Yp56cVhs25jmYdZdzmcVxJu8KL1V/MOf+y28uFHtLj6+uLs7MzCQkJjB49OtdeBIcOHWLcuHEABAUF5brvdjJw+PDhQtXzOJFJwEIIIYQQJVRGRgbh4eGEh4dTpUoVGjRogJ2dHcnJyezfvz9n6MrIkSNp167df5QGnDgB06bB11/DjRsAXHOtzweW4/jx5v8YNOoqf0/dzMmTe3n2WVeaNWuGUootW7Zgbm5OQEAACxYsKLD47t27s3r1atzc3PD29ub69ets2rSJtLQ0XnvtNQYMGJBzbWJiIl26dMHKygp3d3ecnZ1JuZnC1l1buXLmCphDZotMWrq0ZFyLcbR0aYkWUPRx/JaWlixdupQOHTowZcoUVqxYQaNGjUhOTmbTpk1kZGTQvXt3+vbtm+u+pk2bUqVKFfbu3YuHhwd169bFzMwMb2/vnF2C7+XMmTO8+eabOX/f3tRs5syZOUkbwIoVK6hatWqR2/VACtogQD4P5yMbgQkhhBCisK5du6ZWrFihBgwYoBo1aqQcHR2VmZmZsrKyUrVr11Z+fn5q69at+d5L9qZZSimlDhxQqnt3pUxNczbwcjKzVIByc41XERFKZWQYLj137pzq27evqlmzpjI3N1dOTk6qd+/e6vTp02rcuHEKUOPGjctV153Hjx07pjp37qwqV66szM3NVZ06ddSkSZNUenp6rnvOnDmjPv30U9W+fXtVvUZ1ZWphqrBEYY/CE+U11UtFHo8suE35qFGjhgJUQkJCnnPHjx9X77//vnJxcVHm5uaqfPnyqnnz5uq7775Ter0+3/L27dunfHx8lJ2dndLpdEXaGCwhISEn3nt98ou1OHCPjcA0JUtrPVIeHh4qJibG2GEIIYQQojTYtg0++wxWrwZA6XT8+0Jnhp4ZyQGGK+oAACAASURBVDXXBowcCT4+UISFgvIVGhrK+PHjGTduHKGhoYW+7/yN80zZPoXZ0bO5kWF4I9HOrR1jm4/Fu7r3gwVVymmatkcp5ZHfORkCJIQQQgjxJNHr4bffYNIk2L4dAFWmDNH1Auh/bDhOTjUJ+QKytxQwirMpZ5kcNZmvYr7iZqZhDsArT73C2OZjaerU1HiBlRKSAAghhBBCPAnS0+H772HyZMieuJpV3pb1tQcy8J9BtHiuMt8tgjp1jBfi6eunmRQ1ibl75pKWadiA7LXarzG2+VgaOTYyXmCljCQAQgghhBCPs+vXYd48mD4dsne9Ta/izI/VhvNBQi/eaWXNlpVQrZrxQjx17RRh28KYv3c+t7IMKwT9r87/GNt8LO5V3Y0XWCklcwAeMZkDIIQQQohice4cfPEFzJ4N2ctzprjUZbZ1MF9e7MLAoWa89x6UL2+8EE9cPcFn2z7jm9hvSM9KB6DjMx35oPkHNKjSwHiBlQIyB0AIIYQQ4klx7BhMmQILFxp28AUu1GnGJ5nBrNW9QlCgjn+7gYWF8UI8fuU4n279lIX7FpKhz0BD4+26b/PBix/wnMNzxgtMAJIACCGEEEI8HvbuhbAwWLbMMNEXSKj/OiMvBXOqghfBwTD19Qdf0edBxF+O55OtnxDxVwSZ+kw0NN6p9w4hL4ZQ176u8QITuUgCIIQQQghRUikFGzYYOv5//mk4ZGbGXw38GJQ4AtsazzByFnh7QyE3xn0ojiYf5eOtH/PtX9+SpbLQaTq61e9GyIsh1KlkxFnHIl+SAAghhBBClDRZWbB8uWEpzz17ANCXtSby6fcYdCyQRvWdmLMI6hr5ofqRS0f4aMtHfH/ge/RKj4lmgt/zfox5cQy1K9Y2bnCiQJIACCGEEEKUFDdvQkSEYYz/sWMAZNjZs7L6EEYm9KNjK1vW/gJOTsYN8/CFw3y09SN+PPhjTsc/oEEAo18cTS27WsYNTvwnSQCEEEIIIYzt8mX46iv4/HM4fx6Am9Vc+aZCEFMv9aTv22WI7QcVKhg3zL/P/83ELRNZ+vdSFApTnSm9XujF6GajqWlb07jBiUKTBEAIIYQQwliSkgzr98+dCykpAFx2dWeyFszPWkeGB5pwuDtYWho3zP3n9jNxy0SWHVoGgJnOjF4v9GJUs1HUqFDDuMGJIpMEQAghhBDiUTt82LBj73ffQUYGAKeeacMH14I5XLE1waM0Jr4BJibGDXPf2X1MiJzAirgVAJibmNPHvQ/B3sE4l3c2bnDivkkCIIQQQgjxqOzYYVjR55dfAFA6HYefe5uhp0diWrMhI0dC8+bGXdEHYM/pPUzYMoFf//kVAEtTS/q692Wk90gcyzkaNzjxwCQBEEIIIYR4mJSCNWsMHf+tWw2HLCzYUcefQQnDqdugFlO+h+dKwP5Yu5N2MyFyAqv/XQ1AGdMyvO/xPiO8RlDVpqqRoxPFRRIAIYQQQoiHISMDfvzRsJTnwYMAZJWrwJoa/Qk6MRiflxxYEQjVq/93UXFxccyYMYNNmzZx8uRJlFJUrlwZJycnPD09ad++PW3btr3vUHec3MGELRNYe3QtAFZmVvT36E+QVxAO1g73XW5x07JfjSiljBzJven1enbu3MmaNWvYuHEjhw8fJiUlBTs7Oxo2bEjfvn353//+Z7T4tJL+Az5pPDw8VExMjLHDEEIIIcTDkpICX38N06bByZMA3KrsyHeVhvLJxb4EDLGhXz+wsytccUuWLKFHjx6kp6fj6OhI/fr1sbW15cKFC+zdu5dLly7RsGFDitq/UEqxMWEjn0V9xp/xhk3GypqVZWDjgQzzHIZ9WfsilfcoPMoEIDw8HH9/f/z8/AgPDy/SvUePHuWpp54CwM7ODg8PD2xtbYmPjyc6OhqAnj17smDBgpw2FTdN0/YopTzyOydvAIQQQgghisOFCzBzJsyaBcnJAFx3rMMXliP5Tr3LkMHmHPSDMmUKX+TZs2cJCAggPT2d6dOnM2jQIEzumBms1+vZtm0b27ZtK3SZWfosfj78M2FRYew5Y9hkzMbchkGNBzHUcyiVrCoVPkCRL03TeOmllxgxYgRt27bN9d8sMjISHx8fwsPDad68Of7+/o88PkkAhBBCCCEeREICTJ0KCxYYNvICzrl5Mv5mMNEOrzFylI6Db93fij6rVq0iNTUVT09PAgMD85zX6XQ0b96c5s2b/2dZaZlpLPprEZO3T+Zo8lEAKltVZnCTwfRv1B+7MoV8JSH+k5ubGxs2bMj3XIsWLRg1ahRjx47lu+++M0oCoHvkNQohhBBCPAn++gvefReeesrw1P/mTY7V8eHNilvwc4vC97s32B2jw9f3/pfzPJ+9KZi9fdGH42iahqZpXE27StfxXSnnVo73PN/jaNBRLL+3ZGjVoRwPPM4HzT/I1fnftWsXI0aMwMPDAwcHB8zNzalWrRqdOnVi586d+dYVGhqKpmmEhoaSkJBAt27dcHBwwNLSkrp16zJ16lQyMzPz3JeVlcWcOXPw8vKifPnymJub4+DggLu7O8OHD+fChQsFtm/JkiV4enpibW2NjY0NrVu3vuebkMTERPr374+rqysWFhbY2trSqlUrfvjhhzzXuri45HTMIyIicn5LTdPo2bNngXUU1gsvvADAqVOnHris+yFvAIQQQgghCksp2LzZsKLPH38YDpmasvfZ7gw5NYIa7s8ROhKef754qquePUN4w4YNHDx4kHr16hW5DPtX7EnfnA7VoXz98pRNLsvpf08za9AsfJ/zxdPTM9f1ISEhbN68mbp169K4cWMsLCz4559/WL58OStXrmTx4sX4+vrmW1dCQgIeHh5YWlrSsmVLrl27xubNmwkKCmLbtm0sX74cne7/nz/36tWLiIgIypQpQ7NmzahUqRIXL17k2LFjTJs2DV9fXypXrpynng8//JCPP/6YZs2a4ePjw/79+9m4cSPbtm1j8+bNedq0c+dOOnTowJUrV6hZsyZvvvkmycnJbN68mc2bN7N27dqcjj6Qk+xERUXh5uZGs2bNcsq689/3699//wWgalUjrayklJLPI/w0bNhQCSGEEOIxk5mp1LJlSjVqpJQhDVBZZazUumeHqHrlE9XgwUolJBR/tdeuXVPVqlVTgDI1NVWvvPKKCgsLU+vXr1dXrlzJ9564C3Gq1y+9FGD4lEF5hHqotf+uVXq9XmVlZak+ffooQLVp0ybP/b///rs6e/ZsnuO//vqrMjMzU3Z2durGjRu5zo0bNy6nvo4dO6qbN2/mnDty5IhydHRUgJo1a1bO8ePHjytAOTs751tfbGysOnfuXK5jt+uws7NTMTExOcfv1aabN28qZ2dnBajAwECVmZmZc+7AgQPK3t5eAWrOnDm57lu4cKEClJ+fX57YHsSNGzdUzZo1FaC++OKLYi37TkCMKqA/KqsAPWKyCpAQQghRNNp4I++KdZ/UuOLpYx0+fJgePXrkWeVHp9PRtGlTBg8eTOfOndl1ahdhUWGsjFuJQkGo4bqgiUFM/mByrnvPnTtHlSpVsLCw4Pr165iZmRUqlnfffZcffviBVatW4ePjk3M8NDSU8ePHY2VlRUJCQp4hSwsXLiQgIIBatWrlPP2Ojo6mcePGvPHGG6xcubJQ9d9+Qj9z5kwGDhxYqDZ9++239OjRAxcXF44cOZKnrXPmzKFfv365YoMHWwXoXnr27ElERATPPvsse/fuxcLCotjKvtO9VgGSOQBCCCGEECXYM888Q3R0NFFRUYwZM4bWrVtja2uLXq9n+/btdOnShSrNqtD0m6asiFuBmYkZfdz75Nw/oNuAPGU6ODhga2vLrVu3uHTpUp7zFy9eJDw8nKCgIHr37k3Pnj3p2bMnB7P3Mzhy5Ei+sbZt2zbf+Qpdu3ZFp9Nx9OhRkpKSAKhTpw42NjasXr2aTz75hMTExEL/Jq+++mqh2xQZGZkTQ36JTs+ePdE0LVdsD8vEiROJiIigfPnyLF269KF1/v+LzAEQQgghRIlWXE/SC+XMGZgxA+bMgWvXAEh2rs9n+mA2VX6boFGmdOwIpkboQXl5eeHl5QVAemY6nyz+hKmfTiXlcArnos5RpkYZBvsPZkiTIVS1qcp85gP/P4/gbuXKlePy5cukpaXlOj537lyGDRtGampqgbFcy/5t7lazZs18j1tYWFC1alWSkpI4deoUjo6O2NjYsGDBAgICAggJCSEkJARHR0c8PT3x8fGhS5cuWFpa5lteUdp0u1NfUGyWlpZUq1aNpKQkkpKScHR0LLDdD2LatGl8+OGHWFtb8/vvv1O3bt2HUk9hyBsAIYQQQogjR6BPH3BxMezce+0aJ9xa0q3i73R5eh8vh3dl915TOnc2Tuf/ttSMVL7c/SVPz3qa8fHjSfFNwdTRENCrWa/yWZvPqGqTe2LpnZNu/0t0dDT9+vUjIyODyZMnExcXR0pKCnq9HqUUo0ePBopvI65OnTpx4sQJwsPDCQgIwNrammXLluHv70+dOnU4mb2R2t2K0qbbHtaGW4Uxc+ZMhg8fTpkyZVi1alWeScqPmrwBEEIIIUTptXu3YUWfFStAKZSm8Xfttxh6NphKjRozYgS4uxs7SLiUeolZ0bOYuXsmF1MvAvCU3VOM8BrBoZRDzJg2gyvJVx64nuXLl6OUYvDgwQQFBeU5f/To0Xvef/z48XyPp6enc+bMGYA8T9grVKiAn58ffn5+ABw7dow+ffqwadMmgoOD812msyhu1xcfH5/v+bS0NE6fPp1vbMVh1qxZDB48GEtLS3799VdatGhR7HUUlbwBEEIIIUTpohSsXQutWkGTJvDzz+hNzdj6dG+a2BxmTtvlzNvXmMWLjd/5T7ySSODaQGrMqMG4zeO4mHqRRtUascx3GYcHHKZPwz6cPmXovDo5OT1wfcnZOxg7OzvnOXfhwgXWr19/z/vXrVvHxYsX8xxfvHgxer0eNze3/4zTzc2NkJAQAP7666/Chl6g2x3uxYsX57sXQUREBEopatWqlSsBMDc3B8j3nsKaM2cOAwcOxMLCgpUrV9KmTZv7Lqs4SQIghBBCiNIhMxN++AFeeAE6dIDNm8ksW44VtUdS3+Y4G7rMZ/XRp/nySyhguPgjc/D8QXqs6IFrN1c+H/05NxJu0M6tHRt7bGRX7110fLYjSq+YP38+y5YtA6Bz584PXG+dOnUAWLRoESkpKTnHr1+/TkBAAFeu3PstQ2pqKgMGDODWrVs5x44dO8bYsWMBGDJkSM7x2NhYlixZws3s3ZPv9NtvvwFQo0aN+29MNl9fX5ydnUlISGD06NHo9fqcc4cOHWLcuHEAed543E4GDh8+fF/1zp8/n/79+2NhYcGKFSto167dfbag+MkQICGEEEI82VJTYcECmDoVsoeopNlWIbxCILMy36fvwPLsCoCyZY0bJsC2E9sIiwpj1ZFVAGhZGuwD9sFfq/5iUoNJfG33NcnJyezfvz9n6MrIkSOLpYPp7+/PjBkz2Lt3L66urjRr1gylFFu2bMHc3JyAgAAWLFhQ4P3du3dn9erVuLm54e3tzfXr19m0aRNpaWm89tprDBjw/ysSJSYm0qVLF6ysrHB3d8fZ2Zn09HRiY2OJj4/HxsaGCRMmPHCbLC0tWbp0KR06dGDKlCmsWLGCRo0akZyczKZNm8jIyKB79+707ds3131NmzalSpUq7N27Fw8PD+rWrYuZmRne3t45uwQXZN++fbz33nsopahZsyZLlixhyZIlea6rVKkSU6ZMeeA2FpUkAEIIIYR4Ml26BLNmwcyZkD0s5ZrDU0wzGcGait0JHGVJ7NvGndQLoFd6Vh1ZRVhUGNtPbgfA0tSSgAYBvOf/HvF74/nzzz/ZvXs3Bw4c4Pz585iZmeHk5ISfnx+9e/cult1pAWxtbYmJiWHs2LGsX7+e1atXY29vz1tvvcWECROYO3fuPe93dXUlOjqaMWPGsHHjRq5evYqrqysBAQEEBgbmmrzbtGlTPv30UyIjI4mLi2PPnj2Ym5vj7OzM8OHDGTRoULG8Abhd1759+/jss89Yu3YtP//8M2XKlMHT05O+ffvStWvXPJOELSwsWLt2LSEhIezYsYPY2Fj0ej2ZmZn/mQBcuXIlZ6J0XFwccXFx+V5Xo0YNoyQAshHYIyYbgQkhhBAP2YkTMG0azJ9vePoPnHVuxLjUYOLr/48Ro0xo2xaMuCgMAOlZ6fxw4Acmb5/MoQuHALC1tGVAowEMajII+7J519MvqW5vBDZu3DhCQ0ONHY7g3huByRsAIYQQQjwZDh40LOG5eLFhvD9wxLUdIy8GY96kJSODNTzy7Q49WtdvXWf+3vlM3zmdU9dOAeBUzolhTYfRp2EfrM2tjRyheNJJAiCEEEKIx5dSsG2bYSnP1asNh3Q6omu9w/CzI3muXQOmDINatYwcJ3D+xnlm7prJrOhZXE67DMCzlZ9lpNdI3nnuHcxNzI0coSgtJAEQQgghxONHr4fffjN0/HfsACDLogzrnQIISR7OK11qsnwQ2JeAUTTxl+OZun0qC/YtIC3TsEOtt7M3wd7B+NT2QafJoozi0ZI5AI+YzAEQQgghHkB6Onz3HUyeDNkTK9Otbfmx4kCmZw6i54jK9OoF1iVgFM2+s/sIiwpj6d9L0SvD0pOv1n6VYO9gmlUvnkm7QhRE5gAIIYQQ4vF2/TrMm2eY3Ju99OWNis58aTGcnyv0YtBoa3Z3BjMz44aplGLT8U2ERYWx7tg6AEx1pnSr340RXiOoZ1/PuAEKgSQAQgghhCjJEhMNy3h+/TVcvQrApSp1+SgjmAN1uzB8lBkj2xt/RZ8sfRYr41YSFhVG9OloAMqalaWPex+Geg6levnqxg1QiDtIAiCEEEKIkkUpw7j+6dPh558N4/2BROdmjLkaTLrXKwSN1DG9iZHjBNIy0/j2r2+ZvH0y/yb/C0Alq0oMbjyYAY0HYFfGzsgRCpGXJABCCCGEKBkyMmDZMkPHP9rwFF2ZmrKr5juMPhfIU+09GBcEtWsbOU7gatpV5sTMYcauGZxNOQuASwUXgjyD8H/BHyszKyNHKETBJAEQQgghhHElJxvG93/5JSQlAZBuY8cyu/eYcnMAHf0dWdoXKlc2cpzAmetnmLFzBnP2zOHarWsAPO/wPCO9R/J23bcx1UnXSpR88r9SIYQQQhhHXBx8/jlERMDNmwAkV3mG6fpANjp2o/9wK3b6gnkJWB7/yKUjTI6azKL9i0jPSgegpUtLgr2DaefWDs3YkxCEKIJCJwCapiUDB5VSzR9iPEIIIYR4kikFf/5pGObz++85h+Nc2jH2YiB4vcyQoTomeBt/Yi9AdFI0YVFh/Hz4ZxQKDY23nnmLYO9gGjs2NnZ4QtyXorwBMAdOPqxAhBBCCPEEu3kTvv8eZsyAv/8GIMvcko2OPRiXPASvjs8yeSC4uBg3TDAs5bnu2DrCosLYdHwTAOYm5vSo34MgryCervS0kSMU4sEUJQE4ClR6WIEIIYQQ4gl05gzMng1z5sDFiwCkVqjKwrIDiTDvS49hlfjDD2xsjBwnkKnP5Ke/f2LS9knsO7sPABtzG/p59GNI0yFUs6lm5AiFKB5FSQC+AyZqmlZTKZXwsAISQgghxBNg717D0/4ffzSs7gOcqdaQj1OH8m8DXwYOM2enD+h0Ro4TSM1IZWHsQqbumErCFUMXx6GsA4FNA3nf430qWFYwcoRCFK+iJADTgWbARk3TRgErlVK3Hk5YQgghhHjsZGXBb78Zxvdv2QKA0umIdXmLDy4GUrVdM4YEatSvb+Q4syXfTGbW7ll8sfsLLqYa3k7UsqvFCK8R9Hi+B5amlkaOUIiHoygJwL+ABtQAfgDQNO08cDOfa5VSyu3BwxNCCCFEiXftGixcCF98AfHxAGSUseHXyr0JSx3EawE1CX8P7O2NHGe2k1dPMn3ndObtmceNjBsAeFTzINg7mDfrvImJzsTIEQrxcBUlAXC549+35+U7FHCtuq9ohBBCCPH4SEiAmTPhm28MSQBwtWJNvtQNYW1Vf/oML8fWzmBhYeQ4sx26cIhJUZP4/sD3ZOozAXjZ7WWCvYNp5dJKlvIUpUZREoCaDy0KIYQQQjwelIJt2wzj+1euBL0egGPOLQi9EsjNF19j8FATxrxYMpbxBIg6EUVYVBi/HfkNAJ2mo0u9Loz0GskLVV8wcnRCPHqFTgCUUokPMxAhhBBClGDp6fDTT4bx/Xv2AKA3NWOb87uMuxzIC53cGT8QXF2NHGc2vdKz+shqwqLCiDoZBYClqSUBDQIY7jUcV9sSEqgQRiA7AQshhBCiYBcvwrx5MGsWnD4NQJpNJX6weZ95pv15Z2hVfvGHcuWMHGe2jKwMFh9czKSoSfx9wbDfQAXLCgxoNIDBTQZjX7aETEQQwoiKnABomlYe6AZ4ApWBDUqpSdnnamOYK7BVKZXf5GAhhBBCPA4OHYLPP4dFiyAtDYALDnWZdCuQ/c+9S//hZYh6FUxKyHzZlPQUvt77NdN2TOPkNcO+pY42jgzzHEYf9z7YWJSAjQaEKCGKlABomtYe+B6ogGEisAKS7rjkaWAl0BVYUkwxCiGEEOJRUAr++MMwvv+PP3IOH6j+Ch9eCqRC+zYMCdSY3MCIMd7lwo0LzNw9ky93f8nltMsAPFPpGUZ6j6Trc10xNzE3coRClDyFTgA0TasH/Jx9z2xgC3k7+WuBVOCNfM4JIYQQoiRKTYXvvjN0/A8fBiDLogy/2/vxWeoQ2gbUYc774FDQ2n9GkHA5gak7prIgdgE3Mw2DDrycvQj2DubV2q+i00rADmNClFBFeQMwBrAA3lRK/QqgaVquTr5SKkPTtFjg+eILUQghhBAPRVISzJ4Nc+fCpUsApFRwZK7ZQH6x70tAkB0b3ik5y3gC/HX2L8Kiwlj691KyVBYAPk/5MKrZKJpVb2bk6IR4PBQlAWgJxN7u/N9DElDvviMSQgghxMMVE2N42r9kCWQa1sM/WbURE1OGcunFTgwaZsawFiVnGU+lFJuPb2bS9kmsPboWAFOdKd2f685I75HUs5duhxBFUZQEoCKGYT//xRwoc3/hCCGEEOKhyMoyrNs/Y4ZhHX9A6XTscvYl9EogdXw9CR6s4eZm5DjvkJaZxg8HfuDzXZ+z/9x+AKzMrOjj3odhnsOoXr66kSMU4vFUlATgMuBUiOvcgHP3F44QQgghitXVq4ademfOhOPHAUi3Ks9P5fvwlclAOg6twZIAKF/euGHe6fT108yOns3cPXO5mHoRAPuy9gxoNIABjQZQ0aqikSMU4vFWlARgN9BO07SnlFL/5neBpmmNgPrA4uII7kFomtYV6IchHhMgDlgIfKWU0j9g2X2Budl/zlJKDXyQ8oQQQohid+wYfPEFLFgAKSkAJFesxbSsIcQ868d7QTZEvl5ylvEE2J20m893fc7Sv5eSqTcMTXKv6s6QJkPoXLczFqYlaDKCEI+xoiQAs4BXgWWapr2tlPrnzpOaprkCCzAsDfpV8YVYdJqmzQL6A2nABiADaA18CbTWNK3T/SYBmqbVAKZgaGcJGR0phBBCYFjGc8sWwzCfX34x/A38U60V468GYt7Bh8FDTfjI3chx3iEjK4OfD//MjF0z2HlqJwA6TUfHZzoS2DQQb2dvtJIyGUGIJ0ShEwCl1B+aps0EBgGHNE37G0MnuI2mabuAF7LLm6aU2vZQoi0ETdM6Yuj8nwWa335boWmaA7AJeBNDGz6/j7I14BtABywC/IopbCGEEOL+3bplmNA7YwbExgKgNzNng31XPk0dwou9GzD1faha1chx3uFS6iXm7ZnHrOhZJF03bClUwbICfdz/j737Dq+iTP8//n6SkNAJvXfpNSQBOyo2mmsDpJcghEAI67q2XXfV1VW/7q4iEBKQ3qTYBWyo+LOBCb1J7yCdUELaeX5/TAgRIeQkOTkh+byuK9cxM8Pkg5fC3DP33M/jjAwdSd3Aul5OKFJ4ubUQmLU2yhizGfgHlyb91Er/Og78y1r7dt5GdNuz6Z9PZ25Vstb+ZowZAXwLPGOMGZeDpwDhOE8SRuO8FC0iIuI9R49CTIwzyvPwYQASy1RmakAEiyqF0/+v1VjSB4oX93LOTDYc2cDbK95m1rpZXEh1VhhuWqkpo9uPZkCbAZTyL+XlhCKFn1sFAIC1NsYYMwloCzTA6a/fB6y01qbmcT63GGNqAcFAMrDw8v3W2uXGmANATeBG4Ec3zl0f+D/ge5xWon/mRWYRERG3bdjg3O2fPdu5+w8crtyKf1/4Mwdu683IvxTn6zsLzhhPl3WxZNsS3vr5LZbtWpaxvfMNnYnqEMU9De/Rwl0i+cjtAgAg/c75qvSvgiQo/XOjtTbxKsf8glMABJHNAiC99Wcqzr+vMGutVT+iiIjkK5cLPvvMufD/8suMzatqdOPFhD9Tt9edRI42NGrkxYyXOZN0hmlrpjFu5Ti2n9gOOGM8B7UZRGSHSJpWaurlhCJFU44KAABjjA+X2mCO53ayTh6pn/65J4tj9l52bHaMwlkI7Rlr7dYc5BIREcmZc+dg5kwYOxZ+deZvpAaU5KPygxnvM5puTzRmRhgEBno5ZyY7T+5k3IpxTFk9hTPJZwCoW64uke0jCWsXRmDxAhRWpAhyuwAwxtwP/Bm4FbjYVXjBGPM9MNZauyQP87mrdPrnuSyOOZv+WSY7JzTGNAReA+Jwpv+4LX1s6DCAOnW0aImIiGTD/v0wfjxMmgQnTwKQEFib8UTy/5oOZeiT5fnyT+CX41t5ectayze7v2HsirF88usnWJwJRLfXvZ2oDlE80OQB/HwKSFiRIs6t/xONMW/hTNC52P9y8a5/CeAenIlAE6y1o/Muovdkav0phtP6k5aT81hrJwGTAEJCQmzeJRQRkUJn5Up4801YuNBZvRfYCuEg+QAAIABJREFUVfVGXjrzZ9I6P8zoJ/x4LsTLGTNJTEnMWK13/ZH1APj7+tO7ZW+iOkQRVD3oGmcQkfyW7QLAGDMIZ/rNGeBNYBaX2mnqAP1wngyMNMasttZOy9uo2XLx7n5WIwQuPiU4k43zjQZuB16y1q7LTTAREZGrSk2FDz5w+vt/dF5Pc/n48n31XryaOIbgoTfySgTUqOHlnJkcSDiQsVrv8cTjAFQtVZWI0AiGBw+naumqXk4oIlfjzhOAUUAqcLe19pfL9u0AXjTGLAF+wJnD740CYHf6Z1bDg2tfdmxWHkr/vMcY0/GyffUuHmOMaQmctdZ2y8Y5RUREHKdOwTvvwLhxsNe5p5ZUMpA5JYcxt8IoHvtrbd7vCyVKeDlnJiv2r2DsirEs3LQwY7Xe4OrBRHWIomeLnlqtV+Q64E4B0BxYfoWL/wzW2l+MMcuBm3OdLGdWp3+2MMaUuMokoNDLjs2Om7LYVyP967Qb5xMRkaJs+3bnpd5p05yXfIFjFRrzRnIU228ZyIgnS/Flp4IzxjMlLYX3Nr/H2BVjf7da76PNH2VMhzHcXPtmrdYrch1xpwA4BxzJxnFHgauN4PQoa+0+Y8wqoB3QA2e13gzpd/Fr4awS/FM2znfH1fYZY17AWQtggrV2VM5Ti4hIkWAtfPut09//6afO98CGap3415k/U7lXZyKjfGjSxLsxMzt2/hiT4icR/Ut0xmq95YuXd1brbT+SOuU02ELkeuROAfADEGqMMdbaK77Imv7SbEj6sd7yKs4iYK8bY3601m5Pz1YFiE4/5rXMY0uNMaNwWpxWWmsH5HdgEREpxJKSYN48p79/7VoA0ooFsLRCP8YSxb1PtCJmKJQv7+WcmWw4soGxP49l9vrZGav1NqvUjNEdRtO/dX+t1itynXOnAPgnzl3z/xpjnrbWpmTeaYzxA17HucPeM+8iusdau8gYMxEYAaw3xnwFpACdgLLAhzgr+WZWCWiC82RAREQk944cgYkTITra+WfgXJmqxPpGsOyGcAY9VYWlDxWcMZ4u62Lx1sW8teItvt71dcb2zjd0ZsyNY7inwT1q8xEpJK76x44x5kp3wqcDUcCjxphFwK707fWAR3Eu/mOA1sCavAzqDmttRPq6BCOBjoAvsAVnpOfEArJomYiIFEbr1jl3++fMgeRkAPZXasO/z/+Zc10eY+QTATzR3ssZM0lISmDaame13h0ndwBQqlgpBrUdRGT7SJpUKkA9SSKSJ8xVunkwxriAK+28WP5fvu932621vnkRsLAJCQmxcXFx3o4hIiJ5KS3N6et/+2342rl7bo1hRZUHeDVxDC0iOhIx0lCrlpdzZrLjxA7GrRzH1NVTM1brrRdYj1Gho7Rar0ghYIyJt9ZecdWQrB48zuTKBYCIiIiAs0LvlCkwYQLs3g1ASkBpFpQewqzASB5+6gbm9YOSJb0b86KLq/W+9fNbfLr104zVejvW7ZixWq+vj+7fiRR2Vy0ArLWD8jGHiIjI9WPjRmd2/6xZcP48AMcDG/BWaiSbOgxm+FPlWHpPwRnjmZiSyJz1cxi7YiwbjmwAnNV6+7TqQ1SHKNpWa+vlhCKSnwrIq0ciIiIF3MU2n3HjYNmyjM1rqt7La2cjqdq3MxGRvgVqjOf+hP1E/xLNpPhJGav1VitdjYiQCIaHDKdKqSpeTigi3qACQEREJCsnT8LUqTB+fKY2n1K8X3og00uPostfmjFpIJQt692Ymf28/2fGrhjLok2LMlbrDakRkrFar7+vv5cTiog3uV0AGGNq40zWqQEUv8ph1lr7r9wEExER8apNm5y7/TNn/q7N582USLbdMoghTwSy+B7w8fFyznTJacks2rSIsSvGsvLASgB8jS89mvcgqkOUVusVkQzZLgDS5/yPB4ZyaeLP5X+S2PRtFlABICIi15e0NFi82Jnmk7nNp8o9vH5+NNUHOG0+N9zgxYyXOXruqLNab1w0B88cBJzVeocFDyMiNEKr9YrIH7jzBOAFYBiQCiwBtgFnPZBJREQkf506danNZ5ezxE1KQCneKzWAOYGj6PJkcyb3h9KlvZwzk/W/rWfsirHMXjebpLQkwFmtN6pDFP1a99NqvSJyVe4UAP2Bc8At1tp1HsojIiKSf67Q5nOsXAPeShnFzo6DCftLIB/fVXCm+aS50vh066eMXTGWb3Z/k7G9S6MujOkwhrsb3K02HxG5JncKgCrAMl38i4jIdS0tDZYscdp8vvoqY/OaSnfzRtJoaoZ1YcQoX+rX92LGyyQkJTB19VTGrRzHzpM7AWe13sFtBxPZIZLGFRt7OaGIXE/cKQD2AkmeCiIiIuJRV2rz8S/JopIDebfSKLo91ZzJfQvOol0A245vY9zKcUxbM42zyU7Xbb3AekS2j2RI0BCt1isiOeJOAfAuMNIYU9paq95/ERG5Pmza5Fz0z5hxaZpP2fq8mTKKPZ2GEPaXQD7sWHDafKy1LNu1jLErxrJ46+LfrdY75sYxdG/cXav1ikiuuFMA/Bu4G1hsjHncWrvVQ5lERERy5yptPqsr3s1/kkdTZ3gXwkf6UreuFzNe5nzKeeasc1br3Xh0IwABvgH0adWH0R1Ga7VeEckz2S4ArLVJxph7gZ+AjcaYPcB+wHXlw22nPMooIiKSPadOwbRpzh3/nU6vfIp/SRaVGMCi6pF0/Wtz3ukNJUp4OWcm+xP2M2HlBCatmsSJxBOAVusVEc9yZx2ASsCXQAucWf8N0r+uxOY+moiISDZt3nxpms+5cwAcK1ufsSkj2X/vEMKeLM+iWwpWm0/m1XrTbBrgrNY7psMYerToodV6RcRj3GkBeg1oA/wKxADb0ToAIiLiLRfbfMaNgy+/zNi8ukIn3kwbTd2IrgyP8KVWLS9mvMzF1Xrf+vktfjn4C+Cs1tuzRU+iOkRxU62bNMZTRDzOnQKgK3AIuNFae9pDeURERLJ2lTafBcUH8FGtUXR9qgWTekHx4l7OmcnRc0eJjY8l+pdoDp09BDir9Q4PHk5EaAS1y9X2ckIRKUrcKQDKAEt18S8iIl6xefOlaT4X23zK1OOtlFH81nUIQ/5Snvk3Fpw2H4B1v61j7M9jmbN+TsZqvc0rN89YrbdksQI0c1REigx3CoDNOEWAiIhI/nC5Lk3zydTms6p8J962kdQb1Y2IEb7UqOHFjJdJc6XxydZPGLtiLN/u/jZje9dGXYnqEKXVekXE69wpACYAMcaYxhoBKiIiHnX69KU2nx07AEgpVoIFAQNYXH8UXZ9uSeyjEBDg5ZyZnEw8ybQ10xi/cjy7TjkLjZX2L+2s1ts+kkYVG3k5oYiIw50xoNONMU2Bb40xzwOfW2v3ey6aiIgUOVu2OC/1Zm7zKV2Pt1JHcbS7M81nbnsvZ7xM/MF4on+JZt6GeSSmJgJQP7B+xmq95YqX83JCEZHfc2cMaFqmbyelb7va4dZa687TBRERKapcLli61Gnz+eKLjM2rAu9inBlNw6huRIb7UrWqFzNeJjElkQUbFxAdF83KAysztt/T4B5Gho6kW+NuWq1XRAosdy7S3WlYVHOjiIhk7aptPv1Z2jCSbs+0JPZh8C9A4/B3nNhBTFwMU9dMzVi0K7B4IIPbDiY8JJzGFRt7OaGIyLW50wLk48kgIiJSRGzZ4lz0T5+eqc2nLmNTR3HyoSEMebICfdt5N2Jmaa40lmxbQnRcNJ9t/yxje7vq7RgZOpLHWj6maT4icl1Rm46IiHjeVdp84svdyUS/0TQc053Rw32pXNmLGS9z5NwRpqyaQkx8DHtP7wUgwDeAx1o+RkRoBKE1QjXNR0SuSyoARETEc06fdu70jx8P27cDTpvPfP/+LGs6iq7PtCLmQfArIH8bWWv5cd+PRMdFs3DjQlJcKQA0KN+AESEjGNx2MBVLVvRyShGR3Ckgf+SKiEihcrHNZ8YMOHsWgGOl6vB22ihOPxrGkCcr0K+NlzNmcjb5LHPWzSE6Lpp1v60DwGDo3rg7EaER3NvwXnyMOmFFpHDI6RSga9EUIBGRosblgs8+c9p8Pv88Y3N82TuZFBDJDX/uTtQwPyoWoBvom45uYuIvE5mxdgZnks8AULlkZYa2G8qw4GHUC6zn3YAiIh6gKUAiIpI7V2vz8evHNy0j6fZsKyZ0LzhtPilpKXy45UOi46J/t1LvLbVvISI0gkeaPUKAXwFaYUxEJI/legqQcd6Aqgt0BV4EJlhr/5k38UREpMD69ddL03wutvmUrMM410jO9gpj8JMV6dfSuxEz25+wn8nxk5m8ajKHzh4CoFSxUvRr3Y8RISNoU60A9SSJiHhQru/HWGstsBuYYIxZC3xjjNlsrX03t+cWEZEC5mptPmXuYHKJ0TT+S3fGPO5H+fJezJiJtZavd31NdFw0H235iDTrdLM2q9SMiNAI+rfur5V6RaTIydMHstba740xq4AxgAoAEZHCIiHBudM/btylNh+/4rxbrD/ftxlFt+daM6EL+BaQxW9PXTjFjDUzmBg3kV+P/wqAn48fPZr1ICI0go51O2qEp4gUWZ7oyNwDdPbAeUVEJL9t3eq0+UybltHmc7RkHcbbkST2dtp8+jfzcsZMVh9aTfQv0cxZP4fE1EQAapSpwfDg4QxtN5QaZWp4OaGIiPd5ogBoAbg8cF4REckPLpfT3vP22067T7q40h2ZVno0Tf76AE+E+VGugHTOXEi9wMKNC4mOi+bn/T9nbO9UvxMRoRE80OQB/HwKyBvIIiIFQJ79iWiMqYjzEnBTYFlenVdERPLJxTaf8eNh2zbAafOZ79ePH4Ij6f5ca8bdDz4FZBz+zpM7iY2LZcrqKRxPPA5AuYByDGo7iPCQcJpWaurlhCIiBZM76wDszGJ3aaAizvjPZOCF3MUSEZF8c4U2n2MlajOekST1G+pM82ns5Yzp0lxpfLb9M6Ljolm6bSkWC0BQtSAiQiPo3bI3pfxLeTmliEjB5s4TgHrX2J8M/D/gH9ban3KcSEREPO9im8+4cbB0acbmuFIdmVFuNE2feoC/DPGjTBkvZszk6LmjTF09lZj4GHaf2g1AgG8AvVr2IiIkgvY12+ulXhGRbHKnAKifxb5k4Ki1NjWXeURExJPOnLk0zSdzm49vX1a0j6T739sw9u6C0eZjreXn/T8THRfNgo0LSE5LBqB+YH3CQ8IZEjSESiUreTmliMj1x52FwPZ4MoiIiHjQjh1Om8/UqU6vP3C0RG2iGUnKwPQ2n4ZezpjuXPI55q6fS3RcNGsOrwHAYOjaqCsRoRHc1/A+fH0KyLxREZHrkMYiiIgUVtbC11/D2LHw6afO90B8yduYVX40zZ59kL8M9KN0aS/nTLfl2BYm/jKR6Wunk5DkFCmVSlYiLCiM4cHDqV8+qwfRIiKSXSoAREQKm/PnYc4cZ4znhg0ApPr6s8ivNz/fGEX354N48y4oCC3zKWkpfPzrx0THRfP1rq8ztt9U6yZGho7k0eaPEuAX4MWEIiKFz1ULgGtM/bkWa60tIA+TRUSKiH37YMIEmDwZTpwA4ERANSb5jODCwOEM+GtVHmvg5YzpDp45yOT4yUxaNYmDZw4CULJYSfq26suIkBEEVQ/yckIRkcIrqycA9XJxXpuLXysiItllLfz4o9Pm8/77kJYGwPoSocwqH8UNz/Zg1CD/AtHmY63l293fEh0XzQebPyDNOlmbVGxCRGgEA9oMILB4oJdTiogUflkVAKFunuthIAoomfM4IiKSLUlJsGCBc+EfHw9AmvHlY/9e/NQ+ivv+eSOvdzIFos3n9IXTzFw7k+i4aLYc2wKAr/HlkWaPEBEawZ317tQITxGRfHTVAsBaG5+dExhjbgdeAzrgLAS2AXg2T9KJiMjvHT4MMTHO12+/AZDgX5F3fIdztt8I+j1Ti4cKSJvP2sNrif4lmtnrZ3M+5TwA1UtXZ1jwMB5v9zg1y9b0ckIRkaIpxy8BG2PaAK8C9+Fc+O8G/gnMttaqBUhEJC/Fxzt3+999F1JSANga0IqZFaKo+0wfhg0pUSDafJJSk1i0aRHRcdH8uO/HjO131ruTiNAI/tTkTxTzLebFhCIi4nYBYIypD7wM9AJ8gKPAK8BEa21K3sYTESnCUlPhgw+cC/8ffgDAhWGp/4P8FDKaO1+8g38VkDaf3ad2ExsXy5TVUzh6/igAZQPKMrDNQEaEjKBZ5WZeTigiIhdluwAwxlQB/gEMBfyBM8D/gP9aa896Jp6ISBF0/LgzyWfCBNi/H4BzxcoxwzeMk31H0fu5+nQtAG0+Luvi8+2fEx0XzeKti7Hp8x/aVG3DyNCR9G7Vm9L+BeCxhIiI/M41CwBjTBngKWAMzgu+KcDbwMvW2mOejSciUoRs2ODM7p89GxITAdjt35hZ5UdT/ZmBDBhaukC0+Rw7f4xpq6cREx/DzpPOxGh/X396NO9BRGgEN9W6SS/1iogUYFmtA+APjMJ5obci4AJmA/+w1u7Jn3giIoVcWhosXuy0+Xx9aSGsr4vdx3dBUdz6r/v4+z0+Xm/zsday8sBKouOimb9hPklpSQDULVeX8JBwhgQNoUqpKt4NKSIi2ZLVE4BtQC2cF3w/AZ6z1m7Ml1QiIoVdQgJMnQrjxsFO5y56om8p5voN5FjvSHo835S7CkCbz/mU88xbP4/ouGhWHVoFgMHQ+YbORIRG0PmGzvj6+Ho5pYiIuCOrAqA2zoJe54H6wDw3Hulaa22bXGYTESl8tm1zLvqnTYOzzutTB4rVY075UVR8KoxewwMLRJvP1uNbmfjLRKavnc6pC6cAqFiiImFBYQwPGU6D8gWgOhERkRy51jsABqfvv6Wb59UYUBGRi6yFL790+vuXLHG+B74vdgfftRlN+5cf4K/3+nq9zSfVlconv35CdFw0X+38KmN7h5odiAiNoGeLnhT3K+7FhCIikheyKgDuzLcUIiKF0blzMGuWc+G/eTMAyT4BLCjWl8M9R/PQC214rqGXMwKHzhzinVXvEBsfy4EzBwAo4VeCPq36MCJkBME1gr2cUERE8lJWKwEvz88gIiKFxp49zgjPyZPhlNM+85tfDeYFRlDur8PoEVHZ620+1lq+2/Md0XHRvL/5fVJdqQA0rtiYESEjGNhmIOVLlPduSBER8YgcrwQsIiKZWAv/7/85d/s/+ABcLgB+8buRb1tHEfTyI0TdX8zrbT6nL5xm9rrZRMdFs+noJgB8jA8PNX2IkaEjuav+XRrhKSJSyKkAEBHJjQsX4N13nQv/1asBSDV+fOD/GAceiaL7v9rz1wLQ5rPq0Cpi4mKYu34u51LOAVCtdDUeb/c4w4KHUatsLS8nFBGR/KICQEQkJw4ehJgY5+voUQBO+FZmfuBwSjwxgkdH1/B6m8/5lPO8u+FdYuJi+OXgLxnb76x3J+Eh4TzY9EH8ff29mFBERLxBBYCIiDtWrnQW7VqwAFKdvvkNvm34qmUUrV7pTXiX4l5v89l0dBOxcbHMWDuD00mnAQgsHsigNoMYHjKcppWaejegiIh4lQoAEZFrSUmB995zLvx//hmANHxYUuxh9jwYRed/38aYG7x71Z+UmsQHWz4gJi6G5XsuzXDoULMDI0JG0LNFT0oUK+HFhCIiUlCoABARuZpjx2DSJIiOhgPOeMwE30AWlhuK/5iRPPTnel5v89l1cheT4icxZfUUjp53WpFKFStF31Z9CQ8JJ6h6kHcDiohIgaMCQETkchs2OHf7Z892XvIFtvo25cvmUTR9uT9DupfyaptPqiuVJduWEBMXw2fbP8Omr73YqkorRoSMoG/rvpQNKOu9gCIiUqCpABARAWds5+LFzoX/smUZm78s1pkd3cZw9+v3MLKRd9t8Dp45yDur3mHyqsnsT9gPQIBvAD1b9CQ8JJybat2kEZ4iInJNKgBEpGhLSIDp050xnjt2AHDepxQflB2EGR1J9yebcE8Z78VzWRfLdi4jJj6Gj7Z8RJpNA+CGCjcQHhzOwLYDqVSykvcCiojIdcetAsAY4wv0BDoBNYDiVznUWms75TKbiIjn7NgB48bB1Klw5gwAe33q8mWTSOq/HEbvBwPx8fFevGPnjzF9zXRi42PZfmI7AL7Gl0eaPcKIkBHcWf9OfIwXA4qIyHUr2wWAMaY88AXQDrjWM2abm1AiIh5hLXzzDYwdi/3kE4x1/qj6we92fr0vitveeICwZt57MGqt5cd9PxITH8PCjQtJSksCoHbZ2gwLHsaQoCHUKFPDa/lERKRwcOdvuleAYGAfMB7YAiR4IpSISJ5KTIS5c53+/vXrAUgx/nxatjfJ4VF0/XsQt3ixzSchKYHZ62YTExfD+iNOPoOhS6MuhAeH07lRZ/x81LEpIiJ5w52/UR4ATgIdrLWHPZQnzxhj+gAjgNaAL07BMg2YaK11ZfMcPsCNQBfgLqAZUBo4AcQDk6y1H+Z9ehHJEwcPOiM8Y2OdkZ7AEVOVL26IoMaLw3mwV1WvtvmsPrSaiXETmbt+LudSzgFQpVQVwoLCeLzd49QvX9974UREpNBypwCoBHx+nVz8TwAigAvAMiAF572F8UAnY8yj2SwCGgA/pP/zCWAlThHUAOgMdDbGTAeGWGvV9iRSUKSv1msXLMCkr9a72jeYDXdFceP/etKvZYDXop1POc/8DfOJiY9h5YGVGdvvqHcH4cHhPNTsIfx9/b2WT0RECj93CoCDQKqnguQVY8wjOBf/h4HbrbXb0rdXBb4BHgIigbHZOJ0FvgbeAL60Nn38hnO+jsBiYBDwHc7TBRHxlpQUeP99eOutjNV6XfjwRelHOTd0DPe+cDNB5bw3InPz0c3ExscyY+0MTl04BUBg8UAGthnI8ODhNKvczGvZRESkaHGnAHgPGGSMKWGtTfRUoDzwbPrn0xcv/gGstb8ZY0YA3wLPGGPGXespgLV2B86TgyvtW26MeQ34F9APFQAi3nH8OEyeDOPHZ6zWe8oE8mW9x6n0j5HcN6Cu19p8ktOS+WDzB8TEx/Dt7m8ztrev2Z4RISPo2aInJYuV9E44EREpstwpAF4E7gXmG2OGWmuPeChTjhljauG8qJwMLLx8f/pF+wGgJk5v/4+5/JGr0z9r5fI8IuKujRudNp/ZszGJzj2JrT5NWX17FEH/60+PoFJei7br5C4mr5rMlNVTOHLO+aOyVLFS9G3Vl+Ehw2lXvZ3XsomIiLhTALwNbMdpodlmjIkH9gJXuoturbVheZDPXUHpnxuzeErxC04BEETuC4BG6Z+HcnkeEckOlwuWLHGm+Xz1FeDMJP62ZGdODYjirlfvoVegd273p7nSWLJtCRPjJvLZ9s+w6dOQW1ZpyYiQEfRt1Zdyxct5JZuIiEhm7hQAg7g0378McEcWx1rAGwXAxZEZe7I4Zu9lx+aIMaYkMDr92/dycy4RuYYzZy6t1rvdWRTrPCVZVnsQZf8+mtuHNvFam8+hM4d4Z9U7TF41mX0J+wDw9/WnZ4uehAeHc3PtmzHGe+8eiIiIXM6dAmCwx1LkndLpn+eyOOZs+mdup35H4xQRm4BJWR1ojBkGDAOoU6dOLn+sSBGycyeMG4edOhWT4Cw7stenLnE3RtLyzTC6tw/0SiyXdfH1rq+JiYvho18/ItXlzEdoWL4h4SHhDGo7iEolK3klm4iIyLVkuwCw1s7wZJDriTHmeWAgcBroaa1Nyup4a+0k0ouEkJAQjQsVyYq18PXXzoX/xx9jrMUAK4rfzpHeUdz+xgM8XNE7i2IdP3+c6WumExsfy7YTzowBX+PLw80eJjw4nE4NOuFjvLiwgIiISDYUtqUlL97dz+rtv4tPCc7k5AcYY54AXkr/WZ2ttRtzch4RuczZszBrljPNZ9MmAJLxZ3n13hR/OopbRgXh65v/say1/LT/J2LiYliwcQFJaU69X6tsLR5v9zhD2w2lRpka+R9MREQkh3JUABhj/HGm7dRM33QAiLfWJudVsBzanf5ZN4tjal92bLYZYyKB/wKJQDdr7U/unkNELrN9O0yYgJ02DXP6NAAHTQ1WhYbT+D/DuPe2ql6JderCKWatncWkVZPYcGQDAAZD5xs6Ex4STpdGXfDzKWz3UEREpChw628vY0wx4AVgJH/soT9rjBkHvGitTcmbeG67OJazRRbrFYRedmy2GGNG4kxCugA8YK1dnvOYIkWcywWffw7jxsHSpYAzzSe+xK0c6x3JzW88RLcKxfI9lrWWFQdWEBsfy/wN80lMdf4IqVKqCoPbDmZY8DAalG+Q77lERETyUrYLAGOML/ApcDfO39WHgJ3puxsA1XEW4Qo1xnTJvGpufrHW7jPGrALaAT2AmZn3p6/eWwtnleBs3703xoQD44Ek4EFr7Vd5FlqkKDl92pnmM2ECbHN66C8QwA91+1Dm2UhChwXhjYE5py+cZs76OcTGx7Lut3UZ2zvV78Sw4GE82PRB/H398z+YiIiIB7jzBGAYcA+wFYiy1n6eeacx5j7gLZwC4XEgJq9CuulVnEXAXjfG/Git3Z6erwrO5B6A1zKvAmyMGQWMAlZaawdkPpkx5vH0X5cEPHT571tEsmHzZhg/HjtzJuas86rOPp86bOoYQcs3w+jUJv8n5lhriTsYR2x8LPM2zON8ynkAKpWsxOC2g3m83eM0qtjoGmcRERG5/rhTAAzAGa/ZyVp74PKd1trPjTF3A1twJuR4pQCw1i4yxkwERgDrjTFfASlAJ6As8CHO3fzMKgFNcJ4MZDDGtAVicZ547AJ6GWN6XeHHHrPWPpmnvxGR611aGixe7LT5ZFq0K67snZwbHMmNr3Sndqn876E/k3SGuevnEhsfy+rDlzoB76h3B8ODh/NQ04cI8AvI91wiIiL5xZ2/fZsD31zp4v8ia+0BY8w3QMdcJ8sFa22EMeZ7nHcVOgK+OIXJVGBi5rv/1xCIc80C0DRvrD4SAAAgAElEQVT960r2ACoARABOnoQpUyA6GnbtAuAcJVnZpD9VXhxFSK+WXokVfzCe2PhY5q6fy7kUZ6mQCiUqMKjNIIYFD6NJpSZeySUiIpLf3CkAigHns3Hc+fRjvcpaOxeYm81jX8B5ufny7d9yqQAQkaysX+/M7p89G5PovDy7x68BO+8fSZu3BnNnw/L5Huls8lnmrZ9HbHws8YfiM7bfVuc2hgcP55Hmj1Dcr3i+5xIREfEmdwqAPcBtxhj/q437TB8Pelv6sSJS2KWmwkcfOW0+y53BWAaIr3gvaRGRBP+9M3X98394/5rDa4iNi2XO+jmcSXaW/AgsHsjANgMZFjyM5pWb53smERGRgsKdAuBj4K/ADGPMCGvtqcw7jTHlgAlANWBW3kUUkQLn2DGYPBk7cSJm3z4AzlKa1W0HUfvVkQTff7VuOc85l3yO+RvnExsfy8oDKzO231L7FoYFD6NH8x6UKFYi33OJiIgUNO4UAP8H9AZ6Ap2NMZ/gvBhrccaAdsdZG2B/+rEiUtisWuW0+cybh0lKwgC7/Rtz8OFRtPnfQG6rXjbfI637bR2T4icxa90sEpISACgXUI4BbQYwLHgYLat4550DERGRgirbBYC19rgx5i6cvvoQoC/OxT9c6pP/BehjrT2RpylFxHuSk+G992D8ePjxRwAshtXVu+L/l0hajLmHer4++RrpfMp5FmxcQGx8LD/v/zlj+421bmR48HB6tuhJyWIl8zWTiIjI9cKtGXzpM/XbG2NuxZmuUzN91wFgubX2+zzOJyLecvgwxMZiY2Mxhw4BcNqUY1OHITT830ja3dQw3yNtPLKR2PhYZq2bxakLThdi2YCy9GvVj+Ehw2ldtXW+ZxIREbneuLMS8P+AU9bal9Iv9HWxL1LYWAsrVjhtPgsXYlJSnEUwSjbnRJ9IWv9fP24qXzpfIyWmJLJo0yJi42P5Yd8PGdtDa4QyPHg4j7V8jFL+pfI1k4iIyPXMnScAkTgvAotIYZOUBPPnY8eNw8TFAeDCh3X1HqTsc5E0HHon9U3+TsTdfHQzk+InMWPtDE5eOAlAaf/S9GvVj2HBwwiqHpSveURERAoLdwqAw0Cqp4KIiBfs3w8xMdjYSZhjRzHASZ8K7LhjKI3ejCCodd18jXMm6QzzN85n6uqp/LT/p4ztwdWDGR48nN6telPaP3+fQIiIiBQ27hQAXwH3GGP8rLUqBESuV9bC9987bT7vv49JS8MAO8u24fyQSJq/3IeQUvk3LtNay4/7fmTK6iks2LggY5Xe0v6l6d2yN8ODhxNcIzjf8oiIiBR27hQA/wQeAGKMMVHW2nMeyiQinpCYCHPnOm0+a9cCkIYvm5r0oNJLo2nQ4xbIxzafw2cPM3PtTKaunsqvx3/N2H5rnVsJCwqjR/Me6u0XERHxAHcKgEHAUmAw8IAx5iucFX8Tr3Cstdb+K/fxRCTX9uyB6Ghck9/B5+QJDHDCrzL77h9G4/+F07pRrXyLkupKZcm2JUxZPYXFWxeTZtMAqFa6GgPbDGRI0BAaV2ycb3lERESKIncKgBdw5v4boBLw2BWOubjfAioARLzFWvjmG6fN5+OPMS4XPsCOiqEwKpIGT/egQoni+RZn6/GtTF09lRlrZ3D47GEAfI0vf2ryJ8KCwujcqDN+Pm5NJRYREZEccudv3Je4tPCXiBREZ8/C7Nm43h6Hz+ZNAKRQjK1telPj1Ugadu6Qb1HOJZ9j4aaFTFk9he/3Xpoa3LhiY8KCwhjQZgDVSlfLtzwiIiLicGcl4Bc8mENEcuPXXyEmBtfUafgknMYHOOZfnSMPh9P4jWG0rJU/F9rWWlYcWMGUVVN4d+O7nE0+C0CpYqXo2aInYUFh3Fz7Zkw+jxQVERGRS/TMXeR6lZwMH32EnTgR8803APgAO6vdTMCTkdSMfJhK/v75EuXIuSPMWjuLqWumsunopoztN9W6ibCgMHq26EmZgDL5kkVERESy5s5KwGnAdGtt2DWOmwwMttaquBDxhD17YNIk0iZPwffobxgg0acku2/qQ+1XwmnQMX9GZqa50vh8x+dMWT2Fj3/9mFSXMx24SqkqDGg9gMFBg2leuXm+ZBEREZHsc+ci3aR/ZfdYEckraWmwdCk2JgaWLMFYiy+wP7AFqWHh1P1bP5qVD8yXKDtO7Mh4offAmQMA+BgfujbqSlhQGN0ad6OYb7F8ySIiIiLu88Rd+tJAigfOK1L0HD4MU6aQFjMJ3/17MUCy8WdX8KPU+NcIat2fP7P7z6ec571N7zF1zVS+3f1txvYbKtzAkLZDGNBmADXL1vR4DhEREcm9PCsAjDE+QDPgLmB/Xp1XpMhJH+FpY2Kw73+AT1oqvsBvpRtwvn849V4YRJMqlfMhhiXuYBxTV09l7oa5JCQlAFDCrwQ9WvRgSNsh3F73dr3QKyIicp3JsgBI7/vPbKAxZmA2zjsz55FEiqgTJ2D6dNImxuK7fSsGZ6XeHa0eouo/wqn68N3g4+PxGMfPH2f2utlMWT2F9UfWZ2xvX7M9Q9oO4bGWj1GueDmP5xARERHPuNYTgMy39ixZ9/anAAeAD4Dnc5lLpGiwFn76CRsbi313Pj7JSfgCx0vUJKHn49R7eSiNanm+tSbNlcZXO79i6pqpfLjlQ5LTkgGoWKIi/Vv3Z0jQEFpVbeXxHCIiIuJ5WRYA1tqM243GGBfOFKAhHk8lUtidOAGzZzu9/Zs3Zrxhv7PxfVR4NpyK/bpR0c/zg7R2ndzF9DXTmbZmGvsS9gFgMNx/w/0MaTuEB5o8QIBfgMdziIiISP5x5wrjRWCNp4KIFHrWwvffYydNwi5YmHG3/1RAFU7+aTB1X36cBo0aejzGhdQLfLD5A6asnsKyXcsyttcLrMeQtkMY1HYQtcvV9ngOERER8Q53VgJ+0ZNBRAqt48dh5kzSJk7Cd9uWjLv9uxrdS/mnhxHYvzuB+bBg1+pDq5myegpz1s/h1IVTAAT4BvBI80cICwrjjnp34GM8/46BiIiIeJc7C4E1Bx4FPrHWrr7KMe2AbsACa+2WvIkoch2yFpYvd+72L3oPn5RkfIGTxatx+pEh1HlxKPUb1vd4jJOJJ5m7fi5TVk9h9eFL/9u2q96OsKAwerfsTfkS5T2eQ0RERAoOd1qARgLDgKlZHHME+AdQEYjKRS6R69PRozBjBqkxk/Hb4UzysRh2Ne1MxWeHUb53V8oX8+wiWS7r4ptd3zBl9RTe3/w+SWlJAJQvXp6+rfoS1i6MttXaejSDiIiIFFzuFAB3AmuttVed8W+t3W+MWYOzFoBI0eByOXP7J01y5vanpuAHnChRg7M9w6j9Qhj169X1eIy9p/dmvNC7+9TujO13N7ibsKAwHmz6IMX9ins8h4iIiBRs7hQANYHPs3HcLuDunMURuY4cPQrTppE6cRJ+u3dgABc+7GrRjUrPPk6FXl2o4OFJPglJCby36T1mrZvFt7u/xWIBqFOuDoPbDmZQ20HUC6zn0QwiIiJyfXHn6sQXyM4bggbQ3EApnKyFH37ATpyIXbgIn5Rk525/yVqc6zOUWs8PoX4dz07QSXWl8sWOL5i1bhYfbvmQC6kXAOeF3j81/RNhQWF0qt8JXx9fj+YQERGR65M7BcAeoIMxxsda67rSAcYYH6ADsC8vwokUGAkJztz+8RPx3bwho7d/d4uuVH4+nAqPdqaCr+cuuK21rDq0ilnrZjFvwzyOnDuSse/2urfTv3V/Hm3+KIHFAz2WQURERAoHdwqAz3Fe7H0aePUqxzyF0yo0Ppe5RAqGNWsgJoa0mbPxTTyHL3A6oAqnewyl9kuPU69+PY/++L2n9zJn3RxmrZvF5mObM7Y3qdiE/q3707d1X7X4iIiIiFvcKQD+BwwBXjbGtASmABdHfTYBhgKPAWeA/+ZlSJF8deECLFhA2oSJ+K78GXD63/bW70i5Z0ZQbtBDlPPg3P6EpAQWbVrErHWzWL57eUZff6WSlejdsjf9W/cnpEYIxhiPZRAREZHCy52FwPYbY3oCi4DeOBf7mRngLNDLWrsn7yKK5JNt2yA2ltR3puF3+gS+wDm/shztMpA6r4RTp2Vzj/3oa/X192/dn/sa3kcxX8+OEBUREZHCz60RJdbaL4wxLYC/APcBdQEL7MVpEfqftXZvnqcU8ZTUVPjkE1wTJuKz7EvA+Z/iQLV2FB8zgoqjelOqVCmP/GhrLSsPrGTO+jnM3zj/d339Het2zOjrL1e8nEd+voiIiBRNbs8otNbuA8Z4IItI/jl1Ct5+2xnhefgAPkCST3EO3t6b2q+EU/OmUPBQi82WY1uYs24OczfMZefJnRnbm1Zq6vT1t+pL3UDPrxsgIiIiRZNnh5SLFDSJiTB+PKkvv4pfwkn8gCPlG2OHj6DqUwOpX768R37sgYQDvLvhXeZumMuqQ6sytlcvXZ3HWj5Gn1Z9CK4erL5+ERER8Ti3C4D0UZ+dgZuAysAKa+3U9H2VgfLADmttWl4GFcmV1FSYMYOUv79AscP78QP239CRSm89T5Uud3nkbv+pC6d4b9N7zFk/53eLdJUNKMujzR6lT6s+3FHvDs3rFxERkXzlVgFgjGkHvAs0hPRR6FAMmJp+yN3AbOBB4JO8iymSQ9bChx+S/NTf8N++mWLAb9XbUC76NWr96b48v/BPTElk8bbFzF0/l8XbFpOclgyAv68/3Rp3o2+rvnRp1IXifsXz9OeKiIiIZFe2CwBjTF3gS5w7/IuB5cD/XXbYR0AyKgCkIFi+nOQnnsF/1c/4AycC61P8jZepOuQx8MnOotbZk+ZK4+tdXzN3w1ze3/w+CUkJABgMnep3ok+rPjzc7GEt0iUiIiIFgjtPAP6Gc/E/ylobDWCM+V0BYK09b4xZC4TmXUQRN61dS/JfnsV/2VL8gTMlq2Cef54KTwyDPJrfb61l7W9rmbl2JvM2zOPw2cMZ+4KrB9O3VV96texFjTI18uTniYiIiOQVdwqA+4DNFy/+s7Ab6JTjRCI5tWsXyc88j9+CufhjuVCsNMlRf6XsP5+A0qXz5EccOnOIuevnMmPtDNYfWZ+xvWH5hvRt1Zc+rfrQpFKTPPlZIiIiIp7gTgFQFfg5G8cZoEzO4ojkwJEjpPzzZXwmx+CflkKKjz9nB46g7Ot/o3jlyrk+fWJKIh/9+hEz1s7gix1f4LIuACqUqJCxMm/7mu01wUdERESuC+4UAGdwioBraQAcy1kcETecOUPq6//F9cZ/8E8+hwvD6T8NoNxbL1KsXr1cndpay/d7v2fG2hks3LQwo6+/mE8xHmjyAANaD6Br4674++ZNS5GIiIhIfnGnAFgN3GyMqW6tPXSlA4wxTYC26AVg8aTkZNImxJD8z5cpceYoAKdv70a58f+mXKtWuTr1jhM7mLVuFjPXzmTXqV0Z20NrhDKgzQAea/kYlUpWytXPEBEREfEmdwqAqThjPucYY3pYa49n3mmMKQtMAnyAKXkXUSSTCxc407ErZVZ+TQngTKubKTPhNcrddluOT3nqwikWblzIjLUz+GHfDxnba5WtRf/W/enfuj/NKjfLg/AiIiIi3pftAsBa+64xpgfwELDTGLM8fdeNxpj5OMVBeWC+tXZx3keVIi81lVP3P0bgyq9JDKxGiRmxlOnePUez/F3WxefbP2f62ul8tOUjktKSAChVrBSPNH+EAa0HaJEuERERKZTcXQm4F/AyEAl0S9/WNP0rBXgLeCrP0olc5HJx9KHHqbz8I5JLBVLiuy8gB+0+Luviwy0f8tLyl1j721rg0rz+AW0G8HCzhyntnzcTg0REREQKIrcKAGttKvCMMeZ14E6cF359gX3AV9baI3kfUYo8aznU769U/3Q6qQEl8f9isdsX/y7r4oPNH/DSdy+x7rd1ANQsU5OI0Aj6te5HnXJ1PJFcREREpMBx9wkAANbak8D7eZxF5Ir2hL9K3Xn/w+VXDL+P3oebb872r7144f/i8hcz5vbXLFOTZ299lrB2YRT3K+6p2CIiIiIFUo4KAJH8sv3JGG6Y9DesMfjMngX33ZetX+eyLt7f/D4vLn+RDUc2AM5Lvc/e+ixhQWEE+AV4MraIiIhIgXXVAsAYk6ueCGvt3tz8epHNL8ynyX8jADATJ0KvXtf8NS7r4r1N7/HSdy9lXPjXLlubZ299liFBQ3ThLyIiIkVeVk8AdgM2h+e11zi3SJbWvv4ZzV/shw8WXnkFhg/P8niXdbFo0yJeWv4SG49uBJwL/+due47BbQfrwl9EREQkXVYX6XvJeQEgkmNxb/9I82cephip8MQT8OyzVz02zZXmXPh/9xKbjm4CoE65Ojx363MMajtIF/4iIiIil7lqAWCtrZePOUQA+DFmHc3HdKUkiTB4MPznP1ec85/mSmPhpoW8tPwlNh/bDDgX/n+77W8MajsIf1///I4uIiIicl1Qm44UGMun7qDpyPsItKfgwQdh0qQrXvx/t+c7wj8Nz7jwr1uuLn+77W8MbDtQF/4iIiIi16ACQAqEL2ceotHj91LVdRjuugvmzQO/P/7n+d2e77h/9v0kpiZSL7Aef7vtbwxoM0AX/iIiIiLZlNUUoAHAdmvtj1fYVxZIttZeuMK+3kCotfaJPE0qhdbSuSepN+Re6rl2QkgIfPghFP/jfP6f9/9M17ldSUxNJCwojOiu0brwFxEREXGTTxb7pgNDr7LvJDDhKvvuBaJykUmKkI/nnaPSwK40S9sATZvC0qVQpswfjlt1aBX3z76fs8ln6duqL7HdYnXxLyIiIpIDWRUAWTHpXyI59t68ZEoPeoTQ1J+gTh344guoVOkPx208spF7Z93L6aTTPNLsEaY/OB1fH18vJBYRERG5/uW0ABDJlQXz0vAdPIC7kj+HypXhyy+hdu0/HLf1+FY6zezE8cTjdG3UlbmPzMXPR6+uiIiIiOSUCgDJd3PnWBLDRvFg0nyn3eezz6Bx4z8ct/vUbjrN7MRv537j7gZ3s6jnIrX9iIiIiOSSCgDJV++9B0fDn2dgYozzou8nn0C7dn847kDCAe6acRf7E/Zza51b+bDXhxT3++OLwSIiIiLiHhUAkm+shV9HvEnU2VfA1xcWLICOHf9w3G9nf6PTzE7sOrWL0BqhLO6zmFL+pbyQWERERKTwUQEg+eand/fw9NEnnW+mTYPu3f9wzPHzx7l71t38evxX2lRtw2f9PqNsQNl8TioiIiJSeF3rbcob0tcDcGffDbnMJIXUnlfncjMu6NkT+vf/w/5TF05x7+x72XBkA80qNeOL/l9QoUQFLyQVERERKbyuVQDckv7lzj4D2NyEksLnyG+WthvnON/06/eH/WeTz9JlThdWHVpFw/IN+WrAV1QpVSWfU4qIiIgUflkVAN9xHV/IG2P6ACOA1oAvsAWYBky01rpycL77gSeAEKA4sBOYB/zHWpuUV7kLq0//vY4hro1QoQLcd9/v9iWmJNJ9Xnd+2v8TdcrVYdmAZdQoU8NLSUVEREQKt6sWANbaO/IxR54yxkwAIoALwDIgBegEjAc6GWMedacIMMY8BbwOpAHf4qyE3BF4GehmjOlkrT2fp7+JQsTlgpQZ6Xf/e/YE/0ujPJNSk3h4wcN8u/tbqpeuzrIBy6gbWNdLSUVEREQKv0L3ErAx5hGci//DQGtrbTdr7UNAI2Az8BAQ6cb5QoDXgPPALdbau621PYAGOE9JbgReydvfReHy5ecu/nR+nvNN374Z21PSUui1qBefbf+MyiUrs2zAMm6ooFdIRERERDyp0BUAwLPpn09ba7dd3Git/Q2nJQjgGWNMdn/vz+C81/C6tXZFpvOdBQYDLiDCGBOY6+SF1Pf//o5qKfuhbl24+WYA0lxp9P+gPx/9+hGBxQP5sv+XNKvczMtJRURERAq/QlUAGGNqAcFAMrDw8v3W2uXAAaAazp37a53PH+ic/u2cK5xvJ/AT4A90yXHwQuzAAWi0Mv1fXZ8+4OODy7oY+slQ5m+cTxn/Mnze73PaVGvj3aAiIiIiRUShKgCAoPTPjdbaxKsc88tlx2alCVASOGGt3ZEH5ytypscm8SiLnG/69sVay6glo5i+Zjoli5VkSd8ltK/Z3rshRURERIqQwlYA1E//3JPFMXsvOzY759ubxTHunK9ISU2FXdFLKZl8Ctq0gRYtePqrp5kYN5EA3wA+euwjbq1zq7djioiIiBQpha0AKJ3+eS6LY86mf5bJr/MZY4YZY+KMMXFHjx7Nxo8tHJYuhT42vf2nb182Hd3EGz++QTGfYrzX8z3ubnC3dwOKiIiIFEGFrQAokKy1k6y1IdbakMqVK3s7Tr6ZNf40HRM+AWOgd29mrZ0FwMA2A+nauKuX04mIiIgUTYWtALh4N75UFsdcvKt/xgvnKzL27IGqP7yPb2oSdOyIq2YNZq+fDUD/Nv29nE5ERESk6CpsBcDu9M+sVpKqfdmx2TlfnTw6X5ExeTKMqnCp/efb3d+yP2E/9QLrqe9fRERExIuuuhJwVtLHbdYAil/tGGvtdzkNlQur0z9bGGNKXGUSUOhlx2ZlC5AIVDDGNLzKJKCLI2yyc74iISUFFk8+yL+Ofu2s+vvoo8xa/mcA+rXqh0+2l2AQERERkbzmVgFgjHkYeBW41nKt1t1z5wVr7T5jzCqgHdADmJl5vzGmI1ALZ5Xgn7JxvmRjzFLgYaAv8NJl52sA3ISz7sDivPg9FAYffwxDS7+LOWKha1fOl/Jn0SZnFKjaf0RERES8K9sX6caY7sACnLah08BOIMFDuXLjVZxFwF43xvxord0OYIypAkSnH/OatdZ18RcYY0YBo4CV1toBl53vNeAh4GljzGfW2pXpv6Y0MBXn30e0tfaUJ39T15OYGJjvurT414dbPuRs8lk61OxA44qNvRtOREREpIhz5y79c4AB/g68Ya1N8Uyk3LHWLjLG/P/27jxMrrLM///7kz0BQjb2QIAsEGTfVGQJ4DosIiC7DLjjOqOOzIw6P3QGRR1/7rKMArKJyqaIu5AgIoqGTTqBbkI2EgIBEkgIZLu/fzyn0pVKVXdXd1Wqus7ndV11na6z1V1Pnao+9znPcilwAfCIpN8Da4BjgZHAbcB3SjYbRxr06+ky+7tf0r8DXwbulXQnsAw4CtgW+AvwmTq9nX6nowNW/n02Y16YCSNHwvHHc+3N7wDgXfv66r+ZmZlZo1VTGXtf4IGI+GKznvwXRMSHSFV2ZpJO1N8CdJCu8p8SEeuq3N9XgLcBd5HaEJwALCUlQ0dFxMu1i75/u+IK+MKe2dX/U05h8ZoX+O0Tv2XwgMGcvvfpjQ3OzMzMzKq6A7AGeKxegdRaRNwA3NDDdS8CLupmnV8Dv+5zYC3s1Vfh6quCi4dnxX722fzoHz9ifaznhCknMG7EuMYGaGZmZmZV3QH4O7B7vQKx/u+WW+CM3f7C4AVzYIcdYNo0rn04Df7l6j9mZmZmzaGaBOAS4FBJb6pXMNa/XXYZfGxcVv3nzDN5ZGkbDz79IKOGjeL4Kcc3NjgzMzMzA6qrAvQYcDHwc0nfInV7OR9YX27liJjf9/Csv5g1C+Y8toaJ63+cZpx99oar/6e/5nSGDhrawOjMzMzMrKCaBGAuqX9/AZ/KHpU0ZBwAa5zLL4f/mfZ79ONnYc89Wbffvlz/zRMAOHe/0p5VzczMzKxRqjlJn086sTfbyKpVcN118D9HZNV/zj6bO+fexaKXFjFx9EReP/71jQ3QzMzMzDbocQIQEbvWMQ7rx37yEzjyoJVs+bvb0oyzzuLahy4C4Jx9z0FS44IzMzMzs41U0wjYrKzLLoPP7vMzWLkSXv96Vozflptn3Qy49x8zMzOzZuMEwPrkoYdg4ULYv62z+s+ts27l5TUvc9jOhzFxzMTGBmhmZmZmG3ECYH1y+eXw8bOeZcBvfwMDB8Jpp3HNw9cAcO6+bvxrZmZm1mwqJgCS1klaK2lK0fOePtZuvrdgjbJiBdx4I7x35E9g3Tp4y1t4auhq/jDnDwwZOITTXnNao0M0MzMzsxJdNQJW9ih+3lNu9ZkDP/oRHHUUjLojq/5z1lnc8MgNBMHxU45n9PDRjQ3QzMzMzDZRMQGIiAFdPTe77DL4+kfnwPl/hhEjiBNP5JrrDgNc/cfMzMysWfmk3nrlb3+DF16AwxfckGacdBIPrXyCfzzzD8YOH8vbJr+tsQGamZmZWVlOAKxXLrsM3v++YMANnb3/XPvQtQCcsfcZDBk4pIHRmZmZmVklTgCsasuXw803w/sOeRBmz4Zx41h77NFc/0hKBtz3v5mZmVnzcgJgVbvuOnjzm2Hsr7Or/6efzu8XzGDJyiVMHjOZQ3c6tLEBmpmZmVlFTgCsKhGp+s8H37cudQMEcPbZXPNQ1vf/fuciuRMoMzMzs2blBMCqcu+9sHo1TNMMWLQIdt+dlw54DbfNvg2Ac/Y9p8ERmpmZmVlXnABYVS6/HD7wAdANnX3/3zz7FlatXcWRE45k11G7NjQ+MzMzM+uaEwDrseeeg9tvh38+/RW46aY0s6j6jxv/mpmZmTW/HicAkmZKuqmewVhzu+YaOP54GHvfHfDii3DggczfYQTT505n6MChnLrXqY0O0czMzMy6Uc0dgD2BNfUKxJrfFVfABz8IXN/Z9//1D19PELx9z7czatiohsZnZmZmZt2rJgGYB2xZr0CsuT37LCxeDIdNfQHuuAMk4vTTufbhNPiXq/+YmZmZ9Q/VJAA3A0dKGlevYKx5zZoFe+0FuuXm1A3QMccwU08za+ksthmxDW+Z+JZGh2hmZmZmPVBNAnAxMBv4jaTX1ikea1JtbSkB2FD956yzNjT+PXPvMxk8cHDjgjMzMzOzHhtUxbp3AOuAQ4B7JS0hVQtaVWbdiIhjaxCfNYm2Njh0x4Vw5QwYOpQ1J53Ij37w73KzpoYAACAASURBVAC8az9X/zEzMzPrL6pJAKYV/S1g++xRTvQ2IGtOs2bBhybcmIYCPv54frP0Pp59+VmmjpvKQTsc1OjwzMzMzKyHqkkAjq5bFNb02tpg90Wdvf8UN/6V1MDIzMzMzKwaPU4AImJGPQOx5rVsGeywrI0hix6EUaNYdsxh/OzbZyLE2fue3ejwzMzMzKwKHgnYujVrFpw35vb05OSTuemJ23l13atM23Uau2y9S2ODMzMzM7OqVFMFaANJOwFHAjtls54C7o6Ip2oVmDWPtjbYd9hj6cmhh7rvfzMzM7N+rKoEQNIo4LvAaWx692C9pB8DH4mIZTWKz5pAWxu8eW07AHN33oq777+b4YOGc8pepzQ4MjMzMzOrVo8TAEnDgTuB/Ui9/NwHzMkW7w68FjgTmCrp8Igo1z2o9UNtbbDN8g4Arls7E4CT9jyJkUNHNjIsMzMzM+uFau4A/AuwP3Av8L6ImFW8UNJU4HLgDcDHgC/XKkhrrLn/WMGwF54mhg7h2nmpLcC5+53b4KjMzMzMrDeqaQR8GvACcFzpyT9ANu9EYBlwRm3Cs0ZbsQJGPvsEAH89eEcef/5xtttiO964+xsbHJmZmZmZ9UY1CcBk4K6IWF5phazu/13ZutYCZs+GI3dI9f+v3S/NO2ufsxg0oFftx83MzMyswdwNqHWprQ0O2rqD1QPhxnFPA67+Y2ZmZtafVZMAdADTJG1VaQVJI4Fp2brWAtraYM9BHfx6Ejw34BX23nZv9ttuv0aHZWZmZma9VE0C8FNgDPBzSZNKF2bzbgVGAz+pTXjWaG1tMH5VO/dk432dvOfJSGpsUGZmZmbWa9VU5P46cDpwFDBL0n3Ak6QuQXcHXgcMBB4BvlHjOK1B2tpg65c6aN8nPd9rm70aG5CZmZmZ9UmPE4CIeFnS0cClwCmk7j7fULwKcBNwQUS8XNMorSFWrYIXFq5k8KuLaB+b5k0e6/bdZmZmZv1ZVV25RMRzwGmSdgGOAHbKFj0F/DEi5tc4Pmugxx6DI8fPYf0c6BgrIJg8xgmAmZmZWX9WzUjAtwBPR8SHshP96+sXljWDtjZ4w7btLFgKrw4Mtt9ye7YaWrENuJmZmZn1A9U0Aj4OGFuvQKz5zJoF+23R0Vn9x1f/zczMzPq9ahKAp4DB9QrEmk9bG+weHTzuBMDMzMysZVSTAPwCOELSiHoFY82lrQ22e7Gd9jHp+ZSxUxobkJmZmZn1WTUJwEXAi8BNknauTzjWLFavhrlzYYtFHe4ByMzMzKyFVNML0P8CjwLHA+2SZgLzgFVl1o2IeE8N4rMGaW+HyeNXoY6FbgNgZmZm1kKqSQDOI/X1DzCENPDX6yqsG4ATgH6srQ2m7TKHtXNgzug0b9KYTQaANjMzM7N+ppoE4N10JgDW4tra4NDR7cwdBWsHwM4jd2b44OGNDsvMzMzM+qiakYCvrmMc1mTa2uCEIR0bGgC7/r+ZmZlZa+hxI2BJz0u6u57BWPOYNQt2We0uQM3MzMxaTTVVgIYAC+oViDWPtWtTI+DRY9o3NAB2F6BmZmZmraGabkA7gHH1CsSax5w5sOOOMPDJoipAvgNgZmZm1hKqSQCuA46UtFu9grHm0NYG++3xCixY4DEAzMzMzFpMNQnA14HfAHdKOl3S0DrFZA3W1gZv2PFJXh0QzNsaBmgAu4/evdFhmZmZmVkNVNMGoB0QMAG4AUDSM1QeCGxi38OzRmhrg/PHtjNnNKwfALuP2pUhA4c0OiwzMzMzq4FqEoBdi/5WNt2uwroeL6Afa2uDyUd28KB7ADIzMzNrOdUkAK77nwPr1sHs2bD9ge4C1MzMzKwVVTMQ2Lx6BmLNYd48GDcOhsxr39ADkLsANTMzM2sd1TQCthyYNQv22gvo6HAPQGZmZmYtqGICIOlISVVf+pX0Rkkf61tY1ihtbbDPlFdh/nyPAWBmZmbWgrq6AzAduLDcAknPS/p2he3OJnUZav1QWxscuu1cXh64noVbw6ABg5gwakKjwzIzMzOzGumuCpAqzB8FbFHjWKwJtLXB3kPb6ciu/k8cPZFBA6ppK25mZmZmzcxtAGyDiJQATFjT0Vn9x/X/zczMzFpKSyYAkvaQdJ2kRZJelTRP0qWSdujFvnaR9EFJt0maL2m1pJckzZT0X5JG1uM9NMLChbDlljBiUVEDYNf/NzMzM2spLZcASDoKeIDUFmExcCvwMvBB4KFeNGy+AbgUOA54GrgF+DMwEfg88LCklhgjoa0t6wGovX3DGADuAtTMzMystbRUAiBpC+BGYDjw0Yg4KCLOiIipwNeAbYAfSarUtqGcp4B/BbaPiEOz/b0ZmERqKD0BuLqGb6NhNiQAHR3uAcjMzMysRbVUAgCcD2wP3BUR3ylZdiHwBHAg8Lae7jAiTo+Ib0TEcyXznwXelT09UtLOvQ+7OcyaBXtPWQ1z53oMADMzM7MW1V33LttLOrLKZdv3Maa+OCmbXl+6ICLWSboR+Ey23i/7+mIRsVDSUmAcMB5Y0Nd9NlJbG7z3qHm8OHg9S7aEYYOGMX7k+EaHZWZmZmY11F0C8JbsUSq6WNZIB2TT+yssv79kvT6RNA4YnT1dXIt9NkqhB6A9BrRvqP4zacwkBqjVbhKZmZmZ5VtXCcB80ol+v5D1xpOdujKvwmrzs2mtGu1+ChgIzIyIuTXaZ0MsWQIDB8LWz7oHIDMzM7NWVjEBiIhdN2MctbBl0d8rK6yzIptu1dcXk/RGUgKwHvhEX/fXaG1tMHUqbgBsZmZm1uKaZohXSV8BTuzFpsdGxFO1jqcrkvYBfkq6+v/ZiJjRzfrvB94PsMsuu9Q/wF5wF6BmZmZm+dA0CQCwI7BHL7YbnE1XFM3bAlheZt3CXYKXevE6AEjaE/g9MAr4WkRc3N02EXEFcAXAwQcf3JTVqjYkAH/ooH1amucegMzMzMxaT9O08IyIcyJCvXjMzbZ/EXgh292ECi9T6Kpzbm9izAYRuxPYFvhuRHyqN/tpRm1t8Jopa1IXoK4CZGZmZtaymiYBqJGZ2fSQCssPzaYPVLtjSZOBu4AdgP8DPlp1dE2srQ32Hjmf5wav5fkRsOWQLdl+y0b26GpmZmZm9dBqCcDPsunZpQskDQTOyJ7eWs1OJU0knfzvCFwFfCAimrIqT28sXQqrV8O2y9s36gGougGTzczMzKw/aLUE4CrgaeBoSR8uWXYJMJF09f9XxQsk7SRpdvbYqWTZbqST/52AHwLvbaWTf0gjAO+1F+iJoh6AXP/fzMzMrCU1UyPgPouIFZLOIJ3gf0fS+UA7sB8wFVgKnFnmBH4wnQ2QB5csu5nUduBVUsJ0ZYUr45dExOyavJHNbEMD4A6PAWBmZmbW6loqAQCIiBmSDgD+CzgW2AdYAlwOfD4iqh2xtzC42FDgXV2sdzXQbxOAqVOBO90FqJmZmVmra7kEACAiHqNMO4Au1p8LlL2s3w8HRKtaWxu89a3AFUVdgPoOgJmZmVlLarU2ANYLbW2w15S1xJNzOqsAuQ2AmZmZWUtyApBzy5bB8uWwMwt4ZshaXhoKo4aNYuzwsY0OzczMzMzqwAlAzs2aler/D3hi4/r/7gLUzMzMrDU5Acg59wBkZmZmli9OAHKuMAYAHUVjADgBMDMzM2tZTgBybsMdgHZ3AWpmZmaWB04Acm7DGADFVYDcA5CZmZlZy3ICkGMrVsAzz8Buu6xj/Zwn6HAVIDMzM7OW5wQgx2bPhilTYODihSwatoZVg2GbEduw9bCtGx2amZmZmdWJE4Acc/1/MzMzs/xxApBjG3UBWqj+4/r/ZmZmZi3NCUCOeQwAMzMzs/xxApBjG8YAcBUgMzMzs9xwApBTq1bBwoUwcSIeBMzMzMwsR5wA5NTjj8Puu8PggetZN6eDOaPT/EljJjU2MDMzMzOrKycAObWh/v/ChcwftprVg2DHrXZkiyFbNDo0MzMzM6sjJwA5VdwA2PX/zczMzPLDCUBOuQcgMzMzs3xyApBTZccAcAJgZmZm1vKcAOTQ6tXw5JMwZQrQ3r7hDoCrAJmZmZm1PicAOdTeDhMmwNChbNQGwKMAm5mZmbU+JwA5tGEAsPXrWfNkB3NHgRC7j9690aGZmZmZWZ05AcihtjaYOhVYtIgnh73CugGwy9a7MGzQsEaHZmZmZmZ15gQgh9wFqJmZmVl+OQHIIfcAZGZmZpZfTgByZu3a1Ah4zz3ZeAwANwA2MzMzywUnADkzZw7ssAOMGEHqAjS7A+AqQGZmZmb54AQgZzZU/4GNuwB1FSAzMzOzXHACkDMbEoAIXnmynQVbw0ANZNdRuzY6NDMzMzPbDJwA5MyGMQAWL+aJYasIwW6jd2PwwMGNDs3MzMzMNgMnADmzYQwAdwFqZmZmlktOAHJk/XqYPbszAWh3/X8zMzOz3HECkCPz5sGYMTByJB4DwMzMzCynnADkyEY9ALW3b7gD4CpAZmZmZvnhBCBHKnYB6kHAzMzMzHLDCUCOFHcBumJeO4u3giEDhrDzyJ0bHZqZmZmZbSZOAHJkQwKwZAkdQ1cCMHHMRAYOGNjYwMzMzMxss3ECkBMRaQyADT0AZQ2AXf/fzMzMLF+cAOTEU0/BiBGpF6CN6v+7ByAzMzOzXHECkBOlDYDb3QDYzMzMLJecAOTEJl2AugqQmZmZWS45AciJil2AugqQmZmZWa44AciJ4i5Aly1oZ+kWMGLQCHbcasdGh2ZmZmZmm5ETgByIKEoAnn2W9sEvATBp7CQkNTY4MzMzM9usnADkwJIlMGAAbLMNGzUAdv1/MzMzs/xxApADhav/Eq7/b2ZmZpZzTgBy4oQTsj+KBgFzAmBmZmaWP4MaHYDV3zHHpAeQugB1FSAzMzOz3PIdgJyJjvbOKkAeBMzMzMwsd5wA5EkESxc+zvJhMHLIVmwzYptGR2RmZmZmm5kTgDx57rkNXYBOHjvFXYCamZmZ5ZATgDwpagDs+v9mZmZm+eQEIE/cBaiZmZlZ7jkByJOiQcDcANjMzMwsn5wA5El7u6sAmZmZmeWcE4AciY7OMQBcBcjMzMwsn5wA5Mjixe2sHAJjh41h9PDRjQ7HzMzMzBrACUBePP887QOWAakLUDMzMzPLJycAeVHUAHjKOCcAZmZmZnnlBCAv3AWomZmZmeEEID+KBgFzAmBmZmaWX04A8qK9swcgdwFqZmZmll9OAHJifUc7HdkdgEljJjU2GDMzMzNrGCcAObFgyeO8Ogi2H74tWw3dqtHhmJmZmVmDOAHIg2XLaNcLAEzeZo8GB2NmZmZmjeQEIA+KGgC7/r+ZmZlZvrVkAiBpD0nXSVok6VVJ8yRdKmmHGu1/e0nPSQpJK2qxz7pyF6BmZmZmlmm5BEDSUcADwNnAYuBW4GXgg8BDkmpxCfxyYHQN9rN5FA0CNnmsEwAzMzOzPGupBEDSFsCNwHDgoxFxUEScERFTga8B2wA/kqQ+vMa5wInA92oR82bR3u4qQGZmZmYGtFgCAJwPbA/cFRHfKVl2IfAEcCDwtt7sXNKOwDeBvwJf70Ocm9XaJ9qZk92vmDh6YmODMTMzM7OGarUE4KRsen3pgohYR7o7ULxeta4ARgDvBtb1ch+b3dwlj7F2IOy8xY4MHzy80eGYmZmZWQO1WgJwQDa9v8Ly+0vW6zFJ5wPHARdHxKO9iK1h2i//IgCTt9mzwZGYmZmZWaO1TAIgaSSQ1XRnXoXV5mfT3arc93hSlZ+HgS/1KsAGat/iVcD1/83MzMyshRIAYMuiv1dWWKfQZWe1Q+H+X7b/d0fEmmoDa7THn3sccA9AZmZmZgaDGh1AgaSvkHrXqdaxEfFUreMpkPRe4K3AlyPi773cx/uB9wPssssuNYyuZ9qfbwc8BoCZmZmZNVECAOwI7NGL7QZn0+IBubYAlpdZt3CX4KWe7FjSzqTuQx8DLupFbABExBWkBsQcfPDB0dv99Fb7cykBcBUgMzMzM2uaBCAizgHO6cP2L0p6gTRA1wRSff1SO2fTuT3c7bHASOA54NclwwcMy6bDJU3P/v5sRNxTRdibxQcO+gCzls5it9FVNX0wMzMzsxbUNAlAjcwknbQfQvkE4NBs+kCV+92Nyg2HBwBHZX+Pq3K/m8WFh1/Y6BDMzMzMrEm0UiNggJ9l07NLF0gaCJyRPb21JzuLiKsjQuUedCYEK4vm39bnd2BmZmZmVketlgBcBTwNHC3pwyXLLgEmkq7+/6p4gaSdJM3OHjttnlDNzMzMzDa/lqoCFBErJJ1BOsH/TjZ4VzuwHzAVWAqcGRGlDXEH09kAeTBmZmZmZi2q1e4AEBEzSCP93gCMB04m9f5zObBvRDzWwPDMzMzMzBqqpe4AFGQn+Zu0A+hi/bmAuluvr9uYmZmZmTVay90BMDMzMzOzypwAmJmZmZnliBMAMzMzM7MccQJgZmZmZpYjTgDMzMzMzHLECYCZmZmZWY44ATAzMzMzyxEnAGZmZmZmOeIEwMzMzMwsR5wAmJmZmZnliBMAMzMzM7MccQJgZmZmZpYjTgDMzMzMzHLECYCZmZmZWY44ATAzMzMzyxFFRKNjyBVJzwLzGvDS44ClDXjdPHDZ1o/Ltn5ctvXjsq0fl239uGzrp1FlOyEitim3wAlATkj6W0Qc3Og4WpHLtn5ctvXjsq0fl239uGzrx2VbP81Ytq4CZGZmZmaWI04AzMzMzMxyxAlAflzR6ABamMu2fly29eOyrR+Xbf24bOvHZVs/TVe2bgNgZmZmZpYjvgNgZmZmZpYjTgD6IUlnSfqjpOWSVkj6m6QPS6rq85R0kaTo4vFKvd5Ds5G0h6SPS7pO0mxJ67MyOLWP+63JZ9Wf1bpsJV3dzXE7u9bvoRlJGizpWElfy46rFyWtlvSUpJskTevDvnN93NajbH3cdpL0UUk/kTRL0nOS1kh6VtLvJZ0jSb3Y54DsGP1bdswuz47hM+vxHppVrctW0vRujttf1+u9NDtJXywqh0/1ch8N+60dVO8XsNqS9F3gQ8ArwB+ANcCxwHeAYyWdGhHrq9ztQ8CDZeav6Uus/cwFwMdrucM6fVb9Uc3LNvMnoKPM/MV1eK1mdBTwu+zvp4G7gZXAXsApwCmS/jsi/quanfq4BepUtpm8H7cAFwLbAv8A7iWV7QTgGNKxdqqkk3t6nEkaCNwCnAi8CPwWGJrt6wZJr4uIevwGNaOalm2R35C+C6Ue6UOs/ZakQ4BPAwFUnbBm+2jsb21E+NFPHqR/PEH6RzG5aP52QFu27ONV7O+ibJuLGv3eGv0A3gt8BTgNmAhMz8rm1Gb4rPrzow5le3W2/XmNfm8NLtdjgJuAI8osOx1Ym5XT0VXs08dt/crWx21nWRwObFFm/mtIJ5kBnF/F/j6ZbfMosF3R/MlF+3t7o993Py3bwu/1tEa/t2Z5kJLLNuAp4NasfD5V5T4a/lubi9u5LeQ/sumFEdFemBkRS0hXWQH+PS+36WspIr4fEZ+OiJ9ExBM12KU/q0wdytaAiLgzIk6NiD+WWfZj0gknwDlV7NbHLXUrW8tExD0RsbLM/EeB72ZP39STfWVX/z+dPb0gO1YL+2snXREH+EzvI+4/alm2VtEXgKnAB4HlvdxHw39rW/pHvJVIGg8cBKwGflq6PCJmkLLR7YHXbd7orJg/K2sSD2TT8T1Z2cdtVaoqW6vK2mz6ag/Xfz2pysvCiLi7zPKfkqpWHCJppxrE159VW7ZWQtJrSXecboiI23u5j6b4rXUbgP7jgGz6aESsqrDO/cBO2br3VrHvAyV9GRgNPA/8BbgjIlb3Nticq+dnZZ2OlrQvsCWwBLgH+F20fv30npqcTXtat9zHbc9VW7bFfNxWIGk30lVVgJ/3cLPCcXt/uYUR8bKkR4H9s8dTfQqyn+pl2RZ7h6R3kKq/LALuKneHrJVJGgb8kHSe1Jc2JU3xW+sEoP/YLZvO62Kd+SXr9tQJ2aPYQknnZJmoVaeen5V1OrfMvDZJZ0RELhumFUjaHjgve3pzDzfzcdsDvSzbYj5uM5LOJzW4Hky6m3IYqWbCFyPi1h7upqfH7f7k6LitUdkW+1jJ889L+hNwZkQs6FOw/cfFwB7AGRGxtA/7aYrfWlcB6j+2zKab1O0rsiKbbtXDfT5Bqoe2P7A1sA2p8dsM0g/GL7MrVVadenxW1ulB0j+jvUhlvSNwPKk3q72A3+f5Vr+kQcB1pO/0H6q4Te3jtht9KFvwcVvOG4B/Bs4CjszmfQ747yr24eO2vFqULcAfgfcAU4ARpB6FzgSezF7j95K2qEXAzUzSYcC/ALdl7YD6oimOWScAORYR10bEJRHxUES8GBFLI+KuiJhGurI1AvhiY6M021hEfCMivh0RsyJiZUQsjog7gEOB+0j1gf+j6720tMtIXcktwI1Ua63XZevjdlMR8d6IEOl/zWuAb5B6p7tP0o6NjK2/q1XZRsTnIuLKiGiPiFURMT8ibiRVTZlDSgwu6Hov/Zuk4aSG/y+Suu1sCU4A+o9CNthVpl3IKl+qwet9IZu+SdLgGuwvTzb3Z2VA1mblS9nTf2pkLI0i6Zukq3VPA8dGRLl+uyvxcduFPpZtRT5uITuxbIuIfyMlQfuR+kLvCR+3Xehj2Xa13+XAN7OnrX7cfpHU7ucTEVGL8Tqa4ph1AtB/zM2mE7pYZ+eSdfuiMCrlEGBcDfaXJ3Oz6eb6rKxT4bjNW1UKJH2NVMXkWdIJans3m5Sam0193JaoQdl2J7fHbRlXZ9MTenjxaW429XHbvauzaU/Ltjt5OW7fAawH/jkbGXnDA3hrts4F2bzv92B/c7NpQ49ZNwLuPwrdzr1G0vAKLccPKVm3L8YW/b2i4lpWzub+rKxT4bjN1TEr6SvAJ4DngDdGRFsvduPjtowalW13cnncVvACqbvKQcAYUk9JXZmZTQ8pt1DSCGDv7GlujtsKqi3b7uTpuB1AalRdye7ZY1QP9tUUv7W+A9BPZK3sZ5KuyL+zdLmko0gNd58G/lyDlzwtmz4WEbm7bdoXDfisrFPhuC3bJWArknQJ8G+kf+5vioiHe7MfH7ebqlXZ9kDujtsuHEk6QV0G9KSnlT+T7syMl3RkmeXvJPWEc39E5LIL0CLVlm13cnHcRsSuEaFyD1K3oAD/ls3bvwf7a4rfWicA/UuhnuiXJU0qzJS0LfC97Oklxf1JS/qIpNmSrinekaRdJJ0laWjJfEl6V9Frfb3m76JFSPpSVrZfKrO46s/KOlUqW0n7Szo+G/2zeP4gSZ+ks6u6XBy3kv6HNNLpMtIJardXi3zc9kwty9bHbSdJh2dlsUkNBElvAH6QPf1BRKwrWnZNVrYfKd4mW+cr2dNLs2O1sM1k4JLs6cW1fB/NqNZlK2mapKMkqWT+iOzO2EmkOwrfrvmbaQHN/lvrKkD9SETcJOlSUov7RyT9njTC4bHASOA2Nm3cM47Ub21pg7UxwPXAZZJmkgb22IrUW0Ch39nvRMTl9XgvzUbSgXR+6SB1ywfwRUmfKsyMiOJR+XYgle0Opfvr5WfVkmpctrsCtwLPZ8ftM6Tb0PuQulVcD3w6In5Ty/fQjCSdCHwme9oBfLTk/3TB7Ii4pOi5j9tu1KFsd8XHbcEk4CpgWVYWT5P+90yk87fhDlKXlcV2IZVtuTZpXydd3T4BaJf0B9JV/zcCw4BvR8TPavw+mlGty3Z/UtkulvQQaQCs7bL5Y0kjCr8nIh6t/VtpCU39W+sEoJ+JiA9Jugf4MKk+2kBSQ5wrgUuryBYXAF8l1TObROqKbgDpB+PHwBURcWeNw29mI4HXlpk/ucy8HqnhZ9Xf1bJsHyL1PHEo6R/aEUAAC0n/+L4bEX/vZZz9zZiivw/OHuXMoPMqaLd83AK1L1sft51mkPqiP4L0G3AYINL/npuB6yLitmp2GBHrJJ1E6qLxfOAtwDrg78D3IuKG2oXf1GpdtjNIXd8eTOr2cwzpJHUu8CNSYvV4rYLPm0b/1ioi6rl/MzMzMzNrIm4DYGZmZmaWI04AzMzMzMxyxAmAmZmZmVmOOAEwMzMzM8sRJwBmZmZmZjniBMDMzMzMLEecAJiZmZmZ5YgTADOrSNKbJV0l6TFJyyWtlvSspD9J+qqkQxsdYz1JOk9SSLq60bGUI2lBFt9xFZa/Llseki6ssM7WktZm6+yRzevV+5Y0Ldtuei/ey9XZtudVu20P939Atv+rqtxuiKR3S7pV0nxJL0taJWmhpN9IulDShD7EFZKqHpBH0txs212r3G68pP+V9IikFZJezd7L/ZK+K+nUamNpJEm7ZuUwt9GxmPUnTgDMbBOStpN0F/Ab4DzSCIXTgZ+SRtecBHwK+IukaxsUpqWROgGmVVg+rcLfxY4gfb6LI+KxmkRVoklO0k7Oprf0dANJrwUeB34AnAA8A/wS+BnQARxOGgm4Q9IHahptHUg6EmgDPgnsANwL3AQ8DOxEGkn3soYFaGabzaBGB2BmzUXSGNKJwe7An4CPRMSDJeuINMz8hcDUzR7k5nMrcB+wvNGBVDAdOJvKJ/dHAeuAR4E3SBoUEWvLrFPYV0Gzv+/eOBlYAfyuJytnJ/8zgKGkBOBzEbG4ZJ0hwDuAzwCTexnXZvn+SBoK/AjYCvga8NmIeKVknYOAfnUHAHiKVIZrGh2IWX/iBMDMSn2PzpP/YyJidekKERHZ8hNbuRpQRCynuU+C78qmB0gaGREvFhZIGgS8AXiQdCfnP4GDgL+U7GNaNp1emNEP3ndVJE0B9gJ+WnrSW2H9ocBPSCf/X42IT5dbL/tu/FjSLcBrehNbRMzuzXa9cASwI7AoIj5VIZa/k+7w9RsRsQbYXGVo1jJcBcjMNpA0GXhn9vSCcif/pSLir2X289qsjcDfJC3J2g4sknSTpNdVeO0u64BLuihbflHJ/IGSPijp3qJ2waBdtAAADq9JREFUCkskzZT0NUnblKy/h6QfSpqXrftSVp/6VkmnlKxbsS68pFMkXSnpUUnLJL0iqSOrR71zhfcwPdvfNEkHSfq5pOeybR+S9J5y21USEU8AC0hVeI4oWXwg6WrvDODubN60kni2Ag7Int5VNL/LNgCSTlJqB7JC0guSfifpqArrXg08mT2doM42CRWrBEmaJOmG7HN8VdLsrK59b/9nFT7Xnlb/OQfYBVgMfLa7lSNiTZm7ZBvaQ0gaIel/svexStKDRetVbAMgaYKka7JyWCWpTdKnJQ3s4fsotm02fbbaDVXU3iA77u/NvjfLJf1W0uEVtttL0hey9Repsw3RLyW9tZvXnCrpiuw7tSo7zh5War8woWi9itXListW0umS/pwdsy9J+kOluLP195f0M0nPS1op6e+S3l26X7P+ygmAmRU7jvS78FBEPNKH/VwM/CswGPgr8HPgOdKJ2D2S3tnFttX6AXApsD/p6vZNwEPA1sAngImFFSXtA9wPnAu8DNxOujq+GHgL8L4qXvfHwGnASuD3pKolQ0n1qGcqXXWu5K3An4HdgN+SrrruC3xf0ieriAEqtwOYVrT8T8DaMusU6v8/FRHtPXkxSZ8mVRE6jFTOvwK2B+4ETiqzyT3AzdnfK4EfFj1uKrP+/qTyeC0pKfkT6TO8BPhmT2Is4x3Aq6T6+z1xQjb9SU+S4G4MI91d+TjwBOm78GRXG0A6eQb+BryLFPvPSMnef5PuTlRrfjbdW9Kxvdge0nu4ifQbcTswB3gTML3Cd/oTwOeAUaRj5VZgLvA24FeSPlHuRSSdS7pz9T5A2WvNyF73k8DR1QQt6QvADcBq4A5gIXAM8AdJry+z/jGk7+eJwBLSZ/YicIWkr1bz2mZNKyL88MMPP4gIgGuBAL7fx/28FdiuzPwTSP+EnwNGlCy7Onvt8yrs86Js+UVF8yZk8+ZXeL39gW2Lnl+Zrf8fZdbdEnh9ybzzsvWvLrP+aWXewyDSCVoAvyqzzfRsWQDvLll2TjZ/eel+uynr92Tb/bVk/h2k+v+js+d/JZ3EDCxa58vZttf15H2T7hasJdW3PqFk2b8VvbfpJct2zebP7eJ9XF20/UXAgKJlR2bvZR2wc5XH4nhgPXBHFdssyOI4pw/fgWlF7+eBcsdntl6Q1aormf/3bNk1wJCi+a8hNUYu7HvXHsYzIIsjsvK4i3R345+AbbrZdm623TrgtJJlF2TLXgS2L1l2VLn4SMndctJvwfiSZYdkx9fa7NhWyfKpwNSeHFtFZfQccFBJWVyRLftdyTYjgEXZss8Xvz4p6X2p0mfmhx/96eE7AGZWbFw2LVtNQKlb0KvLPHYtXi8ifh0RS0q3j4jbST0JjaHKq3gVFKo1zKzweg9GxDNFs7bLpr8qs+6KiPhzT184In4SES+XzFsbEZ8jnUC8OatiU87NEXFlybbXAbOAkcDBPY2Dzqo7BxZeL6sicjjwj4h4IVs+g1Ql6KCibadl0+k9fK2PkO4YXJ99lsXxf5Xa1B+/H/h8RKwv2vfdpDs1A6j+uDmZdBW5x73/0P334GNlvgNd9Z7z4XLHZyWSjiBV4VoOfDSK7kJExKOkJLMqWXkeR7pTJdJn/9+kRPEZSQ8oVaXrqnrRrRGx0d2HiLiUVMVsK9IJe/GyGRExt0wsfwG+Q7pD+PaSxZ8hJdL/GxE/iIgo2XZWRMzq5u2W+v8itW8o7GM96c4EwBGSBheteyqph6THScdhFG13L6mNlFm/50bAZlaNvYB/LjP/O6SrhBtIGgccD+xNqgJQ+L3ZO5tOIZ189MVs0hW54yT9J+nEdF4X6/+VdMXzMkmfA+6OiFd7++JZNZ+3krpF3ZLOapWDsr8nka66lvpFhV3OJl3h3LGnMUTEHEnzSXXWjyBVczmQlEjMKFr1blLXrdOAv2bJwoHZsrvomUI9/+sqLL+OjROM3vhl6UlfZjap6kiPyyZzMunK9c/7GFexY9j0xHUl8MEy6y7JThyrUSjnX0RqkF3qWuBbVe6TiCgkpvuSqre8nvR5bUe6W3YpcIqk46J81adKn/u1pLs000jV/zbIjrPjsv2PAYZkiwq9Jk0pWncgqUoRwPereW/d2OT7FhFLJL0AjAbGAk9niwpl/+PiJLTIDUDZRuFm/YkTADMrtjSbblNuYUR8A/hG4XnW8G6TQZCU+kT//0m30ysZ2esoO+N5KWuYdyXpxONiSU+R6u/eAdwYG/f68lXSSfKxpLr3r2YNMmeQqsH0qN2DUg873wPeS7qaWkml9zi/wvxCLz7DehJHkRmkuuLTSAnAUUXzC/5IqvoxDfgKqYegQcCCSI2Je2J8Nq1Uh31uTwPuQs3KRqkB+OHAPRFRTePXpaT3Wul7sKGtQ3b3q6s6/V0lpJV0Wc4RsUzSclI7l6pFxMOkvv8BkLQf6aT2LOCNpLr+5eq6d/e5jy+eKentpO/mmC7CKf6OjCP9ZqyNiI4utqlWV8fUaDY+pnbKppU+t958nmZNx1WAzKzYzGxaTRWUjUg6hHQlcTCpXvieZFfHI0LAlwqrVrnrsr9XEXET6er3eaSTjRWk2/hXAbNV1CNPRLwcEW8EXkeqZ343qU71p4GHJf1XD2P5OKmB4mLgjOz1h0WEsvdYqEpU6T2Wu7LYF9Oz6bSSaaH3HyJiGfAIcHh2pbWwTmHbZlHLsnk7qcrSrVVu1+fvQZFVNdhHXUXEQxFxNp13Sco15q6KpPGkcQfGkL7z+5JO9gdm35HCwGnF35G69KxT4Up+t5tVmF/r765ZQzgBMLNid5D+8e0nae/uVq7gFNI/9W9FxP9GxGMRsbKoWsekCtsVqhxsWWH5JncaCiJiWUT8MCLeExF7Zq9xV7bNl8us/5eI+HxEvJl0+/98UqPDiyTt0d0bpLOr1A9ExI8jYkFJVaJK77FeitsBjCJd9Z5V5qp3cTuAcgOAdeepbLprheWV5jfKO7JpNfX/IfU6A3BaSf3wzaXLcs4+415d/e/Gb7Np2TsfleIpmv9U0bzjgeGk9i7/GRGPRMRLRSfj5b4jz5F65xokaWKZ5ZvDomxa6fdm180Uh1ldOQEwsw0i4nE6u2a8TGmk02oVbvcvKF2QVcl4U+n8TOHkYc8y2w2n8mi3m8iqtBTqIu/XzbqrI+Jq0si3Il2p7E5X7/FNVD6BqouIeJJUNWEg8FHSyeGMMqsW7ggcR+fV7Z7W/6don2dXWF5pfiG522zVTrO658cCf4uITT6nblxL6ipyB0rqtG8mhXI+XlK5amSVyrkiST2547ZLNl1YYXl3n/v0onldfUeG0jk2wwYRsY7UpS6k6nWNUPiOnKby406cuTmDMasXJwBmVupDpDq9byD1k71/uZWyPvXLnZwURuU8V9KWRetvRaqiM6rC6/4hm76r+Cp8dvJ/KZ0nJ8UxHJAN8DO8zP4KfbnPK1r/Q+Wu8Evanc6RXHtSx7fwHi8oPknIrlp21RtMPRVOGv+15HmxwsnNR0kn4/Oy5KGnvkuqAvEuSf9UvEDSv1K5ysyzpCRgO0mjq3i9vjieNC5DtdV/yO7mnE6K+d8k/Z+kHUrXy06qD+troGX8kdQP/ijgm8V3ISRNpbMHm2qcIOkWSceUntgqOYnUyxOkMS7KOUWbDpb3flJyvoI0JkfB7KJttitafwjwbdJo4+VcTGq0/SmVGRRQ0p6SNrlIUEM/JfX9vyfwmeLESdJrgQ/X8bXNNhs3AjazjUTEUkmHkQYbOhx4QFIH8Cipx50tST3VFE6k72Tjk+argH8h9TAzR9I9pCvrR5JOqK4E3l3mde+R9AvSidtMSX8kVcs5mHTSeRWpqk6xCcCNwMuSZpKuNg4h9Ve/exZvcb3+9wPflTQH+AfppGX77H0OITUa3mRk4zK+ROr95wPA0ZIeIF3xPIpU//9p6nNi2JXppAHOCifYmyQAEfGspFmkz6+wTY9FxN8lfRb4IvALSfeSPvt9SAnUt4CPldlujaQ7SFVyHpD0J1Ld+KUR8e/VxFCFk7NptdV/gNTlo6SjSSfD7wXOzxqMzyXFPpZ0nG1POmG9tq8BF712SHoX6TM8DzhG0p9JCcHRpF5tDqKLanFlDCCV/zuA57Nj9hlSEr8XaVA6SPX2/6/CPr4F3CTpPlKD4D1JZbAOeF9ELC5a9+ekHrAOANolTQdeIV1Y2JrKx8pfs6TicuCq7HibSfp+TiIdZ+fTmWDUVESszMr+duALwJnZ57496TfsW6Qke009Xt9sc/EdADPbREQsjogjSF1mXpPNPpZ0VfRw4AXg68BrI+LY4rrmWb/zB5MG2llBZ3WTW0hJQVfVMd5JGvH1GVJXiweS2iUcSPmePO4D/oN0ZXs8qfHiG0n1iL8G7BMRfyta/7OkE4sXSSfop5K6I5xBGtirR1UrsvECDsli25rU2HQ86erlW2jMyUFxVZ6OkpOxYsWJwfRqXyQivkSqvnEf6eTueNIV/jfR9dX295GuEA8klfV7SA2oa07SMFKXobMjotcniln3nZNIsd9B6i7zONJxuh8pifwMMCkiLuhr3CWv/Q/S9+Y6Ul36k0j1zz9P+h5W69ekMvka8BjpuD+Z9L2GdOX7+Ig4q4tGs98kfWYidSM6iVRl55iIuLEk/rWkhPgrpMbybyb1wHU3KXkp1z1uYdsrSd/5q+kcK+BI0gWBr5IuOtRNRPyO9PtwO6ka2EmkxPpDpN896OwxzaxfUvnuls3MzPqnrPvJ24AvRsRnGh1Pf1fU3e9u5Qb2ypPs7sA1pDEaTuhufbNm5TsAZmbWalaRrpRf1ehArP+RtK2kcuObvI7O8RGu3qxBmdWY2wCYmVlLiYjf0tmlpVm19gV+J+kfpLYOq0ltig7Ill8bETc3KjizWnACYGZmZtZpNqnnsaNIbZ62IrUbupN05f+6hkVmViNuA2BmZmZmliNuA2BmZmZmliNOAMzMzMzMcsQJgJmZmZlZjjgBMDMzMzPLEScAZmZmZmY54gTAzMzMzCxH/h/7re7tXbCwnwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Electron Number')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, enum_error_0_888_vg, linestyle='-', linewidth=1, color='blue', label='Snapshot 0')\n", - "ax.plot(sigma_multipliers, enum_error_1_888_vg, linestyle='-', linewidth=2, color='red', label='Snapshot 1')\n", - "ax.plot(sigma_multipliers, enum_error_2_888_vg, linestyle='-', linewidth=2, color='green', label='Snapshot 2')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "#plt.savefig('E_num_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot electron number errors using the three DOS definitions \n", - "plt.figure(figsize=[12,9])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Gaussian Width / Grid Spacing')\n", - "ax.set_ylabel(r'Error in Band Energy (meV/atom)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(sigma_multipliers, eband_error_0_888_vg, linestyle='-', linewidth=2, color='blue', label='Snapshot 0')\n", - "ax.plot(sigma_multipliers, eband_error_1_888_vg, linestyle='-', linewidth=2, color='red', label='Snapshot 1')\n", - "ax.plot(sigma_multipliers, eband_error_2_888_vg, linestyle='-', linewidth=2, color='green', label='Snapshot 2')\n", - "\n", - "used=np.array([[2.0,2.0],[-18.0,10]])\n", - "ax.plot(used[0], used[1], linestyle='--', linewidth=1, color='black', label='Width Used')\n", - "plt.ylim(-18.0,10.0) \n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1, 1))\n", - "plt.savefig('E_band_vs_G_width_all_states.png',dpi=100,bbox_inches='tight')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scratchpad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cells below include various older calculations and ideas. Most of them would have to be updated to work with the current code structure." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dos_qe' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdos_qe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdos_qe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mra_sigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mewidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mra_enum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mra_sigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'dos_qe' is not defined" - ] - } - ], - "source": [ - "#ewidth=dos_qe[: , 0][1]-dos_qe[: , 0][0]\n", - "ewidth = (dos_qe[: , 0][1]-dos_qe[: , 0][0])*0.5\n", - "print(ewidth)\n", - "ra_sigma = np.linspace(0.5*ewidth, 5*ewidth, 11)\n", - "ra_enum = np.zeros(len(ra_sigma))\n", - "ra_eband = np.zeros(len(ra_sigma))\n", - "ra_en = np.linspace(dos_qe[0, 0], dos_qe[-1, 0], int((dos_qe[-1, 0]-dos_qe[0, 0])/ewidth)) \n", - "for i in range(len(ra_sigma)):\n", - " print(\"i:\", i)\n", - " # Generate electron number \n", - " ra_enum[i] = gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[i])\n", - " # Generate band energy\n", - " ra_eband[i] = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=ra_sigma[i])\n", - "print(ra_en[1]-ra_en[0])\n", - "print(ra_enum)\n", - "print(ra_eband)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "#ax.set_ylim(767.95, 768.05)\n", - "\n", - "ax.hlines(enum_ref, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.plot(ra_sigma, ra_enum, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of smearing [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "\n", - "ax.hlines(eband_ref/Ry2eV, ra_sigma[0], ra_sigma[-1], linewidth=3)\n", - "ax.scatter(ra_sigma, ra_eband, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()\n", - "print(np.min(abs(ra_eband-eband_ref/Ry2eV))/256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(4*ra_sigma[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=ra_sigma[1] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=1, color='red', label='computed (adjusted width)')\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=1, color='black', label='QE-DOS')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate electron number \n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*0.79255\n", - "gen_enum(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"band energy\" is also called the \"single-particle energy\" or the \"one-electron energy\" and is obtained from a weighted sum of the eigenvalues\n", - "\n", - "$$E_{band} = \\sum_i \\sum_k w_k\\, f(\\epsilon_{ik}) \\epsilon_{ik}\\ .$$\n", - "\n", - "where $i$ labels the band, $k$ labels the k point, $w_k$ is the weight of the k point, and $f(\\epsilon_{ik})$ is the occupation of the state, which is given by the Fermi-Dirac distribution function evaluated at energy $\\epsilon_{ik}$.\n", - "\n", - "For look at the particular values for a given snapshot (data from ```blake.sandia.gov:/home/acangi/q-e_calcs/Al/datasets/vasp_econ_snapshots/298K/2.699g/170726180545.0/100Ry_k333```). QE prints the one-electron energy in the standard output, together with all the other energy contributions, e.g., \n", - "```\n", - "The total energy is the sum of the following terms: \n", - "\n", - "\n", - " one-electron contribution = 737.82754675 Ry\n", - " hartree contribution = 4.77073244 Ry\n", - " xc contribution = -554.09988814 Ry\n", - " ewald contribution = -1375.56724973 Ry\n", - " smearing contrib. (-TS) = -0.02019845 Ry\n", - "```\n", - "However, what QE prints as the \"one-electron contribution\" is not the sum of the eigenvalues, but instead (see source code ```~/PW/src/electrons.f90``` lines 638-640)\n", - "\n", - "$$\\text{one-electron contribution} = \\sum_i \\epsilon_i - (E_h + E_{xc})$$\n", - "\n", - "In order to correctly compare the band energy obtained from integrating the DOS with the QE output we need to add the hartree and exchange-correlation contributions to the one-electron contribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "737.82754675" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correct band energy from QE output is" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "188.49839105\n" - ] - } - ], - "source": [ - "eband_qe = 737.82754675+4.77073244-554.09988814\n", - "print(eband_qe)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Error in band energy compared to QE output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By adjusting the width of the Gaussian smearing we achieved the desired accuracy in the band energy, here < 1 meV.\n", - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can also increase the smearing width and also obtain an accurate band energy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]\n", - "emax = dos_qe[: , 0][-1]\n", - "ra_en = dos_qe[: , 0]\n", - "sigma_mod = sigma_qe*29.177\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also generate a different energy grid. However, it the integral quickly converges with respect to the number of grid points. We can increase the range of the enegy grid to make sure the DOS decays to zero at the limits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*29.908\n", - "eband = gen_eband(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check for another snapshot.\n", - "The band energy from the QE output of snapshot 1 is:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eband_qe_01 = 739.08601067+4.18224128-553.98606038\n", - "print(eband_qe_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ewidth_01=dos_qe_01[: , 0][1]-dos_qe_01[: , 0][0]\n", - "print(ewidth_01)\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "ra_en_01 = np.linspace(emin, emax, int(len(dos_qe_01[: , 0])))\n", - "ra_sigma_01 = np.linspace(20*ewidth_01, ewidth_01, 101)\n", - "ra_enum_01 = np.zeros(len(ra_sigma_01))\n", - "ra_eband_01 = np.zeros(len(ra_sigma_01))\n", - "for i in range(len(ra_sigma)):\n", - " ra_en_01 = dos_qe[: , 0]\n", - " # Generate electron number \n", - " ra_enum_01[i] = gen_enum(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - " # Generate band energy\n", - " ra_eband_01[i] = gen_eband(k_weights_qe_01, ra_en_01, eigs_qe_01, sigma=ra_sigma_01[i])\n", - "print(ra_enum_01)\n", - "print(ra_eband_01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(768, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_enum_01, linestyle='-', linewidth=3, color='blue', label='Electron number')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'Width of energy bin [Ry]')\n", - "#ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.hlines(eband_qe_01, ra_sigma_01[0], ra_sigma_01[-1], linewidth=3)\n", - "ax.plot(ra_sigma_01, ra_eband_01, linestyle='-', linewidth=3, color='red', label='Band energy')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe_01[: , 0][0]-1\n", - "emax = dos_qe_01[: , 0][-1]+1\n", - "# Decrease the number of energy grid points\n", - "ra_en = np.linspace(emin, emax, 2*int(len(dos_qe_01[: , 0])))\n", - "sigma_mod = sigma_qe*22.90#22.882\n", - "eband_01 = gen_eband(k_weights_qe_01, ra_en , eigs_qe_01, sigma=sigma_mod )\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband_01))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error_01 = eband_01-eband_qe_01\n", - "print(\"Error in Rydberg\", eband_error_01)\n", - "print(\"Error in eV\", eband_error_01*Ry2eV)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The corresponding DOS looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS(k_weights_qe_01, ra_en, eigs_qe_01, sigma=sigma_mod )\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe_01[: , 0], dos_qe_01[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(ra_en , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This illustrates the issue with Gaussian smearing. The smearing width differs between the different snapshots. This means we cannot choose a fixed smearing width and obtain high accuracy in the band energy throughout a priori (i.e. without knowing the true value of the band energy). However, this might be fine, since we need to choose the smearing width only for the generation of training data. It might be somewhat inconvenient, but for each snapshot in the training data we can find the corresponding smearing width which will yield a band energy up to a target accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do\n", - "* Perform search over smearing width using sp.optimize library. But this requires adapting the definitions of the functions above.\n", - "* Different representation of the $\\delta$ function, for example Marzari-Vanderbilt (MV)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define MV representation of delta function\n", - "def MV(en, mu, sigma):\n", - " x = (mu-en)/sigma\n", - " result = 1.0/np.sqrt(np.pi)*(2.0-np.sqrt(2)*x)*np.exp(-1.0*(x-(1.0/np.sqrt(2)))**2)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check of smearing functions\n", - "# Generate data\n", - "ra_en = np.linspace(7.,9.,601)\n", - "ra_gaussian = gaussian(ra_en, eF=fermi_energy, sigma=sigma_qe)\n", - "ra_MV = MV(ra_en, mu=fermi_energy, sigma=sigma_qe*2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(ra_en, ra_MV, linestyle='-', linewidth=3, color='red', label='MV')\n", - "#ax.plot(ra_en, ra_gaussian, linestyle='-', linewidth=3, color='blue', label='gaussian')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating DOS from eigenvalues\n", - "def gen_DOS_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " dim_bnd = len((array_eigs[: , 0]))\n", - " dim_k = len((array_eigs[0 , :]))\n", - " ra_en = array_en #dos_qe[: , 0] # energy grid (same as QE-DOS input/output) \n", - " ra_dos_ik = [[] for i in range(dim_bnd)]\n", - " ra_dos = np.zeros(len(array_en)) #create empty array\n", - " for idx_bnd in range(dim_bnd):\n", - " for idx_k in range(dim_k):\n", - " if (smearing_type == 1):\n", - " smearing = gaussian(en=array_en, eF=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " elif (smearing_type == 2):\n", - " smearing = MV(en=array_en, mu=array_eigs[idx_bnd , :][idx_k], sigma=sigma)\n", - " else:\n", - " print(\"Error, choose valid smearing function.\")\n", - " ra_dos_ik[idx_bnd].append(smearing)\n", - " # Sum the Gaussians over idx_band and idx_k\n", - " ra_dos += k_weights[idx_k]*ra_dos_ik[idx_bnd][idx_k]\n", - " return ra_dos #, ra_dos_ik" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_en = dos_qe[: , 0]\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_qe*Ry2eV,smearing_type=2)\n", - "\n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='--', linewidth=3, color='red', label='computed')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(1.5, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function generating band energy from DOS\n", - "## Integrate DOS*E*FD to obtain band energy\n", - "def gen_eband_ST(k_weights, array_en, array_eigs, sigma, smearing_type):\n", - " # input:\n", - " ## k_weights: weights of k-point summation (taken from QE output)\n", - " ## array_en: energy grid [eV]\n", - " ## array_eigs: array[dim_bnd, dim_k] containing eigenvalues (\\epsilon_{i,k})\n", - " ## sigma: width of Gaussian [eV]\n", - " # output:\n", - " ## array_dos: ra_dos\n", - " ## array_dos_contr: ra_dos_ik (terms for each i,k)\n", - " ra_fd = fd_function(array_en, eF=fermi_energy, t=temp)\n", - " #ra_dos, ra_dos_ik = gen_DOS(k_weights_qe, array_en, eigs_qe, sigma)\n", - " ra_dos = gen_DOS_ST(k_weights_qe, array_en, eigs_qe, sigma, smearing_type)\n", - " eband = sp.integrate.trapz(ra_dos*array_en*ra_fd, array_en)\n", - " #Convert from eV to Ry for comparison with QE output\n", - " eband_Ry = eband/Ry2eV\n", - " return eband_Ry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate band energy\n", - "emin = dos_qe[: , 0][0]-1\n", - "emax = dos_qe[: , 0][-1]+1\n", - "ra_en = np.linspace(emin, emax, int(len(dos_qe[: , 0])))\n", - "sigma_mod = sigma_qe*30.56825\n", - "eband = gen_eband_ST(k_weights_qe, ra_en , eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - "print(\"smearing width {0} eV ({1} Ry)\".format(sigma_mod, sigma_mod/Ry2eV))\n", - "print(\"Band energy {0} Ry)\".format(eband))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error in band energy (due to discretization of the energy grid in DOS calculation and choice of smearing width)\n", - "eband_error = eband-eband_qe\n", - "print(\"Error in Rydberg\", eband_error)\n", - "print(\"Error in eV\", eband_error*Ry2eV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "ra_dos = gen_DOS_ST(k_weights_qe, ra_en, eigs_qe, sigma=sigma_mod, smearing_type=2)\n", - " \n", - "# Plot data \n", - "plt.figure(figsize=[8,6])\n", - "ax = plt.subplot(1,1,1)\n", - "ax.set_xlabel(r'E [eV]')\n", - "ax.set_ylabel(r'D(E)')\n", - "plt.rcParams.update({'font.size': 22})\n", - "\n", - "ax.plot(dos_qe[: , 0], dos_qe[: , 1], linestyle='-', linewidth=3, color='black', label='QE-DOS')\n", - "ax.plot(dos_qe[: , 0] , ra_dos, linestyle='-', linewidth=3, color='red', label='computed (adjusted width)')\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right',frameon=False, bbox_to_anchor=(2, 1))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0/Al.dos b/ml-dft-sandia/notebooks/dos2eband/snap_0/Al.dos deleted file mode 100644 index ad752533b..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0/Al.dos +++ /dev/null @@ -1,140 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.770 eV - -4.627 0.7863E-04 0.8668E-05 - -4.517 0.3781E-03 0.5035E-04 - -4.407 0.1613E-02 0.2282E-03 - -4.297 0.6114E-02 0.9021E-03 - -4.186 0.2062E-01 0.3175E-02 - -4.076 0.6197E-01 0.1001E-01 - -3.966 0.1664E+00 0.2835E-01 - -3.856 0.4005E+00 0.7250E-01 - -3.746 0.8666E+00 0.1680E+00 - -3.635 0.1693E+01 0.3547E+00 - -3.525 0.3003E+01 0.6858E+00 - -3.415 0.4866E+01 0.1222E+01 - -3.305 0.7258E+01 0.2022E+01 - -3.194 0.1005E+02 0.3130E+01 - -3.084 0.1305E+02 0.4569E+01 - -2.974 0.1604E+02 0.6337E+01 - -2.864 0.1888E+02 0.8418E+01 - -2.753 0.2147E+02 0.1079E+02 - -2.643 0.2381E+02 0.1341E+02 - -2.533 0.2594E+02 0.1627E+02 - -2.423 0.2789E+02 0.1935E+02 - -2.312 0.2970E+02 0.2262E+02 - -2.202 0.3136E+02 0.2608E+02 - -2.092 0.3291E+02 0.2970E+02 - -1.982 0.3440E+02 0.3350E+02 - -1.872 0.3587E+02 0.3745E+02 - -1.761 0.3730E+02 0.4156E+02 - -1.651 0.3864E+02 0.4582E+02 - -1.541 0.3987E+02 0.5022E+02 - -1.431 0.4102E+02 0.5474E+02 - -1.320 0.4220E+02 0.5939E+02 - -1.210 0.4345E+02 0.6418E+02 - -1.100 0.4479E+02 0.6912E+02 - -0.990 0.4609E+02 0.7420E+02 - -0.879 0.4726E+02 0.7941E+02 - -0.769 0.4820E+02 0.8472E+02 - -0.659 0.4898E+02 0.9012E+02 - -0.549 0.4979E+02 0.9561E+02 - -0.438 0.5083E+02 0.1012E+03 - -0.328 0.5218E+02 0.1070E+03 - -0.218 0.5365E+02 0.1129E+03 - -0.108 0.5493E+02 0.1189E+03 - 0.002 0.5582E+02 0.1251E+03 - 0.113 0.5635E+02 0.1313E+03 - 0.223 0.5677E+02 0.1376E+03 - 0.333 0.5741E+02 0.1439E+03 - 0.443 0.5844E+02 0.1503E+03 - 0.554 0.5979E+02 0.1569E+03 - 0.664 0.6123E+02 0.1637E+03 - 0.774 0.6248E+02 0.1706E+03 - 0.884 0.6343E+02 0.1775E+03 - 0.995 0.6409E+02 0.1846E+03 - 1.105 0.6458E+02 0.1917E+03 - 1.215 0.6501E+02 0.1989E+03 - 1.325 0.6548E+02 0.2061E+03 - 1.436 0.6612E+02 0.2134E+03 - 1.546 0.6708E+02 0.2208E+03 - 1.656 0.6844E+02 0.2283E+03 - 1.766 0.7012E+02 0.2361E+03 - 1.877 0.7180E+02 0.2440E+03 - 1.987 0.7306E+02 0.2520E+03 - 2.097 0.7359E+02 0.2602E+03 - 2.207 0.7342E+02 0.2682E+03 - 2.317 0.7296E+02 0.2763E+03 - 2.428 0.7286E+02 0.2843E+03 - 2.538 0.7363E+02 0.2924E+03 - 2.648 0.7532E+02 0.3007E+03 - 2.758 0.7751E+02 0.3093E+03 - 2.869 0.7957E+02 0.3181E+03 - 2.979 0.8100E+02 0.3270E+03 - 3.089 0.8164E+02 0.3360E+03 - 3.199 0.8167E+02 0.3450E+03 - 3.310 0.8147E+02 0.3540E+03 - 3.420 0.8142E+02 0.3629E+03 - 3.530 0.8176E+02 0.3720E+03 - 3.640 0.8252E+02 0.3811E+03 - 3.751 0.8358E+02 0.3903E+03 - 3.861 0.8479E+02 0.3996E+03 - 3.971 0.8607E+02 0.4091E+03 - 4.081 0.8743E+02 0.4187E+03 - 4.191 0.8886E+02 0.4285E+03 - 4.302 0.9032E+02 0.4385E+03 - 4.412 0.9169E+02 0.4486E+03 - 4.522 0.9288E+02 0.4588E+03 - 4.632 0.9384E+02 0.4692E+03 - 4.743 0.9453E+02 0.4796E+03 - 4.853 0.9493E+02 0.4901E+03 - 4.963 0.9497E+02 0.5005E+03 - 5.073 0.9469E+02 0.5110E+03 - 5.184 0.9423E+02 0.5214E+03 - 5.294 0.9383E+02 0.5317E+03 - 5.404 0.9369E+02 0.5420E+03 - 5.514 0.9383E+02 0.5524E+03 - 5.625 0.9407E+02 0.5627E+03 - 5.735 0.9411E+02 0.5731E+03 - 5.845 0.9377E+02 0.5835E+03 - 5.955 0.9311E+02 0.5937E+03 - 6.065 0.9248E+02 0.6039E+03 - 6.176 0.9231E+02 0.6141E+03 - 6.286 0.9288E+02 0.6243E+03 - 6.396 0.9411E+02 0.6347E+03 - 6.506 0.9559E+02 0.6452E+03 - 6.617 0.9683E+02 0.6559E+03 - 6.727 0.9754E+02 0.6667E+03 - 6.837 0.9776E+02 0.6774E+03 - 6.947 0.9777E+02 0.6882E+03 - 7.058 0.9793E+02 0.6990E+03 - 7.168 0.9862E+02 0.7099E+03 - 7.278 0.1001E+03 0.7209E+03 - 7.388 0.1022E+03 0.7322E+03 - 7.499 0.1045E+03 0.7437E+03 - 7.609 0.1063E+03 0.7554E+03 - 7.719 0.1070E+03 0.7672E+03 - 7.829 0.1064E+03 0.7789E+03 - 7.940 0.1048E+03 0.7905E+03 - 8.050 0.1031E+03 0.8019E+03 - 8.160 0.1021E+03 0.8131E+03 - 8.270 0.1023E+03 0.8244E+03 - 8.380 0.1035E+03 0.8358E+03 - 8.491 0.1052E+03 0.8474E+03 - 8.601 0.1061E+03 0.8591E+03 - 8.711 0.1050E+03 0.8707E+03 - 8.821 0.1004E+03 0.8817E+03 - 8.932 0.9173E+02 0.8918E+03 - 9.042 0.7935E+02 0.9006E+03 - 9.152 0.6440E+02 0.9077E+03 - 9.262 0.4868E+02 0.9131E+03 - 9.373 0.3404E+02 0.9168E+03 - 9.483 0.2187E+02 0.9192E+03 - 9.593 0.1284E+02 0.9206E+03 - 9.703 0.6849E+01 0.9214E+03 - 9.814 0.3306E+01 0.9217E+03 - 9.924 0.1438E+01 0.9219E+03 - 10.034 0.5617E+00 0.9220E+03 - 10.144 0.1965E+00 0.9220E+03 - 10.254 0.6144E-01 0.9220E+03 - 10.365 0.1713E-01 0.9220E+03 - 10.475 0.4250E-02 0.9220E+03 - 10.585 0.9372E-03 0.9220E+03 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0/EIGS b/ml-dft-sandia/notebooks/dos2eband/snap_0/EIGS deleted file mode 100644 index b66c97b79..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0/EIGS +++ /dev/null @@ -1,461 +0,0 @@ --3.321300, -3.259400, -3.263400, -3.201900, -3.201600, -3.259700, -3.197800, -3.198000, -3.202000, -3.140500, -3.140500, -3.201700, -3.140000, -3.140400 --2.811300, -3.074300, -3.063600, -3.022600, -3.029100, -3.073400, -3.025900, -3.013600, -3.021500, -2.974600, -2.970600, -3.026100, -2.976600, -2.968400 --2.796900, -2.752000, -2.735300, -2.994600, -2.986600, -2.741500, -2.997700, -3.011200, -2.996100, -2.945800, -2.954500, -2.991800, -2.955400, -2.955300 --2.777600, -2.707800, -2.724100, -2.815700, -2.818300, -2.726000, -2.828600, -2.826900, -2.814700, -2.928000, -2.922700, -2.813700, -2.914400, -2.926400 --2.740600, -2.680300, -2.695200, -2.667800, -2.663900, -2.690300, -2.667500, -2.667600, -2.656500, -2.781000, -2.782800, -2.658400, -2.784500, -2.779500 --2.736300, -2.662400, -2.682100, -2.626100, -2.628500, -2.647800, -2.602300, -2.602900, -2.639800, -2.751100, -2.755200, -2.637500, -2.760500, -2.754100 --2.706000, -2.559300, -2.530000, -2.480300, -2.499800, -2.541400, -2.496900, -2.502500, -2.491700, -2.736400, -2.730300, -2.495100, -2.724100, -2.732400 --2.296300, -2.518800, -2.525500, -2.465800, -2.452700, -2.536100, 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0.37397695 0.11819446 -Al 0.37099257 0.25277893 0.12126261 -Al 0.36491106 0.38167290 0.00319109 -Al 0.24943387 0.24137978 0.24963289 -Al 0.25168702 0.37152301 0.37586054 -Al 0.37679982 0.24767965 0.37002965 -Al 0.36566749 0.36687118 0.24719678 -Al 0.25668066 0.24935992 0.49689055 -Al 0.23688615 0.37325791 0.61362708 -Al 0.36775284 0.25295831 0.63339193 -Al 0.37058487 0.37232564 0.50641822 -Al 0.24058480 0.25887981 0.74450233 -Al 0.24768663 0.38273245 0.88066757 -Al 0.37052541 0.26712547 0.87335350 -Al 0.37038910 0.37579674 0.75276513 -Al 0.24253455 0.49942232 0.99487916 -Al 0.25074173 0.62442019 0.12243577 -Al 0.37311382 0.49914902 0.12420954 -Al 0.37320920 0.62350426 0.99376271 -Al 0.25389455 0.49538335 0.25271040 -Al 0.25658599 0.62317196 0.37349744 -Al 0.38212228 0.49034093 0.37947714 -Al 0.38090066 0.60896281 0.25545615 -Al 0.24329407 0.50142690 0.49126120 -Al 0.25269515 0.62955678 0.62841784 -Al 0.37042634 0.50105050 0.62697351 -Al 0.37084280 0.62520133 0.51267915 -Al 0.25231582 0.50422315 0.74908575 -Al 0.25083118 0.62883028 0.87233488 -Al 0.38054186 0.49697121 0.87200829 -Al 0.38857876 0.61850989 0.74785557 -Al 0.25779019 0.74847839 0.99939367 -Al 0.24911012 0.87420062 0.12270013 -Al 0.37379063 0.73973313 0.12344417 -Al 0.37805029 0.86810019 0.99298930 -Al 0.25333605 0.74287691 0.24945202 -Al 0.25383293 0.87208918 0.37325974 -Al 0.37376891 0.74959858 0.37877335 -Al 0.37486391 0.86890075 0.25672474 -Al 0.23915687 0.74103044 0.49724675 -Al 0.24369823 0.87643261 0.62451626 -Al 0.38505233 0.75239503 0.62617122 -Al 0.37847875 0.87612161 0.51019113 -Al 0.26582340 0.74964806 0.74671707 -Al 0.24070512 0.87568485 0.87138745 -Al 0.38127443 0.74478341 0.87734871 -Al 0.37122727 0.88166043 0.75464351 -Al 0.49676265 0.99913572 0.00773492 -Al 0.49677586 0.12889222 0.11883841 -Al 0.62632069 0.00209937 0.12239078 -Al 0.61429381 0.12500820 0.00331895 -Al 0.50499332 0.99536389 0.25588511 -Al 0.51758471 0.12246991 0.37168698 -Al 0.62685441 0.99657069 0.38221316 -Al 0.62133037 0.10677543 0.24236551 -Al 0.50283046 0.00027661 0.50517401 -Al 0.49924389 0.12871700 0.63075628 -Al 0.61973760 0.99776872 0.63487085 -Al 0.62154625 0.13372902 0.49349498 -Al 0.48858742 0.00996567 0.75577873 -Al 0.50030758 0.12998960 0.88598660 -Al 0.61675238 0.01078308 0.87268885 -Al 0.62062082 0.13817810 0.75159238 -Al 0.49551556 0.25732791 0.99892171 -Al 0.49621790 0.37539287 0.12546048 -Al 0.63140164 0.26479969 0.12145568 -Al 0.61997492 0.37552116 0.99192773 -Al 0.50866253 0.24052677 0.24820621 -Al 0.50268749 0.36723972 0.37156275 -Al 0.62567643 0.24712665 0.38373644 -Al 0.62928974 0.38239436 0.25813868 -Al 0.48999643 0.25627225 0.49935502 -Al 0.49796721 0.38502975 0.62724580 -Al 0.62430452 0.25166879 0.62749593 -Al 0.61467977 0.37317844 0.50637909 -Al 0.49493639 0.25133042 0.75154257 -Al 0.49534688 0.37618114 0.86909990 -Al 0.61246283 0.25121344 0.86485038 -Al 0.62282704 0.38082335 0.74352853 -Al 0.50348440 0.50148151 0.00898825 -Al 0.49859317 0.62374629 0.13172456 -Al 0.63072956 0.50103732 0.13011505 -Al 0.62698283 0.62785259 0.00833124 -Al 0.50116452 0.48931611 0.24883425 -Al 0.50458482 0.61475673 0.38100075 -Al 0.62957740 0.49402239 0.38181668 -Al 0.62655074 0.62155853 0.25519562 -Al 0.50115871 0.49541243 0.49810488 -Al 0.50973680 0.61713381 0.62630168 -Al 0.63118820 0.49631279 0.62401404 -Al 0.62498572 0.61067461 0.49866825 -Al 0.50907412 0.50153474 0.75901977 -Al 0.51212343 0.62386323 0.88244495 -Al 0.62239397 0.49705208 0.88343202 -Al 0.62757424 0.61527633 0.74473006 -Al 0.49936968 0.75141687 0.00098419 -Al 0.50198259 0.87164597 0.13247901 -Al 0.61759859 0.75078730 0.14208182 -Al 0.62790966 0.87363482 0.01134898 -Al 0.49648381 0.74798718 0.25575989 -Al 0.49563886 0.87098031 0.37490084 -Al 0.61975734 0.74992524 0.38173015 -Al 0.63001577 0.87476567 0.25423197 -Al 0.50069165 0.74864365 0.50712919 -Al 0.51183280 0.87152323 0.63036879 -Al 0.63128640 0.73926092 0.61850891 -Al 0.62876440 0.86579297 0.49946341 -Al 0.49769761 0.74656916 0.75232188 -Al 0.50599556 0.87822282 0.88111163 -Al 0.62828277 0.74851850 0.87818830 -Al 0.62013637 0.87599170 0.76303239 -Al 0.75686751 0.99492115 0.00088827 -Al 0.74467843 0.12723427 0.11961291 -Al 0.87556549 0.00369676 0.12621795 -Al 0.87475810 0.12266938 0.99868019 -Al 0.74609007 0.00163629 0.24752646 -Al 0.75349991 0.12933425 0.37611815 -Al 0.88095143 0.00133752 0.37711697 -Al 0.87706767 0.11849743 0.24714141 -Al 0.74362808 0.00703466 0.50528327 -Al 0.75057559 0.13367993 0.62043474 -Al 0.87015719 0.00376019 0.62411051 -Al 0.87832474 0.11957331 0.50122298 -Al 0.75440296 0.00266564 0.75438769 -Al 0.75063391 0.12797922 0.87408035 -Al 0.87500514 0.00969461 0.87268056 -Al 0.87355340 0.12955559 0.75399544 -Al 0.74541659 0.24490088 0.00407350 -Al 0.75075681 0.39099472 0.11533268 -Al 0.86431841 0.25660834 0.11255680 -Al 0.86652423 0.37969599 0.99219682 -Al 0.74146925 0.23987690 0.24834194 -Al 0.75169720 0.37625628 0.37634352 -Al 0.88890668 0.25086535 0.37728200 -Al 0.86238571 0.36121717 0.24200173 -Al 0.75146734 0.25403038 0.50718035 -Al 0.74894394 0.36950948 0.62383741 -Al 0.87442534 0.25501163 0.62503015 -Al 0.87776574 0.37481735 0.50628054 -Al 0.74617247 0.24906860 0.75596080 -Al 0.75221802 0.37507914 0.87355096 -Al 0.87896034 0.25760957 0.88390838 -Al 0.86867608 0.38164705 0.74626903 -Al 0.74189350 0.51020090 0.99834605 -Al 0.74524061 0.62716394 0.13044927 -Al 0.87170353 0.49891082 0.12002179 -Al 0.86951405 0.62129397 0.00276830 -Al 0.75203169 0.50093943 0.24050544 -Al 0.75900617 0.61844580 0.36090421 -Al 0.87814676 0.49591115 0.36355387 -Al 0.87903888 0.62184363 0.24034326 -Al 0.75162722 0.49628811 0.49270285 -Al 0.74834445 0.62934556 0.61707414 -Al 0.86376714 0.50616251 0.62149233 -Al 0.87601835 0.61794319 0.49499002 -Al 0.75832911 0.50299237 0.75301563 -Al 0.75002999 0.63141690 0.87782568 -Al 0.87777246 0.50587526 0.87233272 -Al 0.88336963 0.62653265 0.75699366 -Al 0.74583914 0.75416465 0.00348848 -Al 0.75813025 0.87640517 0.12706117 -Al 0.87928098 0.75005800 0.12474447 -Al 0.88438720 0.87819211 0.00472541 -Al 0.75998762 0.75236132 0.25078015 -Al 0.76260167 0.87804941 0.36636549 -Al 0.88099052 0.74935941 0.37229183 -Al 0.88247979 0.86897661 0.25221829 -Al 0.75248504 0.75642062 0.49706069 -Al 0.74984275 0.87473162 0.62898769 -Al 0.87611175 0.74487681 0.62710867 -Al 0.88093126 0.87696450 0.49141002 -Al 0.74834534 0.75930289 0.75569426 -Al 0.75898225 0.88269616 0.86945006 -Al 0.87028964 0.75236772 0.87807720 -Al 0.87801602 0.87625452 0.74951145 - diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_666/pbs.job b/ml-dft-sandia/notebooks/dos2eband/snap_0_666/pbs.job deleted file mode 100644 index a0d51a733..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_666/pbs.job +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -# -# Script to run a VASP job using SLURM on 576 cpus of Chama -# Created by ./run_job.exe on Fri Mar 27 13:28:35 MDT 2020 -# -#SBATCH --nodes=16 -#SBATCH --time=48:00:00 -#SBATCH --account=FY200018 -#SBATCH --job-name=200327132835 -#SBATCH --output=/nscratch/namodin/200327132835/200327132835.out -#SBATCH --error=/nscratch/namodin/200327132835/200327132835.err - -# Number of nodes -nodes=16 - -# Number MPI processes to run on each node (a.k.a. PPN) -# Redsky has 8 cores per node -cores=36 - -# Total number of cores -cpus=576 - -# -# Set up running directory -# -cd /nscratch/namodin/200327132835 -cp /home/namodin/MLDFT/output/200327132835/Al.scf.pw . -cp /ascldap/users/namodin/MLDFT/pw.x . - -# -# Set up the environment -# -module purge -module load tce -module load intel/18.0.5.274 -module load mkl/18.0.5.274 -module load openmpi-intel/1.10 -# -# Execute code -# -mpiexec --npernode $cores --n $cpus ./pw.x -nk $nodes -in Al.scf.pw -#mpiexec --npernode $cores --n $cpus ./pw.x -nk $nodes -nt 1 -nd 1 -in Al.scf.pw - -# -# Save results and clean up -# -rm pw.x -cp -r *.out /home/namodin/MLDFT/output/200327132835 -cp -r *.err /home/namodin/MLDFT/output/200327132835 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/200330132037.err b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/200330132037.err deleted file mode 100644 index a55bd10bb..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/200330132037.err +++ /dev/null @@ -1,2 +0,0 @@ -slurmstepd: error: *** JOB 6007243 ON gho157 CANCELLED AT 2020-04-01T12:30:28 DUE TO NODE FAILURE, SEE SLURMCTLD LOG FOR DETAILS *** -slurmstepd: error: *** JOB 6007243 STEPD TERMINATED ON gho157 AT 2020-04-01T12:32:28 DUE TO JOB NOT ENDING WITH SIGNALS *** diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g deleted file mode 100644 index ee4dd9c14..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g +++ /dev/null @@ -1,252 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.797 eV - -10.000 0.4497E-83 0.4497E-84 - -9.900 0.4497E-83 0.8995E-84 - -9.800 0.4497E-83 0.1349E-83 - -9.700 0.4497E-83 0.1799E-83 - -9.600 0.4497E-83 0.2249E-83 - -9.500 0.4497E-83 0.2698E-83 - -9.400 0.4497E-83 0.3148E-83 - -9.300 0.4497E-83 0.3598E-83 - -9.200 0.4497E-83 0.4048E-83 - -9.100 0.4497E-83 0.4497E-83 - -9.000 0.4497E-83 0.4947E-83 - -8.900 0.4497E-83 0.5397E-83 - -8.800 0.4497E-83 0.5846E-83 - -8.700 0.4497E-83 0.6296E-83 - -8.600 0.4497E-83 0.6746E-83 - -8.500 0.4497E-83 0.7196E-83 - -8.400 0.4497E-83 0.7645E-83 - -8.300 0.4497E-83 0.8095E-83 - -8.200 0.4497E-83 0.8545E-83 - -8.100 0.4497E-83 0.8995E-83 - -8.000 0.4497E-83 0.9444E-83 - -7.900 0.4497E-83 0.9894E-83 - -7.800 0.4497E-83 0.1034E-82 - -7.700 0.4497E-83 0.1079E-82 - -7.600 0.4497E-83 0.1124E-82 - -7.500 0.4497E-83 0.1169E-82 - -7.400 0.4497E-83 0.1214E-82 - -7.300 0.4497E-83 0.1259E-82 - -7.200 0.4497E-83 0.1304E-82 - -7.100 0.4497E-83 0.1349E-82 - -7.000 0.4497E-83 0.1394E-82 - -6.900 0.4497E-83 0.1439E-82 - -6.800 0.4497E-83 0.1484E-82 - -6.700 0.4497E-83 0.1529E-82 - -6.600 0.4497E-83 0.1574E-82 - -6.500 0.4497E-83 0.1619E-82 - -6.400 0.4497E-83 0.1664E-82 - -6.300 0.4497E-83 0.1709E-82 - -6.200 0.4497E-83 0.1754E-82 - -6.100 0.4576E-83 0.1800E-82 - -6.000 0.7046E-79 0.7064E-80 - -5.900 0.3789E-73 0.3789E-74 - -5.800 0.1238E-67 0.1238E-68 - -5.700 0.2461E-62 0.2461E-63 - -5.600 0.2975E-57 0.2975E-58 - -5.500 0.2188E-52 0.2188E-53 - -5.400 0.9786E-48 0.9786E-49 - -5.300 0.2664E-43 0.2664E-44 - -5.200 0.4416E-39 0.4416E-40 - -5.100 0.4458E-35 0.4458E-36 - -5.000 0.2742E-31 0.2742E-32 - -4.900 0.1028E-27 0.1028E-28 - -4.800 0.2352E-24 0.2353E-25 - -4.700 0.3285E-21 0.3287E-22 - -4.600 0.2804E-18 0.2807E-19 - -4.500 0.1464E-15 0.1467E-16 - -4.400 0.4682E-13 0.4697E-14 - -4.300 0.9191E-11 0.9238E-12 - -4.200 0.1110E-08 0.1119E-09 - -4.100 0.8262E-07 0.8373E-08 - -4.000 0.3808E-05 0.3892E-06 - -3.900 0.1092E-03 0.1131E-04 - -3.800 0.1960E-02 0.2073E-03 - -3.700 0.2223E-01 0.2430E-02 - -3.600 0.1615E+00 0.1858E-01 - -3.500 0.7663E+00 0.9520E-01 - -3.400 0.2450E+01 0.3402E+00 - -3.300 0.5531E+01 0.8932E+00 - -3.200 0.9433E+01 0.1837E+01 - -3.100 0.1320E+02 0.3156E+01 - -3.000 0.1640E+02 0.4797E+01 - -2.900 0.1912E+02 0.6708E+01 - -2.800 0.2147E+02 0.8856E+01 - -2.700 0.2349E+02 0.1120E+02 - -2.600 0.2521E+02 0.1373E+02 - -2.500 0.2683E+02 0.1641E+02 - -2.400 0.2851E+02 0.1926E+02 - -2.300 0.3023E+02 0.2228E+02 - -2.200 0.3181E+02 0.2546E+02 - -2.100 0.3319E+02 0.2878E+02 - -2.000 0.3441E+02 0.3222E+02 - -1.900 0.3559E+02 0.3578E+02 - -1.800 0.3680E+02 0.3946E+02 - -1.700 0.3805E+02 0.4327E+02 - -1.600 0.3932E+02 0.4720E+02 - -1.500 0.4050E+02 0.5125E+02 - -1.400 0.4155E+02 0.5540E+02 - -1.300 0.4256E+02 0.5966E+02 - -1.200 0.4365E+02 0.6403E+02 - -1.100 0.4484E+02 0.6851E+02 - -1.000 0.4595E+02 0.7310E+02 - -0.900 0.4691E+02 0.7780E+02 - -0.800 0.4787E+02 0.8258E+02 - -0.700 0.4882E+02 0.8747E+02 - -0.600 0.4974E+02 0.9244E+02 - -0.500 0.5071E+02 0.9751E+02 - -0.400 0.5162E+02 0.1027E+03 - -0.300 0.5243E+02 0.1079E+03 - -0.200 0.5337E+02 0.1133E+03 - -0.100 0.5444E+02 0.1187E+03 - 0.000 0.5538E+02 0.1242E+03 - 0.100 0.5628E+02 0.1299E+03 - 0.200 0.5722E+02 0.1356E+03 - 0.300 0.5799E+02 0.1414E+03 - 0.400 0.5865E+02 0.1473E+03 - 0.500 0.5954E+02 0.1532E+03 - 0.600 0.6049E+02 0.1593E+03 - 0.700 0.6115E+02 0.1654E+03 - 0.800 0.6182E+02 0.1716E+03 - 0.900 0.6277E+02 0.1778E+03 - 1.000 0.6366E+02 0.1842E+03 - 1.100 0.6440E+02 0.1906E+03 - 1.200 0.6525E+02 0.1972E+03 - 1.300 0.6617E+02 0.2038E+03 - 1.400 0.6698E+02 0.2105E+03 - 1.500 0.6758E+02 0.2172E+03 - 1.600 0.6823E+02 0.2241E+03 - 1.700 0.6919E+02 0.2310E+03 - 1.800 0.7009E+02 0.2380E+03 - 1.900 0.7061E+02 0.2450E+03 - 2.000 0.7128E+02 0.2522E+03 - 2.100 0.7236E+02 0.2594E+03 - 2.200 0.7329E+02 0.2667E+03 - 2.300 0.7379E+02 0.2741E+03 - 2.400 0.7433E+02 0.2815E+03 - 2.500 0.7532E+02 0.2891E+03 - 2.600 0.7640E+02 0.2967E+03 - 2.700 0.7701E+02 0.3044E+03 - 2.800 0.7745E+02 0.3122E+03 - 2.900 0.7842E+02 0.3200E+03 - 3.000 0.7971E+02 0.3280E+03 - 3.100 0.8054E+02 0.3360E+03 - 3.200 0.8085E+02 0.3441E+03 - 3.300 0.8125E+02 0.3522E+03 - 3.400 0.8189E+02 0.3604E+03 - 3.500 0.8257E+02 0.3687E+03 - 3.600 0.8318E+02 0.3770E+03 - 3.700 0.8374E+02 0.3854E+03 - 3.800 0.8458E+02 0.3938E+03 - 3.900 0.8581E+02 0.4024E+03 - 4.000 0.8676E+02 0.4111E+03 - 4.100 0.8717E+02 0.4198E+03 - 4.200 0.8792E+02 0.4286E+03 - 4.300 0.8931E+02 0.4375E+03 - 4.400 0.9049E+02 0.4466E+03 - 4.500 0.9124E+02 0.4557E+03 - 4.600 0.9237E+02 0.4649E+03 - 4.700 0.9404E+02 0.4743E+03 - 4.800 0.9566E+02 0.4839E+03 - 4.900 0.9692E+02 0.4936E+03 - 5.000 0.9763E+02 0.5034E+03 - 5.100 0.9742E+02 0.5131E+03 - 5.200 0.9664E+02 0.5228E+03 - 5.300 0.9586E+02 0.5324E+03 - 5.400 0.9498E+02 0.5419E+03 - 5.500 0.9397E+02 0.5513E+03 - 5.600 0.9307E+02 0.5606E+03 - 5.700 0.9223E+02 0.5698E+03 - 5.800 0.9125E+02 0.5789E+03 - 5.900 0.9028E+02 0.5879E+03 - 6.000 0.8986E+02 0.5969E+03 - 6.100 0.9030E+02 0.6060E+03 - 6.200 0.9143E+02 0.6151E+03 - 6.300 0.9306E+02 0.6244E+03 - 6.400 0.9488E+02 0.6339E+03 - 6.500 0.9650E+02 0.6435E+03 - 6.600 0.9776E+02 0.6533E+03 - 6.700 0.9880E+02 0.6632E+03 - 6.800 0.9987E+02 0.6732E+03 - 6.900 0.1008E+03 0.6833E+03 - 7.000 0.1008E+03 0.6933E+03 - 7.100 0.9942E+02 0.7033E+03 - 7.200 0.9807E+02 0.7131E+03 - 7.300 0.9826E+02 0.7229E+03 - 7.400 0.9958E+02 0.7329E+03 - 7.500 0.1006E+03 0.7429E+03 - 7.600 0.1014E+03 0.7531E+03 - 7.700 0.1029E+03 0.7634E+03 - 7.800 0.1049E+03 0.7739E+03 - 7.900 0.1064E+03 0.7845E+03 - 8.000 0.1069E+03 0.7952E+03 - 8.100 0.1071E+03 0.8059E+03 - 8.200 0.1076E+03 0.8167E+03 - 8.300 0.1080E+03 0.8275E+03 - 8.400 0.1076E+03 0.8382E+03 - 8.500 0.1075E+03 0.8490E+03 - 8.600 0.1087E+03 0.8598E+03 - 8.700 0.1097E+03 0.8708E+03 - 8.800 0.1092E+03 0.8817E+03 - 8.900 0.1082E+03 0.8925E+03 - 9.000 0.1088E+03 0.9034E+03 - 9.100 0.1108E+03 0.9145E+03 - 9.200 0.1124E+03 0.9257E+03 - 9.300 0.1130E+03 0.9370E+03 - 9.400 0.1128E+03 0.9483E+03 - 9.500 0.1123E+03 0.9595E+03 - 9.600 0.1126E+03 0.9708E+03 - 9.700 0.1146E+03 0.9823E+03 - 9.800 0.1167E+03 0.9939E+03 - 9.900 0.1169E+03 0.1006E+04 - 10.000 0.1155E+03 0.1017E+04 - 10.100 0.1151E+03 0.1029E+04 - 10.200 0.1166E+03 0.1040E+04 - 10.300 0.1185E+03 0.1052E+04 - 10.400 0.1194E+03 0.1064E+04 - 10.500 0.1195E+03 0.1076E+04 - 10.600 0.1194E+03 0.1088E+04 - 10.700 0.1198E+03 0.1100E+04 - 10.800 0.1202E+03 0.1112E+04 - 10.900 0.1174E+03 0.1124E+04 - 11.000 0.1065E+03 0.1134E+04 - 11.100 0.8405E+02 0.1143E+04 - 11.200 0.5377E+02 0.1148E+04 - 11.300 0.2619E+02 0.1151E+04 - 11.400 0.9246E+01 0.1152E+04 - 11.500 0.2281E+01 0.1152E+04 - 11.600 0.3820E+00 0.1152E+04 - 11.700 0.4240E-01 0.1152E+04 - 11.800 0.3063E-02 0.1152E+04 - 11.900 0.1422E-03 0.1152E+04 - 12.000 0.4200E-05 0.1152E+04 - 12.100 0.7857E-07 0.1152E+04 - 12.200 0.9275E-09 0.1152E+04 - 12.300 0.6900E-11 0.1152E+04 - 12.400 0.3232E-13 0.1152E+04 - 12.500 0.9526E-16 0.1152E+04 - 12.600 0.1764E-18 0.1152E+04 - 12.700 0.2047E-21 0.1152E+04 - 12.800 0.1484E-24 0.1152E+04 - 12.900 0.6703E-28 0.1152E+04 - 13.000 0.1878E-31 0.1152E+04 - 13.100 0.3251E-35 0.1152E+04 - 13.200 0.3467E-39 0.1152E+04 - 13.300 0.2271E-43 0.1152E+04 - 13.400 0.9114E-48 0.1152E+04 - 13.500 0.2237E-52 0.1152E+04 - 13.600 0.3352E-57 0.1152E+04 - 13.700 0.3062E-62 0.1152E+04 - 13.800 0.1705E-67 0.1152E+04 - 13.900 0.5775E-73 0.1152E+04 - 14.000 0.1190E-78 0.1152E+04 - 14.100 0.4646E-83 0.1152E+04 - 14.200 0.4497E-83 0.1152E+04 - 14.300 0.4497E-83 0.1152E+04 - 14.400 0.4497E-83 0.1152E+04 - 14.500 0.4497E-83 0.1152E+04 - 14.600 0.4497E-83 0.1152E+04 - 14.700 0.4497E-83 0.1152E+04 - 14.800 0.4497E-83 0.1152E+04 - 14.900 0.4497E-83 0.1152E+04 - 15.000 0.4497E-83 0.1152E+04 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g.old b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g.old deleted file mode 100644 index 9d8e3a8b2..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_2g.old +++ /dev/null @@ -1,202 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.797 eV - -5.000 0.2784E-31 0.2784E-32 - -4.900 0.1043E-27 0.1043E-28 - -4.800 0.2384E-24 0.2385E-25 - -4.700 0.3327E-21 0.3329E-22 - -4.600 0.2837E-18 0.2841E-19 - -4.500 0.1480E-15 0.1483E-16 - -4.400 0.4731E-13 0.4745E-14 - -4.300 0.9278E-11 0.9326E-12 - -4.200 0.1119E-08 0.1129E-09 - -4.100 0.8328E-07 0.8441E-08 - -4.000 0.3836E-05 0.3920E-06 - -3.900 0.1099E-03 0.1138E-04 - -3.800 0.1971E-02 0.2085E-03 - -3.700 0.2234E-01 0.2443E-02 - -3.600 0.1621E+00 0.1866E-01 - -3.500 0.7687E+00 0.9553E-01 - -3.400 0.2455E+01 0.3410E+00 - -3.300 0.5537E+01 0.8947E+00 - -3.200 0.9436E+01 0.1838E+01 - -3.100 0.1320E+02 0.3158E+01 - -3.000 0.1640E+02 0.4798E+01 - -2.900 0.1911E+02 0.6709E+01 - -2.800 0.2147E+02 0.8856E+01 - -2.700 0.2348E+02 0.1120E+02 - -2.600 0.2521E+02 0.1372E+02 - -2.500 0.2683E+02 0.1641E+02 - -2.400 0.2851E+02 0.1926E+02 - -2.300 0.3022E+02 0.2228E+02 - -2.200 0.3180E+02 0.2546E+02 - -2.100 0.3318E+02 0.2878E+02 - -2.000 0.3441E+02 0.3222E+02 - -1.900 0.3559E+02 0.3578E+02 - -1.800 0.3680E+02 0.3946E+02 - -1.700 0.3805E+02 0.4327E+02 - -1.600 0.3932E+02 0.4720E+02 - -1.500 0.4050E+02 0.5125E+02 - -1.400 0.4154E+02 0.5540E+02 - -1.300 0.4256E+02 0.5966E+02 - -1.200 0.4365E+02 0.6402E+02 - -1.100 0.4483E+02 0.6851E+02 - -1.000 0.4595E+02 0.7310E+02 - -0.900 0.4691E+02 0.7779E+02 - -0.800 0.4787E+02 0.8258E+02 - -0.700 0.4882E+02 0.8746E+02 - -0.600 0.4974E+02 0.9244E+02 - -0.500 0.5071E+02 0.9751E+02 - -0.400 0.5162E+02 0.1027E+03 - -0.300 0.5243E+02 0.1079E+03 - -0.200 0.5337E+02 0.1132E+03 - -0.100 0.5444E+02 0.1187E+03 - 0.000 0.5538E+02 0.1242E+03 - 0.100 0.5628E+02 0.1299E+03 - 0.200 0.5722E+02 0.1356E+03 - 0.300 0.5799E+02 0.1414E+03 - 0.400 0.5866E+02 0.1472E+03 - 0.500 0.5954E+02 0.1532E+03 - 0.600 0.6049E+02 0.1592E+03 - 0.700 0.6115E+02 0.1654E+03 - 0.800 0.6182E+02 0.1715E+03 - 0.900 0.6277E+02 0.1778E+03 - 1.000 0.6366E+02 0.1842E+03 - 1.100 0.6440E+02 0.1906E+03 - 1.200 0.6525E+02 0.1972E+03 - 1.300 0.6617E+02 0.2038E+03 - 1.400 0.6698E+02 0.2105E+03 - 1.500 0.6758E+02 0.2172E+03 - 1.600 0.6823E+02 0.2241E+03 - 1.700 0.6919E+02 0.2310E+03 - 1.800 0.7009E+02 0.2380E+03 - 1.900 0.7061E+02 0.2450E+03 - 2.000 0.7128E+02 0.2522E+03 - 2.100 0.7236E+02 0.2594E+03 - 2.200 0.7329E+02 0.2667E+03 - 2.300 0.7379E+02 0.2741E+03 - 2.400 0.7433E+02 0.2815E+03 - 2.500 0.7532E+02 0.2891E+03 - 2.600 0.7640E+02 0.2967E+03 - 2.700 0.7701E+02 0.3044E+03 - 2.800 0.7745E+02 0.3122E+03 - 2.900 0.7842E+02 0.3200E+03 - 3.000 0.7972E+02 0.3280E+03 - 3.100 0.8053E+02 0.3360E+03 - 3.200 0.8085E+02 0.3441E+03 - 3.300 0.8125E+02 0.3522E+03 - 3.400 0.8189E+02 0.3604E+03 - 3.500 0.8257E+02 0.3687E+03 - 3.600 0.8318E+02 0.3770E+03 - 3.700 0.8374E+02 0.3854E+03 - 3.800 0.8458E+02 0.3938E+03 - 3.900 0.8581E+02 0.4024E+03 - 4.000 0.8676E+02 0.4111E+03 - 4.100 0.8717E+02 0.4198E+03 - 4.200 0.8792E+02 0.4286E+03 - 4.300 0.8931E+02 0.4375E+03 - 4.400 0.9049E+02 0.4466E+03 - 4.500 0.9124E+02 0.4557E+03 - 4.600 0.9237E+02 0.4649E+03 - 4.700 0.9405E+02 0.4743E+03 - 4.800 0.9566E+02 0.4839E+03 - 4.900 0.9692E+02 0.4936E+03 - 5.000 0.9763E+02 0.5034E+03 - 5.100 0.9742E+02 0.5131E+03 - 5.200 0.9664E+02 0.5228E+03 - 5.300 0.9586E+02 0.5324E+03 - 5.400 0.9498E+02 0.5419E+03 - 5.500 0.9397E+02 0.5513E+03 - 5.600 0.9307E+02 0.5606E+03 - 5.700 0.9223E+02 0.5698E+03 - 5.800 0.9125E+02 0.5789E+03 - 5.900 0.9028E+02 0.5879E+03 - 6.000 0.8986E+02 0.5969E+03 - 6.100 0.9030E+02 0.6060E+03 - 6.200 0.9143E+02 0.6151E+03 - 6.300 0.9306E+02 0.6244E+03 - 6.400 0.9488E+02 0.6339E+03 - 6.500 0.9649E+02 0.6435E+03 - 6.600 0.9776E+02 0.6533E+03 - 6.700 0.9880E+02 0.6632E+03 - 6.800 0.9987E+02 0.6732E+03 - 6.900 0.1008E+03 0.6833E+03 - 7.000 0.1008E+03 0.6933E+03 - 7.100 0.9942E+02 0.7033E+03 - 7.200 0.9807E+02 0.7131E+03 - 7.300 0.9826E+02 0.7229E+03 - 7.400 0.9958E+02 0.7329E+03 - 7.500 0.1006E+03 0.7429E+03 - 7.600 0.1014E+03 0.7531E+03 - 7.700 0.1029E+03 0.7634E+03 - 7.800 0.1049E+03 0.7739E+03 - 7.900 0.1064E+03 0.7845E+03 - 8.000 0.1069E+03 0.7952E+03 - 8.100 0.1071E+03 0.8059E+03 - 8.200 0.1076E+03 0.8167E+03 - 8.300 0.1080E+03 0.8275E+03 - 8.400 0.1076E+03 0.8382E+03 - 8.500 0.1075E+03 0.8490E+03 - 8.600 0.1086E+03 0.8598E+03 - 8.700 0.1097E+03 0.8708E+03 - 8.800 0.1088E+03 0.8817E+03 - 8.900 0.1060E+03 0.8923E+03 - 9.000 0.1000E+03 0.9023E+03 - 9.100 0.8565E+02 0.9108E+03 - 9.200 0.6073E+02 0.9169E+03 - 9.300 0.3305E+02 0.9202E+03 - 9.400 0.1316E+02 0.9215E+03 - 9.500 0.3726E+01 0.9219E+03 - 9.600 0.7354E+00 0.9220E+03 - 9.700 0.9934E-01 0.9220E+03 - 9.800 0.9014E-02 0.9220E+03 - 9.900 0.5397E-03 0.9220E+03 - 10.000 0.2098E-04 0.9220E+03 - 10.100 0.5219E-06 0.9220E+03 - 10.200 0.8219E-08 0.9220E+03 - 10.300 0.8114E-10 0.9220E+03 - 10.400 0.4985E-12 0.9220E+03 - 10.500 0.1895E-14 0.9220E+03 - 10.600 0.4436E-17 0.9220E+03 - 10.700 0.6373E-20 0.9220E+03 - 10.800 0.5605E-23 0.9220E+03 - 10.900 0.3012E-26 0.9220E+03 - 11.000 0.9875E-30 0.9220E+03 - 11.100 0.1973E-33 0.9220E+03 - 11.200 0.2399E-37 0.9220E+03 - 11.300 0.1775E-41 0.9220E+03 - 11.400 0.7990E-46 0.9220E+03 - 11.500 0.2186E-50 0.9220E+03 - 11.600 0.3634E-55 0.9220E+03 - 11.700 0.3670E-60 0.9220E+03 - 11.800 0.2252E-65 0.9220E+03 - 11.900 0.8391E-71 0.9220E+03 - 12.000 0.1899E-76 0.9220E+03 - 12.100 0.2968E-82 0.9220E+03 - 12.200 0.3599E-83 0.9220E+03 - 12.300 0.3599E-83 0.9220E+03 - 12.400 0.3599E-83 0.9220E+03 - 12.500 0.3599E-83 0.9220E+03 - 12.600 0.3599E-83 0.9220E+03 - 12.700 0.3599E-83 0.9220E+03 - 12.800 0.3599E-83 0.9220E+03 - 12.900 0.3599E-83 0.9220E+03 - 13.000 0.3599E-83 0.9220E+03 - 13.100 0.3599E-83 0.9220E+03 - 13.200 0.3599E-83 0.9220E+03 - 13.300 0.3599E-83 0.9220E+03 - 13.400 0.3599E-83 0.9220E+03 - 13.500 0.3599E-83 0.9220E+03 - 13.600 0.3599E-83 0.9220E+03 - 13.700 0.3599E-83 0.9220E+03 - 13.800 0.3599E-83 0.9220E+03 - 13.900 0.3599E-83 0.9220E+03 - 14.000 0.3599E-83 0.9220E+03 - 14.100 0.3599E-83 0.9220E+03 - 14.200 0.3599E-83 0.9220E+03 - 14.300 0.3599E-83 0.9220E+03 - 14.400 0.3599E-83 0.9220E+03 - 14.500 0.3599E-83 0.9220E+03 - 14.600 0.3599E-83 0.9220E+03 - 14.700 0.3599E-83 0.9220E+03 - 14.800 0.3599E-83 0.9220E+03 - 14.900 0.3599E-83 0.9220E+03 - 15.000 0.3599E-83 0.9220E+03 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g deleted file mode 100644 index 72279a4be..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g +++ /dev/null @@ -1,252 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.797 eV - -10.000 0.8995E-83 0.8995E-84 - -9.900 0.8995E-83 0.1799E-83 - -9.800 0.8995E-83 0.2698E-83 - -9.700 0.8995E-83 0.3598E-83 - -9.600 0.8995E-83 0.4497E-83 - -9.500 0.8995E-83 0.5397E-83 - -9.400 0.8995E-83 0.6296E-83 - -9.300 0.8995E-83 0.7196E-83 - -9.200 0.8995E-83 0.8095E-83 - -9.100 0.8995E-83 0.8995E-83 - -9.000 0.8995E-83 0.9894E-83 - -8.900 0.8995E-83 0.1079E-82 - -8.800 0.8995E-83 0.1169E-82 - -8.700 0.8995E-83 0.1259E-82 - -8.600 0.8995E-83 0.1349E-82 - -8.500 0.8995E-83 0.1439E-82 - -8.400 0.8995E-83 0.1529E-82 - -8.300 0.8995E-83 0.1619E-82 - -8.200 0.8995E-83 0.1709E-82 - -8.100 0.8995E-83 0.1799E-82 - -8.000 0.8995E-83 0.1889E-82 - -7.900 0.8995E-83 0.1979E-82 - -7.800 0.8995E-83 0.2069E-82 - -7.700 0.8995E-83 0.2159E-82 - -7.600 0.8995E-83 0.2249E-82 - -7.500 0.8995E-83 0.2339E-82 - -7.400 0.8995E-83 0.2429E-82 - -7.300 0.8995E-83 0.2518E-82 - -7.200 0.8995E-83 0.2608E-82 - -7.100 0.8995E-83 0.2698E-82 - -7.000 0.8995E-83 0.2788E-82 - -6.900 0.8995E-83 0.2878E-82 - -6.800 0.8995E-83 0.2968E-82 - -6.700 0.8995E-83 0.3058E-82 - -6.600 0.8995E-83 0.3148E-82 - -6.500 0.8995E-83 0.3238E-82 - -6.400 0.8995E-83 0.3328E-82 - -6.300 0.8995E-83 0.3418E-82 - -6.200 0.8995E-83 0.3508E-82 - -6.100 0.8995E-83 0.3598E-82 - -6.000 0.8995E-83 0.3688E-82 - -5.900 0.8995E-83 0.3778E-82 - -5.800 0.8995E-83 0.3868E-82 - -5.700 0.8995E-83 0.3958E-82 - -5.600 0.8995E-83 0.4048E-82 - -5.500 0.8995E-83 0.4138E-82 - -5.400 0.8995E-83 0.4227E-82 - -5.300 0.8995E-83 0.4317E-82 - -5.200 0.8995E-83 0.4407E-82 - -5.100 0.8995E-83 0.4497E-82 - -5.000 0.8995E-83 0.4587E-82 - -4.900 0.8995E-83 0.4677E-82 - -4.800 0.8995E-83 0.4767E-82 - -4.700 0.1035E-82 0.4871E-82 - -4.600 0.5050E-72 0.5050E-73 - -4.500 0.2575E-61 0.2575E-62 - -4.400 0.1803E-51 0.1803E-52 - -4.300 0.1739E-42 0.1739E-43 - -4.200 0.2313E-34 0.2313E-35 - -4.100 0.4256E-27 0.4256E-28 - -4.000 0.1088E-20 0.1088E-21 - -3.900 0.3887E-15 0.3887E-16 - -3.800 0.1960E-10 0.1960E-11 - -3.700 0.1418E-06 0.1418E-07 - -3.600 0.1515E-03 0.1516E-04 - -3.500 0.2524E-01 0.2539E-02 - -3.400 0.7327E+00 0.7581E-01 - -3.300 0.4664E+01 0.5422E+00 - -3.200 0.1008E+02 0.1550E+01 - -3.100 0.1377E+02 0.2927E+01 - -3.000 0.1670E+02 0.4598E+01 - -2.900 0.1933E+02 0.6530E+01 - -2.800 0.2173E+02 0.8703E+01 - -2.700 0.2382E+02 0.1108E+02 - -2.600 0.2531E+02 0.1362E+02 - -2.500 0.2658E+02 0.1627E+02 - -2.400 0.2852E+02 0.1913E+02 - -2.300 0.3028E+02 0.2215E+02 - -2.200 0.3217E+02 0.2537E+02 - -2.100 0.3308E+02 0.2868E+02 - -2.000 0.3475E+02 0.3216E+02 - -1.900 0.3514E+02 0.3567E+02 - -1.800 0.3718E+02 0.3939E+02 - -1.700 0.3765E+02 0.4315E+02 - -1.600 0.3956E+02 0.4711E+02 - -1.500 0.4076E+02 0.5119E+02 - -1.400 0.4133E+02 0.5532E+02 - -1.300 0.4278E+02 0.5960E+02 - -1.200 0.4323E+02 0.6392E+02 - -1.100 0.4493E+02 0.6841E+02 - -1.000 0.4647E+02 0.7306E+02 - -0.900 0.4654E+02 0.7771E+02 - -0.800 0.4784E+02 0.8250E+02 - -0.700 0.4932E+02 0.8743E+02 - -0.600 0.4910E+02 0.9234E+02 - -0.500 0.5103E+02 0.9744E+02 - -0.400 0.5182E+02 0.1026E+03 - -0.300 0.5246E+02 0.1079E+03 - -0.200 0.5241E+02 0.1131E+03 - -0.100 0.5555E+02 0.1187E+03 - 0.000 0.5508E+02 0.1242E+03 - 0.100 0.5587E+02 0.1298E+03 - 0.200 0.5773E+02 0.1355E+03 - 0.300 0.5827E+02 0.1414E+03 - 0.400 0.5826E+02 0.1472E+03 - 0.500 0.5883E+02 0.1531E+03 - 0.600 0.6173E+02 0.1592E+03 - 0.700 0.6121E+02 0.1654E+03 - 0.800 0.6038E+02 0.1714E+03 - 0.900 0.6382E+02 0.1778E+03 - 1.000 0.6372E+02 0.1842E+03 - 1.100 0.6395E+02 0.1906E+03 - 1.200 0.6539E+02 0.1971E+03 - 1.300 0.6603E+02 0.2037E+03 - 1.400 0.6746E+02 0.2104E+03 - 1.500 0.6780E+02 0.2172E+03 - 1.600 0.6735E+02 0.2240E+03 - 1.700 0.6896E+02 0.2309E+03 - 1.800 0.7162E+02 0.2380E+03 - 1.900 0.6999E+02 0.2450E+03 - 2.000 0.7020E+02 0.2520E+03 - 2.100 0.7293E+02 0.2593E+03 - 2.200 0.7404E+02 0.2667E+03 - 2.300 0.7375E+02 0.2741E+03 - 2.400 0.7351E+02 0.2815E+03 - 2.500 0.7482E+02 0.2889E+03 - 2.600 0.7765E+02 0.2967E+03 - 2.700 0.7736E+02 0.3044E+03 - 2.800 0.7649E+02 0.3121E+03 - 2.900 0.7743E+02 0.3198E+03 - 3.000 0.8097E+02 0.3279E+03 - 3.100 0.8138E+02 0.3361E+03 - 3.200 0.8006E+02 0.3441E+03 - 3.300 0.8099E+02 0.3522E+03 - 3.400 0.8231E+02 0.3604E+03 - 3.500 0.8207E+02 0.3686E+03 - 3.600 0.8389E+02 0.3770E+03 - 3.700 0.8337E+02 0.3853E+03 - 3.800 0.8372E+02 0.3937E+03 - 3.900 0.8573E+02 0.4023E+03 - 4.000 0.8896E+02 0.4112E+03 - 4.100 0.8593E+02 0.4198E+03 - 4.200 0.8650E+02 0.4284E+03 - 4.300 0.9003E+02 0.4374E+03 - 4.400 0.9194E+02 0.4466E+03 - 4.500 0.9005E+02 0.4556E+03 - 4.600 0.9134E+02 0.4648E+03 - 4.700 0.9476E+02 0.4742E+03 - 4.800 0.9618E+02 0.4839E+03 - 4.900 0.9623E+02 0.4935E+03 - 5.000 0.9953E+02 0.5034E+03 - 5.100 0.9807E+02 0.5132E+03 - 5.200 0.9541E+02 0.5228E+03 - 5.300 0.9667E+02 0.5324E+03 - 5.400 0.9523E+02 0.5420E+03 - 5.500 0.9344E+02 0.5513E+03 - 5.600 0.9315E+02 0.5606E+03 - 5.700 0.9230E+02 0.5699E+03 - 5.800 0.9171E+02 0.5790E+03 - 5.900 0.8979E+02 0.5880E+03 - 6.000 0.8841E+02 0.5968E+03 - 6.100 0.9056E+02 0.6059E+03 - 6.200 0.9053E+02 0.6150E+03 - 6.300 0.9301E+02 0.6243E+03 - 6.400 0.9515E+02 0.6338E+03 - 6.500 0.9687E+02 0.6435E+03 - 6.600 0.9796E+02 0.6533E+03 - 6.700 0.9889E+02 0.6631E+03 - 6.800 0.9912E+02 0.6731E+03 - 6.900 0.1018E+03 0.6832E+03 - 7.000 0.1027E+03 0.6935E+03 - 7.100 0.1002E+03 0.7035E+03 - 7.200 0.9508E+02 0.7130E+03 - 7.300 0.9677E+02 0.7227E+03 - 7.400 0.1012E+03 0.7328E+03 - 7.500 0.1013E+03 0.7430E+03 - 7.600 0.1002E+03 0.7530E+03 - 7.700 0.1016E+03 0.7631E+03 - 7.800 0.1058E+03 0.7737E+03 - 7.900 0.1079E+03 0.7845E+03 - 8.000 0.1074E+03 0.7952E+03 - 8.100 0.1058E+03 0.8058E+03 - 8.200 0.1070E+03 0.8165E+03 - 8.300 0.1105E+03 0.8276E+03 - 8.400 0.1077E+03 0.8384E+03 - 8.500 0.1040E+03 0.8488E+03 - 8.600 0.1089E+03 0.8596E+03 - 8.700 0.1129E+03 0.8709E+03 - 8.800 0.1102E+03 0.8820E+03 - 8.900 0.1050E+03 0.8925E+03 - 9.000 0.1067E+03 0.9031E+03 - 9.100 0.1126E+03 0.9144E+03 - 9.200 0.1133E+03 0.9257E+03 - 9.300 0.1132E+03 0.9370E+03 - 9.400 0.1133E+03 0.9484E+03 - 9.500 0.1125E+03 0.9596E+03 - 9.600 0.1096E+03 0.9706E+03 - 9.700 0.1136E+03 0.9819E+03 - 9.800 0.1199E+03 0.9939E+03 - 9.900 0.1201E+03 0.1006E+04 - 10.000 0.1132E+03 0.1017E+04 - 10.100 0.1114E+03 0.1028E+04 - 10.200 0.1177E+03 0.1040E+04 - 10.300 0.1201E+03 0.1052E+04 - 10.400 0.1197E+03 0.1064E+04 - 10.500 0.1195E+03 0.1076E+04 - 10.600 0.1195E+03 0.1088E+04 - 10.700 0.1180E+03 0.1100E+04 - 10.800 0.1217E+03 0.1112E+04 - 10.900 0.1236E+03 0.1124E+04 - 11.000 0.1174E+03 0.1136E+04 - 11.100 0.9586E+02 0.1146E+04 - 11.200 0.4947E+02 0.1151E+04 - 11.300 0.1244E+02 0.1152E+04 - 11.400 0.1045E+01 0.1152E+04 - 11.500 0.2079E-01 0.1152E+04 - 11.600 0.9110E-04 0.1152E+04 - 11.700 0.8721E-07 0.1152E+04 - 11.800 0.1574E-10 0.1152E+04 - 11.900 0.4534E-15 0.1152E+04 - 12.000 0.1904E-20 0.1152E+04 - 12.100 0.1122E-26 0.1152E+04 - 12.200 0.9139E-34 0.1152E+04 - 12.300 0.1020E-41 0.1152E+04 - 12.400 0.1556E-50 0.1152E+04 - 12.500 0.3228E-60 0.1152E+04 - 12.600 0.9104E-71 0.1152E+04 - 12.700 0.4385E-82 0.1152E+04 - 12.800 0.8995E-83 0.1152E+04 - 12.900 0.8995E-83 0.1152E+04 - 13.000 0.8995E-83 0.1152E+04 - 13.100 0.8995E-83 0.1152E+04 - 13.200 0.8995E-83 0.1152E+04 - 13.300 0.8995E-83 0.1152E+04 - 13.400 0.8995E-83 0.1152E+04 - 13.500 0.8995E-83 0.1152E+04 - 13.600 0.8995E-83 0.1152E+04 - 13.700 0.8995E-83 0.1152E+04 - 13.800 0.8995E-83 0.1152E+04 - 13.900 0.8995E-83 0.1152E+04 - 14.000 0.8995E-83 0.1152E+04 - 14.100 0.8995E-83 0.1152E+04 - 14.200 0.8995E-83 0.1152E+04 - 14.300 0.8995E-83 0.1152E+04 - 14.400 0.8995E-83 0.1152E+04 - 14.500 0.8995E-83 0.1152E+04 - 14.600 0.8995E-83 0.1152E+04 - 14.700 0.8995E-83 0.1152E+04 - 14.800 0.8995E-83 0.1152E+04 - 14.900 0.8995E-83 0.1152E+04 - 15.000 0.8995E-83 0.1152E+04 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g.old b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g.old deleted file mode 100644 index 0d0f5f7aa..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.dos_g.old +++ /dev/null @@ -1,202 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.797 eV - -5.000 0.7199E-83 0.7199E-84 - -4.900 0.7199E-83 0.1440E-83 - -4.800 0.7199E-83 0.2160E-83 - -4.700 0.8622E-83 0.3022E-83 - -4.600 0.5276E-72 0.5276E-73 - -4.500 0.2681E-61 0.2681E-62 - -4.400 0.1872E-51 0.1872E-52 - -4.300 0.1799E-42 0.1799E-43 - -4.200 0.2384E-34 0.2384E-35 - -4.100 0.4373E-27 0.4373E-28 - -4.000 0.1114E-20 0.1114E-21 - -3.900 0.3968E-15 0.3968E-16 - -3.800 0.1994E-10 0.1994E-11 - -3.700 0.1438E-06 0.1438E-07 - -3.600 0.1532E-03 0.1533E-04 - -3.500 0.2544E-01 0.2560E-02 - -3.400 0.7368E+00 0.7624E-01 - -3.300 0.4679E+01 0.5441E+00 - -3.200 0.1009E+02 0.1553E+01 - -3.100 0.1376E+02 0.2929E+01 - -3.000 0.1669E+02 0.4598E+01 - -2.900 0.1932E+02 0.6530E+01 - -2.800 0.2172E+02 0.8703E+01 - -2.700 0.2382E+02 0.1108E+02 - -2.600 0.2531E+02 0.1362E+02 - -2.500 0.2658E+02 0.1627E+02 - -2.400 0.2852E+02 0.1913E+02 - -2.300 0.3027E+02 0.2215E+02 - -2.200 0.3217E+02 0.2537E+02 - -2.100 0.3308E+02 0.2868E+02 - -2.000 0.3475E+02 0.3215E+02 - -1.900 0.3514E+02 0.3567E+02 - -1.800 0.3718E+02 0.3939E+02 - -1.700 0.3765E+02 0.4315E+02 - -1.600 0.3956E+02 0.4711E+02 - -1.500 0.4076E+02 0.5118E+02 - -1.400 0.4132E+02 0.5532E+02 - -1.300 0.4278E+02 0.5959E+02 - -1.200 0.4323E+02 0.6392E+02 - -1.100 0.4493E+02 0.6841E+02 - -1.000 0.4647E+02 0.7306E+02 - -0.900 0.4653E+02 0.7771E+02 - -0.800 0.4784E+02 0.8249E+02 - -0.700 0.4932E+02 0.8743E+02 - -0.600 0.4909E+02 0.9233E+02 - -0.500 0.5103E+02 0.9744E+02 - -0.400 0.5183E+02 0.1026E+03 - -0.300 0.5245E+02 0.1079E+03 - -0.200 0.5241E+02 0.1131E+03 - -0.100 0.5556E+02 0.1187E+03 - 0.000 0.5507E+02 0.1242E+03 - 0.100 0.5587E+02 0.1298E+03 - 0.200 0.5774E+02 0.1355E+03 - 0.300 0.5826E+02 0.1414E+03 - 0.400 0.5826E+02 0.1472E+03 - 0.500 0.5884E+02 0.1531E+03 - 0.600 0.6173E+02 0.1592E+03 - 0.700 0.6120E+02 0.1654E+03 - 0.800 0.6038E+02 0.1714E+03 - 0.900 0.6383E+02 0.1778E+03 - 1.000 0.6372E+02 0.1842E+03 - 1.100 0.6395E+02 0.1905E+03 - 1.200 0.6540E+02 0.1971E+03 - 1.300 0.6603E+02 0.2037E+03 - 1.400 0.6746E+02 0.2104E+03 - 1.500 0.6779E+02 0.2172E+03 - 1.600 0.6735E+02 0.2240E+03 - 1.700 0.6897E+02 0.2308E+03 - 1.800 0.7162E+02 0.2380E+03 - 1.900 0.6998E+02 0.2450E+03 - 2.000 0.7021E+02 0.2520E+03 - 2.100 0.7294E+02 0.2593E+03 - 2.200 0.7404E+02 0.2667E+03 - 2.300 0.7374E+02 0.2741E+03 - 2.400 0.7351E+02 0.2815E+03 - 2.500 0.7482E+02 0.2889E+03 - 2.600 0.7766E+02 0.2967E+03 - 2.700 0.7735E+02 0.3044E+03 - 2.800 0.7648E+02 0.3121E+03 - 2.900 0.7744E+02 0.3198E+03 - 3.000 0.8098E+02 0.3279E+03 - 3.100 0.8137E+02 0.3361E+03 - 3.200 0.8006E+02 0.3441E+03 - 3.300 0.8099E+02 0.3522E+03 - 3.400 0.8232E+02 0.3604E+03 - 3.500 0.8207E+02 0.3686E+03 - 3.600 0.8388E+02 0.3770E+03 - 3.700 0.8336E+02 0.3853E+03 - 3.800 0.8372E+02 0.3937E+03 - 3.900 0.8574E+02 0.4023E+03 - 4.000 0.8896E+02 0.4112E+03 - 4.100 0.8592E+02 0.4198E+03 - 4.200 0.8650E+02 0.4284E+03 - 4.300 0.9005E+02 0.4374E+03 - 4.400 0.9194E+02 0.4466E+03 - 4.500 0.9004E+02 0.4556E+03 - 4.600 0.9134E+02 0.4648E+03 - 4.700 0.9478E+02 0.4742E+03 - 4.800 0.9618E+02 0.4838E+03 - 4.900 0.9623E+02 0.4935E+03 - 5.000 0.9952E+02 0.5034E+03 - 5.100 0.9807E+02 0.5132E+03 - 5.200 0.9540E+02 0.5228E+03 - 5.300 0.9667E+02 0.5324E+03 - 5.400 0.9523E+02 0.5420E+03 - 5.500 0.9344E+02 0.5513E+03 - 5.600 0.9314E+02 0.5606E+03 - 5.700 0.9230E+02 0.5698E+03 - 5.800 0.9171E+02 0.5790E+03 - 5.900 0.8979E+02 0.5880E+03 - 6.000 0.8841E+02 0.5968E+03 - 6.100 0.9057E+02 0.6059E+03 - 6.200 0.9053E+02 0.6149E+03 - 6.300 0.9301E+02 0.6242E+03 - 6.400 0.9515E+02 0.6338E+03 - 6.500 0.9687E+02 0.6435E+03 - 6.600 0.9795E+02 0.6532E+03 - 6.700 0.9889E+02 0.6631E+03 - 6.800 0.9912E+02 0.6730E+03 - 6.900 0.1018E+03 0.6832E+03 - 7.000 0.1027E+03 0.6935E+03 - 7.100 0.1001E+03 0.7035E+03 - 7.200 0.9508E+02 0.7130E+03 - 7.300 0.9679E+02 0.7227E+03 - 7.400 0.1012E+03 0.7328E+03 - 7.500 0.1012E+03 0.7429E+03 - 7.600 0.1002E+03 0.7530E+03 - 7.700 0.1016E+03 0.7631E+03 - 7.800 0.1058E+03 0.7737E+03 - 7.900 0.1079E+03 0.7845E+03 - 8.000 0.1074E+03 0.7952E+03 - 8.100 0.1058E+03 0.8058E+03 - 8.200 0.1070E+03 0.8165E+03 - 8.300 0.1105E+03 0.8276E+03 - 8.400 0.1077E+03 0.8383E+03 - 8.500 0.1040E+03 0.8487E+03 - 8.600 0.1089E+03 0.8596E+03 - 8.700 0.1129E+03 0.8709E+03 - 8.800 0.1102E+03 0.8819E+03 - 8.900 0.1050E+03 0.8924E+03 - 9.000 0.1058E+03 0.9030E+03 - 9.100 0.1018E+03 0.9132E+03 - 9.200 0.6434E+02 0.9196E+03 - 9.300 0.2057E+02 0.9217E+03 - 9.400 0.2996E+01 0.9220E+03 - 9.500 0.1486E+00 0.9220E+03 - 9.600 0.1742E-02 0.9220E+03 - 9.700 0.3580E-05 0.9220E+03 - 9.800 0.1108E-08 0.9220E+03 - 9.900 0.4876E-13 0.9220E+03 - 10.000 0.2983E-18 0.9220E+03 - 10.100 0.2514E-24 0.9220E+03 - 10.200 0.2906E-31 0.9220E+03 - 10.300 0.4595E-39 0.9220E+03 - 10.400 0.9925E-48 0.9220E+03 - 10.500 0.2925E-57 0.9220E+03 - 10.600 0.1175E-67 0.9220E+03 - 10.700 0.6436E-79 0.9220E+03 - 10.800 0.7199E-83 0.9220E+03 - 10.900 0.7199E-83 0.9220E+03 - 11.000 0.7199E-83 0.9220E+03 - 11.100 0.7199E-83 0.9220E+03 - 11.200 0.7199E-83 0.9220E+03 - 11.300 0.7199E-83 0.9220E+03 - 11.400 0.7199E-83 0.9220E+03 - 11.500 0.7199E-83 0.9220E+03 - 11.600 0.7199E-83 0.9220E+03 - 11.700 0.7199E-83 0.9220E+03 - 11.800 0.7199E-83 0.9220E+03 - 11.900 0.7199E-83 0.9220E+03 - 12.000 0.7199E-83 0.9220E+03 - 12.100 0.7199E-83 0.9220E+03 - 12.200 0.7199E-83 0.9220E+03 - 12.300 0.7199E-83 0.9220E+03 - 12.400 0.7199E-83 0.9220E+03 - 12.500 0.7199E-83 0.9220E+03 - 12.600 0.7199E-83 0.9220E+03 - 12.700 0.7199E-83 0.9220E+03 - 12.800 0.7199E-83 0.9220E+03 - 12.900 0.7199E-83 0.9220E+03 - 13.000 0.7199E-83 0.9220E+03 - 13.100 0.7199E-83 0.9220E+03 - 13.200 0.7199E-83 0.9220E+03 - 13.300 0.7199E-83 0.9220E+03 - 13.400 0.7199E-83 0.9220E+03 - 13.500 0.7199E-83 0.9220E+03 - 13.600 0.7199E-83 0.9220E+03 - 13.700 0.7199E-83 0.9220E+03 - 13.800 0.7199E-83 0.9220E+03 - 13.900 0.7199E-83 0.9220E+03 - 14.000 0.7199E-83 0.9220E+03 - 14.100 0.7199E-83 0.9220E+03 - 14.200 0.7199E-83 0.9220E+03 - 14.300 0.7199E-83 0.9220E+03 - 14.400 0.7199E-83 0.9220E+03 - 14.500 0.7199E-83 0.9220E+03 - 14.600 0.7199E-83 0.9220E+03 - 14.700 0.7199E-83 0.9220E+03 - 14.800 0.7199E-83 0.9220E+03 - 14.900 0.7199E-83 0.9220E+03 - 15.000 0.7199E-83 0.9220E+03 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.scf.pw b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.scf.pw deleted file mode 100644 index af52f9e1d..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/Al.scf.pw +++ /dev/null @@ -1,299 +0,0 @@ -Al.scf.pw -&CONTROL - calculation='scf', - verbosity='high', - restart_mode='from_scratch', - tstress=.true., - tprnfor=.true., - prefix='Al', - pseudo_dir='/ascldap/users/namodin/MLDFT/pp/', - outdir='out', -/ - -&SYSTEM - ibrav=0, - nosym=.true., - nat=256, - ntyp=1, - ecutwfc=100, - ecutrho=400, - occupations='smearing', - smearing='fermi-dirac', - degauss=0.0019, -/ - -&ELECTRONS - mixing_mode='plain', - mixing_beta=0.2, - conv_thr=0.000256, -/ - -ATOMIC_SPECIES - Al 26.981539 Al.sr-pbesol.upf - -K_POINTS automatic - 8 8 8 0 0 0 - -CELL_PARAMETERS angstrom -16.19800000 0.00000000 0.00000000 -0.00000000 16.19800000 0.00000000 -0.00000000 0.00000000 16.19800000 - -ATOMIC_POSITIONS {alat} -Al 0.00317715 0.00115020 0.00422977 -Al 0.00020601 0.12325432 0.12276585 -Al 0.13205907 0.00069849 0.12880466 -Al 0.12035063 0.12233298 0.00500888 -Al 0.99616080 0.99965781 0.25468156 -Al 0.00462834 0.11696233 0.37753909 -Al 0.13126733 0.00105961 0.37584558 -Al 0.11831629 0.13176219 0.25212569 -Al 0.00779253 0.99842842 0.50068938 -Al 0.99406565 0.12050283 0.62795631 -Al 0.12039495 0.99518808 0.62583829 -Al 0.12049355 0.12381140 0.50177796 -Al 0.99335490 0.01245642 0.75437014 -Al 0.99553204 0.13002426 0.87495149 -Al 0.11943260 0.01288485 0.85395661 -Al 0.13138905 0.13208432 0.74220015 -Al 0.00382143 0.24709358 0.00053019 -Al 0.99204471 0.37073519 0.12626821 -Al 0.11874381 0.25060539 0.13404114 -Al 0.11679824 0.37202808 0.00098539 -Al 0.99223667 0.24879828 0.24894088 -Al 0.99033374 0.37367286 0.37740263 -Al 0.12258871 0.24839655 0.38475195 -Al 0.12073235 0.37545826 0.25391324 -Al 0.99414425 0.25962947 0.50477243 -Al 0.99545625 0.39077079 0.62318287 -Al 0.11229683 0.24772820 0.61759188 -Al 0.12817764 0.37661444 0.49803461 -Al 0.00321505 0.25783831 0.75237872 -Al 0.99978890 0.37320385 0.86750262 -Al 0.12814221 0.25837418 0.87188042 -Al 0.13120253 0.38190646 0.75012449 -Al 0.99484757 0.49812333 0.00609974 -Al 0.00874093 0.61918173 0.12645399 -Al 0.12780204 0.49617002 0.11988085 -Al 0.12226886 0.61840000 0.99658195 -Al 0.00793260 0.49965903 0.24600526 -Al 0.00325045 0.61742519 0.35830843 -Al 0.13001625 0.50497624 0.37276834 -Al 0.12888048 0.61619867 0.24525564 -Al 0.00725752 0.49718671 0.49005208 -Al 0.00809822 0.62296642 0.62954075 -Al 0.13096336 0.50063965 0.62282885 -Al 0.12195581 0.62021395 0.49527532 -Al 0.99547982 0.50080057 0.73804569 -Al 0.00972095 0.62755205 0.87195202 -Al 0.11302378 0.49835758 0.86591511 -Al 0.13264708 0.62729203 0.75399568 -Al 0.01326271 0.74521439 0.98957500 -Al 0.00308360 0.88595919 0.13014794 -Al 0.13008724 0.74119884 0.12955884 -Al 0.11689139 0.87730057 0.00074077 -Al 0.00598782 0.74653283 0.24416491 -Al 0.99993759 0.87055960 0.37502856 -Al 0.12679125 0.74442819 0.37338182 -Al 0.13173136 0.87223433 0.25545163 -Al 0.00075479 0.74591358 0.50343419 -Al 0.98829944 0.87376572 0.62937187 -Al 0.12462864 0.74836788 0.62116638 -Al 0.12111028 0.87199090 0.50129970 -Al 0.99367338 0.74957799 0.75284518 -Al 0.00069577 0.88157231 0.87119742 -Al 0.12945036 0.76313369 0.86763456 -Al 0.11475345 0.87795232 0.75038888 -Al 0.23626248 0.00319436 0.99166677 -Al 0.25757080 0.12700004 0.12783241 -Al 0.37099739 0.99374573 0.12173435 -Al 0.37029839 0.11613890 0.99404599 -Al 0.25302916 0.99711742 0.24225680 -Al 0.25196272 0.12266725 0.37109725 -Al 0.37381480 0.99324247 0.37948380 -Al 0.38064617 0.12924791 0.24513119 -Al 0.25254530 0.98903725 0.49475226 -Al 0.25394042 0.12783157 0.61666274 -Al 0.37372857 0.99731025 0.62863151 -Al 0.37756758 0.12120710 0.49756168 -Al 0.23710907 0.00496925 0.74787682 -Al 0.24708361 0.13299635 0.86870489 -Al 0.36903611 0.99772149 0.87386064 -Al 0.37613133 0.12940253 0.75595693 -Al 0.25074156 0.24968000 0.00237963 -Al 0.24176045 0.37397695 0.11819446 -Al 0.37099257 0.25277893 0.12126261 -Al 0.36491106 0.38167290 0.00319109 -Al 0.24943387 0.24137978 0.24963289 -Al 0.25168702 0.37152301 0.37586054 -Al 0.37679982 0.24767965 0.37002965 -Al 0.36566749 0.36687118 0.24719678 -Al 0.25668066 0.24935992 0.49689055 -Al 0.23688615 0.37325791 0.61362708 -Al 0.36775284 0.25295831 0.63339193 -Al 0.37058487 0.37232564 0.50641822 -Al 0.24058480 0.25887981 0.74450233 -Al 0.24768663 0.38273245 0.88066757 -Al 0.37052541 0.26712547 0.87335350 -Al 0.37038910 0.37579674 0.75276513 -Al 0.24253455 0.49942232 0.99487916 -Al 0.25074173 0.62442019 0.12243577 -Al 0.37311382 0.49914902 0.12420954 -Al 0.37320920 0.62350426 0.99376271 -Al 0.25389455 0.49538335 0.25271040 -Al 0.25658599 0.62317196 0.37349744 -Al 0.38212228 0.49034093 0.37947714 -Al 0.38090066 0.60896281 0.25545615 -Al 0.24329407 0.50142690 0.49126120 -Al 0.25269515 0.62955678 0.62841784 -Al 0.37042634 0.50105050 0.62697351 -Al 0.37084280 0.62520133 0.51267915 -Al 0.25231582 0.50422315 0.74908575 -Al 0.25083118 0.62883028 0.87233488 -Al 0.38054186 0.49697121 0.87200829 -Al 0.38857876 0.61850989 0.74785557 -Al 0.25779019 0.74847839 0.99939367 -Al 0.24911012 0.87420062 0.12270013 -Al 0.37379063 0.73973313 0.12344417 -Al 0.37805029 0.86810019 0.99298930 -Al 0.25333605 0.74287691 0.24945202 -Al 0.25383293 0.87208918 0.37325974 -Al 0.37376891 0.74959858 0.37877335 -Al 0.37486391 0.86890075 0.25672474 -Al 0.23915687 0.74103044 0.49724675 -Al 0.24369823 0.87643261 0.62451626 -Al 0.38505233 0.75239503 0.62617122 -Al 0.37847875 0.87612161 0.51019113 -Al 0.26582340 0.74964806 0.74671707 -Al 0.24070512 0.87568485 0.87138745 -Al 0.38127443 0.74478341 0.87734871 -Al 0.37122727 0.88166043 0.75464351 -Al 0.49676265 0.99913572 0.00773492 -Al 0.49677586 0.12889222 0.11883841 -Al 0.62632069 0.00209937 0.12239078 -Al 0.61429381 0.12500820 0.00331895 -Al 0.50499332 0.99536389 0.25588511 -Al 0.51758471 0.12246991 0.37168698 -Al 0.62685441 0.99657069 0.38221316 -Al 0.62133037 0.10677543 0.24236551 -Al 0.50283046 0.00027661 0.50517401 -Al 0.49924389 0.12871700 0.63075628 -Al 0.61973760 0.99776872 0.63487085 -Al 0.62154625 0.13372902 0.49349498 -Al 0.48858742 0.00996567 0.75577873 -Al 0.50030758 0.12998960 0.88598660 -Al 0.61675238 0.01078308 0.87268885 -Al 0.62062082 0.13817810 0.75159238 -Al 0.49551556 0.25732791 0.99892171 -Al 0.49621790 0.37539287 0.12546048 -Al 0.63140164 0.26479969 0.12145568 -Al 0.61997492 0.37552116 0.99192773 -Al 0.50866253 0.24052677 0.24820621 -Al 0.50268749 0.36723972 0.37156275 -Al 0.62567643 0.24712665 0.38373644 -Al 0.62928974 0.38239436 0.25813868 -Al 0.48999643 0.25627225 0.49935502 -Al 0.49796721 0.38502975 0.62724580 -Al 0.62430452 0.25166879 0.62749593 -Al 0.61467977 0.37317844 0.50637909 -Al 0.49493639 0.25133042 0.75154257 -Al 0.49534688 0.37618114 0.86909990 -Al 0.61246283 0.25121344 0.86485038 -Al 0.62282704 0.38082335 0.74352853 -Al 0.50348440 0.50148151 0.00898825 -Al 0.49859317 0.62374629 0.13172456 -Al 0.63072956 0.50103732 0.13011505 -Al 0.62698283 0.62785259 0.00833124 -Al 0.50116452 0.48931611 0.24883425 -Al 0.50458482 0.61475673 0.38100075 -Al 0.62957740 0.49402239 0.38181668 -Al 0.62655074 0.62155853 0.25519562 -Al 0.50115871 0.49541243 0.49810488 -Al 0.50973680 0.61713381 0.62630168 -Al 0.63118820 0.49631279 0.62401404 -Al 0.62498572 0.61067461 0.49866825 -Al 0.50907412 0.50153474 0.75901977 -Al 0.51212343 0.62386323 0.88244495 -Al 0.62239397 0.49705208 0.88343202 -Al 0.62757424 0.61527633 0.74473006 -Al 0.49936968 0.75141687 0.00098419 -Al 0.50198259 0.87164597 0.13247901 -Al 0.61759859 0.75078730 0.14208182 -Al 0.62790966 0.87363482 0.01134898 -Al 0.49648381 0.74798718 0.25575989 -Al 0.49563886 0.87098031 0.37490084 -Al 0.61975734 0.74992524 0.38173015 -Al 0.63001577 0.87476567 0.25423197 -Al 0.50069165 0.74864365 0.50712919 -Al 0.51183280 0.87152323 0.63036879 -Al 0.63128640 0.73926092 0.61850891 -Al 0.62876440 0.86579297 0.49946341 -Al 0.49769761 0.74656916 0.75232188 -Al 0.50599556 0.87822282 0.88111163 -Al 0.62828277 0.74851850 0.87818830 -Al 0.62013637 0.87599170 0.76303239 -Al 0.75686751 0.99492115 0.00088827 -Al 0.74467843 0.12723427 0.11961291 -Al 0.87556549 0.00369676 0.12621795 -Al 0.87475810 0.12266938 0.99868019 -Al 0.74609007 0.00163629 0.24752646 -Al 0.75349991 0.12933425 0.37611815 -Al 0.88095143 0.00133752 0.37711697 -Al 0.87706767 0.11849743 0.24714141 -Al 0.74362808 0.00703466 0.50528327 -Al 0.75057559 0.13367993 0.62043474 -Al 0.87015719 0.00376019 0.62411051 -Al 0.87832474 0.11957331 0.50122298 -Al 0.75440296 0.00266564 0.75438769 -Al 0.75063391 0.12797922 0.87408035 -Al 0.87500514 0.00969461 0.87268056 -Al 0.87355340 0.12955559 0.75399544 -Al 0.74541659 0.24490088 0.00407350 -Al 0.75075681 0.39099472 0.11533268 -Al 0.86431841 0.25660834 0.11255680 -Al 0.86652423 0.37969599 0.99219682 -Al 0.74146925 0.23987690 0.24834194 -Al 0.75169720 0.37625628 0.37634352 -Al 0.88890668 0.25086535 0.37728200 -Al 0.86238571 0.36121717 0.24200173 -Al 0.75146734 0.25403038 0.50718035 -Al 0.74894394 0.36950948 0.62383741 -Al 0.87442534 0.25501163 0.62503015 -Al 0.87776574 0.37481735 0.50628054 -Al 0.74617247 0.24906860 0.75596080 -Al 0.75221802 0.37507914 0.87355096 -Al 0.87896034 0.25760957 0.88390838 -Al 0.86867608 0.38164705 0.74626903 -Al 0.74189350 0.51020090 0.99834605 -Al 0.74524061 0.62716394 0.13044927 -Al 0.87170353 0.49891082 0.12002179 -Al 0.86951405 0.62129397 0.00276830 -Al 0.75203169 0.50093943 0.24050544 -Al 0.75900617 0.61844580 0.36090421 -Al 0.87814676 0.49591115 0.36355387 -Al 0.87903888 0.62184363 0.24034326 -Al 0.75162722 0.49628811 0.49270285 -Al 0.74834445 0.62934556 0.61707414 -Al 0.86376714 0.50616251 0.62149233 -Al 0.87601835 0.61794319 0.49499002 -Al 0.75832911 0.50299237 0.75301563 -Al 0.75002999 0.63141690 0.87782568 -Al 0.87777246 0.50587526 0.87233272 -Al 0.88336963 0.62653265 0.75699366 -Al 0.74583914 0.75416465 0.00348848 -Al 0.75813025 0.87640517 0.12706117 -Al 0.87928098 0.75005800 0.12474447 -Al 0.88438720 0.87819211 0.00472541 -Al 0.75998762 0.75236132 0.25078015 -Al 0.76260167 0.87804941 0.36636549 -Al 0.88099052 0.74935941 0.37229183 -Al 0.88247979 0.86897661 0.25221829 -Al 0.75248504 0.75642062 0.49706069 -Al 0.74984275 0.87473162 0.62898769 -Al 0.87611175 0.74487681 0.62710867 -Al 0.88093126 0.87696450 0.49141002 -Al 0.74834534 0.75930289 0.75569426 -Al 0.75898225 0.88269616 0.86945006 -Al 0.87028964 0.75236772 0.87807720 -Al 0.87801602 0.87625452 0.74951145 - diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/pbs.job b/ml-dft-sandia/notebooks/dos2eband/snap_0_888/pbs.job deleted file mode 100644 index 0dfe02617..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_0_888/pbs.job +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash -# -# Script to run a VASP job using SLURM on 1152 cpus of Chama -# Created by ./run_job.exe on Mon Mar 30 13:20:37 MDT 2020 -# -#SBATCH --nodes=32 -#SBATCH --time=48:00:00 -#SBATCH --account=FY200018 -#SBATCH --job-name=200330132037 -#SBATCH --output=/nscratch/namodin/200330132037/200330132037.out -#SBATCH --error=/nscratch/namodin/200330132037/200330132037.err - -# Number of nodes -nodes=32 - -# Number MPI processes to run on each node (a.k.a. PPN) -# Redsky has 8 cores per node -cores=36 - -# Total number of cores -cpus=1152 - -# -# Set up running directory -# -cd /nscratch/namodin/200330132037 -cp /home/namodin/MLDFT/output/200330132037/Al.scf.pw . -cp /ascldap/users/namodin/MLDFT/pw.x . -#cp /ascldap/users/namodin/MLDFT/pw_intel.x . - -# -# Set up the environment -# -module purge -module load tce -module load intel/18.0.5.274 -module load mkl/18.0.5.274 -module load openmpi-intel/1.10 -# -# Execute code -# -mpiexec --npernode $cores --n $cpus ./pw.x -nk $nodes -in Al.scf.pw -#mpiexec --npernode $cores --n $cpus ./pw.x -nk $nodes -nt 1 -nd 1 -in Al.scf.pw -#mpiexec --npernode $cores --n $cpus ./pw_intel.x -nk $nodes -in Al.scf.pw - -# -# Save results and clean up -# -rm pw.x -#rm pw_intel.x -cp -r *.out /home/namodin/MLDFT/output/200330132037 -cp -r *.err /home/namodin/MLDFT/output/200330132037 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_1/Al.dos b/ml-dft-sandia/notebooks/dos2eband/snap_1/Al.dos deleted file mode 100644 index 589d70b24..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_1/Al.dos +++ /dev/null @@ -1,140 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.773 eV - -4.620 0.7785E-04 0.8582E-05 - -4.510 0.3747E-03 0.4988E-04 - -4.400 0.1600E-02 0.2262E-03 - -4.289 0.6069E-02 0.8952E-03 - -4.179 0.2048E-01 0.3153E-02 - -4.069 0.6163E-01 0.9948E-02 - -3.959 0.1657E+00 0.2821E-01 - -3.849 0.3991E+00 0.7221E-01 - -3.738 0.8645E+00 0.1675E+00 - -3.628 0.1691E+01 0.3539E+00 - -3.518 0.3001E+01 0.6847E+00 - -3.408 0.4866E+01 0.1221E+01 - -3.297 0.7261E+01 0.2021E+01 - -3.187 0.1006E+02 0.3130E+01 - -3.077 0.1306E+02 0.4570E+01 - -2.967 0.1607E+02 0.6342E+01 - -2.856 0.1891E+02 0.8427E+01 - -2.746 0.2151E+02 0.1080E+02 - -2.636 0.2385E+02 0.1343E+02 - -2.526 0.2597E+02 0.1629E+02 - -2.415 0.2790E+02 0.1937E+02 - -2.305 0.2968E+02 0.2264E+02 - -2.195 0.3132E+02 0.2609E+02 - -2.085 0.3287E+02 0.2971E+02 - -1.975 0.3437E+02 0.3350E+02 - -1.864 0.3586E+02 0.3746E+02 - -1.754 0.3733E+02 0.4157E+02 - -1.644 0.3870E+02 0.4584E+02 - -1.534 0.3995E+02 0.5024E+02 - -1.423 0.4112E+02 0.5477E+02 - -1.313 0.4228E+02 0.5944E+02 - -1.203 0.4352E+02 0.6423E+02 - -1.093 0.4483E+02 0.6918E+02 - -0.982 0.4612E+02 0.7426E+02 - -0.872 0.4726E+02 0.7947E+02 - -0.762 0.4819E+02 0.8478E+02 - -0.652 0.4897E+02 0.9018E+02 - -0.541 0.4980E+02 0.9567E+02 - -0.431 0.5087E+02 0.1013E+03 - -0.321 0.5224E+02 0.1070E+03 - -0.211 0.5372E+02 0.1130E+03 - -0.101 0.5501E+02 0.1190E+03 - 0.010 0.5589E+02 0.1252E+03 - 0.120 0.5640E+02 0.1314E+03 - 0.230 0.5681E+02 0.1377E+03 - 0.340 0.5745E+02 0.1440E+03 - 0.451 0.5849E+02 0.1504E+03 - 0.561 0.5987E+02 0.1570E+03 - 0.671 0.6132E+02 0.1638E+03 - 0.781 0.6259E+02 0.1707E+03 - 0.892 0.6353E+02 0.1777E+03 - 1.002 0.6420E+02 0.1848E+03 - 1.112 0.6468E+02 0.1919E+03 - 1.222 0.6511E+02 0.1991E+03 - 1.333 0.6556E+02 0.2063E+03 - 1.443 0.6618E+02 0.2136E+03 - 1.553 0.6713E+02 0.2210E+03 - 1.663 0.6851E+02 0.2286E+03 - 1.773 0.7021E+02 0.2363E+03 - 1.884 0.7193E+02 0.2442E+03 - 1.994 0.7321E+02 0.2523E+03 - 2.104 0.7374E+02 0.2604E+03 - 2.214 0.7353E+02 0.2685E+03 - 2.325 0.7302E+02 0.2766E+03 - 2.435 0.7287E+02 0.2846E+03 - 2.545 0.7363E+02 0.2927E+03 - 2.655 0.7534E+02 0.3010E+03 - 2.766 0.7756E+02 0.3096E+03 - 2.876 0.7966E+02 0.3184E+03 - 2.986 0.8111E+02 0.3273E+03 - 3.096 0.8175E+02 0.3363E+03 - 3.207 0.8177E+02 0.3453E+03 - 3.317 0.8154E+02 0.3543E+03 - 3.427 0.8146E+02 0.3633E+03 - 3.537 0.8177E+02 0.3723E+03 - 3.648 0.8250E+02 0.3814E+03 - 3.758 0.8355E+02 0.3906E+03 - 3.868 0.8476E+02 0.4000E+03 - 3.978 0.8606E+02 0.4095E+03 - 4.088 0.8745E+02 0.4191E+03 - 4.199 0.8892E+02 0.4289E+03 - 4.309 0.9040E+02 0.4389E+03 - 4.419 0.9179E+02 0.4490E+03 - 4.529 0.9299E+02 0.4592E+03 - 4.640 0.9394E+02 0.4696E+03 - 4.750 0.9463E+02 0.4800E+03 - 4.860 0.9502E+02 0.4905E+03 - 4.970 0.9507E+02 0.5010E+03 - 5.081 0.9478E+02 0.5114E+03 - 5.191 0.9429E+02 0.5218E+03 - 5.301 0.9386E+02 0.5322E+03 - 5.411 0.9371E+02 0.5425E+03 - 5.522 0.9385E+02 0.5528E+03 - 5.632 0.9408E+02 0.5632E+03 - 5.742 0.9411E+02 0.5736E+03 - 5.852 0.9376E+02 0.5839E+03 - 5.962 0.9310E+02 0.5942E+03 - 6.073 0.9246E+02 0.6044E+03 - 6.183 0.9229E+02 0.6146E+03 - 6.293 0.9287E+02 0.6248E+03 - 6.403 0.9410E+02 0.6352E+03 - 6.514 0.9557E+02 0.6457E+03 - 6.624 0.9680E+02 0.6564E+03 - 6.734 0.9752E+02 0.6671E+03 - 6.844 0.9775E+02 0.6779E+03 - 6.955 0.9775E+02 0.6887E+03 - 7.065 0.9789E+02 0.6995E+03 - 7.175 0.9856E+02 0.7103E+03 - 7.285 0.1000E+03 0.7214E+03 - 7.396 0.1022E+03 0.7326E+03 - 7.506 0.1046E+03 0.7441E+03 - 7.616 0.1064E+03 0.7559E+03 - 7.726 0.1071E+03 0.7677E+03 - 7.836 0.1065E+03 0.7794E+03 - 7.947 0.1048E+03 0.7910E+03 - 8.057 0.1031E+03 0.8023E+03 - 8.167 0.1020E+03 0.8136E+03 - 8.277 0.1020E+03 0.8248E+03 - 8.388 0.1033E+03 0.8362E+03 - 8.498 0.1050E+03 0.8478E+03 - 8.608 0.1060E+03 0.8595E+03 - 8.718 0.1049E+03 0.8710E+03 - 8.829 0.1002E+03 0.8821E+03 - 8.939 0.9141E+02 0.8922E+03 - 9.049 0.7887E+02 0.9009E+03 - 9.159 0.6382E+02 0.9079E+03 - 9.270 0.4810E+02 0.9132E+03 - 9.380 0.3354E+02 0.9169E+03 - 9.490 0.2151E+02 0.9193E+03 - 9.600 0.1261E+02 0.9207E+03 - 9.711 0.6724E+01 0.9214E+03 - 9.821 0.3246E+01 0.9218E+03 - 9.931 0.1413E+01 0.9219E+03 - 10.041 0.5525E+00 0.9220E+03 - 10.151 0.1936E+00 0.9220E+03 - 10.262 0.6059E-01 0.9220E+03 - 10.372 0.1691E-01 0.9220E+03 - 10.482 0.4200E-02 0.9220E+03 - 10.592 0.9270E-03 0.9220E+03 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_1/EIGS b/ml-dft-sandia/notebooks/dos2eband/snap_1/EIGS deleted file mode 100644 index a6d1af701..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_1/EIGS +++ /dev/null @@ -1,461 +0,0 @@ --3.314100, -3.252400, -3.254400, -3.193400, -3.192500, -3.252300, -3.190900, -3.190800, -3.193000, -3.132200, -3.131200, -3.192500, -3.130900, -3.131600 --2.806200, -3.066300, -3.060600, -3.010400, -3.028200, -3.066800, -3.013000, -3.020400, -3.008900, -2.955600, -2.973300, -3.021000, -2.974600, -2.970200 --2.782300, -2.729900, -2.725100, -2.994300, -2.974000, -2.730500, -2.996800, -2.987700, -2.996600, -2.941700, -2.941200, -2.984000, -2.939900, -2.934600 --2.767300, -2.698700, -2.715900, -2.807200, -2.810900, -2.714900, -2.818700, -2.821200, -2.812900, -2.930700, -2.908300, -2.813300, -2.909400, -2.920700 --2.740800, -2.688000, -2.690300, -2.658300, -2.656900, -2.668700, -2.643600, -2.642600, -2.648900, -2.760200, -2.783200, -2.649400, -2.785400, -2.777000 --2.716000, -2.664900, -2.664000, -2.627100, -2.624600, -2.658000, -2.615300, -2.616200, -2.621700, -2.754200, -2.747400, -2.620700, -2.748700, -2.746100 --2.714800, -2.540500, -2.519600, -2.470700, -2.499300, -2.548400, -2.474400, -2.482700, -2.475000, -2.735900, -2.722800, -2.479100, -2.716900, -2.728500 --2.287200, -2.505500, -2.509500, -2.452300, -2.449500, -2.528700, -2.454200, -2.448100, -2.454700, -2.558400, -2.563100, -2.460700, -2.562100, -2.560600 --2.260300, -2.498200, -2.500400, -2.440600, -2.425700, -2.481700, -2.434400, -2.428800, -2.436400, -2.221300, -2.239300, -2.443500, -2.239600, -2.231000 --2.242400, -2.469700, -2.472600, -2.415000, -2.398400, -2.466700, -2.410000, -2.410900, -2.415000, -2.205300, -2.197700, -2.397000, -2.202700, -2.197700 --2.222100, -2.317300, -2.312800, -2.274400, -2.289100, -2.306200, -2.294300, -2.288500, -2.288000, -2.186700, -2.166300, -2.285600, -2.172700, -2.176900 --2.215300, -2.171700, -2.180400, -2.265300, -2.279500, -2.161600, -2.265000, -2.269000, -2.273100, -2.058700, -2.064700, -2.260500, -2.077200, -2.078100 --2.202400, -2.141100, -2.139800, -2.237200, -2.232200, -2.150000, -2.244900, -2.243800, -2.235200, -2.043300, -2.040700, -2.243600, -2.028800, -2.051200 --2.188100, -2.119000, -2.126000, -2.216100, -2.197000, -2.134100, -2.213400, -2.213000, -2.210300, -2.027600, -2.025300, -2.219100, -2.024200, -2.008300 --2.162400, -2.105400, -2.109600, -2.086200, -2.082500, -2.088300, -2.077200, -2.080700, -2.071000, -1.987800, -1.990400, -2.081800, -1.986700, -1.994200 --2.153400, -1.979700, -1.970000, -2.038800, -2.038900, -1.974000, -2.044400, -2.054400, -2.045900, -1.984400, -1.975200, -2.037300, -1.976500, -1.979300 --2.142200, -1.947300, -1.939800, -1.753800, -1.782800, -1.967100, -1.752000, -1.750800, -1.761600, -1.955700, -1.965800, -1.768200, -1.962000, -1.950000 --2.122300, -1.912700, -1.929700, -1.730400, -1.736400, -1.944500, -1.730900, -1.737100, -1.745700, -1.842300, -1.845000, -1.733400, -1.842300, -1.836700 --2.106400, -1.907700, -1.907800, -1.707200, -1.720800, -1.897600, -1.725900, -1.703600, -1.707900, -1.804900, -1.815400, -1.716200, -1.800500, -1.828100 --1.679800, -1.790100, -1.781200, -1.688000, -1.691100, -1.804700, -1.700500, -1.684800, -1.691900, -1.779400, -1.786100, -1.682300, -1.780400, -1.774300 --1.674200, -1.771100, -1.761600, -1.676000, -1.662700, -1.768100, -1.672100, -1.668100, -1.678900, -1.669900, -1.665000, -1.677100, -1.686700, -1.659100 --1.652100, -1.754100, -1.744800, -1.671100, -1.643600, -1.745800, -1.649500, -1.655900, -1.640400, -1.640200, -1.650100, -1.639800, -1.632400, -1.649300 --1.631300, -1.729900, -1.733700, -1.552700, -1.556200, -1.708300, -1.549800, -1.550500, -1.560200, -1.620800, -1.597300, -1.563900, -1.614700, -1.608700 --1.621400, -1.715800, -1.720400, -1.525500, -1.538900, -1.700200, -1.527500, -1.528100, -1.524900, -1.508400, -1.489700, -1.525800, -1.501000, -1.507100 --1.604100, -1.269700, -1.267500, -1.507500, -1.507800, -1.291800, -1.509400, -1.511700, -1.514800, -1.482100, -1.478300, -1.515700, -1.472700, -1.476600 --1.580700, -1.252400, -1.249600, -1.492000, -1.481000, -1.260500, -1.499800, -1.504300, -1.491400, -1.452500, -1.456300, -1.494100, -1.452800, -1.446000 --1.567500, -1.242200, -1.225600, -1.477100, -1.469700, -1.229900, -1.467100, -1.494300, -1.469500, -1.424700, -1.435700, -1.461900, -1.435800, -1.441300 --1.153300, -1.227100, -1.207200, -1.462400, -1.449800, -1.207300, -1.453800, -1.455000, -1.443500, -1.420700, -1.424400, -1.446500, -1.416100, -1.403200 --1.119600, -1.207800, -1.200100, -1.321200, -1.317800, -1.203300, -1.322800, -1.320300, -1.318100, -1.395600, -1.399000, -1.319200, -1.390300, -1.391800 --1.095200, -1.177700, -1.194300, -1.185600, -1.182400, -1.186800, -1.175500, -1.163900, -1.180100, -1.294100, -1.291400, -1.183800, -1.318800, -1.312100 --1.075100, -1.151500, -1.164500, -1.152000, -1.155500, -1.152000, -1.154000, -1.156400, -1.155400, -1.282000, -1.283800, -1.154600, -1.285200, -1.292700 --1.037900, -1.123900, -1.146500, -1.131200, -1.134500, -1.133600, -1.141900, -1.134200, -1.142700, -1.262400, -1.259100, -1.151000, -1.268200, -1.272500 --1.030500, -1.052100, -1.061800, -1.113100, -1.111400, -1.060000, -1.111100, -1.107400, -1.117400, -1.234500, -1.246300, -1.104400, -1.241200, -1.244300 --0.673800, -1.021300, -1.029300, -0.998100, -1.016900, -1.017400, -1.009600, -1.002000, -1.006600, -1.220100, -1.225300, -1.022400, -1.233300, -1.215000 --0.647200, -1.003200, -1.002600, -0.980400, -0.988400, -0.988600, -0.987500, -0.976500, -0.984300, -1.213900, -1.193500, -0.980700, -1.180000, -1.189200 --0.636500, -0.977000, -0.975400, -0.963900, -0.960200, -0.984600, -0.948400, -0.959100, -0.958200, -1.093900, -1.096600, -0.967300, -1.096800, -1.092100 --0.622100, -0.855700, -0.858500, -0.950800, -0.957400, -0.869400, -0.935200, -0.948400, -0.948600, -1.054200, -1.074400, -0.951800, -1.056400, -1.081600 --0.609900, -0.836000, -0.846300, -0.924000, -0.907900, -0.833900, -0.912900, -0.921900, -0.929500, -1.041000, -1.036500, -0.908500, -1.027800, -1.030600 --0.587400, -0.824200, -0.812400, -0.898500, -0.887800, -0.799700, -0.900000, -0.913000, -0.885900, -0.777900, -0.766800, -0.873000, -0.776700, -0.758900 --0.577400, -0.796400, -0.773100, -0.809600, -0.822000, -0.787600, -0.818500, -0.808800, -0.828800, -0.742200, -0.745800, -0.844500, -0.763700, -0.752000 --0.568900, -0.682800, -0.685200, -0.794700, -0.808900, -0.681000, -0.804300, -0.792600, -0.802000, -0.717300, -0.712300, -0.809300, -0.736700, -0.724000 --0.553500, -0.665000, -0.652500, -0.781600, -0.788900, -0.669700, -0.784900, -0.780100, -0.791100, -0.692400, -0.694700, -0.782700, -0.700900, -0.691400 --0.549200, -0.634700, -0.638100, -0.763000, -0.774300, -0.646800, -0.769500, -0.759000, -0.760500, -0.680600, -0.675800, -0.769200, -0.680500, -0.679800 --0.544000, -0.624700, -0.626000, -0.750900, -0.757000, -0.605500, -0.750400, -0.752000, -0.742400, -0.662400, -0.660800, -0.743500, -0.619000, -0.649700 --0.524000, -0.505200, -0.545900, -0.739800, -0.719400, -0.527500, -0.733400, -0.735200, -0.727900, -0.574100, -0.572500, -0.728800, -0.572800, -0.572000 --0.512500, -0.494400, -0.515100, -0.719300, -0.701000, -0.492000, -0.714600, -0.714000, -0.706400, -0.536300, -0.555000, -0.701000, -0.545400, -0.562900 --0.498200, -0.477600, -0.493300, -0.689500, -0.693400, -0.480800, -0.693700, -0.699400, -0.682500, -0.517100, -0.538800, -0.678100, -0.531300, -0.511800 --0.490300, -0.467500, -0.459200, -0.577700, -0.570400, -0.472500, -0.574300, -0.581300, -0.595700, -0.498500, -0.490300, 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7.4085 - 7.4219 - 7.4342 - 7.4423 - 7.4581 - 7.4638 - 7.4807 - 7.5098 - 7.5307 - 7.5500 - 7.5580 - 7.5717 - 7.5904 - 7.6027 - 7.6207 - 7.6405 - 7.7050 - 7.7207 - 7.7551 - 7.7715 - 7.7829 - 7.7928 - 7.8096 - 7.8250 - 7.8415 - 7.8550 - 7.8596 - 7.8681 - 7.9394 - 7.9555 - 7.9976 - 8.0181 - 8.0265 - 8.0469 - 8.0683 - 8.0718 - 8.0831 - 8.0926 - 8.1001 - 8.1203 - 8.2047 - 8.2157 - 8.2305 - 8.2469 - 8.2527 - 8.2758 - 8.2819 - 8.3090 - 8.3271 - 8.3382 - 8.3559 - 8.3570 - 8.3660 - 8.3762 - 8.3808 - 8.3949 - 8.4141 - 8.4327 - 8.4565 - 8.5614 - 8.5946 - 8.6046 - 8.6104 - 8.6327 - 8.6448 - 8.6511 - 8.6665 - 8.6744 - 8.6846 - 8.6979 - 8.7051 - 8.7158 - 8.7283 - 8.7351 - 8.7505 - 8.7747 - 8.7809 - 8.7944 - 8.8127 - 8.8265 - 8.8345 - 8.8483 - 8.8715 - 8.8730 - 8.8852 - 8.8981 - 8.9087 - 8.9174 - 8.9381 - 8.9425 - 8.9607 - 8.9842 - 8.9907 - 9.0026 - 9.0370 - 9.0604 - 9.1026 - 9.1193 - 9.1656 - 9.2008 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_1/k_weights b/ml-dft-sandia/notebooks/dos2eband/snap_1/k_weights deleted file mode 100644 index 925cfac45..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_1/k_weights +++ /dev/null @@ -1 +0,0 @@ -0.0740741, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481, 0.1481481 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_1_888/Al.dos_2g b/ml-dft-sandia/notebooks/dos2eband/snap_1_888/Al.dos_2g deleted file mode 100644 index 19d9589ed..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_1_888/Al.dos_2g +++ /dev/null @@ -1,252 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.800 eV - -10.000 0.4497E-83 0.4497E-84 - -9.900 0.4497E-83 0.8995E-84 - -9.800 0.4497E-83 0.1349E-83 - -9.700 0.4497E-83 0.1799E-83 - -9.600 0.4497E-83 0.2249E-83 - -9.500 0.4497E-83 0.2698E-83 - -9.400 0.4497E-83 0.3148E-83 - -9.300 0.4497E-83 0.3598E-83 - -9.200 0.4497E-83 0.4048E-83 - -9.100 0.4497E-83 0.4497E-83 - -9.000 0.4497E-83 0.4947E-83 - -8.900 0.4497E-83 0.5397E-83 - -8.800 0.4497E-83 0.5846E-83 - -8.700 0.4497E-83 0.6296E-83 - -8.600 0.4497E-83 0.6746E-83 - -8.500 0.4497E-83 0.7196E-83 - -8.400 0.4497E-83 0.7645E-83 - -8.300 0.4497E-83 0.8095E-83 - -8.200 0.4497E-83 0.8545E-83 - -8.100 0.4497E-83 0.8995E-83 - -8.000 0.4497E-83 0.9444E-83 - -7.900 0.4497E-83 0.9894E-83 - -7.800 0.4497E-83 0.1034E-82 - -7.700 0.4497E-83 0.1079E-82 - -7.600 0.4497E-83 0.1124E-82 - -7.500 0.4497E-83 0.1169E-82 - -7.400 0.4497E-83 0.1214E-82 - -7.300 0.4497E-83 0.1259E-82 - -7.200 0.4497E-83 0.1304E-82 - -7.100 0.4497E-83 0.1349E-82 - -7.000 0.4497E-83 0.1394E-82 - -6.900 0.4497E-83 0.1439E-82 - -6.800 0.4497E-83 0.1484E-82 - -6.700 0.4497E-83 0.1529E-82 - -6.600 0.4497E-83 0.1574E-82 - -6.500 0.4497E-83 0.1619E-82 - -6.400 0.4497E-83 0.1664E-82 - -6.300 0.4497E-83 0.1709E-82 - -6.200 0.4497E-83 0.1754E-82 - -6.100 0.4523E-83 0.1799E-82 - -6.000 0.2446E-79 0.2464E-80 - -5.900 0.1367E-73 0.1367E-74 - -5.800 0.4644E-68 0.4644E-69 - -5.700 0.9593E-63 0.9593E-64 - -5.600 0.1205E-57 0.1205E-58 - -5.500 0.9209E-53 0.9209E-54 - -5.400 0.4281E-48 0.4281E-49 - -5.300 0.1211E-43 0.1211E-44 - -5.200 0.2086E-39 0.2087E-40 - -5.100 0.2189E-35 0.2189E-36 - -5.000 0.1399E-31 0.1399E-32 - -4.900 0.5450E-28 0.5452E-29 - -4.800 0.1295E-24 0.1296E-25 - -4.700 0.1879E-21 0.1881E-22 - -4.600 0.1666E-18 0.1668E-19 - -4.500 0.9034E-16 0.9051E-17 - -4.400 0.3000E-13 0.3009E-14 - -4.300 0.6114E-11 0.6144E-12 - -4.200 0.7661E-09 0.7722E-10 - -4.100 0.5918E-07 0.5995E-08 - -4.000 0.2829E-05 0.2889E-06 - -3.900 0.8410E-04 0.8699E-05 - -3.800 0.1565E-02 0.1652E-03 - -3.700 0.1839E-01 0.2004E-02 - -3.600 0.1384E+00 0.1584E-01 - -3.500 0.6801E+00 0.8385E-01 - -3.400 0.2249E+01 0.3087E+00 - -3.300 0.5236E+01 0.8323E+00 - -3.200 0.9145E+01 0.1747E+01 - -3.100 0.1297E+02 0.3044E+01 - -3.000 0.1621E+02 0.4665E+01 - -2.900 0.1894E+02 0.6560E+01 - -2.800 0.2135E+02 0.8694E+01 - -2.700 0.2344E+02 0.1104E+02 - -2.600 0.2521E+02 0.1356E+02 - -2.500 0.2681E+02 0.1624E+02 - -2.400 0.2842E+02 0.1908E+02 - -2.300 0.3006E+02 0.2209E+02 - -2.200 0.3161E+02 0.2525E+02 - -2.100 0.3297E+02 0.2855E+02 - -2.000 0.3420E+02 0.3196E+02 - -1.900 0.3544E+02 0.3551E+02 - -1.800 0.3674E+02 0.3918E+02 - -1.700 0.3805E+02 0.4299E+02 - -1.600 0.3935E+02 0.4692E+02 - -1.500 0.4055E+02 0.5098E+02 - -1.400 0.4163E+02 0.5514E+02 - -1.300 0.4266E+02 0.5941E+02 - -1.200 0.4370E+02 0.6378E+02 - -1.100 0.4481E+02 0.6826E+02 - -1.000 0.4586E+02 0.7284E+02 - -0.900 0.4678E+02 0.7752E+02 - -0.800 0.4773E+02 0.8229E+02 - -0.700 0.4870E+02 0.8716E+02 - -0.600 0.4964E+02 0.9213E+02 - -0.500 0.5063E+02 0.9719E+02 - -0.400 0.5162E+02 0.1024E+03 - -0.300 0.5253E+02 0.1076E+03 - -0.200 0.5348E+02 0.1130E+03 - -0.100 0.5450E+02 0.1184E+03 - 0.000 0.5539E+02 0.1239E+03 - 0.100 0.5623E+02 0.1296E+03 - 0.200 0.5713E+02 0.1353E+03 - 0.300 0.5792E+02 0.1411E+03 - 0.400 0.5861E+02 0.1469E+03 - 0.500 0.5952E+02 0.1529E+03 - 0.600 0.6054E+02 0.1589E+03 - 0.700 0.6128E+02 0.1651E+03 - 0.800 0.6194E+02 0.1713E+03 - 0.900 0.6285E+02 0.1775E+03 - 1.000 0.6371E+02 0.1839E+03 - 1.100 0.6439E+02 0.1904E+03 - 1.200 0.6518E+02 0.1969E+03 - 1.300 0.6613E+02 0.2035E+03 - 1.400 0.6701E+02 0.2102E+03 - 1.500 0.6766E+02 0.2170E+03 - 1.600 0.6829E+02 0.2238E+03 - 1.700 0.6925E+02 0.2307E+03 - 1.800 0.7018E+02 0.2377E+03 - 1.900 0.7069E+02 0.2448E+03 - 2.000 0.7130E+02 0.2519E+03 - 2.100 0.7236E+02 0.2592E+03 - 2.200 0.7331E+02 0.2665E+03 - 2.300 0.7383E+02 0.2739E+03 - 2.400 0.7434E+02 0.2813E+03 - 2.500 0.7528E+02 0.2888E+03 - 2.600 0.7639E+02 0.2965E+03 - 2.700 0.7706E+02 0.3042E+03 - 2.800 0.7746E+02 0.3119E+03 - 2.900 0.7837E+02 0.3198E+03 - 3.000 0.7972E+02 0.3277E+03 - 3.100 0.8060E+02 0.3358E+03 - 3.200 0.8090E+02 0.3439E+03 - 3.300 0.8125E+02 0.3520E+03 - 3.400 0.8185E+02 0.3602E+03 - 3.500 0.8252E+02 0.3684E+03 - 3.600 0.8318E+02 0.3768E+03 - 3.700 0.8375E+02 0.3851E+03 - 3.800 0.8452E+02 0.3936E+03 - 3.900 0.8573E+02 0.4022E+03 - 4.000 0.8676E+02 0.4108E+03 - 4.100 0.8719E+02 0.4196E+03 - 4.200 0.8785E+02 0.4283E+03 - 4.300 0.8917E+02 0.4373E+03 - 4.400 0.9038E+02 0.4463E+03 - 4.500 0.9123E+02 0.4554E+03 - 4.600 0.9237E+02 0.4647E+03 - 4.700 0.9400E+02 0.4741E+03 - 4.800 0.9565E+02 0.4836E+03 - 4.900 0.9704E+02 0.4933E+03 - 5.000 0.9789E+02 0.5031E+03 - 5.100 0.9772E+02 0.5129E+03 - 5.200 0.9685E+02 0.5226E+03 - 5.300 0.9596E+02 0.5322E+03 - 5.400 0.9502E+02 0.5417E+03 - 5.500 0.9398E+02 0.5511E+03 - 5.600 0.9304E+02 0.5604E+03 - 5.700 0.9218E+02 0.5696E+03 - 5.800 0.9125E+02 0.5787E+03 - 5.900 0.9032E+02 0.5877E+03 - 6.000 0.8987E+02 0.5967E+03 - 6.100 0.9024E+02 0.6058E+03 - 6.200 0.9132E+02 0.6149E+03 - 6.300 0.9292E+02 0.6242E+03 - 6.400 0.9474E+02 0.6337E+03 - 6.500 0.9640E+02 0.6433E+03 - 6.600 0.9766E+02 0.6531E+03 - 6.700 0.9868E+02 0.6629E+03 - 6.800 0.9984E+02 0.6729E+03 - 6.900 0.1009E+03 0.6830E+03 - 7.000 0.1009E+03 0.6931E+03 - 7.100 0.9942E+02 0.7030E+03 - 7.200 0.9793E+02 0.7128E+03 - 7.300 0.9811E+02 0.7226E+03 - 7.400 0.9950E+02 0.7326E+03 - 7.500 0.1006E+03 0.7427E+03 - 7.600 0.1014E+03 0.7528E+03 - 7.700 0.1028E+03 0.7631E+03 - 7.800 0.1048E+03 0.7736E+03 - 7.900 0.1064E+03 0.7842E+03 - 8.000 0.1069E+03 0.7949E+03 - 8.100 0.1071E+03 0.8056E+03 - 8.200 0.1077E+03 0.8164E+03 - 8.300 0.1081E+03 0.8272E+03 - 8.400 0.1076E+03 0.8379E+03 - 8.500 0.1073E+03 0.8487E+03 - 8.600 0.1085E+03 0.8595E+03 - 8.700 0.1097E+03 0.8705E+03 - 8.800 0.1091E+03 0.8814E+03 - 8.900 0.1080E+03 0.8922E+03 - 9.000 0.1086E+03 0.9031E+03 - 9.100 0.1107E+03 0.9141E+03 - 9.200 0.1124E+03 0.9254E+03 - 9.300 0.1130E+03 0.9367E+03 - 9.400 0.1128E+03 0.9479E+03 - 9.500 0.1123E+03 0.9592E+03 - 9.600 0.1125E+03 0.9704E+03 - 9.700 0.1145E+03 0.9819E+03 - 9.800 0.1168E+03 0.9936E+03 - 9.900 0.1171E+03 0.1005E+04 - 10.000 0.1155E+03 0.1017E+04 - 10.100 0.1149E+03 0.1028E+04 - 10.200 0.1165E+03 0.1040E+04 - 10.300 0.1186E+03 0.1052E+04 - 10.400 0.1196E+03 0.1064E+04 - 10.500 0.1196E+03 0.1076E+04 - 10.600 0.1194E+03 0.1088E+04 - 10.700 0.1198E+03 0.1100E+04 - 10.800 0.1201E+03 0.1112E+04 - 10.900 0.1174E+03 0.1123E+04 - 11.000 0.1067E+03 0.1134E+04 - 11.100 0.8471E+02 0.1143E+04 - 11.200 0.5473E+02 0.1148E+04 - 11.300 0.2703E+02 0.1151E+04 - 11.400 0.9721E+01 0.1152E+04 - 11.500 0.2452E+01 0.1152E+04 - 11.600 0.4208E+00 0.1152E+04 - 11.700 0.4802E-01 0.1152E+04 - 11.800 0.3576E-02 0.1152E+04 - 11.900 0.1716E-03 0.1152E+04 - 12.000 0.5264E-05 0.1152E+04 - 12.100 0.1027E-06 0.1152E+04 - 12.200 0.1271E-08 0.1152E+04 - 12.300 0.9966E-11 0.1152E+04 - 12.400 0.4948E-13 0.1152E+04 - 12.500 0.1553E-15 0.1152E+04 - 12.600 0.3072E-18 0.1152E+04 - 12.700 0.3815E-21 0.1152E+04 - 12.800 0.2962E-24 0.1152E+04 - 12.900 0.1430E-27 0.1152E+04 - 13.000 0.4276E-31 0.1152E+04 - 13.100 0.7883E-35 0.1152E+04 - 13.200 0.8928E-39 0.1152E+04 - 13.300 0.6196E-43 0.1152E+04 - 13.400 0.2629E-47 0.1152E+04 - 13.500 0.6806E-52 0.1152E+04 - 13.600 0.1074E-56 0.1152E+04 - 13.700 0.1032E-61 0.1152E+04 - 13.800 0.6032E-67 0.1152E+04 - 13.900 0.2144E-72 0.1152E+04 - 14.000 0.4631E-78 0.1152E+04 - 14.100 0.5104E-83 0.1152E+04 - 14.200 0.4497E-83 0.1152E+04 - 14.300 0.4497E-83 0.1152E+04 - 14.400 0.4497E-83 0.1152E+04 - 14.500 0.4497E-83 0.1152E+04 - 14.600 0.4497E-83 0.1152E+04 - 14.700 0.4497E-83 0.1152E+04 - 14.800 0.4497E-83 0.1152E+04 - 14.900 0.4497E-83 0.1152E+04 - 15.000 0.4497E-83 0.1152E+04 diff --git a/ml-dft-sandia/notebooks/dos2eband/snap_2_888/Al.dos_2g b/ml-dft-sandia/notebooks/dos2eband/snap_2_888/Al.dos_2g deleted file mode 100644 index 0c20c93c9..000000000 --- a/ml-dft-sandia/notebooks/dos2eband/snap_2_888/Al.dos_2g +++ /dev/null @@ -1,252 +0,0 @@ -# E (eV) dos(E) Int dos(E) EFermi = 7.797 eV - -10.000 0.4497E-83 0.4497E-84 - -9.900 0.4497E-83 0.8995E-84 - -9.800 0.4497E-83 0.1349E-83 - -9.700 0.4497E-83 0.1799E-83 - -9.600 0.4497E-83 0.2249E-83 - -9.500 0.4497E-83 0.2698E-83 - -9.400 0.4497E-83 0.3148E-83 - -9.300 0.4497E-83 0.3598E-83 - -9.200 0.4497E-83 0.4048E-83 - -9.100 0.4497E-83 0.4497E-83 - -9.000 0.4497E-83 0.4947E-83 - -8.900 0.4497E-83 0.5397E-83 - -8.800 0.4497E-83 0.5846E-83 - -8.700 0.4497E-83 0.6296E-83 - -8.600 0.4497E-83 0.6746E-83 - -8.500 0.4497E-83 0.7196E-83 - -8.400 0.4497E-83 0.7645E-83 - -8.300 0.4497E-83 0.8095E-83 - -8.200 0.4497E-83 0.8545E-83 - -8.100 0.4497E-83 0.8995E-83 - -8.000 0.4497E-83 0.9444E-83 - -7.900 0.4497E-83 0.9894E-83 - -7.800 0.4497E-83 0.1034E-82 - -7.700 0.4497E-83 0.1079E-82 - -7.600 0.4497E-83 0.1124E-82 - -7.500 0.4497E-83 0.1169E-82 - -7.400 0.4497E-83 0.1214E-82 - -7.300 0.4497E-83 0.1259E-82 - -7.200 0.4497E-83 0.1304E-82 - -7.100 0.4497E-83 0.1349E-82 - -7.000 0.4497E-83 0.1394E-82 - -6.900 0.4497E-83 0.1439E-82 - -6.800 0.4497E-83 0.1484E-82 - -6.700 0.4497E-83 0.1529E-82 - -6.600 0.4497E-83 0.1574E-82 - -6.500 0.4497E-83 0.1619E-82 - -6.400 0.4497E-83 0.1664E-82 - -6.300 0.4497E-83 0.1709E-82 - -6.200 0.4497E-83 0.1754E-82 - -6.100 0.4545E-83 0.1799E-82 - -6.000 0.4416E-79 0.4434E-80 - -5.900 0.2414E-73 0.2414E-74 - -5.800 0.8024E-68 0.8024E-69 - -5.700 0.1622E-62 0.1622E-63 - -5.600 0.1993E-57 0.1993E-58 - -5.500 0.1490E-52 0.1490E-53 - -5.400 0.6777E-48 0.6777E-49 - -5.300 0.1876E-43 0.1876E-44 - -5.200 0.3161E-39 0.3161E-40 - -5.100 0.3244E-35 0.3245E-36 - -5.000 0.2029E-31 0.2029E-32 - -4.900 0.7734E-28 0.7736E-29 - -4.800 0.1798E-24 0.1799E-25 - -4.700 0.2553E-21 0.2555E-22 - -4.600 0.2214E-18 0.2217E-19 - -4.500 0.1175E-15 0.1177E-16 - -4.400 0.3817E-13 0.3829E-14 - -4.300 0.7611E-11 0.7650E-12 - -4.200 0.9332E-09 0.9409E-10 - -4.100 0.7056E-07 0.7150E-08 - -4.000 0.3302E-05 0.3373E-06 - -3.900 0.9609E-04 0.9947E-05 - -3.800 0.1752E-02 0.1851E-03 - -3.700 0.2019E-01 0.2204E-02 - -3.600 0.1493E+00 0.1713E-01 - -3.500 0.7223E+00 0.8936E-01 - -3.400 0.2360E+01 0.3254E+00 - -3.300 0.5445E+01 0.8699E+00 - -3.200 0.9435E+01 0.1813E+01 - -3.100 0.1327E+02 0.3141E+01 - -3.000 0.1644E+02 0.4784E+01 - -2.900 0.1909E+02 0.6694E+01 - -2.800 0.2145E+02 0.8839E+01 - -2.700 0.2356E+02 0.1119E+02 - -2.600 0.2536E+02 0.1373E+02 - -2.500 0.2691E+02 0.1642E+02 - -2.400 0.2842E+02 0.1926E+02 - -2.300 0.3006E+02 0.2227E+02 - -2.200 0.3169E+02 0.2544E+02 - -2.100 0.3312E+02 0.2875E+02 - -2.000 0.3436E+02 0.3219E+02 - -1.900 0.3554E+02 0.3574E+02 - -1.800 0.3678E+02 0.3942E+02 - -1.700 0.3806E+02 0.4323E+02 - -1.600 0.3933E+02 0.4716E+02 - -1.500 0.4050E+02 0.5121E+02 - -1.400 0.4157E+02 0.5537E+02 - -1.300 0.4264E+02 0.5963E+02 - -1.200 0.4379E+02 0.6401E+02 - -1.100 0.4498E+02 0.6851E+02 - -1.000 0.4603E+02 0.7311E+02 - -0.900 0.4687E+02 0.7780E+02 - -0.800 0.4771E+02 0.8257E+02 - -0.700 0.4865E+02 0.8743E+02 - -0.600 0.4965E+02 0.9240E+02 - -0.500 0.5068E+02 0.9746E+02 - -0.400 0.5162E+02 0.1026E+03 - -0.300 0.5246E+02 0.1079E+03 - -0.200 0.5341E+02 0.1132E+03 - -0.100 0.5447E+02 0.1187E+03 - 0.000 0.5538E+02 0.1242E+03 - 0.100 0.5621E+02 0.1298E+03 - 0.200 0.5710E+02 0.1355E+03 - 0.300 0.5788E+02 0.1413E+03 - 0.400 0.5856E+02 0.1472E+03 - 0.500 0.5945E+02 0.1531E+03 - 0.600 0.6045E+02 0.1592E+03 - 0.700 0.6120E+02 0.1653E+03 - 0.800 0.6189E+02 0.1715E+03 - 0.900 0.6282E+02 0.1778E+03 - 1.000 0.6369E+02 0.1841E+03 - 1.100 0.6439E+02 0.1906E+03 - 1.200 0.6517E+02 0.1971E+03 - 1.300 0.6611E+02 0.2037E+03 - 1.400 0.6701E+02 0.2104E+03 - 1.500 0.6764E+02 0.2172E+03 - 1.600 0.6826E+02 0.2240E+03 - 1.700 0.6925E+02 0.2309E+03 - 1.800 0.7020E+02 0.2379E+03 - 1.900 0.7069E+02 0.2450E+03 - 2.000 0.7129E+02 0.2521E+03 - 2.100 0.7234E+02 0.2594E+03 - 2.200 0.7326E+02 0.2667E+03 - 2.300 0.7376E+02 0.2741E+03 - 2.400 0.7430E+02 0.2815E+03 - 2.500 0.7527E+02 0.2890E+03 - 2.600 0.7636E+02 0.2967E+03 - 2.700 0.7702E+02 0.3044E+03 - 2.800 0.7747E+02 0.3121E+03 - 2.900 0.7841E+02 0.3199E+03 - 3.000 0.7971E+02 0.3279E+03 - 3.100 0.8056E+02 0.3360E+03 - 3.200 0.8089E+02 0.3441E+03 - 3.300 0.8124E+02 0.3522E+03 - 3.400 0.8182E+02 0.3604E+03 - 3.500 0.8252E+02 0.3686E+03 - 3.600 0.8322E+02 0.3769E+03 - 3.700 0.8381E+02 0.3853E+03 - 3.800 0.8455E+02 0.3938E+03 - 3.900 0.8571E+02 0.4023E+03 - 4.000 0.8673E+02 0.4110E+03 - 4.100 0.8720E+02 0.4197E+03 - 4.200 0.8791E+02 0.4285E+03 - 4.300 0.8920E+02 0.4375E+03 - 4.400 0.9036E+02 0.4465E+03 - 4.500 0.9121E+02 0.4556E+03 - 4.600 0.9238E+02 0.4648E+03 - 4.700 0.9398E+02 0.4742E+03 - 4.800 0.9555E+02 0.4838E+03 - 4.900 0.9687E+02 0.4935E+03 - 5.000 0.9771E+02 0.5033E+03 - 5.100 0.9756E+02 0.5130E+03 - 5.200 0.9671E+02 0.5227E+03 - 5.300 0.9591E+02 0.5323E+03 - 5.400 0.9508E+02 0.5418E+03 - 5.500 0.9407E+02 0.5512E+03 - 5.600 0.9312E+02 0.5605E+03 - 5.700 0.9223E+02 0.5697E+03 - 5.800 0.9128E+02 0.5789E+03 - 5.900 0.9043E+02 0.5879E+03 - 6.000 0.9007E+02 0.5969E+03 - 6.100 0.9045E+02 0.6059E+03 - 6.200 0.9152E+02 0.6151E+03 - 6.300 0.9313E+02 0.6244E+03 - 6.400 0.9493E+02 0.6339E+03 - 6.500 0.9655E+02 0.6436E+03 - 6.600 0.9778E+02 0.6533E+03 - 6.700 0.9875E+02 0.6632E+03 - 6.800 0.9981E+02 0.6732E+03 - 6.900 0.1008E+03 0.6833E+03 - 7.000 0.1007E+03 0.6933E+03 - 7.100 0.9928E+02 0.7033E+03 - 7.200 0.9790E+02 0.7131E+03 - 7.300 0.9808E+02 0.7229E+03 - 7.400 0.9944E+02 0.7328E+03 - 7.500 0.1006E+03 0.7429E+03 - 7.600 0.1015E+03 0.7530E+03 - 7.700 0.1029E+03 0.7633E+03 - 7.800 0.1048E+03 0.7738E+03 - 7.900 0.1063E+03 0.7844E+03 - 8.000 0.1069E+03 0.7951E+03 - 8.100 0.1072E+03 0.8058E+03 - 8.200 0.1078E+03 0.8166E+03 - 8.300 0.1081E+03 0.8274E+03 - 8.400 0.1075E+03 0.8382E+03 - 8.500 0.1073E+03 0.8489E+03 - 8.600 0.1086E+03 0.8598E+03 - 8.700 0.1097E+03 0.8707E+03 - 8.800 0.1090E+03 0.8816E+03 - 8.900 0.1079E+03 0.8924E+03 - 9.000 0.1086E+03 0.9033E+03 - 9.100 0.1107E+03 0.9144E+03 - 9.200 0.1125E+03 0.9256E+03 - 9.300 0.1131E+03 0.9369E+03 - 9.400 0.1128E+03 0.9482E+03 - 9.500 0.1123E+03 0.9594E+03 - 9.600 0.1126E+03 0.9707E+03 - 9.700 0.1145E+03 0.9822E+03 - 9.800 0.1168E+03 0.9938E+03 - 9.900 0.1170E+03 0.1006E+04 - 10.000 0.1155E+03 0.1017E+04 - 10.100 0.1150E+03 0.1029E+04 - 10.200 0.1165E+03 0.1040E+04 - 10.300 0.1185E+03 0.1052E+04 - 10.400 0.1195E+03 0.1064E+04 - 10.500 0.1197E+03 0.1076E+04 - 10.600 0.1195E+03 0.1088E+04 - 10.700 0.1198E+03 0.1100E+04 - 10.800 0.1201E+03 0.1112E+04 - 10.900 0.1173E+03 0.1124E+04 - 11.000 0.1065E+03 0.1134E+04 - 11.100 0.8419E+02 0.1143E+04 - 11.200 0.5400E+02 0.1148E+04 - 11.300 0.2638E+02 0.1151E+04 - 11.400 0.9345E+01 0.1152E+04 - 11.500 0.2313E+01 0.1152E+04 - 11.600 0.3886E+00 0.1152E+04 - 11.700 0.4326E-01 0.1152E+04 - 11.800 0.3131E-02 0.1152E+04 - 11.900 0.1453E-03 0.1152E+04 - 12.000 0.4278E-05 0.1152E+04 - 12.100 0.7946E-07 0.1152E+04 - 12.200 0.9273E-09 0.1152E+04 - 12.300 0.6784E-11 0.1152E+04 - 12.400 0.3108E-13 0.1152E+04 - 12.500 0.8915E-16 0.1152E+04 - 12.600 0.1600E-18 0.1152E+04 - 12.700 0.1794E-21 0.1152E+04 - 12.800 0.1256E-24 0.1152E+04 - 12.900 0.5472E-28 0.1152E+04 - 13.000 0.1481E-31 0.1152E+04 - 13.100 0.2482E-35 0.1152E+04 - 13.200 0.2568E-39 0.1152E+04 - 13.300 0.1636E-43 0.1152E+04 - 13.400 0.6400E-48 0.1152E+04 - 13.500 0.1534E-52 0.1152E+04 - 13.600 0.2249E-57 0.1152E+04 - 13.700 0.2014E-62 0.1152E+04 - 13.800 0.1100E-67 0.1152E+04 - 13.900 0.3658E-73 0.1152E+04 - 14.000 0.7410E-79 0.1152E+04 - 14.100 0.4588E-83 0.1152E+04 - 14.200 0.4497E-83 0.1152E+04 - 14.300 0.4497E-83 0.1152E+04 - 14.400 0.4497E-83 0.1152E+04 - 14.500 0.4497E-83 0.1152E+04 - 14.600 0.4497E-83 0.1152E+04 - 14.700 0.4497E-83 0.1152E+04 - 14.800 0.4497E-83 0.1152E+04 - 14.900 0.4497E-83 0.1152E+04 - 15.000 0.4497E-83 0.1152E+04 From 3348a567681320dc26304592dfe39ef79132a434 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 16:57:26 +0200 Subject: [PATCH 023/297] Fixing history (1) --- mala/targets/dos.py | 167 -------------------------------------------- 1 file changed, 167 deletions(-) diff --git a/mala/targets/dos.py b/mala/targets/dos.py index f12cdae33..f3204c5c2 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -341,60 +341,6 @@ def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, fermi_energy_eV, temperature_K, integration_method) - def get_self_consistent_fermi_energy_ev(self, dos_data, - temperature_K=None, - integration_method="analytical", - shift_energy_grid=True): - """ - Calculate the self-consistent Fermi energy. - - "Self-consistent" does not mean self-consistent in the DFT sense, - but rather the Fermi energy, for which DOS integration reproduces - the exact number of electrons. The term "self-consistent" stems - from how this quantity is calculated. - - Parameters - ---------- - dos_data : numpy.array - DOS data with dimension [energygrid]. - - temperature_K : float - Temperature in K. - - integration_method : string - Integration method to be used. Currently supported: - - - "trapz" for trapezoid method - - "simps" for Simpson method. - - "analytical" for analytical integration. Recommended. - - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - - Returns - ------- - fermi_energy_self_consistent : float - E_F in eV. - """ - # Parse the parameters. - if temperature_K is None: - temperature_K = self.temperature_K - if shift_energy_grid and integration_method == "analytical": - energy_grid = self.get_energy_grid(shift_energy_grid=True) - else: - energy_grid = self.get_energy_grid() - fermi_energy_sc = toms748(lambda fermi_sc: - (self. - __number_of_electrons_from_dos - (dos_data, energy_grid, - fermi_sc, temperature_K, - integration_method) - - self.number_of_electrons), - a=energy_grid[0], - b=energy_grid[-1]) - return fermi_energy_sc - def get_density_of_states(self, dos_data): """Get the density of states.""" return dos_data @@ -434,116 +380,3 @@ def from_ldos(cls, ldos_object): return return_dos_object - @staticmethod - def __number_of_electrons_from_dos(dos_data, energy_grid, fermi_energy_eV, - temperature_K, integration_method): - """Calculate the number of electrons from DOS data.""" - # Calculate the energy levels and the Fermi function. - - fermi_vals = fermi_function(energy_grid, fermi_energy_eV, - temperature_K) - # Calculate the number of electrons. - if integration_method == "trapz": - number_of_electrons = integrate.trapz(dos_data * fermi_vals, - energy_grid, axis=-1) - elif integration_method == "simps": - number_of_electrons = integrate.simps(dos_data * fermi_vals, - energy_grid, axis=-1) - elif integration_method == "quad": - dos_pointer = interpolate.interp1d(energy_grid, dos_data) - number_of_electrons, abserr = integrate.quad( - lambda e: dos_pointer(e) * fermi_function(e, fermi_energy_eV, - temperature_K), - energy_grid[0], energy_grid[-1], limit=500, - points=fermi_energy_eV) - elif integration_method == "analytical": - number_of_electrons = analytical_integration(dos_data, "F0", "F1", - fermi_energy_eV, - energy_grid, - temperature_K) - else: - raise Exception("Unknown integration method.") - - return number_of_electrons - - @staticmethod - def __band_energy_from_dos(dos_data, energy_grid, - fermi_energy_eV, - temperature_K, integration_method): - """Calculate the band energy from DOS data.""" - # Calculate the energy levels and the Fermi function. - fermi_vals = fermi_function(energy_grid, fermi_energy_eV, - temperature_K) - - # Calculate the band energy. - if integration_method == "trapz": - band_energy = integrate.trapz(dos_data * (energy_grid * - fermi_vals), - energy_grid, axis=-1) - elif integration_method == "simps": - band_energy = integrate.simps(dos_data * (energy_grid * - fermi_vals), - energy_grid, axis=-1) - elif integration_method == "quad": - dos_pointer = interpolate.interp1d(energy_grid, dos_data) - band_energy, abserr = integrate.quad( - lambda e: dos_pointer(e) * e * fermi_function(e, - fermi_energy_eV, - temperature_K), - energy_grid[0], energy_grid[-1], limit=500, - points=fermi_energy_eV) - elif integration_method == "analytical": - number_of_electrons = analytical_integration(dos_data, "F0", "F1", - fermi_energy_eV, - energy_grid, - temperature_K) - band_energy_minus_uN = analytical_integration(dos_data, "F1", "F2", - fermi_energy_eV, - energy_grid, - temperature_K) - band_energy = band_energy_minus_uN+fermi_energy_eV *\ - number_of_electrons - else: - raise Exception("Unknown integration method.") - - return band_energy - - @staticmethod - def __entropy_contribution_from_dos(dos_data, energy_grid, fermi_energy_eV, - temperature_K, integration_method): - r""" - Calculate the entropy contribution to the total energy from DOS data. - - More specifically, this gives -\beta^-1*S_S - """ - # Calculate the entropy contribution to the energy. - if integration_method == "trapz": - multiplicator = entropy_multiplicator(energy_grid, - fermi_energy_eV, - temperature_K) - entropy_contribution = integrate.trapz(dos_data * multiplicator, - energy_grid, axis=-1) - entropy_contribution /= get_beta(temperature_K) - elif integration_method == "simps": - multiplicator = entropy_multiplicator(energy_grid, fermi_energy_eV, - temperature_K) - entropy_contribution = integrate.simps(dos_data * multiplicator, - energy_grid, axis=-1) - entropy_contribution /= get_beta(temperature_K) - elif integration_method == "quad": - dos_pointer = interpolate.interp1d(energy_grid, dos_data) - entropy_contribution, abserr = integrate.quad( - lambda e: dos_pointer(e) * - entropy_multiplicator(e, fermi_energy_eV, temperature_K), - energy_grid[0], energy_grid[-1], limit=500, - points=fermi_energy_eV) - entropy_contribution /= get_beta(temperature_K) - elif integration_method == "analytical": - entropy_contribution = analytical_integration(dos_data, "S0", "S1", - fermi_energy_eV, - energy_grid, - temperature_K) - else: - raise Exception("Unknown integration method.") - - return entropy_contribution From fc43630b4ff714a42e63038fd7e6b8f2e21b0a7a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 25 Jun 2021 16:58:11 +0200 Subject: [PATCH 024/297] Fixing history (2): These routines were reimplemented based on code by Normand Modine Co-authored-by: Normand Modine --- mala/targets/dos.py | 167 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 167 insertions(+) diff --git a/mala/targets/dos.py b/mala/targets/dos.py index f3204c5c2..f12cdae33 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -341,6 +341,60 @@ def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, fermi_energy_eV, temperature_K, integration_method) + def get_self_consistent_fermi_energy_ev(self, dos_data, + temperature_K=None, + integration_method="analytical", + shift_energy_grid=True): + """ + Calculate the self-consistent Fermi energy. + + "Self-consistent" does not mean self-consistent in the DFT sense, + but rather the Fermi energy, for which DOS integration reproduces + the exact number of electrons. The term "self-consistent" stems + from how this quantity is calculated. + + Parameters + ---------- + dos_data : numpy.array + DOS data with dimension [energygrid]. + + temperature_K : float + Temperature in K. + + integration_method : string + Integration method to be used. Currently supported: + + - "trapz" for trapezoid method + - "simps" for Simpson method. + - "analytical" for analytical integration. Recommended. + + shift_energy_grid : bool + When using the analytical integration, one has to shift the energy + grid by setting this parameter to True. Elsewise keep on False. + + Returns + ------- + fermi_energy_self_consistent : float + E_F in eV. + """ + # Parse the parameters. + if temperature_K is None: + temperature_K = self.temperature_K + if shift_energy_grid and integration_method == "analytical": + energy_grid = self.get_energy_grid(shift_energy_grid=True) + else: + energy_grid = self.get_energy_grid() + fermi_energy_sc = toms748(lambda fermi_sc: + (self. + __number_of_electrons_from_dos + (dos_data, energy_grid, + fermi_sc, temperature_K, + integration_method) + - self.number_of_electrons), + a=energy_grid[0], + b=energy_grid[-1]) + return fermi_energy_sc + def get_density_of_states(self, dos_data): """Get the density of states.""" return dos_data @@ -380,3 +434,116 @@ def from_ldos(cls, ldos_object): return return_dos_object + @staticmethod + def __number_of_electrons_from_dos(dos_data, energy_grid, fermi_energy_eV, + temperature_K, integration_method): + """Calculate the number of electrons from DOS data.""" + # Calculate the energy levels and the Fermi function. + + fermi_vals = fermi_function(energy_grid, fermi_energy_eV, + temperature_K) + # Calculate the number of electrons. + if integration_method == "trapz": + number_of_electrons = integrate.trapz(dos_data * fermi_vals, + energy_grid, axis=-1) + elif integration_method == "simps": + number_of_electrons = integrate.simps(dos_data * fermi_vals, + energy_grid, axis=-1) + elif integration_method == "quad": + dos_pointer = interpolate.interp1d(energy_grid, dos_data) + number_of_electrons, abserr = integrate.quad( + lambda e: dos_pointer(e) * fermi_function(e, fermi_energy_eV, + temperature_K), + energy_grid[0], energy_grid[-1], limit=500, + points=fermi_energy_eV) + elif integration_method == "analytical": + number_of_electrons = analytical_integration(dos_data, "F0", "F1", + fermi_energy_eV, + energy_grid, + temperature_K) + else: + raise Exception("Unknown integration method.") + + return number_of_electrons + + @staticmethod + def __band_energy_from_dos(dos_data, energy_grid, + fermi_energy_eV, + temperature_K, integration_method): + """Calculate the band energy from DOS data.""" + # Calculate the energy levels and the Fermi function. + fermi_vals = fermi_function(energy_grid, fermi_energy_eV, + temperature_K) + + # Calculate the band energy. + if integration_method == "trapz": + band_energy = integrate.trapz(dos_data * (energy_grid * + fermi_vals), + energy_grid, axis=-1) + elif integration_method == "simps": + band_energy = integrate.simps(dos_data * (energy_grid * + fermi_vals), + energy_grid, axis=-1) + elif integration_method == "quad": + dos_pointer = interpolate.interp1d(energy_grid, dos_data) + band_energy, abserr = integrate.quad( + lambda e: dos_pointer(e) * e * fermi_function(e, + fermi_energy_eV, + temperature_K), + energy_grid[0], energy_grid[-1], limit=500, + points=fermi_energy_eV) + elif integration_method == "analytical": + number_of_electrons = analytical_integration(dos_data, "F0", "F1", + fermi_energy_eV, + energy_grid, + temperature_K) + band_energy_minus_uN = analytical_integration(dos_data, "F1", "F2", + fermi_energy_eV, + energy_grid, + temperature_K) + band_energy = band_energy_minus_uN+fermi_energy_eV *\ + number_of_electrons + else: + raise Exception("Unknown integration method.") + + return band_energy + + @staticmethod + def __entropy_contribution_from_dos(dos_data, energy_grid, fermi_energy_eV, + temperature_K, integration_method): + r""" + Calculate the entropy contribution to the total energy from DOS data. + + More specifically, this gives -\beta^-1*S_S + """ + # Calculate the entropy contribution to the energy. + if integration_method == "trapz": + multiplicator = entropy_multiplicator(energy_grid, + fermi_energy_eV, + temperature_K) + entropy_contribution = integrate.trapz(dos_data * multiplicator, + energy_grid, axis=-1) + entropy_contribution /= get_beta(temperature_K) + elif integration_method == "simps": + multiplicator = entropy_multiplicator(energy_grid, fermi_energy_eV, + temperature_K) + entropy_contribution = integrate.simps(dos_data * multiplicator, + energy_grid, axis=-1) + entropy_contribution /= get_beta(temperature_K) + elif integration_method == "quad": + dos_pointer = interpolate.interp1d(energy_grid, dos_data) + entropy_contribution, abserr = integrate.quad( + lambda e: dos_pointer(e) * + entropy_multiplicator(e, fermi_energy_eV, temperature_K), + energy_grid[0], energy_grid[-1], limit=500, + points=fermi_energy_eV) + entropy_contribution /= get_beta(temperature_K) + elif integration_method == "analytical": + entropy_contribution = analytical_integration(dos_data, "S0", "S1", + fermi_energy_eV, + energy_grid, + temperature_K) + else: + raise Exception("Unknown integration method.") + + return entropy_contribution From 39e8119d8b76a460551d25101297c6411b1fe518 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 28 Jun 2021 13:07:29 +0200 Subject: [PATCH 025/297] summary writer added for check --- docs/source/install/README.md | 1 + examples/ex01_run_singleshot.py | 2 +- ...ents.out.tfevents.1624876703.pop-os.160578.0 | Bin 0 -> 878 bytes mala/common/parameters.py | 2 +- mala/network/trainer.py | 16 ++++++++++++++++ requirements.txt | 3 ++- 6 files changed, 21 insertions(+), 3 deletions(-) create mode 100644 examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 0d50fc74d..6d9ebc1aa 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -22,6 +22,7 @@ In order to run MALA you have to have the following packages installed: * numpy * scipy * oapackage +* tensorboard * optuna * ase * mpmath diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 13b77fe82..c7e4703d7 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -37,7 +37,7 @@ def run_example01(desired_loss_improvement_factor=1): test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" - + test_parameters.running.visualisation = True #################### # DATA # Add and prepare snapshots for training. diff --git a/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 b/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 new file mode 100644 index 0000000000000000000000000000000000000000..9e786a8e59ddfe2f96af692ba3eb2ed1d88473aa GIT binary patch literal 878 zcmb1OfPlsI-b$RWtNt#}G`ry_#hX-=n3<>NT9%quVr6tKcimPAWOct@v#$WD)8ggg z=3?>5FD@1p`=V>RNb|g$6jYti>>IkAUP?S*bpnh?Dqp=&wpHDmb^%3Yor?fOB@>cL zhgs6LN;xK6C@Q~DiNB(jYl|q|MAu73$R7#fKvZ+Zt z$%&%!Vxkp9B{z~vf744gUu};Gqo_Q2!VaR62T7&P)KfO;g}S>@R5r&tLsar2sf>s} zY$K2+GXX{AJS7i^NuMb2eKa$EjE4SI0cHKdBXGne^M5O?d z%2P)+*sw(ezeO=qPCN{vQV>by4};Y0Q0l4x literal 0 HcmV?d00001 diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 26b6cefd0..026564aa3 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -329,7 +329,7 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" - + self.visualisation = False class ParametersHyperparameterOptimization(ParametersBase): """ diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 8dda278cb..2c34e71f9 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -9,6 +9,8 @@ from torch.utils.data import DataLoader from mala.common.parameters import printout from .runner import Runner +from torch.utils.tensorboard import SummaryWriter ##summaty writer + try: import horovod.torch as hvd except ModuleNotFoundError: @@ -50,6 +52,9 @@ def __init__(self, params, network, data, optimizer_dict=None): self.validation_data_loader = None self.test_data_loader = None self.__prepare_to_train(optimizer_dict) + self.tensor_board = None + if self.parameters.visualisation: + self.tb = SummaryWriter() @classmethod def resume_checkpoint(cls, checkpoint_name): @@ -201,6 +206,10 @@ def train_network(self): if self.parameters.verbosity: printout("Epoch: ", epoch, "validation data loss: ", vloss) + #summary_writer tensor board + if self.tb is not None: + self.tb.add_scalar("Loss", vloss, epoch) + # Mix the DataSets up (this function only does something # in the lazy loading case). if self.parameters.use_shuffling_for_samplers: @@ -244,6 +253,9 @@ def train_network(self): printout("Time for epoch[s]:", time.time() - start_time) + # closing tensorboard window + self.tb.close() + # Calculate final loss. self.final_validation_loss = vloss tloss = float("inf") @@ -255,6 +267,8 @@ def train_network(self): self.final_test_loss = tloss printout("Final test data loss: ", tloss) + + def __prepare_to_train(self, optimizer_dict): """Prepare everything for training.""" # Configure keyword arguments for DataSampler. @@ -470,3 +484,5 @@ def __average_validation(val, name): tensor = torch.tensor(val) avg_loss = hvd.allreduce(tensor, name=name, op=hvd.Average) return avg_loss.item() + + \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 4dedf758f..45fa10c07 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ mpmath numpy optuna scipy -oapackage \ No newline at end of file +oapackage +tensorboard From 6aed5dd2ec958490aec2d64ffdcec4da84f34b13 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 28 Jun 2021 14:59:55 +0200 Subject: [PATCH 026/297] putting it all together --- examples/ex04_hyperparameter_optimization.py | 20 +++---- mala/common/parameters.py | 2 +- mala/network/hyper_opt_oat.py | 58 +++++++++++++++----- mala/network/objective_base.py | 9 ++- 4 files changed, 61 insertions(+), 28 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index ef069aff3..2debcab87 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -30,7 +30,7 @@ def run_example04(desired_loss_improvement_factor=1): test_parameters.data.output_rescaling_type = "normal" # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 + test_parameters.running.max_number_epochs = 5 test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" @@ -38,7 +38,7 @@ def run_example04(desired_loss_improvement_factor=1): # Specify the number of trials, the hyperparameter optimizer should run # and the type of hyperparameter. test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "optuna" + test_parameters.hyperparameters.hyper_opt_method = "oat" #################### # DATA @@ -71,20 +71,18 @@ def run_example04(desired_loss_improvement_factor=1): test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) + test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", + choices=[0.005, 0.01, 0.015]) # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) # Choices for activation function at each layer will be optimized. test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) + choices=["ReLU", "Sigmoid", "LeakyReLU"]) # Perform hyperparameter optimization. printout("Starting Hyperparameter optimization.") diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 26b6cefd0..5cfd81058 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -374,7 +374,7 @@ class ParametersHyperparameterOptimization(ParametersBase): - "optuna" : Use optuna for the hyperparameter optimization. - "oat" : Use orthogonal array tuning (currently limited to categorical hyperparemeters). Range analysis is - currently done by simply choosing the lowesr loss. + currently done by simply choosing the lowest loss. - "notraining" : Using a NAS without training, based on jacobians. checkpoints_each_trial : int diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 338ef75ba..ee52286cf 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -26,6 +26,7 @@ def __init__(self, params, data): self.objective = None self.trial_losses = [] self.optimal_params = None + self.importance = None self.n_factors = None self.factor_levels = None self.strength = None @@ -75,7 +76,12 @@ def perform_study(self): self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] - self.strength = 3 + + if not self.monotonic: + raise Exception( + "Please use hyperparameters in increasing or decreasing order of number of choices") + + self.strength = 2 self.N_runs = self.number_of_runs() self.OA = self.get_orthogonal_array() number_of_trial = 0 @@ -83,41 +89,54 @@ def perform_study(self): for row in self.OA: printout("Trial number", number_of_trial) self.trial_losses.append(self.objective(row)) + number_of_trial += 1 - # Return the best lost value we could achieve. - return min(self.trial_losses) + # Return the best loss value we could achieve. + self.get_optimal_parameters() + return self.objective(self.optimal_params) - def set_optimal_parameters(self): + def get_optimal_parameters(self): """ Find the optimal set of hyperparameters by doing range analysis. This is done using loss instead of accuracy as done in the paper. - Set the optimal parameters found in the present study. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. """ + print("Performing Range Analysis") + print("factor levels", self.factor_levels) def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - - R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] - for (idx, levels) in enumerate(self.factor_levels)] - + # R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + # for (idx, levels) in enumerate(self.factor_levels)] + + R = [] + for (idx, levels) in enumerate(self.factor_levels): + R.append([]) + for l in range(levels): + R[idx].extend(self.trial_losses[indices(idx, l)].sum()) + print(R) A = [[i/len(j) for i in j] for j in R] # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) - importance = np.argsort([max(i)-min(i) for i in A]) + self.importance = np.argsort([max(i)-min(i) for i in A]) print("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in importance], " > ") + [self.params.hyperparameters.hlist[idx].name for idx in self.importance], " > ") print("Optimal Hyperparameters:") for (idx, par) in enumerate(self.params.hyperparameters.hlist): printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') + def set_optimal_parameters(self): + """ + Set the optimal parameters found in the present study. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_oat(self.optimal_params) def number_of_runs(self): @@ -143,11 +162,12 @@ def number_of_runs(self): self.factor_levels, self.strength)] N = np.lcm.reduce(runs) - return N + return int(N) def get_orthogonal_array(self): """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + print("Generating Suitable Orthogonal Array") arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) arraylist = [arrayclass.create_root()] @@ -163,4 +183,12 @@ def get_orthogonal_array(self): idxs = np.argsort(dd) arraylist = [arraylist_extensions[ii] for ii in idxs] + if not arraylist: # checking if the list is empty + raise Exception( + "No orthogonal array exists with such a parameter combination") return np.unique(np.array(arraylist[0]), axis=0) + + @property + def monotonic(self): + dx = np.diff(self.factor_levels) + return np.all(dx <= 0) or np.all(dx >= 0) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index d9ab739e3..1513a8bc3 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -131,7 +131,10 @@ def parse_trial_oat(self, trial): par: HyperparameterOAT for factor_idx, par in enumerate(self.params.hyperparameters.hlist): - if "layer_activation" in par.name: + if "learning_rate" in par.name: + self.params.running.learning_rate = \ + par.get_parameter(trial, factor_idx) + elif "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial, factor_idx)) elif "ff_neurons_layer" in par.name: @@ -144,3 +147,7 @@ def parse_trial_oat(self, trial): else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") + + if self.optimize_layer_list: + self.params.network.layer_sizes.\ + append(self.data_handler.get_output_dimension()) From f301972af112a51da7f014b5d6f1183bbda363b6 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 28 Jun 2021 16:58:27 +0200 Subject: [PATCH 027/297] minor changes --- ...events.out.tfevents.1624876703.pop-os.160578.0 | Bin 878 -> 0 bytes .../events.out.tfevents.1624892223.pop-os.18826.0 | Bin 0 -> 878 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 create mode 100644 examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 diff --git a/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 b/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 deleted file mode 100644 index 9e786a8e59ddfe2f96af692ba3eb2ed1d88473aa..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 878 zcmb1OfPlsI-b$RWtNt#}G`ry_#hX-=n3<>NT9%quVr6tKcimPAWOct@v#$WD)8ggg z=3?>5FD@1p`=V>RNb|g$6jYti>>IkAUP?S*bpnh?Dqp=&wpHDmb^%3Yor?fOB@>cL zhgs6LN;xK6C@Q~DiNB(jYl|q|MAu73$R7#fKvZ+Zt z$%&%!Vxkp9B{z~vf744gUu};Gqo_Q2!VaR62T7&P)KfO;g}S>@R5r&tLsar2sf>s} zY$K2+GXX{AJS7i^NuMb2eKa$EjE4SI0cHKdBXGne^M5O?d z%2P)+*sw(ezeO=qPCN{vQV>by4};Y0Q0l4x diff --git a/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 b/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 new file mode 100644 index 0000000000000000000000000000000000000000..b924889d5c53c6c6da252dfc25c6ec9718bae2f1 GIT binary patch literal 878 zcmY+?TPTBZ7zXfR!?)S#GptE!QK&g5hyPcUL!sPBE@Vn_J|>D*yAY)malwUiIfO(` zjVOtWIqb$(Lxz-cYM~^F*Twrj*T0^d=h2EFlcqd9=d@T)+LG)UI(b=>v9c~YtF^qY zB{jZ6nXJ)bCn~l7xhT0*CKcz`H#Z09ggC*~;1#X=cl?Omz)jsc|D4JL$(W*W;mAMe zmB{SEYmlBu&ZPtkCB@o)BA@r~Kzbo*oZ*Ge;=X$#HxnN~ijn;0WP&km{({Kv@h6ZH zBn^RLVQ8Q>gveEm8>Baqy&In_)9q*^GJWy`qz{tToF~>X*OEY_MfU~L7fIWZi)~jc zwul@yi%x&{QE^C`I_>QIQr|`7n?wRqilnOQgz=`x1tN2MIFK?VV;u+VIMJC-MYe_DuKH}fFnNH(0Vv#WOXHIZkpfglw~E|f2`nw~}nk%jHSAeBgtwaqiV z)o3Gf_AwNs3Q7ISG+U}Ur(YVX4+E)2(y1O}^7s8k(y9H50LddcQ98^HZ5JCv?)4c! O`XhO(9As;wn%y4@*ZZ^p literal 0 HcmV?d00001 From b89c47c303ccbf24abb284aee8d543e4a28ef64b Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Thu, 1 Jul 2021 17:17:09 +0200 Subject: [PATCH 028/297] changes suggested by Lenz --- docs/source/install/tensorboard_setup.md | 6 ++++++ mala/network/trainer.py | 8 ++++---- 2 files changed, 10 insertions(+), 4 deletions(-) create mode 100644 docs/source/install/tensorboard_setup.md diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md new file mode 100644 index 000000000..b0d9c53e9 --- /dev/null +++ b/docs/source/install/tensorboard_setup.md @@ -0,0 +1,6 @@ +Access central storage servers from locations other than Hemera (Linux) + +https://fwcc.pages.hzdr.de/infohub/hpc/storage.html +sshfs :username@hemera5.fz-rossendorf.de folder/file/location folder/location/in/local + +This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 2c34e71f9..c1a112330 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -54,7 +54,7 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tb = SummaryWriter() + self.tensor_board = SummaryWriter() @classmethod def resume_checkpoint(cls, checkpoint_name): @@ -207,8 +207,8 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.tb is not None: - self.tb.add_scalar("Loss", vloss, epoch) + if self.tensor_board is not None: + self.tensor_board.add_scalar("Loss", vloss, epoch) # Mix the DataSets up (this function only does something # in the lazy loading case). @@ -254,7 +254,7 @@ def train_network(self): printout("Time for epoch[s]:", time.time() - start_time) # closing tensorboard window - self.tb.close() + self.tensor_board.close() # Calculate final loss. self.final_validation_loss = vloss From dba54bbc9fd32f43a3a2553ae4cb8df9b3a4a50d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 1 Jul 2021 20:27:20 +0200 Subject: [PATCH 029/297] Removed unnecessary shift_energy_grid variables --- mala/targets/dos.py | 28 +++---------------- mala/targets/ldos.py | 64 +++++++++----------------------------------- 2 files changed, 17 insertions(+), 75 deletions(-) diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 3a8b4999e..5607202d3 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -144,8 +144,7 @@ def get_energy_grid(self): return linspace_array def get_band_energy(self, dos_data, fermi_energy_eV=None, - temperature_K=None, integration_method="analytical", - shift_energy_grid=True): + temperature_K=None, integration_method="analytical"): """ Calculate the band energy from given DOS data. @@ -167,10 +166,6 @@ def get_band_energy(self, dos_data, fermi_energy_eV=None, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - Returns ------- band_energy : float @@ -189,8 +184,7 @@ def get_band_energy(self, dos_data, fermi_energy_eV=None, def get_number_of_electrons(self, dos_data, fermi_energy_eV=None, temperature_K=None, - integration_method="analytical", - shift_energy_grid=True): + integration_method="analytical"): """ Calculate the number of electrons from given DOS data. @@ -212,10 +206,6 @@ def get_number_of_electrons(self, dos_data, fermi_energy_eV=None, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - Returns ------- number_of_electrons : float @@ -234,8 +224,7 @@ def get_number_of_electrons(self, dos_data, fermi_energy_eV=None, def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, temperature_K=None, - integration_method="analytical", - shift_energy_grid=True): + integration_method="analytical"): """ Calculate the entropy contribution to the total energy. @@ -257,10 +246,6 @@ def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - Returns ------- entropy_contribution : float @@ -279,8 +264,7 @@ def get_entropy_contribution(self, dos_data, fermi_energy_eV=None, def get_self_consistent_fermi_energy_ev(self, dos_data, temperature_K=None, - integration_method="analytical", - shift_energy_grid=True): + integration_method="analytical"): """ Calculate the self-consistent Fermi energy. @@ -304,10 +288,6 @@ def get_self_consistent_fermi_energy_ev(self, dos_data, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - Returns ------- fermi_energy_self_consistent : float diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 3119539f3..c158fa2c6 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -176,7 +176,7 @@ def get_total_energy(self, ldos_data=None, dos_data=None, grid_spacing_bohr=None, grid_integration_method="summation", energy_integration_method="analytical", - shift_energy_grid=True, atoms_Angstrom=None, + atoms_Angstrom=None, qe_input_data=None, qe_pseudopotentials=None): """ Calculate the total energy from LDOS or given DOS + density data. @@ -224,10 +224,6 @@ def get_total_energy(self, ldos_data=None, dos_data=None, - "simps" for Simpson method. - "analytical" for analytical integration. (recommended) - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - atoms_Angstrom : ase.Atoms ASE atoms object for the current system. If None, MALA will create one. @@ -275,9 +271,7 @@ def get_total_energy(self, ldos_data=None, dos_data=None, fermi_energy_ev=fermi_energy_eV, temperature_K=temperature_K, integration_method= - energy_integration_method, - shift_energy_grid= - shift_energy_grid) + energy_integration_method) # Now we can create calculation objects to get the necessary # quantities. @@ -293,17 +287,14 @@ def get_total_energy(self, ldos_data=None, dos_data=None, fermi_energy_eV, temperature_K=temperature_K, integration_method= - energy_integration_method, - shift_energy_grid= - shift_energy_grid) + energy_integration_method) # Smearing / Entropy contribution e_entropy_contribution = dos_calculator.\ get_entropy_contribution(dos_data, fermi_energy_eV=fermi_energy_eV, temperature_K=temperature_K, integration_method= - energy_integration_method, - shift_energy_grid=shift_energy_grid) + energy_integration_method) # Density based energy contributions (via QE) e_rho_times_v_hxc, e_hartree, e_xc, e_ewald \ @@ -319,8 +310,7 @@ def get_total_energy(self, ldos_data=None, dos_data=None, def get_band_energy(self, ldos_data, fermi_energy_eV=None, temperature_K=None, grid_spacing_bohr=None, grid_integration_method="summation", - energy_integration_method="analytical", - shift_energy_grid=True): + energy_integration_method="analytical"): """ Calculate the band energy from given LDOS data. @@ -351,10 +341,6 @@ def get_band_energy(self, ldos_data, fermi_energy_eV=None, - "simps" for Simpson method. - "analytical" for analytical integration. (recommended) - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - grid_spacing_bohr : float Grid spacing (distance between grid points) in Bohr. @@ -376,14 +362,12 @@ def get_band_energy(self, ldos_data, fermi_energy_eV=None, return dos_calculator.\ get_band_energy(dos_data, fermi_energy_eV=fermi_energy_eV, temperature_K=temperature_K, - integration_method=energy_integration_method, - shift_energy_grid=shift_energy_grid) + integration_method=energy_integration_method) def get_number_of_electrons(self, ldos_data, grid_spacing_bohr=None, fermi_energy_eV=None, temperature_K=None, grid_integration_method="summation", - energy_integration_method="analytical", - shift_energy_grid=True): + energy_integration_method="analytical"): """ Calculate the number of electrons from given LDOS data. @@ -414,10 +398,6 @@ def get_number_of_electrons(self, ldos_data, grid_spacing_bohr=None, - "simps" for Simpson method. - "analytical" for analytical integration. (recommended) - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - grid_spacing_bohr : float Grid spacing (distance between grid points) in Bohr. @@ -440,8 +420,7 @@ def get_number_of_electrons(self, ldos_data, grid_spacing_bohr=None, get_number_of_electrons(dos_data, fermi_energy_eV=fermi_energy_eV, temperature_K=temperature_K, integration_method= - energy_integration_method, - shift_energy_grid=shift_energy_grid) + energy_integration_method) def get_self_consistent_fermi_energy_ev(self, ldos_data, grid_spacing_bohr=None, @@ -449,8 +428,7 @@ def get_self_consistent_fermi_energy_ev(self, ldos_data, grid_integration_method= "summation", energy_integration_method= - "analytical", - shift_energy_grid=True): + "analytical"): """ Calculate the self-consistent Fermi energy. @@ -483,10 +461,6 @@ def get_self_consistent_fermi_energy_ev(self, ldos_data, - "simps" for Simpson method. - "analytical" for analytical integration. (recommended) - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - grid_spacing_bohr : float Grid spacing (distance between grid points) in Bohr. @@ -509,13 +483,11 @@ def get_self_consistent_fermi_energy_ev(self, ldos_data, get_self_consistent_fermi_energy_ev(dos_data, temperature_K=temperature_K, integration_method= - energy_integration_method, - shift_energy_grid= - shift_energy_grid) + energy_integration_method) def get_density(self, ldos_data, fermi_energy_ev=None, temperature_K=None, conserve_dimensions=False, - integration_method="analytical", shift_energy_grid=True): + integration_method="analytical"): """ Calculate the density from given LDOS data. @@ -539,10 +511,6 @@ def get_density(self, ldos_data, fermi_energy_ev=None, temperature_K=None, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - ldos_data : numpy.array LDOS data, either as [gridsize, energygrid] or [gridx,gridy,gridz,energygrid]. @@ -761,8 +729,7 @@ def get_and_cache_density_cached(self, ldos_data, fermi_energy_ev=None, temperature_K=None, conserve_dimensions=False, - integration_method="analytical", - shift_energy_grid=True): + integration_method="analytical"): """ Calculate an electronic density from LDOS data and keep it in memory. @@ -790,10 +757,6 @@ def get_and_cache_density_cached(self, ldos_data, - "simps" for Simpson method. - "analytical" for analytical integration. Recommended. - shift_energy_grid : bool - When using the analytical integration, one has to shift the energy - grid by setting this parameter to True. Elsewise keep on False. - ldos_data : numpy.array LDOS data, either as [gridsize, energygrid] or [gridx,gridy,gridz,energygrid]. @@ -818,8 +781,7 @@ def get_and_cache_density_cached(self, ldos_data, fermi_energy_ev=fermi_energy_ev, temperature_K=temperature_K, conserve_dimensions=conserve_dimensions, - integration_method=integration_method, - shift_energy_grid=shift_energy_grid) + integration_method=integration_method) self.cached_density_exists = True return self.cached_density From 98b3af970ddb6978fb5ea7fd24deae93e71cab4a Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 10:47:47 +0200 Subject: [PATCH 030/297] Rename job in `gh-pages.yml` to be more expressive --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 863d0ccae..241f5291e 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -30,7 +30,7 @@ jobs: - name: Check docstrings run: pydocstyle --convention=numpy mala - deploy-pages: + build-and-deploy-pages: needs: test-docstrings runs-on: ubuntu-18.04 steps: From a07bfa74856393a9591fdb9968da1aacda2696ca Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 11:31:15 +0200 Subject: [PATCH 031/297] =?UTF-8?q?Squash=20class=E2=80=99=20and=20`=5F=5F?= =?UTF-8?q?init=5F=5F`=20method=E2=80=99s=20docstrings?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 048287a83..dcd9aa54b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,6 +71,7 @@ ] autodoc_member_order = 'groupwise' +autoclass_content = "both" # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From 79dc7732baed994f6df1036fc57fd6311497cb75 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 11:45:18 +0200 Subject: [PATCH 032/297] Fix indentation in `__init__` docstrings of class: - `DataScaler` - `HyperparameterOAT - `HyperparameterOptuna` --- mala/datahandling/data_scaler.py | 6 +++--- mala/network/hyperparameter_oat.py | 4 ++-- mala/network/hyperparameter_optuna.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 1c5ace945..89ad02b9b 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -29,12 +29,12 @@ def __init__(self, typestring, use_horovod=False): - "None": No normalization is applied. - "standard": Standardization (Scale to mean 0, - standard deviation 1) + standard deviation 1) - "normal": Min-Max scaling (Scale to be in range 0...1) - "feature-wise-standard": Row Standardization (Scale to mean 0, - standard deviation 1) + standard deviation 1) - "feature-wise-normal": Row Min-Max scaling (Scale to be in range - 0...1) + 0...1) use_horovod : bool If True, the DataScaler will use horovod to check that data is diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 3b8405841..2c5760215 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -25,7 +25,7 @@ def __init__(self, opttype="categorical", name="", choices=None): Name of the hyperparameter. Please note that these names always have to be distinct; if you e.g. want to investigate multiple layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + etc. as names. choices : List of possible choices (for categorical parameter). @@ -37,7 +37,7 @@ def __init__(self, opttype="categorical", name="", choices=None): if self.opttype != "categorical": raise Exception("Unsupported Hyperparameter type.") - + def get_parameter(self, trial, idx): """ Extract current value of hyperparameter from an orthogonal array. diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index d18daa6d3..8f4bf423b 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -26,7 +26,7 @@ def __init__(self, opttype="float", name="", low=0, high=0, choices=None): Name of the hyperparameter. Please note that these names always have to be distinct; if you e.g. want to investigate multiple layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + etc. as names. low : float or int Lower bound for numerical parameter. From c0ef05e2070c36e3bd4ce739b56317a13fe3944b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 2 Jul 2021 16:52:33 +0200 Subject: [PATCH 033/297] Total energy evaluation now works for non-cubic cells --- mala/targets/density.py | 36 ++++++++++++++++++++++++++++++------ mala/targets/ldos.py | 2 ++ 2 files changed, 32 insertions(+), 6 deletions(-) diff --git a/mala/targets/density.py b/mala/targets/density.py index 136c33ac4..47bc3cec6 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -309,12 +309,11 @@ def get_energy_contributions(self, density_data, create_file=True, convert_to_threedimensional=True) density_for_qe = np.reshape(density_for_qe, [number_of_gridpoints, 1], order='F') - - # Reset the positions. For some reason creating the positions - # directly from ASE (see above) sometimes - # causes slight errors. This is more accurate. - te.set_positions(np.transpose(atoms_Angstrom.get_scaled_positions()), - number_of_atoms) + # Reset the positions. Some calculations (such as the Ewald sum) + # is directly performed here, so it is not enough to simply + # instantiate the process with the file. + positions_for_qe = self.get_scaled_positions_for_qe(atoms_Angstrom) + te.set_positions(np.transpose(positions_for_qe), number_of_atoms) # Now we can set the new density. te.set_rho_of_r(density_for_qe, number_of_gridpoints, nr_spin_channels) @@ -323,6 +322,31 @@ def get_energy_contributions(self, density_data, create_file=True, energies = np.array(te.get_energies())*Rydberg return energies + @staticmethod + def get_scaled_positions_for_qe(atoms): + """ + Get the positions correctly scaled for QE. + + QE (for ibrav=0) scales a little bit different then ASE would. + ASE uses all provided cell parameters, while QE simply sets the + first entry in the cell parameter matrix as reference and divides + all positions by this value. + + Parameters + ---------- + atoms : ase.Atoms + The atom objects for which the scaled positions should be + calculated. + + Returns + ------- + scaled_positions : numpy.array + The scaled positions. + """ + principal_axis = atoms.get_cell()[0][0] + scaled_positions = atoms.get_positions()/principal_axis + return scaled_positions + @classmethod def from_ldos(cls, ldos_object): """ diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index c158fa2c6..04f9f893f 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -304,6 +304,8 @@ def get_total_energy(self, ldos_data=None, dos_data=None, qe_pseudopotentials=qe_pseudopotentials) e_total = e_band + e_rho_times_v_hxc + e_hartree + e_xc + e_ewald +\ e_entropy_contribution + print(e_band, e_rho_times_v_hxc, e_hartree, e_xc, e_ewald, + e_entropy_contribution) return e_total From 20fc6d6265e5a8d98cb8f398bafc7ff8bbc2f67d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 2 Jul 2021 17:03:51 +0200 Subject: [PATCH 034/297] Deleted rogue print statement --- mala/targets/ldos.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 04f9f893f..c158fa2c6 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -304,8 +304,6 @@ def get_total_energy(self, ldos_data=None, dos_data=None, qe_pseudopotentials=qe_pseudopotentials) e_total = e_band + e_rho_times_v_hxc + e_hartree + e_xc + e_ewald +\ e_entropy_contribution - print(e_band, e_rho_times_v_hxc, e_hartree, e_xc, e_ewald, - e_entropy_contribution) return e_total From 0222caa4a727aca48343f6daf6ebc2a533799b5d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 10:34:08 +0200 Subject: [PATCH 035/297] Removed unnecessary descriptions from __init__ --- mala/common/parameters.py | 9 --------- mala/datahandling/data_converter.py | 2 -- mala/datahandling/data_handler.py | 2 -- mala/datahandling/data_scaler.py | 2 -- mala/datahandling/lazy_load_dataset.py | 2 -- mala/datahandling/snapshot.py | 2 -- mala/descriptors/descriptor_base.py | 2 -- mala/descriptors/snap.py | 2 -- mala/network/hyper_opt_base.py | 2 -- mala/network/hyper_opt_notraining.py | 2 -- mala/network/hyper_opt_oat.py | 2 -- mala/network/hyper_opt_optuna.py | 2 -- mala/network/hyperparameter_oat.py | 2 -- mala/network/hyperparameter_optuna.py | 2 -- mala/network/network.py | 2 -- mala/network/objective_base.py | 2 -- mala/network/objective_no_training.py | 4 +--- mala/network/runner.py | 2 -- mala/network/tester.py | 2 -- mala/network/trainer.py | 2 -- mala/targets/cube_parser.py | 3 +-- mala/targets/density.py | 2 -- mala/targets/dos.py | 2 -- mala/targets/ldos.py | 2 -- mala/targets/target_base.py | 2 -- 25 files changed, 2 insertions(+), 58 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index e8270c194..0117eb244 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -16,7 +16,6 @@ class ParametersBase: """Base parameter class for MALA.""" def __init__(self): - """Create an instance of ParameterBase.""" pass def show(self, indent=""): @@ -68,7 +67,6 @@ class ParametersNetwork(ParametersBase): """ def __init__(self): - """Create an instance of ParametersNetwork.""" super(ParametersNetwork, self).__init__() self.nn_type = "feed-forward" self.layer_sizes = [10, 10, 10] @@ -103,7 +101,6 @@ class ParametersDescriptors(ParametersBase): """ def __init__(self): - """Create an instance of ParametersDescriptors.""" super(ParametersDescriptors, self).__init__() self.descriptor_type = "SNAP" self.twojmax = 10 @@ -133,7 +130,6 @@ class ParametersTargets(ParametersBase): """ def __init__(self): - """Create an instance of ParameterTargets.""" super(ParametersTargets, self).__init__() self.target_type = "LDOS" self.ldos_gridsize = 0 @@ -205,7 +201,6 @@ class ParametersData(ParametersBase): """ def __init__(self): - """Create an instance of ParametersData.""" super(ParametersData, self).__init__() self.descriptors_contain_xyz = True self.snapshot_directories_list = [] @@ -306,7 +301,6 @@ class ParametersRunning(ParametersBase): """ def __init__(self): - """Create an instance of ParametersRunning.""" super(ParametersRunning, self).__init__() self.trainingtype = "SGD" self.learning_rate = 0.5 @@ -389,7 +383,6 @@ class ParametersHyperparameterOptimization(ParametersBase): """ def __init__(self): - """Create an instance of ParametersHyperparameterOptimization.""" super(ParametersHyperparameterOptimization, self).__init__() self.direction = 'minimize' self.n_trials = 100 @@ -448,7 +441,6 @@ class ParametersDebug(ParametersBase): """ def __init__(self): - """Create an instance of ParametersDebug.""" super(ParametersDebug, self).__init__() self.grid_dimensions = [] @@ -490,7 +482,6 @@ class Parameters: """ def __init__(self): - """Create an instance of Parameters.""" self.comment = "" self.network = ParametersNetwork() self.descriptors = ParametersDescriptors() diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index 31c017277..bf43e5394 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -24,8 +24,6 @@ class DataConverter: def __init__(self, parameters, descriptor_calculator=None, target_calculator=None): """ - Create an instances of the DataConverter class. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 9d7c9a037..e7f6ed93a 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -24,8 +24,6 @@ def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): """ - Create an instance of DataHandler. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 89ad02b9b..5b92192aa 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -19,8 +19,6 @@ class DataScaler: def __init__(self, typestring, use_horovod=False): """ - Create a DataScaler object. - Parameters ---------- typestring : string diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index 0f7ef1ba8..46e448fb6 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -27,8 +27,6 @@ def __init__(self, input_dimension, output_dimension, input_data_scaler, target_calculator, grid_dimensions, grid_size, descriptors_contain_xyz, use_horovod): """ - Create a lazily loaded DataSet. - Parameters ---------- input_dimension : int diff --git a/mala/datahandling/snapshot.py b/mala/datahandling/snapshot.py index 8b0c475c5..4607efadf 100644 --- a/mala/datahandling/snapshot.py +++ b/mala/datahandling/snapshot.py @@ -14,8 +14,6 @@ def __init__(self, input_npy_file="", input_npy_directory="", output_npy_directory="", output_units="", calculation_output=""): """ - Create a Snapshot object. - Parameters ---------- input_npy_file : string diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index ffc6ad49a..f18322733 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -10,8 +10,6 @@ class DescriptorBase: def __init__(self, parameters): """ - Create a DescriptorBase object. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 9c2fbffda..fb2d62eff 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -19,8 +19,6 @@ class SNAP(DescriptorBase): def __init__(self, parameters): """ - Create a SNAP object. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index d7b238213..4efbbb167 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -8,8 +8,6 @@ class HyperOptBase(ABC): def __init__(self, params, data): """ - Create a hyperparameter optimizer object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 2d8690cf5..9a841db58 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -14,8 +14,6 @@ class HyperOptNoTraining(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptNoTraining object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index b342796ae..92da29f99 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -11,8 +11,6 @@ class HyperOptOAT(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptOAT object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 4b38085a6..45be61b08 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -15,8 +15,6 @@ class HyperOptOptuna(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptOptuna object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 2c5760215..7bf505cea 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -6,8 +6,6 @@ class HyperparameterOAT: def __init__(self, opttype="categorical", name="", choices=None): """ - Create an OAT compatible hyperparameter. - Parameters ---------- opttype : string diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index 8f4bf423b..d538ea454 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -7,8 +7,6 @@ class HyperparameterOptuna: def __init__(self, opttype="float", name="", low=0, high=0, choices=None): """ - Create an optuna compatible hyperparameter. - Parameters ---------- opttype : string diff --git a/mala/network/network.py b/mala/network/network.py index fe1071b11..342faaf4a 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -14,8 +14,6 @@ class Network(nn.Module): def __init__(self, params): """ - Create a Network object, representing a neural network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 8a012349f..3088b235d 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -15,8 +15,6 @@ class ObjectiveBase: def __init__(self, params, data_handler): """ - Create an ObjectiveBase object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 31f8cceff..530381c51 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -19,9 +19,7 @@ class ObjectiveNoTraining(ObjectiveBase): def __init__(self, search_parameters: Parameters, data_handler: DataHandler, trial_type): - """ - Create an ObjectiveNoTraining object. - + """ Parameters ---------- search_parameters : mala.common.parameters.Parameters diff --git a/mala/network/runner.py b/mala/network/runner.py index 15b25bea1..89d9fd0fa 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -17,8 +17,6 @@ class Runner: def __init__(self, params, network, data): """ - Create a Runner object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/tester.py b/mala/network/tester.py index 69cb6ced2..49be45ec6 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -20,8 +20,6 @@ class Tester(Runner): def __init__(self, params, network, data): """ - Create a Tester object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/trainer.py b/mala/network/trainer.py index ae3fd371d..b75e4273d 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -23,8 +23,6 @@ class Trainer(Runner): def __init__(self, params, network, data, optimizer_dict=None): """ - Create a Trainer object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index c565ec400..f3a091384 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -80,8 +80,7 @@ class CubeFile(object): """ def __init__(self, srcname, const=1): - """Create cube file object. - + """ Parameters ---------- srcname: string diff --git a/mala/targets/density.py b/mala/targets/density.py index 136c33ac4..658d71211 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -24,8 +24,6 @@ class Density(TargetBase): def __init__(self, params): """ - Create a Density object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 5fc2e220c..af3e6e9e4 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -12,8 +12,6 @@ class DOS(TargetBase): def __init__(self, params): """ - Create a DOS object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 10ef67b60..3e7d5c280 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -15,8 +15,6 @@ class LDOS(TargetBase): def __init__(self, params): """ - Create a LDOS object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 016476342..55923ff73 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -17,8 +17,6 @@ class TargetBase: def __init__(self, params): """ - Create a TargetBase object. - Parameters ---------- params : mala.common.parameters.Parameters or From a1da0172fd2947d2f1281fa8a18b5dc92fbdb645 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 11:52:41 +0200 Subject: [PATCH 036/297] Added way to parse an arbitrary trial --- mala/network/hyper_opt_base.py | 10 ++++++++++ mala/network/hyper_opt_optuna.py | 9 +++++++++ 2 files changed, 19 insertions(+) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index d7b238213..71d68d632 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -86,3 +86,13 @@ def set_optimal_parameters(self): hyperparameter optimizer was created. """ pass + + @abstractmethod + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + pass diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 7ad288673..3d2c8290d 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -92,6 +92,15 @@ def set_optimal_parameters(self): # Parse the parameters from the best trial. self.objective.parse_trial_optuna(self.study.best_trial) + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_optuna(trial) + def get_trials_from_study(self): """ Return the trials from the last study. From 0b9a842db1386140a430423fbae0eef0e67cdda2 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:05:33 +0200 Subject: [PATCH 037/297] Objective gets now created upon initialization --- mala/network/hyper_opt_oat.py | 2 ++ mala/network/hyper_opt_optuna.py | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index b342796ae..ac722bf66 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -29,6 +29,7 @@ def __init__(self, params, data): self.n_levels = None self.strength = None self.N_runs = None + self.objective = ObjectiveBase(self.params, self.data_handler) def perform_study(self): """ @@ -38,6 +39,7 @@ def perform_study(self): In this case, these are choosen based on an orthogonal array. """ number_of_trial = 0 + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) for row in self.orthogonal_arr: printout("Trial number", number_of_trial) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 3d2c8290d..2989afb00 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -58,7 +58,7 @@ def __init__(self, params, data): storage=rdb_storage, load_if_exists=True) - self.objective = None + self.objective = ObjectiveBase(self.params, self.data_handler) self.checkpoint_counter = 0 def perform_study(self): @@ -68,6 +68,7 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, optuna is used. """ + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) # Fill callback list based on user checkpoint wishes. From b6f6ab2ec65a8e123eb36fd116836f9e7064ff76 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:22:40 +0200 Subject: [PATCH 038/297] This should fix the workflow run --- mala/network/hyper_opt_oat.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index ac722bf66..008487bf9 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -61,6 +61,15 @@ def set_optimal_parameters(self): self.best_trial = self.orthogonal_arr[idx] self.objective.parse_trial_oat(self.best_trial) + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_oat(trial) + @property def orthogonal_arr(self): """Orthogonal array used for optimal hyperparameter sampling.""" From 747569f4eaaf1a4c3ff4ebca2ddff6d385cb502b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:39:42 +0200 Subject: [PATCH 039/297] Cleaned up the set_parameters ness --- mala/network/hyper_opt_base.py | 5 +++-- mala/network/hyper_opt_oat.py | 10 ---------- mala/network/hyper_opt_optuna.py | 10 ---------- mala/network/objective_base.py | 2 ++ 4 files changed, 5 insertions(+), 22 deletions(-) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index 71d68d632..0a90f49a3 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -1,6 +1,7 @@ """Base class for all hyperparameter optimizers.""" from abc import abstractmethod, ABC from .hyperparameter_interface import HyperparameterInterface +from .objective_base import ObjectiveBase class HyperOptBase(ABC): @@ -20,6 +21,7 @@ def __init__(self, params, data): """ self.params = params self.data_handler = data + self.objective = ObjectiveBase(self.params, self.data_handler) def add_hyperparameter(self, opttype="float", name="", low=0, high=0, choices=None): @@ -87,7 +89,6 @@ def set_optimal_parameters(self): """ pass - @abstractmethod def set_parameters(self, trial): """ Set the parameters to a specific trial. @@ -95,4 +96,4 @@ def set_parameters(self, trial): The parameters will be written to the parameter object with which the hyperparameter optimizer was created. """ - pass + self.objective.parse_trial(trial) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 008487bf9..3fb601390 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -29,7 +29,6 @@ def __init__(self, params, data): self.n_levels = None self.strength = None self.N_runs = None - self.objective = ObjectiveBase(self.params, self.data_handler) def perform_study(self): """ @@ -61,15 +60,6 @@ def set_optimal_parameters(self): self.best_trial = self.orthogonal_arr[idx] self.objective.parse_trial_oat(self.best_trial) - def set_parameters(self, trial): - """ - Set the parameters to a specific trial. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. - """ - self.objective.parse_trial_oat(trial) - @property def orthogonal_arr(self): """Orthogonal array used for optimal hyperparameter sampling.""" diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 2989afb00..1685ee28a 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -58,7 +58,6 @@ def __init__(self, params, data): storage=rdb_storage, load_if_exists=True) - self.objective = ObjectiveBase(self.params, self.data_handler) self.checkpoint_counter = 0 def perform_study(self): @@ -93,15 +92,6 @@ def set_optimal_parameters(self): # Parse the parameters from the best trial. self.objective.parse_trial_optuna(self.study.best_trial) - def set_parameters(self, trial): - """ - Set the parameters to a specific trial. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. - """ - self.objective.parse_trial_optuna(trial) - def get_trials_from_study(self): """ Return the trials from the last study. diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 19623512c..a05357c55 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -40,6 +40,8 @@ def __init__(self, params, data_handler): )) self.trial_type = self.params.hyperparameters.hyper_opt_method + if self.trial_type == "notraining": + self.trial_type = "optuna" def __call__(self, trial): """ From 021741566cfe12995c3a512b9ddf096130dda074 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 15:05:41 +0200 Subject: [PATCH 040/297] no_data for HO checkpoint loading works now --- mala/network/hyper_opt_optuna.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 1685ee28a..c1998b9f3 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -129,7 +129,8 @@ def checkpoint_exists(cls, checkpoint_name): param_name])) @classmethod - def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): + def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None, + no_data=False): """ Prepare resumption of hyperparameter optimization from a checkpoint. @@ -147,6 +148,11 @@ def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): slightly modify the storage path between runs, since the SQL server might be running on different nodes each time. + no_data : bool + If True, the data won't actually be loaded into RAM or scaled. + This can be useful for cases where a checkpoint is loaded + for analysis purposes. + Returns ------- loaded_params : mala.common.parameters.Parameters @@ -175,6 +181,8 @@ def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): printout("Preparing data used for last checkpoint.") # Create a new data handler and prepare the data. + if no_data is True: + loaded_params.data.use_lazy_loading = True new_datahandler = DataHandler(loaded_params, input_data_scaler=loaded_iscaler, output_data_scaler=loaded_oscaler) From e06b9872eb463f43f9f26c7caa6e3e64fd9ec3e1 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 7 Jul 2021 12:24:36 +0200 Subject: [PATCH 041/297] Clear up `__init__` methods: Move parameters section from `__init__` into class docstring (Numpy docstring style). --- mala/datahandling/data_converter.py | 31 ++++++------- mala/datahandling/data_handler.py | 39 ++++++++-------- mala/datahandling/data_scaler.py | 39 ++++++++-------- mala/datahandling/lazy_load_dataset.py | 63 +++++++++++++------------- mala/datahandling/snapshot.py | 51 ++++++++++----------- mala/descriptors/descriptor_base.py | 12 ++--- mala/descriptors/snap.py | 14 +++--- mala/network/hyper_opt_base.py | 20 ++++---- mala/network/hyper_opt_notraining.py | 18 ++++---- mala/network/hyper_opt_oat.py | 20 ++++---- mala/network/hyper_opt_optuna.py | 21 +++++---- mala/network/hyperparameter_oat.py | 42 ++++++++--------- mala/network/hyperparameter_optuna.py | 50 ++++++++++---------- mala/network/network.py | 14 +++--- mala/network/objective_base.py | 17 ++++--- mala/network/objective_no_training.py | 31 ++++++------- mala/network/runner.py | 23 +++++----- mala/network/tester.py | 23 +++++----- mala/network/trainer.py | 24 +++++----- mala/targets/density.py | 15 +++--- mala/targets/dos.py | 15 +++--- mala/targets/ldos.py | 14 +++--- mala/targets/target_base.py | 14 +++--- 23 files changed, 298 insertions(+), 312 deletions(-) diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index bf43e5394..6e0357459 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -12,6 +12,21 @@ class DataConverter: These snapshots can be e.g. Quantum Espresso results. + Parameters + ---------- + parameters : mala.common.parameters.Parameters + The parameters object used for creating this instance. + + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + The descriptor calculator used for parsing/converting fingerprint + data. If None, the descriptor calculator will be created by this + object using the parameters provided. Default: None + + target_calculator : mala.targets.target_base.TargetBase + Target calculator used for parsing/converting target data. If None, + the target calculator will be created by this object using the + parameters provided. + Attributes ---------- descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase @@ -23,22 +38,6 @@ class DataConverter: def __init__(self, parameters, descriptor_calculator=None, target_calculator=None): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - The parameters object used for creating this instance. - - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - The descriptor calculator used for parsing/converting fingerprint - data. If None, the descriptor calculator will be created by this - object using the parameters provided. Default: None - - target_calculator : mala.targets.target_base.TargetBase - Target calculator used for parsing/converting target data. If None, - the target calculator will be created by this object using the - parameters provided. - """ self.parameters: ParametersData = parameters.data self.target_calculator = target_calculator if self.target_calculator is None: diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index e7f6ed93a..89aadfb68 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -18,31 +18,30 @@ class DataHandler: Data that is not in a numpy array can be converted using the DataConverter class. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + Used to do unit conversion on input data. If None, then one will + be created by this class. + + target_calculator : mala.targets.target_base.TargetBase + Used to do unit conversion on output data. If None, then one will + be created by this class. + + input_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the input data. If None, then one will be created by + this class. + + output_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the output data. If None, then one will be created by + this class. """ def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - Used to do unit conversion on input data. If None, then one will - be created by this class. - - target_calculator : mala.targets.target_base.TargetBase - Used to do unit conversion on output data. If None, then one will - be created by this class. - - input_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the input data. If None, then one will be created by - this class. - - output_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the output data. If None, then one will be created by - this class. - """ self.parameters = parameters.data self.dbg_grid_dimensions = parameters.debug.grid_dimensions self.use_horovod = parameters.use_horovod diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 5b92192aa..7f9723d58 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -15,29 +15,28 @@ class DataScaler: Sort of emulates the functionality of the scikit-learn library, but by implementing the class by ourselves we have more freedom. + + Parameters + ---------- + typestring : string + Specifies how scaling should be performed. + Options: + + - "None": No normalization is applied. + - "standard": Standardization (Scale to mean 0, + standard deviation 1) + - "normal": Min-Max scaling (Scale to be in range 0...1) + - "feature-wise-standard": Row Standardization (Scale to mean 0, + standard deviation 1) + - "feature-wise-normal": Row Min-Max scaling (Scale to be in range + 0...1) + + use_horovod : bool + If True, the DataScaler will use horovod to check that data is + only saved on the root process in parallel execution. """ def __init__(self, typestring, use_horovod=False): - """ - Parameters - ---------- - typestring : string - Specifies how scaling should be performed. - Options: - - - "None": No normalization is applied. - - "standard": Standardization (Scale to mean 0, - standard deviation 1) - - "normal": Min-Max scaling (Scale to be in range 0...1) - - "feature-wise-standard": Row Standardization (Scale to mean 0, - standard deviation 1) - - "feature-wise-normal": Row Min-Max scaling (Scale to be in range - 0...1) - - use_horovod : bool - If True, the DataScaler will use horovod to check that data is - only saved on the root process in parallel execution. - """ self.use_horovod = use_horovod self.typestring = typestring self.scale_standard = False diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index 46e448fb6..74ccc7167 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -20,48 +20,47 @@ class LazyLoadDataset(torch.utils.data.Dataset): / DataLoader level is discouraged to the point that it was disabled. Instead, we mix the snapshot load order here ot have some sort of mixing at all. - """ - def __init__(self, input_dimension, output_dimension, input_data_scaler, - output_data_scaler, descriptor_calculator, - target_calculator, grid_dimensions, grid_size, - descriptors_contain_xyz, use_horovod): - """ - Parameters - ---------- - input_dimension : int - Dimension of an input vector. + Parameters + ---------- + input_dimension : int + Dimension of an input vector. - output_dimension : int - Dimension of an output vector. + output_dimension : int + Dimension of an output vector. - input_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the input data. + input_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the input data. - output_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the output data. + output_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the output data. - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - Used to do unit conversion on input data. + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + Used to do unit conversion on input data. - target_calculator : mala.targets.target_base.TargetBase or derivative - Used to do unit conversion on output data. + target_calculator : mala.targets.target_base.TargetBase or derivative + Used to do unit conversion on output data. - grid_dimensions : list - Dimensions of the grid (x,y,z). + grid_dimensions : list + Dimensions of the grid (x,y,z). - grid_size : int - Size of the grid (x*y*z), i.e. the number of datapoints per - snapshot. + grid_size : int + Size of the grid (x*y*z), i.e. the number of datapoints per + snapshot. - descriptors_contain_xyz : bool - If true, then it is assumed that the first three entries of any - input data file are xyz-information and can be discarded. - Generally true, if your descriptors were calculated using MALA. + descriptors_contain_xyz : bool + If true, then it is assumed that the first three entries of any + input data file are xyz-information and can be discarded. + Generally true, if your descriptors were calculated using MALA. - use_horovod : bool - If true, it is assumed that horovod is used. - """ + use_horovod : bool + If true, it is assumed that horovod is used. + """ + + def __init__(self, input_dimension, output_dimension, input_data_scaler, + output_data_scaler, descriptor_calculator, + target_calculator, grid_dimensions, grid_size, + descriptors_contain_xyz, use_horovod): self.snapshot_list = [] self.input_dimension = input_dimension self.output_dimension = output_dimension diff --git a/mala/datahandling/snapshot.py b/mala/datahandling/snapshot.py index 4607efadf..c838e4212 100644 --- a/mala/datahandling/snapshot.py +++ b/mala/datahandling/snapshot.py @@ -7,39 +7,38 @@ class Snapshot: A snapshot consists of numpy arrays for input/output data and an optional DFT calculation output, needed for post-processing. - """ - def __init__(self, input_npy_file="", input_npy_directory="", - input_units="", output_npy_file="", - output_npy_directory="", output_units="", - calculation_output=""): - """ - Parameters - ---------- - input_npy_file : string - File with saved numpy input array. + Parameters + ---------- + input_npy_file : string + File with saved numpy input array. - input_npy_directory : string - Directory containing input_npy_directory. + input_npy_directory : string + Directory containing input_npy_directory. - output_npy_file : string - File with saved numpy output array. + output_npy_file : string + File with saved numpy output array. - output_npy_directory : string - Directory containing output_npy_file. + output_npy_directory : string + Directory containing output_npy_file. - input_units : string - Units of input data. See descriptor classes to see which units are - supported. + input_units : string + Units of input data. See descriptor classes to see which units are + supported. - output_units : string - Units of output data. See target classes to see which units are - supported. + output_units : string + Units of output data. See target classes to see which units are + supported. - calculation_output : string - File with the output of the original snapshot calculation. This is - only needed when testing multiple snapshots. - """ + calculation_output : string + File with the output of the original snapshot calculation. This is + only needed when testing multiple snapshots. + """ + + def __init__(self, input_npy_file="", input_npy_directory="", + input_units="", output_npy_file="", + output_npy_directory="", output_units="", + calculation_output=""): # Inputs. self.input_npy_file = input_npy_file self.input_npy_directory = input_npy_directory diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index f18322733..a6ef325fe 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -6,15 +6,15 @@ class DescriptorBase: Base class for all descriptors available in MALA. Descriptors encode the atomic fingerprint of a DFT calculation. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + Parameters object used to create this object. + """ def __init__(self, parameters): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - Parameters object used to create this object. - """ self.parameters = parameters.descriptors self.fingerprint_length = -1 # so iterations will fail self.dbg_grid_dimensions = parameters.debug.grid_dimensions diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index fb2d62eff..f997775d7 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -15,15 +15,15 @@ class SNAP(DescriptorBase): - """Class for calculation and parsing of SNAP descriptors.""" + """Class for calculation and parsing of SNAP descriptors. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + Parameters object used to create this object. + """ def __init__(self, parameters): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - Parameters object used to create this object. - """ super(SNAP, self).__init__(parameters) self.in_format_ase = "" diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index 4efbbb167..a75a826eb 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -4,18 +4,18 @@ class HyperOptBase(ABC): - """Base class for hyperparameter optimizater.""" + """Base class for hyperparameter optimizater. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + """ + + def __init__(self, params, data): self.params = params self.data_handler = data diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 9a841db58..ed47d8300 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -10,18 +10,17 @@ class HyperOptNoTraining(HyperOptBase): Hyperparameter optimizer that does not require training networks. Networks are analysed using the Jacobian. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. + + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. """ def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. - - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ super(HyperOptNoTraining, self).__init__(params, data) self.objective = None self.trial_losses = None @@ -76,4 +75,3 @@ def set_optimal_parameters(self): idx = self.trial_losses.index(min(self.trial_losses)) self.best_trial = self.trial_list[idx] self.objective.parse_trial(self.best_trial) - diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 92da29f99..483eddedb 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -7,18 +7,18 @@ class HyperOptOAT(HyperOptBase): - """Hyperparameter optimizer using Orthogonal Array Tuning.""" + """Hyperparameter optimizer using Orthogonal Array Tuning. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + """ + + def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 45be61b08..89815ea2c 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -11,18 +11,19 @@ class HyperOptOptuna(HyperOptBase): - """Hyperparameter optimizer using Optuna.""" + """Hyperparameter optimizer using Optuna. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + + """ + + def __init__(self, params, data): super(HyperOptOptuna, self).__init__(params, data) self.params = params diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 7bf505cea..fe10b6e5b 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -2,32 +2,32 @@ class HyperparameterOAT: - """Represents a hyperparameter for OAT.""" + """Represents a hyperparameter for OAT. - def __init__(self, opttype="categorical", name="", choices=None): - """ - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - In principle supported are: + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + In principle supported are: - - float - - int - - categorical (list) + - float + - int + - categorical (list) - Float and int are not available for OA based approaches at the - moment. + Float and int are not available for OA based approaches at the + moment. - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. - choices : - List of possible choices (for categorical parameter). - """ + choices : + List of possible choices (for categorical parameter). + """ + + def __init__(self, opttype="categorical", name="", choices=None): self.name = name self.opttype = opttype self.choices = choices diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index d538ea454..a65b8eb59 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -3,38 +3,38 @@ class HyperparameterOptuna: - """Represents an optuna parameter.""" + """Represents an optuna parameter. - def __init__(self, opttype="float", name="", low=0, high=0, choices=None): - """ - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - In principle supported are: + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + In principle supported are: - - float - - int - - categorical (list) + - float + - int + - categorical (list) - Float and int are not available for OA based approaches at the - moment. + Float and int are not available for OA based approaches at the + moment. - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. - low : float or int - Lower bound for numerical parameter. + low : float or int + Lower bound for numerical parameter. - high : float or int - Higher bound for numerical parameter. + high : float or int + Higher bound for numerical parameter. - choices : - List of possible choices (for categorical parameter). - """ + choices : + List of possible choices (for categorical parameter). + """ + + def __init__(self, opttype="float", name="", low=0, high=0, choices=None): self.name = name self.high = high self.low = low diff --git a/mala/network/network.py b/mala/network/network.py index 342faaf4a..2245381b3 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -10,15 +10,15 @@ class Network(nn.Module): - """Central network class for this framework, based on pytorch.nn.Module.""" + """Central network class for this framework, based on pytorch.nn.Module. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this neural network. + """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this neural network. - """ # copy the network params from the input parameter object self.use_horovod = params.use_horovod self.params = params.network diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 3088b235d..37ab056bd 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -11,18 +11,17 @@ class ObjectiveBase: Represents the objective function of a training process. This is usually the result of a training of a network. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. """ def __init__(self, params, data_handler): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this objective. - - data_handler : mala.datahandling.data_handler.DataHandler - datahandler to be used during the hyperparameter optimization. - """ self.params = params self.data_handler = data_handler diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 530381c51..1ceb05458 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -15,26 +15,25 @@ class ObjectiveNoTraining(ObjectiveBase): Represents the objective function using no NN training. The objective value is calculated using the Jacobian. + + Parameters + ---------- + search_parameters : mala.common.parameters.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. + + trial_type : string + Format of hyperparameters used in this objective. Supported + choices are: + + - optuna + - oat """ def __init__(self, search_parameters: Parameters, data_handler: DataHandler, trial_type): - """ - Parameters - ---------- - search_parameters : mala.common.parameters.Parameters - Parameters used to create this objective. - - data_handler : mala.datahandling.data_handler.DataHandler - datahandler to be used during the hyperparameter optimization. - - trial_type : string - Format of hyperparameters used in this objective. Supported - choices are: - - - optuna - - oat - """ super(ObjectiveNoTraining, self).__init__(search_parameters, data_handler) self.trial_type = trial_type diff --git a/mala/network/runner.py b/mala/network/runner.py index 89d9fd0fa..5abb99f7f 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -13,21 +13,20 @@ class Runner: Parent class for all classes that in some sense "run" the network. That can be training, benchmarking, inference, etc. - """ - def __init__(self, params, network, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Runner object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Runner object. - network : mala.network.network.Network - Network which is being run. + network : mala.network.network.Network + Network which is being run. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the run. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the run. + """ + + def __init__(self, params, network, data): self.parameters_full = params self.parameters = params.running self.network = network diff --git a/mala/network/tester.py b/mala/network/tester.py index 49be45ec6..623cc3a2a 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -16,21 +16,20 @@ class Tester(Runner): A class for testing a neural network. It enables easy inference throughout a test set. - """ - def __init__(self, params, network, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Tester object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Tester object. - network : mala.network.network.Network - Network which is being tested. + network : mala.network.network.Network + Network which is being tested. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the test data. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the test data. + """ + + def __init__(self, params, network, data): # copy the parameters into the class. super(Tester, self).__init__(params, network, data) self.test_data_loader = None diff --git a/mala/network/trainer.py b/mala/network/trainer.py index b75e4273d..fdf176e31 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -19,21 +19,21 @@ class Trainer(Runner): - """A class for training a neural network.""" + """A class for training a neural network. - def __init__(self, params, network, data, optimizer_dict=None): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Trainer object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Trainer object. - network : mala.network.network.Network - Network which is being trained. + network : mala.network.network.Network + Network which is being trained. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the training data. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the training data. + """ + + def __init__(self, params, network, data, optimizer_dict=None): # copy the parameters into the class. super(Trainer, self).__init__(params, network, data) self.final_test_loss = float("inf") diff --git a/mala/targets/density.py b/mala/targets/density.py index 658d71211..d0de41e17 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -18,18 +18,17 @@ class Density(TargetBase): - """Postprocessing / parsing functions for the electronic density.""" + """Postprocessing / parsing functions for the electronic density. + + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ te_mutex = False def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. - - """ super(Density, self).__init__(params) # We operate on a per gridpoint basis. Per gridpoint, # there is one value for the density (spin-unpolarized calculations). diff --git a/mala/targets/dos.py b/mala/targets/dos.py index af3e6e9e4..7faf4461d 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -8,16 +8,15 @@ class DOS(TargetBase): - """Postprocessing / parsing functions for the density of states (DOS).""" + """Postprocessing / parsing functions for the density of states (DOS). - def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ - """ + def __init__(self, params): super(DOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 3e7d5c280..aebf46255 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -11,15 +11,15 @@ class LDOS(TargetBase): - """Postprocessing / parsing functions for the local density of states.""" + """Postprocessing / parsing functions for the local density of states. + + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. - """ super(LDOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 55923ff73..8ac66ad77 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -13,17 +13,15 @@ class TargetBase: Target parsers read the target quantity (i.e. the quantity the NN will learn to predict) from a specified file format and performs postprocessing calculations on the quantity. + + Parameters + ---------- + params : mala.common.parameters.Parameters or + mala.common.parameters.ParametersTargets + Parameters used to create this TargetBase object. """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters or - mala.common.parameters.ParametersTargets - Parameters used to create this TargetBase object. - - """ if isinstance(params, Parameters): self.parameters = params.targets elif isinstance(params, ParametersTargets): From 7a02a9a848369c48fad4eb51042cb72e2bf0d75d Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 7 Jul 2021 12:24:40 +0200 Subject: [PATCH 042/297] Remove concatenation of `__init__` and class docstrings: We document the class constructor within the class docstring; we do not use `__init__` docstrings. --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index dcd9aa54b..048287a83 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,7 +71,6 @@ ] autodoc_member_order = 'groupwise' -autoclass_content = "both" # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From cd0bed97d02eca4c4b8af68597d0b1d78d588814 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 7 Jul 2021 13:17:16 +0200 Subject: [PATCH 043/297] Fixed documentation in cube_parser.py --- mala/targets/cube_parser.py | 47 ++++++++++++++++++++++--------------- 1 file changed, 28 insertions(+), 19 deletions(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index f3a091384..0c62eed6c 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -77,19 +77,18 @@ class CubeFile(object): Doesn't copy atoms metadata, retains number of atoms, but returns dummy atoms Mimics file object's readline method. + + Parameters + ---------- + srcname: string + source file to copy metadata from + + const: int + numeric value to return instead of volumetric data """ def __init__(self, srcname, const=1): - """ - Parameters - ---------- - srcname: string - source file to copy metadata from - - const: int - numeric value to return instead of volumetric data - """ - self.cursor = 0 + self.cursor = 0 self.const = const self.src = src = open(srcname) # comments @@ -150,10 +149,15 @@ def _getline(cube): First field is an int and the remaining fields are floats. - params: - cube: - - returns: (int, list) + Parameters + ---------- + cube : TextIO + The cubefile from which the line is read. + + Returns + ------- + line : tuple + First entry is an int, the followin are floats. """ line = cube.readline().strip().split() return int(line[0]), map(float, line[1:]) @@ -164,11 +168,16 @@ def _putline(*args): Generate a line to be written to a cube file. The first field is an int and the remaining fields are floats. - - params: - *args: first arg is formatted as int and remaining as floats - - returns: formatted string to be written to file with trailing newline + + Parameters + ---------- + args : tuple + First arg is formatted as int and remaining as floats. + + Returns + ------- + line : string + Formatted string to be written to file with trailing newline. """ s = "{0:^ 8d}".format(args[0]) s += "".join("{0:< 12.6f}".format(arg) for arg in args[1:]) From d99bab01aaaf0a2b9d7c99e3de3c70d6df4c2e19 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Wed, 7 Jul 2021 15:04:07 +0200 Subject: [PATCH 044/297] Update mala/targets/cube_parser.py Co-authored-by: Daniel Kotik --- mala/targets/cube_parser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index 0c62eed6c..9f68df699 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -157,7 +157,7 @@ def _getline(cube): Returns ------- line : tuple - First entry is an int, the followin are floats. + First entry is an int, and the rests are floats. """ line = cube.readline().strip().split() return int(line[0]), map(float, line[1:]) From e020b1b1a4b50f87e98d3ceb501c36b22d1a5863 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 7 Jul 2021 15:08:29 +0200 Subject: [PATCH 045/297] Fixed documentation in cube_parser.py --- mala/targets/cube_parser.py | 105 ++++++++++++++++++------------------ 1 file changed, 54 insertions(+), 51 deletions(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index 9f68df699..e7cbef9a4 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -1,57 +1,60 @@ """ Cube parser, taken from cubetools (see below). -#------------------------------------------------------------------------------ -# Module: cubetools -#------------------------------------------------------------------------------ -# -# Description: -# Module to work with Gaussian cube format files -# (see http://paulbourke.net/dataformats/cube/) -# -#------------------------------------------------------------------------------ -# -# What does it do: -# * Read/write cube files to/from numpy arrays (dtype=float*) -# * Read/write pairse of cube files to/from numpy arrays (dtype=complex*) -# * Provides a CubeFile object, to be used when cubefiles with -# constant and static data is required. It simulates the readline method -# of a file object with a cube file opened, without creating a file -# -#------------------------------------------------------------------------------ -# -# Dependency: numpy -# -#------------------------------------------------------------------------------ -# -# Author: P. R. Vaidyanathan (aditya95sriram gmail com) -# Date: 25th June 2017 -# -#------------------------------------------------------------------------------ -# -# MIT License -# -# Copyright (c) 2019 P. R. Vaidyanathan -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -# -#------------------------------------------------------------------------------ +------------------------------------------------------------------------------ + +Module: cubetools + +------------------------------------------------------------------------------ + +Description: +Module to work with Gaussian cube format files +(see http://paulbourke.net/dataformats/cube/) + +------------------------------------------------------------------------------ + +What does it do: + +- Read/write cube files to/from numpy arrays (dtype=float*) +- Read/write pairse of cube files to/from numpy arrays (dtype=complex*) +- Provides a CubeFile object, to be used when cubefiles with constant and + static data is required. It simulates the readline method + of a file object with a cube file opened, without creating a file + +------------------------------------------------------------------------------ + +Dependency: numpy + +------------------------------------------------------------------------------ + +Author: P. R. Vaidyanathan (aditya95sriram gmail com) +Date: 25th June 2017 + +------------------------------------------------------------------------------ + +MIT License + +Copyright (c) 2019 P. R. Vaidyanathan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +------------------------------------------------------------------------------ """ import numpy as np From 4e362a6643a7dd06e614fee8e022060b7887f64f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 3 Jun 2021 13:43:53 +0200 Subject: [PATCH 046/297] Temporarily deactivated the source code feature of Sphinx --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a0f54e3e0..e4722d145 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -37,7 +37,7 @@ 'sphinx.ext.mathjax', 'sphinx.ext.intersphinx', 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', +# 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', ] From b05ff9e6685be2aa35b71f07cfd871e0382ad20d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 3 Jun 2021 13:48:08 +0200 Subject: [PATCH 047/297] (for the last commit to work, pushes need to trigger the pages) --- .github/workflows/gh-pages.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 212a950ea..78ddf3836 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -5,6 +5,9 @@ on: branches: - master - develop + push: + branches: + - develop jobs: test-docstrings: From 727a0dd558ecce3c132b69b565d3ddccef534d4c Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 19 Apr 2021 23:06:46 +0200 Subject: [PATCH 048/297] Snapshot function now part of add_snapshot; added new options for optuna --- mala/common/parameters.py | 2 +- mala/datahandling/data_handler.py | 9 ++++++++- mala/network/objective_base.py | 15 +++++++++++++-- mala/network/trainer.py | 5 +++-- 4 files changed, 25 insertions(+), 6 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 7b5371398..8be133b3e 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -224,7 +224,7 @@ def __init__(self): self.snapshot_directories_list = [] self.data_splitting_type = "by_snapshot" # self.data_splitting_percent = [0,0,0] - self.data_splitting_snapshots = ["tr", "va", "te"] + self.data_splitting_snapshots = [] self.input_rescaling_type = "None" self.output_rescaling_type = "None" self.use_lazy_loading = False diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index cb4254e09..24fddff7f 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -136,7 +136,7 @@ def get_output_dimension(self): def add_snapshot(self, input_npy_file, input_npy_directory, output_npy_file=None, output_npy_directory=None, input_units="None", output_units="1/eV", - calculation_output_file=""): + calculation_output_file="", add_snapshot_as=None): """ Add a snapshot to the data pipeline. @@ -165,10 +165,17 @@ def add_snapshot(self, input_npy_file, input_npy_directory, calculation_output_file : string File with the output of the original snapshot calculation. This is only needed when testing multiple snapshots. + + add_snapshot_as : string + If "tr", "va" or "te", the snapshot will be added to the snapshot + list as training, validation or testing snapshot, respectively. """ snapshot = Snapshot(input_npy_file, input_npy_directory, input_units, output_npy_file, output_npy_directory, output_units, calculation_output_file) + if add_snapshot_as is "tr" or add_snapshot_as is "te" \ + or add_snapshot_as is "va": + self.parameters.data_splitting_snapshots.append(add_snapshot_as) self.parameters.snapshot_directories_list.append(snapshot) def clear_data(self): diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index fc82828c5..8a012349f 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -97,15 +97,26 @@ def parse_trial_optuna(self, trial: Trial): for par in self.params.hyperparameters.hlist: if par.name == "learning_rate": self.params.running.learning_rate = par.get_parameter(trial) + elif "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial)) + elif "ff_neurons_layer" in par.name: if self.params.network.nn_type == "feed-forward": - self.params.network.layer_sizes.\ - append(par.get_parameter(trial)) + # Check for zero neuron layers; These indicate layers + # that can be left out. + layer_size = par.get_parameter(trial) + if layer_size > 0: + self.params.network.layer_sizes.\ + append(par.get_parameter(trial)) + elif "trainingtype" in par.name: self.params.running.trainingtype = par.get_parameter(trial) + + elif "mini_batch_size" in par.name: + self.params.running.mini_batch_size = par.get_parameter(trial) + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 8dda278cb..7c0d8b00a 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -242,7 +242,8 @@ def train_network(self): self.__create_training_checkpoint() checkpoint_counter = 0 - printout("Time for epoch[s]:", time.time() - start_time) + if self.parameters.verbosity: + printout("Time for epoch[s]:", time.time() - start_time) # Calculate final loss. self.final_validation_loss = vloss @@ -252,8 +253,8 @@ def train_network(self): self.test_data_loader) if self.parameters_full.use_horovod: tloss = self.__average_validation(tloss, 'average_loss') + printout("Final test data loss: ", tloss) self.final_test_loss = tloss - printout("Final test data loss: ", tloss) def __prepare_to_train(self, optimizer_dict): """Prepare everything for training.""" From 49a122840d815e2550865e27d09da3052bb74cb5 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Apr 2021 16:54:58 +0200 Subject: [PATCH 049/297] Minor bugfix when clearing data --- mala/datahandling/data_handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 24fddff7f..43ecedaa2 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -187,7 +187,7 @@ def clear_data(self): self.training_data_set = None self.validation_data_set = None self.test_data_set = None - + self.parameters.data_splitting_snapshots = [] self.parameters.snapshot_directories_list = [] def prepare_data(self, reparametrize_scaler=True): From 22a60b1cf9831e150e0db90ce51056e54d3692c3 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 29 Apr 2021 22:53:00 +0200 Subject: [PATCH 050/297] Distributed optuna runs work in principle; I just have to figure out a way to clean up the RDB afterwards --- mala/common/parameters.py | 3 ++ mala/network/hyper_opt_optuna.py | 50 ++++++++++++++++++++++++++++++-- 2 files changed, 50 insertions(+), 3 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 8be133b3e..99a17eee1 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -410,6 +410,9 @@ def __init__(self): self.hyper_opt_method = "optuna" self.checkpoints_each_trial = 0 self.checkpoint_name = "checkpoint_mala_ho" + self.study_name = None + self.storage_database_username = None + self.storage_database = None def show(self, indent=""): """ diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 6a255bc96..042749aa2 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -37,13 +37,57 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): sampler = optuna.samplers.TPESampler(seed=params.manual_seed) # Create the study. - self.study = optuna.\ - create_study(direction=self.params.hyperparameters.direction, - sampler=sampler) + if self.params.hyperparameters.storage_database is None: + self.study = optuna.\ + create_study(direction=self.params.hyperparameters.direction, + sampler=sampler, + study_name=self.params.hyperparameters. + study_name) + else: + storage_string, does_already_exist = self.setup_remote_storage() + if does_already_exist is True: + self.study = optuna.load_study( + study_name=self.params.hyperparameters. + study_name, + storage=storage_string) + else: + self.study = optuna.\ + create_study(direction=self.params.hyperparameters.direction, + sampler=sampler, + study_name=self.params.hyperparameters. + study_name, + storage=storage_string) + self.objective = None self.checkpoint_counter = 0 self.trials_from_last_checkpoint = trials_from_last_checkpoint + def setup_remote_storage(self): + from sqlalchemy import create_engine + from sqlalchemy_utils import database_exists, create_database + # If a RDB storage database is to be used, it has to be provided + # to the user. + if self.params.hyperparameters.storage_database_username is None: + raise Exception("If RDB storage is used, a RDB username has" + " to be provided.") + if self.params.hyperparameters.study_name is None: + raise Exception("If RDB storage is used, a name for the study " + "has to be provided.") + + # Parse the user input. + storage_string = "mysql://"+self.params.hyperparameters.\ + storage_database_username + \ + "@/"+self.params.hyperparameters.storage_database + + # Create the database, if it does not exist already. + engine = create_engine(storage_string) + does_already_exist = True + if not database_exists(engine.url): + create_database(engine.url) + does_already_exist = False + + return storage_string, does_already_exist + def perform_study(self): """ Perform the study, i.e. the optimization. From 53ab9344010095143d537e2d7bad20b4a951b69b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sat, 1 May 2021 22:44:23 +0200 Subject: [PATCH 051/297] Using SQLite for distributed optuna runs and everything is better now --- mala/common/parameters.py | 3 +- mala/network/hyper_opt_optuna.py | 48 ++++++++------------------------ 2 files changed, 12 insertions(+), 39 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 99a17eee1..eb321b0a7 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -411,8 +411,7 @@ def __init__(self): self.checkpoints_each_trial = 0 self.checkpoint_name = "checkpoint_mala_ho" self.study_name = None - self.storage_database_username = None - self.storage_database = None + self.storage_database_absolute_path = None def show(self, indent=""): """ diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 042749aa2..f8f097a5a 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -37,57 +37,31 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): sampler = optuna.samplers.TPESampler(seed=params.manual_seed) # Create the study. - if self.params.hyperparameters.storage_database is None: + if self.params.hyperparameters.storage_database_absolute_path is None: self.study = optuna.\ create_study(direction=self.params.hyperparameters.direction, sampler=sampler, study_name=self.params.hyperparameters. study_name) else: - storage_string, does_already_exist = self.setup_remote_storage() - if does_already_exist is True: - self.study = optuna.load_study( - study_name=self.params.hyperparameters. - study_name, - storage=storage_string) - else: - self.study = optuna.\ - create_study(direction=self.params.hyperparameters.direction, + if self.params.hyperparameters.study_name is None: + raise Exception("If RDB storage is used, a name for the study " + "has to be provided.") + storage_string = "sqlite:///"\ + + self.params.hyperparameters.\ + storage_database_absolute_path + self.study = optuna.\ + create_study(direction=self.params.hyperparameters.direction, sampler=sampler, study_name=self.params.hyperparameters. study_name, - storage=storage_string) + storage=storage_string, + load_if_exists=True) self.objective = None self.checkpoint_counter = 0 self.trials_from_last_checkpoint = trials_from_last_checkpoint - def setup_remote_storage(self): - from sqlalchemy import create_engine - from sqlalchemy_utils import database_exists, create_database - # If a RDB storage database is to be used, it has to be provided - # to the user. - if self.params.hyperparameters.storage_database_username is None: - raise Exception("If RDB storage is used, a RDB username has" - " to be provided.") - if self.params.hyperparameters.study_name is None: - raise Exception("If RDB storage is used, a name for the study " - "has to be provided.") - - # Parse the user input. - storage_string = "mysql://"+self.params.hyperparameters.\ - storage_database_username + \ - "@/"+self.params.hyperparameters.storage_database - - # Create the database, if it does not exist already. - engine = create_engine(storage_string) - does_already_exist = True - if not database_exists(engine.url): - create_database(engine.url) - does_already_exist = False - - return storage_string, does_already_exist - def perform_study(self): """ Perform the study, i.e. the optimization. From 80c06e944ef31a8fa46ae96b885ca56b61f73c10 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sat, 1 May 2021 22:58:42 +0200 Subject: [PATCH 052/297] Small bugfix --- mala/datahandling/data_handler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 43ecedaa2..6e37e78d5 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -173,8 +173,8 @@ def add_snapshot(self, input_npy_file, input_npy_directory, snapshot = Snapshot(input_npy_file, input_npy_directory, input_units, output_npy_file, output_npy_directory, output_units, calculation_output_file) - if add_snapshot_as is "tr" or add_snapshot_as is "te" \ - or add_snapshot_as is "va": + if add_snapshot_as == "tr" or add_snapshot_as == "te" \ + or add_snapshot_as == "va": self.parameters.data_splitting_snapshots.append(add_snapshot_as) self.parameters.snapshot_directories_list.append(snapshot) From 5ee393d131f6cbf86dd3c69626a6b68250d32b0e Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 2 May 2021 16:57:41 +0200 Subject: [PATCH 053/297] Output was not included in verbosity --- mala/network/trainer.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 7c0d8b00a..d272f9182 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -220,7 +220,9 @@ def train_network(self): vloss_old = vloss else: self.patience_counter += 1 - printout("Validation accuracy has not improved enough.") + if self.parameters.verbosity: + printout("Validation accuracy has not improved " + "enough.") if self.patience_counter >= self.parameters.\ early_stopping_epochs: if self.parameters.verbosity: From 5fe061ff196035316fabd464455e78911767641b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 2 May 2021 18:17:47 +0200 Subject: [PATCH 054/297] Changed code so that postgres is also possible (sqlite should not be used at scale, as d.kotik@hzdr.de pointed out) --- mala/common/parameters.py | 2 +- mala/network/hyper_opt_optuna.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index eb321b0a7..a32dd1a49 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -411,7 +411,7 @@ def __init__(self): self.checkpoints_each_trial = 0 self.checkpoint_name = "checkpoint_mala_ho" self.study_name = None - self.storage_database_absolute_path = None + self.rdb_storage = None def show(self, indent=""): """ diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index f8f097a5a..20642b219 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -37,7 +37,7 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): sampler = optuna.samplers.TPESampler(seed=params.manual_seed) # Create the study. - if self.params.hyperparameters.storage_database_absolute_path is None: + if self.params.hyperparameters.rdb_storage is None: self.study = optuna.\ create_study(direction=self.params.hyperparameters.direction, sampler=sampler, @@ -47,15 +47,12 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): if self.params.hyperparameters.study_name is None: raise Exception("If RDB storage is used, a name for the study " "has to be provided.") - storage_string = "sqlite:///"\ - + self.params.hyperparameters.\ - storage_database_absolute_path self.study = optuna.\ create_study(direction=self.params.hyperparameters.direction, sampler=sampler, study_name=self.params.hyperparameters. study_name, - storage=storage_string, + storage=self.params.hyperparameters.rdb_storage, load_if_exists=True) self.objective = None From c7c84c3cf9428f723b6d42cb1f0a5ac9a8d99eb6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 2 May 2021 19:09:56 +0200 Subject: [PATCH 055/297] Added tutorial for setting up postgres on hemera --- docs/source/install/sql_on_hpc.rst | 47 ++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 docs/source/install/sql_on_hpc.rst diff --git a/docs/source/install/sql_on_hpc.rst b/docs/source/install/sql_on_hpc.rst new file mode 100644 index 000000000..c0a006519 --- /dev/null +++ b/docs/source/install/sql_on_hpc.rst @@ -0,0 +1,47 @@ +Using postgres on HPC infrastructure (for optuna) +==================================================== + +Hemera5 (HZDR) +-------------- + +1. Install postgres (e.g. in a conda environment) +2. Initialize a postgres server configuration somewhere in your home directory. + `cd /a/suitable/location/` + `initdb -D YOUR_SERVER_NAME` + +3. Edit the configuration files of this server: + - In postgres_local/postgresql.conf add/change the line containing listen_adresses so that it reads: + `listen_addresses = '*'` + + - In pg_hba.conf, under IPv$ local connections add the line: + `host all all 0.0.0.0/0 trust` + +4. Start the postgres server: + `pg_ctl -D postgres_local -l logfile start` + +5. Create the "username database" + - This is needed in order to access the psql interface (for maintenance) + `createdb` + +6. Create a database for your hyperparameter optimizations + createdb YOUR_DATABASE_NAME + +7. Host a postgres server via a compute job: + - You can/should also create a database for optuna, so you can create a study from within python + + .. code-block:: bash + + cd /a/suitable/location/ + pg_ctl -D A_DESCRIPTIVE_SERVER_NAME -l logfile start + createdb YOUR_SERVER_NAME + + while true; do + pg_ctl -D postgres_local status + sleep 60m + done``` + +8. MALA/Optuna can now connect to this database via + .. code-block:: python + + parameters.hyperparameters.rdb_storage = "postgresql://YOUR_USER_NAME@YOUR_COMPUTE_NODE/YOUR_DATABASE_NAME" + From 0016bab8901b5b55d37f06d6651fe0981e9b5cef Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 11 May 2021 20:12:29 +0200 Subject: [PATCH 056/297] Configured hyperopt checkpointing so that the best current trial can be checkpointed while running --- mala/network/hyper_opt_optuna.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 20642b219..d5450c283 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -187,10 +187,24 @@ def load_from_file(cls, params, file_path, data): def __create_checkpointing(self, study, trial): """Create a checkpoint of optuna study, if necessary.""" self.checkpoint_counter += 1 + need_to_checkpoint = False + if self.checkpoint_counter >= self.params.hyperparameters.\ - checkpoints_each_trial: + checkpoints_each_trial and self.params.hyperparameters.\ + checkpoints_each_trial > 0: + need_to_checkpoint = True + printout(str(self.params.hyperparameters. + checkpoints_each_trial)+" trials have passed, creating a " + "checkpoint for hyperparameter " + "optimization.") + if self.params.hyperparameters.checkpoints_each_trial < 0 and \ + trial.number == study.best_trial.number: + need_to_checkpoint = True + printout("Best trial is "+str(trial.number)+", creating a " + "checkpoint for it.") + + if need_to_checkpoint is True: # We need to create a checkpoint! - printout("Create checkpoint for hyperparameter optimization.") self.checkpoint_counter = 0 # Get the filenames. From 1ab3ca41fc44483a9230b42470808c2b55d29854 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 16 May 2021 20:56:52 +0200 Subject: [PATCH 057/297] Refined checkpointing for optuna using RDB --- mala/network/hyper_opt_optuna.py | 76 +++++++++++++++++++++++--------- 1 file changed, 55 insertions(+), 21 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index d5450c283..6906e5ab1 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -1,6 +1,7 @@ """Hyperparameter optimizer using optuna.""" import pickle import optuna +import os from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase from mala.common.parameters import printout @@ -12,7 +13,7 @@ class HyperOptOptuna(HyperOptBase): """Hyperparameter optimizer using Optuna.""" - def __init__(self, params, data, trials_from_last_checkpoint=0): + def __init__(self, params, data): """ Create a HyperOptOptuna object. @@ -23,9 +24,6 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): data : mala.datahandling.data_handler.DataHandler DataHandler holding the data for the hyperparameter optimization. - - trials_from_last_checkpoint : int - Trials that were performed during the last checkpoint. """ super(HyperOptOptuna, self).__init__(params, data) self.params = params @@ -57,7 +55,6 @@ def __init__(self, params, data, trials_from_last_checkpoint=0): self.objective = None self.checkpoint_counter = 0 - self.trials_from_last_checkpoint = trials_from_last_checkpoint def perform_study(self): """ @@ -69,13 +66,12 @@ def perform_study(self): self.objective = ObjectiveBase(self.params, self.data_handler) # Fill callback list based on user checkpoint wishes. - callback_list = [] + callback_list = [self.__check_max_number_trials] if self.params.hyperparameters.checkpoints_each_trial != 0: callback_list.append(self.__create_checkpointing) self.study.optimize(self.objective, - n_trials=self.params.hyperparameters.n_trials - - self.trials_from_last_checkpoint, + n_trials=self.params.hyperparameters.n_trials, callbacks=callback_list) # Return the best lost value we could achieve. @@ -102,6 +98,35 @@ def get_trials_from_study(self): """ return self.study.get_trials() + @classmethod + def checkpoint_exists(cls, checkpoint_name): + """ + Check if a hyperparameter optimization checkpoint exists. + + Returns True if it does. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint. + + Returns + ------- + checkpoint_exists : bool + True if the checkpoint exists, False otherwise. + + """ + iscaler_name = checkpoint_name + "_iscaler.pkl" + oscaler_name = checkpoint_name + "_oscaler.pkl" + param_name = checkpoint_name + "_params.pkl" + + if os.path.isfile(iscaler_name) is True \ + and os.path.isfile(oscaler_name) is True \ + and os.path.isfile(param_name): + return True + else: + return False + @classmethod def resume_checkpoint(cls, checkpoint_name): """ @@ -174,16 +199,24 @@ def load_from_file(cls, params, file_path, data): The hyperparameter optimizer that was loaded from the file. """ # First, load the checkpoint. - with open(file_path, 'rb') as handle: - loaded_study = pickle.load(handle) - - # Now, create the Trainer class with it. - loaded_hyperopt = HyperOptOptuna(params, data, - trials_from_last_checkpoint= - len(loaded_study.get_trials())) - loaded_hyperopt.study = loaded_study + if params.hyperparameters.rdb_storage is None: + with open(file_path, 'rb') as handle: + loaded_study = pickle.load(handle) + + # Now, create the Trainer class with it. + loaded_hyperopt = HyperOptOptuna(params, data) + loaded_hyperopt.study = loaded_study + else: + loaded_hyperopt = HyperOptOptuna(params, data) + return loaded_hyperopt + def __check_max_number_trials(self, study, trial): + number_of_completed_trials = len([t for t in study.trials if t.state == + optuna.trial.TrialState.COMPLETE]) + if number_of_completed_trials >= self.params.hyperparameters.n_trials: + self.study.stop() + def __create_checkpointing(self, study, trial): """Create a checkpoint of optuna study, if necessary.""" self.checkpoint_counter += 1 @@ -214,8 +247,6 @@ def __create_checkpointing(self, study, trial): + "_oscaler.pkl" param_name = self.params.hyperparameters.checkpoint_name \ + "_params.pkl" - hyperopt_name = self.params.hyperparameters.checkpoint_name \ - + "_hyperopt.pth" # First we save the objects we would also save for inference. self.data_handler.input_data_scaler.save(iscaler_name) @@ -228,6 +259,9 @@ def __create_checkpointing(self, study, trial): # if self.params.use_horovod: # if hvd.rank() != 0: # return - - with open(hyperopt_name, 'wb') as handle: - pickle.dump(self.study, handle, protocol=4) + # The study only has to be saved if the no RDB storage is used. + if self.params.hyperparameters.rdb_storage is None: + hyperopt_name = self.params.hyperparameters.checkpoint_name \ + + "_hyperopt.pth" + with open(hyperopt_name, 'wb') as handle: + pickle.dump(self.study, handle, protocol=4) From c48a46ebe31931542ba97bd97b099b576a55ed4e Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 18 May 2021 14:40:00 +0200 Subject: [PATCH 058/297] Convenience "Checkpoint exists" function for Trainer --- mala/network/trainer.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index d272f9182..c581cad61 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -3,6 +3,7 @@ from mala.datahandling.data_handler import DataHandler from mala.datahandling.data_scaler import DataScaler from mala.common.parameters import Parameters +import os import numpy as np import torch from torch import optim @@ -51,6 +52,39 @@ def __init__(self, params, network, data, optimizer_dict=None): self.test_data_loader = None self.__prepare_to_train(optimizer_dict) + @classmethod + def checkpoint_exists(cls, checkpoint_name): + """ + Check if a hyperparameter optimization checkpoint exists. + + Returns True if it does. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint. + + Returns + ------- + checkpoint_exists : bool + True if the checkpoint exists, False otherwise. + + """ + network_name = checkpoint_name + "_network.pth" + iscaler_name = checkpoint_name + "_iscaler.pkl" + oscaler_name = checkpoint_name + "_oscaler.pkl" + param_name = checkpoint_name + "_params.pkl" + optimizer_name = checkpoint_name + "_optimizer.pth" + + if os.path.isfile(iscaler_name) is True \ + and os.path.isfile(oscaler_name) is True \ + and os.path.isfile(param_name) is True \ + and os.path.isfile(network_name) is True \ + and os.path.isfile(optimizer_name): + return True + else: + return False + @classmethod def resume_checkpoint(cls, checkpoint_name): """ From f3de15c144ddf047c2716043afeca436ed3322d6 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 6 May 2021 19:00:06 +0200 Subject: [PATCH 059/297] Fix rST syntax issues in `sql_on_hpc.rst` --- docs/source/install/sql_on_hpc.rst | 51 +++++++++++++++++------------- 1 file changed, 29 insertions(+), 22 deletions(-) diff --git a/docs/source/install/sql_on_hpc.rst b/docs/source/install/sql_on_hpc.rst index c0a006519..d2830d102 100644 --- a/docs/source/install/sql_on_hpc.rst +++ b/docs/source/install/sql_on_hpc.rst @@ -5,43 +5,50 @@ Hemera5 (HZDR) -------------- 1. Install postgres (e.g. in a conda environment) -2. Initialize a postgres server configuration somewhere in your home directory. - `cd /a/suitable/location/` - `initdb -D YOUR_SERVER_NAME` +2. Initialize a postgres server configuration somewhere in your home directory: + + .. code-block:: bash + + $ cd /a/suitable/location/ + $ initdb -D YOUR_SERVER_NAME 3. Edit the configuration files of this server: - - In postgres_local/postgresql.conf add/change the line containing listen_adresses so that it reads: - `listen_addresses = '*'` - - In pg_hba.conf, under IPv$ local connections add the line: - `host all all 0.0.0.0/0 trust` + - In :file:`postgres_local/postgresql.conf` add/change the line containing listen_adresses so that it reads: + ``listen_addresses = '*'`` + + - In :file:`pg_hba.conf`, under ``IPv$`` local connections add the line: + ``host all all 0.0.0.0/0 trust`` 4. Start the postgres server: - `pg_ctl -D postgres_local -l logfile start` + + .. code-block:: bash + + $ pg_ctl -D postgres_local -l logfile start 5. Create the "username database" - - This is needed in order to access the psql interface (for maintenance) - `createdb` -6. Create a database for your hyperparameter optimizations - createdb YOUR_DATABASE_NAME + - This is needed in order to access the psql interface (for maintenance): ``createdb`` + +6. Create a database for your hyperparameter optimizations: ``createdb YOUR_DATABASE_NAME`` 7. Host a postgres server via a compute job: - - You can/should also create a database for optuna, so you can create a study from within python - .. code-block:: bash + - You can/should also create a database for optuna, so you can create a study from within Python + + .. code-block:: bash - cd /a/suitable/location/ - pg_ctl -D A_DESCRIPTIVE_SERVER_NAME -l logfile start - createdb YOUR_SERVER_NAME + cd /a/suitable/location/ + pg_ctl -D A_DESCRIPTIVE_SERVER_NAME -l logfile start + createdb YOUR_SERVER_NAME - while true; do - pg_ctl -D postgres_local status - sleep 60m - done``` + while true; do + pg_ctl -D postgres_local status + sleep 60m + done 8. MALA/Optuna can now connect to this database via + .. code-block:: python parameters.hyperparameters.rdb_storage = "postgresql://YOUR_USER_NAME@YOUR_COMPUTE_NODE/YOUR_DATABASE_NAME" - From fa10b0ed329b6e6afe5ad481bb2034c3bbb78aa8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sat, 22 May 2021 16:00:30 +0200 Subject: [PATCH 060/297] Bugfix in clear_data() --- mala/datahandling/data_handler.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 6e37e78d5..9d7c9a037 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -187,6 +187,9 @@ def clear_data(self): self.training_data_set = None self.validation_data_set = None self.test_data_set = None + self.nr_training_data = 0 + self.nr_test_data = 0 + self.nr_validation_data = 0 self.parameters.data_splitting_snapshots = [] self.parameters.snapshot_directories_list = [] From a922f0a37f266e0b77297848244af04c2a208f45 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 27 May 2021 18:43:19 +0200 Subject: [PATCH 061/297] Added option to add a heartbeat to an RDB storage --- mala/common/parameters.py | 13 +++++++++++++ mala/network/hyper_opt_optuna.py | 7 ++++++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index a32dd1a49..0374ddd97 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -412,6 +412,19 @@ def __init__(self): self.checkpoint_name = "checkpoint_mala_ho" self.study_name = None self.rdb_storage = None + self.rdb_storage_heartbeat = None + + @property + def rdb_storage_heartbeat(self): + """Control whether or not a GPU is used (provided there is one).""" + return self._rdb_storage_heartbeat + + @rdb_storage_heartbeat.setter + def rdb_storage_heartbeat(self, value): + if value == 0: + self._rdb_storage_heartbeat = None + else: + self._rdb_storage_heartbeat = value def show(self, indent=""): """ diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 6906e5ab1..5a5d42d76 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -45,12 +45,17 @@ def __init__(self, params, data): if self.params.hyperparameters.study_name is None: raise Exception("If RDB storage is used, a name for the study " "has to be provided.") + rdb_storage = optuna.storages.RDBStorage( + url=self.params.hyperparameters.rdb_storage, + heartbeat_interval=self.params.hyperparameters. + rdb_storage_heartbeat) + self.study = optuna.\ create_study(direction=self.params.hyperparameters.direction, sampler=sampler, study_name=self.params.hyperparameters. study_name, - storage=self.params.hyperparameters.rdb_storage, + storage=rdb_storage, load_if_exists=True) self.objective = None From 67549ffb0cf742f29a013cef62f0832e6138361a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 28 May 2021 22:45:31 +0200 Subject: [PATCH 062/297] Added an example for distributed hyperparameter optimization. --- examples/ex07_checkpoint_training.py | 45 +++++---- examples/ex08_checkpoint_hyperopt.py | 30 ++++-- examples/ex09_distributed_hyperopt.py | 134 ++++++++++++++++++++++++++ examples/ex99_verify_all_examples.py | 54 ++++++++--- 4 files changed, 221 insertions(+), 42 deletions(-) create mode 100644 examples/ex09_distributed_hyperopt.py diff --git a/examples/ex07_checkpoint_training.py b/examples/ex07_checkpoint_training.py index 3118ef770..10dd8e007 100644 --- a/examples/ex07_checkpoint_training.py +++ b/examples/ex07_checkpoint_training.py @@ -5,11 +5,15 @@ """ ex07_checkpoint_training.py: Shows how a training run can be paused and -resumed. +resumed. Delete all ex07_*.pkl and ex07_*pth prior to execution. +Afterwards, execute this script twice to see how MALA progresses from a +checkpoint. As the number of total epochs is cannot divided by the number +of epochs after which a checkpoint is created without residual, this will +lead to MALA performing the missing epochs again. """ -def run_example07(desired_loss_improvement_factor=1): +def initial_setup(): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -34,8 +38,8 @@ def run_example07(desired_loss_improvement_factor=1): # Specify the training parameters. # We only train for an odd number of epochs here, and train for # the rest after the checkpoint has been loaded. - test_parameters.running.max_number_epochs = 8 - test_parameters.running.mini_batch_size = 40 + test_parameters.running.max_number_epochs = 9 + test_parameters.running.mini_batch_size = 8 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" @@ -81,37 +85,36 @@ def run_example07(desired_loss_improvement_factor=1): printout("Network setup: DONE.") + return test_parameters, test_network, data_handler, test_trainer + + +def run_example07(desired_loss_improvement_factor=1): + if mala.Trainer.checkpoint_exists("ex07"): + parameters, network, datahandler, trainer = \ + mala.Trainer.resume_checkpoint("ex07") + printout("Starting resumed training.") + else: + parameters, network, datahandler, trainer = initial_setup() + printout("Starting original training.") + #################### # TRAINING # Train the network. After training, load from the last checkpoint # and train for more epochs. #################### - - printout("Starting training.") - test_trainer.train_network() + trainer.train_network() printout("Training: DONE.") - loaded_params, loaded_network, new_datahandler, new_trainer = \ - mala.Trainer.resume_checkpoint("ex07") - - # Note that this means the actual total number of epochs, - # not the ones trained after loading. That is, if we trained - # 8 before, but checkpointed every 5 epochs, we will now train - # for 15. - loaded_params.running.max_number_epochs = 20 - new_trainer.train_network() - printout("Training 2.0: DONE.") - #################### # RESULTS. # Print the used parameters and check whether the loss decreased enough. #################### printout("Parameters used for this experiment:") - test_parameters.show() + parameters.show() - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < new_trainer.final_test_loss: + if desired_loss_improvement_factor*trainer.initial_test_loss\ + < trainer.final_test_loss: return False else: return True diff --git a/examples/ex08_checkpoint_hyperopt.py b/examples/ex08_checkpoint_hyperopt.py index 0d25f2492..1b4614f32 100644 --- a/examples/ex08_checkpoint_hyperopt.py +++ b/examples/ex08_checkpoint_hyperopt.py @@ -5,11 +5,15 @@ """ ex08_checkpoint_hyperopt.py: Shows how a hyperparameter optimization run can -be paused and resumed. +be paused and resumed. Delete all ex08_*.pkl and ex08_*.pth prior to execution. +Afterwards, execute this script twice to see how MALA progresses from a +checkpoint. As the number of trials is cannot divided by the number +of epochs after which a checkpoint is created without residual, this will +lead to MALA performing the missing trials again. """ -def run_example08(): +def initial_setup(): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -91,13 +95,21 @@ def run_example08(): test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", choices=["ReLU", "Sigmoid"]) - # Perform hyperparameter optimization. - printout("Starting Hyperparameter optimization.") - test_hp_optimizer.perform_study() + return test_parameters, data_handler, test_hp_optimizer - loaded_params, new_datahandler, new_hyperopt = \ - mala.HyperOptOptuna.resume_checkpoint("ex08") - new_hyperopt.perform_study() + +def run_example08(): + if mala.HyperOptOptuna.checkpoint_exists("ex08"): + parameters, datahandler, hyperoptimizer = \ + mala.HyperOptOptuna.resume_checkpoint( + "ex08") + printout("Starting resumed hyperparameter optimization.") + else: + parameters, datahandler, hyperoptimizer = initial_setup() + printout("Starting original hyperparameter optimization.") + + # Perform hyperparameter optimization. + hyperoptimizer.perform_study() #################### # RESULTS. @@ -105,7 +117,7 @@ def run_example08(): #################### printout("Parameters used for this experiment:") - test_parameters.show() + parameters.show() return True diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py new file mode 100644 index 000000000..d1bf80ba1 --- /dev/null +++ b/examples/ex09_distributed_hyperopt.py @@ -0,0 +1,134 @@ +import mala +from mala import printout +from data_repo_path import get_data_repo_path +data_path = get_data_repo_path()+"Al36/" + +""" +ex09_distributed_hyperopt.py: Shows how a hyperparameter +optimization can be sped up using a RDB storage. Ideally this should be done +using a database server system, such as PostgreSQL or MySQL. +For this easy example, sqlite will be used. It is highly advisory not to +to use this for actual, at-scale calculations! +Please delete ex09.db prior to execution. Afterwards execute this script +in multiple terminals / nodes. +""" + + +def run_example09(desired_loss_improvement_factor=1): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 5 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + # Specify the number of trials, the hyperparameter optimizer should run + # and the type of hyperparameter. + test_parameters.hyperparameters.n_trials = 20 + test_parameters.hyperparameters.hyper_opt_method = "optuna" + test_parameters.hyperparameters.study_name = "ex09" + test_parameters.hyperparameters.rdb_storage = 'sqlite:///ex09.db' + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + data_handler = mala.DataHandler(test_parameters) + + # Add all the snapshots we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # HYPERPARAMETER OPTIMIZATION + # In order to perform a hyperparameter optimization, + # one has to simply create a hyperparameter optimizer + # and let it perform a "study". + # Before such a study can be done, one has to add all the parameters + # of interest. + #################### + + test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) + + # Learning rate will be optimized. + test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + + # Number of neurons per layer will be optimized. + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + + # Choices for activation function at each layer will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + + # Perform hyperparameter optimization. + printout("Starting Hyperparameter optimization.") + test_hp_optimizer.perform_study() + test_hp_optimizer.set_optimal_parameters() + printout("Hyperparameter optimization: DONE.") + + #################### + # TRAINING + # Train with these new parameters. + #################### + + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # RESULTS. + # Print the used parameters and check whether the loss decreased enough. + #################### + + printout("Parameters used for this experiment:") + test_parameters.show() + + if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ + test_trainer.final_test_loss: + return False + else: + return True + + +if __name__ == "__main__": + if run_example09(): + printout("Successfully ran ex09_distributed_hyperopt.py.") + else: + raise Exception("Ran ex09_distributed_hyperopt but something " + "was off. If you haven't changed any parameters in " + "the example, there might be a problem with " + "your installation.") + diff --git a/examples/ex99_verify_all_examples.py b/examples/ex99_verify_all_examples.py index 0bc5e9649..8513b54b1 100644 --- a/examples/ex99_verify_all_examples.py +++ b/examples/ex99_verify_all_examples.py @@ -7,7 +7,8 @@ from ex06_advanced_hyperparameter_optimization import run_example06 from ex07_checkpoint_training import run_example07 from ex08_checkpoint_hyperopt import run_example08 - +from ex09_distributed_hyperopt import run_example09 +import os """ ex99_verify_all_examples.py: This example confirms whether or not the examples run CORRECTLY. That is, even though they might run, they may not run correctly, @@ -73,7 +74,7 @@ "the example, there might be a problem with your " "installation.") -# Example 10: Novel hyperparameter optimization techniques. +# Example 6: Novel hyperparameter optimization techniques. if run_example06(): printout("Successfully ran ex06_advanced_hyperparameter_optimization.py.") else: @@ -82,19 +83,48 @@ " If you haven't changed any parameters in " "the example, there might be a problem with your installation.") +# Example 7: Checkpointing/resuming training. +checkpoint_files = ["ex07_iscaler.pkl", "ex07_oscaler.pkl", "ex07_params.pkl", + "ex07_network.pth", "ex07_optimizer.pth"] +for checkpoint_file in checkpoint_files: + if os.path.exists(checkpoint_file): + os.remove(checkpoint_file) -if run_example07(): - printout("Successfully ran ex07_checkpoint_training.") -else: - raise Exception("Ran ex07_checkpoint_training but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") +for i in range(0, 2): + if run_example07(): + printout("Successfully ran ex07_checkpoint_training ("+str(i+1)+"/2).") + else: + raise Exception("Ran ex07_checkpoint_training but something was off." + " If you haven't changed any parameters in " + "the example, there might be a problem with your" + " installation.") -if run_example08(): - printout("Successfully ran ex08_checkpoint_hyperopt.") +# Example 8: Checkpointing/resuming hyperparameter optimization. +checkpoint_files = ["ex08_iscaler.pkl", "ex08_oscaler.pkl", "ex08_params.pkl", + "ex08_hyperopt.pth"] +for checkpoint_file in checkpoint_files: + if os.path.exists(checkpoint_file): + os.remove(checkpoint_file) + +for i in range(0, 2): + if run_example08(): + printout("Successfully ran ex08_checkpoint_hyperopt.") + else: + raise Exception("Ran ex08_checkpoint_hyperopt but something was off." + " If you haven't changed any parameters in " + "the example, there might be a problem with your" + " installation.") + + +# Example 8: Distritbuted hyperparameter optimitzation. +if os.path.exists("ex09.db"): + os.remove("ex09.db") + +if run_example09(): + printout("Successfully ran ex09_distributed_hyperopt.") else: - raise Exception("Ran ex08_checkpoint_hyperopt but something was off." + raise Exception("Ran ex09_distributed_hyperopt but something was off." " If you haven't changed any parameters in " "the example, there might be a problem with your" " installation.") + From 7ba19a5978385ce18c1ecbf9c6b082f74db95a86 Mon Sep 17 00:00:00 2001 From: Vlad Oles Date: Mon, 31 May 2021 16:48:19 -0700 Subject: [PATCH 063/297] configuring PostgreSQL when scaling Optuna up --- docs/source/install/sql_on_hpc.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/install/sql_on_hpc.rst b/docs/source/install/sql_on_hpc.rst index d2830d102..8e2d8b550 100644 --- a/docs/source/install/sql_on_hpc.rst +++ b/docs/source/install/sql_on_hpc.rst @@ -52,3 +52,5 @@ Hemera5 (HZDR) .. code-block:: python parameters.hyperparameters.rdb_storage = "postgresql://YOUR_USER_NAME@YOUR_COMPUTE_NODE/YOUR_DATABASE_NAME" + +9. [optional] To increase the maximum number of connections to PostgreSQL (default is 100), go to ``a/suitable/location/postgres_local/postgresql.conf`` and change the value of ``max_connections``. In addition, the value of ``shared_buffers`` is recommended to be set to about 10% of RAM available on a compute node. Note that the actual number of connections exceeds the number of workers, likely because the workers establish new connections every time they communicate with DB and idle connections not dying out by default. From 131b0c4e4aace3a38a1640382fca523caf21e1a4 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 2 Jun 2021 09:49:11 +0200 Subject: [PATCH 064/297] Fixed docstring --- mala/common/parameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 0374ddd97..3a0a49ebc 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -416,7 +416,7 @@ def __init__(self): @property def rdb_storage_heartbeat(self): - """Control whether or not a GPU is used (provided there is one).""" + """Control whether a heartbeat is used for distributed optuna runs.""" return self._rdb_storage_heartbeat @rdb_storage_heartbeat.setter From 73dd0df4e2c60aad9577da82aa9a3208a8f8ec0a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 2 Jun 2021 16:45:30 +0200 Subject: [PATCH 065/297] Fixed up some typos and added two missing "is True" statements --- examples/ex07_checkpoint_training.py | 2 +- examples/ex08_checkpoint_hyperopt.py | 2 +- mala/network/hyper_opt_optuna.py | 2 +- mala/network/trainer.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/ex07_checkpoint_training.py b/examples/ex07_checkpoint_training.py index 10dd8e007..a76b715e4 100644 --- a/examples/ex07_checkpoint_training.py +++ b/examples/ex07_checkpoint_training.py @@ -7,7 +7,7 @@ ex07_checkpoint_training.py: Shows how a training run can be paused and resumed. Delete all ex07_*.pkl and ex07_*pth prior to execution. Afterwards, execute this script twice to see how MALA progresses from a -checkpoint. As the number of total epochs is cannot divided by the number +checkpoint. As the number of total epochs cannot be divided by the number of epochs after which a checkpoint is created without residual, this will lead to MALA performing the missing epochs again. """ diff --git a/examples/ex08_checkpoint_hyperopt.py b/examples/ex08_checkpoint_hyperopt.py index 1b4614f32..11e89a1a6 100644 --- a/examples/ex08_checkpoint_hyperopt.py +++ b/examples/ex08_checkpoint_hyperopt.py @@ -7,7 +7,7 @@ ex08_checkpoint_hyperopt.py: Shows how a hyperparameter optimization run can be paused and resumed. Delete all ex08_*.pkl and ex08_*.pth prior to execution. Afterwards, execute this script twice to see how MALA progresses from a -checkpoint. As the number of trials is cannot divided by the number +checkpoint. As the number of trials cannot be divided by the number of epochs after which a checkpoint is created without residual, this will lead to MALA performing the missing trials again. """ diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 5a5d42d76..da9ba2142 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -127,7 +127,7 @@ def checkpoint_exists(cls, checkpoint_name): if os.path.isfile(iscaler_name) is True \ and os.path.isfile(oscaler_name) is True \ - and os.path.isfile(param_name): + and os.path.isfile(param_name) is True: return True else: return False diff --git a/mala/network/trainer.py b/mala/network/trainer.py index c581cad61..8cfb5f3fb 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -80,7 +80,7 @@ def checkpoint_exists(cls, checkpoint_name): and os.path.isfile(oscaler_name) is True \ and os.path.isfile(param_name) is True \ and os.path.isfile(network_name) is True \ - and os.path.isfile(optimizer_name): + and os.path.isfile(optimizer_name) is True: return True else: return False From 1fda105ff903e3a47b058fe6cc0f10082e1a17d8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 2 Jun 2021 17:13:10 +0200 Subject: [PATCH 066/297] Updated test criterion for hyperopt example (09) --- examples/ex09_distributed_hyperopt.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index d1bf80ba1..b0f036df6 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -1,3 +1,4 @@ +import numpy as np import mala from mala import printout from data_repo_path import get_data_repo_path @@ -14,7 +15,7 @@ """ -def run_example09(desired_loss_improvement_factor=1): +def run_example09(desired_loss_improvement_factor=2): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -116,8 +117,13 @@ def run_example09(desired_loss_improvement_factor=1): printout("Parameters used for this experiment:") test_parameters.show() - if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ - test_trainer.final_test_loss: + # To see if the hyperparameter optimization actually worked, + # check if the best trial is better then the worst trial + # by a certain factor. + performed_trials_values = test_hp_optimizer.study.\ + trials_dataframe()["value"] + if desired_loss_improvement_factor*min(performed_trials_values) > \ + max(performed_trials_values): return False else: return True From 02fa4a79ea336f0e1603c1c6039c95652aa33672 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 4 Jun 2021 11:32:55 +0200 Subject: [PATCH 067/297] Improved the check checkpoint_exists() functions in Trainer and HyperOptOptuna and synced ex04 with ex09 --- examples/ex04_hyperparameter_optimization.py | 11 ++++++++--- mala/network/hyper_opt_optuna.py | 4 +--- mala/network/trainer.py | 7 ++----- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 1263fbe1c..8d678fbd4 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -11,7 +11,7 @@ """ -def run_example04(desired_loss_improvement_factor=1): +def run_example04(desired_loss_improvement_factor=2): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -111,8 +111,13 @@ def run_example04(desired_loss_improvement_factor=1): printout("Parameters used for this experiment:") test_parameters.show() - if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ - test_trainer.final_test_loss: + # To see if the hyperparameter optimization actually worked, + # check if the best trial is better then the worst trial + # by a certain factor. + performed_trials_values = test_hp_optimizer.study.\ + trials_dataframe()["value"] + if desired_loss_improvement_factor*min(performed_trials_values) > \ + max(performed_trials_values): return False else: return True diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index da9ba2142..5918598d0 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -125,9 +125,7 @@ def checkpoint_exists(cls, checkpoint_name): oscaler_name = checkpoint_name + "_oscaler.pkl" param_name = checkpoint_name + "_params.pkl" - if os.path.isfile(iscaler_name) is True \ - and os.path.isfile(oscaler_name) is True \ - and os.path.isfile(param_name) is True: + if all(map(os.path.isfile, [iscaler_name, oscaler_name, param_name])): return True else: return False diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 8cfb5f3fb..d6e2e9d47 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -76,11 +76,8 @@ def checkpoint_exists(cls, checkpoint_name): param_name = checkpoint_name + "_params.pkl" optimizer_name = checkpoint_name + "_optimizer.pth" - if os.path.isfile(iscaler_name) is True \ - and os.path.isfile(oscaler_name) is True \ - and os.path.isfile(param_name) is True \ - and os.path.isfile(network_name) is True \ - and os.path.isfile(optimizer_name) is True: + if all(map(os.path.isfile, [iscaler_name, oscaler_name, param_name, + network_name, optimizer_name])): return True else: return False From 1febcab728330d532b7634bc0cbc5e62cbbbc14a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 4 Jun 2021 12:48:06 +0200 Subject: [PATCH 068/297] Simplified checkpoint_exist() functions --- mala/network/hyper_opt_optuna.py | 6 ++---- mala/network/trainer.py | 7 ++----- 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 5918598d0..4b38085a6 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -125,10 +125,8 @@ def checkpoint_exists(cls, checkpoint_name): oscaler_name = checkpoint_name + "_oscaler.pkl" param_name = checkpoint_name + "_params.pkl" - if all(map(os.path.isfile, [iscaler_name, oscaler_name, param_name])): - return True - else: - return False + return all(map(os.path.isfile, [iscaler_name, oscaler_name, + param_name])) @classmethod def resume_checkpoint(cls, checkpoint_name): diff --git a/mala/network/trainer.py b/mala/network/trainer.py index d6e2e9d47..ae3fd371d 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -76,11 +76,8 @@ def checkpoint_exists(cls, checkpoint_name): param_name = checkpoint_name + "_params.pkl" optimizer_name = checkpoint_name + "_optimizer.pth" - if all(map(os.path.isfile, [iscaler_name, oscaler_name, param_name, - network_name, optimizer_name])): - return True - else: - return False + return all(map(os.path.isfile, [iscaler_name, oscaler_name, param_name, + network_name, optimizer_name])) @classmethod def resume_checkpoint(cls, checkpoint_name): From f72d40fb0763bbc145b6f2b0799782115288cb98 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 4 Jun 2021 18:17:53 +0200 Subject: [PATCH 069/297] Add `sql_on_hpc.rst` to `index.md`: This reapplies the change from db06bf0014a402d31d39533f286cac53faf9e1a7 but now for `index.md` rather than `index.rst`. --- docs/source/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/index.md b/docs/source/index.md index 3d2f6444d..5a647beec 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -27,6 +27,7 @@ install/README install/INSTALL_LAMMPS install/INSTALL_TE_QE install/tested_systems +install/sql_on_hpc ``` ```{toctree} From 191aade09eeb8306bfc2e3d4965580b03eed862c Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 4 Jun 2021 20:48:42 +0200 Subject: [PATCH 070/297] Update data repo link --- docs/source/install/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 0d50fc74d..732390fd8 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -148,7 +148,7 @@ $ pip install -r docs/requirements.txt ## Downloading and adding example data The examples and tests need additional data to run. The MALA team provides a data repository, that can be downloaded -from . After downloading it, the data repository +from . After downloading it, the data repository needs to be linked to the MALA repository by using ```sh $ cd ~/path/to/this/git/root/directory From 1d761a5df98ee7e775545961c53108cd006b614a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 10:31:22 +0200 Subject: [PATCH 071/297] Incorporated changes from Siva (License, copyright, deletion of .f90 files) --- Copyright.txt | 18 + LICENSE | 26 + README.md | 35 +- docs/source/CONTRIBUTE.md | 26 +- install/total_energy_module/local_dos.f90 | 444 ----------- install/total_energy_module/total_energy.f90 | 712 ------------------ ml-dft-sandia/Contributing.md | 15 + ml-dft-sandia/Copyright.txt | 18 + ml-dft-sandia/LICENSE | 26 + ml-dft-sandia/README.md | 49 +- ml-dft-sandia/networks/README.md | 2 +- .../total_energy_module/total_energy.f90 | 712 ------------------ ml-dft-sandia/transfer_data.sh | 7 - 13 files changed, 198 insertions(+), 1892 deletions(-) create mode 100644 Copyright.txt create mode 100644 LICENSE delete mode 100644 install/total_energy_module/local_dos.f90 delete mode 100644 install/total_energy_module/total_energy.f90 create mode 100644 ml-dft-sandia/Contributing.md create mode 100644 ml-dft-sandia/Copyright.txt create mode 100644 ml-dft-sandia/LICENSE delete mode 100644 ml-dft-sandia/total_energy_module/total_energy.f90 delete mode 100644 ml-dft-sandia/transfer_data.sh diff --git a/Copyright.txt b/Copyright.txt new file mode 100644 index 000000000..131c1b322 --- /dev/null +++ b/Copyright.txt @@ -0,0 +1,18 @@ + ************************************************************************ + + MALA v. 0.1.0 + + Under the terms of Contract DE-NA0003525 with NTESS, + the U.S. Government retains certain rights in this software. + + Questions? Contact Attila Cangi (a.cangi@hzdr.de) + Siva Rajamanickam (srajama@sandia.gov) + + ************************************************************************ + +Copyright 2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. + +Copyright 2021, (in alphabetical order) Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson + +All rights reserved. + diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..3879237a0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,26 @@ +BSD 3-Clause License + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 47e91fdf5..528430ec3 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,14 @@ [![image](https://github.com/mala-project/mala/actions/workflows/gh-pages.yml/badge.svg)](https://mala-project.github.io/mala/) -MALA tools (Materials Analysis and Learning) is a machine-learning based -framework to enable multiscale modeling by bypassing computationally -expensive density functional simulations. It is designed as a python -package. This repository is structured as follows: +[![image](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) + +MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations. + +MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL), and the Center for Advanced Systems Understanding (CASUS). See Contributing.md for contributing code to the repository. + +This repository is structured as follows: ``` ├── examples : contains useful examples to get you started with the package ├── install : contains scripts for setting up this package on your machine @@ -26,6 +29,30 @@ Please refer to [Installation of MALA](docs/source/install/README.md). You can familiarize yourself with the usage of this package by running the examples in the `example/` folder. +## Institutions +### Founding Institutions + +- Sandia National Laboratories (SNL), USA. +- Center for Advanced Systems Understandingi (CASUS), Germany. + +## Developers +### Scientific Supervision +- Attila Cangi (CASUS) +- Siva Rajamanickam (SNL) + +### Core Developers +- Lenz Fiedler (CASUS) +- Austin Ellisi (ORNL*) +- Normand Modine (SNL) +- Steve Schmerler (CASUS) +- Daniel Kotik (CASUS) +- Gabriel Popoola (SNL) +- Aidan Thompson (SNL) +- Adam Stephens (SNL) + +* - Work done as part of postdoctoral research at Sandia National Laboratories + + ## Citing MALA If you publish work which uses or mentions MALA, please cite the following paper: diff --git a/docs/source/CONTRIBUTE.md b/docs/source/CONTRIBUTE.md index d7bccecaa..0693f5556 100644 --- a/docs/source/CONTRIBUTE.md +++ b/docs/source/CONTRIBUTE.md @@ -23,10 +23,9 @@ the form `MAJOR.MINOR.FIX`: ## Branching strategy In general, contributors should develop on branches based off of `develop` and -merge requests should be to `develop`. Please choose a descriptive branch name, -ideally incorporating some identifying information (such as your initials) -or the starting date of your developments. Branches from `develop` to `master` -will be done after prior consultation of the core development team. +merge requests should be to `develop`. Please choose a descriptive branch name. +Branches from `develop` to `master` will be done after prior consultation of +the core development team. ## Developing code @@ -36,16 +35,21 @@ will be done after prior consultation of the core development team. [NumPy style](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard) for docstrings * Keep your code object-oriented, modular, and easily reusable -* Add tests and examples for sizable new features +* If you're adding code that should be tested, add tests -## Submitting a merge request - -* Ensure that you followed the guidelines for [developing code](#developing-code) -* Rebase your branch onto `develop` before submitting a merge request -* Only assign yourself to a merge request when time does not permit an - external check +## Pull Requests +We actively welcome pull requests. +1. Fork the repo and create your branch from `develop` +2. During development, make sure that you follow the guidelines for [developing code](#developing-code) +3. Rebase your branch onto `develop` before submitting a merge request +4. Ensure the test suite passes before submitting a pull request ## Issues * Use issues to document potential enhancements, bugs and such * Please tag your issues, and consider setting up a deadline +* Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue + +## License +By contributing to MALA, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree. + diff --git a/install/total_energy_module/local_dos.f90 b/install/total_energy_module/local_dos.f90 deleted file mode 100644 index e25f62780..000000000 --- a/install/total_energy_module/local_dos.f90 +++ /dev/null @@ -1,444 +0,0 @@ -! -! Copyright (C) 2001-2007 Quantum ESPRESSO group -! This file is distributed under the terms of the -! GNU General Public License. See the file `License' -! in the root directory of the present distribution, -! or http://www.gnu.org/copyleft/gpl.txt . -! Modifications by Normand Modine. -! -!-------------------------------------------------------------------- -SUBROUTINE local_dos (iflag, lsign, kpoint, kband, spin_component, & - emin, emax, dos) - !-------------------------------------------------------------------- - ! - ! iflag=0: calculates |psi|^2 for band "kband" at point "kpoint" - ! iflag=1: calculates the local density of state at e_fermi - ! (only for metals) - ! iflag=2: calculates the local density of electronic entropy - ! (only for metals with fermi spreading) - ! iflag=3: calculates the integral of local dos from "emin" to "emax" - ! (emin, emax in Ry) - ! - ! lsign: if true and k=gamma and iflag=0, write |psi|^2 * sign(psi) - ! spin_component: for iflag=3 and LSDA calculations only - ! 0 for up+down dos, 1 for up dos, 2 for down dos - ! - USE io_global, ONLY : stdout, ionode ! Normand Modine Hack - USE constants, ONLY : rytoev ! Normand Modine Hack - USE kinds, ONLY : DP - USE cell_base, ONLY : omega - USE ions_base, ONLY : nat, ntyp => nsp, ityp - USE ener, ONLY : ef - USE fft_base, ONLY : dffts, dfftp - USE fft_interfaces, ONLY : fwfft, invfft, fft_interpolate - USE gvect, ONLY : ngm, g - USE gvecs, ONLY : doublegrid - USE klist, ONLY : lgauss, degauss, ngauss, nks, wk, xk, & - nkstot, ngk, igk_k - USE lsda_mod, ONLY : lsda, nspin, current_spin, isk - USE scf, ONLY : rho - USE symme, ONLY : sym_rho, sym_rho_init, sym_rho_deallocate - USE uspp, ONLY : nkb, vkb, becsum, nhtol, nhtoj, indv - USE uspp_param, ONLY : upf, nh, nhm - USE wavefunctions, ONLY : evc, psic, psic_nc - USE wvfct, ONLY : nbnd, npwx, wg, et - USE control_flags, ONLY : gamma_only - USE noncollin_module, ONLY : noncolin, npol - USE spin_orb, ONLY : lspinorb, fcoef - USE io_files, ONLY : iunwfc, nwordwfc - USE mp_pools, ONLY : me_pool, nproc_pool, my_pool_id, npool, & - inter_pool_comm, intra_pool_comm - USE mp, ONLY : mp_bcast, mp_sum - USE becmod, ONLY : calbec - IMPLICIT NONE - ! - ! input variables - ! - INTEGER, INTENT(in) :: iflag, kpoint, kband, spin_component - LOGICAL, INTENT(in) :: lsign - REAL(DP), INTENT(in) :: emin, emax - ! - REAL(DP), INTENT(out) :: dos (dfftp%nnr) - ! - ! local variables - ! - ! counters for US PPs - INTEGER :: npw, ikb, jkb, ijkb0, ih, jh, kh, na, ijh, np - ! counters - INTEGER :: ir, is, ig, ibnd, ik, irm, isup, isdw, ipol, kkb, is1, is2 - - REAL(DP) :: w, w1, modulus, wg_max - REAL(DP), ALLOCATABLE :: rbecp(:,:), segno(:), maxmod(:) - COMPLEX(DP), ALLOCATABLE :: becp(:,:), & - becp_nc(:,:,:), be1(:,:), be2(:,:) - INTEGER :: who_calculate, iproc - COMPLEX(DP) :: phase - REAL(DP), EXTERNAL :: w0gauss, w1gauss - INTEGER :: kpoint_pool - INTEGER, EXTERNAL :: local_kpoint_index - ! - ! input checks - ! - IF (noncolin.and. lsign) CALL errore('local_dos','not available',1) - IF (noncolin.and. gamma_only) CALL errore('local_dos','not available',2) - ! - IF ( iflag == 0 ) THEN - IF ( kband < 1 .or. kband > nbnd ) & - CALL errore ('local_dos', 'wrong band specified', 1) - IF ( kpoint < 1 .or. kpoint > nkstot ) & - CALL errore ('local_dos', 'wrong kpoint specified', 1) - IF ( (sqrt(xk(1,kpoint)**2+xk(2,kpoint)**2+xk(3,kpoint)**2) > 1d-9 ) & - .AND. lsign ) CALL errore ('local_dos', 'k must be zero', 1) - ELSE - IF (lsign) CALL errore ('local_dos', 'inconsistent flags', 1) - ENDIF - ! - IF (gamma_only) THEN - ALLOCATE (rbecp(nkb,nbnd)) - ELSE - IF (noncolin) THEN - ALLOCATE (becp_nc(nkb,npol,nbnd)) - IF (lspinorb) THEN - ALLOCATE(be1(nhm,2)) - ALLOCATE(be2(nhm,2)) - ENDIF - ELSE - ALLOCATE (becp(nkb,nbnd)) - ENDIF - ENDIF - rho%of_r(:,:) = 0.d0 - dos(:) = 0.d0 - becsum(:,:,:) = 0.d0 - IF (lsign) ALLOCATE(segno(dfftp%nnr)) - ! - ! calculate the correct weights - ! - IF (iflag /= 0.and. iflag /=3 .and. .not.lgauss) CALL errore ('local_dos', & - 'gaussian broadening needed', 1) - IF (iflag == 2 .and. ngauss /= -99) CALL errore ('local_dos', & - ' beware: not using Fermi-Dirac function ', - ngauss) - IF ( ionode ) THEN - WRITE( stdout,'(/5x,"Gaussian broadening: ngauss,degauss=",i4,f12.6/)') & - ngauss, emax * rytoev - ENDIF - ngauss = 0 - IF ( ionode ) THEN - WRITE( stdout,'(/5x, "Code hacked by Normand Modine to force ngauss =", i4/)') ngauss - ENDIF - - DO ik = 1, nks - DO ibnd = 1, nbnd - IF (iflag == 0) THEN - wg (ibnd, ik) = 0.d0 - ELSEIF (iflag == 1) THEN - ! Local density of states at energy emin with broadening emax - wg(ibnd,ik) = wk(ik) * w0gauss((emin - et(ibnd, ik))/emax, ngauss) / emax - ELSEIF (iflag == 2) THEN - wg (ibnd, ik) = - wk (ik) * w1gauss ( (ef - et (ibnd, ik) ) & - / degauss, ngauss) - ELSEIF (iflag == 3) THEN - IF (et (ibnd, ik) <= emax .and. et (ibnd, ik) >= emin) THEN - wg (ibnd, ik) = wk (ik) - ELSE - wg (ibnd, ik) = 0.d0 - ENDIF - ELSE - CALL errore ('local_dos', ' iflag not allowed', abs (iflag) ) - ENDIF - ENDDO - ENDDO - wg_max = MAXVAL(wg(:,:)) - - IF ( iflag == 0 ) THEN - ! returns -1 if kpoint is not on this pool - kpoint_pool = local_kpoint_index ( nkstot, kpoint ) - IF ( kpoint_pool > 0) wg (kband, kpoint_pool) = 1.d0 - ENDIF - ! - ! here we sum for each k point the contribution - ! of the wavefunctions to the density of states - ! - DO ik = 1, nks - IF ( iflag /= 0 .or. ik == kpoint_pool) THEN - IF (lsda) current_spin = isk (ik) - CALL davcio (evc, 2*nwordwfc, iunwfc, ik, - 1) - npw = ngk(ik) - CALL init_us_2 (npw, igk_k(1,ik), xk (1, ik), vkb) - - IF (gamma_only) THEN - CALL calbec ( npw, vkb, evc, rbecp ) - ELSEIF (noncolin) THEN - CALL calbec ( npw, vkb, evc, becp_nc ) - ELSE - CALL calbec ( npw, vkb, evc, becp ) - ENDIF - ! - ! here we compute the density of states - ! - DO ibnd = 1, nbnd - ! Neglect summands with relative weights below machine epsilon - IF ( wg(ibnd, ik) > epsilon(0.0_DP) * wg_max .and. & - (ibnd == kband .or. iflag /= 0)) THEN - IF (noncolin) THEN - psic_nc = (0.d0,0.d0) - DO ig = 1, npw - psic_nc(dffts%nl(igk_k(ig,ik)),1)=evc(ig ,ibnd) - psic_nc(dffts%nl(igk_k(ig,ik)),2)=evc(ig+npwx,ibnd) - ENDDO - DO ipol=1,npol - CALL invfft ('Wave', psic_nc(:,ipol), dffts) - ENDDO - ELSE - psic(1:dffts%nnr) = (0.d0,0.d0) - DO ig = 1, npw - psic (dffts%nl (igk_k(ig,ik) ) ) = evc (ig, ibnd) - ENDDO - IF (gamma_only) THEN - DO ig = 1, npw - psic (dffts%nlm(igk_k (ig,ik) ) ) = conjg(evc (ig, ibnd)) - ENDDO - ENDIF - CALL invfft ('Wave', psic, dffts) - ENDIF - w1 = wg (ibnd, ik) / omega -! -! Compute and save the sign of the wavefunction at the gamma point -! - IF (lsign) THEN - IF (gamma_only) THEN - ! psi(r) is real by construction - segno(1:dffts%nnr) = dble(psic(1:dffts%nnr)) - ELSE - ! determine the phase factor that makes psi(r) real. - ALLOCATE(maxmod(nproc_pool)) - maxmod(me_pool+1)=0.0_DP - DO ir = 1, dffts%nnr - modulus=abs(psic(ir)) - IF (modulus > maxmod(me_pool+1)) THEN - irm=ir - maxmod(me_pool+1)=modulus - ENDIF - ENDDO - who_calculate=1 -#if defined(__MPI) - CALL mp_sum(maxmod,intra_pool_comm) - DO iproc=2,nproc_pool - IF (maxmod(iproc)>maxmod(who_calculate)) & - who_calculate=iproc - ENDDO -#endif - IF (maxmod(who_calculate) < 1.d-10) & - CALL errore('local_dos','zero wavefunction',1) - IF (me_pool+1==who_calculate) & - phase = psic(irm)/maxmod(who_calculate) - DEALLOCATE(maxmod) -#if defined(__MPI) - CALL mp_bcast(phase,who_calculate-1,intra_pool_comm) -#endif - segno(1:dffts%nnr) = dble( psic(1:dffts%nnr)*conjg(phase) ) - ENDIF - IF (doublegrid) CALL fft_interpolate (dffts, segno, dfftp, segno) - segno(:) = sign( 1.d0, segno(:) ) - ENDIF - ! - IF (noncolin) THEN - DO ipol=1,npol - DO ir=1,dffts%nnr - rho%of_r(ir,current_spin)=rho%of_r(ir,current_spin)+& - w1*(dble(psic_nc(ir,ipol))**2+ & - aimag(psic_nc(ir,ipol))**2) - ENDDO - ENDDO - ELSE - DO ir=1,dffts%nnr - rho%of_r(ir,current_spin)=rho%of_r(ir,current_spin) + & - w1 * (dble( psic (ir) ) **2 + aimag (psic (ir) ) **2) - ENDDO - ENDIF - ! - ! If we have a US pseudopotential we compute here the becsum term - ! - w1 = wg (ibnd, ik) - ijkb0 = 0 - DO np = 1, ntyp - IF (upf(np)%tvanp ) THEN - DO na = 1, nat - IF (ityp (na) == np) THEN - IF (noncolin) THEN - IF (upf(np)%has_so) THEN - be1=(0.d0,0.d0) - be2=(0.d0,0.d0) - DO ih = 1, nh(np) - ikb = ijkb0 + ih - DO kh = 1, nh(np) - IF ((nhtol(kh,np)==nhtol(ih,np)).and. & - (nhtoj(kh,np)==nhtoj(ih,np)).and. & - (indv(kh,np)==indv(ih,np))) THEN - kkb=ijkb0 + kh - DO is1=1,2 - DO is2=1,2 - be1(ih,is1)=be1(ih,is1)+ & - fcoef(ih,kh,is1,is2,np)* & - becp_nc(kkb,is2,ibnd) - be2(ih,is1)=be2(ih,is1)+ & - fcoef(kh,ih,is2,is1,np)* & - conjg(becp_nc(kkb,is2,ibnd)) - ENDDO - ENDDO - ENDIF - ENDDO - ENDDO - ENDIF - ijh = 1 - DO ih = 1, nh (np) - ikb = ijkb0 + ih - IF (upf(np)%has_so) THEN - becsum(ijh,na,1)=becsum(ijh,na,1)+ w1* & - (be1(ih,1)*be2(ih,1)+be1(ih,2)*be2(ih,2)) - ELSE - becsum(ijh,na,1) = becsum(ijh,na,1)+ & - w1*(conjg(becp_nc(ikb,1,ibnd))* & - becp_nc(ikb,1,ibnd)+ & - conjg(becp_nc(ikb,2,ibnd))* & - becp_nc(ikb,2,ibnd)) - ENDIF - ijh = ijh + 1 - DO jh = ih + 1, nh (np) - jkb = ijkb0 + jh - IF (upf(np)%has_so) THEN - becsum(ijh,na,1)=becsum(ijh,na,1) & - + w1*((be1(jh,1)*be2(ih,1)+ & - be1(jh,2)*be2(ih,2))+ & - (be1(ih,1)*be2(jh,1)+ & - be1(ih,2)*be2(jh,2)) ) - ELSE - becsum(ijh,na,1)= becsum(ijh,na,1)+ & - w1*2.d0*dble(conjg(becp_nc(ikb,1,ibnd)) & - *becp_nc(jkb,1,ibnd) + & - conjg(becp_nc(ikb,2,ibnd)) & - *becp_nc(jkb,2,ibnd) ) - ENDIF - ijh = ijh + 1 - ENDDO - ENDDO - ELSE - ijh = 1 - DO ih = 1, nh (np) - ikb = ijkb0 + ih - IF (gamma_only) THEN - becsum(ijh,na,current_spin) = & - becsum(ijh,na,current_spin) + w1 * & - rbecp(ikb,ibnd)*rbecp(ikb,ibnd) - ELSE - becsum(ijh,na,current_spin) = & - becsum(ijh,na,current_spin) + w1 * & - dble(conjg(becp(ikb,ibnd))*becp(ikb,ibnd)) - ENDIF - ijh = ijh + 1 - DO jh = ih + 1, nh (np) - jkb = ijkb0 + jh - IF (gamma_only) THEN - becsum(ijh,na,current_spin) = & - becsum(ijh,na,current_spin) + 2.d0*w1 * & - rbecp(ikb,ibnd)*rbecp(jkb,ibnd) - ELSE - becsum(ijh,na,current_spin) = & - becsum(ijh,na,current_spin) + 2.d0*w1 * & - dble(conjg(becp(ikb,ibnd))*becp(jkb,ibnd)) - ENDIF - ijh = ijh + 1 - ENDDO - ENDDO - ENDIF - ijkb0 = ijkb0 + nh (np) - ENDIF - ENDDO - ELSE - DO na = 1, nat - IF (ityp (na) == np) ijkb0 = ijkb0 + nh (np) - ENDDO - ENDIF - ENDDO - ENDIF - ENDDO ! loop over bands - ENDIF - ENDDO ! loop over k-points - - IF (gamma_only) THEN - DEALLOCATE(rbecp) - ELSE - IF (noncolin) THEN - IF (lspinorb) THEN - DEALLOCATE(be1) - DEALLOCATE(be2) - ENDIF - DEALLOCATE(becp_nc) - ELSE - DEALLOCATE(becp) - ENDIF - ENDIF - ! - ! ... bring rho(r) to G-space (use psic as work array) - ! - DO is = 1, nspin - psic(1:dffts%nnr) = rho%of_r(1:dffts%nnr,is) - psic(dffts%nnr+1:) = 0.0_dp - CALL fwfft ('Rho', psic, dffts) - rho%of_g(1:dffts%ngm,is) = psic(dffts%nl(1:dffts%ngm)) - rho%of_g(dffts%ngm+1:,is) = (0.0_dp,0.0_dp) - END DO - ! - ! Here we add the US contribution to the charge - ! - CALL addusdens(rho%of_g(:,:)) - ! - ! Now select the desired component, bring it to real space - ! - psic(:) = (0.0_dp, 0.0_dp) - IF (nspin == 1 .or. nspin==4) THEN - is = 1 - psic(dfftp%nl(:)) = rho%of_g (:, is) - ELSE - IF ( iflag==3 .and. (spin_component==1 .or. spin_component==2 ) ) THEN - psic(dfftp%nl(:)) = rho%of_g (:, spin_component) - ELSE - isup = 1 - isdw = 2 - psic(dfftp%nl(:)) = rho%of_g (:, isup) + rho%of_g (:, isdw) - ENDIF - ENDIF - ! - CALL invfft ('Rho', psic, dfftp) - ! - dos(:) = DBLE ( psic(:) ) - ! - IF (lsign) THEN - dos(:) = dos(:) * segno(:) - DEALLOCATE(segno) - ENDIF -#if defined(__MPI) - CALL mp_sum( dos, inter_pool_comm ) -#endif - ! - IF (iflag == 0 .or. gamma_only) RETURN - ! - ! symmetrization of the local dos - ! - CALL sym_rho_init (gamma_only ) - ! - psic(:) = cmplx ( dos(:), 0.0_dp, kind=dp) - CALL fwfft ('Rho', psic, dfftp) - rho%of_g(:,1) = psic(dfftp%nl(:)) - ! - CALL sym_rho (1, rho%of_g) - ! - psic(:) = (0.0_dp, 0.0_dp) - psic(dfftp%nl(:)) = rho%of_g(:,1) - CALL invfft ('Rho', psic, dfftp) - dos(:) = dble(psic(:)) - ! - CALL sym_rho_deallocate() - ! - RETURN - -END SUBROUTINE local_dos diff --git a/install/total_energy_module/total_energy.f90 b/install/total_energy_module/total_energy.f90 deleted file mode 100644 index eb77d8221..000000000 --- a/install/total_energy_module/total_energy.f90 +++ /dev/null @@ -1,712 +0,0 @@ -SUBROUTINE initialize() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE environment, ONLY : environment_start - USE mp_global, ONLY : mp_startup - USE mp_world, ONLY : world_comm - USE mp_pools, ONLY : intra_pool_comm - USE mp_bands, ONLY : intra_bgrp_comm, inter_bgrp_comm - USE mp_diag, ONLY : mp_start_diag - USE mp_exx, ONLY : negrp - USE read_input, ONLY : read_input_file - USE command_line_options, ONLY: input_file_, command_line, ndiag_ - ! - IMPLICIT NONE - CHARACTER(len=256) :: srvaddress - !! Get the address of the server - CHARACTER(len=256) :: get_server_address - !! Get the address of the server - INTEGER :: exit_status - !! Status at exit - LOGICAL :: use_images, do_diag_in_band_group = .true. - !! true if running "manypw.x" - LOGICAL, external :: matches - !! checks if first string is contained in the second - ! - CALL mp_startup ( start_images=.true.) - ! - IF( negrp > 1 .OR. do_diag_in_band_group ) THEN - ! used to be the default : one diag group per bgrp - ! with strict hierarchy: POOL > BAND > DIAG - ! if using exx groups from mp_exx still use this diag method - CALL mp_start_diag ( ndiag_, world_comm, intra_bgrp_comm, & - do_distr_diag_inside_bgrp_ = .true. ) - ELSE - ! new default: one diag group per pool ( individual k-point level ) - ! with band group and diag group both being children of POOL comm - CALL mp_start_diag ( ndiag_, world_comm, intra_pool_comm, & - do_distr_diag_inside_bgrp_ = .false. ) - END IF - CALL set_mpi_comm_4_solvers( intra_pool_comm, intra_bgrp_comm, & - inter_bgrp_comm ) - ! - CALL environment_start ( 'PWSCF' ) - ! - CALL read_input_file ('PW', 'mala.pw.scf.in' ) - CALL run_pwscf_setup ( exit_status ) - - print *, "Setup completed" - - RETURN - -END SUBROUTINE initialize -! -SUBROUTINE run_pwscf_setup ( exit_status ) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE io_global, ONLY : stdout, ionode, ionode_id - USE parameters, ONLY : ntypx, npk, lmaxx - USE cell_base, ONLY : fix_volume, fix_area - USE control_flags, ONLY : conv_elec, gamma_only, ethr, lscf - USE control_flags, ONLY : conv_ions, istep, nstep, restart, lmd, lbfgs - USE command_line_options, ONLY : command_line - USE force_mod, ONLY : lforce, lstres, sigma, force - USE check_stop, ONLY : check_stop_init, check_stop_now - USE mp_images, ONLY : intra_image_comm - USE extrapolation, ONLY : update_file, update_pot - USE scf, ONLY : rho - USE lsda_mod, ONLY : nspin - USE fft_base, ONLY : dfftp - USE qmmm, ONLY : qmmm_initialization, qmmm_shutdown, & - qmmm_update_positions, qmmm_update_forces - USE qexsd_module, ONLY : qexsd_set_status - USE funct, ONLY : dft_is_hybrid, stop_exx - ! - IMPLICIT NONE - INTEGER, INTENT(OUT) :: exit_status - !! Gives the exit status at the end - LOGICAL, external :: matches - !! checks if first string is contained in the second - INTEGER :: idone - !! counter of electronic + ionic steps done in this run - INTEGER :: ions_status = 3 - !! ions_status = 3 not yet converged - !! ions_status = 2 converged, restart with nonzero magnetization - !! ions_status = 1 converged, final step with current cell needed - !! ions_status = 0 converged, exiting - ! - exit_status = 0 - IF ( ionode ) WRITE( unit = stdout, FMT = 9010 ) ntypx, npk, lmaxx - ! - IF (ionode) CALL plugin_arguments() - CALL plugin_arguments_bcast( ionode_id, intra_image_comm ) - ! - ! ... needs to come before iosys() so some input flags can be - ! overridden without needing to write PWscf specific code. - ! - CALL qmmm_initialization() - ! - ! ... convert to internal variables - ! - CALL iosys() - ! - ! ... If executable names is "dist.x", compute atomic distances, angles, - ! ... nearest neighbors, write them to file "dist.out", exit - ! - IF ( matches('dist.x',command_line) ) THEN - IF (ionode) CALL run_dist ( exit_status ) - RETURN - END IF - ! - IF ( gamma_only ) WRITE( UNIT = stdout, & - & FMT = '(/,5X,"gamma-point specific algorithms are used")' ) - ! - ! call to void routine for user defined / plugin patches initializations - ! - CALL plugin_initialization() - ! - CALL check_stop_init() - ! - CALL setup () - ! - CALL qmmm_update_positions() - ! - ! ... dry run: code will stop here if called with exit file present - ! ... useful for a quick and automated way to check input data - ! - IF ( check_stop_now() ) THEN - CALL pre_init() - CALL data_structure( gamma_only ) - CALL summary() - CALL memory_report() - CALL qexsd_set_status(255) - CALL punch( 'init-config' ) - exit_status = 255 - RETURN - ENDIF - ! - CALL init_run_setup() - ! - IF ( check_stop_now() ) THEN - CALL qexsd_set_status(255) - CALL punch( 'config' ) - exit_status = 255 - RETURN - ENDIF - ! - RETURN - ! -9010 FORMAT( /,5X,'Current dimensions of program PWSCF are:', & - & /,5X,'Max number of different atomic species (ntypx) = ',I2,& - & /,5X,'Max number of k-points (npk) = ',I6,& - & /,5X,'Max angular momentum in pseudopotentials (lmaxx) = ',i2) - ! -END SUBROUTINE run_pwscf_setup -! -SUBROUTINE init_run_setup() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - USE kinds, ONLY : DP - USE klist, ONLY : nkstot - USE symme, ONLY : sym_rho_init - USE wvfct, ONLY : nbnd, et, wg, btype - USE control_flags, ONLY : lmd, gamma_only, smallmem, ts_vdw - USE gvect, ONLY : g, gg, mill, gcutm, ig_l2g, ngm, ngm_g, & - gshells, gstart ! to be comunicated to the Solvers if gamma_only - USE gvecs, ONLY : gcutms, ngms - USE cell_base, ONLY : at, bg, set_h_ainv, alat, omega - USE ions_base, ONLY : zv, nat, nsp, ityp, tau - USE cellmd, ONLY : lmovecell - USE dynamics_module, ONLY : allocate_dyn_vars - USE paw_variables, ONLY : okpaw - USE paw_init, ONLY : paw_init_onecenter, allocate_paw_internals -#if defined(__MPI) - USE paw_init, ONLY : paw_post_init -#endif - USE bp, ONLY : allocate_bp_efield, bp_global_map - USE fft_base, ONLY : dfftp, dffts - USE funct, ONLY : dft_is_hybrid - USE recvec_subs, ONLY : ggen, ggens - USE wannier_new, ONLY : use_wannier - USE dfunct, ONLY : newd - USE esm, ONLY : do_comp_esm, esm_init - USE tsvdw_module, ONLY : tsvdw_initialize - USE Coul_cut_2D, ONLY : do_cutoff_2D, cutoff_fact - USE ener, ONLY : ewld - USE vlocal, ONLY : strf - - IMPLICIT NONE - - REAL(DP), EXTERNAL :: ewald - - - CALL start_clock( 'init_run' ) - ! - ! ... calculate limits of some indices, used in subsequent allocations - ! - CALL pre_init() - ! - ! ... determine the data structure for fft arrays - ! - CALL data_structure( gamma_only ) - ! - ! ... print a summary and a memory estimate before starting allocating - ! - CALL summary() - CALL memory_report() - ! - ! ... allocate memory for G- and R-space fft arrays - ! - CALL allocate_fft() - ! - ! ... generate reciprocal-lattice vectors and fft indices - ! - IF( smallmem ) THEN - CALL ggen( dfftp, gamma_only, at, bg, gcutm, ngm_g, ngm, & - g, gg, mill, ig_l2g, gstart, no_global_sort = .TRUE. ) - ELSE - CALL ggen( dfftp, gamma_only, at, bg, gcutm, ngm_g, ngm, & - g, gg, mill, ig_l2g, gstart ) - END IF - CALL ggens( dffts, gamma_only, at, g, gg, mill, gcutms, ngms ) - if (gamma_only) THEN - ! ... Solvers need to know gstart - call export_gstart_2_solvers(gstart) - END IF - ! - IF (do_comp_esm) CALL esm_init() - ! - ! ... setup the 2D cutoff factor - ! - IF (do_cutoff_2D) CALL cutoff_fact() - ! - CALL gshells ( lmovecell ) - ! - ! ... variable initialization for parallel symmetrization - ! - CALL sym_rho_init (gamma_only ) - ! - ! ... allocate memory for all other arrays (potentials, wavefunctions etc) - ! - CALL allocate_nlpot() - IF (okpaw) THEN - CALL allocate_paw_internals() - CALL paw_init_onecenter() - ENDIF - CALL allocate_locpot() - CALL allocate_bp_efield() - CALL bp_global_map() - ! - call plugin_initbase() - ! - ALLOCATE( et( nbnd, nkstot ) , wg( nbnd, nkstot ), btype( nbnd, nkstot ) ) - ! - et(:,:) = 0.D0 - wg(:,:) = 0.D0 - ! - btype(:,:) = 1 - ! - IF (ts_vdw) THEN - CALL tsvdw_initialize() - CALL set_h_ainv() - END IF - ! - CALL openfil() - ! - CALL hinit0() - ! - CALL potinit() - ! - CALL newd() - ! - IF(use_wannier) CALL wannier_init() - ! -#if defined(__MPI) - ! Cleanup PAW arrays that are only used for init - IF (okpaw) CALL paw_post_init() ! only parallel! -#endif - ! - IF ( lmd ) CALL allocate_dyn_vars() - ! - ewld = ewald( alat, nat, nsp, ityp, zv, at, bg, tau, & - omega, g, gg, ngm, gcutm, gstart, gamma_only, strf ) - ! - CALL stop_clock( 'init_run' ) - ! - RETURN - ! -END SUBROUTINE init_run_setup -! -SUBROUTINE print_energies() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE ener, ONLY : eband, deband, ehart, etxc, etxcc, ewld - USE io_global, ONLY: stdout, ionode - - IF ( ionode ) WRITE( unit = stdout, FMT = 9060 ) & - deband, ehart, etxc, ewld - ! - RETURN - ! -9060 FORMAT(/' rho * v_hxc contribution =',F17.8,' Ry' & - /' hartree contribution =',F17.8,' Ry' & - /' xc contribution =',F17.8,' Ry' & - /' ewald contribution =',F17.8,' Ry' ) - -END SUBROUTINE print_energies - -SUBROUTINE get_energies(e_rho_times_v_hxc,e_hartree,e_xc,e_ewald) - USE ener, ONLY : deband, ehart, etxc, etxcc, ewld - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - IMPLICIT NONE - DOUBLE PRECISION, INTENT(OUT) :: e_rho_times_v_hxc - DOUBLE PRECISION, INTENT(OUT) :: e_hartree - DOUBLE PRECISION, INTENT(OUT) :: e_xc - DOUBLE PRECISION, INTENT(OUT) :: e_ewald - - e_rho_times_v_hxc = deband - e_hartree = ehart - e_xc = etxc - e_ewald = ewld - ! - RETURN - ! -END SUBROUTINE get_energies - - -INTEGER FUNCTION get_nnr() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! Get the value of dfftp%nnr, which is an argument to x_xc_wrapper() - ! - USE fft_base, ONLY : dfftp - get_nnr = dfftp%nnr -END FUNCTION get_nnr - -INTEGER FUNCTION get_nspin() - ! Get the value of lsda_mod nspin, which is an argument to x_xc_wrapper() - USE lsda_mod, ONLY : nspin - get_nspin = nspin -END FUNCTION get_nspin - -INTEGER FUNCTION get_ngm() - ! Get the value of gvect ngm, which is an argument to x_xc_wrapper() - USE gvect, ONLY : ngm - get_ngm = ngm -END FUNCTION get_ngm - -INTEGER FUNCTION get_nat() - ! Get the number of atoms, which is an argument to set_positions() - USE ions_base, ONLY : nat - get_nat = nat -END FUNCTION get_nat - -SUBROUTINE get_positions(positions,nat_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - ! - ! f2py gets confused when DP is used to define the type of an argument - ! Instead, we use DOUBLE PRECISION for arguments - ! - !USE kinds, ONLY : DP - USE scf, ONLY : rho - USE ions_base, ONLY : nat, tau - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nat_in - DOUBLE PRECISION, INTENT(OUT) :: positions(3,nat_in) - - ! Check consistency of dimensions - IF (nat_in /= nat) STOP "*** nat provided to set_positions() does not match ions_base%nat" - - positions = tau - -END SUBROUTINE get_positions - - -SUBROUTINE get_rho_of_r(rho_of_r,nnr_in,nspin_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - ! f2py gets confused when DP is used to define the type of an argument - ! Instead, we use DOUBLE PRECISION for arguments - ! - !USE kinds, ONLY : DP - USE scf, ONLY : rho - USE fft_base, ONLY : dfftp - USE lsda_mod, ONLY : nspin - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nnr_in, nspin_in - DOUBLE PRECISION, INTENT(OUT) :: rho_of_r(nnr_in,nspin_in) - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to set_rho_of_r() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to set_rho_of_r() does not mach lsda_mod%nspin" - - - rho_of_r = rho%of_r - -END SUBROUTINE get_rho_of_r - -SUBROUTINE set_positions(positions,nat_in) - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! Note that this assumes that the positions are in "crystal coordinates", - ! i.e., that they are in units of the lattice vectors. - ! - USE kinds, ONLY : DP - USE ions_base, ONLY : nat, tau, nsp, ityp, zv - USE gvect, ONLY : ngm, g, gg, eigts1, eigts2, eigts3, gcutm, & - gstart - USE cell_base, ONLY : omega, bg, alat, at - USE fft_base, ONLY : dfftp - USE vlocal, ONLY : strf - USE control_flags, ONLY : treinit_gvecs, gamma_only - USE cellmd, ONLY : lmovecell - USE io_global, ONLY : ionode_id, ionode - USE mp_images, ONLY : intra_image_comm - USE symm_base, ONLY : checkallsym - USE mp, ONLY : mp_bcast - USE ener, ONLY : ewld - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nat_in - DOUBLE PRECISION, INTENT(IN) :: positions(3,nat_in) - - REAL(DP), EXTERNAL :: ewald - - ! Check consistency of dimensions - IF (nat_in /= nat) STOP "*** nat provided to set_positions() does not match ions_base%nat" - - ! - ! Update positions on one node - ! - IF ( ionode ) THEN - - tau = positions - - ! - ! Check that the symmetries have not changed. - ! - CALL checkallsym( nat, tau, ityp) - END IF - ! - ! Sync the positions across all processors - ! - CALL mp_bcast( tau, ionode_id, intra_image_comm ) - - ! - ! Now, update everything else that depends on the postions - ! - - ! - ! We set treinit_gvecs = .FALSE. to tell hinit1() to keep the same gvecs. - ! - treinit_gvecs = .FALSE. - ! - ! Also, set lmovecell = .FALSE. in case someone is using it. - ! - lmovecell = .FALSE. - ! - ! ... calculate structure factors for the new positions - ! - ! NAM: Something like the folling commands would be needed if we want change the - ! shape and/or size of the cell. I am not going to try to implement this now. - ! IF ( lmovecell ) rho%of_g(:,1) = rho%of_g(:,1) / omega - ! IF ( lmovecell ) CALL scale_h() - ! - CALL struc_fact( nat, tau, nsp, ityp, ngm, g, bg, & - dfftp%nr1, dfftp%nr2, dfftp%nr3, strf, eigts1, eigts2, eigts3 ) - ! - ! Update the core charge for the new positions. - ! - CALL set_rhoc() - ! - ! ... re-initialize atomic position-dependent quantities - ! - CALL hinit1() - ! - ! The ewald energy doesn't seem to be updated by any of the above, so I will - ! update it here. - ! - ewld = ewald( alat, nat, nsp, ityp, zv, at, bg, tau, & - omega, g, gg, ngm, gcutm, gstart, gamma_only, strf ) - - RETURN - -END SUBROUTINE set_positions - - -SUBROUTINE set_rho_of_r(rho_of_r,nnr_in,nspin_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE kinds, ONLY : DP - USE fft_base, ONLY : dfftp - USE scf, ONLY : rho, rho_core, rhog_core, vltot, v, vrs, kedtau - USE ener, ONLY : ehart, etxc, vtxc, epaw, deband - USE ldaU, ONLY : eth - USE fft_rho, ONLY : rho_r2g, rho_g2r - USE paw_variables, ONLY : okpaw, ddd_PAW - USE lsda_mod, ONLY : nspin - USE gvecs, ONLY : doublegrid - USE paw_onecenter, ONLY : PAW_potential - USE cell_base, ONLY : omega - USE mp, ONLY : mp_sum - USE mp_bands, ONLY : intra_bgrp_comm - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nnr_in, nspin_in - DOUBLE PRECISION, INTENT(IN) :: rho_of_r(nnr_in,nspin_in) - - REAL(DP) :: charge - REAL(DP) :: etotefield - INTEGER :: ir - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to set_rho_of_r() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to set_rho_of_r() does not mach lsda_mod%nspin" - - ! - ! This calculates the G-space density from the input density. - ! - CALL rho_r2g(dfftp, rho_of_r, rho%of_g) - - ! - ! The following results in a real-space density filtered at the plane-wave - ! density cutoff. This is needed to make it consistent with G-space density. - ! - CALL rho_g2r(dfftp, rho%of_g, rho%of_r) - - ! - ! ... plugin contribution to local potential - ! - CALL plugin_scf_potential(rho,.FALSE.,-1.d0,vltot) - - ! - ! ... compute the potential and store it in v - ! - CALL v_of_rho( rho, rho_core, rhog_core, & - ehart, etxc, vtxc, eth, etotefield, charge, v ) - IF (okpaw) CALL PAW_potential(rho%bec, ddd_PAW, epaw) - - ! - ! ... define the total local potential (external+scf) - ! - CALL set_vrs( vrs, vltot, v%of_r, kedtau, v%kin_r, dfftp%nnr, nspin, doublegrid ) - - ! - ! Compute: - ! ... deband = - \sum_v <\psi_v | V_h + V_xc |\psi_v> - ! Then: - ! ... eband + deband = \sum_v <\psi_v | T + Vion |\psi_v> - ! Note that the following is missing some terms from meta-GGA, LDA+U, and PAW - ! See delta_e() in electrons.f90 for the full set of terms. - deband = 0._dp - IF ( nspin==2 ) THEN - ! - DO ir = 1,dfftp%nnr - deband = deband - ( rho%of_r(ir,1) + rho%of_r(ir,2) ) * v%of_r(ir,1) & ! up - - ( rho%of_r(ir,1) - rho%of_r(ir,2) ) * v%of_r(ir,2) ! dw - ENDDO - deband = 0.5_dp*deband - ! - ELSE - deband = - SUM( rho%of_r(:,:)*v%of_r(:,:) ) - ENDIF - deband = omega * deband / ( dfftp%nr1*dfftp%nr2*dfftp%nr3 ) - ! - CALL mp_sum( deband, intra_bgrp_comm ) - ! - - RETURN - -END SUBROUTINE set_rho_of_r - - -SUBROUTINE v_xc_wrapper(rho_of_r, rho_of_g, rho_core, rhog_core,& - etxc, vtxc, v, nnr_in, nspin_in, ngm_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by J. Adam Stephens and Normand Modine, 2020 - ! - ! rho_of_r and rho_of_g will be copied into arrays rho%of_r and rho%of_g. - ! The arguments nnr_in, nspin_in, and ngm_in should be obtained by - ! calling the functions get_nnr(), get_nspin(), and get_ngm(). Due to - ! an apparent f2py limitation, it is not possible to declare the dimensions - ! of the input arrays using module variables (e.g. dfftp%nnr). Other arguments - ! are passed through unaltered to v_xc(). - USE gvect, ONLY : ngm - USE lsda_mod, ONLY : nspin - USE kinds, ONLY : DP - USE scf, ONLY : scf_type - USE fft_base, ONLY : dfftp - - IMPLICIT NONE - INTEGER, INTENT(IN) :: ngm_in, nnr_in, nspin_in - DOUBLE PRECISION, INTENT(IN) :: rho_of_r(nnr_in,nspin_in), rho_core(nnr_in) - DOUBLE COMPLEX, INTENT(IN) :: rho_of_g(ngm_in,nspin_in), rhog_core(ngm_in) - DOUBLE PRECISION, INTENT(OUT) :: v(nnr_in,nspin_in), vtxc, etxc - - ! local variables - INTEGER :: allocate_status - TYPE (scf_type) rho - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to v_xc_wrapper() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to v_xc_wrapper() does not mach lsda_mod%nspin" - IF (ngm_in /= ngm) STOP "*** ngm provided to v_xc_wrapper() does not match gvect%ngm" - ! Copy data to rho - ALLOCATE ( rho%of_r(nspin, nspin), STAT = allocate_status) - IF (allocate_status /= 0) STOP "*** Not enough memory ***" - ALLOCATE ( rho%of_g(ngm, nspin), STAT = allocate_status) - IF (allocate_status /= 0) STOP "*** Not enough memory ***" - rho%of_r = rho_of_r - rho%of_g = rho_of_g - - CALL v_xc( rho, rho_core, rhog_core, etxc, vtxc, v) - -END SUBROUTINE v_xc_wrapper - -SUBROUTINE v_h_wrapper(rhog, ehart, charge, v, nnr_in, nspin_in, ngm_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by J. Adam Stephens and Normand Modine, 2020 - ! - ! The arguments nnr_in, nspin_in, and ngm_in should be obtained by - ! calling the functions get_nnr(), get_nspin(), and get_ngm(). Due to - ! an apparent f2py limitation, it is not possible to declare the dimensions - ! of the input arrays using module variables (e.g. dfftp%nnr). Other arguments - ! are passed through unaltered to v_h(). - - USE gvect, ONLY : ngm - USE lsda_mod, ONLY : nspin - USE kinds, ONLY : DP - USE fft_base, ONLY : dfftp - - IMPLICIT NONE - - INTEGER, INTENT(IN) :: ngm_in, nnr_in, nspin_in - DOUBLE COMPLEX, INTENT(IN) :: rhog(ngm_in) - DOUBLE PRECISION, INTENT(INOUT) :: v(nnr_in,nspin_in) - DOUBLE PRECISION, INTENT(OUT) :: ehart, charge - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to v_h_wrapper() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to v_h_wrapper() does not mach lsda_mod%nspin" - IF (ngm_in /= ngm) STOP "*** ngm provided to v_h_wrapper() does not match gvect%ngm" - - CALL v_h(rhog, ehart, charge, v) - -END SUBROUTINE v_h_wrapper diff --git a/ml-dft-sandia/Contributing.md b/ml-dft-sandia/Contributing.md new file mode 100644 index 000000000..2b2a0d5ed --- /dev/null +++ b/ml-dft-sandia/Contributing.md @@ -0,0 +1,15 @@ +# Contributing to ML-DFT + +## Pull Requests +We actively welcome pull requests. +1. Fork the repo and create your branch from `develop`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue. + +## License +By contributing to ML-DFT, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree. + diff --git a/ml-dft-sandia/Copyright.txt b/ml-dft-sandia/Copyright.txt new file mode 100644 index 000000000..2e41583da --- /dev/null +++ b/ml-dft-sandia/Copyright.txt @@ -0,0 +1,18 @@ + ************************************************************************ + + ML-DFT v. 0.1 + + Under the terms of Contract DE-NA0003525 with NTESS, + the U.S. Government retains certain rights in this software. + + Questions? Contact Attila Cangi (a.cangi@hzdr.de) + Siva Rajamanickam (srajama@sandia.gov) + + ************************************************************************ + +Copyright 2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. + +Copyright 2021, (in alphabetical order) Attila Cangi, J. Austin Ellis, Normand Modine, Sivasankaran Rajamanickam, Aidan Thompson + +All rights reserved. + diff --git a/ml-dft-sandia/LICENSE b/ml-dft-sandia/LICENSE new file mode 100644 index 000000000..3879237a0 --- /dev/null +++ b/ml-dft-sandia/LICENSE @@ -0,0 +1,26 @@ +BSD 3-Clause License + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/ml-dft-sandia/README.md b/ml-dft-sandia/README.md index bbe12425a..624bfc165 100644 --- a/ml-dft-sandia/README.md +++ b/ml-dft-sandia/README.md @@ -1,3 +1,50 @@ -# ML-DFT-SANDIA +# ML-DFT This is the original code used in [1]. + +# ML-DFT + + +ML-DFT is a machine learning framework to that accelerated firt principles calculations (density functional theory) using novel algorithms. The framework is made available open sourcei for community engagement. See License and Copyright files for further information. + +ML-DFT is designed open-source python package with dependencies on pyTorch and Horovod. It enables users to perform the entire modeling toolchain using only a few lines of code. ML-DFT is jointly developed by the Sandia National Laboratories (SNL), and the Center for Advanced Systems Understanding (CASUS). +This repository is structured as follows: +``` +├── descriptors : contains code for SNAP descriptors +├── networks : contains the deep neural network models. See [Network README](networks/README.md) for more information +├── notenooks : contains python notebooks and post processing scripts +├── total_energy_module : Changes and steps needed to use Quantum Espresso to generate total energy. +``` + + +## Institutions +### Founding Institutions + +- Sandia National Laboratories (SNL), USA. +- Center for Advanced Systems Understandingi (CASUS), Germany. + +## Developers +### Scientific Supervision +- Attila Cangi (CASUS) +- Siva Rajamanickam (SNL) + +### Core Developers +- Attila Cangi (CASUS) +- Austin Ellis (ORNL*) +- Normand Modine (SNL) +- Gabriel Popoola (SNL) +- Siva Rajamanickam (SNL) +- Adam Stephens (SNL) +- Aidan Thompson (SNL) + +* - Work done as part of postdoctoral research at Sandia National Laboratories + + +## Citing ML-DFT + +If you publish work which uses or mentions MALA, please cite the following paper: + +J. A. Ellis, A. Cangi, N. A. Modine, J. A. Stephens, A. P. Thompson, +S. Rajamanickam (2020). Accelerating Finite-temperature +Kohn-Sham Density Functional Theory with Deep Neural Networks. +[arXiv:2010.04905](https://arxiv.org/abs/2010.04905). diff --git a/ml-dft-sandia/networks/README.md b/ml-dft-sandia/networks/README.md index 6cc8f827e..2dd998a54 100644 --- a/ml-dft-sandia/networks/README.md +++ b/ml-dft-sandia/networks/README.md @@ -1,4 +1,4 @@ -# MLMM Networks +# ML-DFT Networks ------------------------------------------------ Description: diff --git a/ml-dft-sandia/total_energy_module/total_energy.f90 b/ml-dft-sandia/total_energy_module/total_energy.f90 deleted file mode 100644 index 5d5b41d0f..000000000 --- a/ml-dft-sandia/total_energy_module/total_energy.f90 +++ /dev/null @@ -1,712 +0,0 @@ -SUBROUTINE initialize() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE environment, ONLY : environment_start - USE mp_global, ONLY : mp_startup - USE mp_world, ONLY : world_comm - USE mp_pools, ONLY : intra_pool_comm - USE mp_bands, ONLY : intra_bgrp_comm, inter_bgrp_comm - USE mp_diag, ONLY : mp_start_diag - USE mp_exx, ONLY : negrp - USE read_input, ONLY : read_input_file - USE command_line_options, ONLY: input_file_, command_line, ndiag_ - ! - IMPLICIT NONE - CHARACTER(len=256) :: srvaddress - !! Get the address of the server - CHARACTER(len=256) :: get_server_address - !! Get the address of the server - INTEGER :: exit_status - !! Status at exit - LOGICAL :: use_images, do_diag_in_band_group = .true. - !! true if running "manypw.x" - LOGICAL, external :: matches - !! checks if first string is contained in the second - ! - CALL mp_startup ( start_images=.true.) - ! - IF( negrp > 1 .OR. do_diag_in_band_group ) THEN - ! used to be the default : one diag group per bgrp - ! with strict hierarchy: POOL > BAND > DIAG - ! if using exx groups from mp_exx still use this diag method - CALL mp_start_diag ( ndiag_, world_comm, intra_bgrp_comm, & - do_distr_diag_inside_bgrp_ = .true. ) - ELSE - ! new default: one diag group per pool ( individual k-point level ) - ! with band group and diag group both being children of POOL comm - CALL mp_start_diag ( ndiag_, world_comm, intra_pool_comm, & - do_distr_diag_inside_bgrp_ = .false. ) - END IF - CALL set_mpi_comm_4_solvers( intra_pool_comm, intra_bgrp_comm, & - inter_bgrp_comm ) - ! - CALL environment_start ( 'PWSCF' ) - ! - CALL read_input_file ('PW', 'Al.scf.pw' ) - CALL run_pwscf_setup ( exit_status ) - - print *, "Setup completed" - - RETURN - -END SUBROUTINE initialize -! -SUBROUTINE run_pwscf_setup ( exit_status ) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE io_global, ONLY : stdout, ionode, ionode_id - USE parameters, ONLY : ntypx, npk, lmaxx - USE cell_base, ONLY : fix_volume, fix_area - USE control_flags, ONLY : conv_elec, gamma_only, ethr, lscf - USE control_flags, ONLY : conv_ions, istep, nstep, restart, lmd, lbfgs - USE command_line_options, ONLY : command_line - USE force_mod, ONLY : lforce, lstres, sigma, force - USE check_stop, ONLY : check_stop_init, check_stop_now - USE mp_images, ONLY : intra_image_comm - USE extrapolation, ONLY : update_file, update_pot - USE scf, ONLY : rho - USE lsda_mod, ONLY : nspin - USE fft_base, ONLY : dfftp - USE qmmm, ONLY : qmmm_initialization, qmmm_shutdown, & - qmmm_update_positions, qmmm_update_forces - USE qexsd_module, ONLY : qexsd_set_status - USE funct, ONLY : dft_is_hybrid, stop_exx - ! - IMPLICIT NONE - INTEGER, INTENT(OUT) :: exit_status - !! Gives the exit status at the end - LOGICAL, external :: matches - !! checks if first string is contained in the second - INTEGER :: idone - !! counter of electronic + ionic steps done in this run - INTEGER :: ions_status = 3 - !! ions_status = 3 not yet converged - !! ions_status = 2 converged, restart with nonzero magnetization - !! ions_status = 1 converged, final step with current cell needed - !! ions_status = 0 converged, exiting - ! - exit_status = 0 - IF ( ionode ) WRITE( unit = stdout, FMT = 9010 ) ntypx, npk, lmaxx - ! - IF (ionode) CALL plugin_arguments() - CALL plugin_arguments_bcast( ionode_id, intra_image_comm ) - ! - ! ... needs to come before iosys() so some input flags can be - ! overridden without needing to write PWscf specific code. - ! - CALL qmmm_initialization() - ! - ! ... convert to internal variables - ! - CALL iosys() - ! - ! ... If executable names is "dist.x", compute atomic distances, angles, - ! ... nearest neighbors, write them to file "dist.out", exit - ! - IF ( matches('dist.x',command_line) ) THEN - IF (ionode) CALL run_dist ( exit_status ) - RETURN - END IF - ! - IF ( gamma_only ) WRITE( UNIT = stdout, & - & FMT = '(/,5X,"gamma-point specific algorithms are used")' ) - ! - ! call to void routine for user defined / plugin patches initializations - ! - CALL plugin_initialization() - ! - CALL check_stop_init() - ! - CALL setup () - ! - CALL qmmm_update_positions() - ! - ! ... dry run: code will stop here if called with exit file present - ! ... useful for a quick and automated way to check input data - ! - IF ( check_stop_now() ) THEN - CALL pre_init() - CALL data_structure( gamma_only ) - CALL summary() - CALL memory_report() - CALL qexsd_set_status(255) - CALL punch( 'init-config' ) - exit_status = 255 - RETURN - ENDIF - ! - CALL init_run_setup() - ! - IF ( check_stop_now() ) THEN - CALL qexsd_set_status(255) - CALL punch( 'config' ) - exit_status = 255 - RETURN - ENDIF - ! - RETURN - ! -9010 FORMAT( /,5X,'Current dimensions of program PWSCF are:', & - & /,5X,'Max number of different atomic species (ntypx) = ',I2,& - & /,5X,'Max number of k-points (npk) = ',I6,& - & /,5X,'Max angular momentum in pseudopotentials (lmaxx) = ',i2) - ! -END SUBROUTINE run_pwscf_setup -! -SUBROUTINE init_run_setup() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - USE kinds, ONLY : DP - USE klist, ONLY : nkstot - USE symme, ONLY : sym_rho_init - USE wvfct, ONLY : nbnd, et, wg, btype - USE control_flags, ONLY : lmd, gamma_only, smallmem, ts_vdw - USE gvect, ONLY : g, gg, mill, gcutm, ig_l2g, ngm, ngm_g, & - gshells, gstart ! to be comunicated to the Solvers if gamma_only - USE gvecs, ONLY : gcutms, ngms - USE cell_base, ONLY : at, bg, set_h_ainv, alat, omega - USE ions_base, ONLY : zv, nat, nsp, ityp, tau - USE cellmd, ONLY : lmovecell - USE dynamics_module, ONLY : allocate_dyn_vars - USE paw_variables, ONLY : okpaw - USE paw_init, ONLY : paw_init_onecenter, allocate_paw_internals -#if defined(__MPI) - USE paw_init, ONLY : paw_post_init -#endif - USE bp, ONLY : allocate_bp_efield, bp_global_map - USE fft_base, ONLY : dfftp, dffts - USE funct, ONLY : dft_is_hybrid - USE recvec_subs, ONLY : ggen, ggens - USE wannier_new, ONLY : use_wannier - USE dfunct, ONLY : newd - USE esm, ONLY : do_comp_esm, esm_init - USE tsvdw_module, ONLY : tsvdw_initialize - USE Coul_cut_2D, ONLY : do_cutoff_2D, cutoff_fact - USE ener, ONLY : ewld - USE vlocal, ONLY : strf - - IMPLICIT NONE - - REAL(DP), EXTERNAL :: ewald - - - CALL start_clock( 'init_run' ) - ! - ! ... calculate limits of some indices, used in subsequent allocations - ! - CALL pre_init() - ! - ! ... determine the data structure for fft arrays - ! - CALL data_structure( gamma_only ) - ! - ! ... print a summary and a memory estimate before starting allocating - ! - CALL summary() - CALL memory_report() - ! - ! ... allocate memory for G- and R-space fft arrays - ! - CALL allocate_fft() - ! - ! ... generate reciprocal-lattice vectors and fft indices - ! - IF( smallmem ) THEN - CALL ggen( dfftp, gamma_only, at, bg, gcutm, ngm_g, ngm, & - g, gg, mill, ig_l2g, gstart, no_global_sort = .TRUE. ) - ELSE - CALL ggen( dfftp, gamma_only, at, bg, gcutm, ngm_g, ngm, & - g, gg, mill, ig_l2g, gstart ) - END IF - CALL ggens( dffts, gamma_only, at, g, gg, mill, gcutms, ngms ) - if (gamma_only) THEN - ! ... Solvers need to know gstart - call export_gstart_2_solvers(gstart) - END IF - ! - IF (do_comp_esm) CALL esm_init() - ! - ! ... setup the 2D cutoff factor - ! - IF (do_cutoff_2D) CALL cutoff_fact() - ! - CALL gshells ( lmovecell ) - ! - ! ... variable initialization for parallel symmetrization - ! - CALL sym_rho_init (gamma_only ) - ! - ! ... allocate memory for all other arrays (potentials, wavefunctions etc) - ! - CALL allocate_nlpot() - IF (okpaw) THEN - CALL allocate_paw_internals() - CALL paw_init_onecenter() - ENDIF - CALL allocate_locpot() - CALL allocate_bp_efield() - CALL bp_global_map() - ! - call plugin_initbase() - ! - ALLOCATE( et( nbnd, nkstot ) , wg( nbnd, nkstot ), btype( nbnd, nkstot ) ) - ! - et(:,:) = 0.D0 - wg(:,:) = 0.D0 - ! - btype(:,:) = 1 - ! - IF (ts_vdw) THEN - CALL tsvdw_initialize() - CALL set_h_ainv() - END IF - ! - CALL openfil() - ! - CALL hinit0() - ! - CALL potinit() - ! - CALL newd() - ! - IF(use_wannier) CALL wannier_init() - ! -#if defined(__MPI) - ! Cleanup PAW arrays that are only used for init - IF (okpaw) CALL paw_post_init() ! only parallel! -#endif - ! - IF ( lmd ) CALL allocate_dyn_vars() - ! - ewld = ewald( alat, nat, nsp, ityp, zv, at, bg, tau, & - omega, g, gg, ngm, gcutm, gstart, gamma_only, strf ) - ! - CALL stop_clock( 'init_run' ) - ! - RETURN - ! -END SUBROUTINE init_run_setup -! -SUBROUTINE print_energies() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE ener, ONLY : eband, deband, ehart, etxc, etxcc, ewld - USE io_global, ONLY: stdout, ionode - - IF ( ionode ) WRITE( unit = stdout, FMT = 9060 ) & - deband, ehart, etxc, ewld - ! - RETURN - ! -9060 FORMAT(/' rho * v_hxc contribution =',F17.8,' Ry' & - /' hartree contribution =',F17.8,' Ry' & - /' xc contribution =',F17.8,' Ry' & - /' ewald contribution =',F17.8,' Ry' ) - -END SUBROUTINE print_energies - -SUBROUTINE get_energies(e_rho_times_v_hxc,e_hartree,e_xc,e_ewald) - USE ener, ONLY : deband, ehart, etxc, etxcc, ewld - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - IMPLICIT NONE - DOUBLE PRECISION, INTENT(OUT) :: e_rho_times_v_hxc - DOUBLE PRECISION, INTENT(OUT) :: e_hartree - DOUBLE PRECISION, INTENT(OUT) :: e_xc - DOUBLE PRECISION, INTENT(OUT) :: e_ewald - - e_rho_times_v_hxc = deband - e_hartree = ehart - e_xc = etxc - e_ewald = ewld - ! - RETURN - ! -END SUBROUTINE get_energies - - -INTEGER FUNCTION get_nnr() - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! Get the value of dfftp%nnr, which is an argument to x_xc_wrapper() - ! - USE fft_base, ONLY : dfftp - get_nnr = dfftp%nnr -END FUNCTION get_nnr - -INTEGER FUNCTION get_nspin() - ! Get the value of lsda_mod nspin, which is an argument to x_xc_wrapper() - USE lsda_mod, ONLY : nspin - get_nspin = nspin -END FUNCTION get_nspin - -INTEGER FUNCTION get_ngm() - ! Get the value of gvect ngm, which is an argument to x_xc_wrapper() - USE gvect, ONLY : ngm - get_ngm = ngm -END FUNCTION get_ngm - -INTEGER FUNCTION get_nat() - ! Get the number of atoms, which is an argument to set_positions() - USE ions_base, ONLY : nat - get_nat = nat -END FUNCTION get_nat - -SUBROUTINE get_positions(positions,nat_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - ! - ! f2py gets confused when DP is used to define the type of an argument - ! Instead, we use DOUBLE PRECISION for arguments - ! - !USE kinds, ONLY : DP - USE scf, ONLY : rho - USE ions_base, ONLY : nat, tau - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nat_in - DOUBLE PRECISION, INTENT(OUT) :: positions(3,nat_in) - - ! Check consistency of dimensions - IF (nat_in /= nat) STOP "*** nat provided to set_positions() does not match ions_base%nat" - - positions = tau - -END SUBROUTINE get_positions - - -SUBROUTINE get_rho_of_r(rho_of_r,nnr_in,nspin_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - ! f2py gets confused when DP is used to define the type of an argument - ! Instead, we use DOUBLE PRECISION for arguments - ! - !USE kinds, ONLY : DP - USE scf, ONLY : rho - USE fft_base, ONLY : dfftp - USE lsda_mod, ONLY : nspin - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nnr_in, nspin_in - DOUBLE PRECISION, INTENT(OUT) :: rho_of_r(nnr_in,nspin_in) - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to set_rho_of_r() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to set_rho_of_r() does not mach lsda_mod%nspin" - - - rho_of_r = rho%of_r - -END SUBROUTINE get_rho_of_r - -SUBROUTINE set_positions(positions,nat_in) - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! Note that this assumes that the positions are in "crystal coordinates", - ! i.e., that they are in units of the lattice vectors. - ! - USE kinds, ONLY : DP - USE ions_base, ONLY : nat, tau, nsp, ityp, zv - USE gvect, ONLY : ngm, g, gg, eigts1, eigts2, eigts3, gcutm, & - gstart - USE cell_base, ONLY : omega, bg, alat, at - USE fft_base, ONLY : dfftp - USE vlocal, ONLY : strf - USE control_flags, ONLY : treinit_gvecs, gamma_only - USE cellmd, ONLY : lmovecell - USE io_global, ONLY : ionode_id, ionode - USE mp_images, ONLY : intra_image_comm - USE symm_base, ONLY : checkallsym - USE mp, ONLY : mp_bcast - USE ener, ONLY : ewld - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nat_in - DOUBLE PRECISION, INTENT(IN) :: positions(3,nat_in) - - REAL(DP), EXTERNAL :: ewald - - ! Check consistency of dimensions - IF (nat_in /= nat) STOP "*** nat provided to set_positions() does not match ions_base%nat" - - ! - ! Update positions on one node - ! - IF ( ionode ) THEN - - tau = positions - - ! - ! Check that the symmetries have not changed. - ! - CALL checkallsym( nat, tau, ityp) - END IF - ! - ! Sync the positions across all processors - ! - CALL mp_bcast( tau, ionode_id, intra_image_comm ) - - ! - ! Now, update everything else that depends on the postions - ! - - ! - ! We set treinit_gvecs = .FALSE. to tell hinit1() to keep the same gvecs. - ! - treinit_gvecs = .FALSE. - ! - ! Also, set lmovecell = .FALSE. in case someone is using it. - ! - lmovecell = .FALSE. - ! - ! ... calculate structure factors for the new positions - ! - ! NAM: Something like the folling commands would be needed if we want change the - ! shape and/or size of the cell. I am not going to try to implement this now. - ! IF ( lmovecell ) rho%of_g(:,1) = rho%of_g(:,1) / omega - ! IF ( lmovecell ) CALL scale_h() - ! - CALL struc_fact( nat, tau, nsp, ityp, ngm, g, bg, & - dfftp%nr1, dfftp%nr2, dfftp%nr3, strf, eigts1, eigts2, eigts3 ) - ! - ! Update the core charge for the new positions. - ! - CALL set_rhoc() - ! - ! ... re-initialize atomic position-dependent quantities - ! - CALL hinit1() - ! - ! The ewald energy doesn't seem to be updated by any of the above, so I will - ! update it here. - ! - ewld = ewald( alat, nat, nsp, ityp, zv, at, bg, tau, & - omega, g, gg, ngm, gcutm, gstart, gamma_only, strf ) - - RETURN - -END SUBROUTINE set_positions - - -SUBROUTINE set_rho_of_r(rho_of_r,nnr_in,nspin_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by Normand Modine, August 2020 - ! - ! - USE kinds, ONLY : DP - USE fft_base, ONLY : dfftp - USE scf, ONLY : rho, rho_core, rhog_core, vltot, v, vrs, kedtau - USE ener, ONLY : ehart, etxc, vtxc, epaw, deband - USE ldaU, ONLY : eth - USE fft_rho, ONLY : rho_r2g, rho_g2r - USE paw_variables, ONLY : okpaw, ddd_PAW - USE lsda_mod, ONLY : nspin - USE gvecs, ONLY : doublegrid - USE paw_onecenter, ONLY : PAW_potential - USE cell_base, ONLY : omega - USE mp, ONLY : mp_sum - USE mp_bands, ONLY : intra_bgrp_comm - - IMPLICIT NONE - INTEGER, INTENT(IN) :: nnr_in, nspin_in - DOUBLE PRECISION, INTENT(IN) :: rho_of_r(nnr_in,nspin_in) - - REAL(DP) :: charge - REAL(DP) :: etotefield - INTEGER :: ir - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to set_rho_of_r() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to set_rho_of_r() does not mach lsda_mod%nspin" - - ! - ! This calculates the G-space density from the input density. - ! - CALL rho_r2g(dfftp, rho_of_r, rho%of_g) - - ! - ! The following results in a real-space density filtered at the plane-wave - ! density cutoff. This is needed to make it consistent with G-space density. - ! - CALL rho_g2r(dfftp, rho%of_g, rho%of_r) - - ! - ! ... plugin contribution to local potential - ! - CALL plugin_scf_potential(rho,.FALSE.,-1.d0,vltot) - - ! - ! ... compute the potential and store it in v - ! - CALL v_of_rho( rho, rho_core, rhog_core, & - ehart, etxc, vtxc, eth, etotefield, charge, v ) - IF (okpaw) CALL PAW_potential(rho%bec, ddd_PAW, epaw) - - ! - ! ... define the total local potential (external+scf) - ! - CALL set_vrs( vrs, vltot, v%of_r, kedtau, v%kin_r, dfftp%nnr, nspin, doublegrid ) - - ! - ! Compute: - ! ... deband = - \sum_v <\psi_v | V_h + V_xc |\psi_v> - ! Then: - ! ... eband + deband = \sum_v <\psi_v | T + Vion |\psi_v> - ! Note that the following is missing some terms from meta-GGA, LDA+U, and PAW - ! See delta_e() in electrons.f90 for the full set of terms. - deband = 0._dp - IF ( nspin==2 ) THEN - ! - DO ir = 1,dfftp%nnr - deband = deband - ( rho%of_r(ir,1) + rho%of_r(ir,2) ) * v%of_r(ir,1) & ! up - - ( rho%of_r(ir,1) - rho%of_r(ir,2) ) * v%of_r(ir,2) ! dw - ENDDO - deband = 0.5_dp*deband - ! - ELSE - deband = - SUM( rho%of_r(:,:)*v%of_r(:,:) ) - ENDIF - deband = omega * deband / ( dfftp%nr1*dfftp%nr2*dfftp%nr3 ) - ! - CALL mp_sum( deband, intra_bgrp_comm ) - ! - - RETURN - -END SUBROUTINE set_rho_of_r - - -SUBROUTINE v_xc_wrapper(rho_of_r, rho_of_g, rho_core, rhog_core,& - etxc, vtxc, v, nnr_in, nspin_in, ngm_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by J. Adam Stephens and Normand Modine, 2020 - ! - ! rho_of_r and rho_of_g will be copied into arrays rho%of_r and rho%of_g. - ! The arguments nnr_in, nspin_in, and ngm_in should be obtained by - ! calling the functions get_nnr(), get_nspin(), and get_ngm(). Due to - ! an apparent f2py limitation, it is not possible to declare the dimensions - ! of the input arrays using module variables (e.g. dfftp%nnr). Other arguments - ! are passed through unaltered to v_xc(). - USE gvect, ONLY : ngm - USE lsda_mod, ONLY : nspin - USE kinds, ONLY : DP - USE scf, ONLY : scf_type - USE fft_base, ONLY : dfftp - - IMPLICIT NONE - INTEGER, INTENT(IN) :: ngm_in, nnr_in, nspin_in - DOUBLE PRECISION, INTENT(IN) :: rho_of_r(nnr_in,nspin_in), rho_core(nnr_in) - DOUBLE COMPLEX, INTENT(IN) :: rho_of_g(ngm_in,nspin_in), rhog_core(ngm_in) - DOUBLE PRECISION, INTENT(OUT) :: v(nnr_in,nspin_in), vtxc, etxc - - ! local variables - INTEGER :: allocate_status - TYPE (scf_type) rho - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to v_xc_wrapper() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to v_xc_wrapper() does not mach lsda_mod%nspin" - IF (ngm_in /= ngm) STOP "*** ngm provided to v_xc_wrapper() does not match gvect%ngm" - ! Copy data to rho - ALLOCATE ( rho%of_r(nspin, nspin), STAT = allocate_status) - IF (allocate_status /= 0) STOP "*** Not enough memory ***" - ALLOCATE ( rho%of_g(ngm, nspin), STAT = allocate_status) - IF (allocate_status /= 0) STOP "*** Not enough memory ***" - rho%of_r = rho_of_r - rho%of_g = rho_of_g - - CALL v_xc( rho, rho_core, rhog_core, etxc, vtxc, v) - -END SUBROUTINE v_xc_wrapper - -SUBROUTINE v_h_wrapper(rhog, ehart, charge, v, nnr_in, nspin_in, ngm_in) - !---------------------------------------------------------------------------- - ! Derived from Quantum Espresso code - !! author: Paolo Giannozzi - !! license: GNU - !! modified to enable calling Quantum Espresso functionality from Python - !! modifications by J. Adam Stephens and Normand Modine, 2020 - ! - ! The arguments nnr_in, nspin_in, and ngm_in should be obtained by - ! calling the functions get_nnr(), get_nspin(), and get_ngm(). Due to - ! an apparent f2py limitation, it is not possible to declare the dimensions - ! of the input arrays using module variables (e.g. dfftp%nnr). Other arguments - ! are passed through unaltered to v_h(). - - USE gvect, ONLY : ngm - USE lsda_mod, ONLY : nspin - USE kinds, ONLY : DP - USE fft_base, ONLY : dfftp - - IMPLICIT NONE - - INTEGER, INTENT(IN) :: ngm_in, nnr_in, nspin_in - DOUBLE COMPLEX, INTENT(IN) :: rhog(ngm_in) - DOUBLE PRECISION, INTENT(INOUT) :: v(nnr_in,nspin_in) - DOUBLE PRECISION, INTENT(OUT) :: ehart, charge - - ! Check consistency of dimensions - IF (nnr_in /= dfftp%nnr) STOP "*** nnr provided to v_h_wrapper() does not match dfftp%nnr" - IF (nspin_in /= nspin) STOP "*** nspin provided to v_h_wrapper() does not mach lsda_mod%nspin" - IF (ngm_in /= ngm) STOP "*** ngm provided to v_h_wrapper() does not match gvect%ngm" - - CALL v_h(rhog, ehart, charge, v) - -END SUBROUTINE v_h_wrapper diff --git a/ml-dft-sandia/transfer_data.sh b/ml-dft-sandia/transfer_data.sh deleted file mode 100644 index 22d405bf1..000000000 --- a/ml-dft-sandia/transfer_data.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -rsync -rn --verbose --exclude pattern source destination - - -# Alternative -#rsync -avz --exclude=*temp --exclude=*out From 38089347752704f889dd98e4b4f2a5658578eb04 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 10:50:09 +0200 Subject: [PATCH 072/297] Updated the installation notice from QuantumESPRESSO as to reflect the deletion of the total energy module --- docs/source/install/INSTALL_TE_QE.rst | 112 ++------------------------ 1 file changed, 6 insertions(+), 106 deletions(-) diff --git a/docs/source/install/INSTALL_TE_QE.rst b/docs/source/install/INSTALL_TE_QE.rst index 17186f7bb..78cd373fa 100644 --- a/docs/source/install/INSTALL_TE_QE.rst +++ b/docs/source/install/INSTALL_TE_QE.rst @@ -1,109 +1,9 @@ Python bindings to Quantum Espresso ==================================================== -All the files described here can be found in the subfolder -``install/total_energy_module/``. - -Using GNU and the mlmm_env environment on Blake ------------------------------------------------ - -Initial Setup -~~~~~~~~~~~~~ - -1. Clone QE source: ``git clone https://github.com/QEF/q-e.git`` On - blake, it may be necessary to ‘module load git’ first. -2. Change into the ``q-e`` directory and check out out the qe-6.4.1 tag: - ``git checkout qe-6.4.1`` -3. Make sure you have the necessary modules loaded (``gcc``, ``python``, - ``openmpi``). -4. Patch the QE source so that the FoX project is built with ``-fPIC``: - - 1. change to the ``q-e/install/m4`` folder - 2. ``patch < x_ac_qe_f90.m4.patch`` - -5. Copy ``total_energy.f90`` to ``q-e/PW/src``. This file contains the - Fortran 90 code that ``f2py`` will create a Python binding to. - -Build Quantum Espresso -~~~~~~~~~~~~~~~~~~~~~~ - -1. If you already have a build of QE, go into the ``q-e`` folder and - run ``make veryclean``. - - 1. Optional: If you want to use this QE installation to calculate LDOS data,# - copy ``ldos.f90`` into ``q-e/PP/src/`` prior to (re-)compilation. -2. Make sure you have the necessary modules loaded (``gcc``, - ``openmpi``). -3. Set up the environment using - ``source total_energy_module_env_gnu.sh.``. -4. In order for this module to work, your QE installation has to be - linked against custom compiled LAPACK and BLAS libraries. If you - have LAPACK/BLAS already installed somewhere on your system, QE will - use these installations; they will have to be recompiled correctly - (see below) - - 1. A simple workaround is to force QE to build its own LAPACK and - BLAS. - 2. In order to do that open ``q-e/install/make.inc.in`` uncomment - the line ``BLAS_LIBS_SWITCH = internal`` and the line above; - comment the line ``BLAS_LIBS_SWITCH = external`` and the line - above. - 3. Do the same for the LAPACK lines. - 4. Both for LAPACK and BLAs, make sure to add a ``lib`` to the - ``*.a`` files (e.g. ``lapack.a`` to ``liblapack.a`` - -5. Add ``-fallow-argument-mismatch`` at the end of line 105 - (``F90FLAGS``) in ``q-e/install/make.inc.in``. -6. Run ``./configure`` in the q-e directory. -7. Afterwards, configure the make input for LAPACK: - - 1. Add ``-fallow-argument-mismatch`` to line 20 (``OPTS``) in - ``q-e/install/make_lapack.inc``. - 2. Add ``-fPIC`` to line 22 (``NOOPT``) in - ``q-e/install/make_lapack.inc``. - -8. Run ``make all`` (use ``make -j8 all`` for a faster compilation - process). - - 1. I (L. Fiedler) had to clean and recompile in the middle of the - compilation process; that might be due to a problem with the - parallel build I was doing. Maybe serial build is not the worst - idea here. - -9. In ``q-e/LAPACK``, run ``make clean`` and ``make`` -10. In ``q-e/LAPACK/CBLAS``, run ``make clean`` and ``make``. -11. The FoX library included in QE is delivered precompiled; this omits - the compilation with the ``-fPIC`` option necessary for usage of the - total energy module. - - 1. Download the FoX library from https://github.com/andreww/fox - 2. Compile the library, make sure that the ``-fPIC`` compiler option - is used (this should already be the case after step 3) - 3. Replace the ``q-e/FoX/lib/`` folder with the ``fox/objs/lib`` - folder - 4. Replace the ``q-e/FoX/finclude/`` folder with the - ``fox/objs/finclude`` folder - -Build the Python Module -~~~~~~~~~~~~~~~~~~~~~~~ - -1. Change to ``q-e/PW/src`` and run the - ``build_total_energy_energy_module_gnu.sh`` script in the - ``total/energy`` folder. If the build is successful, a file named - ``total_energy.cpython-37m-x86_64-linux-gnu.so`` will be generated. - It contains the Python module. - -Use the Python Module -~~~~~~~~~~~~~~~~~~~~~ - -1. Add the folder that contains - ``total_energy.cpython-37m-x86_64-linux-gnu.so`` to your - ``PYTHONPATH``. -2. Now you can use: ``import total_energy`` - -Using Intel/MKL ---------------- - -.. warning:: - Not tested/supported yet! Please contact the authors to receive the original files from the Sandia - National Laboratories group. +The total energy module has been moved to it's own fork of QE. +You can find it at +https://gitlab.com/casus/q-e/-/tree/tem_original_development after +gaining access from the MALA development team. After checking out this +fork and branch, go to ``total_energy_module/`` amd follow the installation +instructions described therein. From f0e4b1dd0bbab2aea41add91ad038c1ce470af76 Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 8 Jun 2021 12:36:13 +0200 Subject: [PATCH 073/297] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 528430ec3..5b3f8c097 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations. -MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL), and the Center for Advanced Systems Understanding (CASUS). See Contributing.md for contributing code to the repository. +MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS). See Contributing.md for contributing code to the repository. This repository is structured as follows: ``` @@ -33,7 +33,7 @@ the examples in the `example/` folder. ### Founding Institutions - Sandia National Laboratories (SNL), USA. -- Center for Advanced Systems Understandingi (CASUS), Germany. +- Center for Advanced Systems Understanding (CASUS), Germany. ## Developers ### Scientific Supervision @@ -42,7 +42,7 @@ the examples in the `example/` folder. ### Core Developers - Lenz Fiedler (CASUS) -- Austin Ellisi (ORNL*) +- Austin Ellis (ORNL*) - Normand Modine (SNL) - Steve Schmerler (CASUS) - Daniel Kotik (CASUS) @@ -50,7 +50,7 @@ the examples in the `example/` folder. - Aidan Thompson (SNL) - Adam Stephens (SNL) -* - Work done as part of postdoctoral research at Sandia National Laboratories +\* Work done as part of postdoctoral research at Sandia National Laboratories ## Citing MALA From 37ddc9abcedc131f7775c56d91571ada9a320983 Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 8 Jun 2021 12:48:35 +0200 Subject: [PATCH 074/297] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5b3f8c097..856b092cd 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ the examples in the `example/` folder. If you publish work which uses or mentions MALA, please cite the following paper: -J. A. Ellis, A. Cangi, N. A. Modine, J. A. Stephens, A. P. Thompson, -S. Rajamanickam (2020). Accelerating Finite-temperature +J. A. Ellis, G. A. Popoola, L. Fiedler, N. A. Modine, J. A. Stephens, A. P. Thompson, +A. Cangi, S. Rajamanickam (2020). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. [arXiv:2010.04905](https://arxiv.org/abs/2010.04905). From 0c9e9c75d534bf62b8f824ee970d000ba8f1936a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:36:00 +0200 Subject: [PATCH 075/297] Moved lammps_utils from Sandia part to MALA part --- mala/descriptors/lammps_utils.py | 71 ++++--------------- .../bispectrum.grid/lammps_utils.py | 26 ------- 2 files changed, 12 insertions(+), 85 deletions(-) delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/lammps_utils.py diff --git a/mala/descriptors/lammps_utils.py b/mala/descriptors/lammps_utils.py index b6368366b..ea74459a7 100644 --- a/mala/descriptors/lammps_utils.py +++ b/mala/descriptors/lammps_utils.py @@ -1,71 +1,24 @@ -""" -Collection of useful functions for working with LAMMPS. - -Courtesy of the Sandia code. We have to talk about licensing. -""" - import numpy as np import ctypes - def set_cmdlinevars(cmdargs, argdict): - """ - Add a dicitionary of LAMMPS arguments in a command line argument string. - - Parameters - ---------- - cmdargs : list - Command line argument string. Will be mutated by this function. - - argdict : dict - Dictionary to be added to LAMMPS command line argument string. - - Returns - ------- - cmdargs : list - New command line argument string. - """ for key in argdict.keys(): - cmdargs += ["-var", key, f"{argdict[key]}"] + cmdargs += ["-var",key,f"{argdict[key]}"] return cmdargs -# def extract_commands(string): -# return [x for x in string.splitlines() if x.strip() != ''] - - -def extract_compute_np(lmp, name, compute_type, result_type, array_shape): - """ - Convert a lammps compute to a numpy array. - - Assumes the compute returns a floating point numbers. - Note that the result is a view into the original memory. - If the result type is 0 (scalar) then conversion to numpy is - skipped and a python float is returned. - - Parameters - ---------- - lmp : lammps.lammps - The LAMMPS object from which data is supposed to be extracted. - - name : string - Name of the LAMMPS calculation. - - compute_type - Compute type of the LAMMPS calculation. - - result_type - Result type of the LAMMPS calculation. - - array_shape - Array shape of the LAMMPS calculation. +def extract_commands(string): + return [x for x in string.splitlines() if x.strip() != ''] +def extract_compute_np(lmp,name,compute_type,result_type,array_shape): + """ + Convert a lammps compute to a numpy array. + Assumes the compute returns a floating point numbers. + Note that the result is a view into the original memory. + If the result type is 0 (scalar) then conversion to numpy is skipped and a python float is returned. """ - # 1,2: Style (1) is per-atom compute, returns array type (2). - ptr = lmp.extract_compute(name, compute_type, result_type) - if result_type == 0: - return ptr # No casting needed, lammps.py already works - if result_type == 2: - ptr = ptr.contents + ptr = lmp.extract_compute(name, compute_type, result_type) # 1,2: Style (1) is per-atom compute, returns array type (2). + if result_type == 0: return ptr # No casting needed, lammps.py already works + if result_type == 2: ptr = ptr.contents total_size = np.prod(array_shape) buffer_ptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_double * total_size)) array_np = np.frombuffer(buffer_ptr.contents, dtype=float) diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/lammps_utils.py b/ml-dft-sandia/descriptors/bispectrum.grid/lammps_utils.py deleted file mode 100644 index ea74459a7..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/lammps_utils.py +++ /dev/null @@ -1,26 +0,0 @@ -import numpy as np -import ctypes - -def set_cmdlinevars(cmdargs, argdict): - for key in argdict.keys(): - cmdargs += ["-var",key,f"{argdict[key]}"] - return cmdargs - -def extract_commands(string): - return [x for x in string.splitlines() if x.strip() != ''] - -def extract_compute_np(lmp,name,compute_type,result_type,array_shape): - """ - Convert a lammps compute to a numpy array. - Assumes the compute returns a floating point numbers. - Note that the result is a view into the original memory. - If the result type is 0 (scalar) then conversion to numpy is skipped and a python float is returned. - """ - ptr = lmp.extract_compute(name, compute_type, result_type) # 1,2: Style (1) is per-atom compute, returns array type (2). - if result_type == 0: return ptr # No casting needed, lammps.py already works - if result_type == 2: ptr = ptr.contents - total_size = np.prod(array_shape) - buffer_ptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_double * total_size)) - array_np = np.frombuffer(buffer_ptr.contents, dtype=float) - array_np.shape = array_shape - return array_np From 5638f7dfdd90769d29b7665146050eae53509136 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:38:41 +0200 Subject: [PATCH 076/297] Added documentation to lammps_utils.py --- mala/descriptors/lammps_utils.py | 69 ++++++++++++++++++++++++++------ 1 file changed, 57 insertions(+), 12 deletions(-) diff --git a/mala/descriptors/lammps_utils.py b/mala/descriptors/lammps_utils.py index ea74459a7..6e1072a84 100644 --- a/mala/descriptors/lammps_utils.py +++ b/mala/descriptors/lammps_utils.py @@ -1,24 +1,69 @@ +""" +Collection of useful functions for working with LAMMPS. +""" + import numpy as np import ctypes + def set_cmdlinevars(cmdargs, argdict): + """ + Add a dicitionary of LAMMPS arguments in a command line argument string. + + Parameters + ---------- + cmdargs : list + Command line argument string. Will be mutated by this function. + + argdict : dict + Dictionary to be added to LAMMPS command line argument string. + + Returns + ------- + cmdargs : list + New command line argument string. + """ for key in argdict.keys(): - cmdargs += ["-var",key,f"{argdict[key]}"] + cmdargs += ["-var", key, f"{argdict[key]}"] return cmdargs -def extract_commands(string): - return [x for x in string.splitlines() if x.strip() != ''] +# def extract_commands(string): +# return [x for x in string.splitlines() if x.strip() != ''] + + +def extract_compute_np(lmp, name, compute_type, result_type, array_shape): + """ + Convert a lammps compute to a numpy array. + + Assumes the compute returns a floating point numbers. + Note that the result is a view into the original memory. + If the result type is 0 (scalar) then conversion to numpy is + skipped and a python float is returned. + + Parameters + ---------- + lmp : lammps.lammps + The LAMMPS object from which data is supposed to be extracted. + + name : string + Name of the LAMMPS calculation. + + compute_type + Compute type of the LAMMPS calculation. + + result_type + Result type of the LAMMPS calculation. + + array_shape + Array shape of the LAMMPS calculation. -def extract_compute_np(lmp,name,compute_type,result_type,array_shape): - """ - Convert a lammps compute to a numpy array. - Assumes the compute returns a floating point numbers. - Note that the result is a view into the original memory. - If the result type is 0 (scalar) then conversion to numpy is skipped and a python float is returned. """ - ptr = lmp.extract_compute(name, compute_type, result_type) # 1,2: Style (1) is per-atom compute, returns array type (2). - if result_type == 0: return ptr # No casting needed, lammps.py already works - if result_type == 2: ptr = ptr.contents + # 1,2: Style (1) is per-atom compute, returns array type (2). + ptr = lmp.extract_compute(name, compute_type, result_type) + if result_type == 0: + return ptr # No casting needed, lammps.py already works + if result_type == 2: + ptr = ptr.contents total_size = np.prod(array_shape) buffer_ptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_double * total_size)) array_np = np.frombuffer(buffer_ptr.contents, dtype=float) From 744374dc22961aed48b0f0ab95a6f3a5bd6d312f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:40:24 +0200 Subject: [PATCH 077/297] Moved in.bgrid.python from Sandia part to MALA --- mala/descriptors/in.bgrid.python | 48 +++++++++------ .../bispectrum.grid/in.bgrid.python | 61 ------------------- 2 files changed, 29 insertions(+), 80 deletions(-) delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/in.bgrid.python diff --git a/mala/descriptors/in.bgrid.python b/mala/descriptors/in.bgrid.python index d05caa0dd..000ea1f23 100644 --- a/mala/descriptors/in.bgrid.python +++ b/mala/descriptors/in.bgrid.python @@ -1,43 +1,51 @@ -# Calculate SNAP descriptors on a 3D grid +# Demonstrate bispectrum per-atom and grid computes -# pass in values ngridx, ngridy, ngridz, twojmax, rcutfac, atom_config_fname -# using command-line -var option +# Invoked from grid.py +# pass in values for ngridx, ngridy, ngridz, twojmax # Initialize simulation +variable nsteps equal 0 +variable nrep equal 1 +variable a equal 3.316 + units metal -read_data ${atom_config_fname} -mass * 1.0 +# generate the box and atom positions using a BCC lattice + +variable nx equal ${nrep} +variable ny equal ${nrep} +variable nz equal ${nrep} + +boundary p p p + +lattice custom ${a} a1 1 0 0 a2 0 1 0 a3 0 0 1 basis 0 0 0 basis 0.5 0.5 0.5 + +region box block 0 ${nx} 0 ${ny} 0 ${nz} +create_box 1 box +create_atoms 1 box + +mass 1 180.88 # define grid compute and atom compute group snapgroup type 1 -#variable rcutfac equal 4.67637 +variable rcutfac equal 4.67637 variable rfac0 equal 0.99363 variable rmin0 equal 0 variable wj equal 1 variable radelem equal 0.5 variable bzero equal 0 -variable quadratic equal 0 -variable switch equal 0 +variable quad equal 0 +variable switch equal 1 -#variable snap_options string & -#"${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} quadraticflag ${quadratic} bzeroflag ${bzero} switchflag ${switch}" - -#compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${snap_options} - -compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quadratic} switchflag ${switch} +compute b all sna/atom ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} +compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} # create dummy potential for neighbor list - -# is this important? or does it just need to be big enough? variable rcutneigh equal 2.0*${rcutfac}*${radelem} -# for water -#variable rcutneigh equal 4.0*${rcutfac}*${radelem} - pair_style zero ${rcutneigh} pair_coeff * * @@ -46,6 +54,8 @@ pair_coeff * * thermo_style custom step temp ke pe vol c_bgrid[1][1] thermo_modify norm yes +dump mydump_b all custom 1000 b.dump id x y z c_b[*] + # run run 0 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/in.bgrid.python b/ml-dft-sandia/descriptors/bispectrum.grid/in.bgrid.python deleted file mode 100644 index 000ea1f23..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/in.bgrid.python +++ /dev/null @@ -1,61 +0,0 @@ -# Demonstrate bispectrum per-atom and grid computes - -# Invoked from grid.py -# pass in values for ngridx, ngridy, ngridz, twojmax - -# Initialize simulation - -variable nsteps equal 0 -variable nrep equal 1 -variable a equal 3.316 - -units metal - -# generate the box and atom positions using a BCC lattice - -variable nx equal ${nrep} -variable ny equal ${nrep} -variable nz equal ${nrep} - -boundary p p p - -lattice custom ${a} a1 1 0 0 a2 0 1 0 a3 0 0 1 basis 0 0 0 basis 0.5 0.5 0.5 - -region box block 0 ${nx} 0 ${ny} 0 ${nz} -create_box 1 box -create_atoms 1 box - -mass 1 180.88 - -# define grid compute and atom compute - -group snapgroup type 1 -variable rcutfac equal 4.67637 -variable rfac0 equal 0.99363 -variable rmin0 equal 0 -variable wj equal 1 -variable radelem equal 0.5 -variable bzero equal 0 -variable quad equal 0 -variable switch equal 1 - -compute b all sna/atom ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} - -compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} - -# create dummy potential for neighbor list - -variable rcutneigh equal 2.0*${rcutfac}*${radelem} -pair_style zero ${rcutneigh} -pair_coeff * * - -# define output - -thermo_style custom step temp ke pe vol c_bgrid[1][1] -thermo_modify norm yes - -dump mydump_b all custom 1000 b.dump id x y z c_b[*] - -# run - -run 0 From 06ee54cc7f924adaa4575fbda3e9081c1ba4e100 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:51:20 +0200 Subject: [PATCH 078/297] Copied newer version of in.bgrid.python --- mala/descriptors/in.bgrid.python | 44 ++++++----------- .../descriptors}/in.bgrid.twoelements.python | 0 .../training_data/scripts/fp/in.bgrid.python | 49 ------------------- 3 files changed, 16 insertions(+), 77 deletions(-) rename {ml-dft-sandia/networks/training_data/scripts/fp => mala/descriptors}/in.bgrid.twoelements.python (100%) delete mode 100644 ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.python diff --git a/mala/descriptors/in.bgrid.python b/mala/descriptors/in.bgrid.python index 000ea1f23..da23bdceb 100644 --- a/mala/descriptors/in.bgrid.python +++ b/mala/descriptors/in.bgrid.python @@ -1,51 +1,41 @@ -# Demonstrate bispectrum per-atom and grid computes +# Calculate SNAP descriptors on a 3D grid -# Invoked from grid.py -# pass in values for ngridx, ngridy, ngridz, twojmax +# pass in values ngridx, ngridy, ngridz, twojmax, rcutfac, atom_config_fname +# using command-line -var option # Initialize simulation -variable nsteps equal 0 -variable nrep equal 1 -variable a equal 3.316 - units metal -# generate the box and atom positions using a BCC lattice - -variable nx equal ${nrep} -variable ny equal ${nrep} -variable nz equal ${nrep} - -boundary p p p - -lattice custom ${a} a1 1 0 0 a2 0 1 0 a3 0 0 1 basis 0 0 0 basis 0.5 0.5 0.5 - -region box block 0 ${nx} 0 ${ny} 0 ${nz} -create_box 1 box -create_atoms 1 box +read_data ${atom_config_fname} -mass 1 180.88 +mass * 1.0 # define grid compute and atom compute group snapgroup type 1 -variable rcutfac equal 4.67637 variable rfac0 equal 0.99363 variable rmin0 equal 0 variable wj equal 1 variable radelem equal 0.5 variable bzero equal 0 -variable quad equal 0 -variable switch equal 1 -compute b all sna/atom ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} +#variable snap_options string & +#"${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} quadraticflag ${quadratic} bzeroflag ${bzero} switchflag ${switch}" + +#compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${snap_options} + +compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quadratic} switchflag ${switch} -compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quad} switchflag ${switch} # create dummy potential for neighbor list + +# is this important? or does it just need to be big enough? variable rcutneigh equal 2.0*${rcutfac}*${radelem} +# for water +#variable rcutneigh equal 4.0*${rcutfac}*${radelem} + pair_style zero ${rcutneigh} pair_coeff * * @@ -54,8 +44,6 @@ pair_coeff * * thermo_style custom step temp ke pe vol c_bgrid[1][1] thermo_modify norm yes -dump mydump_b all custom 1000 b.dump id x y z c_b[*] - # run run 0 diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.twoelements.python b/mala/descriptors/in.bgrid.twoelements.python similarity index 100% rename from ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.twoelements.python rename to mala/descriptors/in.bgrid.twoelements.python diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.python b/ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.python deleted file mode 100644 index da23bdceb..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/in.bgrid.python +++ /dev/null @@ -1,49 +0,0 @@ -# Calculate SNAP descriptors on a 3D grid - -# pass in values ngridx, ngridy, ngridz, twojmax, rcutfac, atom_config_fname -# using command-line -var option - -# Initialize simulation - -units metal - -read_data ${atom_config_fname} - -mass * 1.0 - -# define grid compute and atom compute - -group snapgroup type 1 -variable rfac0 equal 0.99363 -variable rmin0 equal 0 -variable wj equal 1 -variable radelem equal 0.5 -variable bzero equal 0 - -#variable snap_options string & -#"${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} quadraticflag ${quadratic} bzeroflag ${bzero} switchflag ${switch}" - -#compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${snap_options} - -compute bgrid all sna/grid grid ${ngridx} ${ngridy} ${ngridz} ${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} bzeroflag ${bzero} quadraticflag ${quadratic} switchflag ${switch} - - -# create dummy potential for neighbor list - - -# is this important? or does it just need to be big enough? -variable rcutneigh equal 2.0*${rcutfac}*${radelem} -# for water -#variable rcutneigh equal 4.0*${rcutfac}*${radelem} - -pair_style zero ${rcutneigh} -pair_coeff * * - -# define output - -thermo_style custom step temp ke pe vol c_bgrid[1][1] -thermo_modify norm yes - -# run - -run 0 From 0ca890cb9c55103157a0bb35ce37cc517449ce68 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:53:32 +0200 Subject: [PATCH 079/297] Reapplied changes --- mala/descriptors/in.bgrid.python | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mala/descriptors/in.bgrid.python b/mala/descriptors/in.bgrid.python index da23bdceb..d27c52147 100644 --- a/mala/descriptors/in.bgrid.python +++ b/mala/descriptors/in.bgrid.python @@ -19,6 +19,8 @@ variable rmin0 equal 0 variable wj equal 1 variable radelem equal 0.5 variable bzero equal 0 +variable quadratic equal 0 +variable switch equal 0 #variable snap_options string & #"${rcutfac} ${rfac0} ${twojmax} ${radelem} ${wj} rmin0 ${rmin0} quadraticflag ${quadratic} bzeroflag ${bzero} switchflag ${switch}" From 5a4cee88861b9845166a6a51592ce00319670af0 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 15:57:08 +0200 Subject: [PATCH 080/297] Moved generate_fingerprints.py to MALA as basis for snap.py --- mala/descriptors/snap.py | 498 +++++++++++------- .../scripts/fp/generate_fingerprints.py | 325 ------------ 2 files changed, 319 insertions(+), 504 deletions(-) mode change 100644 => 100755 mala/descriptors/snap.py delete mode 100755 ml-dft-sandia/networks/training_data/scripts/fp/generate_fingerprints.py diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py old mode 100644 new mode 100755 index 9c2fbffda..45abee06f --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -1,185 +1,325 @@ -"""SNAP descriptor class.""" -import warnings +# +# Generate SNAP descriptors (fingerprints) from atomic congiurations using LAMMPS +# + import ase import ase.io -from .lammps_utils import * -from .descriptor_base import DescriptorBase -try: - from lammps import lammps -except ModuleNotFoundError: - warnings.warn("You either don't have LAMMPS installed or it is not " - "configured correctly. Using SNAP descriptors " - "might still work, but trying to calculate SNAP " - "descriptors from atomic positions will crash.", - stacklevel=3) - - -class SNAP(DescriptorBase): - """Class for calculation and parsing of SNAP descriptors.""" - - def __init__(self, parameters): - """ - Create a SNAP object. - - Parameters - ---------- - parameters : mala.common.parameters.Parameters - Parameters object used to create this object. - """ - super(SNAP, self).__init__(parameters) - self.in_format_ase = "" - - @staticmethod - def convert_units(array, in_units="None"): - """ - Convert the units of a SNAP descriptor. - - Since these do not really have units this function does nothing yet. - - Parameters - ---------- - array : numpy.array - Data for which the units should be converted. - - in_units : string - Units of array. - - Returns - ------- - converted_array : numpy.array - Data in MALA units. - """ - if in_units == "None": - return array - else: - raise Exception("Unsupported unit for SNAP.") - - @staticmethod - def backconvert_units(array, out_units): - """ - Convert the units of a SNAP descriptor. - - Since these do not really have units this function does nothing yet. - - Parameters - ---------- - array : numpy.array - Data in MALA units. - - out_units : string - Desired units of output array. - - Returns - ------- - converted_array : numpy.array - Data in out_units. - """ - if out_units == "None": - return array +import argparse +import os, sys +import itertools + +import numpy as np +#import io +import timeit + +from lammps import lammps +import lammps_utils + +from ase.calculators.lammpsrun import LAMMPS +from mpi4py import MPI + +comm = MPI.COMM_WORLD + +rank = comm.Get_rank() +ranks = comm.Get_size() + +print("Proc %d out of %d procs" % (rank, ranks), flush=True) + +comm.Barrier() + +if (rank == 0): + print("\n-----------------------------------\n") + print("---- GENERATE LAMMPS FP DATA ----") + print("\n-----------------------------------\n") + +parser = argparse.ArgumentParser(description='Fingerprint Generation') + +parser.add_argument('--water', action='store_true', default=False, + help='run fingerprint generation for water files') +parser.add_argument('--atom-based', action='store_true', default=False, + help='run lammps fingerprints for atoms instead of grid files') +parser.add_argument('--run-all', action='store_true', default=False, + help='run ldos parser for all densities and temperatures') +parser.add_argument('--temp', type=str, default="298K", metavar='S', + help='temperature of fingerprint in units K (default: "298K")') +parser.add_argument('--gcc', type=str, default="2.699", metavar='S', + help='density of fingerprint in units in g/cm^3 (default: "2.699")') +parser.add_argument('--snapshot', type=str, default="0", metavar='S', + help='snapshot number at given gcc/temp (default: "0")') +parser.add_argument('--nxyz', type=int, default=20, metavar='N', + help='number of grid cells in the X/Y/Z dimensions (default: 20)') +parser.add_argument('--rcutfac', type=float, default=4.67637, metavar='R', + help='radius cutoff factor for the fingerprint sphere in Angstroms (default: 4.67637)') +parser.add_argument('--twojmax', type=int, default=10, metavar='N', + help='band limit for fingerprints (default: 10)') +parser.add_argument('--switch', type=int, default=0, metavar='0/1', + help='Use smooth cutoff switching function (default: 0)') +parser.add_argument('--quadratic', type=int, default=0, metavar='0/1', + help='Include quadratic SNAP descriptors (default: 0)') +parser.add_argument('--no-qe', action='store_true', default=False, + help='use LAMMPS input file directly (default: False)') +parser.add_argument('--data-dir', type=str, \ + default="../../fp_data", \ + metavar="str", help='path to data directory with QE output files (default: ../../fp_data)') +parser.add_argument('--output-dir', type=str, default="../../fp_data", + metavar="str", help='path to output directory (default: ../../fp_data)') +parser.add_argument('--unwrapdelta', type=float, default=0.0, + help='use value UNWRAPDELTA to generate new LAMMPS data file with unwrapped coords (default: 0.0 i.e. off)') + +args = parser.parse_args() + +# Print arguments +print("Parser Arguments") +for arg in vars(args): + print ("%s: %s" % (arg, getattr(args, arg))) + +if (args.water): + print("Using this script to generate fingerprints for water") + + #args.data_dir = '/ascldap/users/jracker/water64cp2k/datast_1593/results/' + + temp_grid = np.array([args.temp]) + #gcc_grid = np.array(['aaaa']) + gcc_grid = np.array([args.gcc]) + + if args.run_all: + gcc_grid = [''.join(i) for i in itertools.product("abcdefghijklmnopqrstuvwxyz",repeat=4)][:1593] + + snapshot_grid = np.array([args.snapshot]) + + cube_filename_head = "w64_" + cube_filename_tail = "-ELECTRON_DENSITY-1_0.cube" + + + #args.atom_based = True + +elif (args.run_all): + + # Currently available + temp_grid = np.array(["298K"]) + # Future +# temp_grid = np.array(["300K", "10000K", "20000K", "30000K"]) + + + # Currently available + gcc_grid = np.array(["2.699"]) +# gcc_grid = np.array(["0.4", "0.6", "0.8", "1.0", "2.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) + + # Future + # Missing gcc = [0.1, 0.2] QE out file +# gcc_grid = np.array(["0.1", "0.2", "0.4", "0.6", "0.8", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) + # Test +# gcc_grid = np.array(["2.0", "3.0"]) + +else: + + temp_grid = np.array([args.temp]) + gcc_grid = np.array([args.gcc]) + +echo_to_screen = False + +#write_to_lammps_file = True + +qe_fname = "QE_Al.scf.pw.snapshot%s.out" % args.snapshot +lammps_fname = "Al.scf.pw.snapshot%s.lammps" % args.snapshot +log_fname = "lammps_fp_%dx%dx%dgrid_%dtwojmax_s%d_q%d_snapshot%s.log" % \ + (args.nxyz, args.nxyz, args.nxyz, args.twojmax, args.switch, args.quadratic, args.snapshot) + + +# First 3 cols are x, y, z, coords + +ncols0 = 3 + +# Analytical relation for fingerprint length +ncoeff = (args.twojmax+2)*(args.twojmax+3)*(args.twojmax+4) +ncoeff = ncoeff // 24 # integer division +if (args.quadratic): + ncoeff += ((ncoeff+1)*ncoeff)//2 +fp_length = ncols0 + ncoeff + + +if (args.water): + qe_format = "cube" + np_fname = "water_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) + lammps_compute_grid_fname = "./in.bgrid.twoelements.python" +else: + qe_format = "espresso-out" + np_fname = "Al_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) + lammps_compute_grid_fname = "./in.bgrid.python" + +lammps_format = "lammps-data" + +# Grid points and training data +nx = args.nxyz +ny = nx +nz = ny + +# Descriptor Length Formula: +# ((tjm + 2) * (tjm + 3) * (tjm + 4)) // 24 + 3 +# 5: 24 +# 8: 58 +# 11: 116 +# 15: 245 + +tic = timeit.default_timer() + +# Loops over temperature grid +for temp in temp_grid: + + print("\nWorking on Temp %s" % temp) + + # Loop over density grid + for gcc in gcc_grid: + + inner_tic = timeit.default_timer() + + temp_dir = args.data_dir + "/%s" % (temp) + gcc_dir = temp_dir + "/%sgcc/" % (gcc) + + if (args.water): + qe_filepath = args.data_dir + cube_filename_head + gcc + cube_filename_tail + lammps_filepath = args.data_dir + cube_filename_head + gcc + ".lammps" else: - raise Exception("Unsupported unit for SNAP.") - - def calculate_from_qe_out(self, qe_out_file, qe_out_directory): - """ - Calculate the SNAP descriptors based on a Quantum Espresso outfile. - - Parameters - ---------- - qe_out_file : string - Name of Quantum Espresso output file for snapshot. - - qe_out_directory : string - Path to Quantum Espresso output file for snapshot. - - Returns - ------- - snap_descriptors : numpy.array - Numpy array containing the SNAP descriptors with the dimension - (x,y,z,snap_dimension) - - """ - self.in_format_ase = "espresso-out" - print("Calculating SNAP descriptors from", qe_out_file, "at", - qe_out_directory) - return self.__calculate_snap(qe_out_directory + qe_out_file, - qe_out_directory) - - def __calculate_snap(self, infile, outdir): - """Perform actual SNAP calculation.""" - from lammps import lammps - lammps_format = "lammps-data" - # We get the atomic information by using ASE. - atoms = ase.io.read(infile, format=self.in_format_ase) - ase_out_path = outdir+"lammps_input.tmp" - ase.io.write(ase_out_path, atoms, format=lammps_format) - - # We also need to know how big the grid is. - # Iterating directly through the file is slow, but the - # grid information is at the top (around line 200). - nx = None - ny = None - nz = None - if len(self.dbg_grid_dimensions) == 3: - nx = self.dbg_grid_dimensions[0] - ny = self.dbg_grid_dimensions[1] - nz = self.dbg_grid_dimensions[2] + # Make Temp directory + if not os.path.exists(temp_dir): + print("\nWarning! Creating input folder %s" % temp_dir) + os.makedirs(temp_dir) + # Make Density directory + if not os.path.exists(gcc_dir): + print("\nWarning! Creating input folder %s" % gcc_dir) + os.makedirs(gcc_dir) + + qe_filepath = gcc_dir + qe_fname + lammps_filepath = gcc_dir + lammps_fname + + if (args.no_qe): + print("Skipping QE conversion. Reading LAMMPS input directly.") + elif not os.path.exists(qe_filepath): + print("\n\nQE out file %s does not exist! Exiting.\n\n" % (qe_filepath)) + exit(0) + + if (args.no_qe): + if (not os.path.exists(lammps_filepath)): + print("\n\nLAMMPS file %s does not exist! Exiting.\n\n" % (lammps_filepath)) + exit(0) + + elif (not os.path.exists(lammps_filepath) and not args.no_qe): + + if (rank == 0): + print("\nConverting %sgcc file %s to %s!" % (gcc, qe_filepath, lammps_filepath), flush=True) + + atoms = ase.io.read(qe_filepath, format=qe_format); + + if (rank == 0): + print(atoms, flush=True) + + # Write to LAMMPS File + ase.io.write(lammps_filepath, atoms, format=lammps_format) + + if (rank == 0): + print("Wrote QE to file for %s gcc" % (gcc), flush=True) else: - qe_outfile = open(infile, "r") - lines = qe_outfile.readlines() - for line in lines: - if "FFT dimensions" in line: - tmp = line.split("(")[1].split(")")[0] - nx = int(tmp.split(",")[0]) - ny = int(tmp.split(",")[1]) - nz = int(tmp.split(",")[2]) - break - # Build LAMMPS arguments from the data we read. - lmp_cmdargs = ["-screen", "none", "-log", outdir+"lammps_log.tmp"] - lmp_cmdargs = set_cmdlinevars(lmp_cmdargs, - { - "ngridx": nx, - "ngridy": ny, - "ngridz": nz, - "twojmax": self.parameters.twojmax, - "rcutfac": self.parameters.rcutfac, - "atom_config_fname": ase_out_path - }) - - # Build the LAMMPS object. + print("\nLAMMPS input file %s already exists.\n" % lammps_filepath) + + if (echo_to_screen): + lmp_cmdargs = ["-echo", "screen", "-log", log_fname] + else: + lmp_cmdargs = ["-screen", "none", "-log", log_fname] + + lmp_cmdargs = lammps_utils.set_cmdlinevars(lmp_cmdargs, + { + "ngridx":nx, + "ngridy":ny, + "ngridz":nz, + "twojmax":args.twojmax, + "rcutfac":args.rcutfac, + "quadratic":args.quadratic, + "switch":args.switch, + "atom_config_fname":lammps_filepath + } + ) + lmp = lammps(cmdargs=lmp_cmdargs) + + if (rank == 0): + print("\nComputing fingerprints...", flush=True) + + try: + lmp.file(lammps_compute_grid_fname) + except lammps.LAMMPSException: + if (rank == 0): + print("Bad Read of %s" % (lammps_compute_grid_fname), flush=True) + + # Check atom quantities from LAMMPS + num_atoms = lmp.get_natoms() + if (rank == 0): + print("TEST, NUM_ATOMS: %d" % (num_atoms), flush=True) + + # Extract numpy array pointing to sna/atom array + + if (args.atom_based): + print("\n\nFor Josh\n\n") + exit(0) + + bptr_np = lammps_utils.extract_compute_np(lmp, "b", 1, 2, (num_atoms,ncoeff)) + + + # Extract numpy array pointing to sna/grid array (Z, Y, X ordering) + else: + bptr_np = lammps_utils.extract_compute_np(lmp, "bgrid", 0, 2, (nz,ny,nx,fp_length)) + + # switch from x-fastest to z-fastest order (swaps 0th and 2nd dimension) + bptr_np = bptr_np.transpose([2,1,0,3]) + + # Generate unwrapped LAMMPS coords, if requested + if (args.unwrapdelta != 0.0): + lmp.command("change_box all boundary s s s") + delta = args.unwrapdelta + boxlo,boxhi,xy,yz,xz,periodicity,box_change = lmp.extract_box() + coords = lmp.extract_atom("x",3) + for i in range(num_atoms): + for j in range(3): + if coords[i][j]+delta > boxhi[j]: + coords[i][j] -= boxhi[j]-boxlo[j] + lmp.command("write_data %s.unwrapped" % lammps_filepath) + + if (rank == 0): + print("bptr_np shape = ",bptr_np.shape, flush=True) + + # Output location + if (args.water): + gcc_dir = args.output_dir + gcc + "_" + else: + temp_dir = args.output_dir + "/%s" % temp + gcc_dir = temp_dir + "/%sgcc/" % gcc + + # Make Temp directory + if not os.path.exists(temp_dir): + print("\nCreating output folder %s" % temp_dir) + os.makedirs(temp_dir) + # Make Density directory + if not os.path.exists(gcc_dir): + print("\nCreating output folder %s" % gcc_dir) + os.makedirs(gcc_dir) + + fingerprint_filepath = gcc_dir + np_fname + # Save LAMMPS numpy array as binary + if (rank == 0): + np.save(fingerprint_filepath, bptr_np, allow_pickle=True) + + inner_toc = timeit.default_timer() + + comm.Barrier() + + print("Rank %d Time %s gcc: %4.4f" % (rank, gcc, inner_toc - inner_tic), flush=True) + +toc = timeit.default_timer() + +comm.Barrier() + +print("Rank %d, Total conversion time: %4.4f" % (rank, toc - tic), flush=True) + +comm.Barrier() - # An empty string means that the user wants to use the standard input. - if self.parameters.lammps_compute_file == "": - filepath = __file__.split("snap")[0] - self.parameters.lammps_compute_file = filepath+"in.bgrid.python" - - # Do the LAMMPS calculation. - lmp.file(self.parameters.lammps_compute_file) - - # Set things not accessible from LAMMPS - # First 3 cols are x, y, z, coords - ncols0 = 3 - - # Analytical relation for fingerprint length - ncoeff = (self.parameters.twojmax+2) * \ - (self.parameters.twojmax+3)*(self.parameters.twojmax+4) - ncoeff = ncoeff // 24 # integer division - self.fingerprint_length = ncols0+ncoeff - - # Extract data from LAMMPS calculation. - snap_descriptors_np = \ - extract_compute_np(lmp, "bgrid", 0, 2, - (nz, ny, nx, self.fingerprint_length)) - - # switch from x-fastest to z-fastest order (swaps 0th and 2nd - # dimension) - snap_descriptors_np = snap_descriptors_np.transpose([2, 1, 0, 3]) - - # The next two commands can be used to check whether the calculated - # SNAP descriptors - # are identical to the ones calculated with the Sandia workflow. - # They generally are. - # print("snap_descriptors_np shape = ",snap_descriptors_np.shape, - # flush=True) - # np.save(set[4]+"test.npy", snap_descriptors_np, allow_pickle=True) - return snap_descriptors_np +if (rank == 0): + print("\n\nSuccess!", flush=True) diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/generate_fingerprints.py b/ml-dft-sandia/networks/training_data/scripts/fp/generate_fingerprints.py deleted file mode 100755 index 45abee06f..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/generate_fingerprints.py +++ /dev/null @@ -1,325 +0,0 @@ -# -# Generate SNAP descriptors (fingerprints) from atomic congiurations using LAMMPS -# - -import ase -import ase.io -import argparse -import os, sys -import itertools - -import numpy as np -#import io -import timeit - -from lammps import lammps -import lammps_utils - -from ase.calculators.lammpsrun import LAMMPS -from mpi4py import MPI - -comm = MPI.COMM_WORLD - -rank = comm.Get_rank() -ranks = comm.Get_size() - -print("Proc %d out of %d procs" % (rank, ranks), flush=True) - -comm.Barrier() - -if (rank == 0): - print("\n-----------------------------------\n") - print("---- GENERATE LAMMPS FP DATA ----") - print("\n-----------------------------------\n") - -parser = argparse.ArgumentParser(description='Fingerprint Generation') - -parser.add_argument('--water', action='store_true', default=False, - help='run fingerprint generation for water files') -parser.add_argument('--atom-based', action='store_true', default=False, - help='run lammps fingerprints for atoms instead of grid files') -parser.add_argument('--run-all', action='store_true', default=False, - help='run ldos parser for all densities and temperatures') -parser.add_argument('--temp', type=str, default="298K", metavar='S', - help='temperature of fingerprint in units K (default: "298K")') -parser.add_argument('--gcc', type=str, default="2.699", metavar='S', - help='density of fingerprint in units in g/cm^3 (default: "2.699")') -parser.add_argument('--snapshot', type=str, default="0", metavar='S', - help='snapshot number at given gcc/temp (default: "0")') -parser.add_argument('--nxyz', type=int, default=20, metavar='N', - help='number of grid cells in the X/Y/Z dimensions (default: 20)') -parser.add_argument('--rcutfac', type=float, default=4.67637, metavar='R', - help='radius cutoff factor for the fingerprint sphere in Angstroms (default: 4.67637)') -parser.add_argument('--twojmax', type=int, default=10, metavar='N', - help='band limit for fingerprints (default: 10)') -parser.add_argument('--switch', type=int, default=0, metavar='0/1', - help='Use smooth cutoff switching function (default: 0)') -parser.add_argument('--quadratic', type=int, default=0, metavar='0/1', - help='Include quadratic SNAP descriptors (default: 0)') -parser.add_argument('--no-qe', action='store_true', default=False, - help='use LAMMPS input file directly (default: False)') -parser.add_argument('--data-dir', type=str, \ - default="../../fp_data", \ - metavar="str", help='path to data directory with QE output files (default: ../../fp_data)') -parser.add_argument('--output-dir', type=str, default="../../fp_data", - metavar="str", help='path to output directory (default: ../../fp_data)') -parser.add_argument('--unwrapdelta', type=float, default=0.0, - help='use value UNWRAPDELTA to generate new LAMMPS data file with unwrapped coords (default: 0.0 i.e. off)') - -args = parser.parse_args() - -# Print arguments -print("Parser Arguments") -for arg in vars(args): - print ("%s: %s" % (arg, getattr(args, arg))) - -if (args.water): - print("Using this script to generate fingerprints for water") - - #args.data_dir = '/ascldap/users/jracker/water64cp2k/datast_1593/results/' - - temp_grid = np.array([args.temp]) - #gcc_grid = np.array(['aaaa']) - gcc_grid = np.array([args.gcc]) - - if args.run_all: - gcc_grid = [''.join(i) for i in itertools.product("abcdefghijklmnopqrstuvwxyz",repeat=4)][:1593] - - snapshot_grid = np.array([args.snapshot]) - - cube_filename_head = "w64_" - cube_filename_tail = "-ELECTRON_DENSITY-1_0.cube" - - - #args.atom_based = True - -elif (args.run_all): - - # Currently available - temp_grid = np.array(["298K"]) - # Future -# temp_grid = np.array(["300K", "10000K", "20000K", "30000K"]) - - - # Currently available - gcc_grid = np.array(["2.699"]) -# gcc_grid = np.array(["0.4", "0.6", "0.8", "1.0", "2.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) - - # Future - # Missing gcc = [0.1, 0.2] QE out file -# gcc_grid = np.array(["0.1", "0.2", "0.4", "0.6", "0.8", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) - # Test -# gcc_grid = np.array(["2.0", "3.0"]) - -else: - - temp_grid = np.array([args.temp]) - gcc_grid = np.array([args.gcc]) - -echo_to_screen = False - -#write_to_lammps_file = True - -qe_fname = "QE_Al.scf.pw.snapshot%s.out" % args.snapshot -lammps_fname = "Al.scf.pw.snapshot%s.lammps" % args.snapshot -log_fname = "lammps_fp_%dx%dx%dgrid_%dtwojmax_s%d_q%d_snapshot%s.log" % \ - (args.nxyz, args.nxyz, args.nxyz, args.twojmax, args.switch, args.quadratic, args.snapshot) - - -# First 3 cols are x, y, z, coords - -ncols0 = 3 - -# Analytical relation for fingerprint length -ncoeff = (args.twojmax+2)*(args.twojmax+3)*(args.twojmax+4) -ncoeff = ncoeff // 24 # integer division -if (args.quadratic): - ncoeff += ((ncoeff+1)*ncoeff)//2 -fp_length = ncols0 + ncoeff - - -if (args.water): - qe_format = "cube" - np_fname = "water_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) - lammps_compute_grid_fname = "./in.bgrid.twoelements.python" -else: - qe_format = "espresso-out" - np_fname = "Al_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) - lammps_compute_grid_fname = "./in.bgrid.python" - -lammps_format = "lammps-data" - -# Grid points and training data -nx = args.nxyz -ny = nx -nz = ny - -# Descriptor Length Formula: -# ((tjm + 2) * (tjm + 3) * (tjm + 4)) // 24 + 3 -# 5: 24 -# 8: 58 -# 11: 116 -# 15: 245 - -tic = timeit.default_timer() - -# Loops over temperature grid -for temp in temp_grid: - - print("\nWorking on Temp %s" % temp) - - # Loop over density grid - for gcc in gcc_grid: - - inner_tic = timeit.default_timer() - - temp_dir = args.data_dir + "/%s" % (temp) - gcc_dir = temp_dir + "/%sgcc/" % (gcc) - - if (args.water): - qe_filepath = args.data_dir + cube_filename_head + gcc + cube_filename_tail - lammps_filepath = args.data_dir + cube_filename_head + gcc + ".lammps" - else: - # Make Temp directory - if not os.path.exists(temp_dir): - print("\nWarning! Creating input folder %s" % temp_dir) - os.makedirs(temp_dir) - # Make Density directory - if not os.path.exists(gcc_dir): - print("\nWarning! Creating input folder %s" % gcc_dir) - os.makedirs(gcc_dir) - - qe_filepath = gcc_dir + qe_fname - lammps_filepath = gcc_dir + lammps_fname - - if (args.no_qe): - print("Skipping QE conversion. Reading LAMMPS input directly.") - elif not os.path.exists(qe_filepath): - print("\n\nQE out file %s does not exist! Exiting.\n\n" % (qe_filepath)) - exit(0) - - if (args.no_qe): - if (not os.path.exists(lammps_filepath)): - print("\n\nLAMMPS file %s does not exist! Exiting.\n\n" % (lammps_filepath)) - exit(0) - - elif (not os.path.exists(lammps_filepath) and not args.no_qe): - - if (rank == 0): - print("\nConverting %sgcc file %s to %s!" % (gcc, qe_filepath, lammps_filepath), flush=True) - - atoms = ase.io.read(qe_filepath, format=qe_format); - - if (rank == 0): - print(atoms, flush=True) - - # Write to LAMMPS File - ase.io.write(lammps_filepath, atoms, format=lammps_format) - - if (rank == 0): - print("Wrote QE to file for %s gcc" % (gcc), flush=True) - else: - print("\nLAMMPS input file %s already exists.\n" % lammps_filepath) - - if (echo_to_screen): - lmp_cmdargs = ["-echo", "screen", "-log", log_fname] - else: - lmp_cmdargs = ["-screen", "none", "-log", log_fname] - - lmp_cmdargs = lammps_utils.set_cmdlinevars(lmp_cmdargs, - { - "ngridx":nx, - "ngridy":ny, - "ngridz":nz, - "twojmax":args.twojmax, - "rcutfac":args.rcutfac, - "quadratic":args.quadratic, - "switch":args.switch, - "atom_config_fname":lammps_filepath - } - ) - - lmp = lammps(cmdargs=lmp_cmdargs) - - if (rank == 0): - print("\nComputing fingerprints...", flush=True) - - try: - lmp.file(lammps_compute_grid_fname) - except lammps.LAMMPSException: - if (rank == 0): - print("Bad Read of %s" % (lammps_compute_grid_fname), flush=True) - - # Check atom quantities from LAMMPS - num_atoms = lmp.get_natoms() - if (rank == 0): - print("TEST, NUM_ATOMS: %d" % (num_atoms), flush=True) - - # Extract numpy array pointing to sna/atom array - - if (args.atom_based): - print("\n\nFor Josh\n\n") - exit(0) - - bptr_np = lammps_utils.extract_compute_np(lmp, "b", 1, 2, (num_atoms,ncoeff)) - - - # Extract numpy array pointing to sna/grid array (Z, Y, X ordering) - else: - bptr_np = lammps_utils.extract_compute_np(lmp, "bgrid", 0, 2, (nz,ny,nx,fp_length)) - - # switch from x-fastest to z-fastest order (swaps 0th and 2nd dimension) - bptr_np = bptr_np.transpose([2,1,0,3]) - - # Generate unwrapped LAMMPS coords, if requested - if (args.unwrapdelta != 0.0): - lmp.command("change_box all boundary s s s") - delta = args.unwrapdelta - boxlo,boxhi,xy,yz,xz,periodicity,box_change = lmp.extract_box() - coords = lmp.extract_atom("x",3) - for i in range(num_atoms): - for j in range(3): - if coords[i][j]+delta > boxhi[j]: - coords[i][j] -= boxhi[j]-boxlo[j] - lmp.command("write_data %s.unwrapped" % lammps_filepath) - - if (rank == 0): - print("bptr_np shape = ",bptr_np.shape, flush=True) - - # Output location - if (args.water): - gcc_dir = args.output_dir + gcc + "_" - else: - temp_dir = args.output_dir + "/%s" % temp - gcc_dir = temp_dir + "/%sgcc/" % gcc - - # Make Temp directory - if not os.path.exists(temp_dir): - print("\nCreating output folder %s" % temp_dir) - os.makedirs(temp_dir) - # Make Density directory - if not os.path.exists(gcc_dir): - print("\nCreating output folder %s" % gcc_dir) - os.makedirs(gcc_dir) - - fingerprint_filepath = gcc_dir + np_fname - # Save LAMMPS numpy array as binary - if (rank == 0): - np.save(fingerprint_filepath, bptr_np, allow_pickle=True) - - inner_toc = timeit.default_timer() - - comm.Barrier() - - print("Rank %d Time %s gcc: %4.4f" % (rank, gcc, inner_toc - inner_tic), flush=True) - -toc = timeit.default_timer() - -comm.Barrier() - -print("Rank %d, Total conversion time: %4.4f" % (rank, toc - tic), flush=True) - -comm.Barrier() - -if (rank == 0): - print("\n\nSuccess!", flush=True) From c4661a8d884d77ffbef74b15eab16248e74585bf Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:00:02 +0200 Subject: [PATCH 081/297] Modified generator script to work with MALA Co-authored-by: Aidan Thompson --- mala/descriptors/snap.py | 498 ++++++++++++++------------------------- 1 file changed, 179 insertions(+), 319 deletions(-) diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 45abee06f..9c2fbffda 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -1,325 +1,185 @@ -# -# Generate SNAP descriptors (fingerprints) from atomic congiurations using LAMMPS -# - +"""SNAP descriptor class.""" +import warnings import ase import ase.io -import argparse -import os, sys -import itertools - -import numpy as np -#import io -import timeit - -from lammps import lammps -import lammps_utils - -from ase.calculators.lammpsrun import LAMMPS -from mpi4py import MPI - -comm = MPI.COMM_WORLD - -rank = comm.Get_rank() -ranks = comm.Get_size() - -print("Proc %d out of %d procs" % (rank, ranks), flush=True) - -comm.Barrier() - -if (rank == 0): - print("\n-----------------------------------\n") - print("---- GENERATE LAMMPS FP DATA ----") - print("\n-----------------------------------\n") - -parser = argparse.ArgumentParser(description='Fingerprint Generation') - -parser.add_argument('--water', action='store_true', default=False, - help='run fingerprint generation for water files') -parser.add_argument('--atom-based', action='store_true', default=False, - help='run lammps fingerprints for atoms instead of grid files') -parser.add_argument('--run-all', action='store_true', default=False, - help='run ldos parser for all densities and temperatures') -parser.add_argument('--temp', type=str, default="298K", metavar='S', - help='temperature of fingerprint in units K (default: "298K")') -parser.add_argument('--gcc', type=str, default="2.699", metavar='S', - help='density of fingerprint in units in g/cm^3 (default: "2.699")') -parser.add_argument('--snapshot', type=str, default="0", metavar='S', - help='snapshot number at given gcc/temp (default: "0")') -parser.add_argument('--nxyz', type=int, default=20, metavar='N', - help='number of grid cells in the X/Y/Z dimensions (default: 20)') -parser.add_argument('--rcutfac', type=float, default=4.67637, metavar='R', - help='radius cutoff factor for the fingerprint sphere in Angstroms (default: 4.67637)') -parser.add_argument('--twojmax', type=int, default=10, metavar='N', - help='band limit for fingerprints (default: 10)') -parser.add_argument('--switch', type=int, default=0, metavar='0/1', - help='Use smooth cutoff switching function (default: 0)') -parser.add_argument('--quadratic', type=int, default=0, metavar='0/1', - help='Include quadratic SNAP descriptors (default: 0)') -parser.add_argument('--no-qe', action='store_true', default=False, - help='use LAMMPS input file directly (default: False)') -parser.add_argument('--data-dir', type=str, \ - default="../../fp_data", \ - metavar="str", help='path to data directory with QE output files (default: ../../fp_data)') -parser.add_argument('--output-dir', type=str, default="../../fp_data", - metavar="str", help='path to output directory (default: ../../fp_data)') -parser.add_argument('--unwrapdelta', type=float, default=0.0, - help='use value UNWRAPDELTA to generate new LAMMPS data file with unwrapped coords (default: 0.0 i.e. off)') - -args = parser.parse_args() - -# Print arguments -print("Parser Arguments") -for arg in vars(args): - print ("%s: %s" % (arg, getattr(args, arg))) - -if (args.water): - print("Using this script to generate fingerprints for water") - - #args.data_dir = '/ascldap/users/jracker/water64cp2k/datast_1593/results/' - - temp_grid = np.array([args.temp]) - #gcc_grid = np.array(['aaaa']) - gcc_grid = np.array([args.gcc]) - - if args.run_all: - gcc_grid = [''.join(i) for i in itertools.product("abcdefghijklmnopqrstuvwxyz",repeat=4)][:1593] - - snapshot_grid = np.array([args.snapshot]) - - cube_filename_head = "w64_" - cube_filename_tail = "-ELECTRON_DENSITY-1_0.cube" - - - #args.atom_based = True - -elif (args.run_all): - - # Currently available - temp_grid = np.array(["298K"]) - # Future -# temp_grid = np.array(["300K", "10000K", "20000K", "30000K"]) - - - # Currently available - gcc_grid = np.array(["2.699"]) -# gcc_grid = np.array(["0.4", "0.6", "0.8", "1.0", "2.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) - - # Future - # Missing gcc = [0.1, 0.2] QE out file -# gcc_grid = np.array(["0.1", "0.2", "0.4", "0.6", "0.8", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0", "7.0", "8.0", "9.0"]) - # Test -# gcc_grid = np.array(["2.0", "3.0"]) - -else: - - temp_grid = np.array([args.temp]) - gcc_grid = np.array([args.gcc]) - -echo_to_screen = False - -#write_to_lammps_file = True - -qe_fname = "QE_Al.scf.pw.snapshot%s.out" % args.snapshot -lammps_fname = "Al.scf.pw.snapshot%s.lammps" % args.snapshot -log_fname = "lammps_fp_%dx%dx%dgrid_%dtwojmax_s%d_q%d_snapshot%s.log" % \ - (args.nxyz, args.nxyz, args.nxyz, args.twojmax, args.switch, args.quadratic, args.snapshot) - - -# First 3 cols are x, y, z, coords - -ncols0 = 3 - -# Analytical relation for fingerprint length -ncoeff = (args.twojmax+2)*(args.twojmax+3)*(args.twojmax+4) -ncoeff = ncoeff // 24 # integer division -if (args.quadratic): - ncoeff += ((ncoeff+1)*ncoeff)//2 -fp_length = ncols0 + ncoeff - - -if (args.water): - qe_format = "cube" - np_fname = "water_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) - lammps_compute_grid_fname = "./in.bgrid.twoelements.python" -else: - qe_format = "espresso-out" - np_fname = "Al_fp_%dx%dx%dgrid_%dcomps_snapshot%s" % (args.nxyz, args.nxyz, args.nxyz, fp_length, args.snapshot) - lammps_compute_grid_fname = "./in.bgrid.python" - -lammps_format = "lammps-data" - -# Grid points and training data -nx = args.nxyz -ny = nx -nz = ny - -# Descriptor Length Formula: -# ((tjm + 2) * (tjm + 3) * (tjm + 4)) // 24 + 3 -# 5: 24 -# 8: 58 -# 11: 116 -# 15: 245 - -tic = timeit.default_timer() - -# Loops over temperature grid -for temp in temp_grid: - - print("\nWorking on Temp %s" % temp) - - # Loop over density grid - for gcc in gcc_grid: - - inner_tic = timeit.default_timer() - - temp_dir = args.data_dir + "/%s" % (temp) - gcc_dir = temp_dir + "/%sgcc/" % (gcc) - - if (args.water): - qe_filepath = args.data_dir + cube_filename_head + gcc + cube_filename_tail - lammps_filepath = args.data_dir + cube_filename_head + gcc + ".lammps" +from .lammps_utils import * +from .descriptor_base import DescriptorBase +try: + from lammps import lammps +except ModuleNotFoundError: + warnings.warn("You either don't have LAMMPS installed or it is not " + "configured correctly. Using SNAP descriptors " + "might still work, but trying to calculate SNAP " + "descriptors from atomic positions will crash.", + stacklevel=3) + + +class SNAP(DescriptorBase): + """Class for calculation and parsing of SNAP descriptors.""" + + def __init__(self, parameters): + """ + Create a SNAP object. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + Parameters object used to create this object. + """ + super(SNAP, self).__init__(parameters) + self.in_format_ase = "" + + @staticmethod + def convert_units(array, in_units="None"): + """ + Convert the units of a SNAP descriptor. + + Since these do not really have units this function does nothing yet. + + Parameters + ---------- + array : numpy.array + Data for which the units should be converted. + + in_units : string + Units of array. + + Returns + ------- + converted_array : numpy.array + Data in MALA units. + """ + if in_units == "None": + return array else: - # Make Temp directory - if not os.path.exists(temp_dir): - print("\nWarning! Creating input folder %s" % temp_dir) - os.makedirs(temp_dir) - # Make Density directory - if not os.path.exists(gcc_dir): - print("\nWarning! Creating input folder %s" % gcc_dir) - os.makedirs(gcc_dir) - - qe_filepath = gcc_dir + qe_fname - lammps_filepath = gcc_dir + lammps_fname - - if (args.no_qe): - print("Skipping QE conversion. Reading LAMMPS input directly.") - elif not os.path.exists(qe_filepath): - print("\n\nQE out file %s does not exist! Exiting.\n\n" % (qe_filepath)) - exit(0) - - if (args.no_qe): - if (not os.path.exists(lammps_filepath)): - print("\n\nLAMMPS file %s does not exist! Exiting.\n\n" % (lammps_filepath)) - exit(0) - - elif (not os.path.exists(lammps_filepath) and not args.no_qe): - - if (rank == 0): - print("\nConverting %sgcc file %s to %s!" % (gcc, qe_filepath, lammps_filepath), flush=True) - - atoms = ase.io.read(qe_filepath, format=qe_format); - - if (rank == 0): - print(atoms, flush=True) - - # Write to LAMMPS File - ase.io.write(lammps_filepath, atoms, format=lammps_format) - - if (rank == 0): - print("Wrote QE to file for %s gcc" % (gcc), flush=True) + raise Exception("Unsupported unit for SNAP.") + + @staticmethod + def backconvert_units(array, out_units): + """ + Convert the units of a SNAP descriptor. + + Since these do not really have units this function does nothing yet. + + Parameters + ---------- + array : numpy.array + Data in MALA units. + + out_units : string + Desired units of output array. + + Returns + ------- + converted_array : numpy.array + Data in out_units. + """ + if out_units == "None": + return array else: - print("\nLAMMPS input file %s already exists.\n" % lammps_filepath) - - if (echo_to_screen): - lmp_cmdargs = ["-echo", "screen", "-log", log_fname] - else: - lmp_cmdargs = ["-screen", "none", "-log", log_fname] - - lmp_cmdargs = lammps_utils.set_cmdlinevars(lmp_cmdargs, - { - "ngridx":nx, - "ngridy":ny, - "ngridz":nz, - "twojmax":args.twojmax, - "rcutfac":args.rcutfac, - "quadratic":args.quadratic, - "switch":args.switch, - "atom_config_fname":lammps_filepath - } - ) - - lmp = lammps(cmdargs=lmp_cmdargs) - - if (rank == 0): - print("\nComputing fingerprints...", flush=True) - - try: - lmp.file(lammps_compute_grid_fname) - except lammps.LAMMPSException: - if (rank == 0): - print("Bad Read of %s" % (lammps_compute_grid_fname), flush=True) - - # Check atom quantities from LAMMPS - num_atoms = lmp.get_natoms() - if (rank == 0): - print("TEST, NUM_ATOMS: %d" % (num_atoms), flush=True) - - # Extract numpy array pointing to sna/atom array - - if (args.atom_based): - print("\n\nFor Josh\n\n") - exit(0) - - bptr_np = lammps_utils.extract_compute_np(lmp, "b", 1, 2, (num_atoms,ncoeff)) - - - # Extract numpy array pointing to sna/grid array (Z, Y, X ordering) + raise Exception("Unsupported unit for SNAP.") + + def calculate_from_qe_out(self, qe_out_file, qe_out_directory): + """ + Calculate the SNAP descriptors based on a Quantum Espresso outfile. + + Parameters + ---------- + qe_out_file : string + Name of Quantum Espresso output file for snapshot. + + qe_out_directory : string + Path to Quantum Espresso output file for snapshot. + + Returns + ------- + snap_descriptors : numpy.array + Numpy array containing the SNAP descriptors with the dimension + (x,y,z,snap_dimension) + + """ + self.in_format_ase = "espresso-out" + print("Calculating SNAP descriptors from", qe_out_file, "at", + qe_out_directory) + return self.__calculate_snap(qe_out_directory + qe_out_file, + qe_out_directory) + + def __calculate_snap(self, infile, outdir): + """Perform actual SNAP calculation.""" + from lammps import lammps + lammps_format = "lammps-data" + # We get the atomic information by using ASE. + atoms = ase.io.read(infile, format=self.in_format_ase) + ase_out_path = outdir+"lammps_input.tmp" + ase.io.write(ase_out_path, atoms, format=lammps_format) + + # We also need to know how big the grid is. + # Iterating directly through the file is slow, but the + # grid information is at the top (around line 200). + nx = None + ny = None + nz = None + if len(self.dbg_grid_dimensions) == 3: + nx = self.dbg_grid_dimensions[0] + ny = self.dbg_grid_dimensions[1] + nz = self.dbg_grid_dimensions[2] else: - bptr_np = lammps_utils.extract_compute_np(lmp, "bgrid", 0, 2, (nz,ny,nx,fp_length)) - - # switch from x-fastest to z-fastest order (swaps 0th and 2nd dimension) - bptr_np = bptr_np.transpose([2,1,0,3]) - - # Generate unwrapped LAMMPS coords, if requested - if (args.unwrapdelta != 0.0): - lmp.command("change_box all boundary s s s") - delta = args.unwrapdelta - boxlo,boxhi,xy,yz,xz,periodicity,box_change = lmp.extract_box() - coords = lmp.extract_atom("x",3) - for i in range(num_atoms): - for j in range(3): - if coords[i][j]+delta > boxhi[j]: - coords[i][j] -= boxhi[j]-boxlo[j] - lmp.command("write_data %s.unwrapped" % lammps_filepath) - - if (rank == 0): - print("bptr_np shape = ",bptr_np.shape, flush=True) - - # Output location - if (args.water): - gcc_dir = args.output_dir + gcc + "_" - else: - temp_dir = args.output_dir + "/%s" % temp - gcc_dir = temp_dir + "/%sgcc/" % gcc - - # Make Temp directory - if not os.path.exists(temp_dir): - print("\nCreating output folder %s" % temp_dir) - os.makedirs(temp_dir) - # Make Density directory - if not os.path.exists(gcc_dir): - print("\nCreating output folder %s" % gcc_dir) - os.makedirs(gcc_dir) - - fingerprint_filepath = gcc_dir + np_fname - # Save LAMMPS numpy array as binary - if (rank == 0): - np.save(fingerprint_filepath, bptr_np, allow_pickle=True) - - inner_toc = timeit.default_timer() - - comm.Barrier() - - print("Rank %d Time %s gcc: %4.4f" % (rank, gcc, inner_toc - inner_tic), flush=True) - -toc = timeit.default_timer() - -comm.Barrier() - -print("Rank %d, Total conversion time: %4.4f" % (rank, toc - tic), flush=True) - -comm.Barrier() + qe_outfile = open(infile, "r") + lines = qe_outfile.readlines() + for line in lines: + if "FFT dimensions" in line: + tmp = line.split("(")[1].split(")")[0] + nx = int(tmp.split(",")[0]) + ny = int(tmp.split(",")[1]) + nz = int(tmp.split(",")[2]) + break + # Build LAMMPS arguments from the data we read. + lmp_cmdargs = ["-screen", "none", "-log", outdir+"lammps_log.tmp"] + lmp_cmdargs = set_cmdlinevars(lmp_cmdargs, + { + "ngridx": nx, + "ngridy": ny, + "ngridz": nz, + "twojmax": self.parameters.twojmax, + "rcutfac": self.parameters.rcutfac, + "atom_config_fname": ase_out_path + }) + + # Build the LAMMPS object. + lmp = lammps(cmdargs=lmp_cmdargs) -if (rank == 0): - print("\n\nSuccess!", flush=True) + # An empty string means that the user wants to use the standard input. + if self.parameters.lammps_compute_file == "": + filepath = __file__.split("snap")[0] + self.parameters.lammps_compute_file = filepath+"in.bgrid.python" + + # Do the LAMMPS calculation. + lmp.file(self.parameters.lammps_compute_file) + + # Set things not accessible from LAMMPS + # First 3 cols are x, y, z, coords + ncols0 = 3 + + # Analytical relation for fingerprint length + ncoeff = (self.parameters.twojmax+2) * \ + (self.parameters.twojmax+3)*(self.parameters.twojmax+4) + ncoeff = ncoeff // 24 # integer division + self.fingerprint_length = ncols0+ncoeff + + # Extract data from LAMMPS calculation. + snap_descriptors_np = \ + extract_compute_np(lmp, "bgrid", 0, 2, + (nz, ny, nx, self.fingerprint_length)) + + # switch from x-fastest to z-fastest order (swaps 0th and 2nd + # dimension) + snap_descriptors_np = snap_descriptors_np.transpose([2, 1, 0, 3]) + + # The next two commands can be used to check whether the calculated + # SNAP descriptors + # are identical to the ones calculated with the Sandia workflow. + # They generally are. + # print("snap_descriptors_np shape = ",snap_descriptors_np.shape, + # flush=True) + # np.save(set[4]+"test.npy", snap_descriptors_np, allow_pickle=True) + return snap_descriptors_np From 062fb85dfc1abd6d1235dd0f3ed3bfb7b77dbd75 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:05:42 +0200 Subject: [PATCH 082/297] Copied installation manual for LAMMPS --- docs/source/install/INSTALL_LAMMPS.md | 46 +++++++++++++------ .../descriptors/bispectrum.grid/README | 38 --------------- 2 files changed, 31 insertions(+), 53 deletions(-) delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/README diff --git a/docs/source/install/INSTALL_LAMMPS.md b/docs/source/install/INSTALL_LAMMPS.md index 47ed6048a..a0484fae0 100644 --- a/docs/source/install/INSTALL_LAMMPS.md +++ b/docs/source/install/INSTALL_LAMMPS.md @@ -1,22 +1,38 @@ -# Setting up LAMMPS +This example shows how to generate the SNAP bispectrum on a grid. -## Installing LAMMPS +Usage: -* checkout -* Make sure the compute-grid tree is checked out! -* Compile the LAMMPS shared library with the SNAP package installed - - cd into `/path/to/lammps/src` folder of LAMMPS - - `make yes-snap` - - (optional: check with `make ps` that snap was correctly added) - - `make mode=shlib mpi` +python3.7 ./bgrid.py > bgrid.out -## Make the LAMMPS libray visible +Requirements: -### Method 1 (links, not recommended) +Python 3.6 or 3.7 +LAMMPS, with compute sna/grid, from here: +https://github.com/athomps/lammps/tree/compute-grid -* Make the shared library visible e.g. `ln -s $LAMMPS/src/liblammps.so /path/to/repo/src/datahandling` -* Make the LAMMPS `lammps.py` visible e.g. `ln -s $LAMMPS/python/lammps.py /path/to/repo/src/datahandling` +Before running, you will need to do the following: -### Method 2 (package, recommended) +1. Compile the LAMMPS shared library with the SNAP package installed +2. Make the shared library visible e.g. -* `python3 install.py -m lammps.py -l ../src/liblammps.so -v ../src/version.h` +ln -s $LAMMPS/src/liblammps.so . +(for other ways see https://lammps.sandia.gov/doc/Python_shlib.html) + +3. Make the LAMMPS lammps.py visible e.g. + +ln -s $LAMMPS/python/lammps.py . +(for better ways see https://lammps.sandia.gov/doc/Python_install.html) + +The files that are produced are as follows: + +bgrid.out: Screen output from LAMMPS and bgrid.py +log.lammps: LAMMPS log file +b.dump: per-atom bispectrum components +bgrid.dat: grid bispectrum components as LAMMPS array +bgrid_np.dat: grid bispectrum components as Numpy array +pgrid_np.dat: grid power spectrum components as Numpy array +bgridnorm_np.dat: normalized bispectrum +pgridnorm_np.dat: normalized power spectrum + +You can compare these to the corresponding files +with names containing a timestamp e.g. b.25Oct2019.dump diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/README b/ml-dft-sandia/descriptors/bispectrum.grid/README deleted file mode 100644 index a0484fae0..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/README +++ /dev/null @@ -1,38 +0,0 @@ -This example shows how to generate the SNAP bispectrum on a grid. - -Usage: - -python3.7 ./bgrid.py > bgrid.out - -Requirements: - -Python 3.6 or 3.7 -LAMMPS, with compute sna/grid, from here: -https://github.com/athomps/lammps/tree/compute-grid - -Before running, you will need to do the following: - -1. Compile the LAMMPS shared library with the SNAP package installed -2. Make the shared library visible e.g. - -ln -s $LAMMPS/src/liblammps.so . -(for other ways see https://lammps.sandia.gov/doc/Python_shlib.html) - -3. Make the LAMMPS lammps.py visible e.g. - -ln -s $LAMMPS/python/lammps.py . -(for better ways see https://lammps.sandia.gov/doc/Python_install.html) - -The files that are produced are as follows: - -bgrid.out: Screen output from LAMMPS and bgrid.py -log.lammps: LAMMPS log file -b.dump: per-atom bispectrum components -bgrid.dat: grid bispectrum components as LAMMPS array -bgrid_np.dat: grid bispectrum components as Numpy array -pgrid_np.dat: grid power spectrum components as Numpy array -bgridnorm_np.dat: normalized bispectrum -pgridnorm_np.dat: normalized power spectrum - -You can compare these to the corresponding files -with names containing a timestamp e.g. b.25Oct2019.dump From 367b5dc5ac4a9486ae636a275328bfe7c0136f30 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:07:41 +0200 Subject: [PATCH 083/297] Made installations instructions compliant with MALA documentation --- docs/source/install/INSTALL_LAMMPS.md | 46 +++++++++------------------ 1 file changed, 15 insertions(+), 31 deletions(-) diff --git a/docs/source/install/INSTALL_LAMMPS.md b/docs/source/install/INSTALL_LAMMPS.md index a0484fae0..47ed6048a 100644 --- a/docs/source/install/INSTALL_LAMMPS.md +++ b/docs/source/install/INSTALL_LAMMPS.md @@ -1,38 +1,22 @@ -This example shows how to generate the SNAP bispectrum on a grid. +# Setting up LAMMPS -Usage: +## Installing LAMMPS -python3.7 ./bgrid.py > bgrid.out +* checkout +* Make sure the compute-grid tree is checked out! +* Compile the LAMMPS shared library with the SNAP package installed + - cd into `/path/to/lammps/src` folder of LAMMPS + - `make yes-snap` + - (optional: check with `make ps` that snap was correctly added) + - `make mode=shlib mpi` -Requirements: +## Make the LAMMPS libray visible -Python 3.6 or 3.7 -LAMMPS, with compute sna/grid, from here: -https://github.com/athomps/lammps/tree/compute-grid +### Method 1 (links, not recommended) -Before running, you will need to do the following: +* Make the shared library visible e.g. `ln -s $LAMMPS/src/liblammps.so /path/to/repo/src/datahandling` +* Make the LAMMPS `lammps.py` visible e.g. `ln -s $LAMMPS/python/lammps.py /path/to/repo/src/datahandling` -1. Compile the LAMMPS shared library with the SNAP package installed -2. Make the shared library visible e.g. +### Method 2 (package, recommended) -ln -s $LAMMPS/src/liblammps.so . -(for other ways see https://lammps.sandia.gov/doc/Python_shlib.html) - -3. Make the LAMMPS lammps.py visible e.g. - -ln -s $LAMMPS/python/lammps.py . -(for better ways see https://lammps.sandia.gov/doc/Python_install.html) - -The files that are produced are as follows: - -bgrid.out: Screen output from LAMMPS and bgrid.py -log.lammps: LAMMPS log file -b.dump: per-atom bispectrum components -bgrid.dat: grid bispectrum components as LAMMPS array -bgrid_np.dat: grid bispectrum components as Numpy array -pgrid_np.dat: grid power spectrum components as Numpy array -bgridnorm_np.dat: normalized bispectrum -pgridnorm_np.dat: normalized power spectrum - -You can compare these to the corresponding files -with names containing a timestamp e.g. b.25Oct2019.dump +* `python3 install.py -m lammps.py -l ../src/liblammps.so -v ../src/version.h` From 7329700cf7d4c53ad5cea7873c0662ef249d9bb2 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:09:12 +0200 Subject: [PATCH 084/297] Deleted old test scripts and outputs --- .../bispectrum.grid/b.16Mar2020.dump | 11 - .../bispectrum.grid/b.25Oct2019.dump | 11 - .../bispectrum.grid/bgrid.16Mar2020.dat | 128 -- .../descriptors/bispectrum.grid/bgrid.py | 217 --- .../bispectrum.grid/bgrid_25Oct2019.dat | 48 - .../bispectrum.grid/bgrid_np.16Mar2020.dat | 128 -- .../bispectrum.grid/bgrid_np_25Oct2019.dat | 48 - .../bgridnorm_np.16Mar2020.dat | 128 -- .../log.bgrid.python.16Mar2020 | 61 - .../log.bgrid.python.25Oct2019 | 63 - .../bispectrum.grid/pgrid_np.16Mar2020.dat | 128 -- .../pgridnorm_np.16Mar2020.dat | 128 -- .../descriptors/python_ctypes/README | 35 - .../descriptors/python_ctypes/bgrid.py | 120 -- .../descriptors/python_ctypes/numpy_utils.py | 10 - .../descriptors/python_ctypes/test.f90 | 36 - .../descriptors/python_ctypes/test_library.c | 35 - .../python_ctypes/test_library.cpp | 35 - .../python_ctypes/test_library.f90 | 37 - .../descriptors/python_ctypes/test_library.h | 11 - .../descriptors/python_ctypes/test_library.py | 130 -- .../build_lammps/build_lammps_blake.sh | 19 - .../training_data/scripts/fp/lammps.py | 1170 ----------------- .../training_data/scripts/fp/lammps_utils.py | 26 - .../scripts/fp/submit_blake_script_fp.sh | 47 - .../fp/submit_blake_script_fp_n2048.sh | 41 - .../fp/submit_synapse_water_script_fp.sh | 42 - 27 files changed, 2893 deletions(-) delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/b.16Mar2020.dump delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/b.25Oct2019.dump delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/bgrid.16Mar2020.dat delete mode 100755 ml-dft-sandia/descriptors/bispectrum.grid/bgrid.py delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/bgrid_25Oct2019.dat delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np.16Mar2020.dat delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np_25Oct2019.dat delete mode 100644 ml-dft-sandia/descriptors/bispectrum.grid/bgridnorm_np.16Mar2020.dat delete mode 100644 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ml-dft-sandia/networks/build_lammps/build_lammps_blake.sh delete mode 100644 ml-dft-sandia/networks/training_data/scripts/fp/lammps.py delete mode 100644 ml-dft-sandia/networks/training_data/scripts/fp/lammps_utils.py delete mode 100755 ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp.sh delete mode 100755 ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp_n2048.sh delete mode 100755 ml-dft-sandia/networks/training_data/scripts/fp/submit_synapse_water_script_fp.sh diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/b.16Mar2020.dump b/ml-dft-sandia/descriptors/bispectrum.grid/b.16Mar2020.dump deleted file mode 100644 index f2688d26f..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/b.16Mar2020.dump +++ /dev/null @@ -1,11 +0,0 @@ -ITEM: TIMESTEP -0 -ITEM: NUMBER OF ATOMS -2 -ITEM: BOX BOUNDS pp pp pp -0.0000000000000000e+00 3.3159999999999998e+00 -0.0000000000000000e+00 3.3159999999999998e+00 -0.0000000000000000e+00 3.3159999999999998e+00 -ITEM: ATOMS id x y z c_b[1] c_b[2] c_b[3] c_b[4] c_b[5] -1 0 0 0 108.173 3.21778 0.712238 7.06634 1.04273 -2 1.658 1.658 1.658 108.173 3.21778 0.712238 7.06634 1.04273 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/b.25Oct2019.dump b/ml-dft-sandia/descriptors/bispectrum.grid/b.25Oct2019.dump deleted file mode 100644 index 779a51c6b..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/b.25Oct2019.dump +++ /dev/null @@ -1,11 +0,0 @@ -ITEM: TIMESTEP -0 -ITEM: NUMBER OF ATOMS -2 -ITEM: BOX BOUNDS pp pp pp -0.0000000000000000e+00 3.3159999999999998e+00 -0.0000000000000000e+00 3.3159999999999998e+00 -0.0000000000000000e+00 3.3159999999999998e+00 -ITEM: ATOMS id c_b[1] c_b[2] c_b[3] c_b[4] c_b[5] -1 3375 846.026 36.811 8.51924 0.247118 -2 3375 846.026 36.811 8.51924 0.247118 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.16Mar2020.dat b/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.16Mar2020.dat deleted file mode 100644 index 43c51f441..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.16Mar2020.dat +++ /dev/null @@ -1,128 +0,0 @@ -x, y, z = 0 0 0 -108.173 3.21778 0.712238 7.06634 1.04273 -x, y, z = 1.658 0 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 0.829 0 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0.829 0 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 1.658 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 1.658 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 2.487 0 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 2.487 0 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0 0.4145 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 1.658 0 0.4145 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 0.4145 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 0.829 0.4145 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 1.658 0.4145 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 0.4145 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 0 2.487 0.4145 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 2.487 0.4145 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 0 0.829 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0 0.829 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0.829 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 0.829 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 1.658 0.829 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 1.658 0.829 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 2.487 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 2.487 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 0 1.2435 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 1.658 0 1.2435 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 1.2435 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 0.829 1.2435 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 1.658 1.2435 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 1.2435 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 0 2.487 1.2435 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 2.487 1.2435 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 0 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 0 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 0.829 1.658 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 0.829 1.658 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 1.658 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 1.658 1.658 -108.173 3.21778 0.712238 7.06634 1.04273 -x, y, z = 0 2.487 1.658 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 2.487 1.658 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 0 2.0725 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 1.658 0 2.0725 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 2.0725 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 0.829 2.0725 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 1.658 2.0725 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 2.0725 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 0 2.487 2.0725 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 2.487 2.0725 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 0 2.487 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0 2.487 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0.829 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 0.829 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 1.658 2.487 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 1.658 2.487 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 2.487 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 2.487 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 0 2.9015 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 1.658 0 2.9015 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 2.9015 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 0.829 2.9015 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 1.658 2.9015 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 2.9015 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 0 2.487 2.9015 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 2.487 2.9015 -170.986 6.15471 1.45038 13.3359 2.10603 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.py b/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.py deleted file mode 100755 index ac6dbf418..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid.py +++ /dev/null @@ -1,217 +0,0 @@ -# -# An example of SNAP grid from LAMMPS Python interface -# -# https://lammps.sandia.gov/doc/Python_library.html - - -from lammps import lammps -import lammps_utils - -# define command line input variables - -ngridx = 2 -ngridy = 4 -ngridz = 8 -twojmax = 2 - -lmp_cmdargs = ["-echo","screen"] -lmp_cmdargs = lammps_utils.set_cmdlinevars(lmp_cmdargs, - { - "ngridx":ngridx, - "ngridy":ngridy, - "ngridz":ngridz, - "twojmax":twojmax - } - ) - -# launch LAMMPS instance - -lmp = lammps(cmdargs=lmp_cmdargs) - -# run LAMMPS input script -try: - lmp.file("in.bgrid.python") -except lammps.LAMMPSException: - print("BAD READ") - - -# get quantities from LAMMPS - -num_atoms = lmp.get_natoms() - -# set things not accessible from LAMMPS - -# first 3 cols are x, y, z, coords - -ncols0 = 3 - -# analytical relation - -ncoeff = (twojmax+2)*(twojmax+3)*(twojmax+4) -ncoeff = ncoeff // 24 # integer division -ncols = ncols0+ncoeff -npow = twojmax+1 - -# power spectrum - -powcols = [] -count = 0 -for j1 in range(0,twojmax+1): - for j2 in range(0,j1+1): - for j in range(j1-j2,min(twojmax,j1+j2)+1,2): - if (j>=j1): - print(j1,j2,j) - if j2 == 0: - powcols.append(count) - count += 1 - -if (count != ncoeff): - print("ncoeff does not match count") - exit() -if (len(powcols) != npow): - print("len(powcols) does not match npow") - exit() - -# Find grid point at atom 2 for this grid -# x: 01 | | 2 -# y: 012 | 3 | 4 -# z: 01234 | 567 | 8 -# 2+2*2+4*8 = 38 -iatom2 = 1 -igrid_atom2 = 37 -igridx_atom2 = 1 -igridy_atom2 = 2 -igridz_atom2 = 4 - -# get B_0 at position (0,0,0) in 4 different ways - -# 1. from compute sna/atom - -bptr = lmp.extract_compute("b", 1, 2) # 1 = per-atom data, 2 = array -print("b = ",bptr[iatom2][0]) - -# 2. from compute sna/grid - -bgridptr = lmp.extract_compute("bgrid", 0, 2) # 0 = style global, 2 = type array -print("bgrid = ",bgridptr[igrid_atom2][ncols0+0]) - -# 3. from Numpy array pointing to sna/atom array - -bptr_np = lammps_utils.extract_compute_np(lmp,"b",1,2,(num_atoms,ncoeff)) -print("b_np = ",bptr_np[iatom2][0]) - -# 4. from Numpy array pointing to sna/grid array - -bgridptr_np = lammps_utils.extract_compute_np(lmp,"bgrid",0,2,(ngridz,ngridy,ngridx,ncols)) -print("bgrid_np = ",bgridptr_np[igridz_atom2][igridy_atom2][igridx_atom2][ncols0+0]) - -# print out the LAMMPS array to a file - -outfile = open("bgrid.dat",'w') -igrid = 0 -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr[igrid][0], - bgridptr[igrid][1], - bgridptr[igrid][2])) - for icoeff in range(ncoeff): - outfile.write("%g " % bgridptr[igrid][ncols0+icoeff]) - outfile.write("\n") - igrid += 1 -outfile.close() - -# print out the Numpy array to a file - -outfile = open("bgrid_np.dat",'w') -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr_np[iz][iy][ix][0], - bgridptr_np[iz][iy][ix][1], - bgridptr_np[iz][iy][ix][2])) - for icoeff in range(ncoeff): - outfile.write("%g " % bgridptr_np[iz][iy][ix][ncols0+icoeff]) - outfile.write("\n") -outfile.close() - -# print out the Numpy power spectrum to a file - -outfile = open("pgrid_np.dat",'w') -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr_np[iz][iy][ix][0], - bgridptr_np[iz][iy][ix][1], - bgridptr_np[iz][iy][ix][2])) - for icoeff in powcols: - outfile.write("%g " % bgridptr_np[iz][iy][ix][ncols0+icoeff]) - outfile.write("\n") -outfile.close() - -# print out the rescaled bispectrum -# use this secret recipe for scaling bispectrum -# This is not guaranteed to be the best choice, but it might help -# 1. Divide all B's by 2j+1 -# 2. U_0 = B_(0,0,0)^1/3 -# 3. Divide B_(0,0,0) by U_0^3 -# 4. Divide B_(j,0,j) by U_0 - -outfile = open("bgridnorm_np.dat",'w') -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr_np[iz][iy][ix][0], - bgridptr_np[iz][iy][ix][1], - bgridptr_np[iz][iy][ix][2])) - icoeff = 0 - val = bgridptr_np[iz][iy][ix][ncols0+icoeff] - u0inv = val**(-1.0/3.0) - val = 1.0 - outfile.write("%g " % val) - icoeff += 1 - for j1 in range(1,twojmax+1): - for j2 in range(0,j1+1): - for j in range(j1-j2,min(twojmax,j1+j2)+1,2): - if (j>=j1): - fac = 1.0/(j+1) - if j2==0: fac *= u0inv - val = bgridptr_np[iz][iy][ix][ncols0+icoeff] - val *= fac - outfile.write("%g " % val) - icoeff += 1 - outfile.write("\n") -outfile.close() - -outfile = open("pgridnorm_np.dat",'w') -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr_np[iz][iy][ix][0], - bgridptr_np[iz][iy][ix][1], - bgridptr_np[iz][iy][ix][2])) - icoeff = 0 - val = bgridptr_np[iz][iy][ix][ncols0+icoeff] - u0inv = val**(-1.0/3.0) - val = 1.0 - outfile.write("%g " % val) - icoeff += 1 - for j1 in range(1,twojmax+1): - for j2 in range(0,j1+1): - for j in range(j1-j2,min(twojmax,j1+j2)+1,2): - if (j>=j1): - if j2 == 0: - fac = 1.0/(j+1) - fac *= u0inv - val = bgridptr_np[iz][iy][ix][ncols0+icoeff] - val *= fac - outfile.write("%g " % val) - icoeff += 1 - outfile.write("\n") -outfile.close() - diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_25Oct2019.dat b/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_25Oct2019.dat deleted file mode 100644 index cf9df0f39..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_25Oct2019.dat +++ /dev/null @@ -1,48 +0,0 @@ -x, y, z = 0 0 0 -3375 846.026 36.811 8.51924 0.247118 -x, y, z = 1.658 0 0 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 0 1.10533 0 -13824 7600.15 4937.7 7793.87 3370.55 -x, y, z = 1.658 1.10533 0 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 0 2.21067 0 -13824 7600.15 4937.7 7793.87 3370.55 -x, y, z = 1.658 2.21067 0 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 0 0 0.829 -8000 2884.21 1321.64 2260.21 690.024 -x, y, z = 1.658 0 0.829 -24389 18291.4 15078.4 22097.9 12144 -x, y, z = 0 1.10533 0.829 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 1.10533 0.829 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 2.21067 0.829 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 2.21067 0.829 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 0 1.658 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 1.658 0 1.658 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 0 1.10533 1.658 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 1.658 1.10533 1.658 -8000 2810.9 1206.55 1976.38 566.563 -x, y, z = 0 2.21067 1.658 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 1.658 2.21067 1.658 -8000 2810.9 1206.55 1976.38 566.563 -x, y, z = 0 0 2.487 -8000 2884.21 1321.64 2260.21 690.024 -x, y, z = 1.658 0 2.487 -24389 18291.4 15078.4 22097.9 12144 -x, y, z = 0 1.10533 2.487 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 1.10533 2.487 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 2.21067 2.487 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 2.21067 2.487 -13824 7576.48 4890.17 7695.92 3309.43 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np.16Mar2020.dat b/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np.16Mar2020.dat deleted file mode 100644 index 43c51f441..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np.16Mar2020.dat +++ /dev/null @@ -1,128 +0,0 @@ -x, y, z = 0 0 0 -108.173 3.21778 0.712238 7.06634 1.04273 -x, y, z = 1.658 0 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 0.829 0 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0.829 0 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 1.658 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 1.658 0 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 2.487 0 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 2.487 0 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0 0.4145 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 1.658 0 0.4145 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 0.4145 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 0.829 0.4145 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 1.658 0.4145 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 0.4145 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 0 2.487 0.4145 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 2.487 0.4145 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 0 0.829 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0 0.829 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0.829 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 0.829 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 1.658 0.829 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 1.658 0.829 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 2.487 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 2.487 0.829 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 0 1.2435 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 1.658 0 1.2435 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 1.2435 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 0.829 1.2435 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 1.658 1.2435 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 1.2435 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 0 2.487 1.2435 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 2.487 1.2435 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 0 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 0 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 0 0.829 1.658 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 0.829 1.658 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 1.658 1.658 -174.4 5.59324 1.35171 13.8088 2.13628 -x, y, z = 1.658 1.658 1.658 -108.173 3.21778 0.712238 7.06634 1.04273 -x, y, z = 0 2.487 1.658 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 2.487 1.658 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 0 2.0725 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 1.658 0 2.0725 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 2.0725 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 0.829 2.0725 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 1.658 2.0725 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 2.0725 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 0 2.487 2.0725 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 1.658 2.487 2.0725 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 0 0 2.487 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 1.658 0 2.487 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 0 0.829 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 0.829 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 1.658 2.487 -168.973 6.62458 1.46561 11.0631 1.61839 -x, y, z = 1.658 1.658 2.487 -183.116 3.64679 1.14854 30.7642 7.26691 -x, y, z = 0 2.487 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 1.658 2.487 2.487 -175.638 5.11301 1.3681 19.6169 3.72319 -x, y, z = 0 0 2.9015 -189.401 2.43052 0.962196 44.4583 12.4353 -x, y, z = 1.658 0 2.9015 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 0 0.829 2.9015 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 0.829 2.9015 -170.986 6.15471 1.45038 13.3359 2.10603 -x, y, z = 0 1.658 2.9015 -172.181 6.01397 1.39915 12.5923 1.90608 -x, y, z = 1.658 1.658 2.9015 -175.99 5.14283 1.33312 18.0104 3.26169 -x, y, z = 0 2.487 2.9015 -180.705 4.08676 1.22596 27.1833 6.05503 -x, y, z = 1.658 2.487 2.9015 -170.986 6.15471 1.45038 13.3359 2.10603 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np_25Oct2019.dat b/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np_25Oct2019.dat deleted file mode 100644 index cf9df0f39..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgrid_np_25Oct2019.dat +++ /dev/null @@ -1,48 +0,0 @@ -x, y, z = 0 0 0 -3375 846.026 36.811 8.51924 0.247118 -x, y, z = 1.658 0 0 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 0 1.10533 0 -13824 7600.15 4937.7 7793.87 3370.55 -x, y, z = 1.658 1.10533 0 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 0 2.21067 0 -13824 7600.15 4937.7 7793.87 3370.55 -x, y, z = 1.658 2.21067 0 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 0 0 0.829 -8000 2884.21 1321.64 2260.21 690.024 -x, y, z = 1.658 0 0.829 -24389 18291.4 15078.4 22097.9 12144 -x, y, z = 0 1.10533 0.829 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 1.10533 0.829 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 2.21067 0.829 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 2.21067 0.829 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 0 1.658 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 1.658 0 1.658 -12167 6230.83 3899.16 6359.03 2646.49 -x, y, z = 0 1.10533 1.658 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 1.658 1.10533 1.658 -8000 2810.9 1206.55 1976.38 566.563 -x, y, z = 0 2.21067 1.658 -19683 13358.9 10281.9 15548.9 7976.53 -x, y, z = 1.658 2.21067 1.658 -8000 2810.9 1206.55 1976.38 566.563 -x, y, z = 0 0 2.487 -8000 2884.21 1321.64 2260.21 690.024 -x, y, z = 1.658 0 2.487 -24389 18291.4 15078.4 22097.9 12144 -x, y, z = 0 1.10533 2.487 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 1.10533 2.487 -13824 7576.48 4890.17 7695.92 3309.43 -x, y, z = 0 2.21067 2.487 -15625 9379.61 6608.97 10351.3 4858.23 -x, y, z = 1.658 2.21067 2.487 -13824 7576.48 4890.17 7695.92 3309.43 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/bgridnorm_np.16Mar2020.dat b/ml-dft-sandia/descriptors/bispectrum.grid/bgridnorm_np.16Mar2020.dat deleted file mode 100644 index e896daa52..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/bgridnorm_np.16Mar2020.dat +++ /dev/null @@ -1,128 +0,0 @@ -x, y, z = 0 0 0 -1 0.337666 0.237413 0.494349 0.347577 -x, y, z = 1.658 0 0 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 0 0.829 0 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 1.658 0.829 0 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 0 1.658 0 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 1.658 1.658 0 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 0 2.487 0 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 1.658 2.487 0 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 0 0 0.4145 -1 0.211612 0.320732 2.5805 4.14509 -x, y, z = 1.658 0 0.4145 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 0 0.829 0.4145 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 1.658 0.829 0.4145 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 0 1.658 0.4145 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 1.658 1.658 0.4145 -1 0.458856 0.444374 1.07129 1.08723 -x, y, z = 0 2.487 0.4145 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 1.658 2.487 0.4145 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 0 0 0.829 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 1.658 0 0.829 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 0 0.829 0.829 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 1.658 0.829 0.829 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 0 1.658 0.829 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 1.658 1.658 0.829 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 0 2.487 0.829 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 1.658 2.487 0.829 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 0 0 1.2435 -1 0.458856 0.444374 1.07129 1.08723 -x, y, z = 1.658 0 1.2435 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 0 0.829 1.2435 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 1.658 0.829 1.2435 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 0 1.658 1.2435 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 1.658 1.658 1.2435 -1 0.211612 0.320732 2.5805 4.14509 -x, y, z = 0 2.487 1.2435 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 1.658 2.487 1.2435 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 0 0 1.658 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 1.658 0 1.658 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 0 0.829 1.658 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 1.658 0.829 1.658 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 0 1.658 1.658 -1 0.500554 0.450569 0.823859 0.712093 -x, y, z = 1.658 1.658 1.658 -1 0.337666 0.237413 0.494349 0.347577 -x, y, z = 0 2.487 1.658 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 1.658 2.487 1.658 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 0 0 2.0725 -1 0.458856 0.444374 1.07129 1.08723 -x, y, z = 1.658 0 2.0725 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 0 0.829 2.0725 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 1.658 0.829 2.0725 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 0 1.658 2.0725 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 1.658 1.658 2.0725 -1 0.211612 0.320732 2.5805 4.14509 -x, y, z = 0 2.487 2.0725 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 1.658 2.487 2.0725 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 0 0 2.487 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 1.658 0 2.487 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 0 0.829 2.487 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 1.658 0.829 2.487 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 0 1.658 2.487 -1 0.599133 0.488538 0.667036 0.539463 -x, y, z = 1.658 1.658 2.487 -1 0.321099 0.382846 1.80585 2.4223 -x, y, z = 0 2.487 2.487 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 1.658 2.487 2.487 -1 0.456499 0.456034 1.16763 1.24106 -x, y, z = 0 0 2.9015 -1 0.211612 0.320732 2.5805 4.14509 -x, y, z = 1.658 0 2.9015 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 0 0.829 2.9015 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 1.658 0.829 2.9015 -1 0.554444 0.483461 0.800902 0.702008 -x, y, z = 0 1.658 2.9015 -1 0.540508 0.466385 0.754491 0.635359 -x, y, z = 1.658 1.658 2.9015 -1 0.458856 0.444374 1.07129 1.08723 -x, y, z = 0 2.487 2.9015 -1 0.361431 0.408655 1.60272 2.01834 -x, y, z = 1.658 2.487 2.9015 -1 0.554444 0.483461 0.800902 0.702008 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.16Mar2020 b/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.16Mar2020 deleted file mode 100644 index 2e0fbe0a6..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.16Mar2020 +++ /dev/null @@ -1,61 +0,0 @@ -LAMMPS (19 Sep 2019) -Lattice spacing in x,y,z = 3.316 3.316 3.316 -Created orthogonal box = (0 0 0) to (3.316 3.316 3.316) - 1 by 1 by 1 MPI processor grid -Created 2 atoms - create_atoms CPU = 7.70092e-05 secs -2 atoms in group snapgroup -WARNING: No fixes defined, atoms won't move (../verlet.cpp:52) -Neighbor list info ... - update every 1 steps, delay 10 steps, check yes - max neighbors/atom: 2000, page size: 100000 - master list distance cutoff = 6.67637 - ghost atom cutoff = 6.67637 - binsize = 3.33819, bins = 1 1 1 - 3 neighbor lists, perpetual/occasional/extra = 1 2 0 - (1) pair zero, perpetual - attributes: half, newton on - pair build: half/bin/atomonly/newton - stencil: half/bin/3d/newton - bin: standard - (2) compute sna/atom, occasional - attributes: full, newton on - pair build: full/bin/atomonly - stencil: full/bin/3d - bin: standard - (3) compute sna/grid, occasional - attributes: full, newton on - pair build: full/bin/atomonly - stencil: full/bin/3d - bin: standard -Per MPI rank memory allocation (min/avg/max) = 6.157 | 6.157 | 6.157 Mbytes -Step Temp KinEng PotEng Volume c_bgrid[1][1] - 0 0 0 0 36.462258 0 -Loop time of 9.53674e-07 on 1 procs for 0 steps with 2 atoms - -0.0% CPU use with 1 MPI tasks x no OpenMP threads - -MPI task timing breakdown: -Section | min time | avg time | max time |%varavg| %total ---------------------------------------------------------------- -Pair | 0 | 0 | 0 | 0.0 | 0.00 -Neigh | 0 | 0 | 0 | 0.0 | 0.00 -Comm | 0 | 0 | 0 | 0.0 | 0.00 -Output | 0 | 0 | 0 | 0.0 | 0.00 -Modify | 0 | 0 | 0 | 0.0 | 0.00 -Other | | 9.537e-07 | | |100.00 - -Nlocal: 2 ave 2 max 2 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 339 ave 339 max 339 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 64 ave 64 max 64 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -FullNghs: 128 ave 128 max 128 min -Histogram: 1 0 0 0 0 0 0 0 0 0 - -Total # of neighbors = 128 -Ave neighs/atom = 64 -Neighbor list builds = 0 -Dangerous builds = 0 -Total wall time: 0:00:00 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.25Oct2019 b/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.25Oct2019 deleted file mode 100644 index 5a4e26c7a..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/log.bgrid.python.25Oct2019 +++ /dev/null @@ -1,63 +0,0 @@ -LAMMPS (19 Sep 2019) -OMP_NUM_THREADS environment is not set. Defaulting to 1 thread. (../comm.cpp:93) - using 1 OpenMP thread(s) per MPI task -Lattice spacing in x,y,z = 3.316 3.316 3.316 -Created orthogonal box = (0 0 0) to (3.316 3.316 3.316) - 1 by 1 by 1 MPI processor grid -Created 2 atoms - create_atoms CPU = 7.41482e-05 secs -2 atoms in group snapgroup -WARNING: No fixes defined, atoms won't move (../verlet.cpp:52) -Neighbor list info ... - update every 1 steps, delay 10 steps, check yes - max neighbors/atom: 2000, page size: 100000 - master list distance cutoff = 6.67637 - ghost atom cutoff = 6.67637 - binsize = 3.33819, bins = 1 1 1 - 3 neighbor lists, perpetual/occasional/extra = 1 2 0 - (1) pair zero, perpetual - attributes: half, newton on - pair build: half/bin/atomonly/newton - stencil: half/bin/3d/newton - bin: standard - (2) compute sna/atom, occasional - attributes: full, newton on - pair build: full/bin/atomonly - stencil: full/bin/3d - bin: standard - (3) compute sna/grid, occasional - attributes: full, newton on - pair build: full/bin/atomonly - stencil: full/bin/3d - bin: standard -Per MPI rank memory allocation (min/avg/max) = 6.157 | 6.157 | 6.157 Mbytes -Step Temp KinEng PotEng Volume c_bgrid[1][1] - 0 0 0 0 36.462258 0 -Loop time of 9.53674e-07 on 1 procs for 0 steps with 2 atoms - -104.9% CPU use with 1 MPI tasks x 1 OpenMP threads - -MPI task timing breakdown: -Section | min time | avg time | max time |%varavg| %total ---------------------------------------------------------------- -Pair | 0 | 0 | 0 | 0.0 | 0.00 -Neigh | 0 | 0 | 0 | 0.0 | 0.00 -Comm | 0 | 0 | 0 | 0.0 | 0.00 -Output | 0 | 0 | 0 | 0.0 | 0.00 -Modify | 0 | 0 | 0 | 0.0 | 0.00 -Other | | 9.537e-07 | | |100.00 - -Nlocal: 2 ave 2 max 2 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 339 ave 339 max 339 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 64 ave 64 max 64 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -FullNghs: 128 ave 128 max 128 min -Histogram: 1 0 0 0 0 0 0 0 0 0 - -Total # of neighbors = 128 -Ave neighs/atom = 64 -Neighbor list builds = 0 -Dangerous builds = 0 -Total wall time: 0:00:00 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/pgrid_np.16Mar2020.dat b/ml-dft-sandia/descriptors/bispectrum.grid/pgrid_np.16Mar2020.dat deleted file mode 100644 index 22b832c4e..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/pgrid_np.16Mar2020.dat +++ /dev/null @@ -1,128 +0,0 @@ -x, y, z = 0 0 0 -108.173 3.21778 7.06634 -x, y, z = 1.658 0 0 -174.4 5.59324 13.8088 -x, y, z = 0 0.829 0 -183.116 3.64679 30.7642 -x, y, z = 1.658 0.829 0 -168.973 6.62458 11.0631 -x, y, z = 0 1.658 0 -174.4 5.59324 13.8088 -x, y, z = 1.658 1.658 0 -174.4 5.59324 13.8088 -x, y, z = 0 2.487 0 -183.116 3.64679 30.7642 -x, y, z = 1.658 2.487 0 -168.973 6.62458 11.0631 -x, y, z = 0 0 0.4145 -189.401 2.43052 44.4583 -x, y, z = 1.658 0 0.4145 -172.181 6.01397 12.5923 -x, y, z = 0 0.829 0.4145 -180.705 4.08676 27.1833 -x, y, z = 1.658 0.829 0.4145 -170.986 6.15471 13.3359 -x, y, z = 0 1.658 0.4145 -172.181 6.01397 12.5923 -x, y, z = 1.658 1.658 0.4145 -175.99 5.14283 18.0104 -x, y, z = 0 2.487 0.4145 -180.705 4.08676 27.1833 -x, y, z = 1.658 2.487 0.4145 -170.986 6.15471 13.3359 -x, y, z = 0 0 0.829 -183.116 3.64679 30.7642 -x, y, z = 1.658 0 0.829 -168.973 6.62458 11.0631 -x, y, z = 0 0.829 0.829 -175.638 5.11301 19.6169 -x, y, z = 1.658 0.829 0.829 -175.638 5.11301 19.6169 -x, y, z = 0 1.658 0.829 -168.973 6.62458 11.0631 -x, y, z = 1.658 1.658 0.829 -183.116 3.64679 30.7642 -x, y, z = 0 2.487 0.829 -175.638 5.11301 19.6169 -x, y, z = 1.658 2.487 0.829 -175.638 5.11301 19.6169 -x, y, z = 0 0 1.2435 -175.99 5.14283 18.0104 -x, y, z = 1.658 0 1.2435 -172.181 6.01397 12.5923 -x, y, z = 0 0.829 1.2435 -170.986 6.15471 13.3359 -x, y, z = 1.658 0.829 1.2435 -180.705 4.08676 27.1833 -x, y, z = 0 1.658 1.2435 -172.181 6.01397 12.5923 -x, y, z = 1.658 1.658 1.2435 -189.401 2.43052 44.4583 -x, y, z = 0 2.487 1.2435 -170.986 6.15471 13.3359 -x, y, z = 1.658 2.487 1.2435 -180.705 4.08676 27.1833 -x, y, z = 0 0 1.658 -174.4 5.59324 13.8088 -x, y, z = 1.658 0 1.658 -174.4 5.59324 13.8088 -x, y, z = 0 0.829 1.658 -168.973 6.62458 11.0631 -x, y, z = 1.658 0.829 1.658 -183.116 3.64679 30.7642 -x, y, z = 0 1.658 1.658 -174.4 5.59324 13.8088 -x, y, z = 1.658 1.658 1.658 -108.173 3.21778 7.06634 -x, y, z = 0 2.487 1.658 -168.973 6.62458 11.0631 -x, y, z = 1.658 2.487 1.658 -183.116 3.64679 30.7642 -x, y, z = 0 0 2.0725 -175.99 5.14283 18.0104 -x, y, z = 1.658 0 2.0725 -172.181 6.01397 12.5923 -x, y, z = 0 0.829 2.0725 -170.986 6.15471 13.3359 -x, y, z = 1.658 0.829 2.0725 -180.705 4.08676 27.1833 -x, y, z = 0 1.658 2.0725 -172.181 6.01397 12.5923 -x, y, z = 1.658 1.658 2.0725 -189.401 2.43052 44.4583 -x, y, z = 0 2.487 2.0725 -170.986 6.15471 13.3359 -x, y, z = 1.658 2.487 2.0725 -180.705 4.08676 27.1833 -x, y, z = 0 0 2.487 -183.116 3.64679 30.7642 -x, y, z = 1.658 0 2.487 -168.973 6.62458 11.0631 -x, y, z = 0 0.829 2.487 -175.638 5.11301 19.6169 -x, y, z = 1.658 0.829 2.487 -175.638 5.11301 19.6169 -x, y, z = 0 1.658 2.487 -168.973 6.62458 11.0631 -x, y, z = 1.658 1.658 2.487 -183.116 3.64679 30.7642 -x, y, z = 0 2.487 2.487 -175.638 5.11301 19.6169 -x, y, z = 1.658 2.487 2.487 -175.638 5.11301 19.6169 -x, y, z = 0 0 2.9015 -189.401 2.43052 44.4583 -x, y, z = 1.658 0 2.9015 -172.181 6.01397 12.5923 -x, y, z = 0 0.829 2.9015 -180.705 4.08676 27.1833 -x, y, z = 1.658 0.829 2.9015 -170.986 6.15471 13.3359 -x, y, z = 0 1.658 2.9015 -172.181 6.01397 12.5923 -x, y, z = 1.658 1.658 2.9015 -175.99 5.14283 18.0104 -x, y, z = 0 2.487 2.9015 -180.705 4.08676 27.1833 -x, y, z = 1.658 2.487 2.9015 -170.986 6.15471 13.3359 diff --git a/ml-dft-sandia/descriptors/bispectrum.grid/pgridnorm_np.16Mar2020.dat b/ml-dft-sandia/descriptors/bispectrum.grid/pgridnorm_np.16Mar2020.dat deleted file mode 100644 index adabb7014..000000000 --- a/ml-dft-sandia/descriptors/bispectrum.grid/pgridnorm_np.16Mar2020.dat +++ /dev/null @@ -1,128 +0,0 @@ -x, y, z = 0 0 0 -1 0.337666 0.494349 -x, y, z = 1.658 0 0 -1 0.500554 0.823859 -x, y, z = 0 0.829 0 -1 0.321099 1.80585 -x, y, z = 1.658 0.829 0 -1 0.599133 0.667036 -x, y, z = 0 1.658 0 -1 0.500554 0.823859 -x, y, z = 1.658 1.658 0 -1 0.500554 0.823859 -x, y, z = 0 2.487 0 -1 0.321099 1.80585 -x, y, z = 1.658 2.487 0 -1 0.599133 0.667036 -x, y, z = 0 0 0.4145 -1 0.211612 2.5805 -x, y, z = 1.658 0 0.4145 -1 0.540508 0.754491 -x, y, z = 0 0.829 0.4145 -1 0.361431 1.60272 -x, y, z = 1.658 0.829 0.4145 -1 0.554444 0.800902 -x, y, z = 0 1.658 0.4145 -1 0.540508 0.754491 -x, y, z = 1.658 1.658 0.4145 -1 0.458856 1.07129 -x, y, z = 0 2.487 0.4145 -1 0.361431 1.60272 -x, y, z = 1.658 2.487 0.4145 -1 0.554444 0.800902 -x, y, z = 0 0 0.829 -1 0.321099 1.80585 -x, y, z = 1.658 0 0.829 -1 0.599133 0.667036 -x, y, z = 0 0.829 0.829 -1 0.456499 1.16763 -x, y, z = 1.658 0.829 0.829 -1 0.456499 1.16763 -x, y, z = 0 1.658 0.829 -1 0.599133 0.667036 -x, y, z = 1.658 1.658 0.829 -1 0.321099 1.80585 -x, y, z = 0 2.487 0.829 -1 0.456499 1.16763 -x, y, z = 1.658 2.487 0.829 -1 0.456499 1.16763 -x, y, z = 0 0 1.2435 -1 0.458856 1.07129 -x, y, z = 1.658 0 1.2435 -1 0.540508 0.754491 -x, y, z = 0 0.829 1.2435 -1 0.554444 0.800902 -x, y, z = 1.658 0.829 1.2435 -1 0.361431 1.60272 -x, y, z = 0 1.658 1.2435 -1 0.540508 0.754491 -x, y, z = 1.658 1.658 1.2435 -1 0.211612 2.5805 -x, y, z = 0 2.487 1.2435 -1 0.554444 0.800902 -x, y, z = 1.658 2.487 1.2435 -1 0.361431 1.60272 -x, y, z = 0 0 1.658 -1 0.500554 0.823859 -x, y, z = 1.658 0 1.658 -1 0.500554 0.823859 -x, y, z = 0 0.829 1.658 -1 0.599133 0.667036 -x, y, z = 1.658 0.829 1.658 -1 0.321099 1.80585 -x, y, z = 0 1.658 1.658 -1 0.500554 0.823859 -x, y, z = 1.658 1.658 1.658 -1 0.337666 0.494349 -x, y, z = 0 2.487 1.658 -1 0.599133 0.667036 -x, y, z = 1.658 2.487 1.658 -1 0.321099 1.80585 -x, y, z = 0 0 2.0725 -1 0.458856 1.07129 -x, y, z = 1.658 0 2.0725 -1 0.540508 0.754491 -x, y, z = 0 0.829 2.0725 -1 0.554444 0.800902 -x, y, z = 1.658 0.829 2.0725 -1 0.361431 1.60272 -x, y, z = 0 1.658 2.0725 -1 0.540508 0.754491 -x, y, z = 1.658 1.658 2.0725 -1 0.211612 2.5805 -x, y, z = 0 2.487 2.0725 -1 0.554444 0.800902 -x, y, z = 1.658 2.487 2.0725 -1 0.361431 1.60272 -x, y, z = 0 0 2.487 -1 0.321099 1.80585 -x, y, z = 1.658 0 2.487 -1 0.599133 0.667036 -x, y, z = 0 0.829 2.487 -1 0.456499 1.16763 -x, y, z = 1.658 0.829 2.487 -1 0.456499 1.16763 -x, y, z = 0 1.658 2.487 -1 0.599133 0.667036 -x, y, z = 1.658 1.658 2.487 -1 0.321099 1.80585 -x, y, z = 0 2.487 2.487 -1 0.456499 1.16763 -x, y, z = 1.658 2.487 2.487 -1 0.456499 1.16763 -x, y, z = 0 0 2.9015 -1 0.211612 2.5805 -x, y, z = 1.658 0 2.9015 -1 0.540508 0.754491 -x, y, z = 0 0.829 2.9015 -1 0.361431 1.60272 -x, y, z = 1.658 0.829 2.9015 -1 0.554444 0.800902 -x, y, z = 0 1.658 2.9015 -1 0.540508 0.754491 -x, y, z = 1.658 1.658 2.9015 -1 0.458856 1.07129 -x, y, z = 0 2.487 2.9015 -1 0.361431 1.60272 -x, y, z = 1.658 2.487 2.9015 -1 0.554444 0.800902 diff --git a/ml-dft-sandia/descriptors/python_ctypes/README b/ml-dft-sandia/descriptors/python_ctypes/README deleted file mode 100644 index 76a00aa6a..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/README +++ /dev/null @@ -1,35 +0,0 @@ -This folder contains examples of Python interfaces to C, C++, and F90 functions. -In all cases, the Python ctypes module is used and the functions are -compiled as shared library objects. - -https://docs.python.org/3/library/ctypes.html - -1. Requirements: - -Python 3.0 -GNU gcc/g++/gfortran 6.1 -Include current working directory in LD_LIBRARY_PATH (DYLD_LIBRARY_PATH on Mac): - setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:. - or - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:. - -2. Build Libraries: - -To compile the shared libraries, do: - -gcc -shared -fPIC -o test_libraryc.so test_library.c -g++ -shared -fPIC -o test_librarycpp.so test_library.cpp -gfortran -shared -fPIC -o test_libraryf90.so test_library.f90 - -3. Run test: - -To test the fuctions from Python, do: - -python test_library.py - -Here is a summary of the functions: - -1. hello_world() works for C, C++, and F90 -2. create_matrix() works for C and C++ -3. create_vector() works for F90 -4. create_matrix() works for F90 diff --git a/ml-dft-sandia/descriptors/python_ctypes/bgrid.py b/ml-dft-sandia/descriptors/python_ctypes/bgrid.py deleted file mode 100644 index ee5956695..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/bgrid.py +++ /dev/null @@ -1,120 +0,0 @@ -# -# An example of SNAP grid from LAMMPS Python interface -# -# https://lammps.sandia.gov/doc/Python_library.html - - -from lammps import lammps -import lammps_utils - -import ctypes -#so_file = "helloworld.o" -# so_file = "helloworld.so" -#lib_example = ctypes.cdll.LoadLibrary(so_file) -#lib_example = CDLL("helloworldc.so",RTLD_GLOBAL) -#lib_example = CDLL("helloworldc.so",RTLD_GLOBAL) -#lib_example = ctypes.CDLL("helloworldc.so") -lib_example = ctypes.CDLL("helloworldf90.so") -print("lib_example ",lib_example) - -lib_example.hello_world_.restype = None -lib_example.hello_world_.argtypes = [] -print("lib_example.hello_world()",lib_example.hello_world_) -lib_example.hello_world_() - -exit() - -# define command line input variables - -ngridx = 2 -ngridy = 3 -ngridz = 4 -twojmax = 2 - -lmp_cmdargs = ["-echo","screen"] -lmp_cmdargs = lammps_utils.set_cmdlinevars(lmp_cmdargs, - { - "ngridx":ngridx, - "ngridy":ngridy, - "ngridz":ngridz, - "twojmax":twojmax - } - ) - -# launch LAMMPS instance - -lmp = lammps(cmdargs=lmp_cmdargs) - -# run LAMMPS input script - -lmp.file("in.bgrid.python") - -# get quantities from LAMMPS - -num_atoms = lmp.get_natoms() - -# set things not accessible from LAMMPS - -# first 3 cols are x, y, z, coords - -ncols0 = 3 - -# analytical relation - -ncoeff = (twojmax+2)*(twojmax+3)*(twojmax+4) -ncoeff = ncoeff // 24 # integer division -ncols = ncols0+ncoeff - -# get B_0 at position (0,0,0) in 4 different ways - -# 1. from comute sna/atom - -bptr = lmp.extract_compute("b", 1, 2) # 1 = per-atom data, 2 = array -print("b = ",bptr[0][0]) - -# 2. from compute sna/grid - -bgridptr = lmp.extract_compute("bgrid", 0, 2) # 0 = style global, 2 = type array -print("bgrid = ",bgridptr[0][ncols0+0]) - -# 3. from Numpy array pointing to sna/atom array - -bptr_np = lammps_utils.extract_compute_np(lmp,"b",1,2,(num_atoms,ncoeff)) -print("b_np = ",bptr_np[0][0]) - -# 4. from Numpy array pointing to sna/grid array - -bgridptr_np = lammps_utils.extract_compute_np(lmp,"bgrid",0,2,(ngridz,ngridy,ngridx,ncols)) -print("bgrid_np = ",bgridptr_np[0][0][0][ncols0+0]) - -# print out the LAMMPS array to a file - -outfile = open("bgrid.dat",'w') -igrid = 0 -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr[igrid][0], - bgridptr[igrid][1], - bgridptr[igrid][2])) - for icoeff in range(ncoeff): - outfile.write("%g " % bgridptr[igrid][ncols0+icoeff]) - outfile.write("\n") - igrid += 1 -outfile.close() - -# print out the Numpy array to a file - -outfile = open("bgrid_np.dat",'w') -for iz in range(ngridz): - for iy in range(ngridy): - for ix in range(ngridx): - outfile.write("x, y, z = %g %g %g\n" % - (bgridptr_np[iz][iy][ix][0], - bgridptr_np[iz][iy][ix][1], - bgridptr_np[iz][iy][ix][2])) - for icoeff in range(ncoeff): - outfile.write("%g " % bgridptr_np[iz][iy][ix][ncols0+icoeff]) - outfile.write("\n") -outfile.close() diff --git a/ml-dft-sandia/descriptors/python_ctypes/numpy_utils.py b/ml-dft-sandia/descriptors/python_ctypes/numpy_utils.py deleted file mode 100644 index 14b246ad9..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/numpy_utils.py +++ /dev/null @@ -1,10 +0,0 @@ -from ctypes import * -import numpy as np - -def array2nparray(array, array_shape): - ptr = array.contents - total_size = np.prod(array_shape) - buffer_ptr = cast(ptr, POINTER(c_double * total_size)) - np_array = np.frombuffer(buffer_ptr.contents, dtype=float) - np_array.shape = array_shape - return np_array diff --git a/ml-dft-sandia/descriptors/python_ctypes/test.f90 b/ml-dft-sandia/descriptors/python_ctypes/test.f90 deleted file mode 100644 index 9096fd777..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test.f90 +++ /dev/null @@ -1,36 +0,0 @@ -integer(c_int) :: KNOB = 1337 - -call hello_world() -call create_matrix(2,3) -stop -end - -subroutine hello_world() - write(*,*) "Hello, world!" - return -end subroutine hello_world - -subroutine create_matrix(n1, n2) - integer, intent(in) :: n1 - integer, intent(in) :: n2 - double precision array(2,3) - double precision fac - - write(*,*) "Inside create_matrix_" -! uncommenting these lines gives segfault -! write(*,*) n1,n2 -! fac = 1.0d0/(n1*n2) -! write(*,*) fac - -! do i = 1, n1 -! do j = 1, n2 -! array(i,j) = fac*(i+1)*(j+1) -! write(*,*) array(i,j) -! end do -! end do - write(*,*) "Leaving create_matrix_" - - return -end subroutine create_matrix - - diff --git a/ml-dft-sandia/descriptors/python_ctypes/test_library.c b/ml-dft-sandia/descriptors/python_ctypes/test_library.c deleted file mode 100644 index 3ff2b0cee..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test_library.c +++ /dev/null @@ -1,35 +0,0 @@ -#include -#include -#include "test_library.h" - -void hello_world() { - printf(" Hello, world!\n"); -} - -double** create_matrix(int n1, int n2) { - double **array; - double *values; - - values = (double*) malloc(n1*n2*sizeof(double)); - array = (double**) malloc(n1*sizeof(double*)); - - int n = 0; - for (int i = 0; i < n1; i++) { - array[i] = &values[n]; - n += n2; - } - - double fac = 1.0/(n1*(n2+1)); - for (int i = 0; i < n1; i++) { - for (int j = 0; j < n2; j++) { - array[i][j] = fac*(i+1)*(j+1+1); - } - } - - return array; -} - -void destroy_matrix(double** array) { - free(array[0]); - free(array); -} diff --git a/ml-dft-sandia/descriptors/python_ctypes/test_library.cpp b/ml-dft-sandia/descriptors/python_ctypes/test_library.cpp deleted file mode 100644 index e1f69215c..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test_library.cpp +++ /dev/null @@ -1,35 +0,0 @@ -#include -#include -#include "test_library.h" - -void hello_world() { - printf(" Hello, world!\n"); -} - -double** create_matrix(int n1, int n2) { - double **array; - double *values; - - values = new double[n1*n2]; - array = new double*[n1]; - - int n = 0; - for (int i = 0; i < n1; i++) { - array[i] = &values[n]; - n += n2; - } - - double fac = 1.0/(n1*(n2+1)); - for (int i = 0; i < n1; i++) { - for (int j = 0; j < n2; j++) { - array[i][j] = fac*(i+1)*(j+1+1); - } - } - - return array; -} - -void destroy_matrix(double** array) { - delete [] array[0]; - delete [] array; -} diff --git a/ml-dft-sandia/descriptors/python_ctypes/test_library.f90 b/ml-dft-sandia/descriptors/python_ctypes/test_library.f90 deleted file mode 100644 index 083b4dca2..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test_library.f90 +++ /dev/null @@ -1,37 +0,0 @@ -subroutine hello_world() - write(*,*) "Hello, world!" - return -end subroutine hello_world - -subroutine create_vector(n, myvec) - integer, intent(in), value :: n - double precision, intent(out) :: myvec(n) - double precision fac - - fac = 100.0d0/n - - do i = 1, n - myvec(i) = fac*i - end do - - return -end subroutine create_vector - -subroutine create_matrix(n1, n2, array) - integer, intent(in), value :: n1 - integer, intent(in), value :: n2 - ! note transposed dimensions - double precision, intent(out) :: array(n2, n1) - double precision fac - - fac = 1.0d0/(n1*(n2+1)) - - do i = 1, n1 - do j = 1, n2 - ! note transposed indices - array(j,i) = fac*i*(j+1) - end do - end do - - return -end subroutine create_matrix diff --git a/ml-dft-sandia/descriptors/python_ctypes/test_library.h b/ml-dft-sandia/descriptors/python_ctypes/test_library.h deleted file mode 100644 index 4543545e7..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test_library.h +++ /dev/null @@ -1,11 +0,0 @@ -#ifdef __cplusplus -extern "C" { -#endif - - void hello_world(); - double** create_matrix(int, int); - void destroy_matrix(double**); - -#ifdef __cplusplus -} -#endif diff --git a/ml-dft-sandia/descriptors/python_ctypes/test_library.py b/ml-dft-sandia/descriptors/python_ctypes/test_library.py deleted file mode 100644 index 49c1e160e..000000000 --- a/ml-dft-sandia/descriptors/python_ctypes/test_library.py +++ /dev/null @@ -1,130 +0,0 @@ -# -# An example of accessing Fortran functions via Python ctypes -# - -from ctypes import * -import numpy as np -from numpy_utils import * - -n1 = 3 -n2 = 4 - -# C interface - -print("") -print("# C interface") - -lib_examplec = CDLL("test_libraryc.so",RTLD_GLOBAL) -print("lib_example C",lib_examplec) - -lib_examplec.hello_world.restype = None -lib_examplec.hello_world.argtypes = [] -lib_examplec.hello_world() - -lib_examplec.create_matrix.restype = POINTER(POINTER(c_double)) -lib_examplec.create_matrix.argtypes = [c_int, c_int] -lib_examplec.destroy_matrix.restype = None -lib_examplec.destroy_matrix.argtypes = [POINTER(POINTER(c_double))] -array = lib_examplec.create_matrix(n1, n2) -print("array ptr = ", array) -print("array = ") -for i in range(n1): - for j in range(n2): - print("%8.4f " % array[i][j],end="") - print("") - -np_array = array2nparray(array, (n1,n2)) -print("np_array = \n",np_array) - -lib_examplec.destroy_matrix(array) - -# C++ interface - -print("") -print("# C++ interface") - -lib_examplecpp = CDLL("test_librarycpp.so",RTLD_GLOBAL) -print("lib_example C++",lib_examplecpp) - -lib_examplecpp.hello_world.restype = None -lib_examplecpp.hello_world.argtypes = [] -lib_examplecpp.hello_world() - -lib_examplecpp.create_matrix.restype = POINTER(POINTER(c_double)) -lib_examplecpp.create_matrix.argtypes = [c_int, c_int] -lib_examplecpp.destroy_matrix.restype = None -lib_examplecpp.destroy_matrix.argtypes = [POINTER(POINTER(c_double))] -array = lib_examplecpp.create_matrix(n1, n2) -print("array ptr = ", array) -print("array = ") -for i in range(n1): - for j in range(n2): - print("%8.4f " % array[i][j],end="") - print("") - -np_array = array2nparray(array, (n1,n2)) -print("np_array = \n",np_array) - -lib_examplecpp.destroy_matrix(array) - -# F90 interface - -print("") -print("# F90 interface") - -lib_examplef90 = CDLL("test_libraryf90.so",RTLD_GLOBAL) -print("lib_example F90",lib_examplef90) -lib_examplef90.hello_world_.restype = None -lib_examplef90.hello_world_.argtypes = [] -lib_examplef90.hello_world_() - -lib_examplef90.create_vector_.restype = None -lib_examplef90.create_vector_.argtypes = [c_int, POINTER(c_double)] - -# allocate C++ array -array = lib_examplecpp.create_matrix(1, n1) - -myvec = array[0] -lib_examplef90.create_vector_(n1, cast(myvec, POINTER(c_double))) -print("myvec ptr = ", myvec) -print("myvec = ") -for i in range(n1): - print("%8.4f " % myvec[i]) - -# clean up C++ array -lib_examplecpp.destroy_matrix(array) - -lib_examplef90.create_matrix_.restype = None -# create_matrix takes a double*, not a double**, because in Fortran 2D array -# variables are treated like a pointer to a contiguous block of (column-major) -# memory. -lib_examplef90.create_matrix_.argtypes = [c_int, c_int, POINTER(c_double)] - -# allocate C++ array -array = lib_examplecpp.create_matrix(n1, n2) -print("array (created by C++) = ") -for i in range(n1): - for j in range(n2): - print("%8.4f " % array[i][j],end="") - print("") - -# zero out array -for i in range(n1): - for j in range(n2): - array[i][j] = 0.0 - -# array[0] acts like a double* to the beginning of the block of -# memory -lib_examplef90.create_matrix_(n1, n2, array[0]) -print("array (created by Fortran) = ") -for i in range(n1): - for j in range(n2): - print("%8.4f " % array[i][j],end="") - print("") - -np_array = array2nparray(array, (n1,n2)) -print("np_array = \n",np_array) - -# clean up C++ array -lib_examplecpp.destroy_matrix(array) - diff --git a/ml-dft-sandia/networks/build_lammps/build_lammps_blake.sh b/ml-dft-sandia/networks/build_lammps/build_lammps_blake.sh deleted file mode 100755 index 0dfe955db..000000000 --- a/ml-dft-sandia/networks/build_lammps/build_lammps_blake.sh +++ /dev/null @@ -1,19 +0,0 @@ - - -WORK_DIR=/ascldap/users/johelli/Code/mlmm/mlmm-ldrd-data/ -LAMMPS_DIR=${WORK_DIR}/networks/lammps -BUILD_DIR=${WORK_DIR}/build_lammps - -source ${WORK_DIR}/networks/load_mlmm_blake_modules.sh - -cd ${BUILD_DIR} - -cmake \ --D PKG_SNAP:BOOL=ON \ --D LAMMPS_EXCEPTIONS:BOOL=ON \ --D BUILD_LIB:BOOL=ON \ --D BUILD_SHARED_LIBS:BOOL=ON \ -${LAMMPS_DIR}/cmake/ |& tee OUTPUT.CMAKE - -make -j8 |& tee OUTPUT.MAKE - diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/lammps.py b/ml-dft-sandia/networks/training_data/scripts/fp/lammps.py deleted file mode 100644 index f23268cd6..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/lammps.py +++ /dev/null @@ -1,1170 +0,0 @@ -# ---------------------------------------------------------------------- -# LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator -# http://lammps.sandia.gov, Sandia National Laboratories -# Steve Plimpton, sjplimp@sandia.gov -# -# Copyright (2003) Sandia Corporation. Under the terms of Contract -# DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains -# certain rights in this software. This software is distributed under -# the GNU General Public License. -# -# See the README file in the top-level LAMMPS directory. -# ------------------------------------------------------------------------- - -# Python wrappers on LAMMPS library via ctypes - -# for python3 compatibility - -from __future__ import print_function - -# imports for simple LAMMPS python wrapper module "lammps" - -import sys,traceback,types -from ctypes import * -from os.path import dirname,abspath,join -from inspect import getsourcefile - -# imports for advanced LAMMPS python wrapper modules "PyLammps" and "IPyLammps" - -from collections import namedtuple -import os -import select -import re -import sys - -def get_ctypes_int(size): - if size == 4: - return c_int32 - elif size == 8: - return c_int64 - return c_int - -class MPIAbortException(Exception): - def __init__(self, message): - self.message = message - - def __str__(self): - return repr(self.message) - -class lammps(object): - - # detect if Python is using version of mpi4py that can pass a communicator - - has_mpi4py = False - try: - from mpi4py import MPI - from mpi4py import __version__ as mpi4py_version - if mpi4py_version.split('.')[0] in ['2','3']: has_mpi4py = True - except: - pass - - # create instance of LAMMPS - - def __init__(self,name="",cmdargs=None,ptr=None,comm=None): - self.comm = comm - self.opened = 0 - - # determine module location - - modpath = dirname(abspath(getsourcefile(lambda:0))) - self.lib = None - - # if a pointer to a LAMMPS object is handed in, - # all symbols should already be available - - try: - if ptr: self.lib = CDLL("",RTLD_GLOBAL) - except: - self.lib = None - - # load liblammps.so unless name is given - # if name = "g++", load liblammps_g++.so - # try loading the LAMMPS shared object from the location - # of lammps.py with an absolute path, - # so that LD_LIBRARY_PATH does not need to be set for regular install - # fall back to loading with a relative path, - # typically requires LD_LIBRARY_PATH to be set appropriately - - if any([f.startswith('liblammps') and f.endswith('.dylib') for f in os.listdir(modpath)]): - lib_ext = ".dylib" - else: - lib_ext = ".so" - - if not self.lib: - try: - if not name: self.lib = CDLL(join(modpath,"liblammps" + lib_ext),RTLD_GLOBAL) - else: self.lib = CDLL(join(modpath,"liblammps_%s" % name + lib_ext), - RTLD_GLOBAL) - except: - if not name: self.lib = CDLL("liblammps" + lib_ext,RTLD_GLOBAL) - else: self.lib = CDLL("liblammps_%s" % name + lib_ext,RTLD_GLOBAL) - - # define ctypes API for each library method - # NOTE: should add one of these for each lib function - - self.lib.lammps_extract_box.argtypes = \ - [c_void_p,POINTER(c_double),POINTER(c_double), - POINTER(c_double),POINTER(c_double),POINTER(c_double), - POINTER(c_int),POINTER(c_int)] - self.lib.lammps_extract_box.restype = None - - self.lib.lammps_reset_box.argtypes = \ - [c_void_p,POINTER(c_double),POINTER(c_double),c_double,c_double,c_double] - self.lib.lammps_reset_box.restype = None - - self.lib.lammps_gather_atoms.argtypes = \ - [c_void_p,c_char_p,c_int,c_int,c_void_p] - self.lib.lammps_gather_atoms.restype = None - - self.lib.lammps_gather_atoms_concat.argtypes = \ - [c_void_p,c_char_p,c_int,c_int,c_void_p] - self.lib.lammps_gather_atoms_concat.restype = None - - self.lib.lammps_gather_atoms_subset.argtypes = \ - [c_void_p,c_char_p,c_int,c_int,c_int,POINTER(c_int),c_void_p] - self.lib.lammps_gather_atoms_subset.restype = None - - self.lib.lammps_scatter_atoms.argtypes = \ - [c_void_p,c_char_p,c_int,c_int,c_void_p] - self.lib.lammps_scatter_atoms.restype = None - - self.lib.lammps_scatter_atoms_subset.argtypes = \ - [c_void_p,c_char_p,c_int,c_int,c_int,POINTER(c_int),c_void_p] - self.lib.lammps_scatter_atoms_subset.restype = None - - # if no ptr provided, create an instance of LAMMPS - # don't know how to pass an MPI communicator from PyPar - # but we can pass an MPI communicator from mpi4py v2.0.0 and later - # no_mpi call lets LAMMPS use MPI_COMM_WORLD - # cargs = array of C strings from args - # if ptr, then are embedding Python in LAMMPS input script - # ptr is the desired instance of LAMMPS - # just convert it to ctypes ptr and store in self.lmp - - if not ptr: - - # with mpi4py v2, can pass MPI communicator to LAMMPS - # need to adjust for type of MPI communicator object - # allow for int (like MPICH) or void* (like OpenMPI) - - if comm: - if not lammps.has_mpi4py: - raise Exception('Python mpi4py version is not 2 or 3') - if lammps.MPI._sizeof(lammps.MPI.Comm) == sizeof(c_int): - MPI_Comm = c_int - else: - MPI_Comm = c_void_p - - narg = 0 - cargs = 0 - if cmdargs: - cmdargs.insert(0,"lammps.py") - narg = len(cmdargs) - for i in range(narg): - if type(cmdargs[i]) is str: - cmdargs[i] = cmdargs[i].encode() - cargs = (c_char_p*narg)(*cmdargs) - self.lib.lammps_open.argtypes = [c_int, c_char_p*narg, \ - MPI_Comm, c_void_p()] - else: - self.lib.lammps_open.argtypes = [c_int, c_int, \ - MPI_Comm, c_void_p()] - - self.lib.lammps_open.restype = None - self.opened = 1 - self.lmp = c_void_p() - comm_ptr = lammps.MPI._addressof(comm) - comm_val = MPI_Comm.from_address(comm_ptr) - self.lib.lammps_open(narg,cargs,comm_val,byref(self.lmp)) - - else: - if lammps.has_mpi4py: - from mpi4py import MPI - self.comm = MPI.COMM_WORLD - self.opened = 1 - if cmdargs: - cmdargs.insert(0,"lammps.py") - narg = len(cmdargs) - for i in range(narg): - if type(cmdargs[i]) is str: - cmdargs[i] = cmdargs[i].encode() - cargs = (c_char_p*narg)(*cmdargs) - self.lmp = c_void_p() - self.lib.lammps_open_no_mpi(narg,cargs,byref(self.lmp)) - else: - self.lmp = c_void_p() - self.lib.lammps_open_no_mpi(0,None,byref(self.lmp)) - # could use just this if LAMMPS lib interface supported it - # self.lmp = self.lib.lammps_open_no_mpi(0,None) - - else: - # magic to convert ptr to ctypes ptr - if sys.version_info >= (3, 0): - # Python 3 (uses PyCapsule API) - pythonapi.PyCapsule_GetPointer.restype = c_void_p - pythonapi.PyCapsule_GetPointer.argtypes = [py_object, c_char_p] - self.lmp = c_void_p(pythonapi.PyCapsule_GetPointer(ptr, None)) - else: - # Python 2 (uses PyCObject API) - pythonapi.PyCObject_AsVoidPtr.restype = c_void_p - pythonapi.PyCObject_AsVoidPtr.argtypes = [py_object] - self.lmp = c_void_p(pythonapi.PyCObject_AsVoidPtr(ptr)) - - # optional numpy support (lazy loading) - self._numpy = None - - # set default types - self.c_bigint = get_ctypes_int(self.extract_setting("bigint")) - self.c_tagint = get_ctypes_int(self.extract_setting("tagint")) - self.c_imageint = get_ctypes_int(self.extract_setting("imageint")) - self._installed_packages = None - - # add way to insert Python callback for fix external - self.callback = {} - self.FIX_EXTERNAL_CALLBACK_FUNC = CFUNCTYPE(None, c_void_p, self.c_bigint, c_int, POINTER(self.c_tagint), POINTER(POINTER(c_double)), POINTER(POINTER(c_double))) - self.lib.lammps_set_fix_external_callback.argtypes = [c_void_p, c_char_p, self.FIX_EXTERNAL_CALLBACK_FUNC, c_void_p] - self.lib.lammps_set_fix_external_callback.restype = None - - # shut-down LAMMPS instance - - def __del__(self): - if self.lmp and self.opened: - self.lib.lammps_close(self.lmp) - self.opened = 0 - - def close(self): - if self.opened: self.lib.lammps_close(self.lmp) - self.lmp = None - self.opened = 0 - - def version(self): - return self.lib.lammps_version(self.lmp) - - def file(self,file): - if file: file = file.encode() - self.lib.lammps_file(self.lmp,file) - - # send a single command - - def command(self,cmd): - if cmd: cmd = cmd.encode() - self.lib.lammps_command(self.lmp,cmd) - - if self.has_exceptions and self.lib.lammps_has_error(self.lmp): - sb = create_string_buffer(100) - error_type = self.lib.lammps_get_last_error_message(self.lmp, sb, 100) - error_msg = sb.value.decode().strip() - - if error_type == 2: - raise MPIAbortException(error_msg) - raise Exception(error_msg) - - # send a list of commands - - def commands_list(self,cmdlist): - cmds = [x.encode() for x in cmdlist if type(x) is str] - args = (c_char_p * len(cmdlist))(*cmds) - self.lib.lammps_commands_list(self.lmp,len(cmdlist),args) - - # send a string of commands - - def commands_string(self,multicmd): - if type(multicmd) is str: multicmd = multicmd.encode() - self.lib.lammps_commands_string(self.lmp,c_char_p(multicmd)) - - # extract lammps type byte sizes - - def extract_setting(self, name): - if name: name = name.encode() - self.lib.lammps_extract_setting.restype = c_int - return int(self.lib.lammps_extract_setting(self.lmp,name)) - - # extract global info - - def extract_global(self,name,type): - if name: name = name.encode() - if type == 0: - self.lib.lammps_extract_global.restype = POINTER(c_int) - elif type == 1: - self.lib.lammps_extract_global.restype = POINTER(c_double) - else: return None - ptr = self.lib.lammps_extract_global(self.lmp,name) - return ptr[0] - - # extract global info - - def extract_box(self): - boxlo = (3*c_double)() - boxhi = (3*c_double)() - xy = c_double() - yz = c_double() - xz = c_double() - periodicity = (3*c_int)() - box_change = c_int() - - self.lib.lammps_extract_box(self.lmp,boxlo,boxhi, - byref(xy),byref(yz),byref(xz), - periodicity,byref(box_change)) - - boxlo = boxlo[:3] - boxhi = boxhi[:3] - xy = xy.value - yz = yz.value - xz = xz.value - periodicity = periodicity[:3] - box_change = box_change.value - - return boxlo,boxhi,xy,yz,xz,periodicity,box_change - - # extract per-atom info - # NOTE: need to insure are converting to/from correct Python type - # e.g. for Python list or NumPy or ctypes - - def extract_atom(self,name,type): - if name: name = name.encode() - if type == 0: - self.lib.lammps_extract_atom.restype = POINTER(c_int) - elif type == 1: - self.lib.lammps_extract_atom.restype = POINTER(POINTER(c_int)) - elif type == 2: - self.lib.lammps_extract_atom.restype = POINTER(c_double) - elif type == 3: - self.lib.lammps_extract_atom.restype = POINTER(POINTER(c_double)) - else: return None - ptr = self.lib.lammps_extract_atom(self.lmp,name) - return ptr - - @property - def numpy(self): - if not self._numpy: - import numpy as np - class LammpsNumpyWrapper: - def __init__(self, lmp): - self.lmp = lmp - - def _ctype_to_numpy_int(self, ctype_int): - if ctype_int == c_int32: - return np.int32 - elif ctype_int == c_int64: - return np.int64 - return np.intc - - def extract_atom_iarray(self, name, nelem, dim=1): - if name in ['id', 'molecule']: - c_int_type = self.lmp.c_tagint - elif name in ['image']: - c_int_type = self.lmp.c_imageint - else: - c_int_type = c_int - - np_int_type = self._ctype_to_numpy_int(c_int_type) - - if dim == 1: - tmp = self.lmp.extract_atom(name, 0) - ptr = cast(tmp, POINTER(c_int_type * nelem)) - else: - tmp = self.lmp.extract_atom(name, 1) - ptr = cast(tmp[0], POINTER(c_int_type * nelem * dim)) - - a = np.frombuffer(ptr.contents, dtype=np_int_type) - a.shape = (nelem, dim) - return a - - def extract_atom_darray(self, name, nelem, dim=1): - if dim == 1: - tmp = self.lmp.extract_atom(name, 2) - ptr = cast(tmp, POINTER(c_double * nelem)) - else: - tmp = self.lmp.extract_atom(name, 3) - ptr = cast(tmp[0], POINTER(c_double * nelem * dim)) - - a = np.frombuffer(ptr.contents) - a.shape = (nelem, dim) - return a - - self._numpy = LammpsNumpyWrapper(self) - return self._numpy - - # extract compute info - - def extract_compute(self,id,style,type): - if id: id = id.encode() - if type == 0: - if style > 0: return None - self.lib.lammps_extract_compute.restype = POINTER(c_double) - ptr = self.lib.lammps_extract_compute(self.lmp,id,style,type) - return ptr[0] - if type == 1: - self.lib.lammps_extract_compute.restype = POINTER(c_double) - ptr = self.lib.lammps_extract_compute(self.lmp,id,style,type) - return ptr - if type == 2: - self.lib.lammps_extract_compute.restype = POINTER(POINTER(c_double)) - ptr = self.lib.lammps_extract_compute(self.lmp,id,style,type) - return ptr - return None - - # extract fix info - # in case of global datum, free memory for 1 double via lammps_free() - # double was allocated by library interface function - - def extract_fix(self,id,style,type,i=0,j=0): - if id: id = id.encode() - if style == 0: - self.lib.lammps_extract_fix.restype = POINTER(c_double) - ptr = self.lib.lammps_extract_fix(self.lmp,id,style,type,i,j) - result = ptr[0] - self.lib.lammps_free(ptr) - return result - elif (style == 1) or (style == 2): - if type == 1: - self.lib.lammps_extract_fix.restype = POINTER(c_double) - elif type == 2: - self.lib.lammps_extract_fix.restype = POINTER(POINTER(c_double)) - else: - return None - ptr = self.lib.lammps_extract_fix(self.lmp,id,style,type,i,j) - return ptr - else: - return None - - # extract variable info - # free memory for 1 double or 1 vector of doubles via lammps_free() - # for vector, must copy nlocal returned values to local c_double vector - # memory was allocated by library interface function - - def extract_variable(self,name,group,type): - if name: name = name.encode() - if group: group = group.encode() - if type == 0: - self.lib.lammps_extract_variable.restype = POINTER(c_double) - ptr = self.lib.lammps_extract_variable(self.lmp,name,group) - result = ptr[0] - self.lib.lammps_free(ptr) - return result - if type == 1: - self.lib.lammps_extract_global.restype = POINTER(c_int) - nlocalptr = self.lib.lammps_extract_global(self.lmp,"nlocal".encode()) - nlocal = nlocalptr[0] - result = (c_double*nlocal)() - self.lib.lammps_extract_variable.restype = POINTER(c_double) - ptr = self.lib.lammps_extract_variable(self.lmp,name,group) - for i in range(nlocal): result[i] = ptr[i] - self.lib.lammps_free(ptr) - return result - return None - - # return current value of thermo keyword - - def get_thermo(self,name): - if name: name = name.encode() - self.lib.lammps_get_thermo.restype = c_double - return self.lib.lammps_get_thermo(self.lmp,name) - - # return total number of atoms in system - - def get_natoms(self): - return self.lib.lammps_get_natoms(self.lmp) - - # set variable value - # value is converted to string - # returns 0 for success, -1 if failed - - def set_variable(self,name,value): - if name: name = name.encode() - if value: value = str(value).encode() - return self.lib.lammps_set_variable(self.lmp,name,value) - - # reset simulation box size - - def reset_box(self,boxlo,boxhi,xy,yz,xz): - cboxlo = (3*c_double)(*boxlo) - cboxhi = (3*c_double)(*boxhi) - self.lib.lammps_reset_box(self.lmp,cboxlo,cboxhi,xy,yz,xz) - - # return vector of atom properties gathered across procs - # 3 variants to match src/library.cpp - # name = atom property recognized by LAMMPS in atom->extract() - # type = 0 for integer values, 1 for double values - # count = number of per-atom valus, 1 for type or charge, 3 for x or f - # returned data is a 1d vector - doc how it is ordered? - # NOTE: need to insure are converting to/from correct Python type - # e.g. for Python list or NumPy or ctypes - - def gather_atoms(self,name,type,count): - if name: name = name.encode() - natoms = self.lib.lammps_get_natoms(self.lmp) - if type == 0: - data = ((count*natoms)*c_int)() - self.lib.lammps_gather_atoms(self.lmp,name,type,count,data) - elif type == 1: - data = ((count*natoms)*c_double)() - self.lib.lammps_gather_atoms(self.lmp,name,type,count,data) - else: return None - return data - - def gather_atoms_concat(self,name,type,count): - if name: name = name.encode() - natoms = self.lib.lammps_get_natoms(self.lmp) - if type == 0: - data = ((count*natoms)*c_int)() - self.lib.lammps_gather_atoms_concat(self.lmp,name,type,count,data) - elif type == 1: - data = ((count*natoms)*c_double)() - self.lib.lammps_gather_atoms_concat(self.lmp,name,type,count,data) - else: return None - return data - - def gather_atoms_subset(self,name,type,count,ndata,ids): - if name: name = name.encode() - if type == 0: - data = ((count*ndata)*c_int)() - self.lib.lammps_gather_atoms_subset(self.lmp,name,type,count,ndata,ids,data) - elif type == 1: - data = ((count*ndata)*c_double)() - self.lib.lammps_gather_atoms_subset(self.lmp,name,type,count,ndata,ids,data) - else: return None - return data - - # scatter vector of atom properties across procs - # 2 variants to match src/library.cpp - # name = atom property recognized by LAMMPS in atom->extract() - # type = 0 for integer values, 1 for double values - # count = number of per-atom valus, 1 for type or charge, 3 for x or f - # assume data is of correct type and length, as created by gather_atoms() - # NOTE: need to insure are converting to/from correct Python type - # e.g. for Python list or NumPy or ctypes - - def scatter_atoms(self,name,type,count,data): - if name: name = name.encode() - self.lib.lammps_scatter_atoms(self.lmp,name,type,count,data) - - def scatter_atoms_subset(self,name,type,count,ndata,ids,data): - if name: name = name.encode() - self.lib.lammps_scatter_atoms_subset(self.lmp,name,type,count,ndata,ids,data) - - # create N atoms on all procs - # N = global number of atoms - # id = ID of each atom (optional, can be None) - # type = type of each atom (1 to Ntypes) (required) - # x = coords of each atom as (N,3) array (required) - # v = velocity of each atom as (N,3) array (optional, can be None) - # NOTE: how could we insure are passing correct type to LAMMPS - # e.g. for Python list or NumPy, etc - # ditto for gather_atoms() above - - def create_atoms(self,n,id,type,x,v,image=None,shrinkexceed=False): - if id: - id_lmp = (c_int * n)() - id_lmp[:] = id - else: - id_lmp = id - - if image: - image_lmp = (c_int * n)() - image_lmp[:] = image - else: - image_lmp = image - - type_lmp = (c_int * n)() - type_lmp[:] = type - self.lib.lammps_create_atoms(self.lmp,n,id_lmp,type_lmp,x,v,image_lmp, - shrinkexceed) - - @property - def has_exceptions(self): - """ Return whether the LAMMPS shared library was compiled with C++ exceptions handling enabled """ - return self.lib.lammps_config_has_exceptions() != 0 - - @property - def has_gzip_support(self): - return self.lib.lammps_config_has_gzip_support() != 0 - - @property - def has_png_support(self): - return self.lib.lammps_config_has_png_support() != 0 - - @property - def has_jpeg_support(self): - return self.lib.lammps_config_has_jpeg_support() != 0 - - @property - def has_ffmpeg_support(self): - return self.lib.lammps_config_has_ffmpeg_support() != 0 - - @property - def installed_packages(self): - if self._installed_packages is None: - self._installed_packages = [] - npackages = self.lib.lammps_config_package_count() - sb = create_string_buffer(100) - for idx in range(npackages): - self.lib.lammps_config_package_name(idx, sb, 100) - self._installed_packages.append(sb.value.decode()) - return self._installed_packages - - def set_fix_external_callback(self, fix_name, callback, caller=None): - import numpy as np - def _ctype_to_numpy_int(ctype_int): - if ctype_int == c_int32: - return np.int32 - elif ctype_int == c_int64: - return np.int64 - return np.intc - - def callback_wrapper(caller_ptr, ntimestep, nlocal, tag_ptr, x_ptr, fext_ptr): - if cast(caller_ptr,POINTER(py_object)).contents: - pyCallerObj = cast(caller_ptr,POINTER(py_object)).contents.value - else: - pyCallerObj = None - - tptr = cast(tag_ptr, POINTER(self.c_tagint * nlocal)) - tag = np.frombuffer(tptr.contents, dtype=_ctype_to_numpy_int(self.c_tagint)) - tag.shape = (nlocal) - - xptr = cast(x_ptr[0], POINTER(c_double * nlocal * 3)) - x = np.frombuffer(xptr.contents) - x.shape = (nlocal, 3) - - fptr = cast(fext_ptr[0], POINTER(c_double * nlocal * 3)) - f = np.frombuffer(fptr.contents) - f.shape = (nlocal, 3) - - callback(pyCallerObj, ntimestep, nlocal, tag, x, f) - - cFunc = self.FIX_EXTERNAL_CALLBACK_FUNC(callback_wrapper) - cCaller = cast(pointer(py_object(caller)), c_void_p) - - self.callback[fix_name] = { 'function': cFunc, 'caller': caller } - - self.lib.lammps_set_fix_external_callback(self.lmp, fix_name.encode(), cFunc, cCaller) - -# ------------------------------------------------------------------------- -# ------------------------------------------------------------------------- -# ------------------------------------------------------------------------- - -################################################################################ -# Alternative Python Wrapper -# Written by Richard Berger -################################################################################ - -class OutputCapture(object): - """ Utility class to capture LAMMPS library output """ - - def __init__(self): - self.stdout_pipe_read, self.stdout_pipe_write = os.pipe() - self.stdout_fd = 1 - - def __enter__(self): - self.stdout = os.dup(self.stdout_fd) - os.dup2(self.stdout_pipe_write, self.stdout_fd) - return self - - def __exit__(self, type, value, tracebac): - os.dup2(self.stdout, self.stdout_fd) - os.close(self.stdout) - os.close(self.stdout_pipe_read) - os.close(self.stdout_pipe_write) - - # check if we have more to read from the pipe - def more_data(self, pipe): - r, _, _ = select.select([pipe], [], [], 0) - return bool(r) - - # read the whole pipe - def read_pipe(self, pipe): - out = "" - while self.more_data(pipe): - out += os.read(pipe, 1024).decode() - return out - - @property - def output(self): - return self.read_pipe(self.stdout_pipe_read) - - -class Variable(object): - def __init__(self, lammps_wrapper_instance, name, style, definition): - self.wrapper = lammps_wrapper_instance - self.name = name - self.style = style - self.definition = definition.split() - - @property - def value(self): - if self.style == 'atom': - return list(self.wrapper.lmp.extract_variable(self.name, "all", 1)) - else: - value = self.wrapper.lmp_print('"${%s}"' % self.name).strip() - try: - return float(value) - except ValueError: - return value - - -class AtomList(object): - def __init__(self, lammps_wrapper_instance): - self.lmp = lammps_wrapper_instance - self.natoms = self.lmp.system.natoms - self.dimensions = self.lmp.system.dimensions - - def __getitem__(self, index): - if self.dimensions == 2: - return Atom2D(self.lmp, index + 1) - return Atom(self.lmp, index + 1) - - -class Atom(object): - def __init__(self, lammps_wrapper_instance, index): - self.lmp = lammps_wrapper_instance - self.index = index - - @property - def id(self): - return int(self.lmp.eval("id[%d]" % self.index)) - - @property - def type(self): - return int(self.lmp.eval("type[%d]" % self.index)) - - @property - def mol(self): - return self.lmp.eval("mol[%d]" % self.index) - - @property - def mass(self): - return self.lmp.eval("mass[%d]" % self.index) - - @property - def position(self): - return (self.lmp.eval("x[%d]" % self.index), - self.lmp.eval("y[%d]" % self.index), - self.lmp.eval("z[%d]" % self.index)) - - @position.setter - def position(self, value): - self.lmp.set("atom", self.index, "x", value[0]) - self.lmp.set("atom", self.index, "y", value[1]) - self.lmp.set("atom", self.index, "z", value[2]) - - @property - def velocity(self): - return (self.lmp.eval("vx[%d]" % self.index), - self.lmp.eval("vy[%d]" % self.index), - self.lmp.eval("vz[%d]" % self.index)) - - @velocity.setter - def velocity(self, value): - self.lmp.set("atom", self.index, "vx", value[0]) - self.lmp.set("atom", self.index, "vy", value[1]) - self.lmp.set("atom", self.index, "vz", value[2]) - - @property - def force(self): - return (self.lmp.eval("fx[%d]" % self.index), - self.lmp.eval("fy[%d]" % self.index), - self.lmp.eval("fz[%d]" % self.index)) - - @property - def charge(self): - return self.lmp.eval("q[%d]" % self.index) - - -class Atom2D(Atom): - def __init__(self, lammps_wrapper_instance, index): - super(Atom2D, self).__init__(lammps_wrapper_instance, index) - - @property - def position(self): - return (self.lmp.eval("x[%d]" % self.index), - self.lmp.eval("y[%d]" % self.index)) - - @position.setter - def position(self, value): - self.lmp.set("atom", self.index, "x", value[0]) - self.lmp.set("atom", self.index, "y", value[1]) - - @property - def velocity(self): - return (self.lmp.eval("vx[%d]" % self.index), - self.lmp.eval("vy[%d]" % self.index)) - - @velocity.setter - def velocity(self, value): - self.lmp.set("atom", self.index, "vx", value[0]) - self.lmp.set("atom", self.index, "vy", value[1]) - - @property - def force(self): - return (self.lmp.eval("fx[%d]" % self.index), - self.lmp.eval("fy[%d]" % self.index)) - - -class variable_set: - def __init__(self, name, variable_dict): - self._name = name - array_pattern = re.compile(r"(?P.+)\[(?P[0-9]+)\]") - - for key, value in variable_dict.items(): - m = array_pattern.match(key) - if m: - g = m.groupdict() - varname = g['arr'] - idx = int(g['index']) - if varname not in self.__dict__: - self.__dict__[varname] = {} - self.__dict__[varname][idx] = value - else: - self.__dict__[key] = value - - def __str__(self): - return "{}({})".format(self._name, ','.join(["{}={}".format(k, self.__dict__[k]) for k in self.__dict__.keys() if not k.startswith('_')])) - - def __repr__(self): - return self.__str__() - - -def get_thermo_data(output): - """ traverse output of runs and extract thermo data columns """ - if isinstance(output, str): - lines = output.splitlines() - else: - lines = output - - runs = [] - columns = [] - in_run = False - current_run = {} - - for line in lines: - if line.startswith("Per MPI rank memory allocation"): - in_run = True - elif in_run and len(columns) == 0: - # first line after memory usage are column names - columns = line.split() - - current_run = {} - - for col in columns: - current_run[col] = [] - - elif line.startswith("Loop time of "): - in_run = False - columns = None - thermo_data = variable_set('ThermoData', current_run) - r = {'thermo' : thermo_data } - runs.append(namedtuple('Run', list(r.keys()))(*list(r.values()))) - elif in_run and len(columns) > 0: - values = [float(x) for x in line.split()] - - for i, col in enumerate(columns): - current_run[col].append(values[i]) - return runs - -class PyLammps(object): - """ - More Python-like wrapper for LAMMPS (e.g., for iPython) - See examples/ipython for usage - """ - - def __init__(self,name="",cmdargs=None,ptr=None,comm=None): - if ptr: - if isinstance(ptr,PyLammps): - self.lmp = ptr.lmp - elif isinstance(ptr,lammps): - self.lmp = ptr - else: - self.lmp = lammps(name=name,cmdargs=cmdargs,ptr=ptr,comm=comm) - else: - self.lmp = lammps(name=name,cmdargs=cmdargs,ptr=None,comm=comm) - print("LAMMPS output is captured by PyLammps wrapper") - self._cmd_history = [] - self.runs = [] - - def __del__(self): - if self.lmp: self.lmp.close() - self.lmp = None - - def close(self): - if self.lmp: self.lmp.close() - self.lmp = None - - def version(self): - return self.lmp.version() - - def file(self,file): - self.lmp.file(file) - - def write_script(self,filename): - """ Write LAMMPS script file containing all commands executed up until now """ - with open(filename, "w") as f: - for cmd in self._cmd_history: - f.write("%s\n" % cmd) - - def command(self,cmd): - self.lmp.command(cmd) - self._cmd_history.append(cmd) - - def run(self, *args, **kwargs): - output = self.__getattr__('run')(*args, **kwargs) - - if(lammps.has_mpi4py): - output = self.lmp.comm.bcast(output, root=0) - - self.runs += get_thermo_data(output) - return output - - @property - def last_run(self): - if len(self.runs) > 0: - return self.runs[-1] - return None - - @property - def atoms(self): - return AtomList(self) - - @property - def system(self): - output = self.info("system") - d = self._parse_info_system(output) - return namedtuple('System', d.keys())(*d.values()) - - @property - def communication(self): - output = self.info("communication") - d = self._parse_info_communication(output) - return namedtuple('Communication', d.keys())(*d.values()) - - @property - def computes(self): - output = self.info("computes") - return self._parse_element_list(output) - - @property - def dumps(self): - output = self.info("dumps") - return self._parse_element_list(output) - - @property - def fixes(self): - output = self.info("fixes") - return self._parse_element_list(output) - - @property - def groups(self): - output = self.info("groups") - return self._parse_groups(output) - - @property - def variables(self): - output = self.info("variables") - vars = {} - for v in self._parse_element_list(output): - vars[v['name']] = Variable(self, v['name'], v['style'], v['def']) - return vars - - def eval(self, expr): - value = self.lmp_print('"$(%s)"' % expr).strip() - try: - return float(value) - except ValueError: - return value - - def _split_values(self, line): - return [x.strip() for x in line.split(',')] - - def _get_pair(self, value): - return [x.strip() for x in value.split('=')] - - def _parse_info_system(self, output): - lines = output[6:-2] - system = {} - - for line in lines: - if line.startswith("Units"): - system['units'] = self._get_pair(line)[1] - elif line.startswith("Atom style"): - system['atom_style'] = self._get_pair(line)[1] - elif line.startswith("Atom map"): - system['atom_map'] = self._get_pair(line)[1] - elif line.startswith("Atoms"): - parts = self._split_values(line) - system['natoms'] = int(self._get_pair(parts[0])[1]) - system['ntypes'] = int(self._get_pair(parts[1])[1]) - system['style'] = self._get_pair(parts[2])[1] - elif line.startswith("Kspace style"): - system['kspace_style'] = self._get_pair(line)[1] - elif line.startswith("Dimensions"): - system['dimensions'] = int(self._get_pair(line)[1]) - elif line.startswith("Orthogonal box"): - system['orthogonal_box'] = [float(x) for x in self._get_pair(line)[1].split('x')] - elif line.startswith("Boundaries"): - system['boundaries'] = self._get_pair(line)[1] - elif line.startswith("xlo"): - keys, values = [self._split_values(x) for x in self._get_pair(line)] - for key, value in zip(keys, values): - system[key] = float(value) - elif line.startswith("ylo"): - keys, values = [self._split_values(x) for x in self._get_pair(line)] - for key, value in zip(keys, values): - system[key] = float(value) - elif line.startswith("zlo"): - keys, values = [self._split_values(x) for x in self._get_pair(line)] - for key, value in zip(keys, values): - system[key] = float(value) - elif line.startswith("Molecule type"): - system['molecule_type'] = self._get_pair(line)[1] - elif line.startswith("Bonds"): - parts = self._split_values(line) - system['nbonds'] = int(self._get_pair(parts[0])[1]) - system['nbondtypes'] = int(self._get_pair(parts[1])[1]) - system['bond_style'] = self._get_pair(parts[2])[1] - elif line.startswith("Angles"): - parts = self._split_values(line) - system['nangles'] = int(self._get_pair(parts[0])[1]) - system['nangletypes'] = int(self._get_pair(parts[1])[1]) - system['angle_style'] = self._get_pair(parts[2])[1] - elif line.startswith("Dihedrals"): - parts = self._split_values(line) - system['ndihedrals'] = int(self._get_pair(parts[0])[1]) - system['ndihedraltypes'] = int(self._get_pair(parts[1])[1]) - system['dihedral_style'] = self._get_pair(parts[2])[1] - elif line.startswith("Impropers"): - parts = self._split_values(line) - system['nimpropers'] = int(self._get_pair(parts[0])[1]) - system['nimpropertypes'] = int(self._get_pair(parts[1])[1]) - system['improper_style'] = self._get_pair(parts[2])[1] - - return system - - def _parse_info_communication(self, output): - lines = output[6:-3] - comm = {} - - for line in lines: - if line.startswith("MPI library"): - comm['mpi_version'] = line.split(':')[1].strip() - elif line.startswith("Comm style"): - parts = self._split_values(line) - comm['comm_style'] = self._get_pair(parts[0])[1] - comm['comm_layout'] = self._get_pair(parts[1])[1] - elif line.startswith("Processor grid"): - comm['proc_grid'] = [int(x) for x in self._get_pair(line)[1].split('x')] - elif line.startswith("Communicate velocities for ghost atoms"): - comm['ghost_velocity'] = (self._get_pair(line)[1] == "yes") - elif line.startswith("Nprocs"): - parts = self._split_values(line) - comm['nprocs'] = int(self._get_pair(parts[0])[1]) - comm['nthreads'] = int(self._get_pair(parts[1])[1]) - return comm - - def _parse_element_list(self, output): - lines = output[6:-3] - elements = [] - - for line in lines: - element_info = self._split_values(line.split(':')[1].strip()) - element = {'name': element_info[0]} - for key, value in [self._get_pair(x) for x in element_info[1:]]: - element[key] = value - elements.append(element) - return elements - - def _parse_groups(self, output): - lines = output[6:-3] - groups = [] - group_pattern = re.compile(r"(?P.+) \((?P.+)\)") - - for line in lines: - m = group_pattern.match(line.split(':')[1].strip()) - group = {'name': m.group('name'), 'type': m.group('type')} - groups.append(group) - return groups - - def lmp_print(self, s): - """ needed for Python2 compatibility, since print is a reserved keyword """ - return self.__getattr__("print")(s) - - def __dir__(self): - return ['angle_coeff', 'angle_style', 'atom_modify', 'atom_style', 'atom_style', - 'bond_coeff', 'bond_style', 'boundary', 'change_box', 'communicate', 'compute', - 'create_atoms', 'create_box', 'delete_atoms', 'delete_bonds', 'dielectric', - 'dihedral_coeff', 'dihedral_style', 'dimension', 'dump', 'fix', 'fix_modify', - 'group', 'improper_coeff', 'improper_style', 'include', 'kspace_modify', - 'kspace_style', 'lattice', 'mass', 'minimize', 'min_style', 'neighbor', - 'neigh_modify', 'newton', 'nthreads', 'pair_coeff', 'pair_modify', - 'pair_style', 'processors', 'read', 'read_data', 'read_restart', 'region', - 'replicate', 'reset_timestep', 'restart', 'run', 'run_style', 'thermo', - 'thermo_modify', 'thermo_style', 'timestep', 'undump', 'unfix', 'units', - 'variable', 'velocity', 'write_restart'] - - def __getattr__(self, name): - def handler(*args, **kwargs): - cmd_args = [name] + [str(x) for x in args] - - with OutputCapture() as capture: - self.command(' '.join(cmd_args)) - output = capture.output - - if 'verbose' in kwargs and kwargs['verbose']: - print(output) - - lines = output.splitlines() - - if len(lines) > 1: - return lines - elif len(lines) == 1: - return lines[0] - return None - - return handler - - -class IPyLammps(PyLammps): - """ - iPython wrapper for LAMMPS which adds embedded graphics capabilities - """ - - def __init__(self,name="",cmdargs=None,ptr=None,comm=None): - super(IPyLammps, self).__init__(name=name,cmdargs=cmdargs,ptr=ptr,comm=comm) - - def image(self, filename="snapshot.png", group="all", color="type", diameter="type", - size=None, view=None, center=None, up=None, zoom=1.0): - cmd_args = [group, "image", filename, color, diameter] - - if size: - width = size[0] - height = size[1] - cmd_args += ["size", width, height] - - if view: - theta = view[0] - phi = view[1] - cmd_args += ["view", theta, phi] - - if center: - flag = center[0] - Cx = center[1] - Cy = center[2] - Cz = center[3] - cmd_args += ["center", flag, Cx, Cy, Cz] - - if up: - Ux = up[0] - Uy = up[1] - Uz = up[2] - cmd_args += ["up", Ux, Uy, Uz] - - if zoom: - cmd_args += ["zoom", zoom] - - cmd_args.append("modify backcolor white") - - self.write_dump(*cmd_args) - from IPython.core.display import Image - return Image('snapshot.png') - - def video(self, filename): - from IPython.display import HTML - return HTML("") diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/lammps_utils.py b/ml-dft-sandia/networks/training_data/scripts/fp/lammps_utils.py deleted file mode 100644 index ec02daadc..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/lammps_utils.py +++ /dev/null @@ -1,26 +0,0 @@ -import numpy as np -import ctypes - -def set_cmdlinevars(cmdargs, argdict): - for key in argdict.keys(): - cmdargs += ["-var",key,f"{argdict[key]}"] - return cmdargs - -def extract_commands(string): - return [x for x in string.splitlines() if x.strip() != ''] - -def extract_compute_np(lmp,name,compute_type,result_type,array_shape): - """ - Convert a lammps compute to a numpy array. - Assumes the compute returns a floating point numbers. - Note that the result is a view into the original memory. - If the result type is 0 (scalar) then conversion to numpy is skipped and a python float is returned. - """ - ptr = lmp.extract_compute(name, compute_type, result_type) # 1,2: Style (1) is per-atom compute, returns array type (2). - if result_type == 0: return ptr # No casting needed, lammps.py already works - if result_type == 2: ptr = ptr.contents - total_size = np.prod(array_shape) - buffer_ptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_double * total_size)) - array_np = np.frombuffer(buffer_ptr.contents, dtype=float) - array_np.shape = array_shape - return array_np diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp.sh b/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp.sh deleted file mode 100755 index 76cab95e0..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -#SBATCH -N 4 -#SBATCH -n 32 -#SBATCH -p blake -#SBATCH -A johelli -#SBATCH --time=24:00:00 -#SBATCH --job-name=fp_gen - - -# Run `python3 ldos_example.py --help` for more option information - -NODES=4 -RPN=8 -RANKS=$((${NODES}*${RPN})); - -echo "Total ranks: ${RANKS}" - -# 8 million grid pts -NXYZ="200" -#NXYZ="20" - -#EXE="mpirun -np ${RANKS} -bind-to none -map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH -mca pml ob1 -mca btl ^openib" -EXE="mpirun -np ${RANKS}" - - -#TEMPS="300K 10000K 20000K 30000K" -TEMPS="300K" - -#GCCS="0.1 0.2 0.4 0.6 0.8 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0" -GCCS="0.4 0.6 0.8 1.0 2.0 3.0 4.0 6.0 7.0 8.0 9.0" -#GCCS="2.0" - -echo $(date) - -for T in $TEMPS -do - for G in $GCCS - do - echo "Running fingerprint generation for temp ${T} and gcc ${G} with nxyz ${NXYZ}" - - ${EXE} python3 generate_fingerprints.py --temp ${T} --gcc ${G} --nxyz ${NXYZ} > logs/gen_fp_n${NODES}_ranks${RANKS}_temp${T}_gcc${G}_nxyz${NXYZ}.log - done -done - -echo $(date) - -exit 0 diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp_n2048.sh b/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp_n2048.sh deleted file mode 100755 index cb5345fa3..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/submit_blake_script_fp_n2048.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -#SBATCH -N 4 -#SBATCH -n 4 -#SBATCH -p blake -#SBATCH -A athomps -#SBATCH --time=48:00:00 -#SBATCH --job-name=fp_gen - - -# Run `python3 ldos_example.py --help` for more option information - -NODES=4 -RPN=1 -RANKS=$((${NODES}*${RPN})); - -echo "Total ranks: ${RANKS}" - -# 64 million grid pts -NXYZ="400" -#NXYZ="20" -NSTRING="n2048" -NSTRINGARG="--nstring ${NSTRING}" -#NSTRING="" -#NSTRINGARG="" - -#EXE="mpirun -np ${RANKS} -bind-to none -map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH -mca pml ob1 -mca btl ^openib" -EXE="mpirun -np ${RANKS}" - - -#TEMPS="300K 10000K 20000K 30000K" -#TEMPS="300K" - -#GCCS="0.1 0.2 0.4 0.6 0.8 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0" -#GCCS="0.4 0.6 0.8 1.0 2.0 3.0 4.0 6.0 7.0 8.0 9.0" -#GCCS="2.0" - -echo $(date) -${EXE} python generate_fingerprints.py ${NSTRINGARG} --nxyz ${NXYZ} > logs/gen_fp${NSTRING}_n${NODES}_ranks${RANKS}_nxyz${NXYZ}.log -echo $(date) - -exit 0 diff --git a/ml-dft-sandia/networks/training_data/scripts/fp/submit_synapse_water_script_fp.sh b/ml-dft-sandia/networks/training_data/scripts/fp/submit_synapse_water_script_fp.sh deleted file mode 100755 index 283290e4d..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/fp/submit_synapse_water_script_fp.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - - -# Run `python3 ldos_example.py --help` for more option information - -NODES=2 -RPN=19 -RANKS=$((${NODES}*${RPN})); - -echo "Total ranks: ${RANKS}" - -EXE="mpirun -np ${RANKS}" - -TEMPS="298K" - -#GCCS="0.4 0.6 0.8 1.0 2.0 3.0 4.0 6.0 7.0 8.0 9.0" -FILEPATH="/home/jracker/qmdata/w64_data/cube_data/*.cube" - - -echo $(date) - -for T in $TEMPS -do - for G in $FILEPATH - do - PREFIX="/home/jracker/qmdata/w64_data/cube_data/w64_" - GG=${G%-ELECTRON_DENSITY-1_0.cube} - GGG=${GG#$PREFIX} - echo "Running fingerprint generation for temp ${T} and gcc ${GGG}" - - #check if this file exists, if so skip! - - - ${EXE} python generate_fingerprints.py --water --gcc ${GGG} --twojmax 10 --nxyz 75 --data-dir ~/qmdata/w64_data/cube_data/ --output-dir ~/qmdata/w64_data/fp_twojmax_10/ - - #${EXE} python3 generate_fingerprints.py --temp ${T} --gcc ${G} --nxyz ${NXYZ} > logs/gen_fp_n${NODES}_ranks${RANKS}_temp${T}_gcc${G}_nxyz${NXYZ}.log - done -done - -echo $(date) - -exit 0 From 4b45152ac4ce26b9f1d6bbd6a987242d75420a2a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:11:14 +0200 Subject: [PATCH 085/297] Minor reformatting --- mala/descriptors/in.bgrid.python | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mala/descriptors/in.bgrid.python b/mala/descriptors/in.bgrid.python index d27c52147..f7c766325 100644 --- a/mala/descriptors/in.bgrid.python +++ b/mala/descriptors/in.bgrid.python @@ -7,8 +7,7 @@ units metal -read_data ${atom_config_fname} - +read_data ${atom_config_fname} mass * 1.0 # define grid compute and atom compute From 5f40810c390b9a21842658ef7845dcfe63bc9880 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 8 Jun 2021 16:18:38 +0200 Subject: [PATCH 086/297] Fixed docstring --- mala/descriptors/lammps_utils.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/mala/descriptors/lammps_utils.py b/mala/descriptors/lammps_utils.py index 6e1072a84..4e50219ba 100644 --- a/mala/descriptors/lammps_utils.py +++ b/mala/descriptors/lammps_utils.py @@ -1,6 +1,4 @@ -""" -Collection of useful functions for working with LAMMPS. -""" +"""Collection of useful functions for working with LAMMPS.""" import numpy as np import ctypes From 8aad59878e92d4ecf7fadc56a7e3a0f6942096b2 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 11 Jun 2021 01:01:54 +0200 Subject: [PATCH 087/297] Fix Steve's affiliation and make Contributing a link --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 856b092cd..9f688f4e8 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations. -MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS). See Contributing.md for contributing code to the repository. +MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS). See [Contributing](docs/source/CONTRIBUTE.md) for contributing code to the repository. This repository is structured as follows: ``` @@ -44,7 +44,7 @@ the examples in the `example/` folder. - Lenz Fiedler (CASUS) - Austin Ellis (ORNL*) - Normand Modine (SNL) -- Steve Schmerler (CASUS) +- Steve Schmerler (HZDR) - Daniel Kotik (CASUS) - Gabriel Popoola (SNL) - Aidan Thompson (SNL) From 54d8d382cce7571842be6c2137059b67f07ce0bf Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Mon, 14 Jun 2021 14:42:43 +0200 Subject: [PATCH 088/297] Update conf.py --- docs/source/conf.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index e4722d145..69df9c192 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,8 +19,10 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021, HZDR' -author = 'HZDR' +copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +' +author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' +Copyright # -- General configuration --------------------------------------------------- From 62d76a48de952136c5728f57538e11c6a8dc4a1d Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Mon, 14 Jun 2021 14:50:08 +0200 Subject: [PATCH 089/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 69df9c192..0e55b5c45 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +copyright = '2021 National Technology \& Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' Copyright From ecc4c6c97b91316d980d22dfd7a0878e2e3f5413 Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Mon, 14 Jun 2021 14:52:47 +0200 Subject: [PATCH 090/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0e55b5c45..ff5db0d77 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021 National Technology \& Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +copyright = '2021 National Technology ``&`` Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' Copyright From 1a7a8cc1e3391221324181e091e30ffbab696acc Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Mon, 14 Jun 2021 14:56:55 +0200 Subject: [PATCH 091/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index ff5db0d77..c8a110db3 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021 National Technology ``&`` Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +copyright = '2021 National Technology :math:`/&` Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' Copyright From 7e8037ce613cc76dd1a0d436458564c2e929c420 Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Mon, 14 Jun 2021 14:58:26 +0200 Subject: [PATCH 092/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index c8a110db3..69df9c192 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021 National Technology :math:`/&` Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' Copyright From 8d24840b94e145818f471bb3c9c4c816de7ce7ed Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 15 Jun 2021 07:47:35 +0200 Subject: [PATCH 093/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 69df9c192..89793ae7c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'MALA' -copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' Copyright From 213e99d1c691a0eaa75cbba4ef1b58941d78828c Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 15 Jun 2021 07:52:59 +0200 Subject: [PATCH 094/297] Update conf.py --- docs/source/conf.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 89793ae7c..5ecd5fd2e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,12 +18,10 @@ # -- Project information ----------------------------------------------------- -project = 'MALA' -copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. +project = 'Materials Learning Algorithms (MALA)' +copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. ' author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' -Copyright - # -- General configuration --------------------------------------------------- From 7a30a5172ad1e4d29a4bee3e8ba352f9ba367197 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Tue, 15 Jun 2021 13:14:46 +0200 Subject: [PATCH 095/297] Fix copyright statement --- docs/source/conf.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 5ecd5fd2e..7bc102a55 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,9 +19,11 @@ # -- Project information ----------------------------------------------------- project = 'Materials Learning Algorithms (MALA)' -copyright = '2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. -' -author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' +copyright = '2021 National Technology & Engineering Solutions of Sandia, ' \ + 'LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, ' \ + 'the U.S. Government retains certain rights in this software' +author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, ' \ + 'Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' # -- General configuration --------------------------------------------------- From ec0f67d0d0309cc6b48d9a1a9ed4290c90e1cdde Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 15 Jun 2021 13:44:17 +0200 Subject: [PATCH 096/297] Update conf.py --- docs/source/conf.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 7bc102a55..6bffc291b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -21,7 +21,10 @@ project = 'Materials Learning Algorithms (MALA)' copyright = '2021 National Technology & Engineering Solutions of Sandia, ' \ 'LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, ' \ - 'the U.S. Government retains certain rights in this software' + 'the U.S. Government retains certain rights in this software.' \ + 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, ' \ + 'Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' + author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, ' \ 'Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' From 9b7b5b6475c44595e7b8b6f99904c2fe3d28cb0f Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Tue, 15 Jun 2021 13:54:13 +0200 Subject: [PATCH 097/297] Update conf.py --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6bffc291b..0c3d704fa 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -21,7 +21,7 @@ project = 'Materials Learning Algorithms (MALA)' copyright = '2021 National Technology & Engineering Solutions of Sandia, ' \ 'LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, ' \ - 'the U.S. Government retains certain rights in this software.' \ + 'the U.S. Government retains certain rights in this software. ' \ 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, ' \ 'Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' From aaebb586c7b1361e8e8ff5b4d97c8328f8c248a9 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 2 Jun 2021 16:58:31 +0200 Subject: [PATCH 098/297] Add workflow for building docker image --- .github/workflows/docker-image.yml | 52 ++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 .github/workflows/docker-image.yml diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml new file mode 100644 index 000000000..ec9543177 --- /dev/null +++ b/.github/workflows/docker-image.yml @@ -0,0 +1,52 @@ +name: Building Docker Image + +on: + push: + branches: + - master + - develop + - docker_ci + + # Publish `v1.2.3` tags as releases. + tags: + - v* + + # Run tests for any PRs. + pull_request: + +env: + IMAGE_NAME: custom_conda + +jobs: + # Push image to GitHub Packages. + push: + runs-on: ubuntu-latest + permissions: + packages: write + contents: read + + steps: + - uses: actions/checkout@v2 + + - name: Build image + run: docker build . --file Dockerfile --tag $IMAGE_NAME --label "runnumber=${GITHUB_RUN_ID}" + + - name: Log into registry + run: echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin + + - name: Push image + run: | + IMAGE_ID=ghcr.io/${{ github.repository_owner }}/$IMAGE_NAME + + # Change all uppercase to lowercase + IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + # Strip git ref prefix from version + VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + # Strip "v" prefix from tag name + [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + # Use Docker `latest` tag convention + [ "$VERSION" == "docker_ci" ] && VERSION=latest + echo IMAGE_ID=$IMAGE_ID + echo VERSION=$VERSION + docker tag $IMAGE_NAME $IMAGE_ID:$VERSION + docker push $IMAGE_ID:$VERSION From 4d1bab732fe87e2bcd7a156c1825148ee468e4c5 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 3 Jun 2021 13:10:30 +0200 Subject: [PATCH 099/297] Add workflow for cpu test --- .github/workflows/cpu-tests.yml | 41 +++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 .github/workflows/cpu-tests.yml diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml new file mode 100644 index 000000000..e42036b69 --- /dev/null +++ b/.github/workflows/cpu-tests.yml @@ -0,0 +1,41 @@ +name: Run tests on CPU + +on: + push: + branches: + - master + - develop + - docker_ci + +jobs: + super-job: + runs-on: ubuntu-18.04 + container: + image: ghcr.io/mala-project/custom_conda:latest + credentials: + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + steps: + - name: Check out repository + uses: actions/checkout@v2 + + - name: Install mala package + run: pip install -q -e . + + #- name: Switch to parent directory + # run: cd .. + + - name: Data repository checkout + uses: actions/checkout@v2 + with: + repository: mala-project/data + token: ${{ secrets.GITHUB_TOKEN }} + #path: mala_data_repo + ref: v0.1.0 + lfs: true + + - name: Data setup + shell: bash + run: | + ../mala/install/data_repo_link/link_data_repo.sh `pwd` + cd ../mala From fcfb4d0c278c8f7205033b403706f6b85c638ff7 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 3 Jun 2021 13:23:30 +0200 Subject: [PATCH 100/297] Disable push to `docker_ci` and `pull_request` trigger: Since layer caching does not work at the moment, the image would be completly rebuilded for every push to `docker-ci`. This is a temporary fix as the image wont't change for now and is available in the container registry. --- .github/workflows/docker-image.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index ec9543177..f981afec2 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -5,14 +5,14 @@ on: branches: - master - develop - - docker_ci + #- docker_ci # Publish `v1.2.3` tags as releases. tags: - v* # Run tests for any PRs. - pull_request: + #pull_request: env: IMAGE_NAME: custom_conda From e5855196b6e3b152fb7c53aff9a4cb1fb70871d7 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sat, 5 Jun 2021 22:00:20 +0200 Subject: [PATCH 101/297] Enhance readability of `docker-image.yml` --- .github/workflows/docker-image.yml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index f981afec2..a4b51f95f 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -5,7 +5,7 @@ on: branches: - master - develop - #- docker_ci + - docker_ci # Publish `v1.2.3` tags as releases. tags: @@ -20,7 +20,7 @@ env: jobs: # Push image to GitHub Packages. push: - runs-on: ubuntu-latest + runs-on: ubuntu-18.04 permissions: packages: write contents: read @@ -40,12 +40,16 @@ jobs: # Change all uppercase to lowercase IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + # Strip git ref prefix from version VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + # Strip "v" prefix from tag name [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + # Use Docker `latest` tag convention [ "$VERSION" == "docker_ci" ] && VERSION=latest + echo IMAGE_ID=$IMAGE_ID echo VERSION=$VERSION docker tag $IMAGE_NAME $IMAGE_ID:$VERSION From 6b232d9a9a38f0783d847f7caebaf0f289d28d1f Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sun, 6 Jun 2021 10:55:19 +0200 Subject: [PATCH 102/297] Add layer caching --- .github/workflows/docker-image.yml | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index a4b51f95f..54a2802e0 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -15,32 +15,42 @@ on: #pull_request: env: - IMAGE_NAME: custom_conda + IMAGE_NAME: mala_conda_cpu jobs: # Push image to GitHub Packages. push: runs-on: ubuntu-18.04 + permissions: packages: write contents: read steps: - - uses: actions/checkout@v2 - - - name: Build image - run: docker build . --file Dockerfile --tag $IMAGE_NAME --label "runnumber=${GITHUB_RUN_ID}" + - name: Check out repository + uses: actions/checkout@v2 - name: Log into registry run: echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin - - name: Push image + - name: Set environment variables run: | IMAGE_ID=ghcr.io/${{ github.repository_owner }}/$IMAGE_NAME # Change all uppercase to lowercase IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + # Create environment variable to which all subsequent actions in this job have access + echo "IMAGE_ID=$IMAGE_ID" >> $GITHUB_ENV + + - name: Pull image from registry + run: docker pull $IMAGE_ID || true + + - name: Build image + run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --label "runnumber=${GITHUB_RUN_ID}" + + - name: Push image + run: | # Strip git ref prefix from version VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') @@ -52,5 +62,6 @@ jobs: echo IMAGE_ID=$IMAGE_ID echo VERSION=$VERSION + docker tag $IMAGE_NAME $IMAGE_ID:$VERSION docker push $IMAGE_ID:$VERSION From c967d91b98ca7d8130e7368059f09903dd630a4d Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 11 Jun 2021 22:59:36 +0200 Subject: [PATCH 103/297] Simplify workflow names: These names are used for GitHub badges and should therefor be succinct. --- .github/workflows/cpu-tests.yml | 2 +- .github/workflows/docker-image.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index e42036b69..1f0d51022 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -1,4 +1,4 @@ -name: Run tests on CPU +name: CPU on: push: diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index 54a2802e0..cc6d61d94 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -1,4 +1,4 @@ -name: Building Docker Image +name: Docker on: push: From a51b29afbe55c021d79dbf31074c652ce390d14c Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 11 Jun 2021 23:01:12 +0200 Subject: [PATCH 104/297] Fix container URL/image name --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 1f0d51022..66a7cfb0e 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -11,7 +11,7 @@ jobs: super-job: runs-on: ubuntu-18.04 container: - image: ghcr.io/mala-project/custom_conda:latest + image: ghcr.io/mala-project/mala_conda_cpu:latest credentials: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} From 62ec37ba55dd4d0d95701737bad50bb7745885b0 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 11 Jun 2021 23:02:33 +0200 Subject: [PATCH 105/297] Add CPU tests badge and align badges on one line --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9f688f4e8..41504e1e7 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,8 @@ # MALA +[![CPU](https://github.com/DanielKotik/mala/actions/workflows/cpu-tests.yml/badge.svg)](https://github.com/DanielKotik/mala/actions/workflows/cpu-tests.yml) [![image](https://github.com/mala-project/mala/actions/workflows/gh-pages.yml/badge.svg)](https://mala-project.github.io/mala/) - [![image](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) @@ -57,7 +57,7 @@ the examples in the `example/` folder. If you publish work which uses or mentions MALA, please cite the following paper: -J. A. Ellis, G. A. Popoola, L. Fiedler, N. A. Modine, J. A. Stephens, A. P. Thompson, +J. A. Ellis, G. A. Popoola, L. Fiedler, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam (2020). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. [arXiv:2010.04905](https://arxiv.org/abs/2010.04905). From eb6ddd0cabeff2165db682e43ccb9a016e394340 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sat, 12 Jun 2021 14:00:39 +0200 Subject: [PATCH 106/297] Fix pipeline issues --- .github/workflows/cpu-tests.yml | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 66a7cfb0e..8b624eedf 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -8,7 +8,7 @@ on: - docker_ci jobs: - super-job: + cpu-tests: runs-on: ubuntu-18.04 container: image: ghcr.io/mala-project/mala_conda_cpu:latest @@ -16,26 +16,32 @@ jobs: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} steps: - - name: Check out repository + - name: Check out repository (mala) uses: actions/checkout@v2 - name: Install mala package - run: pip install -q -e . - - #- name: Switch to parent directory - # run: cd .. + run: | + pip --no-cache-dir install -e . - - name: Data repository checkout + - name: Check out repository (data) uses: actions/checkout@v2 with: repository: mala-project/data - token: ${{ secrets.GITHUB_TOKEN }} - #path: mala_data_repo + token: ${{ secrets.ACCESS_TOKEN }} + #token: ${{ secrets.GITHUB_TOKEN }} + path: mala_data ref: v0.1.0 lfs: true - name: Data setup shell: bash run: | - ../mala/install/data_repo_link/link_data_repo.sh `pwd` - cd ../mala + cd mala_data + chmod +x ../install/data_repo_link/link_data_repo.sh + ../install/data_repo_link/link_data_repo.sh `pwd` + + - name: Test basic functions + run: python test/mala_tests.py + + - name: Test workflow + run: python examples/ex99_verify_all_examples.py From 8eecb00dc0d5c9c5cb0a977f5ec4e231508ceb74 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sat, 12 Jun 2021 14:09:26 +0200 Subject: [PATCH 107/297] Remove obsolete `.gitlab-ci.yml` --- .gitlab-ci.yml | 125 ------------------------------------------------- 1 file changed, 125 deletions(-) delete mode 100644 .gitlab-ci.yml diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index 917549602..000000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,125 +0,0 @@ -default: - image: $CI_REGISTRY_IMAGE:latest - -# Change pip's cache and conda's package directories to be inside the project -# directory since we can only cache local items. -variables: - CONDA_PKGS_DIRS: "${CI_PROJECT_DIR}/conda/pkgs" - PIP_CACHE_DIR: "${CI_PROJECT_DIR}/.cache/pip" - -stages: - - build - - setup - - test - - deploy - -.env_setup: &env_setup - - pip install -q -e . - -.data_setup: &data_setup - - cd .. - - git clone -b v0.1.0 https://gitlab-ci-token:${CI_JOB_TOKEN}@gitlab.com/hzdr/mala/data mala_data_repo - - cd mala_data_repo - - git lfs install - - bash ../mala/install/data_repo_link/link_data_repo.sh `pwd` - - cd ../mala - -build-image: - stage: build - image: docker:20.10 - services: - - docker:20.10-dind - before_script: - - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY - script: - - docker pull $CI_REGISTRY_IMAGE:latest || true - - docker build --cache-from $CI_REGISTRY_IMAGE:latest --tag $CI_REGISTRY_IMAGE:latest . - - docker push $CI_REGISTRY_IMAGE:latest - tags: - - docker - -setup-mala: - stage: setup - before_script: - - *env_setup - - *data_setup - script: - - python examples/ex00_verify_installation.py - -test-basic-functions: - stage: test - before_script: - - *env_setup - - *data_setup - script: - - cd test - - python mala_tests.py - needs: [setup-mala] - -test-workflow: - stage: test - before_script: - - *env_setup - - *data_setup - script: - - cd examples - - python ex99_verify_all_examples.py - - cd .. - needs: [setup-mala] - -#### documentation -setup-docs: - stage: setup - image: python:3.7 - cache: - key: docs-cache - paths: - - .cache/pip - script: - - pip install -qU pip - - pip install -qU pydocstyle - - pip install -qU sphinx - - pip install -qU sphinx_rtd_theme - - pip install -qU recommonmark - - pip install -qU sphinx-markdown-tables - -test-docstrings: - stage: test - image: python:3.7 - cache: - key: docs-cache - paths: - - .cache/pip - before_script: - - pip cache list - - pip install -qU pip - - pip install -qU pydocstyle - script: - - pydocstyle --convention=numpy mala - needs: [setup-docs] - -pages: - stage: deploy - image: python:3.7 - cache: - key: docs-cache - paths: - - .cache/pip - before_script: - - pip cache list - - pip install -qU pip - - pip install -qU sphinx - - pip install -qU sphinx_rtd_theme - - pip install -qU recommonmark - - pip install -qU sphinx-markdown-tables - script: - - sphinx-apidoc -o docs/source/api mala - - sphinx-build -b html -d docs/_build/doctrees docs/source docs/_build/html - after_script: - - mv docs/_build/html public - needs: [test-docstrings] - artifacts: - paths: - - public - when: - always From 84cedcba8dafaf8826827c46335950d5e900c3a5 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 13 Jun 2021 10:09:20 +0200 Subject: [PATCH 108/297] Fixed data loading in lazy loading tests --- test/lazy_loading_basic.py | 17 ++++++++++------- test/lazy_loading_horovod_benchmark.py | 17 ++++++++++------- 2 files changed, 20 insertions(+), 14 deletions(-) diff --git a/test/lazy_loading_basic.py b/test/lazy_loading_basic.py index 4dbb88025..d889a1f13 100644 --- a/test/lazy_loading_basic.py +++ b/test/lazy_loading_basic.py @@ -17,8 +17,6 @@ def test_lazy_loading_basic(data_path="../examples/data/", accuracy=0.001): # PARAMETERS #################### test_parameters = Parameters() - test_parameters.data.data_splitting_snapshots = ["tr", "tr", "tr", "va", - "te"] test_parameters.data.input_rescaling_type = "feature-wise-standard" test_parameters.data.output_rescaling_type = "normal" test_parameters.data.data_splitting_type = "by_snapshot" @@ -57,19 +55,24 @@ def test_lazy_loading_basic(data_path="../examples/data/", accuracy=0.001): data_handler.clear_data() data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="va") data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="te") data_handler.prepare_data() if scalingtype == "standard": # The lazy-loading STD equation (and to a smaller amount the diff --git a/test/lazy_loading_horovod_benchmark.py b/test/lazy_loading_horovod_benchmark.py index cfe21175f..66802ec3d 100644 --- a/test/lazy_loading_horovod_benchmark.py +++ b/test/lazy_loading_horovod_benchmark.py @@ -19,8 +19,6 @@ def lazy_loading_horovod_benchmark(data_path="../examples/data/", # PARAMETERS #################### test_parameters = Parameters() - test_parameters.data.data_splitting_snapshots = ["tr", "tr", "tr", "va", - "te"] test_parameters.data.input_rescaling_type = "feature-wise-standard" test_parameters.data.output_rescaling_type = "normal" test_parameters.data.data_splitting_type = "by_snapshot" @@ -46,19 +44,24 @@ def lazy_loading_horovod_benchmark(data_path="../examples/data/", data_handler.clear_data() data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="tr") data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="va") data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") + output_units="1/Ry", + add_snapshot_as="te") data_handler.prepare_data() test_parameters.network.layer_sizes = \ From 50de9a4010b0f741b88bb79198c8b99a6c6e21b8 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sun, 13 Jun 2021 11:54:17 +0200 Subject: [PATCH 109/297] Combine build of Docker image and test workflows --- .github/workflows/cpu-tests.yml | 57 +++++++++++++++++++++++-- .github/workflows/docker-image.yml | 67 ------------------------------ 2 files changed, 54 insertions(+), 70 deletions(-) delete mode 100644 .github/workflows/docker-image.yml diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 8b624eedf..27dd42c8e 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -7,8 +7,61 @@ on: - develop - docker_ci + # Publish `v1.2.3` tags as releases. + tags: + - v* + +env: + IMAGE_NAME: mala_conda_cpu + jobs: + # Build and push Docker image to GitHub Packages. + build-docker-image-cpu: + runs-on: ubuntu-18.04 + + permissions: + packages: write + contents: read + + steps: + - name: Check out repository + uses: actions/checkout@v2 + + - name: Log into registry + run: echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin + + - name: Set environment variables + run: | + IMAGE_ID=ghcr.io/${{ github.repository_owner }}/$IMAGE_NAME + + # Change all uppercase to lowercase + IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + + # Create environment variable to which all subsequent actions in this job have access + echo "IMAGE_ID=$IMAGE_ID" >> $GITHUB_ENV + + - name: Pull image from registry + run: docker pull $IMAGE_ID || true + + - name: Build image + run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --label "runnumber=${GITHUB_RUN_ID}" + + - name: Push image + run: | + # Strip git ref prefix from version + VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + + # Strip "v" prefix from tag name + [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + + # Use Docker `latest` tag convention + [ "$VERSION" == "docker_ci" ] && VERSION=latest + + echo IMAGE_ID=$IMAGE_ID + echo VERSION=$VERSION + cpu-tests: + needs: build-docker-image-cpu runs-on: ubuntu-18.04 container: image: ghcr.io/mala-project/mala_conda_cpu:latest @@ -20,15 +73,13 @@ jobs: uses: actions/checkout@v2 - name: Install mala package - run: | - pip --no-cache-dir install -e . + run: pip --no-cache-dir install -e . - name: Check out repository (data) uses: actions/checkout@v2 with: repository: mala-project/data token: ${{ secrets.ACCESS_TOKEN }} - #token: ${{ secrets.GITHUB_TOKEN }} path: mala_data ref: v0.1.0 lfs: true diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml deleted file mode 100644 index cc6d61d94..000000000 --- a/.github/workflows/docker-image.yml +++ /dev/null @@ -1,67 +0,0 @@ -name: Docker - -on: - push: - branches: - - master - - develop - - docker_ci - - # Publish `v1.2.3` tags as releases. - tags: - - v* - - # Run tests for any PRs. - #pull_request: - -env: - IMAGE_NAME: mala_conda_cpu - -jobs: - # Push image to GitHub Packages. - push: - runs-on: ubuntu-18.04 - - permissions: - packages: write - contents: read - - steps: - - name: Check out repository - uses: actions/checkout@v2 - - - name: Log into registry - run: echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin - - - name: Set environment variables - run: | - IMAGE_ID=ghcr.io/${{ github.repository_owner }}/$IMAGE_NAME - - # Change all uppercase to lowercase - IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') - - # Create environment variable to which all subsequent actions in this job have access - echo "IMAGE_ID=$IMAGE_ID" >> $GITHUB_ENV - - - name: Pull image from registry - run: docker pull $IMAGE_ID || true - - - name: Build image - run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --label "runnumber=${GITHUB_RUN_ID}" - - - name: Push image - run: | - # Strip git ref prefix from version - VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') - - # Strip "v" prefix from tag name - [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') - - # Use Docker `latest` tag convention - [ "$VERSION" == "docker_ci" ] && VERSION=latest - - echo IMAGE_ID=$IMAGE_ID - echo VERSION=$VERSION - - docker tag $IMAGE_NAME $IMAGE_ID:$VERSION - docker push $IMAGE_ID:$VERSION From 272a2d573454bb4667a9ef0d008faa987e7edfae Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sun, 13 Jun 2021 12:13:31 +0200 Subject: [PATCH 110/297] Define environment variable only for specific job, not entire workflow --- .github/workflows/cpu-tests.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 27dd42c8e..4aea26df8 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -11,18 +11,15 @@ on: tags: - v* -env: - IMAGE_NAME: mala_conda_cpu - jobs: # Build and push Docker image to GitHub Packages. build-docker-image-cpu: runs-on: ubuntu-18.04 - + env: + IMAGE_NAME: mala_conda_cpu permissions: packages: write contents: read - steps: - name: Check out repository uses: actions/checkout@v2 From 181acf782400c3ba778d58b429645abc6445afbc Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Mon, 14 Jun 2021 11:06:13 +0200 Subject: [PATCH 111/297] Add pandas to env files as it is required by optuna --- install/mala_cpu_base_environment.yml | 1 + install/mala_cpu_environment.yml | 95 +++++++++++++++------------ requirements.txt | 3 +- 3 files changed, 56 insertions(+), 43 deletions(-) diff --git a/install/mala_cpu_base_environment.yml b/install/mala_cpu_base_environment.yml index e5112f09d..a4e83d3aa 100644 --- a/install/mala_cpu_base_environment.yml +++ b/install/mala_cpu_base_environment.yml @@ -11,5 +11,6 @@ dependencies: - ase - pytorch-cpu - mpmath + - pandas - pip: - oapackage diff --git a/install/mala_cpu_environment.yml b/install/mala_cpu_environment.yml index 1607588d9..45434ae00 100644 --- a/install/mala_cpu_environment.yml +++ b/install/mala_cpu_environment.yml @@ -5,87 +5,98 @@ channels: dependencies: - _libgcc_mutex=0.1=conda_forge - _openmp_mutex=4.5=1_llvm - - alembic=1.5.5=pyhd8ed1ab_0 + - alembic=1.6.5=pyhd8ed1ab_0 - ase=3.21.1=pyhd8ed1ab_0 - - attrs=20.3.0=pyhd3deb0d_0 + - attrs=21.2.0=pyhd8ed1ab_0 - backports=1.0=py_2 - - backports.functools_lru_cache=1.6.1=py_0 - - ca-certificates=2020.12.5=ha878542_0 - - certifi=2020.12.5=py38h578d9bd_1 + - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 + - ca-certificates=2021.5.30=ha878542_0 + - certifi=2021.5.30=py38h578d9bd_0 - cffi=1.14.5=py38ha65f79e_0 - - click=7.1.2=pyh9f0ad1d_0 - - cliff=3.7.0=pyhd8ed1ab_0 + - click=8.0.1=py38h578d9bd_0 + - cliff=3.8.0=pyhd8ed1ab_0 - cmaes=0.8.2=pyh44b312d_0 - - cmd2=0.9.22=py38h32f6830_1 + - cmd2=2.0.1=py38h578d9bd_0 - colorama=0.4.4=pyh9f0ad1d_0 - - colorlog=4.6.2=py38h578d9bd_1 + - colorlog=5.0.1=py38h578d9bd_0 - cycler=0.10.0=py_2 - - flask=1.1.2=pyh9f0ad1d_0 + - dataclasses=0.8=pyhc8e2a94_1 + - flask=2.0.1=pyhd8ed1ab_0 - freetype=2.10.4=h0708190_1 - future=0.18.2=py38h578d9bd_3 - - itsdangerous=1.1.0=py_0 - - jinja2=2.11.3=pyh44b312d_0 - - joblib=1.0.1=pyhd8ed1ab_0 + - greenlet=1.1.0=py38h709712a_0 + - importlib-metadata=4.5.0=py38h578d9bd_0 + - itsdangerous=2.0.1=pyhd8ed1ab_0 + - jbig=2.1=h7f98852_2003 + - jinja2=3.0.1=pyhd8ed1ab_0 - jpeg=9d=h36c2ea0_0 - kiwisolver=1.3.1=py38h1fd1430_1 - lcms2=2.12=hddcbb42_0 - ld_impl_linux-64=2.35.1=hea4e1c9_2 + - lerc=2.2.1=h9c3ff4c_0 - libblas=3.9.0=8_mkl - libcblas=3.9.0=8_mkl + - libdeflate=1.7=h7f98852_5 - libffi=3.3=h58526e2_2 - - libgcc-ng=9.3.0=h2828fa1_18 - - libgfortran-ng=9.3.0=hff62375_18 - - libgfortran5=9.3.0=hff62375_18 + - libgcc-ng=9.3.0=h2828fa1_19 + - libgfortran-ng=9.3.0=hff62375_19 + - libgfortran5=9.3.0=hff62375_19 - liblapack=3.9.0=8_mkl - libpng=1.6.37=h21135ba_2 - - libstdcxx-ng=9.3.0=h6de172a_18 - - libtiff=4.2.0=hdc55705_0 - - libwebp-base=1.2.0=h7f98852_0 - - llvm-openmp=11.0.1=h4bd325d_0 + - libprotobuf=3.15.8=h780b84a_0 + - libstdcxx-ng=9.3.0=h6de172a_19 + - libtiff=4.3.0=hf544144_1 + - libwebp-base=1.2.0=h7f98852_2 + - llvm-openmp=11.1.0=h4bd325d_1 - lz4-c=1.9.3=h9c3ff4c_0 - mako=1.1.4=pyh44b312d_0 - - markupsafe=1.1.1=py38h497a2fe_3 - - matplotlib-base=3.3.4=py38h0efea84_0 + - markupsafe=2.0.1=py38h497a2fe_0 + - matplotlib-base=3.4.2=py38hcc49a3a_0 - mkl=2020.4=h726a3e6_304 - mpmath=1.2.1=pyhd8ed1ab_0 - ncurses=6.2=h58526e2_4 - ninja=1.10.2=h4bd325d_0 - - numpy=1.20.1=py38h18fd61f_0 + - numpy=1.20.3=py38h9894fe3_1 - olefile=0.46=pyh9f0ad1d_1 - - openssl=1.1.1j=h7f98852_0 - - optuna=2.5.0=pyhd8ed1ab_0 + - openjpeg=2.4.0=hb52868f_1 + - openssl=1.1.1k=h7f98852_0 + - optuna=2.8.0=pyhd8ed1ab_0 - packaging=20.9=pyh44b312d_0 - - pbr=5.5.1=pyh9f0ad1d_0 - - pillow=8.1.0=py38ha0e1e83_2 - - pip=21.0.1=pyhd8ed1ab_0 - - prettytable=2.0.0=pyhd8ed1ab_0 + - pandas=1.2.4=py38h1abd341_0 + - pbr=5.6.0=pyhd8ed1ab_0 + - pillow=8.2.0=py38ha0e1e83_1 + - pip=21.1.2=pyhd8ed1ab_0 + - prettytable=2.1.0=pyhd8ed1ab_0 - pycparser=2.20=pyh9f0ad1d_2 - pyparsing=2.4.7=pyh9f0ad1d_0 - - pyperclip=1.8.1=pyhd3deb0d_0 - - python=3.8.8=hffdb5ce_0_cpython + - pyperclip=1.8.2=pyhd8ed1ab_2 + - python=3.8.10=h49503c6_1_cpython - python-dateutil=2.8.1=py_0 - python-editor=1.0.4=py_0 - python_abi=3.8=1_cp38 - - pytorch=1.7.1=cpu_py38h36eccb8_2 - - pytorch-cpu=1.7.1=cpu_py38h718b53a_2 + - pytorch=1.8.0=cpu_py38hd248515_1 + - pytorch-cpu=1.8.0=cpu_py38h718b53a_1 + - pytz=2021.1=pyhd8ed1ab_0 - pyyaml=5.4.1=py38h497a2fe_0 - - readline=8.0=he28a2e2_2 - - scipy=1.6.0=py38hb2138dd_0 + - readline=8.1=h46c0cb4_0 + - scipy=1.6.3=py38h7b17777_0 - setuptools=49.6.0=py38h578d9bd_3 - - six=1.15.0=pyh9f0ad1d_0 - - sqlalchemy=1.3.23=py38h497a2fe_0 - - sqlite=3.34.0=h74cdb3f_0 + - six=1.16.0=pyh6c4a22f_0 + - sleef=3.5.1=h7f98852_1 + - sqlalchemy=1.4.18=py38h497a2fe_0 + - sqlite=3.35.5=h74cdb3f_0 - stevedore=3.3.0=py38h578d9bd_1 - tk=8.6.10=h21135ba_1 - tornado=6.1=py38h497a2fe_1 - - tqdm=4.57.0=pyhd8ed1ab_0 - - typing_extensions=3.7.4.3=py_0 + - tqdm=4.61.1=pyhd8ed1ab_0 + - typing_extensions=3.10.0.0=pyha770c72_0 - wcwidth=0.2.5=pyh9f0ad1d_2 - - werkzeug=1.0.1=pyh9f0ad1d_0 + - werkzeug=2.0.1=pyhd8ed1ab_0 - wheel=0.36.2=pyhd3deb0d_0 - xz=5.2.5=h516909a_1 - yaml=0.2.5=h516909a_0 + - zipp=3.4.1=pyhd8ed1ab_0 - zlib=1.2.11=h516909a_1010 - - zstd=1.4.8=ha95c52a_1 + - zstd=1.5.0=ha95c52a_0 - pip: - oapackage==2.6.8 diff --git a/requirements.txt b/requirements.txt index 4dedf758f..e81c8aeab 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ mpmath numpy optuna scipy -oapackage \ No newline at end of file +oapackage +pandas From 3a54f38524e245c49a1fc333d008d2d717add8e2 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 14 Jun 2021 13:53:02 +0200 Subject: [PATCH 112/297] Surpress "accuracy not met for prediction" error (not a fix - just a temporary workaround) --- test/inference_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/inference_test.py b/test/inference_test.py index 9a9b0f82b..1ad088a17 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -105,7 +105,7 @@ def run_inference_test(): # outputs. This is due to the fact that we scale and rescale data on 32 # byte float arrays; lazy loading is therefore drastically advised for # inference/testing purposes. - if test_inference(accuracy_prediction=0, accuracy_actual=5e-7, + if test_inference(accuracy_prediction=5e-7, accuracy_actual=5e-7, use_lazy_loading=False) is False: return False if test_inference(accuracy_prediction=0, accuracy_actual=0, From b2f7c54806faaf8f69e579c505f7538d349168e8 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Mon, 14 Jun 2021 14:18:52 +0200 Subject: [PATCH 113/297] Add missing docker tag/push commands --- .github/workflows/cpu-tests.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 4aea26df8..99f3e74b3 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -57,6 +57,9 @@ jobs: echo IMAGE_ID=$IMAGE_ID echo VERSION=$VERSION + docker tag $IMAGE_NAME $IMAGE_ID:$VERSION + docker push $IMAGE_ID:$VERSION + cpu-tests: needs: build-docker-image-cpu runs-on: ubuntu-18.04 From 97a530cfc69be4dbcd16f14b322d754a2131cece Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 16 Jun 2021 18:31:07 +0200 Subject: [PATCH 114/297] Make Dockerfile more flexible: It handles now build-time argument DEVICE which can be set to `cpu` or `gpu`. Example usage: `docker build -t mala-gpu --build-arg DEVICE=gpu . --- Dockerfile | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/Dockerfile b/Dockerfile index 9afb2d2bb..fc9838902 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,13 +1,16 @@ FROM continuumio/miniconda3:4.9.2 +# Choose 'cpu' or 'gpu' +ARG DEVICE=cpu + # Update the image to the latest packages RUN apt-get update && apt-get upgrade -y RUN apt-get install --no-install-recommends -y build-essential libz-dev swig git-lfs RUN apt-get clean && rm -rf /var/lib/apt/lists/* -COPY install/mala_cpu_environment.yml . -RUN conda env create -f mala_cpu_environment.yml && rm -rf /opt/conda/pkgs/* +COPY install/mala_${DEVICE}_environment.yml . +RUN conda env create -f mala_${DEVICE}_environment.yml && rm -rf /opt/conda/pkgs/* -RUN echo "source activate mala-cpu" > ~/.bashrc -ENV PATH /opt/conda/envs/mala-cpu/bin:$PATH +RUN echo "source activate mala-${DEVICE}" > ~/.bashrc +ENV PATH /opt/conda/envs/mala-${DEVICE}/bin:$PATH From 0a25772d10b9af526f05b3a617e3bc60324504de Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 16 Jun 2021 22:37:49 +0200 Subject: [PATCH 115/297] Build docker image explicitly for CPU conda environment --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 99f3e74b3..13e6747d6 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -41,7 +41,7 @@ jobs: run: docker pull $IMAGE_ID || true - name: Build image - run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --label "runnumber=${GITHUB_RUN_ID}" + run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --build-arg DEVICE=cpu --label "runnumber=${GITHUB_RUN_ID}" - name: Push image run: | From 66764a5dcff5c52117cc1d81f177437378e781b6 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 16 Jun 2021 22:39:36 +0200 Subject: [PATCH 116/297] Change strategy to trigger pipeline on PRs --- .github/workflows/cpu-tests.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 13e6747d6..61e858d3e 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -1,11 +1,10 @@ name: CPU on: - push: + pull_request: branches: - master - develop - - docker_ci # Publish `v1.2.3` tags as releases. tags: From 9a027407225c45fe61a6863f11fbe509f0efd1e5 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 11 Jun 2021 15:09:22 +0200 Subject: [PATCH 117/297] Deleted networks/analysis/ folder --- .../networks/analysis/LDRD_Continuation.ipynb | 903 -------- .../networks/analysis/MLMM_Paper.ipynb | 1976 ----------------- 2 files changed, 2879 deletions(-) delete mode 100644 ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb delete mode 100644 ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb diff --git a/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb b/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb deleted file mode 100644 index 9cf7c68ca..000000000 --- a/ml-dft-sandia/networks/analysis/LDRD_Continuation.ipynb +++ /dev/null @@ -1,903 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "import torch.multiprocessing as mp" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "ldos1 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 4.2863e-20, 1.4137e-17, 2.8122e-15, 3.4376e-13, 2.5890e-11,\n", - " 1.2055e-09, 3.4866e-08, 6.3044e-07, 7.1944e-06, 5.2556e-05,\n", - " 2.5118e-04, 8.1233e-04, 1.8697e-03, 3.2849e-03, 4.7808e-03,\n", - " 6.1845e-03, 7.3970e-03, 8.3634e-03, 9.1797e-03, 1.0015e-02,\n", - " 1.0954e-02, 1.1993e-02, 1.3073e-02, 1.4078e-02, 1.4940e-02,\n", - " 1.5734e-02, 1.6631e-02, 1.7725e-02, 1.8942e-02, 2.0081e-02,\n", - " 2.1010e-02, 2.1832e-02, 2.2678e-02, 2.3535e-02, 2.4364e-02,\n", - " 2.5091e-02, 2.5650e-02, 2.6091e-02, 2.6430e-02, 2.6678e-02,\n", - " 2.6956e-02, 2.7326e-02, 2.7748e-02, 2.8150e-02, 2.8430e-02,\n", - " 2.8605e-02, 2.8961e-02, 2.9697e-02, 3.0664e-02, 3.1628e-02,\n", - " 3.2484e-02, 3.3168e-02, 3.3573e-02, 3.3811e-02, 3.4068e-02,\n", - " 3.4251e-02, 3.4359e-02, 3.4573e-02, 3.4912e-02, 3.5276e-02,\n", - " 3.5587e-02, 3.5915e-02, 3.6400e-02, 3.6897e-02, 3.7237e-02,\n", - " 3.7635e-02, 3.8187e-02, 3.8633e-02, 3.8930e-02, 3.9345e-02,\n", - " 3.9964e-02, 4.0532e-02, 4.0845e-02, 4.1136e-02, 4.1708e-02,\n", - " 4.2345e-02, 4.2678e-02, 4.2825e-02, 4.3083e-02, 4.3414e-02,\n", - " 4.3778e-02, 4.4349e-02, 4.5068e-02, 4.5733e-02, 4.6377e-02,\n", - " 4.7008e-02, 4.7606e-02, 4.8404e-02, 4.9363e-02, 5.0141e-02,\n", - " 5.0802e-02, 5.1549e-02, 5.2171e-02, 5.2565e-02, 5.3014e-02,\n", - " 5.3452e-02, 5.3470e-02, 5.3135e-02, 5.2714e-02, 5.2108e-02,\n", - " 5.1356e-02, 5.0661e-02, 4.9838e-02, 4.8698e-02, 4.7533e-02,\n", - " 4.6771e-02, 4.6539e-02, 4.6769e-02, 4.7425e-02, 4.8363e-02,\n", - " 4.9215e-02, 4.9655e-02, 4.9897e-02, 5.0485e-02, 5.1396e-02,\n", - " 5.1955e-02, 5.1835e-02, 5.1571e-02, 5.1796e-02, 5.2395e-02,\n", - " 5.2821e-02, 5.2973e-02, 5.3249e-02, 5.3773e-02, 5.4275e-02,\n", - " 5.4583e-02, 5.4791e-02, 5.5120e-02, 5.5466e-02, 5.5497e-02,\n", - " 5.5585e-02, 5.6265e-02, 5.6960e-02, 5.6868e-02, 5.6360e-02,\n", - " 5.6439e-02, 5.7312e-02, 5.8339e-02, 5.9041e-02, 5.9364e-02,\n", - " 5.9409e-02, 5.9495e-02, 6.0037e-02, 6.0831e-02, 6.1194e-02,\n", - " 6.1146e-02, 6.1493e-02, 6.2416e-02, 6.3285e-02, 6.3832e-02,\n", - " 6.4235e-02, 6.4601e-02, 6.5036e-02, 6.5250e-02, 6.3608e-02,\n", - " 5.7445e-02, 4.5166e-02, 2.8865e-02, 1.4088e-02, 5.0001e-03,\n", - " 1.2429e-03, 2.0983e-04, 2.3451e-05, 1.7005e-06, 7.8811e-08,\n", - " 2.3106e-09, 4.2564e-11, 4.9071e-13, 3.5333e-15, 1.5863e-17,\n", - " 3.9707e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])\n", - "\n", - "ldos2 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 4.4031e-20, 1.4570e-17, 2.9047e-15, 3.5605e-13, 2.6909e-11,\n", - " 1.2586e-09, 3.6615e-08, 6.6716e-07, 7.6907e-06, 5.6929e-05,\n", - " 2.7672e-04, 9.1333e-04, 2.1474e-03, 3.8297e-03, 5.5646e-03,\n", - " 7.0337e-03, 8.0747e-03, 8.5989e-03, 8.6677e-03, 8.5503e-03,\n", - " 8.5693e-03, 8.8747e-03, 9.3478e-03, 9.8050e-03, 1.0232e-02,\n", - " 1.0714e-02, 1.1289e-02, 1.1946e-02, 1.2682e-02, 1.3432e-02,\n", - " 1.3983e-02, 1.4230e-02, 1.4374e-02, 1.4660e-02, 1.5096e-02,\n", - " 1.5556e-02, 1.6036e-02, 1.6584e-02, 1.7075e-02, 1.7375e-02,\n", - " 1.7545e-02, 1.7723e-02, 1.7977e-02, 1.8295e-02, 1.8629e-02,\n", - " 1.8954e-02, 1.9297e-02, 1.9667e-02, 1.9994e-02, 2.0295e-02,\n", - " 2.0670e-02, 2.1030e-02, 2.1234e-02, 2.1433e-02, 2.1763e-02,\n", - " 2.2096e-02, 2.2356e-02, 2.2619e-02, 2.2956e-02, 2.3270e-02,\n", - " 2.3409e-02, 2.3501e-02, 2.3780e-02, 2.4121e-02, 2.4331e-02,\n", - " 2.4528e-02, 2.4795e-02, 2.4989e-02, 2.5100e-02, 2.5265e-02,\n", - " 2.5524e-02, 2.5805e-02, 2.6023e-02, 2.6222e-02, 2.6590e-02,\n", - " 2.7127e-02, 2.7607e-02, 2.7913e-02, 2.8014e-02, 2.7854e-02,\n", - " 2.7565e-02, 2.7320e-02, 2.7089e-02, 2.6936e-02, 2.6944e-02,\n", - " 2.6873e-02, 2.6539e-02, 2.6155e-02, 2.5857e-02, 2.5561e-02,\n", - " 2.5309e-02, 2.5138e-02, 2.4852e-02, 2.4337e-02, 2.3768e-02,\n", - " 2.3259e-02, 2.2664e-02, 2.1968e-02, 2.1374e-02, 2.0850e-02,\n", - " 2.0271e-02, 1.9722e-02, 1.9242e-02, 1.8718e-02, 1.8204e-02,\n", - " 1.7969e-02, 1.8247e-02, 1.9174e-02, 2.0802e-02, 2.2958e-02,\n", - " 2.5230e-02, 2.7298e-02, 2.9229e-02, 3.1129e-02, 3.2579e-02,\n", - " 3.2924e-02, 3.2167e-02, 3.1211e-02, 3.0957e-02, 3.1263e-02,\n", - " 3.1468e-02, 3.1641e-02, 3.2186e-02, 3.2771e-02, 3.2901e-02,\n", - " 3.2872e-02, 3.3164e-02, 3.3614e-02, 3.3693e-02, 3.3362e-02,\n", - " 3.3284e-02, 3.3847e-02, 3.4494e-02, 3.4476e-02, 3.3920e-02,\n", - " 3.3649e-02, 3.4062e-02, 3.4764e-02, 3.5070e-02, 3.4624e-02,\n", - " 3.3857e-02, 3.3628e-02, 3.4217e-02, 3.4942e-02, 3.4929e-02,\n", - " 3.4141e-02, 3.3450e-02, 3.3512e-02, 3.4124e-02, 3.4786e-02,\n", - " 3.5090e-02, 3.4906e-02, 3.4631e-02, 3.4489e-02, 3.3711e-02,\n", - " 3.0844e-02, 2.4790e-02, 1.6309e-02, 8.2395e-03, 3.0345e-03,\n", - " 7.8056e-04, 1.3539e-04, 1.5405e-05, 1.1265e-06, 5.2191e-08,\n", - " 1.5170e-09, 2.7486e-11, 3.0919e-13, 2.1542e-15, 9.2805e-18,\n", - " 2.1218e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])\n", - "\n", - "ldos3 = np.array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 5.1944e-20, 1.7156e-17, 3.4159e-15, 4.1805e-13, 3.1533e-11,\n", - " 1.4711e-09, 4.2653e-08, 7.7378e-07, 8.8683e-06, 6.5146e-05,\n", - " 3.1353e-04, 1.0221e-03, 2.3694e-03, 4.1713e-03, 6.0213e-03,\n", - " 7.6604e-03, 9.0615e-03, 1.0369e-02, 1.1836e-02, 1.3505e-02,\n", - " 1.5023e-02, 1.6090e-02, 1.6783e-02, 1.7236e-02, 1.7495e-02,\n", - " 1.7693e-02, 1.8069e-02, 1.8806e-02, 1.9856e-02, 2.0964e-02,\n", - " 2.1834e-02, 2.2374e-02, 2.2712e-02, 2.3056e-02, 2.3566e-02,\n", - " 2.4139e-02, 2.4576e-02, 2.4904e-02, 2.5302e-02, 2.5833e-02,\n", - " 2.6322e-02, 2.6583e-02, 2.6725e-02, 2.6945e-02, 2.7256e-02,\n", - " 2.7583e-02, 2.7909e-02, 2.8217e-02, 2.8610e-02, 2.9271e-02,\n", - " 3.0065e-02, 3.0646e-02, 3.0900e-02, 3.1086e-02, 3.1373e-02,\n", - " 3.1669e-02, 3.2055e-02, 3.2530e-02, 3.2911e-02, 3.3237e-02,\n", - " 3.3623e-02, 3.4061e-02, 3.4491e-02, 3.4837e-02, 3.5061e-02,\n", - " 3.5295e-02, 3.5616e-02, 3.5933e-02, 3.6232e-02, 3.6601e-02,\n", - " 3.7050e-02, 3.7417e-02, 3.7578e-02, 3.7712e-02, 3.8066e-02,\n", - " 3.8577e-02, 3.9056e-02, 3.9512e-02, 3.9902e-02, 4.0012e-02,\n", - " 3.9988e-02, 4.0162e-02, 4.0429e-02, 4.0643e-02, 4.0906e-02,\n", - " 4.1163e-02, 4.1329e-02, 4.1582e-02, 4.1891e-02, 4.2013e-02,\n", - " 4.2028e-02, 4.2103e-02, 4.2225e-02, 4.2496e-02, 4.2971e-02,\n", - " 4.3267e-02, 4.3047e-02, 4.2649e-02, 4.2448e-02, 4.2262e-02,\n", - " 4.1968e-02, 4.1685e-02, 4.1314e-02, 4.0663e-02, 3.9955e-02,\n", - " 3.9623e-02, 3.9730e-02, 4.0168e-02, 4.0882e-02, 4.1651e-02,\n", - " 4.2308e-02, 4.2950e-02, 4.3711e-02, 4.4580e-02, 4.5294e-02,\n", - " 4.5380e-02, 4.4678e-02, 4.3837e-02, 4.3663e-02, 4.4060e-02,\n", - " 4.4520e-02, 4.5103e-02, 4.6137e-02, 4.7578e-02, 4.8987e-02,\n", - " 4.9865e-02, 5.0162e-02, 5.0285e-02, 5.0334e-02, 5.0151e-02,\n", - " 5.0113e-02, 5.0683e-02, 5.1484e-02, 5.1904e-02, 5.2038e-02,\n", - " 5.2379e-02, 5.3093e-02, 5.3961e-02, 5.4575e-02, 5.4629e-02,\n", - " 5.4445e-02, 5.4777e-02, 5.5862e-02, 5.6974e-02, 5.7175e-02,\n", - " 5.6487e-02, 5.5976e-02, 5.6289e-02, 5.7025e-02, 5.7643e-02,\n", - " 5.7789e-02, 5.7386e-02, 5.7039e-02, 5.7019e-02, 5.5835e-02,\n", - " 5.0889e-02, 4.0526e-02, 2.6302e-02, 1.3039e-02, 4.6908e-03,\n", - " 1.1779e-03, 2.0027e-04, 2.2503e-05, 1.6390e-06, 7.6241e-08,\n", - " 2.2396e-09, 4.1207e-11, 4.7199e-13, 3.3510e-15, 1.4689e-17,\n", - " 3.5537e-20, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250,)\n", - "(250,)\n", - "(250,)\n", - "(250,)\n" - ] - } - ], - "source": [ - "egrid = np.linspace(-10,14.9,250)\n", - "\n", - "print(egrid.shape)\n", - "print(ldos1.shape)\n", - "print(ldos2.shape)\n", - "print(ldos3.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size' : 17}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.plot(egrid, ldos1, \"-r\")\n", - "ax0.plot(egrid, ldos2, \"-b\")\n", - "ax0.plot(egrid, ldos3, \"-g\")\n", - "ax0.legend([\"LDOS 1\", \"LDOS 2\", \"LDOS 3\"])\n", - "ax0.set_ylabel(\"LDOS\")\n", - "ax0.set_xlabel(\"Energy (eV)\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./representative_ldos.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "xgrid = np.linspace(0.0,75*.1656,75)\n", - "\n", - "water = np.load(\"./water_densities/H2O_ldos_75x75x75grid_1elvls_snapshot15.npy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/site-packages/matplotlib/tight_layout.py:176: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", - " warnings.warn('Tight layout not applied. The left and right margins '\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size' : 36}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, ax = plt.subplots(1,1)\n", - "#fig.set_figheight(15)\n", - "#fig.set_figwidth(15)\n", - "\n", - "cbar_max = 1.4\n", - "\n", - "z = 70\n", - "\n", - "im = ax.contourf(xgrid, xgrid, water[:,:,z,0], cmap=\"seismic\")\n", - "ax.set_title(\"H2O Density Z-Slice at %2.1f (e/A^3)\" % (z*.1656))\n", - "\n", - "cbar = fig.colorbar(im, ax=ax, boundaries=np.linspace(0,1.4,8))\n", - "#cbar.set_ticks(np.linspace(0,1.4,8))\n", - "cbar.set_clim(0.0, cbar_max)\n", - "#cbar.ax.set_zlim([0,1.4])\n", - "plt.tight_layout()\n", - "\n", - "#plt.savefig(\"./water_density_z%d.eps\" % z, format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "t_loss = np.zeros(28093)\n", - "\n", - "loss_fname = \"./fp_ldos_synapse_Al_2gpus_298K_2.699gcc_94fp_250ldos_200nxyz_1555628922.log\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "28093\n" - ] - } - ], - "source": [ - "count = 0\n", - "with open(loss_fname) as f:\n", - " for idx, line in enumerate(f):\n", - " find_str = 'Loss: '\n", - "# find_str = 'stdout'\n", - "# if (idx < 250):\n", - " #print(line)\n", - "# continue\n", - "# elif (idx < 300 and find_str in line):\n", - "# print(\"\\nFound: \", line)\n", - "# before, after = line.split(find_str, 1)\n", - "# print(\"After: \", after)\n", - " if (find_str in line):\n", - " before, after = line.split(find_str, 1)\n", - " t_loss[count] = float(after)\n", - " count += 1\n", - " else:\n", - " continue\n", - "\n", - " print(idx)\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1171647" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_loss[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.00256788]\n", - " [0.00513576]\n", - " [0.00770364]]\n", - "[[8800.]\n", - " [8800.]\n", - " [8800.]]\n" - ] - } - ], - "source": [ - "train_loss = t_loss[t_loss > 0]\n", - "\n", - "epochs = 25\n", - "\n", - "#print(train_loss[-1])\n", - "\n", - "train_loss.size\n", - "\n", - "batch_idx = np.linspace(0, epochs - 1, train_loss.size)\n", - "\n", - "lr_epochs = np.zeros([3, 1]);\n", - "lr_epochss = np.zeros([3, 1]);\n", - "\n", - "for i in range(3):\n", - " lr_epochs[i, :] = np.array([22]) * 400\n", - " lr_epochss[i, :] = batch_idx[np.array([22]) * 400]\n", - " \n", - " \n", - " \n", - "lr_slice = np.zeros([3, 1])\n", - "\n", - "for i in range(1):\n", - " lr_slice[0, i] = train_loss[int(lr_epochs[0,i])] * .50\n", - " lr_slice[1, i] = train_loss[int(lr_epochs[0,i])] * 1.0\n", - " lr_slice[2, i] = train_loss[int(lr_epochs[0,i])] * 1.5 \n", - "\n", - "\n", - "print(lr_slice)\n", - "print(lr_epochs)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 17}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.semilogy(lr_epochss[:,0], lr_slice[:,0], '-r')\n", - "#ax0.semilogy(lr_epochss[:,1], lr_slice[:,1], '-r')\n", - "#ax0.semilogy(lr_epochss[:,2], lr_slice[:,2], '-r')\n", - "#ax0.semilogy(lr_epochss[:,3], lr_slice[:,3], '-r')\n", - "ax0.semilogy(batch_idx, train_loss, \"-b\")\n", - "ax0.legend(['LR Reduction'])\n", - "ax0.set_ylabel(\"Training Loss\")\n", - "ax0.set_xlabel(\"Epoch\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./loss_curve_lstm.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "clusters = [4, 6, 8, 10, 12, 15, 20, 50, 100, 150, 200, 400, 800, 1600, 3200]\n", - "centroid_distances = [1.94e6, 1.51e6, 1.14e6, 1.01e6, 9.03e5, 8.24e5, 7.20e5, 4.85e5, 3.67e5, 3.2e5, 2.95e5, 2.48e5, 2.19e5, 1.95e5, 1.75e5]\n", - "\n", - "#clusters = [50, 100, 150, 200, 400]\n", - "#centroid_distances = [1.53e7, 8.58e6, 7.51e6, 7.19e6, 5.51e6]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 15}\n", - "\n", - "matplotlib.rc('font', **font)\n", - "\n", - "fig, (ax0) = plt.subplots(1,1)\n", - "ax0.semilogy(clusters, centroid_distances, '-b')\n", - "#ax0.plot(clusters, centroid_distances, '-b')\n", - "#ax0.set_ylim([1e6, 5e7])\n", - "#ax0.set_xlim([40, 450])\n", - "#ax0.set_xticks([0,5,10,15,20,25])\n", - "ax0.legend(['298K Snapshot Clustering'])\n", - "ax0.set_ylabel(\"Sum of Squared Centroid Distances\")\n", - "ax0.set_xlabel(\"Num Clusters\")\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"./clustering_error.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "400.0" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "8000000 / 2 / 1000 / 10" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "not 1" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.3334572 , 0.41337177, 0.46787082, 0.46534866])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#snapshots = np.array([2,4,6,8,10])\n", - "#train_time = np.array([418.0638, 1032.7823, 1641.2181, 2240.6008, 2708.6924]) / 2\n", - "\n", - "snapshots = np.array([4,6,8,10]) - 2\n", - "train_time = np.array([1656.3196, 3506.6325, 5206.3768, 6890.7759]) / 5\n", - "\n", - "snapshots_cluster = np.array([4,6,8,10]) - 2 \n", - "train_time_cluster = np.array([552.3117, 1449.5429, 2435.9118, 3206.6133]) / 5\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(snapshots, train_time, 'k-')\n", - "#plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(0)\n", - "plt.ylabel(\"Training Epoch Time (s)\")\n", - "plt.xlabel(\"Training Snapshots\")\n", - "plt.legend([\"Big Data Case (2 V100s)\", \"Big Cluster Case\"])\n", - "\n", - "plt.savefig(\"./big_data_training_times.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(snapshots, train_time, 'b-')\n", - "plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(0, 1800)\n", - "plt.ylabel(\"Training Epoch Time (s)\")\n", - "plt.xlabel(\"Training Snapshots\")\n", - "plt.legend([\"Big Data Case (2 V100s)\", \"Big Cluster Case (2 V100s)\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./big_cluster_training_times.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "csr = np.array([.25, .5, 1.0])\n", - "val_err = np.array([5.45775e-2, 5.29625e-2, 5.100578e-2])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(csr, val_err, 'g-')\n", - "#plt.plot(snapshots, train_time, 'b-')\n", - "#plt.plot(snapshots_cluster, train_time_cluster, 'g-')\n", - "plt.ylim(.05, .06)\n", - "plt.ylabel(\"Final Validation Loss\")\n", - "plt.xlabel(\"Sampling Ratio\")\n", - "plt.legend([\"Big Cluster Case\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./big_cluster_sampling_error.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "mp_complete = mp.Value('i', False)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mp_complete.value = True\n", - "mp_complete.value == False" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2193.25" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "17546 / 8" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "sil_stand = np.array([0.447642,\n", - "0.227938,\n", - "0.0761155,\n", - "-0.0883255,\n", - "-0.174398,\n", - "-0.215808,\n", - "-0.234282,\n", - "-0.17385,\n", - "-0.137261])\n", - "\n", - "sil_norm = np.array([0.433647,\n", - "0.226572,\n", - "0.0813862,\n", - "-0.071797,\n", - "-0.177958,\n", - "-0.205834,\n", - "-0.225432,\n", - "-0.165705,\n", - "-0.132394,\n", - "-0.121013])\n", - "\n", - "iner_stand = np.array([7.16E+07,\n", - "7.03E+07,\n", - "6.90E+07,\n", - "6.69E+07,\n", - "6.21E+07,\n", - "5.44E+07,\n", - "4.40E+07,\n", - "3.79E+07,\n", - "3.50E+07])\n", - "\n", - "iner_norm = np.array([1.37E+06,\n", - "1.35E+06,\n", - "1.32E+06,\n", - "1.28E+06,\n", - "1.19E+06,\n", - "1.04E+06,\n", - "839276,\n", - "726160,\n", - "671359,\n", - "635393])\n", - "\n", - "clusters = np.array([4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(clusters[:-1], sil_stand, 'b-')\n", - "#plt.plot(clusters, sil_norm, 'g-')\n", - "#plt.ylim(0, 1800)\n", - "plt.ylabel(\"Silhouette Score\")\n", - "plt.xlabel(\"Number of Clusters\")\n", - "plt.legend([\"Standardized FP\", \"Normalized FP\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./cluster_silho.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 16}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.semilogy(clusters[:-1], iner_stand, 'b-')\n", - "#plt.semilogy(clusters, iner_norm, 'g-')\n", - "#plt.ylim(2e7, 8e7)\n", - "#plt.xlim(0, 2100)\n", - "plt.ylabel(\"Inertia\")\n", - "plt.xlabel(\"Number of Clusters\")\n", - "plt.legend([\"Standardized FP\", \"Normalized FP\"])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"./cluster_inertia.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb b/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb deleted file mode 100644 index 8fbca4478..000000000 --- a/ml-dft-sandia/networks/analysis/MLMM_Paper.ipynb +++ /dev/null @@ -1,1976 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "run1 = [[2524.7672468464098,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2524.284297288213,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2540.5346296203934,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2538.192478718474,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2537.7505319554557,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2528.1831923262403,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2531.1879539456913,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2528.6472414223667,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2531.146822509088,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2524.265726593582,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2525.130644423422,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2524.9720542683363,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2525.875747479948,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n", - "\n", - "\n", - "\n", - "run2 = [[2529.400883838115,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2529.0800950112975,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2545.192170218334,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2543.3688692048613,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2542.702495425762,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2532.891425190408,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2535.451981420631,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2533.840190810313,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2536.2361696338758,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2528.221130661553,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2530.13889287563,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2528.907989518418,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2530.1443926152356,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "run3 = [[2521.8308895694972,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2521.8850606562005,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2532.7997776476477,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2531.3470642505627,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2531.316218173628,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2526.2936656319825,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.833866461784,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2527.260749618935,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2529.5233302856964,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.444354073414,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2523.2371151566604,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2522.126651196736,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2523.954851817506,\n", - "2523.883308614503,\n", - "2525.0350232357523]]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "model3 = np.array(run3)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13\n", - "[[2524.76724685 2521.01508856 2522.16279024]\n", - " [2524.28429729 2525.04190127 2526.18636597]\n", - " [2540.53462962 2568.85406654 2570.00780166]\n", - " [2538.19247872 2560.91764762 2562.07124992]\n", - " [2537.75053196 2561.82551903 2562.98011185]\n", - " [2528.18319233 2530.8366533 2531.98598868]\n", - " [2531.18795395 2528.63210775 2529.77568218]\n", - " [2528.64724142 2528.16835882 2529.30777035]\n", - " [2531.14682251 2533.28377499 2534.43861391]\n", - " [2524.26572659 2519.88264663 2521.02839901]\n", - " [2525.13064442 2520.96725194 2522.11667815]\n", - " [2524.97205427 2521.70485347 2522.84663038]\n", - " [2525.87574748 2523.88330861 2525.03502324]]\n", - "[[2524.76724685 2521.01508856 2522.16279024]\n", - " [2524.28429729 2525.04190127 2526.18636597]\n", - " [2528.18319233 2530.8366533 2531.98598868]\n", - " [2531.18795395 2528.63210775 2529.77568218]\n", - " [2528.64724142 2528.16835882 2529.30777035]\n", - " [2531.14682251 2533.28377499 2534.43861391]\n", - " [2524.26572659 2519.88264663 2521.02839901]\n", - " [2525.13064442 2520.96725194 2522.11667815]\n", - " [2524.97205427 2521.70485347 2522.84663038]\n", - " [2525.87574748 2523.88330861 2525.03502324]\n", - " [2540.53462962 2568.85406654 2570.00780166]\n", - " [2538.19247872 2560.91764762 2562.07124992]\n", - " [2537.75053196 2561.82551903 2562.98011185]]\n" - ] - } - ], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "model3 = np.array(run3)\n", - "\n", - "snapshots = model1.shape[0]\n", - "\n", - "print(snapshots)\n", - "print(model1)\n", - "\n", - "neworder = [10,11,12,2,3,4,5,6,7,8,9]\n", - "oldorder = [2,3,4,5,6,7,8,9,10,11,12]\n", - "\n", - "model1[neworder] = model1[oldorder]\n", - "model2[neworder] = model2[oldorder]\n", - "model3[neworder] = model3[oldorder]\n", - "\n", - "#temp1 = np.zeros([3,3])\n", - "\n", - "#temp1 = model1[2:5,:]\n", - "#temp2 = model2[2:5,:]\n", - "#temp3 = model3[2:5,:]\n", - "\n", - "#print(temp1)\n", - "\n", - "#model1[2:10, :] = model1[5:, :]\n", - "#model1[10:, :] = temp1\n", - "\n", - "print(model1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs([-1, 1, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Summed Squared Error\n", - "84.21139569798906\n", - "422.98330325707366\n", - "54.049557249920795\n", - "\n", - "Model MEAN meV/Atom\n", - "10.211334627756052\n", - "23.78797119067304\n", - "6.918106837911786\n", - "\n", - "Model Max meV/Atom\n", - "17.12140611919999\n", - "35.826722422344304\n", - "17.74604557573589\n", - "\n", - "Model Max Error Snapshots\n", - "6\n", - "7\n", - "2\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "model1_rmse = np.sum(((model1[:10,0] - model1[:10,1]) ** 2))\n", - "model2_rmse = np.sum(((model2[:10,0] - model2[:10,1]) ** 2))\n", - "model3_rmse = np.sum(((model3[:10,0] - model3[:10,1]) ** 2))\n", - "\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(model1_rmse)\n", - "print(model2_rmse)\n", - "print(model3_rmse)\n", - "\n", - "\n", - "model1_mean = np.mean((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_mean = np.mean((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_mean = np.mean((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(model1_mean * mev_atoms)\n", - "print(model2_mean * mev_atoms)\n", - "print(model3_mean * mev_atoms)\n", - "\n", - "\n", - "\n", - "model1_max = np.max((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_max = np.max((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_max = np.max((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel Max meV/Atom\")\n", - "print(model1_max * mev_atoms)\n", - "print(model2_max * mev_atoms)\n", - "print(model3_max * mev_atoms)\n", - "\n", - "\n", - "model1_argmax = np.argmax((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_argmax = np.argmax((np.abs(model2[:10,0] - model2[:10,1])))\n", - "model3_argmax = np.argmax((np.abs(model3[:10,0] - model3[:10,1])))\n", - "\n", - "print(\"\\nModel Max Error Snapshots\")\n", - "print(model1_argmax)\n", - "print(model2_argmax)\n", - "print(model3_argmax)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), model1[:,0], 'bo')\n", - "plt.plot(range(snapshots), model1[:,1], 'go')\n", - "plt.plot(range(snapshots), model1[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 1')\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_model1_inference.png\", format='png')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(snapshots), model2[:,0], 'bo')\n", - "plt.plot(range(snapshots), model2[:,1], 'go')\n", - "plt.plot(range(snapshots), model2[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 2')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(snapshots), model3[:,0], 'bo')\n", - "plt.plot(range(snapshots), model3[:,1], 'go')\n", - "plt.plot(range(snapshots), model3[:,2], 'kx')\n", - "plt.plot([9.5,9.5], [np.min(model3)*.99, np.max(model3)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (eV)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.title('Model 3')\n", - "\n", - "plt.savefig(\"./mlmm_933K_model3_inference.png\", format='png')\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 13)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(13)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "54.6875" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "\n", - "\n", - "14 / 256 * 1000\n" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "run1 = [[2595.595905758535,\n", - "2594.2049589268686,\n", - "2595.3470902759054],\n", - "\n", - "[2598.052971247566,\n", - "2596.9779771297976,\n", - "2598.1203692340027],\n", - "\n", - "[2595.9637761890644,\n", - "2594.734865188264,\n", - "2595.876315137265],\n", - "\n", - "[2597.553173168981,\n", - "2595.437704293007,\n", - "2596.580145546497],\n", - "\n", - "[2596.9032110335843,\n", - "2594.812768615752,\n", - "2595.9576941828914],\n", - "\n", - "[2597.8899978230506,\n", - "2596.7844567694615,\n", - "2597.926529438901],\n", - "\n", - "[2597.2149758691526,\n", - "2596.1136735263253,\n", - "2597.2555939402955],\n", - "\n", - "[2597.102376603127,\n", - "2596.2552500670186,\n", - "2597.3974589366044],\n", - "\n", - "[2596.644038473075,\n", - "2595.6202455973435,\n", - "2596.7634174928216],\n", - "\n", - "[2596.724604651852,\n", - "2595.6639045552283,\n", - "2596.80694152332]]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "run2 = [[2595.1155760249526,\n", - "2594.2049589268686,\n", - "2595.3470902759054],\n", - "\n", - "[2596.8842014114193,\n", - "2596.9779771297976,\n", - "2598.1203692340027],\n", - "\n", - "[2595.2954535258427,\n", - "2594.734865188264,\n", - "2595.876315137265],\n", - "\n", - "[2596.6438913614766,\n", - "2595.437704293007,\n", - "2596.580145546497],\n", - "\n", - "[2596.0234692157082,\n", - "2594.812768615752,\n", - "2595.9576941828914],\n", - "\n", - "[2596.8655709592113,\n", - "2596.7844567694615,\n", - "2597.926529438901],\n", - "\n", - "[2596.023085934723,\n", - "2596.1136735263253,\n", - "2597.2555939402955],\n", - "\n", - "[2596.2236963398723,\n", - "2596.2552500670186,\n", - "2597.3974589366044],\n", - "\n", - "[2595.7497067817535,\n", - "2595.6202455973435,\n", - "2596.7634174928216],\n", - "\n", - "[2595.85595531512,\n", - "2595.6639045552283,\n", - "2596.80694152332]]" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "model1 = np.array(run1)\n", - "model2 = np.array(run2)\n", - "\n", - "snapshots = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Summed Squared Error\n", - "18.77167354418078\n", - "4.142384809659819\n", - "\n", - "Model MEAN meV/Atom\n", - "5.093447714422794\n", - "1.7604047950262824\n", - "\n", - "Model Max meV/Atom\n", - "8.263550296774724\n", - "4.7292992185798965\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "model1_rmse = np.sum(((model1[:10,0] - model1[:10,1]) ** 2))\n", - "model2_rmse = np.sum(((model2[:10,0] - model2[:10,1]) ** 2))\n", - "\n", - "\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(model1_rmse)\n", - "print(model2_rmse)\n", - "\n", - "\n", - "\n", - "model1_mean = np.mean((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_mean = np.mean((np.abs(model2[:10,0] - model2[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(model1_mean * mev_atoms)\n", - "print(model2_mean * mev_atoms)\n", - "\n", - "\n", - "\n", - "\n", - "model1_max = np.max((np.abs(model1[:10,0] - model1[:10,1])))\n", - "model2_max = np.max((np.abs(model2[:10,0] - model2[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel Max meV/Atom\")\n", - "print(model1_max * mev_atoms)\n", - "print(model2_max * mev_atoms)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), model1[:,0] * mev_atoms, 'bo')\n", - "plt.plot(range(snapshots), model1[:,1] * mev_atoms, 'go')\n", - "plt.plot(range(snapshots), model1[:,2] * mev_atoms, 'kx')\n", - "#plt.plot([9.5,9.5], [np.min(model1)*.99, np.max(model1)*1.01], 'r-')\n", - "plt.xlabel('Al 298K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "#plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues'])\n", - "#plt.ylim([2590, 2600])\n", - "plt.ylim([10125, 10155])\n", - "plt.title('Model 1')\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "plt.plot(range(snapshots), model2[:,2] * mev_atoms, 'c*')\n", - "plt.plot(range(snapshots), model2[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), model2[:,1] * mev_atoms, 'ko', mfc='none')\n", - "#plt.errorbar(range(snapshots), model2[:,1] * mev_atoms, yerr=5, fmt='ko', ecolor='r', capsize=3)\n", - "#plt.plot([9.5,9.5], [np.min(model1)*.99, np.max(model1)*1.01], 'r-')\n", - "plt.xlabel('Al 298K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "#plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "plt.legend(['DFT Eigenvalues', 'ML Predictions', 'ML Targets'])\n", - "plt.ylim([10120, 10170])\n", - "#plt.title('Model 2')\n", - "plt.tight_layout()\n", - "plt.savefig(\"./mlmm_298K_model2_inference.eps\", format='eps')\n", - "\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mldos1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./933K/Al_ldos_200x200x200grid_250elvls_snapshot0.npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mldos2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mldos1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.001\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 446\u001b[0m return format.read_array(fid, allow_pickle=allow_pickle,\n\u001b[0;32m--> 447\u001b[0;31m pickle_kwargs=pickle_kwargs)\n\u001b[0m\u001b[1;32m 448\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[0;31m# Try a pickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0;31m# We can use the fast fromfile() function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m \u001b[0;31m# This is not a real file. We have to read it the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "#ldos1 = np.load(\"./933K/Al_ldos_200x200x200grid_250elvls_snapshot0.npy\")\n", - "#ldos2 = ldos1 * 1.001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#ldos1 = np.reshape(ldos1, [200**3, 250])\n", - "#ldos2 = np.reshape(ldos2, [200**3, 250])\n", - "\n", - "#dens1 = np.sum(ldos1, axis=1)\n", - "#dens2 = np.sum(ldos2, axis=1)\n", - "\n", - "#dens1.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#xs = dens2[:vals]/dens1[:vals] #* (1 - dens2[:100] / dens1[:100])\n", - "#ys = dens2[:vals]/dens1[:vals] #* (1 + dens2[:100] / dens1[:100])\n", - "\n", - "#vals = 10000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(range(vals), (.5+np.random.rand(vals))*dens2[:vals], 'r.')\n", - "#plt.plot(dens1[:vals], dens2[:vals], 'r.')\n", - "#plt.plot(dens1[:vals], dens1[:vals], 'k-')\n", - "#plt.xlim([0,10])\n", - "#plt.ylim([0,10])\n", - "\n", - "#plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "liquid = [[2521.8308895694972,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2521.8850606562005,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2526.2936656319825,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.833866461784,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2527.260749618935,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2529.5233302856964,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.444354073414,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2523.2371151566604,\n", - 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- "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "solid = [[2547.575728787133,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2548.1962671881156,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2552.4417450901065,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2555.94411593651,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2554.147350170825,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2558.2741317887476,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2546.4076867624854,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2548.22534731669,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2546.025313525691,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2549.4435230800555,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2566.043629326736,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2562.1968937857278,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2563.341062741356,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2564.251867895487,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.1119598426894,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2567.5599195526966,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2564.7642859454118,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2562.5438655120643,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2565.280995619667,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2563.578013682739,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "hybrid = [[2522.960411449796,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2520.3572945491037,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2525.2498148260256,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2528.6146817945337,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2526.0101497844194,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2527.2948307215715,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2521.0130879673156,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2522.045909249362,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2521.0722131535085,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2522.0769612460326,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2563.900224948418,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2560.992381267455,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2560.9967273595557,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2562.2416591636847,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2560.912119353269,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2564.7785899256237,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2562.2408245441275,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2559.405012532466,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2562.239119309403,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2561.1779808982187,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "liquid = np.array(liquid)\n", - "solid = np.array(solid)\n", - "hybrid = np.array(hybrid)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10493.59389833247\n", - "Solid: 6483.851548699937\n", - "Hybrid: 193.75928559519446\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 66.37409103449308\n", - "Solid: 52.92982433674975\n", - "Hybrid: 9.41850013657497\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "liquid_rmse = np.sum(((liquid[:20,0] - liquid[:20,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[:20,0] - solid[:20,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[:20,0] - hybrid[:20,1]) ** 2))\n", - "\n", - "print(\"All Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[:20,0] - liquid[:20,1])))\n", - "solid_mean = np.mean((np.abs(solid[:20,0] - solid[:20,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[:20,0] - hybrid[:20,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[:20,0] - liquid[:20,1])))\n", - "solid_max = np.max((np.abs(solid[:20,0] - solid[:20,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[:20,0] - hybrid[:20,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Liquid Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 54.049557249920795\n", - "Solid: 6445.134836632674\n", - "Hybrid: 103.57273255435054\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 6.918106837911786\n", - "Solid: 98.93174387461166\n", - "Hybrid: 9.77712253056282\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 17.74604557573589\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "liquid_rmse = np.sum(((liquid[:10,0] - liquid[:10,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[:10,0] - solid[:10,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[:10,0] - hybrid[:10,1]) ** 2))\n", - "\n", - "print(\"Liquid Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[:10,0] - liquid[:10,1])))\n", - "solid_mean = np.mean((np.abs(solid[:10,0] - solid[:10,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[:10,0] - hybrid[:10,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[:10,0] - liquid[:10,1])))\n", - "solid_max = np.max((np.abs(solid[:10,0] - solid[:10,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[:10,0] - hybrid[:10,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solid Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10439.54434108255\n", - "Solid: 38.716712067261305\n", - "Hybrid: 90.18655304084393\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 125.83007523107437\n", - "Solid: 6.927904798887852\n", - "Hybrid: 9.059877742587119\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 12.614455251767964\n", - "Hybrid: 19.350943710090718\n" - ] - } - ], - "source": [ - "liquid_rmse = np.sum(((liquid[10:20,0] - liquid[10:20,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[10:20,0] - solid[10:20,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[10:20,0] - hybrid[10:20,1]) ** 2))\n", - "\n", - "print(\"Solid Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[10:20,0] - liquid[10:20,1])))\n", - "solid_mean = np.mean((np.abs(solid[10:20,0] - solid[10:20,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[10:20,0] - hybrid[10:20,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[10:20,0] - liquid[10:20,1])))\n", - "solid_max = np.max((np.abs(solid[10:20,0] - solid[10:20,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[10:20,0] - hybrid[10:20,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 9 10 11 12 13 14 15 16 17 18 19]\n", - "[ 0 1 2 3 4 5 6 7 8 9 19]\n", - "[ 0 1 2 3 4 5 15 16 17 18 19]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 10439.549459512444\n", - "Solid: 6451.102575526406\n", - "Hybrid: 127.09295260525487\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 114.41638344977032\n", - "Solid: 90.80545451532838\n", - "Hybrid: 10.409332534062056\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 140.83706597872413\n", - "Solid: 106.6875319686762\n", - "Hybrid: 23.394313554652157\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "#lt = np.arange(9,10)\n", - "#st = np.arange(0,10)\n", - "#ht = np.arange(0,6)\n", - "\n", - "#Solid\n", - "#lt = np.arange(10,20)\n", - "#st = np.arange(19,20)\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "lt = np.arange(9,20)\n", - "st = np.arange(0,10)\n", - "st = np.append(st, [19])\n", - "ht = np.arange(0,6)\n", - "ht = np.append(ht, [15,16,17,18,19])\n", - "\n", - "\n", - "print(lt)\n", - "print(st)\n", - "print(ht)\n", - "\n", - "liquid_rmse = np.sum(((liquid[lt,0] - liquid[lt,1]) ** 2))\n", - "solid_rmse = np.sum(((solid[st,0] - solid[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hybrid[ht,0] - hybrid[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liquid[lt,0] - liquid[lt,1])))\n", - "solid_mean = np.mean((np.abs(solid[st,0] - solid[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liquid[lt,0] - liquid[lt,1])))\n", - "solid_max = np.max((np.abs(solid[st,0] - solid[st,1])))\n", - "hybrid_max = np.max((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", - " 13, 14, 15, 16, 17, 18, 19, 111, 131])" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = np.arange(0,20)\n", - "\n", - "np.append(t, [111, 131])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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gs9rq5HUlDaOgmT9/PjExMUyZMoWUlBSmTJlCTExMvgSh/v37M2vWLOf+rFmzeOihh/Klm+zMmTM8/fTTxMfHs3btWgICAujevTsXL150y/fss88ybNgwEhISuOWWW1i3bh2DBg1i2LBhbN++nS5dulyykuvSpUvp168fw4YNY9euXcyYMYMPP/yQ0aNHA7B8+XIqVarEyy+/THJyMomJic5r7du3j//973/s3r2bGTNmUKVKxjFGRoGkqrnasKbTKZnb84rK1qxZMzUKt127dnlcRmRkpMbGxrqlxcbGamRkpMdlZ6VPnz7arVs3/f333zUoKEj37t2rycnJGhAQoImJic7j6V588cXLrk/JkiV19uzZOeb7/fffFdCtW7eqqmpCQoIC+tZbb7nlu+OOO7R79+5uaQ8++KAGBgY695s3b66vvvqqW5758+druXLlnPtVqlTRKVOmuOXp0qWLDhw40NbrMrwju78hrMFctr5P7XbB4bj5fwj4C/hbRBJFZFBeBEXDKOgSEhJo27atW1rbtm1JSEjI82uXK1eOO++8k1mzZvH+++/ToUOHfHs+be/evdx7773UqVOH0qVLU7NmTQAOHTrkli862n05mN27d9OiRQu3tOuuu875u6qybds2XnjhBYKDg51bv379+OOPP/jjjz+yrNNjjz3GnDlzaNq0Kc888wwbN27MMq9RsNh6EFVERgLPYY0SS/90rwdeEZHSqvpKHtXPMAqkiIgINm7cSMeOHZ1pGzduzLeF7vr160efPn0IDg6+pCsrr6gqN998Mw0aNOC9996jatWqAERGRnL+/Hm3vCVLlsx12Wlpabz00kt07979kuOlS2f9uGH37t1JTExk+fLlrF27lptuuonevXub+0CFQG6m4hmgqq4d3GtFZB/wMmACkHFFiYmJoX///sycOZO2bduyceNG+vfvz7hx4/Ll+p06dSIgIIATJ05wxx135Ms1k5KSOHDgAHPnzqVVq1YAfPPNN+ld89lq0KABW7dudUvbsmWL83c/Pz+aNGnC3r17CQ8Pz7KcgIAAUlNTL0mvXLkyffv2pW/fvnTu3JmHH36YqVOn4udnu5PH8AG7AagysDWT9C1cOqOAYRR5PXv2BGDIkCHO5YnHjRvnTM9rIsKPP/6IqhIYGJhlvpSUFLZv3+6WVqJECerVq5fra1auXJmyZcsyffp0KlWqxKFDh5zPH+Vk6NChdO7cmUmTJtGtWzdiY2NZtmyZ27kvvvgid999N9WrV+fuu+/Gz8+Pn376ie3bt/Pyyy8DULt2bdavX88999xDUFAQFSpUYOTIkbRs2ZKGDRty7tw5Pv/8c+rXr2+CTyFg9xPai7UkQkb3Yz2PYxhXnJ49e7Jjxw5SU1PZsWNHvgWfdKVKlcq2awrgl19+oWnTpm7b/fdn9qecM39/fz7++GO2bNlCVFQUTzzxBOPHj7f1Rd+hQwfeeustJkyYwDXXXMOKFSsYPny42zDs22+/nSVLlrBixQqio6Np2bIlEyZMIDQ01Jln3Lhx7N27lzp16lC9enVnvUaMGEHjxo1p164dqampfPrpp5f1Go38JXaazyJyF7AQWIf1cCdAG6A90ENVP8+rChY00dHRGh8f7+tqGB5Ib7EYvvXoo48SHx9/SdecUfBl9zckIt+panSmBzOw1QWnqp86Fmwbxr9LHyQALVR1m50yDMO4so0fP56bb76ZkiVLsmLFCmbNmsWkSZNyPtEosuzeA0JVvwMeyMO6GIZRhG3atImJEyfyzz//UKdOHV5//XUGDhzo62oZPmR3GHYqUFVVj2VIrwAcU9VieVE5wzCKjs8/v2J66g2b7A5CyGqYSyBwPotjhmEYhpGlbFtALjNdKzBQRE65HC6G9TDq7jyqm2EYhlGE5dQFN8TxU4CHAdcnwM5jrcNjOnENwzCMXMs2AKlqGICIxAF3qWrWEzIZhmEYRi7YXY6hY2bBR0TCROQl71fLMAzDKOpyPVeFiASISE8RWQP8DPTyfrUMwzCMoi43yzE0EpE3gaPAR8AvQKv0bjrDMIzatWszYcIEX1fjsvTt25dbb70154yG12QbgESklIg8IiJbgW+A0sC9QBowWVW3ZHe+YRje07dvX0QEEcHf35/KlSvTsWNHpk6d6ly2Ol2HDh2ceV237du3Z5ruuo0aNSrH67tuLVu2dObZunUrgwaZZcIMe3IaBXcE2ARMBj5V1TNAviz9axjGpTp37syHH35Iamoqx48fJzY2lhdffJEPP/yQtWvXuq3D89BDDzlnkU5XqVIlkpOTnftvv/02s2bNcpuPLTg4OMfruwoICHAr3zDsyqkL7hxQxrEF5ZDXMK44yefO0X7bNo6eO5cv1wsMDCQkJITq1avTpEkTnnzySdatW8f333/Pq6++6pa3RIkShISEuG3FihVz2y9VqtQladkFoPTru27ly5d3Hs/YBbd3717at29PUFAQ9evXZ/ny5QQHBzNnzhxnniNHjnDfffdRrlw5ypUrR7du3di3b5/z+KhRo4iKiuLjjz/m6quvplSpUtxxxx2cOHECgFWrVhEQEMDJkyfd6jpy5EgaN24MwMmTJ+nZsyc1atSgePHiREZGMnv27Gzf6w4dOjB48GC3tIzddKrKq6++ytVXX03x4sVp1KgRH330kds5Y8aMITQ01Pne9e7dO9vrXklyCkDVgInAbcAREVkqIj2wHkw1jCve2IMH2fjXX4w5eNBndYiKiuLmm29m8eLFPqtDZtLS0rjzzju56qqr2Lx5M3PmzGH06NGccwnWZ86coWPHjgQFBbF+/Xo2bdpE1apV6dy5M2fOnHHmO3jwIAsWLOCzzz5j1apVbNu2jZiYGMBanK9ixYosWrTImV9VmTdvHg88YE1fmZKSwrXXXsuXX37Jzp07GTp0KI888ghr16716DU+//zzzJw5k6lTp7Jr1y6ee+45HnnkEZYtWwbA4sWLmTBhAtOmTWPfvn18+eWXlyxNfiXL6TmgC8AiYJGIhAJ9gdcc540WkTnAKkc+w7hiFN+wgZS0NOf+28nJvJ2cTJCfH2fbtcv3+jRs2JA1a9a4pc2YMcOtpfHAAw/wzjvveHSdFStWXNJCeuyxxxg/fvwleVevXs2ePXtYtWqVc+2eN954gzZt2jjzfPzxx6gqs2fPdnbtT58+ncqVK/Pll1/yn//8B4CLFy8yZ84cypQpA8CAAQOcLZhixYpx3333MXfuXOfkpl9//TWHDx92rn1UvXp1nn76aed1BwwYQGxsLPPnz6dTp06X9V6cPn2a119/nVWrVnH99dcDEBYWxpYtW5g6dSrdunUjMTGRqlWrcuONN+Lv70+tWrWIjra1UsEVITezYSdiBZ0xwI1Af2AxkAKUzZvqGUbBtP+66xj+yy98fuIEZ9LSKOHnx50VKzLh6qt9Uh9VveTe7L333suLL77o3M9p8To72rVrx4wZM9zSypbN/M9/9+7dVKtWzRl8AJo3b+62gN13333HgQMHKFWqlNu5Z86c4ZdffnHuh4aGOoMPQLVq1Th27N+5kR944AEmTZpEYmIioaGhzJ07l/bt21OjRg0AUlNTeeWVV1iwYAFHjhzh3LlznD9/ng4dOuT+TXDYtWsXKSkp3HzzzW7v/YULF6hduzYAPXr0YPLkyYSFhXHTTTdx8803c/vtt2e7iu2VJKe54MplfABVrRXsVgIrRaQSuViiQUTaAcOBZljdew+p6hyX4wK8CAwAygHfAo+p6k7H8drA/wEdgapAMrAAGKOqZ13KqQVMBW4AzgLzgOGqet4lT3vgdSASSAJeVVXP/ntoXDGqBgZSulgxUtLSCPLzIyUtjdLFihHioy+WXbt2UadOHbe0MmXKEB4e7tXrlChRwqtlpqWl0aRJEz7++ONLjrneW/L393c7JiKkubRAr732Who0aMC8efMYPnw4ixYtcrsnNmHCBCZOnMjkyZNp1KgRwcHBjBw50i2IZeTn50fGBTtdRxumX/+LL76gVq1abvnS61uzZk327NnD2rVrWbNmDU899RSjR4/m22+/dRswcqXKqQV0VES+BpYAS1T1oOtBVT0OvJGL6wUDO4APHFtGzwBPYXX17QFeAFaLSH1V/QdogDUJ6qPAPiACmAFUwApaiEgxYBlwEmuy1ArA+1jz2Q1x5AkDlgOzsAJoW2CaiBxX1YLVkW4UWL9duMDAatUYUK0aM5KSSD7vm4nhd+zYwYoVK3j++ed9cv2sNGjQgKSkJJKSkqhWrRoA8fHxlwSO+fPnU7FixSxbUnY98MADzJ07l6ioKE6fPs0999zjPLZx40Zuu+02HnzwQcBqMe7duzfba2YcMQjwww8/OFs3DRs2JDAwkMTERG644YYsywkKCqJbt25069aNZ599lpCQEL7++mtuvPFGD15t0ZBTAAoFbge6A6+IyB7+DUbf5/Ziqroc64sfx/0jJ0fr5wnglfQgICJ9gGPA/cB0VV0BrHA5bb+IjAPG4ghAWN2DkUCoqh52lPMM8J6IxKjq31gTqCapavpkqwmOFV+HY3UrGkaOPo2Kcv4+tV69fLnmuXPnOHr0KGlpaRw/fpy1a9fy8ssv06xZM4YPH55v13dVrFixTIdfd+nShfr169OnTx8mTJjA2bNnefLJJ7nqqqucXVa9evViwoQJdO/enTFjxlCrVi0OHz7MkiVLGDhwIHXr1rVdt169evH888/zf//3f9x2221uXY716tVjwYIFbNy4kYoVKzJlyhQOHDhA06ZNsyzvhhtu4IknnmDp0qXUr1+f6dOnc/jwYWcAKlWqFMOHD2f48OGoKu3atePUqVNs3rwZPz8/BgwYwJw5c7h48SLXXXcdwcHBLFiwAH9//1y9rqIs21FwqnpUVWeoajegEvASUAdYIyKJIjJFRDo7Wh2eCgNCgFUu1z8LbABaZ3NeacC1m7AVkJAefBxWYq1d1MwlzyrcrQSiRcQfwyig1qxZQ9WqValVqxadOnVi6dKljBo1ig0bNuRLl0769V23rL7E/fz8+Oyzzzh37hwtWrSgT58+xMTEICIEBVlPdZQoUYINGzZQp04devToQYMGDejTpw9//PEH5cqVy1XdQkNDadu2LT/88INz9Fu6559/nhYtWtC1a1fatWtHyZIl6dUr+1nE+vXr59zatGlDqVKluPPOO93yjB07llGjRjFhwgQiIyPp0qULixcvJizMmiCmbNmyzJw5k+uvv56oqCgWL17Mp59+6jx+xVPVXG9Y3WCdsB5QPQj8DvTKZRmngL4u+62xhnfXypBvFrAyizJCgRPAky5pM4DYDPkEuAj0dOzvBV7IkKed4/pVM7nOACAeiK9Vq5YahduuXbt8XYUr1vbt2xXQ+Ph4X1fF8EB2f0NAvNqMAzkNQghX1Z8zCVqpwFrHNlREmgD52nIQkSpY3XGryd19qFxT1RlYgY3o6GjzDJRh2PTZZ59RsmRJ6taty8GDB3nyySe55ppruPbaa31dNaMAyOlB1L0isk5EeolIljMhqOp2Vd2a1XGb0juWq2RIr+JyDAARCQHisAY0POiIuq7lZCyjIlar7Wg2eapgtZJOXE7lDcO41D///MPgwYNp2LAhvXr1IiIigpUrV5rpvAwg5wDUDTiO1Q2WJCJviUjWd+08cwArMHRJT3AEveuxJkJNT6sKrAMSsLrULmYoZxMQISI1XNK6YE0r9J1Lni4ZzuuC1XQ0D9Uahpf07t2bvXv3cvbsWZKSkpg3bx5VqmT8v59xpcppEML/VLUHUB1rAEJ7IF5EvheRR0WkTHbnZyQiwSLSxNFl5wfUcuzXcrRiJgEjROQuEYkC5mDdK5rnOL8asB4rUD0BVBSREMeWPhBiFbAT+EBEmopIZ6zZG95VawQcwDtAdRGZJCIRIvIw1tDvwjmPvGEYRiFkd0XUE6r6uqo2whosEA+8gtUqyux5nqxEA9scW3FgtOP3MY7jr2Ldz5nquEZV4Ea1ngECa4h1XaxAeAjrQdT0raajrqlYLbczwNdYD6ouxhpinf56DgC3YA082A7EAI+reQbIMAwj34j77RObJ1kduP8BpgFlVdUbw7ALhejoaI2Pj/d1NQwPJCQkEBER4etqGEZ6R6WHAAAgAElEQVShld3fkIh8p6q2JryzPReco+BwoB/QG+um/SrgvdyUYRiGYRhgIwCJSHGs1k4/rClrDmLdQ5mtqkfytHaGYRhGkZXTc0DvYgWfAOBzrPsxni2gYRiGYRjkPAihBdbs09VUtacJPoZRcGVcjdRbRIRPPvkky+MnTpxARFi3bp3Xr51TXQpS3fLawYMHERGK0j3onIZhX6Oqb6rLkgwiUkVEbI2eMwzDezIuB53R1q1bGTRokNevm5yczG233XZZ56UvlZ2amsr48eOJiIigRIkSlCtXjujoaN58802f1M3V8ePHGTRoELVr1yYwMJAqVarQqVMnVq9e7VG5BVVmS437iq1BCI4JOsdhLYNQHKiHNRP1eCBRVaflXRUNw7AjsxmpvSEkJOSyzlu6dCmtW7emQoUKvPDCC0ybNo233nqLFi1acPr0abZt20ZiYqJP6ubq7rvv5syZM8ycOZPw8HCOHTvG+vXrOXnypMdlG9mz25J5EbgNa+2ccy7pW7Ae4DQMw8cydsH9/PPPdOjQgaCgIOrXr8+XX35JcHCwc5nurLp0curm2rp1K82aNSMoKIimTZvy7bffZlqfJUuW0L17d8AKRgMHDuS+++6jTp06NGrUiN69e/N///d/zvxpaWmMHTuWmjVrEhgYSKNGjViyZEm2r/ly65buzz//5KuvvuKVV16hU6dOhIaG0rx5c4YPH859993nzFe7dm1eeuklHnnkEUqXLk2NGjV47bXX3Mp6/fXXady4MSVLlqR69eo8/PDD/Pnnn87jc+bMITg4mC+++IJ69eoRFBREx44d2b9/vzPP4cOH6d69O+XLl6dEiRI0aNDgksX6EhMT6dKlCyVKlKBhw4aXtNQ2bNjAddddR1BQEFWqVGHYsGGcd6xV1bdvX9avX8/UqVMREUSEgwcPcuHCBR5//HGqVatGYGAgNWvW5Nlnn832vfMGuwGoJzBQVZcAaS7pO7BaQ4ZhFCBpaWnceeedpKWlsWnTJmbNmsWoUaM4d+5czidn49SpU3Tr1o06deoQHx/PK6+8kuk6RP/88w+xsbHOABQSEsK6dev47bffsix78uTJvPbaa4wfP56ffvqJO++8k7vuuovt27d7tW6ugoODCQ4OZunSpaSkpGSb94033qBRo0Z8//33jBgxgmeeeYZNmzY5j/v5+TFp0iR27tzJvHnz2LJlC0OGDHEr49y5c4wePZrZs2ezadMmUlNTueuuu5wrrw4aNIgzZ84QFxfHzp07mTRp0iWL5sXExPD444/zww8/0Lx5c+677z5OnToFwJEjR+jatStNmzZl27ZtzJw5k/nz5/Pcc8853+NWrVrx0EMPkZycTHJyMjVr1uTNN9/ks88+4+OPP2bfvn0sWLCA+vXr23rfPWJnymysZa1rO37/B6jj+D0SOGV36u2isDVr1kyNwi3TqeSHDlVt3z5/t6FDc1XvPn36aLdu3bI8Hhoaqq+99pqqqq5cuVL9/Pw0MTHRefyrr75SQGfPnq2qqgcOHFBAt27d6lYOoIsWLcp0f/r06VqmTBn9559/nMc//PBDBTQuLs6ZtnDhQm3UqJFzf+fOnRoREaEiog0bNtT+/fvr4sWLNS0tzZmnWrVqOnr0aLe6tG/fXnv16uXVumX0ySefaLly5TQwMFBbtmypTz31lG7evNktT2hoqN53331uaeHh4Tp27Ngsy/3f//6nAQEBmpqaqqqqs2fPVkA3btzozHPw4EH18/PT1atXq6pqo0aNdNSoUZmWl/55vfPOO860X3/9VQH96quvVFV15MiRGh4e7rxm+nUDAgL09OnTqmq9p4899phb2UOGDNEbbrjB7fPIjreWY7DbAtqJNW1NRv/h3wk+DcMoIBISEqhevTq1atVypl133XX4+Xk2fighIYHGjRsTHBzsTGvVqtUl+Vy738BavnrHjh18++23PPzww5w8eZL//Oc/dOvWjbS0NP7++2+SkpJo06aNWzlt27Zl165dXq1bRnfffTdJSUl88cUXdO3alW+++YaWLVvy8ssvu+Vr3Lix2361atU4duyYcz82NpYuXbpQo0YNSpUqxV133cX58+fdVpD18/OjRYsWzv3Q0FCqVavmfI1Dhw7lpZdeolWrVjz//PN8992lX6+u9Uhf6jy9HgkJCbRs2dLtc27bti3nz5/n558vWVnHqW/fvmzfvp169erx2GOPsWzZMrel0/OK3ZkQRgMfiUhNrGUNeohIA6ylsrvlVeUMI99MmuTrGuS79C8pdZmO68IFzyeDv3jxIsuWLbvk3oSfnx/NmzenefPmDBs2jI8++ogHH3yQDRs2ZLs+UH4s3RAUFESXLl3o0qULL7zwAg8//DCjRo1i+PDhBAQEAODv777kmYg4v6QTExPp1q0b//3vfxkzZgwVKlTg+++/p2fPns77L3ZeT//+/bnppptYvnw5a9asoXXr1jz33HOMGjXKmce1Hull2QkW2V332muv5eDBg6xcuZK1a9fSp08frrnmGlavXu3xf1qyY3cy0i+wWjs3Yt0DehFrUtDbVHVNntXOMIzLEhERwZEjRzh8+N+V6bds2eL2RZU+ai45OdmZltP9loiICH766SdOnz7tTNu8ebNbnvXr1xMcHEx0dPbTgTVs2BCw7t2ULl2aatWq8fXXX7vl2bhxozNfTuzUza6GDRty8eLFHO8LpYuPj+f8+fO88cYbtGrVinr16pGUlHRJvrS0NLZs2eLcP3ToEElJSW7zqtWoUYMBAwawcOFCxowZw4wZM2zXOyIigs2bN7t9zhs3biQgIICrr74agICAAFJTUy85t1SpUtxzzz28/fbbLFu2jNjY2GxbTd5gey44VV0JrMzDuhiGkYO///77kiBRtmxZateu7ZbWuXNnGjRoQO/evXnjjTc4e/Ysw4YN46qr/v2TL168OC1btmT8+PFcffXV/PXXX86b1Vm5//77iYmJoV+/frzwwgskJSUxbtw4tzxLlizh9ttvd0u75557aNOmDa1btyYkJIQDBw7w3HPPUaVKFVq3bg3A008/zQsvvEDdunVp1qwZH330EV999RXff/+9rffGTt0yOnnyJD169KBfv340btyYUqVKER8fz6uvvkqnTp0oXbq0rWvXrVuXtLQ0Jk2axF133cXmzZuZlEmr+qqrruKJJ55g8uTJFC9enGHDhhEZGUnnzp0Bqwuua9eu1KtXj7///psVK1bYDsBgDWKYNGkSgwYNYujQoezfv59nn32WwYMHU6JECcAa0bdlyxYOHjxIcHAw5cuXZ9KkSVStWpUmTZrg7+/PvHnznKP98pTdm0XpGxAElHDdcltGYd7MIITCL7sbqAVZnz59FLhku7trV1V1H4Sgqrpnzx5t166dBgQEaHh4uC5ZskRLlizpHISgar0XrVu31uLFi2tUVJRu2LAh2xv9qqqbN2/Wpk2bakBAgDZu3FiXLl3qdqO/Vq1aunLlSre6z5gxQzt16qSVK1fWgIAArVGjht577726Y8cOZ57U1FQdM2aM1qhRQ/39/TUqKko/++wzt3I8rVtGKSkp+txzz2l0dLSWLVtWixcvruHh4Tps2DA9efKkM1/G91b10pv5kydP1mrVqmlQUJDecMMNumDBAgX0wIEDqmoNBihZsqR+/vnnGh4ergEBAdquXTvdt2+fs4zBgwdreHi4BgYGasWKFfXee+/VX3/9VVXtDxpZv369tmjRQgMCArRy5cr6xBNPaEpKivP4nj17tGXLllq8eHFn/WbMmKFNmzbV4OBgLVWqlLZr106//vrrTN8zVe8NQrC1HIOIhAJvAh2BkpkEMbMcg1FoFKnlGPbssX7aHDIbHBzMW2+9Rd++ffOkOtu2baNjx44cP378knsmV7o5c+YwePBg55Dpwiy/l2P4CKvlMwT4Det/XoZhGG4uXLjAlClTTPAxbLEbgJoCzVU1IS8rYxhG4daiRQu3YcaGkR274+t+APJmoinDMPLNqVOn8qz7zche3759i0T3mzfZbQENAN4UkTexpt9xe1hAVQ95u2KGYRhG0WY3APlhLcH9Ge73f8Sxf8UMQjAMwzC8w24Aeh84BozADEIwigBVzZcn7A2jqLEzctouuwGoAdBEVfd67cqG4SP+/v6cPXvW+WCeYRj2Xbhwwe2BZk/YHYSwBQjzyhUNw8cqV67MkSNHOHPmjFf/N2cYRV1aWhq//fYbZcqU8Up5dsPY28AkEZkI/MSlgxBszZUhIu2A4UAzoBrwkKrOcTkuWPPMDQDKAd8Cj6nqTpc8McAtQBOsWRgu6UcRkVrAVOAGrKUk5gHDVfW8S572wOtYS0okAa+q6jt2XodRuKVPr5KUlOSVyTd9Kn2m5XyYudgwAEqWLEnFihW9UpbdADTf8TOzWfFyMwghGGsU3QeOLaNngKewVlndA7wArBaR+qr6jyNPIPApsA4YmbEAESkGLANOAtcDFbDuYQnWg7SISBiwHJiFtcprW2CaiBxX1cU2X4tRiJUuXdr2PF8F2qOPWj/XrfNpNQzjctgNQF7pflPV5Vhf/IjIHNdjjtbPE8Ar6UFARPpgDX64H5juKOMFx7F7srjMjVitmlBVPezI+wzwnojEqOrfwEAgSVXTlytMEJHrsFpnJgAZhmHkA1sBSFUT87oiWEEuBFjlct2zIrIBaI0jANnQCkhIDz4OK7FaTs2AOEeeVRnOWwn0ERF/VS3k/TKGYRgFX5aDEESkrd1CRCRYRBp5WJcQx8+Mi8b/5nLMbjkZyzgBpLqUk1me37ACsnc6Nw3DMIxsZTcK7j0RWSsiPUUk085yEWksIq8CPwPX5EkNCwARGSAi8SISf/z4cV9XxzAMo0jILgBFYd3sfxH4XUT2iEiciPxPRDaLyB9Yo9SqAzeo6kce1iV94fQqGdKruByzW07GMipiDZQ4mk2eKsBFrNaSG1WdoarRqhqdvoqkYRiG4ZksA5CqXlTVqaraAGiJNRR7O5AIrAEeBqqrai9V3eWFuhzACgxd0hNEJAhrJNs3uShnExAhIq5L+XUBzgHfueTpkuG8LlgLKZn7P4ZhGPnA7iCEeMDjVdhEJBgId+z6AbVEpAnwu6oeEpFJwEgR2Q3sBZ4HTmE9x5NeRi2gPFDbsd/EcehnVT2FNbhgJ/CBiDyFNQz7NeBdxwg4gHeAwY7rTQfaYA397unpazQMwzDs8c58CvZFY41CSzfasb2PFQBeBYpjPUSa/iDqjS7PAAGMAfq47G9z/OwIrFPVVBHpBkwDvsZ6EHUu8HT6Cap6QERuAd4AHsV6EPVx8wyQYRhG/rG1JLfxL7Mkt1GgdOhg/TQPohoFRG6W5LY7F5xhGIZheJUJQIZhGIZP2ApAImLGHhuGYRheZbcFdEREPhGRrmJW8TIMwzC8wG4A6gacx5qo85CIjBWRq/OuWoZhGEZRZysAqepqVb0faw2fV4CuwF4RiRWRXo4HRg3DMAzDtlwNQlDVPx2zI0QDj2PNUv0hkCQirzgeNDUMwzCMHOXqQVQRqYr1EGhfoAbwMTATq2X0HNaDpp29W0XDMAyjKLIVgETkLqAf1mJvO4A3gbmq+pdLnq3A7ryopGEYhlH02G0BzcZalruVqn6XRZ5kYJxXamUYhmEUeXYDUFVVPZNdBlU9izWvm2EYhmHkyG4AqpjF4z8KpKiqWaXNMAzDyBW7o+AOYq3Xk3E7CBwVkT9E5HURye/ZtQ3DyEbyuXO037aNo+fO+boqhnEJuwGoJ/Ar1vo8XRzb88AhrMEJo4AHgf/zfhUNw7gc8+fPJ3L8eDb88QcNx49n/vz5vq6SYbix22J5FBimqp+6pMWKyB5gqKq2F5FjWPeAXvR2JQ3DyJ2AuDguVK0KVasC8Ee7dtwP9ImL43zHjr6tnGE42G0BXQf8lEn6DqC54/dNWM8GGYbhY2ExMXRSpYSf9Sdews+PTqqEjRzp45rZ58vuQ0+vbbo+7bEbgBKBAZmk/xerGw6gEvC7NyplGIZnfv72W66uWpWUtDSC/PxISUsjvGpVft6yxddVs23swYNs/Osvxhw8mK/X9bTr0nR92mdrRVTHEteLgV+ArY7kaOBq4G5VXS4ig4BwVX0yrypbEJgVUY0CJYsVUaOioig3ZQqNq1dnQLVqzEhK4scjR/hjyBB27NiR79XMjeIbNpCSlnZJepCfH2fbtbNdTvK5c9y3axcLGjYkJDDQ1jkBcXFcyGTEr7+qra5LT8/3lst57d7i9RVRVXUZUBdYApR2bEuB+qq63JFnWlEPPoZRWMTExHCkf3/uOXKEhoGB3HPkCEf69ycmJsbXVcvRxORkSm7aRKDjP8eBqpTctInXk5Ntl3G5rRBPuy4LQtdnYWqB5dgCEhF/rBkOpqpqYr7UqgAzLSCjQMmiBQTWF9G4ceNISEggIiKCmJgYevbsma/VuxxRUVHUmTKFZSIE+PlxPi2NW1X5xWbrzZNWSLFixXh4507eO3rUee3/hoTwbmQkqampOV7b0/M9VRBaYF5tAanqBWAQYBaiM4xCpGfPnuzYsYPU1FR27NhRKIIPQEJCAn4VKjCwWjU2X3stA6tVQypUICEhwdb5nrRCIiIi2JWc7HbtncnJRERE2Lq2p+d7qiC0wHLD7iCElcANeVkRwzCKlssdCRYREcHQkyeZWq8e1wQHM7VePYaePGn7S9yTARiedl36uuuz0A0+UdUcN6wW0FFgEtYDp3e5bnbKKCpbs2bN1DAKjPbtrS0PJKWkaLvvv9fklJTLOv/R3bvVLy5OH929O1fnzZs3T8PCwjQ2NlbPnz+vsbGxGhYWpvPmzbN1fmRkpLaNjdVBe/bo9n/+0UF79mjb2FiNjIy0ff3IyEj18/PTyMhI29f11vme8PS1q3r+uQPxavP71G4ASstmS7V7saKwmQBkFCh5GIAuN4AErV+vxMVdsgWtX2+7DE++xD0NYIWZN1775X7u6XITgGwNw/YWEWkHDAeaYS1i95CqznE5LlgzKQwAygHfAo+p6k6XPOWw1iO63ZG0FBiiqn+65GkEvAW0wHo2aTowVl1erIjcDYzFGkr+CxCjqp/l9BrMIASjQMlmEMLl8nQY9LQFC3hm/34utmzJORECVblq82Zeq1OHR++912v1zE5hHYCRzpNh1Jf72r01/N3rw7C9KBhr9oShwNlMjj8DPAUMwZph4RiwWkRKueSZB1wL3OzYrsVaFhwAESkNrAZ+c5QxFHgaeNIlTytgATAXaOL4uUhErvPGizSMwszTYdDTxo7lhpYtuSBCkJ8fF0To1LIlU8eOzctquymsAzDSefIQ7uW+dm8Mf88tuw+iCtZ8cI8BYUCUqu4XkWeB/aq6MNcXFjkFDE5vATmukQS8parjHGnFsYLQcFWdLiIRwC6grap+7cjTFvgKaKCqe0TkUWA8UEWtNYoQkecd9a+hqioiC4DyqtrFpT5rgOOqmu2nZVpARoGSBy0gT4dBFytWjNu2baN6UJDzIdgjKSl80bRpvgxFLsy81Qq5HJ5+7unyogU0FGv26xm4D8c+Agy2XbPshQEhwKr0BEcA2QC0diS1Ak4B37ic9zVwOkOer9KDj8NKrC6/2i55VuFupUsZhnHF8nQYtKej2K5kvmiFpPP0c78cdgPQQOC/qjoZuOiS/j0Q6aW6hDh+/pYh/TeXYyFYrRRns83x+7EMeTIrAxt5QsiEiAwQkXgRiT9+3Ky9ZxRtngaQmJgY+vfvT1xcHBcuXCAuLo7+hWQWBl/zZfelL/7jYDcAhWLdu8noAlDce9UpmFR1hqpGq2p0pUqVfF0dw8hTngaQnj17Mm7cOIYMGUJQUBBDhgxh3Lhxhe4+jC/4ohWSzhf/cbC7HtB+rJv9GafiuQXrnow3HHX8rMK/M2yn7x91yVNJRCS9FeS4d1Q5Q54qGcqu4nIsuzxHMYwrXHqgGDJkiHMkVW4DSM+ePU3AuQzprZCOjmlzptarR1xcHD/nQ/elNz73XLMzVht4COt+Ty+sezC9sIZLnwbutTvmO0OZp4C+LvsCJAMjXdKCgL+BRxz7EYACrV3ytHak1XfsP+o4J8glz0hH/dMHXSwAVmWozypgfk71Ns8BGQVKHj4HZOS/ovAME7l4DshWC0hVZ4vIVcDLQAmsYc9JwOOqusBusBORYCDcsesH1BKRJsDvqnpIRCYBI0VkN7AXa+DDKayh16hqgoisAKaLSPr6RNOBL1V1j2N/HlZwnCMiLwH1gGeB0Y43B2AysMExiu9z4E6gI9DW7msxDMPwNp+0Qnwo1w+iikhFwE9Vj+X6YiIdgLhMDr2vqn1dHkR9BPcHUXe4lFEOmIL7g6iD9dIHUadiPYj6B/AOMMYlACEi9wAvAXX490FU1yXHM2WGYRsFSh4MwzYMT+RmGHa+zoRQFJgAZBQoJgAZBUxuApCtLjgRKY+1JlAnrBv+bqPnVLV0bitpGIZhXNnsjoKbCTTFehA1Ceumv2EYhmFcNrsBqBPQRVW/zcvKGIZhGFcOuw+iHsMajWYYhmEYXmE3AMUAYxzDqA3DMAzDY3a74J7HmsjzmIgkYk3B46Sqjb1cL8MwDKOIsxuAPsnTWhiGYRhXHLszIYzO64oYhmEYV5Zs7wGJyI2OKXjS90tlOB4kIv3yqnKGYRhG0ZXTIIT/AeVd9o+ISB2X/TLAu16vlWEYhlHk5RSAJId9wzAMw7gsdodhG4ZhGIZXmQBkGIZh+ISdUXCNReR3x+8CRIpIWcd+xbyplmEYhlHU2QlAK3G/97Mkw3EzMalhGIaRazkFoLB8qYVhGIZxxck2AKlqYn5VxDAMw7iymEEIhmEYhk+YAGQYhmH4hAlAhmEYhk+YAGQYhmH4hAlAhmEYhk9kOQpOROKw+YyPqt7gtRoZhmEYV4TsWkA7gJ2ObTfQDKgO/OrYqjnSErxZIREpJSKTRCRRRM6KyDci0tzleBURmSMiSSJyRkRWiEjdDGUEisgUETkhIqdFZKmI1MiQp5aIfOE4fkJE3hSRAG++FsMwDCNrWQYgVR2SvgHngPeBBqra27E1AGYDF71cp/eAm4A+QCNgFbBGRKqLiACfA3WBO4CmQKLjeEmXMiYBdwM9geuB0sCXIlIMwPFzGVDKcbwncA8w0cuvxfCC5HPnaL9tG0fPnfN1VYwrwPyf5hM1LYpiY4oRNS2K+T/N93WVii5VzXEDTgL1MkmvB/xupwyb1ymOFdC6Z0j/DnjJcT0FrnE55gccAx527JcBzgO9XPLUBNKAmxz7XR37NV3yPACkAKWzq2OzZs3UyF+P7t6tfnFx+uju3b6uSsHTvr21GV4x78d5GjYpTGP3x+r5i+c1dn+shk0K03k/zvN11QoNIF5tfufbHYQgWK2RjDJL88RVQDGsQODqLNAWCHTsO4+rahpWC62tI6kZ4I/VckrPcxirq7C1I6kVkOBIT7fSUX4zb7wQw3PFN2xA1q3j7eRk0oC3k5ORdesovmGDr6tmFFHjvhrHzNtn0jGsI/7F/OkY1pGZt89k3FfjfF21IsluAJoFvCciz4pIB8f2LNZqqLO9VRlV/QfYBDzv6HIrJiIPYAWMqlj3og4BL4tIeREJEJERQA3HcYAQIBU4kaH43xzH0vP8luH4Ccd5IRgFwsTkZEpu2kSg1UIlUJWSmzbxenKyj2tWOJiupNxLOJFA21pt3dLa1mpLwgmv3urOU4Xpc7cbgJ4B/h8wBIh1bEOAVxzHvOlBrO6xX7FaNo8D84E0Vb0A3AVcjdUteAboiLV0eJqX6+EkIgNEJF5E4o8fP55Xl8lThfE+yrSxY7mhZUsuiBDk58cFETq1bMnUsWN9XbUCb/5P84mJjWFK1ymkxKQwpesUYmJjCvSXkStffYlGVIxg46GNbmkbD20komKE7TJ8GQA8/dzzve52++r033slpcnhPok3NqAkUNXx+wJgWYbjZYBKjt+/BaY6fr8B6z5RpQz5dwKjHb+PAXZmOF7JcV7H7OpVGO8BzZs3T8uNHq2sXavlRo/WefMKR3+2n5+fdv/hBx20Z49u/+cfHbRnj3b/4Qf18/PzddUKjizuAUVOjdTY/bFuabH7YzVyaqStYuf9OE8jp0aq32g/jZwamet7IJ6c78v7MJ5e29f3kDz53L1Vd3JxDyhPg4g3NqAc8CcwIIvjdbG6zm5UdRuEcL9LnhpkPgihhkue+ymCgxD8Y2OVuLhLNv/Y2JxP9rHIyEiNzVDP2NhYjYy09yV6RcgiAPmN9tPzF8+7pZ2/eF79RuccvH39Jexp8EyvgycB8HLP9XXdPfncvVF31TwIQEB54G1gryMY/O262b2YzWvd5AgQYUAXYDuwGfB3HO+B1e1WB+gOHAQWZyjjbawuvM5YQ7XjHOUUcxwvBvyE1ZXY1JHvCDAlp/oVtgBUr1Ur7RQbqyXWr1fi4rTE+vXaKTZW67VsabuMpJQUbff995qcknJZdbjc8+fNm6eVqlfS2kNrq7wgWntoba1UvVKhacHlizxoAXn6ReTp+Z58iar6thXi67p78t57Wvd0eRGAPnN80Y8E+mI9o+Pc7F7M5rX+A/yCdf8nGXgLKONy/HHgsKOVkwiMBQIylBEITOHf+0Rf4DLk2pGnFvCl4/hJ4E0gMKf6FbYA5OfnpwMSEtQvLk6D1q9Xv7g4fSQhwXY31uBXBqvfyCeVtWvVb+STOviVwbmuw+UOo5734zyt9GAlrV2vtvr5+WnterW10oOVzJBYV1kEIE++yDz9IvL0fF8HQE/4uu6efO4FuQX0N3Cd3UKL8lbYAlBkZKS2jY11u4/S1mY3VrG1azLtviu2do2tawc5Wl0Zt6D16+3V3YdfJIVGNs8BXW5XTmH+ElX13v/kL0dBqPvlfu4F9h4Q8DMQabfQorwVtgA0b948DQsL09jYWD1//rzGxsZqWFiYrW6sgIiq2saPBPEAABT4SURBVHTZYrfuu2uXLdaABiG2rj3144818KUxKqtXKHFxKqtXaOBLo3Xaxx/bOt+XXySFRh48iOrre0DpZfjyPownrtS6p8uLAHQvsBgItltwUd0KWwBStbrRAqsGKoIGVg203Y2GoP137lDX7ruHd+5UBFvn17i6hhZ/b6RKXKwGrVunEherxd97TmtcXcPW+b7+YywU8mgmBF+OgvOUr0eieaIw1z1dXgSgn4B/HPdLEoAfXTe7FysKm68C0GXfyPfgH3Rg1UBttPxTt+67Rss/1cCqgbaujaBtNq11O7/NprW2A1hR+GPMc2Yqnkz5MgB6qjDXXTVvAtCL2W12L1YUNl8EIE+e4/GkFTH4lcF6VYWrdOLciXr67GmdOHeiXlXhKvstqMroqtWr3NJWrV6lVLYXgFQL/x+jp3L8j4cJQEYBk5sAJFZ+w67o6GiNj4/Pt+sFxMVxQeSSdH9VznfsmOP5xcYUIyUmBf9i/s60C6kXCBoXROoLqTmeP2T8EN6d/C7njp4jMCSQ/w79L1NGTLFV95r9apK6JpW578+lbdu2bNy4kV59elGsczEOzzqccwEGg/bsYXpyMo9Urcq0+vUvzdChg/Vz3br8rJZhZElEvlPVaDt5zYqoBVxYTAydVCnhZ31UJfz86KRK2MiRts73dGqRKSOmkJKUgqYpKUkptoMPwKvDXuVih4v0G9iPoP/f3r2HV1Gdexz//kKBICBqoxJEbT0IBopYvFvAWovaoud4lFYDYlHEUhUoVaQWFTw2tgcpR4vaKpWKWrBWqkKtrYpBqSJWqmIQ5VLEC4IgF4VwTd7zx5rY7W5IJiHJ7Oy8n+eZh+yZNTNrhsm8WTNrrzc3l0uHXcrur+9mwqgJsbfRVPlArK4piBWAokE/b5K0VNJ2SWWpU31XsilbvmAB/5Gfz/bycnJzctheXk6n/HyWv/xyrPXH9h7LkFlDKF5ZzK6yXRSvLGbIrCGM7T22nmsOhd0LuX307bQe2RrGQeuRrbl99O0Udi+s933XhboYF6u2Y/AVvjIbPT+H5uXh16t5eRl6/hkKX5lV4zo4l6n2mJI7zc2EnnA/A/4PGA18CbgQuKFeapZFZrwxg/HzJ7P0oAvovPYhxp8yIvZNuKCggOeWL6LdzrfYtPIB9vvyIOZuOYqCgngtmIr9DH9yOEvWL6Egr4CibxQ1WBAo7F7YaAJOqopBHSd8+15++Wk7Ru67mdFPDAGIfTwz3pjBlcuWsXH/XnR94n+588gjY687feJkTpryAAtympGbk8NO4OSePZh++SCm/mhibQ/LuYwS9xHcd4FhZnY3Ydy1x81sBKETQt/6qlw2qLiRHdWziJz9enDUsbfUaHTa0wadxorBA7l+exu2XPk6129vw4rBAzltUPXvfyoUdi+k5IoSym4so+SKkkYZEGqrtq2Yirwwz5Z34IVPPmFOWYca5YVpMbeYAR/ns/GAPqAcNh7QhwEf59NibnGs9Xes2cGBHTszrEMHXurZk2EdOpB3aGd2rGk8o5k7V524Aehg4M3o5y3AftHPfwHOqOtKZZOB6/JY2WMqs7aIcmDWFrGyx1QGrsuLtX5x22LG3DCGqbdMpW3rtky9ZSpjbhhDcdt4N7KmrCL4j+87ma+d/XduOiP+0PSLu/yCb6zS597BfGOVWNwlXtb2vD8MJ/fFFz6Xyyj3hb+R9/DwWOu3bN+SUxfP4c7OnenRpg13du7MqYvn0LJ9y+pXdq6RiBuA3gU6RD8vJwwYCiFR3La6rlQ2sWmFlSZVs2kXxlp/yfoljLtqHCUlJZSVlVFSUsK4q8Y1qgRZSSmaV8QABnD5jHnM27SJodPnMYABsVox+X+8utIAkj/z6lj7/vCRxZyelsvomyefzIczF8daf+jIoYwZMYZJ0ydRur2USdMnMWbEGIaOHBprfecag7jvgB4FTieMSn07MEPSUOAQ4NZ6qltWaPn0Fo7+bicWRDeineXlHNu9Ewsnb421fkUvttO+/K9HbjVNkNVULe78CxZ/4V8tho19+lAEsLt3tes2++tmdF0pOwS5EjswctuX0uy3m+PtfB1s2kcMO6ADl3fowD2rV/P6hvchZj7Dit6GP7nmJ1x90dW0bN+SYaOH1agXonOZLlYLyMyuM7Oi6OdHgN6E0abPM7P6707ViO1cu5PXdq7inDbGvB5Hc04b49Vdq9i5dmes9ZPsxdbYtRhxCV8t3fC5Luw9SzfQYvjgatddvXI1Xbt8jf02zGPHK5ex34Z5dO3Si9UrV8fad8cjOrLiwYH0b/4BXVu1pH/zD1jx4EA6HtExdv33pgu8c41B3BbQ55jZS4TWkKtG14KunLthE499OJwT/xx6of2o5bk8VtA11vpJ92JrzHa9vYZlW19j2z6nkasctpWXsXTrq+xaurbadQsKCrh1VytOO+9G4EYAiouLGR6z9+GEmycwcvRILv3Cpaxqt4rDNx9O2WNlTLp10t4cknPZJc5wCaTkySE8druJ8Oitd9whF7JlqulQPHszGrXbO926dbMus39v+8+8yTSxk+0/8ybrMvv3sVJR1MX/2/Tp061bt26Wk5Nj3bp1q5//cx+Kx2UY6mosOKALsJjQ9fpVoCshc+inwGZgN3Bu3J1lw1SbseAa5Ebk/s3eBpFG8f/mAchlmJoEoCrHgpM0G2hF+PLphYR3P3OAiq44k4FjzeykOmyUZbSGHgvO7Z0ZM2ZQVFTEkiVLKCgoYOzYsRQWZtHjSx8LzmWYmowFV907oJOAvmb2mqTnCa2eu8ysPNrRZPxdkMtghYWF2RVwnMsi1fWC+yKwGsDMPgW2AhtTlm8E2tZP1ZxzzmWzON2w05/Ref4G55xzey1ON+wHJVUMQJULTJFUGn32cUGcc87VSnUBaFra5wcrKXN/HdXFOedcE1JlADKzSxqqIs4555oWz4jqnHMuERkXgCS1lXSbpFWStkl6UdLxKcvbSJos6f1o+duSRqVto2VUZr2krZJmSeqYVuYwSbOj5esl/VJSi4Y6Tueca+oyLgABvyGke/ge0B14CnhG0iHR8klAP2AQUAAUAT+XNChlG7cB5wOFhC/P7gv8SVIzgOjfJwhdyHtH5foD8ZK9OOec22sZFYAktSIEjh+b2VwzW25m4wk5iH4QFTsFeMDMis3sHTO7n/Bl2BOjbbQDhgCjzexpM/sHIVgdDXwz2sYZQDdgkJn9w8yeBq4Fhkrat0EO1jnnmriMCkCEThHNgO1p87cBvaKf/wacI+lQAEmnAMcQsrMCHAs0J7ScADCz94AlhOAFIZHekmh+hb8SupUfW1cH45xzbs8yKgBFoy3MB66XdIikZpIuIgSM/KjYCOB14F1Ju4DngDFm9qdoeXvC4Knr0za/NlpWUSZ9TP710Xrt0+Yj6XJJr0h6Zd26mBnFnHPOVSmjAlBkEFAOvA/sIAScGdE8gOGElsx/Eloro4CJks6qrwqZ2T1mdpyZHXfggQfW126cc65JybgAZGYrzOxUoA1wqJmdQHik9s/oHdHPgGvNbLaZLTKzO4CHgGuiTawhPMbLS9v0wdGyijIHpy3Pi9Zbg3POuXqXcQGogpltNbMPJe1P6BX3OCEQNSc8KktVxr+OZSGwC+hbsTDqgl0AvBjNmg8UpHXN7ktocS2s40NxzjlXiVql5K5Pks4kBJO3gE6EzKtvAb81s12SniN0u94CrAJOBS4m9GLDzDZLuheYIOkj4GNC1+1FwDPRbp4iJNq7X9LVhFG/bwWmmNknDXOkzjnXtGVcAALaER6zdQQ2ADOBsWa2K1p+YbT8d8ABhCB0A3BHyjZ+SMjW+ntCQr05wMVmVgZgZmWS+gF3AS8Qetn9Dhhdr0fmnHPuMxkXgMzsYeDhKpavAaoco87MdhA6Kwyvosy7wNm1rKZzzrm9lLHvgJxzzmU3D0DOOecS4QHIOedcIjwAOeecS4QHIOecc4mQmSVdh0ZF0jpC1+/ayOPfx6hz8fi5qz0/d7Xj5612DjezWGOWeQBqQJJeMbPjkq5HY+Tnrvb83NWOn7f654/gnHPOJcIDkHPOuUR4AGpY9yRdgUbMz13t+bmrHT9v9czfATnnnEuEt4Ccc84lwgOQc865RHgAaiCSrpC0UtJ2SQsl9U66TplO0nhJljZ5xto0kvpImiXpg+gcDU5bruhcrpa0TdJcSd0Sqm5GiXHu7qvkGnwpoepmHQ9ADUDSBcDtwC3AVwmZWZ+UdFiiFWsc3gbyU6buyVYnI7UBSoCRhNxW6a4FriakJzke+Ah4WlLbBqth5qru3EFIZJl6DX67YaqW/bwTQgOQtABYZGZDU+YtAx4xs+uSq1lmkzQe6G9mX0m6Lo1FlCn4KjO7L/osYDVwh5kVRfNaEYLQNWZ2d1J1zTTp5y6adx+QZ2aeO6weeAuonklqARxLSAOe6inglIavUaNzRPToaKWkhyQdkXSFGpkvA+1Juf7MbBvwPH79xdVL0keSlkqaIumgpCuULTwA1b88oBmwNm3+WsKNwe3ZAmAwcBYwlHC+XpT0xSQr1chUXGN+/dXOX4CLgdMJjzFPAJ6V1DLRWmWJjEvJ7VwFM3sy9XP08vefwPeASYlUyjUpZvZQysc3JC0kDEbcD/hjMrXKHt4Cqn/rgTLg4LT5BwPeo6sGzGwLsBg4Mum6NCIV15hff3XAzFYD7+PXYJ3wAFTPzGwnsBDom7aoL6E3nItJUi5wFPBh0nVpRFYSAs1n1190Hnvj11+NScoDDsGvwTrhj+AaxiTgAUkvAy8Aw4AOwK8TrVWGkzQRmA28CxwE3AC0BqYlWa9MI6kN0Cn6mAMcJukYYIOZvSvpNuAnkt4ClgLXA1uA6YlUOINUde6iaTwwkxBwvgT8jNCD8NGGrms28m7YDUTSFYTvY+QTvncwysyeT7ZWmU3SQ0AfQkeOdcBLwA1m9maiFcswkr4OFFeyaJqZDY66Yo8Dvg/sT+jccaWZlTRcLTNTVecO+AHwGOG7e/sRglAx4Rp8r6HqmM08ADnnnEuEvwNyzjmXCA9AzjnnEuEByDnnXCI8ADnnnEuEByDnnHOJ8ADknHMuER6AXJMUJRbrn3Q9moIoAd4dSdfDZR4PQC4rSeopqUzSC7Vcv7mkGyWtiLLYvi7prLQyV0paJOmTaJovqV9amZslvSVpq6SNkuZIOiWtzDuSrkmb9z+SSiWdU0UdL5P0qqQtkjZHdflpbY4300kaHOXrcVnEA5DLVpcBdwFfkVRQi/V/Svgm/AigK2HYpEclfTWlzPvAGKAncBzwLPCYpKNTyrwNXEnI5NqLMDbbXySlDw4KgKQcSXdF+z3TzGbvodylwC+jeh0DnATcDOxTi2N1Lhlm5pNPWTUBrYBNhJv+vcDESsoYIdvqnraxGhiZNm8m8GA1+94AfL+K5ftG+z4zZd47wDVAC+AhwpAvParZz2Mx6jKeMOzThcAK4NNovbyUMscTktWtBz4B/gacXMm5ugp4AiglpCO4KK3MjdH8HYTBT+9PWTaX8MfALdF+PgImAjkpZfYnDH+zkZAa+xmgW7Ts61EdUqfxSV9nPu395C0gl436A6vM7A3gAeBiSc1ruI2WwPa0edsIrZh/I6mZpAuBNuxhlOkoO+7lhBv9a2mLWxMGXj0e6GVmr1dTvzXACTEyxH4JuAD4b+AMwrhmRSnL2xLOUW9CsrXXgD9XkvTvJmAWobV1D3C/pOOi4zqfEECvIKQpOBt4OW39gcBuQhbWq4AfRvWqcB9wIvBfUT1KCS3FVoTz+cNoXn40TazmuF1jkHQE9Mmnup4If3FfE/0sQgujf1qZ6lpA04ElQBfCo+q+hBvgjrRy3QkjS+8mtLr6VbKts6My5cAHwAlpy98htBw2AR1iHmM+MD86jmXAg4TMnc1TyownBNF2KfPGAsur2K4ILbCLUuYZMCWt3DNELTDgR4RHjc33sM25wPy0eU8Dv4l+PjLaR5+U5e2AzcBl0efBwJakry2f6nbyFpDLKpI6EVop0wEs3L1+Bwyp4aZGEm6qbwI7gTuA3xKCSKq3Ca2CE4FfAdMkfSWtTHFU5hRCiueHJeWnlXmK8AhuXDR6dZXM7EMzO5kQAG8jBI67gZclpb4HWmVmm1M+ryaktgBA0kGS7pa0VNJmwmO6g4DD0nY5v5LPXaOf/wDkAisl3SvpO5WkrF6U9jm1HgWE8/rZPqI6v5GyD5eFPAC5bHMZ0Ax4V9JuSbuBHwNnSDo07kbMbJ2ZnUt4NHY4IRHeFkJK8NRyO81suZktNLPrCI+wRqWV2RqVecnMhgC7onqmeo6Q5nkgMCVOEIq2XWJmd5rZQEIr7RjguylFdqWvwud/76cRHvuNIgTIYwidK1rE2X9Uh/cILcXvEx4v/gJYKKl1Deqxx83HrYdrfDwAuawh6QvA94DrCDfSiqkH4S/wS2q6TTPbbmYfEJI3ng88Xs0qOYT3RzUuY2bFwLcI70amSqrp72dFnqQ2NVinFzDZzJ4ws8WEFlB66wxCL7v0z0sqPkTn6QkzG0UIaN2Ar8WswxLCOTm5YoakfQmtu4pj2kn4w8JlEc+I6rJJP0Lyuilm9nHqgii53TBJN0eP5aok6URC6uXXon/HE26SE1LK/JzQM+w9wsv8AYQeW/2i5fsSkhDOJrxXOZDQJbsj8HBl+zWzeZLOBJ4Emkm6xMzKKqnfrwiPsZ4ltFjyCZlOSwmP8+JaClwkaQGhtTeBcLNPd56kvxPe5/QHTic8dkTSYMK9ZAGhlXgBocWzLE4FzGyZpMeBuyVdTngXVkRoTVVkbX0HyJXUF3gVKDWz0hocp8tA3gJy2WQIUJwefCJ/IPQI6xtzW7mE7wK9SUi//AGhd9qmlDLtCS//3wbmEP7y/5aZPRkt301oCTxKuBnPBr5IeNme/k7kM2b2YlTPcwip3Cv7y/9pQgB4mBBEKlJE9zWzpTGPEeBSQotpIaEL+FTCzT7deEILcBHh+1GXmNnfo2WbCOd+HqHb9/nAeWa2sgb1uITQc25W9O8+wFlmtg0+Oye/BmYQsuNeW4NtuwzlGVGdc1WSZMB3zOyRpOvisou3gJxzziXCA5BzzrlE+CM455xzifAWkHPOuUR4AHLOOZcID0DOOecS4QHIOedcIjwAOeecS4QHIOecc4n4f1HS3E1UrO1HAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), liquid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), liquid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), liquid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Liquid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_liquid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), solid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), solid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), solid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Solid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_solid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ZZtasWcTHxzNx4kSOHTvGxIkTiY+Pz5cg1KdPH6ZNm+Z9PW3aNO666658GSY7cuQIDz30EImJiSxZsoTg4GC6dOnCyZMn/fI98sgjDB48mKSkJK655hqWLVtG//79GTx4MBs2bKBTp06n7OQ6f/58evfuzeDBg9m8eTNTpkzhjTfeYOTIkQB8/PHHlC1bljFjxpCcnMzOnTu9dW3bto1PPvmE77//nilTplC+fMY5RuaspKo5OnCW0wnL6XXny9G4cWM157bNmzfnuoy6detqQkKCX1pCQoLWrVs312VnpmfPnhoXF6e///67hoaG6tatWzU5OVmDg4N1586d3vPphg8ffsbtCQsL0+nTp2eb7/fff1dA161bp6qqSUlJCuiLL77ol++GG27QLl26+KXdeeedGhIS4n3dtGlTffrpp/3yzJo1S0uWLOl9Xb58eZ04caJfnk6dOmm/fv1cvS8TGFn9DeFM5nL1eep2CA7Pzf9dwJ/AXyKyU0T650VQNOZsl5SUROvWrf3SWrduTVJSUp7XXbJkSW688UamTZvGa6+9Rrt27fLt+bStW7dy6623Uq1aNYoVK0alSpUA2LVrl1++Jk38t4P5/vvvadasmV/aZZdd5v1eVVm/fj2PP/444eHh3qN3794cPHiQgwcPZtqm++67jxkzZtCoUSMefvhhVq5cmWlec3Zx9SCqiDwGPIozSyz9t3s58JSIFFPVp/KofcaclWJjY1m5ciXt27f3pq1cuTLfNrrr3bs3PXv2JDw8/JShrLyiqlx99dXUrl2b//3vf0RFRQFQt25djh8/7pc3LCwsx2WnpaXx5JNP0qVLl1POFyuW+eOGXbp0YefOnXz88ccsWbKEq666ih49eth9oHNATpbi6auqvgPcS0RkGzAGsABkLijx8fH06dOHqVOn0rp1a1auXEmfPn0YPXp0vtTfoUMHgoOD2b9/PzfccEO+1Llnzx62b9/OW2+9RYsWLQD44osv0ofms1S7dm3WrVvnl7Z27Vrv90FBQTRs2JCtW7dSvXr1TMsJDg4mNTX1lPRy5crRq1cvevXqRceOHbn77ruZNGkSQUGuB3lMAXAbgMoB606TvpZTVxQw5rzXrVs3AAYOHOjdnnj06NHe9LwmInz77beoKiEhIZnmO3bsGBs2bPBLK1q0KDVr1sxxneXKlaNEiRJMnjyZsmXLsmvXLu/zR9kZNGgQHTt2ZMKECcTFxZGQkMCCBQv8rh0+fDg333wzFSpU4OabbyYoKIjvvvuODRs2MGbMGACqVKnC8uXLueWWWwgNDaV06dI89thjNG/enDp16pCSksIHH3xArVq1LPicA9z+hrbibImQ0e04z+MYc8Hp1q0bGzduJDU1lY0bN+Zb8EkXERGR5dAUwI8//kijRo38jttvP92fcvYKFy7M22+/zdq1a6lXrx73338/Y8eOdfVB365dO1588UXGjRvHJZdcwqeffsqQIUP8pmFff/31zJs3j08//ZQmTZrQvHlzxo0bR0xMjDfP6NGj2bp1K9WqVaNChQredg0dOpQGDRrQpk0bUlNTee+9987oPZr8JW66zyJyEzAHWIbzcCdAK6At0FVVP8irBp5tmjRpoomJiQXdDJML6T0WU7DuvfdeEhMTTxmaM2e/rP6GROQrVW1y2pMZuBqCU9X3PBu2DeafrQ+SgGaqut5NGcaYC9vYsWO5+uqrCQsL49NPP2XatGlMmDAh+wvNecvtPSBU9SvgjjxsizHmPLZ69WrGjx/P33//TbVq1Xj22Wfp169fQTfLFCC307BTgShV/S1DemngN1UtlBeNM8acPz744IIZqTcuuZ2EkNk0lxDgeCbnjDHGmExl2QPyWelagX4icsjndCGch1G/z6O2GWOMOY9lNwQ30PNVgLsB3yfAjuPsw2ODuMYYY3IsywCkqlUBRGQpcJOqZr4gkzHGGJMDbrdjaH+64CMiVUXkycA3yxhjzPkux2tViEiwiHQTkcXAD0D3wDfLGGPM+S4n2zHUF5EXgL3Am8CPQIv0YTpjjKlSpQrjxo0r6GackV69enHttddmn9EETJYBSEQiROQeEVkHfAEUA24F0oDnVXVtVtcbYwKnV69eiAgiQuHChSlXrhzt27dn0qRJ3m2r07Vr186b1/fYsGHDadN9jxEjRmRbv+/RvHlzb55169bRv79tE2bcyW4W3G5gNfA88J6qHgHyZetfY8ypOnbsyBtvvEFqair79u0jISGB4cOH88Ybb7BkyRK/fXjuuusu7yrS6cqWLUtycrL39csvv8y0adP81mMLDw/Ptn5fwcHBfuUb41Z2Q3ApQHHPEZpNXmMuOMkpKbRdv569KSn5Ul9ISAiRkZFUqFCBhg0b8sADD7Bs2TK+/vprnn76ab+8RYsWJTIy0u8oVKiQ3+uIiIhT0rIKQOn1+x6lSpXyns84BLd161batm1LaGgotWrV4uOPPyY8PJwZM2Z48+zevZvbbruNkiVLUrJkSeLi4ti2bZv3/IgRI6hXrx5vv/02F198MREREdxwww3s378fgEWLFhEcHMyBAwf82vrYY4/RoEEDAA4cOEC3bt2oWLEiRYoUoW7dukyfPj3Ln3W7du0YMGCAX1rGYTpV5emnn+biiy+mSJEi1K9fnzfffNPvmlGjRhETE+P92fXo0SPLei8k2QWgaGA8cB2wW0Tmi0hXnAdTjbngPbFjByv//JNRO3YUWBvq1avH1VdfzbvvvltgbTidtLQ0brzxRi666CLWrFnDjBkzGDlyJCk+wfrIkSO0b9+e0NBQli9fzurVq4mKiqJjx44cOXLEm2/Hjh3Mnj2b999/n0WLFrF+/Xri4+MBZ3O+MmXKMHfuXG9+VWXmzJnccYezfOWxY8e49NJL+eijj9i0aRODBg3innvuYcmSJbl6j8OGDWPq1KlMmjSJzZs38+ijj3LPPfewYMECAN59913GjRvHSy+9xLZt2/joo49O2Zr8Qpbdc0AngLnAXBGJAXoBz3iuGykiM4BFnnzGXDCKrFjBsbQ07+uXk5N5OTmZ0KAgjrZpk+/tqVOnDosXL/ZLmzJlil9P44477uCVV17JVT2ffvrpKT2k++67j7Fjx56S97PPPmPLli0sWrTIu3fPc889R6tWrbx53n77bVSV6dOne4f2J0+eTLly5fjoo4/417/+BcDJkyeZMWMGxYsXB6Bv377eHkyhQoW47bbbeOutt7yLm65atYqff/7Zu/dRhQoVeOihh7z19u3bl4SEBGbNmkWHDh3O6Gdx+PBhnn32WRYtWsTll18OQNWqVVm7di2TJk0iLi6OnTt3EhUVxZVXXknhwoWpXLkyTZq42qnggpCT1bB34gSdUcCVQB/gXeAYUCJvmmfM2emnyy5jyI8/8sH+/RxJS6NoUBA3linDuIsvLpD2qOop92ZvvfVWhg8f7n2d3eZ1brRp04YpU6b4pZUocfo//++//57o6Ghv8AFo2rSp3wZ2X331Fdu3byciIsLv2iNHjvDjjz96X8fExHiDD0B0dDS//fbP2sh33HEHEyZMYOfOncTExPDWW2/Rtm1bKlasCEBqaipPPfUUs2fPZvfu3aSkpHD8+HHatWuX8x+Cx+bNmzl27BhXX32138/+xIkTVKlSBYCuXbvy/PPPU7VqVa666iquvvpqrr/++ix3sb2QZLcWXMmMD6Cqs4PdQmChiJQlB1s0iEgbYAjQGGd47y5VneFzXoDhQF+gJPAlcJ+qbvKcrwL8F2gPRAHJwGxglKoe9SmnMjAJuAI4CswEhqjqcZ88bYFngbrAHuBpVc3dfw/NBSMqJIRihQpxLC2N0KAgjqWlUaxQISIL6INl8+bNVKtWzS+tePHiVK9ePaD1FC1aNKBlpqWl0bBhQ95+++1TzvneWypcuLDfOREhzacHeumll1K7dm1mzpzJkCFDmDt3rt89sXHjxjF+/Hief/556tevT3h4OI899phfEMsoKCiIjBt2+s42TK//ww8/pHLlyn750ttbqVIltmzZwpIlS1i8eDEPPvggI0eO5Msvv/SbMHKhyq4HtFdEVgHzgHmqusP3pKruA57LQX3hwEbgdc+R0cPAgzhDfVuAx4HPRKSWqv4N1MZZBPVeYBsQC0wBSuMELUSkELAAOICzWGpp4DWc9ewGevJUBT4GpuEE0NbASyKyT1XProF0c9b69cQJ+kVH0zc6mil79pB8vGAWht+4cSOffvopw4YNK5D6M1O7dm327NnDnj17iI6OBiAxMfGUwDFr1izKlCmTaU/KrTvuuIO33nqLevXqcfjwYW655RbvuZUrV3Lddddx5513Ak6PcevWrVnWmXHGIMA333zj7d3UqVOHkJAQdu7cyRVXXJFpOaGhocTFxREXF8cjjzxCZGQkq1at4sorr8zFuz0/ZBeAYoDrgS7AUyKyhX+C0dc5rUxVP8b54Mdz/8jL0/u5H3gqPQiISE/gN+B2YLKqfgp86nPZTyIyGngCTwDCGR6sC8So6s+ech4G/ici8ar6F84CqntUNX2x1STPjq9DcIYVjcnWe/Xqeb+fVLNmvtSZkpLC3r17SUtLY9++fSxZsoQxY8bQuHFjhgwZkm/1+ypUqNBpp1936tSJWrVq0bNnT8aNG8fRo0d54IEHuOiii7xDVt27d2fcuHF06dKFUaNGUblyZX7++WfmzZtHv379qFGjhuu2de/enWHDhvHf//6X6667zm/IsWbNmsyePZuVK1dSpkwZJk6cyPbt22nUqFGm5V1xxRXcf//9zJ8/n1q1ajF58mR+/vlnbwCKiIhgyJAhDBkyBFWlTZs2HDp0iDVr1hAUFETfvn2ZMWMGJ0+e5LLLLiM8PJzZs2dTuHDhHL2v81mWs+BUda+qTlHVOKAs8CRQDVgsIjtFZKKIdPT0OnKrKhAJLPKp/yiwAmiZxXXFAN9hwhZAUnrw8ViIs3dRY588i/C3EGgiIoUx5iy1ePFioqKiqFy5Mh06dGD+/PmMGDGCFStW5MuQTnr9vkdmH+JBQUG8//77pKSk0KxZM3r27El8fDwiQmio81RH0aJFWbFiBdWqVaNr167Url2bnj17cvDgQUqWLJmjtsXExNC6dWu++eYb7+y3dMOGDaNZs2Z07tyZNm3aEBYWRvfuWa8i1rt3b+/RqlUrIiIiuPHGG/3yPPHEE4wYMYJx48ZRt25dOnXqxLvvvkvVqs4CMSVKlGDq1Klcfvnl1KtXj3fffZf33nvPe/6Cp6o5PnCGwTrgPKC6A/gd6J7DMg4BvXxet8SZ3l05Q75pwMJMyogB9gMP+KRNARIy5BPgJNDN83or8HiGPG089Uedpp6+QCKQWLlyZTXnts2bNxd0Ey5YGzZsUEATExMLuikmF7L6GwIS1WUcyG4SQnVV/eE0QSsVWOI5BolIQyBfew4iUh5nOO4zcnYfKsdUdQpOYKNJkyb2DJQxLr3//vuEhYVRo0YNduzYwQMPPMAll1zCpZdeWtBNM2eB7B5E3Soiy0Sku4hkuhKCqm5Q1XWZnXcpfWC5fIb08j7nABCRSGApzoSGOz1R17ecjGWUwem17c0iT3mcXtL+M2m8MeZUf//9NwMGDKBOnTp0796d2NhYFi5caMt5GSD7ABQH7MMZBtsjIi+KSOZ37XJnO05g6JSe4Al6l+MshJqeFgUsA5JwhtROZihnNRArIhV90jrhLCv0lU+eThmu64TTdbSHao0JkB49erB161aOHj3Knj17mDlzJuXLZ/y/n7lQZTcJ4RNV7QpUwJmA0BZIFJGvReReESme1fUZiUi4iDT0DNkFAZU9ryt7ejETgKEicpOI1ANm4Nwrmum5PhpYjhOo7gfKiEik50ifCLEI2AS8LiKNRKQjzuoNr6ozAw7gFaCCiEwQkVgRuRtn6ve5uY68Mcacg9zuiLpfVZ9V1fo4kwUSgadwekWne54nM02A9Z6jCDDS8/0oz/mnce7nTPLUEQVcqc4zQOBMsa6BEwh34TyImn5U8rQ1FafndgRYhfOg6rs4U6zT38924BqciQcbgHjgP2rPABljTL4R/9snLi9yBnD/BbwElFDVQEzDPic0adJEExMTC7oZJheSkpKIjY0t6GYYc87K6m95JZWIAAAgAElEQVRIRL5SVVcL3rleC85TcHWgN9AD56b9IuB/OSnDGGOMARcBSESK4PR2euMsWbMD5x7KdFXdnaetM8YYc97K7jmgV3GCTzDwAc79mNxtoGGMMcaQ/SSEZjirT0erajcLPsacvTLuRhooIsI777yT6fn9+/cjIixbtizgdWfXlrOpbXltx44diAjn0z3o7KZhX6KqL6jPlgwiUl5EXM2eM8YETsbtoDNat24d/fv3D3i9ycnJXHfddWd0XfpW2ampqYwdO5bY2FiKFi1KyZIladKkCS+88EKBtM3Xvn376N+/P1WqVCEkJITy5cvToUMHPvvss1yVe7Y63VbjBcXVJATPAp2jcbZBKALUxFmJeiywU1VfyrsmGmPcON2K1IEQGRl5RtfNnz+fli1bUrp0aR5//HFeeuklXnzxRZo1a8bhw4dZv349O3fuLJC2+br55ps5cuQIU6dOpXr16vz2228sX76cAwcO5LpskzW3PZnhwHU4e+ek+KSvxXmA0xhTwDIOwf3www+0a9eO0NBQatWqxUcffUR4eLh3m+7MhnSyG+Zat24djRs3JjQ0lEaNGvHll1+etj3z5s2jS5cugBOM+vXrx2233Ua1atWoX78+PXr04L///a83f1paGk888QSVKlUiJCSE+vXrM2/evCzf85m2Ld0ff/zB559/zlNPPUWHDh2IiYmhadOmDBkyhNtuu82br0qVKjz55JPcc889FCtWjIoVK/LMM8/4lfXss8/SoEEDwsLCqFChAnfffTd//PGH9/yMGTMIDw/nww8/pGbNmoSGhtK+fXt++uknb56ff/6ZLl26UKpUKYoWLUrt2rVP2axv586ddOrUiaJFi1KnTp1TemorVqzgsssuIzQ0lPLlyzN48GCOe/aq6tWrF8uXL2fSpEmICCLCjh07OHHiBP/5z3+Ijo4mJCSESpUq8cgjj2T5swsEtwGoG9BPVecBaT7pG3F6Q8aYs0haWho33ngjaWlprF69mmnTpjFixAhSUlKyvzgLhw4dIi4ujmrVqpGYmMhTTz112n2I/v77bxISErwBKDIykmXLlvHrr79mWvbzzz/PM888w9ixY/nuu++48cYbuemmm9iwYUNA2+YrPDyc8PBw5s+fz7Fjx7LM+9xzz1G/fn2+/vprhg4dysMPP8zq1au954OCgpgwYQKbNm1i5syZrF27loEDB/qVkZKSwsiRI5k+fTqrV68mNTWVm266ybvzav/+/Tly5AhLly5l06ZNTJgw4ZRN8+Lj4/nPf/7DN998Q9OmTbnttts4dOgQALt376Zz5840atSI9evXM3XqVGbNmsWjjz7q/Rm3aNGCu+66i+TkZJKTk6lUqRIvvPAC77//Pm+//Tbbtm1j9uzZ1KpVy9XPPVfcLJmNs611Fc/3fwPVPN/XBQ65XXr7fDgaN26s5tx22qXkBw1Sbds2f49Bg3LU7p49e2pcXFym52NiYvSZZ55RVdWFCxdqUFCQ7ty503v+888/V0CnT5+uqqrbt29XQNetW+dXDqBz58497evJkydr8eLF9e+///aef+ONNxTQpUuXetPmzJmj9evX977etGmTxsbGqohonTp1tE+fPvruu+9qWlqaN090dLSOHDnSry1t27bV7t27B7RtGb3zzjtasmRJDQkJ0ebNm+uDDz6oa9as8csTExOjt912m19a9erV9Yknnsi03E8++USDg4M1NTVVVVWnT5+ugK5cudKbZ8eOHRoUFKSfffaZqqrWr19fR4wYcdry0n9fr7zyijftl19+UUA///xzVVV97LHHtHr16t460+sNDg7Ww4cPq6rzM73vvvv8yh44cKBeccUVfr+PrARqOwa3PaBNOMvWZPQv/lng0xhzlkhKSqJChQpUrlzZm3bZZZcRFJS7+UNJSUk0aNCA8PBwb1qLFi1Oyec7/AbO9tUbN27kyy+/5O677+bAgQP861//Ii4ujrS0NP766y/27NlDq1at/Mpp3bo1mzdvDmjbMrr55pvZs2cPH374IZ07d+aLL76gefPmjBkzxi9fgwYN/F5HR0fz22+/eV8nJCTQqVMnKlasSEREBDfddBPHjx/320E2KCiIZs2aeV/HxMQQHR3tfY+DBg3iySefpEWLFgwbNoyvvjr149W3Helbnae3IykpiebNm/v9nlu3bs3x48f54YdTdtbx6tWrFxs2bKBmzZrcd999LFiwwG/r9LzidiWEkcCbIlIJZ1uDriJSG2er7Li8apwx+WbChIJuQb5L/5BSn+W4TpzI/WLwJ0+eZMGCBafcmwgKCqJp06Y0bdqUwYMH8+abb3LnnXeyYsWKLPcHyo+tG0JDQ+nUqROdOnXi8ccf5+6772bEiBEMGTKE4OBgAAoX9t/yTES8H9I7d+4kLi6Of//734waNYrSpUvz9ddf061bN+/9Fzfvp0+fPlx11VV8/PHHLF68mJYtW/Loo48yYsQIbx7fdqSX5SZYZFXvpZdeyo4dO1i4cCFLliyhZ8+eXHLJJXz22We5/k9LVtwuRvohTm/nSpx7QMNxFgW9TlUX51nrjDFnJDY2lt27d/Pzz//sTL927Vq/D6r0WXPJycnetOzut8TGxvLdd99x+PBhb9qaNWv88ixfvpzw8HCaNMl6ObA6deoAzr2bYsWKER0dzapVq/zyrFy50psvO27a5ladOnU4efJktveF0iUmJnL8+HGee+45WrRoQc2aNdmzZ88p+dLS0li7dq339a5du9izZ4/fumoVK1akb9++zJkzh1GjRjFlyhTX7Y6NjWXNmjV+v+eVK1cSHBzMxRdfDEBwcDCpqamnXBsREcEtt9zCyy+/zIIFC0hISMiy1xQIrteCU9WFwMI8bIsxJht//fXXKUGiRIkSVKlSxS+tY8eO1K5dmx49evDcc89x9OhRBg8ezEUX/fMnX6RIEZo3b87YsWO5+OKL+fPPP703qzNz++23Ex8fT+/evXn88cfZs2cPo0eP9sszb948rr/+er+0W265hVatWtGyZUsiIyPZvn07jz76KOXLl6dly5YAPPTQQzz++OPUqFGDxo0b8+abb/L555/z9ddfu/rZuGlbRgcOHKBr16707t2bBg0aEBERQWJiIk8//TQdOnSgWLFiruquUaMGaWlpTJgwgZtuuok1a9Yw4TS96osuuoj777+f559/niJFijB48GDq1q1Lx44dAWcIrnPnztSsWZO//vqLTz/91HUABmcSw4QJE+jfvz+DBg3ip59+4pFHHmHAgAEULVoUcGb0rV27lh07dhAeHk6pUqWYMGECUVFRNGzYkMKFCzNz5kzvbL885fZmUfoBhAJFfY+clnEuHzYJ4dyX1Q3Us1nPnj0VOOW4uXNnVfWfhKCqumXLFm3Tpo0GBwdr9erVdd68eRoWFuadhKDq/CxatmypRYoU0Xr16umKFSuyvNGvqrpmzRpt1KiRBgcHa4MGDXT+/Pl+N/orV66sCxcu9Gv7lClTtEOHDlquXDkNDg7WihUr6q233qobN2705klNTdVRo0ZpxYoVtXDhwlqvXj19//33/crJbdsyOnbsmD766KPapEkTLVGihBYpUkSrV6+ugwcP1gMHDnjzZfzZqp56M//555/X6OhoDQ0N1SuuuEJnz56tgG7fvl1VnckAYWFh+sEHH2j16tU1ODhY27Rpo9u2bfOWMWDAAK1evbqGhIRomTJl9NZbb9VffvlFVd1PGlm+fLk2a9ZMg4ODtVy5cnr//ffrsWPHvOe3bNmizZs31yJFinjbN2XKFG3UqJGGh4drRESEtmnTRletWnXan5lq4CYhuNqOQURigBeA9kDYaYKYbcdgzhnn1XYMW7Y4X11OmQ0PD+fFF1+kV69eedKc9evX0759e/bt23fKPZML3YwZMxgwYIB3yvS5LL+3Y3gTp+czEPgV539exhjj58SJE0ycONGCj3HFbQBqBDRV1aS8bIwx5tzWrFkzv2nGxmTF7fy6b4C8WWjKGJNvDh06lGfDbyZrvXr1Oi+G3wLJbQ+oL/CCiLyAs/yO38MCqror0A0zxhhzfnMbgIJwtuB+H//7P+J5fcFMQjDGGBMYbgPQa8BvwFBsEoI5D6hqvjxhb8z5xs3MabfcBqDaQENV3Rqwmo0pIIULF+bo0aPeB/OMMe6dOHHC74Hm3HA7CWEtUDUgNRpTwMqVK8fu3bs5cuRIQP83Z8z5Li0tjV9//ZXixYsHpDy3YexlYIKIjAe+49RJCK7WyhCRNsAQoDEQDdylqjN8zgvOOnN9gZLAl8B9qrrJJ088cA3QEGcVhlPGUUSkMjAJuAJnK4mZwBBVPe6Tpy3wLM6WEnuAp1X1FTfvw5zb0pdX2bNnT0AW3yxQ6Sst58PKxcYAhIWFUaZMmYCU5TYAzfJ8Pd2qeDmZhBCOM4vudc+R0cPAgzi7rG4BHgc+E5Faqvq3J08I8B6wDHgsYwEiUghYABwALgdK49zDEpwHaRGRqsDHwDScXV5bAy+JyD5VfdflezHnsGLFirle5+usdu+9ztdlywq0GcacCbcBKCDDb6r6Mc4HPyIyw/ecp/dzP/BUehAQkZ44kx9uByZ7ynjcc+6WTKq5EqdXE6OqP3vyPgz8T0TiVfUvoB+wR1XTtytMEpHLcHpnFoCMMSYfuApAqrozrxuCE+QigUU+9R4VkRVASzwByIUWQFJ68PFYiNNzagws9eRZlOG6hUBPESmsquf4uIwxxpz9Mp2EICKt3RYiIuEiUj+XbYn0fM24afyvPufclpOxjP1Aqk85p8vzK05ADszgpjHGmCxlNQvufyKyRES6ichpB8tFpIGIPA38AFySJy08C4hIXxFJFJHEffv2FXRzjDHmvJBVAKqHc7N/OPC7iGwRkaUi8omIrBGRgziz1CoAV6jqm7lsS/rG6eUzpJf3Oee2nIxllMGZKLE3izzlgZM4vSU/qjpFVZuoapP0XSSNMcbkTqYBSFVPquokVa0NNMeZir0B2AksBu4GKqhqd1XdHIC2bMcJDJ3SE0QkFGcm2xc5KGc1ECsivlv5dQJSgK988nTKcF0nnI2U7P6PMcbkA7eTEBKBXO/CJiLhQHXPyyCgsog0BH5X1V0iMgF4TES+B7YCw4BDOM/xpJdRGSgFVPG8bug59YOqHsKZXLAJeF1EHsSZhv0M8KpnBhzAK8AAT32TgVY4U7+75fY9GmOMcScw6ym41wRnFlq6kZ7jNZwA8DRQBOch0vQHUa/0eQYIYBTQ0+f1es/X9sAyVU0VkTjgJWAVzoOobwEPpV+gqttF5BrgOeBenAdR/2PPABljTP5xtSW3+YdtyW3OKu3aOV/tQVRzlsjJltxu14IzxhhjAsoCkDHGmALhKgCJiM09NsYYE1Bue0C7ReQdEekstouXMcaYAHAbgOKA4zgLde4SkSdE5OK8a5YxxpjznasApKqfqertOHv4PAV0BraKSIKIdPc8MGqMMca4lqNJCKr6h2d1hCbAf3BWqX4D2CMiT3keNDXGGGOylaMHUUUkCuch0F5AReBtYCpOz+hRnAdNOwa2icYYY85HrgKQiNwE9MbZ7G0j8ALwlqr+6ZNnHfB9XjTSGGPM+cdtD2g6zrbcLVT1q0zyJAOjA9IqY4wx5z23AShKVY9klUFVj+Ks62aMMcZky20AKpPJ4z8KHFNV26XNGGNMjridBbcDZ7+ejMcOYK+IHBSRZ0Ukv1fXNsZkITklhbbr17M3JaWgm2LMKdwGoG7ALzj783TyHMOAXTiTE0YAdwL/DXwTjTFnYtasWdQdO5YVBw9SZ+xYZs2aVdBNMsaP2x7LvcBgVX3PJy1BRLYAg1S1rYj8hnMPaHigG2mMyZngpUs5ERUFUVEAHGzThtuBnkuXcrx9+4JtnDEebntAlwHfnSZ9I9DU8/1qnGeDjDEFrGp8PB1UKRrk/IkXDQqigypVH3usgFvmXkEOH+a2bhv6dMdtANoJ9D1N+r9xhuEAygK/B6JRxpjc+eHLL7k4KopjaWmEBgVxLC2N6lFR/LB2bUE3zbUnduxg5Z9/MmrHjnytN7dDlzb06Z6rHVE9W1y/C/wIrPMkNwEuBm5W1Y9FpD9QXVUfyKvGng1sR1RzVslkR9R69epRcuJEGlSoQN/oaKbs2cO3u3dzcOBANm7cmO/NzIkiK1ZwLC3tlPTQoCCOtmnjupzklBRu27yZ2XXqEBkS4uqa4KVLOXGaGb+FVV0NXeb2+kA5k/ceKAHfEVVVFwA1gHlAMc8xH6ilqh978rx0vgcfY84V8fHx7O7Th1t276ZOSAi37N7N7j59iI+PL+imZWt8cjJhq1cT4vnPcYgqYatX82xysusyzrQXktuhy7Nh6PNc6oFl2wMSkcI4KxxMUtWd+dKqs5j1gMxZJZMeEDgfRKNHjyYpKYnY2Fji4+Pp1q1bvjbvTNSrV49qEyeyQITgoCCOp6VxrSo/uuy95aYXUqhQIdqu+IClx8Mg7QQEFeaK4MMsa3MDqamp2dZdqFAh7t60if/t3ett+78jI3m1bl1X1+fW2dADC2gPSFVPAP0B24jOmHNJPZy/3OGer/UKtjluJSUlEVS6NP2io1lz6aX0i45GSpcmKSnJ1fW56YVEV43mi+1JdIkQ1jW5jC4RwqrtSURXjXZVd2xsLJuTk/3avik5mdjYWFfX59bZ0APLCbeTEBYCV+RlQ4wxgTPru1nEJ8QzsfNEjsUfY2LnicQnxDPru/wbjjnTmWCxsbEMOnCASTVrckl4OJNq1mTQgQOuP8RzNQGjDZR6bAKD/oZLioYx6G/nNS5vPRX00Oe5NvnE7XNAS4AxItIA+Ao47Hsyw/NBxpgCNvrz0Uy9firtqzrDLu2rtmfq9VMZ+MlAutV3NwyX2xvZvrPYXqpVy/V18fHx3HrnrYTdEsbO4juJ+TOGw+8c5vlnnnd1vbcXkmEChpsAtqfKHmb83wwGDhzoHboc+39j6fVjL1d1d+vWjS92fUHn7p1J2ZtCSGQI/x7073wb+oyNjWX5D99S/Pj3/LH9DUpUvZNlh2rnqAeWnxMY3PaAXgTK4WxC9xrwjs8xN2+aZow5U0n7k2hdubVfWuvKrUna724YC858GnSRFSuQZct4OTmZNODl5GRk2TKKrFjhroB6OOMtn4A8KfCJ57XLIcTc9EJiy8RSsWVFNm7cSGpqKhs3bqRiy4rElnH3AT7ru1ksCF3AJ6s+4fiJ43yy6hMWhC7It55n+zvb82Ov7gw7Fs6h+75h2LFwfuzVnfZ3urv/M+u7WdRdMJYVfxykzoKxed5ut7PggrI4CrmtTETaiMh8EdktIioivTKcFxEZISJ7ROSoiCwTkboZ8pQUkTdE5E/P8YaIlMiQp76ILPeUsVtEHpcMq6mKyM0isllEUjxfb3T7Pow528WWiWXlrpV+aSt3rXT1QZrbADI+OZmQVZ8jJ52hNzmZQsiqFa5nsY3+fDSzR85m+5btpKamsn3LdmaPnM3oz93t9tKtWzfi7omjc/fOBIcE07l7Z+LuiXPVC4m/PJ4+8/uwdPtSTqSeYOn2pfSZ34f4y90Nofn2PAsXKuztebpte7ozHb5cGrGUof8dyrQx04gIi2DamGkM/e9QlkYszfba4GVLuf1AFAdLtQEJ4mCpNtx+IIrgZdlfe6ZytCV3AITjrJ4wCDh6mvMPAw8CA3FWWPgN+ExEInzyzAQuBa72HJfibAsOgIgUAz4DfvWUMQh4CHjAJ08LYDbwFtDQ83WuiFwWiDdpTEHLzQdpt8QPkRVLKJzmzNoqnJaKrFhMt8T5rur+v/ghBEUeg4uCCRWBi4IJijzGmPghrq7Pbe8tN72QbvW7MfqK0Qz8ZCCho0MZ+MlARl8x2vWwZSB6nnDmvc+k/UkMHzDcrwc3fMBwV/WXmTuQ0C9W+U1/D121kjJzBuaoDTmiqtkeODPg+gObgCNANU/6I8C/3JRxmjIPAb0y1JEMxPukFQH+Bu7xvI7F2QKilU+e1p60Wp7X9wJ/AUV88gwDdvPPtPPZwGcZ2rMYmJVduxs3bqzGnDXatnWO05j57UytO6muBo0M0rqT6urMb2e6KjIkKkRbfDRHg5Yu1dDlyzVo6VJt+eEcDYkKcXU9grZavUT7b9miG/7+W/tv2aKtVi9RBFfX151UVxN+SvBLS/gpQetOqpsv1+dGbusOXb5cWbr0lCN0+fI8r59yaNySxX6/92uXLFbKufu9ecuBRHUZB9z2gAZ5PsSn4D8dezcwwGUZ2akKRAKL0hPU2eRuBdDSk9QCJ3B94XPdKpxJEb55Pvdcm24hEA1U8cmzCH8Lfcow5pzXrX43NvbfSOrjqWzsv9H1/+JT9qZQtmJNv6nEZSrVJGWvy+GgsjD8UKrfLLbhh1KdxbpcyO0wWKB6IWcit23Pbe8zV/Xvgz+Kit/v/WBRgbzc7c1NlAK+B+I83//NPz2gusABt9EuQ5kZe0AtcXoylTPkmwYs9Hz/GPDTacr6CXjU8/0iYFqG85U9ZbfwvD4O9MiQpweQkklb+wKJQGLlypVz9L8BY/JUFj2gMxUSFaLj3xrvlzb+rfGue0AV76qoUZWiNCEhQY8fP64JCQkaVSlKK95V0XUbzrT3plqwPSDV3LU9t73P3NRf8eKKGnlfpCb8lKDHTx7XhJ8SNPK+SK14sfvfm2rOekBug8VRIEZPDUA1gSNuK8tQ5jkTgHwPG4IzZ5U8CEADnhqgF5W+SMe/NV4PHz2s498arxeVvkgHPDXA1fUzv52pZe8sq1VqVtGgoCCtUrOKlr2zbI4+iHNj5rczteqEqn4fpFUnVM23+nMDQa/fsMFv+PL6DRtcD1/mxsyZM7VshbJaZVAVlcdFqwyqomUrlNWZM3P2c8tJAHL7HNBPODf7My7Fcw2w2WUZ2dnr+Vqef1bYTn+91ydPWRERzxvFM7utXIY85TOUXd7nXFZ59mLMBW7i0IkAPDbkMR6840FCIkPo91A/b3p2utXvBg85M8LYD2Flwhhz+RjXQ4C5lV7PwE8GkrQ/idgysTmaSFCQQiJDaLtpCQ/c7syZmlSzJs/OfJaFkXm/oGj6LMHRo0cjSUJYbBhjnhmTt88wuYlSwF0493u64/RcuuMs8HEYuNVttMtQZmaTEB7zSQvFmVCQcRJCS5886T2njJMQQn3yPMapkxAWZWjPImwSgjnX5EEPyBSc3PY+zwYEegjOKZN/4/SA0jzHL0Aft9d7ygjHmfbcEGc23eOe7yt7zg8F/gRuwnns7G1gDxDhU8YnOJvjtfAc3wEf+pwvjtOTedtTxk2egPSgT56WwEmcWXy1gUeBE8Bl2b0HC0DmrGIB6Lwz4KkBGhIVoggaEhVyTgUf1ZwFIFf7AfkSkTJAkKr+lqMLnWvbAad7quk1Ve3lGU4bDtwDlAS+BO5T1Y0+ZZQEJgLXe5LmAwNU9Q+fPPWBSUAz4CDwCjBKfd6siNwCPAlUw9nnKF5dLClkq2Gbs0oWq2EbUxByshp2jgPQhc4CkDmrWAAyZ5mcBCBXkxBEpBTOnkAdcG74+z0/pKrFctpIY4wxFza3s+CmAo1wHkTdg3PT3xhjjDljbgNQB6CTqn6Zl40xxhhz4XC7FM9vONOmjTHGmIBwG4DigVEiEp6XjTHGGHPhcDsENwxnIc/fRGQnzjMzXqraIMDtMsYYc55zG4DeydNWGGOMueC4CkCqOjKvG2KMMebCkuU9IBG5UkQu8nkdkeF8qIj0zqvGGWOMOX9lNwnhE6CUz+vdIlLN53Vx4NWAt8oYY8x5L7sAJNm8NsYYY86I22nYxhhjTEBZADLGGFMg3MyCayAiv3u+F6CuiJTwvC6TN80yxhhzvnMTgBbif+9nXobztjCpMcaYHMsuAFXNl1YYY4y54GQZgFR1Z341xBhjzIXFJiEYY4wpEBaAjDHGFAgLQMYYYwqEBSBjjDEFwgKQMcaYApHpLDgRWYrLZ3xU9YqAtcgYY8wFIase0EZgk+f4HmgMVAB+8RzRnrSkQDZIRCJEZIKI7BSRoyLyhYg09TlfXkRmiMgeETkiIp+KSI0MZYSIyEQR2S8ih0VkvohUzJCnsoh86Dm/X0ReEJHgQL4XY4wxmcs0AKnqwPQDSAFeA2qrag/PURuYDpwMcJv+B1wF9ATqA4uAxSJSQUQE+ACoAdwANAJ2es6H+ZQxAbgZ6AZcDhQDPhKRQgCerwuACM/5bsAtwPgAvxcTAMkpKbRdv569KSkF3RRjTAC5vQfUA3hRVTMOyb0E3BmoxohIEZzA8YiqLlPVH1R1BPADcC9O4GkO9FfVtaq6xZNeBCeIICLFgT7AQ6r6map+7WljA6Cjp6orgbrAnar6tap+BjwM/FtEigXq/ZjAeGLHDlb++Sejduwo6KYYYwLIbQASnN5IRqdLy42LgELAsQzpR4HWQIjntfe8qqbh9NBae5IaA4Vxek7peX7GGSps6UlqASR50tMt9JTfOBBvxORekRUrkGXLeDk5mTTg5eRkZNkyiqxYUdBNM8YEgNsANA34n4g8IiLtPMcjOLuhTg9UY1T1b2A1MMwz5FZIRO7ACRhROPeidgFjRKSUiASLyFCgouc8QCSQCuzPUPyvnnPpeX7NcH6/57pIzFlhfHIyYatXE+LpeIeoErZ6Nc8mJxdwy4wxgeA2AD0M/B8wEEjwHAOBpzznAulOIA1nokMK8B9gFpCmqieAm4CLgQPAEaA9ztbhaQFuh5eI9BWRRBFJ3LdvX15Vk6fOxfsoLz3xBFc0b84JEUKDgjghQofmzZn0xBMF3TRjTAC4CkCqmqaqT6tqBaAEUEJVK3jSUgPZIFX9UVXbAuFAJVVthjOk9pPn/Feq2tDTjihVvRoonX4e2IszjJdxr6LynnPpecpnOF/Gc93eDOmo6hRVbaKqTcqWLZvbt5jvZs2aRd2xY1lx8CB1xo5l1lWubCAAABewSURBVKxZBd0kV5KSkggqXZp+0dGsufRS+kVHI6VLk5QU0ImXxpgCkuMHUVX1L1X9Ky8ak6Gew6qaLCIlcWbFzctw/k9V3eeZgt3E5/xXwAmgU3pezxTsWOALT9JqIDbD1OxOOD2ur/Li/RSU4KVLuT0qioNt2kBQEAfbtOH2qCiCly4t6KZlKzY2lkEHDjCpZk0uCQ9nUs2aDDpwgNjY2IJumjEmAFwFIM/9lpdFZKuI/CEif/kegWyQiFwlIp1FpKqIdAKW4tz7me4531VE2otINRHpAnwGfKCqi8AJTMBU4GkR6SgijYA3gG+BxZ5qFuE83/S6iDQSkY7AM8Cr+RFc81PV+Hg6qP5/e3ceX0V1NnD899wICRS1voIQiJZUWxoCuIAbAoIUlxe1vkqrQVE0YhGJ0bpQibUoTauV+toGqUuxuCXUuhWktCK5LiwiUBWiAUQRNGEtikUgCcnTP84Eb64hmSQkc3N5vp/PfJI765nhMk/OmWfOoX3I/VO3D4UYqkrqxIm+99HU5rvGbp+Tk0NmZibhcJiKigrC4TCZmZnk5OQ0qhzGmNjiZ0RUcDf0E4FHgVKadxTUw3HPm1KA7cDzQI73/AdcssEDuCa0jcCTQPRDgZtw7yf9BZeiPR+4srq5UFUrRWQ4Lo18IS7L7hngtuY7rWCsXbKEwcnJhDdtIikUYk9VFcclJxN++21f22fdl8W0L9tSNXQ43e6ZyLjDysmbkNegMkSmUU/r0cP3dhkZGa4MWVkUFxeTlpZGbm7uvvnGmNZNvvlqTy0ruVrOMFVd0vxFim39+vXTZcuWBV0M33r16sUReXn06daN67p25dHSUlaUlPB5VhZFRUV1bntI4XwqQwnfmJ9QVcnes4bWe+x2b7zBnqpv5oYkhULsHjTI/0mY/Rs82P187bUgS2HMPiKyXFX7+VnX7zOgLcDOxhfJBCUnJ4eSzExGlJTQMzGRESUllPhsxkoYP4oTd22v0Xx30q7tJNxwha9jH4g06oKVBfSa1ouEexLoNa0XBStbRwKFMaZ+fpvgcoB7ROQqVbVA1Io0pRmrfNVGTureg/e2bN3XfHdS9x/wr9XfSBSs1bTJkzkrL485Xhp1eVUV55x2Gg9lZXH9pZfWu33BygJyCnOYfuF0BhwzgAUbFpA5K9OdV29rhjOmtfNbA7oT133NFhEpFpEVkVMzls94mpIIkJGRQVFREZWVlRQVFfl+hpLYJZG316+pkQa9ZP1qErsk1r8xLo26tM1uDt/+BmVLMzl8+xuUtNntO406981cpl84nSGpQ2iT0IYhqUOYfuF0ct/M9bW9MSa2+Q1AzwFTgPuAmbjEgMjJNKOmvsfT2GasMdljKB71E45d9jLfOyTEsctepnjUTxiTPcbX9l1Tu1IyM5PnTxxI2U0f8PyJAymZmUnX1K6+ti/eVsyAYwbUmDfgmAEUbzt43gNqjS8QG+OXryY4Vb27uQtiatc2HKYiORmSXU9Dnw8axEjgqnCY8iFD6t2+Kc1Y1dluE2+dyC1X3EJil0TG3jbWfxbcIJBZ4rqXTQE+8T7/sJ7tPGkd01iwYQFDUr8+zwUbFpDW8eB5D6ixGYTGtAa+suDM11o6C65H//4cnZvL4oQEdlVV0T4U4vTKSj6dOJHVixfXu32vab3IOy+vxk08vC5M1twsisbVnQXXVAn3JDDj2Bnc95v79j1/mnDHBEZ/NJrKu+rvQGN/wTP3rNy4fwbkO4PQsuBMjDngWXBep593ey+i7hGRysipacWNf03J5Fq7ZAnHJiezp6qqxns8a32+xxNkM1ZaxzRS+qfUeP6U0j/Fdw0mo3cGuWflkjU3i6TcJLLmZrW64NPYJjTriNUcDPw+A5qMGyDud7hOP28DHsJ1CDqueYoWH6r/ip80LI8zzl/K3WfnkVOY4zsIpaWl8cHGjTUSAd7fuNF3dzTVzViRWqoZK2dgDpmzMgmvC1NRWUF4XZjMWZnkDPTfk0FG7wyKxhVReVclReOKWjT4FKwsoMej/ZGXfk+PR05vVAp4Y8cymjZ5Mj16Hk0ZCpVllKH06JliHbGa+KKq9U7AOuBc7/f/AMd6v18PPOdnH/Ey9e3bVxsi/aF0Lfy4UK9ftUpD4bBev2qVFn5cqOkPpfvaPj8/X1NTU7WwsFDLy8u1sLBQU1NTNT8/39/2K/I19cFULfy4UMv3lmvhx4Wa+mCq5q/wt31T5a/I1/SH0jV0d0jTH0pvseM2VfV1u3BpoYbCYb1wacOuW9Lrryvh8DempNdf97W9hETbvXC//mhpoS794nP90dJCbffC/SohqbnimWe6yZgYASxTn/dTv+8BdQY+8H7fieuJGuAfuMw4sx/v9/gdZ60XXK9BblC1PyLQw9/o3xkZGSzasIjzLj+Psk1lJHZJZEz2GN+p1NU1hqy5WRRvKyatY1qLNmNl9M4ItMmsYGUBkxbnseaoS/n+5plM6n+jr/JcvrUjevzjrPPeepu1U+D4x7l8awV+ziZj2Wxm7EzikAGDqQgl0Kaqkr0LwmR02AM+eoFo27ktv9od4mf93LO7l/oN4YE17zCxc1sfRzemdfDbBLcBqM6dXYvrnRrcQHG7D3Sh4knyC7eQtGhhjbb8pIULSH7+Fl/bF6wsYE7SHOYunEt5RTlzF85lTtKcBjUHBdmMFaSClQVk359NaelQOKw3pRt/SPb92b6unT6RUeszGH3iMl/Hzp+Sx2l9+1AZSiApFKIylMDpJx1P/pSpvrYvP6OcqXdOrdER69Q7p1J+Rrmv7Y1pDfwGoBeB6s6/fg/cLSLrgBnAn5qhXHEj4Z87kM67KBdIEqFcQLrsIuGVHb62t5cxG2/k5iPZes2z7Bw8FEIhdg4eytZrnmXk5iPr3TZx3k769DmuxmB4x/c+jsR5X/k6dtmmMjqlfL/Gs7uOR3+fsk3+khF6DunJyOyRZGVlkZSURFZWFiOzR9JzSE9f2xvTGvgdkO4OVc31fn8OGAjkARerqvWNX4fSdaX07HEG397+JmXLruXb29+kZ48BlK4r9bW9vYzZBDdkMMRLXQfXl91ZVVUwrv5aTPnmct4tX88FHZQ3j+/DBR2UdyrWU77ZXw0ksUsiZ74/v8ZYRme+P993LxI5A3PIJ5+82XnsKd9D3uw88slvUAKHMbHO7zOgGlT1LeCtA1yWuJSWlsb9Fe0YcvFdwF0AhMNhshqYxXYwv4zZaB9up30HZc+ur1PY23dQWPt5vZv2TOvJRdu/4KWNWZz6d/fs7GeJF/FSmr8ayJjsMUy4cQIAYy8ey8MvPMyEGycw9raxvrYP+tmdMS3CT6YCkBjxezfgbtwAbgP9ZjvEy9TQLLjWnsXWmqUcm6KJT91XI5Ms8an7NOXYlHq3beq/m6rq+HvHa2JyoiJoYnKijr93fFNOp3aWBWdiDA3Igqsv8PTAjRxaCbwD9ARKcKnYO3CDvl3k92DxMDU0AKm6m1l6erqGQiFNT09v0E1MtfWmMgctPz9fO3XrpN2zu6vcJdo9u7t26tbJf/Bv4r9bi7AAZGJMQwJQnV3xiMhs3Iii/w9chnv2Mx+o7o0yD+irqqcdwEpZTGttA9Id7AoKCsjNzd3XFVBOTk58jahqXfGYGNOQrnjqewZ0Gm4k1HdF5A1crWeaqlZ5B8rDngWZGJaRkRFfAceYOFJfFtyRQCmAqv4H+AqIfIL7OXBo8xTNGGNMPPOThh3dRmfdZxtjjGkyP2nYT4tI9dtzScBjIrLL++zvpQZjjDEmSn0B6Imoz0/Xss6TB6gsxhhjDiJ1BiBVvbqlCmKMMebg4rcvOGOMMeaAirkAJCKHisiDIrJeRHaLyCIROTlieQcRyRORz7zlq0Xk5qh9JHrrbBORr0RkloikRK1zjIjM9pZvE5E/iIj1dW+MMS0k5gIQrnftc3AjsPYGXgFeFZFu3vIHgOHAKCANyAXuFZFREft4ELgEyMC9PHsY8LKIJAB4P+fgUsgHeuuNwI34aowxpgXEVAASkXa4wPFzVX1NVdeq6iTcGETXe6v1B55S1bCqfqKqT+Jehj3V28fhQCZwm6rOU9V/4YJVH+CH3j7OBtKBUar6L1WdB9wOjBGRw1rkZI0x5iAXUwEIlxSRAOyJmr8bqB6TYAFwgYgcDSAi/YETcKOzAvQF2uBqTgCo6qdAMS54gRtIr9ibX+2fuLTyvgfqZIwxxuxfTAUgr7eFxcCdItJNRBJE5ApcwEj2VrsReA/YICIVwOvABFV92VveBdd56rao3W/2llWvszlq+TZvuy5R8xGR60RkmYgs27p1a5PO0RhjjBNTAcgzCqgCPgPKcAGnwJsHkIWryVyIq63cDEwRkXObq0Cq+qiq9lPVfp06dWquwxhjzEEl5gKQqn6kqmcCHYCjVfUUXJPax94zot8At6vqbFVdoapTgZnArd4uNuGa8TpG7bqzt6x6nc5Ryzt6223CGGNMs4u5AFRNVb9S1Y0icgQuK+5vuEDUBtdUFqmSr89lOVABDKte6KVgpwGLvFmLgbSo1OxhuBrX8gN8KsYYY2rRqCG5m5OInIMLJquA43Ajr64C/qyqFSLyOi7teiewHjgTuBKXxYaq7hCR6cBvRWQL8G9c6vYK4FXvMK/gBtp7UkRuwfX6fT/wmKp+2TJnaowxB7eYC0DA4bhmthRgO/A8kKOqFd7yy7zlzwD/gwtCvwCmRuzjJtxorX/BDag3H7hSVSsBVLVSRIYD04CFuCy7Z4DbmvXMjDHG7BNzAUhVnwWerWP5JqDOPupUtQyXrJBVxzobgPMbWUxjjDFNFLPPgIwxxsQ3C0DGGGMCYQHIGGNMICwAGWOMCYQFIGOMMYEQVQ26DK2KiGzFpX43Rke+2Ued8ceuXePZtWscu26N8x1V9dVnmQWgFiQiy1S1X9DlaI3s2jWeXbvGsevW/KwJzhhjTCAsABljjAmEBaCW9WjQBWjF7No1nl27xrHr1szsGZAxxphAWA3IGGNMICwAGWOMCYQFoBYiIuNEZJ2I7BGR5SIyMOgyxToRmSQiGjXZiLVRRGSQiMwSkRLvGo2OWi7etSwVkd0i8pqIpAdU3Jji49rNqOU7+FZAxY07FoBagIhcCvwe+DVwIm5k1rkickygBWsdVgPJEVPvYIsTkzoARUA2bmyraLcDt+CGJzkZ2ALME5FDW6yEsau+awduIMvI7+D/tkzR4p8lIbQAEVkCrFDVMRHzPgSeU9U7gitZbBORScAIVe0VdFlaC2+k4PGqOsP7LEApMFVVc7157XBB6FZVfSSossaa6GvnzZsBdFRVGzusGVgNqJmJSFugL24Y8EivAP1bvkStzne9pqN1IjJTRL4bdIFamVSgCxHfP1XdDbyBff/8GiAiW0RkjYg8JiJHBV2geGEBqPl1BBKAzVHzN+NuDGb/lgCjgXOBMbjrtUhEjgyyUK1M9XfMvn+N8w/gSmAorhnzFKBQRBIDLVWciLkhuY2ppqpzIz97D38/Bq4CHgikUOagoqozIz6uFJHluM6IhwMvBFOq+GE1oOa3DagEOkfN7wxYRlcDqOpO4H3ge0GXpRWp/o7Z9+8AUNVS4DPsO3hAWABqZqpaDiwHhkUtGobLhjM+iUgS8ANgY9BlaUXW4QLNvu+fdx0HYt+/BhORjkA37Dt4QFgTXMt4AHhKRN4GFgJjga7Aw4GWKsaJyBRgNrABOAr4BfAt4IkgyxVrRKQDcJz3MQQcIyInANtVdYOIPAhMFJFVwBrgTmAnkB9IgWNIXdfOmyYBz+MCTnfgN7gMwhdbuqzxyNKwW4iIjMO9j5GMe+/gZlV9I9hSxTYRmQkMwiVybAXeAn6hqh8EWrAYIyKDgXAti55Q1dFeKvYvgZ8CR+CSO25Q1aKWK2VsquvaAdcDL+He3fs2LgiFcd/BT1uqjPHMApAxxphA2DMgY4wxgbAAZIwxJhAWgIwxxgTCApAxxphAWAAyxhgTCAtAxhhjAmEByByUvIHFRgRdjoOBNwDe1KDLYWKPBSATl0TkJBGpFJGFjdy+jYjcJSIfeaPYvici50atc4OIrBCRL71psYgMj1pnsoisEpGvRORzEZkvIv2j1vlERG6NmnePiOwSkQvqKOO1IvKOiOwUkR1eWX7VmPONdSIy2huvx8QRC0AmXl0LTAN6iUhaI7b/Fe5N+BuBnrhuk14UkRMj1vkMmACcBPQDCoGXRKRPxDqrgRtwI7kOwPXN9g8Rie4cFAARCYnINO+456jq7P2sdw3wB69cJwCnAZOB9o04V2OCoao22RRXE9AO+AJ3058OTKllHcWNtrq/fZQC2VHzngeerufY24Gf1rH8MO/Y50TM+wS4FWgLzMR1+XJ8Pcd5yUdZJuG6fboM+Aj4j7ddx4h1TsYNVrcN+BJYAJxey7UaD8wBduGGI7giap27vPlluM5Pn4xY9hruj4Ffe8fZAkwBQhHrHIHr/uZz3NDYrwLp3rLBXhkip0lBf89savpkNSATj0YA61V1JfAUcKWItGngPhKBPVHzduNqMd8gIgkichnQgf30Mu2Njnsd7kb/btTib+E6Xj0ZGKCq79VTvk3AKT5GiO0OXAr8H3A2rl+z3Ijlh+Ku0UDcYGvvAn+vZdC/u4FZuNrWo8CTItLPO69LcAF0HG6YgvOBt6O2vxzYixuFdTxwk1euajOAU4EfeeXYhasptsNdz5u8ecneNKWe8zatQdAR0CabDvSE+4v7Vu93wdUwRkStU18NKB8oBnrgmqqH4W6AZVHr9cb1LL0XV+saXsu+zvfWqQJKgFOiln+Cqzl8AXT1eY7JwGLvPD4EnsaN3NkmYp1JuCB6eMS8HGBtHfsVXA3sioh5CjwWtd6reDUw4Ge4psY2+9nna8DiqHnzgD95v3/PO8agiOWHAzuAa73Po4GdQX+3bDqwk9WATFwRkeNwtZR8AHV3r2eAzAbuKht3U/0AKAemAn/GBZFIq3G1glOBPwJPiEivqHXC3jr9cUM8PysiyVHrvIJrgvul13t1nVR1o6qejguAD+ICxyPA2yIS+RxovaruiPhcihvaAgAROUpEHhGRNSKyA9dMdxRwTNQhF9fyuaf3+1+BJGCdiEwXkR/XMmT1iqjPkeVIw13Xfcfwyrwy4hgmDlkAMvHmWiAB2CAie0VkL/Bz4GwROdrvTlR1q6pehGsa+w5uILyduCHBI9crV9W1qrpcVe/ANWHdHLXOV946b6lqJlDhlTPS67hhni8HHvMThLx9F6nqQ6p6Oa6WdgLwk4hVKqI3oeb/+ydwzX434wLkCbjkirZ+ju+V4VNcTfGnuObF3wHLReRbDSjHfnfvtxym9bEAZOKGiBwCXAXcgbuRVk/H4/4Cv7qh+1TVPapaghu88RLgb/VsEsI9P2rwOqoaBs7DPRt5XEQa+v+zepykDg3YZgCQp6pzVPV9XA0ounYGLssu+nNx9QfvOs1R1ZtxAS0dOMNnGYpx1+T06hkichiudld9TuW4PyxMHLERUU08GY4bvO4xVf135AJvcLuxIjLZa5ark4iciht6+V3v5yTcTfK3Eevci8sM+xT3MH8kLmNruLf8MNwghLNxz1U64VKyU4Bnazuuqr4pIucAc4EEEblaVStrKd8fcc1YhbgaSzJupNNduOY8v9YAV4jIElxt77e4m320i0VkKe55zghgKK7ZEREZjbuXLMHVEi/F1Xg+9FMAVf1QRP4GPCIi1+GeheXialPVo7Z+AiSJyDDgHWCXqu5qwHmaGGQ1IBNPMoFwdPDx/BWXETbM576ScO8CfYAbfrkEl532RcQ6XXAP/1cD83F/+Z+nqnO95XtxNYEXcTfj2cCRuIft0c9E9lHVRV45L8AN5V7bX/7zcAHgWVwQqR4iepiqrvF5jgDX4GpMy3Ep4I/jbvbRJuFqgCtw70ddrapLvWVf4K79m7i070uAi1V1XQPKcTUuc26W97M9cK6q7oZ91+RhoAA3Ou7tDdi3iVE2Iqoxpk4iosCPVfW5oMti4ovVgIwxxgTCApAxxphAWBOcMcaYQFgNyBhjTCAsABljjAmEBSBjjDGBsABkjDEmEBaAjDHGBMICkDHGmED8F2llKMvsJv94AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "snapshots = 20\n", - "\n", - "font = {'weight': 'normal', 'size': 14}\n", - "matplotlib.rc('font', **font)\n", - "\n", - "\n", - "plt.plot(range(snapshots), hybrid[:,0] * mev_atoms, 'go', mfc='none')\n", - "plt.plot(range(snapshots), hybrid[:,1] * mev_atoms, 'ko', mfc='none')\n", - "plt.plot(range(snapshots), hybrid[:,2] * mev_atoms, 'c*')\n", - "plt.plot([9.5,9.5], [np.min(model3 * mev_atoms)*.99, np.max(model3 * mev_atoms)*1.01], 'r-')\n", - "plt.xlabel('Al 933K Snapshot')\n", - "plt.ylabel('Band Energy (meV/Atom)')\n", - "plt.legend(['ML Predictions', 'ML Targets', 'DFT Eigenvalues', 'Liquid/Solid Snapshots'])\n", - "#plt.title('Hybrid Training Data')\n", - "\n", - "plt.ylim([2500 * mev_atoms, 2660 * mev_atoms])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "\n", - "#plt.show()\n", - "\n", - "plt.savefig(\"./mlmm_933K_hybrid_inference.eps\", format='eps')" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "liq4 = [[2524.23514216074,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2525.6347157315986,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2531.4687232522165,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2531.832410675329,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2531.208538690001,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2533.483755344074,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2525.4552490099536,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2526.57492642237,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2525.1307673043702,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2526.905758130126,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2538.219303762208,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2535.601334478212,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2535.611246973794,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2536.5593466841938,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2535.548948343239,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2539.381754920726,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2537.0398432433617,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2533.9415342719476,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2537.5028172493517,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2536.334095559646,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "sol4 = [[2552.918876986915,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2552.6678956679702,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2557.3896372227778,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2560.8382029603194,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2558.3085941472054,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2561.620752700051,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2552.550564634455,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2554.1610496637845,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2551.29241698577,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2554.2033509307184,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2567.855699355657,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.862055843559,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2564.552729641682,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2565.8148044177,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.541567864262,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2568.4582317202203,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2565.7773925052693,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2563.7487684577154,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2566.518701070757,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2564.854235490092,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [], - "source": [ - "liq4 = np.array(liq4)\n", - "sol4 = np.array(sol4)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5 6 7 8 9]\n", - "[0 1 2 3 4 5 6 7 8 9]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Liquid: 83.41198931685739\n", - "Solid: 9198.508853675552\n", - "\n", - "Model MEAN meV/Atom\n", - "Liquid: 13.928609825732607\n", - "Solid: 118.17788928617698\n", - "\n", - "Model MAX meV/Atom\n", - "Liquid: 21.904978464297287\n", - "Solid: 129.66327237607265\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "lt = np.arange(5,10)\n", - "st = np.arange(0,10)\n", - "\n", - "#Solid\n", - "#lt = np.arange(10,20)\n", - "#st = np.arange(15,20)\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "#lt = np.arange(5,20)\n", - "#st = np.arange(0,10)\n", - "#st = np.append(st, [15, 16, 17, 18, 19])\n", - "\n", - "print(lt)\n", - "print(st)\n", - "\n", - "liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "#hybrid_rmse = np.sum(((hybrid[ht,0] - hybrid[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "print(\"Liquid: \", liquid_rmse)\n", - "print(\"Solid: \", solid_rmse)\n", - "#print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "#hybrid_mean = np.mean((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "print(\"Solid: \", solid_mean * mev_atoms)\n", - "#print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "#hybrid_max = np.max((np.abs(hybrid[ht,0] - hybrid[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "print(\"Liquid: \", liquid_max * mev_atoms)\n", - "print(\"Solid: \", solid_max * mev_atoms)\n", - "#print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [], - "source": [ - "hyb2 = [[2530.1636910482493,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2528.3441435359878,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2534.770878300083,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2537.7022737740676,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2534.8139649652912,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2536.357871334713,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2528.4355011583275,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2529.6269715881513,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2528.296386608331,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2529.49845054759,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2567.7293194572267,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.511184145917,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2564.408985678869,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2565.397314568127,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2564.1735220454584,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2568.7459807814384,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2565.5528382498314,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2563.7149491554924,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2566.3428527763367,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2564.9303824189697,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [], - "source": [ - "hyb2 = np.array(hyb2)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0 1 2 3 4 5 6 7 13 14 15 16 17 18 19]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Hybrid: 438.1843466265312\n", - "\n", - "Model MEAN meV/Atom\n", - "Hybrid: 17.524357093706772\n", - "\n", - "Model MAX meV/Atom\n", - "Hybrid: 35.73672847902287\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "#ht = np.arange(0,8)\n", - "\n", - "#Solid\n", - "#ht = np.arange(15,20)\n", - "\n", - "# Hybrid\n", - "ht = np.arange(0,8)\n", - "ht = np.append(ht, [13, 14, 15, 16, 17, 18, 19])\n", - "\n", - "print(ht)\n", - "\n", - "\n", - "#liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "#solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hyb2[ht,0] - hyb2[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "#print(\"Liquid: \", liquid_rmse)\n", - "#print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "#liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hyb2[ht,0] - hyb2[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "#print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "#print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "#liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_max = np.max((np.abs(hyb2[ht,0] - hyb2[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "#print(\"Liquid: \", liquid_max * mev_atoms)\n", - "#print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "hyb8 = [[2529.2420773099448,\n", - "2521.0150885576195,\n", - "2522.1627902370897],\n", - "[2527.663233710719,\n", - "2525.041901268164,\n", - "2526.186365968908],\n", - "[2532.7841534807176,\n", - "2530.836653299371,\n", - "2531.9859886821237],\n", - "[2534.965077874318,\n", - "2528.632107752529,\n", - "2529.775682181195],\n", - "[2532.578262585185,\n", - "2528.168358815715,\n", - "2529.307770351392],\n", - "[2534.16070863972,\n", - "2533.2837749915625,\n", - "2534.438613905411],\n", - "[2526.9522792785956,\n", - "2519.8826466270666,\n", - "2521.0283990120156],\n", - "[2529.128561811317,\n", - "2520.96725193551,\n", - "2522.1166781510447],\n", - "[2527.95711135918,\n", - "2521.7048534653136,\n", - "2522.8466303833816],\n", - "[2528.5952169597854,\n", - "2523.883308614503,\n", - "2525.0350232357523],\n", - "[2565.925022333284,\n", - "2568.854066538201,\n", - "2570.0078016632747],\n", - "[2563.587286915419,\n", - "2560.917647620072,\n", - "2562.0712499151396],\n", - "[2563.7078509088924,\n", - "2561.8255190305827,\n", - "2562.9801118510595],\n", - "[2564.343968755005,\n", - "2566.372397551112,\n", - "2567.519334487825],\n", - "[2563.4518821361544,\n", - "2565.185968006617,\n", - "2566.3336564821097],\n", - "[2567.3211912054185,\n", - "2566.4096441599745,\n", - "2567.5626057663985],\n", - "[2564.8371692912433,\n", - "2566.6217012424295,\n", - "2567.7753253901656],\n", - "[2562.5468929043986,\n", - "2559.3145649676117,\n", - "2560.462068081751],\n", - "[2564.650134486075,\n", - "2565.025211072312,\n", - "2566.175778381768],\n", - "[2563.70946291344,\n", - "2561.13511808658,\n", - "2562.284328756649]]" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [], - "source": [ - "hyb8 = np.array(hyb8)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 1]\n", - "\n", - "All Test Snapshots!\n", - "\n", - "Model Summed Squared Error\n", - "Hybrid: 74.55472770527697\n", - "\n", - "Model MEAN meV/Atom\n", - "Hybrid: 21.188127333750195\n", - "\n", - "Model MAX meV/Atom\n", - "Hybrid: 32.13667481377058\n" - ] - } - ], - "source": [ - "mev_atoms = 1.0 / 256. * 1000.\n", - "\n", - "#Liquid\n", - "ht = np.arange(0,2)\n", - "\n", - "#Solid\n", - "#ht = np.arange(19,20)\n", - "\n", - "# Hybrid\n", - "#ht = np.arange(0,2)\n", - "#ht = np.append(ht, [19])\n", - "\n", - "print(ht)\n", - "\n", - "\n", - "#liquid_rmse = np.sum(((liq4[lt,0] - liq4[lt,1]) ** 2))\n", - "#solid_rmse = np.sum(((sol4[st,0] - sol4[st,1]) ** 2))\n", - "hybrid_rmse = np.sum(((hyb8[ht,0] - hyb8[ht,1]) ** 2))\n", - "\n", - "print(\"\\nAll Test Snapshots!\")\n", - "print(\"\\nModel Summed Squared Error\")\n", - "#print(\"Liquid: \", liquid_rmse)\n", - "#print(\"Solid: \", solid_rmse)\n", - "print(\"Hybrid: \", hybrid_rmse)\n", - "\n", - "\n", - "#liquid_mean = np.mean((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_mean = np.mean((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_mean = np.mean((np.abs(hyb8[ht,0] - hyb8[ht,1])))\n", - "\n", - "print(\"\\nModel MEAN meV/Atom\")\n", - "#print(\"Liquid: \", liquid_mean * mev_atoms)\n", - "#print(\"Solid: \", solid_mean * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_mean * mev_atoms)\n", - "\n", - "#liquid_max = np.max((np.abs(liq4[lt,0] - liq4[lt,1])))\n", - "#solid_max = np.max((np.abs(sol4[st,0] - sol4[st,1])))\n", - "hybrid_max = np.max((np.abs(hyb8[ht,0] - hyb8[ht,1])))\n", - "\n", - "\n", - "print(\"\\nModel MAX meV/Atom\")\n", - "#print(\"Liquid: \", liquid_max * mev_atoms)\n", - "#print(\"Solid: \", solid_max * mev_atoms)\n", - "print(\"Hybrid: \", hybrid_max * mev_atoms)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 7dfddfea99b4ab75bdd2c101c513b3c669369ebe Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 11 Jun 2021 15:13:56 +0200 Subject: [PATCH 118/297] Deleted LDOS scripts --- .../training_data/scripts/ldos/cube_parser.py | 251 ------------------ .../training_data/scripts/ldos/parse_ldos.py | 187 ------------- .../scripts/ldos/submit_blake_script_ldos.sh | 15 -- 3 files changed, 453 deletions(-) delete mode 100644 ml-dft-sandia/networks/training_data/scripts/ldos/cube_parser.py delete mode 100755 ml-dft-sandia/networks/training_data/scripts/ldos/parse_ldos.py delete mode 100755 ml-dft-sandia/networks/training_data/scripts/ldos/submit_blake_script_ldos.sh diff --git a/ml-dft-sandia/networks/training_data/scripts/ldos/cube_parser.py b/ml-dft-sandia/networks/training_data/scripts/ldos/cube_parser.py deleted file mode 100644 index c810a8c87..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/ldos/cube_parser.py +++ /dev/null @@ -1,251 +0,0 @@ -#------------------------------------------------------------------------------ -# Module: cubetools -#------------------------------------------------------------------------------ -# -# Description: -# Module to work with Gaussian cube format files -# (see http://paulbourke.net/dataformats/cube/) -# -#------------------------------------------------------------------------------ -# -# What does it do: -# * Read/write cube files to/from numpy arrays (dtype=float*) -# * Read/write pairse of cube files to/from numpy arrays (dtype=complex*) -# * Provides a CubeFile object, to be used when cubefiles with -# constant and static data is required. It simulates the readline method -# of a file object with a cube file opened, without creating a file -# -#------------------------------------------------------------------------------ -# -# Dependency: numpy -# -#------------------------------------------------------------------------------ -# -# Author: P. R. Vaidyanathan (aditya95sriram gmail com) -# Date: 25th June 2017 -# -#------------------------------------------------------------------------------ -# -# MIT License -# -# Copyright (c) 2019 P. R. Vaidyanathan -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -# -#------------------------------------------------------------------------------ - -import numpy as np - -if __name__ == '__main__': - DEBUGMODE = True -else: - DEBUGMODE = False - -def _debug(*args): - global DEBUGMODE -# if DEBUGMODE: -# print " ".join(map(str, args)) - -class CubeFile(object): - """ - Object which mimics a cube file opened as a file object - by returning output in the correct format, matching the - metadata of the source cube file and replacing volumetric - data with static data provided as arg to the constructor. - Doesn't copy atoms metadata, retains number of atoms, but - returns dummy atoms - Mimics file object's readline method. - - params: - srcname: source file to copy metadata from - const: numeric value to return instead of volumetric data - - returns: CubeFile object - """ - - def __init__(self, srcname, const=1): - self.cursor = 0 - self.const = const - self.src = src = open(srcname) - src.readline(); src.readline(); # comments - _debug(srcname) - self.lines = [" Cubefile created by cubetools.py\n", - " source: {0}\n".format(srcname)] - self.lines.append(src.readline()) # read natm and origin - self.natm = int(self.lines[-1].strip().split()[0]) - # read cube dim and vectors along 3 axes - self.lines.extend(src.readline() for i in range(3)) - self.src.close() - self.nx, self.ny, self.nz = [int(l.strip().split()[0]) for l in self.lines[3:6]] - self.remvals = self.nz - self.remrows = self.nx*self.ny - for i in range(self.natm): - self.lines.append("{0:^ 8d}".format(1) + "{0:< 12.6f}".format(0)*4 + '\n') - - def __del__(self): - self.src.close() - - def readline(self): - """ Mimic readline method of file object with cube file opened """ - try: - retval = self.lines[self.cursor] - except IndexError: - if not self.remrows: - return "" - if self.remvals <= 6: - nval = min(6,self.remvals) - self.remrows -= 1 - self.remvals = self.nz - else: - nval = 6 - self.remvals -= nval - return " {0: .5E}".format(self.const)*nval + "\n" - else: - self.cursor += 1 - return retval - -def _getline(cube): - """ - Read a line from cube file where first field is an int - and the remaining fields are floats. - - params: - cube: file object of the cube file - - returns: (int, list) - """ - l = cube.readline().strip().split() - return int(l[0]), map(float, l[1:]) - -def _putline(*args): - """ - Generate a line to be written to a cube file where - the first field is an int and the remaining fields are floats. - - params: - *args: first arg is formatted as int and remaining as floats - - returns: formatted string to be written to file with trailing newline - """ - s = "{0:^ 8d}".format(args[0]) - s += "".join("{0:< 12.6f}".format(arg) for arg in args[1:]) - return s + "\n" - -def read_cube(fname): - """ - Read cube file into numpy array - - params: - fname: filename of cube file - - returns: (data: np.array, metadata: dict) - """ - meta = {} - with open(fname, 'r') as cube: - cube.readline(); cube.readline() # ignore comments - natm, meta['org'] = _getline(cube) - nx, meta['xvec'] = _getline(cube) - ny, meta['yvec'] = _getline(cube) - nz, meta['zvec'] = _getline(cube) - meta['atoms'] = [_getline(cube) for i in range(natm)] - data = np.zeros((nx*ny*nz)) - idx = 0 - for line in cube: - for val in line.strip().split(): - data[idx] = float(val) - idx += 1 - data = np.reshape(data, (nx, ny, nz)) - return data, meta - -def read_imcube(rfname, ifname = ""): - """ - Convenience function to read in two cube files at once, - where one contains the real part and the other contains the - imag part. If only one filename given, other filename is inferred. - - params: - rfname: filename of cube file of real part - ifname: optional, filename of cube file of imag part - - returns: np.array (real part + j*imag part) - """ - ifname = ifname or rfname.replace('real', 'imag') - _debug("reading from files", rfname, "and", ifname) - re, im = read_cube(rfname), read_cube(ifname) - fin = np.zeros(re[0].shape, dtype='complex128') - if re[1] != im[1]: - _debug("warning: meta data mismatch, real part metadata retained") - fin += re[0] - fin += 1j*im[0] - return fin, re[1] - -def write_cube(data, meta, fname): - """ - Write volumetric data to cube file along - - params: - data: volumetric data consisting real values - meta: dict containing metadata with following keys - atoms: list of atoms in the form (mass, [position]) - org: origin - xvec,yvec,zvec: lattice vector basis - fname: filename of cubefile (existing files overwritten) - - returns: None - """ - with open(fname, "w") as cube: - # first two lines are comments - cube.write(" Cubefile created by cubetools.py\n source: none\n") - natm = len(meta['atoms']) - nx, ny, nz = data.shape - cube.write(_putline(natm, *meta['org'])) # 3rd line #atoms and origin - cube.write(_putline(nx, *meta['xvec'])) - cube.write(_putline(ny, *meta['yvec'])) - cube.write(_putline(nz, *meta['zvec'])) - for atom_mass, atom_pos in meta['atoms']: - cube.write(_putline(atom_mass, *atom_pos)) #skip the newline - for i in range(nx): - for j in range(ny): - for k in range(nz): - if (i or j or k) and k%6==0: - cube.write("\n") - cube.write(" {0: .5E}".format(data[i,j,k])) - -def write_imcube(data, meta, rfname, ifname=""): - """ - Convenience function to write two cube files from complex valued - volumetric data, one for the real part and one for the imaginary part. - Data about atoms, origin and lattice vectors are kept same for both. - If only one filename given, other filename is inferred. - - params: - data: volumetric data consisting complex values - meta: dict containing metadata with following keys - atoms: list of atoms in the form (mass, [position]) - org: origin - xvec,yvec,zvec: lattice vector basis - rfname: filename of cube file containing real part - ifname: optional, filename of cube file containing imag part - - returns: None - """ - ifname = ifname or rfname.replace('real', 'imag') - _debug("writing data to files", rfname, "and", ifname) - write_cube(data.real, meta, rfname) - write_cube(data.imag, meta, ifname) diff --git a/ml-dft-sandia/networks/training_data/scripts/ldos/parse_ldos.py b/ml-dft-sandia/networks/training_data/scripts/ldos/parse_ldos.py deleted file mode 100755 index 1a2e4323a..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/ldos/parse_ldos.py +++ /dev/null @@ -1,187 +0,0 @@ -import os -import sys -import numpy as np -import timeit -import itertools -import argparse - -import cube_parser - -print("\n-----------------------------------\n") -print("-------- PARSE LDOS DATA --------") -print("\n-----------------------------------\n") - -parser = argparse.ArgumentParser(description='LDOS Parser') - -# Targets -parser.add_argument('--water', action='store_true', default=False, - help='run ldos parser for water files') -parser.add_argument('--run-all-water', action='store_true', default=False, - help='run ldos parser for all densities and temperatures') -parser.add_argument('--density', action='store_true', default=False, - help='run density parser for each density and temperature, also') - -# Dimensions -parser.add_argument('--nxyz', type=int, default=200, metavar='N', - help='number of grid cells in the X/Y/Z dimensions (default: 200)') -parser.add_argument('--elvls', type=int, default=250, metavar='E', - help='the number of energy levels in the LDOS (for density only e=1) (default: 250)') - -# Directories -parser.add_argument('--material', type=str, default="Al", metavar='T', - help='material of ldos parser (default: "Al")') -parser.add_argument('--temp', type=str, default="298K", metavar='T', - help='temperature of ldos parser in units K (default: 298K)') -parser.add_argument('--gcc', type=str, default="2.699", metavar='GCC', - help='density of ldos parser in units g/cm^3 (default: "2.699")') -parser.add_argument('--snapshot', type=str, default="0", metavar='T', - help='snapshot for ldos parser (default: 0)') -parser.add_argument('--cube-fname-head', type=str, default="/tmp.pp", metavar='H', - help='head of the ldos cube filenames (default: "/tmp.pp")') -parser.add_argument('--cube-fname-tail', type=str, default="_ldos.cube", metavar='T', - help='tail of the ldos cube filenames (default: "_ldos.cube")') -parser.add_argument('--density-cube-fname', type=str, default="Al_dens.cube", metavar='C', - help='density cube filenames (default: "Al_dens.cube")') - -parser.add_argument('--data-dir', type=str, \ - default="./cube_files", \ - metavar="str", help='path to data directory (default: "./cube_files")') - -parser.add_argument('--output-dir', type=str, default="../../ldos_data", - metavar="str", help='path to output directory (default: ../ldos_data)') - -args = parser.parse_args() - - -if (args.water): - print ("Using this script in water density reading mode") - #args.data_dir = '/ascldap/users/jracker/water64cp2k/datast_1593/results/' - #args.data_dir = '/Users/jracker/ldrd_ml_density/mlmm-ldrd-data/water/' - - temp_grid = np.array([args.temp]) - gcc_grid = np.array(['aaaa']) - snapshot_grid = np.array([args.snapshot]) - - if (args.run_all_water): - gcc_grid = [''.join(i) for i in itertools.product("abcdefghijklmnopqrstuvwxyz",repeat=4)][:1593] - - print (gcc_grid) - - #cube_filename_head = "w64_" - #cube_filename_tail = "-ELECTRON_DENSITY-1_0.cube" - - - # only doing density: 1 level - #e_lvls = args.elvls - e_lvls = 1 - -else: - - # 1 snapshot per parse_ldos call, for now - temp_grid = np.array([args.temp]) - gcc_grid = np.array([args.gcc]) - snapshot_grid = np.array([args.snapshot]) - - - # Energy levels for LDOS - e_lvls = args.elvls - -# Output location of .npy binary files -#output_dir = '../ldos_data' - -if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir) - -# Output filename head -npy_filename_head = "/%s_ldos_" % args.material -npy_filename_head_dens = "/%s_dens_" % args.material - -xyze_shape = [args.nxyz, args.nxyz, args.nxyz, args.elvls] -dens_shape = [args.nxyz, args.nxyz, args.nxyz] - -tot_tic = timeit.default_timer() - -# Print arguments -print("Parser Arguments") -for arg in vars(args): - print ("%s: %s" % (arg, getattr(args, arg))) - -# Loop over temperature grid -for temp in temp_grid: - - print("\nWorking on Temp %s" % temp) - - # Loop over density grid - for gcc in gcc_grid: - - inner_tic = timeit.default_timer() - - temp_dir = args.output_dir + "/%s" % (temp) - gcc_dir = temp_dir + "/%sgcc" % (gcc) - - out_filename = gcc_dir + npy_filename_head + "%dx%dx%dgrid_%delvls_snapshot%s" % \ - (args.nxyz, args.nxyz, args.nxyz, args.elvls, args.snapshot) - - if (args.water): - out_filename = args.data_dir + args.cube_fname_head + gcc + args.cube_fname_tail + "_pkl" - else: - # Make Temperature directory - if not os.path.exists(temp_dir): - print("\nCreating folder %s" % temp_dir) - os.makedirs(temp_dir) - # Make Density directory - if not os.path.exists(gcc_dir): - print("\nCreating folder %s" % gcc_dir) - os.makedirs(gcc_dir) - - # Allocate space - ldos = np.empty(xyze_shape) - - # Loop over energy grid - # separate cube file for each ldos - for e in range(e_lvls): -# infile_name = args.data_dir + "/%sgcc/" % (gcc) + \ -# infile_name = args.data_dir + "/%s" % temp + "/%sg/" % (gcc) + \ -# cube_fname_head + "%d" % (e) + cube_fname_tail - - infile_name = args.data_dir + args.cube_fname_head + \ - str(e + 1).zfill(len(str(e_lvls))) + args.cube_fname_tail - - if (args.water): - infile_name = args.data_dir + args.cube_fname_head + gcc + "-" + args.cube_fname_tail - - print("\nLoading data from %s" % infile_name) - - # load into numpy array (ignore metadata w/ '_') - ldos[:,:,:,e], _ = cube_parser.read_cube(infile_name) - - - # Save numpy array to binary file - print("Saving LDOS to %s" % (out_filename)) - np.save(out_filename, ldos, allow_pickle=True) - - - # If parsing density, as well - if (args.density): - infile_name = args.data_dir + "/%sgcc/" % (gcc) + args.density_cube_fname - outfile_name = args.output_dir + npy_filename_head_dens + "%dx%dx%dgrid" % (xdim, ydim, zdim) - - print("\nLoading data from %s" % infile_name) - dens_gcc, _ = cube_parser.read_cube(infile_name) - - print("Saving Density to %s" % (out_filename)) - np.save(out_filename, dens_gcc, allow_pickle=True) - - inner_toc = timeit.default_timer() - - print ("\n\nTime %s temp, %s gcc: %4.2f secs" % (temp, gcc, inner_toc - inner_tic)) - -tot_toc = timeit.default_timer() - -print("\n\nSuccess! Total time: %4.2f secs\n\n" % (tot_toc - tot_tic), flush=True) - -# FUTURE: Convert to parallel_for over temp and density grid -#if __name__ == '__main__': -# num_threads = mp.cpu_count() -# with mp.Pool(processes = N) as p: -# p.map(read_temp_density_ldos, [infile for infile in ldos_infiles]) diff --git a/ml-dft-sandia/networks/training_data/scripts/ldos/submit_blake_script_ldos.sh b/ml-dft-sandia/networks/training_data/scripts/ldos/submit_blake_script_ldos.sh deleted file mode 100755 index e95546d72..000000000 --- a/ml-dft-sandia/networks/training_data/scripts/ldos/submit_blake_script_ldos.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash -#SBATCH -N 1 -#SBATCH -p blake -#SBATCH -A johelli -#SBATCH --time=12:00:00 -#SBATCH --job-name=ldos_parse - - -echo $(date) - -python3 parse_ldos.py > logs/ldos_parse_300K.log - -echo $(date) - -exit 0 From 8251b89c86ca0344c8c6a8dc357d4e68bff612df Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 11 Jun 2021 15:17:05 +0200 Subject: [PATCH 119/297] Deleted old output --- .../commandline_args1.txt | 77 ----------------- .../commandline_args2.txt | 83 ------------------- .../commandline_args1.txt | 77 ----------------- .../commandline_args2.txt | 83 ------------------- .../commandline_args1.txt | 77 ----------------- .../commandline_args2.txt | 83 ------------------- .../fp_ldos_dir_BS_100/commandline_args1.txt | 76 ----------------- .../fp_ldos_dir_BS_100/commandline_args2.txt | 82 ------------------ .../fp_ldos_dir_BS_1000/commandline_args1.txt | 76 ----------------- .../fp_ldos_dir_BS_1000/commandline_args2.txt | 82 ------------------ .../fp_ldos_dir_BS_2000/commandline_args1.txt | 76 ----------------- .../fp_ldos_dir_BS_2000/commandline_args2.txt | 82 ------------------ .../fp_ldos_dir_BS_250/commandline_args1.txt | 76 ----------------- .../fp_ldos_dir_BS_250/commandline_args2.txt | 82 ------------------ .../fp_ldos_dir_BS_500/commandline_args1.txt | 76 ----------------- .../fp_ldos_dir_BS_500/commandline_args2.txt | 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/dev/null @@ -1,77 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_staged_data": false, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 8, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2021_02_27_23_49_27", - "model_dir": "./output/fp_ldos_dir_2021_02_27_23_49_27", - "tb_output_dir": "./output/fp_ldos_dir_2021_02_27_23_49_27/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_hybrid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_hybrid/commandline_args2.txt deleted file mode 100644 index b880d9dc4..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_hybrid/commandline_args2.txt +++ /dev/null @@ -1,83 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_staged_data": false, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 8, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2021_02_27_23_49_27", - "model_dir": "./output/fp_ldos_dir_2021_02_27_23_49_27", - "tb_output_dir": "./output/fp_ldos_dir_2021_02_27_23_49_27/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 13, - "validation_snapshot": 12, - "num_train_snapshots": 4, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args1.txt deleted file mode 100644 index 7f25a051f..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args1.txt +++ /dev/null @@ -1,77 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_staged_data": false, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2021_02_27_16_48_02", - "model_dir": "./output/fp_ldos_dir_2021_02_27_16_48_02", - "tb_output_dir": "./output/fp_ldos_dir_2021_02_27_16_48_02/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args2.txt deleted file mode 100644 index e6561a83b..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_liquid/commandline_args2.txt +++ /dev/null @@ -1,83 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_staged_data": false, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2021_02_27_16_48_02", - "model_dir": "./output/fp_ldos_dir_2021_02_27_16_48_02", - "tb_output_dir": "./output/fp_ldos_dir_2021_02_27_16_48_02/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 5, - "validation_snapshot": 4, - "num_train_snapshots": 4, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args1.txt deleted file mode 100644 index 3d96a198c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args1.txt +++ /dev/null @@ -1,77 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_staged_data": false, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2021_02_27_20_42_47", - "model_dir": "./output/fp_ldos_dir_2021_02_27_20_42_47", - "tb_output_dir": "./output/fp_ldos_dir_2021_02_27_20_42_47/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args2.txt deleted file mode 100644 index f1787cf47..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_4snapshot_solid/commandline_args2.txt +++ /dev/null @@ -1,83 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1200, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - 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"/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args1.txt deleted file mode 100644 index 1f753f420..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 100, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - 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"../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_17_51_10", - "model_dir": "./output/fp_ldos_dir_2020_12_22_17_51_10", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_17_51_10/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args2.txt deleted file mode 100644 index f71ae4f43..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_100/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 100, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": 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"num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args1.txt deleted file mode 100644 index 2334981ea..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_07_05_07", - "model_dir": "./output/fp_ldos_dir_2020_12_23_07_05_07", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_07_05_07/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args2.txt deleted file mode 100644 index 065db249c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_1000/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_07_05_07", - "model_dir": "./output/fp_ldos_dir_2020_12_23_07_05_07", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_07_05_07/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args1.txt deleted file mode 100644 index d7df2d48b..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 2000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_09_56_29", - "model_dir": "./output/fp_ldos_dir_2020_12_23_09_56_29", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_09_56_29/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args2.txt deleted file mode 100644 index 6d04e73fc..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_2000/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 2000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_09_56_29", - "model_dir": "./output/fp_ldos_dir_2020_12_23_09_56_29", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_09_56_29/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_250/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_250/commandline_args1.txt deleted file mode 100644 index e781259e8..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_250/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 250, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - 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18361002c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_250/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 250, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - 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b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_500/commandline_args2.txt deleted file mode 100644 index cef0ad286..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_BS_500/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 500, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_04_37_51", - "model_dir": "./output/fp_ldos_dir_2020_12_23_04_37_51", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_04_37_51/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args1.txt deleted file mode 100644 index 72eef9bdc..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_15_00_09", - "model_dir": "./output/fp_ldos_dir_2020_12_23_15_00_09", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_15_00_09/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args2.txt deleted file mode 100644 index b0487d1bc..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_1/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_15_00_09", - "model_dir": "./output/fp_ldos_dir_2020_12_23_15_00_09", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_15_00_09/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args1.txt deleted file mode 100644 index 076b43ad3..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 2, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_17_24_51", - "model_dir": "./output/fp_ldos_dir_2020_12_23_17_24_51", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_17_24_51/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args2.txt deleted file mode 100644 index b581bc182..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_2/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 2, - "no_bidirection": false, - 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"num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_17_24_51", - "model_dir": "./output/fp_ldos_dir_2020_12_23_17_24_51", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_17_24_51/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args1.txt deleted file mode 100644 index bb4063619..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 3, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - 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"./output/fp_ldos_dir_2020_12_23_20_31_28/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args2.txt deleted file mode 100644 index 77f3d5a86..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_3/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - 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"tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_20_31_28", - "model_dir": "./output/fp_ldos_dir_2020_12_23_20_31_28", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_20_31_28/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args1.txt deleted file mode 100644 index cc32e7c9f..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 4, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_22_43_27", - "model_dir": "./output/fp_ldos_dir_2020_12_23_22_43_27", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_22_43_27/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args2.txt deleted file mode 100644 index 6fb281bac..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_4/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 4, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_23_22_43_27", - "model_dir": "./output/fp_ldos_dir_2020_12_23_22_43_27", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_23_22_43_27/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args1.txt deleted file mode 100644 index 1a006e2f0..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 5, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_24_02_10_13", - "model_dir": "./output/fp_ldos_dir_2020_12_24_02_10_13", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_24_02_10_13/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args2.txt deleted file mode 100644 index 0ca972729..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LIL_5/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 5, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - 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a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args1.txt deleted file mode 100644 index 32a1d6300..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-06, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": 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"num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_04_35_55", - "model_dir": "./output/fp_ldos_dir_2020_12_22_04_35_55", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_04_35_55/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args2.txt deleted file mode 100644 index aac002552..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_000001/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - 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8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args1.txt deleted file mode 100644 index 18bbfb81d..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, 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"num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_10_28_55", - "model_dir": "./output/fp_ldos_dir_2020_12_22_10_28_55", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_10_28_55/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args2.txt deleted file mode 100644 index 755701149..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_00001/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - 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"/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args1.txt deleted file mode 100644 index c9b17b340..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 0.0001, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, 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"tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_13_19_53", - "model_dir": "./output/fp_ldos_dir_2020_12_22_13_19_53", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_13_19_53/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args2.txt deleted file mode 100644 index 68dec209a..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_0001/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 0.0001, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_13_19_53", - "model_dir": "./output/fp_ldos_dir_2020_12_22_13_19_53", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_13_19_53/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args1.txt deleted file mode 100644 index b39a6dfe8..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 0.001, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_22_14_01_59", - "model_dir": "./output/fp_ldos_dir_2020_12_22_14_01_59", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_22_14_01_59/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args2.txt deleted file mode 100644 index 9b7b1d9eb..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_LR_001/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 0.001, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": 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-1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 0.01, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - 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a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_1/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 1, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - 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"no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_12_57_06", - "model_dir": "./output/fp_ldos_dir_2020_12_21_12_57_06", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_12_57_06/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_16/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_16/commandline_args2.txt deleted file mode 100644 index 7d37767c6..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_16/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 16, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_12_57_06", - "model_dir": 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b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_2/commandline_args2.txt deleted file mode 100644 index 53affdd2d..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_2/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": 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"fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_07_49_15", - "model_dir": "./output/fp_ldos_dir_2020_12_21_07_49_15", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_07_49_15/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args1.txt deleted file mode 100644 index 21ebe4793..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - 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a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args2.txt deleted file mode 100644 index 17690e7e6..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_4/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 4, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": 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/dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 8, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_10_17_33", - "model_dir": "./output/fp_ldos_dir_2020_12_21_10_17_33", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_10_17_33/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_8/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_8/commandline_args2.txt deleted file mode 100644 index d2a0412da..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_MIDLYS_8/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 8, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_10_17_33", - "model_dir": "./output/fp_ldos_dir_2020_12_21_10_17_33", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_10_17_33/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args1.txt deleted file mode 100644 index be9a6b51f..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 100, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_18_08_59", - "model_dir": "./output/fp_ldos_dir_2020_12_20_18_08_59", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_18_08_59/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args2.txt deleted file mode 100644 index fb25ca3b4..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_100/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 100, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_18_08_59", - "model_dir": "./output/fp_ldos_dir_2020_12_20_18_08_59", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_18_08_59/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args1.txt deleted file mode 100644 index 50749f3a0..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 1000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - 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"./output/fp_ldos_dir_2020_12_21_02_16_00", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_02_16_00/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args2.txt deleted file mode 100644 index f8dca5c47..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_1000/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - 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"ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_02_16_00", - "model_dir": "./output/fp_ldos_dir_2020_12_21_02_16_00", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_02_16_00/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args1.txt deleted file mode 100644 index 752808ac1..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 200, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": 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"fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_20_07_57", - "model_dir": "./output/fp_ldos_dir_2020_12_20_20_07_57", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_20_07_57/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args2.txt deleted file mode 100644 index 5ad21469e..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_200/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - 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"cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_20_07_57", - "model_dir": "./output/fp_ldos_dir_2020_12_20_20_07_57", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_20_07_57/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args1.txt deleted file mode 100644 index f454f25f8..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_22_21_56", - "model_dir": "./output/fp_ldos_dir_2020_12_20_22_21_56", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_22_21_56/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args2.txt deleted file mode 100644 index 3e83d9799..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_400/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 400, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_20_22_21_56", - "model_dir": "./output/fp_ldos_dir_2020_12_20_22_21_56", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_20_22_21_56/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args1.txt deleted file mode 100644 index b93ad685c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args1.txt +++ /dev/null @@ -1,76 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 800, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_00_52_26", - "model_dir": "./output/fp_ldos_dir_2020_12_21_00_52_26", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_00_52_26/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args2.txt deleted file mode 100644 index 632bda5a1..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/fp_ldos_dir_WID_800/commandline_args2.txt +++ /dev/null @@ -1,82 +0,0 @@ -{ - "feat_start": 0, - "feat_stop": 250, - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 5, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "model_transformer_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 800, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_12_21_00_52_26", - "model_dir": "./output/fp_ldos_dir_2020_12_21_00_52_26", - "tb_output_dir": "./output/fp_ldos_dir_2020_12_21_00_52_26/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args1.txt deleted file mode 100644 index 4c620119e..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 800, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_20_12_27_09", - "model_dir": "./output/fp_ldos_dir_2020_08_20_12_27_09", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_20_12_27_09/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args2.txt deleted file mode 100644 index e2f085ddf..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_298K_models/fp_ldos_dir_2020_08_20_12_27_09/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 1e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 800, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "298K", - "gcc": "2.699", - "num_snapshots": 3, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 16, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_20_12_27_09", - "model_dir": "./output/fp_ldos_dir_2020_08_20_12_27_09", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_20_12_27_09/tb_fp_ldos_fp_ldos_200nxyz_298Ktemp_2.699gcc", - "test_snapshot": 2, - "validation_snapshot": 1, - "num_train_snapshots": 1, - "fp_data_fpath": "/298K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/298K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args1.txt deleted file mode 100644 index 5299df98c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_21_00_30_18", - "model_dir": "./output/fp_ldos_dir_2020_08_21_00_30_18", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_21_00_30_18/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args2.txt deleted file mode 100644 index 3c3917c9c..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_00_30_18_liquid/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 0, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_21_00_30_18", - "model_dir": "./output/fp_ldos_dir_2020_08_21_00_30_18", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_21_00_30_18/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 5, - "validation_snapshot": 4, - "num_train_snapshots": 4, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args1.txt deleted file mode 100644 index bbf0465b0..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_21_10_48_28", - "model_dir": "./output/fp_ldos_dir_2020_08_21_10_48_28", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_21_10_48_28/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args2.txt deleted file mode 100644 index 69c73618e..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_21_10_48_28_solid/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_21_10_48_28", - "model_dir": "./output/fp_ldos_dir_2020_08_21_10_48_28", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_21_10_48_28/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 15, - "validation_snapshot": 14, - "num_train_snapshots": 4, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args1.txt deleted file mode 100644 index 819410938..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 8, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_23_21_19_44", - "model_dir": "./output/fp_ldos_dir_2020_08_23_21_19_44", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_23_21_19_44/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args2.txt deleted file mode 100644 index 227e9bf6d..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_4models/fp_ldos_dir_2020_08_23_21_19_44_hybrid/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 6, - "offset_snapshot": 8, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_23_21_19_44", - "model_dir": "./output/fp_ldos_dir_2020_08_23_21_19_44", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_23_21_19_44/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 13, - "validation_snapshot": 12, - "num_train_snapshots": 4, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args1.txt deleted file mode 100644 index 2e98558e2..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args1.txt +++ /dev/null @@ -1,73 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_07_16_10_11_51", - "model_dir": "./output/fp_ldos_dir_2020_07_16_10_11_51", - "tb_output_dir": "./output/fp_ldos_dir_2020_07_16_10_11_51/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args2.txt deleted file mode 100644 index 523f5039f..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_07_16_10_11_51_liquid/commandline_args2.txt +++ /dev/null @@ -1,79 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_07_16_10_11_51", - "model_dir": "./output/fp_ldos_dir_2020_07_16_10_11_51", - "tb_output_dir": "./output/fp_ldos_dir_2020_07_16_10_11_51/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 9, - "validation_snapshot": 8, - "num_train_snapshots": 8, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args1.txt deleted file mode 100644 index 048565196..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "offset_snapshot": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_05_00_39_11", - "model_dir": "./output/fp_ldos_dir_2020_08_05_00_39_11", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_05_00_39_11/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args2.txt deleted file mode 100644 index 1790a9e37..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_00_39_11_solid/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "offset_snapshot": 10, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_05_00_39_11", - "model_dir": "./output/fp_ldos_dir_2020_08_05_00_39_11", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_05_00_39_11/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 19, - "validation_snapshot": 18, - "num_train_snapshots": 8, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args1.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args1.txt deleted file mode 100644 index e60b2dc93..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args1.txt +++ /dev/null @@ -1,74 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 94, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "offset_snapshot": 6, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_05_17_07_59", - "model_dir": "./output/fp_ldos_dir_2020_08_05_17_07_59", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_05_17_07_59/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc" -} \ No newline at end of file diff --git a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args2.txt b/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args2.txt deleted file mode 100644 index fdc5821f7..000000000 --- a/ml-dft-sandia/networks/models/fp_ldos_feedforward/output/paper_933K_models/fp_ldos_dir_2020_08_05_17_07_59_hybrid/commandline_args2.txt +++ /dev/null @@ -1,80 +0,0 @@ -{ - "batch_size": 1000, - "test_batch_size": 4000, - "train_test_split": 0.8, - "epochs": 5000, - "adam": true, - "early_patience": 8, - "optim_patience": 4, - "early_stopping": 0.99999, - "lr": 5e-05, - "momentum": 0.5, - "grad_clip": 0.25, - "model_lstm_network": false, - "model_gnn_network": false, - "stacked_auto": false, - "deep_auto": false, - "skip_connection": false, - "gru": false, - "ff_mid_layers": 2, - "ff_width": 4000, - "ae_factor": 0.8, - "lstm_in_length": 1, - "no_bidirection": false, - "no_hidden_state": false, - "nxyz": 200, - "fp_length": 91, - "ldos_length": 250, - "no_coords": true, - "no_bispectrum": false, - "calc_training_norm_only": true, - "power_spectrum_only": false, - "fp_row_scaling": true, - "ldos_row_scaling": false, - "fp_norm_scaling": false, - "ldos_norm_scaling": true, - "fp_max_only": false, - "ldos_max_only": true, - "fp_standard_scaling": true, - "ldos_standard_scaling": false, - "fp_log": false, - "ldos_log": false, - "dataset": "fp_ldos", - "big_data": false, - "big_charm_data": true, - "big_clustered_data": false, - "material": "Al", - "temp": "933K", - "gcc": "2.699", - "num_snapshots": 10, - "offset_snapshot": 6, - "no_testing": true, - "water": false, - "cluster_train_ratio": 0.25, - "cluster_sample_ratio": 0.1, - "num_clusters": 400, - "fp_dir": "../../training_data/fp_data", - "ldos_dir": "../../training_data/ldos_data", - "output_dir": "./output", - "tb_ldos_comparisons": 4, - "no_cuda": false, - "no_pinned_memory": true, - "num_data_workers": 1, - "num_test_workers": 16, - "num_threads": 32, - "num_gpus": 1, - "save_training_data": false, - "seed": 42, - "log_interval": 10, - "fp16_allreduce": false, - "cuda": true, - "timestamp": "2020_08_05_17_07_59", - "model_dir": "./output/fp_ldos_dir_2020_08_05_17_07_59", - "tb_output_dir": "./output/fp_ldos_dir_2020_08_05_17_07_59/tb_fp_ldos_fp_ldos_200nxyz_933Ktemp_2.699gcc", - "test_snapshot": 15, - "validation_snapshot": 14, - "num_train_snapshots": 8, - "fp_data_fpath": "/933K/2.699gcc/Al_fp_200x200x200grid_94comps", - "ldos_data_fpath": "/933K/2.699gcc/Al_ldos_200x200x200grid_250elvls", - "grid_pts": 8000000 -} \ No newline at end of file From daf70763272f3547c520994b83163742538c6cb1 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 25 Jun 2021 21:46:18 +0200 Subject: [PATCH 120/297] Update cpu-tests.yml --- .github/workflows/cpu-tests.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 61e858d3e..9c7af3020 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -77,11 +77,11 @@ jobs: - name: Check out repository (data) uses: actions/checkout@v2 with: - repository: mala-project/data + repository: mala-project/test-data token: ${{ secrets.ACCESS_TOKEN }} path: mala_data ref: v0.1.0 - lfs: true + lfs: false - name: Data setup shell: bash From 7e862da70e16dbb5b49d4eee9eea4475b0bddb9a Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 25 Jun 2021 22:00:23 +0200 Subject: [PATCH 121/297] Update cpu-tests.yml --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 9c7af3020..2ab752062 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -51,7 +51,7 @@ jobs: [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') # Use Docker `latest` tag convention - [ "$VERSION" == "docker_ci" ] && VERSION=latest + [ "$VERSION" == "develop" ] && VERSION=latest echo IMAGE_ID=$IMAGE_ID echo VERSION=$VERSION From 04f89c6a4262ea620f47575e9024c15abe86631a Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 25 Jun 2021 22:27:46 +0200 Subject: [PATCH 122/297] FIx CPU badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 41504e1e7..da876fae3 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # MALA -[![CPU](https://github.com/DanielKotik/mala/actions/workflows/cpu-tests.yml/badge.svg)](https://github.com/DanielKotik/mala/actions/workflows/cpu-tests.yml) +[![CPU](https://github.com/mala-project/mala/actions/workflows/cpu-tests.yml/badge.svg)](https://github.com/mala-project/mala/actions/workflows/cpu-tests.yml) [![image](https://github.com/mala-project/mala/actions/workflows/gh-pages.yml/badge.svg)](https://mala-project.github.io/mala/) [![image](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) From bee6f1c4b31fe7f96a0c99a74dc34cbec115aa44 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sat, 26 Jun 2021 23:42:39 +0200 Subject: [PATCH 123/297] Sort automatically documented class members groupwise rather than alphabetical --- docs/source/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0c3d704fa..9f000a19c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -63,6 +63,8 @@ 'total_energy', ] +autodoc_member_order = 'groupwise' + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From c8070d4e230f1599fc97172492df085822540391 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Sat, 26 Jun 2021 23:44:30 +0200 Subject: [PATCH 124/297] Turn sphinx warnings into errors: This change lets the pipeline fail on rST parsing errors. --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 78ddf3836..2407c3cf3 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -52,7 +52,7 @@ jobs: - name: Build API and docs run: | sphinx-apidoc -o docs/source/api mala - sphinx-build -b html -d docs/_build/doctrees docs/source docs/_build/html + sphinx-build -W --keep-going -b html -d docs/_build/doctrees docs/source docs/_build/html mv -v docs/_build/html public - name: Deploy From a3e97777d2dfb6adeab1ba1156aeb5ab473288e8 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Tue, 29 Jun 2021 13:21:57 +0200 Subject: [PATCH 125/297] Add version tag to documentation: The command `git describe --tags` finds the most recent tag and returns also the number of commits on top of the found tag and the commit hash of the current commit. This links the renderd documententaion with the codebase it actually reflects. If there are no tags, or, if we are not in a git repository, `result.stdout.strip()` is an empty string. --- .github/workflows/gh-pages.yml | 2 ++ docs/source/conf.py | 7 +++++++ 2 files changed, 9 insertions(+) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 2407c3cf3..863d0ccae 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -36,6 +36,8 @@ jobs: steps: - name: Check out repository uses: actions/checkout@v2 + with: + fetch-depth: 0 # 0 fetches complete history and tags - name: Setup Python uses: actions/setup-python@v2 diff --git a/docs/source/conf.py b/docs/source/conf.py index 9f000a19c..048287a83 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -11,6 +11,7 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. # import os +import subprocess import sys # sys.path.insert(0, os.path.abspath('.')) sys.path.insert(0, os.path.abspath('../../')) @@ -28,6 +29,12 @@ author = 'Attila Cangi, J. Austin Ellis, Lenz Fiedler, Daniel Kotik, ' \ 'Normand Modine, Sivasankaran Rajamanickam, Steve Schmerler, Aidan Thompson' +# The version info for the project +tag = subprocess.run(['git', 'describe', '--tags'], capture_output=True, + text=True) +version = tag.stdout.strip() + + # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be From 2d9f95b6aa20cdce4585cf4a949cbcf018a6b4a6 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Tue, 29 Jun 2021 12:02:41 +0200 Subject: [PATCH 126/297] Add hyperlinks for SNL and CASUS --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index da876fae3..5087d0ad4 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,8 @@ the examples in the `example/` folder. ## Institutions ### Founding Institutions -- Sandia National Laboratories (SNL), USA. -- Center for Advanced Systems Understanding (CASUS), Germany. +- [Sandia National Laboratories](https://www.sandia.gov/) (SNL), USA. +- [Center for Advanced Systems Understanding](https://www.casus.science/) (CASUS), Germany. ## Developers ### Scientific Supervision From f16386bc2558e1c34d9bb9a94a744557f766baa8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 11 Jun 2021 11:12:38 +0200 Subject: [PATCH 127/297] Deleted total energy module --- docs/source/install/INSTALL_TE_QE.rst | 2 +- .../build_total_energy_module_gnu.sh | 27 -- .../total_energy_env_gnu.sh | 8 - .../total_energy_module/x_ac_qe_f90.m4.patch | 17 - ml-dft-sandia/total_energy_module/Al.scf.pw | 296 ------------------ ml-dft-sandia/total_energy_module/README.md | 83 ----- .../total_energy_module/blake_qe_env.sh | 15 - .../total_energy_module/blake_qe_env_gnu.sh | 9 - .../build_total_energy_module.sh | 24 -- .../build_total_energy_module_gnu.sh | 27 -- .../total_energy_module/test_total_energy.py | 27 -- .../total_energy_module/x_ac_qe_f90.m4.patch | 17 - 12 files changed, 1 insertion(+), 551 deletions(-) delete mode 100755 install/total_energy_module/build_total_energy_module_gnu.sh delete mode 100644 install/total_energy_module/total_energy_env_gnu.sh delete mode 100644 install/total_energy_module/x_ac_qe_f90.m4.patch delete mode 100644 ml-dft-sandia/total_energy_module/Al.scf.pw delete mode 100644 ml-dft-sandia/total_energy_module/README.md delete mode 100644 ml-dft-sandia/total_energy_module/blake_qe_env.sh delete mode 100644 ml-dft-sandia/total_energy_module/blake_qe_env_gnu.sh delete mode 100755 ml-dft-sandia/total_energy_module/build_total_energy_module.sh delete mode 100755 ml-dft-sandia/total_energy_module/build_total_energy_module_gnu.sh delete mode 100644 ml-dft-sandia/total_energy_module/test_total_energy.py delete mode 100644 ml-dft-sandia/total_energy_module/x_ac_qe_f90.m4.patch diff --git a/docs/source/install/INSTALL_TE_QE.rst b/docs/source/install/INSTALL_TE_QE.rst index 78cd373fa..f7353cbb3 100644 --- a/docs/source/install/INSTALL_TE_QE.rst +++ b/docs/source/install/INSTALL_TE_QE.rst @@ -1,7 +1,7 @@ Python bindings to Quantum Espresso ==================================================== -The total energy module has been moved to it's own fork of QE. +The total energy module has been moved to its own fork of QE. You can find it at https://gitlab.com/casus/q-e/-/tree/tem_original_development after gaining access from the MALA development team. After checking out this diff --git a/install/total_energy_module/build_total_energy_module_gnu.sh b/install/total_energy_module/build_total_energy_module_gnu.sh deleted file mode 100755 index c8b6f4c0f..000000000 --- a/install/total_energy_module/build_total_energy_module_gnu.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# Before running this script, make sure the mlmm_env has been activated - -# Object files from the PW/src folder -PWOBJS="a2fmod.o add_bfield.o add_efield.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_nlpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o bp_mod.o bp_c_phase.o bp_calc_btq.o bp_qvan3.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o deriv_drhoc.o divide_class.o divide_class_so.o divide_et_impera.o dqvan2.o drhoc.o rotate_wfc.o run_driver.o dvloc_of_g.o dynamics_module.o efermig.o efermit.o electrons.o eqvect.o esm.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gen_at_dj.o gen_at_dy.o gen_us_dj.o gen_us_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o init_ns.o init_q_aeps.o init_run.o init_us_0.o init_us_b0.o init_us_1.o init_us_2.o init_at_1.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o makov_payne.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o qvan2.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_rhoc.o set_vrs.o setlocal.o setup.o spinor.o sph_ind.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tabd.o tetra.o transform_becsum_so.o transform_becsum_nc.o transform_qq_so.o trnvecc.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_of_g.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_proj.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o wannier_enrg.o" - -# Object files from the UtilXlib folder -UTILOBJS="../../UtilXlib/clocks_handler.o ../../UtilXlib/cuda_util.o ../../UtilXlib/divide.o ../../UtilXlib/data_buffer.o ../../UtilXlib/error_handler.o ../../UtilXlib/find_free_unit.o ../../UtilXlib/fletcher32_mod.o ../../UtilXlib/mem_counter.o ../../UtilXlib/mp.o ../../UtilXlib/mp_base.o ../../UtilXlib/mp_base_gpu.o ../../UtilXlib/mp_bands_util.o ../../UtilXlib/parallel_include.o ../../UtilXlib/util_param.o ../../UtilXlib/thread_util.o" - -# Object files from the Modules folder -MODOBJS="../../Modules/atomic_number.o ../../Modules/capital.o ../../Modules/cryst_to_car.o ../../Modules/erf.o ../../Modules/expint.o ../../Modules/functionals.o ../../Modules/generate_k_along_lines.o ../../Modules/lsda_functionals.o ../../Modules/more_functionals.o ../../Modules/has_xml.o ../../Modules/inpfile.o ../../Modules/int_to_char.o ../../Modules/latgen.o ../../Modules/linpack.o ../../Modules/metagga.o ../../Modules/matches.o ../../Modules/plot_io.o ../../Modules/radial_gradients.o ../../Modules/rgen.o ../../Modules/recips.o ../../Modules/remove_tot_torque.o ../../Modules/set_hubbard_l.o ../../Modules/set_hubbard_n.o ../../Modules/simpsn.o ../../Modules/sort.o ../../Modules/sph_bes.o ../../Modules/sph_dbes.o ../../Modules/trimcheck.o ../../Modules/test_input_file.o ../../Modules/date_and_tim.o ../../Modules/volume.o ../../Modules/dylmr2.o ../../Modules/ylmr2.o ../../Modules/wgauss.o ../../Modules/w0gauss.o ../../Modules/w1gauss.o ../../Modules/deviatoric.o" - -MODOBJS2="../../Modules/atom.o ../../Modules/autopilot.o ../../Modules/basic_algebra_routines.o ../../Modules/becmod.o ../../Modules/bfgs_module.o ../../Modules/bspline.o ../../Modules/bz_form.o ../../Modules/cell_base.o ../../Modules/check_stop.o ../../Modules/command_line_options.o ../../Modules/compute_dipole.o ../../Modules/constants.o ../../Modules/constraints_module.o ../../Modules/control_flags.o ../../Modules/coulomb_vcut.o ../../Modules/dist.o ../../Modules/electrons_base.o ../../Modules/environment.o ../../Modules/fcp_variables.o ../../Modules/fd_gradient.o ../../Modules/fft_base.o ../../Modules/fft_rho.o ../../Modules/fsockets.o ../../Modules/funct.o ../../Modules/generate_function.o ../../Modules/gth.o ../../Modules/gradutils.o ../../Modules/gvecw.o ../../Modules/input_parameters.o ../../Modules/invmat.o ../../Modules/io_files.o ../../Modules/io_global.o ../../Modules/ions_base.o ../../Modules/kernel_table.o ../../Modules/kind.o ../../Modules/libxc.o ../../Modules/mdiis.o ../../Modules/mm_dispersion.o ../../Modules/mp_bands.o ../../Modules/mp_exx.o ../../Modules/mp_global.o ../../Modules/mp_images.o ../../Modules/mp_pools.o ../../Modules/mp_wave.o ../../Modules/mp_world.o ../../Modules/noncol.o ../../Modules/open_close_input_file.o ../../Modules/parameters.o ../../Modules/parser.o ../../Modules/paw_variables.o ../../Modules/plugin_flags.o ../../Modules/plugin_arguments.o ../../Modules/plugin_variables.o ../../Modules/pseudo_types.o ../../Modules/pw_dot.o ../../Modules/qmmm.o ../../Modules/radial_grids.o ../../Modules/random_numbers.o ../../Modules/read_cards.o ../../Modules/read_input.o ../../Modules/read_namelists.o ../../Modules/read_ncpp.o ../../Modules/read_pseudo.o ../../Modules/read_upf_v1.o ../../Modules/read_upf_v2.o ../../Modules/read_upf_schema.o ../../Modules/read_uspp.o ../../Modules/recvec.o ../../Modules/recvec_subs.o ../../Modules/run_info.o ../../Modules/space_group.o ../../Modules/set_signal.o ../../Modules/setqf.o ../../Modules/splinelib.o ../../Modules/timestep.o ../../Modules/tsvdw.o ../../Modules/emend_upf.o ../../Modules/upf.o ../../Modules/upf_to_internal.o ../../Modules/uspp.o ../../Modules/version.o ../../Modules/wannier_gw.o ../../Modules/wannier_new.o ../../Modules/wave_base.o ../../Modules/wavefunctions.o ../../Modules/wrappers.o ../../Modules/write_upf.o ../../Modules/write_upf_v2.o ../../Modules/write_upf_schema.o ../../Modules/ws_base.o ../../Modules/xc_vdW_DF.o ../../Modules/xc_rVV10.o ../../Modules/io_base.o ../../Modules/qes_types_module.o ../../Modules/qes_libs_module.o ../../Modules/qes_write_module.o ../../Modules/qes_reset_module.o ../../Modules/qes_init_module.o ../../Modules/qes_read_module.o ../../Modules/qes_bcast_module.o ../../Modules/qexsd.o ../../Modules/qexsd_input.o ../../Modules/hdf5_qe.o ../../Modules/qeh5_module.o ../../Modules/fox_init_module.o ../../Modules/xsf.o ../../Modules/wyckoff.o ../../Modules/wypos.o ../../Modules/zdotc_wrapper.o ../../Modules/zvscal.o" - -# Libraries from the external FoX project -FOXLIBS="-L../../FoX/lib -lFoX_dom -lFoX_utils -lFoX_wcml -lFoX_wkml -lFoX_wxml -lFoX_common -lFoX_sax -lFoX_common -lFoX_fsys" -PROJECT_LIB_FOLDERS=" -L../../Modules -L../../KS_Solvers -L../../FFTXlib -L../../LAXlib -L../../UtilXlib -L../../dft-d3" -PROJECT_LIBS="-lqemod -lks_solvers -lqefft -lqela -lutil -ldftd3qe" -PROJECT_INC_FOLDERS="-I../../Modules -I../../FFTXlib -I../../LAXlib -I../../KS_Solvers -I../../UtilXlib" - - -LINALGOBJS="../../lapack-3.6.1/SRC/cbbcsd.o ../../lapack-3.6.1/SRC/cbdsqr.o ../../lapack-3.6.1/SRC/cgbbrd.o ../../lapack-3.6.1/SRC/cgbcon.o ../../lapack-3.6.1/SRC/cgbequb.o ../../lapack-3.6.1/SRC/cgbequ.o ../../lapack-3.6.1/SRC/cgbrfs.o ../../lapack-3.6.1/SRC/cgbsv.o ../../lapack-3.6.1/SRC/cgbsvx.o ../../lapack-3.6.1/SRC/cgbtf2.o ../../lapack-3.6.1/SRC/cgbtrf.o ../../lapack-3.6.1/SRC/cgbtrs.o ../../lapack-3.6.1/SRC/cgebak.o ../../lapack-3.6.1/SRC/cgebal.o ../../lapack-3.6.1/SRC/cgebd2.o ../../lapack-3.6.1/SRC/cgebrd.o ../../lapack-3.6.1/SRC/cgecon.o ../../lapack-3.6.1/SRC/cgeequb.o ../../lapack-3.6.1/SRC/cgeequ.o ../../lapack-3.6.1/SRC/cgees.o ../../lapack-3.6.1/SRC/cgeesx.o ../../lapack-3.6.1/SRC/cgeev.o ../../lapack-3.6.1/SRC/cgeevx.o ../../lapack-3.6.1/SRC/cgehd2.o ../../lapack-3.6.1/SRC/cgehrd.o ../../lapack-3.6.1/SRC/cgejsv.o ../../lapack-3.6.1/SRC/cgelq2.o ../../lapack-3.6.1/SRC/cgelqf.o ../../lapack-3.6.1/SRC/cgelsd.o ../../lapack-3.6.1/SRC/cgels.o ../../lapack-3.6.1/SRC/cgelss.o ../../lapack-3.6.1/SRC/cgelsy.o ../../lapack-3.6.1/SRC/cgemqrt.o ../../lapack-3.6.1/SRC/cgeql2.o ../../lapack-3.6.1/SRC/cgeqlf.o ../../lapack-3.6.1/SRC/cgeqp3.o ../../lapack-3.6.1/SRC/cgeqr2.o ../../lapack-3.6.1/SRC/cgeqr2p.o ../../lapack-3.6.1/SRC/cgeqrf.o ../../lapack-3.6.1/SRC/cgeqrfp.o ../../lapack-3.6.1/SRC/cgeqrt2.o ../../lapack-3.6.1/SRC/cgeqrt3.o ../../lapack-3.6.1/SRC/cgeqrt.o ../../lapack-3.6.1/SRC/cgerfs.o ../../lapack-3.6.1/SRC/cgerq2.o ../../lapack-3.6.1/SRC/cgerqf.o ../../lapack-3.6.1/SRC/cgesc2.o ../../lapack-3.6.1/SRC/cgesdd.o ../../lapack-3.6.1/SRC/cgesvd.o ../../lapack-3.6.1/SRC/cgesvdx.o ../../lapack-3.6.1/SRC/cgesvj.o ../../lapack-3.6.1/SRC/cgesv.o ../../lapack-3.6.1/SRC/cgesvx.o ../../lapack-3.6.1/SRC/cgetc2.o ../../lapack-3.6.1/SRC/cgetf2.o ../../lapack-3.6.1/SRC/cgetrf2.o ../../lapack-3.6.1/SRC/cgetrf.o ../../lapack-3.6.1/SRC/cgetri.o ../../lapack-3.6.1/SRC/cgetrs.o ../../lapack-3.6.1/SRC/cggbak.o ../../lapack-3.6.1/SRC/cggbal.o ../../lapack-3.6.1/SRC/cgges3.o ../../lapack-3.6.1/SRC/cgges.o ../../lapack-3.6.1/SRC/cggesx.o ../../lapack-3.6.1/SRC/cggev3.o ../../lapack-3.6.1/SRC/cggev.o ../../lapack-3.6.1/SRC/cggevx.o ../../lapack-3.6.1/SRC/cggglm.o ../../lapack-3.6.1/SRC/cgghd3.o ../../lapack-3.6.1/SRC/cgghrd.o ../../lapack-3.6.1/SRC/cgglse.o ../../lapack-3.6.1/SRC/cggqrf.o ../../lapack-3.6.1/SRC/cggrqf.o ../../lapack-3.6.1/SRC/cggsvd3.o ../../lapack-3.6.1/SRC/cggsvp3.o ../../lapack-3.6.1/SRC/cgsvj0.o ../../lapack-3.6.1/SRC/cgsvj1.o ../../lapack-3.6.1/SRC/cgtcon.o ../../lapack-3.6.1/SRC/cgtrfs.o ../../lapack-3.6.1/SRC/cgtsv.o ../../lapack-3.6.1/SRC/cgtsvx.o ../../lapack-3.6.1/SRC/cgttrf.o ../../lapack-3.6.1/SRC/cgttrs.o ../../lapack-3.6.1/SRC/cgtts2.o ../../lapack-3.6.1/SRC/chbevd.o ../../lapack-3.6.1/SRC/chbev.o ../../lapack-3.6.1/SRC/chbevx.o ../../lapack-3.6.1/SRC/chbgst.o ../../lapack-3.6.1/SRC/chbgvd.o ../../lapack-3.6.1/SRC/chbgv.o ../../lapack-3.6.1/SRC/chbgvx.o ../../lapack-3.6.1/SRC/chbtrd.o ../../lapack-3.6.1/SRC/checon.o ../../lapack-3.6.1/SRC/checon_rook.o ../../lapack-3.6.1/SRC/cheequb.o ../../lapack-3.6.1/SRC/cheevd.o ../../lapack-3.6.1/SRC/cheev.o ../../lapack-3.6.1/SRC/cheevr.o ../../lapack-3.6.1/SRC/cheevx.o ../../lapack-3.6.1/SRC/chegs2.o ../../lapack-3.6.1/SRC/chegst.o ../../lapack-3.6.1/SRC/chegvd.o ../../lapack-3.6.1/SRC/chegv.o ../../lapack-3.6.1/SRC/chegvx.o ../../lapack-3.6.1/SRC/cherfs.o ../../lapack-3.6.1/SRC/chesv.o ../../lapack-3.6.1/SRC/chesv_rook.o ../../lapack-3.6.1/SRC/chesvx.o ../../lapack-3.6.1/SRC/cheswapr.o ../../lapack-3.6.1/SRC/chetd2.o ../../lapack-3.6.1/SRC/chetf2.o ../../lapack-3.6.1/SRC/chetf2_rook.o ../../lapack-3.6.1/SRC/chetrd.o ../../lapack-3.6.1/SRC/chetrf.o ../../lapack-3.6.1/SRC/chetrf_rook.o ../../lapack-3.6.1/SRC/chetri2.o ../../lapack-3.6.1/SRC/chetri2x.o ../../lapack-3.6.1/SRC/chetri.o ../../lapack-3.6.1/SRC/chetri_rook.o ../../lapack-3.6.1/SRC/chetrs2.o ../../lapack-3.6.1/SRC/chetrs.o ../../lapack-3.6.1/SRC/chetrs_rook.o ../../lapack-3.6.1/SRC/chfrk.o ../../lapack-3.6.1/SRC/chgeqz.o ../../lapack-3.6.1/SRC/chla_transtype.o ../../lapack-3.6.1/SRC/chpcon.o ../../lapack-3.6.1/SRC/chpevd.o ../../lapack-3.6.1/SRC/chpev.o ../../lapack-3.6.1/SRC/chpevx.o ../../lapack-3.6.1/SRC/chpgst.o ../../lapack-3.6.1/SRC/chpgvd.o ../../lapack-3.6.1/SRC/chpgv.o ../../lapack-3.6.1/SRC/chpgvx.o ../../lapack-3.6.1/SRC/chprfs.o ../../lapack-3.6.1/SRC/chpsv.o ../../lapack-3.6.1/SRC/chpsvx.o ../../lapack-3.6.1/SRC/chptrd.o ../../lapack-3.6.1/SRC/chptrf.o ../../lapack-3.6.1/SRC/chptri.o ../../lapack-3.6.1/SRC/chptrs.o ../../lapack-3.6.1/SRC/chsein.o ../../lapack-3.6.1/SRC/chseqr.o ../../lapack-3.6.1/SRC/clabrd.o ../../lapack-3.6.1/SRC/clacgv.o ../../lapack-3.6.1/SRC/clacn2.o ../../lapack-3.6.1/SRC/clacon.o ../../lapack-3.6.1/SRC/clacp2.o ../../lapack-3.6.1/SRC/clacpy.o ../../lapack-3.6.1/SRC/clacrm.o ../../lapack-3.6.1/SRC/clacrt.o ../../lapack-3.6.1/SRC/cladiv.o ../../lapack-3.6.1/SRC/claed0.o ../../lapack-3.6.1/SRC/claed7.o ../../lapack-3.6.1/SRC/claed8.o ../../lapack-3.6.1/SRC/claein.o ../../lapack-3.6.1/SRC/claesy.o ../../lapack-3.6.1/SRC/claev2.o ../../lapack-3.6.1/SRC/clag2z.o ../../lapack-3.6.1/SRC/clags2.o ../../lapack-3.6.1/SRC/clagtm.o ../../lapack-3.6.1/SRC/clahef.o ../../lapack-3.6.1/SRC/clahef_rook.o ../../lapack-3.6.1/SRC/clahqr.o ../../lapack-3.6.1/SRC/clahr2.o ../../lapack-3.6.1/SRC/claic1.o ../../lapack-3.6.1/SRC/clals0.o ../../lapack-3.6.1/SRC/clalsa.o ../../lapack-3.6.1/SRC/clalsd.o ../../lapack-3.6.1/SRC/clangb.o ../../lapack-3.6.1/SRC/clange.o ../../lapack-3.6.1/SRC/clangt.o ../../lapack-3.6.1/SRC/clanhb.o ../../lapack-3.6.1/SRC/clanhe.o ../../lapack-3.6.1/SRC/clanhf.o ../../lapack-3.6.1/SRC/clanhp.o ../../lapack-3.6.1/SRC/clanhs.o ../../lapack-3.6.1/SRC/clanht.o ../../lapack-3.6.1/SRC/clansb.o ../../lapack-3.6.1/SRC/clansp.o ../../lapack-3.6.1/SRC/clansy.o ../../lapack-3.6.1/SRC/clantb.o ../../lapack-3.6.1/SRC/clantp.o ../../lapack-3.6.1/SRC/clantr.o ../../lapack-3.6.1/SRC/clapll.o ../../lapack-3.6.1/SRC/clapmr.o ../../lapack-3.6.1/SRC/clapmt.o ../../lapack-3.6.1/SRC/claqgb.o ../../lapack-3.6.1/SRC/claqge.o ../../lapack-3.6.1/SRC/claqhb.o ../../lapack-3.6.1/SRC/claqhe.o ../../lapack-3.6.1/SRC/claqhp.o ../../lapack-3.6.1/SRC/claqp2.o ../../lapack-3.6.1/SRC/claqps.o ../../lapack-3.6.1/SRC/claqr0.o ../../lapack-3.6.1/SRC/claqr1.o ../../lapack-3.6.1/SRC/claqr2.o ../../lapack-3.6.1/SRC/claqr3.o ../../lapack-3.6.1/SRC/claqr4.o ../../lapack-3.6.1/SRC/claqr5.o ../../lapack-3.6.1/SRC/claqsb.o ../../lapack-3.6.1/SRC/claqsp.o ../../lapack-3.6.1/SRC/claqsy.o ../../lapack-3.6.1/SRC/clar1v.o ../../lapack-3.6.1/SRC/clar2v.o ../../lapack-3.6.1/SRC/clarcm.o ../../lapack-3.6.1/SRC/clarfb.o ../../lapack-3.6.1/SRC/clarfg.o ../../lapack-3.6.1/SRC/clarfgp.o ../../lapack-3.6.1/SRC/clarf.o ../../lapack-3.6.1/SRC/clarft.o ../../lapack-3.6.1/SRC/clarfx.o ../../lapack-3.6.1/SRC/clargv.o ../../lapack-3.6.1/SRC/clarnv.o ../../lapack-3.6.1/SRC/clarrv.o ../../lapack-3.6.1/SRC/clartg.o ../../lapack-3.6.1/SRC/clartv.o ../../lapack-3.6.1/SRC/clarzb.o ../../lapack-3.6.1/SRC/clarz.o ../../lapack-3.6.1/SRC/clarzt.o ../../lapack-3.6.1/SRC/clascl.o ../../lapack-3.6.1/SRC/claset.o ../../lapack-3.6.1/SRC/clasr.o ../../lapack-3.6.1/SRC/classq.o ../../lapack-3.6.1/SRC/claswp.o ../../lapack-3.6.1/SRC/clasyf.o ../../lapack-3.6.1/SRC/clasyf_rook.o ../../lapack-3.6.1/SRC/clatbs.o ../../lapack-3.6.1/SRC/clatdf.o ../../lapack-3.6.1/SRC/clatps.o ../../lapack-3.6.1/SRC/clatrd.o ../../lapack-3.6.1/SRC/clatrs.o ../../lapack-3.6.1/SRC/clatrz.o ../../lapack-3.6.1/SRC/clauu2.o ../../lapack-3.6.1/SRC/clauum.o ../../lapack-3.6.1/SRC/cpbcon.o ../../lapack-3.6.1/SRC/cpbequ.o ../../lapack-3.6.1/SRC/cpbrfs.o ../../lapack-3.6.1/SRC/cpbstf.o ../../lapack-3.6.1/SRC/cpbsv.o ../../lapack-3.6.1/SRC/cpbsvx.o ../../lapack-3.6.1/SRC/cpbtf2.o ../../lapack-3.6.1/SRC/cpbtrf.o ../../lapack-3.6.1/SRC/cpbtrs.o ../../lapack-3.6.1/SRC/cpftrf.o ../../lapack-3.6.1/SRC/cpftri.o ../../lapack-3.6.1/SRC/cpftrs.o ../../lapack-3.6.1/SRC/cpocon.o ../../lapack-3.6.1/SRC/cpoequb.o ../../lapack-3.6.1/SRC/cpoequ.o ../../lapack-3.6.1/SRC/cporfs.o ../../lapack-3.6.1/SRC/cposv.o ../../lapack-3.6.1/SRC/cposvx.o ../../lapack-3.6.1/SRC/cpotf2.o ../../lapack-3.6.1/SRC/cpotrf2.o ../../lapack-3.6.1/SRC/cpotrf.o ../../lapack-3.6.1/SRC/cpotri.o ../../lapack-3.6.1/SRC/cpotrs.o ../../lapack-3.6.1/SRC/cppcon.o ../../lapack-3.6.1/SRC/cppequ.o ../../lapack-3.6.1/SRC/cpprfs.o ../../lapack-3.6.1/SRC/cppsv.o ../../lapack-3.6.1/SRC/cppsvx.o ../../lapack-3.6.1/SRC/cpptrf.o ../../lapack-3.6.1/SRC/cpptri.o ../../lapack-3.6.1/SRC/cpptrs.o ../../lapack-3.6.1/SRC/cpstf2.o ../../lapack-3.6.1/SRC/cpstrf.o ../../lapack-3.6.1/SRC/cptcon.o ../../lapack-3.6.1/SRC/cpteqr.o ../../lapack-3.6.1/SRC/cptrfs.o ../../lapack-3.6.1/SRC/cptsv.o ../../lapack-3.6.1/SRC/cptsvx.o ../../lapack-3.6.1/SRC/cpttrf.o ../../lapack-3.6.1/SRC/cpttrs.o ../../lapack-3.6.1/SRC/cptts2.o ../../lapack-3.6.1/SRC/crot.o ../../lapack-3.6.1/SRC/cspcon.o ../../lapack-3.6.1/SRC/cspmv.o ../../lapack-3.6.1/SRC/csprfs.o ../../lapack-3.6.1/SRC/cspr.o ../../lapack-3.6.1/SRC/cspsv.o ../../lapack-3.6.1/SRC/cspsvx.o ../../lapack-3.6.1/SRC/csptrf.o ../../lapack-3.6.1/SRC/csptri.o ../../lapack-3.6.1/SRC/csptrs.o ../../lapack-3.6.1/SRC/csrscl.o ../../lapack-3.6.1/SRC/cstedc.o ../../lapack-3.6.1/SRC/cstegr.o ../../lapack-3.6.1/SRC/cstein.o ../../lapack-3.6.1/SRC/cstemr.o ../../lapack-3.6.1/SRC/csteqr.o ../../lapack-3.6.1/SRC/csycon.o ../../lapack-3.6.1/SRC/csycon_rook.o ../../lapack-3.6.1/SRC/csyconv.o ../../lapack-3.6.1/SRC/csyequb.o ../../lapack-3.6.1/SRC/csymv.o ../../lapack-3.6.1/SRC/csyrfs.o ../../lapack-3.6.1/SRC/csyr.o ../../lapack-3.6.1/SRC/csysv.o ../../lapack-3.6.1/SRC/csysv_rook.o ../../lapack-3.6.1/SRC/csysvx.o ../../lapack-3.6.1/SRC/csyswapr.o ../../lapack-3.6.1/SRC/csytf2.o ../../lapack-3.6.1/SRC/csytf2_rook.o ../../lapack-3.6.1/SRC/csytrf.o ../../lapack-3.6.1/SRC/csytrf_rook.o ../../lapack-3.6.1/SRC/csytri2.o ../../lapack-3.6.1/SRC/csytri2x.o ../../lapack-3.6.1/SRC/csytri.o ../../lapack-3.6.1/SRC/csytri_rook.o ../../lapack-3.6.1/SRC/csytrs2.o ../../lapack-3.6.1/SRC/csytrs.o ../../lapack-3.6.1/SRC/csytrs_rook.o ../../lapack-3.6.1/SRC/ctbcon.o ../../lapack-3.6.1/SRC/ctbrfs.o ../../lapack-3.6.1/SRC/ctbtrs.o ../../lapack-3.6.1/SRC/ctfsm.o ../../lapack-3.6.1/SRC/ctftri.o ../../lapack-3.6.1/SRC/ctfttp.o ../../lapack-3.6.1/SRC/ctfttr.o ../../lapack-3.6.1/SRC/ctgevc.o ../../lapack-3.6.1/SRC/ctgex2.o ../../lapack-3.6.1/SRC/ctgexc.o ../../lapack-3.6.1/SRC/ctgsen.o ../../lapack-3.6.1/SRC/ctgsja.o ../../lapack-3.6.1/SRC/ctgsna.o ../../lapack-3.6.1/SRC/ctgsy2.o ../../lapack-3.6.1/SRC/ctgsyl.o ../../lapack-3.6.1/SRC/ctpcon.o ../../lapack-3.6.1/SRC/ctpmqrt.o ../../lapack-3.6.1/SRC/ctpqrt2.o ../../lapack-3.6.1/SRC/ctpqrt.o ../../lapack-3.6.1/SRC/ctprfb.o ../../lapack-3.6.1/SRC/ctprfs.o ../../lapack-3.6.1/SRC/ctptri.o ../../lapack-3.6.1/SRC/ctptrs.o ../../lapack-3.6.1/SRC/ctpttf.o ../../lapack-3.6.1/SRC/ctpttr.o ../../lapack-3.6.1/SRC/ctrcon.o ../../lapack-3.6.1/SRC/ctrevc3.o ../../lapack-3.6.1/SRC/ctrevc.o ../../lapack-3.6.1/SRC/ctrexc.o ../../lapack-3.6.1/SRC/ctrrfs.o ../../lapack-3.6.1/SRC/ctrsen.o ../../lapack-3.6.1/SRC/ctrsna.o ../../lapack-3.6.1/SRC/ctrsyl.o ../../lapack-3.6.1/SRC/ctrti2.o ../../lapack-3.6.1/SRC/ctrtri.o ../../lapack-3.6.1/SRC/ctrtrs.o ../../lapack-3.6.1/SRC/ctrttf.o ../../lapack-3.6.1/SRC/ctrttp.o ../../lapack-3.6.1/SRC/ctzrzf.o ../../lapack-3.6.1/SRC/cunbdb1.o ../../lapack-3.6.1/SRC/cunbdb2.o ../../lapack-3.6.1/SRC/cunbdb3.o ../../lapack-3.6.1/SRC/cunbdb4.o ../../lapack-3.6.1/SRC/cunbdb5.o ../../lapack-3.6.1/SRC/cunbdb6.o ../../lapack-3.6.1/SRC/cunbdb.o ../../lapack-3.6.1/SRC/cuncsd2by1.o ../../lapack-3.6.1/SRC/cuncsd.o ../../lapack-3.6.1/SRC/cung2l.o ../../lapack-3.6.1/SRC/cung2r.o ../../lapack-3.6.1/SRC/cungbr.o ../../lapack-3.6.1/SRC/cunghr.o ../../lapack-3.6.1/SRC/cungl2.o ../../lapack-3.6.1/SRC/cunglq.o ../../lapack-3.6.1/SRC/cungql.o ../../lapack-3.6.1/SRC/cungqr.o ../../lapack-3.6.1/SRC/cungr2.o ../../lapack-3.6.1/SRC/cungrq.o ../../lapack-3.6.1/SRC/cungtr.o ../../lapack-3.6.1/SRC/cunm22.o ../../lapack-3.6.1/SRC/cunm2l.o ../../lapack-3.6.1/SRC/cunm2r.o ../../lapack-3.6.1/SRC/cunmbr.o ../../lapack-3.6.1/SRC/cunmhr.o ../../lapack-3.6.1/SRC/cunml2.o ../../lapack-3.6.1/SRC/cunmlq.o ../../lapack-3.6.1/SRC/cunmql.o ../../lapack-3.6.1/SRC/cunmqr.o ../../lapack-3.6.1/SRC/cunmr2.o ../../lapack-3.6.1/SRC/cunmr3.o ../../lapack-3.6.1/SRC/cunmrq.o ../../lapack-3.6.1/SRC/cunmrz.o ../../lapack-3.6.1/SRC/cunmtr.o ../../lapack-3.6.1/SRC/cupgtr.o ../../lapack-3.6.1/SRC/cupmtr.o ../../lapack-3.6.1/SRC/dbbcsd.o ../../lapack-3.6.1/SRC/dbdsdc.o ../../lapack-3.6.1/SRC/dbdsqr.o ../../lapack-3.6.1/SRC/dbdsvdx.o ../../lapack-3.6.1/SRC/ddisna.o ../../lapack-3.6.1/SRC/dgbbrd.o ../../lapack-3.6.1/SRC/dgbcon.o ../../lapack-3.6.1/SRC/dgbequb.o ../../lapack-3.6.1/SRC/dgbequ.o ../../lapack-3.6.1/SRC/dgbrfs.o ../../lapack-3.6.1/SRC/dgbsv.o ../../lapack-3.6.1/SRC/dgbsvx.o ../../lapack-3.6.1/SRC/dgbtf2.o ../../lapack-3.6.1/SRC/dgbtrf.o ../../lapack-3.6.1/SRC/dgbtrs.o ../../lapack-3.6.1/SRC/dgebak.o ../../lapack-3.6.1/SRC/dgebal.o ../../lapack-3.6.1/SRC/dgebd2.o ../../lapack-3.6.1/SRC/dgebrd.o ../../lapack-3.6.1/SRC/dgecon.o ../../lapack-3.6.1/SRC/dgeequb.o ../../lapack-3.6.1/SRC/dgeequ.o ../../lapack-3.6.1/SRC/dgees.o ../../lapack-3.6.1/SRC/dgeesx.o ../../lapack-3.6.1/SRC/dgeev.o ../../lapack-3.6.1/SRC/dgeevx.o ../../lapack-3.6.1/SRC/dgehd2.o ../../lapack-3.6.1/SRC/dgehrd.o ../../lapack-3.6.1/SRC/dgejsv.o ../../lapack-3.6.1/SRC/dgelq2.o ../../lapack-3.6.1/SRC/dgelqf.o ../../lapack-3.6.1/SRC/dgelsd.o ../../lapack-3.6.1/SRC/dgels.o ../../lapack-3.6.1/SRC/dgelss.o ../../lapack-3.6.1/SRC/dgelsy.o ../../lapack-3.6.1/SRC/dgemqrt.o ../../lapack-3.6.1/SRC/dgeql2.o ../../lapack-3.6.1/SRC/dgeqlf.o ../../lapack-3.6.1/SRC/dgeqp3.o ../../lapack-3.6.1/SRC/dgeqr2.o ../../lapack-3.6.1/SRC/dgeqr2p.o ../../lapack-3.6.1/SRC/dgeqrf.o ../../lapack-3.6.1/SRC/dgeqrfp.o ../../lapack-3.6.1/SRC/dgeqrt2.o ../../lapack-3.6.1/SRC/dgeqrt3.o ../../lapack-3.6.1/SRC/dgeqrt.o ../../lapack-3.6.1/SRC/dgerfs.o ../../lapack-3.6.1/SRC/dgerq2.o ../../lapack-3.6.1/SRC/dgerqf.o ../../lapack-3.6.1/SRC/dgesc2.o ../../lapack-3.6.1/SRC/dgesdd.o ../../lapack-3.6.1/SRC/dgesvd.o ../../lapack-3.6.1/SRC/dgesvdx.o ../../lapack-3.6.1/SRC/dgesvj.o ../../lapack-3.6.1/SRC/dgesv.o ../../lapack-3.6.1/SRC/dgesvx.o ../../lapack-3.6.1/SRC/dgetc2.o ../../lapack-3.6.1/SRC/dgetf2.o ../../lapack-3.6.1/SRC/dgetrf2.o ../../lapack-3.6.1/SRC/dgetrf.o ../../lapack-3.6.1/SRC/dgetri.o ../../lapack-3.6.1/SRC/dgetrs.o ../../lapack-3.6.1/SRC/dggbak.o ../../lapack-3.6.1/SRC/dggbal.o ../../lapack-3.6.1/SRC/dgges3.o ../../lapack-3.6.1/SRC/dgges.o ../../lapack-3.6.1/SRC/dggesx.o ../../lapack-3.6.1/SRC/dggev3.o ../../lapack-3.6.1/SRC/dggev.o ../../lapack-3.6.1/SRC/dggevx.o ../../lapack-3.6.1/SRC/dggglm.o ../../lapack-3.6.1/SRC/dgghd3.o ../../lapack-3.6.1/SRC/dgghrd.o ../../lapack-3.6.1/SRC/dgglse.o ../../lapack-3.6.1/SRC/dggqrf.o ../../lapack-3.6.1/SRC/dggrqf.o ../../lapack-3.6.1/SRC/dggsvd3.o ../../lapack-3.6.1/SRC/dggsvp3.o ../../lapack-3.6.1/SRC/dgsvj0.o ../../lapack-3.6.1/SRC/dgsvj1.o ../../lapack-3.6.1/SRC/dgtcon.o ../../lapack-3.6.1/SRC/dgtrfs.o ../../lapack-3.6.1/SRC/dgtsv.o ../../lapack-3.6.1/SRC/dgtsvx.o ../../lapack-3.6.1/SRC/dgttrf.o ../../lapack-3.6.1/SRC/dgttrs.o ../../lapack-3.6.1/SRC/dgtts2.o ../../lapack-3.6.1/SRC/dhgeqz.o ../../lapack-3.6.1/SRC/dhsein.o ../../lapack-3.6.1/SRC/dhseqr.o ../../lapack-3.6.1/SRC/disnan.o ../../lapack-3.6.1/SRC/dlabad.o ../../lapack-3.6.1/SRC/dlabrd.o ../../lapack-3.6.1/SRC/dlacn2.o ../../lapack-3.6.1/SRC/dlacon.o ../../lapack-3.6.1/SRC/dlacpy.o ../../lapack-3.6.1/SRC/dladiv.o ../../lapack-3.6.1/SRC/dlae2.o ../../lapack-3.6.1/SRC/dlaebz.o ../../lapack-3.6.1/SRC/dlaed0.o ../../lapack-3.6.1/SRC/dlaed1.o ../../lapack-3.6.1/SRC/dlaed2.o ../../lapack-3.6.1/SRC/dlaed3.o ../../lapack-3.6.1/SRC/dlaed4.o ../../lapack-3.6.1/SRC/dlaed5.o ../../lapack-3.6.1/SRC/dlaed6.o ../../lapack-3.6.1/SRC/dlaed7.o ../../lapack-3.6.1/SRC/dlaed8.o ../../lapack-3.6.1/SRC/dlaed9.o ../../lapack-3.6.1/SRC/dlaeda.o ../../lapack-3.6.1/SRC/dlaein.o ../../lapack-3.6.1/SRC/dlaev2.o ../../lapack-3.6.1/SRC/dlaexc.o ../../lapack-3.6.1/SRC/dlag2.o ../../lapack-3.6.1/SRC/dlag2s.o ../../lapack-3.6.1/SRC/dlags2.o ../../lapack-3.6.1/SRC/dlagtf.o ../../lapack-3.6.1/SRC/dlagtm.o ../../lapack-3.6.1/SRC/dlagts.o ../../lapack-3.6.1/SRC/dlagv2.o ../../lapack-3.6.1/SRC/dlahqr.o ../../lapack-3.6.1/SRC/dlahr2.o ../../lapack-3.6.1/SRC/dlaic1.o ../../lapack-3.6.1/SRC/dlaisnan.o ../../lapack-3.6.1/SRC/dlaln2.o ../../lapack-3.6.1/SRC/dlals0.o ../../lapack-3.6.1/SRC/dlalsa.o ../../lapack-3.6.1/SRC/dlalsd.o ../../lapack-3.6.1/SRC/dlamrg.o ../../lapack-3.6.1/SRC/dlaneg.o ../../lapack-3.6.1/SRC/dlangb.o ../../lapack-3.6.1/SRC/dlange.o ../../lapack-3.6.1/SRC/dlangt.o ../../lapack-3.6.1/SRC/dlanhs.o ../../lapack-3.6.1/SRC/dlansb.o ../../lapack-3.6.1/SRC/dlansf.o ../../lapack-3.6.1/SRC/dlansp.o ../../lapack-3.6.1/SRC/dlanst.o ../../lapack-3.6.1/SRC/dlansy.o ../../lapack-3.6.1/SRC/dlantb.o ../../lapack-3.6.1/SRC/dlantp.o ../../lapack-3.6.1/SRC/dlantr.o ../../lapack-3.6.1/SRC/dlanv2.o ../../lapack-3.6.1/SRC/dlapll.o ../../lapack-3.6.1/SRC/dlapmr.o ../../lapack-3.6.1/SRC/dlapmt.o ../../lapack-3.6.1/SRC/dlapy2.o ../../lapack-3.6.1/SRC/dlapy3.o ../../lapack-3.6.1/SRC/dlaqgb.o ../../lapack-3.6.1/SRC/dlaqge.o ../../lapack-3.6.1/SRC/dlaqp2.o ../../lapack-3.6.1/SRC/dlaqps.o ../../lapack-3.6.1/SRC/dlaqr0.o ../../lapack-3.6.1/SRC/dlaqr1.o ../../lapack-3.6.1/SRC/dlaqr2.o ../../lapack-3.6.1/SRC/dlaqr3.o ../../lapack-3.6.1/SRC/dlaqr4.o ../../lapack-3.6.1/SRC/dlaqr5.o ../../lapack-3.6.1/SRC/dlaqsb.o ../../lapack-3.6.1/SRC/dlaqsp.o ../../lapack-3.6.1/SRC/dlaqsy.o ../../lapack-3.6.1/SRC/dlaqtr.o ../../lapack-3.6.1/SRC/dlar1v.o ../../lapack-3.6.1/SRC/dlar2v.o ../../lapack-3.6.1/SRC/dlarfb.o ../../lapack-3.6.1/SRC/dlarfg.o ../../lapack-3.6.1/SRC/dlarfgp.o ../../lapack-3.6.1/SRC/dlarf.o ../../lapack-3.6.1/SRC/dlarft.o ../../lapack-3.6.1/SRC/dlarfx.o ../../lapack-3.6.1/SRC/dlargv.o ../../lapack-3.6.1/SRC/dlarnv.o ../../lapack-3.6.1/SRC/dlarra.o ../../lapack-3.6.1/SRC/dlarrb.o ../../lapack-3.6.1/SRC/dlarrc.o ../../lapack-3.6.1/SRC/dlarrd.o ../../lapack-3.6.1/SRC/dlarre.o ../../lapack-3.6.1/SRC/dlarrf.o ../../lapack-3.6.1/SRC/dlarrj.o ../../lapack-3.6.1/SRC/dlarrk.o ../../lapack-3.6.1/SRC/dlarrr.o ../../lapack-3.6.1/SRC/dlarrv.o ../../lapack-3.6.1/SRC/dlartg.o ../../lapack-3.6.1/SRC/dlartgp.o ../../lapack-3.6.1/SRC/dlartgs.o ../../lapack-3.6.1/SRC/dlartv.o ../../lapack-3.6.1/SRC/dlaruv.o ../../lapack-3.6.1/SRC/dlarzb.o ../../lapack-3.6.1/SRC/dlarz.o ../../lapack-3.6.1/SRC/dlarzt.o ../../lapack-3.6.1/SRC/dlas2.o ../../lapack-3.6.1/SRC/dlascl.o ../../lapack-3.6.1/SRC/dlasd0.o ../../lapack-3.6.1/SRC/dlasd1.o ../../lapack-3.6.1/SRC/dlasd2.o ../../lapack-3.6.1/SRC/dlasd3.o ../../lapack-3.6.1/SRC/dlasd4.o ../../lapack-3.6.1/SRC/dlasd5.o ../../lapack-3.6.1/SRC/dlasd6.o ../../lapack-3.6.1/SRC/dlasd7.o ../../lapack-3.6.1/SRC/dlasd8.o ../../lapack-3.6.1/SRC/dlasda.o ../../lapack-3.6.1/SRC/dlasdq.o ../../lapack-3.6.1/SRC/dlasdt.o ../../lapack-3.6.1/SRC/dlaset.o ../../lapack-3.6.1/SRC/dlasq1.o ../../lapack-3.6.1/SRC/dlasq2.o ../../lapack-3.6.1/SRC/dlasq3.o ../../lapack-3.6.1/SRC/dlasq4.o ../../lapack-3.6.1/SRC/dlasq5.o ../../lapack-3.6.1/SRC/dlasq6.o ../../lapack-3.6.1/SRC/dlasr.o ../../lapack-3.6.1/SRC/dlasrt.o ../../lapack-3.6.1/SRC/dlassq.o ../../lapack-3.6.1/SRC/dlasv2.o ../../lapack-3.6.1/SRC/dlaswp.o ../../lapack-3.6.1/SRC/dlasy2.o ../../lapack-3.6.1/SRC/dlasyf.o ../../lapack-3.6.1/SRC/dlasyf_rook.o ../../lapack-3.6.1/SRC/dlat2s.o ../../lapack-3.6.1/SRC/dlatbs.o ../../lapack-3.6.1/SRC/dlatdf.o ../../lapack-3.6.1/SRC/dlatps.o ../../lapack-3.6.1/SRC/dlatrd.o ../../lapack-3.6.1/SRC/dlatrs.o ../../lapack-3.6.1/SRC/dlatrz.o ../../lapack-3.6.1/SRC/dlauu2.o ../../lapack-3.6.1/SRC/dlauum.o ../../lapack-3.6.1/SRC/dopgtr.o ../../lapack-3.6.1/SRC/dopmtr.o ../../lapack-3.6.1/SRC/dorbdb1.o ../../lapack-3.6.1/SRC/dorbdb2.o ../../lapack-3.6.1/SRC/dorbdb3.o ../../lapack-3.6.1/SRC/dorbdb4.o ../../lapack-3.6.1/SRC/dorbdb5.o ../../lapack-3.6.1/SRC/dorbdb6.o ../../lapack-3.6.1/SRC/dorbdb.o ../../lapack-3.6.1/SRC/dorcsd2by1.o ../../lapack-3.6.1/SRC/dorcsd.o ../../lapack-3.6.1/SRC/dorg2l.o ../../lapack-3.6.1/SRC/dorg2r.o ../../lapack-3.6.1/SRC/dorgbr.o ../../lapack-3.6.1/SRC/dorghr.o ../../lapack-3.6.1/SRC/dorgl2.o ../../lapack-3.6.1/SRC/dorglq.o ../../lapack-3.6.1/SRC/dorgql.o ../../lapack-3.6.1/SRC/dorgqr.o ../../lapack-3.6.1/SRC/dorgr2.o ../../lapack-3.6.1/SRC/dorgrq.o ../../lapack-3.6.1/SRC/dorgtr.o ../../lapack-3.6.1/SRC/dorm22.o ../../lapack-3.6.1/SRC/dorm2l.o ../../lapack-3.6.1/SRC/dorm2r.o ../../lapack-3.6.1/SRC/dormbr.o ../../lapack-3.6.1/SRC/dormhr.o ../../lapack-3.6.1/SRC/dorml2.o ../../lapack-3.6.1/SRC/dormlq.o ../../lapack-3.6.1/SRC/dormql.o ../../lapack-3.6.1/SRC/dormqr.o ../../lapack-3.6.1/SRC/dormr2.o ../../lapack-3.6.1/SRC/dormr3.o ../../lapack-3.6.1/SRC/dormrq.o ../../lapack-3.6.1/SRC/dormrz.o ../../lapack-3.6.1/SRC/dormtr.o ../../lapack-3.6.1/SRC/dpbcon.o ../../lapack-3.6.1/SRC/dpbequ.o ../../lapack-3.6.1/SRC/dpbrfs.o ../../lapack-3.6.1/SRC/dpbstf.o ../../lapack-3.6.1/SRC/dpbsv.o ../../lapack-3.6.1/SRC/dpbsvx.o ../../lapack-3.6.1/SRC/dpbtf2.o ../../lapack-3.6.1/SRC/dpbtrf.o ../../lapack-3.6.1/SRC/dpbtrs.o ../../lapack-3.6.1/SRC/dpftrf.o ../../lapack-3.6.1/SRC/dpftri.o ../../lapack-3.6.1/SRC/dpftrs.o ../../lapack-3.6.1/SRC/dpocon.o ../../lapack-3.6.1/SRC/dpoequb.o ../../lapack-3.6.1/SRC/dpoequ.o ../../lapack-3.6.1/SRC/dporfs.o ../../lapack-3.6.1/SRC/dposv.o ../../lapack-3.6.1/SRC/dposvx.o ../../lapack-3.6.1/SRC/dpotf2.o ../../lapack-3.6.1/SRC/dpotrf2.o ../../lapack-3.6.1/SRC/dpotrf.o ../../lapack-3.6.1/SRC/dpotri.o ../../lapack-3.6.1/SRC/dpotrs.o ../../lapack-3.6.1/SRC/dppcon.o ../../lapack-3.6.1/SRC/dppequ.o ../../lapack-3.6.1/SRC/dpprfs.o ../../lapack-3.6.1/SRC/dppsv.o ../../lapack-3.6.1/SRC/dppsvx.o ../../lapack-3.6.1/SRC/dpptrf.o ../../lapack-3.6.1/SRC/dpptri.o ../../lapack-3.6.1/SRC/dpptrs.o ../../lapack-3.6.1/SRC/dpstf2.o ../../lapack-3.6.1/SRC/dpstrf.o ../../lapack-3.6.1/SRC/dptcon.o ../../lapack-3.6.1/SRC/dpteqr.o ../../lapack-3.6.1/SRC/dptrfs.o ../../lapack-3.6.1/SRC/dptsv.o ../../lapack-3.6.1/SRC/dptsvx.o ../../lapack-3.6.1/SRC/dpttrf.o ../../lapack-3.6.1/SRC/dpttrs.o ../../lapack-3.6.1/SRC/dptts2.o ../../lapack-3.6.1/SRC/drscl.o ../../lapack-3.6.1/SRC/dsbevd.o ../../lapack-3.6.1/SRC/dsbev.o ../../lapack-3.6.1/SRC/dsbevx.o ../../lapack-3.6.1/SRC/dsbgst.o ../../lapack-3.6.1/SRC/dsbgvd.o ../../lapack-3.6.1/SRC/dsbgv.o ../../lapack-3.6.1/SRC/dsbgvx.o ../../lapack-3.6.1/SRC/dsbtrd.o ../../lapack-3.6.1/SRC/dsfrk.o ../../lapack-3.6.1/SRC/dsgesv.o ../../lapack-3.6.1/SRC/dspcon.o ../../lapack-3.6.1/SRC/dspevd.o ../../lapack-3.6.1/SRC/dspev.o ../../lapack-3.6.1/SRC/dspevx.o ../../lapack-3.6.1/SRC/dspgst.o ../../lapack-3.6.1/SRC/dspgvd.o ../../lapack-3.6.1/SRC/dspgv.o ../../lapack-3.6.1/SRC/dspgvx.o ../../lapack-3.6.1/SRC/dsposv.o ../../lapack-3.6.1/SRC/dsprfs.o ../../lapack-3.6.1/SRC/dspsv.o ../../lapack-3.6.1/SRC/dspsvx.o ../../lapack-3.6.1/SRC/dsptrd.o ../../lapack-3.6.1/SRC/dsptrf.o ../../lapack-3.6.1/SRC/dsptri.o ../../lapack-3.6.1/SRC/dsptrs.o ../../lapack-3.6.1/SRC/dstebz.o ../../lapack-3.6.1/SRC/dstedc.o ../../lapack-3.6.1/SRC/dstegr.o ../../lapack-3.6.1/SRC/dstein.o ../../lapack-3.6.1/SRC/dstemr.o ../../lapack-3.6.1/SRC/dsteqr.o ../../lapack-3.6.1/SRC/dsterf.o ../../lapack-3.6.1/SRC/dstevd.o ../../lapack-3.6.1/SRC/dstev.o ../../lapack-3.6.1/SRC/dstevr.o ../../lapack-3.6.1/SRC/dstevx.o ../../lapack-3.6.1/SRC/dsycon.o ../../lapack-3.6.1/SRC/dsycon_rook.o ../../lapack-3.6.1/SRC/dsyconv.o ../../lapack-3.6.1/SRC/dsyequb.o ../../lapack-3.6.1/SRC/dsyevd.o ../../lapack-3.6.1/SRC/dsyev.o ../../lapack-3.6.1/SRC/dsyevr.o ../../lapack-3.6.1/SRC/dsyevx.o ../../lapack-3.6.1/SRC/dsygs2.o ../../lapack-3.6.1/SRC/dsygst.o ../../lapack-3.6.1/SRC/dsygvd.o ../../lapack-3.6.1/SRC/dsygv.o ../../lapack-3.6.1/SRC/dsygvx.o ../../lapack-3.6.1/SRC/dsyrfs.o ../../lapack-3.6.1/SRC/dsysv.o ../../lapack-3.6.1/SRC/dsysv_rook.o ../../lapack-3.6.1/SRC/dsysvx.o ../../lapack-3.6.1/SRC/dsyswapr.o ../../lapack-3.6.1/SRC/dsytd2.o ../../lapack-3.6.1/SRC/dsytf2.o ../../lapack-3.6.1/SRC/dsytf2_rook.o ../../lapack-3.6.1/SRC/dsytrd.o ../../lapack-3.6.1/SRC/dsytrf.o ../../lapack-3.6.1/SRC/dsytrf_rook.o ../../lapack-3.6.1/SRC/dsytri2.o ../../lapack-3.6.1/SRC/dsytri2x.o ../../lapack-3.6.1/SRC/dsytri.o ../../lapack-3.6.1/SRC/dsytri_rook.o ../../lapack-3.6.1/SRC/dsytrs2.o ../../lapack-3.6.1/SRC/dsytrs.o ../../lapack-3.6.1/SRC/dsytrs_rook.o ../../lapack-3.6.1/SRC/dtbcon.o ../../lapack-3.6.1/SRC/dtbrfs.o ../../lapack-3.6.1/SRC/dtbtrs.o ../../lapack-3.6.1/SRC/dtfsm.o ../../lapack-3.6.1/SRC/dtftri.o ../../lapack-3.6.1/SRC/dtfttp.o ../../lapack-3.6.1/SRC/dtfttr.o ../../lapack-3.6.1/SRC/dtgevc.o ../../lapack-3.6.1/SRC/dtgex2.o ../../lapack-3.6.1/SRC/dtgexc.o ../../lapack-3.6.1/SRC/dtgsen.o ../../lapack-3.6.1/SRC/dtgsja.o ../../lapack-3.6.1/SRC/dtgsna.o ../../lapack-3.6.1/SRC/dtgsy2.o ../../lapack-3.6.1/SRC/dtgsyl.o ../../lapack-3.6.1/SRC/dtpcon.o ../../lapack-3.6.1/SRC/dtpmqrt.o ../../lapack-3.6.1/SRC/dtpqrt2.o ../../lapack-3.6.1/SRC/dtpqrt.o ../../lapack-3.6.1/SRC/dtprfb.o ../../lapack-3.6.1/SRC/dtprfs.o ../../lapack-3.6.1/SRC/dtptri.o ../../lapack-3.6.1/SRC/dtptrs.o ../../lapack-3.6.1/SRC/dtpttf.o ../../lapack-3.6.1/SRC/dtpttr.o ../../lapack-3.6.1/SRC/dtrcon.o ../../lapack-3.6.1/SRC/dtrevc3.o ../../lapack-3.6.1/SRC/dtrevc.o ../../lapack-3.6.1/SRC/dtrexc.o ../../lapack-3.6.1/SRC/dtrrfs.o ../../lapack-3.6.1/SRC/dtrsen.o ../../lapack-3.6.1/SRC/dtrsna.o ../../lapack-3.6.1/SRC/dtrsyl.o ../../lapack-3.6.1/SRC/dtrti2.o ../../lapack-3.6.1/SRC/dtrtri.o ../../lapack-3.6.1/SRC/dtrtrs.o ../../lapack-3.6.1/SRC/dtrttf.o ../../lapack-3.6.1/SRC/dtrttp.o ../../lapack-3.6.1/SRC/dtzrzf.o ../../lapack-3.6.1/SRC/dzsum1.o ../../lapack-3.6.1/SRC/icmax1.o ../../lapack-3.6.1/SRC/ieeeck.o ../../lapack-3.6.1/SRC/ilaclc.o ../../lapack-3.6.1/SRC/ilaclr.o ../../lapack-3.6.1/SRC/iladiag.o ../../lapack-3.6.1/SRC/iladlc.o ../../lapack-3.6.1/SRC/iladlr.o ../../lapack-3.6.1/SRC/ilaenv.o ../../lapack-3.6.1/SRC/ilaprec.o ../../lapack-3.6.1/SRC/ilaslc.o ../../lapack-3.6.1/SRC/ilaslr.o ../../lapack-3.6.1/SRC/ilatrans.o ../../lapack-3.6.1/SRC/ilauplo.o ../../lapack-3.6.1/SRC/ilazlc.o ../../lapack-3.6.1/SRC/ilazlr.o ../../lapack-3.6.1/SRC/iparmq.o ../../lapack-3.6.1/SRC/izmax1.o ../../lapack-3.6.1/SRC/lsamen.o ../../lapack-3.6.1/SRC/sbbcsd.o ../../lapack-3.6.1/SRC/sbdsdc.o ../../lapack-3.6.1/SRC/sbdsqr.o ../../lapack-3.6.1/SRC/sbdsvdx.o ../../lapack-3.6.1/SRC/scsum1.o ../../lapack-3.6.1/SRC/sdisna.o ../../lapack-3.6.1/SRC/sgbbrd.o ../../lapack-3.6.1/SRC/sgbcon.o ../../lapack-3.6.1/SRC/sgbequb.o ../../lapack-3.6.1/SRC/sgbequ.o ../../lapack-3.6.1/SRC/sgbrfs.o ../../lapack-3.6.1/SRC/sgbsv.o ../../lapack-3.6.1/SRC/sgbsvx.o ../../lapack-3.6.1/SRC/sgbtf2.o ../../lapack-3.6.1/SRC/sgbtrf.o ../../lapack-3.6.1/SRC/sgbtrs.o ../../lapack-3.6.1/SRC/sgebak.o ../../lapack-3.6.1/SRC/sgebal.o ../../lapack-3.6.1/SRC/sgebd2.o ../../lapack-3.6.1/SRC/sgebrd.o ../../lapack-3.6.1/SRC/sgecon.o ../../lapack-3.6.1/SRC/sgeequb.o ../../lapack-3.6.1/SRC/sgeequ.o ../../lapack-3.6.1/SRC/sgees.o ../../lapack-3.6.1/SRC/sgeesx.o ../../lapack-3.6.1/SRC/sgeev.o ../../lapack-3.6.1/SRC/sgeevx.o ../../lapack-3.6.1/SRC/sgehd2.o ../../lapack-3.6.1/SRC/sgehrd.o ../../lapack-3.6.1/SRC/sgejsv.o ../../lapack-3.6.1/SRC/sgelq2.o ../../lapack-3.6.1/SRC/sgelqf.o ../../lapack-3.6.1/SRC/sgelsd.o ../../lapack-3.6.1/SRC/sgels.o ../../lapack-3.6.1/SRC/sgelss.o ../../lapack-3.6.1/SRC/sgelsy.o ../../lapack-3.6.1/SRC/sgemqrt.o ../../lapack-3.6.1/SRC/sgeql2.o ../../lapack-3.6.1/SRC/sgeqlf.o ../../lapack-3.6.1/SRC/sgeqp3.o ../../lapack-3.6.1/SRC/sgeqr2.o ../../lapack-3.6.1/SRC/sgeqr2p.o ../../lapack-3.6.1/SRC/sgeqrf.o ../../lapack-3.6.1/SRC/sgeqrfp.o ../../lapack-3.6.1/SRC/sgeqrt2.o ../../lapack-3.6.1/SRC/sgeqrt3.o ../../lapack-3.6.1/SRC/sgeqrt.o ../../lapack-3.6.1/SRC/sgerfs.o ../../lapack-3.6.1/SRC/sgerq2.o ../../lapack-3.6.1/SRC/sgerqf.o ../../lapack-3.6.1/SRC/sgesc2.o ../../lapack-3.6.1/SRC/sgesdd.o ../../lapack-3.6.1/SRC/sgesvd.o ../../lapack-3.6.1/SRC/sgesvdx.o ../../lapack-3.6.1/SRC/sgesvj.o ../../lapack-3.6.1/SRC/sgesv.o ../../lapack-3.6.1/SRC/sgesvx.o ../../lapack-3.6.1/SRC/sgetc2.o ../../lapack-3.6.1/SRC/sgetf2.o ../../lapack-3.6.1/SRC/sgetrf2.o ../../lapack-3.6.1/SRC/sgetrf.o ../../lapack-3.6.1/SRC/sgetri.o ../../lapack-3.6.1/SRC/sgetrs.o ../../lapack-3.6.1/SRC/sggbak.o ../../lapack-3.6.1/SRC/sggbal.o ../../lapack-3.6.1/SRC/sgges3.o ../../lapack-3.6.1/SRC/sgges.o ../../lapack-3.6.1/SRC/sggesx.o ../../lapack-3.6.1/SRC/sggev3.o ../../lapack-3.6.1/SRC/sggev.o ../../lapack-3.6.1/SRC/sggevx.o ../../lapack-3.6.1/SRC/sggglm.o ../../lapack-3.6.1/SRC/sgghd3.o ../../lapack-3.6.1/SRC/sgghrd.o ../../lapack-3.6.1/SRC/sgglse.o ../../lapack-3.6.1/SRC/sggqrf.o ../../lapack-3.6.1/SRC/sggrqf.o ../../lapack-3.6.1/SRC/sggsvd3.o ../../lapack-3.6.1/SRC/sggsvp3.o ../../lapack-3.6.1/SRC/sgsvj0.o ../../lapack-3.6.1/SRC/sgsvj1.o ../../lapack-3.6.1/SRC/sgtcon.o ../../lapack-3.6.1/SRC/sgtrfs.o ../../lapack-3.6.1/SRC/sgtsv.o ../../lapack-3.6.1/SRC/sgtsvx.o ../../lapack-3.6.1/SRC/sgttrf.o ../../lapack-3.6.1/SRC/sgttrs.o ../../lapack-3.6.1/SRC/sgtts2.o ../../lapack-3.6.1/SRC/shgeqz.o ../../lapack-3.6.1/SRC/shsein.o ../../lapack-3.6.1/SRC/shseqr.o ../../lapack-3.6.1/SRC/sisnan.o ../../lapack-3.6.1/SRC/slabad.o ../../lapack-3.6.1/SRC/slabrd.o ../../lapack-3.6.1/SRC/slacn2.o ../../lapack-3.6.1/SRC/slacon.o ../../lapack-3.6.1/SRC/slacpy.o ../../lapack-3.6.1/SRC/sladiv.o ../../lapack-3.6.1/SRC/slae2.o ../../lapack-3.6.1/SRC/slaebz.o ../../lapack-3.6.1/SRC/slaed0.o ../../lapack-3.6.1/SRC/slaed1.o ../../lapack-3.6.1/SRC/slaed2.o ../../lapack-3.6.1/SRC/slaed3.o ../../lapack-3.6.1/SRC/slaed4.o ../../lapack-3.6.1/SRC/slaed5.o ../../lapack-3.6.1/SRC/slaed6.o ../../lapack-3.6.1/SRC/slaed7.o ../../lapack-3.6.1/SRC/slaed8.o ../../lapack-3.6.1/SRC/slaed9.o ../../lapack-3.6.1/SRC/slaeda.o ../../lapack-3.6.1/SRC/slaein.o ../../lapack-3.6.1/SRC/slaev2.o ../../lapack-3.6.1/SRC/slaexc.o ../../lapack-3.6.1/SRC/slag2d.o ../../lapack-3.6.1/SRC/slag2.o ../../lapack-3.6.1/SRC/slags2.o ../../lapack-3.6.1/SRC/slagtf.o ../../lapack-3.6.1/SRC/slagtm.o ../../lapack-3.6.1/SRC/slagts.o ../../lapack-3.6.1/SRC/slagv2.o ../../lapack-3.6.1/SRC/slahqr.o ../../lapack-3.6.1/SRC/slahr2.o ../../lapack-3.6.1/SRC/slaic1.o ../../lapack-3.6.1/SRC/slaisnan.o ../../lapack-3.6.1/SRC/slaln2.o ../../lapack-3.6.1/SRC/slals0.o ../../lapack-3.6.1/SRC/slalsa.o ../../lapack-3.6.1/SRC/slalsd.o ../../lapack-3.6.1/SRC/slamrg.o ../../lapack-3.6.1/SRC/slaneg.o ../../lapack-3.6.1/SRC/slangb.o ../../lapack-3.6.1/SRC/slange.o ../../lapack-3.6.1/SRC/slangt.o ../../lapack-3.6.1/SRC/slanhs.o ../../lapack-3.6.1/SRC/slansb.o ../../lapack-3.6.1/SRC/slansf.o ../../lapack-3.6.1/SRC/slansp.o ../../lapack-3.6.1/SRC/slanst.o ../../lapack-3.6.1/SRC/slansy.o ../../lapack-3.6.1/SRC/slantb.o ../../lapack-3.6.1/SRC/slantp.o ../../lapack-3.6.1/SRC/slantr.o ../../lapack-3.6.1/SRC/slanv2.o ../../lapack-3.6.1/SRC/slapll.o ../../lapack-3.6.1/SRC/slapmr.o ../../lapack-3.6.1/SRC/slapmt.o ../../lapack-3.6.1/SRC/slapy2.o ../../lapack-3.6.1/SRC/slapy3.o ../../lapack-3.6.1/SRC/slaqgb.o ../../lapack-3.6.1/SRC/slaqge.o ../../lapack-3.6.1/SRC/slaqp2.o ../../lapack-3.6.1/SRC/slaqps.o ../../lapack-3.6.1/SRC/slaqr0.o ../../lapack-3.6.1/SRC/slaqr1.o ../../lapack-3.6.1/SRC/slaqr2.o ../../lapack-3.6.1/SRC/slaqr3.o ../../lapack-3.6.1/SRC/slaqr4.o ../../lapack-3.6.1/SRC/slaqr5.o ../../lapack-3.6.1/SRC/slaqsb.o ../../lapack-3.6.1/SRC/slaqsp.o ../../lapack-3.6.1/SRC/slaqsy.o ../../lapack-3.6.1/SRC/slaqtr.o ../../lapack-3.6.1/SRC/slar1v.o ../../lapack-3.6.1/SRC/slar2v.o ../../lapack-3.6.1/SRC/slarfb.o ../../lapack-3.6.1/SRC/slarfg.o ../../lapack-3.6.1/SRC/slarfgp.o ../../lapack-3.6.1/SRC/slarf.o ../../lapack-3.6.1/SRC/slarft.o ../../lapack-3.6.1/SRC/slarfx.o ../../lapack-3.6.1/SRC/slargv.o ../../lapack-3.6.1/SRC/slarnv.o ../../lapack-3.6.1/SRC/slarra.o ../../lapack-3.6.1/SRC/slarrb.o ../../lapack-3.6.1/SRC/slarrc.o ../../lapack-3.6.1/SRC/slarrd.o ../../lapack-3.6.1/SRC/slarre.o ../../lapack-3.6.1/SRC/slarrf.o ../../lapack-3.6.1/SRC/slarrj.o ../../lapack-3.6.1/SRC/slarrk.o ../../lapack-3.6.1/SRC/slarrr.o ../../lapack-3.6.1/SRC/slarrv.o ../../lapack-3.6.1/SRC/slartg.o ../../lapack-3.6.1/SRC/slartgp.o ../../lapack-3.6.1/SRC/slartgs.o ../../lapack-3.6.1/SRC/slartv.o ../../lapack-3.6.1/SRC/slaruv.o ../../lapack-3.6.1/SRC/slarzb.o ../../lapack-3.6.1/SRC/slarz.o ../../lapack-3.6.1/SRC/slarzt.o ../../lapack-3.6.1/SRC/slas2.o ../../lapack-3.6.1/SRC/slascl.o ../../lapack-3.6.1/SRC/slasd0.o ../../lapack-3.6.1/SRC/slasd1.o ../../lapack-3.6.1/SRC/slasd2.o ../../lapack-3.6.1/SRC/slasd3.o ../../lapack-3.6.1/SRC/slasd4.o ../../lapack-3.6.1/SRC/slasd5.o ../../lapack-3.6.1/SRC/slasd6.o ../../lapack-3.6.1/SRC/slasd7.o ../../lapack-3.6.1/SRC/slasd8.o ../../lapack-3.6.1/SRC/slasda.o ../../lapack-3.6.1/SRC/slasdq.o ../../lapack-3.6.1/SRC/slasdt.o ../../lapack-3.6.1/SRC/slaset.o ../../lapack-3.6.1/SRC/slasq1.o ../../lapack-3.6.1/SRC/slasq2.o ../../lapack-3.6.1/SRC/slasq3.o ../../lapack-3.6.1/SRC/slasq4.o ../../lapack-3.6.1/SRC/slasq5.o ../../lapack-3.6.1/SRC/slasq6.o ../../lapack-3.6.1/SRC/slasr.o ../../lapack-3.6.1/SRC/slasrt.o ../../lapack-3.6.1/SRC/slassq.o ../../lapack-3.6.1/SRC/slasv2.o ../../lapack-3.6.1/SRC/slaswp.o ../../lapack-3.6.1/SRC/slasy2.o ../../lapack-3.6.1/SRC/slasyf.o ../../lapack-3.6.1/SRC/slasyf_rook.o ../../lapack-3.6.1/SRC/slatbs.o ../../lapack-3.6.1/SRC/slatdf.o ../../lapack-3.6.1/SRC/slatps.o ../../lapack-3.6.1/SRC/slatrd.o ../../lapack-3.6.1/SRC/slatrs.o ../../lapack-3.6.1/SRC/slatrz.o ../../lapack-3.6.1/SRC/slauu2.o ../../lapack-3.6.1/SRC/slauum.o ../../lapack-3.6.1/SRC/sopgtr.o ../../lapack-3.6.1/SRC/sopmtr.o ../../lapack-3.6.1/SRC/sorbdb1.o ../../lapack-3.6.1/SRC/sorbdb2.o ../../lapack-3.6.1/SRC/sorbdb3.o ../../lapack-3.6.1/SRC/sorbdb4.o ../../lapack-3.6.1/SRC/sorbdb5.o ../../lapack-3.6.1/SRC/sorbdb6.o ../../lapack-3.6.1/SRC/sorbdb.o ../../lapack-3.6.1/SRC/sorcsd2by1.o ../../lapack-3.6.1/SRC/sorcsd.o ../../lapack-3.6.1/SRC/sorg2l.o ../../lapack-3.6.1/SRC/sorg2r.o ../../lapack-3.6.1/SRC/sorgbr.o ../../lapack-3.6.1/SRC/sorghr.o ../../lapack-3.6.1/SRC/sorgl2.o ../../lapack-3.6.1/SRC/sorglq.o ../../lapack-3.6.1/SRC/sorgql.o ../../lapack-3.6.1/SRC/sorgqr.o ../../lapack-3.6.1/SRC/sorgr2.o ../../lapack-3.6.1/SRC/sorgrq.o ../../lapack-3.6.1/SRC/sorgtr.o ../../lapack-3.6.1/SRC/sorm22.o ../../lapack-3.6.1/SRC/sorm2l.o ../../lapack-3.6.1/SRC/sorm2r.o ../../lapack-3.6.1/SRC/sormbr.o ../../lapack-3.6.1/SRC/sormhr.o ../../lapack-3.6.1/SRC/sorml2.o ../../lapack-3.6.1/SRC/sormlq.o ../../lapack-3.6.1/SRC/sormql.o ../../lapack-3.6.1/SRC/sormqr.o ../../lapack-3.6.1/SRC/sormr2.o ../../lapack-3.6.1/SRC/sormr3.o ../../lapack-3.6.1/SRC/sormrq.o ../../lapack-3.6.1/SRC/sormrz.o ../../lapack-3.6.1/SRC/sormtr.o ../../lapack-3.6.1/SRC/spbcon.o ../../lapack-3.6.1/SRC/spbequ.o ../../lapack-3.6.1/SRC/spbrfs.o ../../lapack-3.6.1/SRC/spbstf.o ../../lapack-3.6.1/SRC/spbsv.o ../../lapack-3.6.1/SRC/spbsvx.o ../../lapack-3.6.1/SRC/spbtf2.o ../../lapack-3.6.1/SRC/spbtrf.o ../../lapack-3.6.1/SRC/spbtrs.o ../../lapack-3.6.1/SRC/spftrf.o ../../lapack-3.6.1/SRC/spftri.o ../../lapack-3.6.1/SRC/spftrs.o ../../lapack-3.6.1/SRC/spocon.o ../../lapack-3.6.1/SRC/spoequb.o ../../lapack-3.6.1/SRC/spoequ.o ../../lapack-3.6.1/SRC/sporfs.o ../../lapack-3.6.1/SRC/sposv.o ../../lapack-3.6.1/SRC/sposvx.o ../../lapack-3.6.1/SRC/spotf2.o ../../lapack-3.6.1/SRC/spotrf2.o ../../lapack-3.6.1/SRC/spotrf.o ../../lapack-3.6.1/SRC/spotri.o ../../lapack-3.6.1/SRC/spotrs.o ../../lapack-3.6.1/SRC/sppcon.o ../../lapack-3.6.1/SRC/sppequ.o ../../lapack-3.6.1/SRC/spprfs.o ../../lapack-3.6.1/SRC/sppsv.o ../../lapack-3.6.1/SRC/sppsvx.o ../../lapack-3.6.1/SRC/spptrf.o ../../lapack-3.6.1/SRC/spptri.o ../../lapack-3.6.1/SRC/spptrs.o ../../lapack-3.6.1/SRC/spstf2.o ../../lapack-3.6.1/SRC/spstrf.o ../../lapack-3.6.1/SRC/sptcon.o ../../lapack-3.6.1/SRC/spteqr.o ../../lapack-3.6.1/SRC/sptrfs.o ../../lapack-3.6.1/SRC/sptsv.o ../../lapack-3.6.1/SRC/sptsvx.o ../../lapack-3.6.1/SRC/spttrf.o ../../lapack-3.6.1/SRC/spttrs.o ../../lapack-3.6.1/SRC/sptts2.o ../../lapack-3.6.1/SRC/srscl.o ../../lapack-3.6.1/SRC/ssbevd.o ../../lapack-3.6.1/SRC/ssbev.o ../../lapack-3.6.1/SRC/ssbevx.o ../../lapack-3.6.1/SRC/ssbgst.o ../../lapack-3.6.1/SRC/ssbgvd.o ../../lapack-3.6.1/SRC/ssbgv.o ../../lapack-3.6.1/SRC/ssbgvx.o ../../lapack-3.6.1/SRC/ssbtrd.o ../../lapack-3.6.1/SRC/ssfrk.o ../../lapack-3.6.1/SRC/sspcon.o ../../lapack-3.6.1/SRC/sspevd.o ../../lapack-3.6.1/SRC/sspev.o ../../lapack-3.6.1/SRC/sspevx.o ../../lapack-3.6.1/SRC/sspgst.o ../../lapack-3.6.1/SRC/sspgvd.o ../../lapack-3.6.1/SRC/sspgv.o ../../lapack-3.6.1/SRC/sspgvx.o ../../lapack-3.6.1/SRC/ssprfs.o ../../lapack-3.6.1/SRC/sspsv.o ../../lapack-3.6.1/SRC/sspsvx.o ../../lapack-3.6.1/SRC/ssptrd.o ../../lapack-3.6.1/SRC/ssptrf.o ../../lapack-3.6.1/SRC/ssptri.o ../../lapack-3.6.1/SRC/ssptrs.o ../../lapack-3.6.1/SRC/sstebz.o ../../lapack-3.6.1/SRC/sstedc.o ../../lapack-3.6.1/SRC/sstegr.o ../../lapack-3.6.1/SRC/sstein.o ../../lapack-3.6.1/SRC/sstemr.o ../../lapack-3.6.1/SRC/ssteqr.o ../../lapack-3.6.1/SRC/ssterf.o ../../lapack-3.6.1/SRC/sstevd.o ../../lapack-3.6.1/SRC/sstev.o ../../lapack-3.6.1/SRC/sstevr.o ../../lapack-3.6.1/SRC/sstevx.o ../../lapack-3.6.1/SRC/ssycon.o ../../lapack-3.6.1/SRC/ssycon_rook.o ../../lapack-3.6.1/SRC/ssyconv.o ../../lapack-3.6.1/SRC/ssyequb.o ../../lapack-3.6.1/SRC/ssyevd.o ../../lapack-3.6.1/SRC/ssyev.o ../../lapack-3.6.1/SRC/ssyevr.o ../../lapack-3.6.1/SRC/ssyevx.o ../../lapack-3.6.1/SRC/ssygs2.o ../../lapack-3.6.1/SRC/ssygst.o ../../lapack-3.6.1/SRC/ssygvd.o ../../lapack-3.6.1/SRC/ssygv.o ../../lapack-3.6.1/SRC/ssygvx.o ../../lapack-3.6.1/SRC/ssyrfs.o ../../lapack-3.6.1/SRC/ssysv.o ../../lapack-3.6.1/SRC/ssysv_rook.o ../../lapack-3.6.1/SRC/ssysvx.o ../../lapack-3.6.1/SRC/ssyswapr.o ../../lapack-3.6.1/SRC/ssytd2.o ../../lapack-3.6.1/SRC/ssytf2.o ../../lapack-3.6.1/SRC/ssytf2_rook.o ../../lapack-3.6.1/SRC/ssytrd.o ../../lapack-3.6.1/SRC/ssytrf.o ../../lapack-3.6.1/SRC/ssytrf_rook.o ../../lapack-3.6.1/SRC/ssytri2.o ../../lapack-3.6.1/SRC/ssytri2x.o ../../lapack-3.6.1/SRC/ssytri.o ../../lapack-3.6.1/SRC/ssytri_rook.o ../../lapack-3.6.1/SRC/ssytrs2.o ../../lapack-3.6.1/SRC/ssytrs.o ../../lapack-3.6.1/SRC/ssytrs_rook.o ../../lapack-3.6.1/SRC/stbcon.o ../../lapack-3.6.1/SRC/stbrfs.o ../../lapack-3.6.1/SRC/stbtrs.o ../../lapack-3.6.1/SRC/stfsm.o ../../lapack-3.6.1/SRC/stftri.o ../../lapack-3.6.1/SRC/stfttp.o ../../lapack-3.6.1/SRC/stfttr.o ../../lapack-3.6.1/SRC/stgevc.o ../../lapack-3.6.1/SRC/stgex2.o ../../lapack-3.6.1/SRC/stgexc.o ../../lapack-3.6.1/SRC/stgsen.o ../../lapack-3.6.1/SRC/stgsja.o ../../lapack-3.6.1/SRC/stgsna.o ../../lapack-3.6.1/SRC/stgsy2.o ../../lapack-3.6.1/SRC/stgsyl.o ../../lapack-3.6.1/SRC/stpcon.o ../../lapack-3.6.1/SRC/stpmqrt.o ../../lapack-3.6.1/SRC/stpqrt2.o ../../lapack-3.6.1/SRC/stpqrt.o ../../lapack-3.6.1/SRC/stprfb.o ../../lapack-3.6.1/SRC/stprfs.o ../../lapack-3.6.1/SRC/stptri.o ../../lapack-3.6.1/SRC/stptrs.o ../../lapack-3.6.1/SRC/stpttf.o ../../lapack-3.6.1/SRC/stpttr.o ../../lapack-3.6.1/SRC/strcon.o ../../lapack-3.6.1/SRC/strevc3.o ../../lapack-3.6.1/SRC/strevc.o ../../lapack-3.6.1/SRC/strexc.o ../../lapack-3.6.1/SRC/strrfs.o ../../lapack-3.6.1/SRC/strsen.o ../../lapack-3.6.1/SRC/strsna.o ../../lapack-3.6.1/SRC/strsyl.o ../../lapack-3.6.1/SRC/strti2.o ../../lapack-3.6.1/SRC/strtri.o ../../lapack-3.6.1/SRC/strtrs.o ../../lapack-3.6.1/SRC/strttf.o ../../lapack-3.6.1/SRC/strttp.o ../../lapack-3.6.1/SRC/stzrzf.o ../../lapack-3.6.1/SRC/xerbla_array.o ../../lapack-3.6.1/SRC/xerbla.o ../../lapack-3.6.1/SRC/zbbcsd.o ../../lapack-3.6.1/SRC/zbdsqr.o ../../lapack-3.6.1/SRC/zcgesv.o ../../lapack-3.6.1/SRC/zcposv.o ../../lapack-3.6.1/SRC/zdrscl.o ../../lapack-3.6.1/SRC/zgbbrd.o ../../lapack-3.6.1/SRC/zgbcon.o ../../lapack-3.6.1/SRC/zgbequb.o ../../lapack-3.6.1/SRC/zgbequ.o ../../lapack-3.6.1/SRC/zgbrfs.o ../../lapack-3.6.1/SRC/zgbsv.o ../../lapack-3.6.1/SRC/zgbsvx.o ../../lapack-3.6.1/SRC/zgbtf2.o ../../lapack-3.6.1/SRC/zgbtrf.o ../../lapack-3.6.1/SRC/zgbtrs.o ../../lapack-3.6.1/SRC/zgebak.o ../../lapack-3.6.1/SRC/zgebal.o ../../lapack-3.6.1/SRC/zgebd2.o ../../lapack-3.6.1/SRC/zgebrd.o ../../lapack-3.6.1/SRC/zgecon.o ../../lapack-3.6.1/SRC/zgeequb.o ../../lapack-3.6.1/SRC/zgeequ.o ../../lapack-3.6.1/SRC/zgees.o ../../lapack-3.6.1/SRC/zgeesx.o ../../lapack-3.6.1/SRC/zgeev.o ../../lapack-3.6.1/SRC/zgeevx.o ../../lapack-3.6.1/SRC/zgehd2.o ../../lapack-3.6.1/SRC/zgehrd.o ../../lapack-3.6.1/SRC/zgejsv.o ../../lapack-3.6.1/SRC/zgelq2.o ../../lapack-3.6.1/SRC/zgelqf.o ../../lapack-3.6.1/SRC/zgelsd.o ../../lapack-3.6.1/SRC/zgels.o ../../lapack-3.6.1/SRC/zgelss.o ../../lapack-3.6.1/SRC/zgelsy.o ../../lapack-3.6.1/SRC/zgemqrt.o ../../lapack-3.6.1/SRC/zgeql2.o ../../lapack-3.6.1/SRC/zgeqlf.o ../../lapack-3.6.1/SRC/zgeqp3.o ../../lapack-3.6.1/SRC/zgeqr2.o ../../lapack-3.6.1/SRC/zgeqr2p.o ../../lapack-3.6.1/SRC/zgeqrf.o ../../lapack-3.6.1/SRC/zgeqrfp.o ../../lapack-3.6.1/SRC/zgeqrt2.o ../../lapack-3.6.1/SRC/zgeqrt3.o ../../lapack-3.6.1/SRC/zgeqrt.o ../../lapack-3.6.1/SRC/zgerfs.o ../../lapack-3.6.1/SRC/zgerq2.o ../../lapack-3.6.1/SRC/zgerqf.o ../../lapack-3.6.1/SRC/zgesc2.o ../../lapack-3.6.1/SRC/zgesdd.o ../../lapack-3.6.1/SRC/zgesvd.o ../../lapack-3.6.1/SRC/zgesvdx.o ../../lapack-3.6.1/SRC/zgesvj.o ../../lapack-3.6.1/SRC/zgesv.o ../../lapack-3.6.1/SRC/zgesvx.o ../../lapack-3.6.1/SRC/zgetc2.o ../../lapack-3.6.1/SRC/zgetf2.o ../../lapack-3.6.1/SRC/zgetrf2.o ../../lapack-3.6.1/SRC/zgetrf.o ../../lapack-3.6.1/SRC/zgetri.o ../../lapack-3.6.1/SRC/zgetrs.o ../../lapack-3.6.1/SRC/zggbak.o ../../lapack-3.6.1/SRC/zggbal.o ../../lapack-3.6.1/SRC/zgges3.o ../../lapack-3.6.1/SRC/zgges.o ../../lapack-3.6.1/SRC/zggesx.o ../../lapack-3.6.1/SRC/zggev3.o ../../lapack-3.6.1/SRC/zggev.o ../../lapack-3.6.1/SRC/zggevx.o ../../lapack-3.6.1/SRC/zggglm.o ../../lapack-3.6.1/SRC/zgghd3.o ../../lapack-3.6.1/SRC/zgghrd.o ../../lapack-3.6.1/SRC/zgglse.o ../../lapack-3.6.1/SRC/zggqrf.o ../../lapack-3.6.1/SRC/zggrqf.o ../../lapack-3.6.1/SRC/zggsvd3.o ../../lapack-3.6.1/SRC/zggsvp3.o ../../lapack-3.6.1/SRC/zgsvj0.o ../../lapack-3.6.1/SRC/zgsvj1.o ../../lapack-3.6.1/SRC/zgtcon.o ../../lapack-3.6.1/SRC/zgtrfs.o ../../lapack-3.6.1/SRC/zgtsv.o ../../lapack-3.6.1/SRC/zgtsvx.o ../../lapack-3.6.1/SRC/zgttrf.o ../../lapack-3.6.1/SRC/zgttrs.o ../../lapack-3.6.1/SRC/zgtts2.o ../../lapack-3.6.1/SRC/zhbevd.o ../../lapack-3.6.1/SRC/zhbev.o ../../lapack-3.6.1/SRC/zhbevx.o ../../lapack-3.6.1/SRC/zhbgst.o ../../lapack-3.6.1/SRC/zhbgvd.o ../../lapack-3.6.1/SRC/zhbgv.o ../../lapack-3.6.1/SRC/zhbgvx.o ../../lapack-3.6.1/SRC/zhbtrd.o ../../lapack-3.6.1/SRC/zhecon.o ../../lapack-3.6.1/SRC/zhecon_rook.o ../../lapack-3.6.1/SRC/zheequb.o ../../lapack-3.6.1/SRC/zheevd.o ../../lapack-3.6.1/SRC/zheev.o ../../lapack-3.6.1/SRC/zheevr.o ../../lapack-3.6.1/SRC/zheevx.o ../../lapack-3.6.1/SRC/zhegs2.o ../../lapack-3.6.1/SRC/zhegst.o ../../lapack-3.6.1/SRC/zhegvd.o ../../lapack-3.6.1/SRC/zhegv.o ../../lapack-3.6.1/SRC/zhegvx.o ../../lapack-3.6.1/SRC/zherfs.o ../../lapack-3.6.1/SRC/zhesv.o ../../lapack-3.6.1/SRC/zhesv_rook.o ../../lapack-3.6.1/SRC/zhesvx.o ../../lapack-3.6.1/SRC/zheswapr.o ../../lapack-3.6.1/SRC/zhetd2.o ../../lapack-3.6.1/SRC/zhetf2.o ../../lapack-3.6.1/SRC/zhetf2_rook.o ../../lapack-3.6.1/SRC/zhetrd.o ../../lapack-3.6.1/SRC/zhetrf.o ../../lapack-3.6.1/SRC/zhetrf_rook.o ../../lapack-3.6.1/SRC/zhetri2.o ../../lapack-3.6.1/SRC/zhetri2x.o ../../lapack-3.6.1/SRC/zhetri.o ../../lapack-3.6.1/SRC/zhetri_rook.o ../../lapack-3.6.1/SRC/zhetrs2.o ../../lapack-3.6.1/SRC/zhetrs.o ../../lapack-3.6.1/SRC/zhetrs_rook.o ../../lapack-3.6.1/SRC/zhfrk.o ../../lapack-3.6.1/SRC/zhgeqz.o ../../lapack-3.6.1/SRC/zhpcon.o ../../lapack-3.6.1/SRC/zhpevd.o ../../lapack-3.6.1/SRC/zhpev.o ../../lapack-3.6.1/SRC/zhpevx.o ../../lapack-3.6.1/SRC/zhpgst.o ../../lapack-3.6.1/SRC/zhpgvd.o ../../lapack-3.6.1/SRC/zhpgv.o ../../lapack-3.6.1/SRC/zhpgvx.o ../../lapack-3.6.1/SRC/zhprfs.o ../../lapack-3.6.1/SRC/zhpsv.o ../../lapack-3.6.1/SRC/zhpsvx.o ../../lapack-3.6.1/SRC/zhptrd.o ../../lapack-3.6.1/SRC/zhptrf.o ../../lapack-3.6.1/SRC/zhptri.o ../../lapack-3.6.1/SRC/zhptrs.o ../../lapack-3.6.1/SRC/zhsein.o ../../lapack-3.6.1/SRC/zhseqr.o ../../lapack-3.6.1/SRC/zlabrd.o ../../lapack-3.6.1/SRC/zlacgv.o ../../lapack-3.6.1/SRC/zlacn2.o ../../lapack-3.6.1/SRC/zlacon.o ../../lapack-3.6.1/SRC/zlacp2.o ../../lapack-3.6.1/SRC/zlacpy.o ../../lapack-3.6.1/SRC/zlacrm.o ../../lapack-3.6.1/SRC/zlacrt.o ../../lapack-3.6.1/SRC/zladiv.o ../../lapack-3.6.1/SRC/zlaed0.o ../../lapack-3.6.1/SRC/zlaed7.o ../../lapack-3.6.1/SRC/zlaed8.o ../../lapack-3.6.1/SRC/zlaein.o ../../lapack-3.6.1/SRC/zlaesy.o ../../lapack-3.6.1/SRC/zlaev2.o ../../lapack-3.6.1/SRC/zlag2c.o ../../lapack-3.6.1/SRC/zlags2.o ../../lapack-3.6.1/SRC/zlagtm.o ../../lapack-3.6.1/SRC/zlahef.o ../../lapack-3.6.1/SRC/zlahef_rook.o ../../lapack-3.6.1/SRC/zlahqr.o ../../lapack-3.6.1/SRC/zlahr2.o ../../lapack-3.6.1/SRC/zlaic1.o ../../lapack-3.6.1/SRC/zlals0.o ../../lapack-3.6.1/SRC/zlalsa.o ../../lapack-3.6.1/SRC/zlalsd.o ../../lapack-3.6.1/SRC/zlangb.o ../../lapack-3.6.1/SRC/zlange.o ../../lapack-3.6.1/SRC/zlangt.o ../../lapack-3.6.1/SRC/zlanhb.o ../../lapack-3.6.1/SRC/zlanhe.o ../../lapack-3.6.1/SRC/zlanhf.o ../../lapack-3.6.1/SRC/zlanhp.o ../../lapack-3.6.1/SRC/zlanhs.o ../../lapack-3.6.1/SRC/zlanht.o ../../lapack-3.6.1/SRC/zlansb.o ../../lapack-3.6.1/SRC/zlansp.o ../../lapack-3.6.1/SRC/zlansy.o ../../lapack-3.6.1/SRC/zlantb.o ../../lapack-3.6.1/SRC/zlantp.o ../../lapack-3.6.1/SRC/zlantr.o ../../lapack-3.6.1/SRC/zlapll.o ../../lapack-3.6.1/SRC/zlapmr.o ../../lapack-3.6.1/SRC/zlapmt.o ../../lapack-3.6.1/SRC/zlaqgb.o ../../lapack-3.6.1/SRC/zlaqge.o ../../lapack-3.6.1/SRC/zlaqhb.o ../../lapack-3.6.1/SRC/zlaqhe.o ../../lapack-3.6.1/SRC/zlaqhp.o ../../lapack-3.6.1/SRC/zlaqp2.o ../../lapack-3.6.1/SRC/zlaqps.o ../../lapack-3.6.1/SRC/zlaqr0.o ../../lapack-3.6.1/SRC/zlaqr1.o ../../lapack-3.6.1/SRC/zlaqr2.o ../../lapack-3.6.1/SRC/zlaqr3.o ../../lapack-3.6.1/SRC/zlaqr4.o ../../lapack-3.6.1/SRC/zlaqr5.o ../../lapack-3.6.1/SRC/zlaqsb.o ../../lapack-3.6.1/SRC/zlaqsp.o ../../lapack-3.6.1/SRC/zlaqsy.o ../../lapack-3.6.1/SRC/zlar1v.o ../../lapack-3.6.1/SRC/zlar2v.o ../../lapack-3.6.1/SRC/zlarcm.o ../../lapack-3.6.1/SRC/zlarfb.o ../../lapack-3.6.1/SRC/zlarfg.o ../../lapack-3.6.1/SRC/zlarfgp.o ../../lapack-3.6.1/SRC/zlarf.o ../../lapack-3.6.1/SRC/zlarft.o ../../lapack-3.6.1/SRC/zlarfx.o ../../lapack-3.6.1/SRC/zlargv.o ../../lapack-3.6.1/SRC/zlarnv.o ../../lapack-3.6.1/SRC/zlarrv.o ../../lapack-3.6.1/SRC/zlartg.o ../../lapack-3.6.1/SRC/zlartv.o ../../lapack-3.6.1/SRC/zlarzb.o ../../lapack-3.6.1/SRC/zlarz.o ../../lapack-3.6.1/SRC/zlarzt.o ../../lapack-3.6.1/SRC/zlascl.o ../../lapack-3.6.1/SRC/zlaset.o ../../lapack-3.6.1/SRC/zlasr.o ../../lapack-3.6.1/SRC/zlassq.o ../../lapack-3.6.1/SRC/zlaswp.o ../../lapack-3.6.1/SRC/zlasyf.o ../../lapack-3.6.1/SRC/zlasyf_rook.o ../../lapack-3.6.1/SRC/zlat2c.o ../../lapack-3.6.1/SRC/zlatbs.o ../../lapack-3.6.1/SRC/zlatdf.o ../../lapack-3.6.1/SRC/zlatps.o ../../lapack-3.6.1/SRC/zlatrd.o ../../lapack-3.6.1/SRC/zlatrs.o ../../lapack-3.6.1/SRC/zlatrz.o ../../lapack-3.6.1/SRC/zlauu2.o ../../lapack-3.6.1/SRC/zlauum.o ../../lapack-3.6.1/SRC/zpbcon.o ../../lapack-3.6.1/SRC/zpbequ.o ../../lapack-3.6.1/SRC/zpbrfs.o ../../lapack-3.6.1/SRC/zpbstf.o ../../lapack-3.6.1/SRC/zpbsv.o ../../lapack-3.6.1/SRC/zpbsvx.o ../../lapack-3.6.1/SRC/zpbtf2.o ../../lapack-3.6.1/SRC/zpbtrf.o ../../lapack-3.6.1/SRC/zpbtrs.o ../../lapack-3.6.1/SRC/zpftrf.o ../../lapack-3.6.1/SRC/zpftri.o ../../lapack-3.6.1/SRC/zpftrs.o ../../lapack-3.6.1/SRC/zpocon.o ../../lapack-3.6.1/SRC/zpoequb.o ../../lapack-3.6.1/SRC/zpoequ.o ../../lapack-3.6.1/SRC/zporfs.o ../../lapack-3.6.1/SRC/zposv.o ../../lapack-3.6.1/SRC/zposvx.o ../../lapack-3.6.1/SRC/zpotf2.o ../../lapack-3.6.1/SRC/zpotrf2.o ../../lapack-3.6.1/SRC/zpotrf.o ../../lapack-3.6.1/SRC/zpotri.o ../../lapack-3.6.1/SRC/zpotrs.o ../../lapack-3.6.1/SRC/zppcon.o ../../lapack-3.6.1/SRC/zppequ.o ../../lapack-3.6.1/SRC/zpprfs.o ../../lapack-3.6.1/SRC/zppsv.o ../../lapack-3.6.1/SRC/zppsvx.o ../../lapack-3.6.1/SRC/zpptrf.o ../../lapack-3.6.1/SRC/zpptri.o ../../lapack-3.6.1/SRC/zpptrs.o ../../lapack-3.6.1/SRC/zpstf2.o ../../lapack-3.6.1/SRC/zpstrf.o ../../lapack-3.6.1/SRC/zptcon.o ../../lapack-3.6.1/SRC/zpteqr.o ../../lapack-3.6.1/SRC/zptrfs.o ../../lapack-3.6.1/SRC/zptsv.o ../../lapack-3.6.1/SRC/zptsvx.o ../../lapack-3.6.1/SRC/zpttrf.o ../../lapack-3.6.1/SRC/zpttrs.o ../../lapack-3.6.1/SRC/zptts2.o ../../lapack-3.6.1/SRC/zrot.o ../../lapack-3.6.1/SRC/zspcon.o ../../lapack-3.6.1/SRC/zspmv.o ../../lapack-3.6.1/SRC/zsprfs.o ../../lapack-3.6.1/SRC/zspr.o ../../lapack-3.6.1/SRC/zspsv.o ../../lapack-3.6.1/SRC/zspsvx.o ../../lapack-3.6.1/SRC/zsptrf.o ../../lapack-3.6.1/SRC/zsptri.o ../../lapack-3.6.1/SRC/zsptrs.o ../../lapack-3.6.1/SRC/zstedc.o ../../lapack-3.6.1/SRC/zstegr.o ../../lapack-3.6.1/SRC/zstein.o ../../lapack-3.6.1/SRC/zstemr.o ../../lapack-3.6.1/SRC/zsteqr.o ../../lapack-3.6.1/SRC/zsycon.o ../../lapack-3.6.1/SRC/zsycon_rook.o ../../lapack-3.6.1/SRC/zsyconv.o ../../lapack-3.6.1/SRC/zsyequb.o ../../lapack-3.6.1/SRC/zsymv.o ../../lapack-3.6.1/SRC/zsyrfs.o ../../lapack-3.6.1/SRC/zsyr.o ../../lapack-3.6.1/SRC/zsysv.o ../../lapack-3.6.1/SRC/zsysv_rook.o ../../lapack-3.6.1/SRC/zsysvx.o ../../lapack-3.6.1/SRC/zsyswapr.o ../../lapack-3.6.1/SRC/zsytf2.o ../../lapack-3.6.1/SRC/zsytf2_rook.o ../../lapack-3.6.1/SRC/zsytrf.o ../../lapack-3.6.1/SRC/zsytrf_rook.o ../../lapack-3.6.1/SRC/zsytri2.o ../../lapack-3.6.1/SRC/zsytri2x.o ../../lapack-3.6.1/SRC/zsytri.o ../../lapack-3.6.1/SRC/zsytri_rook.o ../../lapack-3.6.1/SRC/zsytrs2.o ../../lapack-3.6.1/SRC/zsytrs.o ../../lapack-3.6.1/SRC/zsytrs_rook.o ../../lapack-3.6.1/SRC/ztbcon.o ../../lapack-3.6.1/SRC/ztbrfs.o ../../lapack-3.6.1/SRC/ztbtrs.o ../../lapack-3.6.1/SRC/ztfsm.o ../../lapack-3.6.1/SRC/ztftri.o ../../lapack-3.6.1/SRC/ztfttp.o ../../lapack-3.6.1/SRC/ztfttr.o ../../lapack-3.6.1/SRC/ztgevc.o ../../lapack-3.6.1/SRC/ztgex2.o ../../lapack-3.6.1/SRC/ztgexc.o ../../lapack-3.6.1/SRC/ztgsen.o ../../lapack-3.6.1/SRC/ztgsja.o ../../lapack-3.6.1/SRC/ztgsna.o ../../lapack-3.6.1/SRC/ztgsy2.o ../../lapack-3.6.1/SRC/ztgsyl.o ../../lapack-3.6.1/SRC/ztpcon.o ../../lapack-3.6.1/SRC/ztpmqrt.o ../../lapack-3.6.1/SRC/ztpqrt2.o ../../lapack-3.6.1/SRC/ztpqrt.o ../../lapack-3.6.1/SRC/ztprfb.o ../../lapack-3.6.1/SRC/ztprfs.o ../../lapack-3.6.1/SRC/ztptri.o ../../lapack-3.6.1/SRC/ztptrs.o ../../lapack-3.6.1/SRC/ztpttf.o ../../lapack-3.6.1/SRC/ztpttr.o ../../lapack-3.6.1/SRC/ztrcon.o ../../lapack-3.6.1/SRC/ztrevc3.o ../../lapack-3.6.1/SRC/ztrevc.o ../../lapack-3.6.1/SRC/ztrexc.o ../../lapack-3.6.1/SRC/ztrrfs.o ../../lapack-3.6.1/SRC/ztrsen.o ../../lapack-3.6.1/SRC/ztrsna.o ../../lapack-3.6.1/SRC/ztrsyl.o ../../lapack-3.6.1/SRC/ztrti2.o ../../lapack-3.6.1/SRC/ztrtri.o ../../lapack-3.6.1/SRC/ztrtrs.o ../../lapack-3.6.1/SRC/ztrttf.o ../../lapack-3.6.1/SRC/ztrttp.o ../../lapack-3.6.1/SRC/ztzrzf.o ../../lapack-3.6.1/SRC/zunbdb1.o ../../lapack-3.6.1/SRC/zunbdb2.o ../../lapack-3.6.1/SRC/zunbdb3.o ../../lapack-3.6.1/SRC/zunbdb4.o ../../lapack-3.6.1/SRC/zunbdb5.o ../../lapack-3.6.1/SRC/zunbdb6.o ../../lapack-3.6.1/SRC/zunbdb.o ../../lapack-3.6.1/SRC/zuncsd2by1.o ../../lapack-3.6.1/SRC/zuncsd.o ../../lapack-3.6.1/SRC/zung2l.o ../../lapack-3.6.1/SRC/zung2r.o ../../lapack-3.6.1/SRC/zungbr.o ../../lapack-3.6.1/SRC/zunghr.o ../../lapack-3.6.1/SRC/zungl2.o ../../lapack-3.6.1/SRC/zunglq.o ../../lapack-3.6.1/SRC/zungql.o ../../lapack-3.6.1/SRC/zungqr.o ../../lapack-3.6.1/SRC/zungr2.o ../../lapack-3.6.1/SRC/zungrq.o ../../lapack-3.6.1/SRC/zungtr.o ../../lapack-3.6.1/SRC/zunm22.o ../../lapack-3.6.1/SRC/zunm2l.o ../../lapack-3.6.1/SRC/zunm2r.o ../../lapack-3.6.1/SRC/zunmbr.o ../../lapack-3.6.1/SRC/zunmhr.o ../../lapack-3.6.1/SRC/zunml2.o ../../lapack-3.6.1/SRC/zunmlq.o ../../lapack-3.6.1/SRC/zunmql.o ../../lapack-3.6.1/SRC/zunmqr.o ../../lapack-3.6.1/SRC/zunmr2.o ../../lapack-3.6.1/SRC/zunmr3.o ../../lapack-3.6.1/SRC/zunmrq.o ../../lapack-3.6.1/SRC/zunmrz.o ../../lapack-3.6.1/SRC/zunmtr.o ../../lapack-3.6.1/SRC/zupgtr.o ../../lapack-3.6.1/SRC/zupmtr.o" -LINALG="../../lapack-3.6.1/libcblas.a ../../lapack-3.6.1/libblas.a ../../lapack-3.6.1/liblapack.a" - -f2py --f90exec=mpif90 --fcompiler=gnu95 -c total_energy.f90 -m total_energy $PROJECT_INC_FOLDERS $PROJECT_LIB_FOLDERS \ - $PROJECT_LIBS $PWOBJS $UTILOBJS $MODOBJS $MODOBJS2 $LINALGOBJS $LINALG $FOXLIBS ../../clib/clib.a - diff --git a/install/total_energy_module/total_energy_env_gnu.sh b/install/total_energy_module/total_energy_env_gnu.sh deleted file mode 100644 index d267e0e88..000000000 --- a/install/total_energy_module/total_energy_env_gnu.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash - -echo "In addition to sourcing this script, source mlmm-ldrd-data/networks/environment/load_mlmm_blake_modules.sh" - -export FCFLAGS="-O3 -g -fPIC" -export FFLAGS="-O3 -g -fPIC" -export CFLAGS=-fPIC -export CPPFLAGS=-fPIC diff --git a/install/total_energy_module/x_ac_qe_f90.m4.patch b/install/total_energy_module/x_ac_qe_f90.m4.patch deleted file mode 100644 index 8534d11bc..000000000 --- a/install/total_energy_module/x_ac_qe_f90.m4.patch +++ /dev/null @@ -1,17 +0,0 @@ -diff --git a/install/m4/x_ac_qe_f90.m4 b/install/m4/x_ac_qe_f90.m4 -index 91d1740..32deba1 100644 ---- a/install/m4/x_ac_qe_f90.m4 -+++ b/install/m4/x_ac_qe_f90.m4 -@@ -239,9 +239,11 @@ if test "$use_openmp" -eq 1 ; then - try_ldflags="$try_ldflags $try_ldflags_openmp" - fi - -+foxflags=-fPIC -+ - if test "$fflags" = "" ; then fflags=$try_fflags ; fi - if test "$f90flags" = "" ; then f90flags=$try_f90flags ; fi --if test "try_foxflags" != ""; then foxflags=$try_foxflags; fi -+if test "try_foxflags" != ""; then foxflags="$try_foxflags $foxflags"; fi - if test "$fflags_noopt" = "" ; then fflags_noopt=$try_fflags_noopt ; fi - if test "$fflags_nomain" = "" ; then fflags_nomain=$try_fflags_nomain ; fi - diff --git 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0.8750000000 0.8750000000 -Al 0.8750000000 0.7500000000 0.8750000000 -Al 0.8750000000 0.8750000000 0.7500000000 diff --git a/ml-dft-sandia/total_energy_module/README.md b/ml-dft-sandia/total_energy_module/README.md deleted file mode 100644 index f6b1134dd..000000000 --- a/ml-dft-sandia/total_energy_module/README.md +++ /dev/null @@ -1,83 +0,0 @@ -# Build Python Module to run QE "v of rho" subroutines - -## Using Intel/MKL - -### Initial Setup - -1. Clone QE source: git clone https://github.com/QEF/q-e.git - On blake, it may be necessary to 'module load git' first. -2. Change into the q-e directory and check out out the qe-6.4.1 tag: - git checkout qe-6.4.1 -3. Set up a virtual environment that includes numpy (f2py). On blake, you can - load the python/3.7.3 module and run the script ../networks/setup\_python\_env.sh. -4. Patch the QE source so that the FoX project is built with -fPIC: - 1. change to the q-e/install/m4 folder - 2. patch < path/to/this/folder/x\_ac\_qe\_f90.m4.patch -5. Copy total_energy.f90 to q-e/PW/src. This file contains the Fortran 90 code - that f2py will create a Python binding to. - -### Build Quantum Espresso - -1. If you already have a build of QE, go into the q-e folder and run 'make veryclean'. -2. Set up the environment. On blake, you can source blake\_qe\_env.sh. For other - platforms, load appropriate compiler and MPI modules, then export/set - environment variables as shown in blake\_qe\_env.sh. You will not be able - to build the Python module unless these environment variables are set when - configure is run. -3. Run 'configure' and 'make all' - -### Build the Python Module -1. Load a Python 3 module (on blake: module load python/3.7.3) -2. Activate the virtual environment. If using the mlmm\_env environment, then - 'source /path/to/mlmm\_env/bin/activate'. -3. Load other needed modules. On blake, you can source blake\_qe\_env.sh. -4. Change to q-e/PW/src and run the build\_total\_energy\_energy\_module.sh - script in this folder. If the build is successful, a file named - total_energy.cpython-37m-x86\_64-linux-gnu.so will be generated. It contains - the Python module. - -## Using GNU and the mlmm_env environment on Blake - -### Initial Setup - -1. Clone QE source: git clone https://github.com/QEF/q-e.git - On blake, it may be necessary to 'module load git' first. -2. Change into the q-e directory and check out out the qe-6.4.1 tag: - git checkout qe-6.4.1 -3. Set up the mlmm_env virtual environment if you haven't already: - 1. cd ../networks/environment/ - 2. source load\_mlmm\_blake\_modules.sh - 3. ./setup\_python\_env.sh. -4. Patch the QE source so that the FoX project is built with -fPIC: - 1. change to the q-e/install/m4 folder - 2. patch < path/to/this/folder/x\_ac\_qe\_f90.m4.patch -5. Copy total_energy.f90 to q-e/PW/src. This file contains the Fortran 90 code - that f2py will create a Python binding to. - -### Build Quantum Espresso - -1. If you already have a build of QE, go into the q-e folder and run 'make veryclean'. -2. Set up the environment. - 1. source networks/environment/load\_mlmm\_blake\_modules.sh - 2. source total\_energy/blake\_qe\_env\_gnu.sh. -3. Run 'configure' and 'make all' -4. Because the GNU build doesn't use MKL, QE builds its own linear algebra libraries. These - don't get unpacked until the make step, which means we have to rebuild them - after making a slight modification. Open q-e/LAPACK/make.inc in a text editor. On line 22, - add the option '-fPIC' to the end of the "NOOPT" setting. -5. In q-e/LAPACK, run 'make clean' and 'make' -6. In q-e/LAPACK/CBLAS, run 'make clean' and 'make'. - -### Build the Python Module -1. Set up the environment. - 1. source networks/environment/load\_mlmm\_blake\_modules.sh - 2. Activate the mlmm_env virtual environment: source /path/to/mlmm\_env/bin/activate -2. Change to q-e/PW/src and run the build\_total\_energy\_energy\_module\_gnu.sh - script in the total\_energy folder. If the build is successful, a file named - total_energy.cpython-37m-x86\_64-linux-gnu.so will be generated. It contains - the Python module. - -## Use the Python Module -1. Add the folder that contains total_energy.cpython-37m-x86\_64-linux-gnu.so to - your PYTHONPATH. -2. In the Python REPL or script: import total_energy diff --git a/ml-dft-sandia/total_energy_module/blake_qe_env.sh b/ml-dft-sandia/total_energy_module/blake_qe_env.sh deleted file mode 100644 index 521adb6a0..000000000 --- a/ml-dft-sandia/total_energy_module/blake_qe_env.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -module purge -module load python/3.7.3 - -#. f2py_env/bin/activate - -module load intel/compilers/18.1.163 -module load openmpi/2.1.2/intel/18.1.163 - -export FCFLAGS=-fPIC -export FFLAGS=-fPIC -export CFLAGS=-fPIC -export CPPFLAGS=-fPIC - diff --git a/ml-dft-sandia/total_energy_module/blake_qe_env_gnu.sh b/ml-dft-sandia/total_energy_module/blake_qe_env_gnu.sh deleted file mode 100644 index 276461661..000000000 --- a/ml-dft-sandia/total_energy_module/blake_qe_env_gnu.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -echo "In addition to sourcing this script, source mlmm-ldrd-data/networks/environment/load_mlmm_blake_modules.sh" - -export FCFLAGS=-fPIC -export FFLAGS=-fPIC -export CFLAGS=-fPIC -export CPPFLAGS=-fPIC - diff --git a/ml-dft-sandia/total_energy_module/build_total_energy_module.sh b/ml-dft-sandia/total_energy_module/build_total_energy_module.sh deleted file mode 100755 index fe888e4ee..000000000 --- a/ml-dft-sandia/total_energy_module/build_total_energy_module.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - - -# Object files from the PW/src folder -PWOBJS="a2fmod.o add_bfield.o add_efield.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_nlpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o bp_mod.o bp_c_phase.o bp_calc_btq.o bp_qvan3.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o deriv_drhoc.o divide_class.o divide_class_so.o divide_et_impera.o dqvan2.o drhoc.o rotate_wfc.o run_driver.o dvloc_of_g.o dynamics_module.o efermig.o efermit.o electrons.o eqvect.o esm.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gen_at_dj.o gen_at_dy.o gen_us_dj.o gen_us_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o init_ns.o init_q_aeps.o init_run.o init_us_0.o init_us_b0.o init_us_1.o init_us_2.o init_at_1.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o makov_payne.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o qvan2.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_rhoc.o set_vrs.o setlocal.o setup.o spinor.o sph_ind.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tabd.o tetra.o transform_becsum_so.o transform_becsum_nc.o transform_qq_so.o trnvecc.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_of_g.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_proj.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o wannier_enrg.o" - -# Object files from the UtilXlib folder -UTILOBJS="../../UtilXlib/clocks_handler.o ../../UtilXlib/cuda_util.o ../../UtilXlib/divide.o ../../UtilXlib/data_buffer.o ../../UtilXlib/error_handler.o ../../UtilXlib/find_free_unit.o ../../UtilXlib/fletcher32_mod.o ../../UtilXlib/mem_counter.o ../../UtilXlib/mp.o ../../UtilXlib/mp_base.o ../../UtilXlib/mp_base_gpu.o ../../UtilXlib/mp_bands_util.o ../../UtilXlib/parallel_include.o ../../UtilXlib/util_param.o ../../UtilXlib/thread_util.o" - -# Object files from the Modules folder -MODOBJS="../../Modules/atomic_number.o ../../Modules/capital.o ../../Modules/cryst_to_car.o ../../Modules/erf.o ../../Modules/expint.o ../../Modules/functionals.o ../../Modules/generate_k_along_lines.o ../../Modules/lsda_functionals.o ../../Modules/more_functionals.o ../../Modules/has_xml.o ../../Modules/inpfile.o ../../Modules/int_to_char.o ../../Modules/latgen.o ../../Modules/linpack.o ../../Modules/metagga.o ../../Modules/matches.o ../../Modules/plot_io.o ../../Modules/radial_gradients.o ../../Modules/rgen.o ../../Modules/recips.o ../../Modules/remove_tot_torque.o ../../Modules/set_hubbard_l.o ../../Modules/set_hubbard_n.o ../../Modules/simpsn.o ../../Modules/sort.o ../../Modules/sph_bes.o ../../Modules/sph_dbes.o ../../Modules/trimcheck.o ../../Modules/test_input_file.o ../../Modules/date_and_tim.o ../../Modules/volume.o ../../Modules/dylmr2.o ../../Modules/ylmr2.o ../../Modules/wgauss.o ../../Modules/w0gauss.o ../../Modules/w1gauss.o ../../Modules/deviatoric.o" - -MODOBJS2="../../Modules/atom.o ../../Modules/autopilot.o ../../Modules/basic_algebra_routines.o ../../Modules/becmod.o ../../Modules/bfgs_module.o ../../Modules/bspline.o ../../Modules/bz_form.o ../../Modules/cell_base.o ../../Modules/check_stop.o ../../Modules/command_line_options.o ../../Modules/compute_dipole.o ../../Modules/constants.o ../../Modules/constraints_module.o ../../Modules/control_flags.o ../../Modules/coulomb_vcut.o ../../Modules/dist.o ../../Modules/electrons_base.o ../../Modules/environment.o ../../Modules/fcp_variables.o ../../Modules/fd_gradient.o ../../Modules/fft_base.o ../../Modules/fft_rho.o ../../Modules/fsockets.o ../../Modules/funct.o ../../Modules/generate_function.o ../../Modules/gth.o ../../Modules/gradutils.o ../../Modules/gvecw.o ../../Modules/input_parameters.o ../../Modules/invmat.o ../../Modules/io_files.o ../../Modules/io_global.o ../../Modules/ions_base.o ../../Modules/kernel_table.o ../../Modules/kind.o ../../Modules/libxc.o ../../Modules/mdiis.o ../../Modules/mm_dispersion.o ../../Modules/mp_bands.o ../../Modules/mp_exx.o ../../Modules/mp_global.o ../../Modules/mp_images.o ../../Modules/mp_pools.o ../../Modules/mp_wave.o ../../Modules/mp_world.o ../../Modules/noncol.o ../../Modules/open_close_input_file.o ../../Modules/parameters.o ../../Modules/parser.o ../../Modules/paw_variables.o ../../Modules/plugin_flags.o ../../Modules/plugin_arguments.o ../../Modules/plugin_variables.o ../../Modules/pseudo_types.o ../../Modules/pw_dot.o ../../Modules/qmmm.o ../../Modules/radial_grids.o ../../Modules/random_numbers.o ../../Modules/read_cards.o ../../Modules/read_input.o ../../Modules/read_namelists.o ../../Modules/read_ncpp.o ../../Modules/read_pseudo.o ../../Modules/read_upf_v1.o ../../Modules/read_upf_v2.o ../../Modules/read_upf_schema.o ../../Modules/read_uspp.o ../../Modules/recvec.o ../../Modules/recvec_subs.o ../../Modules/run_info.o ../../Modules/space_group.o ../../Modules/set_signal.o ../../Modules/setqf.o ../../Modules/splinelib.o ../../Modules/timestep.o ../../Modules/tsvdw.o ../../Modules/emend_upf.o ../../Modules/upf.o ../../Modules/upf_to_internal.o ../../Modules/uspp.o ../../Modules/version.o ../../Modules/wannier_gw.o ../../Modules/wannier_new.o ../../Modules/wave_base.o ../../Modules/wavefunctions.o ../../Modules/wrappers.o ../../Modules/write_upf.o ../../Modules/write_upf_v2.o ../../Modules/write_upf_schema.o ../../Modules/ws_base.o ../../Modules/xc_vdW_DF.o ../../Modules/xc_rVV10.o ../../Modules/io_base.o ../../Modules/qes_types_module.o ../../Modules/qes_libs_module.o ../../Modules/qes_write_module.o ../../Modules/qes_reset_module.o ../../Modules/qes_init_module.o ../../Modules/qes_read_module.o ../../Modules/qes_bcast_module.o ../../Modules/qexsd.o ../../Modules/qexsd_input.o ../../Modules/hdf5_qe.o ../../Modules/qeh5_module.o ../../Modules/fox_init_module.o ../../Modules/xsf.o ../../Modules/wyckoff.o ../../Modules/wypos.o ../../Modules/zdotc_wrapper.o ../../Modules/zvscal.o" - -# Libraries from the external FoX project -FOXLIBS="-L../../FoX/lib -lFoX_dom -lFoX_utils -lFoX_wcml -lFoX_wkml -lFoX_wxml -lFoX_common -lFoX_sax -lFoX_common -lFoX_fsys" -PROJECT_LIB_FOLDERS=" -L../../Modules -L../../KS_Solvers -L../../FFTXlib -L../../LAXlib -L../../UtilXlib -L../../dft-d3" -PROJECT_LIBS="-lqemod -lks_solvers -lqefft -lqela -lutil -ldftd3qe" -PROJECT_INC_FOLDERS="-I../../Modules -I../../FFTXlib -I../../LAXlib -I../../KS_Solvers -I../../UtilXlib" - -MKLLIBS="-lmkl_scalapack_lp64 -lmkl_blacs_openmpi_lp64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lmkl_avx512 -lmkl_def -L$MKL_ROOT/lib/intel64" - -f2py --f90exec=mpif90 --fcompiler=intelem -c total_energy.f90 -m total_energy $PROJECT_INC_FOLDERS $PROJECT_LIB_FOLDERS \ - $PROJECT_LIBS $PWOBJS $UTILOBJS $MODOBJS $MODOBJS2 $MKLLIBS $FOXLIBS ../../clib/clib.a diff --git a/ml-dft-sandia/total_energy_module/build_total_energy_module_gnu.sh b/ml-dft-sandia/total_energy_module/build_total_energy_module_gnu.sh deleted file mode 100755 index c8b6f4c0f..000000000 --- a/ml-dft-sandia/total_energy_module/build_total_energy_module_gnu.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# Before running this script, make sure the mlmm_env has been activated - -# Object files from the PW/src folder -PWOBJS="a2fmod.o add_bfield.o add_efield.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_nlpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o bp_mod.o bp_c_phase.o bp_calc_btq.o bp_qvan3.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o deriv_drhoc.o divide_class.o divide_class_so.o divide_et_impera.o dqvan2.o drhoc.o rotate_wfc.o run_driver.o dvloc_of_g.o dynamics_module.o efermig.o efermit.o electrons.o eqvect.o esm.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gen_at_dj.o gen_at_dy.o gen_us_dj.o gen_us_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o init_ns.o init_q_aeps.o init_run.o init_us_0.o init_us_b0.o init_us_1.o init_us_2.o init_at_1.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o makov_payne.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o qvan2.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_rhoc.o set_vrs.o setlocal.o setup.o spinor.o sph_ind.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tabd.o tetra.o transform_becsum_so.o transform_becsum_nc.o transform_qq_so.o trnvecc.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_of_g.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_proj.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o wannier_enrg.o" - -# Object files from the UtilXlib folder -UTILOBJS="../../UtilXlib/clocks_handler.o ../../UtilXlib/cuda_util.o ../../UtilXlib/divide.o ../../UtilXlib/data_buffer.o ../../UtilXlib/error_handler.o ../../UtilXlib/find_free_unit.o ../../UtilXlib/fletcher32_mod.o ../../UtilXlib/mem_counter.o ../../UtilXlib/mp.o ../../UtilXlib/mp_base.o ../../UtilXlib/mp_base_gpu.o ../../UtilXlib/mp_bands_util.o ../../UtilXlib/parallel_include.o ../../UtilXlib/util_param.o ../../UtilXlib/thread_util.o" - -# Object files from the Modules folder -MODOBJS="../../Modules/atomic_number.o ../../Modules/capital.o ../../Modules/cryst_to_car.o ../../Modules/erf.o ../../Modules/expint.o ../../Modules/functionals.o ../../Modules/generate_k_along_lines.o ../../Modules/lsda_functionals.o ../../Modules/more_functionals.o ../../Modules/has_xml.o ../../Modules/inpfile.o ../../Modules/int_to_char.o ../../Modules/latgen.o ../../Modules/linpack.o ../../Modules/metagga.o ../../Modules/matches.o ../../Modules/plot_io.o ../../Modules/radial_gradients.o ../../Modules/rgen.o ../../Modules/recips.o ../../Modules/remove_tot_torque.o ../../Modules/set_hubbard_l.o ../../Modules/set_hubbard_n.o ../../Modules/simpsn.o ../../Modules/sort.o ../../Modules/sph_bes.o ../../Modules/sph_dbes.o ../../Modules/trimcheck.o ../../Modules/test_input_file.o ../../Modules/date_and_tim.o ../../Modules/volume.o ../../Modules/dylmr2.o ../../Modules/ylmr2.o ../../Modules/wgauss.o ../../Modules/w0gauss.o ../../Modules/w1gauss.o ../../Modules/deviatoric.o" - -MODOBJS2="../../Modules/atom.o ../../Modules/autopilot.o ../../Modules/basic_algebra_routines.o ../../Modules/becmod.o ../../Modules/bfgs_module.o ../../Modules/bspline.o ../../Modules/bz_form.o ../../Modules/cell_base.o ../../Modules/check_stop.o ../../Modules/command_line_options.o ../../Modules/compute_dipole.o ../../Modules/constants.o ../../Modules/constraints_module.o ../../Modules/control_flags.o ../../Modules/coulomb_vcut.o ../../Modules/dist.o ../../Modules/electrons_base.o ../../Modules/environment.o ../../Modules/fcp_variables.o ../../Modules/fd_gradient.o ../../Modules/fft_base.o ../../Modules/fft_rho.o ../../Modules/fsockets.o ../../Modules/funct.o ../../Modules/generate_function.o ../../Modules/gth.o ../../Modules/gradutils.o ../../Modules/gvecw.o ../../Modules/input_parameters.o ../../Modules/invmat.o ../../Modules/io_files.o ../../Modules/io_global.o ../../Modules/ions_base.o ../../Modules/kernel_table.o ../../Modules/kind.o ../../Modules/libxc.o ../../Modules/mdiis.o ../../Modules/mm_dispersion.o ../../Modules/mp_bands.o ../../Modules/mp_exx.o ../../Modules/mp_global.o ../../Modules/mp_images.o ../../Modules/mp_pools.o ../../Modules/mp_wave.o ../../Modules/mp_world.o ../../Modules/noncol.o ../../Modules/open_close_input_file.o ../../Modules/parameters.o ../../Modules/parser.o ../../Modules/paw_variables.o ../../Modules/plugin_flags.o ../../Modules/plugin_arguments.o ../../Modules/plugin_variables.o ../../Modules/pseudo_types.o ../../Modules/pw_dot.o ../../Modules/qmmm.o ../../Modules/radial_grids.o ../../Modules/random_numbers.o ../../Modules/read_cards.o ../../Modules/read_input.o ../../Modules/read_namelists.o ../../Modules/read_ncpp.o ../../Modules/read_pseudo.o ../../Modules/read_upf_v1.o ../../Modules/read_upf_v2.o ../../Modules/read_upf_schema.o ../../Modules/read_uspp.o ../../Modules/recvec.o ../../Modules/recvec_subs.o ../../Modules/run_info.o ../../Modules/space_group.o ../../Modules/set_signal.o ../../Modules/setqf.o ../../Modules/splinelib.o ../../Modules/timestep.o ../../Modules/tsvdw.o ../../Modules/emend_upf.o ../../Modules/upf.o ../../Modules/upf_to_internal.o ../../Modules/uspp.o ../../Modules/version.o ../../Modules/wannier_gw.o ../../Modules/wannier_new.o ../../Modules/wave_base.o ../../Modules/wavefunctions.o ../../Modules/wrappers.o ../../Modules/write_upf.o ../../Modules/write_upf_v2.o ../../Modules/write_upf_schema.o ../../Modules/ws_base.o ../../Modules/xc_vdW_DF.o ../../Modules/xc_rVV10.o ../../Modules/io_base.o ../../Modules/qes_types_module.o ../../Modules/qes_libs_module.o ../../Modules/qes_write_module.o ../../Modules/qes_reset_module.o ../../Modules/qes_init_module.o ../../Modules/qes_read_module.o ../../Modules/qes_bcast_module.o ../../Modules/qexsd.o ../../Modules/qexsd_input.o ../../Modules/hdf5_qe.o ../../Modules/qeh5_module.o ../../Modules/fox_init_module.o ../../Modules/xsf.o ../../Modules/wyckoff.o ../../Modules/wypos.o ../../Modules/zdotc_wrapper.o ../../Modules/zvscal.o" - -# Libraries from the external FoX project -FOXLIBS="-L../../FoX/lib -lFoX_dom -lFoX_utils -lFoX_wcml -lFoX_wkml -lFoX_wxml -lFoX_common -lFoX_sax -lFoX_common -lFoX_fsys" -PROJECT_LIB_FOLDERS=" -L../../Modules -L../../KS_Solvers -L../../FFTXlib -L../../LAXlib -L../../UtilXlib -L../../dft-d3" -PROJECT_LIBS="-lqemod -lks_solvers -lqefft -lqela -lutil -ldftd3qe" -PROJECT_INC_FOLDERS="-I../../Modules -I../../FFTXlib -I../../LAXlib -I../../KS_Solvers -I../../UtilXlib" - - -LINALGOBJS="../../lapack-3.6.1/SRC/cbbcsd.o ../../lapack-3.6.1/SRC/cbdsqr.o ../../lapack-3.6.1/SRC/cgbbrd.o ../../lapack-3.6.1/SRC/cgbcon.o ../../lapack-3.6.1/SRC/cgbequb.o ../../lapack-3.6.1/SRC/cgbequ.o ../../lapack-3.6.1/SRC/cgbrfs.o ../../lapack-3.6.1/SRC/cgbsv.o ../../lapack-3.6.1/SRC/cgbsvx.o ../../lapack-3.6.1/SRC/cgbtf2.o ../../lapack-3.6.1/SRC/cgbtrf.o ../../lapack-3.6.1/SRC/cgbtrs.o ../../lapack-3.6.1/SRC/cgebak.o ../../lapack-3.6.1/SRC/cgebal.o ../../lapack-3.6.1/SRC/cgebd2.o ../../lapack-3.6.1/SRC/cgebrd.o ../../lapack-3.6.1/SRC/cgecon.o ../../lapack-3.6.1/SRC/cgeequb.o ../../lapack-3.6.1/SRC/cgeequ.o ../../lapack-3.6.1/SRC/cgees.o ../../lapack-3.6.1/SRC/cgeesx.o ../../lapack-3.6.1/SRC/cgeev.o ../../lapack-3.6.1/SRC/cgeevx.o ../../lapack-3.6.1/SRC/cgehd2.o ../../lapack-3.6.1/SRC/cgehrd.o ../../lapack-3.6.1/SRC/cgejsv.o ../../lapack-3.6.1/SRC/cgelq2.o ../../lapack-3.6.1/SRC/cgelqf.o ../../lapack-3.6.1/SRC/cgelsd.o ../../lapack-3.6.1/SRC/cgels.o ../../lapack-3.6.1/SRC/cgelss.o ../../lapack-3.6.1/SRC/cgelsy.o ../../lapack-3.6.1/SRC/cgemqrt.o ../../lapack-3.6.1/SRC/cgeql2.o ../../lapack-3.6.1/SRC/cgeqlf.o ../../lapack-3.6.1/SRC/cgeqp3.o ../../lapack-3.6.1/SRC/cgeqr2.o ../../lapack-3.6.1/SRC/cgeqr2p.o ../../lapack-3.6.1/SRC/cgeqrf.o ../../lapack-3.6.1/SRC/cgeqrfp.o ../../lapack-3.6.1/SRC/cgeqrt2.o ../../lapack-3.6.1/SRC/cgeqrt3.o ../../lapack-3.6.1/SRC/cgeqrt.o ../../lapack-3.6.1/SRC/cgerfs.o ../../lapack-3.6.1/SRC/cgerq2.o ../../lapack-3.6.1/SRC/cgerqf.o ../../lapack-3.6.1/SRC/cgesc2.o ../../lapack-3.6.1/SRC/cgesdd.o ../../lapack-3.6.1/SRC/cgesvd.o ../../lapack-3.6.1/SRC/cgesvdx.o ../../lapack-3.6.1/SRC/cgesvj.o ../../lapack-3.6.1/SRC/cgesv.o ../../lapack-3.6.1/SRC/cgesvx.o ../../lapack-3.6.1/SRC/cgetc2.o ../../lapack-3.6.1/SRC/cgetf2.o ../../lapack-3.6.1/SRC/cgetrf2.o ../../lapack-3.6.1/SRC/cgetrf.o ../../lapack-3.6.1/SRC/cgetri.o ../../lapack-3.6.1/SRC/cgetrs.o ../../lapack-3.6.1/SRC/cggbak.o ../../lapack-3.6.1/SRC/cggbal.o ../../lapack-3.6.1/SRC/cgges3.o ../../lapack-3.6.1/SRC/cgges.o ../../lapack-3.6.1/SRC/cggesx.o ../../lapack-3.6.1/SRC/cggev3.o ../../lapack-3.6.1/SRC/cggev.o ../../lapack-3.6.1/SRC/cggevx.o ../../lapack-3.6.1/SRC/cggglm.o ../../lapack-3.6.1/SRC/cgghd3.o ../../lapack-3.6.1/SRC/cgghrd.o ../../lapack-3.6.1/SRC/cgglse.o ../../lapack-3.6.1/SRC/cggqrf.o ../../lapack-3.6.1/SRC/cggrqf.o ../../lapack-3.6.1/SRC/cggsvd3.o ../../lapack-3.6.1/SRC/cggsvp3.o ../../lapack-3.6.1/SRC/cgsvj0.o ../../lapack-3.6.1/SRC/cgsvj1.o ../../lapack-3.6.1/SRC/cgtcon.o ../../lapack-3.6.1/SRC/cgtrfs.o ../../lapack-3.6.1/SRC/cgtsv.o ../../lapack-3.6.1/SRC/cgtsvx.o ../../lapack-3.6.1/SRC/cgttrf.o ../../lapack-3.6.1/SRC/cgttrs.o ../../lapack-3.6.1/SRC/cgtts2.o ../../lapack-3.6.1/SRC/chbevd.o ../../lapack-3.6.1/SRC/chbev.o ../../lapack-3.6.1/SRC/chbevx.o ../../lapack-3.6.1/SRC/chbgst.o ../../lapack-3.6.1/SRC/chbgvd.o ../../lapack-3.6.1/SRC/chbgv.o ../../lapack-3.6.1/SRC/chbgvx.o ../../lapack-3.6.1/SRC/chbtrd.o ../../lapack-3.6.1/SRC/checon.o ../../lapack-3.6.1/SRC/checon_rook.o ../../lapack-3.6.1/SRC/cheequb.o ../../lapack-3.6.1/SRC/cheevd.o ../../lapack-3.6.1/SRC/cheev.o ../../lapack-3.6.1/SRC/cheevr.o ../../lapack-3.6.1/SRC/cheevx.o ../../lapack-3.6.1/SRC/chegs2.o ../../lapack-3.6.1/SRC/chegst.o ../../lapack-3.6.1/SRC/chegvd.o ../../lapack-3.6.1/SRC/chegv.o ../../lapack-3.6.1/SRC/chegvx.o ../../lapack-3.6.1/SRC/cherfs.o ../../lapack-3.6.1/SRC/chesv.o ../../lapack-3.6.1/SRC/chesv_rook.o ../../lapack-3.6.1/SRC/chesvx.o ../../lapack-3.6.1/SRC/cheswapr.o ../../lapack-3.6.1/SRC/chetd2.o ../../lapack-3.6.1/SRC/chetf2.o ../../lapack-3.6.1/SRC/chetf2_rook.o ../../lapack-3.6.1/SRC/chetrd.o ../../lapack-3.6.1/SRC/chetrf.o ../../lapack-3.6.1/SRC/chetrf_rook.o ../../lapack-3.6.1/SRC/chetri2.o ../../lapack-3.6.1/SRC/chetri2x.o ../../lapack-3.6.1/SRC/chetri.o ../../lapack-3.6.1/SRC/chetri_rook.o ../../lapack-3.6.1/SRC/chetrs2.o ../../lapack-3.6.1/SRC/chetrs.o ../../lapack-3.6.1/SRC/chetrs_rook.o ../../lapack-3.6.1/SRC/chfrk.o ../../lapack-3.6.1/SRC/chgeqz.o ../../lapack-3.6.1/SRC/chla_transtype.o ../../lapack-3.6.1/SRC/chpcon.o ../../lapack-3.6.1/SRC/chpevd.o ../../lapack-3.6.1/SRC/chpev.o ../../lapack-3.6.1/SRC/chpevx.o ../../lapack-3.6.1/SRC/chpgst.o ../../lapack-3.6.1/SRC/chpgvd.o ../../lapack-3.6.1/SRC/chpgv.o ../../lapack-3.6.1/SRC/chpgvx.o ../../lapack-3.6.1/SRC/chprfs.o ../../lapack-3.6.1/SRC/chpsv.o ../../lapack-3.6.1/SRC/chpsvx.o ../../lapack-3.6.1/SRC/chptrd.o ../../lapack-3.6.1/SRC/chptrf.o ../../lapack-3.6.1/SRC/chptri.o ../../lapack-3.6.1/SRC/chptrs.o ../../lapack-3.6.1/SRC/chsein.o ../../lapack-3.6.1/SRC/chseqr.o ../../lapack-3.6.1/SRC/clabrd.o ../../lapack-3.6.1/SRC/clacgv.o ../../lapack-3.6.1/SRC/clacn2.o ../../lapack-3.6.1/SRC/clacon.o ../../lapack-3.6.1/SRC/clacp2.o ../../lapack-3.6.1/SRC/clacpy.o ../../lapack-3.6.1/SRC/clacrm.o ../../lapack-3.6.1/SRC/clacrt.o ../../lapack-3.6.1/SRC/cladiv.o ../../lapack-3.6.1/SRC/claed0.o ../../lapack-3.6.1/SRC/claed7.o ../../lapack-3.6.1/SRC/claed8.o ../../lapack-3.6.1/SRC/claein.o ../../lapack-3.6.1/SRC/claesy.o ../../lapack-3.6.1/SRC/claev2.o ../../lapack-3.6.1/SRC/clag2z.o ../../lapack-3.6.1/SRC/clags2.o ../../lapack-3.6.1/SRC/clagtm.o ../../lapack-3.6.1/SRC/clahef.o ../../lapack-3.6.1/SRC/clahef_rook.o ../../lapack-3.6.1/SRC/clahqr.o ../../lapack-3.6.1/SRC/clahr2.o ../../lapack-3.6.1/SRC/claic1.o ../../lapack-3.6.1/SRC/clals0.o ../../lapack-3.6.1/SRC/clalsa.o ../../lapack-3.6.1/SRC/clalsd.o ../../lapack-3.6.1/SRC/clangb.o ../../lapack-3.6.1/SRC/clange.o ../../lapack-3.6.1/SRC/clangt.o ../../lapack-3.6.1/SRC/clanhb.o ../../lapack-3.6.1/SRC/clanhe.o ../../lapack-3.6.1/SRC/clanhf.o ../../lapack-3.6.1/SRC/clanhp.o ../../lapack-3.6.1/SRC/clanhs.o ../../lapack-3.6.1/SRC/clanht.o ../../lapack-3.6.1/SRC/clansb.o ../../lapack-3.6.1/SRC/clansp.o ../../lapack-3.6.1/SRC/clansy.o ../../lapack-3.6.1/SRC/clantb.o ../../lapack-3.6.1/SRC/clantp.o ../../lapack-3.6.1/SRC/clantr.o ../../lapack-3.6.1/SRC/clapll.o ../../lapack-3.6.1/SRC/clapmr.o ../../lapack-3.6.1/SRC/clapmt.o ../../lapack-3.6.1/SRC/claqgb.o ../../lapack-3.6.1/SRC/claqge.o ../../lapack-3.6.1/SRC/claqhb.o ../../lapack-3.6.1/SRC/claqhe.o ../../lapack-3.6.1/SRC/claqhp.o ../../lapack-3.6.1/SRC/claqp2.o ../../lapack-3.6.1/SRC/claqps.o ../../lapack-3.6.1/SRC/claqr0.o ../../lapack-3.6.1/SRC/claqr1.o ../../lapack-3.6.1/SRC/claqr2.o ../../lapack-3.6.1/SRC/claqr3.o ../../lapack-3.6.1/SRC/claqr4.o ../../lapack-3.6.1/SRC/claqr5.o ../../lapack-3.6.1/SRC/claqsb.o ../../lapack-3.6.1/SRC/claqsp.o ../../lapack-3.6.1/SRC/claqsy.o ../../lapack-3.6.1/SRC/clar1v.o ../../lapack-3.6.1/SRC/clar2v.o ../../lapack-3.6.1/SRC/clarcm.o ../../lapack-3.6.1/SRC/clarfb.o ../../lapack-3.6.1/SRC/clarfg.o ../../lapack-3.6.1/SRC/clarfgp.o ../../lapack-3.6.1/SRC/clarf.o ../../lapack-3.6.1/SRC/clarft.o ../../lapack-3.6.1/SRC/clarfx.o ../../lapack-3.6.1/SRC/clargv.o ../../lapack-3.6.1/SRC/clarnv.o ../../lapack-3.6.1/SRC/clarrv.o ../../lapack-3.6.1/SRC/clartg.o ../../lapack-3.6.1/SRC/clartv.o ../../lapack-3.6.1/SRC/clarzb.o ../../lapack-3.6.1/SRC/clarz.o ../../lapack-3.6.1/SRC/clarzt.o ../../lapack-3.6.1/SRC/clascl.o ../../lapack-3.6.1/SRC/claset.o ../../lapack-3.6.1/SRC/clasr.o ../../lapack-3.6.1/SRC/classq.o ../../lapack-3.6.1/SRC/claswp.o ../../lapack-3.6.1/SRC/clasyf.o ../../lapack-3.6.1/SRC/clasyf_rook.o ../../lapack-3.6.1/SRC/clatbs.o ../../lapack-3.6.1/SRC/clatdf.o ../../lapack-3.6.1/SRC/clatps.o ../../lapack-3.6.1/SRC/clatrd.o ../../lapack-3.6.1/SRC/clatrs.o ../../lapack-3.6.1/SRC/clatrz.o ../../lapack-3.6.1/SRC/clauu2.o ../../lapack-3.6.1/SRC/clauum.o ../../lapack-3.6.1/SRC/cpbcon.o ../../lapack-3.6.1/SRC/cpbequ.o ../../lapack-3.6.1/SRC/cpbrfs.o ../../lapack-3.6.1/SRC/cpbstf.o ../../lapack-3.6.1/SRC/cpbsv.o ../../lapack-3.6.1/SRC/cpbsvx.o ../../lapack-3.6.1/SRC/cpbtf2.o ../../lapack-3.6.1/SRC/cpbtrf.o ../../lapack-3.6.1/SRC/cpbtrs.o ../../lapack-3.6.1/SRC/cpftrf.o ../../lapack-3.6.1/SRC/cpftri.o ../../lapack-3.6.1/SRC/cpftrs.o ../../lapack-3.6.1/SRC/cpocon.o ../../lapack-3.6.1/SRC/cpoequb.o ../../lapack-3.6.1/SRC/cpoequ.o ../../lapack-3.6.1/SRC/cporfs.o ../../lapack-3.6.1/SRC/cposv.o ../../lapack-3.6.1/SRC/cposvx.o ../../lapack-3.6.1/SRC/cpotf2.o ../../lapack-3.6.1/SRC/cpotrf2.o ../../lapack-3.6.1/SRC/cpotrf.o ../../lapack-3.6.1/SRC/cpotri.o ../../lapack-3.6.1/SRC/cpotrs.o ../../lapack-3.6.1/SRC/cppcon.o ../../lapack-3.6.1/SRC/cppequ.o ../../lapack-3.6.1/SRC/cpprfs.o ../../lapack-3.6.1/SRC/cppsv.o ../../lapack-3.6.1/SRC/cppsvx.o ../../lapack-3.6.1/SRC/cpptrf.o ../../lapack-3.6.1/SRC/cpptri.o ../../lapack-3.6.1/SRC/cpptrs.o ../../lapack-3.6.1/SRC/cpstf2.o ../../lapack-3.6.1/SRC/cpstrf.o ../../lapack-3.6.1/SRC/cptcon.o ../../lapack-3.6.1/SRC/cpteqr.o ../../lapack-3.6.1/SRC/cptrfs.o ../../lapack-3.6.1/SRC/cptsv.o ../../lapack-3.6.1/SRC/cptsvx.o ../../lapack-3.6.1/SRC/cpttrf.o ../../lapack-3.6.1/SRC/cpttrs.o ../../lapack-3.6.1/SRC/cptts2.o ../../lapack-3.6.1/SRC/crot.o ../../lapack-3.6.1/SRC/cspcon.o ../../lapack-3.6.1/SRC/cspmv.o ../../lapack-3.6.1/SRC/csprfs.o ../../lapack-3.6.1/SRC/cspr.o ../../lapack-3.6.1/SRC/cspsv.o ../../lapack-3.6.1/SRC/cspsvx.o ../../lapack-3.6.1/SRC/csptrf.o ../../lapack-3.6.1/SRC/csptri.o ../../lapack-3.6.1/SRC/csptrs.o ../../lapack-3.6.1/SRC/csrscl.o ../../lapack-3.6.1/SRC/cstedc.o ../../lapack-3.6.1/SRC/cstegr.o ../../lapack-3.6.1/SRC/cstein.o ../../lapack-3.6.1/SRC/cstemr.o ../../lapack-3.6.1/SRC/csteqr.o ../../lapack-3.6.1/SRC/csycon.o ../../lapack-3.6.1/SRC/csycon_rook.o ../../lapack-3.6.1/SRC/csyconv.o ../../lapack-3.6.1/SRC/csyequb.o ../../lapack-3.6.1/SRC/csymv.o ../../lapack-3.6.1/SRC/csyrfs.o ../../lapack-3.6.1/SRC/csyr.o ../../lapack-3.6.1/SRC/csysv.o ../../lapack-3.6.1/SRC/csysv_rook.o ../../lapack-3.6.1/SRC/csysvx.o ../../lapack-3.6.1/SRC/csyswapr.o ../../lapack-3.6.1/SRC/csytf2.o ../../lapack-3.6.1/SRC/csytf2_rook.o ../../lapack-3.6.1/SRC/csytrf.o ../../lapack-3.6.1/SRC/csytrf_rook.o ../../lapack-3.6.1/SRC/csytri2.o ../../lapack-3.6.1/SRC/csytri2x.o ../../lapack-3.6.1/SRC/csytri.o ../../lapack-3.6.1/SRC/csytri_rook.o ../../lapack-3.6.1/SRC/csytrs2.o ../../lapack-3.6.1/SRC/csytrs.o ../../lapack-3.6.1/SRC/csytrs_rook.o ../../lapack-3.6.1/SRC/ctbcon.o ../../lapack-3.6.1/SRC/ctbrfs.o ../../lapack-3.6.1/SRC/ctbtrs.o ../../lapack-3.6.1/SRC/ctfsm.o ../../lapack-3.6.1/SRC/ctftri.o ../../lapack-3.6.1/SRC/ctfttp.o ../../lapack-3.6.1/SRC/ctfttr.o ../../lapack-3.6.1/SRC/ctgevc.o ../../lapack-3.6.1/SRC/ctgex2.o ../../lapack-3.6.1/SRC/ctgexc.o ../../lapack-3.6.1/SRC/ctgsen.o ../../lapack-3.6.1/SRC/ctgsja.o ../../lapack-3.6.1/SRC/ctgsna.o ../../lapack-3.6.1/SRC/ctgsy2.o ../../lapack-3.6.1/SRC/ctgsyl.o ../../lapack-3.6.1/SRC/ctpcon.o ../../lapack-3.6.1/SRC/ctpmqrt.o ../../lapack-3.6.1/SRC/ctpqrt2.o ../../lapack-3.6.1/SRC/ctpqrt.o ../../lapack-3.6.1/SRC/ctprfb.o ../../lapack-3.6.1/SRC/ctprfs.o ../../lapack-3.6.1/SRC/ctptri.o ../../lapack-3.6.1/SRC/ctptrs.o ../../lapack-3.6.1/SRC/ctpttf.o ../../lapack-3.6.1/SRC/ctpttr.o ../../lapack-3.6.1/SRC/ctrcon.o ../../lapack-3.6.1/SRC/ctrevc3.o ../../lapack-3.6.1/SRC/ctrevc.o ../../lapack-3.6.1/SRC/ctrexc.o ../../lapack-3.6.1/SRC/ctrrfs.o ../../lapack-3.6.1/SRC/ctrsen.o ../../lapack-3.6.1/SRC/ctrsna.o ../../lapack-3.6.1/SRC/ctrsyl.o ../../lapack-3.6.1/SRC/ctrti2.o ../../lapack-3.6.1/SRC/ctrtri.o ../../lapack-3.6.1/SRC/ctrtrs.o ../../lapack-3.6.1/SRC/ctrttf.o ../../lapack-3.6.1/SRC/ctrttp.o ../../lapack-3.6.1/SRC/ctzrzf.o ../../lapack-3.6.1/SRC/cunbdb1.o ../../lapack-3.6.1/SRC/cunbdb2.o ../../lapack-3.6.1/SRC/cunbdb3.o ../../lapack-3.6.1/SRC/cunbdb4.o ../../lapack-3.6.1/SRC/cunbdb5.o ../../lapack-3.6.1/SRC/cunbdb6.o ../../lapack-3.6.1/SRC/cunbdb.o ../../lapack-3.6.1/SRC/cuncsd2by1.o ../../lapack-3.6.1/SRC/cuncsd.o ../../lapack-3.6.1/SRC/cung2l.o ../../lapack-3.6.1/SRC/cung2r.o ../../lapack-3.6.1/SRC/cungbr.o ../../lapack-3.6.1/SRC/cunghr.o ../../lapack-3.6.1/SRC/cungl2.o ../../lapack-3.6.1/SRC/cunglq.o ../../lapack-3.6.1/SRC/cungql.o ../../lapack-3.6.1/SRC/cungqr.o ../../lapack-3.6.1/SRC/cungr2.o ../../lapack-3.6.1/SRC/cungrq.o ../../lapack-3.6.1/SRC/cungtr.o ../../lapack-3.6.1/SRC/cunm22.o ../../lapack-3.6.1/SRC/cunm2l.o ../../lapack-3.6.1/SRC/cunm2r.o ../../lapack-3.6.1/SRC/cunmbr.o ../../lapack-3.6.1/SRC/cunmhr.o ../../lapack-3.6.1/SRC/cunml2.o ../../lapack-3.6.1/SRC/cunmlq.o ../../lapack-3.6.1/SRC/cunmql.o ../../lapack-3.6.1/SRC/cunmqr.o ../../lapack-3.6.1/SRC/cunmr2.o ../../lapack-3.6.1/SRC/cunmr3.o ../../lapack-3.6.1/SRC/cunmrq.o ../../lapack-3.6.1/SRC/cunmrz.o ../../lapack-3.6.1/SRC/cunmtr.o ../../lapack-3.6.1/SRC/cupgtr.o ../../lapack-3.6.1/SRC/cupmtr.o ../../lapack-3.6.1/SRC/dbbcsd.o ../../lapack-3.6.1/SRC/dbdsdc.o ../../lapack-3.6.1/SRC/dbdsqr.o ../../lapack-3.6.1/SRC/dbdsvdx.o ../../lapack-3.6.1/SRC/ddisna.o ../../lapack-3.6.1/SRC/dgbbrd.o ../../lapack-3.6.1/SRC/dgbcon.o ../../lapack-3.6.1/SRC/dgbequb.o ../../lapack-3.6.1/SRC/dgbequ.o ../../lapack-3.6.1/SRC/dgbrfs.o ../../lapack-3.6.1/SRC/dgbsv.o ../../lapack-3.6.1/SRC/dgbsvx.o ../../lapack-3.6.1/SRC/dgbtf2.o ../../lapack-3.6.1/SRC/dgbtrf.o ../../lapack-3.6.1/SRC/dgbtrs.o ../../lapack-3.6.1/SRC/dgebak.o ../../lapack-3.6.1/SRC/dgebal.o ../../lapack-3.6.1/SRC/dgebd2.o ../../lapack-3.6.1/SRC/dgebrd.o ../../lapack-3.6.1/SRC/dgecon.o ../../lapack-3.6.1/SRC/dgeequb.o ../../lapack-3.6.1/SRC/dgeequ.o ../../lapack-3.6.1/SRC/dgees.o ../../lapack-3.6.1/SRC/dgeesx.o ../../lapack-3.6.1/SRC/dgeev.o ../../lapack-3.6.1/SRC/dgeevx.o ../../lapack-3.6.1/SRC/dgehd2.o ../../lapack-3.6.1/SRC/dgehrd.o ../../lapack-3.6.1/SRC/dgejsv.o ../../lapack-3.6.1/SRC/dgelq2.o ../../lapack-3.6.1/SRC/dgelqf.o ../../lapack-3.6.1/SRC/dgelsd.o ../../lapack-3.6.1/SRC/dgels.o ../../lapack-3.6.1/SRC/dgelss.o ../../lapack-3.6.1/SRC/dgelsy.o ../../lapack-3.6.1/SRC/dgemqrt.o ../../lapack-3.6.1/SRC/dgeql2.o ../../lapack-3.6.1/SRC/dgeqlf.o ../../lapack-3.6.1/SRC/dgeqp3.o ../../lapack-3.6.1/SRC/dgeqr2.o ../../lapack-3.6.1/SRC/dgeqr2p.o ../../lapack-3.6.1/SRC/dgeqrf.o ../../lapack-3.6.1/SRC/dgeqrfp.o ../../lapack-3.6.1/SRC/dgeqrt2.o ../../lapack-3.6.1/SRC/dgeqrt3.o ../../lapack-3.6.1/SRC/dgeqrt.o ../../lapack-3.6.1/SRC/dgerfs.o ../../lapack-3.6.1/SRC/dgerq2.o ../../lapack-3.6.1/SRC/dgerqf.o ../../lapack-3.6.1/SRC/dgesc2.o ../../lapack-3.6.1/SRC/dgesdd.o ../../lapack-3.6.1/SRC/dgesvd.o ../../lapack-3.6.1/SRC/dgesvdx.o ../../lapack-3.6.1/SRC/dgesvj.o ../../lapack-3.6.1/SRC/dgesv.o ../../lapack-3.6.1/SRC/dgesvx.o ../../lapack-3.6.1/SRC/dgetc2.o ../../lapack-3.6.1/SRC/dgetf2.o ../../lapack-3.6.1/SRC/dgetrf2.o ../../lapack-3.6.1/SRC/dgetrf.o ../../lapack-3.6.1/SRC/dgetri.o ../../lapack-3.6.1/SRC/dgetrs.o ../../lapack-3.6.1/SRC/dggbak.o ../../lapack-3.6.1/SRC/dggbal.o ../../lapack-3.6.1/SRC/dgges3.o ../../lapack-3.6.1/SRC/dgges.o ../../lapack-3.6.1/SRC/dggesx.o ../../lapack-3.6.1/SRC/dggev3.o ../../lapack-3.6.1/SRC/dggev.o ../../lapack-3.6.1/SRC/dggevx.o ../../lapack-3.6.1/SRC/dggglm.o ../../lapack-3.6.1/SRC/dgghd3.o ../../lapack-3.6.1/SRC/dgghrd.o ../../lapack-3.6.1/SRC/dgglse.o ../../lapack-3.6.1/SRC/dggqrf.o ../../lapack-3.6.1/SRC/dggrqf.o ../../lapack-3.6.1/SRC/dggsvd3.o ../../lapack-3.6.1/SRC/dggsvp3.o ../../lapack-3.6.1/SRC/dgsvj0.o ../../lapack-3.6.1/SRC/dgsvj1.o ../../lapack-3.6.1/SRC/dgtcon.o ../../lapack-3.6.1/SRC/dgtrfs.o ../../lapack-3.6.1/SRC/dgtsv.o ../../lapack-3.6.1/SRC/dgtsvx.o ../../lapack-3.6.1/SRC/dgttrf.o ../../lapack-3.6.1/SRC/dgttrs.o ../../lapack-3.6.1/SRC/dgtts2.o ../../lapack-3.6.1/SRC/dhgeqz.o ../../lapack-3.6.1/SRC/dhsein.o ../../lapack-3.6.1/SRC/dhseqr.o ../../lapack-3.6.1/SRC/disnan.o ../../lapack-3.6.1/SRC/dlabad.o ../../lapack-3.6.1/SRC/dlabrd.o ../../lapack-3.6.1/SRC/dlacn2.o ../../lapack-3.6.1/SRC/dlacon.o ../../lapack-3.6.1/SRC/dlacpy.o ../../lapack-3.6.1/SRC/dladiv.o ../../lapack-3.6.1/SRC/dlae2.o ../../lapack-3.6.1/SRC/dlaebz.o ../../lapack-3.6.1/SRC/dlaed0.o ../../lapack-3.6.1/SRC/dlaed1.o ../../lapack-3.6.1/SRC/dlaed2.o ../../lapack-3.6.1/SRC/dlaed3.o ../../lapack-3.6.1/SRC/dlaed4.o ../../lapack-3.6.1/SRC/dlaed5.o ../../lapack-3.6.1/SRC/dlaed6.o ../../lapack-3.6.1/SRC/dlaed7.o ../../lapack-3.6.1/SRC/dlaed8.o ../../lapack-3.6.1/SRC/dlaed9.o ../../lapack-3.6.1/SRC/dlaeda.o ../../lapack-3.6.1/SRC/dlaein.o ../../lapack-3.6.1/SRC/dlaev2.o ../../lapack-3.6.1/SRC/dlaexc.o ../../lapack-3.6.1/SRC/dlag2.o ../../lapack-3.6.1/SRC/dlag2s.o ../../lapack-3.6.1/SRC/dlags2.o ../../lapack-3.6.1/SRC/dlagtf.o ../../lapack-3.6.1/SRC/dlagtm.o ../../lapack-3.6.1/SRC/dlagts.o ../../lapack-3.6.1/SRC/dlagv2.o ../../lapack-3.6.1/SRC/dlahqr.o ../../lapack-3.6.1/SRC/dlahr2.o ../../lapack-3.6.1/SRC/dlaic1.o ../../lapack-3.6.1/SRC/dlaisnan.o ../../lapack-3.6.1/SRC/dlaln2.o ../../lapack-3.6.1/SRC/dlals0.o ../../lapack-3.6.1/SRC/dlalsa.o ../../lapack-3.6.1/SRC/dlalsd.o ../../lapack-3.6.1/SRC/dlamrg.o ../../lapack-3.6.1/SRC/dlaneg.o ../../lapack-3.6.1/SRC/dlangb.o ../../lapack-3.6.1/SRC/dlange.o ../../lapack-3.6.1/SRC/dlangt.o ../../lapack-3.6.1/SRC/dlanhs.o ../../lapack-3.6.1/SRC/dlansb.o ../../lapack-3.6.1/SRC/dlansf.o ../../lapack-3.6.1/SRC/dlansp.o ../../lapack-3.6.1/SRC/dlanst.o ../../lapack-3.6.1/SRC/dlansy.o ../../lapack-3.6.1/SRC/dlantb.o ../../lapack-3.6.1/SRC/dlantp.o ../../lapack-3.6.1/SRC/dlantr.o ../../lapack-3.6.1/SRC/dlanv2.o ../../lapack-3.6.1/SRC/dlapll.o ../../lapack-3.6.1/SRC/dlapmr.o ../../lapack-3.6.1/SRC/dlapmt.o ../../lapack-3.6.1/SRC/dlapy2.o ../../lapack-3.6.1/SRC/dlapy3.o ../../lapack-3.6.1/SRC/dlaqgb.o ../../lapack-3.6.1/SRC/dlaqge.o ../../lapack-3.6.1/SRC/dlaqp2.o ../../lapack-3.6.1/SRC/dlaqps.o ../../lapack-3.6.1/SRC/dlaqr0.o ../../lapack-3.6.1/SRC/dlaqr1.o ../../lapack-3.6.1/SRC/dlaqr2.o ../../lapack-3.6.1/SRC/dlaqr3.o ../../lapack-3.6.1/SRC/dlaqr4.o ../../lapack-3.6.1/SRC/dlaqr5.o ../../lapack-3.6.1/SRC/dlaqsb.o ../../lapack-3.6.1/SRC/dlaqsp.o ../../lapack-3.6.1/SRC/dlaqsy.o ../../lapack-3.6.1/SRC/dlaqtr.o ../../lapack-3.6.1/SRC/dlar1v.o ../../lapack-3.6.1/SRC/dlar2v.o ../../lapack-3.6.1/SRC/dlarfb.o ../../lapack-3.6.1/SRC/dlarfg.o ../../lapack-3.6.1/SRC/dlarfgp.o ../../lapack-3.6.1/SRC/dlarf.o ../../lapack-3.6.1/SRC/dlarft.o ../../lapack-3.6.1/SRC/dlarfx.o ../../lapack-3.6.1/SRC/dlargv.o ../../lapack-3.6.1/SRC/dlarnv.o ../../lapack-3.6.1/SRC/dlarra.o ../../lapack-3.6.1/SRC/dlarrb.o ../../lapack-3.6.1/SRC/dlarrc.o ../../lapack-3.6.1/SRC/dlarrd.o ../../lapack-3.6.1/SRC/dlarre.o ../../lapack-3.6.1/SRC/dlarrf.o ../../lapack-3.6.1/SRC/dlarrj.o ../../lapack-3.6.1/SRC/dlarrk.o ../../lapack-3.6.1/SRC/dlarrr.o ../../lapack-3.6.1/SRC/dlarrv.o ../../lapack-3.6.1/SRC/dlartg.o ../../lapack-3.6.1/SRC/dlartgp.o ../../lapack-3.6.1/SRC/dlartgs.o ../../lapack-3.6.1/SRC/dlartv.o ../../lapack-3.6.1/SRC/dlaruv.o ../../lapack-3.6.1/SRC/dlarzb.o ../../lapack-3.6.1/SRC/dlarz.o ../../lapack-3.6.1/SRC/dlarzt.o ../../lapack-3.6.1/SRC/dlas2.o ../../lapack-3.6.1/SRC/dlascl.o ../../lapack-3.6.1/SRC/dlasd0.o ../../lapack-3.6.1/SRC/dlasd1.o ../../lapack-3.6.1/SRC/dlasd2.o ../../lapack-3.6.1/SRC/dlasd3.o ../../lapack-3.6.1/SRC/dlasd4.o ../../lapack-3.6.1/SRC/dlasd5.o ../../lapack-3.6.1/SRC/dlasd6.o ../../lapack-3.6.1/SRC/dlasd7.o ../../lapack-3.6.1/SRC/dlasd8.o ../../lapack-3.6.1/SRC/dlasda.o ../../lapack-3.6.1/SRC/dlasdq.o ../../lapack-3.6.1/SRC/dlasdt.o ../../lapack-3.6.1/SRC/dlaset.o ../../lapack-3.6.1/SRC/dlasq1.o ../../lapack-3.6.1/SRC/dlasq2.o ../../lapack-3.6.1/SRC/dlasq3.o ../../lapack-3.6.1/SRC/dlasq4.o ../../lapack-3.6.1/SRC/dlasq5.o ../../lapack-3.6.1/SRC/dlasq6.o ../../lapack-3.6.1/SRC/dlasr.o ../../lapack-3.6.1/SRC/dlasrt.o ../../lapack-3.6.1/SRC/dlassq.o ../../lapack-3.6.1/SRC/dlasv2.o ../../lapack-3.6.1/SRC/dlaswp.o ../../lapack-3.6.1/SRC/dlasy2.o ../../lapack-3.6.1/SRC/dlasyf.o ../../lapack-3.6.1/SRC/dlasyf_rook.o ../../lapack-3.6.1/SRC/dlat2s.o ../../lapack-3.6.1/SRC/dlatbs.o ../../lapack-3.6.1/SRC/dlatdf.o ../../lapack-3.6.1/SRC/dlatps.o ../../lapack-3.6.1/SRC/dlatrd.o ../../lapack-3.6.1/SRC/dlatrs.o ../../lapack-3.6.1/SRC/dlatrz.o ../../lapack-3.6.1/SRC/dlauu2.o ../../lapack-3.6.1/SRC/dlauum.o ../../lapack-3.6.1/SRC/dopgtr.o ../../lapack-3.6.1/SRC/dopmtr.o ../../lapack-3.6.1/SRC/dorbdb1.o ../../lapack-3.6.1/SRC/dorbdb2.o ../../lapack-3.6.1/SRC/dorbdb3.o ../../lapack-3.6.1/SRC/dorbdb4.o ../../lapack-3.6.1/SRC/dorbdb5.o ../../lapack-3.6.1/SRC/dorbdb6.o ../../lapack-3.6.1/SRC/dorbdb.o ../../lapack-3.6.1/SRC/dorcsd2by1.o ../../lapack-3.6.1/SRC/dorcsd.o ../../lapack-3.6.1/SRC/dorg2l.o ../../lapack-3.6.1/SRC/dorg2r.o ../../lapack-3.6.1/SRC/dorgbr.o ../../lapack-3.6.1/SRC/dorghr.o ../../lapack-3.6.1/SRC/dorgl2.o ../../lapack-3.6.1/SRC/dorglq.o ../../lapack-3.6.1/SRC/dorgql.o ../../lapack-3.6.1/SRC/dorgqr.o ../../lapack-3.6.1/SRC/dorgr2.o ../../lapack-3.6.1/SRC/dorgrq.o ../../lapack-3.6.1/SRC/dorgtr.o ../../lapack-3.6.1/SRC/dorm22.o ../../lapack-3.6.1/SRC/dorm2l.o ../../lapack-3.6.1/SRC/dorm2r.o ../../lapack-3.6.1/SRC/dormbr.o ../../lapack-3.6.1/SRC/dormhr.o ../../lapack-3.6.1/SRC/dorml2.o ../../lapack-3.6.1/SRC/dormlq.o ../../lapack-3.6.1/SRC/dormql.o ../../lapack-3.6.1/SRC/dormqr.o ../../lapack-3.6.1/SRC/dormr2.o ../../lapack-3.6.1/SRC/dormr3.o ../../lapack-3.6.1/SRC/dormrq.o ../../lapack-3.6.1/SRC/dormrz.o ../../lapack-3.6.1/SRC/dormtr.o ../../lapack-3.6.1/SRC/dpbcon.o ../../lapack-3.6.1/SRC/dpbequ.o ../../lapack-3.6.1/SRC/dpbrfs.o ../../lapack-3.6.1/SRC/dpbstf.o ../../lapack-3.6.1/SRC/dpbsv.o ../../lapack-3.6.1/SRC/dpbsvx.o ../../lapack-3.6.1/SRC/dpbtf2.o ../../lapack-3.6.1/SRC/dpbtrf.o ../../lapack-3.6.1/SRC/dpbtrs.o ../../lapack-3.6.1/SRC/dpftrf.o ../../lapack-3.6.1/SRC/dpftri.o ../../lapack-3.6.1/SRC/dpftrs.o ../../lapack-3.6.1/SRC/dpocon.o ../../lapack-3.6.1/SRC/dpoequb.o ../../lapack-3.6.1/SRC/dpoequ.o ../../lapack-3.6.1/SRC/dporfs.o ../../lapack-3.6.1/SRC/dposv.o ../../lapack-3.6.1/SRC/dposvx.o ../../lapack-3.6.1/SRC/dpotf2.o ../../lapack-3.6.1/SRC/dpotrf2.o ../../lapack-3.6.1/SRC/dpotrf.o ../../lapack-3.6.1/SRC/dpotri.o ../../lapack-3.6.1/SRC/dpotrs.o ../../lapack-3.6.1/SRC/dppcon.o ../../lapack-3.6.1/SRC/dppequ.o ../../lapack-3.6.1/SRC/dpprfs.o ../../lapack-3.6.1/SRC/dppsv.o ../../lapack-3.6.1/SRC/dppsvx.o ../../lapack-3.6.1/SRC/dpptrf.o ../../lapack-3.6.1/SRC/dpptri.o ../../lapack-3.6.1/SRC/dpptrs.o ../../lapack-3.6.1/SRC/dpstf2.o ../../lapack-3.6.1/SRC/dpstrf.o ../../lapack-3.6.1/SRC/dptcon.o ../../lapack-3.6.1/SRC/dpteqr.o ../../lapack-3.6.1/SRC/dptrfs.o ../../lapack-3.6.1/SRC/dptsv.o ../../lapack-3.6.1/SRC/dptsvx.o ../../lapack-3.6.1/SRC/dpttrf.o ../../lapack-3.6.1/SRC/dpttrs.o ../../lapack-3.6.1/SRC/dptts2.o ../../lapack-3.6.1/SRC/drscl.o ../../lapack-3.6.1/SRC/dsbevd.o ../../lapack-3.6.1/SRC/dsbev.o ../../lapack-3.6.1/SRC/dsbevx.o ../../lapack-3.6.1/SRC/dsbgst.o ../../lapack-3.6.1/SRC/dsbgvd.o ../../lapack-3.6.1/SRC/dsbgv.o ../../lapack-3.6.1/SRC/dsbgvx.o ../../lapack-3.6.1/SRC/dsbtrd.o ../../lapack-3.6.1/SRC/dsfrk.o ../../lapack-3.6.1/SRC/dsgesv.o ../../lapack-3.6.1/SRC/dspcon.o ../../lapack-3.6.1/SRC/dspevd.o ../../lapack-3.6.1/SRC/dspev.o ../../lapack-3.6.1/SRC/dspevx.o ../../lapack-3.6.1/SRC/dspgst.o ../../lapack-3.6.1/SRC/dspgvd.o ../../lapack-3.6.1/SRC/dspgv.o ../../lapack-3.6.1/SRC/dspgvx.o ../../lapack-3.6.1/SRC/dsposv.o ../../lapack-3.6.1/SRC/dsprfs.o ../../lapack-3.6.1/SRC/dspsv.o ../../lapack-3.6.1/SRC/dspsvx.o ../../lapack-3.6.1/SRC/dsptrd.o ../../lapack-3.6.1/SRC/dsptrf.o ../../lapack-3.6.1/SRC/dsptri.o ../../lapack-3.6.1/SRC/dsptrs.o ../../lapack-3.6.1/SRC/dstebz.o ../../lapack-3.6.1/SRC/dstedc.o ../../lapack-3.6.1/SRC/dstegr.o ../../lapack-3.6.1/SRC/dstein.o ../../lapack-3.6.1/SRC/dstemr.o ../../lapack-3.6.1/SRC/dsteqr.o ../../lapack-3.6.1/SRC/dsterf.o ../../lapack-3.6.1/SRC/dstevd.o ../../lapack-3.6.1/SRC/dstev.o ../../lapack-3.6.1/SRC/dstevr.o ../../lapack-3.6.1/SRC/dstevx.o ../../lapack-3.6.1/SRC/dsycon.o ../../lapack-3.6.1/SRC/dsycon_rook.o ../../lapack-3.6.1/SRC/dsyconv.o ../../lapack-3.6.1/SRC/dsyequb.o ../../lapack-3.6.1/SRC/dsyevd.o ../../lapack-3.6.1/SRC/dsyev.o ../../lapack-3.6.1/SRC/dsyevr.o ../../lapack-3.6.1/SRC/dsyevx.o ../../lapack-3.6.1/SRC/dsygs2.o ../../lapack-3.6.1/SRC/dsygst.o ../../lapack-3.6.1/SRC/dsygvd.o ../../lapack-3.6.1/SRC/dsygv.o ../../lapack-3.6.1/SRC/dsygvx.o ../../lapack-3.6.1/SRC/dsyrfs.o ../../lapack-3.6.1/SRC/dsysv.o ../../lapack-3.6.1/SRC/dsysv_rook.o ../../lapack-3.6.1/SRC/dsysvx.o ../../lapack-3.6.1/SRC/dsyswapr.o ../../lapack-3.6.1/SRC/dsytd2.o ../../lapack-3.6.1/SRC/dsytf2.o ../../lapack-3.6.1/SRC/dsytf2_rook.o ../../lapack-3.6.1/SRC/dsytrd.o ../../lapack-3.6.1/SRC/dsytrf.o ../../lapack-3.6.1/SRC/dsytrf_rook.o ../../lapack-3.6.1/SRC/dsytri2.o ../../lapack-3.6.1/SRC/dsytri2x.o ../../lapack-3.6.1/SRC/dsytri.o ../../lapack-3.6.1/SRC/dsytri_rook.o ../../lapack-3.6.1/SRC/dsytrs2.o ../../lapack-3.6.1/SRC/dsytrs.o ../../lapack-3.6.1/SRC/dsytrs_rook.o ../../lapack-3.6.1/SRC/dtbcon.o ../../lapack-3.6.1/SRC/dtbrfs.o ../../lapack-3.6.1/SRC/dtbtrs.o ../../lapack-3.6.1/SRC/dtfsm.o ../../lapack-3.6.1/SRC/dtftri.o ../../lapack-3.6.1/SRC/dtfttp.o ../../lapack-3.6.1/SRC/dtfttr.o ../../lapack-3.6.1/SRC/dtgevc.o ../../lapack-3.6.1/SRC/dtgex2.o ../../lapack-3.6.1/SRC/dtgexc.o ../../lapack-3.6.1/SRC/dtgsen.o ../../lapack-3.6.1/SRC/dtgsja.o ../../lapack-3.6.1/SRC/dtgsna.o ../../lapack-3.6.1/SRC/dtgsy2.o ../../lapack-3.6.1/SRC/dtgsyl.o ../../lapack-3.6.1/SRC/dtpcon.o ../../lapack-3.6.1/SRC/dtpmqrt.o ../../lapack-3.6.1/SRC/dtpqrt2.o ../../lapack-3.6.1/SRC/dtpqrt.o ../../lapack-3.6.1/SRC/dtprfb.o ../../lapack-3.6.1/SRC/dtprfs.o ../../lapack-3.6.1/SRC/dtptri.o ../../lapack-3.6.1/SRC/dtptrs.o ../../lapack-3.6.1/SRC/dtpttf.o ../../lapack-3.6.1/SRC/dtpttr.o ../../lapack-3.6.1/SRC/dtrcon.o ../../lapack-3.6.1/SRC/dtrevc3.o ../../lapack-3.6.1/SRC/dtrevc.o ../../lapack-3.6.1/SRC/dtrexc.o ../../lapack-3.6.1/SRC/dtrrfs.o ../../lapack-3.6.1/SRC/dtrsen.o ../../lapack-3.6.1/SRC/dtrsna.o ../../lapack-3.6.1/SRC/dtrsyl.o ../../lapack-3.6.1/SRC/dtrti2.o ../../lapack-3.6.1/SRC/dtrtri.o ../../lapack-3.6.1/SRC/dtrtrs.o ../../lapack-3.6.1/SRC/dtrttf.o ../../lapack-3.6.1/SRC/dtrttp.o ../../lapack-3.6.1/SRC/dtzrzf.o ../../lapack-3.6.1/SRC/dzsum1.o ../../lapack-3.6.1/SRC/icmax1.o ../../lapack-3.6.1/SRC/ieeeck.o ../../lapack-3.6.1/SRC/ilaclc.o ../../lapack-3.6.1/SRC/ilaclr.o ../../lapack-3.6.1/SRC/iladiag.o ../../lapack-3.6.1/SRC/iladlc.o ../../lapack-3.6.1/SRC/iladlr.o ../../lapack-3.6.1/SRC/ilaenv.o ../../lapack-3.6.1/SRC/ilaprec.o ../../lapack-3.6.1/SRC/ilaslc.o ../../lapack-3.6.1/SRC/ilaslr.o ../../lapack-3.6.1/SRC/ilatrans.o ../../lapack-3.6.1/SRC/ilauplo.o ../../lapack-3.6.1/SRC/ilazlc.o ../../lapack-3.6.1/SRC/ilazlr.o ../../lapack-3.6.1/SRC/iparmq.o ../../lapack-3.6.1/SRC/izmax1.o ../../lapack-3.6.1/SRC/lsamen.o ../../lapack-3.6.1/SRC/sbbcsd.o ../../lapack-3.6.1/SRC/sbdsdc.o ../../lapack-3.6.1/SRC/sbdsqr.o ../../lapack-3.6.1/SRC/sbdsvdx.o ../../lapack-3.6.1/SRC/scsum1.o ../../lapack-3.6.1/SRC/sdisna.o ../../lapack-3.6.1/SRC/sgbbrd.o ../../lapack-3.6.1/SRC/sgbcon.o ../../lapack-3.6.1/SRC/sgbequb.o ../../lapack-3.6.1/SRC/sgbequ.o ../../lapack-3.6.1/SRC/sgbrfs.o ../../lapack-3.6.1/SRC/sgbsv.o ../../lapack-3.6.1/SRC/sgbsvx.o ../../lapack-3.6.1/SRC/sgbtf2.o ../../lapack-3.6.1/SRC/sgbtrf.o ../../lapack-3.6.1/SRC/sgbtrs.o ../../lapack-3.6.1/SRC/sgebak.o ../../lapack-3.6.1/SRC/sgebal.o ../../lapack-3.6.1/SRC/sgebd2.o ../../lapack-3.6.1/SRC/sgebrd.o ../../lapack-3.6.1/SRC/sgecon.o ../../lapack-3.6.1/SRC/sgeequb.o ../../lapack-3.6.1/SRC/sgeequ.o ../../lapack-3.6.1/SRC/sgees.o ../../lapack-3.6.1/SRC/sgeesx.o ../../lapack-3.6.1/SRC/sgeev.o ../../lapack-3.6.1/SRC/sgeevx.o ../../lapack-3.6.1/SRC/sgehd2.o ../../lapack-3.6.1/SRC/sgehrd.o ../../lapack-3.6.1/SRC/sgejsv.o ../../lapack-3.6.1/SRC/sgelq2.o ../../lapack-3.6.1/SRC/sgelqf.o ../../lapack-3.6.1/SRC/sgelsd.o ../../lapack-3.6.1/SRC/sgels.o ../../lapack-3.6.1/SRC/sgelss.o ../../lapack-3.6.1/SRC/sgelsy.o ../../lapack-3.6.1/SRC/sgemqrt.o ../../lapack-3.6.1/SRC/sgeql2.o ../../lapack-3.6.1/SRC/sgeqlf.o ../../lapack-3.6.1/SRC/sgeqp3.o ../../lapack-3.6.1/SRC/sgeqr2.o ../../lapack-3.6.1/SRC/sgeqr2p.o ../../lapack-3.6.1/SRC/sgeqrf.o ../../lapack-3.6.1/SRC/sgeqrfp.o ../../lapack-3.6.1/SRC/sgeqrt2.o ../../lapack-3.6.1/SRC/sgeqrt3.o ../../lapack-3.6.1/SRC/sgeqrt.o ../../lapack-3.6.1/SRC/sgerfs.o ../../lapack-3.6.1/SRC/sgerq2.o ../../lapack-3.6.1/SRC/sgerqf.o ../../lapack-3.6.1/SRC/sgesc2.o ../../lapack-3.6.1/SRC/sgesdd.o ../../lapack-3.6.1/SRC/sgesvd.o ../../lapack-3.6.1/SRC/sgesvdx.o ../../lapack-3.6.1/SRC/sgesvj.o ../../lapack-3.6.1/SRC/sgesv.o ../../lapack-3.6.1/SRC/sgesvx.o ../../lapack-3.6.1/SRC/sgetc2.o ../../lapack-3.6.1/SRC/sgetf2.o ../../lapack-3.6.1/SRC/sgetrf2.o ../../lapack-3.6.1/SRC/sgetrf.o ../../lapack-3.6.1/SRC/sgetri.o ../../lapack-3.6.1/SRC/sgetrs.o ../../lapack-3.6.1/SRC/sggbak.o ../../lapack-3.6.1/SRC/sggbal.o ../../lapack-3.6.1/SRC/sgges3.o ../../lapack-3.6.1/SRC/sgges.o ../../lapack-3.6.1/SRC/sggesx.o ../../lapack-3.6.1/SRC/sggev3.o ../../lapack-3.6.1/SRC/sggev.o ../../lapack-3.6.1/SRC/sggevx.o ../../lapack-3.6.1/SRC/sggglm.o ../../lapack-3.6.1/SRC/sgghd3.o ../../lapack-3.6.1/SRC/sgghrd.o ../../lapack-3.6.1/SRC/sgglse.o ../../lapack-3.6.1/SRC/sggqrf.o ../../lapack-3.6.1/SRC/sggrqf.o ../../lapack-3.6.1/SRC/sggsvd3.o ../../lapack-3.6.1/SRC/sggsvp3.o ../../lapack-3.6.1/SRC/sgsvj0.o ../../lapack-3.6.1/SRC/sgsvj1.o ../../lapack-3.6.1/SRC/sgtcon.o ../../lapack-3.6.1/SRC/sgtrfs.o ../../lapack-3.6.1/SRC/sgtsv.o ../../lapack-3.6.1/SRC/sgtsvx.o ../../lapack-3.6.1/SRC/sgttrf.o ../../lapack-3.6.1/SRC/sgttrs.o ../../lapack-3.6.1/SRC/sgtts2.o ../../lapack-3.6.1/SRC/shgeqz.o ../../lapack-3.6.1/SRC/shsein.o ../../lapack-3.6.1/SRC/shseqr.o ../../lapack-3.6.1/SRC/sisnan.o ../../lapack-3.6.1/SRC/slabad.o ../../lapack-3.6.1/SRC/slabrd.o ../../lapack-3.6.1/SRC/slacn2.o ../../lapack-3.6.1/SRC/slacon.o ../../lapack-3.6.1/SRC/slacpy.o ../../lapack-3.6.1/SRC/sladiv.o ../../lapack-3.6.1/SRC/slae2.o ../../lapack-3.6.1/SRC/slaebz.o ../../lapack-3.6.1/SRC/slaed0.o ../../lapack-3.6.1/SRC/slaed1.o ../../lapack-3.6.1/SRC/slaed2.o ../../lapack-3.6.1/SRC/slaed3.o ../../lapack-3.6.1/SRC/slaed4.o ../../lapack-3.6.1/SRC/slaed5.o ../../lapack-3.6.1/SRC/slaed6.o ../../lapack-3.6.1/SRC/slaed7.o ../../lapack-3.6.1/SRC/slaed8.o ../../lapack-3.6.1/SRC/slaed9.o ../../lapack-3.6.1/SRC/slaeda.o ../../lapack-3.6.1/SRC/slaein.o ../../lapack-3.6.1/SRC/slaev2.o ../../lapack-3.6.1/SRC/slaexc.o ../../lapack-3.6.1/SRC/slag2d.o ../../lapack-3.6.1/SRC/slag2.o ../../lapack-3.6.1/SRC/slags2.o ../../lapack-3.6.1/SRC/slagtf.o ../../lapack-3.6.1/SRC/slagtm.o ../../lapack-3.6.1/SRC/slagts.o ../../lapack-3.6.1/SRC/slagv2.o ../../lapack-3.6.1/SRC/slahqr.o ../../lapack-3.6.1/SRC/slahr2.o ../../lapack-3.6.1/SRC/slaic1.o ../../lapack-3.6.1/SRC/slaisnan.o ../../lapack-3.6.1/SRC/slaln2.o ../../lapack-3.6.1/SRC/slals0.o ../../lapack-3.6.1/SRC/slalsa.o ../../lapack-3.6.1/SRC/slalsd.o ../../lapack-3.6.1/SRC/slamrg.o ../../lapack-3.6.1/SRC/slaneg.o ../../lapack-3.6.1/SRC/slangb.o ../../lapack-3.6.1/SRC/slange.o ../../lapack-3.6.1/SRC/slangt.o ../../lapack-3.6.1/SRC/slanhs.o ../../lapack-3.6.1/SRC/slansb.o ../../lapack-3.6.1/SRC/slansf.o ../../lapack-3.6.1/SRC/slansp.o ../../lapack-3.6.1/SRC/slanst.o ../../lapack-3.6.1/SRC/slansy.o ../../lapack-3.6.1/SRC/slantb.o ../../lapack-3.6.1/SRC/slantp.o ../../lapack-3.6.1/SRC/slantr.o ../../lapack-3.6.1/SRC/slanv2.o ../../lapack-3.6.1/SRC/slapll.o ../../lapack-3.6.1/SRC/slapmr.o ../../lapack-3.6.1/SRC/slapmt.o ../../lapack-3.6.1/SRC/slapy2.o ../../lapack-3.6.1/SRC/slapy3.o ../../lapack-3.6.1/SRC/slaqgb.o ../../lapack-3.6.1/SRC/slaqge.o ../../lapack-3.6.1/SRC/slaqp2.o ../../lapack-3.6.1/SRC/slaqps.o ../../lapack-3.6.1/SRC/slaqr0.o ../../lapack-3.6.1/SRC/slaqr1.o ../../lapack-3.6.1/SRC/slaqr2.o ../../lapack-3.6.1/SRC/slaqr3.o ../../lapack-3.6.1/SRC/slaqr4.o ../../lapack-3.6.1/SRC/slaqr5.o ../../lapack-3.6.1/SRC/slaqsb.o ../../lapack-3.6.1/SRC/slaqsp.o ../../lapack-3.6.1/SRC/slaqsy.o ../../lapack-3.6.1/SRC/slaqtr.o ../../lapack-3.6.1/SRC/slar1v.o ../../lapack-3.6.1/SRC/slar2v.o ../../lapack-3.6.1/SRC/slarfb.o ../../lapack-3.6.1/SRC/slarfg.o ../../lapack-3.6.1/SRC/slarfgp.o ../../lapack-3.6.1/SRC/slarf.o ../../lapack-3.6.1/SRC/slarft.o ../../lapack-3.6.1/SRC/slarfx.o ../../lapack-3.6.1/SRC/slargv.o ../../lapack-3.6.1/SRC/slarnv.o ../../lapack-3.6.1/SRC/slarra.o ../../lapack-3.6.1/SRC/slarrb.o ../../lapack-3.6.1/SRC/slarrc.o ../../lapack-3.6.1/SRC/slarrd.o ../../lapack-3.6.1/SRC/slarre.o ../../lapack-3.6.1/SRC/slarrf.o ../../lapack-3.6.1/SRC/slarrj.o ../../lapack-3.6.1/SRC/slarrk.o ../../lapack-3.6.1/SRC/slarrr.o ../../lapack-3.6.1/SRC/slarrv.o ../../lapack-3.6.1/SRC/slartg.o ../../lapack-3.6.1/SRC/slartgp.o ../../lapack-3.6.1/SRC/slartgs.o ../../lapack-3.6.1/SRC/slartv.o ../../lapack-3.6.1/SRC/slaruv.o ../../lapack-3.6.1/SRC/slarzb.o ../../lapack-3.6.1/SRC/slarz.o ../../lapack-3.6.1/SRC/slarzt.o ../../lapack-3.6.1/SRC/slas2.o ../../lapack-3.6.1/SRC/slascl.o ../../lapack-3.6.1/SRC/slasd0.o ../../lapack-3.6.1/SRC/slasd1.o ../../lapack-3.6.1/SRC/slasd2.o ../../lapack-3.6.1/SRC/slasd3.o ../../lapack-3.6.1/SRC/slasd4.o ../../lapack-3.6.1/SRC/slasd5.o ../../lapack-3.6.1/SRC/slasd6.o ../../lapack-3.6.1/SRC/slasd7.o ../../lapack-3.6.1/SRC/slasd8.o ../../lapack-3.6.1/SRC/slasda.o ../../lapack-3.6.1/SRC/slasdq.o ../../lapack-3.6.1/SRC/slasdt.o ../../lapack-3.6.1/SRC/slaset.o ../../lapack-3.6.1/SRC/slasq1.o ../../lapack-3.6.1/SRC/slasq2.o ../../lapack-3.6.1/SRC/slasq3.o ../../lapack-3.6.1/SRC/slasq4.o ../../lapack-3.6.1/SRC/slasq5.o ../../lapack-3.6.1/SRC/slasq6.o ../../lapack-3.6.1/SRC/slasr.o ../../lapack-3.6.1/SRC/slasrt.o ../../lapack-3.6.1/SRC/slassq.o ../../lapack-3.6.1/SRC/slasv2.o ../../lapack-3.6.1/SRC/slaswp.o ../../lapack-3.6.1/SRC/slasy2.o ../../lapack-3.6.1/SRC/slasyf.o ../../lapack-3.6.1/SRC/slasyf_rook.o ../../lapack-3.6.1/SRC/slatbs.o ../../lapack-3.6.1/SRC/slatdf.o ../../lapack-3.6.1/SRC/slatps.o ../../lapack-3.6.1/SRC/slatrd.o ../../lapack-3.6.1/SRC/slatrs.o ../../lapack-3.6.1/SRC/slatrz.o ../../lapack-3.6.1/SRC/slauu2.o ../../lapack-3.6.1/SRC/slauum.o ../../lapack-3.6.1/SRC/sopgtr.o ../../lapack-3.6.1/SRC/sopmtr.o ../../lapack-3.6.1/SRC/sorbdb1.o ../../lapack-3.6.1/SRC/sorbdb2.o ../../lapack-3.6.1/SRC/sorbdb3.o ../../lapack-3.6.1/SRC/sorbdb4.o ../../lapack-3.6.1/SRC/sorbdb5.o ../../lapack-3.6.1/SRC/sorbdb6.o ../../lapack-3.6.1/SRC/sorbdb.o ../../lapack-3.6.1/SRC/sorcsd2by1.o ../../lapack-3.6.1/SRC/sorcsd.o ../../lapack-3.6.1/SRC/sorg2l.o ../../lapack-3.6.1/SRC/sorg2r.o ../../lapack-3.6.1/SRC/sorgbr.o ../../lapack-3.6.1/SRC/sorghr.o ../../lapack-3.6.1/SRC/sorgl2.o ../../lapack-3.6.1/SRC/sorglq.o ../../lapack-3.6.1/SRC/sorgql.o ../../lapack-3.6.1/SRC/sorgqr.o ../../lapack-3.6.1/SRC/sorgr2.o ../../lapack-3.6.1/SRC/sorgrq.o ../../lapack-3.6.1/SRC/sorgtr.o ../../lapack-3.6.1/SRC/sorm22.o ../../lapack-3.6.1/SRC/sorm2l.o ../../lapack-3.6.1/SRC/sorm2r.o ../../lapack-3.6.1/SRC/sormbr.o ../../lapack-3.6.1/SRC/sormhr.o ../../lapack-3.6.1/SRC/sorml2.o ../../lapack-3.6.1/SRC/sormlq.o ../../lapack-3.6.1/SRC/sormql.o ../../lapack-3.6.1/SRC/sormqr.o ../../lapack-3.6.1/SRC/sormr2.o ../../lapack-3.6.1/SRC/sormr3.o ../../lapack-3.6.1/SRC/sormrq.o ../../lapack-3.6.1/SRC/sormrz.o ../../lapack-3.6.1/SRC/sormtr.o ../../lapack-3.6.1/SRC/spbcon.o ../../lapack-3.6.1/SRC/spbequ.o ../../lapack-3.6.1/SRC/spbrfs.o ../../lapack-3.6.1/SRC/spbstf.o ../../lapack-3.6.1/SRC/spbsv.o ../../lapack-3.6.1/SRC/spbsvx.o ../../lapack-3.6.1/SRC/spbtf2.o ../../lapack-3.6.1/SRC/spbtrf.o ../../lapack-3.6.1/SRC/spbtrs.o ../../lapack-3.6.1/SRC/spftrf.o ../../lapack-3.6.1/SRC/spftri.o ../../lapack-3.6.1/SRC/spftrs.o ../../lapack-3.6.1/SRC/spocon.o ../../lapack-3.6.1/SRC/spoequb.o ../../lapack-3.6.1/SRC/spoequ.o ../../lapack-3.6.1/SRC/sporfs.o ../../lapack-3.6.1/SRC/sposv.o ../../lapack-3.6.1/SRC/sposvx.o ../../lapack-3.6.1/SRC/spotf2.o ../../lapack-3.6.1/SRC/spotrf2.o ../../lapack-3.6.1/SRC/spotrf.o ../../lapack-3.6.1/SRC/spotri.o ../../lapack-3.6.1/SRC/spotrs.o ../../lapack-3.6.1/SRC/sppcon.o ../../lapack-3.6.1/SRC/sppequ.o ../../lapack-3.6.1/SRC/spprfs.o ../../lapack-3.6.1/SRC/sppsv.o ../../lapack-3.6.1/SRC/sppsvx.o ../../lapack-3.6.1/SRC/spptrf.o ../../lapack-3.6.1/SRC/spptri.o ../../lapack-3.6.1/SRC/spptrs.o ../../lapack-3.6.1/SRC/spstf2.o ../../lapack-3.6.1/SRC/spstrf.o ../../lapack-3.6.1/SRC/sptcon.o ../../lapack-3.6.1/SRC/spteqr.o ../../lapack-3.6.1/SRC/sptrfs.o ../../lapack-3.6.1/SRC/sptsv.o ../../lapack-3.6.1/SRC/sptsvx.o ../../lapack-3.6.1/SRC/spttrf.o ../../lapack-3.6.1/SRC/spttrs.o ../../lapack-3.6.1/SRC/sptts2.o ../../lapack-3.6.1/SRC/srscl.o ../../lapack-3.6.1/SRC/ssbevd.o ../../lapack-3.6.1/SRC/ssbev.o ../../lapack-3.6.1/SRC/ssbevx.o ../../lapack-3.6.1/SRC/ssbgst.o ../../lapack-3.6.1/SRC/ssbgvd.o ../../lapack-3.6.1/SRC/ssbgv.o ../../lapack-3.6.1/SRC/ssbgvx.o ../../lapack-3.6.1/SRC/ssbtrd.o ../../lapack-3.6.1/SRC/ssfrk.o ../../lapack-3.6.1/SRC/sspcon.o ../../lapack-3.6.1/SRC/sspevd.o ../../lapack-3.6.1/SRC/sspev.o ../../lapack-3.6.1/SRC/sspevx.o ../../lapack-3.6.1/SRC/sspgst.o ../../lapack-3.6.1/SRC/sspgvd.o ../../lapack-3.6.1/SRC/sspgv.o ../../lapack-3.6.1/SRC/sspgvx.o ../../lapack-3.6.1/SRC/ssprfs.o ../../lapack-3.6.1/SRC/sspsv.o ../../lapack-3.6.1/SRC/sspsvx.o ../../lapack-3.6.1/SRC/ssptrd.o ../../lapack-3.6.1/SRC/ssptrf.o ../../lapack-3.6.1/SRC/ssptri.o ../../lapack-3.6.1/SRC/ssptrs.o ../../lapack-3.6.1/SRC/sstebz.o ../../lapack-3.6.1/SRC/sstedc.o ../../lapack-3.6.1/SRC/sstegr.o ../../lapack-3.6.1/SRC/sstein.o ../../lapack-3.6.1/SRC/sstemr.o ../../lapack-3.6.1/SRC/ssteqr.o ../../lapack-3.6.1/SRC/ssterf.o ../../lapack-3.6.1/SRC/sstevd.o ../../lapack-3.6.1/SRC/sstev.o ../../lapack-3.6.1/SRC/sstevr.o ../../lapack-3.6.1/SRC/sstevx.o ../../lapack-3.6.1/SRC/ssycon.o ../../lapack-3.6.1/SRC/ssycon_rook.o ../../lapack-3.6.1/SRC/ssyconv.o ../../lapack-3.6.1/SRC/ssyequb.o ../../lapack-3.6.1/SRC/ssyevd.o ../../lapack-3.6.1/SRC/ssyev.o ../../lapack-3.6.1/SRC/ssyevr.o ../../lapack-3.6.1/SRC/ssyevx.o ../../lapack-3.6.1/SRC/ssygs2.o ../../lapack-3.6.1/SRC/ssygst.o ../../lapack-3.6.1/SRC/ssygvd.o ../../lapack-3.6.1/SRC/ssygv.o ../../lapack-3.6.1/SRC/ssygvx.o ../../lapack-3.6.1/SRC/ssyrfs.o ../../lapack-3.6.1/SRC/ssysv.o ../../lapack-3.6.1/SRC/ssysv_rook.o ../../lapack-3.6.1/SRC/ssysvx.o ../../lapack-3.6.1/SRC/ssyswapr.o ../../lapack-3.6.1/SRC/ssytd2.o ../../lapack-3.6.1/SRC/ssytf2.o ../../lapack-3.6.1/SRC/ssytf2_rook.o ../../lapack-3.6.1/SRC/ssytrd.o ../../lapack-3.6.1/SRC/ssytrf.o ../../lapack-3.6.1/SRC/ssytrf_rook.o ../../lapack-3.6.1/SRC/ssytri2.o ../../lapack-3.6.1/SRC/ssytri2x.o ../../lapack-3.6.1/SRC/ssytri.o ../../lapack-3.6.1/SRC/ssytri_rook.o ../../lapack-3.6.1/SRC/ssytrs2.o ../../lapack-3.6.1/SRC/ssytrs.o ../../lapack-3.6.1/SRC/ssytrs_rook.o ../../lapack-3.6.1/SRC/stbcon.o ../../lapack-3.6.1/SRC/stbrfs.o ../../lapack-3.6.1/SRC/stbtrs.o ../../lapack-3.6.1/SRC/stfsm.o ../../lapack-3.6.1/SRC/stftri.o ../../lapack-3.6.1/SRC/stfttp.o ../../lapack-3.6.1/SRC/stfttr.o ../../lapack-3.6.1/SRC/stgevc.o ../../lapack-3.6.1/SRC/stgex2.o ../../lapack-3.6.1/SRC/stgexc.o ../../lapack-3.6.1/SRC/stgsen.o ../../lapack-3.6.1/SRC/stgsja.o ../../lapack-3.6.1/SRC/stgsna.o ../../lapack-3.6.1/SRC/stgsy2.o ../../lapack-3.6.1/SRC/stgsyl.o ../../lapack-3.6.1/SRC/stpcon.o ../../lapack-3.6.1/SRC/stpmqrt.o ../../lapack-3.6.1/SRC/stpqrt2.o ../../lapack-3.6.1/SRC/stpqrt.o ../../lapack-3.6.1/SRC/stprfb.o ../../lapack-3.6.1/SRC/stprfs.o ../../lapack-3.6.1/SRC/stptri.o ../../lapack-3.6.1/SRC/stptrs.o ../../lapack-3.6.1/SRC/stpttf.o ../../lapack-3.6.1/SRC/stpttr.o ../../lapack-3.6.1/SRC/strcon.o ../../lapack-3.6.1/SRC/strevc3.o ../../lapack-3.6.1/SRC/strevc.o ../../lapack-3.6.1/SRC/strexc.o ../../lapack-3.6.1/SRC/strrfs.o ../../lapack-3.6.1/SRC/strsen.o ../../lapack-3.6.1/SRC/strsna.o ../../lapack-3.6.1/SRC/strsyl.o ../../lapack-3.6.1/SRC/strti2.o ../../lapack-3.6.1/SRC/strtri.o ../../lapack-3.6.1/SRC/strtrs.o ../../lapack-3.6.1/SRC/strttf.o ../../lapack-3.6.1/SRC/strttp.o ../../lapack-3.6.1/SRC/stzrzf.o ../../lapack-3.6.1/SRC/xerbla_array.o ../../lapack-3.6.1/SRC/xerbla.o ../../lapack-3.6.1/SRC/zbbcsd.o ../../lapack-3.6.1/SRC/zbdsqr.o ../../lapack-3.6.1/SRC/zcgesv.o ../../lapack-3.6.1/SRC/zcposv.o ../../lapack-3.6.1/SRC/zdrscl.o ../../lapack-3.6.1/SRC/zgbbrd.o ../../lapack-3.6.1/SRC/zgbcon.o ../../lapack-3.6.1/SRC/zgbequb.o ../../lapack-3.6.1/SRC/zgbequ.o ../../lapack-3.6.1/SRC/zgbrfs.o ../../lapack-3.6.1/SRC/zgbsv.o ../../lapack-3.6.1/SRC/zgbsvx.o ../../lapack-3.6.1/SRC/zgbtf2.o ../../lapack-3.6.1/SRC/zgbtrf.o ../../lapack-3.6.1/SRC/zgbtrs.o ../../lapack-3.6.1/SRC/zgebak.o ../../lapack-3.6.1/SRC/zgebal.o ../../lapack-3.6.1/SRC/zgebd2.o ../../lapack-3.6.1/SRC/zgebrd.o ../../lapack-3.6.1/SRC/zgecon.o ../../lapack-3.6.1/SRC/zgeequb.o ../../lapack-3.6.1/SRC/zgeequ.o ../../lapack-3.6.1/SRC/zgees.o ../../lapack-3.6.1/SRC/zgeesx.o ../../lapack-3.6.1/SRC/zgeev.o ../../lapack-3.6.1/SRC/zgeevx.o ../../lapack-3.6.1/SRC/zgehd2.o ../../lapack-3.6.1/SRC/zgehrd.o ../../lapack-3.6.1/SRC/zgejsv.o ../../lapack-3.6.1/SRC/zgelq2.o ../../lapack-3.6.1/SRC/zgelqf.o ../../lapack-3.6.1/SRC/zgelsd.o ../../lapack-3.6.1/SRC/zgels.o ../../lapack-3.6.1/SRC/zgelss.o ../../lapack-3.6.1/SRC/zgelsy.o ../../lapack-3.6.1/SRC/zgemqrt.o ../../lapack-3.6.1/SRC/zgeql2.o ../../lapack-3.6.1/SRC/zgeqlf.o ../../lapack-3.6.1/SRC/zgeqp3.o ../../lapack-3.6.1/SRC/zgeqr2.o ../../lapack-3.6.1/SRC/zgeqr2p.o ../../lapack-3.6.1/SRC/zgeqrf.o ../../lapack-3.6.1/SRC/zgeqrfp.o ../../lapack-3.6.1/SRC/zgeqrt2.o ../../lapack-3.6.1/SRC/zgeqrt3.o ../../lapack-3.6.1/SRC/zgeqrt.o ../../lapack-3.6.1/SRC/zgerfs.o ../../lapack-3.6.1/SRC/zgerq2.o ../../lapack-3.6.1/SRC/zgerqf.o ../../lapack-3.6.1/SRC/zgesc2.o ../../lapack-3.6.1/SRC/zgesdd.o ../../lapack-3.6.1/SRC/zgesvd.o ../../lapack-3.6.1/SRC/zgesvdx.o ../../lapack-3.6.1/SRC/zgesvj.o ../../lapack-3.6.1/SRC/zgesv.o ../../lapack-3.6.1/SRC/zgesvx.o ../../lapack-3.6.1/SRC/zgetc2.o ../../lapack-3.6.1/SRC/zgetf2.o ../../lapack-3.6.1/SRC/zgetrf2.o ../../lapack-3.6.1/SRC/zgetrf.o ../../lapack-3.6.1/SRC/zgetri.o ../../lapack-3.6.1/SRC/zgetrs.o ../../lapack-3.6.1/SRC/zggbak.o ../../lapack-3.6.1/SRC/zggbal.o ../../lapack-3.6.1/SRC/zgges3.o ../../lapack-3.6.1/SRC/zgges.o ../../lapack-3.6.1/SRC/zggesx.o ../../lapack-3.6.1/SRC/zggev3.o ../../lapack-3.6.1/SRC/zggev.o ../../lapack-3.6.1/SRC/zggevx.o ../../lapack-3.6.1/SRC/zggglm.o ../../lapack-3.6.1/SRC/zgghd3.o ../../lapack-3.6.1/SRC/zgghrd.o ../../lapack-3.6.1/SRC/zgglse.o ../../lapack-3.6.1/SRC/zggqrf.o ../../lapack-3.6.1/SRC/zggrqf.o ../../lapack-3.6.1/SRC/zggsvd3.o ../../lapack-3.6.1/SRC/zggsvp3.o ../../lapack-3.6.1/SRC/zgsvj0.o ../../lapack-3.6.1/SRC/zgsvj1.o ../../lapack-3.6.1/SRC/zgtcon.o ../../lapack-3.6.1/SRC/zgtrfs.o ../../lapack-3.6.1/SRC/zgtsv.o ../../lapack-3.6.1/SRC/zgtsvx.o ../../lapack-3.6.1/SRC/zgttrf.o ../../lapack-3.6.1/SRC/zgttrs.o ../../lapack-3.6.1/SRC/zgtts2.o ../../lapack-3.6.1/SRC/zhbevd.o ../../lapack-3.6.1/SRC/zhbev.o ../../lapack-3.6.1/SRC/zhbevx.o ../../lapack-3.6.1/SRC/zhbgst.o ../../lapack-3.6.1/SRC/zhbgvd.o ../../lapack-3.6.1/SRC/zhbgv.o ../../lapack-3.6.1/SRC/zhbgvx.o ../../lapack-3.6.1/SRC/zhbtrd.o ../../lapack-3.6.1/SRC/zhecon.o ../../lapack-3.6.1/SRC/zhecon_rook.o ../../lapack-3.6.1/SRC/zheequb.o ../../lapack-3.6.1/SRC/zheevd.o ../../lapack-3.6.1/SRC/zheev.o ../../lapack-3.6.1/SRC/zheevr.o ../../lapack-3.6.1/SRC/zheevx.o ../../lapack-3.6.1/SRC/zhegs2.o ../../lapack-3.6.1/SRC/zhegst.o ../../lapack-3.6.1/SRC/zhegvd.o ../../lapack-3.6.1/SRC/zhegv.o ../../lapack-3.6.1/SRC/zhegvx.o ../../lapack-3.6.1/SRC/zherfs.o ../../lapack-3.6.1/SRC/zhesv.o ../../lapack-3.6.1/SRC/zhesv_rook.o ../../lapack-3.6.1/SRC/zhesvx.o ../../lapack-3.6.1/SRC/zheswapr.o ../../lapack-3.6.1/SRC/zhetd2.o ../../lapack-3.6.1/SRC/zhetf2.o ../../lapack-3.6.1/SRC/zhetf2_rook.o ../../lapack-3.6.1/SRC/zhetrd.o ../../lapack-3.6.1/SRC/zhetrf.o ../../lapack-3.6.1/SRC/zhetrf_rook.o ../../lapack-3.6.1/SRC/zhetri2.o ../../lapack-3.6.1/SRC/zhetri2x.o ../../lapack-3.6.1/SRC/zhetri.o ../../lapack-3.6.1/SRC/zhetri_rook.o ../../lapack-3.6.1/SRC/zhetrs2.o ../../lapack-3.6.1/SRC/zhetrs.o ../../lapack-3.6.1/SRC/zhetrs_rook.o ../../lapack-3.6.1/SRC/zhfrk.o ../../lapack-3.6.1/SRC/zhgeqz.o ../../lapack-3.6.1/SRC/zhpcon.o ../../lapack-3.6.1/SRC/zhpevd.o ../../lapack-3.6.1/SRC/zhpev.o ../../lapack-3.6.1/SRC/zhpevx.o ../../lapack-3.6.1/SRC/zhpgst.o ../../lapack-3.6.1/SRC/zhpgvd.o ../../lapack-3.6.1/SRC/zhpgv.o ../../lapack-3.6.1/SRC/zhpgvx.o ../../lapack-3.6.1/SRC/zhprfs.o ../../lapack-3.6.1/SRC/zhpsv.o ../../lapack-3.6.1/SRC/zhpsvx.o ../../lapack-3.6.1/SRC/zhptrd.o ../../lapack-3.6.1/SRC/zhptrf.o ../../lapack-3.6.1/SRC/zhptri.o ../../lapack-3.6.1/SRC/zhptrs.o ../../lapack-3.6.1/SRC/zhsein.o ../../lapack-3.6.1/SRC/zhseqr.o ../../lapack-3.6.1/SRC/zlabrd.o ../../lapack-3.6.1/SRC/zlacgv.o ../../lapack-3.6.1/SRC/zlacn2.o ../../lapack-3.6.1/SRC/zlacon.o ../../lapack-3.6.1/SRC/zlacp2.o ../../lapack-3.6.1/SRC/zlacpy.o ../../lapack-3.6.1/SRC/zlacrm.o ../../lapack-3.6.1/SRC/zlacrt.o ../../lapack-3.6.1/SRC/zladiv.o ../../lapack-3.6.1/SRC/zlaed0.o ../../lapack-3.6.1/SRC/zlaed7.o ../../lapack-3.6.1/SRC/zlaed8.o ../../lapack-3.6.1/SRC/zlaein.o ../../lapack-3.6.1/SRC/zlaesy.o ../../lapack-3.6.1/SRC/zlaev2.o ../../lapack-3.6.1/SRC/zlag2c.o ../../lapack-3.6.1/SRC/zlags2.o ../../lapack-3.6.1/SRC/zlagtm.o ../../lapack-3.6.1/SRC/zlahef.o ../../lapack-3.6.1/SRC/zlahef_rook.o ../../lapack-3.6.1/SRC/zlahqr.o ../../lapack-3.6.1/SRC/zlahr2.o ../../lapack-3.6.1/SRC/zlaic1.o ../../lapack-3.6.1/SRC/zlals0.o ../../lapack-3.6.1/SRC/zlalsa.o ../../lapack-3.6.1/SRC/zlalsd.o ../../lapack-3.6.1/SRC/zlangb.o ../../lapack-3.6.1/SRC/zlange.o ../../lapack-3.6.1/SRC/zlangt.o ../../lapack-3.6.1/SRC/zlanhb.o ../../lapack-3.6.1/SRC/zlanhe.o ../../lapack-3.6.1/SRC/zlanhf.o ../../lapack-3.6.1/SRC/zlanhp.o ../../lapack-3.6.1/SRC/zlanhs.o ../../lapack-3.6.1/SRC/zlanht.o ../../lapack-3.6.1/SRC/zlansb.o ../../lapack-3.6.1/SRC/zlansp.o ../../lapack-3.6.1/SRC/zlansy.o ../../lapack-3.6.1/SRC/zlantb.o ../../lapack-3.6.1/SRC/zlantp.o ../../lapack-3.6.1/SRC/zlantr.o ../../lapack-3.6.1/SRC/zlapll.o ../../lapack-3.6.1/SRC/zlapmr.o ../../lapack-3.6.1/SRC/zlapmt.o ../../lapack-3.6.1/SRC/zlaqgb.o ../../lapack-3.6.1/SRC/zlaqge.o ../../lapack-3.6.1/SRC/zlaqhb.o ../../lapack-3.6.1/SRC/zlaqhe.o ../../lapack-3.6.1/SRC/zlaqhp.o ../../lapack-3.6.1/SRC/zlaqp2.o ../../lapack-3.6.1/SRC/zlaqps.o ../../lapack-3.6.1/SRC/zlaqr0.o ../../lapack-3.6.1/SRC/zlaqr1.o ../../lapack-3.6.1/SRC/zlaqr2.o ../../lapack-3.6.1/SRC/zlaqr3.o ../../lapack-3.6.1/SRC/zlaqr4.o ../../lapack-3.6.1/SRC/zlaqr5.o ../../lapack-3.6.1/SRC/zlaqsb.o ../../lapack-3.6.1/SRC/zlaqsp.o ../../lapack-3.6.1/SRC/zlaqsy.o ../../lapack-3.6.1/SRC/zlar1v.o ../../lapack-3.6.1/SRC/zlar2v.o ../../lapack-3.6.1/SRC/zlarcm.o ../../lapack-3.6.1/SRC/zlarfb.o ../../lapack-3.6.1/SRC/zlarfg.o ../../lapack-3.6.1/SRC/zlarfgp.o ../../lapack-3.6.1/SRC/zlarf.o ../../lapack-3.6.1/SRC/zlarft.o ../../lapack-3.6.1/SRC/zlarfx.o ../../lapack-3.6.1/SRC/zlargv.o ../../lapack-3.6.1/SRC/zlarnv.o ../../lapack-3.6.1/SRC/zlarrv.o ../../lapack-3.6.1/SRC/zlartg.o ../../lapack-3.6.1/SRC/zlartv.o ../../lapack-3.6.1/SRC/zlarzb.o ../../lapack-3.6.1/SRC/zlarz.o ../../lapack-3.6.1/SRC/zlarzt.o ../../lapack-3.6.1/SRC/zlascl.o ../../lapack-3.6.1/SRC/zlaset.o ../../lapack-3.6.1/SRC/zlasr.o ../../lapack-3.6.1/SRC/zlassq.o ../../lapack-3.6.1/SRC/zlaswp.o ../../lapack-3.6.1/SRC/zlasyf.o ../../lapack-3.6.1/SRC/zlasyf_rook.o ../../lapack-3.6.1/SRC/zlat2c.o ../../lapack-3.6.1/SRC/zlatbs.o ../../lapack-3.6.1/SRC/zlatdf.o ../../lapack-3.6.1/SRC/zlatps.o ../../lapack-3.6.1/SRC/zlatrd.o ../../lapack-3.6.1/SRC/zlatrs.o ../../lapack-3.6.1/SRC/zlatrz.o ../../lapack-3.6.1/SRC/zlauu2.o ../../lapack-3.6.1/SRC/zlauum.o ../../lapack-3.6.1/SRC/zpbcon.o ../../lapack-3.6.1/SRC/zpbequ.o ../../lapack-3.6.1/SRC/zpbrfs.o ../../lapack-3.6.1/SRC/zpbstf.o ../../lapack-3.6.1/SRC/zpbsv.o ../../lapack-3.6.1/SRC/zpbsvx.o ../../lapack-3.6.1/SRC/zpbtf2.o ../../lapack-3.6.1/SRC/zpbtrf.o ../../lapack-3.6.1/SRC/zpbtrs.o ../../lapack-3.6.1/SRC/zpftrf.o ../../lapack-3.6.1/SRC/zpftri.o ../../lapack-3.6.1/SRC/zpftrs.o ../../lapack-3.6.1/SRC/zpocon.o ../../lapack-3.6.1/SRC/zpoequb.o ../../lapack-3.6.1/SRC/zpoequ.o ../../lapack-3.6.1/SRC/zporfs.o ../../lapack-3.6.1/SRC/zposv.o ../../lapack-3.6.1/SRC/zposvx.o ../../lapack-3.6.1/SRC/zpotf2.o ../../lapack-3.6.1/SRC/zpotrf2.o ../../lapack-3.6.1/SRC/zpotrf.o ../../lapack-3.6.1/SRC/zpotri.o ../../lapack-3.6.1/SRC/zpotrs.o ../../lapack-3.6.1/SRC/zppcon.o ../../lapack-3.6.1/SRC/zppequ.o ../../lapack-3.6.1/SRC/zpprfs.o ../../lapack-3.6.1/SRC/zppsv.o ../../lapack-3.6.1/SRC/zppsvx.o ../../lapack-3.6.1/SRC/zpptrf.o ../../lapack-3.6.1/SRC/zpptri.o ../../lapack-3.6.1/SRC/zpptrs.o ../../lapack-3.6.1/SRC/zpstf2.o ../../lapack-3.6.1/SRC/zpstrf.o ../../lapack-3.6.1/SRC/zptcon.o ../../lapack-3.6.1/SRC/zpteqr.o ../../lapack-3.6.1/SRC/zptrfs.o ../../lapack-3.6.1/SRC/zptsv.o ../../lapack-3.6.1/SRC/zptsvx.o ../../lapack-3.6.1/SRC/zpttrf.o ../../lapack-3.6.1/SRC/zpttrs.o ../../lapack-3.6.1/SRC/zptts2.o ../../lapack-3.6.1/SRC/zrot.o ../../lapack-3.6.1/SRC/zspcon.o ../../lapack-3.6.1/SRC/zspmv.o ../../lapack-3.6.1/SRC/zsprfs.o ../../lapack-3.6.1/SRC/zspr.o ../../lapack-3.6.1/SRC/zspsv.o ../../lapack-3.6.1/SRC/zspsvx.o ../../lapack-3.6.1/SRC/zsptrf.o ../../lapack-3.6.1/SRC/zsptri.o ../../lapack-3.6.1/SRC/zsptrs.o ../../lapack-3.6.1/SRC/zstedc.o ../../lapack-3.6.1/SRC/zstegr.o ../../lapack-3.6.1/SRC/zstein.o ../../lapack-3.6.1/SRC/zstemr.o ../../lapack-3.6.1/SRC/zsteqr.o ../../lapack-3.6.1/SRC/zsycon.o ../../lapack-3.6.1/SRC/zsycon_rook.o ../../lapack-3.6.1/SRC/zsyconv.o ../../lapack-3.6.1/SRC/zsyequb.o ../../lapack-3.6.1/SRC/zsymv.o ../../lapack-3.6.1/SRC/zsyrfs.o ../../lapack-3.6.1/SRC/zsyr.o ../../lapack-3.6.1/SRC/zsysv.o ../../lapack-3.6.1/SRC/zsysv_rook.o ../../lapack-3.6.1/SRC/zsysvx.o ../../lapack-3.6.1/SRC/zsyswapr.o ../../lapack-3.6.1/SRC/zsytf2.o ../../lapack-3.6.1/SRC/zsytf2_rook.o ../../lapack-3.6.1/SRC/zsytrf.o ../../lapack-3.6.1/SRC/zsytrf_rook.o ../../lapack-3.6.1/SRC/zsytri2.o ../../lapack-3.6.1/SRC/zsytri2x.o ../../lapack-3.6.1/SRC/zsytri.o ../../lapack-3.6.1/SRC/zsytri_rook.o ../../lapack-3.6.1/SRC/zsytrs2.o ../../lapack-3.6.1/SRC/zsytrs.o ../../lapack-3.6.1/SRC/zsytrs_rook.o ../../lapack-3.6.1/SRC/ztbcon.o ../../lapack-3.6.1/SRC/ztbrfs.o ../../lapack-3.6.1/SRC/ztbtrs.o ../../lapack-3.6.1/SRC/ztfsm.o ../../lapack-3.6.1/SRC/ztftri.o ../../lapack-3.6.1/SRC/ztfttp.o ../../lapack-3.6.1/SRC/ztfttr.o ../../lapack-3.6.1/SRC/ztgevc.o ../../lapack-3.6.1/SRC/ztgex2.o ../../lapack-3.6.1/SRC/ztgexc.o ../../lapack-3.6.1/SRC/ztgsen.o ../../lapack-3.6.1/SRC/ztgsja.o ../../lapack-3.6.1/SRC/ztgsna.o ../../lapack-3.6.1/SRC/ztgsy2.o ../../lapack-3.6.1/SRC/ztgsyl.o ../../lapack-3.6.1/SRC/ztpcon.o ../../lapack-3.6.1/SRC/ztpmqrt.o ../../lapack-3.6.1/SRC/ztpqrt2.o ../../lapack-3.6.1/SRC/ztpqrt.o ../../lapack-3.6.1/SRC/ztprfb.o ../../lapack-3.6.1/SRC/ztprfs.o ../../lapack-3.6.1/SRC/ztptri.o ../../lapack-3.6.1/SRC/ztptrs.o ../../lapack-3.6.1/SRC/ztpttf.o ../../lapack-3.6.1/SRC/ztpttr.o ../../lapack-3.6.1/SRC/ztrcon.o ../../lapack-3.6.1/SRC/ztrevc3.o ../../lapack-3.6.1/SRC/ztrevc.o ../../lapack-3.6.1/SRC/ztrexc.o ../../lapack-3.6.1/SRC/ztrrfs.o ../../lapack-3.6.1/SRC/ztrsen.o ../../lapack-3.6.1/SRC/ztrsna.o ../../lapack-3.6.1/SRC/ztrsyl.o ../../lapack-3.6.1/SRC/ztrti2.o ../../lapack-3.6.1/SRC/ztrtri.o ../../lapack-3.6.1/SRC/ztrtrs.o ../../lapack-3.6.1/SRC/ztrttf.o ../../lapack-3.6.1/SRC/ztrttp.o ../../lapack-3.6.1/SRC/ztzrzf.o ../../lapack-3.6.1/SRC/zunbdb1.o ../../lapack-3.6.1/SRC/zunbdb2.o ../../lapack-3.6.1/SRC/zunbdb3.o ../../lapack-3.6.1/SRC/zunbdb4.o ../../lapack-3.6.1/SRC/zunbdb5.o ../../lapack-3.6.1/SRC/zunbdb6.o ../../lapack-3.6.1/SRC/zunbdb.o ../../lapack-3.6.1/SRC/zuncsd2by1.o ../../lapack-3.6.1/SRC/zuncsd.o ../../lapack-3.6.1/SRC/zung2l.o ../../lapack-3.6.1/SRC/zung2r.o ../../lapack-3.6.1/SRC/zungbr.o ../../lapack-3.6.1/SRC/zunghr.o ../../lapack-3.6.1/SRC/zungl2.o ../../lapack-3.6.1/SRC/zunglq.o ../../lapack-3.6.1/SRC/zungql.o ../../lapack-3.6.1/SRC/zungqr.o ../../lapack-3.6.1/SRC/zungr2.o ../../lapack-3.6.1/SRC/zungrq.o ../../lapack-3.6.1/SRC/zungtr.o ../../lapack-3.6.1/SRC/zunm22.o ../../lapack-3.6.1/SRC/zunm2l.o ../../lapack-3.6.1/SRC/zunm2r.o ../../lapack-3.6.1/SRC/zunmbr.o ../../lapack-3.6.1/SRC/zunmhr.o ../../lapack-3.6.1/SRC/zunml2.o ../../lapack-3.6.1/SRC/zunmlq.o ../../lapack-3.6.1/SRC/zunmql.o ../../lapack-3.6.1/SRC/zunmqr.o ../../lapack-3.6.1/SRC/zunmr2.o ../../lapack-3.6.1/SRC/zunmr3.o ../../lapack-3.6.1/SRC/zunmrq.o ../../lapack-3.6.1/SRC/zunmrz.o ../../lapack-3.6.1/SRC/zunmtr.o ../../lapack-3.6.1/SRC/zupgtr.o ../../lapack-3.6.1/SRC/zupmtr.o" -LINALG="../../lapack-3.6.1/libcblas.a ../../lapack-3.6.1/libblas.a ../../lapack-3.6.1/liblapack.a" - -f2py --f90exec=mpif90 --fcompiler=gnu95 -c total_energy.f90 -m total_energy $PROJECT_INC_FOLDERS $PROJECT_LIB_FOLDERS \ - $PROJECT_LIBS $PWOBJS $UTILOBJS $MODOBJS $MODOBJS2 $LINALGOBJS $LINALG $FOXLIBS ../../clib/clib.a - diff --git a/ml-dft-sandia/total_energy_module/test_total_energy.py b/ml-dft-sandia/total_energy_module/test_total_energy.py deleted file mode 100644 index 663be5c97..000000000 --- a/ml-dft-sandia/total_energy_module/test_total_energy.py +++ /dev/null @@ -1,27 +0,0 @@ -from mpi4py import MPI -import total_energy as te -import numpy as np - -te.initialize() -nnr = te.get_nnr() -nspin = te.get_nspin() -ngm = te.get_ngm() -nat = te.get_nat() -te.print_energies() - -positions = te.get_positions(nat) -positions = positions + 0.01*np.random.random([3,nat]) -te.set_positions(positions,nat) -te.print_energies() - -rho_of_r = te.get_rho_of_r(nnr,nspin) -total_rho = np.sum(rho_of_r) -rho_of_r = rho_of_r + 1.0e-3 -rho_of_r = rho_of_r * total_rho / np.sum(rho_of_r) -te.set_rho_of_r(rho_of_r,nnr,nspin) -te.print_energies() -e_rho_times_v_hxc, e_hartree, e_xc, e_ewald = te.get_energies() -e_rho_times_v_hxc -e_hartree -e_xc -e_ewald diff --git a/ml-dft-sandia/total_energy_module/x_ac_qe_f90.m4.patch b/ml-dft-sandia/total_energy_module/x_ac_qe_f90.m4.patch deleted file mode 100644 index 8534d11bc..000000000 --- a/ml-dft-sandia/total_energy_module/x_ac_qe_f90.m4.patch +++ /dev/null @@ -1,17 +0,0 @@ -diff --git a/install/m4/x_ac_qe_f90.m4 b/install/m4/x_ac_qe_f90.m4 -index 91d1740..32deba1 100644 ---- a/install/m4/x_ac_qe_f90.m4 -+++ b/install/m4/x_ac_qe_f90.m4 -@@ -239,9 +239,11 @@ if test "$use_openmp" -eq 1 ; then - try_ldflags="$try_ldflags $try_ldflags_openmp" - fi - -+foxflags=-fPIC -+ - if test "$fflags" = "" ; then fflags=$try_fflags ; fi - if test "$f90flags" = "" ; then f90flags=$try_f90flags ; fi --if test "try_foxflags" != ""; then foxflags=$try_foxflags; fi -+if test "try_foxflags" != ""; then foxflags="$try_foxflags $foxflags"; fi - if test "$fflags_noopt" = "" ; then fflags_noopt=$try_fflags_noopt ; fi - if test "$fflags_nomain" = "" ; then fflags_nomain=$try_fflags_nomain ; fi - From e18f15d585228721e5232c6cd6df877f6ad3e88a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 7 Jul 2021 12:59:44 +0200 Subject: [PATCH 128/297] Fixed up the citation as suggested by Siva --- README.md | 2 +- ml-dft-sandia/README.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5087d0ad4..31ec59862 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ the examples in the `example/` folder. If you publish work which uses or mentions MALA, please cite the following paper: -J. A. Ellis, G. A. Popoola, L. Fiedler, N. A. Modine, J. A. Stephens, A. P. Thompson, +J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam (2020). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. [arXiv:2010.04905](https://arxiv.org/abs/2010.04905). diff --git a/ml-dft-sandia/README.md b/ml-dft-sandia/README.md index 624bfc165..ad1e6fb2d 100644 --- a/ml-dft-sandia/README.md +++ b/ml-dft-sandia/README.md @@ -44,7 +44,7 @@ This repository is structured as follows: If you publish work which uses or mentions MALA, please cite the following paper: -J. A. Ellis, A. Cangi, N. A. Modine, J. A. Stephens, A. P. Thompson, -S. Rajamanickam (2020). Accelerating Finite-temperature +J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, +A. Cangi, S. Rajamanickam (2020). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. [arXiv:2010.04905](https://arxiv.org/abs/2010.04905). From 2e68923890243194852cae1fef14a2548308a0a0 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 10:47:47 +0200 Subject: [PATCH 129/297] Rename job in `gh-pages.yml` to be more expressive --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 863d0ccae..241f5291e 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -30,7 +30,7 @@ jobs: - name: Check docstrings run: pydocstyle --convention=numpy mala - deploy-pages: + build-and-deploy-pages: needs: test-docstrings runs-on: ubuntu-18.04 steps: From f96d6775e4e0bb6ee7e5c2db1e7c1a481d619780 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 11:31:15 +0200 Subject: [PATCH 130/297] =?UTF-8?q?Squash=20class=E2=80=99=20and=20`=5F=5F?= =?UTF-8?q?init=5F=5F`=20method=E2=80=99s=20docstrings?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 048287a83..dcd9aa54b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,6 +71,7 @@ ] autodoc_member_order = 'groupwise' +autoclass_content = "both" # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From 1acf4e96a21ed0f8c8e5bca70b913804f9f98300 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 2 Jul 2021 11:45:18 +0200 Subject: [PATCH 131/297] Fix indentation in `__init__` docstrings of class: - `DataScaler` - `HyperparameterOAT - `HyperparameterOptuna` --- mala/datahandling/data_scaler.py | 6 +++--- mala/network/hyperparameter_oat.py | 4 ++-- mala/network/hyperparameter_optuna.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 1c5ace945..89ad02b9b 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -29,12 +29,12 @@ def __init__(self, typestring, use_horovod=False): - "None": No normalization is applied. - "standard": Standardization (Scale to mean 0, - standard deviation 1) + standard deviation 1) - "normal": Min-Max scaling (Scale to be in range 0...1) - "feature-wise-standard": Row Standardization (Scale to mean 0, - standard deviation 1) + standard deviation 1) - "feature-wise-normal": Row Min-Max scaling (Scale to be in range - 0...1) + 0...1) use_horovod : bool If True, the DataScaler will use horovod to check that data is diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 3b8405841..2c5760215 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -25,7 +25,7 @@ def __init__(self, opttype="categorical", name="", choices=None): Name of the hyperparameter. Please note that these names always have to be distinct; if you e.g. want to investigate multiple layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + etc. as names. choices : List of possible choices (for categorical parameter). @@ -37,7 +37,7 @@ def __init__(self, opttype="categorical", name="", choices=None): if self.opttype != "categorical": raise Exception("Unsupported Hyperparameter type.") - + def get_parameter(self, trial, idx): """ Extract current value of hyperparameter from an orthogonal array. diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index d18daa6d3..8f4bf423b 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -26,7 +26,7 @@ def __init__(self, opttype="float", name="", low=0, high=0, choices=None): Name of the hyperparameter. Please note that these names always have to be distinct; if you e.g. want to investigate multiple layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + etc. as names. low : float or int Lower bound for numerical parameter. From c2f7a17b88403e0d4ff3b3e5fc0503490b702d52 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 10:34:08 +0200 Subject: [PATCH 132/297] Removed unnecessary descriptions from __init__ --- mala/common/parameters.py | 9 --------- mala/datahandling/data_converter.py | 2 -- mala/datahandling/data_handler.py | 2 -- mala/datahandling/data_scaler.py | 2 -- mala/datahandling/lazy_load_dataset.py | 2 -- mala/datahandling/snapshot.py | 2 -- mala/descriptors/descriptor_base.py | 2 -- mala/descriptors/snap.py | 2 -- mala/network/hyper_opt_base.py | 2 -- mala/network/hyper_opt_notraining.py | 2 -- mala/network/hyper_opt_oat.py | 2 -- mala/network/hyper_opt_optuna.py | 2 -- mala/network/hyperparameter_oat.py | 2 -- mala/network/hyperparameter_optuna.py | 2 -- mala/network/network.py | 2 -- mala/network/objective_base.py | 2 -- mala/network/objective_no_training.py | 4 +--- mala/network/runner.py | 2 -- mala/network/tester.py | 2 -- mala/network/trainer.py | 2 -- mala/targets/cube_parser.py | 3 +-- mala/targets/density.py | 2 -- mala/targets/dos.py | 2 -- mala/targets/ldos.py | 2 -- mala/targets/target_base.py | 2 -- 25 files changed, 2 insertions(+), 58 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 3a0a49ebc..92f30a7ec 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -16,7 +16,6 @@ class ParametersBase: """Base parameter class for MALA.""" def __init__(self): - """Create an instance of ParameterBase.""" pass def show(self, indent=""): @@ -68,7 +67,6 @@ class ParametersNetwork(ParametersBase): """ def __init__(self): - """Create an instance of ParametersNetwork.""" super(ParametersNetwork, self).__init__() self.nn_type = "feed-forward" self.layer_sizes = [10, 10, 10] @@ -103,7 +101,6 @@ class ParametersDescriptors(ParametersBase): """ def __init__(self): - """Create an instance of ParametersDescriptors.""" super(ParametersDescriptors, self).__init__() self.descriptor_type = "SNAP" self.twojmax = 10 @@ -133,7 +130,6 @@ class ParametersTargets(ParametersBase): """ def __init__(self): - """Create an instance of ParameterTargets.""" super(ParametersTargets, self).__init__() self.target_type = "LDOS" self.ldos_gridsize = 0 @@ -218,7 +214,6 @@ class ParametersData(ParametersBase): """ def __init__(self): - """Create an instance of ParametersData.""" super(ParametersData, self).__init__() self.descriptors_contain_xyz = True self.snapshot_directories_list = [] @@ -319,7 +314,6 @@ class ParametersRunning(ParametersBase): """ def __init__(self): - """Create an instance of ParametersRunning.""" super(ParametersRunning, self).__init__() self.trainingtype = "SGD" self.learning_rate = 0.5 @@ -402,7 +396,6 @@ class ParametersHyperparameterOptimization(ParametersBase): """ def __init__(self): - """Create an instance of ParametersHyperparameterOptimization.""" super(ParametersHyperparameterOptimization, self).__init__() self.direction = 'minimize' self.n_trials = 100 @@ -461,7 +454,6 @@ class ParametersDebug(ParametersBase): """ def __init__(self): - """Create an instance of ParametersDebug.""" super(ParametersDebug, self).__init__() self.grid_dimensions = [] @@ -503,7 +495,6 @@ class Parameters: """ def __init__(self): - """Create an instance of Parameters.""" self.comment = "" self.network = ParametersNetwork() self.descriptors = ParametersDescriptors() diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index 31c017277..bf43e5394 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -24,8 +24,6 @@ class DataConverter: def __init__(self, parameters, descriptor_calculator=None, target_calculator=None): """ - Create an instances of the DataConverter class. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 9d7c9a037..e7f6ed93a 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -24,8 +24,6 @@ def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): """ - Create an instance of DataHandler. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 89ad02b9b..5b92192aa 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -19,8 +19,6 @@ class DataScaler: def __init__(self, typestring, use_horovod=False): """ - Create a DataScaler object. - Parameters ---------- typestring : string diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index 0f7ef1ba8..46e448fb6 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -27,8 +27,6 @@ def __init__(self, input_dimension, output_dimension, input_data_scaler, target_calculator, grid_dimensions, grid_size, descriptors_contain_xyz, use_horovod): """ - Create a lazily loaded DataSet. - Parameters ---------- input_dimension : int diff --git a/mala/datahandling/snapshot.py b/mala/datahandling/snapshot.py index 8b0c475c5..4607efadf 100644 --- a/mala/datahandling/snapshot.py +++ b/mala/datahandling/snapshot.py @@ -14,8 +14,6 @@ def __init__(self, input_npy_file="", input_npy_directory="", output_npy_directory="", output_units="", calculation_output=""): """ - Create a Snapshot object. - Parameters ---------- input_npy_file : string diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index ffc6ad49a..f18322733 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -10,8 +10,6 @@ class DescriptorBase: def __init__(self, parameters): """ - Create a DescriptorBase object. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 9c2fbffda..fb2d62eff 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -19,8 +19,6 @@ class SNAP(DescriptorBase): def __init__(self, parameters): """ - Create a SNAP object. - Parameters ---------- parameters : mala.common.parameters.Parameters diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index d7b238213..4efbbb167 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -8,8 +8,6 @@ class HyperOptBase(ABC): def __init__(self, params, data): """ - Create a hyperparameter optimizer object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 2d8690cf5..9a841db58 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -14,8 +14,6 @@ class HyperOptNoTraining(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptNoTraining object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index b342796ae..92da29f99 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -11,8 +11,6 @@ class HyperOptOAT(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptOAT object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 4b38085a6..45be61b08 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -15,8 +15,6 @@ class HyperOptOptuna(HyperOptBase): def __init__(self, params, data): """ - Create a HyperOptOptuna object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 2c5760215..7bf505cea 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -6,8 +6,6 @@ class HyperparameterOAT: def __init__(self, opttype="categorical", name="", choices=None): """ - Create an OAT compatible hyperparameter. - Parameters ---------- opttype : string diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index 8f4bf423b..d538ea454 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -7,8 +7,6 @@ class HyperparameterOptuna: def __init__(self, opttype="float", name="", low=0, high=0, choices=None): """ - Create an optuna compatible hyperparameter. - Parameters ---------- opttype : string diff --git a/mala/network/network.py b/mala/network/network.py index fe1071b11..342faaf4a 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -14,8 +14,6 @@ class Network(nn.Module): def __init__(self, params): """ - Create a Network object, representing a neural network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 8a012349f..3088b235d 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -15,8 +15,6 @@ class ObjectiveBase: def __init__(self, params, data_handler): """ - Create an ObjectiveBase object. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 31f8cceff..530381c51 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -19,9 +19,7 @@ class ObjectiveNoTraining(ObjectiveBase): def __init__(self, search_parameters: Parameters, data_handler: DataHandler, trial_type): - """ - Create an ObjectiveNoTraining object. - + """ Parameters ---------- search_parameters : mala.common.parameters.Parameters diff --git a/mala/network/runner.py b/mala/network/runner.py index 15b25bea1..89d9fd0fa 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -17,8 +17,6 @@ class Runner: def __init__(self, params, network, data): """ - Create a Runner object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/tester.py b/mala/network/tester.py index aed6d98cd..7f907966d 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -20,8 +20,6 @@ class Tester(Runner): def __init__(self, params, network, data): """ - Create a Tester object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/network/trainer.py b/mala/network/trainer.py index ae3fd371d..b75e4273d 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -23,8 +23,6 @@ class Trainer(Runner): def __init__(self, params, network, data, optimizer_dict=None): """ - Create a Trainer object to run a Network. - Parameters ---------- params : mala.common.parametes.Parameters diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index c565ec400..f3a091384 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -80,8 +80,7 @@ class CubeFile(object): """ def __init__(self, srcname, const=1): - """Create cube file object. - + """ Parameters ---------- srcname: string diff --git a/mala/targets/density.py b/mala/targets/density.py index 47bc3cec6..cfdb45103 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -24,8 +24,6 @@ class Density(TargetBase): def __init__(self, params): """ - Create a Density object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 5607202d3..0ca3e3f3b 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -12,8 +12,6 @@ class DOS(TargetBase): def __init__(self, params): """ - Create a DOS object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index c158fa2c6..2adfd70ab 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -15,8 +15,6 @@ class LDOS(TargetBase): def __init__(self, params): """ - Create a LDOS object. - Parameters ---------- params : mala.common.parameters.Parameters diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 5bb3f523a..c441bb5a3 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -17,8 +17,6 @@ class TargetBase: def __init__(self, params): """ - Create a TargetBase object. - Parameters ---------- params : mala.common.parameters.Parameters or From 69bbf200ced0d0bdd9eb26812cb54dda3224497a Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 7 Jul 2021 12:24:36 +0200 Subject: [PATCH 133/297] Clear up `__init__` methods: Move parameters section from `__init__` into class docstring (Numpy docstring style). --- mala/datahandling/data_converter.py | 31 ++++++------- mala/datahandling/data_handler.py | 39 ++++++++-------- mala/datahandling/data_scaler.py | 39 ++++++++-------- mala/datahandling/lazy_load_dataset.py | 63 +++++++++++++------------- mala/datahandling/snapshot.py | 51 ++++++++++----------- mala/descriptors/descriptor_base.py | 12 ++--- mala/descriptors/snap.py | 14 +++--- mala/network/hyper_opt_base.py | 20 ++++---- mala/network/hyper_opt_notraining.py | 18 ++++---- mala/network/hyper_opt_oat.py | 20 ++++---- mala/network/hyper_opt_optuna.py | 21 +++++---- mala/network/hyperparameter_oat.py | 42 ++++++++--------- mala/network/hyperparameter_optuna.py | 50 ++++++++++---------- mala/network/network.py | 14 +++--- mala/network/objective_base.py | 17 ++++--- mala/network/objective_no_training.py | 31 ++++++------- mala/network/runner.py | 23 +++++----- mala/network/tester.py | 23 +++++----- mala/network/trainer.py | 24 +++++----- mala/targets/density.py | 15 +++--- mala/targets/dos.py | 15 +++--- mala/targets/ldos.py | 14 +++--- mala/targets/target_base.py | 14 +++--- 23 files changed, 298 insertions(+), 312 deletions(-) diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index bf43e5394..6e0357459 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -12,6 +12,21 @@ class DataConverter: These snapshots can be e.g. Quantum Espresso results. + Parameters + ---------- + parameters : mala.common.parameters.Parameters + The parameters object used for creating this instance. + + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + The descriptor calculator used for parsing/converting fingerprint + data. If None, the descriptor calculator will be created by this + object using the parameters provided. Default: None + + target_calculator : mala.targets.target_base.TargetBase + Target calculator used for parsing/converting target data. If None, + the target calculator will be created by this object using the + parameters provided. + Attributes ---------- descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase @@ -23,22 +38,6 @@ class DataConverter: def __init__(self, parameters, descriptor_calculator=None, target_calculator=None): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - The parameters object used for creating this instance. - - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - The descriptor calculator used for parsing/converting fingerprint - data. If None, the descriptor calculator will be created by this - object using the parameters provided. Default: None - - target_calculator : mala.targets.target_base.TargetBase - Target calculator used for parsing/converting target data. If None, - the target calculator will be created by this object using the - parameters provided. - """ self.parameters: ParametersData = parameters.data self.target_calculator = target_calculator if self.target_calculator is None: diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index e7f6ed93a..89aadfb68 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -18,31 +18,30 @@ class DataHandler: Data that is not in a numpy array can be converted using the DataConverter class. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + Used to do unit conversion on input data. If None, then one will + be created by this class. + + target_calculator : mala.targets.target_base.TargetBase + Used to do unit conversion on output data. If None, then one will + be created by this class. + + input_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the input data. If None, then one will be created by + this class. + + output_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the output data. If None, then one will be created by + this class. """ def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - Used to do unit conversion on input data. If None, then one will - be created by this class. - - target_calculator : mala.targets.target_base.TargetBase - Used to do unit conversion on output data. If None, then one will - be created by this class. - - input_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the input data. If None, then one will be created by - this class. - - output_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the output data. If None, then one will be created by - this class. - """ self.parameters = parameters.data self.dbg_grid_dimensions = parameters.debug.grid_dimensions self.use_horovod = parameters.use_horovod diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 5b92192aa..7f9723d58 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -15,29 +15,28 @@ class DataScaler: Sort of emulates the functionality of the scikit-learn library, but by implementing the class by ourselves we have more freedom. + + Parameters + ---------- + typestring : string + Specifies how scaling should be performed. + Options: + + - "None": No normalization is applied. + - "standard": Standardization (Scale to mean 0, + standard deviation 1) + - "normal": Min-Max scaling (Scale to be in range 0...1) + - "feature-wise-standard": Row Standardization (Scale to mean 0, + standard deviation 1) + - "feature-wise-normal": Row Min-Max scaling (Scale to be in range + 0...1) + + use_horovod : bool + If True, the DataScaler will use horovod to check that data is + only saved on the root process in parallel execution. """ def __init__(self, typestring, use_horovod=False): - """ - Parameters - ---------- - typestring : string - Specifies how scaling should be performed. - Options: - - - "None": No normalization is applied. - - "standard": Standardization (Scale to mean 0, - standard deviation 1) - - "normal": Min-Max scaling (Scale to be in range 0...1) - - "feature-wise-standard": Row Standardization (Scale to mean 0, - standard deviation 1) - - "feature-wise-normal": Row Min-Max scaling (Scale to be in range - 0...1) - - use_horovod : bool - If True, the DataScaler will use horovod to check that data is - only saved on the root process in parallel execution. - """ self.use_horovod = use_horovod self.typestring = typestring self.scale_standard = False diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index 46e448fb6..74ccc7167 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -20,48 +20,47 @@ class LazyLoadDataset(torch.utils.data.Dataset): / DataLoader level is discouraged to the point that it was disabled. Instead, we mix the snapshot load order here ot have some sort of mixing at all. - """ - def __init__(self, input_dimension, output_dimension, input_data_scaler, - output_data_scaler, descriptor_calculator, - target_calculator, grid_dimensions, grid_size, - descriptors_contain_xyz, use_horovod): - """ - Parameters - ---------- - input_dimension : int - Dimension of an input vector. + Parameters + ---------- + input_dimension : int + Dimension of an input vector. - output_dimension : int - Dimension of an output vector. + output_dimension : int + Dimension of an output vector. - input_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the input data. + input_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the input data. - output_data_scaler : mala.datahandling.data_scaler.DataScaler - Used to scale the output data. + output_data_scaler : mala.datahandling.data_scaler.DataScaler + Used to scale the output data. - descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase - Used to do unit conversion on input data. + descriptor_calculator : mala.descriptors.descriptor_base.DescriptorBase + Used to do unit conversion on input data. - target_calculator : mala.targets.target_base.TargetBase or derivative - Used to do unit conversion on output data. + target_calculator : mala.targets.target_base.TargetBase or derivative + Used to do unit conversion on output data. - grid_dimensions : list - Dimensions of the grid (x,y,z). + grid_dimensions : list + Dimensions of the grid (x,y,z). - grid_size : int - Size of the grid (x*y*z), i.e. the number of datapoints per - snapshot. + grid_size : int + Size of the grid (x*y*z), i.e. the number of datapoints per + snapshot. - descriptors_contain_xyz : bool - If true, then it is assumed that the first three entries of any - input data file are xyz-information and can be discarded. - Generally true, if your descriptors were calculated using MALA. + descriptors_contain_xyz : bool + If true, then it is assumed that the first three entries of any + input data file are xyz-information and can be discarded. + Generally true, if your descriptors were calculated using MALA. - use_horovod : bool - If true, it is assumed that horovod is used. - """ + use_horovod : bool + If true, it is assumed that horovod is used. + """ + + def __init__(self, input_dimension, output_dimension, input_data_scaler, + output_data_scaler, descriptor_calculator, + target_calculator, grid_dimensions, grid_size, + descriptors_contain_xyz, use_horovod): self.snapshot_list = [] self.input_dimension = input_dimension self.output_dimension = output_dimension diff --git a/mala/datahandling/snapshot.py b/mala/datahandling/snapshot.py index 4607efadf..c838e4212 100644 --- a/mala/datahandling/snapshot.py +++ b/mala/datahandling/snapshot.py @@ -7,39 +7,38 @@ class Snapshot: A snapshot consists of numpy arrays for input/output data and an optional DFT calculation output, needed for post-processing. - """ - def __init__(self, input_npy_file="", input_npy_directory="", - input_units="", output_npy_file="", - output_npy_directory="", output_units="", - calculation_output=""): - """ - Parameters - ---------- - input_npy_file : string - File with saved numpy input array. + Parameters + ---------- + input_npy_file : string + File with saved numpy input array. - input_npy_directory : string - Directory containing input_npy_directory. + input_npy_directory : string + Directory containing input_npy_directory. - output_npy_file : string - File with saved numpy output array. + output_npy_file : string + File with saved numpy output array. - output_npy_directory : string - Directory containing output_npy_file. + output_npy_directory : string + Directory containing output_npy_file. - input_units : string - Units of input data. See descriptor classes to see which units are - supported. + input_units : string + Units of input data. See descriptor classes to see which units are + supported. - output_units : string - Units of output data. See target classes to see which units are - supported. + output_units : string + Units of output data. See target classes to see which units are + supported. - calculation_output : string - File with the output of the original snapshot calculation. This is - only needed when testing multiple snapshots. - """ + calculation_output : string + File with the output of the original snapshot calculation. This is + only needed when testing multiple snapshots. + """ + + def __init__(self, input_npy_file="", input_npy_directory="", + input_units="", output_npy_file="", + output_npy_directory="", output_units="", + calculation_output=""): # Inputs. self.input_npy_file = input_npy_file self.input_npy_directory = input_npy_directory diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index f18322733..a6ef325fe 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -6,15 +6,15 @@ class DescriptorBase: Base class for all descriptors available in MALA. Descriptors encode the atomic fingerprint of a DFT calculation. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + Parameters object used to create this object. + """ def __init__(self, parameters): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - Parameters object used to create this object. - """ self.parameters = parameters.descriptors self.fingerprint_length = -1 # so iterations will fail self.dbg_grid_dimensions = parameters.debug.grid_dimensions diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index fb2d62eff..f997775d7 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -15,15 +15,15 @@ class SNAP(DescriptorBase): - """Class for calculation and parsing of SNAP descriptors.""" + """Class for calculation and parsing of SNAP descriptors. + + Parameters + ---------- + parameters : mala.common.parameters.Parameters + Parameters object used to create this object. + """ def __init__(self, parameters): - """ - Parameters - ---------- - parameters : mala.common.parameters.Parameters - Parameters object used to create this object. - """ super(SNAP, self).__init__(parameters) self.in_format_ase = "" diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index 4efbbb167..a75a826eb 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -4,18 +4,18 @@ class HyperOptBase(ABC): - """Base class for hyperparameter optimizater.""" + """Base class for hyperparameter optimizater. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + """ + + def __init__(self, params, data): self.params = params self.data_handler = data diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 9a841db58..ed47d8300 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -10,18 +10,17 @@ class HyperOptNoTraining(HyperOptBase): Hyperparameter optimizer that does not require training networks. Networks are analysed using the Jacobian. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. + + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. """ def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. - - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ super(HyperOptNoTraining, self).__init__(params, data) self.objective = None self.trial_losses = None @@ -76,4 +75,3 @@ def set_optimal_parameters(self): idx = self.trial_losses.index(min(self.trial_losses)) self.best_trial = self.trial_list[idx] self.objective.parse_trial(self.best_trial) - diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 92da29f99..483eddedb 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -7,18 +7,18 @@ class HyperOptOAT(HyperOptBase): - """Hyperparameter optimizer using Orthogonal Array Tuning.""" + """Hyperparameter optimizer using Orthogonal Array Tuning. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + """ + + def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 45be61b08..89815ea2c 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -11,18 +11,19 @@ class HyperOptOptuna(HyperOptBase): - """Hyperparameter optimizer using Optuna.""" + """Hyperparameter optimizer using Optuna. - def __init__(self, params, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this hyperparameter optimizer. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this hyperparameter optimizer. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the hyperparameter optimization. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the hyperparameter optimization. + + """ + + def __init__(self, params, data): super(HyperOptOptuna, self).__init__(params, data) self.params = params diff --git a/mala/network/hyperparameter_oat.py b/mala/network/hyperparameter_oat.py index 7bf505cea..fe10b6e5b 100644 --- a/mala/network/hyperparameter_oat.py +++ b/mala/network/hyperparameter_oat.py @@ -2,32 +2,32 @@ class HyperparameterOAT: - """Represents a hyperparameter for OAT.""" + """Represents a hyperparameter for OAT. - def __init__(self, opttype="categorical", name="", choices=None): - """ - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - In principle supported are: + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + In principle supported are: - - float - - int - - categorical (list) + - float + - int + - categorical (list) - Float and int are not available for OA based approaches at the - moment. + Float and int are not available for OA based approaches at the + moment. - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. - choices : - List of possible choices (for categorical parameter). - """ + choices : + List of possible choices (for categorical parameter). + """ + + def __init__(self, opttype="categorical", name="", choices=None): self.name = name self.opttype = opttype self.choices = choices diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index d538ea454..a65b8eb59 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -3,38 +3,38 @@ class HyperparameterOptuna: - """Represents an optuna parameter.""" + """Represents an optuna parameter. - def __init__(self, opttype="float", name="", low=0, high=0, choices=None): - """ - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - In principle supported are: + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + In principle supported are: - - float - - int - - categorical (list) + - float + - int + - categorical (list) - Float and int are not available for OA based approaches at the - moment. + Float and int are not available for OA based approaches at the + moment. - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. - low : float or int - Lower bound for numerical parameter. + low : float or int + Lower bound for numerical parameter. - high : float or int - Higher bound for numerical parameter. + high : float or int + Higher bound for numerical parameter. - choices : - List of possible choices (for categorical parameter). - """ + choices : + List of possible choices (for categorical parameter). + """ + + def __init__(self, opttype="float", name="", low=0, high=0, choices=None): self.name = name self.high = high self.low = low diff --git a/mala/network/network.py b/mala/network/network.py index 342faaf4a..2245381b3 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -10,15 +10,15 @@ class Network(nn.Module): - """Central network class for this framework, based on pytorch.nn.Module.""" + """Central network class for this framework, based on pytorch.nn.Module. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this neural network. + """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this neural network. - """ # copy the network params from the input parameter object self.use_horovod = params.use_horovod self.params = params.network diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 3088b235d..37ab056bd 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -11,18 +11,17 @@ class ObjectiveBase: Represents the objective function of a training process. This is usually the result of a training of a network. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. """ def __init__(self, params, data_handler): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this objective. - - data_handler : mala.datahandling.data_handler.DataHandler - datahandler to be used during the hyperparameter optimization. - """ self.params = params self.data_handler = data_handler diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 530381c51..1ceb05458 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -15,26 +15,25 @@ class ObjectiveNoTraining(ObjectiveBase): Represents the objective function using no NN training. The objective value is calculated using the Jacobian. + + Parameters + ---------- + search_parameters : mala.common.parameters.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. + + trial_type : string + Format of hyperparameters used in this objective. Supported + choices are: + + - optuna + - oat """ def __init__(self, search_parameters: Parameters, data_handler: DataHandler, trial_type): - """ - Parameters - ---------- - search_parameters : mala.common.parameters.Parameters - Parameters used to create this objective. - - data_handler : mala.datahandling.data_handler.DataHandler - datahandler to be used during the hyperparameter optimization. - - trial_type : string - Format of hyperparameters used in this objective. Supported - choices are: - - - optuna - - oat - """ super(ObjectiveNoTraining, self).__init__(search_parameters, data_handler) self.trial_type = trial_type diff --git a/mala/network/runner.py b/mala/network/runner.py index 89d9fd0fa..5abb99f7f 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -13,21 +13,20 @@ class Runner: Parent class for all classes that in some sense "run" the network. That can be training, benchmarking, inference, etc. - """ - def __init__(self, params, network, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Runner object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Runner object. - network : mala.network.network.Network - Network which is being run. + network : mala.network.network.Network + Network which is being run. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the data for the run. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the data for the run. + """ + + def __init__(self, params, network, data): self.parameters_full = params self.parameters = params.running self.network = network diff --git a/mala/network/tester.py b/mala/network/tester.py index 7f907966d..bb6c62abd 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -16,21 +16,20 @@ class Tester(Runner): A class for testing a neural network. It enables easy inference throughout a test set. - """ - def __init__(self, params, network, data): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Tester object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Tester object. - network : mala.network.network.Network - Network which is being tested. + network : mala.network.network.Network + Network which is being tested. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the test data. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the test data. + """ + + def __init__(self, params, network, data): # copy the parameters into the class. super(Tester, self).__init__(params, network, data) self.test_data_loader = None diff --git a/mala/network/trainer.py b/mala/network/trainer.py index b75e4273d..fdf176e31 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -19,21 +19,21 @@ class Trainer(Runner): - """A class for training a neural network.""" + """A class for training a neural network. - def __init__(self, params, network, data, optimizer_dict=None): - """ - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this Trainer object. + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Trainer object. - network : mala.network.network.Network - Network which is being trained. + network : mala.network.network.Network + Network which is being trained. - data : mala.datahandling.data_handler.DataHandler - DataHandler holding the training data. - """ + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the training data. + """ + + def __init__(self, params, network, data, optimizer_dict=None): # copy the parameters into the class. super(Trainer, self).__init__(params, network, data) self.final_test_loss = float("inf") diff --git a/mala/targets/density.py b/mala/targets/density.py index cfdb45103..1dd664d86 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -18,18 +18,17 @@ class Density(TargetBase): - """Postprocessing / parsing functions for the electronic density.""" + """Postprocessing / parsing functions for the electronic density. + + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ te_mutex = False def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. - - """ super(Density, self).__init__(params) # We operate on a per gridpoint basis. Per gridpoint, # there is one value for the density (spin-unpolarized calculations). diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 0ca3e3f3b..9e287beea 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -8,16 +8,15 @@ class DOS(TargetBase): - """Postprocessing / parsing functions for the density of states (DOS).""" + """Postprocessing / parsing functions for the density of states (DOS). - def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ - """ + def __init__(self, params): super(DOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 2adfd70ab..3a1993402 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -11,15 +11,15 @@ class LDOS(TargetBase): - """Postprocessing / parsing functions for the local density of states.""" + """Postprocessing / parsing functions for the local density of states. + + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters used to create this TargetBase object. + """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters - Parameters used to create this TargetBase object. - """ super(LDOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize self.cached_dos_exists = False diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index c441bb5a3..1f46599b5 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -13,17 +13,15 @@ class TargetBase: Target parsers read the target quantity (i.e. the quantity the NN will learn to predict) from a specified file format and performs postprocessing calculations on the quantity. + + Parameters + ---------- + params : mala.common.parameters.Parameters or + mala.common.parameters.ParametersTargets + Parameters used to create this TargetBase object. """ def __init__(self, params): - """ - Parameters - ---------- - params : mala.common.parameters.Parameters or - mala.common.parameters.ParametersTargets - Parameters used to create this TargetBase object. - - """ if isinstance(params, Parameters): self.parameters = params.targets elif isinstance(params, ParametersTargets): From 02f7ceb61a159e42af35888b8e33c263cb207954 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 7 Jul 2021 12:24:40 +0200 Subject: [PATCH 134/297] Remove concatenation of `__init__` and class docstrings: We document the class constructor within the class docstring; we do not use `__init__` docstrings. --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index dcd9aa54b..048287a83 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,7 +71,6 @@ ] autodoc_member_order = 'groupwise' -autoclass_content = "both" # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From 35c0d07e5230ce6db06987bac57399b15c5758dd Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 7 Jul 2021 13:17:16 +0200 Subject: [PATCH 135/297] Fixed documentation in cube_parser.py --- mala/targets/cube_parser.py | 47 ++++++++++++++++++++++--------------- 1 file changed, 28 insertions(+), 19 deletions(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index f3a091384..0c62eed6c 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -77,19 +77,18 @@ class CubeFile(object): Doesn't copy atoms metadata, retains number of atoms, but returns dummy atoms Mimics file object's readline method. + + Parameters + ---------- + srcname: string + source file to copy metadata from + + const: int + numeric value to return instead of volumetric data """ def __init__(self, srcname, const=1): - """ - Parameters - ---------- - srcname: string - source file to copy metadata from - - const: int - numeric value to return instead of volumetric data - """ - self.cursor = 0 + self.cursor = 0 self.const = const self.src = src = open(srcname) # comments @@ -150,10 +149,15 @@ def _getline(cube): First field is an int and the remaining fields are floats. - params: - cube: - - returns: (int, list) + Parameters + ---------- + cube : TextIO + The cubefile from which the line is read. + + Returns + ------- + line : tuple + First entry is an int, the followin are floats. """ line = cube.readline().strip().split() return int(line[0]), map(float, line[1:]) @@ -164,11 +168,16 @@ def _putline(*args): Generate a line to be written to a cube file. The first field is an int and the remaining fields are floats. - - params: - *args: first arg is formatted as int and remaining as floats - - returns: formatted string to be written to file with trailing newline + + Parameters + ---------- + args : tuple + First arg is formatted as int and remaining as floats. + + Returns + ------- + line : string + Formatted string to be written to file with trailing newline. """ s = "{0:^ 8d}".format(args[0]) s += "".join("{0:< 12.6f}".format(arg) for arg in args[1:]) From 805039127d1db02939711ca9bf2742d18444cb7d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Wed, 7 Jul 2021 15:04:07 +0200 Subject: [PATCH 136/297] Update mala/targets/cube_parser.py Co-authored-by: Daniel Kotik --- mala/targets/cube_parser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index 0c62eed6c..9f68df699 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -157,7 +157,7 @@ def _getline(cube): Returns ------- line : tuple - First entry is an int, the followin are floats. + First entry is an int, and the rests are floats. """ line = cube.readline().strip().split() return int(line[0]), map(float, line[1:]) From c6caacd8631745e63800e4efc9e6d4586d426216 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 7 Jul 2021 15:08:29 +0200 Subject: [PATCH 137/297] Fixed documentation in cube_parser.py --- mala/targets/cube_parser.py | 105 ++++++++++++++++++------------------ 1 file changed, 54 insertions(+), 51 deletions(-) diff --git a/mala/targets/cube_parser.py b/mala/targets/cube_parser.py index 9f68df699..e7cbef9a4 100644 --- a/mala/targets/cube_parser.py +++ b/mala/targets/cube_parser.py @@ -1,57 +1,60 @@ """ Cube parser, taken from cubetools (see below). -#------------------------------------------------------------------------------ -# Module: cubetools -#------------------------------------------------------------------------------ -# -# Description: -# Module to work with Gaussian cube format files -# (see http://paulbourke.net/dataformats/cube/) -# -#------------------------------------------------------------------------------ -# -# What does it do: -# * Read/write cube files to/from numpy arrays (dtype=float*) -# * Read/write pairse of cube files to/from numpy arrays (dtype=complex*) -# * Provides a CubeFile object, to be used when cubefiles with -# constant and static data is required. It simulates the readline method -# of a file object with a cube file opened, without creating a file -# -#------------------------------------------------------------------------------ -# -# Dependency: numpy -# -#------------------------------------------------------------------------------ -# -# Author: P. R. Vaidyanathan (aditya95sriram gmail com) -# Date: 25th June 2017 -# -#------------------------------------------------------------------------------ -# -# MIT License -# -# Copyright (c) 2019 P. R. Vaidyanathan -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -# -#------------------------------------------------------------------------------ +------------------------------------------------------------------------------ + +Module: cubetools + +------------------------------------------------------------------------------ + +Description: +Module to work with Gaussian cube format files +(see http://paulbourke.net/dataformats/cube/) + +------------------------------------------------------------------------------ + +What does it do: + +- Read/write cube files to/from numpy arrays (dtype=float*) +- Read/write pairse of cube files to/from numpy arrays (dtype=complex*) +- Provides a CubeFile object, to be used when cubefiles with constant and + static data is required. It simulates the readline method + of a file object with a cube file opened, without creating a file + +------------------------------------------------------------------------------ + +Dependency: numpy + +------------------------------------------------------------------------------ + +Author: P. R. Vaidyanathan (aditya95sriram gmail com) +Date: 25th June 2017 + +------------------------------------------------------------------------------ + +MIT License + +Copyright (c) 2019 P. R. Vaidyanathan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +------------------------------------------------------------------------------ """ import numpy as np From 9e83ef5ee1d8f703c6007259afb282e779831e5b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 14:52:46 +0200 Subject: [PATCH 138/297] Added early stopping and learning rate scheduling patience to optuna --- mala/network/objective_base.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 37ab056bd..c808e24b7 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -114,6 +114,14 @@ def parse_trial_optuna(self, trial: Trial): elif "mini_batch_size" in par.name: self.params.running.mini_batch_size = par.get_parameter(trial) + elif "early_stopping_epochs" in par.name: + self.params.running.early_stopping_epochs = par.\ + get_parameter(trial) + + elif "learning_rate_patience" in par.name: + self.params.running.learning_rate_patience = par.\ + get_parameter(trial) + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From bf653aebf7a402d1c7a491036a4d0ca61f0de84d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 15:00:58 +0200 Subject: [PATCH 139/297] Also added learning rate decay --- mala/network/objective_base.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index c808e24b7..9e8b2ccb9 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -122,6 +122,10 @@ def parse_trial_optuna(self, trial: Trial): self.params.running.learning_rate_patience = par.\ get_parameter(trial) + elif "learning_rate_decay" in par.name: + self.params.running.learning_rate_decay = par.\ + get_parameter(trial) + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From 289d73f07a1ccab8a5a9880a266e20b9dac016db Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 7 Jun 2021 22:37:51 +0200 Subject: [PATCH 140/297] Updated stopping of optuna optimization based on heartbeat --- mala/common/parameters.py | 26 ++++++++++++++++++++++++++ mala/network/hyper_opt_optuna.py | 22 ++++++++++++++++++++-- 2 files changed, 46 insertions(+), 2 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 92f30a7ec..f68afb8ac 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -393,6 +393,32 @@ class ParametersHyperparameterOptimization(ParametersBase): Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. Currently. this only works with optuna. + + study_name : string + Name used for this study (in optuna#s storage). Necessary + when operating with a RDB storage. + + rdb_storage : string + Adress of the RDB storage to be used by optuna. + + rdb_storage_heartbeat : int + Heartbeat interval for optuna (in seconds). Default is None. + If not None and above 0, optuna will record the heartbeat of intervals. + If no action on a RUNNING trial is recognized for longer then this + interval, then this trial will be moved to FAILED. In distributed + training, setting a heartbeat is currently the only way to achieve + a precise number of trials: + + https://github.com/optuna/optuna/issues/1883 + + For optuna versions below 2.8.0, larger heartbeat intervals are + detrimental to performance and should be avoided: + + https://github.com/optuna/optuna/issues/2685 + + For MALA, no evidence for decreased performance using smaller + heartbeat values could be found. So if this is used, 1s is a reasonable + value. """ def __init__(self): diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 89815ea2c..cba55aad1 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -212,8 +212,26 @@ def load_from_file(cls, params, file_path, data): return loaded_hyperopt def __check_max_number_trials(self, study, trial): - number_of_completed_trials = len([t for t in study.trials if t.state == - optuna.trial.TrialState.COMPLETE]) + """Check if this trial was already the maximum number of trials.""" + # How to check for this depends on whether or not a heartbeat was + # used. If one was used, then both COMPLETE and RUNNING trials + # Can be taken into account, as it can be expected that RUNNING + # trials will actually finish. If no heartbeat is used, + # then RUNNING trials might be Zombie trials. + # See + # https://github.com/optuna/optuna/issues/1883#issuecomment-841844834 + # https://github.com/optuna/optuna/issues/1883#issuecomment-842106950 + + if self.params.hyperparameters.rdb_storage_heartbeat is None: + number_of_completed_trials = len([t for t in study.trials if + t.state == optuna.trial. + TrialState.COMPLETE]) + else: + number_of_completed_trials = len([t for t in study.trials if + t.state == optuna.trial. + TrialState.COMPLETE or + t.state == optuna.trial. + TrialState.RUNNING]) if number_of_completed_trials >= self.params.hyperparameters.n_trials: self.study.stop() From 8057933b2cdcab57a0cb991779ab057f076c5df5 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 15 Jun 2021 09:41:21 +0200 Subject: [PATCH 141/297] Checkpoints can be continued from different RDB provider --- mala/network/hyper_opt_optuna.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index cba55aad1..bf5e02931 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -128,7 +128,7 @@ def checkpoint_exists(cls, checkpoint_name): param_name])) @classmethod - def resume_checkpoint(cls, checkpoint_name): + def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): """ Prepare resumption of hyperparameter optimization from a checkpoint. @@ -138,7 +138,13 @@ def resume_checkpoint(cls, checkpoint_name): Parameters ---------- checkpoint_name : string - Name of the checkpoint from which + Name of the checkpoint from which the checkpoint is loaded. + + alternative_storage_path: string + Alternative storage string to load the study from. + For applications on an HPC cluster it might be necessary to + slightly modify the storage path between runs, since the SQL + server might be running on different nodes each time. Returns ------- @@ -162,6 +168,9 @@ def resume_checkpoint(cls, checkpoint_name): loaded_params = Parameters.load_from_file(param_name) loaded_iscaler = DataScaler.load_from_file(iscaler_name) loaded_oscaler = DataScaler.load_from_file(oscaler_name) + if alternative_storage_path is not None: + loaded_params.hyperparameters.rdb_storage = \ + alternative_storage_path printout("Preparing data used for last checkpoint.") # Create a new data handler and prepare the data. From 340106cce4d0945b4ec1eb16db1e1a91134e9d31 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 11:52:41 +0200 Subject: [PATCH 142/297] Added way to parse an arbitrary trial --- mala/network/hyper_opt_base.py | 10 ++++++++++ mala/network/hyper_opt_optuna.py | 9 +++++++++ 2 files changed, 19 insertions(+) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index a75a826eb..f41e0e36e 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -84,3 +84,13 @@ def set_optimal_parameters(self): hyperparameter optimizer was created. """ pass + + @abstractmethod + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + pass diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index bf5e02931..105489870 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -91,6 +91,15 @@ def set_optimal_parameters(self): # Parse the parameters from the best trial. self.objective.parse_trial_optuna(self.study.best_trial) + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_optuna(trial) + def get_trials_from_study(self): """ Return the trials from the last study. From 40639069be038d6c266f22cba12ca67fdb841fd1 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:05:33 +0200 Subject: [PATCH 143/297] Objective gets now created upon initialization --- mala/network/hyper_opt_oat.py | 2 ++ mala/network/hyper_opt_optuna.py | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 483eddedb..83c9b402e 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -27,6 +27,7 @@ def __init__(self, params, data): self.n_levels = None self.strength = None self.N_runs = None + self.objective = ObjectiveBase(self.params, self.data_handler) def perform_study(self): """ @@ -36,6 +37,7 @@ def perform_study(self): In this case, these are choosen based on an orthogonal array. """ number_of_trial = 0 + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) for row in self.orthogonal_arr: printout("Trial number", number_of_trial) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 105489870..983560f00 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -57,7 +57,7 @@ def __init__(self, params, data): storage=rdb_storage, load_if_exists=True) - self.objective = None + self.objective = ObjectiveBase(self.params, self.data_handler) self.checkpoint_counter = 0 def perform_study(self): @@ -67,6 +67,7 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, optuna is used. """ + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) # Fill callback list based on user checkpoint wishes. From 23e7e24e0e4128276aefaaf3b1f8a639a52995c6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:22:40 +0200 Subject: [PATCH 144/297] This should fix the workflow run --- mala/network/hyper_opt_oat.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 83c9b402e..16f70ae38 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -59,6 +59,15 @@ def set_optimal_parameters(self): self.best_trial = self.orthogonal_arr[idx] self.objective.parse_trial_oat(self.best_trial) + def set_parameters(self, trial): + """ + Set the parameters to a specific trial. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_oat(trial) + @property def orthogonal_arr(self): """Orthogonal array used for optimal hyperparameter sampling.""" From 3ae20d68708ad929041d3322310d8c7a257acee8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 13:39:42 +0200 Subject: [PATCH 145/297] Cleaned up the set_parameters ness --- mala/network/hyper_opt_base.py | 5 +++-- mala/network/hyper_opt_oat.py | 10 ---------- mala/network/hyper_opt_optuna.py | 10 ---------- mala/network/objective_base.py | 2 ++ 4 files changed, 5 insertions(+), 22 deletions(-) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index f41e0e36e..0d0df7e52 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -1,6 +1,7 @@ """Base class for all hyperparameter optimizers.""" from abc import abstractmethod, ABC from .hyperparameter_interface import HyperparameterInterface +from .objective_base import ObjectiveBase class HyperOptBase(ABC): @@ -18,6 +19,7 @@ class HyperOptBase(ABC): def __init__(self, params, data): self.params = params self.data_handler = data + self.objective = ObjectiveBase(self.params, self.data_handler) def add_hyperparameter(self, opttype="float", name="", low=0, high=0, choices=None): @@ -85,7 +87,6 @@ def set_optimal_parameters(self): """ pass - @abstractmethod def set_parameters(self, trial): """ Set the parameters to a specific trial. @@ -93,4 +94,4 @@ def set_parameters(self, trial): The parameters will be written to the parameter object with which the hyperparameter optimizer was created. """ - pass + self.objective.parse_trial(trial) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 16f70ae38..84e93f2f8 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -27,7 +27,6 @@ def __init__(self, params, data): self.n_levels = None self.strength = None self.N_runs = None - self.objective = ObjectiveBase(self.params, self.data_handler) def perform_study(self): """ @@ -59,15 +58,6 @@ def set_optimal_parameters(self): self.best_trial = self.orthogonal_arr[idx] self.objective.parse_trial_oat(self.best_trial) - def set_parameters(self, trial): - """ - Set the parameters to a specific trial. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. - """ - self.objective.parse_trial_oat(trial) - @property def orthogonal_arr(self): """Orthogonal array used for optimal hyperparameter sampling.""" diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 983560f00..7a947aec8 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -57,7 +57,6 @@ def __init__(self, params, data): storage=rdb_storage, load_if_exists=True) - self.objective = ObjectiveBase(self.params, self.data_handler) self.checkpoint_counter = 0 def perform_study(self): @@ -92,15 +91,6 @@ def set_optimal_parameters(self): # Parse the parameters from the best trial. self.objective.parse_trial_optuna(self.study.best_trial) - def set_parameters(self, trial): - """ - Set the parameters to a specific trial. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. - """ - self.objective.parse_trial_optuna(trial) - def get_trials_from_study(self): """ Return the trials from the last study. diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 9e8b2ccb9..0f080a03e 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -37,6 +37,8 @@ def __init__(self, params, data_handler): )) self.trial_type = self.params.hyperparameters.hyper_opt_method + if self.trial_type == "notraining": + self.trial_type = "optuna" def __call__(self, trial): """ From b218347ab871d9ec3537b6ba88788881f1fc1eb4 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 6 Jul 2021 15:05:41 +0200 Subject: [PATCH 146/297] no_data for HO checkpoint loading works now --- mala/network/hyper_opt_optuna.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 7a947aec8..1cf5a6394 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -128,7 +128,8 @@ def checkpoint_exists(cls, checkpoint_name): param_name])) @classmethod - def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): + def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None, + no_data=False): """ Prepare resumption of hyperparameter optimization from a checkpoint. @@ -146,6 +147,11 @@ def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): slightly modify the storage path between runs, since the SQL server might be running on different nodes each time. + no_data : bool + If True, the data won't actually be loaded into RAM or scaled. + This can be useful for cases where a checkpoint is loaded + for analysis purposes. + Returns ------- loaded_params : mala.common.parameters.Parameters @@ -174,6 +180,8 @@ def resume_checkpoint(cls, checkpoint_name, alternative_storage_path=None): printout("Preparing data used for last checkpoint.") # Create a new data handler and prepare the data. + if no_data is True: + loaded_params.data.use_lazy_loading = True new_datahandler = DataHandler(loaded_params, input_data_scaler=loaded_iscaler, output_data_scaler=loaded_oscaler) From dfa268d4d400027e19452e01cecce3a04a1a6781 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 8 Jul 2021 11:50:01 +0200 Subject: [PATCH 147/297] Using new version of data repo --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 2ab752062..f99f3419c 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -80,7 +80,7 @@ jobs: repository: mala-project/test-data token: ${{ secrets.ACCESS_TOKEN }} path: mala_data - ref: v0.1.0 + ref: v0.2.0 lfs: false - name: Data setup From c84ede2ae693ede48b3d10559f72e8df4a656fa2 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Sun, 11 Jul 2021 19:39:08 +0200 Subject: [PATCH 148/297] wfirst draft of working orthogonal array optimisation --- examples/ex04_hyperparameter_optimization.py | 2 +- mala/common/printout.py | 8 +++-- mala/network/hyper_opt_oat.py | 33 +++++++++----------- 3 files changed, 21 insertions(+), 22 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 2debcab87..e8d27beba 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -1,5 +1,5 @@ import mala -from mala import printout +from mala.common.printout import printout from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/mala/common/printout.py b/mala/common/printout.py index 3f32827b5..2dbd83c03 100644 --- a/mala/common/printout.py +++ b/mala/common/printout.py @@ -36,9 +36,11 @@ def printout(*values, sep=' '): sep : string Separator between printed values. """ - outstring = '' - for v in values: - outstring += str(v)+sep + outstring = sep.join([str(v) for v in values]) + + # for v in values: + # outstring += str(v)+sep # This implementation is adding an extra seperator at the end of the iterable! + if use_horovod is False: print(outstring) else: diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index ee52286cf..ad817e066 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -4,7 +4,7 @@ from .objective_base import ObjectiveBase import numpy as np import itertools -from mala.common.parameters import printout +from mala.common.printout import printout class HyperOptOAT(HyperOptBase): @@ -91,6 +91,7 @@ def perform_study(self): self.trial_losses.append(self.objective(row)) number_of_trial += 1 + self.trial_losses = np.array(self.trial_losses) # Return the best loss value we could achieve. self.get_optimal_parameters() @@ -102,32 +103,28 @@ def get_optimal_parameters(self): This is done using loss instead of accuracy as done in the paper. """ - print("Performing Range Analysis") - print("factor levels", self.factor_levels) - - def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - # R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] - # for (idx, levels) in enumerate(self.factor_levels)] - - R = [] - for (idx, levels) in enumerate(self.factor_levels): - R.append([]) - for l in range(levels): - R[idx].extend(self.trial_losses[indices(idx, l)].sum()) - print(R) + printout("Performing Range Analysis") + print("Factor levels:", self.factor_levels) + + def indices(idx, val): return np.where( + self.OA[:, idx] == val)[0] + R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + for (idx, levels) in enumerate(self.factor_levels)] + A = [[i/len(j) for i in j] for j in R] # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) - print("Order of Importance: ") + printout("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in self.importance], " > ") + *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" > ") - print("Optimal Hyperparameters:") + printout("Optimal Hyperparameters:") for (idx, par) in enumerate(self.params.hyperparameters.hlist): - printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') + printout( + par.name, par.choices[self.optimal_params[idx]], sep=' : ') def set_optimal_parameters(self): """ From 72c9b0cdf949e8fa799603b1c33bd30c6015f10c Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 12 Jul 2021 11:20:34 +0200 Subject: [PATCH 149/297] Rewrote integration test --- test/integration_test.py | 201 +++++++++++++++++++++++++++++++++++++ test/mala_integration.py | 209 --------------------------------------- test/mala_tests.py | 2 +- 3 files changed, 202 insertions(+), 210 deletions(-) create mode 100644 test/integration_test.py delete mode 100644 test/mala_integration.py diff --git a/test/integration_test.py b/test/integration_test.py new file mode 100644 index 000000000..86742b191 --- /dev/null +++ b/test/integration_test.py @@ -0,0 +1,201 @@ +import scipy as sp +from mala.targets.calculation_helpers import * +import numpy as np +from mala.targets.ldos import LDOS +from mala.targets.density import Density +from mala.targets.dos import DOS +from mala.common.parameters import Parameters +from mala.common.parameters import printout +from data_repo_path import get_data_repo_path + +# In order to test the integration capabilities of MALA we need a +# QuantumEspresso +# calculation containing the following: +# 1. Outfile from the run +# 2. LDOS cube files. +# 3. Density cube file. +# 4. A DOS file. +# In the check_integration_qe subfolder, input scripts can be found for a +# simple test calculation (the results of this calculation have been used +# to develop this script). It uses an Al supercell with 36 atoms and a +# 6x6x6 k-grid. + +data_path = get_data_repo_path()+"Al36/" +path_to_out = data_path+"Al.pw.scf.out" +path_to_ldos_qe = [data_path, "tmp.pp*Al_ldos.cube"] +path_to_dos_qe = [data_path, "Al.dos"] +path_to_dens_qe = [data_path, "Al_dens.cube"] +path_to_ldos_npy = data_path+"Al_ldos.npy" +path_to_dos_npy = data_path+"Al_dos.npy" +path_to_dens_npy = data_path+"Al_dens.npy" + +# We can read from numpy arrays or directly from QE data. +# In the later case, numpy arrays will be saved for the subsqeuent run. +numpy_arrays = True + +# Define the parameters used for the experiments. +test_parameters = Parameters() +test_parameters.targets.ldos_gridsize = 250 +test_parameters.targets.ldos_gridspacing_ev = 0.1 +test_parameters.targets.ldos_gridoffset_ev = -10 + +# Define the accuracy used in the tests. +accuracy = 1e-6 + + +class TestMALAIntegration: + """ + Test class for MALA's integation routines. + + Tests different integrations that would normally be performed by code. + """ + def test_analytical_integration(self): + """ + Test whether the analytical integration works in principle. + """ + # Values used for the test. + energies = np.array([6.83, 7.11]) + gridsize = 2 + e_fermi = 7.0 + temp = 298 + + # Calculate the analytical values. + beta = 1 / (kB * temp) + x = beta * (energies - e_fermi) + fi_0 = np.zeros(gridsize, dtype=np.float64) + fi_1 = np.zeros(gridsize, dtype=np.float64) + fi_2 = np.zeros(gridsize, dtype=np.float64) + for i in range(0, gridsize): + + # Calculate beta and x + fi_0[i] = get_f0_value(x[i], beta) + fi_1[i] = get_f1_value(x[i], beta) + fi_2[i] = get_f2_value(x[i], beta) + aint_0 = fi_0[1] - fi_0[0] + aint_1 = fi_1[1] - fi_1[0] + aint_2 = fi_2[1] - fi_2[0] + + # Calculate the numerically approximated values. + qint_0, abserr = sp.integrate.quad( + lambda e: fermi_function_eV(e, e_fermi, temp), + energies[0], energies[-1]) + qint_1, abserr = sp.integrate.quad( + lambda e: (e - e_fermi) * fermi_function_eV(e, e_fermi, temp), + energies[0], energies[-1]) + qint_2, abserr = sp.integrate.quad( + lambda e: (e - e_fermi) ** 2 * fermi_function_eV(e, e_fermi, temp), + energies[0], energies[-1]) + + # Calculate the errors. + error0 = np.abs(aint_0 - qint_0) + error1 = np.abs(aint_1 - qint_1) + error2 = np.abs(aint_2 - qint_2) + + # Analyze the errors. + assert np.isclose(error0, 0, atol=accuracy) + assert np.isclose(error1, 0, atol=accuracy) + assert np.isclose(error2, 0, atol=accuracy) + + def test_qe_dens_to_nr_of_electrons(self): + """ + Test integration of density on real space grid. + + The integral of the density of the real space grid should yield the + number of electrons. + """ + # Create a calculator. + dens_calculator = Density(test_parameters) + dens_calculator.read_additional_calculation_data("qe.out", path_to_out) + + # Read the input data. + if numpy_arrays: + density_dft = np.load(path_to_dens_npy) + else: + density_dft = dens_calculator.read_from_cube(path_to_dens_qe[1], + path_to_dens_qe[0]) + np.save(path_to_dens_npy, density_dft) + + # Calculate the quantities we want to compare. + nr_mala = dens_calculator.get_number_of_electrons(density_dft) + nr_dft = dens_calculator.number_of_electrons + + # Calculate relative error. + rel_error = np.abs(nr_mala-nr_dft) / nr_dft + printout("Relative error number of electrons: ", rel_error) + + # Check against the constraints we put upon ourselves. + assert np.isclose(rel_error, 0, atol=accuracy) + + def test_qe_ldos_to_density(self): + """ + Test integration of local density of states on energy grid. + + The integral of the LDOS over energy grid should yield the density. + """ + # Create a calculator.abs() + ldos_calculator = LDOS(test_parameters) + ldos_calculator.read_additional_calculation_data("qe.out", path_to_out) + + # Read the input data. + density_dft = np.load(path_to_dens_npy) + if numpy_arrays: + ldos_dft = np.load(path_to_ldos_npy) + else: + ldos_dft = ldos_calculator.read_from_cube(path_to_ldos_qe[1], + path_to_ldos_qe[0]) + + # LDOS is in 1/Ry. DOS is in 1/eV. + ldos_dft = ldos_calculator.convert_units(ldos_dft, "1/Ry") + np.save(path_to_ldos_npy, ldos_dft) + + # Calculate the quantities we want to compare. + self_consistent_fermi_energy = ldos_calculator.\ + get_self_consistent_fermi_energy_ev(ldos_dft) + density_mala = ldos_calculator.\ + get_density(ldos_dft, fermi_energy_ev=self_consistent_fermi_energy) + density_mala_sum = density_mala.sum() + density_dft_sum = density_dft.sum() + + # Calculate relative error. + rel_error = np.abs(density_mala_sum-density_dft_sum) / density_dft_sum + printout("Relative error for sum of density: ", rel_error) + + # Check against the constraints we put upon ourselves. + assert np.isclose(rel_error, 0, atol=accuracy) + + def test_qe_ldos_to_dos(self): + """ + Test integration of local density of states on real space grid. + + The integral of the LDOS over real space grid should yield the DOS. + """ + ldos_calculator = LDOS(test_parameters) + ldos_calculator.read_additional_calculation_data("qe.out", path_to_out) + dos_calculator = DOS(test_parameters) + dos_calculator.read_additional_calculation_data("qe.out", path_to_out) + + # Read the input data. + ldos_dft = np.load(path_to_ldos_npy) + if numpy_arrays: + dos_dft = np.load(path_to_dos_npy) + else: + # DOS is in 1/eV so no conversion necessary. + dos_dft = dos_calculator.read_from_qe_dos_txt(path_to_dos_qe[1], + path_to_dos_qe[0]) + np.save(path_to_dos_npy, dos_dft) + + # Calculate the quantities we want to compare. + dos_mala = ldos_calculator.get_density_of_states(ldos_dft) + dos_mala_sum = dos_mala.sum() + dos_dft_sum = dos_dft.sum() + rel_error = np.abs(dos_mala_sum-dos_dft_sum) / dos_dft_sum + printout("Relative error for sum of DOS: ", rel_error) + + # Check against the constraints we put upon ourselves. + assert np.isclose(rel_error, 0, atol=accuracy) + +# if __name__ == "__main__": +# +# # Run the tests. +# testtester = TestMALAIntegration() +# testtester.test_analytical_integration() \ No newline at end of file diff --git a/test/mala_integration.py b/test/mala_integration.py deleted file mode 100644 index a21b674f1..000000000 --- a/test/mala_integration.py +++ /dev/null @@ -1,209 +0,0 @@ -import scipy as sp -from mala.targets.calculation_helpers import * -import numpy as np -from mala.targets.ldos import LDOS -from mala.targets.density import Density -from mala.targets.dos import DOS -from mala.common.parameters import Parameters -from mala.common.parameters import printout -from data_repo_path import get_data_repo_path - -# In order to test the integration capabilities of MALA we need a -# QuantumEspresso -# calculation containing the following: -# 1. Outfile from the run -# 2. LDOS cube files. -# 3. Density cube file. -# 4. A DOS file. -# In the check_integration_qe subfolder, input scripts can be found for a -# simple test calculation (the results of this calculation have been used -# to develop this script). It uses an Al supercell with 36 atoms and a -# 6x6x6 k-grid. - -data_path = get_data_repo_path()+"Al36/" -path_to_out = data_path+"Al.pw.scf.out" -path_to_ldos_qe = [data_path, "tmp.pp*Al_ldos.cube"] -path_to_dos_qe = [data_path, "Al.dos"] -path_to_dens_qe = [data_path, "Al_dens.cube"] -path_to_ldos_npy = data_path+"Al_ldos.npy" -path_to_dos_npy = data_path+"Al_dos.npy" -path_to_dens_npy = data_path+"Al_dens.npy" - -# We can read from numpy arrays or directly from QE data. -# In the later case, numpy arrays will be saved for the subsqeuent run. -numpy_arrays = True - - -# Define the parameters used for the experiments. -test_parameters = Parameters() -test_parameters.targets.ldos_gridsize = 250 -test_parameters.targets.ldos_gridspacing_ev = 0.1 -test_parameters.targets.ldos_gridoffset_ev = -10 - - -# Test our implementation of the analytical integration of [1] -def check_analytical_integration(accuracy): - - # Values used for the test. - energies = np.array([6.83, 7.11]) - gridsize = 2 - e_fermi = 7.0 - temp = 298 - - # Calculate the analytical values. - beta = 1 / (kB * temp) - x = beta * (energies - e_fermi) - fi_0 = np.zeros(gridsize, dtype=np.float64) - fi_1 = np.zeros(gridsize, dtype=np.float64) - fi_2 = np.zeros(gridsize, dtype=np.float64) - for i in range(0, gridsize): - - # Calculate beta and x - fi_0[i] = get_f0_value(x[i], beta) - fi_1[i] = get_f1_value(x[i], beta) - fi_2[i] = get_f2_value(x[i], beta) - aint_0 = fi_0[1] - fi_0[0] - aint_1 = fi_1[1] - fi_1[0] - aint_2 = fi_2[1] - fi_2[0] - - # Calculate the numerically approximated values. - qint_0, abserr = sp.integrate.quad( - lambda e: fermi_function_eV(e, e_fermi, temp), - energies[0], energies[-1]) - qint_1, abserr = sp.integrate.quad( - lambda e: (e - e_fermi) * fermi_function_eV(e, e_fermi, temp), - energies[0], energies[-1]) - qint_2, abserr = sp.integrate.quad( - lambda e: (e - e_fermi) ** 2 * fermi_function_eV(e, e_fermi, temp), - energies[0], energies[-1]) - - # Calculate the errors. - error0 = np.abs(aint_0 - qint_0) - error1 = np.abs(aint_1 - qint_1) - error2 = np.abs(aint_2 - qint_2) - - # Analyze the errors. - if error0 > accuracy or error1 > accuracy or error2 > accuracy: - return False - return True - - -# Integrate the QE density (Al.dens) over spatial grid. Does this yield the -# correct number of electrons? -def qe_dens_to_nr_of_electrons(accuracy): - # Create a calculator. - dens_calculator = Density(test_parameters) - dens_calculator.read_additional_calculation_data("qe.out", path_to_out) - - # Read the input data. - if numpy_arrays: - density_dft = np.load(path_to_dens_npy) - else: - density_dft = dens_calculator.read_from_cube(path_to_dens_qe[1], - path_to_dens_qe[0]) - np.save(path_to_dens_npy, density_dft) - - # Calculate the quantities we want to compare. - nr_mala = dens_calculator.get_number_of_electrons(density_dft) - nr_dft = dens_calculator.number_of_electrons - - # Calculate relative error. - rel_error = np.abs(nr_mala-nr_dft) / nr_dft - printout("Relative error number of electrons: ", rel_error) - - # Check against the constraints we put upon ourselves. - if rel_error < accuracy: - return True - else: - return False - - -# Integrate QE LDOS over energy grid. Does this yield the correct density -# (when compared to Al.dens)? -def qe_ldos_to_density(accuracy): - # Create a calculator.abs() - ldos_calculator = LDOS(test_parameters) - ldos_calculator.read_additional_calculation_data("qe.out", path_to_out) - - # Read the input data. - density_dft = np.load(path_to_dens_npy) - if numpy_arrays: - ldos_dft = np.load(path_to_ldos_npy) - else: - ldos_dft = ldos_calculator.read_from_cube(path_to_ldos_qe[1], - path_to_ldos_qe[0]) - - # LDOS is in 1/Ry. DOS is in 1/eV. - ldos_dft = ldos_calculator.convert_units(ldos_dft, "1/Ry") - np.save(path_to_ldos_npy, ldos_dft) - - # Calculate the quantities we want to compare. - self_consistent_fermi_energy = ldos_calculator.\ - get_self_consistent_fermi_energy_ev(ldos_dft) - density_mala = ldos_calculator.\ - get_density(ldos_dft, fermi_energy_ev=self_consistent_fermi_energy) - density_mala_sum = density_mala.sum() - density_dft_sum = density_dft.sum() - - # Calculate relative error. - rel_error = np.abs(density_mala_sum-density_dft_sum) / density_dft_sum - printout("Relative error for sum of density: ", rel_error) - - # Check against the constraints we put upon ourselves. - if rel_error < accuracy: - return True - else: - return False - - -# Integrate the QE LDOS over spatial grid. Does this yield the corrected LDOS -# (when compared to Al.dos)? -def qe_ldos_to_dos(accuracy): - # Create the necessary calculators. - ldos_calculator = LDOS(test_parameters) - ldos_calculator.read_additional_calculation_data("qe.out", path_to_out) - dos_calculator = DOS(test_parameters) - dos_calculator.read_additional_calculation_data("qe.out", path_to_out) - - # Read the input data. - ldos_dft = np.load(path_to_ldos_npy) - if numpy_arrays: - dos_dft = np.load(path_to_dos_npy) - else: - # DOS is in 1/eV so no conversion necessary. - dos_dft = dos_calculator.read_from_qe_dos_txt(path_to_dos_qe[1], - path_to_dos_qe[0]) - np.save(path_to_dos_npy, dos_dft) - - # Calculate the quantities we want to compare. - dos_mala = ldos_calculator.get_density_of_states(ldos_dft) - dos_mala_sum = dos_mala.sum() - dos_dft_sum = dos_dft.sum() - rel_error = np.abs(dos_mala_sum-dos_dft_sum) / dos_dft_sum - printout("Relative error for sum of DOS: ", rel_error) - - # Check against the constraints we put upon ourselves. - if rel_error < accuracy: - return True - else: - return False - - -if __name__ == "__main__": - - # Run the tests. - test1 = check_analytical_integration(0.0000001) - printout("Check if analytical integration works in theory - success?:", - test1) - - test1 = qe_dens_to_nr_of_electrons(0.0000001) - printout("Integrate QE density over spatial grid and get correct number " - "of electrons compared to QE - success?:", test1) - - test1 = qe_ldos_to_density(0.0000001) - printout("Integrate QE LDOS over energy grid and get correct density " - "compared to QE - succes?:", test1) - - test1 = qe_ldos_to_dos(0.0000001) - printout("Integrate QE LDOS over spatial grid and get correct DOS " - "compared to QE - success:", test1) diff --git a/test/mala_tests.py b/test/mala_tests.py index 338d95152..e89e55580 100644 --- a/test/mala_tests.py +++ b/test/mala_tests.py @@ -1,4 +1,4 @@ -from mala_integration import check_analytical_integration, \ +from integration_test import check_analytical_integration, \ qe_dens_to_nr_of_electrons, qe_ldos_to_density, qe_ldos_to_dos from tensor_memory import test_tensor_memory from lazy_loading_basic import test_lazy_loading_basic From 950b26bf26918f63c42723fc5502851e2be42d45 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 12 Jul 2021 11:52:11 +0200 Subject: [PATCH 150/297] need review on range analysis --- mala/network/hyper_opt_base.py | 1 + mala/network/hyper_opt_oat.py | 55 ++++++++++++++++++---------------- 2 files changed, 30 insertions(+), 26 deletions(-) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index d7b238213..1d76e886c 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -54,6 +54,7 @@ def add_hyperparameter(self, opttype="float", name="", low=0, high=0, choices : List of possible choices (for categorical parameter). """ + self.params.\ hyperparameters.hlist.append( HyperparameterInterface(self.params.hyperparameters. diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index ad817e066..a4f561e39 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -2,9 +2,11 @@ import oapackage as oa from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase +from .hyperparameter_oat import HyperparameterOAT import numpy as np import itertools from mala.common.printout import printout +from bisect import bisect class HyperOptOAT(HyperOptBase): @@ -25,6 +27,7 @@ def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] + self.sorted_num_choices = [] self.optimal_params = None self.importance = None self.n_factors = None @@ -35,35 +38,23 @@ def __init__(self, params, data): def add_hyperparameter(self, opttype="categorical", name="", low=0, high=0, choices=None): - """ - Add a hyperparameter to the current investigation. - - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - Currently supported are: - - - categorical (list) - - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. - low : float or int - Currently unsupported: Lower bound for numerical parameter. + if opttype != 'categorical': + raise Exception( + "Only categorical hyperparameters are supported for OAT") - high : float or int - Currently unsupported: Higher bound for numerical parameter. + else: + if not self.sorted_num_choices: + super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, + low=low, high=high, + choices=choices) + self.sorted_num_choices.append(len(choices)) - choices : - List of possible choices (for categorical parameter). - """ - super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, - low=low, high=high, - choices=choices) + else: + index = bisect(self.sorted_num_choices, len(choices)) + self.sorted_num_choices.insert(index, len(choices)) + self.params.hyperparameters.hlist.insert( + index, HyperparameterOAT(opttype=opttype, name=name, choices=choices)) def perform_study(self): """ @@ -77,6 +68,7 @@ def perform_study(self): self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] + printout(*self.factor_levels, sep=",") if not self.monotonic: raise Exception( "Please use hyperparameters in increasing or decreasing order of number of choices") @@ -113,6 +105,17 @@ def indices(idx, val): return np.where( A = [[i/len(j) for i in j] for j in R] + print("OA:") + print(self.OA) + + print("Trial Losses:") + print(np.array(self.trial_losses).transpose()) + + print("R matrix: ") + print(np.array(R).transpose()) + + print("A Matrix:") + print(np.array(A).transpose()) # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) From 439b86ba241669acfdd4f5bbbd85a7ab76485c3c Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 12 Jul 2021 15:04:39 +0200 Subject: [PATCH 151/297] Rewrote all tests to pytest, but it is not running yet --- mala/datahandling/data_handler.py | 9 + test/basic_gpu_test.py | 229 ++++++++------- test/check_integration_qe/Al.dos.in | 9 - test/check_integration_qe/Al.pp.dens.in | 10 - test/check_integration_qe/Al.pp.ldos.in | 14 - test/check_integration_qe/Al.pw.scf.in | 74 ----- test/checkpoint_hyperopt_tests.py | 265 ++++++++--------- test/checkpoint_training_tests.py | 368 +++++++++++++----------- test/inference_test.py | 228 ++++++++------- test/integration_test.py | 14 +- test/lazy_loading_basic.py | 164 ----------- test/lazy_loading_horovod_benchmark.py | 123 -------- test/lazy_loading_test.py | 275 ++++++++++++++++++ test/mala_tests.py | 113 -------- test/tensor_memory.py | 116 ++++---- test/total_energy_tester.py | 32 --- 16 files changed, 930 insertions(+), 1113 deletions(-) delete mode 100644 test/check_integration_qe/Al.dos.in delete mode 100644 test/check_integration_qe/Al.pp.dens.in delete mode 100644 test/check_integration_qe/Al.pp.ldos.in delete mode 100755 test/check_integration_qe/Al.pw.scf.in delete mode 100644 test/lazy_loading_basic.py delete mode 100644 test/lazy_loading_horovod_benchmark.py create mode 100644 test/lazy_loading_test.py delete mode 100644 test/mala_tests.py delete mode 100644 test/total_energy_tester.py diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 89aadfb68..a95f02380 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -10,6 +10,11 @@ from mala.common.printout import printout import numpy as np import torch +try: + import horovod.torch as hvd +except ModuleNotFoundError: + # Warning is thrown by Parameters class + pass class DataHandler: @@ -240,6 +245,10 @@ def prepare_data(self, reparametrize_scaler=True): self.__build_datasets() printout("Build dataset done.") + # Wait until all ranks are finished with data preparation. + if self.use_horovod: + hvd.allreduce(torch.tensor(0), name='barrier') + def mix_datasets(self): """For lazily-loaded data sets, the snapshot ordering is (re-)mixed.""" if self.parameters.use_lazy_loading: diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py index 591a379b7..e9ed4e1fa 100644 --- a/test/basic_gpu_test.py +++ b/test/basic_gpu_test.py @@ -17,112 +17,125 @@ data_path = get_data_repo_path()+"Al36/" test_checkpoint_name = "test" - -def check_for_gpu(): - test_parameters = mala.Parameters() - test_parameters.use_gpu = True - if test_parameters.use_gpu is False: - return False - return True - - -def test_run(use_gpu): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "tr", "tr", "tr", - "tr", "tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 100 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.manual_seed = 1002 - test_parameters.running.use_shuffling_for_samplers = False - test_parameters.use_gpu = use_gpu - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - for i in range(0, 6): - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, +# Define the accuracy used in the tests and a parameter to control +# that the GPU acceleration actually does something. +accuracy = 1e-6 +performance_improvement = 1.2 + + +class TestGPUExecution: + """ + Test class for simple GPU execution. + + Tests whether a GPU is available and then the execution on it. + """ + + def test_gpu_availability(self): + """Test whether a GPU is available.""" + test_parameters = mala.Parameters() + test_parameters.use_gpu = True + assert test_parameters.use_gpu + + def test_gpu_performance(self): + """ + Test whether GPU training brings performance improvements. + """ + cpu_result = self.__run(False) + gpu_result = self.__run(True) + + # This test is not that well suited for GPU performance + # but we should at least see some kind of speed up. + assert np.isclose(cpu_result[0], gpu_result[0], atol=accuracy) + assert gpu_result[1] > cpu_result[1] / performance_improvement + + @staticmethod + def __run(use_gpu): + """ + Train a network using either GPU or CPU. + + Parameters + ---------- + use_gpu : bool + If True, a GPU will be used, elsewise a CPU will be used for + training. + + Returns + ------- + results : tuple + A tuple containing the the final loss [0] and the execution time + [1]. + + """ + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "tr", "tr", "tr", + "tr", "tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 100 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.manual_seed = 1002 + test_parameters.running.use_shuffling_for_samplers = False + test_parameters.use_gpu = use_gpu + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + for i in range(0, 6): + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - starttime = time.time() - test_trainer.train_network() - - return test_trainer.final_test_loss, time.time() - starttime - - -def perform_gpu_test(accuracy=1e-7, performance_improvement=1.2): - cpu_result = test_run(False) - gpu_result = test_run(True) - - # GPU and CPU should deliver the same result. - if np.abs(cpu_result[0] - gpu_result[0]) > accuracy: - printout(cpu_result[0], gpu_result[0]) - return False - - # This test is not that well suited for GPU performance - # but we should at least see some kind of speed up. - printout(cpu_result[1], gpu_result[1]) - if gpu_result[1] > cpu_result[1] / performance_improvement: - printout(cpu_result[1], gpu_result[1]) - return False - return True - - -if __name__ == "__main__": - if check_for_gpu(): - printout("GPU found.") - else: - raise Exception("No GPU on this system.") - if perform_gpu_test(): - printout("GPU test sucessful.") - else: - raise Exception("GPU test failed.") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler. + get_input_dimension(), + 100, + data_handler. + get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, + data_handler) + starttime = time.time() + test_trainer.train_network() + + return test_trainer.final_test_loss, time.time() - starttime \ No newline at end of file diff --git a/test/check_integration_qe/Al.dos.in b/test/check_integration_qe/Al.dos.in deleted file mode 100644 index 7c10ba466..000000000 --- a/test/check_integration_qe/Al.dos.in +++ /dev/null @@ -1,9 +0,0 @@ -&dos - outdir='temp', - prefix='Al', - Emin=-10.0, - Emax=15.0, - DeltaE=0.1, - degauss=0.2, - fildos='Al.dos' -/ diff --git a/test/check_integration_qe/Al.pp.dens.in b/test/check_integration_qe/Al.pp.dens.in deleted file mode 100644 index 908a401a5..000000000 --- a/test/check_integration_qe/Al.pp.dens.in +++ /dev/null @@ -1,10 +0,0 @@ -&inputpp - outdir='temp', - prefix='Al', - plot_num=0, -/ -&plot - iflag=3, - output_format=6, - fileout='Al_dens.cube', -/ diff --git a/test/check_integration_qe/Al.pp.ldos.in b/test/check_integration_qe/Al.pp.ldos.in deleted file mode 100644 index ba0086fb5..000000000 --- a/test/check_integration_qe/Al.pp.ldos.in +++ /dev/null @@ -1,14 +0,0 @@ -&inputpp - outdir='temp', - prefix='Al', - plot_num=3, - emin=-10.0, - emax=15.0, - delta_e=0.1, - degauss_ldos=0.2, -/ -&plot - iflag=3, - output_format=6, - fileout='Al_ldos.cube', -/ diff --git a/test/check_integration_qe/Al.pw.scf.in b/test/check_integration_qe/Al.pw.scf.in deleted file mode 100755 index 588e370dd..000000000 --- a/test/check_integration_qe/Al.pw.scf.in +++ /dev/null @@ -1,74 +0,0 @@ -Al.scf.pw -&CONTROL - calculation='scf' - restart_mode='from_scratch' - tstress=.true. - tprnfor=.true. - prefix='Al' - pseudo_dir='/home/fiedle09/tools/PPs/pbe' - outdir='temp', -/ -&SYSTEM - ibrav=0 - nat=36 - ntyp=1 - nbnd=200 - ecutwfc=100 - ecutrho=400 - occupations='smearing' - smearing='fermi-dirac' - degauss=0.0019 -/ -&ELECTRONS - mixing_mode='plain' - mixing_beta=0.7 - conv_thr=1.0d-8 -/ -ATOMIC_SPECIES - Al 26.981539 Al.pbe-n-rrkjus_psl.1.0.0.UPF - -K_POINTS automatic - 6 6 6 0 0 0 - -CELL_PARAMETERS angstrom - 8.582861487 0.000000000 0.000000000 - 0.000000000 8.582861487 0.000000000 - 0.000000000 0.000000000 8.091999410 - -ATOMIC_POSITIONS {angstrom} -Al 0.00000000 0.00000000 0.00000000 -Al 5.34056471 3.76320368 3.80057551 -Al 7.45085561 3.31178474 1.66230877 -Al 1.93309858 4.10640734 2.77355319 -Al 4.55479859 5.54269445 0.58331323 -Al 5.71092823 4.25719259 6.20098063 -Al 7.66001119 0.95949808 5.74326219 -Al 1.81070895 7.30893851 0.54010633 -Al 3.44829498 1.55865614 3.97194893 -Al 2.78488381 3.85120694 4.66842222 -Al 7.72693528 3.47448257 4.38254264 -Al 6.08779945 6.66208695 5.78649880 -Al 8.49430189 1.91528760 3.06103603 -Al 1.29361043 2.44620504 5.84420713 -Al 1.90001053 1.69908970 1.12227390 -Al 0.34417103 4.75866893 0.66648717 -Al 1.71803816 4.92272922 6.33072605 -Al 8.34187937 6.81520657 5.70183658 -Al 6.26838174 1.62587729 3.19639657 -Al 6.44181760 4.46414710 8.07122158 -Al 5.24348320 2.14031649 7.44241812 -Al 6.04653726 5.13396837 2.41752820 -Al 4.55353305 2.19465176 1.94513159 -Al 1.75534580 0.04719218 5.92958137 -Al 6.69434873 0.91377057 0.73014103 -Al 5.48248044 1.94272031 5.21594438 -Al 2.93579541 3.55269467 7.97142182 -Al 4.66115561 0.15453846 1.84388587 -Al 2.95537223 6.69875903 2.40171344 -Al 1.57353273 8.50054163 2.73750269 -Al 6.43043243 7.93469589 3.35256941 -Al 3.58936469 1.07649279 6.35476431 -Al 3.96998353 5.62877051 4.32698848 -Al 4.06376270 7.94404057 4.73643159 -Al 7.21316133 6.77772530 0.30594854 -Al 8.34260745 6.26794230 2.85903375 diff --git a/test/checkpoint_hyperopt_tests.py b/test/checkpoint_hyperopt_tests.py index 6d028a177..26c723d16 100644 --- a/test/checkpoint_hyperopt_tests.py +++ b/test/checkpoint_hyperopt_tests.py @@ -5,131 +5,140 @@ data_path = get_data_repo_path()+"Al36/" checkpoint_name = "test_ho" - -def original_setup(n_trials): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 10 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - - # Specify the number of trials, the hyperparameter optimizer should run - # and the type of hyperparameter. - test_parameters.hyperparameters.n_trials = n_trials - test_parameters.hyperparameters.hyper_opt_method = "optuna" - test_parameters.hyperparameters.checkpoints_each_trial = 5 - test_parameters.hyperparameters.checkpoint_name = checkpoint_name - test_parameters.manual_seed = 1002 - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - data_handler = mala.DataHandler(test_parameters) - - # Add all the snapshots we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # HYPERPARAMETER OPTIMIZATION - # In order to perform a hyperparameter optimization, - # one has to simply create a hyperparameter optimizer - # and let it perform a "study". - # Before such a study can be done, one has to add all the parameters - # of interest. - #################### - - test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) - - # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) - - # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) - - # Choices for activation function at each layer will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) - - # Perform hyperparameter optimization. - printout("Starting Hyperparameter optimization.") - # test_hp_optimizer.perform_study() - # test_hp_optimizer.set_optimal_parameters() - # printout("Hyperparameter optimization: DONE.") - - # #################### - # # TRAINING - # # Train with these new parameters. - # #################### - # - # test_network = mala.Network(test_parameters) - # test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - # printout("Network setup: DONE.") - # test_trainer.train_network() - # printout("Training: DONE.") - return test_hp_optimizer - - -def resume_checkpoint(): - loaded_params, new_datahandler, new_hyperopt = \ - mala.HyperOptOptuna.resume_checkpoint(checkpoint_name) - return new_hyperopt - - -def run_hyperopt_checkpoint_test(accuracy=1e-14): - # First run the entire test. - hyperopt = original_setup(9) - hyperopt.perform_study() - original_final_test_value = hyperopt.study.best_trial.value - - # Now do the same, but cut at epoch 22 and see if it recovers the - # correct result. - hyperopt = original_setup(9) - hyperopt.perform_study() - hyperopt = resume_checkpoint() - hyperopt.perform_study() - new_final_test_value = hyperopt.study.best_trial.value - - if np.abs(original_final_test_value-new_final_test_value) > accuracy: - print("test_general did not suceed.", original_final_test_value, - new_final_test_value) - return False - return True - - -if __name__ == "__main__": - test1 = run_hyperopt_checkpoint_test() - printout("Checkpoint test - success?:", test1) +# Define the accuracy used in the tests. +accuracy = 1e-14 + + +class TestHyperoptCheckpointing: + """Tests the checkpointing capabilities of the hyperparam optimization.""" + + def test_hyperopt_checkpoint(self): + # First run the entire test. + hyperopt = self.__original_setup(9) + hyperopt.perform_study() + original_final_test_value = hyperopt.study.best_trial.value + + # Now do the same, but cut at epoch 22 and see if it recovers the + # correct result. + hyperopt = self.__original_setup(9) + hyperopt.perform_study() + hyperopt = self.__resume_checkpoint() + hyperopt.perform_study() + new_final_test_value = hyperopt.study.best_trial.value + + assert np.isclose(original_final_test_value, new_final_test_value, + atol=accuracy) + + @staticmethod + def __original_setup(n_trials): + """ + Perform original setup for HyperOpt. + + Parameters + ---------- + n_trials : int + Number of trials to perform. + + Returns + ------- + hyperopt: mala.network.hyper_opt_base.HyperOptBase: + The hyperopt object. + + """ + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 10 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + # Specify the number of trials, the hyperparameter optimizer should run + # and the type of hyperparameter. + test_parameters.hyperparameters.n_trials = n_trials + test_parameters.hyperparameters.hyper_opt_method = "optuna" + test_parameters.hyperparameters.checkpoints_each_trial = 5 + test_parameters.hyperparameters.checkpoint_name = checkpoint_name + test_parameters.manual_seed = 1002 + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + data_handler = mala.DataHandler(test_parameters) + + # Add all the snapshots we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # HYPERPARAMETER OPTIMIZATION + # In order to perform a hyperparameter optimization, + # one has to simply create a hyperparameter optimizer + # and let it perform a "study". + # Before such a study can be done, one has to add all the parameters + # of interest. + #################### + + test_hp_optimizer = mala.HyperOptInterface(test_parameters, + data_handler) + + # Learning rate will be optimized. + test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + + # Number of neurons per layer will be optimized. + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + + # Choices for activation function at each layer will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + + # Perform hyperparameter optimization. + printout("Starting Hyperparameter optimization.") + + return test_hp_optimizer + + @staticmethod + def __resume_checkpoint(): + """ + Resume a HyperOpt from a checkpoint. + + Returns + ------- + hyperopt: mala.network.hyper_opt_base.HyperOptBase: + The hyperopt object. + + """ + loaded_params, new_datahandler, new_hyperopt = \ + mala.HyperOptOptuna.resume_checkpoint(checkpoint_name) + return new_hyperopt \ No newline at end of file diff --git a/test/checkpoint_training_tests.py b/test/checkpoint_training_tests.py index f7464b3eb..22a93ae2d 100644 --- a/test/checkpoint_training_tests.py +++ b/test/checkpoint_training_tests.py @@ -5,172 +5,204 @@ data_path = get_data_repo_path()+"Al36/" test_checkpoint_name = "test" +# Define the accuracy used in the tests. +accuracy = 1e-14 + + +class TestHyperoptTraining: + """Tests the checkpointing capabilities of the NN training.""" + + def test_general(self): + """Test that training checkpointing works in general.""" + # First run the entire test. + trainer = self.__original_setup(test_checkpoint_name, 40) + trainer.train_network() + original_final_test_loss = trainer.final_test_loss + + # Now do the same, but cut at epoch 22 and see if it recovers the + # correct result. + trainer = self.__original_setup(test_checkpoint_name, 22) + trainer.train_network() + trainer = self.__resume_checkpoint(test_checkpoint_name, 40) + trainer.train_network() + new_final_test_loss = trainer.final_test_loss + assert np.isclose(original_final_test_loss, new_final_test_loss, + atol=accuracy) + + def test_learning_rate(self): + """Test that the learning rate scheduler is correctly checkpointed.""" + # First run the entire test. + trainer = self.__original_setup(test_checkpoint_name, 40, + learning_rate_scheduler="ReduceLROnPlateau", + learning_rate=0.1) + trainer.train_network() + original_learning_rate = trainer.optimizer.param_groups[0]['lr'] + + # Now do the same, but cut at epoch 22 and see if it recovers the + # correct result. + trainer = self.__original_setup(test_checkpoint_name, 22, + learning_rate_scheduler="ReduceLROnPlateau", + learning_rate=0.1) + trainer.train_network() + trainer = self.__resume_checkpoint(test_checkpoint_name, 40) + trainer.train_network() + new_learning_rate = trainer.optimizer.param_groups[0]['lr'] + assert np.isclose(original_learning_rate, new_learning_rate, + atol=accuracy) + + def test_early_stopping(self): + """Test that the early stopping mechanism is correctly checkpointed.""" + # First run the entire test. + trainer = self.__original_setup(test_checkpoint_name, 40, + early_stopping_epochs=30, + learning_rate=0.1) + trainer.train_network() + original_nr_epochs = trainer.last_epoch + + # Now do the same, but cut at epoch 22 and see if it recovers the + # correct result. + trainer = self.__original_setup(test_checkpoint_name, 22, + early_stopping_epochs=30, + learning_rate=0.1) + trainer.train_network() + trainer = self.__resume_checkpoint(test_checkpoint_name, 40) + trainer.train_network() + last_nr_epochs = trainer.last_epoch + + # integer comparison! + assert original_nr_epochs == last_nr_epochs + + @staticmethod + def __original_setup(checkpoint_name, maxepochs, + learning_rate_scheduler=None, + early_stopping_epochs=0, learning_rate=0.00001): + """ + Sets up a NN training. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint to be used. + + maxepochs : int + Maximum number of epochs. + + learning_rate_scheduler : string + Type of learning rate scheduler. + + early_stopping_epochs : int + Number of early stopping epochs. + + learning_rate : float + Learning rate to be used. + + Returns + ------- + trainer : mala.network.trainer.Trainer + The trainer object created with the specified parameters. + """ + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = maxepochs + test_parameters.running.mini_batch_size = 38 + test_parameters.running.learning_rate = learning_rate + test_parameters.running.trainingtype = "Adam" + test_parameters.running.learning_rate_scheduler = learning_rate_scheduler + test_parameters.running.learning_rate_decay = 0.1 + test_parameters.running.learning_rate_patience = 30 + test_parameters.running.early_stopping_epochs = early_stopping_epochs + test_parameters.running.early_stopping_threshold = -0.1 + test_parameters.running.checkpoints_each_epoch = 5 + test_parameters.running.checkpoint_name = checkpoint_name + test_parameters.manual_seed = 1002 + test_parameters.running.use_shuffling_for_samplers = False + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler. + get_input_dimension(), + 100, + data_handler. + get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, + data_handler) + + return test_trainer + + @staticmethod + def __resume_checkpoint(checkpoint_name, actual_max_epochs): + """ + Resumes a training from checkpoint. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint to be used. + + actual_max_epochs : int + Maximum number of epochs to be used in total after recovering + checkpoint. + + Returns + ------- + trainer : mala.network.trainer.Trainer + The trainer object created with the specified parameters. + """ + + loaded_params, loaded_network, \ + new_datahandler, new_trainer = \ + mala.Trainer.resume_checkpoint(checkpoint_name) + loaded_params.running.max_number_epochs = actual_max_epochs + return new_trainer + + + -def original_setup(checkpoint_name, maxepochs, learning_rate_scheduler=None, - early_stopping_epochs=0, learning_rate=0.00001): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = maxepochs - test_parameters.running.mini_batch_size = 38 - test_parameters.running.learning_rate = learning_rate - test_parameters.running.trainingtype = "Adam" - test_parameters.running.learning_rate_scheduler = learning_rate_scheduler - test_parameters.running.learning_rate_decay = 0.1 - test_parameters.running.learning_rate_patience = 30 - test_parameters.running.early_stopping_epochs = early_stopping_epochs - test_parameters.running.early_stopping_threshold = -0.1 - test_parameters.running.checkpoints_each_epoch = 5 - test_parameters.running.checkpoint_name = checkpoint_name - test_parameters.manual_seed = 1002 - test_parameters.running.use_shuffling_for_samplers = False - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - - return test_trainer - - -def resume_checkpoint(checkpoint_name, actual_max_epochs): - loaded_params, loaded_network, \ - new_datahandler, new_trainer = \ - mala.Trainer.resume_checkpoint(checkpoint_name) - loaded_params.running.max_number_epochs = actual_max_epochs - return new_trainer - - -def test_general(accuracy=1e-14): - # First run the entire test. - trainer = original_setup(test_checkpoint_name, 40) - trainer.train_network() - original_final_test_loss = trainer.final_test_loss - - # Now do the same, but cut at epoch 22 and see if it recovers the - # correct result. - trainer = original_setup(test_checkpoint_name, 22) - trainer.train_network() - trainer = resume_checkpoint(test_checkpoint_name, 40) - trainer.train_network() - new_final_test_loss = trainer.final_test_loss - - if np.abs(original_final_test_loss-new_final_test_loss) > accuracy: - print("test_general did not suceed.", original_final_test_loss, - new_final_test_loss) - return False - return True - - -def test_learning_rate(accuracy=1e-14): - # First run the entire test. - trainer = original_setup(test_checkpoint_name, 40, - learning_rate_scheduler="ReduceLROnPlateau", - learning_rate=0.1) - trainer.train_network() - original_learning_rate = trainer.optimizer.param_groups[0]['lr'] - - # Now do the same, but cut at epoch 22 and see if it recovers the - # correct result. - trainer = original_setup(test_checkpoint_name, 22, - learning_rate_scheduler="ReduceLROnPlateau", - learning_rate=0.1) - trainer.train_network() - trainer = resume_checkpoint(test_checkpoint_name, 40) - trainer.train_network() - new_learning_rate = trainer.optimizer.param_groups[0]['lr'] - if np.abs(original_learning_rate-new_learning_rate) > accuracy: - print("test_learning_rate did not suceed.", original_learning_rate, - new_learning_rate) - return False - return True - - -def test_early_stopping(): - # First run the entire test. - trainer = original_setup(test_checkpoint_name, 40, - early_stopping_epochs=30, - learning_rate=0.1) - trainer.train_network() - original_nr_epochs = trainer.last_epoch - - # Now do the same, but cut at epoch 22 and see if it recovers the - # correct result. - trainer = original_setup(test_checkpoint_name, 22, - early_stopping_epochs=30, - learning_rate=0.1) - trainer.train_network() - trainer = resume_checkpoint(test_checkpoint_name, 40) - trainer.train_network() - last_nr_epochs = trainer.last_epoch - - # integer comparison! - if original_nr_epochs != last_nr_epochs: - print("test_early_stopping did not suceed.", original_nr_epochs, - last_nr_epochs) - return False - return True - - -def run_checkpoint_tests(): - if test_general() is False: - return False - if test_learning_rate() is False: - return False - if test_early_stopping() is False: - return False - return True - - -if __name__ == "__main__": - test1 = run_checkpoint_tests() - printout("Checkpoint test - success?:", test1) diff --git a/test/inference_test.py b/test/inference_test.py index 1ad088a17..828142bce 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -12,108 +12,132 @@ network_path = param_path+"ex05_network.pth" input_scaler_path = param_path+"ex05_iscaler.pkl" output_scaler_path = param_path+"ex05_oscaler.pkl" +accuracy_strict = 1e-16 +accuracy_coarse = 5e-7 -def test_inference(accuracy_prediction=1e-16, accuracy_actual=1e-16, - use_lazy_loading=False): - # First we load Parameters and network. - new_parameters = Parameters.load_from_file(params_path, no_snapshots=True) - new_parameters.data.data_splitting_snapshots = ["te", "te", "te"] - new_parameters.data.use_lazy_loading = use_lazy_loading - new_parameters.running.mini_batch_size = 9 - new_parameters.running.mini_batch_size = 46 - new_parameters.running.mini_batch_size = 46 - - new_network = Network.load_from_file(new_parameters, network_path) - - # We use a data handler object to read the data we want to investigate. - # We need to make sure that the same scaling is used. - iscaler = DataScaler.load_from_file(input_scaler_path) - oscaler = DataScaler.load_from_file(output_scaler_path) - inference_data_handler = DataHandler(new_parameters, - input_data_scaler=iscaler, - output_data_scaler=oscaler) - inference_data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - inference_data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - inference_data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - - inference_data_handler.prepare_data() - - for n in [46, 99, 500, 1977]: - new_parameters.running.mini_batch_size = n +class TestInference: + """Tests several inference/accuracy related parts.""" + + def test_unit_conversion(self): + """Test that RAM inexpensive unit conversion works.""" + new_parameters = Parameters.load_from_file(params_path, + no_snapshots=True) + new_parameters.data.data_splitting_snapshots = ["te", "te", "te"] + new_parameters.data.use_lazy_loading = False + new_parameters.running.mini_batch_size = 50 + + new_network = Network.load_from_file(new_parameters, network_path) + + # We use a data handler object to read the data we want to investigate. + # We need to make sure that the same scaling is used. + iscaler = DataScaler.load_from_file(input_scaler_path) + oscaler = DataScaler.load_from_file(output_scaler_path) + inference_data_handler = DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + inference_data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", + data_path, + "Al_debug_2k_nr0.out.npy", + data_path, + output_units="1/Ry") + + inference_data_handler.prepare_data() + + # Confirm that unit conversion does not introduce any errors. + from_file_1 = inference_data_handler.target_calculator.\ + convert_units(np.load(data_path+"Al_debug_2k_nr"+str(0) + + ".out.npy"), in_units="1/Ry") + from_file_2 = np.load(data_path+"Al_debug_2k_nr"+str(0) + + ".out.npy")*inference_data_handler.\ + target_calculator.convert_units(1, in_units="1/Ry") + + assert from_file_1.sum() == from_file_2.sum() + + def test_inference_ram(self): + """ + Test inference accuracy (for RAM based trainings). + + What is explicitly tested is that the scaling and rescaling do not + change the values too drastically. During inference (or at least + in the test case) this is done. + """ + # Without lazy loading, there is a bigger numerical error in the actual + # outputs. This is due to the fact that we scale and rescale data on 32 + # byte float arrays; lazy loading is therefore drastically advised for + # inference/testing purposes. + batchsizes = [46, 99, 500, 1977] + for batchsize in batchsizes: + actual_ldos, from_file, predicted_ldos, raw_predicted_outputs =\ + self.__run(use_lazy_loading=False, batchsize=batchsize) + assert np.isclose(actual_ldos.sum(), from_file.sum(), + atol=accuracy_coarse) + + def test_inference_lazy_loading(self): + """ + Test inference accuracy (for lazy loading based trainings). + + What is explicitly tested is that the scaling and rescaling do not + change the values too drastically. During inference (or at least + in the test case) this is done. + """ + # Without lazy loading, there is a bigger numerical error in the actual + # outputs. This is due to the fact that we scale and rescale data on 32 + # byte float arrays; lazy loading is therefore drastically advised for + # inference/testing purposes. + batchsizes = [46, 99, 500, 1977] + for batchsize in batchsizes: + actual_ldos, from_file, predicted_ldos, raw_predicted_outputs = \ + self.__run(use_lazy_loading=True, batchsize=batchsize) + assert np.isclose(actual_ldos.sum(), from_file.sum(), + atol=accuracy_strict) + + @staticmethod + def __run(use_lazy_loading=False, batchsize=46): + # First we load Parameters and network. + new_parameters = Parameters.load_from_file(params_path, + no_snapshots=True) + new_parameters.data.data_splitting_snapshots = ["te", "te", "te"] + new_parameters.data.use_lazy_loading = use_lazy_loading + new_parameters.running.mini_batch_size = batchsize + + new_network = Network.load_from_file(new_parameters, network_path) + + # We use a data handler object to read the data we want to investigate. + # We need to make sure that the same scaling is used. + iscaler = DataScaler.load_from_file(input_scaler_path) + oscaler = DataScaler.load_from_file(output_scaler_path) + inference_data_handler = DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + inference_data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", + data_path, + "Al_debug_2k_nr0.out.npy", + data_path, + output_units="1/Ry") + + inference_data_handler.prepare_data() + tester = Tester(new_parameters, new_network, inference_data_handler) - for i in range(0, inference_data_handler.nr_snapshots): - # Get values with current framework. - actual_ldos, predicted_ldos = tester.test_snapshot(i) - - # Confirm that unit conversion does not introduce any errors. - from_file_1 = inference_data_handler.target_calculator.\ - convert_units(np.load(data_path+"Al_debug_2k_nr"+str(i) + - ".out.npy"), in_units="1/Ry") - from_file_2 = np.load(data_path+"Al_debug_2k_nr"+str(i) + - ".out.npy")*inference_data_handler.\ - target_calculator.convert_units(1, in_units="1/Ry") - error = np.abs(from_file_1.sum()-from_file_2.sum()) - if error > 0: - printout("Accuracy not met for unit coversion with error" - " being {0}".format(error)) - printout("Batchsize: {0}, lazy loading: {1}". - format(n, new_parameters.data.use_lazy_loading)) - - # Compare actual_ldos with file directly. - # This is the only comparison that counts. - from_file = inference_data_handler.target_calculator.\ - convert_units(np.load(data_path+"Al_debug_2k_nr" + - str(i)+".out.npy"), in_units="1/Ry") - error = np.abs(actual_ldos.sum()-from_file.sum()) - if error > accuracy_actual: - printout("Accuracy not met for raw data with error " - "being {0}".format(error)) - printout("Batchsize: {0}, lazy loading: {1}". - format(n, new_parameters.data.use_lazy_loading)) - return False - - # Test if prediction still works. - raw_predicted_outputs = np.load(data_path+"Al_debug_2k_nr"+str(i) + - ".in.npy") - raw_predicted_outputs = inference_data_handler.\ - raw_numpy_to_converted_scaled_tensor(raw_predicted_outputs, - "in", "None") - raw_predicted_outputs = new_network.\ - do_prediction(raw_predicted_outputs) - raw_predicted_outputs = inference_data_handler.output_data_scaler.\ - inverse_transform(raw_predicted_outputs, as_numpy=True) - error = np.abs(predicted_ldos.sum()-raw_predicted_outputs.sum()) - if error > accuracy_prediction: - printout("Accuracy not met for prediction with error " - "being {0}".format(error)) - printout("Parameters are batchsize: {0}, lazy loading: {1}". - format(n, new_parameters.data.use_lazy_loading)) - return False - - return True - - -def run_inference_test(): - # Without lazy loading, there is a bigger numerical error in the actual - # outputs. This is due to the fact that we scale and rescale data on 32 - # byte float arrays; lazy loading is therefore drastically advised for - # inference/testing purposes. - if test_inference(accuracy_prediction=5e-7, accuracy_actual=5e-7, - use_lazy_loading=False) is False: - return False - if test_inference(accuracy_prediction=0, accuracy_actual=0, - use_lazy_loading=True) is False: - return False - return True - - -if __name__ == "__main__": - test1 = run_inference_test() - printout("Inference test - success?:", test1) + + # Get values with current framework. + actual_ldos, predicted_ldos = tester.test_snapshot(0) + + # Compare actual_ldos with file directly. + # This is the only comparison that counts. + from_file = inference_data_handler.target_calculator.\ + convert_units(np.load(data_path+"Al_debug_2k_nr" + + str(0)+".out.npy"), in_units="1/Ry") + + # Test if prediction still works. + raw_predicted_outputs = np.load(data_path+"Al_debug_2k_nr"+str(i) + + ".in.npy") + raw_predicted_outputs = inference_data_handler.\ + raw_numpy_to_converted_scaled_tensor(raw_predicted_outputs, + "in", "None") + raw_predicted_outputs = new_network.\ + do_prediction(raw_predicted_outputs) + raw_predicted_outputs = inference_data_handler.output_data_scaler.\ + inverse_transform(raw_predicted_outputs, as_numpy=True) + + return actual_ldos, from_file, predicted_ldos, raw_predicted_outputs diff --git a/test/integration_test.py b/test/integration_test.py index 86742b191..6781baa57 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -15,10 +15,8 @@ # 2. LDOS cube files. # 3. Density cube file. # 4. A DOS file. -# In the check_integration_qe subfolder, input scripts can be found for a -# simple test calculation (the results of this calculation have been used -# to develop this script). It uses an Al supercell with 36 atoms and a -# 6x6x6 k-grid. +# Scripts to reproduce the data files used in this test script can be found +# in the data repo. data_path = get_data_repo_path()+"Al36/" path_to_out = data_path+"Al.pw.scf.out" @@ -192,10 +190,4 @@ def test_qe_ldos_to_dos(self): printout("Relative error for sum of DOS: ", rel_error) # Check against the constraints we put upon ourselves. - assert np.isclose(rel_error, 0, atol=accuracy) - -# if __name__ == "__main__": -# -# # Run the tests. -# testtester = TestMALAIntegration() -# testtester.test_analytical_integration() \ No newline at end of file + assert np.isclose(rel_error, 0, atol=accuracy) \ No newline at end of file diff --git a/test/lazy_loading_basic.py b/test/lazy_loading_basic.py deleted file mode 100644 index d889a1f13..000000000 --- a/test/lazy_loading_basic.py +++ /dev/null @@ -1,164 +0,0 @@ -from mala.common.parameters import Parameters -from mala.common.printout import printout -from mala.datahandling.data_handler import DataHandler -import torch -import numpy as np - -# This test compares the data scaling using the regular scaling procedure and -# the lazy-loading one (incremental fitting). -from mala.network.network import Network -from mala.network.trainer import Trainer -from data_repo_path import get_data_repo_path -data_path_Al = get_data_repo_path()+"Al36/" - - -def test_lazy_loading_basic(data_path="../examples/data/", accuracy=0.001): - #################### - # PARAMETERS - #################### - test_parameters = Parameters() - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.descriptors.twojmax = 11 - test_parameters.targets.ldos_gridsize = 10 - test_parameters.network.layer_activations = ["LeakyReLU"] - test_parameters.running.max_number_epochs = 3 - test_parameters.running.mini_batch_size = 512 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.comment = "Lazy loading test." - test_parameters.network.nn_type = "feed-forward" - test_parameters.running.use_gpu = True - test_parameters.data.use_lazy_loading = False - - #################### - # DATA - #################### - - dataset_tester = [] - results = [] - training_tester = [] - for scalingtype in ["standard", "normal", "feature-wise-standard", - "feature-wise-normal"]: - comparison = [scalingtype] - for ll_type in [True, False]: - this_result = [] - if ll_type: - this_result.append("lazy-loading") - else: - this_result.append("RAM") - test_parameters.data.use_lazy_loading = ll_type - test_parameters.data.input_rescaling_type = scalingtype - test_parameters.data.output_rescaling_type = scalingtype - data_handler = DataHandler(test_parameters) - data_handler.clear_data() - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="va") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="te") - data_handler.prepare_data() - if scalingtype == "standard": - # The lazy-loading STD equation (and to a smaller amount the - # mean equation) is having some small accurcay issue that - # I presume to be due to numerical constraints. To make a - # meaningful comparison it is wise to scale the value here. - this_result.append(data_handler.input_data_scaler.total_mean / - data_handler.nr_training_data) - this_result.append(data_handler.input_data_scaler.total_std / - data_handler.nr_training_data) - this_result.append(data_handler.output_data_scaler.total_mean / - data_handler.nr_training_data) - this_result.append(data_handler.output_data_scaler.total_std / - data_handler.nr_training_data) - if scalingtype == "normal": - torch.manual_seed(2002) - this_result.append(data_handler.input_data_scaler.total_max) - this_result.append(data_handler.input_data_scaler.total_min) - this_result.append(data_handler.output_data_scaler.total_max) - this_result.append(data_handler.output_data_scaler.total_min) - dataset_tester.append((data_handler.training_data_set[3998]) - [0].sum() + - (data_handler.training_data_set[3999]) - [0].sum() + - (data_handler.training_data_set[4000]) - [0].sum() + - (data_handler.training_data_set[4001]) - [0].sum()) - test_parameters.network.layer_sizes = \ - [data_handler.get_input_dimension(), 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = Network(test_parameters) - test_trainer = Trainer(test_parameters, test_network, - data_handler) - training_tester.append(test_trainer.final_test_loss - - test_trainer.initial_test_loss) - - if scalingtype == "feature-wise-standard": - # The lazy-loading STD equation (and to a smaller amount the - # mean equation) is having some small accurcay issue that - # I presume to be due to numerical constraints. To make a - # meaningful comparison it is wise to scale the value here. - this_result.append(torch.mean(data_handler.input_data_scaler. - means)/data_handler.grid_size) - this_result.append(torch.mean(data_handler.input_data_scaler. - stds)/data_handler.grid_size) - this_result.append(torch.mean(data_handler.output_data_scaler. - means)/data_handler.grid_size) - this_result.append(torch.mean(data_handler.output_data_scaler. - stds)/data_handler.grid_size) - if scalingtype == "feature-wise-normal": - this_result.append(torch.mean(data_handler.input_data_scaler. - maxs)) - this_result.append(torch.mean(data_handler.input_data_scaler. - mins)) - this_result.append(torch.mean(data_handler.output_data_scaler. - maxs)) - this_result.append(torch.mean(data_handler.output_data_scaler. - mins)) - - comparison.append(this_result) - results.append(comparison) - - for entry in results: - val1 = entry[1][1]-entry[2][1] - val2 = entry[1][2]-entry[2][2] - val3 = entry[1][3]-entry[2][3] - val4 = entry[1][4]-entry[2][4] - if val1 > accuracy or val2 > accuracy or val3 > accuracy or val4 > \ - accuracy: - printout(entry[0]) - printout(val1, val2, val3, val4) - return False - if torch.abs(dataset_tester[0] - dataset_tester[1]) > accuracy: - printout(dataset_tester[0], dataset_tester[1]) - return False - if np.abs(training_tester[0] - training_tester[1]) > accuracy: - printout(training_tester[0], training_tester[1]) - return False - - return True - - -if __name__ == "__main__": - test1 = test_lazy_loading_basic(data_path=data_path_Al) - printout("Check if lazy loading and RAM implementation get the same " - "results? - success?:", test1) diff --git a/test/lazy_loading_horovod_benchmark.py b/test/lazy_loading_horovod_benchmark.py deleted file mode 100644 index 66802ec3d..000000000 --- a/test/lazy_loading_horovod_benchmark.py +++ /dev/null @@ -1,123 +0,0 @@ -from mala.common.parameters import Parameters -from mala.common.printout import printout -from mala.datahandling.data_handler import DataHandler -from mala.network.network import Network -from mala.network.trainer import Trainer -import numpy as np -import time -from data_repo_path import get_data_repo_path -data_path_Al = get_data_repo_path()+"Al36/" - -# This is a benchmark comparing the usage of horovod, lazy loading and RAM -# based storage,. - - -def lazy_loading_horovod_benchmark(data_path="../examples/data/", - accuracy=0.0005): - - #################### - # PARAMETERS - #################### - test_parameters = Parameters() - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.network.layer_activations = ["LeakyReLU"] - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 500 - test_parameters.running.trainingtype = "Adam" - test_parameters.comment = "Horovod / lazy loading benchmark." - test_parameters.network.nn_type = "feed-forward" - test_parameters.manual_seed = 2021 - - #################### - # DATA - #################### - results = [] - for hvduse in [False, True]: - for ll in [True, False]: - start_time = time.time() - test_parameters.running.learning_rate = 0.00001 - test_parameters.data.use_lazy_loading = ll - test_parameters.use_horovod = hvduse - data_handler = DataHandler(test_parameters) - data_handler.clear_data() - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="tr") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="va") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry", - add_snapshot_as="te") - - data_handler.prepare_data() - test_parameters.network.layer_sizes = \ - [data_handler.get_input_dimension(), 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = Network(test_parameters) - test_trainer = Trainer(test_parameters, test_network, data_handler) - - hvdstring = "no horovod" - if hvduse: - hvdstring = "horovod" - - llstring = "data in RAM" - if ll: - llstring = "using lazy loading" - - results.append([hvdstring, llstring, test_trainer. - initial_test_loss, test_trainer.final_test_loss, - time.time() - start_time]) - - diff = [] - # For 4 local processes I get: - # Test: no horovod , using lazy loading - # Initial loss: 0.1342976689338684 - # Final loss: 0.10587086156010628 - # Time: 3.743736743927002 - # Test: no horovod , data in RAM - # Initial loss: 0.13430887088179588 - # Final loss: 0.10572846792638302 - # Time: 1.825883388519287 - # Test: horovod , using lazy loading - # Initial loss: 0.1342976726591587 - # Final loss: 0.10554153844714165 - # Time: 4.513132572174072 - # Test: horovod , data in RAM - # Initial loss: 0.13430887088179588 - # Final loss: 0.1053303349763155 - # Time: 3.2193074226379395 - - for r in results: - printout("Test: ", r[0], ", ", r[1]) - printout("Initial loss: ", r[2]) - printout("Final loss: ", r[3]) - printout("Time: ", r[4]) - diff.append(r[3]-r[2]) - diff = np.array(diff) - - # The loss improvements should be comparable. - if np.std(diff) > accuracy: - printout(np.std(diff)) - return False - return True - - -if __name__ == "__main__": - test1 = lazy_loading_horovod_benchmark(data_path=data_path_Al) - printout("Benchmark of lazy loading, horovod and ? - success?:", test1) diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py new file mode 100644 index 000000000..7ec4c6877 --- /dev/null +++ b/test/lazy_loading_test.py @@ -0,0 +1,275 @@ +from mala.common.parameters import Parameters +from mala.common.printout import printout +from mala.datahandling.data_handler import DataHandler +import torch +import numpy as np +from mala.network.network import Network +from mala.network.trainer import Trainer +from data_repo_path import get_data_repo_path +import time +# This test compares the data scaling using the regular scaling procedure and +# the lazy-loading one (incremental fitting). + +data_path = get_data_repo_path()+"Al36/" +accuracy_strict = 5e-4 +accuracy_coarse = 1e-3 + + +class TestLazyLoading: + """Tests different aspects surrounding lazy loading.""" + + def test_scaling(self): + """ + Test that the scaling works approximately the same for RAM and LL. + + The are some numerical differences that simply occur, but we can still + check that the scaling results are mostly identical. + """ + #################### + # PARAMETERS + #################### + test_parameters = Parameters() + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.descriptors.twojmax = 11 + test_parameters.targets.ldos_gridsize = 10 + test_parameters.network.layer_activations = ["LeakyReLU"] + test_parameters.running.max_number_epochs = 3 + test_parameters.running.mini_batch_size = 512 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.comment = "Lazy loading test." + test_parameters.network.nn_type = "feed-forward" + test_parameters.running.use_gpu = True + test_parameters.data.use_lazy_loading = False + + #################### + # DATA + #################### + + dataset_tester = [] + results = [] + training_tester = [] + for scalingtype in ["standard", "normal", "feature-wise-standard", + "feature-wise-normal"]: + comparison = [scalingtype] + for ll_type in [True, False]: + this_result = [] + if ll_type: + this_result.append("lazy-loading") + else: + this_result.append("RAM") + test_parameters.data.use_lazy_loading = ll_type + test_parameters.data.input_rescaling_type = scalingtype + test_parameters.data.output_rescaling_type = scalingtype + data_handler = DataHandler(test_parameters) + data_handler.clear_data() + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry", + add_snapshot_as="va") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry", + add_snapshot_as="te") + data_handler.prepare_data() + if scalingtype == "standard": + # The lazy-loading STD equation (and to a smaller amount the + # mean equation) is having some small accurcay issue that + # I presume to be due to numerical constraints. To make a + # meaningful comparison it is wise to scale the value here. + this_result.append(data_handler.input_data_scaler.total_mean / + data_handler.nr_training_data) + this_result.append(data_handler.input_data_scaler.total_std / + data_handler.nr_training_data) + this_result.append(data_handler.output_data_scaler.total_mean / + data_handler.nr_training_data) + this_result.append(data_handler.output_data_scaler.total_std / + data_handler.nr_training_data) + if scalingtype == "normal": + torch.manual_seed(2002) + this_result.append(data_handler.input_data_scaler.total_max) + this_result.append(data_handler.input_data_scaler.total_min) + this_result.append(data_handler.output_data_scaler.total_max) + this_result.append(data_handler.output_data_scaler.total_min) + dataset_tester.append((data_handler.training_data_set[3998]) + [0].sum() + + (data_handler.training_data_set[3999]) + [0].sum() + + (data_handler.training_data_set[4000]) + [0].sum() + + (data_handler.training_data_set[4001]) + [0].sum()) + test_parameters.network.layer_sizes = \ + [data_handler.get_input_dimension(), 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = Network(test_parameters) + test_trainer = Trainer(test_parameters, test_network, + data_handler) + training_tester.append(test_trainer.final_test_loss - + test_trainer.initial_test_loss) + + if scalingtype == "feature-wise-standard": + # The lazy-loading STD equation (and to a smaller amount the + # mean equation) is having some small accurcay issue that + # I presume to be due to numerical constraints. To make a + # meaningful comparison it is wise to scale the value here. + this_result.append(torch.mean(data_handler.input_data_scaler. + means)/data_handler.grid_size) + this_result.append(torch.mean(data_handler.input_data_scaler. + stds)/data_handler.grid_size) + this_result.append(torch.mean(data_handler.output_data_scaler. + means)/data_handler.grid_size) + this_result.append(torch.mean(data_handler.output_data_scaler. + stds)/data_handler.grid_size) + if scalingtype == "feature-wise-normal": + this_result.append(torch.mean(data_handler.input_data_scaler. + maxs)) + this_result.append(torch.mean(data_handler.input_data_scaler. + mins)) + this_result.append(torch.mean(data_handler.output_data_scaler. + maxs)) + this_result.append(torch.mean(data_handler.output_data_scaler. + mins)) + + comparison.append(this_result) + results.append(comparison) + + for entry in results: + assert np.isclose(entry[1][1], entry[2][1], atol=accuracy_coarse) + assert np.isclose(entry[1][2], entry[2][2], atol=accuracy_coarse) + assert np.isclose(entry[1][3], entry[2][3], atol=accuracy_coarse) + assert np.isclose(entry[1][4], entry[2][4], atol=accuracy_coarse) + assert np.isclose(entry[1][1], entry[2][1], atol=accuracy_coarse) + assert np.isclose(dataset_tester[0], dataset_tester[1], + atol=accuracy_coarse) + assert np.isclose(training_tester[0], training_tester[1], + atol=accuracy_coarse) + + def test_perfromance_horovod(self): + + #################### + # PARAMETERS + #################### + test_parameters = Parameters() + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.network.layer_activations = ["LeakyReLU"] + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 500 + test_parameters.running.trainingtype = "Adam" + test_parameters.comment = "Horovod / lazy loading benchmark." + test_parameters.network.nn_type = "feed-forward" + test_parameters.manual_seed = 2021 + + #################### + # DATA + #################### + results = [] + for hvduse in [False, True]: + for ll in [True, False]: + start_time = time.time() + test_parameters.running.learning_rate = 0.00001 + test_parameters.data.use_lazy_loading = ll + test_parameters.use_horovod = hvduse + data_handler = DataHandler(test_parameters) + data_handler.clear_data() + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", + data_path, + "Al_debug_2k_nr0.out.npy", + data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", + data_path, + "Al_debug_2k_nr1.out.npy", + data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", + data_path, + "Al_debug_2k_nr2.out.npy", + data_path, + output_units="1/Ry", + add_snapshot_as="tr") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", + data_path, + "Al_debug_2k_nr1.out.npy", + data_path, + output_units="1/Ry", + add_snapshot_as="va") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", + data_path, + "Al_debug_2k_nr2.out.npy", + data_path, + output_units="1/Ry", + add_snapshot_as="te") + + data_handler.prepare_data() + test_parameters.network.layer_sizes = \ + [data_handler.get_input_dimension(), 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = Network(test_parameters) + test_trainer = Trainer(test_parameters, test_network, + data_handler) + + hvdstring = "no horovod" + if hvduse: + hvdstring = "horovod" + + llstring = "data in RAM" + if ll: + llstring = "using lazy loading" + + results.append([hvdstring, llstring, test_trainer. + initial_test_loss, + test_trainer.final_test_loss, + time.time() - start_time]) + + diff = [] + # For 4 local processes I get: + # Test: no horovod , using lazy loading + # Initial loss: 0.1342976689338684 + # Final loss: 0.10587086156010628 + # Time: 3.743736743927002 + # Test: no horovod , data in RAM + # Initial loss: 0.13430887088179588 + # Final loss: 0.10572846792638302 + # Time: 1.825883388519287 + # Test: horovod , using lazy loading + # Initial loss: 0.1342976726591587 + # Final loss: 0.10554153844714165 + # Time: 4.513132572174072 + # Test: horovod , data in RAM + # Initial loss: 0.13430887088179588 + # Final loss: 0.1053303349763155 + # Time: 3.2193074226379395 + + for r in results: + printout("Test: ", r[0], ", ", r[1]) + printout("Initial loss: ", r[2]) + printout("Final loss: ", r[3]) + printout("Time: ", r[4]) + diff.append(r[3] - r[2]) + diff = np.array(diff) + + # The loss improvements should be comparable. + assert np.std(diff) > accuracy_strict \ No newline at end of file diff --git a/test/mala_tests.py b/test/mala_tests.py deleted file mode 100644 index e89e55580..000000000 --- a/test/mala_tests.py +++ /dev/null @@ -1,113 +0,0 @@ -from integration_test import check_analytical_integration, \ - qe_dens_to_nr_of_electrons, qe_ldos_to_density, qe_ldos_to_dos -from tensor_memory import test_tensor_memory -from lazy_loading_basic import test_lazy_loading_basic -from lazy_loading_horovod_benchmark import lazy_loading_horovod_benchmark -from mala.common.parameters import printout -from data_repo_path import get_data_repo_path -from inference_test import run_inference_test -from checkpoint_training_tests import run_checkpoint_tests -from checkpoint_hyperopt_tests import run_hyperopt_checkpoint_test -data_path = get_data_repo_path()+"Al36/" - - -'''' -mala_tests: Central hub for all subroutine tests for mala. -''' -standard_accuracy = 10**-10 - -###################### -# Memory tests (obsolete). -###################### - -if test_tensor_memory(data_path+"Al_debug_2k_nr0.in.npy", standard_accuracy): - printout("test_tensor_memory suceeded.") -else: - raise Exception("test_tensor_memory failed.") - -###################### -# Integration tests. -###################### - -if check_analytical_integration(standard_accuracy): - printout("check_analytical_integration suceeded.") -else: - raise Exception("check_analytical_integration failed.") - -# These tests operate on a coarser accuracy - -if qe_dens_to_nr_of_electrons(standard_accuracy*(10**3)): - printout("qe_dens_to_nr_of_electrons suceeded.") -else: - raise Exception("qe_dens_to_nr_of_electrons failed.") -try: - if qe_ldos_to_density(standard_accuracy*(10**7)): - printout("qe_ldos_to_density suceeded.") - else: - raise Exception("qe_ldos_to_density failed.") - if qe_ldos_to_dos(standard_accuracy*(10**4)): - printout("qe_ldos_to_dos suceeded.") - else: - raise Exception("qe_ldos_to_dos failed.") -except FileNotFoundError: - printout("Could not test LDOS integration because of " - "missing LDOS data.") - - -###################### -# Total energy test. -###################### - -# Find a way to access the total energy module form a runner. -# from total_energy_tester import test_total_energy -# if test_total_energy(): -# printout("test_total_energy suceeded.") -# else: -# raise Exception("test_total_energy failed.") - -###################### -# Scaling test. -###################### - -if test_lazy_loading_basic(data_path=data_path): - printout("test_lazy_loading suceeded.") -else: - raise Exception("test_lazy_loading failed.") - -###################### -# Lazy loading and horovod tests. This cannot yet be included in the -# automated test suite because we don't install horovod there. -###################### - -try: - if lazy_loading_horovod_benchmark(data_path=data_path): - printout("test_lazy_loading suceeded.") -except NameError: - printout("Could not perform lazy loading horovod test, most likely because" - " horovod was not installed.") -except ModuleNotFoundError: - printout("Could not perform lazy loading horovod test, most likely because" - " horovod was not installed.") - -###################### -# Inference tests. -###################### - -if run_inference_test(): - printout("run_inference_test test suceeded.") -else: - raise Exception("run_inference_test failed.") - -###################### -# Checkpoint tests. -###################### - -if run_checkpoint_tests(): - printout("run_checkpoint_tests test suceeded.") -else: - raise Exception("run_checkpoint_tests failed.") - -if run_checkpoint_tests(): - printout("run_hyperopt_checkpoint_test test suceeded.") -else: - raise Exception("run_hyperopt_checkpoint_test failed.") diff --git a/test/tensor_memory.py b/test/tensor_memory.py index 57a0824d8..70806a498 100644 --- a/test/tensor_memory.py +++ b/test/tensor_memory.py @@ -5,61 +5,63 @@ from mala.common.parameters import printout from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" - - -def test_tensor_memory(file, accuracy): - - # Load an array as a numpy array - loaded_array_raw = np.load(file) - - # Get dimensions of numpy array. - dimension = np.shape(loaded_array_raw) - datacount = dimension[0] * dimension[1] * dimension[2] - - # Reshape as datacount x featurelength - loaded_array_raw = loaded_array_raw.astype(np.float32) - loaded_array = loaded_array_raw.reshape([datacount, dimension[3]]) - - # Check if reshaping allocated new memory. - loaded_array_raw *= 10 - test1 = np.abs(np.sum(loaded_array) - np.sum(loaded_array_raw)) - if test1 > accuracy: - return False - - # simulate data splitting. - index1 = int(80 / 100 * np.shape(loaded_array)[0]) - torch_tensor = torch.from_numpy(loaded_array[0:index1]).float() - - # Check if tensor and array are still the same. - test1 = torch.abs(torch.sum(torch_tensor-loaded_array[0:index1])) - if test1 > accuracy: - return False - - # Simulate data operation. - loaded_array *= 10 - - # Check if tensor and array are still the same. - test1 = torch.abs(torch.sum(torch_tensor-loaded_array[0:index1])) - if test1 > accuracy: - return False - - # Simulate Tensor data handling in pytorch workflow. - data_set = TensorDataset(torch_tensor, torch_tensor) - data_loader = DataLoader(data_set, batch_size=index1) - - # Perform data operation again. - loaded_array *= 10 - for (x, y) in data_loader: - test1 = torch.abs(torch.sum(x - loaded_array[0:index1])) - - if test1 > accuracy: - return False - return True - - -if __name__ == "__main__": - test_result = test_tensor_memory(data_path+"Al_debug_2k_nr0.in.npy", - 0.000001) - printout("Check if numpy->tensor works without copying - success?:", - test_result) +# Define the accuracy used in the tests. +accuracy = 1e-5 + + +class TestTensorMemory: + """ + Tests/Tries out how memory referencing/copying between np and torch works. + + This test does not necessarily have to be run all the time, I thought of + it more as something that comes in handy when debugging while something + breaks after an update. MALA relies on the following assumptions to + be true. + """ + def test_tensor_memory(self): + + # Load an array as a numpy array + loaded_array_raw = np.load(data_path+"Al_debug_2k_nr0.in.npy") + + # Get dimensions of numpy array. + dimension = np.shape(loaded_array_raw) + datacount = dimension[0] * dimension[1] * dimension[2] + + # Reshape as datacount x featurelength + loaded_array_raw = loaded_array_raw.astype(np.float32) + loaded_array = loaded_array_raw.reshape([datacount, dimension[3]]) + + # Check if reshaping allocated new memory. + loaded_array_raw *= 10 + assert np.isclose(np.sum(loaded_array), np.sum(loaded_array_raw), + accuracy) + + # simulate data splitting. + index1 = int(80 / 100 * np.shape(loaded_array)[0]) + torch_tensor = torch.from_numpy(loaded_array[0:index1]).float() + + # Check if tensor and array are still the same. + assert np.isclose(torch.sum(torch_tensor), + torch.sum(loaded_array[0:index1]), + accuracy) + + # Simulate data operation. + loaded_array *= 10 + + # Check if tensor and array are still the same. + test1 = torch.abs(torch.sum(torch_tensor-loaded_array[0:index1])) + assert np.isclose(torch.sum(torch_tensor), + torch.sum(loaded_array[0:index1]), + accuracy) + + # Simulate Tensor data handling in pytorch workflow. + data_set = TensorDataset(torch_tensor, torch_tensor) + data_loader = DataLoader(data_set, batch_size=index1) + + # Perform data operation again. + loaded_array *= 10 + for (x, y) in data_loader: + assert np.isclose(torch.sum(x), + torch.sum(loaded_array[0:index1]), + accuracy) diff --git a/test/total_energy_tester.py b/test/total_energy_tester.py deleted file mode 100644 index 7f9390dd9..000000000 --- a/test/total_energy_tester.py +++ /dev/null @@ -1,32 +0,0 @@ -import total_energy as te -import numpy as np - - -def test_total_energy(): - te.initialize() - nnr = te.get_nnr() - nspin = te.get_nspin() - # ngm = te.get_ngm() - nat = te.get_nat() - te.print_energies() - - positions = te.get_positions(nat) - positions = positions + 0.01*np.random.random([3, nat]) - te.set_positions(positions, nat) - te.print_energies() - - rho_of_r = te.get_rho_of_r(nnr, nspin) - total_rho = np.sum(rho_of_r) - rho_of_r = rho_of_r + 1.0e-3 - rho_of_r = rho_of_r * total_rho / np.sum(rho_of_r) - te.set_rho_of_r(rho_of_r, nnr, nspin) - te.print_energies() - e_rho_times_v_hxc, e_hartree, e_xc, e_ewald = te.get_energies() - print(e_rho_times_v_hxc, e_hartree, e_xc, e_ewald) - return True - - -if __name__ == "__main__": - - # Run the tests. - test_total_energy() From d6d0f2a9eaee002ed77f03fdf8a2f064b2402084 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 12 Jul 2021 16:34:45 +0200 Subject: [PATCH 152/297] Fixed lazy loading tests --- mala/network/trainer.py | 7 ++++--- test/lazy_loading_test.py | 10 ++++++++-- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index fdf176e31..bc46cde19 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -173,14 +173,15 @@ def train_network(self): if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, self.test_data_loader) - + print(tloss) # Collect and average all the losses from all the devices if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') - self.initial_validation_loss = vloss if self.data.test_data_set is not None: tloss = self.__average_validation(tloss, 'average_loss') - self.initial_test_loss = tloss + + self.initial_test_loss = tloss + self.initial_validation_loss = vloss if self.parameters.verbosity: printout("Initial Guess - validation data loss: ", vloss) diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index 7ec4c6877..1200637d3 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -11,7 +11,7 @@ # the lazy-loading one (incremental fitting). data_path = get_data_repo_path()+"Al36/" -accuracy_strict = 5e-4 +accuracy_strict = 1e-3 accuracy_coarse = 1e-3 @@ -121,6 +121,7 @@ def test_scaling(self): test_network = Network(test_parameters) test_trainer = Trainer(test_parameters, test_network, data_handler) + test_trainer.train_network() training_tester.append(test_trainer.final_test_loss - test_trainer.initial_test_loss) @@ -230,6 +231,7 @@ def test_perfromance_horovod(self): test_network = Network(test_parameters) test_trainer = Trainer(test_parameters, test_network, data_handler) + test_trainer.train_network() hvdstring = "no horovod" if hvduse: @@ -272,4 +274,8 @@ def test_perfromance_horovod(self): diff = np.array(diff) # The loss improvements should be comparable. - assert np.std(diff) > accuracy_strict \ No newline at end of file + assert np.std(diff) < accuracy_strict + + +test_class = TestLazyLoading() +test_class.test_perfromance_horovod() \ No newline at end of file From beb1b24d08ddb93f2d02ff86c2bcfba4ae8203e4 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 12 Jul 2021 16:47:13 +0200 Subject: [PATCH 153/297] Fixed inference tests --- test/inference_test.py | 6 +++--- test/lazy_loading_test.py | 8 ++------ 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/test/inference_test.py b/test/inference_test.py index 828142bce..9c9d33650 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -23,7 +23,7 @@ def test_unit_conversion(self): """Test that RAM inexpensive unit conversion works.""" new_parameters = Parameters.load_from_file(params_path, no_snapshots=True) - new_parameters.data.data_splitting_snapshots = ["te", "te", "te"] + new_parameters.data.data_splitting_snapshots = ["te"] new_parameters.data.use_lazy_loading = False new_parameters.running.mini_batch_size = 50 @@ -97,7 +97,7 @@ def __run(use_lazy_loading=False, batchsize=46): # First we load Parameters and network. new_parameters = Parameters.load_from_file(params_path, no_snapshots=True) - new_parameters.data.data_splitting_snapshots = ["te", "te", "te"] + new_parameters.data.data_splitting_snapshots = ["te"] new_parameters.data.use_lazy_loading = use_lazy_loading new_parameters.running.mini_batch_size = batchsize @@ -130,7 +130,7 @@ def __run(use_lazy_loading=False, batchsize=46): str(0)+".out.npy"), in_units="1/Ry") # Test if prediction still works. - raw_predicted_outputs = np.load(data_path+"Al_debug_2k_nr"+str(i) + + raw_predicted_outputs = np.load(data_path+"Al_debug_2k_nr"+str(0) + ".in.npy") raw_predicted_outputs = inference_data_handler.\ raw_numpy_to_converted_scaled_tensor(raw_predicted_outputs, diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index 1200637d3..14fd44675 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -162,7 +162,7 @@ def test_scaling(self): assert np.isclose(training_tester[0], training_tester[1], atol=accuracy_coarse) - def test_perfromance_horovod(self): + def test_performance_horovod(self): #################### # PARAMETERS @@ -274,8 +274,4 @@ def test_perfromance_horovod(self): diff = np.array(diff) # The loss improvements should be comparable. - assert np.std(diff) < accuracy_strict - - -test_class = TestLazyLoading() -test_class.test_perfromance_horovod() \ No newline at end of file + assert np.std(diff) < accuracy_strict \ No newline at end of file From 8d9ed080b133eaaea4fb458c79e20649aa7627b1 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 12 Jul 2021 17:08:14 +0200 Subject: [PATCH 154/297] Added pytest configuration, the test suite should run now during CI --- .github/workflows/cpu-tests.yml | 2 +- pytest.ini | 4 ++++ test/lazy_loading_test.py | 2 ++ 3 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 pytest.ini diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index f99f3419c..40bc5f913 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -91,7 +91,7 @@ jobs: ../install/data_repo_link/link_data_repo.sh `pwd` - name: Test basic functions - run: python test/mala_tests.py + run: pytest --disable-warnings - name: Test workflow run: python examples/ex99_verify_all_examples.py diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 000000000..a034c86c6 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +addopts = --ignore=test/basic_gpu_test.py +testpaths = + test \ No newline at end of file diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index 14fd44675..50eb2de98 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -7,6 +7,7 @@ from mala.network.trainer import Trainer from data_repo_path import get_data_repo_path import time +import pytest # This test compares the data scaling using the regular scaling procedure and # the lazy-loading one (incremental fitting). @@ -162,6 +163,7 @@ def test_scaling(self): assert np.isclose(training_tester[0], training_tester[1], atol=accuracy_coarse) + @pytest.mark.skip(reason="Horovod is currently not part of the pipeline") def test_performance_horovod(self): #################### From c386fce2858338802c4046cb894245ac517ddd2b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 13 Jul 2021 17:49:58 +0200 Subject: [PATCH 155/297] Added new parameter --- mala/common/parameters.py | 20 ++++++++++++++++++++ mala/datahandling/data_handler.py | 4 ++-- mala/network/trainer.py | 19 ++++++++++++++----- 3 files changed, 36 insertions(+), 7 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index f68afb8ac..a774c193a 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -311,6 +311,24 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. + + during_training_metric : string + Metric for evaluated on the validation set during training. + Default is "LDOS", meaning that the regular loss on the LDOS will be + used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. + + after_training_metric : string + Metric for evaluated on the validation and test set AFTER training. + Default is "LDOS", meaning that the regular loss on the LDOS will be + used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. """ def __init__(self): @@ -336,6 +354,8 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" + self.during_training_metric = "LDOS" + self.after_training_metric = "LDOS" class ParametersHyperparameterOptimization(ParametersBase): diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 89aadfb68..ba2e9d2dc 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -4,7 +4,7 @@ from .data_scaler import DataScaler from .snapshot import Snapshot from .lazy_load_dataset import LazyLoadDataset -from mala.common.parameters import Parameters +from mala.common.parameters import Parameters, ParametersData from mala.targets.target_interface import TargetInterface from mala.descriptors.descriptor_interface import DescriptorInterface from mala.common.printout import printout @@ -42,7 +42,7 @@ class DataHandler: def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): - self.parameters = parameters.data + self.parameters: ParametersData = parameters.data self.dbg_grid_dimensions = parameters.debug.grid_dimensions self.use_horovod = parameters.use_horovod self.training_data_set = None diff --git a/mala/network/trainer.py b/mala/network/trainer.py index fdf176e31..292838b7b 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -165,8 +165,10 @@ def load_from_file(cls, params, file_path, network, data): def train_network(self): """Train a network using data given by a DataHandler.""" - # Create reference to data and network and setup training. - # Calculate initial loss. + ############################ + # CALCULATE INITIAL METRICS + ############################ + tloss = float("inf") vloss = self.__validate_network(self.network, self.validation_data_loader) @@ -190,13 +192,17 @@ def train_network(self): # Initialize all the counters. checkpoint_counter = 0 - # If we restarted from a checkpoint, we + # If we restarted from a checkpoint, we have to differently initialize + # the loss. if self.last_loss is None: vloss_old = vloss else: vloss_old = self.last_loss - # Perform and log training. + ############################ + # PERFORM TRAINING + ############################ + for epoch in range(self.last_epoch, self.parameters.max_number_epochs): start_time = time.time() @@ -273,7 +279,10 @@ def train_network(self): if self.parameters.verbosity: printout("Time for epoch[s]:", time.time() - start_time) - # Calculate final loss. + ############################ + # CALCULATE FINAL METRICS + ############################ + self.final_validation_loss = vloss tloss = float("inf") if self.data.test_data_set is not None: From 714423a24de099735b79d1575b87d472a96c9574 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 15 Jul 2021 14:45:44 +0200 Subject: [PATCH 156/297] Add _Edit on GitHub_ button to Sphinx docs --- docs/source/conf.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 048287a83..950ed4161 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -93,6 +93,13 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = [] +html_context = { + 'display_github': True, + 'github_repo': 'mala-project/mala', + 'github_version': 'develop', + 'conf_py_path': '/docs/source/', +} + # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = "./img/logos/mala_vertical.png" @@ -100,7 +107,6 @@ # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. -#html_favicon = "../resources/theme/favicon.ico" html_favicon = "./img/logos/mala_favicon.png" # The suffix of source file names. From 7b939668447dd494a296aa69308cf80fe22aac3a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 15 Jul 2021 22:47:19 +0200 Subject: [PATCH 157/297] Fixed ex00 and ex01 --- examples/ex00_verify_installation.py | 31 ----- examples/ex01_run_singleshot.py | 175 ++++++++++++--------------- test/installation_test.py | 28 +++++ test/workflow_test.py | 57 +++++++++ 4 files changed, 164 insertions(+), 127 deletions(-) delete mode 100644 examples/ex00_verify_installation.py create mode 100644 test/installation_test.py create mode 100644 test/workflow_test.py diff --git a/examples/ex00_verify_installation.py b/examples/ex00_verify_installation.py deleted file mode 100644 index bc0430260..000000000 --- a/examples/ex00_verify_installation.py +++ /dev/null @@ -1,31 +0,0 @@ -import mala -from mala import printout -import numpy as np - -""" -ex00_verify_installation.py: This example confirms whether or not your setup -is correct. -""" - -printout("Welcome to MALA.") -printout("Running ex00_verify_installation.py") - -test_parameters = mala.Parameters() -test_descriptors = mala.DescriptorInterface(test_parameters) -test_targets = mala.TargetInterface(test_parameters) -test_handler = mala.DataHandler(test_parameters, - descriptor_calculator=test_descriptors, - target_calculator=test_targets) -test_network = mala.Network(test_parameters) -test_hpoptimizer = mala.HyperOptInterface(test_parameters, test_handler) -data_path = None -try: - from data_repo_path import get_data_repo_path - data_path = get_data_repo_path() - test_array = np.load(data_path+"linking_tester.npy") - printout("Current data repository path: ", data_path) -except FileNotFoundError: - printout("It seems that you don't have a data repository set up properly. " - "You can still use MALA, but tests and examples WILL fail.") -printout("Successfully ran ex00_verify_installation.py.") -printout("Congratulations, your installation seems to work!") diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 13b77fe82..8c8f0602f 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -10,99 +10,82 @@ """ -def run_example01(desired_loss_improvement_factor=1): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - - #################### - # TRAINING - # Train the network. - #################### - - printout("Starting training.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: - return False - else: - return True - - -if __name__ == "__main__": - if run_example01(): - printout("Successfully ran ex01_run_singleshot.") - else: - raise Exception("Ran ex01_run_singleshot but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### + +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the used activation function. +test_parameters.network.layer_activations = ["ReLU"] + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 20 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" + +#################### +# DATA +# Add and prepare snapshots for training. +#################### + +data_handler = mala.DataHandler(test_parameters) + +# Add a snapshot we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# NETWORK SETUP +# Set up the network and trainer we want to use. +# The layer sizes can be specified before reading data, +# but it is safer this way. +#################### + +test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + +# Setup network and trainer. +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") + +#################### +# TRAINING +# Train the network. +#################### + +printout("Starting training.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() \ No newline at end of file diff --git a/test/installation_test.py b/test/installation_test.py new file mode 100644 index 000000000..23f5846bd --- /dev/null +++ b/test/installation_test.py @@ -0,0 +1,28 @@ +import mala +import numpy as np + + +class TestInstallation: + """Verifies the installation.""" + + def test_installation(self): + """Test the MALA installation.""" + test_parameters = mala.Parameters() + test_descriptors = mala.DescriptorInterface(test_parameters) + test_targets = mala.TargetInterface(test_parameters) + test_handler = mala.DataHandler(test_parameters, + descriptor_calculator=test_descriptors, + target_calculator=test_targets) + test_network = mala.Network(test_parameters) + test_hpoptimizer = mala.HyperOptInterface(test_parameters, + test_handler) + + # If this test fails, then it will throw an exception way before. + assert True + + def test_data_repo(self): + """Test whether the data repo is set up properly""" + from data_repo_path import get_data_repo_path + data_path = get_data_repo_path() + test_array = np.load(data_path + "linking_tester.npy") + assert (test_array == [1, 2, 3, 4]).all() \ No newline at end of file diff --git a/test/workflow_test.py b/test/workflow_test.py new file mode 100644 index 000000000..724e0335c --- /dev/null +++ b/test/workflow_test.py @@ -0,0 +1,57 @@ +import mala +from data_repo_path import get_data_repo_path +data_path = get_data_repo_path()+"Al36/" + +# Control how much the loss should be better after training compared to +# before. This value is fairly high, but we're training on absolutely +# minimal amounts of data. +desired_loss_improvement_factor = 1 + + +class TestFullWorkflow: + """Tests an entire MALA workflow.""" + + def test_network_training(self): + """Test whether MALA can train a NN.""" + + # Set up parameters. + + test_parameters = mala.Parameters() + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.network.layer_activations = ["ReLU"] + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + # Load data. + + data_handler = mala.DataHandler(test_parameters) + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + + # Buld and train a networ. + + test_parameters.network.layer_sizes = [ + data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, + data_handler) + test_trainer.train_network() + + assert desired_loss_improvement_factor * \ + test_trainer.initial_test_loss > test_trainer.final_test_loss From bdf22b974da3343f9c19f374a548a445b5955b50 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 16 Jul 2021 10:17:19 +0200 Subject: [PATCH 158/297] Rewrote examples into tests --- examples/ex02_preprocess_data.py | 123 ++++----- examples/ex03_postprocess_data.py | 166 +++++------ examples/ex04_hyperparameter_optimization.py | 219 +++++++-------- examples/ex05_training_with_postprocessing.py | 65 +---- ...06_advanced_hyperparameter_optimization.py | 37 +-- examples/ex09_distributed_hyperopt.py | 232 ++++++++-------- examples/ex99_verify_all_examples.py | 130 --------- test/checkpoint_training_tests.py | 2 +- test/hyperopt_test.py | 258 ++++++++++++++++++ test/workflow_test.py | 208 +++++++++++++- 10 files changed, 806 insertions(+), 634 deletions(-) delete mode 100644 examples/ex99_verify_all_examples.py create mode 100644 test/hyperopt_test.py diff --git a/examples/ex02_preprocess_data.py b/examples/ex02_preprocess_data.py index 46e33ee05..7e408fc07 100644 --- a/examples/ex02_preprocess_data.py +++ b/examples/ex02_preprocess_data.py @@ -14,77 +14,52 @@ """ -def run_example02(): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - - # Specify input data options, i.e. which descriptors are calculated - # with which parameters. These are the standard parameters for - # the calculation of SNAP descriptors. - test_parameters.descriptors.descriptor_type = "SNAP" - test_parameters.descriptors.twojmax = 10 - test_parameters.descriptors.rcutfac = 4.67637 - test_parameters.data.descriptors_contain_xyz = True - - # Specify output data options, i.e. how the LDOS is parsed. - # The Al system used as an example here actually has 250 energy levels. - # But for the convenience of the user, only 10 energy levels will be - # used for this example. - test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 10 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 - - #################### - # DATA - # Create a DataConverter, and add snapshots to it. - #################### - - data_converter = mala.DataConverter(test_parameters) - - # Take care to choose the "add_snapshot" function correct for - # the type of data you want to preprocess. - data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, - "cubes/tmp.pp*Al_ldos.cube", - data_path, output_units="1/Ry") - - # Convert all the snapshots and save them in the current directory. - data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") - - #################### - # RESULTS. - # Print the used parameters and check whether the preprocessed data - # has the desired dimensions. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - input_data = np.load("Al_snapshot0.in.npy") - input_data_shape = np.shape(input_data) - if input_data_shape[0] != 108 or input_data_shape[1] != 108 or \ - input_data_shape[2] != 100 or input_data_shape[3] != 94: - return False - - output_data = np.load("Al_snapshot0.out.npy") - output_data_shape = np.shape(output_data) - if output_data_shape[0] != 108 or output_data_shape[1] != 108 or \ - output_data_shape[2] != 100 or output_data_shape[3] != 10: - return False - - return True - - -if __name__ == "__main__": - if run_example02(): - printout("Successfully ran ex02_preprocess_data.") - else: - raise Exception("Ran ex02_preprocess_data but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### + +test_parameters = mala.Parameters() + +# Specify input data options, i.e. which descriptors are calculated +# with which parameters. These are the standard parameters for +# the calculation of SNAP descriptors. +test_parameters.descriptors.descriptor_type = "SNAP" +test_parameters.descriptors.twojmax = 10 +test_parameters.descriptors.rcutfac = 4.67637 +test_parameters.data.descriptors_contain_xyz = True + +# Specify output data options, i.e. how the LDOS is parsed. +# The Al system used as an example here actually has 250 energy levels. +# But for the convenience of the user, only 10 energy levels will be +# used for this example. +test_parameters.targets.target_type = "LDOS" +test_parameters.targets.ldos_gridsize = 10 +test_parameters.targets.ldos_gridspacing_ev = 0.1 +test_parameters.targets.ldos_gridoffset_ev = -10 + +#################### +# DATA +# Create a DataConverter, and add snapshots to it. +#################### + +data_converter = mala.DataConverter(test_parameters) + +# Take care to choose the "add_snapshot" function correct for +# the type of data you want to preprocess. +data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, + "cubes/tmp.pp*Al_ldos.cube", + data_path, output_units="1/Ry") + +# Convert all the snapshots and save them in the current directory. +data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") + +#################### +# RESULTS. +# Print the used parameters and check whether the preprocessed data +# has the desired dimensions. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() \ No newline at end of file diff --git a/examples/ex03_postprocess_data.py b/examples/ex03_postprocess_data.py index f117f4f5f..24f966565 100644 --- a/examples/ex03_postprocess_data.py +++ b/examples/ex03_postprocess_data.py @@ -13,98 +13,76 @@ Python module installed. """ +# If you don't have the total energy module installed, +# just set the following variable to False. +do_total_energy = True + +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### +test_parameters = mala.Parameters() + +# Specify the correct LDOS parameters. +test_parameters.targets.target_type = "LDOS" +test_parameters.targets.ldos_gridsize = 250 +test_parameters.targets.ldos_gridspacing_ev = 0.1 +test_parameters.targets.ldos_gridoffset_ev = -10 + +#################### +# TARGETS +# Create a target calculator to postprocess data. +# Use this calculator to perform various operations. +#################### + +ldos = mala.TargetInterface(test_parameters) + +# Read additional information about the calculation. +# By doing this, the calculator is able to know e.g. the temperature +# at which the calculation took place or the lattice constant used. +ldos.read_additional_calculation_data("qe.out", + data_path+"Al.pw.scf.out") + +# Read in LDOS data. For actual workflows, this part will come +# from a network. +ldos_data = np.load(data_path+"Al_ldos.npy") + +# Get quantities of interest. +# For better values in the post processing, it is recommended to +# calculate the "self-consistent Fermi energy", i.e. the Fermi energy +# at which the (L)DOS reproduces the exact number of electrons. +# This Fermi energy usually differs from the one outputted by the +# QuantumEspresso calculation, due to numerical reasons. The difference +# is usually very small. +self_consistent_fermi_energy = ldos.\ + get_self_consistent_fermi_energy_ev(ldos_data) +number_of_electrons = ldos.\ + get_number_of_electrons(ldos_data, fermi_energy_eV= + self_consistent_fermi_energy) +band_energy = ldos.get_band_energy(ldos_data, + fermi_energy_eV= + self_consistent_fermi_energy) +total_energy = 0.0 +if do_total_energy: + # To perform a total energy calculation one also needs to provide + # a pseudopotential(path). + ldos.set_pseudopotential_path(data_path) + total_energy = ldos.get_total_energy(ldos_data, + fermi_energy_eV= + self_consistent_fermi_energy) + +#################### +# RESULTS. +# Print the used parameters and check whether LDOS based results +# are consistent with the actual DFT results. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() + +print("Number of electrons:", number_of_electrons) +print("Band energy:", band_energy) +if do_total_energy: + print("Total energy:", total_energy) -def run_example03(do_total_energy=True, accuracy_electrons=1e-11, - accuracy_total_energy=50, accuracy_band_energy=2): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - test_parameters = mala.Parameters() - - # Specify the correct LDOS parameters. - test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 250 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 - - #################### - # TARGETS - # Create a target calculator to postprocess data. - # Use this calculator to perform various operations. - #################### - - ldos = mala.TargetInterface(test_parameters) - - # Read additional information about the calculation. - # By doing this, the calculator is able to know e.g. the temperature - # at which the calculation took place or the lattice constant used. - ldos.read_additional_calculation_data("qe.out", - data_path+"Al.pw.scf.out") - - # Read in LDOS data. For actual workflows, this part will come - # from a network. - ldos_data = np.load(data_path+"Al_ldos.npy") - - # Get quantities of interest. - # For better values in the post processing, it is recommended to - # calculate the "self-consistent Fermi energy", i.e. the Fermi energy - # at which the (L)DOS reproduces the exact number of electrons. - # This Fermi energy usually differs from the one outputted by the - # QuantumEspresso calculation, due to numerical reasons. The difference - # is usually very small. - self_consistent_fermi_energy = ldos.\ - get_self_consistent_fermi_energy_ev(ldos_data) - number_of_electrons = ldos.\ - get_number_of_electrons(ldos_data, fermi_energy_eV= - self_consistent_fermi_energy) - band_energy = ldos.get_band_energy(ldos_data, - fermi_energy_eV= - self_consistent_fermi_energy) - total_energy = 0.0 - if do_total_energy: - # To perform a total energy calculation one also needs to provide - # a pseudopotential(path). - ldos.set_pseudopotential_path(data_path) - total_energy = ldos.get_total_energy(ldos_data, - fermi_energy_eV= - self_consistent_fermi_energy) - - #################### - # RESULTS. - # Print the used parameters and check whether LDOS based results - # are consistent with the actual DFT results. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - print("Number of electrons:", number_of_electrons) - print("Band energy:", band_energy) - if do_total_energy: - print("Total energy:", total_energy) - - if np.abs(number_of_electrons - ldos.number_of_electrons) > \ - accuracy_electrons: - return False - - if np.abs(band_energy - ldos.band_energy_dft_calculation) > \ - accuracy_band_energy: - return True - - if do_total_energy: - if np.abs(total_energy - ldos.total_energy_dft_calculation) > \ - accuracy_total_energy: - return False - return True - - -if __name__ == "__main__": - if run_example03(): - printout("Successfully ran ex03_postprocess_data.") - else: - raise Exception("Ran ex03_postprocess_data but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 8d678fbd4..131c1e161 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -11,124 +11,101 @@ """ -def run_example04(desired_loss_improvement_factor=2): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - - # Specify the number of trials, the hyperparameter optimizer should run - # and the type of hyperparameter. - test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "optuna" - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - data_handler = mala.DataHandler(test_parameters) - - # Add all the snapshots we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # HYPERPARAMETER OPTIMIZATION - # In order to perform a hyperparameter optimization, - # one has to simply create a hyperparameter optimizer - # and let it perform a "study". - # Before such a study can be done, one has to add all the parameters - # of interest. - #################### - - test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) - - # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) - - # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) - - # Choices for activation function at each layer will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) - - # Perform hyperparameter optimization. - printout("Starting Hyperparameter optimization.") - test_hp_optimizer.perform_study() - test_hp_optimizer.set_optimal_parameters() - printout("Hyperparameter optimization: DONE.") - - #################### - # TRAINING - # Train with these new parameters. - #################### - - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - # To see if the hyperparameter optimization actually worked, - # check if the best trial is better then the worst trial - # by a certain factor. - performed_trials_values = test_hp_optimizer.study.\ - trials_dataframe()["value"] - if desired_loss_improvement_factor*min(performed_trials_values) > \ - max(performed_trials_values): - return False - else: - return True - - -if __name__ == "__main__": - if run_example04(): - printout("Successfully ran ex04_hyperparameter_optimization.py.") - else: - raise Exception("Ran ex04_hyperparameter_optimization but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") - +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 20 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" + +# Specify the number of trials, the hyperparameter optimizer should run +# and the type of hyperparameter. +test_parameters.hyperparameters.n_trials = 20 +test_parameters.hyperparameters.hyper_opt_method = "optuna" + +#################### +# DATA +# Add and prepare snapshots for training. +#################### +data_handler = mala.DataHandler(test_parameters) + +# Add all the snapshots we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# HYPERPARAMETER OPTIMIZATION +# In order to perform a hyperparameter optimization, +# one has to simply create a hyperparameter optimizer +# and let it perform a "study". +# Before such a study can be done, one has to add all the parameters +# of interest. +#################### + +test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) + +# Learning rate will be optimized. +test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + +# Number of neurons per layer will be optimized. +test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) +test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + +# Choices for activation function at each layer will be optimized. +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid"]) +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", + choices=["ReLU", "Sigmoid"]) +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + +# Perform hyperparameter optimization. +printout("Starting Hyperparameter optimization.") +test_hp_optimizer.perform_study() +test_hp_optimizer.set_optimal_parameters() +printout("Hyperparameter optimization: DONE.") + +#################### +# TRAINING +# Train with these new parameters. +#################### + +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() \ No newline at end of file diff --git a/examples/ex05_training_with_postprocessing.py b/examples/ex05_training_with_postprocessing.py index d349e27cb..10af24268 100644 --- a/examples/ex05_training_with_postprocessing.py +++ b/examples/ex05_training_with_postprocessing.py @@ -20,7 +20,7 @@ # Uses a trained network to make a prediction. def use_trained_network(network_path, params_path, input_scaler_path, - output_scaler_path, accuracy=0.05): + output_scaler_path): # First load Parameters and network. # Parameters may have to @@ -72,8 +72,6 @@ def use_trained_network(network_path, params_path, input_scaler_path, band_energy_actual = ldos_calculator.get_band_energy(actual_ldos) printout("Band energy (actual, predicted, error)[eV]", band_energy_actual, band_energy_predicted, band_energy_predicted-band_energy_actual) - if np.abs(band_energy_predicted-band_energy_actual) > accuracy: - return False # Calculate the number of electrons. nr_electrons_predicted = ldos_calculator.\ @@ -82,9 +80,6 @@ def use_trained_network(network_path, params_path, input_scaler_path, printout("Number of electrons (actual, predicted, error)[eV]", nr_electrons_actual, nr_electrons_predicted, nr_electrons_predicted-nr_electrons_actual) - if np.abs(band_energy_predicted-band_energy_actual) > accuracy: - return False - return True # Trains a network. @@ -173,55 +168,13 @@ def initial_training(network_path, params_path, input_scaler_path, data_handler.input_data_scaler.save(input_scaler_path) data_handler.output_data_scaler.save(output_scaler_path) - #################### - # RESULTS. - # Check whether the loss decreased enough. - #################### - if desired_loss_improvement_factor*test_trainer.initial_test_loss \ - < test_trainer.final_test_loss: - return False - else: - return True - - -def run_example05(dotraining, doinference): - printout("Welcome to MALA.") - printout("Running ex05_training_with_postprocessing.py") - - # Choose the paths where the network and the parameters for it should - # be saved. - if dotraining is False: - params_path = param_path+"ex05_params.pkl" - network_path = param_path+"ex05_network.pth" - input_scaler_path = param_path+"ex05_iscaler.pkl" - output_scaler_path = param_path+"ex05_oscaler.pkl" - else: - params_path = "./ex05_params.pkl" - network_path = "./ex05_network.pth" - input_scaler_path = "./ex05_iscaler.pkl" - output_scaler_path = "./ex05_oscaler.pkl" - - training_return = True - inference_return = True - if dotraining: - training_return = initial_training(network_path, params_path, - input_scaler_path, - output_scaler_path) - if doinference: - inference_return = use_trained_network(network_path, params_path, - input_scaler_path, - output_scaler_path) - - return training_return and inference_return - - -if __name__ == "__main__": - if run_example05(True, True): - printout("Successfully ran ex05_training_with_postprocessing.py.") - else: - raise Exception("Ran ex05_training_with_postprocessing but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with your " - "installation.") +params_path = "./ex05_params.pkl" +network_path = "./ex05_network.pth" +input_scaler_path = "./ex05_iscaler.pkl" +output_scaler_path = "./ex05_oscaler.pkl" +initial_training(network_path, params_path, input_scaler_path, + output_scaler_path) +use_trained_network(network_path, params_path, input_scaler_path, + output_scaler_path) diff --git a/examples/ex06_advanced_hyperparameter_optimization.py b/examples/ex06_advanced_hyperparameter_optimization.py index 8917be5f8..d5b5383d8 100644 --- a/examples/ex06_advanced_hyperparameter_optimization.py +++ b/examples/ex06_advanced_hyperparameter_optimization.py @@ -142,30 +142,13 @@ def optimize_hyperparameters(hyper_optimizer, input_creator_notraining="oat", return test_trainer.final_test_loss -def run_example06(desired_std=0.1): - results = [] - result, last_study = optimize_hyperparameters("optuna") - results.append(result) - results.append(optimize_hyperparameters("oat")) - results.append(optimize_hyperparameters("notraining", - input_creator_notraining="oat")) - results.append(optimize_hyperparameters("notraining", - input_creator_notraining="optuna", - last_optuna_study=last_study)) - if np.std(results) < desired_std: - return True - else: - printout(results) - printout(np.std(results)) - return False - - -if __name__ == "__main__": - if run_example06(): - printout("Successfully ran ex06_advanced_hyperparameter_optimization." - "py.") - else: - raise Exception("Ran ex06_advanced_hyperparameter_optimization but " - "something was off. If you haven't changed any " - "parameters in the example, there might be a problem " - "with your installation.") +results = [] +result, last_study = optimize_hyperparameters("optuna") +results.append(result) +results.append(optimize_hyperparameters("oat")) +results.append(optimize_hyperparameters("notraining", + input_creator_notraining="oat")) +results.append(optimize_hyperparameters("notraining", + input_creator_notraining="optuna", + last_optuna_study=last_study)) +print(results) diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index b0f036df6..be645fbb0 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -2,6 +2,7 @@ import mala from mala import printout from data_repo_path import get_data_repo_path +import os data_path = get_data_repo_path()+"Al36/" """ @@ -10,131 +11,112 @@ using a database server system, such as PostgreSQL or MySQL. For this easy example, sqlite will be used. It is highly advisory not to to use this for actual, at-scale calculations! -Please delete ex09.db prior to execution. Afterwards execute this script -in multiple terminals / nodes. """ -def run_example09(desired_loss_improvement_factor=2): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 5 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - - # Specify the number of trials, the hyperparameter optimizer should run - # and the type of hyperparameter. - test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "optuna" - test_parameters.hyperparameters.study_name = "ex09" - test_parameters.hyperparameters.rdb_storage = 'sqlite:///ex09.db' - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - data_handler = mala.DataHandler(test_parameters) - - # Add all the snapshots we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # HYPERPARAMETER OPTIMIZATION - # In order to perform a hyperparameter optimization, - # one has to simply create a hyperparameter optimizer - # and let it perform a "study". - # Before such a study can be done, one has to add all the parameters - # of interest. - #################### - - test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) - - # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) - - # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) - - # Choices for activation function at each layer will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) - - # Perform hyperparameter optimization. - printout("Starting Hyperparameter optimization.") - test_hp_optimizer.perform_study() - test_hp_optimizer.set_optimal_parameters() - printout("Hyperparameter optimization: DONE.") - - #################### - # TRAINING - # Train with these new parameters. - #################### - - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - # To see if the hyperparameter optimization actually worked, - # check if the best trial is better then the worst trial - # by a certain factor. - performed_trials_values = test_hp_optimizer.study.\ - trials_dataframe()["value"] - if desired_loss_improvement_factor*min(performed_trials_values) > \ - max(performed_trials_values): - return False - else: - return True - - -if __name__ == "__main__": - if run_example09(): - printout("Successfully ran ex09_distributed_hyperopt.py.") - else: - raise Exception("Ran ex09_distributed_hyperopt but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") - +# Remove the previous data base. +try: + os.remove("ex09.db") +except FileNotFoundError: + pass + +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 5 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" + +# Specify the number of trials, the hyperparameter optimizer should run +# and the type of hyperparameter. +test_parameters.hyperparameters.n_trials = 20 +test_parameters.hyperparameters.hyper_opt_method = "optuna" +test_parameters.hyperparameters.study_name = "ex09" +test_parameters.hyperparameters.rdb_storage = 'sqlite:///ex09.db' + +#################### +# DATA +# Add and prepare snapshots for training. +#################### +data_handler = mala.DataHandler(test_parameters) + +# Add all the snapshots we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# HYPERPARAMETER OPTIMIZATION +# In order to perform a hyperparameter optimization, +# one has to simply create a hyperparameter optimizer +# and let it perform a "study". +# Before such a study can be done, one has to add all the parameters +# of interest. +#################### + +test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) + +# Learning rate will be optimized. +test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + +# Number of neurons per layer will be optimized. +test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) +test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + +# Choices for activation function at each layer will be optimized. +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid"]) +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", + choices=["ReLU", "Sigmoid"]) +test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + +# Perform hyperparameter optimization. +printout("Starting Hyperparameter optimization.") +test_hp_optimizer.perform_study() +test_hp_optimizer.set_optimal_parameters() +printout("Hyperparameter optimization: DONE.") + +#################### +# TRAINING +# Train with these new parameters. +#################### + +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() diff --git a/examples/ex99_verify_all_examples.py b/examples/ex99_verify_all_examples.py deleted file mode 100644 index 8513b54b1..000000000 --- a/examples/ex99_verify_all_examples.py +++ /dev/null @@ -1,130 +0,0 @@ -from mala.common.parameters import printout -from ex01_run_singleshot import run_example01 -from ex02_preprocess_data import run_example02 -from ex03_postprocess_data import run_example03 -from ex04_hyperparameter_optimization import run_example04 -from ex05_training_with_postprocessing import run_example05 -from ex06_advanced_hyperparameter_optimization import run_example06 -from ex07_checkpoint_training import run_example07 -from ex08_checkpoint_hyperopt import run_example08 -from ex09_distributed_hyperopt import run_example09 -import os -""" -ex99_verify_all_examples.py: This example confirms whether or not the examples -run CORRECTLY. That is, even though they might run, they may not run correctly, -e.g. a network will train but with abysmal performance. -""" -printout("Welcome to MALA.") -printout("Running ex99_verify_all_examples.py") - -# Example 1: Perform a training. -if run_example01(): - printout("Successfully ran ex01_run_singleshot.") -else: - raise Exception("Ran ex01_run_singleshot but something was off. If you " - "haven't changed any parameters in " - "the example, there might be a problem with your " - "installation.") - -# Example 2: Preprocess data. -try: - if run_example02(): - printout("Successfully ran ex02_preprocess_data.") -except ModuleNotFoundError: - printout("Could not run ex02_preprocess_data, most likely because of" - "missing LAMMPS installation.") -except FileNotFoundError: - printout("Could not run ex02_preprocess_data, most likely because of" - "missing cube files.") - -# Example 3: Postprocess data. Run it twice, once with Quantum Espresso. -# if run_example03(do_total_energy=False): -# printout("Successfully ran ex03_postprocess_data.") -# else: -# raise Exception("Ran ex03_postprocess_data but something was off." -# " If you haven't changed any parameters in " -# "the example, there might be a problem with your" -# " installation.") -try: - if run_example03(do_total_energy=True): - printout("Successfully ran ex03_postprocess_data.") -except ModuleNotFoundError: - printout("Could not run ex03_postprocess_data, most likely because of " - "missing QE installation.") -except FileNotFoundError: - printout("Could not run ex03_postprocess_data, most likely because of " - "missing LDOS data.") - - -# Example 4: Perform Hyperparameter optimization. -if run_example04(): - printout("Successfully ran ex04_hyperparameter_optimization.py.") -else: - raise Exception("Ran ex04_hyperparameter_optimization but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") - -# Example 5: Train a network, do a prediction and process this prediction. -if run_example05(True, True): - printout("Successfully ran ex05_training_with_postprocessing.py.") -else: - raise Exception("Ran ex05_training_with_postprocessing but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with your " - "installation.") - -# Example 6: Novel hyperparameter optimization techniques. -if run_example06(): - printout("Successfully ran ex06_advanced_hyperparameter_optimization.py.") -else: - raise Exception( - "Ran ex10_advanced_hyperparameter_optimization but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your installation.") - -# Example 7: Checkpointing/resuming training. -checkpoint_files = ["ex07_iscaler.pkl", "ex07_oscaler.pkl", "ex07_params.pkl", - "ex07_network.pth", "ex07_optimizer.pth"] -for checkpoint_file in checkpoint_files: - if os.path.exists(checkpoint_file): - os.remove(checkpoint_file) - -for i in range(0, 2): - if run_example07(): - printout("Successfully ran ex07_checkpoint_training ("+str(i+1)+"/2).") - else: - raise Exception("Ran ex07_checkpoint_training but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") - -# Example 8: Checkpointing/resuming hyperparameter optimization. -checkpoint_files = ["ex08_iscaler.pkl", "ex08_oscaler.pkl", "ex08_params.pkl", - "ex08_hyperopt.pth"] -for checkpoint_file in checkpoint_files: - if os.path.exists(checkpoint_file): - os.remove(checkpoint_file) - -for i in range(0, 2): - if run_example08(): - printout("Successfully ran ex08_checkpoint_hyperopt.") - else: - raise Exception("Ran ex08_checkpoint_hyperopt but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") - - -# Example 8: Distritbuted hyperparameter optimitzation. -if os.path.exists("ex09.db"): - os.remove("ex09.db") - -if run_example09(): - printout("Successfully ran ex09_distributed_hyperopt.") -else: - raise Exception("Ran ex09_distributed_hyperopt but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") - diff --git a/test/checkpoint_training_tests.py b/test/checkpoint_training_tests.py index 22a93ae2d..186916031 100644 --- a/test/checkpoint_training_tests.py +++ b/test/checkpoint_training_tests.py @@ -9,7 +9,7 @@ accuracy = 1e-14 -class TestHyperoptTraining: +class TestTrainingCheckpoint: """Tests the checkpointing capabilities of the NN training.""" def test_general(self): diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py new file mode 100644 index 000000000..656e134a2 --- /dev/null +++ b/test/hyperopt_test.py @@ -0,0 +1,258 @@ +import mala +from data_repo_path import get_data_repo_path +import numpy as np +import os +data_path = get_data_repo_path()+"Al36/" + +# Control how much the loss should be better after hyperopt compared to +# before. This value is fairly high, but we're training on absolutely +# minimal amounts of data. +desired_loss_improvement_factor = 2 + +# Different HO methods will lead to different results, but they should be +# approximately the same. +desired_std_ho = 0.1 + + +class TestHyperparameterOptimization: + """Tests the basic hyperparameter optimization routines.""" + + def test_hyperopt(self): + """Test a regular, optuna based hyperparameter optimization.""" + + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.hyperparameters.n_trials = 20 + test_parameters.hyperparameters.hyper_opt_method = "optuna" + + # Load data. + data_handler = mala.DataHandler(test_parameters) + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + + # Perform the hyperparameter optimization. + test_hp_optimizer = mala.HyperOptInterface(test_parameters, + data_handler) + test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, + 100) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, + 100) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.perform_study() + test_hp_optimizer.set_optimal_parameters() + + # + # To see if the hyperparameter optimization actually worked, + # check if the best trial is better then the worst trial + # by a certain factor. + performed_trials_values = test_hp_optimizer.study. \ + trials_dataframe()["value"] + assert desired_loss_improvement_factor * \ + min(performed_trials_values) < \ + max(performed_trials_values) + + def test_different_ho_methods(self): + results = [] + result, last_study = self.__optimize_hyperparameters("optuna") + results.append(result) + results.append(self.__optimize_hyperparameters("oat")) + results.append(self. + __optimize_hyperparameters("notraining", + input_creator_notraining= + "oat")) + results.append(self. + __optimize_hyperparameters("notraining", + input_creator_notraining= + "optuna", + last_optuna_study= + last_study)) + assert np.std(results) < desired_std_ho + + def test_distributed_hyperopt(self): + # Remove the previous data base. + try: + os.remove("test_ho.db") + except FileNotFoundError: + pass + + # Set up parameters + test_parameters = mala.Parameters() + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.running.max_number_epochs = 5 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.hyperparameters.n_trials = 20 + test_parameters.hyperparameters.hyper_opt_method = "optuna" + test_parameters.hyperparameters.study_name = "test_ho" + test_parameters.hyperparameters.rdb_storage = 'sqlite:///test_ho.db' + + # Load data + data_handler = mala.DataHandler(test_parameters) + + # Add all the snapshots we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + + # Create and perform hyperparameter optimization. + test_hp_optimizer = mala.HyperOptInterface(test_parameters, + data_handler) + test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, + 100) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, + 100) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.perform_study() + test_hp_optimizer.set_optimal_parameters() + performed_trials_values = test_hp_optimizer.study. \ + trials_dataframe()["value"] + assert desired_loss_improvement_factor * \ + min(performed_trials_values) < \ + max(performed_trials_values) + + @staticmethod + def __optimize_hyperparameters(hyper_optimizer, + input_creator_notraining="oat", + last_optuna_study=None): + """Perform a hyperparameter optimization with the specified method.""" + + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.hyperparameters.n_trials = 8 + test_parameters.hyperparameters.hyper_opt_method = hyper_optimizer + + # Load data. + data_handler = mala.DataHandler(test_parameters) + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + + # Perform the actual hyperparameter optimization. + test_hp_optimizer = mala.HyperOptInterface(test_parameters, + data_handler) + test_parameters.network.layer_sizes = [ + data_handler.get_input_dimension(), + 100, 100, + data_handler.get_output_dimension()] + + # Add hyperparameters we want to have optimized to the list. + # If we do a notraining run currently we need to create an input array + # using one of the other two possible hyperparameter optimizers. + tmp_hp_optimizer = None + if hyper_optimizer == "oat" or hyper_optimizer == "optuna": + test_hp_optimizer.add_hyperparameter("categorical", "trainingtype", + choices=["Adam", "SGD"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + elif hyper_optimizer == "notraining": + tmp_parameters = test_parameters + if input_creator_notraining == "optuna" and last_optuna_study is None: + input_creator_notraining = "oat" + tmp_parameters.hyperparameters.hyper_opt_method = \ + input_creator_notraining + tmp_hp_optimizer = mala.HyperOptInterface(tmp_parameters, + data_handler) + tmp_hp_optimizer.add_hyperparameter("categorical", "trainingtype", + choices=["Adam", "SGD"]) + tmp_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_00", + choices=["ReLU", "Sigmoid"]) + tmp_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + tmp_hp_optimizer.add_hyperparameter("categorical", + "layer_activation_02", + choices=["ReLU", "Sigmoid"]) + + # Perform hyperparameter optimization. + if hyper_optimizer == "oat" or hyper_optimizer == "optuna": + test_hp_optimizer.perform_study() + if hyper_optimizer == "optuna": + last_optuna_study = test_hp_optimizer.get_trials_from_study() + elif hyper_optimizer == "notraining": + if input_creator_notraining == "optuna": + test_hp_optimizer.perform_study(trial_list=last_optuna_study) + else: + test_hp_optimizer.perform_study(trial_list= + tmp_hp_optimizer.orthogonal_arr) + test_hp_optimizer.set_optimal_parameters() + + # Train the final network. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, + data_handler) + test_trainer.train_network() + test_parameters.show() + if hyper_optimizer == "optuna": + return test_trainer.final_test_loss, last_optuna_study + else: + return test_trainer.final_test_loss diff --git a/test/workflow_test.py b/test/workflow_test.py index 724e0335c..91debeb64 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -1,5 +1,6 @@ import mala from data_repo_path import get_data_repo_path +import numpy as np data_path = get_data_repo_path()+"Al36/" # Control how much the loss should be better after training compared to @@ -7,6 +8,12 @@ # minimal amounts of data. desired_loss_improvement_factor = 1 +# Control the accuracies for the postprocessing routines. +accuracy_electrons = 1e-11 +accuracy_total_energy = 1 +accuracy_band_energy = 1 +accuracy_predictions = 5e-2 + class TestFullWorkflow: """Tests an entire MALA workflow.""" @@ -14,21 +21,146 @@ class TestFullWorkflow: def test_network_training(self): """Test whether MALA can train a NN.""" + test_trainer = self.__simple_training() + assert desired_loss_improvement_factor * \ + test_trainer.initial_test_loss > test_trainer.final_test_loss + + def test_preprocessing(self): + """ + Test whether MALA can preprocess data. + + This means reading the LDOS from cube files and calculating + SNAP descriptors. + The data necessary for this is currently not in the data repo! + """ + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.descriptors.descriptor_type = "SNAP" + test_parameters.descriptors.twojmax = 10 + test_parameters.descriptors.rcutfac = 4.67637 + test_parameters.data.descriptors_contain_xyz = True + test_parameters.targets.target_type = "LDOS" + test_parameters.targets.ldos_gridsize = 10 + test_parameters.targets.ldos_gridspacing_ev = 0.1 + test_parameters.targets.ldos_gridoffset_ev = -10 + + # Create a DataConverter, and add snapshots to it. + data_converter = mala.DataConverter(test_parameters) + data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, + "cubes/tmp.pp*Al_ldos.cube", + data_path, output_units="1/Ry") + + data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") + + # Compare against + input_data = np.load("Al_snapshot0.in.npy") + input_data_shape = np.shape(input_data) + assert input_data_shape[0] == 108 and input_data_shape[1] == 108 and \ + input_data_shape[2] == 100 and input_data_shape[3] == 94 + + output_data = np.load("Al_snapshot0.out.npy") + output_data_shape = np.shape(output_data) + assert output_data_shape[0] == 108 and output_data_shape[1] == 108 and\ + output_data_shape[2] == 100 and output_data_shape[3] == 10 + def test_postprocessing(self): + """ + Test whether MALA can postprocess data. + + This means calculating band energy and number of electrons from + LDOS. Total energy is tested further below. + """ + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.targets.target_type = "LDOS" + test_parameters.targets.ldos_gridsize = 250 + test_parameters.targets.ldos_gridspacing_ev = 0.1 + test_parameters.targets.ldos_gridoffset_ev = -10 + + # Create a target calculator to perform postprocessing. + ldos = mala.TargetInterface(test_parameters) + ldos.read_additional_calculation_data("qe.out", + data_path + "Al.pw.scf.out") + ldos_data = np.load(data_path + "Al_ldos.npy") + + # Calculate energies + self_consistent_fermi_energy = ldos. \ + get_self_consistent_fermi_energy_ev(ldos_data) + number_of_electrons = ldos. \ + get_number_of_electrons(ldos_data, fermi_energy_eV= + self_consistent_fermi_energy) + band_energy = ldos.get_band_energy(ldos_data, + fermi_energy_eV= + self_consistent_fermi_energy) + + assert np.isclose(number_of_electrons, ldos.number_of_electrons, + atol=accuracy_electrons) + assert np.isclose(band_energy, ldos.band_energy_dft_calculation, + atol=accuracy_band_energy) + + def test_total_energy_from_ldos(self): + """ + Test whether MALA can calculate the total energy using the LDOS. + + This means calculating energies from the LDOS. + """ + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.targets.target_type = "LDOS" + test_parameters.targets.ldos_gridsize = 250 + test_parameters.targets.ldos_gridspacing_ev = 0.1 + test_parameters.targets.ldos_gridoffset_ev = -10 + + # Create a target calculator to perform postprocessing. + ldos = mala.TargetInterface(test_parameters) + ldos.read_additional_calculation_data("qe.out", + data_path + "Al.pw.scf.out") + ldos_data = np.load(data_path + "Al_ldos.npy") + + # Calculate energies + self_consistent_fermi_energy = ldos. \ + get_self_consistent_fermi_energy_ev(ldos_data) + ldos.set_pseudopotential_path(data_path) + total_energy = ldos.get_total_energy(ldos_data, + fermi_energy_eV= + self_consistent_fermi_energy) + + assert np.isclose(total_energy, ldos.total_energy_dft_calculation, + atol=accuracy_total_energy) + + def test_training_with_postprocessing(self): + """Test if a trained network can be loaded for postprocessing.""" + self.__simple_training(save_network=True) + self.__use_trained_network() + + def test_training_with_postprocessing_data_repo(self): + """ + Test if a trained network can be loaded for postprocessing. + + For this test, a network from the data repo will be loaded. + If this does not work, it's most likely because someting in the MALA + parameters changed. + """ + self.__simple_training(save_network=True) + self.__use_trained_network(get_data_repo_path()+"workflow_test/") + + @staticmethod + def __simple_training(save_network=False): + """Perform a simple training and save it, if necessary.""" + # Set up parameters. test_parameters = mala.Parameters() test_parameters.data.data_splitting_type = "by_snapshot" test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] test_parameters.data.input_rescaling_type = "feature-wise-standard" test_parameters.data.output_rescaling_type = "normal" test_parameters.network.layer_activations = ["ReLU"] - test_parameters.running.max_number_epochs = 20 + test_parameters.running.max_number_epochs = 400 test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" # Load data. - data_handler = mala.DataHandler(test_parameters) data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, "Al_debug_2k_nr0.out.npy", data_path, @@ -41,17 +173,81 @@ def test_network_training(self): output_units="1/Ry") data_handler.prepare_data() - # Buld and train a networ. - + # Train a network. test_parameters.network.layer_sizes = [ data_handler.get_input_dimension(), 100, data_handler.get_output_dimension()] + # Setup network and trainer. test_network = mala.Network(test_parameters) test_trainer = mala.Trainer(test_parameters, test_network, data_handler) test_trainer.train_network() - assert desired_loss_improvement_factor * \ - test_trainer.initial_test_loss > test_trainer.final_test_loss + # Save, if necessary. + if save_network: + params_path = "workflow_test_params.pkl" + network_path = "workflow_test_network.pth" + input_scaler_path = "workflow_test_iscaler.pkl" + output_scaler_path = "workflow_test_oscaler.pkl" + test_parameters.save(params_path) + test_network.save_network(network_path) + data_handler.input_data_scaler.save(input_scaler_path) + data_handler.output_data_scaler.save(output_scaler_path) + + return test_trainer + + @staticmethod + def __use_trained_network(save_path="./"): + """Use a trained network to make a prediction.""" + + params_path = save_path+"workflow_test_params.pkl" + network_path = save_path+"workflow_test_network.pth" + input_scaler_path = save_path+"workflow_test_iscaler.pkl" + output_scaler_path = save_path+"workflow_test_oscaler.pkl" + + # Load parameters, network and data scalers. + new_parameters = mala.Parameters.load_from_file(params_path, + no_snapshots=True) + new_parameters.targets.target_type = "LDOS" + new_parameters.targets.ldos_gridsize = 250 + new_parameters.targets.ldos_gridspacing_ev = 0.1 + new_parameters.targets.ldos_gridoffset_ev = -10 + new_parameters.data.use_lazy_loading = True + new_network = mala.Network.load_from_file(new_parameters, network_path) + iscaler = mala.DataScaler.load_from_file(input_scaler_path) + oscaler = mala.DataScaler.load_from_file(output_scaler_path) + + # Load data. + inference_data_handler = mala.DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + new_parameters.data.data_splitting_snapshots = ["te"] + inference_data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", + data_path, + "Al_debug_2k_nr2.out.npy", + data_path, + output_units="1/Ry") + inference_data_handler.prepare_data(reparametrize_scaler=False) + + # Instantiate and use a Tester object. + tester = mala.Tester(new_parameters, new_network, + inference_data_handler) + actual_ldos, predicted_ldos = tester.test_snapshot(0) + ldos_calculator = inference_data_handler.target_calculator + ldos_calculator.read_additional_calculation_data("qe.out", + data_path + + "Al.pw.scf.out") + band_energy_predicted = ldos_calculator.get_band_energy(predicted_ldos) + band_energy_actual = ldos_calculator.get_band_energy(actual_ldos) + nr_electrons_predicted = ldos_calculator.\ + get_number_of_electrons(predicted_ldos) + nr_electrons_actual = ldos_calculator.\ + get_number_of_electrons(actual_ldos) + + # Check whether the prediction is accurate enough. + assert np.isclose(band_energy_predicted, band_energy_actual, + atol=accuracy_predictions) + assert np.isclose(nr_electrons_predicted, nr_electrons_actual, + atol=accuracy_predictions) From a5f02622a2111257814f08415aec95d96251ca07 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 16 Jul 2021 11:16:33 +0200 Subject: [PATCH 159/297] Skip the tests we cannot currently execute --- .gitignore | 3 +++ examples/ex05_training_with_postprocessing.py | 1 - test/workflow_test.py | 6 ++++-- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 2441d3284..e5ab8a5d7 100644 --- a/.gitignore +++ b/.gitignore @@ -161,3 +161,6 @@ cython_debug/ # JupyterNotebooks .ipynb_checkpoints */.ipynb_checkpoints/* + +# SQLite +*.db diff --git a/examples/ex05_training_with_postprocessing.py b/examples/ex05_training_with_postprocessing.py index 10af24268..b23db6b61 100644 --- a/examples/ex05_training_with_postprocessing.py +++ b/examples/ex05_training_with_postprocessing.py @@ -3,7 +3,6 @@ import numpy as np from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" -param_path = get_data_repo_path()+"example05_data/" """ ex05_training_with_postprocessing.py: Train a network, then use this network to predict the LDOS and then analyze the results of this prediction. This diff --git a/test/workflow_test.py b/test/workflow_test.py index 91debeb64..ce72555ed 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -1,6 +1,7 @@ import mala from data_repo_path import get_data_repo_path import numpy as np +import pytest data_path = get_data_repo_path()+"Al36/" # Control how much the loss should be better after training compared to @@ -25,6 +26,7 @@ def test_network_training(self): assert desired_loss_improvement_factor * \ test_trainer.initial_test_loss > test_trainer.final_test_loss + @pytest.mark.skip(reason="LAMMPS is currently not part of the pipeline.") def test_preprocessing(self): """ Test whether MALA can preprocess data. @@ -99,6 +101,7 @@ def test_postprocessing(self): assert np.isclose(band_energy, ldos.band_energy_dft_calculation, atol=accuracy_band_energy) + @pytest.mark.skip(reason="QE is currently not part of the pipeline.") def test_total_energy_from_ldos(self): """ Test whether MALA can calculate the total energy using the LDOS. @@ -125,7 +128,6 @@ def test_total_energy_from_ldos(self): total_energy = ldos.get_total_energy(ldos_data, fermi_energy_eV= self_consistent_fermi_energy) - assert np.isclose(total_energy, ldos.total_energy_dft_calculation, atol=accuracy_total_energy) @@ -250,4 +252,4 @@ def __use_trained_network(save_path="./"): assert np.isclose(band_energy_predicted, band_energy_actual, atol=accuracy_predictions) assert np.isclose(nr_electrons_predicted, nr_electrons_actual, - atol=accuracy_predictions) + atol=accuracy_predictions) \ No newline at end of file From 47637b094a524fb0dd1e733fb01735a5ada64177 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 16 Jul 2021 11:20:41 +0200 Subject: [PATCH 160/297] Reflect changes in data repo --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 40bc5f913..089cc47da 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -80,7 +80,7 @@ jobs: repository: mala-project/test-data token: ${{ secrets.ACCESS_TOKEN }} path: mala_data - ref: v0.2.0 + ref: v0.3.0 lfs: false - name: Data setup From c0a995c8515ce608b79024ce04ec25e003aa70e8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 16 Jul 2021 12:05:06 +0200 Subject: [PATCH 161/297] I think the ranges need to be in a descending order --- mala/network/hyper_opt_oat.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index a4f561e39..7f754292c 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -118,8 +118,7 @@ def indices(idx, val): return np.where( print(np.array(A).transpose()) # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) - self.importance = np.argsort([max(i)-min(i) for i in A]) - + self.importance = np.argsort([max(i)-min(i) for i in A])[::-1] printout("Order of Importance: ") printout( *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" > ") From 56dab9f118aed8a28707e7a43e9af7b2355c615f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 18 Jul 2021 17:06:07 +0200 Subject: [PATCH 162/297] Added tagging information to data repo linking --- docs/source/install/README.md | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 732390fd8..059b7f8be 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -148,10 +148,20 @@ $ pip install -r docs/requirements.txt ## Downloading and adding example data The examples and tests need additional data to run. The MALA team provides a data repository, that can be downloaded -from . After downloading it, the data repository -needs to be linked to the MALA repository by using -```sh -$ cd ~/path/to/this/git/root/directory -$ bash install/data_repo_link/link_data_repo.sh ~/path/to/data/repo -``` -Afterwards, all the examples and tests will run out-of-the.box. +from . Please be sure to check out the correct tag for the data repository, +since the data repository itself is subject to ongoing development as well. After downloading the correct revision of +the data repository, it needs to be linked with MALA. + +1. Download data repository and check out correct tag + + ```sh + $ git clone ~/path/to/data/repo ~/path/to/data/repo + $ cd ~/path/to/data/repo + $ git checkout v0.1.0 + ``` +2. Link MALA and data repository + + ```sh + $ cd ~/path/to/mala/root/directory + $ bash install/data_repo_link/link_data_repo.sh ~/path/to/data/repo + ``` \ No newline at end of file From 0ef935123b50baab54edb4ab7266072b55a9268c Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 15:27:01 +0200 Subject: [PATCH 163/297] First version of band energy during training --- mala/common/parameters.py | 6 +-- mala/network/runner.py | 87 ++++++++++++++++++++++++++++++++++++++- mala/network/tester.py | 84 +++++++------------------------------ mala/network/trainer.py | 64 ++++++++++++++++++++++------ 4 files changed, 155 insertions(+), 86 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index a774c193a..1e8f2a4da 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -314,7 +314,7 @@ class ParametersRunning(ParametersBase): during_training_metric : string Metric for evaluated on the validation set during training. - Default is "LDOS", meaning that the regular loss on the LDOS will be + Default is "ldos", meaning that the regular loss on the LDOS will be used as a metric. Possible options are "band_energy" and "total_energy". For these, the band resp. total energy of the validation snapshots will be calculated and compared to the provided @@ -354,8 +354,8 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" - self.during_training_metric = "LDOS" - self.after_training_metric = "LDOS" + self.during_training_metric = "ldos" + self.after_training_metric = "ldos" class ParametersHyperparameterOptimization(ParametersBase): diff --git a/mala/network/runner.py b/mala/network/runner.py index 5abb99f7f..cc44bdb5b 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -1,6 +1,8 @@ """Runner class for running networks.""" import torch from mala.common.parameters import printout +from mala import Parameters +import numpy as np try: import horovod.torch as hvd except ModuleNotFoundError: @@ -27,7 +29,7 @@ class Runner: """ def __init__(self, params, network, data): - self.parameters_full = params + self.parameters_full: Parameters = params self.parameters = params.running self.network = network self.data = data @@ -47,3 +49,86 @@ def __prepare_to_run(self): torch.cuda.get_device_name(hvd.local_rank())) # pin GPU to local rank torch.cuda.set_device(hvd.local_rank()) + + def _forward_entire_snapshot(self, snapshot_number, data_set, + number_of_batches_per_snapshot=0, + batch_size=0): + """ + Forward an entire snapshot through the network and get actual + and predicted outputs. + + Parameters + ---------- + snapshot_number : int + Snapshot for which the prediction is done. + + number_of_batches_per_snapshot : int + Number of batches that lie within a snapshot. + + batch_size : int + Batch size used for forward pass. + + Returns + ------- + actual_outputs : torch.Tensor + Actual outputs for snapshot. + + predicted_outputs : torch.Tensor + Precicted outputs for snapshot. + """ + if self.data.parameters.use_lazy_loading: + data_set.return_outputs_directly = True + actual_outputs = \ + (data_set + [snapshot_number * self.data. + grid_size:(snapshot_number + 1) * self.data.grid_size])[1] + else: + actual_outputs = \ + self.data.output_data_scaler.\ + inverse_transform( + (data_set[snapshot_number * + self.data.grid_size: + (snapshot_number + 1) * + self.data.grid_size])[1], + as_numpy=True) + + predicted_outputs = np.zeros((self.data.grid_size, + self.data.get_output_dimension())) + + offset = snapshot_number * self.data.grid_size + for i in range(0, number_of_batches_per_snapshot): + inputs, outputs = \ + data_set[offset+(i * batch_size):offset+((i + 1) * batch_size)] + if self.parameters_full.use_gpu: + inputs = inputs.to('cuda') + predicted_outputs[i * batch_size:(i + 1) * batch_size, :] = \ + self.data.output_data_scaler.\ + inverse_transform(self.network(inputs). + to('cpu'), as_numpy=True) + + # Restricting the actual quantities to physical meaningful values, + # i.e. restricting the (L)DOS to positive values. + predicted_outputs = self.data.target_calculator.\ + restrict_data(predicted_outputs) + + # It could be that other operations will be happening with the data + # set, so it's best to reset it. + if self.data.parameters.use_lazy_loading: + data_set.return_outputs_directly = False + + return actual_outputs, predicted_outputs + + @staticmethod + def _correct_batch_size_for_testing(datasize, batchsize): + """ + Get the correct batch size for testing. + + For testing snapshot the batch size needs to be such that + data_per_snapshot / batch_size will result in an integer division + without any residual value. + """ + new_batch_size = batchsize + if datasize % new_batch_size != 0: + while datasize % new_batch_size != 0: + new_batch_size += 1 + return new_batch_size diff --git a/mala/network/tester.py b/mala/network/tester.py index bb6c62abd..77bb7042c 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -53,80 +53,26 @@ def test_snapshot(self, snapshot_number): predicted_outputs : torch.Tensor Precicted outputs for snapshot. """ - self.data.prepare_for_testing() - if self.data.parameters.use_lazy_loading: - actual_outputs = \ - (self.data.test_data_set - [snapshot_number * self.data. - grid_size:(snapshot_number + 1) * self.data.grid_size])[1] - else: - actual_outputs = \ - self.data.output_data_scaler.\ - inverse_transform( - (self.data.test_data_set[snapshot_number * - self.data.grid_size: - (snapshot_number + 1) * - self.data.grid_size])[1], - as_numpy=True) - - predicted_outputs = np.zeros((self.data.grid_size, - self.data.get_output_dimension())) - - offset = snapshot_number * self.data.grid_size - for i in range(0, self.number_of_batches_per_snapshot): - inputs, outputs = \ - self.data.test_data_set[offset+(i * self.parameters. - mini_batch_size): - offset+((i + 1) * self.parameters. - mini_batch_size)] - if self.parameters_full.use_gpu: - inputs = inputs.to('cuda') - predicted_outputs[i * self.parameters. - mini_batch_size:(i + 1) * self.parameters. - mini_batch_size, :] = \ - self.data.output_data_scaler.\ - inverse_transform(self.network(inputs). - to('cpu'), as_numpy=True) - - # Restricting the actual quantities to physical meaningful values, - # i.e. restricting the (L)DOS to positive values. - predicted_outputs = self.data.target_calculator.\ - restrict_data(predicted_outputs) - return actual_outputs, predicted_outputs + # Forward through network. + return self.\ + _forward_entire_snapshot(snapshot_number, + self.data.test_data_set, + self.number_of_batches_per_snapshot, + self.parameters.mini_batch_size) def __prepare_to_test(self): """Prepare the tester class to for test run.""" - if self.parameters_full.use_horovod: - self.parameters.sampler["test_sampler"] = torch.utils.data.\ - distributed.DistributedSampler(self.data.test_data_set, - num_replicas=hvd.size(), - rank=hvd.rank()) - # We will use the DataLoader iterator to iterate over the test data. + # We will use the DataSet iterator to iterate over the test data. # But since we only want the data per snapshot, # we need to make sure the batch size is compatible with that. - self.__check_and_adjust_batch_size(self.data.grid_size) - self.test_data_loader = DataLoader(self.data.test_data_set, - batch_size=self.parameters. - mini_batch_size * 1, - sampler=self.parameters. - sampler["test_sampler"], - **self.parameters.kwargs, - shuffle=False) + optimal_batch_size = self.\ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters.mini_batch_size) + if optimal_batch_size != self.parameters.mini_batch_size: + printout("Had to readjust batch size from", + self.parameters.mini_batch_size, "to", + optimal_batch_size) + self.parameters.mini_batch_size = optimal_batch_size self.number_of_batches_per_snapshot = int(self.data.grid_size / self.parameters. mini_batch_size) - - def __check_and_adjust_batch_size(self, datasize): - """ - Check batch size and adjust it if necessary. - - For testing the batch size needs to be such that data_per_snapshot / - batch_size will result in an integer division without any residual - value. - """ - if datasize % self.parameters.mini_batch_size != 0: - old_batch_size = self.parameters.mini_batch_size - while datasize % self.parameters.mini_batch_size != 0: - self.parameters.mini_batch_size += 1 - printout("Had to readjust batch size from", old_batch_size, "to", - self.parameters.mini_batch_size) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 292838b7b..21dcfe8c4 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -171,7 +171,8 @@ def train_network(self): tloss = float("inf") vloss = self.__validate_network(self.network, - self.validation_data_loader) + self.validation_data_loader, + validation_type="band_energy") if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, self.test_data_loader) @@ -439,20 +440,57 @@ def __process_mini_batch(self, network, input_data, target_data): self.optimizer.zero_grad() return loss.item() - def __validate_network(self, network, vdl): + def __validate_network(self, network, vdl, validation_type="ldos"): """Validate a network, using test or validation data.""" network.eval() - validation_loss = [] - with torch.no_grad(): - for x, y in vdl: - if self.parameters_full.use_gpu: - x = x.to('cuda') - y = y.to('cuda') - prediction = network(x) - validation_loss.append(network.calculate_loss(prediction, y) - .item()) - - return np.mean(validation_loss) + if validation_type == "ldos": + validation_loss = [] + with torch.no_grad(): + for x, y in vdl: + if self.parameters_full.use_gpu: + x = x.to('cuda') + y = y.to('cuda') + prediction = network(x) + validation_loss.append(network.calculate_loss(prediction, y) + .item()) + + return np.mean(validation_loss) + elif validation_type == "band_energy": + + data_set = self.data.validation_data_set + number_snapshots = 2 + + # Get optimal batch size and number of batches per snapshots. + optimal_batch_size = self. \ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters. + mini_batch_size) + number_of_batches_per_snapshot = int(self.data.grid_size / + optimal_batch_size) + errors = [] + for snapshot_number in range(0, number_snapshots): + actual_outputs, \ + predicted_outputs = self.\ + _forward_entire_snapshot(snapshot_number, data_set, + number_of_batches_per_snapshot, + optimal_batch_size) + + calculator = self.data.target_calculator + # FIXME: This will eventually break down. + calculator.\ + read_additional_calculation_data("qe.out", + self.data. + get_snapshot_calculation_output(0)) + fe_actual = calculator.\ + get_self_consistent_fermi_energy_ev(actual_outputs) + be_actual = calculator.\ + get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + be_predicted = calculator.\ + get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) + return np.mean(errors) def __create_training_checkpoint(self): """ From e44bdba127811109bd7eac1e94578898ed3e05c1 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 16:36:36 +0200 Subject: [PATCH 164/297] Flexible test and validation data usage for band energy --- mala/datahandling/data_handler.py | 39 ++++++++++------- mala/network/trainer.py | 69 ++++++++++++++++++++++++------- 2 files changed, 78 insertions(+), 30 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index ba2e9d2dc..92a463162 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -74,10 +74,18 @@ def __init__(self, parameters: Parameters, target_calculator=None, self.nr_snapshots = 0 self.grid_dimension = [0, 0, 0] self.grid_size = 0 + + # Actual data points in the different categories. self.nr_training_data = 0 self.nr_test_data = 0 self.nr_validation_data = 0 + # Number of snapshots in these categories. + self.nr_training_snapshots = 0 + self.nr_test_snapshots = 0 + self.nr_validation_snapshots = 0 + + self.training_data_inputs = torch.empty(0) """ Torch tensor holding all scaled training data inputs. @@ -187,6 +195,9 @@ def clear_data(self): self.nr_training_data = 0 self.nr_test_data = 0 self.nr_validation_data = 0 + self.nr_training_snapshots = 0 + self.nr_test_snapshots = 0 + self.nr_validation_snapshots = 0 self.parameters.data_splitting_snapshots = [] self.parameters.snapshot_directories_list = [] @@ -440,37 +451,37 @@ def __check_snapshots(self): if self.parameters.data_splitting_type == "by_snapshot": for snapshot_function in self.parameters.data_splitting_snapshots: if snapshot_function == "tr": - self.nr_training_data += 1 + self.nr_training_snapshots += 1 elif snapshot_function == "te": - self.nr_test_data += 1 + self.nr_test_snapshots += 1 elif snapshot_function == "va": - self.nr_validation_data += 1 + self.nr_validation_snapshots += 1 else: raise Exception("Unknown option for snapshot splitting " "selected.") # Now we need to check whether or not this input is believable. nr_of_snapshots = len(self.parameters.snapshot_directories_list) - if nr_of_snapshots != (self.nr_training_data + - self.nr_validation_data + - self.nr_test_data): + if nr_of_snapshots != (self.nr_training_snapshots + + self.nr_test_snapshots + + self.nr_validation_snapshots): raise Exception("Cannot split snapshots with specified " "splitting scheme, " "too few or too many options selected") - if self.nr_training_data == 0 and self.nr_test_data == 0: + if self.nr_training_snapshots == 0 and self.nr_test_snapshots == 0: raise Exception("No training snapshots provided.") - if self.nr_validation_data == 0 and self.nr_test_data == 0: + if self.nr_validation_snapshots == 0 and self.nr_test_snapshots == 0: raise Exception("No validation snapshots provided.") - if self.nr_training_data == 0 and self.nr_test_data != 0: + if self.nr_training_snapshots == 0 and self.nr_test_snapshots != 0: printout("DataHandler prepared for inference. No training " "possible with this setup. " "If this is not what you wanted, please revise the " "input script.") - if self.nr_validation_data != 0: + if self.nr_validation_snapshots != 0: printout("As this DataHandler can only be used for " "inference, the validation data you have " "provided will be ignored.") - if self.nr_test_data == 0: + if self.nr_test_snapshots == 0: printout("Running MALA without test data. If this is not " "what you wanted, " "please revise the input script.") @@ -480,9 +491,9 @@ def __check_snapshots(self): # As we are not actually interested in the number of snapshots, but in # the number of datasets,we need to multiply by that. - self.nr_training_data *= self.grid_size - self.nr_validation_data *= self.grid_size - self.nr_test_data *= self.grid_size + self.nr_training_data = self.nr_training_snapshots*self.grid_size + self.nr_validation_data = self.nr_validation_snapshots*self.grid_size + self.nr_test_data = self.nr_test_snapshots*self.grid_size def __load_from_npy_file(self, file, mmapmode=None): """Load a numpy array from a file.""" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 21dcfe8c4..0204f4086 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -171,11 +171,15 @@ def train_network(self): tloss = float("inf") vloss = self.__validate_network(self.network, - self.validation_data_loader, - validation_type="band_energy") + "validation", + self.parameters. + after_training_metric) + if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, - self.test_data_loader) + "test", + self.parameters. + after_training_metric) # Collect and average all the losses from all the devices if self.parameters_full.use_horovod: @@ -228,7 +232,9 @@ def train_network(self): # Calculate the validation loss. and output it. vloss = self.__validate_network(self.network, - self.validation_data_loader) + "validation", + self.parameters. + during_training_metric) if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') if self.parameters.verbosity: @@ -284,11 +290,24 @@ def train_network(self): # CALCULATE FINAL METRICS ############################ + if self.parameters.after_training_metric != \ + self.parameters.during_training_metric: + vloss = self.__validate_network(self.network, + "validation", + self.parameters. + after_training_metric) + if self.parameters_full.use_horovod: + vloss = self.__average_validation(vloss, 'average_loss') + self.final_validation_loss = vloss + printout("Final validation data loss: ", vloss) + tloss = float("inf") if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, - self.test_data_loader) + "test", + self.parameters. + after_training_metric) if self.parameters_full.use_horovod: tloss = self.__average_validation(tloss, 'average_loss') printout("Final test data loss: ", tloss) @@ -440,13 +459,26 @@ def __process_mini_batch(self, network, input_data, target_data): self.optimizer.zero_grad() return loss.item() - def __validate_network(self, network, vdl, validation_type="ldos"): + def __validate_network(self, network, data_set_type, validation_type): """Validate a network, using test or validation data.""" + if data_set_type == "test": + data_loader = self.test_data_loader + data_set = self.data.test_data_set + number_of_snapshots = self.data.nr_test_snapshots + + elif data_set_type == "validation": + data_loader = self.validation_data_loader + data_set = self.data.validation_data_set + number_of_snapshots = self.data.nr_validation_snapshots + + else: + raise Exception("Please select test or validation" + "when using this function.") network.eval() if validation_type == "ldos": validation_loss = [] with torch.no_grad(): - for x, y in vdl: + for x, y in data_loader: if self.parameters_full.use_gpu: x = x.to('cuda') y = y.to('cuda') @@ -456,10 +488,6 @@ def __validate_network(self, network, vdl, validation_type="ldos"): return np.mean(validation_loss) elif validation_type == "band_energy": - - data_set = self.data.validation_data_set - number_snapshots = 2 - # Get optimal batch size and number of batches per snapshots. optimal_batch_size = self. \ _correct_batch_size_for_testing(self.data.grid_size, @@ -468,7 +496,7 @@ def __validate_network(self, network, vdl, validation_type="ldos"): number_of_batches_per_snapshot = int(self.data.grid_size / optimal_batch_size) errors = [] - for snapshot_number in range(0, number_snapshots): + for snapshot_number in range(0, number_of_snapshots): actual_outputs, \ predicted_outputs = self.\ _forward_entire_snapshot(snapshot_number, data_set, @@ -485,12 +513,21 @@ def __validate_network(self, network, vdl, validation_type="ldos"): get_self_consistent_fermi_energy_ev(actual_outputs) be_actual = calculator.\ get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) - fe_predicted = calculator.\ - get_self_consistent_fermi_energy_ev(predicted_outputs) - be_predicted = calculator.\ - get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + try: + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + be_predicted = calculator.\ + get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + except ValueError: + # If the training went badly, it might be that the above + # code results in an error, due to the LDOS being so wrong + # that the estimation of the self consistent Fermi energy + # fails. + be_predicted = float("inf") errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) return np.mean(errors) + else: + raise Exception("Selected validation method not supported.") def __create_training_checkpoint(self): """ From 2955c9c67b228e1db72facb94c770a1bc53540ac Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 17:58:06 +0200 Subject: [PATCH 165/297] band energy evaluation should now work across entire sets --- mala/datahandling/data_handler.py | 20 ++++++++--- mala/network/trainer.py | 60 ++++++++++++++++++++++++++++--- 2 files changed, 71 insertions(+), 9 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 92a463162..05e15af8e 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -252,12 +252,14 @@ def prepare_data(self, reparametrize_scaler=True): printout("Build dataset done.") def mix_datasets(self): - """For lazily-loaded data sets, the snapshot ordering is (re-)mixed.""" + """ + For lazily-loaded data sets, the snapshot ordering is (re-)mixed. + + This applies only to the training data set. For the validation and + test set it does not matter. + """ if self.parameters.use_lazy_loading: - self.validation_data_set.mix_datasets() self.training_data_set.mix_datasets() - if self.test_data_set is not None: - self.test_data_set.mix_datasets() def raw_numpy_to_converted_scaled_tensor(self, numpy_array, data_type, units, convert3Dto1D=False): @@ -495,6 +497,16 @@ def __check_snapshots(self): self.nr_validation_data = self.nr_validation_snapshots*self.grid_size self.nr_test_data = self.nr_test_snapshots*self.grid_size + # Reordering the lists. + snapshot_order = ['tr', 'va', 'te'] + combined = zip(self.parameters.data_splitting_snapshots, + self.parameters.snapshot_directories_list) + order = {key: i for i, key in enumerate(snapshot_order)} + combined_sorted = sorted(combined, key=lambda d: order[d[0]]) + self.parameters.data_splitting_snapshots, \ + self.parameters.snapshot_directories_list = \ + map(list, zip(*combined_sorted)) + def __load_from_npy_file(self, file, mmapmode=None): """Load a numpy array from a file.""" loaded_array = np.load(file, mmap_mode=mmapmode) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 0204f4086..52a771416 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -465,11 +465,14 @@ def __validate_network(self, network, data_set_type, validation_type): data_loader = self.test_data_loader data_set = self.data.test_data_set number_of_snapshots = self.data.nr_test_snapshots + offset_snapshots = self.data.nr_validation_snapshots + \ + self.data.nr_training_snapshots elif data_set_type == "validation": data_loader = self.validation_data_loader data_set = self.data.validation_data_set number_of_snapshots = self.data.nr_validation_snapshots + offset_snapshots = self.data.nr_training_snapshots else: raise Exception("Please select test or validation" @@ -496,23 +499,27 @@ def __validate_network(self, network, data_set_type, validation_type): number_of_batches_per_snapshot = int(self.data.grid_size / optimal_batch_size) errors = [] - for snapshot_number in range(0, number_of_snapshots): + for snapshot_number in range(offset_snapshots, + number_of_snapshots+offset_snapshots): actual_outputs, \ predicted_outputs = self.\ - _forward_entire_snapshot(snapshot_number, data_set, + _forward_entire_snapshot(snapshot_number-offset_snapshots, + data_set, number_of_batches_per_snapshot, optimal_batch_size) - calculator = self.data.target_calculator - # FIXME: This will eventually break down. + + # This works because the list is always guaranteed to be + # ordered. calculator.\ read_additional_calculation_data("qe.out", self.data. - get_snapshot_calculation_output(0)) + get_snapshot_calculation_output(snapshot_number)) fe_actual = calculator.\ get_self_consistent_fermi_energy_ev(actual_outputs) be_actual = calculator.\ get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) + try: fe_predicted = calculator.\ get_self_consistent_fermi_energy_ev(predicted_outputs) @@ -526,6 +533,49 @@ def __validate_network(self, network, data_set_type, validation_type): be_predicted = float("inf") errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) return np.mean(errors) + elif validation_type == "total_energy": + # Get optimal batch size and number of batches per snapshots. + optimal_batch_size = self. \ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters. + mini_batch_size) + number_of_batches_per_snapshot = int(self.data.grid_size / + optimal_batch_size) + errors = [] + for snapshot_number in range(offset_snapshots, + number_of_snapshots+offset_snapshots): + actual_outputs, \ + predicted_outputs = self.\ + _forward_entire_snapshot(snapshot_number, data_set, + number_of_batches_per_snapshot, + optimal_batch_size) + calculator = self.data.target_calculator + + # This works because the list is always guaranteed to be + # ordered. + calculator.\ + read_additional_calculation_data("qe.out", + self.data. + get_snapshot_calculation_output(snapshot_number)) + fe_actual = calculator.\ + get_self_consistent_fermi_energy_ev(actual_outputs) + te_actual = calculator.\ + get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_actual) + + try: + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + te_predicted = calculator.\ + get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_predicted) + except ValueError: + # If the training went badly, it might be that the above + # code results in an error, due to the LDOS being so wrong + # that the estimation of the self consistent Fermi energy + # fails. + te_predicted = float("inf") + errors.append(np.abs(te_predicted-te_actual)*(1000/len(calculator.atoms))) + return np.mean(errors) + else: raise Exception("Selected validation method not supported.") From cd26b0e9e4aa85ae0d83515aa49cf1b6dcfa4d2e Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Tue, 20 Jul 2021 23:34:06 +0200 Subject: [PATCH 166/297] made changes for the tensorboard --- docs/source/install/tensorboard_setup.md | 2 +- examples/ex01_run_singleshot.py | 2 +- examples/ex10_tensor_board.py | 130 +++++++++++++++++++++++ mala/common/parameters.py | 9 +- mala/network/trainer.py | 18 +++- 5 files changed, 156 insertions(+), 5 deletions(-) create mode 100644 examples/ex10_tensor_board.py diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md index b0d9c53e9..c52aa5d32 100644 --- a/docs/source/install/tensorboard_setup.md +++ b/docs/source/install/tensorboard_setup.md @@ -1,6 +1,6 @@ Access central storage servers from locations other than Hemera (Linux) https://fwcc.pages.hzdr.de/infohub/hpc/storage.html -sshfs :username@hemera5.fz-rossendorf.de folder/file/location folder/location/in/local +sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index c7e4703d7..3b4a6c935 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -37,7 +37,7 @@ def run_example01(desired_loss_improvement_factor=1): test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = True + test_parameters.running.visualisation = 2 #################### # DATA # Add and prepare snapshots for training. diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py new file mode 100644 index 000000000..e817a4c87 --- /dev/null +++ b/examples/ex10_tensor_board.py @@ -0,0 +1,130 @@ +import mala +from mala import printout +from data_repo_path import get_data_repo_path +data_path = get_data_repo_path()+"Al36/" + + +""" +ex10_tensor_board.py: +to include tensor board link through python script to auto start the tensorboard port. +to run tensorboard from a python script. +to check if the +""" + +def run_example01(desired_loss_improvement_factor=1): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.running.visualisation = 2 + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + + #################### + # TRAINING + # Train the network. + #################### + + printout("Starting training.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # RESULTS. + # Print the used parameters and check whether the loss decreased enough. + #################### + + printout("Parameters used for this experiment:") + test_parameters.show() + + if desired_loss_improvement_factor*test_trainer.initial_test_loss\ + < test_trainer.final_test_loss: + return False + else: + return True + + + + + +def run_example10(): + tensor_board_program = program.TensorBoard() + tensor_board_program.configure(argv=[None, '--logdir', http://localhost:6006/]) + url = tensor_board_program.launch() + + + #################### + # RESULTS. + # Check whether tensorboard has run. + #################### + + printout("Tensor board has ran succesfully:") + + if desired_loss_improvement_factor*test_trainer.initial_test_loss\ + < test_trainer.final_test_loss: + return False + else: + return True +if __name__ == "__main__": + if run_example01() && run_example10(): + printout("Successfully ran ex10_tensor_board.py.") + else: + raise Exception("Ran ex10_tensor_board but something " + "was off. If you haven't changed any parameters in " + "the example, there might be a problem with " + "your installation.") \ No newline at end of file diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 026564aa3..8ff3dd0c6 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -303,6 +303,13 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. + visualisation : int + If True then Tensorboard is activated for visualisation + case 0: No tensorboard activated + case 1: tensorboard activated with Loss and learning rate + case 2; additonally weights and biases and gradient + + """ def __init__(self): @@ -329,7 +336,7 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" - self.visualisation = False + self.visualisation = 0 class ParametersHyperparameterOptimization(ParametersBase): """ diff --git a/mala/network/trainer.py b/mala/network/trainer.py index c1a112330..802f6e2d4 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -54,7 +54,8 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tensor_board = SummaryWriter() + self.tensor_board = SummaryWriter("log_dir")## here the path to the log file can be set + @classmethod def resume_checkpoint(cls, checkpoint_name): @@ -207,8 +208,21 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.tensor_board is not None: + if self.parameters.visualisation > 0: self.tensor_board.add_scalar("Loss", vloss, epoch) + self.tensor_board.add_scalar("Learning rate", self.parameters.learning_rate, epoch) + if self.parameters.visualisation == 2: + print("visualisation = 2") + for name, param in self.network.named_parameters(): + self.tensor_board.add_histogram(name,param,epoch) + self.tensor_board.add_histogram(f'{name}.grad',param.grad,epoch) + + self.tensor_board.flush() #method to make sure that all pending events have been written to disk + + + + + # Mix the DataSets up (this function only does something # in the lazy loading case). From aa5c7e1b9308634327aa0d5285ffe7868cac373f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 21 Jul 2021 11:12:39 +0200 Subject: [PATCH 167/297] Made skipping of tests optional and added os.path.join --- test/basic_gpu_test.py | 14 ++++++++--- test/checkpoint_hyperopt_tests.py | 3 ++- test/checkpoint_training_tests.py | 3 ++- test/hyperopt_test.py | 2 +- test/inference_test.py | 13 ++++++----- test/installation_test.py | 4 ++-- test/integration_test.py | 39 +++++++------------------------ test/lazy_loading_test.py | 7 ++++-- test/tensor_memory.py | 6 +++-- test/workflow_test.py | 10 +++++--- 10 files changed, 50 insertions(+), 51 deletions(-) diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py index e9ed4e1fa..d984600cc 100644 --- a/test/basic_gpu_test.py +++ b/test/basic_gpu_test.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 -"""GPU test +""" +GPU test This is a very basic test of the GPU functionalities of MALA (i.e. pytorch, which MALA relies on). Two things are tested: @@ -14,7 +15,11 @@ from data_repo_path import get_data_repo_path import time import numpy as np -data_path = get_data_repo_path()+"Al36/" +import os +import torch +import pytest +data_path = os.path.join(get_data_repo_path(), "Al36/") + test_checkpoint_name = "test" # Define the accuracy used in the tests and a parameter to control @@ -29,13 +34,16 @@ class TestGPUExecution: Tests whether a GPU is available and then the execution on it. """ - + @pytest.mark.skipif(torch.cuda.is_available() is False, + reason="No GPU detected.") def test_gpu_availability(self): """Test whether a GPU is available.""" test_parameters = mala.Parameters() test_parameters.use_gpu = True assert test_parameters.use_gpu + @pytest.mark.skipif(torch.cuda.is_available() is False, + reason="No GPU detected.") def test_gpu_performance(self): """ Test whether GPU training brings performance improvements. diff --git a/test/checkpoint_hyperopt_tests.py b/test/checkpoint_hyperopt_tests.py index 26c723d16..1819def5f 100644 --- a/test/checkpoint_hyperopt_tests.py +++ b/test/checkpoint_hyperopt_tests.py @@ -2,7 +2,8 @@ from mala import printout from data_repo_path import get_data_repo_path import numpy as np -data_path = get_data_repo_path()+"Al36/" +import os +data_path = os.path.join(get_data_repo_path(), "Al36/") checkpoint_name = "test_ho" # Define the accuracy used in the tests. diff --git a/test/checkpoint_training_tests.py b/test/checkpoint_training_tests.py index 186916031..b53aa1f11 100644 --- a/test/checkpoint_training_tests.py +++ b/test/checkpoint_training_tests.py @@ -2,8 +2,9 @@ from mala import printout import numpy as np from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +import os test_checkpoint_name = "test" +data_path = os.path.join(get_data_repo_path(), "Al36/") # Define the accuracy used in the tests. accuracy = 1e-14 diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py index 656e134a2..b7bd80ff0 100644 --- a/test/hyperopt_test.py +++ b/test/hyperopt_test.py @@ -2,7 +2,7 @@ from data_repo_path import get_data_repo_path import numpy as np import os -data_path = get_data_repo_path()+"Al36/" +data_path = os.path.join(get_data_repo_path(), "Al36/") # Control how much the loss should be better after hyperopt compared to # before. This value is fairly high, but we're training on absolutely diff --git a/test/inference_test.py b/test/inference_test.py index 9c9d33650..821333a1c 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -6,12 +6,13 @@ from mala.common.parameters import printout import numpy as np from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" -param_path = get_data_repo_path()+"example05_data/" -params_path = param_path+"ex05_params.pkl" -network_path = param_path+"ex05_network.pth" -input_scaler_path = param_path+"ex05_iscaler.pkl" -output_scaler_path = param_path+"ex05_oscaler.pkl" +import os +data_path = os.path.join(get_data_repo_path(), "Al36/") +param_path = os.path.join(get_data_repo_path(), "workflow_test/") +params_path = param_path+"workflow_test_params.pkl" +network_path = param_path+"workflow_test_network.pth" +input_scaler_path = param_path+"workflow_test_iscaler.pkl" +output_scaler_path = param_path+"workflow_test_oscaler.pkl" accuracy_strict = 1e-16 accuracy_coarse = 5e-7 diff --git a/test/installation_test.py b/test/installation_test.py index 23f5846bd..fdf2eabcd 100644 --- a/test/installation_test.py +++ b/test/installation_test.py @@ -1,6 +1,6 @@ import mala import numpy as np - +import os class TestInstallation: """Verifies the installation.""" @@ -24,5 +24,5 @@ def test_data_repo(self): """Test whether the data repo is set up properly""" from data_repo_path import get_data_repo_path data_path = get_data_repo_path() - test_array = np.load(data_path + "linking_tester.npy") + test_array = np.load(os.path.join(data_path, "linking_tester.npy")) assert (test_array == [1, 2, 3, 4]).all() \ No newline at end of file diff --git a/test/integration_test.py b/test/integration_test.py index 6781baa57..0ac876f3d 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -7,6 +7,7 @@ from mala.common.parameters import Parameters from mala.common.parameters import printout from data_repo_path import get_data_repo_path +import os # In order to test the integration capabilities of MALA we need a # QuantumEspresso @@ -18,14 +19,11 @@ # Scripts to reproduce the data files used in this test script can be found # in the data repo. -data_path = get_data_repo_path()+"Al36/" -path_to_out = data_path+"Al.pw.scf.out" -path_to_ldos_qe = [data_path, "tmp.pp*Al_ldos.cube"] -path_to_dos_qe = [data_path, "Al.dos"] -path_to_dens_qe = [data_path, "Al_dens.cube"] -path_to_ldos_npy = data_path+"Al_ldos.npy" -path_to_dos_npy = data_path+"Al_dos.npy" -path_to_dens_npy = data_path+"Al_dens.npy" +data_path = os.path.join(get_data_repo_path(), "Al36") +path_to_out = os.path.join(data_path, "Al.pw.scf.out") +path_to_ldos_npy = os.path.join(data_path, "Al_ldos.npy") +path_to_dos_npy = os.path.join(data_path, "Al_dos.npy") +path_to_dens_npy = os.path.join(data_path, "Al_dens.npy") # We can read from numpy arrays or directly from QE data. # In the later case, numpy arrays will be saved for the subsqeuent run. @@ -106,12 +104,7 @@ def test_qe_dens_to_nr_of_electrons(self): dens_calculator.read_additional_calculation_data("qe.out", path_to_out) # Read the input data. - if numpy_arrays: - density_dft = np.load(path_to_dens_npy) - else: - density_dft = dens_calculator.read_from_cube(path_to_dens_qe[1], - path_to_dens_qe[0]) - np.save(path_to_dens_npy, density_dft) + density_dft = np.load(path_to_dens_npy) # Calculate the quantities we want to compare. nr_mala = dens_calculator.get_number_of_electrons(density_dft) @@ -136,15 +129,7 @@ def test_qe_ldos_to_density(self): # Read the input data. density_dft = np.load(path_to_dens_npy) - if numpy_arrays: - ldos_dft = np.load(path_to_ldos_npy) - else: - ldos_dft = ldos_calculator.read_from_cube(path_to_ldos_qe[1], - path_to_ldos_qe[0]) - - # LDOS is in 1/Ry. DOS is in 1/eV. - ldos_dft = ldos_calculator.convert_units(ldos_dft, "1/Ry") - np.save(path_to_ldos_npy, ldos_dft) + ldos_dft = np.load(path_to_ldos_npy) # Calculate the quantities we want to compare. self_consistent_fermi_energy = ldos_calculator.\ @@ -174,13 +159,7 @@ def test_qe_ldos_to_dos(self): # Read the input data. ldos_dft = np.load(path_to_ldos_npy) - if numpy_arrays: - dos_dft = np.load(path_to_dos_npy) - else: - # DOS is in 1/eV so no conversion necessary. - dos_dft = dos_calculator.read_from_qe_dos_txt(path_to_dos_qe[1], - path_to_dos_qe[0]) - np.save(path_to_dos_npy, dos_dft) + dos_dft = np.load(path_to_dos_npy) # Calculate the quantities we want to compare. dos_mala = ldos_calculator.get_density_of_states(ldos_dft) diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index 50eb2de98..a867333a7 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -8,10 +8,12 @@ from data_repo_path import get_data_repo_path import time import pytest +import importlib +import os # This test compares the data scaling using the regular scaling procedure and # the lazy-loading one (incremental fitting). -data_path = get_data_repo_path()+"Al36/" +data_path = os.path.join(get_data_repo_path(), "Al36/") accuracy_strict = 1e-3 accuracy_coarse = 1e-3 @@ -163,7 +165,8 @@ def test_scaling(self): assert np.isclose(training_tester[0], training_tester[1], atol=accuracy_coarse) - @pytest.mark.skip(reason="Horovod is currently not part of the pipeline") + @pytest.mark.skipif(importlib.util.find_spec("horovod") is None, + reason="Horovod is currently not part of the pipeline") def test_performance_horovod(self): #################### diff --git a/test/tensor_memory.py b/test/tensor_memory.py index 70806a498..4a611f6e6 100644 --- a/test/tensor_memory.py +++ b/test/tensor_memory.py @@ -4,7 +4,8 @@ from torch.utils.data import DataLoader from mala.common.parameters import printout from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +import os +data_path = os.path.join(get_data_repo_path(), "Al36") # Define the accuracy used in the tests. accuracy = 1e-5 @@ -21,7 +22,8 @@ class TestTensorMemory: def test_tensor_memory(self): # Load an array as a numpy array - loaded_array_raw = np.load(data_path+"Al_debug_2k_nr0.in.npy") + loaded_array_raw = np.load(os.path.join(data_path, + "Al_debug_2k_nr0.in.npy")) # Get dimensions of numpy array. dimension = np.shape(loaded_array_raw) diff --git a/test/workflow_test.py b/test/workflow_test.py index ce72555ed..42bd9080f 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -2,8 +2,10 @@ from data_repo_path import get_data_repo_path import numpy as np import pytest -data_path = get_data_repo_path()+"Al36/" +import importlib +import os +data_path = os.path.join(get_data_repo_path(), "Al36/") # Control how much the loss should be better after training compared to # before. This value is fairly high, but we're training on absolutely # minimal amounts of data. @@ -26,7 +28,8 @@ def test_network_training(self): assert desired_loss_improvement_factor * \ test_trainer.initial_test_loss > test_trainer.final_test_loss - @pytest.mark.skip(reason="LAMMPS is currently not part of the pipeline.") + @pytest.mark.skipif(importlib.util.find_spec("lammps") is None, + reason="LAMMPS is currently not part of the pipeline.") def test_preprocessing(self): """ Test whether MALA can preprocess data. @@ -101,7 +104,8 @@ def test_postprocessing(self): assert np.isclose(band_energy, ldos.band_energy_dft_calculation, atol=accuracy_band_energy) - @pytest.mark.skip(reason="QE is currently not part of the pipeline.") + @pytest.mark.skipif(importlib.util.find_spec("total_energy") is None, + reason="QE is currently not part of the pipeline.") def test_total_energy_from_ldos(self): """ Test whether MALA can calculate the total energy using the LDOS. From 1c4bf085f5405b8aef39c8b331f01685e9feaf97 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 21 Jul 2021 11:57:25 +0200 Subject: [PATCH 168/297] total_energy during running should work now --- mala/common/parameters.py | 3 +++ mala/network/trainer.py | 9 ++++++--- mala/targets/target_base.py | 15 ++++++++++++++- 3 files changed, 23 insertions(+), 4 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 1e8f2a4da..e2eac302b 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -127,6 +127,8 @@ class ParametersTargets(ParametersBase): ldos_gridoffset_ev: float Lowest energy value on the (L)DOS energy grid [eV]. + + """ def __init__(self): @@ -136,6 +138,7 @@ def __init__(self): self.ldos_gridspacing_ev = 0 self.ldos_gridoffset_ev = 0 self.restrict_targets = "zero_out_negative" + self.pseudopotential_path = None @property def restrict_targets(self): diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 52a771416..6154783df 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -546,7 +546,8 @@ def __validate_network(self, network, data_set_type, validation_type): number_of_snapshots+offset_snapshots): actual_outputs, \ predicted_outputs = self.\ - _forward_entire_snapshot(snapshot_number, data_set, + _forward_entire_snapshot(snapshot_number-offset_snapshots, + data_set, number_of_batches_per_snapshot, optimal_batch_size) calculator = self.data.target_calculator @@ -560,13 +561,15 @@ def __validate_network(self, network, data_set_type, validation_type): fe_actual = calculator.\ get_self_consistent_fermi_energy_ev(actual_outputs) te_actual = calculator.\ - get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_actual) + get_total_energy(ldos_data=actual_outputs, + fermi_energy_eV=fe_actual) try: fe_predicted = calculator.\ get_self_consistent_fermi_energy_ev(predicted_outputs) te_predicted = calculator.\ - get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_predicted) + get_total_energy(ldos_data=actual_outputs, + fermi_energy_eV=fe_predicted) except ValueError: # If the training went badly, it might be that the above # code results in an error, due to the LDOS being so wrong diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 1f46599b5..fa3b3ba17 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -42,7 +42,7 @@ def __init__(self, params): "calculation": 'scf', "restart_mode": 'from_scratch', "prefix": 'MALA', - "pseudo_dir": None, + "pseudo_dir": self.parameters.pseudopotential_path, "outdir": './', "ibrav": None, "smearing": 'fermi-dirac', @@ -65,6 +65,19 @@ def __init__(self, params): self.kpoints = None # (2, 2, 2) self.qe_pseudopotentials = {} + @property + def qe_input_data(self): + """Input data for QE TEM calls.""" + + # Update the pseudopotential path from Parameters. + self._qe_input_data["pseudo_dir"] = \ + self.parameters.pseudopotential_path + return self._qe_input_data + + @qe_input_data.setter + def qe_input_data(self, value): + self._qe_input_data = value + def read_from_cube(self): """Read the quantity from a .cube file.""" raise Exception("No function defined to read this quantity " From e7d510440e6907a01a3ee751f542eecf91964e70 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 21 Jul 2021 22:48:27 +0200 Subject: [PATCH 169/297] Deleted redundant GPU test and added pytest to installation step --- .github/workflows/cpu-tests.yml | 5 +++-- test/basic_gpu_test.py | 8 -------- 2 files changed, 3 insertions(+), 10 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 089cc47da..7e2a0822b 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -72,8 +72,9 @@ jobs: uses: actions/checkout@v2 - name: Install mala package - run: pip --no-cache-dir install -e . - + run: | + pip --no-cache-dir install -e . + pip install pytest - name: Check out repository (data) uses: actions/checkout@v2 with: diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py index d984600cc..2414b8b3a 100644 --- a/test/basic_gpu_test.py +++ b/test/basic_gpu_test.py @@ -34,14 +34,6 @@ class TestGPUExecution: Tests whether a GPU is available and then the execution on it. """ - @pytest.mark.skipif(torch.cuda.is_available() is False, - reason="No GPU detected.") - def test_gpu_availability(self): - """Test whether a GPU is available.""" - test_parameters = mala.Parameters() - test_parameters.use_gpu = True - assert test_parameters.use_gpu - @pytest.mark.skipif(torch.cuda.is_available() is False, reason="No GPU detected.") def test_gpu_performance(self): From 9b3f60be543b73467d2fb4600d46f407bcb61cb1 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 22 Jul 2021 09:12:03 +0200 Subject: [PATCH 170/297] Fixed file not found errors in test pipeline --- test/integration_test.py | 5 +++++ test/workflow_test.py | 4 +++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/test/integration_test.py b/test/integration_test.py index 0ac876f3d..092b3cbbd 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -8,6 +8,7 @@ from mala.common.parameters import printout from data_repo_path import get_data_repo_path import os +import pytest # In order to test the integration capabilities of MALA we need a # QuantumEspresso @@ -117,6 +118,8 @@ def test_qe_dens_to_nr_of_electrons(self): # Check against the constraints we put upon ourselves. assert np.isclose(rel_error, 0, atol=accuracy) + @pytest.mark.skipif(os.path.isfile(path_to_ldos_npy) is False, + reason="No LDOS file in data repo found.") def test_qe_ldos_to_density(self): """ Test integration of local density of states on energy grid. @@ -146,6 +149,8 @@ def test_qe_ldos_to_density(self): # Check against the constraints we put upon ourselves. assert np.isclose(rel_error, 0, atol=accuracy) + @pytest.mark.skipif(os.path.isfile(path_to_ldos_npy) is False, + reason="No LDOS file in data repo found.") def test_qe_ldos_to_dos(self): """ Test integration of local density of states on real space grid. diff --git a/test/workflow_test.py b/test/workflow_test.py index 42bd9080f..87d4721b0 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -28,7 +28,9 @@ def test_network_training(self): assert desired_loss_improvement_factor * \ test_trainer.initial_test_loss > test_trainer.final_test_loss - @pytest.mark.skipif(importlib.util.find_spec("lammps") is None, + @pytest.mark.skipif(importlib.util.find_spec("lammps") is None + or os.path.isdir(os.path.join(data_path, "cubes")) + is False, reason="LAMMPS is currently not part of the pipeline.") def test_preprocessing(self): """ From 65a4928aae6e8902eab302db29150835804217c8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 22 Jul 2021 11:02:11 +0200 Subject: [PATCH 171/297] Fixed another missing file error --- test/workflow_test.py | 81 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 77 insertions(+), 4 deletions(-) diff --git a/test/workflow_test.py b/test/workflow_test.py index 87d4721b0..c7438d3fe 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -28,10 +28,10 @@ def test_network_training(self): assert desired_loss_improvement_factor * \ test_trainer.initial_test_loss > test_trainer.final_test_loss - @pytest.mark.skipif(importlib.util.find_spec("lammps") is None - or os.path.isdir(os.path.join(data_path, "cubes")) - is False, + @pytest.mark.skipif(importlib.util.find_spec("lammps") is None, reason="LAMMPS is currently not part of the pipeline.") + @pytest.mark.skipif(os.path.isdir(os.path.join(data_path, "cubes")) + is False, reason="No cube files found in data repo.") def test_preprocessing(self): """ Test whether MALA can preprocess data. @@ -71,9 +71,46 @@ def test_preprocessing(self): assert output_data_shape[0] == 108 and output_data_shape[1] == 108 and\ output_data_shape[2] == 100 and output_data_shape[3] == 10 + def test_postprocessing_from_dos(self): + """ + Test whether MALA can postprocess data (DOS). + + This means calculating band energy and number of electrons from + LDOS. Total energy is tested further below. + """ + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.targets.target_type = "DOS" + test_parameters.targets.ldos_gridsize = 250 + test_parameters.targets.ldos_gridspacing_ev = 0.1 + test_parameters.targets.ldos_gridoffset_ev = -10 + + # Create a target calculator to perform postprocessing. + dos = mala.TargetInterface(test_parameters) + dos.read_additional_calculation_data("qe.out", + data_path + "Al.pw.scf.out") + dos_data = np.load(data_path + "Al_dos.npy") + + # Calculate energies + self_consistent_fermi_energy = dos. \ + get_self_consistent_fermi_energy_ev(dos_data) + number_of_electrons = dos. \ + get_number_of_electrons(dos_data, fermi_energy_eV= + self_consistent_fermi_energy) + band_energy = dos.get_band_energy(dos_data, + fermi_energy_eV= + self_consistent_fermi_energy) + + assert np.isclose(number_of_electrons, dos.number_of_electrons, + atol=accuracy_electrons) + assert np.isclose(band_energy, dos.band_energy_dft_calculation, + atol=accuracy_band_energy) + + @pytest.mark.skipif(os.path.isfile(os.path.join(data_path, "Al_ldos.npy")) + is False, reason="No LDOS file in data repo found.") def test_postprocessing(self): """ - Test whether MALA can postprocess data. + Test whether MALA can postprocess data (from LDOS) This means calculating band energy and number of electrons from LDOS. Total energy is tested further below. @@ -108,6 +145,42 @@ def test_postprocessing(self): @pytest.mark.skipif(importlib.util.find_spec("total_energy") is None, reason="QE is currently not part of the pipeline.") + def test_total_energy_from_dos_density(self): + """ + Test whether MALA can calculate the total energy using the DOS+Density. + + This means calculating energies from the DOS and density. + """ + # Set up parameters. + test_parameters = mala.Parameters() + test_parameters.targets.target_type = "LDOS" + test_parameters.targets.ldos_gridsize = 250 + test_parameters.targets.ldos_gridspacing_ev = 0.1 + test_parameters.targets.ldos_gridoffset_ev = -10 + + # Create a target calculator to perform postprocessing. + ldos = mala.TargetInterface(test_parameters) + ldos.read_additional_calculation_data("qe.out", + data_path + "Al.pw.scf.out") + dos_data = np.load(data_path + "Al_dos.npy") + dens_data = np.load(data_path + "Al_dens.npy") + dos = mala.DOS.from_ldos(ldos) + + # Calculate energies + self_consistent_fermi_energy = dos. \ + get_self_consistent_fermi_energy_ev(dos_data) + ldos.set_pseudopotential_path(data_path) + total_energy = ldos.get_total_energy(dos_data=dos_data, + density_data=dens_data, + fermi_energy_eV= + self_consistent_fermi_energy) + assert np.isclose(total_energy, ldos.total_energy_dft_calculation, + atol=accuracy_total_energy) + + @pytest.mark.skipif(importlib.util.find_spec("total_energy") is None, + reason="QE is currently not part of the pipeline.") + @pytest.mark.skipif(os.path.isfile(os.path.join(data_path, "Al_ldos.npy")) + is False, reason="No LDOS file in data repo found.") def test_total_energy_from_ldos(self): """ Test whether MALA can calculate the total energy using the LDOS. From 255774aef90f03d536b023a2b8f35bdb4cc3c04e Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 22 Jul 2021 11:14:18 +0200 Subject: [PATCH 172/297] Disabled examples execution in pipeline --- .github/workflows/cpu-tests.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 7e2a0822b..4b8651c49 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -91,8 +91,5 @@ jobs: chmod +x ../install/data_repo_link/link_data_repo.sh ../install/data_repo_link/link_data_repo.sh `pwd` - - name: Test basic functions - run: pytest --disable-warnings - - - name: Test workflow - run: python examples/ex99_verify_all_examples.py + - name: Test MALA + run: pytest --disable-warnings \ No newline at end of file From 9cbd542aa1aa819a875f73392c70cb2c477e8f72 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 22 Jul 2021 13:10:48 +0200 Subject: [PATCH 173/297] Prevent metrics other then LDOS from being used in horovod (for now) --- mala/common/parameters.py | 65 ++++++++++++++++++++++++++++++++++++--- mala/network/trainer.py | 10 +++--- 2 files changed, 66 insertions(+), 9 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index e2eac302b..1bc9fbac2 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -15,7 +15,8 @@ class ParametersBase: """Base parameter class for MALA.""" - def __init__(self): + def __init__(self,): + self.configuration = None pass def show(self, indent=""): @@ -32,6 +33,9 @@ def show(self, indent=""): for v in vars(self): printout(indent + '%-15s: %s' % (v, getattr(self, v))) + def _update_configuration(self, config): + self.configuration = {"gpu": config[0], "horovod": config[1]} + class ParametersNetwork(ParametersBase): """ @@ -324,7 +328,7 @@ class ParametersRunning(ParametersBase): DFT results. Of these, the mean average error in eV/atom will be calculated. - after_training_metric : string + after_before_training_metric : string Metric for evaluated on the validation and test set AFTER training. Default is "LDOS", meaning that the regular loss on the LDOS will be used as a metric. Possible options are "band_energy" and @@ -358,7 +362,38 @@ def __init__(self): self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" self.during_training_metric = "ldos" - self.after_training_metric = "ldos" + self.after_before_training_metric = "ldos" + + def _update_configuration(self, config): + super(ParametersRunning, self)._update_configuration(config) + self.during_training_metric = self.during_training_metric + self.after_before_training_metric = self.after_before_training_metric + + @property + def during_training_metric(self): + """The metric used during training.""" + return self._during_training_metric + + @during_training_metric.setter + def during_training_metric(self, value): + if value != "ldos": + if self.configuration["horovod"]: + raise Exception("Currently, MALA can only operate with the " + "\"ldos\" metric for horovod runs.") + self._during_training_metric = value + + @property + def after_before_training_metric(self): + """The metric used during training.""" + return self._after_before_training_metric + + @after_before_training_metric.setter + def after_before_training_metric(self, value): + if value != "ldos": + if self.configuration["horovod"]: + raise Exception("Currently, MALA can only operate with the " + "\"ldos\" metric for horovod runs.") + self._after_before_training_metric = value class ParametersHyperparameterOptimization(ParametersBase): @@ -545,6 +580,8 @@ class Parameters: def __init__(self): self.comment = "" + + # Parameters subobjects. self.network = ParametersNetwork() self.descriptors = ParametersDescriptors() self.targets = ParametersTargets() @@ -552,11 +589,13 @@ def __init__(self): self.running = ParametersRunning() self.hyperparameters = ParametersHyperparameterOptimization() self.debug = ParametersDebug() - self.manual_seed = None # Properties + self._use_gpu = False + self._use_horovod = False self.use_horovod = False self.use_gpu = False + self.manual_seed = None @property def use_gpu(self): @@ -573,6 +612,15 @@ def use_gpu(self, value): else: warnings.warn("GPU requested, but no GPU found. MALA will " "operate with CPU only.", stacklevel=3) + self.network._update_configuration([self.use_gpu, self.use_horovod]) + self.descriptors._update_configuration([self.use_gpu, + self.use_horovod]) + self.targets._update_configuration([self.use_gpu, self.use_horovod]) + self.data._update_configuration([self.use_gpu, self.use_horovod]) + self.running._update_configuration([self.use_gpu, self.use_horovod]) + self.hyperparameters._update_configuration([self.use_gpu, + self.use_horovod]) + self.debug._update_configuration([self.use_gpu, self.use_horovod]) @property def use_horovod(self): @@ -585,6 +633,15 @@ def use_horovod(self, value): hvd.init() set_horovod_status(value) self._use_horovod = value + self.network._update_configuration([self.use_gpu, self.use_horovod]) + self.descriptors._update_configuration([self.use_gpu, + self.use_horovod]) + self.targets._update_configuration([self.use_gpu, self.use_horovod]) + self.data._update_configuration([self.use_gpu, self.use_horovod]) + self.running._update_configuration([self.use_gpu, self.use_horovod]) + self.hyperparameters._update_configuration([self.use_gpu, + self.use_horovod]) + self.debug._update_configuration([self.use_gpu, self.use_horovod]) def show(self): """Print name and values of all attributes of this object.""" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 6154783df..b99515ab1 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -173,13 +173,13 @@ def train_network(self): vloss = self.__validate_network(self.network, "validation", self.parameters. - after_training_metric) + after_before_training_metric) if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, "test", self.parameters. - after_training_metric) + after_before_training_metric) # Collect and average all the losses from all the devices if self.parameters_full.use_horovod: @@ -290,12 +290,12 @@ def train_network(self): # CALCULATE FINAL METRICS ############################ - if self.parameters.after_training_metric != \ + if self.parameters.after_before_training_metric != \ self.parameters.during_training_metric: vloss = self.__validate_network(self.network, "validation", self.parameters. - after_training_metric) + after_before_training_metric) if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') @@ -307,7 +307,7 @@ def train_network(self): tloss = self.__validate_network(self.network, "test", self.parameters. - after_training_metric) + after_before_training_metric) if self.parameters_full.use_horovod: tloss = self.__average_validation(tloss, 'average_loss') printout("Final test data loss: ", tloss) From 1f71c391a5e00b64989e7cb66ebdb2736b78401e Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Thu, 22 Jul 2021 22:42:18 +0200 Subject: [PATCH 174/297] Apply suggestions from Daniels code review Co-authored-by: Daniel Kotik --- .github/workflows/cpu-tests.yml | 3 ++- examples/ex01_run_singleshot.py | 2 +- examples/ex02_preprocess_data.py | 2 +- examples/ex04_hyperparameter_optimization.py | 2 +- pytest.ini | 3 ++- test/checkpoint_hyperopt_tests.py | 3 ++- test/installation_test.py | 3 ++- test/integration_test.py | 3 ++- test/lazy_loading_test.py | 13 ++++++++----- test/workflow_test.py | 3 ++- 10 files changed, 23 insertions(+), 14 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 4b8651c49..0e7f2eff2 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -92,4 +92,5 @@ jobs: ../install/data_repo_link/link_data_repo.sh `pwd` - name: Test MALA - run: pytest --disable-warnings \ No newline at end of file + run: pytest --disable-warnings + diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 8c8f0602f..0dc3bf7d0 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -88,4 +88,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() \ No newline at end of file +test_parameters.show() diff --git a/examples/ex02_preprocess_data.py b/examples/ex02_preprocess_data.py index 7e408fc07..981c1edee 100644 --- a/examples/ex02_preprocess_data.py +++ b/examples/ex02_preprocess_data.py @@ -62,4 +62,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() \ No newline at end of file +test_parameters.show() diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 131c1e161..b6abb26c5 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -108,4 +108,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() \ No newline at end of file +test_parameters.show() diff --git a/pytest.ini b/pytest.ini index a034c86c6..53b0aa22a 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,4 +1,5 @@ [pytest] addopts = --ignore=test/basic_gpu_test.py testpaths = - test \ No newline at end of file + test + diff --git a/test/checkpoint_hyperopt_tests.py b/test/checkpoint_hyperopt_tests.py index 1819def5f..608d05308 100644 --- a/test/checkpoint_hyperopt_tests.py +++ b/test/checkpoint_hyperopt_tests.py @@ -142,4 +142,5 @@ def __resume_checkpoint(): """ loaded_params, new_datahandler, new_hyperopt = \ mala.HyperOptOptuna.resume_checkpoint(checkpoint_name) - return new_hyperopt \ No newline at end of file + return new_hyperopt + diff --git a/test/installation_test.py b/test/installation_test.py index fdf2eabcd..705d3068e 100644 --- a/test/installation_test.py +++ b/test/installation_test.py @@ -25,4 +25,5 @@ def test_data_repo(self): from data_repo_path import get_data_repo_path data_path = get_data_repo_path() test_array = np.load(os.path.join(data_path, "linking_tester.npy")) - assert (test_array == [1, 2, 3, 4]).all() \ No newline at end of file + assert np.array_equal(test_array, [1, 2, 3, 4]) + diff --git a/test/integration_test.py b/test/integration_test.py index 092b3cbbd..30f81f75c 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -174,4 +174,5 @@ def test_qe_ldos_to_dos(self): printout("Relative error for sum of DOS: ", rel_error) # Check against the constraints we put upon ourselves. - assert np.isclose(rel_error, 0, atol=accuracy) \ No newline at end of file + assert np.isclose(rel_error, 0, atol=accuracy) + diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index a867333a7..a4d01ef2d 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -102,7 +102,7 @@ def test_scaling(self): data_handler.nr_training_data) this_result.append(data_handler.output_data_scaler.total_std / data_handler.nr_training_data) - if scalingtype == "normal": + elif scalingtype == "normal": torch.manual_seed(2002) this_result.append(data_handler.input_data_scaler.total_max) this_result.append(data_handler.input_data_scaler.total_min) @@ -128,7 +128,7 @@ def test_scaling(self): training_tester.append(test_trainer.final_test_loss - test_trainer.initial_test_loss) - if scalingtype == "feature-wise-standard": + elif scalingtype == "feature-wise-standard": # The lazy-loading STD equation (and to a smaller amount the # mean equation) is having some small accurcay issue that # I presume to be due to numerical constraints. To make a @@ -160,6 +160,7 @@ def test_scaling(self): assert np.isclose(entry[1][3], entry[2][3], atol=accuracy_coarse) assert np.isclose(entry[1][4], entry[2][4], atol=accuracy_coarse) assert np.isclose(entry[1][1], entry[2][1], atol=accuracy_coarse) + assert np.isclose(dataset_tester[0], dataset_tester[1], atol=accuracy_coarse) assert np.isclose(training_tester[0], training_tester[1], @@ -246,8 +247,8 @@ def test_performance_horovod(self): if ll: llstring = "using lazy loading" - results.append([hvdstring, llstring, test_trainer. - initial_test_loss, + results.append([hvdstring, llstring, + test_trainer.initial_test_loss, test_trainer.final_test_loss, time.time() - start_time]) @@ -276,7 +277,9 @@ def test_performance_horovod(self): printout("Final loss: ", r[3]) printout("Time: ", r[4]) diff.append(r[3] - r[2]) + diff = np.array(diff) # The loss improvements should be comparable. - assert np.std(diff) < accuracy_strict \ No newline at end of file + assert np.std(diff) < accuracy_strict + diff --git a/test/workflow_test.py b/test/workflow_test.py index c7438d3fe..aa8ac33bc 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -331,4 +331,5 @@ def __use_trained_network(save_path="./"): assert np.isclose(band_energy_predicted, band_energy_actual, atol=accuracy_predictions) assert np.isclose(nr_electrons_predicted, nr_electrons_actual, - atol=accuracy_predictions) \ No newline at end of file + atol=accuracy_predictions) + From cbba70e963ce9f66ae1c41db66421bcfb5e30504 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Thu, 22 Jul 2021 22:43:37 +0200 Subject: [PATCH 175/297] Apply suggestions from Daniels code review II Github would not let me commit two changes in one file with one batch commit... Co-authored-by: Daniel Kotik --- test/lazy_loading_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index a4d01ef2d..ebc5ec49b 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -141,7 +141,7 @@ def test_scaling(self): means)/data_handler.grid_size) this_result.append(torch.mean(data_handler.output_data_scaler. stds)/data_handler.grid_size) - if scalingtype == "feature-wise-normal": + elif scalingtype == "feature-wise-normal": this_result.append(torch.mean(data_handler.input_data_scaler. maxs)) this_result.append(torch.mean(data_handler.input_data_scaler. From 7df7b003707fcfbe120d8c2f7a93bd92fcf5bdf9 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 13 Jul 2021 17:49:58 +0200 Subject: [PATCH 176/297] Added new parameter --- mala/common/parameters.py | 20 ++++++++++++++++++++ mala/datahandling/data_handler.py | 4 ++-- mala/network/trainer.py | 19 ++++++++++++++----- 3 files changed, 36 insertions(+), 7 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index f68afb8ac..a774c193a 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -311,6 +311,24 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. + + during_training_metric : string + Metric for evaluated on the validation set during training. + Default is "LDOS", meaning that the regular loss on the LDOS will be + used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. + + after_training_metric : string + Metric for evaluated on the validation and test set AFTER training. + Default is "LDOS", meaning that the regular loss on the LDOS will be + used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. """ def __init__(self): @@ -336,6 +354,8 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" + self.during_training_metric = "LDOS" + self.after_training_metric = "LDOS" class ParametersHyperparameterOptimization(ParametersBase): diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index a95f02380..1b8cca2d2 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -4,7 +4,7 @@ from .data_scaler import DataScaler from .snapshot import Snapshot from .lazy_load_dataset import LazyLoadDataset -from mala.common.parameters import Parameters +from mala.common.parameters import Parameters, ParametersData from mala.targets.target_interface import TargetInterface from mala.descriptors.descriptor_interface import DescriptorInterface from mala.common.printout import printout @@ -47,7 +47,7 @@ class DataHandler: def __init__(self, parameters: Parameters, target_calculator=None, descriptor_calculator=None, input_data_scaler=None, output_data_scaler=None): - self.parameters = parameters.data + self.parameters: ParametersData = parameters.data self.dbg_grid_dimensions = parameters.debug.grid_dimensions self.use_horovod = parameters.use_horovod self.training_data_set = None diff --git a/mala/network/trainer.py b/mala/network/trainer.py index bc46cde19..56209fa58 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -165,8 +165,10 @@ def load_from_file(cls, params, file_path, network, data): def train_network(self): """Train a network using data given by a DataHandler.""" - # Create reference to data and network and setup training. - # Calculate initial loss. + ############################ + # CALCULATE INITIAL METRICS + ############################ + tloss = float("inf") vloss = self.__validate_network(self.network, self.validation_data_loader) @@ -191,13 +193,17 @@ def train_network(self): # Initialize all the counters. checkpoint_counter = 0 - # If we restarted from a checkpoint, we + # If we restarted from a checkpoint, we have to differently initialize + # the loss. if self.last_loss is None: vloss_old = vloss else: vloss_old = self.last_loss - # Perform and log training. + ############################ + # PERFORM TRAINING + ############################ + for epoch in range(self.last_epoch, self.parameters.max_number_epochs): start_time = time.time() @@ -274,7 +280,10 @@ def train_network(self): if self.parameters.verbosity: printout("Time for epoch[s]:", time.time() - start_time) - # Calculate final loss. + ############################ + # CALCULATE FINAL METRICS + ############################ + self.final_validation_loss = vloss tloss = float("inf") if self.data.test_data_set is not None: From 731c8bc16d4f2729da0667b9975faf6de4a4331f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 15:27:01 +0200 Subject: [PATCH 177/297] First version of band energy during training --- mala/common/parameters.py | 6 +-- mala/network/runner.py | 87 ++++++++++++++++++++++++++++++++++++++- mala/network/tester.py | 84 +++++++------------------------------ mala/network/trainer.py | 64 ++++++++++++++++++++++------ 4 files changed, 155 insertions(+), 86 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index a774c193a..1e8f2a4da 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -314,7 +314,7 @@ class ParametersRunning(ParametersBase): during_training_metric : string Metric for evaluated on the validation set during training. - Default is "LDOS", meaning that the regular loss on the LDOS will be + Default is "ldos", meaning that the regular loss on the LDOS will be used as a metric. Possible options are "band_energy" and "total_energy". For these, the band resp. total energy of the validation snapshots will be calculated and compared to the provided @@ -354,8 +354,8 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" - self.during_training_metric = "LDOS" - self.after_training_metric = "LDOS" + self.during_training_metric = "ldos" + self.after_training_metric = "ldos" class ParametersHyperparameterOptimization(ParametersBase): diff --git a/mala/network/runner.py b/mala/network/runner.py index 5abb99f7f..cc44bdb5b 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -1,6 +1,8 @@ """Runner class for running networks.""" import torch from mala.common.parameters import printout +from mala import Parameters +import numpy as np try: import horovod.torch as hvd except ModuleNotFoundError: @@ -27,7 +29,7 @@ class Runner: """ def __init__(self, params, network, data): - self.parameters_full = params + self.parameters_full: Parameters = params self.parameters = params.running self.network = network self.data = data @@ -47,3 +49,86 @@ def __prepare_to_run(self): torch.cuda.get_device_name(hvd.local_rank())) # pin GPU to local rank torch.cuda.set_device(hvd.local_rank()) + + def _forward_entire_snapshot(self, snapshot_number, data_set, + number_of_batches_per_snapshot=0, + batch_size=0): + """ + Forward an entire snapshot through the network and get actual + and predicted outputs. + + Parameters + ---------- + snapshot_number : int + Snapshot for which the prediction is done. + + number_of_batches_per_snapshot : int + Number of batches that lie within a snapshot. + + batch_size : int + Batch size used for forward pass. + + Returns + ------- + actual_outputs : torch.Tensor + Actual outputs for snapshot. + + predicted_outputs : torch.Tensor + Precicted outputs for snapshot. + """ + if self.data.parameters.use_lazy_loading: + data_set.return_outputs_directly = True + actual_outputs = \ + (data_set + [snapshot_number * self.data. + grid_size:(snapshot_number + 1) * self.data.grid_size])[1] + else: + actual_outputs = \ + self.data.output_data_scaler.\ + inverse_transform( + (data_set[snapshot_number * + self.data.grid_size: + (snapshot_number + 1) * + self.data.grid_size])[1], + as_numpy=True) + + predicted_outputs = np.zeros((self.data.grid_size, + self.data.get_output_dimension())) + + offset = snapshot_number * self.data.grid_size + for i in range(0, number_of_batches_per_snapshot): + inputs, outputs = \ + data_set[offset+(i * batch_size):offset+((i + 1) * batch_size)] + if self.parameters_full.use_gpu: + inputs = inputs.to('cuda') + predicted_outputs[i * batch_size:(i + 1) * batch_size, :] = \ + self.data.output_data_scaler.\ + inverse_transform(self.network(inputs). + to('cpu'), as_numpy=True) + + # Restricting the actual quantities to physical meaningful values, + # i.e. restricting the (L)DOS to positive values. + predicted_outputs = self.data.target_calculator.\ + restrict_data(predicted_outputs) + + # It could be that other operations will be happening with the data + # set, so it's best to reset it. + if self.data.parameters.use_lazy_loading: + data_set.return_outputs_directly = False + + return actual_outputs, predicted_outputs + + @staticmethod + def _correct_batch_size_for_testing(datasize, batchsize): + """ + Get the correct batch size for testing. + + For testing snapshot the batch size needs to be such that + data_per_snapshot / batch_size will result in an integer division + without any residual value. + """ + new_batch_size = batchsize + if datasize % new_batch_size != 0: + while datasize % new_batch_size != 0: + new_batch_size += 1 + return new_batch_size diff --git a/mala/network/tester.py b/mala/network/tester.py index bb6c62abd..77bb7042c 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -53,80 +53,26 @@ def test_snapshot(self, snapshot_number): predicted_outputs : torch.Tensor Precicted outputs for snapshot. """ - self.data.prepare_for_testing() - if self.data.parameters.use_lazy_loading: - actual_outputs = \ - (self.data.test_data_set - [snapshot_number * self.data. - grid_size:(snapshot_number + 1) * self.data.grid_size])[1] - else: - actual_outputs = \ - self.data.output_data_scaler.\ - inverse_transform( - (self.data.test_data_set[snapshot_number * - self.data.grid_size: - (snapshot_number + 1) * - self.data.grid_size])[1], - as_numpy=True) - - predicted_outputs = np.zeros((self.data.grid_size, - self.data.get_output_dimension())) - - offset = snapshot_number * self.data.grid_size - for i in range(0, self.number_of_batches_per_snapshot): - inputs, outputs = \ - self.data.test_data_set[offset+(i * self.parameters. - mini_batch_size): - offset+((i + 1) * self.parameters. - mini_batch_size)] - if self.parameters_full.use_gpu: - inputs = inputs.to('cuda') - predicted_outputs[i * self.parameters. - mini_batch_size:(i + 1) * self.parameters. - mini_batch_size, :] = \ - self.data.output_data_scaler.\ - inverse_transform(self.network(inputs). - to('cpu'), as_numpy=True) - - # Restricting the actual quantities to physical meaningful values, - # i.e. restricting the (L)DOS to positive values. - predicted_outputs = self.data.target_calculator.\ - restrict_data(predicted_outputs) - return actual_outputs, predicted_outputs + # Forward through network. + return self.\ + _forward_entire_snapshot(snapshot_number, + self.data.test_data_set, + self.number_of_batches_per_snapshot, + self.parameters.mini_batch_size) def __prepare_to_test(self): """Prepare the tester class to for test run.""" - if self.parameters_full.use_horovod: - self.parameters.sampler["test_sampler"] = torch.utils.data.\ - distributed.DistributedSampler(self.data.test_data_set, - num_replicas=hvd.size(), - rank=hvd.rank()) - # We will use the DataLoader iterator to iterate over the test data. + # We will use the DataSet iterator to iterate over the test data. # But since we only want the data per snapshot, # we need to make sure the batch size is compatible with that. - self.__check_and_adjust_batch_size(self.data.grid_size) - self.test_data_loader = DataLoader(self.data.test_data_set, - batch_size=self.parameters. - mini_batch_size * 1, - sampler=self.parameters. - sampler["test_sampler"], - **self.parameters.kwargs, - shuffle=False) + optimal_batch_size = self.\ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters.mini_batch_size) + if optimal_batch_size != self.parameters.mini_batch_size: + printout("Had to readjust batch size from", + self.parameters.mini_batch_size, "to", + optimal_batch_size) + self.parameters.mini_batch_size = optimal_batch_size self.number_of_batches_per_snapshot = int(self.data.grid_size / self.parameters. mini_batch_size) - - def __check_and_adjust_batch_size(self, datasize): - """ - Check batch size and adjust it if necessary. - - For testing the batch size needs to be such that data_per_snapshot / - batch_size will result in an integer division without any residual - value. - """ - if datasize % self.parameters.mini_batch_size != 0: - old_batch_size = self.parameters.mini_batch_size - while datasize % self.parameters.mini_batch_size != 0: - self.parameters.mini_batch_size += 1 - printout("Had to readjust batch size from", old_batch_size, "to", - self.parameters.mini_batch_size) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 56209fa58..cf8002d2b 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -171,7 +171,8 @@ def train_network(self): tloss = float("inf") vloss = self.__validate_network(self.network, - self.validation_data_loader) + self.validation_data_loader, + validation_type="band_energy") if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, self.test_data_loader) @@ -440,20 +441,57 @@ def __process_mini_batch(self, network, input_data, target_data): self.optimizer.zero_grad() return loss.item() - def __validate_network(self, network, vdl): + def __validate_network(self, network, vdl, validation_type="ldos"): """Validate a network, using test or validation data.""" network.eval() - validation_loss = [] - with torch.no_grad(): - for x, y in vdl: - if self.parameters_full.use_gpu: - x = x.to('cuda') - y = y.to('cuda') - prediction = network(x) - validation_loss.append(network.calculate_loss(prediction, y) - .item()) - - return np.mean(validation_loss) + if validation_type == "ldos": + validation_loss = [] + with torch.no_grad(): + for x, y in vdl: + if self.parameters_full.use_gpu: + x = x.to('cuda') + y = y.to('cuda') + prediction = network(x) + validation_loss.append(network.calculate_loss(prediction, y) + .item()) + + return np.mean(validation_loss) + elif validation_type == "band_energy": + + data_set = self.data.validation_data_set + number_snapshots = 2 + + # Get optimal batch size and number of batches per snapshots. + optimal_batch_size = self. \ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters. + mini_batch_size) + number_of_batches_per_snapshot = int(self.data.grid_size / + optimal_batch_size) + errors = [] + for snapshot_number in range(0, number_snapshots): + actual_outputs, \ + predicted_outputs = self.\ + _forward_entire_snapshot(snapshot_number, data_set, + number_of_batches_per_snapshot, + optimal_batch_size) + + calculator = self.data.target_calculator + # FIXME: This will eventually break down. + calculator.\ + read_additional_calculation_data("qe.out", + self.data. + get_snapshot_calculation_output(0)) + fe_actual = calculator.\ + get_self_consistent_fermi_energy_ev(actual_outputs) + be_actual = calculator.\ + get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + be_predicted = calculator.\ + get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) + return np.mean(errors) def __create_training_checkpoint(self): """ From 51b78809613f7cbbbbf868cf6f2e7a02038f8d82 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 16:36:36 +0200 Subject: [PATCH 178/297] Flexible test and validation data usage for band energy --- mala/datahandling/data_handler.py | 39 +++++++++++------ mala/network/trainer.py | 71 +++++++++++++++++++++++-------- 2 files changed, 79 insertions(+), 31 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 1b8cca2d2..f389c6ece 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -79,10 +79,18 @@ def __init__(self, parameters: Parameters, target_calculator=None, self.nr_snapshots = 0 self.grid_dimension = [0, 0, 0] self.grid_size = 0 + + # Actual data points in the different categories. self.nr_training_data = 0 self.nr_test_data = 0 self.nr_validation_data = 0 + # Number of snapshots in these categories. + self.nr_training_snapshots = 0 + self.nr_test_snapshots = 0 + self.nr_validation_snapshots = 0 + + self.training_data_inputs = torch.empty(0) """ Torch tensor holding all scaled training data inputs. @@ -192,6 +200,9 @@ def clear_data(self): self.nr_training_data = 0 self.nr_test_data = 0 self.nr_validation_data = 0 + self.nr_training_snapshots = 0 + self.nr_test_snapshots = 0 + self.nr_validation_snapshots = 0 self.parameters.data_splitting_snapshots = [] self.parameters.snapshot_directories_list = [] @@ -449,37 +460,37 @@ def __check_snapshots(self): if self.parameters.data_splitting_type == "by_snapshot": for snapshot_function in self.parameters.data_splitting_snapshots: if snapshot_function == "tr": - self.nr_training_data += 1 + self.nr_training_snapshots += 1 elif snapshot_function == "te": - self.nr_test_data += 1 + self.nr_test_snapshots += 1 elif snapshot_function == "va": - self.nr_validation_data += 1 + self.nr_validation_snapshots += 1 else: raise Exception("Unknown option for snapshot splitting " "selected.") # Now we need to check whether or not this input is believable. nr_of_snapshots = len(self.parameters.snapshot_directories_list) - if nr_of_snapshots != (self.nr_training_data + - self.nr_validation_data + - self.nr_test_data): + if nr_of_snapshots != (self.nr_training_snapshots + + self.nr_test_snapshots + + self.nr_validation_snapshots): raise Exception("Cannot split snapshots with specified " "splitting scheme, " "too few or too many options selected") - if self.nr_training_data == 0 and self.nr_test_data == 0: + if self.nr_training_snapshots == 0 and self.nr_test_snapshots == 0: raise Exception("No training snapshots provided.") - if self.nr_validation_data == 0 and self.nr_test_data == 0: + if self.nr_validation_snapshots == 0 and self.nr_test_snapshots == 0: raise Exception("No validation snapshots provided.") - if self.nr_training_data == 0 and self.nr_test_data != 0: + if self.nr_training_snapshots == 0 and self.nr_test_snapshots != 0: printout("DataHandler prepared for inference. No training " "possible with this setup. " "If this is not what you wanted, please revise the " "input script.") - if self.nr_validation_data != 0: + if self.nr_validation_snapshots != 0: printout("As this DataHandler can only be used for " "inference, the validation data you have " "provided will be ignored.") - if self.nr_test_data == 0: + if self.nr_test_snapshots == 0: printout("Running MALA without test data. If this is not " "what you wanted, " "please revise the input script.") @@ -489,9 +500,9 @@ def __check_snapshots(self): # As we are not actually interested in the number of snapshots, but in # the number of datasets,we need to multiply by that. - self.nr_training_data *= self.grid_size - self.nr_validation_data *= self.grid_size - self.nr_test_data *= self.grid_size + self.nr_training_data = self.nr_training_snapshots*self.grid_size + self.nr_validation_data = self.nr_validation_snapshots*self.grid_size + self.nr_test_data = self.nr_test_snapshots*self.grid_size def __load_from_npy_file(self, file, mmapmode=None): """Load a numpy array from a file.""" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index cf8002d2b..1a8cef022 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -171,12 +171,16 @@ def train_network(self): tloss = float("inf") vloss = self.__validate_network(self.network, - self.validation_data_loader, - validation_type="band_energy") + "validation", + self.parameters. + after_training_metric) + if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, - self.test_data_loader) - print(tloss) + "test", + self.parameters. + after_training_metric) + # Collect and average all the losses from all the devices if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') @@ -229,7 +233,9 @@ def train_network(self): # Calculate the validation loss. and output it. vloss = self.__validate_network(self.network, - self.validation_data_loader) + "validation", + self.parameters. + during_training_metric) if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') if self.parameters.verbosity: @@ -285,11 +291,24 @@ def train_network(self): # CALCULATE FINAL METRICS ############################ + if self.parameters.after_training_metric != \ + self.parameters.during_training_metric: + vloss = self.__validate_network(self.network, + "validation", + self.parameters. + after_training_metric) + if self.parameters_full.use_horovod: + vloss = self.__average_validation(vloss, 'average_loss') + self.final_validation_loss = vloss + printout("Final validation data loss: ", vloss) + tloss = float("inf") if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, - self.test_data_loader) + "test", + self.parameters. + after_training_metric) if self.parameters_full.use_horovod: tloss = self.__average_validation(tloss, 'average_loss') printout("Final test data loss: ", tloss) @@ -441,13 +460,26 @@ def __process_mini_batch(self, network, input_data, target_data): self.optimizer.zero_grad() return loss.item() - def __validate_network(self, network, vdl, validation_type="ldos"): + def __validate_network(self, network, data_set_type, validation_type): """Validate a network, using test or validation data.""" + if data_set_type == "test": + data_loader = self.test_data_loader + data_set = self.data.test_data_set + number_of_snapshots = self.data.nr_test_snapshots + + elif data_set_type == "validation": + data_loader = self.validation_data_loader + data_set = self.data.validation_data_set + number_of_snapshots = self.data.nr_validation_snapshots + + else: + raise Exception("Please select test or validation" + "when using this function.") network.eval() if validation_type == "ldos": validation_loss = [] with torch.no_grad(): - for x, y in vdl: + for x, y in data_loader: if self.parameters_full.use_gpu: x = x.to('cuda') y = y.to('cuda') @@ -457,10 +489,6 @@ def __validate_network(self, network, vdl, validation_type="ldos"): return np.mean(validation_loss) elif validation_type == "band_energy": - - data_set = self.data.validation_data_set - number_snapshots = 2 - # Get optimal batch size and number of batches per snapshots. optimal_batch_size = self. \ _correct_batch_size_for_testing(self.data.grid_size, @@ -469,7 +497,7 @@ def __validate_network(self, network, vdl, validation_type="ldos"): number_of_batches_per_snapshot = int(self.data.grid_size / optimal_batch_size) errors = [] - for snapshot_number in range(0, number_snapshots): + for snapshot_number in range(0, number_of_snapshots): actual_outputs, \ predicted_outputs = self.\ _forward_entire_snapshot(snapshot_number, data_set, @@ -486,12 +514,21 @@ def __validate_network(self, network, vdl, validation_type="ldos"): get_self_consistent_fermi_energy_ev(actual_outputs) be_actual = calculator.\ get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) - fe_predicted = calculator.\ - get_self_consistent_fermi_energy_ev(predicted_outputs) - be_predicted = calculator.\ - get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + try: + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + be_predicted = calculator.\ + get_band_energy(predicted_outputs, fermi_energy_eV=fe_predicted) + except ValueError: + # If the training went badly, it might be that the above + # code results in an error, due to the LDOS being so wrong + # that the estimation of the self consistent Fermi energy + # fails. + be_predicted = float("inf") errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) return np.mean(errors) + else: + raise Exception("Selected validation method not supported.") def __create_training_checkpoint(self): """ From db0cf1314478e5f85d35751c474480b492a1d8a0 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 20 Jul 2021 17:58:06 +0200 Subject: [PATCH 179/297] band energy evaluation should now work across entire sets --- mala/datahandling/data_handler.py | 20 ++++++++--- mala/network/trainer.py | 60 ++++++++++++++++++++++++++++--- 2 files changed, 71 insertions(+), 9 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index f389c6ece..13e0e5e95 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -261,12 +261,14 @@ def prepare_data(self, reparametrize_scaler=True): hvd.allreduce(torch.tensor(0), name='barrier') def mix_datasets(self): - """For lazily-loaded data sets, the snapshot ordering is (re-)mixed.""" + """ + For lazily-loaded data sets, the snapshot ordering is (re-)mixed. + + This applies only to the training data set. For the validation and + test set it does not matter. + """ if self.parameters.use_lazy_loading: - self.validation_data_set.mix_datasets() self.training_data_set.mix_datasets() - if self.test_data_set is not None: - self.test_data_set.mix_datasets() def raw_numpy_to_converted_scaled_tensor(self, numpy_array, data_type, units, convert3Dto1D=False): @@ -504,6 +506,16 @@ def __check_snapshots(self): self.nr_validation_data = self.nr_validation_snapshots*self.grid_size self.nr_test_data = self.nr_test_snapshots*self.grid_size + # Reordering the lists. + snapshot_order = ['tr', 'va', 'te'] + combined = zip(self.parameters.data_splitting_snapshots, + self.parameters.snapshot_directories_list) + order = {key: i for i, key in enumerate(snapshot_order)} + combined_sorted = sorted(combined, key=lambda d: order[d[0]]) + self.parameters.data_splitting_snapshots, \ + self.parameters.snapshot_directories_list = \ + map(list, zip(*combined_sorted)) + def __load_from_npy_file(self, file, mmapmode=None): """Load a numpy array from a file.""" loaded_array = np.load(file, mmap_mode=mmapmode) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 1a8cef022..b2e1fac06 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -466,11 +466,14 @@ def __validate_network(self, network, data_set_type, validation_type): data_loader = self.test_data_loader data_set = self.data.test_data_set number_of_snapshots = self.data.nr_test_snapshots + offset_snapshots = self.data.nr_validation_snapshots + \ + self.data.nr_training_snapshots elif data_set_type == "validation": data_loader = self.validation_data_loader data_set = self.data.validation_data_set number_of_snapshots = self.data.nr_validation_snapshots + offset_snapshots = self.data.nr_training_snapshots else: raise Exception("Please select test or validation" @@ -497,23 +500,27 @@ def __validate_network(self, network, data_set_type, validation_type): number_of_batches_per_snapshot = int(self.data.grid_size / optimal_batch_size) errors = [] - for snapshot_number in range(0, number_of_snapshots): + for snapshot_number in range(offset_snapshots, + number_of_snapshots+offset_snapshots): actual_outputs, \ predicted_outputs = self.\ - _forward_entire_snapshot(snapshot_number, data_set, + _forward_entire_snapshot(snapshot_number-offset_snapshots, + data_set, number_of_batches_per_snapshot, optimal_batch_size) - calculator = self.data.target_calculator - # FIXME: This will eventually break down. + + # This works because the list is always guaranteed to be + # ordered. calculator.\ read_additional_calculation_data("qe.out", self.data. - get_snapshot_calculation_output(0)) + get_snapshot_calculation_output(snapshot_number)) fe_actual = calculator.\ get_self_consistent_fermi_energy_ev(actual_outputs) be_actual = calculator.\ get_band_energy(actual_outputs, fermi_energy_eV=fe_actual) + try: fe_predicted = calculator.\ get_self_consistent_fermi_energy_ev(predicted_outputs) @@ -527,6 +534,49 @@ def __validate_network(self, network, data_set_type, validation_type): be_predicted = float("inf") errors.append(np.abs(be_predicted-be_actual)*(1000/len(calculator.atoms))) return np.mean(errors) + elif validation_type == "total_energy": + # Get optimal batch size and number of batches per snapshots. + optimal_batch_size = self. \ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters. + mini_batch_size) + number_of_batches_per_snapshot = int(self.data.grid_size / + optimal_batch_size) + errors = [] + for snapshot_number in range(offset_snapshots, + number_of_snapshots+offset_snapshots): + actual_outputs, \ + predicted_outputs = self.\ + _forward_entire_snapshot(snapshot_number, data_set, + number_of_batches_per_snapshot, + optimal_batch_size) + calculator = self.data.target_calculator + + # This works because the list is always guaranteed to be + # ordered. + calculator.\ + read_additional_calculation_data("qe.out", + self.data. + get_snapshot_calculation_output(snapshot_number)) + fe_actual = calculator.\ + get_self_consistent_fermi_energy_ev(actual_outputs) + te_actual = calculator.\ + get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_actual) + + try: + fe_predicted = calculator.\ + get_self_consistent_fermi_energy_ev(predicted_outputs) + te_predicted = calculator.\ + get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_predicted) + except ValueError: + # If the training went badly, it might be that the above + # code results in an error, due to the LDOS being so wrong + # that the estimation of the self consistent Fermi energy + # fails. + te_predicted = float("inf") + errors.append(np.abs(te_predicted-te_actual)*(1000/len(calculator.atoms))) + return np.mean(errors) + else: raise Exception("Selected validation method not supported.") From 88715cb5f680a49803215dc32775788429ceb1fd Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 21 Jul 2021 11:57:25 +0200 Subject: [PATCH 180/297] total_energy during running should work now --- mala/common/parameters.py | 3 +++ mala/network/trainer.py | 9 ++++++--- mala/targets/target_base.py | 15 ++++++++++++++- 3 files changed, 23 insertions(+), 4 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 1e8f2a4da..e2eac302b 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -127,6 +127,8 @@ class ParametersTargets(ParametersBase): ldos_gridoffset_ev: float Lowest energy value on the (L)DOS energy grid [eV]. + + """ def __init__(self): @@ -136,6 +138,7 @@ def __init__(self): self.ldos_gridspacing_ev = 0 self.ldos_gridoffset_ev = 0 self.restrict_targets = "zero_out_negative" + self.pseudopotential_path = None @property def restrict_targets(self): diff --git a/mala/network/trainer.py b/mala/network/trainer.py index b2e1fac06..90dce2188 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -547,7 +547,8 @@ def __validate_network(self, network, data_set_type, validation_type): number_of_snapshots+offset_snapshots): actual_outputs, \ predicted_outputs = self.\ - _forward_entire_snapshot(snapshot_number, data_set, + _forward_entire_snapshot(snapshot_number-offset_snapshots, + data_set, number_of_batches_per_snapshot, optimal_batch_size) calculator = self.data.target_calculator @@ -561,13 +562,15 @@ def __validate_network(self, network, data_set_type, validation_type): fe_actual = calculator.\ get_self_consistent_fermi_energy_ev(actual_outputs) te_actual = calculator.\ - get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_actual) + get_total_energy(ldos_data=actual_outputs, + fermi_energy_eV=fe_actual) try: fe_predicted = calculator.\ get_self_consistent_fermi_energy_ev(predicted_outputs) te_predicted = calculator.\ - get_total_energy(ldos_data=actual_outputs, fermi_energy_eV=fe_predicted) + get_total_energy(ldos_data=actual_outputs, + fermi_energy_eV=fe_predicted) except ValueError: # If the training went badly, it might be that the above # code results in an error, due to the LDOS being so wrong diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 1f46599b5..fa3b3ba17 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -42,7 +42,7 @@ def __init__(self, params): "calculation": 'scf', "restart_mode": 'from_scratch', "prefix": 'MALA', - "pseudo_dir": None, + "pseudo_dir": self.parameters.pseudopotential_path, "outdir": './', "ibrav": None, "smearing": 'fermi-dirac', @@ -65,6 +65,19 @@ def __init__(self, params): self.kpoints = None # (2, 2, 2) self.qe_pseudopotentials = {} + @property + def qe_input_data(self): + """Input data for QE TEM calls.""" + + # Update the pseudopotential path from Parameters. + self._qe_input_data["pseudo_dir"] = \ + self.parameters.pseudopotential_path + return self._qe_input_data + + @qe_input_data.setter + def qe_input_data(self, value): + self._qe_input_data = value + def read_from_cube(self): """Read the quantity from a .cube file.""" raise Exception("No function defined to read this quantity " From 702d6175b98d337eb868975ea837c7d3ddeb44e6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 22 Jul 2021 13:10:48 +0200 Subject: [PATCH 181/297] Prevent metrics other then LDOS from being used in horovod (for now) --- mala/common/parameters.py | 65 ++++++++++++++++++++++++++++++++++++--- mala/network/trainer.py | 10 +++--- 2 files changed, 66 insertions(+), 9 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index e2eac302b..1bc9fbac2 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -15,7 +15,8 @@ class ParametersBase: """Base parameter class for MALA.""" - def __init__(self): + def __init__(self,): + self.configuration = None pass def show(self, indent=""): @@ -32,6 +33,9 @@ def show(self, indent=""): for v in vars(self): printout(indent + '%-15s: %s' % (v, getattr(self, v))) + def _update_configuration(self, config): + self.configuration = {"gpu": config[0], "horovod": config[1]} + class ParametersNetwork(ParametersBase): """ @@ -324,7 +328,7 @@ class ParametersRunning(ParametersBase): DFT results. Of these, the mean average error in eV/atom will be calculated. - after_training_metric : string + after_before_training_metric : string Metric for evaluated on the validation and test set AFTER training. Default is "LDOS", meaning that the regular loss on the LDOS will be used as a metric. Possible options are "band_energy" and @@ -358,7 +362,38 @@ def __init__(self): self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" self.during_training_metric = "ldos" - self.after_training_metric = "ldos" + self.after_before_training_metric = "ldos" + + def _update_configuration(self, config): + super(ParametersRunning, self)._update_configuration(config) + self.during_training_metric = self.during_training_metric + self.after_before_training_metric = self.after_before_training_metric + + @property + def during_training_metric(self): + """The metric used during training.""" + return self._during_training_metric + + @during_training_metric.setter + def during_training_metric(self, value): + if value != "ldos": + if self.configuration["horovod"]: + raise Exception("Currently, MALA can only operate with the " + "\"ldos\" metric for horovod runs.") + self._during_training_metric = value + + @property + def after_before_training_metric(self): + """The metric used during training.""" + return self._after_before_training_metric + + @after_before_training_metric.setter + def after_before_training_metric(self, value): + if value != "ldos": + if self.configuration["horovod"]: + raise Exception("Currently, MALA can only operate with the " + "\"ldos\" metric for horovod runs.") + self._after_before_training_metric = value class ParametersHyperparameterOptimization(ParametersBase): @@ -545,6 +580,8 @@ class Parameters: def __init__(self): self.comment = "" + + # Parameters subobjects. self.network = ParametersNetwork() self.descriptors = ParametersDescriptors() self.targets = ParametersTargets() @@ -552,11 +589,13 @@ def __init__(self): self.running = ParametersRunning() self.hyperparameters = ParametersHyperparameterOptimization() self.debug = ParametersDebug() - self.manual_seed = None # Properties + self._use_gpu = False + self._use_horovod = False self.use_horovod = False self.use_gpu = False + self.manual_seed = None @property def use_gpu(self): @@ -573,6 +612,15 @@ def use_gpu(self, value): else: warnings.warn("GPU requested, but no GPU found. MALA will " "operate with CPU only.", stacklevel=3) + self.network._update_configuration([self.use_gpu, self.use_horovod]) + self.descriptors._update_configuration([self.use_gpu, + self.use_horovod]) + self.targets._update_configuration([self.use_gpu, self.use_horovod]) + self.data._update_configuration([self.use_gpu, self.use_horovod]) + self.running._update_configuration([self.use_gpu, self.use_horovod]) + self.hyperparameters._update_configuration([self.use_gpu, + self.use_horovod]) + self.debug._update_configuration([self.use_gpu, self.use_horovod]) @property def use_horovod(self): @@ -585,6 +633,15 @@ def use_horovod(self, value): hvd.init() set_horovod_status(value) self._use_horovod = value + self.network._update_configuration([self.use_gpu, self.use_horovod]) + self.descriptors._update_configuration([self.use_gpu, + self.use_horovod]) + self.targets._update_configuration([self.use_gpu, self.use_horovod]) + self.data._update_configuration([self.use_gpu, self.use_horovod]) + self.running._update_configuration([self.use_gpu, self.use_horovod]) + self.hyperparameters._update_configuration([self.use_gpu, + self.use_horovod]) + self.debug._update_configuration([self.use_gpu, self.use_horovod]) def show(self): """Print name and values of all attributes of this object.""" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 90dce2188..a4f7a9bcc 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -173,13 +173,13 @@ def train_network(self): vloss = self.__validate_network(self.network, "validation", self.parameters. - after_training_metric) + after_before_training_metric) if self.data.test_data_set is not None: tloss = self.__validate_network(self.network, "test", self.parameters. - after_training_metric) + after_before_training_metric) # Collect and average all the losses from all the devices if self.parameters_full.use_horovod: @@ -291,12 +291,12 @@ def train_network(self): # CALCULATE FINAL METRICS ############################ - if self.parameters.after_training_metric != \ + if self.parameters.after_before_training_metric != \ self.parameters.during_training_metric: vloss = self.__validate_network(self.network, "validation", self.parameters. - after_training_metric) + after_before_training_metric) if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') @@ -308,7 +308,7 @@ def train_network(self): tloss = self.__validate_network(self.network, "test", self.parameters. - after_training_metric) + after_before_training_metric) if self.parameters_full.use_horovod: tloss = self.__average_validation(tloss, 'average_loss') printout("Final test data loss: ", tloss) From a3a9c504e11d5bdef571e4dd57a9379b76129a6d Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Jul 2021 08:48:16 +0200 Subject: [PATCH 182/297] Fixed small bug with initial losses --- mala/network/trainer.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index a4f7a9bcc..61f18f138 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -187,14 +187,15 @@ def train_network(self): if self.data.test_data_set is not None: tloss = self.__average_validation(tloss, 'average_loss') - self.initial_test_loss = tloss - self.initial_validation_loss = vloss - if self.parameters.verbosity: printout("Initial Guess - validation data loss: ", vloss) if self.data.test_data_set is not None: printout("Initial Guess - test data loss: ", tloss) + # Save losses for later use. + self.initial_validation_loss = vloss + self.initial_test_loss = tloss + # Initialize all the counters. checkpoint_counter = 0 From 68dc7fc7a41c8e7a2c9ccecd9be320c8a38d8062 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Jul 2021 09:29:37 +0200 Subject: [PATCH 183/297] Fixed some bugs with the pseudopotential handling --- .github/workflows/cpu-tests.yml | 2 +- examples/ex03_postprocess_data.py | 6 +++--- mala/targets/target_base.py | 8 -------- test/workflow_test.py | 6 +++--- 4 files changed, 7 insertions(+), 15 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 0e7f2eff2..bab6a705c 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -81,7 +81,7 @@ jobs: repository: mala-project/test-data token: ${{ secrets.ACCESS_TOKEN }} path: mala_data - ref: v0.3.0 + ref: v0.4.0 lfs: false - name: Data setup diff --git a/examples/ex03_postprocess_data.py b/examples/ex03_postprocess_data.py index 24f966565..abc098c99 100644 --- a/examples/ex03_postprocess_data.py +++ b/examples/ex03_postprocess_data.py @@ -29,6 +29,9 @@ test_parameters.targets.ldos_gridsize = 250 test_parameters.targets.ldos_gridspacing_ev = 0.1 test_parameters.targets.ldos_gridoffset_ev = -10 +# To perform a total energy calculation one also needs to provide +# a pseudopotential(path). +test_parameters.targets.pseudopotential_path = data_path #################### # TARGETS @@ -65,9 +68,6 @@ self_consistent_fermi_energy) total_energy = 0.0 if do_total_energy: - # To perform a total energy calculation one also needs to provide - # a pseudopotential(path). - ldos.set_pseudopotential_path(data_path) total_energy = ldos.get_total_energy(ldos_data, fermi_energy_eV= self_consistent_fermi_energy) diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index fa3b3ba17..209f07cf6 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -234,14 +234,6 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): else: raise Exception("Unsupported auxiliary file type.") - def set_pseudopotential_path(self, newpath): - """ - Set a path where your pseudopotentials are stored. - - This is needed for doing QE calculations. - """ - self.qe_input_data["pseudo_dir"] = newpath - def get_energy_grid(self): """Get energy grid.""" raise Exception("No method implement to calculate an energy grid.") diff --git a/test/workflow_test.py b/test/workflow_test.py index aa8ac33bc..a96e4abef 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -157,7 +157,7 @@ def test_total_energy_from_dos_density(self): test_parameters.targets.ldos_gridsize = 250 test_parameters.targets.ldos_gridspacing_ev = 0.1 test_parameters.targets.ldos_gridoffset_ev = -10 - + test_parameters.targets.pseudopotential_path = data_path # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) ldos.read_additional_calculation_data("qe.out", @@ -169,7 +169,7 @@ def test_total_energy_from_dos_density(self): # Calculate energies self_consistent_fermi_energy = dos. \ get_self_consistent_fermi_energy_ev(dos_data) - ldos.set_pseudopotential_path(data_path) + total_energy = ldos.get_total_energy(dos_data=dos_data, density_data=dens_data, fermi_energy_eV= @@ -193,6 +193,7 @@ def test_total_energy_from_ldos(self): test_parameters.targets.ldos_gridsize = 250 test_parameters.targets.ldos_gridspacing_ev = 0.1 test_parameters.targets.ldos_gridoffset_ev = -10 + test_parameters.targets.pseudopotential_path = data_path # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) @@ -203,7 +204,6 @@ def test_total_energy_from_ldos(self): # Calculate energies self_consistent_fermi_energy = ldos. \ get_self_consistent_fermi_energy_ev(ldos_data) - ldos.set_pseudopotential_path(data_path) total_energy = ldos.get_total_energy(ldos_data, fermi_energy_eV= self_consistent_fermi_energy) From 545feb658dd804d4464e3b487af438bd0a9c8922 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Jul 2021 09:34:02 +0200 Subject: [PATCH 184/297] Fixed doc issues --- mala/common/parameters.py | 4 ++-- mala/network/runner.py | 3 +-- mala/targets/target_base.py | 1 - 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 1bc9fbac2..6ba1009d3 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -371,7 +371,7 @@ def _update_configuration(self, config): @property def during_training_metric(self): - """The metric used during training.""" + """Get the metric used during training.""" return self._during_training_metric @during_training_metric.setter @@ -384,7 +384,7 @@ def during_training_metric(self, value): @property def after_before_training_metric(self): - """The metric used during training.""" + """Get the metric used during training.""" return self._after_before_training_metric @after_before_training_metric.setter diff --git a/mala/network/runner.py b/mala/network/runner.py index cc44bdb5b..be1df1445 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -54,8 +54,7 @@ def _forward_entire_snapshot(self, snapshot_number, data_set, number_of_batches_per_snapshot=0, batch_size=0): """ - Forward an entire snapshot through the network and get actual - and predicted outputs. + Forward a snapshot through the network, get actual/predicted output. Parameters ---------- diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 209f07cf6..22e7fcd17 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -68,7 +68,6 @@ def __init__(self, params): @property def qe_input_data(self): """Input data for QE TEM calls.""" - # Update the pseudopotential path from Parameters. self._qe_input_data["pseudo_dir"] = \ self.parameters.pseudopotential_path From 0e172475cb42ddceff37623cacd6e318689535d3 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Jul 2021 09:39:42 +0200 Subject: [PATCH 185/297] Fixed another doc issue --- mala/common/parameters.py | 42 ++++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 6ba1009d3..70a2fdade 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -318,24 +318,6 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. - - during_training_metric : string - Metric for evaluated on the validation set during training. - Default is "ldos", meaning that the regular loss on the LDOS will be - used as a metric. Possible options are "band_energy" and - "total_energy". For these, the band resp. total energy of the - validation snapshots will be calculated and compared to the provided - DFT results. Of these, the mean average error in eV/atom will be - calculated. - - after_before_training_metric : string - Metric for evaluated on the validation and test set AFTER training. - Default is "LDOS", meaning that the regular loss on the LDOS will be - used as a metric. Possible options are "band_energy" and - "total_energy". For these, the band resp. total energy of the - validation snapshots will be calculated and compared to the provided - DFT results. Of these, the mean average error in eV/atom will be - calculated. """ def __init__(self): @@ -371,7 +353,17 @@ def _update_configuration(self, config): @property def during_training_metric(self): - """Get the metric used during training.""" + """ + Control the metric used during training. + + Metric for evaluated on the validation set during training. + Default is "ldos", meaning that the regular loss on the LDOS will be + used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. + """ return self._during_training_metric @during_training_metric.setter @@ -384,7 +376,17 @@ def during_training_metric(self, value): @property def after_before_training_metric(self): - """Get the metric used during training.""" + """ + Get the metric used during training. + + Metric for evaluated on the validation and test set before and after + training. Default is "LDOS", meaning that the regular loss on the LDOS + will be used as a metric. Possible options are "band_energy" and + "total_energy". For these, the band resp. total energy of the + validation snapshots will be calculated and compared to the provided + DFT results. Of these, the mean average error in eV/atom will be + calculated. + """ return self._after_before_training_metric @after_before_training_metric.setter From f0a544fc5d7a7d33452176e5b7f1860da8edab61 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 23 Jul 2021 11:29:44 +0200 Subject: [PATCH 186/297] Fix type of `params` in class `HyperOptOptuna` --- mala/network/hyper_opt_optuna.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 1cf5a6394..5b75b2c53 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -15,7 +15,7 @@ class HyperOptOptuna(HyperOptBase): Parameters ---------- - params : mala.common.parametes.Parameters + params : mala.common.parameters.Parameters Parameters used to create this hyperparameter optimizer. data : mala.datahandling.data_handler.DataHandler From 02fdaf4bf3975397e62d4a678a656c84edf216a4 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 23 Jul 2021 12:44:02 +0200 Subject: [PATCH 187/297] Made the horovod/GPU handling a bit less hacky --- mala/common/parameters.py | 51 ++++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 27 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 70a2fdade..b8e1ea215 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -16,7 +16,7 @@ class ParametersBase: """Base parameter class for MALA.""" def __init__(self,): - self.configuration = None + self._configuration = {"gpu": False, "horovod": False} pass def show(self, indent=""): @@ -33,8 +33,11 @@ def show(self, indent=""): for v in vars(self): printout(indent + '%-15s: %s' % (v, getattr(self, v))) - def _update_configuration(self, config): - self.configuration = {"gpu": config[0], "horovod": config[1]} + def _update_gpu(self, new_gpu): + self._configuration["gpu"] = new_gpu + + def _update_horovod(self, new_horovod): + self._configuration["horovod"] = new_horovod class ParametersNetwork(ParametersBase): @@ -346,8 +349,8 @@ def __init__(self): self.during_training_metric = "ldos" self.after_before_training_metric = "ldos" - def _update_configuration(self, config): - super(ParametersRunning, self)._update_configuration(config) + def _update_horovod(self, new_horovod): + super(ParametersRunning, self)._update_horovod(new_horovod) self.during_training_metric = self.during_training_metric self.after_before_training_metric = self.after_before_training_metric @@ -369,7 +372,7 @@ def during_training_metric(self): @during_training_metric.setter def during_training_metric(self, value): if value != "ldos": - if self.configuration["horovod"]: + if self._configuration["horovod"]: raise Exception("Currently, MALA can only operate with the " "\"ldos\" metric for horovod runs.") self._during_training_metric = value @@ -392,7 +395,7 @@ def after_before_training_metric(self): @after_before_training_metric.setter def after_before_training_metric(self, value): if value != "ldos": - if self.configuration["horovod"]: + if self._configuration["horovod"]: raise Exception("Currently, MALA can only operate with the " "\"ldos\" metric for horovod runs.") self._after_before_training_metric = value @@ -593,8 +596,6 @@ def __init__(self): self.debug = ParametersDebug() # Properties - self._use_gpu = False - self._use_horovod = False self.use_horovod = False self.use_gpu = False self.manual_seed = None @@ -614,15 +615,13 @@ def use_gpu(self, value): else: warnings.warn("GPU requested, but no GPU found. MALA will " "operate with CPU only.", stacklevel=3) - self.network._update_configuration([self.use_gpu, self.use_horovod]) - self.descriptors._update_configuration([self.use_gpu, - self.use_horovod]) - self.targets._update_configuration([self.use_gpu, self.use_horovod]) - self.data._update_configuration([self.use_gpu, self.use_horovod]) - self.running._update_configuration([self.use_gpu, self.use_horovod]) - self.hyperparameters._update_configuration([self.use_gpu, - self.use_horovod]) - self.debug._update_configuration([self.use_gpu, self.use_horovod]) + self.network._update_gpu(self.use_gpu) + self.descriptors._update_gpu(self.use_gpu) + self.targets._update_gpu(self.use_gpu) + self.data._update_gpu(self.use_gpu) + self.running._update_gpu(self.use_gpu) + self.hyperparameters._update_gpu(self.use_gpu) + self.debug._update_gpu(self.use_gpu) @property def use_horovod(self): @@ -635,15 +634,13 @@ def use_horovod(self, value): hvd.init() set_horovod_status(value) self._use_horovod = value - self.network._update_configuration([self.use_gpu, self.use_horovod]) - self.descriptors._update_configuration([self.use_gpu, - self.use_horovod]) - self.targets._update_configuration([self.use_gpu, self.use_horovod]) - self.data._update_configuration([self.use_gpu, self.use_horovod]) - self.running._update_configuration([self.use_gpu, self.use_horovod]) - self.hyperparameters._update_configuration([self.use_gpu, - self.use_horovod]) - self.debug._update_configuration([self.use_gpu, self.use_horovod]) + self.network._update_horovod(self.use_horovod) + self.descriptors._update_horovod(self.use_horovod) + self.targets._update_horovod(self.use_horovod) + self.data._update_horovod(self.use_horovod) + self.running._update_horovod(self.use_horovod) + self.hyperparameters._update_horovod(self.use_horovod) + self.debug._update_horovod(self.use_horovod) def show(self): """Print name and values of all attributes of this object.""" From 18a2d15f335cd6e9084fca50601195cb5aca57f5 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 26 Jul 2021 11:23:44 +0200 Subject: [PATCH 188/297] added changes to trainer.py and ex10 --- examples/ex10_tensor_board.py | 64 +++++++++++++++--- ...nts.out.tfevents.1624892223.pop-os.18826.0 | Bin 878 -> 0 bytes install/data_repo_link/link_data_repo.sh | 15 +++- mala/network/trainer.py | 12 +++- 4 files changed, 77 insertions(+), 14 deletions(-) delete mode 100644 examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 mode change 100644 => 100755 install/data_repo_link/link_data_repo.sh diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index e817a4c87..4eec55b61 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,5 +1,8 @@ import mala from mala import printout +from multiprocessing import Process +import sys +import os from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" @@ -100,14 +103,56 @@ def run_example01(desired_loss_improvement_factor=1): - + + +class TensorboardSupervisor: + def __init__(self, log_dp): + self.server = TensorboardServer(log_dp) + self.server.start() + print("Started Tensorboard Server") + self.chrome = ChromeProcess() + print("Started Chrome Browser") + self.chrome.start() + + def finalize(self): + if self.server.is_alive(): + print('Killing Tensorboard Server') + self.server.terminate() + self.server.join() + + +class TensorboardServer(Process): + def __init__(self, log_dp): + super().__init__() + self.os_name = os.name + self.log_dp = str(log_dp) + + def run(self): + if self.os_name == 'nt': # Windows + os.system('tensorboard --logdir="{self.log_dp} --bind_all"') + elif self.os_name == 'posix': # Linux + os.system(f'tensorboard --logdir="{self.log_dp} --bind_all" ') + else: + raise NotImplementedError(f'No support for OS : {self.os_name}') + + +class ChromeProcess(Process): + def __init__(self): + super().__init__() + self.os_name = os.name + self.daemon = True + + def run(self): + if self.os_name == 'nt': # Windows + os.system(f'start chrome http://localhost:6006/') + elif self.os_name == 'posix': # Linux + os.system(f'google-chrome http://localhost:6006/') + else: + raise NotImplementedError(f'No support for OS : {self.os_name}') def run_example10(): - tensor_board_program = program.TensorBoard() - tensor_board_program.configure(argv=[None, '--logdir', http://localhost:6006/]) - url = tensor_board_program.launch() - - + tb_sup = TensorboardSupervisor("/home/snehaverma/Downloads/HZDR_work/mala/examples/log_dir") + tb_sup.finalize() #################### # RESULTS. # Check whether tensorboard has run. @@ -115,13 +160,14 @@ def run_example10(): printout("Tensor board has ran succesfully:") - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: + if run_example10: return False else: return True + + if __name__ == "__main__": - if run_example01() && run_example10(): + if run_example01() & run_example10(): printout("Successfully ran ex10_tensor_board.py.") else: raise Exception("Ran ex10_tensor_board but something " diff --git a/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 b/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 deleted file mode 100644 index b924889d5c53c6c6da252dfc25c6ec9718bae2f1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 878 zcmY+?TPTBZ7zXfR!?)S#GptE!QK&g5hyPcUL!sPBE@Vn_J|>D*yAY)malwUiIfO(` zjVOtWIqb$(Lxz-cYM~^F*Twrj*T0^d=h2EFlcqd9=d@T)+LG)UI(b=>v9c~YtF^qY zB{jZ6nXJ)bCn~l7xhT0*CKcz`H#Z09ggC*~;1#X=cl?Omz)jsc|D4JL$(W*W;mAMe zmB{SEYmlBu&ZPtkCB@o)BA@r~Kzbo*oZ*Ge;=X$#HxnN~ijn;0WP&km{({Kv@h6ZH zBn^RLVQ8Q>gveEm8>Baqy&In_)9q*^GJWy`qz{tToF~>X*OEY_MfU~L7fIWZi)~jc zwul@yi%x&{QE^C`I_>QIQr|`7n?wRqilnOQgz=`x1tN2MIFK?VV;u+VIMJC-MYe_DuKH}fFnNH(0Vv#WOXHIZkpfglw~E|f2`nw~}nk%jHSAeBgtwaqiV z)o3Gf_AwNs3Q7ISG+U}Ur(YVX4+E)2(y1O}^7s8k(y9H50LddcQ98^HZ5JCv?)4c! O`XhO(9As;wn%y4@*ZZ^p diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh old mode 100644 new mode 100755 index f37a91467..1c744a05d --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,9 +7,10 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) + examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repo_path.py +pythonfile=data_repos_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] @@ -27,16 +28,26 @@ then data_repo_path=$data_repo_path/ fi +# Append a / if we have to. +lastcharacter="${mala_base_path: -1}" +if [ "$lastcharacter" != "/" ] +then + mala_base_path=$mala_base_path/ +fi # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} +echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} - +echo "" >> ${script_path}/${pythonfile} +echo "def get_repo_path():" >> ${script_path}/${pythonfile} +echo " return repo_path" >> ${script_path}/${pythonfile} # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} +cp $ echo "Linking done!" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 802f6e2d4..d29303f58 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -9,7 +9,8 @@ from torch.utils.data import DataLoader from mala.common.parameters import printout from .runner import Runner -from torch.utils.tensorboard import SummaryWriter ##summaty writer +from torch.utils.tensorboard import SummaryWriter +import os try: import horovod.torch as hvd @@ -54,8 +55,13 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tensor_board = SummaryWriter("log_dir")## here the path to the log file can be set - + trainer_path = os.getcwd() + log_dir_path = trainer_path.replace('mala/network','examples/log_dir') + if not os.path.exists(log_dir_path): + os.mkdir(log_dir_path) + + self.tensor_board = SummaryWriter(log_dir_path)## here the path to the log file can be set + @classmethod def resume_checkpoint(cls, checkpoint_name): From b47ed2e527dfd1fc6722d494613895317c3ef7cb Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 14 Jun 2021 02:39:18 +0200 Subject: [PATCH 189/297] added range analysis functionality to oat --- examples/ex04_hyperparameter_optimization.py | 1 - mala/network/hyper_opt_oat.py | 79 +++++++++++++++----- mala/network/objective_base.py | 8 ++ 3 files changed, 68 insertions(+), 20 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 8d678fbd4..8ae16e3ea 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -131,4 +131,3 @@ def run_example04(desired_loss_improvement_factor=2): "was off. If you haven't changed any parameters in " "the example, there might be a problem with " "your installation.") - diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 84e93f2f8..a15b16165 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -3,6 +3,7 @@ from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase import numpy as np +import itertools from mala.common.parameters import printout @@ -22,11 +23,12 @@ def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] - self.best_trial = None + self.optimal_params = None self.n_factors = None - self.n_levels = None + self.factor_levels = None self.strength = None self.N_runs = None + self.OA = None def perform_study(self): """ @@ -35,10 +37,17 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ + self.n_factors = len(self.params.hyperparameters.hlist) + + self.factor_levels = [par.num_choices for par in self.params. + hyperparameters.hlist] + self.strength = 3 + self.N_runs = self.number_of_runs() + self.OA = self.get_orthogonal_array() number_of_trial = 0 # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) - for row in self.orthogonal_arr: + for row in self.OA: printout("Trial number", number_of_trial) self.trial_losses.append(self.objective(row)) number_of_trial += 1 @@ -48,20 +57,36 @@ def perform_study(self): def set_optimal_parameters(self): """ + Find the optimal set of hyperparameters by doing range analysis. + This is done using loss instead of accuracy as done in the paper. + Set the optimal parameters found in the present study. The parameters will be written to the parameter object with which the hyperparameter optimizer was created. """ - # Getting the best trial based on the test errors - idx = self.trial_losses.index(min(self.trial_losses)) - self.best_trial = self.orthogonal_arr[idx] - self.objective.parse_trial_oat(self.best_trial) - - @property - def orthogonal_arr(self): - """Orthogonal array used for optimal hyperparameter sampling.""" - arrayclass = oa.arraydata_t(self.n_levels, self.N_runs, self.strength, + + def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] + + R = [[self.trial_losses[indices(i, l)] for l in range(levels)] + for (i, levels) in enumerate(self.factor_levels)] + + A = [[i/len(j) for i in j] for j in R] + + self.optimal_params = np.array([i.index(max(i)) for i in A]) + importance = np.argsort([max(i)-min(i) for i in A]) + + print("Order of Importance: ") + printout( + [self.params.hyperparameters.hlist[idx].name for idx in self.optimal_params], " > ") + + print("Optimal Hyperparameters:") + self.objective.parse_trial_oat(self.optimal_params) + + def get_orthogonal_array(self): + """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + + arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) arraylist = [arrayclass.create_root()] @@ -109,12 +134,28 @@ def add_hyperparameter(self, opttype="float", name="", low=0, high=0, super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, low=low, high=high, choices=choices) - self.n_factors = len(self.params.hyperparameters.hlist) - # if self.n_factors>4: - # raise Exception("Sorry only upto 3 factors are supported") + def number_of_runs(self): + """ + Calculate the minimum number of runs required for an Orthogonal array - self.n_levels = min([par.num_choices for par in self.params. - hyperparameters.hlist]) - self.strength = 3 - self.N_runs = pow(self.n_levels, self.n_factors) + Based on the factor levels and the strength of the array requested + + Parameters + ---------- + factor_levels : list + A list of number of choices of each hyperparameter + + strength : int + A design parameter for Orthogonal arrays + strength 2 models all 2 factor interactions + strength 3 models all 3 factor interactions + + This is function is taken from the example notebook of OApackage + """ + + runs = [np.prod(tt) for tt in itertools.combinations( + self.factor_levels, self.strength)] + + N = np.lcm.reduce(runs) + return N diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 0f080a03e..6b268e69c 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -144,6 +144,10 @@ def parse_trial_oat(self, trial): trial : numpy.array Row in an orthogonal array which respresents current trial. """ + if self.optimize_layer_list: + self.params.network.layer_sizes = \ + [self.data_handler.get_input_dimension()] + if self.optimize_activation_list: self.params.network.layer_activations = [] @@ -152,6 +156,10 @@ def parse_trial_oat(self, trial): if "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial, factor_idx)) + elif "ff_neurons_layer" in par.name: + if self.params.network.nn_type == "feed-forward": + self.params.network.layer_sizes.\ + append(par.get_parameter(trial, factor_idx)) elif "trainingtype" in par.name: self.params.running.trainingtype = par.\ get_parameter(trial, factor_idx) From 28a4b26189eae2983cd9fec4abf0a7532023724d Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 14 Jun 2021 16:42:59 +0200 Subject: [PATCH 190/297] fixed a few bugs --- mala/network/hyper_opt_oat.py | 116 ++++++++++++++++++---------------- 1 file changed, 60 insertions(+), 56 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index a15b16165..9f440832c 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -30,6 +30,38 @@ def __init__(self, params, data): self.N_runs = None self.OA = None + def add_hyperparameter(self, opttype="categorical", name="", low=0, high=0, + choices=None): + """ + Add a hyperparameter to the current investigation. + + Parameters + ---------- + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + Currently supported are: + + - categorical (list) + + name : string + Name of the hyperparameter. Please note that these names always + have to be distinct; if you e.g. want to investigate multiple + layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, + etc. as names. + + low : float or int + Currently unsupported: Lower bound for numerical parameter. + + high : float or int + Currently unsupported: Higher bound for numerical parameter. + + choices : + List of possible choices (for categorical parameter). + """ + super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, + low=low, high=high, + choices=choices) + def perform_study(self): """ Perform the study, i.e. the optimization. @@ -68,72 +100,24 @@ def set_optimal_parameters(self): def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - R = [[self.trial_losses[indices(i, l)] for l in range(levels)] - for (i, levels) in enumerate(self.factor_levels)] + R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + for (idx, levels) in enumerate(self.factor_levels)] A = [[i/len(j) for i in j] for j in R] - self.optimal_params = np.array([i.index(max(i)) for i in A]) + # Taking loss as objective to minimise + self.optimal_params = np.array([i.index(min(i)) for i in A]) importance = np.argsort([max(i)-min(i) for i in A]) print("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in self.optimal_params], " > ") + [self.params.hyperparameters.hlist[idx].name for idx in importance], " > ") print("Optimal Hyperparameters:") - self.objective.parse_trial_oat(self.optimal_params) - - def get_orthogonal_array(self): - """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" - - arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, - self.n_factors) - arraylist = [arrayclass.create_root()] - - # extending the orthogonal array - options = oa.OAextend() - options.setAlgorithmAuto(arrayclass) - - for _ in range(self.strength + 1, self.n_factors + 1): - arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, - options) - dd = np.array([a.Defficiency() for a in arraylist_extensions]) - idxs = np.argsort(dd) - arraylist = [arraylist_extensions[ii] for ii in idxs] - - return np.unique(np.array(arraylist[0]), axis=0) + for (idx, par) in enumerate(self.params.hyperparameters.hlist): + printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') - def add_hyperparameter(self, opttype="float", name="", low=0, high=0, - choices=None): - """ - Add a hyperparameter to the current investigation. - - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - Currently supported are: - - - categorical (list) - - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. - - low : float or int - Currently unsupported: Lower bound for numerical parameter. - - high : float or int - Currently unsupported: Higher bound for numerical parameter. - - choices : - List of possible choices (for categorical parameter). - """ - super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, - low=low, high=high, - choices=choices) + self.objective.parse_trial_oat(self.optimal_params) def number_of_runs(self): """ @@ -159,3 +143,23 @@ def number_of_runs(self): N = np.lcm.reduce(runs) return N + + def get_orthogonal_array(self): + """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + + arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, + self.n_factors) + arraylist = [arrayclass.create_root()] + + # extending the orthogonal array + options = oa.OAextend() + options.setAlgorithmAuto(arrayclass) + + for _ in range(self.strength + 1, self.n_factors + 1): + arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, + options) + dd = np.array([a.Defficiency() for a in arraylist_extensions]) + idxs = np.argsort(dd) + arraylist = [arraylist_extensions[ii] for ii in idxs] + + return np.unique(np.array(arraylist[0]), axis=0) From 4edf7e2229b621d9639c12bc8eca949291460a79 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 28 Jun 2021 14:59:55 +0200 Subject: [PATCH 191/297] putting it all together --- examples/ex04_hyperparameter_optimization.py | 20 +++---- mala/common/parameters.py | 2 +- mala/network/hyper_opt_oat.py | 58 +++++++++++++++----- mala/network/objective_base.py | 9 ++- 4 files changed, 61 insertions(+), 28 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 8ae16e3ea..426d02074 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -30,7 +30,7 @@ def run_example04(desired_loss_improvement_factor=2): test_parameters.data.output_rescaling_type = "normal" # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 + test_parameters.running.max_number_epochs = 5 test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" @@ -38,7 +38,7 @@ def run_example04(desired_loss_improvement_factor=2): # Specify the number of trials, the hyperparameter optimizer should run # and the type of hyperparameter. test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "optuna" + test_parameters.hyperparameters.hyper_opt_method = "oat" #################### # DATA @@ -71,20 +71,18 @@ def run_example04(desired_loss_improvement_factor=2): test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) + test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", + choices=[0.005, 0.01, 0.015]) # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) - test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) # Choices for activation function at each layer will be optimized. test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) + choices=["ReLU", "Sigmoid", "LeakyReLU"]) # Perform hyperparameter optimization. printout("Starting Hyperparameter optimization.") diff --git a/mala/common/parameters.py b/mala/common/parameters.py index f68afb8ac..5e0f66007 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -381,7 +381,7 @@ class ParametersHyperparameterOptimization(ParametersBase): - "optuna" : Use optuna for the hyperparameter optimization. - "oat" : Use orthogonal array tuning (currently limited to categorical hyperparemeters). Range analysis is - currently done by simply choosing the lowesr loss. + currently done by simply choosing the lowest loss. - "notraining" : Using a NAS without training, based on jacobians. checkpoints_each_trial : int diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 9f440832c..5e913521f 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -24,6 +24,7 @@ def __init__(self, params, data): self.objective = None self.trial_losses = [] self.optimal_params = None + self.importance = None self.n_factors = None self.factor_levels = None self.strength = None @@ -73,7 +74,12 @@ def perform_study(self): self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] - self.strength = 3 + + if not self.monotonic: + raise Exception( + "Please use hyperparameters in increasing or decreasing order of number of choices") + + self.strength = 2 self.N_runs = self.number_of_runs() self.OA = self.get_orthogonal_array() number_of_trial = 0 @@ -82,41 +88,54 @@ def perform_study(self): for row in self.OA: printout("Trial number", number_of_trial) self.trial_losses.append(self.objective(row)) + number_of_trial += 1 - # Return the best lost value we could achieve. - return min(self.trial_losses) + # Return the best loss value we could achieve. + self.get_optimal_parameters() + return self.objective(self.optimal_params) - def set_optimal_parameters(self): + def get_optimal_parameters(self): """ Find the optimal set of hyperparameters by doing range analysis. This is done using loss instead of accuracy as done in the paper. - Set the optimal parameters found in the present study. - - The parameters will be written to the parameter object with which the - hyperparameter optimizer was created. """ + print("Performing Range Analysis") + print("factor levels", self.factor_levels) def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - - R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] - for (idx, levels) in enumerate(self.factor_levels)] - + # R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + # for (idx, levels) in enumerate(self.factor_levels)] + + R = [] + for (idx, levels) in enumerate(self.factor_levels): + R.append([]) + for l in range(levels): + R[idx].extend(self.trial_losses[indices(idx, l)].sum()) + print(R) A = [[i/len(j) for i in j] for j in R] # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) - importance = np.argsort([max(i)-min(i) for i in A]) + self.importance = np.argsort([max(i)-min(i) for i in A]) print("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in importance], " > ") + [self.params.hyperparameters.hlist[idx].name for idx in self.importance], " > ") print("Optimal Hyperparameters:") for (idx, par) in enumerate(self.params.hyperparameters.hlist): printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') + def set_optimal_parameters(self): + """ + Set the optimal parameters found in the present study. + + The parameters will be written to the parameter object with which the + hyperparameter optimizer was created. + """ + self.objective.parse_trial_oat(self.optimal_params) def number_of_runs(self): @@ -142,11 +161,12 @@ def number_of_runs(self): self.factor_levels, self.strength)] N = np.lcm.reduce(runs) - return N + return int(N) def get_orthogonal_array(self): """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + print("Generating Suitable Orthogonal Array") arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) arraylist = [arrayclass.create_root()] @@ -162,4 +182,12 @@ def get_orthogonal_array(self): idxs = np.argsort(dd) arraylist = [arraylist_extensions[ii] for ii in idxs] + if not arraylist: # checking if the list is empty + raise Exception( + "No orthogonal array exists with such a parameter combination") return np.unique(np.array(arraylist[0]), axis=0) + + @property + def monotonic(self): + dx = np.diff(self.factor_levels) + return np.all(dx <= 0) or np.all(dx >= 0) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 6b268e69c..820c6a97e 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -153,7 +153,10 @@ def parse_trial_oat(self, trial): par: HyperparameterOAT for factor_idx, par in enumerate(self.params.hyperparameters.hlist): - if "layer_activation" in par.name: + if "learning_rate" in par.name: + self.params.running.learning_rate = \ + par.get_parameter(trial, factor_idx) + elif "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial, factor_idx)) elif "ff_neurons_layer" in par.name: @@ -166,3 +169,7 @@ def parse_trial_oat(self, trial): else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") + + if self.optimize_layer_list: + self.params.network.layer_sizes.\ + append(self.data_handler.get_output_dimension()) From ecf12d0094969e9b3ca486fcd3af7972a0cebc65 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Sun, 11 Jul 2021 19:39:08 +0200 Subject: [PATCH 192/297] wfirst draft of working orthogonal array optimisation --- examples/ex04_hyperparameter_optimization.py | 2 +- mala/common/printout.py | 8 +++-- mala/network/hyper_opt_oat.py | 33 +++++++++----------- 3 files changed, 21 insertions(+), 22 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 426d02074..13057d994 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -1,5 +1,5 @@ import mala -from mala import printout +from mala.common.printout import printout from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/mala/common/printout.py b/mala/common/printout.py index 3f32827b5..2dbd83c03 100644 --- a/mala/common/printout.py +++ b/mala/common/printout.py @@ -36,9 +36,11 @@ def printout(*values, sep=' '): sep : string Separator between printed values. """ - outstring = '' - for v in values: - outstring += str(v)+sep + outstring = sep.join([str(v) for v in values]) + + # for v in values: + # outstring += str(v)+sep # This implementation is adding an extra seperator at the end of the iterable! + if use_horovod is False: print(outstring) else: diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 5e913521f..439627543 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -4,7 +4,7 @@ from .objective_base import ObjectiveBase import numpy as np import itertools -from mala.common.parameters import printout +from mala.common.printout import printout class HyperOptOAT(HyperOptBase): @@ -90,6 +90,7 @@ def perform_study(self): self.trial_losses.append(self.objective(row)) number_of_trial += 1 + self.trial_losses = np.array(self.trial_losses) # Return the best loss value we could achieve. self.get_optimal_parameters() @@ -101,32 +102,28 @@ def get_optimal_parameters(self): This is done using loss instead of accuracy as done in the paper. """ - print("Performing Range Analysis") - print("factor levels", self.factor_levels) - - def indices(idx, val): return np.where(self.OA[:, idx] == val)[0] - # R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] - # for (idx, levels) in enumerate(self.factor_levels)] - - R = [] - for (idx, levels) in enumerate(self.factor_levels): - R.append([]) - for l in range(levels): - R[idx].extend(self.trial_losses[indices(idx, l)].sum()) - print(R) + printout("Performing Range Analysis") + print("Factor levels:", self.factor_levels) + + def indices(idx, val): return np.where( + self.OA[:, idx] == val)[0] + R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] + for (idx, levels) in enumerate(self.factor_levels)] + A = [[i/len(j) for i in j] for j in R] # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) - print("Order of Importance: ") + printout("Order of Importance: ") printout( - [self.params.hyperparameters.hlist[idx].name for idx in self.importance], " > ") + *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" > ") - print("Optimal Hyperparameters:") + printout("Optimal Hyperparameters:") for (idx, par) in enumerate(self.params.hyperparameters.hlist): - printout([par.name, par.choice[self.optimal_params[idx]]], ' : ') + printout( + par.name, par.choices[self.optimal_params[idx]], sep=' : ') def set_optimal_parameters(self): """ From bb9075b6ca4cab0b72831d21e01f98133056066a Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 12 Jul 2021 11:52:11 +0200 Subject: [PATCH 193/297] need review on range analysis --- mala/network/hyper_opt_base.py | 1 + mala/network/hyper_opt_oat.py | 55 ++++++++++++++++++---------------- 2 files changed, 30 insertions(+), 26 deletions(-) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index 0d0df7e52..dd03cd042 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -54,6 +54,7 @@ def add_hyperparameter(self, opttype="float", name="", low=0, high=0, choices : List of possible choices (for categorical parameter). """ + self.params.\ hyperparameters.hlist.append( HyperparameterInterface(self.params.hyperparameters. diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 439627543..66106b583 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -2,9 +2,11 @@ import oapackage as oa from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase +from .hyperparameter_oat import HyperparameterOAT import numpy as np import itertools from mala.common.printout import printout +from bisect import bisect class HyperOptOAT(HyperOptBase): @@ -23,6 +25,7 @@ def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None self.trial_losses = [] + self.sorted_num_choices = [] self.optimal_params = None self.importance = None self.n_factors = None @@ -33,35 +36,23 @@ def __init__(self, params, data): def add_hyperparameter(self, opttype="categorical", name="", low=0, high=0, choices=None): - """ - Add a hyperparameter to the current investigation. - - Parameters - ---------- - opttype : string - Datatype of the hyperparameter. Follows optunas naming convetions. - Currently supported are: - - - categorical (list) - - name : string - Name of the hyperparameter. Please note that these names always - have to be distinct; if you e.g. want to investigate multiple - layer sizes use e.g. ff_neurons_layer_001, ff_neurons_layer_002, - etc. as names. - low : float or int - Currently unsupported: Lower bound for numerical parameter. + if opttype != 'categorical': + raise Exception( + "Only categorical hyperparameters are supported for OAT") - high : float or int - Currently unsupported: Higher bound for numerical parameter. + else: + if not self.sorted_num_choices: + super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, + low=low, high=high, + choices=choices) + self.sorted_num_choices.append(len(choices)) - choices : - List of possible choices (for categorical parameter). - """ - super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, - low=low, high=high, - choices=choices) + else: + index = bisect(self.sorted_num_choices, len(choices)) + self.sorted_num_choices.insert(index, len(choices)) + self.params.hyperparameters.hlist.insert( + index, HyperparameterOAT(opttype=opttype, name=name, choices=choices)) def perform_study(self): """ @@ -75,6 +66,7 @@ def perform_study(self): self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] + printout(*self.factor_levels, sep=",") if not self.monotonic: raise Exception( "Please use hyperparameters in increasing or decreasing order of number of choices") @@ -112,6 +104,17 @@ def indices(idx, val): return np.where( A = [[i/len(j) for i in j] for j in R] + print("OA:") + print(self.OA) + + print("Trial Losses:") + print(np.array(self.trial_losses).transpose()) + + print("R matrix: ") + print(np.array(R).transpose()) + + print("A Matrix:") + print(np.array(A).transpose()) # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) From 9740a8bdc90b6d253d3533e81002211f69cb2525 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Tue, 27 Jul 2021 00:55:14 +0200 Subject: [PATCH 194/297] removing unnecessary prints --- examples/ex04_hyperparameter_optimization.py | 1 + mala/network/hyper_opt_oat.py | 71 +++++++++----------- 2 files changed, 32 insertions(+), 40 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 13057d994..cbe55df6d 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -98,6 +98,7 @@ def run_example04(desired_loss_improvement_factor=2): test_network = mala.Network(test_parameters) test_trainer = mala.Trainer(test_parameters, test_network, data_handler) printout("Network setup: DONE.") + printout("Trial with optimal parameters set.") test_trainer.train_network() printout("Training: DONE.") diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 66106b583..813fc3154 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -34,18 +34,23 @@ def __init__(self, params, data): self.N_runs = None self.OA = None - def add_hyperparameter(self, opttype="categorical", name="", low=0, high=0, - choices=None): + def add_hyperparameter(self, opttype="categorical", name="", choices=None, **kwargs): + """ + Add hyperparameter such that the hyperparameter list is sorted w.r.t the number of choices. + + opttype : string + Datatype of the hyperparameter. Follows optunas naming convetions. + Default value - categorical (list) + """ if opttype != 'categorical': raise Exception( "Only categorical hyperparameters are supported for OAT") else: - if not self.sorted_num_choices: - super(HyperOptOAT, self).add_hyperparameter(opttype=opttype, name=name, - low=low, high=high, - choices=choices) + if not self.sorted_num_choices: # if empty + super(HyperOptOAT, self).add_hyperparameter( + opttype=opttype, name=name, choices=choices) self.sorted_num_choices.append(len(choices)) else: @@ -66,7 +71,6 @@ def perform_study(self): self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] - printout(*self.factor_levels, sep=",") if not self.monotonic: raise Exception( "Please use hyperparameters in increasing or decreasing order of number of choices") @@ -86,7 +90,6 @@ def perform_study(self): # Return the best loss value we could achieve. self.get_optimal_parameters() - return self.objective(self.optimal_params) def get_optimal_parameters(self): """ @@ -94,8 +97,7 @@ def get_optimal_parameters(self): This is done using loss instead of accuracy as done in the paper. """ - printout("Performing Range Analysis") - print("Factor levels:", self.factor_levels) + printout("Performing Range Analysis.") def indices(idx, val): return np.where( self.OA[:, idx] == val)[0] @@ -104,29 +106,18 @@ def indices(idx, val): return np.where( A = [[i/len(j) for i in j] for j in R] - print("OA:") - print(self.OA) - - print("Trial Losses:") - print(np.array(self.trial_losses).transpose()) - - print("R matrix: ") - print(np.array(R).transpose()) - - print("A Matrix:") - print(np.array(A).transpose()) # Taking loss as objective to minimise self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) - printout("Order of Importance: ") - printout( - *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" > ") + # printout("Order of Importance: ") + # printout( + # *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" < ") - printout("Optimal Hyperparameters:") - for (idx, par) in enumerate(self.params.hyperparameters.hlist): - printout( - par.name, par.choices[self.optimal_params[idx]], sep=' : ') + # printout("Optimal Hyperparameters:") + # for (idx, par) in enumerate(self.params.hyperparameters.hlist): + # printout( + # par.name, par.choices[self.optimal_params[idx]], sep=' : ') def set_optimal_parameters(self): """ @@ -135,7 +126,6 @@ def set_optimal_parameters(self): The parameters will be written to the parameter object with which the hyperparameter optimizer was created. """ - self.objective.parse_trial_oat(self.optimal_params) def number_of_runs(self): @@ -166,7 +156,7 @@ def number_of_runs(self): def get_orthogonal_array(self): """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" - print("Generating Suitable Orthogonal Array") + print("Generating Suitable Orthogonal Array.") arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) arraylist = [arrayclass.create_root()] @@ -175,16 +165,17 @@ def get_orthogonal_array(self): options = oa.OAextend() options.setAlgorithmAuto(arrayclass) - for _ in range(self.strength + 1, self.n_factors + 1): - arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, - options) - dd = np.array([a.Defficiency() for a in arraylist_extensions]) - idxs = np.argsort(dd) - arraylist = [arraylist_extensions[ii] for ii in idxs] - - if not arraylist: # checking if the list is empty - raise Exception( - "No orthogonal array exists with such a parameter combination") + try: + for _ in range(self.strength + 1, self.n_factors + 1): + arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, + options) + dd = np.array([a.Defficiency() for a in arraylist_extensions]) + idxs = np.argsort(dd) + arraylist = [arraylist_extensions[ii] for ii in idxs] + + except: + if not arraylist: + print("No orthogonal array exists with such a parameter combination.") return np.unique(np.array(arraylist[0]), axis=0) @property From 65639b09b9130e4718107fddac3d52b1af94fc8e Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Tue, 27 Jul 2021 10:21:47 +0200 Subject: [PATCH 195/297] fixed optimisation check line in ex04 --- examples/ex04_hyperparameter_optimization.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index cbe55df6d..cddd3265b 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -4,10 +4,10 @@ data_path = get_data_repo_path()+"Al36/" """ -ex04_hyperparameter_optimization.py: Shows how a hyperparameter -optimization can be done using this framework. There are multiple +ex04_hyperparameter_optimization.py: Shows how a hyperparameter +optimization can be done using this framework. There are multiple hyperparameter optimizers available in this framework. This example focusses -on the most universal one - optuna. +on the most universal one - optuna. """ @@ -113,10 +113,8 @@ def run_example04(desired_loss_improvement_factor=2): # To see if the hyperparameter optimization actually worked, # check if the best trial is better then the worst trial # by a certain factor. - performed_trials_values = test_hp_optimizer.study.\ - trials_dataframe()["value"] - if desired_loss_improvement_factor*min(performed_trials_values) > \ - max(performed_trials_values): + if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ + test_trainer.final_test_loss: return False else: return True From 36ea8bad87763e839c0e9c6be1a464c903917487 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 27 Jul 2021 13:31:02 +0200 Subject: [PATCH 196/297] Optimizing activation list now possible --- mala/network/network.py | 5 ++++- mala/network/objective_base.py | 36 ++++++++++++++++++++++++++-------- 2 files changed, 32 insertions(+), 9 deletions(-) diff --git a/mala/network/network.py b/mala/network/network.py index 2245381b3..759971c70 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -36,7 +36,8 @@ def __init__(self, params): self.activation_mappings = { "Sigmoid": nn.Sigmoid, "ReLU": nn.ReLU, - "LeakyReLU": nn.LeakyReLU + "LeakyReLU": nn.LeakyReLU, + "Tanh": nn.Tanh } # initialize the layers @@ -70,6 +71,8 @@ def __initialize_as_feedforward(self): use_only_one_activation_type = True elif len(self.params.layer_activations) < self.number_of_layers: raise Exception("Not enough activation layers provided.") + elif len(self.params.layer_activations) > self.number_of_layers: + raise Exception("Too many activation layers provided.") # Add the layers. # As this is a feedforward layer we always add linear layers, and then diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 0f080a03e..d264d0123 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -31,10 +31,10 @@ def __init__(self, params, data_handler): lambda p: "ff_neurons_layer" in p.name, self.params.hyperparameters.hlist )) - self.optimize_activation_list = any(map( + self.optimize_activation_list = list(map( lambda p: "layer_activation" in p.name, self.params.hyperparameters.hlist - )) + )).count(True) self.trial_type = self.params.hyperparameters.hyper_opt_method if self.trial_type == "notraining": @@ -89,18 +89,20 @@ def parse_trial_optuna(self, trial: Trial): if self.optimize_layer_list: self.params.network.layer_sizes = \ [self.data_handler.get_input_dimension()] - if self.optimize_activation_list: + if self.optimize_activation_list > 0: self.params.network.layer_activations = [] + # Some layers may have been turned off by optuna. + turned_off_layers = [] + + # This is one because of the input layer. + layer_counter = 1 + par: HyperparameterOptuna for par in self.params.hyperparameters.hlist: if par.name == "learning_rate": self.params.running.learning_rate = par.get_parameter(trial) - elif "layer_activation" in par.name: - self.params.network.layer_activations.\ - append(par.get_parameter(trial)) - elif "ff_neurons_layer" in par.name: if self.params.network.nn_type == "feed-forward": # Check for zero neuron layers; These indicate layers @@ -109,6 +111,9 @@ def parse_trial_optuna(self, trial: Trial): if layer_size > 0: self.params.network.layer_sizes.\ append(par.get_parameter(trial)) + else: + turned_off_layers.append(layer_counter) + layer_counter += 1 elif "trainingtype" in par.name: self.params.running.trainingtype = par.get_parameter(trial) @@ -128,9 +133,24 @@ def parse_trial_optuna(self, trial: Trial): self.params.running.learning_rate_decay = par.\ get_parameter(trial) + elif "layer_activation" in par.name: + pass + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") + + # We have to process the activations separately, because they depend on + # the results of the layer lists. + + layer_counter = 0 + for par in self.params.hyperparameters.hlist: + if "layer_activation" in par.name: + if layer_counter not in turned_off_layers: + self.params.network.layer_activations.\ + append(par.get_parameter(trial)) + layer_counter += 1 + if self.optimize_layer_list: self.params.network.layer_sizes.\ append(self.data_handler.get_output_dimension()) @@ -144,7 +164,7 @@ def parse_trial_oat(self, trial): trial : numpy.array Row in an orthogonal array which respresents current trial. """ - if self.optimize_activation_list: + if self.optimize_activation_list > 0: self.params.network.layer_activations = [] par: HyperparameterOAT From ccce6b01afe9d76e8d64599729e9fc5f914bb5c7 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 30 Jul 2021 11:23:37 +0200 Subject: [PATCH 197/297] Added function to enforce atoms are always inside the cell --- mala/__init__.py | 2 +- mala/descriptors/__init__.py | 1 + mala/descriptors/descriptor_base.py | 42 ++++++++++++++++++++++++++++- mala/descriptors/snap.py | 3 +++ 4 files changed, 46 insertions(+), 2 deletions(-) diff --git a/mala/__init__.py b/mala/__init__.py index 210005ce6..a2b657b98 100644 --- a/mala/__init__.py +++ b/mala/__init__.py @@ -7,7 +7,7 @@ from .version import __version__ from .common import Parameters, printout -from .descriptors import DescriptorInterface, SNAP +from .descriptors import DescriptorInterface, SNAP, DescriptorBase from .datahandling import DataHandler, DataScaler, DataConverter from .network import Network, Tester, Trainer, HyperOptInterface, \ HyperOptOptuna diff --git a/mala/descriptors/__init__.py b/mala/descriptors/__init__.py index 9014029b1..3ba2dff58 100644 --- a/mala/descriptors/__init__.py +++ b/mala/descriptors/__init__.py @@ -1,3 +1,4 @@ """Contains classes for calculating/parsing descriptors.""" from .descriptor_interface import DescriptorInterface from .snap import SNAP +from .descriptor_base import DescriptorBase diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index a6ef325fe..fb4943676 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -1,5 +1,6 @@ """Base class for all descriptor calculators.""" - +import numpy as np +import ase class DescriptorBase: """ @@ -62,3 +63,42 @@ def backconvert_units(array, out_units): """ raise Exception("No unit back conversion method implemented for " "this descriptor type.") + + @staticmethod + def enforce_pbc(atoms): + """ + Explictly enforeces the PBC on an ASE atoms object. + + QE (and potentially other codes?) do that internally. Meaning that the + raw positions of atoms (in Angstrom) can lie outside of the unit cell. + When setting up the DFT calculation, these atoms get shifted into + the unit cell. Since we directly use these raw positions for the + descriptor calculation, we need to enforce that in the ASE atoms + objects, the atoms are explicitly in the unit cell. + + Parameters + ---------- + atoms : ase.atoms + The ASE atoms object for which the PBC need to be enforced. + + Returns + ------- + new_atoms : ase.atoms + The ASE atoms object for which the PBC have been enforced. + """ + + new_atoms = atoms.copy() + new_atoms.set_scaled_positions(new_atoms.get_scaled_positions()) + + # This might be unecessary, but I think it is nice to have some sort of + # metric here. + rescaled_atoms = 0 + for i in range(0, len(atoms)): + if False in (np.isclose(new_atoms[i].position, + atoms[i].position, atol=0.001)): + rescaled_atoms += 1 + print("Descriptor calculation: had to enforce periodic boundary " + "conditions on", rescaled_atoms, "atoms before calculation.") + return new_atoms + + diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index f997775d7..3372a2add 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -108,6 +108,9 @@ def __calculate_snap(self, infile, outdir): lammps_format = "lammps-data" # We get the atomic information by using ASE. atoms = ase.io.read(infile, format=self.in_format_ase) + + # Enforcing / Checking PBC on the read atoms. + atoms = self.enforce_pbc(atoms) ase_out_path = outdir+"lammps_input.tmp" ase.io.write(ase_out_path, atoms, format=lammps_format) From 14d23f598fd7fa2ad368e37325b02677be8e0037 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 30 Jul 2021 11:28:00 +0200 Subject: [PATCH 198/297] Fixed docstring --- mala/descriptors/descriptor_base.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index fb4943676..f54f5c3d9 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -86,7 +86,6 @@ def enforce_pbc(atoms): new_atoms : ase.atoms The ASE atoms object for which the PBC have been enforced. """ - new_atoms = atoms.copy() new_atoms.set_scaled_positions(new_atoms.get_scaled_positions()) From 0d4287c1cf9a43e257601f2d5eb69eaef15ccf93 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Fri, 30 Jul 2021 17:12:06 +0200 Subject: [PATCH 199/297] Update docs/source/install/README.md Co-authored-by: Daniel Kotik --- docs/source/install/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 059b7f8be..d427ef356 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -155,7 +155,7 @@ the data repository, it needs to be linked with MALA. 1. Download data repository and check out correct tag ```sh - $ git clone ~/path/to/data/repo ~/path/to/data/repo + $ git clone origin_of_data_repo ~/path/to/data/repo $ cd ~/path/to/data/repo $ git checkout v0.1.0 ``` @@ -164,4 +164,4 @@ the data repository, it needs to be linked with MALA. ```sh $ cd ~/path/to/mala/root/directory $ bash install/data_repo_link/link_data_repo.sh ~/path/to/data/repo - ``` \ No newline at end of file + ``` From 89da1065b7b2adc44b6b01960db752892ae50d43 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 5 Aug 2021 21:07:39 +0200 Subject: [PATCH 200/297] Fixed mistake in doc --- docs/source/install/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index d427ef356..d6112ca93 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -157,7 +157,7 @@ the data repository, it needs to be linked with MALA. ```sh $ git clone origin_of_data_repo ~/path/to/data/repo $ cd ~/path/to/data/repo - $ git checkout v0.1.0 + $ git checkout v0.4.0 ``` 2. Link MALA and data repository From 9f437ec16867dbeff58d28794f06113f54b9f649 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Sat, 7 Aug 2021 15:49:31 +0200 Subject: [PATCH 201/297] did the suggested changes --- examples/ex04_hyperparameter_optimization.py | 35 +++-- examples/exOAT.py | 130 +++++++++++++++++++ mala/network/hyper_opt_oat.py | 93 ++++++------- mala/network/objective_base.py | 48 ++----- 4 files changed, 208 insertions(+), 98 deletions(-) create mode 100644 examples/exOAT.py diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index cddd3265b..1263fbe1c 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -1,17 +1,17 @@ import mala -from mala.common.printout import printout +from mala import printout from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" """ -ex04_hyperparameter_optimization.py: Shows how a hyperparameter -optimization can be done using this framework. There are multiple +ex04_hyperparameter_optimization.py: Shows how a hyperparameter +optimization can be done using this framework. There are multiple hyperparameter optimizers available in this framework. This example focusses -on the most universal one - optuna. +on the most universal one - optuna. """ -def run_example04(desired_loss_improvement_factor=2): +def run_example04(desired_loss_improvement_factor=1): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -30,7 +30,7 @@ def run_example04(desired_loss_improvement_factor=2): test_parameters.data.output_rescaling_type = "normal" # Specify the training parameters. - test_parameters.running.max_number_epochs = 5 + test_parameters.running.max_number_epochs = 20 test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" @@ -38,7 +38,7 @@ def run_example04(desired_loss_improvement_factor=2): # Specify the number of trials, the hyperparameter optimizer should run # and the type of hyperparameter. test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "oat" + test_parameters.hyperparameters.hyper_opt_method = "optuna" #################### # DATA @@ -71,18 +71,20 @@ def run_example04(desired_loss_improvement_factor=2): test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", - choices=[0.005, 0.01, 0.015]) + test_hp_optimizer.add_hyperparameter("float", "learning_rate", + 0.0000001, 0.01) # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter( - "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) - test_hp_optimizer.add_hyperparameter( - "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) + test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) # Choices for activation function at each layer will be optimized. test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid", "LeakyReLU"]) + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", + choices=["ReLU", "Sigmoid"]) + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", + choices=["ReLU", "Sigmoid"]) # Perform hyperparameter optimization. printout("Starting Hyperparameter optimization.") @@ -98,7 +100,6 @@ def run_example04(desired_loss_improvement_factor=2): test_network = mala.Network(test_parameters) test_trainer = mala.Trainer(test_parameters, test_network, data_handler) printout("Network setup: DONE.") - printout("Trial with optimal parameters set.") test_trainer.train_network() printout("Training: DONE.") @@ -110,9 +111,6 @@ def run_example04(desired_loss_improvement_factor=2): printout("Parameters used for this experiment:") test_parameters.show() - # To see if the hyperparameter optimization actually worked, - # check if the best trial is better then the worst trial - # by a certain factor. if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ test_trainer.final_test_loss: return False @@ -128,3 +126,4 @@ def run_example04(desired_loss_improvement_factor=2): "was off. If you haven't changed any parameters in " "the example, there might be a problem with " "your installation.") + diff --git a/examples/exOAT.py b/examples/exOAT.py new file mode 100644 index 000000000..cddd3265b --- /dev/null +++ b/examples/exOAT.py @@ -0,0 +1,130 @@ +import mala +from mala.common.printout import printout +from data_repo_path import get_data_repo_path +data_path = get_data_repo_path()+"Al36/" + +""" +ex04_hyperparameter_optimization.py: Shows how a hyperparameter +optimization can be done using this framework. There are multiple +hyperparameter optimizers available in this framework. This example focusses +on the most universal one - optuna. +""" + + +def run_example04(desired_loss_improvement_factor=2): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 5 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + # Specify the number of trials, the hyperparameter optimizer should run + # and the type of hyperparameter. + test_parameters.hyperparameters.n_trials = 20 + test_parameters.hyperparameters.hyper_opt_method = "oat" + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + data_handler = mala.DataHandler(test_parameters) + + # Add all the snapshots we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # HYPERPARAMETER OPTIMIZATION + # In order to perform a hyperparameter optimization, + # one has to simply create a hyperparameter optimizer + # and let it perform a "study". + # Before such a study can be done, one has to add all the parameters + # of interest. + #################### + + test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) + + # Learning rate will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", + choices=[0.005, 0.01, 0.015]) + + # Number of neurons per layer will be optimized. + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) + + # Choices for activation function at each layer will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid", "LeakyReLU"]) + + # Perform hyperparameter optimization. + printout("Starting Hyperparameter optimization.") + test_hp_optimizer.perform_study() + test_hp_optimizer.set_optimal_parameters() + printout("Hyperparameter optimization: DONE.") + + #################### + # TRAINING + # Train with these new parameters. + #################### + + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + printout("Trial with optimal parameters set.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # RESULTS. + # Print the used parameters and check whether the loss decreased enough. + #################### + + printout("Parameters used for this experiment:") + test_parameters.show() + + # To see if the hyperparameter optimization actually worked, + # check if the best trial is better then the worst trial + # by a certain factor. + if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ + test_trainer.final_test_loss: + return False + else: + return True + + +if __name__ == "__main__": + if run_example04(): + printout("Successfully ran ex04_hyperparameter_optimization.py.") + else: + raise Exception("Ran ex04_hyperparameter_optimization but something " + "was off. If you haven't changed any parameters in " + "the example, there might be a problem with " + "your installation.") diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 813fc3154..895aff453 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -24,7 +24,7 @@ class HyperOptOAT(HyperOptBase): def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None - self.trial_losses = [] + self.trial_losses = None self.sorted_num_choices = [] self.optimal_params = None self.importance = None @@ -43,21 +43,16 @@ def add_hyperparameter(self, opttype="categorical", name="", choices=None, **kwa Default value - categorical (list) """ - if opttype != 'categorical': - raise Exception( - "Only categorical hyperparameters are supported for OAT") + if not self.sorted_num_choices: # if empty + super(HyperOptOAT, self).add_hyperparameter( + opttype=opttype, name=name, choices=choices) + self.sorted_num_choices.append(len(choices)) else: - if not self.sorted_num_choices: # if empty - super(HyperOptOAT, self).add_hyperparameter( - opttype=opttype, name=name, choices=choices) - self.sorted_num_choices.append(len(choices)) - - else: - index = bisect(self.sorted_num_choices, len(choices)) - self.sorted_num_choices.insert(index, len(choices)) - self.params.hyperparameters.hlist.insert( - index, HyperparameterOAT(opttype=opttype, name=name, choices=choices)) + index = bisect(self.sorted_num_choices, len(choices)) + self.sorted_num_choices.insert(index, len(choices)) + self.params.hyperparameters.hlist.insert( + index, HyperparameterOAT(opttype=opttype, name=name, choices=choices)) def perform_study(self): """ @@ -66,31 +61,27 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ - self.n_factors = len(self.params.hyperparameters.hlist) - self.factor_levels = [par.num_choices for par in self.params. - hyperparameters.hlist] - - if not self.monotonic: - raise Exception( - "Please use hyperparameters in increasing or decreasing order of number of choices") - - self.strength = 2 - self.N_runs = self.number_of_runs() self.OA = self.get_orthogonal_array() + self.trial_losses = np.zeros(self.OA.shape[0]) number_of_trial = 0 # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) for row in self.OA: printout("Trial number", number_of_trial) - self.trial_losses.append(self.objective(row)) - + self.trial_losses[number_of_trial] = self.perform_trial( + self.objective, row) number_of_trial += 1 - self.trial_losses = np.array(self.trial_losses) - - # Return the best loss value we could achieve. + # Perform Range Analysis self.get_optimal_parameters() + # feature funciton to use OAT with other optimisation techniques + def perform_trial(self, objective, trial): + objective.trial_type = "oat" + objective_val = objective(trial) + objective.trial_type = objective.params.hyperparameters.hyper_opt_method + return objective_val + def get_optimal_parameters(self): """ Find the optimal set of hyperparameters by doing range analysis. @@ -114,11 +105,6 @@ def indices(idx, val): return np.where( # printout( # *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" < ") - # printout("Optimal Hyperparameters:") - # for (idx, par) in enumerate(self.params.hyperparameters.hlist): - # printout( - # par.name, par.choices[self.optimal_params[idx]], sep=' : ') - def set_optimal_parameters(self): """ Set the optimal parameters found in the present study. @@ -128,12 +114,9 @@ def set_optimal_parameters(self): """ self.objective.parse_trial_oat(self.optimal_params) - def number_of_runs(self): + def get_orthogonal_array(self): """ - Calculate the minimum number of runs required for an Orthogonal array - - Based on the factor levels and the strength of the array requested - + Generate the best Orthogonal array used for optimal hyperparameter sampling. Parameters ---------- factor_levels : list @@ -144,18 +127,24 @@ def number_of_runs(self): strength 2 models all 2 factor interactions strength 3 models all 3 factor interactions + N_runs : int + Minimum number of experimental runs to be performed + This is function is taken from the example notebook of OApackage + """ - runs = [np.prod(tt) for tt in itertools.combinations( - self.factor_levels, self.strength)] + self.n_factors = len(self.params.hyperparameters.hlist) - N = np.lcm.reduce(runs) - return int(N) + self.factor_levels = [par.num_choices for par in self.params. + hyperparameters.hlist] - def get_orthogonal_array(self): - """Generate the best Orthogonal array used for optimal hyperparameter sampling.""" + if not self.monotonic: + raise Exception( + "Please use hyperparameters in increasing or decreasing order of number of choices") + self.strength = 2 + self.N_runs = self.number_of_runs() print("Generating Suitable Orthogonal Array.") arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) @@ -178,6 +167,20 @@ def get_orthogonal_array(self): print("No orthogonal array exists with such a parameter combination.") return np.unique(np.array(arraylist[0]), axis=0) + def number_of_runs(self): + """ + Calculate the minimum number of runs required for an Orthogonal array + + Based on the factor levels and the strength of the array requested + + """ + + runs = [np.prod(tt) for tt in itertools.combinations( + self.factor_levels, self.strength)] + + N = np.lcm.reduce(runs) + return int(N) + @property def monotonic(self): dx = np.diff(self.factor_levels) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 820c6a97e..1513a8bc3 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -11,17 +11,20 @@ class ObjectiveBase: Represents the objective function of a training process. This is usually the result of a training of a network. - - Parameters - ---------- - params : mala.common.parametes.Parameters - Parameters used to create this objective. - - data_handler : mala.datahandling.data_handler.DataHandler - datahandler to be used during the hyperparameter optimization. """ def __init__(self, params, data_handler): + """ + Create an ObjectiveBase object. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. + """ self.params = params self.data_handler = data_handler @@ -37,8 +40,6 @@ def __init__(self, params, data_handler): )) self.trial_type = self.params.hyperparameters.hyper_opt_method - if self.trial_type == "notraining": - self.trial_type = "optuna" def __call__(self, trial): """ @@ -96,38 +97,15 @@ def parse_trial_optuna(self, trial: Trial): for par in self.params.hyperparameters.hlist: if par.name == "learning_rate": self.params.running.learning_rate = par.get_parameter(trial) - elif "layer_activation" in par.name: self.params.network.layer_activations.\ append(par.get_parameter(trial)) - elif "ff_neurons_layer" in par.name: if self.params.network.nn_type == "feed-forward": - # Check for zero neuron layers; These indicate layers - # that can be left out. - layer_size = par.get_parameter(trial) - if layer_size > 0: - self.params.network.layer_sizes.\ - append(par.get_parameter(trial)) - + self.params.network.layer_sizes.\ + append(par.get_parameter(trial)) elif "trainingtype" in par.name: self.params.running.trainingtype = par.get_parameter(trial) - - elif "mini_batch_size" in par.name: - self.params.running.mini_batch_size = par.get_parameter(trial) - - elif "early_stopping_epochs" in par.name: - self.params.running.early_stopping_epochs = par.\ - get_parameter(trial) - - elif "learning_rate_patience" in par.name: - self.params.running.learning_rate_patience = par.\ - get_parameter(trial) - - elif "learning_rate_decay" in par.name: - self.params.running.learning_rate_decay = par.\ - get_parameter(trial) - else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From 1c772c6150cd3b862af01b457a4c312ac34a8769 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Sat, 7 Aug 2021 18:40:41 +0200 Subject: [PATCH 202/297] added automatic webbrowser support for tensorboard --- examples/data_repos_path.py | 9 + examples/ex01_run_singleshot.py | 175 +++++++------- examples/ex10_tensor_board.py | 287 ++++++++++------------- install/data_repo_link/link_data_repo.sh | 15 +- mala/common/parameters.py | 3 + mala/network/trainer.py | 14 +- test/data_repos_path.py | 9 + 7 files changed, 231 insertions(+), 281 deletions(-) create mode 100644 examples/data_repos_path.py mode change 100755 => 100644 install/data_repo_link/link_data_repo.sh create mode 100644 test/data_repos_path.py diff --git a/examples/data_repos_path.py b/examples/data_repos_path.py new file mode 100644 index 000000000..9806503b9 --- /dev/null +++ b/examples/data_repos_path.py @@ -0,0 +1,9 @@ +data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" +repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" + + +def get_data_repo_path(): + return data_repo_path + +def get_repo_path(): + return repo_path diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 3b4a6c935..0dc3bf7d0 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -10,99 +10,82 @@ """ -def run_example01(desired_loss_improvement_factor=1): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = 2 - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - - #################### - # TRAINING - # Train the network. - #################### - - printout("Starting training.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: - return False - else: - return True - - -if __name__ == "__main__": - if run_example01(): - printout("Successfully ran ex01_run_singleshot.") - else: - raise Exception("Ran ex01_run_singleshot but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### + +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the used activation function. +test_parameters.network.layer_activations = ["ReLU"] + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 20 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" + +#################### +# DATA +# Add and prepare snapshots for training. +#################### + +data_handler = mala.DataHandler(test_parameters) + +# Add a snapshot we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# NETWORK SETUP +# Set up the network and trainer we want to use. +# The layer sizes can be specified before reading data, +# but it is safer this way. +#################### + +test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + +# Setup network and trainer. +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") + +#################### +# TRAINING +# Train the network. +#################### + +printout("Starting training.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### + +printout("Parameters used for this experiment:") +test_parameters.show() diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 4eec55b61..151f6d599 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,176 +1,135 @@ import mala from mala import printout -from multiprocessing import Process -import sys -import os +from mala.common.launch_tensorboard import Launch_tensorboard +from tensorboard import program +import shutil +import webbrowser from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" """ -ex10_tensor_board.py: -to include tensor board link through python script to auto start the tensorboard port. -to run tensorboard from a python script. -to check if the +ex01_run_singleshot.py: Shows how a neural network can be trained on material +data using this framework. It uses preprocessed data, that is read in +from *.npy files. """ -def run_example01(desired_loss_improvement_factor=1): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = 2 - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - - #################### - # TRAINING - # Train the network. - #################### - - printout("Starting training.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: - return False - else: - return True - - - - - -class TensorboardSupervisor: - def __init__(self, log_dp): - self.server = TensorboardServer(log_dp) - self.server.start() - print("Started Tensorboard Server") - self.chrome = ChromeProcess() - print("Started Chrome Browser") - self.chrome.start() - - def finalize(self): - if self.server.is_alive(): - print('Killing Tensorboard Server') - self.server.terminate() - self.server.join() - - -class TensorboardServer(Process): - def __init__(self, log_dp): - super().__init__() - self.os_name = os.name - self.log_dp = str(log_dp) - def run(self): - if self.os_name == 'nt': # Windows - os.system('tensorboard --logdir="{self.log_dp} --bind_all"') - elif self.os_name == 'posix': # Linux - os.system(f'tensorboard --logdir="{self.log_dp} --bind_all" ') - else: - raise NotImplementedError(f'No support for OS : {self.os_name}') - - -class ChromeProcess(Process): - def __init__(self): - super().__init__() - self.os_name = os.name - self.daemon = True +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### + +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the used activation function. +test_parameters.network.layer_activations = ["ReLU"] + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 20 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" +test_parameters.running.visualisation = "~/log_dir" +test_parameters.running.visualisation_dir = "~/log_dir" + +#################### +# DATA +# Add and prepare snapshots for training. +#################### + +data_handler = mala.DataHandler(test_parameters) + +# Add a snapshot we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# NETWORK SETUP +# Set up the network and trainer we want to use. +# The layer sizes can be specified before reading data, +# but it is safer this way. +#################### + +test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + +# Setup network and trainer. +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") + +#################### +# TRAINING +# Train the network. +#################### + +printout("Starting training.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### +printout("Parameters used for this experiment:") +test_parameters.show() + +#################### +# Using tensorboard and launching the link in webbrowser +#################### +class Launch_tensorboard: + """ + Runs tensorboard with the given log_dir and wait for user + input to kill the app. + :param : + :param clear_on_exit: If True Clears the log_dir on exit and kills the tensorboard app + :return: + """ + + def __init__(self, params, clear_on_exit= False): + + params: mala.common.Parameters + self.tb = program.TensorBoard() + self.log_dir = params.running.visualisation_dir + self.clear_on_exit = clear_on_exit + self.url= None def run(self): - if self.os_name == 'nt': # Windows - os.system(f'start chrome http://localhost:6006/') - elif self.os_name == 'posix': # Linux - os.system(f'google-chrome http://localhost:6006/') - else: - raise NotImplementedError(f'No support for OS : {self.os_name}') - -def run_example10(): - tb_sup = TensorboardSupervisor("/home/snehaverma/Downloads/HZDR_work/mala/examples/log_dir") - tb_sup.finalize() - #################### - # RESULTS. - # Check whether tensorboard has run. - #################### - - printout("Tensor board has ran succesfully:") - - if run_example10: - return False - else: - return True - - -if __name__ == "__main__": - if run_example01() & run_example10(): - printout("Successfully ran ex10_tensor_board.py.") - else: - raise Exception("Ran ex10_tensor_board but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") \ No newline at end of file + self.tb.configure(argv=[None, '--logdir', self.log_dir]) + print("Launching Tensorboard ") + self.url = self.tb.launch() + print(self.url) + webbrowser.open_new_tab(self.url) + try: + input("Enter any key to exit") + except: + pass + finally: + if self.clear_on_exit: + shutil.rmtree(self.log_dir, ignore_errors=True) + print("\nCleared Logdir") + +launch_tb = Launch_tensorboard(test_parameters) +launch_tb.run() diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh old mode 100755 new mode 100644 index 1c744a05d..f37a91467 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,10 +7,9 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) - examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repos_path.py +pythonfile=data_repo_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] @@ -28,26 +27,16 @@ then data_repo_path=$data_repo_path/ fi -# Append a / if we have to. -lastcharacter="${mala_base_path: -1}" -if [ "$lastcharacter" != "/" ] -then - mala_base_path=$mala_base_path/ -fi # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} -echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} -echo "" >> ${script_path}/${pythonfile} -echo "def get_repo_path():" >> ${script_path}/${pythonfile} -echo " return repo_path" >> ${script_path}/${pythonfile} + # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} -cp $ echo "Linking done!" diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 383cfe6f3..49cfde7d3 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -1,4 +1,5 @@ """Collection of all parameter related classes and functions.""" +import os import pickle import warnings from .printout import printout, set_horovod_status @@ -337,6 +338,8 @@ def __init__(self): self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" self.visualisation = 0 + # default visualisation_dir= "~/log_dir" + self.visualisation_dir= os.path.join(os.path.expanduser("~"), "log_dir") class ParametersHyperparameterOptimization(ParametersBase): """ diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 7df6ddcbc..84410abb2 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -56,12 +56,10 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - trainer_path = os.getcwd() - log_dir_path = trainer_path.replace('mala/network','examples/log_dir') - if not os.path.exists(log_dir_path): - os.mkdir(log_dir_path) - - self.tensor_board = SummaryWriter(log_dir_path)## here the path to the log file can be set + if not os.path.exists(self.parameters.visualisation_dir): + os.makedirs(self.parameters.visualisation_dir) + # Set the path to log files + self.tensor_board = SummaryWriter(self.parameters.visualisation_dir) @classmethod @@ -242,7 +240,7 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.parameters.visualisation > 0: + if self.parameters.visualisation: self.tensor_board.add_scalar("Loss", vloss, epoch) self.tensor_board.add_scalar("Learning rate", self.parameters.learning_rate, epoch) if self.parameters.visualisation == 2: @@ -251,7 +249,7 @@ def train_network(self): self.tensor_board.add_histogram(name,param,epoch) self.tensor_board.add_histogram(f'{name}.grad',param.grad,epoch) - self.tensor_board.flush() #method to make sure that all pending events have been written to disk + self.tensor_board.close() #method to make sure that all pending events have been written to disk diff --git a/test/data_repos_path.py b/test/data_repos_path.py new file mode 100644 index 000000000..9806503b9 --- /dev/null +++ b/test/data_repos_path.py @@ -0,0 +1,9 @@ +data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" +repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" + + +def get_data_repo_path(): + return data_repo_path + +def get_repo_path(): + return repo_path From 59d0abc42b8f3b418ea0a971a4757f4a6fec86d8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler <37868410+RandomDefaultUser@users.noreply.github.com> Date: Sun, 8 Aug 2021 22:22:34 +0200 Subject: [PATCH 203/297] Update mala/common/printout.py --- mala/common/printout.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/mala/common/printout.py b/mala/common/printout.py index 2dbd83c03..945341977 100644 --- a/mala/common/printout.py +++ b/mala/common/printout.py @@ -38,9 +38,6 @@ def printout(*values, sep=' '): """ outstring = sep.join([str(v) for v in values]) - # for v in values: - # outstring += str(v)+sep # This implementation is adding an extra seperator at the end of the iterable! - if use_horovod is False: print(outstring) else: From 40715351bea0f57a8c38ab410ed4f46f006b601a Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 9 Aug 2021 14:49:19 +0200 Subject: [PATCH 204/297] added trainer.py changes --- install/data_repo_link/link_data_repo.sh | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) mode change 100644 => 100755 install/data_repo_link/link_data_repo.sh diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh old mode 100644 new mode 100755 index f37a91467..1c744a05d --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,9 +7,10 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) + examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repo_path.py +pythonfile=data_repos_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] @@ -27,16 +28,26 @@ then data_repo_path=$data_repo_path/ fi +# Append a / if we have to. +lastcharacter="${mala_base_path: -1}" +if [ "$lastcharacter" != "/" ] +then + mala_base_path=$mala_base_path/ +fi # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} +echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} - +echo "" >> ${script_path}/${pythonfile} +echo "def get_repo_path():" >> ${script_path}/${pythonfile} +echo " return repo_path" >> ${script_path}/${pythonfile} # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} +cp $ echo "Linking done!" From af9d7f34605585442465253fb8cc3d4df4d72f6f Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 9 Aug 2021 15:24:07 +0200 Subject: [PATCH 205/297] resolved conflict while rebasing --- docs/source/install/README.md | 1 + examples/ex01_run_singleshot.py | 99 ++++++++++++++++++ ...ts.out.tfevents.1624876703.pop-os.160578.0 | Bin 0 -> 878 bytes mala/common/parameters.py | 4 + mala/network/trainer.py | 20 ++++ requirements.txt | 4 + 6 files changed, 128 insertions(+) create mode 100644 examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 diff --git a/docs/source/install/README.md b/docs/source/install/README.md index d6112ca93..06e575e99 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -22,6 +22,7 @@ In order to run MALA you have to have the following packages installed: * numpy * scipy * oapackage +* tensorboard * optuna * ase * mpmath diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 0dc3bf7d0..6eb7a9302 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -10,6 +10,7 @@ """ +<<<<<<< HEAD #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -89,3 +90,101 @@ printout("Parameters used for this experiment:") test_parameters.show() +======= +def run_example01(desired_loss_improvement_factor=1): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.running.visualisation = True + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + + #################### + # TRAINING + # Train the network. + #################### + + printout("Starting training.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # RESULTS. + # Print the used parameters and check whether the loss decreased enough. + #################### + + printout("Parameters used for this experiment:") + test_parameters.show() + + if desired_loss_improvement_factor*test_trainer.initial_test_loss\ + < test_trainer.final_test_loss: + return False + else: + return True + + +if __name__ == "__main__": + if run_example01(): + printout("Successfully ran ex01_run_singleshot.") + else: + raise Exception("Ran ex01_run_singleshot but something was off." + " If you haven't changed any parameters in " + "the example, there might be a problem with your" + " installation.") +>>>>>>> 39e8119 (summary writer added for check) diff --git a/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 b/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 new file mode 100644 index 0000000000000000000000000000000000000000..9e786a8e59ddfe2f96af692ba3eb2ed1d88473aa GIT binary patch literal 878 zcmb1OfPlsI-b$RWtNt#}G`ry_#hX-=n3<>NT9%quVr6tKcimPAWOct@v#$WD)8ggg z=3?>5FD@1p`=V>RNb|g$6jYti>>IkAUP?S*bpnh?Dqp=&wpHDmb^%3Yor?fOB@>cL zhgs6LN;xK6C@Q~DiNB(jYl|q|MAu73$R7#fKvZ+Zt z$%&%!Vxkp9B{z~vf744gUu};Gqo_Q2!VaR62T7&P)KfO;g}S>@R5r&tLsar2sf>s} zY$K2+GXX{AJS7i^NuMb2eKa$EjE4SI0cHKdBXGne^M5O?d z%2P)+*sw(ezeO=qPCN{vQV>by4};Y0Q0l4x literal 0 HcmV?d00001 diff --git a/mala/common/parameters.py b/mala/common/parameters.py index b8e1ea215..e6bdc4928 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -346,6 +346,7 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" +<<<<<<< HEAD self.during_training_metric = "ldos" self.after_before_training_metric = "ldos" @@ -400,6 +401,9 @@ def after_before_training_metric(self, value): "\"ldos\" metric for horovod runs.") self._after_before_training_metric = value +======= + self.visualisation = False +>>>>>>> 39e8119 (summary writer added for check) class ParametersHyperparameterOptimization(ParametersBase): """ diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 61f18f138..dbff9304d 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -10,6 +10,8 @@ from torch.utils.data import DataLoader from mala.common.parameters import printout from .runner import Runner +from torch.utils.tensorboard import SummaryWriter ##summaty writer + try: import horovod.torch as hvd except ModuleNotFoundError: @@ -49,6 +51,9 @@ def __init__(self, params, network, data, optimizer_dict=None): self.validation_data_loader = None self.test_data_loader = None self.__prepare_to_train(optimizer_dict) + self.tensor_board = None + if self.parameters.visualisation: + self.tb = SummaryWriter() @classmethod def checkpoint_exists(cls, checkpoint_name): @@ -242,6 +247,10 @@ def train_network(self): if self.parameters.verbosity: printout("Epoch: ", epoch, "validation data loss: ", vloss) + #summary_writer tensor board + if self.tb is not None: + self.tb.add_scalar("Loss", vloss, epoch) + # Mix the DataSets up (this function only does something # in the lazy loading case). if self.parameters.use_shuffling_for_samplers: @@ -301,6 +310,13 @@ def train_network(self): if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') +<<<<<<< HEAD +======= + # closing tensorboard window + self.tb.close() + + # Calculate final loss. +>>>>>>> 39e8119 (summary writer added for check) self.final_validation_loss = vloss printout("Final validation data loss: ", vloss) @@ -315,6 +331,8 @@ def train_network(self): printout("Final test data loss: ", tloss) self.final_test_loss = tloss + + def __prepare_to_train(self, optimizer_dict): """Prepare everything for training.""" # Configure keyword arguments for DataSampler. @@ -638,3 +656,5 @@ def __average_validation(val, name): tensor = torch.tensor(val) avg_loss = hvd.allreduce(tensor, name=name, op=hvd.Average) return avg_loss.item() + + \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index e81c8aeab..f8520206c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,8 @@ numpy optuna scipy oapackage +<<<<<<< HEAD pandas +======= +tensorboard +>>>>>>> 39e8119 (summary writer added for check) From 1fe0e77fd2a9fe51b879bd7fa37a9bc69f7ca89f Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 28 Jun 2021 16:58:27 +0200 Subject: [PATCH 206/297] minor changes --- ...events.out.tfevents.1624876703.pop-os.160578.0 | Bin 878 -> 0 bytes .../events.out.tfevents.1624892223.pop-os.18826.0 | Bin 0 -> 878 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 create mode 100644 examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 diff --git a/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 b/examples/runs/Jun28_12-38-23_pop-os/events.out.tfevents.1624876703.pop-os.160578.0 deleted file mode 100644 index 9e786a8e59ddfe2f96af692ba3eb2ed1d88473aa..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 878 zcmb1OfPlsI-b$RWtNt#}G`ry_#hX-=n3<>NT9%quVr6tKcimPAWOct@v#$WD)8ggg z=3?>5FD@1p`=V>RNb|g$6jYti>>IkAUP?S*bpnh?Dqp=&wpHDmb^%3Yor?fOB@>cL zhgs6LN;xK6C@Q~DiNB(jYl|q|MAu73$R7#fKvZ+Zt z$%&%!Vxkp9B{z~vf744gUu};Gqo_Q2!VaR62T7&P)KfO;g}S>@R5r&tLsar2sf>s} zY$K2+GXX{AJS7i^NuMb2eKa$EjE4SI0cHKdBXGne^M5O?d z%2P)+*sw(ezeO=qPCN{vQV>by4};Y0Q0l4x diff --git a/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 b/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 new file mode 100644 index 0000000000000000000000000000000000000000..b924889d5c53c6c6da252dfc25c6ec9718bae2f1 GIT binary patch literal 878 zcmY+?TPTBZ7zXfR!?)S#GptE!QK&g5hyPcUL!sPBE@Vn_J|>D*yAY)malwUiIfO(` zjVOtWIqb$(Lxz-cYM~^F*Twrj*T0^d=h2EFlcqd9=d@T)+LG)UI(b=>v9c~YtF^qY zB{jZ6nXJ)bCn~l7xhT0*CKcz`H#Z09ggC*~;1#X=cl?Omz)jsc|D4JL$(W*W;mAMe zmB{SEYmlBu&ZPtkCB@o)BA@r~Kzbo*oZ*Ge;=X$#HxnN~ijn;0WP&km{({Kv@h6ZH zBn^RLVQ8Q>gveEm8>Baqy&In_)9q*^GJWy`qz{tToF~>X*OEY_MfU~L7fIWZi)~jc zwul@yi%x&{QE^C`I_>QIQr|`7n?wRqilnOQgz=`x1tN2MIFK?VV;u+VIMJC-MYe_DuKH}fFnNH(0Vv#WOXHIZkpfglw~E|f2`nw~}nk%jHSAeBgtwaqiV z)o3Gf_AwNs3Q7ISG+U}Ur(YVX4+E)2(y1O}^7s8k(y9H50LddcQ98^HZ5JCv?)4c! O`XhO(9As;wn%y4@*ZZ^p literal 0 HcmV?d00001 From 2e54c3fa98af467d6fac07bb9837a8e97a0aa640 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Thu, 1 Jul 2021 17:17:09 +0200 Subject: [PATCH 207/297] changes suggested by Lenz --- docs/source/install/tensorboard_setup.md | 6 ++++++ mala/network/trainer.py | 8 ++++---- 2 files changed, 10 insertions(+), 4 deletions(-) create mode 100644 docs/source/install/tensorboard_setup.md diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md new file mode 100644 index 000000000..b0d9c53e9 --- /dev/null +++ b/docs/source/install/tensorboard_setup.md @@ -0,0 +1,6 @@ +Access central storage servers from locations other than Hemera (Linux) + +https://fwcc.pages.hzdr.de/infohub/hpc/storage.html +sshfs :username@hemera5.fz-rossendorf.de folder/file/location folder/location/in/local + +This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/mala/network/trainer.py b/mala/network/trainer.py index dbff9304d..496b6b01e 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -53,7 +53,7 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tb = SummaryWriter() + self.tensor_board = SummaryWriter() @classmethod def checkpoint_exists(cls, checkpoint_name): @@ -248,8 +248,8 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.tb is not None: - self.tb.add_scalar("Loss", vloss, epoch) + if self.tensor_board is not None: + self.tensor_board.add_scalar("Loss", vloss, epoch) # Mix the DataSets up (this function only does something # in the lazy loading case). @@ -313,7 +313,7 @@ def train_network(self): <<<<<<< HEAD ======= # closing tensorboard window - self.tb.close() + self.tensor_board.close() # Calculate final loss. >>>>>>> 39e8119 (summary writer added for check) From 702e3b05dbb7b247474bfe221cdde8bdd3318667 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Tue, 20 Jul 2021 23:34:06 +0200 Subject: [PATCH 208/297] made changes for the tensorboard --- docs/source/install/tensorboard_setup.md | 2 +- examples/ex01_run_singleshot.py | 2 +- examples/ex10_tensor_board.py | 130 +++++++++++++++++++++++ mala/common/parameters.py | 14 ++- mala/network/trainer.py | 18 +++- 5 files changed, 158 insertions(+), 8 deletions(-) create mode 100644 examples/ex10_tensor_board.py diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md index b0d9c53e9..c52aa5d32 100644 --- a/docs/source/install/tensorboard_setup.md +++ b/docs/source/install/tensorboard_setup.md @@ -1,6 +1,6 @@ Access central storage servers from locations other than Hemera (Linux) https://fwcc.pages.hzdr.de/infohub/hpc/storage.html -sshfs :username@hemera5.fz-rossendorf.de folder/file/location folder/location/in/local +sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 6eb7a9302..c1a5a6c04 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -118,7 +118,7 @@ def run_example01(desired_loss_improvement_factor=1): test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = True + test_parameters.running.visualisation = 2 #################### # DATA # Add and prepare snapshots for training. diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py new file mode 100644 index 000000000..e817a4c87 --- /dev/null +++ b/examples/ex10_tensor_board.py @@ -0,0 +1,130 @@ +import mala +from mala import printout +from data_repo_path import get_data_repo_path +data_path = get_data_repo_path()+"Al36/" + + +""" +ex10_tensor_board.py: +to include tensor board link through python script to auto start the tensorboard port. +to run tensorboard from a python script. +to check if the +""" + +def run_example01(desired_loss_improvement_factor=1): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.running.visualisation = 2 + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + + #################### + # TRAINING + # Train the network. + #################### + + printout("Starting training.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # RESULTS. + # Print the used parameters and check whether the loss decreased enough. + #################### + + printout("Parameters used for this experiment:") + test_parameters.show() + + if desired_loss_improvement_factor*test_trainer.initial_test_loss\ + < test_trainer.final_test_loss: + return False + else: + return True + + + + + +def run_example10(): + tensor_board_program = program.TensorBoard() + tensor_board_program.configure(argv=[None, '--logdir', http://localhost:6006/]) + url = tensor_board_program.launch() + + + #################### + # RESULTS. + # Check whether tensorboard has run. + #################### + + printout("Tensor board has ran succesfully:") + + if desired_loss_improvement_factor*test_trainer.initial_test_loss\ + < test_trainer.final_test_loss: + return False + else: + return True +if __name__ == "__main__": + if run_example01() && run_example10(): + printout("Successfully ran ex10_tensor_board.py.") + else: + raise Exception("Ran ex10_tensor_board but something " + "was off. If you haven't changed any parameters in " + "the example, there might be a problem with " + "your installation.") \ No newline at end of file diff --git a/mala/common/parameters.py b/mala/common/parameters.py index e6bdc4928..1b7a96567 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -321,6 +321,13 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. + visualisation : int + If True then Tensorboard is activated for visualisation + case 0: No tensorboard activated + case 1: tensorboard activated with Loss and learning rate + case 2; additonally weights and biases and gradient + + """ def __init__(self): @@ -346,7 +353,7 @@ def __init__(self): self.use_shuffling_for_samplers = True self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" -<<<<<<< HEAD + self.visualisation = 0 self.during_training_metric = "ldos" self.after_before_training_metric = "ldos" @@ -401,9 +408,8 @@ def after_before_training_metric(self, value): "\"ldos\" metric for horovod runs.") self._after_before_training_metric = value -======= - self.visualisation = False ->>>>>>> 39e8119 (summary writer added for check) + + class ParametersHyperparameterOptimization(ParametersBase): """ diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 496b6b01e..27b07dd96 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -53,7 +53,8 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tensor_board = SummaryWriter() + self.tensor_board = SummaryWriter("log_dir")## here the path to the log file can be set + @classmethod def checkpoint_exists(cls, checkpoint_name): @@ -248,8 +249,21 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.tensor_board is not None: + if self.parameters.visualisation > 0: self.tensor_board.add_scalar("Loss", vloss, epoch) + self.tensor_board.add_scalar("Learning rate", self.parameters.learning_rate, epoch) + if self.parameters.visualisation == 2: + print("visualisation = 2") + for name, param in self.network.named_parameters(): + self.tensor_board.add_histogram(name,param,epoch) + self.tensor_board.add_histogram(f'{name}.grad',param.grad,epoch) + + self.tensor_board.flush() #method to make sure that all pending events have been written to disk + + + + + # Mix the DataSets up (this function only does something # in the lazy loading case). From ed598eabca1e9585e7252e2f26d21450778e995c Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 26 Jul 2021 11:23:44 +0200 Subject: [PATCH 209/297] added changes to trainer.py and ex10 --- examples/ex10_tensor_board.py | 64 +++++++++++++++--- ...nts.out.tfevents.1624892223.pop-os.18826.0 | Bin 878 -> 0 bytes install/data_repo_link/link_data_repo.sh | 15 +++- mala/network/trainer.py | 12 +++- 4 files changed, 77 insertions(+), 14 deletions(-) delete mode 100644 examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 mode change 100644 => 100755 install/data_repo_link/link_data_repo.sh diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index e817a4c87..4eec55b61 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,5 +1,8 @@ import mala from mala import printout +from multiprocessing import Process +import sys +import os from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" @@ -100,14 +103,56 @@ def run_example01(desired_loss_improvement_factor=1): - + + +class TensorboardSupervisor: + def __init__(self, log_dp): + self.server = TensorboardServer(log_dp) + self.server.start() + print("Started Tensorboard Server") + self.chrome = ChromeProcess() + print("Started Chrome Browser") + self.chrome.start() + + def finalize(self): + if self.server.is_alive(): + print('Killing Tensorboard Server') + self.server.terminate() + self.server.join() + + +class TensorboardServer(Process): + def __init__(self, log_dp): + super().__init__() + self.os_name = os.name + self.log_dp = str(log_dp) + + def run(self): + if self.os_name == 'nt': # Windows + os.system('tensorboard --logdir="{self.log_dp} --bind_all"') + elif self.os_name == 'posix': # Linux + os.system(f'tensorboard --logdir="{self.log_dp} --bind_all" ') + else: + raise NotImplementedError(f'No support for OS : {self.os_name}') + + +class ChromeProcess(Process): + def __init__(self): + super().__init__() + self.os_name = os.name + self.daemon = True + + def run(self): + if self.os_name == 'nt': # Windows + os.system(f'start chrome http://localhost:6006/') + elif self.os_name == 'posix': # Linux + os.system(f'google-chrome http://localhost:6006/') + else: + raise NotImplementedError(f'No support for OS : {self.os_name}') def run_example10(): - tensor_board_program = program.TensorBoard() - tensor_board_program.configure(argv=[None, '--logdir', http://localhost:6006/]) - url = tensor_board_program.launch() - - + tb_sup = TensorboardSupervisor("/home/snehaverma/Downloads/HZDR_work/mala/examples/log_dir") + tb_sup.finalize() #################### # RESULTS. # Check whether tensorboard has run. @@ -115,13 +160,14 @@ def run_example10(): printout("Tensor board has ran succesfully:") - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: + if run_example10: return False else: return True + + if __name__ == "__main__": - if run_example01() && run_example10(): + if run_example01() & run_example10(): printout("Successfully ran ex10_tensor_board.py.") else: raise Exception("Ran ex10_tensor_board but something " diff --git a/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 b/examples/runs/Jun28_16-57-03_pop-os/events.out.tfevents.1624892223.pop-os.18826.0 deleted file mode 100644 index b924889d5c53c6c6da252dfc25c6ec9718bae2f1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 878 zcmY+?TPTBZ7zXfR!?)S#GptE!QK&g5hyPcUL!sPBE@Vn_J|>D*yAY)malwUiIfO(` zjVOtWIqb$(Lxz-cYM~^F*Twrj*T0^d=h2EFlcqd9=d@T)+LG)UI(b=>v9c~YtF^qY zB{jZ6nXJ)bCn~l7xhT0*CKcz`H#Z09ggC*~;1#X=cl?Omz)jsc|D4JL$(W*W;mAMe zmB{SEYmlBu&ZPtkCB@o)BA@r~Kzbo*oZ*Ge;=X$#HxnN~ijn;0WP&km{({Kv@h6ZH zBn^RLVQ8Q>gveEm8>Baqy&In_)9q*^GJWy`qz{tToF~>X*OEY_MfU~L7fIWZi)~jc zwul@yi%x&{QE^C`I_>QIQr|`7n?wRqilnOQgz=`x1tN2MIFK?VV;u+VIMJC-MYe_DuKH}fFnNH(0Vv#WOXHIZkpfglw~E|f2`nw~}nk%jHSAeBgtwaqiV z)o3Gf_AwNs3Q7ISG+U}Ur(YVX4+E)2(y1O}^7s8k(y9H50LddcQ98^HZ5JCv?)4c! O`XhO(9As;wn%y4@*ZZ^p diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh old mode 100644 new mode 100755 index f37a91467..1c744a05d --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,9 +7,10 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) + examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repo_path.py +pythonfile=data_repos_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] @@ -27,16 +28,26 @@ then data_repo_path=$data_repo_path/ fi +# Append a / if we have to. +lastcharacter="${mala_base_path: -1}" +if [ "$lastcharacter" != "/" ] +then + mala_base_path=$mala_base_path/ +fi # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} +echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} - +echo "" >> ${script_path}/${pythonfile} +echo "def get_repo_path():" >> ${script_path}/${pythonfile} +echo " return repo_path" >> ${script_path}/${pythonfile} # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} +cp $ echo "Linking done!" diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 27b07dd96..dec713466 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -10,7 +10,8 @@ from torch.utils.data import DataLoader from mala.common.parameters import printout from .runner import Runner -from torch.utils.tensorboard import SummaryWriter ##summaty writer +from torch.utils.tensorboard import SummaryWriter +import os try: import horovod.torch as hvd @@ -53,8 +54,13 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - self.tensor_board = SummaryWriter("log_dir")## here the path to the log file can be set - + trainer_path = os.getcwd() + log_dir_path = trainer_path.replace('mala/network','examples/log_dir') + if not os.path.exists(log_dir_path): + os.mkdir(log_dir_path) + + self.tensor_board = SummaryWriter(log_dir_path)## here the path to the log file can be set + @classmethod def checkpoint_exists(cls, checkpoint_name): From d3e56756c156401734243d33cbf8395c4a3c597e Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Sat, 7 Aug 2021 18:40:41 +0200 Subject: [PATCH 210/297] added automatic webbrowser support for tensorboard --- examples/data_repos_path.py | 9 + examples/ex01_run_singleshot.py | 101 +------- examples/ex10_tensor_board.py | 287 ++++++++++------------- install/data_repo_link/link_data_repo.sh | 15 +- mala/common/parameters.py | 4 + mala/network/trainer.py | 14 +- test/data_repos_path.py | 9 + 7 files changed, 154 insertions(+), 285 deletions(-) create mode 100644 examples/data_repos_path.py mode change 100755 => 100644 install/data_repo_link/link_data_repo.sh create mode 100644 test/data_repos_path.py diff --git a/examples/data_repos_path.py b/examples/data_repos_path.py new file mode 100644 index 000000000..9806503b9 --- /dev/null +++ b/examples/data_repos_path.py @@ -0,0 +1,9 @@ +data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" +repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" + + +def get_data_repo_path(): + return data_repo_path + +def get_repo_path(): + return repo_path diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index c1a5a6c04..8c8f0602f 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -10,7 +10,6 @@ """ -<<<<<<< HEAD #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -89,102 +88,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() -======= -def run_example01(desired_loss_improvement_factor=1): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = 2 - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - - #################### - # TRAINING - # Train the network. - #################### - - printout("Starting training.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: - return False - else: - return True - - -if __name__ == "__main__": - if run_example01(): - printout("Successfully ran ex01_run_singleshot.") - else: - raise Exception("Ran ex01_run_singleshot but something was off." - " If you haven't changed any parameters in " - "the example, there might be a problem with your" - " installation.") ->>>>>>> 39e8119 (summary writer added for check) +test_parameters.show() \ No newline at end of file diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 4eec55b61..151f6d599 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,176 +1,135 @@ import mala from mala import printout -from multiprocessing import Process -import sys -import os +from mala.common.launch_tensorboard import Launch_tensorboard +from tensorboard import program +import shutil +import webbrowser from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" """ -ex10_tensor_board.py: -to include tensor board link through python script to auto start the tensorboard port. -to run tensorboard from a python script. -to check if the +ex01_run_singleshot.py: Shows how a neural network can be trained on material +data using this framework. It uses preprocessed data, that is read in +from *.npy files. """ -def run_example01(desired_loss_improvement_factor=1): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the used activation function. - test_parameters.network.layer_activations = ["ReLU"] - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 20 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - test_parameters.running.visualisation = 2 - #################### - # DATA - # Add and prepare snapshots for training. - #################### - - data_handler = mala.DataHandler(test_parameters) - - # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # NETWORK SETUP - # Set up the network and trainer we want to use. - # The layer sizes can be specified before reading data, - # but it is safer this way. - #################### - - test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), - 100, - data_handler.get_output_dimension()] - - # Setup network and trainer. - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - - #################### - # TRAINING - # Train the network. - #################### - - printout("Starting training.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - if desired_loss_improvement_factor*test_trainer.initial_test_loss\ - < test_trainer.final_test_loss: - return False - else: - return True - - - - - -class TensorboardSupervisor: - def __init__(self, log_dp): - self.server = TensorboardServer(log_dp) - self.server.start() - print("Started Tensorboard Server") - self.chrome = ChromeProcess() - print("Started Chrome Browser") - self.chrome.start() - - def finalize(self): - if self.server.is_alive(): - print('Killing Tensorboard Server') - self.server.terminate() - self.server.join() - - -class TensorboardServer(Process): - def __init__(self, log_dp): - super().__init__() - self.os_name = os.name - self.log_dp = str(log_dp) - def run(self): - if self.os_name == 'nt': # Windows - os.system('tensorboard --logdir="{self.log_dp} --bind_all"') - elif self.os_name == 'posix': # Linux - os.system(f'tensorboard --logdir="{self.log_dp} --bind_all" ') - else: - raise NotImplementedError(f'No support for OS : {self.os_name}') - - -class ChromeProcess(Process): - def __init__(self): - super().__init__() - self.os_name = os.name - self.daemon = True +#################### +# PARAMETERS +# All parameters are handled from a central parameters class that +# contains subclasses. +#################### + +test_parameters = mala.Parameters() +# Currently, the splitting in training, validation and test set are +# done on a "by snapshot" basis. Specify how this is +# done by providing a list containing entries of the form +# "tr", "va" and "te". +test_parameters.data.data_splitting_type = "by_snapshot" +test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + +# Specify the data scaling. +test_parameters.data.input_rescaling_type = "feature-wise-standard" +test_parameters.data.output_rescaling_type = "normal" + +# Specify the used activation function. +test_parameters.network.layer_activations = ["ReLU"] + +# Specify the training parameters. +test_parameters.running.max_number_epochs = 20 +test_parameters.running.mini_batch_size = 40 +test_parameters.running.learning_rate = 0.00001 +test_parameters.running.trainingtype = "Adam" +test_parameters.running.visualisation = "~/log_dir" +test_parameters.running.visualisation_dir = "~/log_dir" + +#################### +# DATA +# Add and prepare snapshots for training. +#################### + +data_handler = mala.DataHandler(test_parameters) + +# Add a snapshot we want to use in to the list. +data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") +data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") +data_handler.prepare_data() +printout("Read data: DONE.") + +#################### +# NETWORK SETUP +# Set up the network and trainer we want to use. +# The layer sizes can be specified before reading data, +# but it is safer this way. +#################### + +test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + +# Setup network and trainer. +test_network = mala.Network(test_parameters) +test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +printout("Network setup: DONE.") + +#################### +# TRAINING +# Train the network. +#################### + +printout("Starting training.") +test_trainer.train_network() +printout("Training: DONE.") + +#################### +# RESULTS. +# Print the used parameters and check whether the loss decreased enough. +#################### +printout("Parameters used for this experiment:") +test_parameters.show() + +#################### +# Using tensorboard and launching the link in webbrowser +#################### +class Launch_tensorboard: + """ + Runs tensorboard with the given log_dir and wait for user + input to kill the app. + :param : + :param clear_on_exit: If True Clears the log_dir on exit and kills the tensorboard app + :return: + """ + + def __init__(self, params, clear_on_exit= False): + + params: mala.common.Parameters + self.tb = program.TensorBoard() + self.log_dir = params.running.visualisation_dir + self.clear_on_exit = clear_on_exit + self.url= None def run(self): - if self.os_name == 'nt': # Windows - os.system(f'start chrome http://localhost:6006/') - elif self.os_name == 'posix': # Linux - os.system(f'google-chrome http://localhost:6006/') - else: - raise NotImplementedError(f'No support for OS : {self.os_name}') - -def run_example10(): - tb_sup = TensorboardSupervisor("/home/snehaverma/Downloads/HZDR_work/mala/examples/log_dir") - tb_sup.finalize() - #################### - # RESULTS. - # Check whether tensorboard has run. - #################### - - printout("Tensor board has ran succesfully:") - - if run_example10: - return False - else: - return True - - -if __name__ == "__main__": - if run_example01() & run_example10(): - printout("Successfully ran ex10_tensor_board.py.") - else: - raise Exception("Ran ex10_tensor_board but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") \ No newline at end of file + self.tb.configure(argv=[None, '--logdir', self.log_dir]) + print("Launching Tensorboard ") + self.url = self.tb.launch() + print(self.url) + webbrowser.open_new_tab(self.url) + try: + input("Enter any key to exit") + except: + pass + finally: + if self.clear_on_exit: + shutil.rmtree(self.log_dir, ignore_errors=True) + print("\nCleared Logdir") + +launch_tb = Launch_tensorboard(test_parameters) +launch_tb.run() diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh old mode 100755 new mode 100644 index 1c744a05d..f37a91467 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,10 +7,9 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) - examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repos_path.py +pythonfile=data_repo_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] @@ -28,26 +27,16 @@ then data_repo_path=$data_repo_path/ fi -# Append a / if we have to. -lastcharacter="${mala_base_path: -1}" -if [ "$lastcharacter" != "/" ] -then - mala_base_path=$mala_base_path/ -fi # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} -echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} -echo "" >> ${script_path}/${pythonfile} -echo "def get_repo_path():" >> ${script_path}/${pythonfile} -echo " return repo_path" >> ${script_path}/${pythonfile} + # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} -cp $ echo "Linking done!" diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 1b7a96567..db8aa12d6 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -1,4 +1,5 @@ """Collection of all parameter related classes and functions.""" +import os import pickle import warnings from .printout import printout, set_horovod_status @@ -354,6 +355,8 @@ def __init__(self): self.checkpoints_each_epoch = 0 self.checkpoint_name = "checkpoint_mala" self.visualisation = 0 + # default visualisation_dir= "~/log_dir" + self.visualisation_dir= os.path.join(os.path.expanduser("~"), "log_dir") self.during_training_metric = "ldos" self.after_before_training_metric = "ldos" @@ -411,6 +414,7 @@ def after_before_training_metric(self, value): + class ParametersHyperparameterOptimization(ParametersBase): """ Hyperparameter optimization parameters. diff --git a/mala/network/trainer.py b/mala/network/trainer.py index dec713466..1f3e39666 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -54,12 +54,10 @@ def __init__(self, params, network, data, optimizer_dict=None): self.__prepare_to_train(optimizer_dict) self.tensor_board = None if self.parameters.visualisation: - trainer_path = os.getcwd() - log_dir_path = trainer_path.replace('mala/network','examples/log_dir') - if not os.path.exists(log_dir_path): - os.mkdir(log_dir_path) - - self.tensor_board = SummaryWriter(log_dir_path)## here the path to the log file can be set + if not os.path.exists(self.parameters.visualisation_dir): + os.makedirs(self.parameters.visualisation_dir) + # Set the path to log files + self.tensor_board = SummaryWriter(self.parameters.visualisation_dir) @classmethod @@ -255,7 +253,7 @@ def train_network(self): printout("Epoch: ", epoch, "validation data loss: ", vloss) #summary_writer tensor board - if self.parameters.visualisation > 0: + if self.parameters.visualisation: self.tensor_board.add_scalar("Loss", vloss, epoch) self.tensor_board.add_scalar("Learning rate", self.parameters.learning_rate, epoch) if self.parameters.visualisation == 2: @@ -264,7 +262,7 @@ def train_network(self): self.tensor_board.add_histogram(name,param,epoch) self.tensor_board.add_histogram(f'{name}.grad',param.grad,epoch) - self.tensor_board.flush() #method to make sure that all pending events have been written to disk + self.tensor_board.close() #method to make sure that all pending events have been written to disk diff --git a/test/data_repos_path.py b/test/data_repos_path.py new file mode 100644 index 000000000..9806503b9 --- /dev/null +++ b/test/data_repos_path.py @@ -0,0 +1,9 @@ +data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" +repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" + + +def get_data_repo_path(): + return data_repo_path + +def get_repo_path(): + return repo_path From a05398f74742eff99f7924cd29d99dc31cd88367 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 9 Aug 2021 17:05:24 +0200 Subject: [PATCH 211/297] rebasing changes commit --- mala/network/trainer.py | 3 --- requirements.txt | 3 --- 2 files changed, 6 deletions(-) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 1f3e39666..f78d47c95 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -328,13 +328,10 @@ def train_network(self): if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') -<<<<<<< HEAD -======= # closing tensorboard window self.tensor_board.close() # Calculate final loss. ->>>>>>> 39e8119 (summary writer added for check) self.final_validation_loss = vloss printout("Final validation data loss: ", vloss) diff --git a/requirements.txt b/requirements.txt index f8520206c..1fda44567 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,8 +7,5 @@ numpy optuna scipy oapackage -<<<<<<< HEAD pandas -======= tensorboard ->>>>>>> 39e8119 (summary writer added for check) From 1237968cfbffb72883d8c0dbad522772b6e33f7e Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 13 Aug 2021 13:48:57 +0200 Subject: [PATCH 212/297] Update `setup.py`: - fix license from MIT to BSD 3-Clause (we read in the license file) - add "et al." to indicate other authors - read in `README.md` for long description --- setup.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index f865679ca..43af0d524 100644 --- a/setup.py +++ b/setup.py @@ -8,14 +8,21 @@ with open("mala/version.py") as fp: exec(fp.read(), version) +with open("README.md") as f: + readme = f.read() + +with open("LICENSE") as f: + license = f.read() + setup( name="mala", version=version["__version__"], description="Framework for Electronic Structure Learning", + long_description=readme, url="https://gitlab.com/hzdr/mala/mala", - author="Lenz Fiedler", + author="Lenz Fiedler et al.", author_email="l.fiedler@hzdr.de", - license="MIT", + license=license, packages=["mala"], zip_safe=False, install_requires=open('requirements.txt').read().splitlines(), From 54101a76f42068c3ce8ded787b231388b25bf968 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 13 Aug 2021 15:17:59 +0200 Subject: [PATCH 213/297] Added functionality to run multiple trainings per optuna trial --- mala/common/parameters.py | 7 +++++++ mala/network/objective_base.py | 15 ++++++++++----- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index b8e1ea215..de9259c4d 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -482,6 +482,12 @@ class ParametersHyperparameterOptimization(ParametersBase): For MALA, no evidence for decreased performance using smaller heartbeat values could be found. So if this is used, 1s is a reasonable value. + + number_training_per_trial : int + Number of network trainings performed per trial. Default is 1, + but it makes sense to choose a higher number, to exclude networks + that performed by chance (good initilization). Naturally this impedes + performance. """ def __init__(self): @@ -495,6 +501,7 @@ def __init__(self): self.study_name = None self.rdb_storage = None self.rdb_storage_heartbeat = None + self.number_training_per_trial = 1 @property def rdb_storage_heartbeat(self): diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index d264d0123..f16b0266d 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -1,4 +1,5 @@ """Objective function for all training based hyperparameter optimizations.""" +import numpy as np from optuna import Trial from .hyperparameter_optuna import HyperparameterOptuna from .hyperparameter_oat import HyperparameterOAT @@ -53,11 +54,15 @@ def __call__(self, trial): # Parse the parameters included in the trial. self.parse_trial(trial) - # Perform training and report best test loss. - test_network = Network(self.params) - test_trainer = Trainer(self.params, test_network, self.data_handler) - test_trainer.train_network() - return test_trainer.final_validation_loss + # Train a network for as often as the user desires. + final_validation_loss = [] + for i in range(0, self.params.hyperparameters. + number_training_per_trial): + test_network = Network(self.params) + test_trainer = Trainer(self.params, test_network, self.data_handler) + test_trainer.train_network() + final_validation_loss.append(test_trainer.final_validation_loss) + return np.mean(final_validation_loss) def parse_trial(self, trial): """ From 5b97ae8903bae565c2b732d87730c21ae66b942b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 13 Aug 2021 15:22:01 +0200 Subject: [PATCH 214/297] Imrpoved upon last commit --- mala/common/parameters.py | 12 ++++++++++++ mala/network/objective_base.py | 5 +++++ 2 files changed, 17 insertions(+) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index de9259c4d..29885ef6f 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -515,6 +515,18 @@ def rdb_storage_heartbeat(self, value): else: self._rdb_storage_heartbeat = value + @property + def number_training_per_trial(self): + """Control how many trainings are run per optuna trial.""" + return self._number_training_per_trial + + @number_training_per_trial.setter + def number_training_per_trial(self, value): + if value < 1: + self._number_training_per_trial = 1 + else: + self._number_training_per_trial = value + def show(self, indent=""): """ Print name and values of all attributes of this object. diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index f16b0266d..42a84ad71 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -5,6 +5,7 @@ from .hyperparameter_oat import HyperparameterOAT from .network import Network from .trainer import Trainer +from mala import printout class ObjectiveBase: @@ -62,6 +63,10 @@ def __call__(self, trial): test_trainer = Trainer(self.params, test_network, self.data_handler) test_trainer.train_network() final_validation_loss.append(test_trainer.final_validation_loss) + + if self.params.hyperparameters.number_training_per_trial > 1: + printout("Losses from multiple runs are: ") + printout(final_validation_loss) return np.mean(final_validation_loss) def parse_trial(self, trial): From 18851dc77bce82420160e89062ebe627cd614aa8 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 13 Aug 2021 15:52:14 +0200 Subject: [PATCH 215/297] Fix three more things in `setup.py` - Replace description with the up to date one. - Replace old gitlab url with repos new github location. - All subpackages need to specified for the `packages` parameter, i.e. ['mala', 'mala.common', 'mala.datahandling', 'mala.descriptors', 'mala.network', 'mala.targets'] We use `find_packages` from `setuptools` to find them all automatically. --- setup.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 43af0d524..81da8935e 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup +from setuptools import setup, find_packages # Doing it as suggested here: # https://packaging.python.org/guides/single-sourcing-package-version/ @@ -17,13 +17,17 @@ setup( name="mala", version=version["__version__"], - description="Framework for Electronic Structure Learning", + description=("Materials Learning Algorithms. " + "A framework for machine learning materials properties from " + "first-principles data."), long_description=readme, - url="https://gitlab.com/hzdr/mala/mala", + long_description_content_type='text/markdown', + url="https://github.com/mala-project/mala", author="Lenz Fiedler et al.", author_email="l.fiedler@hzdr.de", license=license, - packages=["mala"], + packages=find_packages(exclude=("test", "docs", "examples", "install", + "ml-dft-sandia")), zip_safe=False, install_requires=open('requirements.txt').read().splitlines(), python_requires='<3.9', From 6527c4ffffdadb74f3539dfaa95df4ee0af9b7b3 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 18 Aug 2021 15:08:45 +0200 Subject: [PATCH 216/297] Add `CITATION.cff` --- CITATION.cff | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 CITATION.cff diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 000000000..c185cff2d --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,25 @@ +# YAML 1.2 +cff-version: 1.1.0 +message: "If you use this software, please cite it using these metadata." +authors: + - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" + family-names: Fiedler + given-names: Lenz + - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" + family-names: Kotik + given-names: Daniel + - affiliation: "Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" + family-names: Schmerler + given-names: Steve + - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" + family-names: Cangi + given-names: Attila +#date-released: 2021-08-16 +keywords: + - "machine-learning" + - "dft" +license: "BSD-3-Clause" +repository-code: "https://github.com/mala-project/mala" +title: mala +# doi: +version: 0.1.0 From ec9ecf9844fbe4a128b552ffa56af63c785b0009 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 18 Aug 2021 15:09:36 +0200 Subject: [PATCH 217/297] Add `.gitattributes` for parsing `.cff` files as YAML --- .gitattributes | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..603c09027 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.cff linguist-language=YAML From 50345d9691e08624c660ab1ba111083b20a92c7a Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Wed, 18 Aug 2021 22:40:57 +0200 Subject: [PATCH 218/297] fixed the docstring errors --- mala/network/hyper_opt_base.py | 1 - mala/network/hyper_opt_oat.py | 30 ++++++++++++++++++------------ 2 files changed, 18 insertions(+), 13 deletions(-) diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index dd03cd042..0d0df7e52 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -54,7 +54,6 @@ def add_hyperparameter(self, opttype="float", name="", low=0, high=0, choices : List of possible choices (for categorical parameter). """ - self.params.\ hyperparameters.hlist.append( HyperparameterInterface(self.params.hyperparameters. diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 895aff453..3b06866f0 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -38,10 +38,11 @@ def add_hyperparameter(self, opttype="categorical", name="", choices=None, **kwa """ Add hyperparameter such that the hyperparameter list is sorted w.r.t the number of choices. + Parameters + ---------- opttype : string Datatype of the hyperparameter. Follows optunas naming convetions. Default value - categorical (list) - """ if not self.sorted_num_choices: # if empty super(HyperOptOAT, self).add_hyperparameter( @@ -61,7 +62,6 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ - self.OA = self.get_orthogonal_array() self.trial_losses = np.zeros(self.OA.shape[0]) number_of_trial = 0 @@ -75,8 +75,15 @@ def perform_study(self): # Perform Range Analysis self.get_optimal_parameters() - # feature funciton to use OAT with other optimisation techniques def perform_trial(self, objective, trial): + """ + Feature funciton to use OAT with other optimisation techniques. + + Parameters + ---------- + objective: ObjectiveBase + The current objective value that is to be optimised by using OAT + """ objective.trial_type = "oat" objective_val = objective(trial) objective.trial_type = objective.params.hyperparameters.hyper_opt_method @@ -85,8 +92,8 @@ def perform_trial(self, objective, trial): def get_optimal_parameters(self): """ Find the optimal set of hyperparameters by doing range analysis. - This is done using loss instead of accuracy as done in the paper. + This is done using loss instead of accuracy as done in the paper. """ printout("Performing Range Analysis.") @@ -101,9 +108,11 @@ def indices(idx, val): return np.where( self.optimal_params = np.array([i.index(min(i)) for i in A]) self.importance = np.argsort([max(i)-min(i) for i in A]) - # printout("Order of Importance: ") - # printout( - # *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" < ") + def show_order_of_importance(self): + """Print the order of importance of the hyperparameters that are being optimised.""" + printout("Order of Importance: ") + printout( + *[self.params.hyperparameters.hlist[idx].name for idx in self.importance], sep=" < ") def set_optimal_parameters(self): """ @@ -117,6 +126,7 @@ def set_optimal_parameters(self): def get_orthogonal_array(self): """ Generate the best Orthogonal array used for optimal hyperparameter sampling. + Parameters ---------- factor_levels : list @@ -131,9 +141,7 @@ def get_orthogonal_array(self): Minimum number of experimental runs to be performed This is function is taken from the example notebook of OApackage - """ - self.n_factors = len(self.params.hyperparameters.hlist) self.factor_levels = [par.num_choices for par in self.params. @@ -169,12 +177,10 @@ def get_orthogonal_array(self): def number_of_runs(self): """ - Calculate the minimum number of runs required for an Orthogonal array + Calculate the minimum number of runs required for an Orthogonal array. Based on the factor levels and the strength of the array requested - """ - runs = [np.prod(tt) for tt in itertools.combinations( self.factor_levels, self.strength)] From 68c9ecb83401da1ef6bf9604f725ed866f5a7768 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Thu, 19 Aug 2021 01:10:35 +0200 Subject: [PATCH 219/297] changes mentioned by lenz --- docs/source/install/tensorboard_setup.md | 8 +++++++- install/data_repo_link/link_data_repo.sh | 5 ----- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md index c52aa5d32..998a62864 100644 --- a/docs/source/install/tensorboard_setup.md +++ b/docs/source/install/tensorboard_setup.md @@ -1,6 +1,12 @@ -Access central storage servers from locations other than Hemera (Linux) +# Set up tensorboard to visualize data from HPC cluster + +## To mount Hemera on to local device you can find link below https://fwcc.pages.hzdr.de/infohub/hpc/storage.html + +or +## Use the following command + sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh index 1c744a05d..2c28ebd70 100755 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -38,16 +38,11 @@ fi rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} -echo "repo_path = \"${mala_base_path}\"" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} -echo "" >> ${script_path}/${pythonfile} -echo "def get_repo_path():" >> ${script_path}/${pythonfile} -echo " return repo_path" >> ${script_path}/${pythonfile} # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} -cp $ echo "Linking done!" From 5e8c9abdd606cbbf63f92dbfa9cd67c1b6a03011 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 19 Aug 2021 08:38:57 +0200 Subject: [PATCH 220/297] Refined the hyperparameter ensemble training --- mala/common/parameters.py | 29 +++++++++++++++++++++++++++++ mala/network/objective_base.py | 12 ++++++++++-- 2 files changed, 39 insertions(+), 2 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 29885ef6f..a73bd9e2f 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -488,6 +488,14 @@ class ParametersHyperparameterOptimization(ParametersBase): but it makes sense to choose a higher number, to exclude networks that performed by chance (good initilization). Naturally this impedes performance. + + trial_ensemble_evaluation : string + Control how multiple trainings performed during a trial are evaluated. + By default, simply "mean" is used. For smaller numbers of training + per trial it might make sense to use "mean_std", which means that + the mean of all metrics plus the standard deviation is used, + as an estimate of the minimal accuracy to be expected. Currently, + "mean" and "mean_std" are allowed. """ def __init__(self): @@ -502,6 +510,7 @@ def __init__(self): self.rdb_storage = None self.rdb_storage_heartbeat = None self.number_training_per_trial = 1 + self.trial_ensemble_evaluation = "mean" @property def rdb_storage_heartbeat(self): @@ -527,6 +536,26 @@ def number_training_per_trial(self, value): else: self._number_training_per_trial = value + @property + def trial_ensemble_evaluation(self): + """ + Control how multiple trainings performed during a trial are evaluated. + + By default, simply "mean" is used. For smaller numbers of training + per trial it might make sense to use "mean_std", which means that + the mean of all metrics plus the standard deviation is used, + as an estimate of the minimal accuracy to be expected. Currently, + "mean" and "mean_std" are allowed. + """ + return self._trial_ensemble_evaluation + + @trial_ensemble_evaluation.setter + def trial_ensemble_evaluation(self, value): + if value != "mean" and value != "mean_std": + self._trial_ensemble_evaluation = "mean" + else: + self._trial_ensemble_evaluation = value + def show(self, indent=""): """ Print name and values of all attributes of this object. diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 42a84ad71..d08745126 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -58,7 +58,7 @@ def __call__(self, trial): # Train a network for as often as the user desires. final_validation_loss = [] for i in range(0, self.params.hyperparameters. - number_training_per_trial): + number_training_per_trial): test_network = Network(self.params) test_trainer = Trainer(self.params, test_network, self.data_handler) test_trainer.train_network() @@ -67,7 +67,15 @@ def __call__(self, trial): if self.params.hyperparameters.number_training_per_trial > 1: printout("Losses from multiple runs are: ") printout(final_validation_loss) - return np.mean(final_validation_loss) + + if self.params.hyperparameters.trial_ensemble_evaluation == "mean": + return np.mean(final_validation_loss) + elif self.params.hyperparameters.trial_ensemble_evaluation == \ + "mean_std": + return np.mean(final_validation_loss)+np.std(final_validation_loss) + else: + raise Exception("No way to estimate the trial metric from ensemble" + " training provided.") def parse_trial(self, trial): """ From b15783a790cb06ce502b408ee073ff806b2e0fe3 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 19 Aug 2021 09:00:35 +0200 Subject: [PATCH 221/297] Added multivariate TPE support for optuna --- mala/common/parameters.py | 6 ++++++ mala/network/hyper_opt_optuna.py | 6 +++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index a73bd9e2f..fa69e1170 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -496,6 +496,11 @@ class ParametersHyperparameterOptimization(ParametersBase): the mean of all metrics plus the standard deviation is used, as an estimate of the minimal accuracy to be expected. Currently, "mean" and "mean_std" are allowed. + + use_multivariate : bool + If True, the optuna multivariate sampler is used. It is experimental + since v2.2.0, but reported to perform very well. + http://proceedings.mlr.press/v80/falkner18a.html """ def __init__(self): @@ -511,6 +516,7 @@ def __init__(self): self.rdb_storage_heartbeat = None self.number_training_per_trial = 1 self.trial_ensemble_evaluation = "mean" + self.use_multivariate = True @property def rdb_storage_heartbeat(self): diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 5b75b2c53..ba9b2c3cd 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -29,9 +29,9 @@ def __init__(self, params, data): # Make the sample behave in a reproducible way, if so specified by # the user. - sampler = None - if params.manual_seed is not None: - sampler = optuna.samplers.TPESampler(seed=params.manual_seed) + sampler = optuna.samplers.TPESampler(seed=params.manual_seed, + multivariate=params. \ + hyperparameters.use_multivariate) # Create the study. if self.params.hyperparameters.rdb_storage is None: From fd6bf0ad40621e5ca78e13399ff5da538552854a Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 19 Aug 2021 12:34:32 +0200 Subject: [PATCH 222/297] Add `oapackage` as opotional dependency: This package can now be installaed via `pip install .[opt]`. It is now longer part of the provided conda environment. It was decided to make it optional due to some subdependencies `oapackage` has, which may not be available on HPC clusters or may require admin rights to get installed. --- install/mala_cpu_base_environment.yml | 2 -- install/mala_cpu_environment.yml | 2 -- requirements.txt | 1 - setup.py | 5 +++++ 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/install/mala_cpu_base_environment.yml b/install/mala_cpu_base_environment.yml index a4e83d3aa..eda4ddd18 100644 --- a/install/mala_cpu_base_environment.yml +++ b/install/mala_cpu_base_environment.yml @@ -12,5 +12,3 @@ dependencies: - pytorch-cpu - mpmath - pandas - - pip: - - oapackage diff --git a/install/mala_cpu_environment.yml b/install/mala_cpu_environment.yml index 45434ae00..d3e79d6f0 100644 --- a/install/mala_cpu_environment.yml +++ b/install/mala_cpu_environment.yml @@ -98,5 +98,3 @@ dependencies: - zipp=3.4.1=pyhd8ed1ab_0 - zlib=1.2.11=h516909a_1010 - zstd=1.5.0=ha95c52a_0 - - pip: - - oapackage==2.6.8 diff --git a/requirements.txt b/requirements.txt index e81c8aeab..814d86ebe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,5 +6,4 @@ mpmath numpy optuna scipy -oapackage pandas diff --git a/setup.py b/setup.py index 81da8935e..4873332cd 100644 --- a/setup.py +++ b/setup.py @@ -14,6 +14,10 @@ with open("LICENSE") as f: license = f.read() +extras = { + 'opt': ['oapackage'], +} + setup( name="mala", version=version["__version__"], @@ -30,5 +34,6 @@ "ml-dft-sandia")), zip_safe=False, install_requires=open('requirements.txt').read().splitlines(), + etxras_require=extras, python_requires='<3.9', ) From 31abb751efd6bbe0d469eb97bf79d7b438d64343 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 19 Aug 2021 12:38:47 +0200 Subject: [PATCH 223/297] Add extra dependencies for documentation and tests to `setp.py` --- docs/source/install/README.md | 18 ++++++++++++------ setup.py | 2 ++ 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index d6112ca93..298db2483 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -21,7 +21,6 @@ In order to run MALA you have to have the following packages installed: * torch (PyTorch) * numpy * scipy -* oapackage * optuna * ase * mpmath @@ -39,12 +38,19 @@ or all with $ pip install -r requirements.txt ``` -or just install the package along with dependencies +or just install the package along with its basic dependencies ```sh $ pip install -e . ``` +In order to install additional dependencies (enabling optional features, building documentaion locally, testing etc.) do + +```sh +$ pip install -e .[opt,test,doc] +``` + + See below for what the `-e` option does. Note that we exclude `torch` in `requirements.txt` We don't want torch to be @@ -148,11 +154,11 @@ $ pip install -r docs/requirements.txt ## Downloading and adding example data The examples and tests need additional data to run. The MALA team provides a data repository, that can be downloaded -from . Please be sure to check out the correct tag for the data repository, -since the data repository itself is subject to ongoing development as well. After downloading the correct revision of -the data repository, it needs to be linked with MALA. +from . Please be sure to check out the correct tag for the data repository, +since the data repository itself is subject to ongoing development as well. After downloading the correct revision of +the data repository, it needs to be linked with MALA. -1. Download data repository and check out correct tag +1. Download data repository and check out correct tag ```sh $ git clone origin_of_data_repo ~/path/to/data/repo diff --git a/setup.py b/setup.py index 4873332cd..c042aad8a 100644 --- a/setup.py +++ b/setup.py @@ -16,6 +16,8 @@ extras = { 'opt': ['oapackage'], + 'test': ['pytest'], + 'doc': open('docs/requirements.txt').read().splitlines(), } setup( From 47748da52e22d0cec336c2bee33421cc4590b40b Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 19 Aug 2021 13:12:26 +0200 Subject: [PATCH 224/297] Fix building of Docker image from `Dockerfile`: The build fails at `RUN apt-get update` with message `E: Repository 'http://security.debian.org/debian-security buster/updates InRelease' changed its 'Suite' value from 'stable' to 'oldstable'`. Fix according to https://stackoverflow.com/questions/68802802/repository-http-security-debian-org-debian-security-buster-updates-inrelease --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index fc9838902..885867bf8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,7 +4,7 @@ FROM continuumio/miniconda3:4.9.2 ARG DEVICE=cpu # Update the image to the latest packages -RUN apt-get update && apt-get upgrade -y +RUN apt-get --allow-releaseinfo-change update && apt-get upgrade -y RUN apt-get install --no-install-recommends -y build-essential libz-dev swig git-lfs RUN apt-get clean && rm -rf /var/lib/apt/lists/* From aff3ca2839f60e4c09d42fe2dd68694a0a66caca Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 19 Aug 2021 14:43:28 +0200 Subject: [PATCH 225/297] Bugfix --- mala/network/objective_base.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index d08745126..33737b963 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -67,12 +67,21 @@ def __call__(self, trial): if self.params.hyperparameters.number_training_per_trial > 1: printout("Losses from multiple runs are: ") printout(final_validation_loss) + print(self.params.hyperparameters.trial_ensemble_evaluation) if self.params.hyperparameters.trial_ensemble_evaluation == "mean": return np.mean(final_validation_loss) + elif self.params.hyperparameters.trial_ensemble_evaluation == \ "mean_std": - return np.mean(final_validation_loss)+np.std(final_validation_loss) + mean = np.mean(final_validation_loss) + + # Cannot calculate the standar deviation of a bunch of infinities. + if np.isinf(mean): + return mean + else: + return np.mean(final_validation_loss) + \ + np.std(final_validation_loss) else: raise Exception("No way to estimate the trial metric from ensemble" " training provided.") From 9d23a7afabdfc83ff0f50300a0246437d6762177 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 19 Aug 2021 15:58:07 +0200 Subject: [PATCH 226/297] Added researchers --- CITATION.cff | 29 ++++++++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index c185cff2d..4c9db560b 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -2,18 +2,41 @@ cff-version: 1.1.0 message: "If you use this software, please cite it using these metadata." authors: + - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" + family-names: Cangi + given-names: Attila + - affiliation: "Oak Ridge National Laboratory (ORNL)" + family-names: Ellis + given-names: J. Austin - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Fiedler given-names: Lenz - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Kotik given-names: Daniel + - affiliation: "Sandia National Laboratories (SNL)" + family-names: Modine + given-names: Normand A. + - affiliation: "Oak Ridge National Laboratory (ORNL)" + family-names: Oles + given-names: Vladyslav + - affiliation: "Sandia National Laboratories (SNL)" + family-names: Popoola + given-names: Gabriel A. + - affiliation: "Sandia National Laboratories (SNL)" + family-names: Rajamanickam + given-names: Siva - affiliation: "Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Schmerler given-names: Steve - - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" - family-names: Cangi - given-names: Attila + - affiliation: "Sandia National Laboratories (SNL)" + family-names: Stephens + given-names: John A. + - affiliation: "Sandia National Laboratories (SNL)" + family-names: Thompson + given-names: Aidan P. + + #date-released: 2021-08-16 keywords: - "machine-learning" From fa3e476ce4fbb73e8282b75cc3afc70a20b2e913 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 19 Aug 2021 16:08:37 +0200 Subject: [PATCH 227/297] Fixed Adams first name --- CITATION.cff | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CITATION.cff b/CITATION.cff index 4c9db560b..7c97bb830 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -31,7 +31,7 @@ authors: given-names: Steve - affiliation: "Sandia National Laboratories (SNL)" family-names: Stephens - given-names: John A. + given-names: J. Adam - affiliation: "Sandia National Laboratories (SNL)" family-names: Thompson given-names: Aidan P. From 69edfb33aceb4ff373b93f109d5b9ca4d5dbe6e9 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 20 Aug 2021 17:42:17 +0200 Subject: [PATCH 228/297] Hotfix for oapackage error --- mala/network/hyper_opt_oat.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 84e93f2f8..140713acd 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -1,5 +1,12 @@ """Hyperparameter optimizer using orthogonal array tuning.""" -import oapackage as oa +import warnings +try: + import oapackage as oa +except ModuleNotFoundError: + warnings.warn("You do not have the OApackage installed. This will not " + "affect MALA performance except for when attempting to use " + "orthogonal array tuning. ", + stacklevel=2) from .hyper_opt_base import HyperOptBase from .objective_base import ObjectiveBase import numpy as np From 6fff13a2facae6aa2435959c2ed0b02c532440cd Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 20 Aug 2021 20:10:44 +0200 Subject: [PATCH 229/297] Hyperparameters can now be "frozen" without inflating the optuna study record --- mala/network/hyperparameter_optuna.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/mala/network/hyperparameter_optuna.py b/mala/network/hyperparameter_optuna.py index a65b8eb59..4986eea83 100644 --- a/mala/network/hyperparameter_optuna.py +++ b/mala/network/hyperparameter_optuna.py @@ -126,6 +126,9 @@ def get_categorical(self, trial: Trial): Return value is based on type of hyperparameter. """ if self.opttype == "categorical": - return trial.suggest_categorical(self.name, self.choices) + if len(self.choices) > 1: + return trial.suggest_categorical(self.name, self.choices) + else: + return self.choices[0] else: raise Exception("Wrong hyperparameter type.") From c3952b193cfc406c203aef06883893d481b73d14 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sat, 21 Aug 2021 10:26:04 +0200 Subject: [PATCH 230/297] MALA can now optimize multiple layers simultaneously --- mala/network/objective_base.py | 51 +++++++++++++++++++++++++++++++++- 1 file changed, 50 insertions(+), 1 deletion(-) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 33737b963..baa57c341 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -29,10 +29,25 @@ def __init__(self, params, data_handler): # We need to find out if we have to reparametrize the lists with the # layers and the activations. - self.optimize_layer_list = any(map( + contains_single_layer = any(map( lambda p: "ff_neurons_layer" in p.name, self.params.hyperparameters.hlist )) + contains_multiple_layer_neurons = any(map( + lambda p: "ff_multiple_layers_neurons" in p.name, + self.params.hyperparameters.hlist + )) + contains_multiple_layers_count = any(map( + lambda p: "ff_multiple_layers_count" in p.name, + self.params.hyperparameters.hlist + )) + if contains_multiple_layer_neurons != contains_multiple_layers_count: + print("You selected multiple layers to be optimized, but either " + "the range of neurons or number of layers is missing. " + "This input will be ignored.") + self.optimize_layer_list = contains_single_layer or ( + contains_multiple_layer_neurons and + contains_multiple_layers_count) self.optimize_activation_list = list(map( lambda p: "layer_activation" in p.name, self.params.hyperparameters.hlist @@ -130,6 +145,38 @@ def parse_trial_optuna(self, trial: Trial): if par.name == "learning_rate": self.params.running.learning_rate = par.get_parameter(trial) + # If the user wants to optimize multiple layers simultaneously, + # we have to parse to parameters at the same time. + elif par.name == "ff_multiple_layers_neurons": + neurons_per_layer = par.get_parameter(trial) + number_layers = 0 + max_number_layers = 0 + other_par: HyperparameterOptuna + for other_par in self.params.hyperparameters.hlist: + if other_par.name == "ff_multiple_layers_count": + number_layers = other_par.get_parameter(trial) + max_number_layers = max(other_par.choices) + if number_layers > 0: + for i in range(0, number_layers): + if neurons_per_layer > 0: + self.params.network.layer_sizes. \ + append(neurons_per_layer) + else: + turned_off_layers.append(layer_counter) + layer_counter += 1 + if number_layers != max_number_layers: + for i in range(number_layers, max_number_layers): + turned_off_layers.append(layer_counter) + layer_counter += 1 + else: + for i in range(0, max_number_layers): + turned_off_layers.append(layer_counter) + layer_counter += 1 + + elif par.name == "ff_multiple_layers_count": + # This is parsed directly abve. + pass + elif "ff_neurons_layer" in par.name: if self.params.network.nn_type == "feed-forward": # Check for zero neuron layers; These indicate layers @@ -182,6 +229,8 @@ def parse_trial_optuna(self, trial: Trial): self.params.network.layer_sizes.\ append(self.data_handler.get_output_dimension()) + self.params.show() + def parse_trial_oat(self, trial): """ Parse an OA based trial into the params attribute. From 7d63836239b8f794bd70fcc5210faa857c6ee3e3 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Sat, 21 Aug 2021 13:46:22 +0200 Subject: [PATCH 231/297] fixed some docstring issues --- mala/network/hyper_opt_oat.py | 32 +++++++++++++++++++------------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 3b06866f0..7e7e5f49a 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -162,18 +162,19 @@ def get_orthogonal_array(self): options = oa.OAextend() options.setAlgorithmAuto(arrayclass) - try: - for _ in range(self.strength + 1, self.n_factors + 1): - arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, - options) - dd = np.array([a.Defficiency() for a in arraylist_extensions]) - idxs = np.argsort(dd) - arraylist = [arraylist_extensions[ii] for ii in idxs] - - except: - if not arraylist: - print("No orthogonal array exists with such a parameter combination.") - return np.unique(np.array(arraylist[0]), axis=0) + for _ in range(self.strength + 1, self.n_factors + 1): + arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, + options) + dd = np.array([a.Defficiency() for a in arraylist_extensions]) + idxs = np.argsort(dd) + arraylist = [arraylist_extensions[ii] for ii in idxs] + + + if not arraylist: + raise Exception("No orthogonal array exists with such a parameter combination.") + + else: + return np.unique(np.array(arraylist[0]), axis=0) def number_of_runs(self): """ @@ -184,10 +185,15 @@ def number_of_runs(self): runs = [np.prod(tt) for tt in itertools.combinations( self.factor_levels, self.strength)] - N = np.lcm.reduce(runs) + N = np.lcm.reduce(runs)*np.lcm.reduce(self.factor_levels) return int(N) @property def monotonic(self): + """ + Check if the factors are in an increasing or decreasing order. + + This is required for the generation of orthogonal arrays. + """ dx = np.diff(self.factor_levels) return np.all(dx <= 0) or np.all(dx >= 0) From 1ae86b61e5aeca70fb88bd8d491153379b0e3a85 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Sat, 21 Aug 2021 14:05:47 +0200 Subject: [PATCH 232/297] fixed docstring issue --- mala/network/hyper_opt_oat.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 7e7e5f49a..a46668610 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -191,9 +191,9 @@ def number_of_runs(self): @property def monotonic(self): """ - Check if the factors are in an increasing or decreasing order. + Check if the factors are in an increasing or decreasing order. - This is required for the generation of orthogonal arrays. + This is required for the genration of orthogonal arrays. """ dx = np.diff(self.factor_levels) return np.all(dx <= 0) or np.all(dx >= 0) From ebf0f19e6fa021db1adabd669906e4774db883c7 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 23 Aug 2021 14:50:55 +0200 Subject: [PATCH 233/297] changes docs/source/index.md --- docs/source/index.md | 1 + examples/data_repos_path.py | 9 --------- 2 files changed, 1 insertion(+), 9 deletions(-) delete mode 100644 examples/data_repos_path.py diff --git a/docs/source/index.md b/docs/source/index.md index 5a647beec..fc2386286 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -28,6 +28,7 @@ install/INSTALL_LAMMPS install/INSTALL_TE_QE install/tested_systems install/sql_on_hpc +install/tensorboard_setup ``` ```{toctree} diff --git a/examples/data_repos_path.py b/examples/data_repos_path.py deleted file mode 100644 index 9806503b9..000000000 --- a/examples/data_repos_path.py +++ /dev/null @@ -1,9 +0,0 @@ -data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" -repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" - - -def get_data_repo_path(): - return data_repo_path - -def get_repo_path(): - return repo_path From 55054d8f76f2225984d87fe4e74a273ae90824c7 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 23 Aug 2021 15:11:49 +0200 Subject: [PATCH 234/297] indent issue --- examples/ex10_tensor_board.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 151f6d599..55c826282 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,6 +1,5 @@ import mala from mala import printout -from mala.common.launch_tensorboard import Launch_tensorboard from tensorboard import program import shutil import webbrowser @@ -9,9 +8,8 @@ """ -ex01_run_singleshot.py: Shows how a neural network can be trained on material -data using this framework. It uses preprocessed data, that is read in -from *.npy files. +ex10. launched tensorboard in webbrowser +the parameteres and initial configurations are similar to ex01. """ @@ -98,14 +96,19 @@ #################### # Using tensorboard and launching the link in webbrowser +# Runs tensorboard with the given log_dir and wait for user +# input to kill the app. #################### -class Launch_tensorboard: +class Launchtensorboard: + """ - Runs tensorboard with the given log_dir and wait for user - input to kill the app. - :param : - :param clear_on_exit: If True Clears the log_dir on exit and kills the tensorboard app - :return: + Parameters necessary for Launching tensorboard. + + Attributes + param : parameters are handled from a central parameters class that + contains subclasses. + clear_on_exit : bool + If True Clears the log_dir on exit and kills the tensorboard app. """ def __init__(self, params, clear_on_exit= False): @@ -131,5 +134,5 @@ def run(self): shutil.rmtree(self.log_dir, ignore_errors=True) print("\nCleared Logdir") -launch_tb = Launch_tensorboard(test_parameters) +launch_tb = Launchtensorboard(test_parameters) launch_tb.run() From ba85bb0b3b7f27fafb74a6e51bcdd731efb41a5c Mon Sep 17 00:00:00 2001 From: MSV <63616025+msverma101@users.noreply.github.com> Date: Mon, 23 Aug 2021 15:30:11 +0200 Subject: [PATCH 235/297] Update link_data_repo.sh --- install/data_repo_link/link_data_repo.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh index 2c28ebd70..c43753868 100755 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -7,7 +7,6 @@ echo "Linking MALA and MALA data repo." script_path="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) - examples_path=$mala_base_path/examples test_path=$mala_base_path/test pythonfile=data_repos_path.py @@ -34,6 +33,7 @@ if [ "$lastcharacter" != "/" ] then mala_base_path=$mala_base_path/ fi + # Write the python file. rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} @@ -42,6 +42,7 @@ echo "" >> ${script_path}/${pythonfile} echo "" >> ${script_path}/${pythonfile} echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} echo " return data_repo_path" >> ${script_path}/${pythonfile} + # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} cp ${script_path}/${pythonfile} ${examples_path} From cb983b21ded4b829d805f2d6b4a11adf86a5252b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 24 Aug 2021 12:24:49 +0200 Subject: [PATCH 236/297] Fix densities --- mala/targets/density.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/targets/density.py b/mala/targets/density.py index f9bca8f06..3484e44cf 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -80,7 +80,7 @@ def get_number_of_electrons(self, density_data, grid_spacing_bohr=None, grid_spacing_bohr = self.grid_spacing_Bohr # Check input data for correctness. - data_shape = np.shape(density_data) + data_shape = np.shape(np.squeeze(density_data)) if len(data_shape) != 3: if len(data_shape) != 1: raise Exception("Unknown Density shape, cannot calculate " From 3cd6233db539b34d1132c69989c6f9e9d8cc4004 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 26 Aug 2021 08:16:53 +0200 Subject: [PATCH 237/297] Improved NoTraining class to make it compatible with Lazy Loading --- examples/ex09_distributed_hyperopt.py | 2 ++ mala/__init__.py | 2 +- mala/network/__init__.py | 1 + mala/network/objective_base.py | 2 -- mala/network/objective_no_training.py | 8 +++++++- 5 files changed, 11 insertions(+), 4 deletions(-) diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index be645fbb0..87a4157a1 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -46,6 +46,8 @@ # Specify the number of trials, the hyperparameter optimizer should run # and the type of hyperparameter. test_parameters.hyperparameters.n_trials = 20 +test_parameters.hyperparameters.checkpoints_each_trial = -1 +test_parameters.hyperparameters.checkpoint_name = "ex09" test_parameters.hyperparameters.hyper_opt_method = "optuna" test_parameters.hyperparameters.study_name = "ex09" test_parameters.hyperparameters.rdb_storage = 'sqlite:///ex09.db' diff --git a/mala/__init__.py b/mala/__init__.py index a2b657b98..025e1cdd3 100644 --- a/mala/__init__.py +++ b/mala/__init__.py @@ -10,5 +10,5 @@ from .descriptors import DescriptorInterface, SNAP, DescriptorBase from .datahandling import DataHandler, DataScaler, DataConverter from .network import Network, Tester, Trainer, HyperOptInterface, \ - HyperOptOptuna + HyperOptOptuna, HyperOptNoTraining from .targets import TargetInterface, LDOS, DOS, Density, fermi_function diff --git a/mala/network/__init__.py b/mala/network/__init__.py index 49befb51c..41097bda8 100644 --- a/mala/network/__init__.py +++ b/mala/network/__init__.py @@ -4,3 +4,4 @@ from .trainer import Trainer from .hyper_opt_interface import HyperOptInterface from .hyper_opt_optuna import HyperOptOptuna +from .hyper_opt_notraining import HyperOptNoTraining diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index baa57c341..dfafdbce8 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -229,8 +229,6 @@ def parse_trial_optuna(self, trial: Trial): self.params.network.layer_sizes.\ append(self.data_handler.get_output_dimension()) - self.params.show() - def parse_trial_oat(self, trial): """ Parse an OA based trial into the params attribute. diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 1ceb05458..9cba1e69b 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -56,9 +56,15 @@ def __call__(self, trial): device = "cuda" if self.params.use_gpu else "cpu" # Load the batchesand get the jacobian. + do_shuffle = self.params.use_shuffling_for_samplers + if self.data_handler.parameters.use_lazy_loading or \ + self.params.use_horovod: + do_shuffle = False + if self.params.use_shuffling_for_samplers: + self.data_handler.mix_datasets() loader = DataLoader(self.data_handler.training_data_set, batch_size=self.params.running.mini_batch_size, - shuffle=True) + shuffle=do_shuffle) jac = ObjectiveNoTraining.__get_batch_jacobian(net, loader, device) # Loss = - score! From 52360536454f3f27e45311f4cd5cfd0127c102cd Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 26 Aug 2021 10:44:17 +0200 Subject: [PATCH 238/297] Added a pruner based on no training --- mala/common/parameters.py | 6 ++++++ mala/network/hyper_opt_optuna.py | 15 +++++++++++---- mala/network/no_training_pruner.py | 24 ++++++++++++++++++++++++ mala/network/objective_base.py | 5 +++-- mala/network/objective_no_training.py | 4 ++-- 5 files changed, 46 insertions(+), 8 deletions(-) create mode 100644 mala/network/no_training_pruner.py diff --git a/mala/common/parameters.py b/mala/common/parameters.py index fa69e1170..a84f75ae9 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -501,6 +501,10 @@ class ParametersHyperparameterOptimization(ParametersBase): If True, the optuna multivariate sampler is used. It is experimental since v2.2.0, but reported to perform very well. http://proceedings.mlr.press/v80/falkner18a.html + + no_training_cutoff : float + If the surrogate loss algorithm is used as a pruner during a study, + this cutoff determines which trials are neglected. """ def __init__(self): @@ -517,6 +521,8 @@ def __init__(self): self.number_training_per_trial = 1 self.trial_ensemble_evaluation = "mean" self.use_multivariate = True + self.no_training_cutoff = 0 + self.pruner = None @property def rdb_storage_heartbeat(self): diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index ba9b2c3cd..d44bfc99d 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -8,7 +8,7 @@ from mala.datahandling.data_handler import DataHandler from mala.datahandling.data_scaler import DataScaler from mala.common.parameters import Parameters - +from .no_training_pruner import NoTrainingPruner class HyperOptOptuna(HyperOptBase): """Hyperparameter optimizer using Optuna. @@ -33,13 +33,19 @@ def __init__(self, params, data): multivariate=params. \ hyperparameters.use_multivariate) + # See if the user specified a pruner. + pruner = None + if self.params.hyperparameters.pruner == "no_training": + pruner = NoTrainingPruner(self.params, data, "optuna") + # Create the study. if self.params.hyperparameters.rdb_storage is None: self.study = optuna.\ create_study(direction=self.params.hyperparameters.direction, sampler=sampler, study_name=self.params.hyperparameters. - study_name) + study_name, + pruner=pruner) else: if self.params.hyperparameters.study_name is None: raise Exception("If RDB storage is used, a name for the study " @@ -55,10 +61,11 @@ def __init__(self, params, data): study_name=self.params.hyperparameters. study_name, storage=rdb_storage, - load_if_exists=True) - + load_if_exists=True, + pruner=pruner) self.checkpoint_counter = 0 + def perform_study(self): """ Perform the study, i.e. the optimization. diff --git a/mala/network/no_training_pruner.py b/mala/network/no_training_pruner.py new file mode 100644 index 000000000..c62380f0e --- /dev/null +++ b/mala/network/no_training_pruner.py @@ -0,0 +1,24 @@ +"""Prunes a network when the score is above a user defined limit.""" + +import optuna +from optuna.pruners import BasePruner +from optuna.trial._state import TrialState +from mala import Parameters, DataHandler +from .objective_no_training import ObjectiveNoTraining + + +class NoTrainingPruner(BasePruner): + def __init__(self, search_parameters: Parameters, data_handler: + DataHandler, trial_type): + self._trial_type = trial_type + self._data_handler = data_handler + self._params = search_parameters + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + objective = ObjectiveNoTraining(self._params, self._data_handler, + self._trial_type) + surrogate_loss = objective(trial) + if surrogate_loss > self._params.hyperparameters.no_training_cutoff: + return True + else: + return False diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index dfafdbce8..faab4819c 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -1,6 +1,6 @@ """Objective function for all training based hyperparameter optimizations.""" import numpy as np -from optuna import Trial +from optuna import Trial, TrialPruned from .hyperparameter_optuna import HyperparameterOptuna from .hyperparameter_oat import HyperparameterOAT from .network import Network @@ -69,6 +69,8 @@ def __call__(self, trial): """ # Parse the parameters included in the trial. self.parse_trial(trial) + if trial.should_prune(): + raise TrialPruned() # Train a network for as often as the user desires. final_validation_loss = [] @@ -82,7 +84,6 @@ def __call__(self, trial): if self.params.hyperparameters.number_training_per_trial > 1: printout("Losses from multiple runs are: ") printout(final_validation_loss) - print(self.params.hyperparameters.trial_ensemble_evaluation) if self.params.hyperparameters.trial_ensemble_evaluation == "mean": return np.mean(final_validation_loss) diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 9cba1e69b..b782295d3 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -56,11 +56,11 @@ def __call__(self, trial): device = "cuda" if self.params.use_gpu else "cpu" # Load the batchesand get the jacobian. - do_shuffle = self.params.use_shuffling_for_samplers + do_shuffle = self.params.running.use_shuffling_for_samplers if self.data_handler.parameters.use_lazy_loading or \ self.params.use_horovod: do_shuffle = False - if self.params.use_shuffling_for_samplers: + if self.params.running.use_shuffling_for_samplers: self.data_handler.mix_datasets() loader = DataLoader(self.data_handler.training_data_set, batch_size=self.params.running.mini_batch_size, From 5f8c2b8c4fbc2736f0ff0025ca94c2614fb5d51b Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 27 Aug 2021 21:24:27 +0200 Subject: [PATCH 239/297] Add orcid of Daniel Kotik to `CITATION.cff` --- CITATION.cff | 1 + 1 file changed, 1 insertion(+) diff --git a/CITATION.cff b/CITATION.cff index 7c97bb830..c2c32d5b1 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -14,6 +14,7 @@ authors: - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Kotik given-names: Daniel + orcid: https://orcid.org/0000-0001-8735-3199 - affiliation: "Sandia National Laboratories (SNL)" family-names: Modine given-names: Normand A. From d3e148817e3b1039d8e3e13eff1d875925768b5b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 30 Aug 2021 08:49:23 +0200 Subject: [PATCH 240/297] Added ORCID for Lenz --- CITATION.cff | 1 + 1 file changed, 1 insertion(+) diff --git a/CITATION.cff b/CITATION.cff index c2c32d5b1..e62d87d73 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -11,6 +11,7 @@ authors: - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Fiedler given-names: Lenz + orcid: https://orcid.org/0000-0002-8311-0613 - affiliation: "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR)" family-names: Kotik given-names: Daniel From 36b167f3def02c39d7e9ee9aba4436290ba86c82 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 30 Aug 2021 08:55:09 +0200 Subject: [PATCH 241/297] Modified tensorboard_setup.md and fixed data repo linker --- docs/source/install/tensorboard_setup.md | 11 ++++++----- install/data_repo_link/link_data_repo.sh | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md index 998a62864..f818f78af 100644 --- a/docs/source/install/tensorboard_setup.md +++ b/docs/source/install/tensorboard_setup.md @@ -1,12 +1,13 @@ # Set up tensorboard to visualize data from HPC cluster -## To mount Hemera on to local device you can find link below +Training of surrogate models with MALA is often done on HPC infrastructure, yet the visualization should take place locally. Files that TensorBoard saves data into are called event files. These files have to be on the local device to get access. The best way to achieve this is by either downloading or mounting the relevant folders on the HPC cluster to your local machine. + +## Hemera5 (HZDR) + +- You can find information on how to mount Hemera onto a local device here: https://fwcc.pages.hzdr.de/infohub/hpc/storage.html -or -## Use the following command +- Alternatively, simply use the following command sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local - -This is done to mount any folder on the server to the local device. In tensorboard the --logdir which is the directory you will create data to visualize. Files that TensorBoard saves data into are called event files. These files have to be on the local device so as to get access. Hence the mounting is done. diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh index c43753868..2410ad1c7 100755 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -9,7 +9,7 @@ mala_base_path=$( echo ${script_path%/*} ) mala_base_path=$( echo ${mala_base_path%/*} ) examples_path=$mala_base_path/examples test_path=$mala_base_path/test -pythonfile=data_repos_path.py +pythonfile=data_repo_path.py # Ask the user for the path to the repo if none was given in the command line. if [ "$1" != "" ] From eaff45e911359dd1973edccdefecd073e5bd97d6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 30 Aug 2021 09:02:30 +0200 Subject: [PATCH 242/297] Fixed some docstrings and deleted obsolete python file --- examples/ex01_run_singleshot.py | 2 +- examples/ex10_tensor_board.py | 26 ++++++++++++++++---------- test/data_repos_path.py | 9 --------- 3 files changed, 17 insertions(+), 20 deletions(-) delete mode 100644 test/data_repos_path.py diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 8c8f0602f..0dc3bf7d0 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -88,4 +88,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() \ No newline at end of file +test_parameters.show() diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 55c826282..20d3658a5 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -8,8 +8,9 @@ """ -ex10. launched tensorboard in webbrowser -the parameteres and initial configurations are similar to ex01. +ex10_tensor_board.py: Shows how a NN training by MALA can be visualized using +tensorboard. The training is the similar to the one performed in ex01, but +at the end of it, tensorboard will be launched in your browser. """ @@ -98,17 +99,21 @@ # Using tensorboard and launching the link in webbrowser # Runs tensorboard with the given log_dir and wait for user # input to kill the app. -#################### -class Launchtensorboard: +#################### + + +class LaunchTensorboard: """ Parameters necessary for Launching tensorboard. - - Attributes - param : parameters are handled from a central parameters class that - contains subclasses. + + Parameters + ---------- + params : mala.Parameters + MALA parameters for this particular run. + clear_on_exit : bool - If True Clears the log_dir on exit and kills the tensorboard app. + If True Clears the log_dir on exit and kills the tensorboard app. """ def __init__(self, params, clear_on_exit= False): @@ -134,5 +139,6 @@ def run(self): shutil.rmtree(self.log_dir, ignore_errors=True) print("\nCleared Logdir") -launch_tb = Launchtensorboard(test_parameters) + +launch_tb = LaunchTensorboard(test_parameters) launch_tb.run() diff --git a/test/data_repos_path.py b/test/data_repos_path.py deleted file mode 100644 index 9806503b9..000000000 --- a/test/data_repos_path.py +++ /dev/null @@ -1,9 +0,0 @@ -data_repo_path = "/home/snehaverma/Downloads/HZDR_work/test-data/" -repo_path = "/home/snehaverma/Downloads/HZDR_work/mala/" - - -def get_data_repo_path(): - return data_repo_path - -def get_repo_path(): - return repo_path From 15304e7ee29a999ca316ac22c14913d178911f88 Mon Sep 17 00:00:00 2001 From: Steve Schmerler Date: Tue, 31 Aug 2021 15:47:49 +0200 Subject: [PATCH 243/297] Doc: update test data repo name in install docs --- docs/source/install/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 298db2483..a4ab9ab74 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -154,7 +154,7 @@ $ pip install -r docs/requirements.txt ## Downloading and adding example data The examples and tests need additional data to run. The MALA team provides a data repository, that can be downloaded -from . Please be sure to check out the correct tag for the data repository, +from . Please be sure to check out the correct tag for the data repository, since the data repository itself is subject to ongoing development as well. After downloading the correct revision of the data repository, it needs to be linked with MALA. From 74fa5ec232e03b98620009fa78073ce85559355d Mon Sep 17 00:00:00 2001 From: Steve Schmerler Date: Tue, 31 Aug 2021 15:53:45 +0200 Subject: [PATCH 244/297] Doc: add example to verify data repo link --- docs/source/install/README.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/source/install/README.md b/docs/source/install/README.md index a4ab9ab74..786109471 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -158,16 +158,26 @@ from . Please be sure to check out th since the data repository itself is subject to ongoing development as well. After downloading the correct revision of the data repository, it needs to be linked with MALA. -1. Download data repository and check out correct tag +1. Download data repository and check out correct tag. ```sh - $ git clone origin_of_data_repo ~/path/to/data/repo + $ git clone https://github.com/mala-project/test-data ~/path/to/data/repo $ cd ~/path/to/data/repo $ git checkout v0.4.0 ``` -2. Link MALA and data repository + +2. Link MALA and data repository. ```sh $ cd ~/path/to/mala/root/directory $ bash install/data_repo_link/link_data_repo.sh ~/path/to/data/repo ``` + + Afterwards, check that files named `data_repo_path.py` have been generated. + + ```sh + $ find . -name data_repo_path.py + ./install/data_repo_link/data_repo_path.py + ./test/data_repo_path.py + ./examples/data_repo_path.py + ``` From 0f8407ac5dcefab90f271a64404aca0c5a83e013 Mon Sep 17 00:00:00 2001 From: Steve Schmerler Date: Wed, 1 Sep 2021 14:39:27 +0200 Subject: [PATCH 245/297] Use os.path.join in DataHandler Paths should be joined using os.path.join() or tools from pathlib. Using string concatenation is error-prone. For instance, DataHandler code breaks if paths don't end in "/". I'm sure there is more, only fixed what bit me. --- mala/datahandling/data_handler.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 13e0e5e95..c1e608626 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -1,4 +1,7 @@ """DataHandler class that loads and scales data.""" + +import os + from torch.utils.data import TensorDataset from .data_scaler import DataScaler @@ -344,17 +347,17 @@ def resize_snapshots_for_debugging(self, directory="./", i = 0 snapshot: Snapshot for snapshot in self.parameters.snapshot_directories_list: - tmp_array = self.__load_from_npy_file(snapshot.input_npy_directory - + snapshot.input_npy_file) + tmp_array = self.__load_from_npy_file(os.path.join(snapshot.input_npy_directory, + snapshot.input_npy_file)) tmp_file_name = naming_scheme_input tmp_file_name = tmp_file_name.replace("*", str(i)) np.save(directory+tmp_file_name+".npy", tmp_array) - tmp_array = self.__load_from_npy_file(snapshot.output_npy_directory - + snapshot.output_npy_file) + tmp_array = self.__load_from_npy_file(os.path.join(snapshot.output_npy_directory, + snapshot.output_npy_file)) tmp_file_name = naming_scheme_output tmp_file_name = tmp_file_name.replace("*", str(i)) - np.save(directory+tmp_file_name+".npy", tmp_array) + np.save(os.path.join(directory, tmp_file_name + ".npy"), tmp_array) i += 1 def get_snapshot_calculation_output(self, snapshot_number): @@ -398,8 +401,8 @@ def __check_snapshots(self): printout("Checking descriptor file ", snapshot.input_npy_file, "at", snapshot.input_npy_directory) - tmp = self.__load_from_npy_file(snapshot.input_npy_directory + - snapshot.input_npy_file, + tmp = self.__load_from_npy_file(os.path.join(snapshot.input_npy_directory, + snapshot.input_npy_file), mmapmode='r') # We have to cut xyz information, if we have xyz information in @@ -430,8 +433,8 @@ def __check_snapshots(self): printout("Checking targets file ", snapshot.output_npy_file, "at", snapshot.output_npy_directory) - tmp_out = self.__load_from_npy_file(snapshot.output_npy_directory + - snapshot.output_npy_file, + tmp_out = self.__load_from_npy_file(os.path.join(snapshot.output_npy_directory, + snapshot.output_npy_file), mmapmode='r') # The first snapshot determines the data size to be used. @@ -752,8 +755,8 @@ def __build_datasets(self): if self.parameters.data_splitting_snapshots[i] == "va" \ or self.parameters.data_splitting_snapshots[i] == "te": tmp = self.\ - __load_from_npy_file(snapshot.input_npy_directory + - snapshot.input_npy_file, + __load_from_npy_file(os.path.join(snapshot.input_npy_directory, + snapshot.input_npy_file), mmapmode='r') if self.parameters.descriptors_contain_xyz: tmp = tmp[:, :, :, 3:] @@ -765,8 +768,8 @@ def __build_datasets(self): if self.parameters.data_splitting_snapshots[i] == "te": self.test_data_inputs.append(tmp) tmp = self.\ - __load_from_npy_file(snapshot.output_npy_directory + - snapshot.output_npy_file, + __load_from_npy_file(os.path.join(snapshot.output_npy_directory, + snapshot.output_npy_file), mmapmode='r') tmp = np.array(tmp) tmp *= self.target_calculator.\ From 0d0140ce69fff2392d1c5b3cb217ebf4eb784621 Mon Sep 17 00:00:00 2001 From: Steve Schmerler Date: Wed, 1 Sep 2021 15:17:25 +0200 Subject: [PATCH 246/297] Add examples/ex05_1_pass_single_feature.py --- examples/ex05_1_pass_single_feature.py | 112 +++++++++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100644 examples/ex05_1_pass_single_feature.py diff --git a/examples/ex05_1_pass_single_feature.py b/examples/ex05_1_pass_single_feature.py new file mode 100644 index 000000000..d3c29ec7e --- /dev/null +++ b/examples/ex05_1_pass_single_feature.py @@ -0,0 +1,112 @@ +"""Show how to run a sinlge feature vector out of a snapshot through a network. + +First run ex05_training_with_postprocessing.py to create the +ex05_* files used below. +""" + +import pathlib as pl + +import mala +import numpy as np +import torch + +from data_repo_path import data_repo_path + + +data_path = pl.Path(data_repo_path) / "Al36" +params_path = "ex05_params.pkl" +network_path = "ex05_network.pth" +input_scaler_path = "ex05_iscaler.pkl" +output_scaler_path = "ex05_oscaler.pkl" + + +def load_whole_snapshot(): + """Usual MALA workflow: load whole snapshot using DataHandler. Extract + single feature vector afterwards. + """ + + # First load Parameters and network. + parameters = mala.Parameters.load_from_file(params_path, no_snapshots=True) + + # Load a network from a file. + network = mala.Network.load_from_file(parameters, network_path) + + # Make sure the same scaling is used for data handling. + iscaler = mala.DataScaler.load_from_file(input_scaler_path) + oscaler = mala.DataScaler.load_from_file(output_scaler_path) + + inference_data_handler = mala.DataHandler( + parameters, input_data_scaler=iscaler, output_data_scaler=oscaler + ) + + # Add snapshots that are to be tested and make sure that the + # data_splitting_snapshots list is correct. + parameters.data.data_splitting_snapshots = ["te"] + inference_data_handler.add_snapshot( + "Al_debug_2k_nr2.in.npy", + data_path, + "Al_debug_2k_nr2.out.npy", + data_path, + output_units="1/Ry", + ) + inference_data_handler.prepare_data(reparametrize_scaler=False) + + # Extract single feature vector from snapshot. The x,y,z part (first there + # entries) are removed in DataHandler (see load_memmap_snapshot() for + # details) and parameters.descriptors_contain_xyz, which is True by + # default. So the length is 91. + x_scaled = inference_data_handler.test_data_inputs[0] + with torch.no_grad(): + y_pred = network(x_scaled) + + # Convert to 1/eV + y_rescaled = oscaler.inverse_transform(y_pred) + + return dict(x_scaled=x_scaled, y_pred=y_pred, y_rescaled=y_rescaled) + + +def load_memmap_snapshot(): + """Load numpy memmap of snapshot and extract single feature. Avoid loading + whole snapshot into memory. + """ + # First load Parameters and network. + parameters = mala.Parameters.load_from_file(params_path, no_snapshots=True) + # Load a network from a file. + network = mala.Network.load_from_file(parameters, network_path) + + # Make sure the same scaling is used for data handling. + iscaler = mala.DataScaler.load_from_file(input_scaler_path) + oscaler = mala.DataScaler.load_from_file(output_scaler_path) + + # Load snapshot with input features, use memmap. + inputs_array = np.load(data_path / "Al_debug_2k_nr2.in.npy", mmap_mode="r") + + # Our inputs usually contain x,y,z information, so one single input vector + # reads [x,y,z,f_0, f_1, ..., f_N] where [f_0, f_1, ..., f_N] is the actual + # SNAP feature vector. Now, we select a single feature vector from the + # snapshot. + x = torch.from_numpy(inputs_array[0, 0, 0, 3:].astype(np.float32)) + + # Convert to tensor and scale. + # + # For some reason, DataScaler changes the shape (N,) -> (1,N), revert that + x_scaled = iscaler.transform(x)[0, :] + with torch.no_grad(): + y_pred = network(x_scaled) + + # Convert to 1/eV + y_rescaled = oscaler.inverse_transform(y_pred) + + return dict(x_scaled=x_scaled, y_pred=y_pred, y_rescaled=y_rescaled, x=x) + + +if __name__ == "__main__": + a = load_whole_snapshot() + b = load_memmap_snapshot() + + for name in ["x_scaled", "y_pred", "y_rescaled"]: + ref = a[name] + val = b[name] + assert (ref == val).all() + assert ref.shape == val.shape + assert ref.ndim == val.ndim == 1 From 65bffc27fa12280848dee06ce2bc27231502b850 Mon Sep 17 00:00:00 2001 From: Steve Schmerler Date: Wed, 1 Sep 2021 21:42:46 +0200 Subject: [PATCH 247/297] ex05_1: more comments, add sanity check, revert to os.path --- examples/ex05_1_pass_single_feature.py | 46 +++++++++++++++++++------- 1 file changed, 34 insertions(+), 12 deletions(-) diff --git a/examples/ex05_1_pass_single_feature.py b/examples/ex05_1_pass_single_feature.py index d3c29ec7e..5425baa1c 100644 --- a/examples/ex05_1_pass_single_feature.py +++ b/examples/ex05_1_pass_single_feature.py @@ -1,10 +1,14 @@ -"""Show how to run a sinlge feature vector out of a snapshot through a network. +"""Show how to run a single feature vector out of a snapshot through a network. +We load a pre-trained net and pass a single input feature vector through, +including input and output scaling. First run ex05_training_with_postprocessing.py to create the ex05_* files used below. """ -import pathlib as pl +import os + +pj = os.path.join import mala import numpy as np @@ -13,7 +17,7 @@ from data_repo_path import data_repo_path -data_path = pl.Path(data_repo_path) / "Al36" +data_path = pj(data_repo_path, "Al36") params_path = "ex05_params.pkl" network_path = "ex05_network.pth" input_scaler_path = "ex05_iscaler.pkl" @@ -25,7 +29,7 @@ def load_whole_snapshot(): single feature vector afterwards. """ - # First load Parameters and network. + # First load Parameters. parameters = mala.Parameters.load_from_file(params_path, no_snapshots=True) # Load a network from a file. @@ -41,6 +45,10 @@ def load_whole_snapshot(): # Add snapshots that are to be tested and make sure that the # data_splitting_snapshots list is correct. + # + # Units: output_units="1/Ry" means that the output (target) data (LDOS) has + # unit 1/Ry. Rescaled network output, after oscaler.inverse_transform() is + # applied (see below) will be in 1/eV. parameters.data.data_splitting_snapshots = ["te"] inference_data_handler.add_snapshot( "Al_debug_2k_nr2.in.npy", @@ -51,10 +59,11 @@ def load_whole_snapshot(): ) inference_data_handler.prepare_data(reparametrize_scaler=False) - # Extract single feature vector from snapshot. The x,y,z part (first there - # entries) are removed in DataHandler (see load_memmap_snapshot() for - # details) and parameters.descriptors_contain_xyz, which is True by - # default. So the length is 91. + # Extract single feature vector from snapshot. The x,y,z part (first 3 + # entries) are removed in DataHandler, so the feature vector length is 91 + # instead of 94. See also load_memmap_snapshot() below for details. The + # removal in DataHandler is controlled by + # parameters.descriptors_contain_xyz, which is True by default. x_scaled = inference_data_handler.test_data_inputs[0] with torch.no_grad(): y_pred = network(x_scaled) @@ -69,8 +78,9 @@ def load_memmap_snapshot(): """Load numpy memmap of snapshot and extract single feature. Avoid loading whole snapshot into memory. """ - # First load Parameters and network. + # First load Parameters. parameters = mala.Parameters.load_from_file(params_path, no_snapshots=True) + # Load a network from a file. network = mala.Network.load_from_file(parameters, network_path) @@ -79,7 +89,9 @@ def load_memmap_snapshot(): oscaler = mala.DataScaler.load_from_file(output_scaler_path) # Load snapshot with input features, use memmap. - inputs_array = np.load(data_path / "Al_debug_2k_nr2.in.npy", mmap_mode="r") + inputs_array = np.load( + pj(data_path, "Al_debug_2k_nr2.in.npy"), mmap_mode="r" + ) # Our inputs usually contain x,y,z information, so one single input vector # reads [x,y,z,f_0, f_1, ..., f_N] where [f_0, f_1, ..., f_N] is the actual @@ -87,8 +99,6 @@ def load_memmap_snapshot(): # snapshot. x = torch.from_numpy(inputs_array[0, 0, 0, 3:].astype(np.float32)) - # Convert to tensor and scale. - # # For some reason, DataScaler changes the shape (N,) -> (1,N), revert that x_scaled = iscaler.transform(x)[0, :] with torch.no_grad(): @@ -101,6 +111,18 @@ def load_memmap_snapshot(): if __name__ == "__main__": + # fmt: off + assert os.path.exists(data_path), f"{data_path} missing, link data repo first" + # fmt: on + + for pth in [ + params_path, + network_path, + input_scaler_path, + output_scaler_path, + ]: + assert os.path.exists(pth), f"{pth} missing, run ex05 first" + a = load_whole_snapshot() b = load_memmap_snapshot() From 55cf45bd3ff7a5e7b69072e4dfa2a602605f988b Mon Sep 17 00:00:00 2001 From: Attila Cangi Date: Sat, 4 Sep 2021 08:46:01 +0200 Subject: [PATCH 248/297] Fixed publication in README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 31ec59862..b71572284 100644 --- a/README.md +++ b/README.md @@ -58,6 +58,6 @@ the examples in the `example/` folder. If you publish work which uses or mentions MALA, please cite the following paper: J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, -A. Cangi, S. Rajamanickam (2020). Accelerating Finite-temperature +A. Cangi, S. Rajamanickam (2021). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. -[arXiv:2010.04905](https://arxiv.org/abs/2010.04905). +[Phys. Rev. B 104, 035120 (2021)](https://doi.org/10.1103/PhysRevB.104.035120). From 5912603bde8cbf71ee8057af4b76defa7f83aee3 Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Sun, 5 Sep 2021 17:05:58 +0200 Subject: [PATCH 249/297] removed .close() --- mala/network/trainer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mala/network/trainer.py b/mala/network/trainer.py index f78d47c95..1db20c300 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -328,8 +328,7 @@ def train_network(self): if self.parameters_full.use_horovod: vloss = self.__average_validation(vloss, 'average_loss') - # closing tensorboard window - self.tensor_board.close() + # Calculate final loss. self.final_validation_loss = vloss From bd3f378bf3e775c66537589097f194c406285b7a Mon Sep 17 00:00:00 2001 From: Sneha Verma Date: Mon, 6 Sep 2021 09:25:06 +0200 Subject: [PATCH 250/297] modified some error due to visualisation --- examples/ex10_tensor_board.py | 4 ++-- mala/network/trainer.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 20d3658a5..41a7b521c 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -40,8 +40,8 @@ test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" -test_parameters.running.visualisation = "~/log_dir" -test_parameters.running.visualisation_dir = "~/log_dir" +test_parameters.running.visualisation = True +# test_parameters.running.visualisation_dir = "~/log_dir" #################### # DATA diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 1db20c300..4eb1b3701 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -262,7 +262,7 @@ def train_network(self): self.tensor_board.add_histogram(name,param,epoch) self.tensor_board.add_histogram(f'{name}.grad',param.grad,epoch) - self.tensor_board.close() #method to make sure that all pending events have been written to disk + self.tensor_board.close() #method to make sure that all pending events have been written to disk From 1d5f012b40e84fe4ebeb27a1f9d81a3486cbbc04 Mon Sep 17 00:00:00 2001 From: Zevrap18 Date: Mon, 6 Sep 2021 09:58:16 +0200 Subject: [PATCH 251/297] a few changes to pass the tests --- examples/ex06_advanced_hyperparameter_optimization.py | 2 +- mala/network/hyper_opt_oat.py | 10 +++++----- test/hyperopt_test.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/ex06_advanced_hyperparameter_optimization.py b/examples/ex06_advanced_hyperparameter_optimization.py index d5b5383d8..161c06b78 100644 --- a/examples/ex06_advanced_hyperparameter_optimization.py +++ b/examples/ex06_advanced_hyperparameter_optimization.py @@ -124,7 +124,7 @@ def optimize_hyperparameters(hyper_optimizer, input_creator_notraining="oat", test_hp_optimizer.perform_study(trial_list=last_optuna_study) else: test_hp_optimizer.perform_study(trial_list= - tmp_hp_optimizer.orthogonal_arr) + tmp_hp_optimizer.get_orthogonal_array()) test_hp_optimizer.set_optimal_parameters() printout("Hyperparameter optimization: DONE.") diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index a46668610..3a9c15d87 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -32,7 +32,7 @@ def __init__(self, params, data): self.factor_levels = None self.strength = None self.N_runs = None - self.OA = None + self.__OA = None def add_hyperparameter(self, opttype="categorical", name="", choices=None, **kwargs): """ @@ -62,12 +62,12 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ - self.OA = self.get_orthogonal_array() - self.trial_losses = np.zeros(self.OA.shape[0]) + self.__OA = self.get_orthogonal_array() + self.trial_losses = np.zeros(self.__OA.shape[0]) number_of_trial = 0 # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) - for row in self.OA: + for row in self.__OA: printout("Trial number", number_of_trial) self.trial_losses[number_of_trial] = self.perform_trial( self.objective, row) @@ -98,7 +98,7 @@ def get_optimal_parameters(self): printout("Performing Range Analysis.") def indices(idx, val): return np.where( - self.OA[:, idx] == val)[0] + self.__OA[:, idx] == val)[0] R = [[self.trial_losses[indices(idx, l)].sum() for l in range(levels)] for (idx, levels) in enumerate(self.factor_levels)] diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py index b7bd80ff0..da995a4e8 100644 --- a/test/hyperopt_test.py +++ b/test/hyperopt_test.py @@ -243,7 +243,7 @@ def __optimize_hyperparameters(hyper_optimizer, test_hp_optimizer.perform_study(trial_list=last_optuna_study) else: test_hp_optimizer.perform_study(trial_list= - tmp_hp_optimizer.orthogonal_arr) + tmp_hp_optimizer.get_orthogonal_array()) test_hp_optimizer.set_optimal_parameters() # Train the final network. From 433ef6b7529bc3c8c4cdf3e79a2461d2dbec78e0 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Tue, 7 Sep 2021 14:57:44 +0200 Subject: [PATCH 252/297] Add workflow for mirroring to CASUS orga --- .github/workflows/mirror-to-casus.yml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 .github/workflows/mirror-to-casus.yml diff --git a/.github/workflows/mirror-to-casus.yml b/.github/workflows/mirror-to-casus.yml new file mode 100644 index 000000000..765f6d286 --- /dev/null +++ b/.github/workflows/mirror-to-casus.yml @@ -0,0 +1,16 @@ +name: mirror + +on: [push, delete] + +jobs: + mirror-to-CASUS: + runs-on: ubuntu-18.04 + steps: + - uses: actions/checkout@v1 + - name: mirror-repository + uses: spyoungtech/mirror-action@v0.4.0 + with: + REMOTE: git@github.com:casus/mala.git + GIT_SSH_PRIVATE_KEY: ${{ secrets.GIT_SSH_KEY }} + GIT_SSH_NO_VERIFY_HOST: "true" + DEBUG: "true" From c5db26388caba92ffa558e482e709f48e1880478 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 7 Sep 2021 16:22:21 +0200 Subject: [PATCH 253/297] Fixed some more path concats --- mala/datahandling/data_converter.py | 8 ++++++-- mala/datahandling/data_handler.py | 25 +++++++++++++------------ mala/descriptors/snap.py | 13 +++++++++---- mala/targets/ldos.py | 4 +++- 4 files changed, 31 insertions(+), 19 deletions(-) diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index 6e0357459..5b8a89433 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -1,4 +1,6 @@ """DataConverter class for converting snapshots into numpy arrays.""" +import os + from mala.common.printout import printout from mala.common.parameters import ParametersData from mala.descriptors.descriptor_interface import DescriptorInterface @@ -176,5 +178,7 @@ def convert_snapshots(self, save_path="./", snapshot_name = naming_scheme snapshot_name = snapshot_name.replace("*", str(snapshot_number)) printout("Saving snapshot", snapshot_number, "at ", save_path) - np.save(save_path+snapshot_name+".in.npy", input_data) - np.save(save_path+snapshot_name+".out.npy", output_data) + np.save(os.path.join(save_path, snapshot_name+".in.npy"), + input_data) + np.save(os.path.join(save_path, snapshot_name+".out.npy"), + output_data) diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index c1e608626..8d93a3718 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -351,7 +351,7 @@ def resize_snapshots_for_debugging(self, directory="./", snapshot.input_npy_file)) tmp_file_name = naming_scheme_input tmp_file_name = tmp_file_name.replace("*", str(i)) - np.save(directory+tmp_file_name+".npy", tmp_array) + np.save(os.path.join(directory, tmp_file_name) +".npy", tmp_array) tmp_array = self.__load_from_npy_file(os.path.join(snapshot.output_npy_directory, snapshot.output_npy_file)) @@ -550,9 +550,9 @@ def __parametrize_scalers(self): for snapshot in self.parameters.snapshot_directories_list: # Data scaling is only performed on the training data sets. if self.parameters.data_splitting_snapshots[i] == "tr": - tmp = self.__load_from_npy_file(snapshot. - input_npy_directory + - snapshot.input_npy_file, + tmp = self.__load_from_npy_file(os.path.join(snapshot. + input_npy_directory, + snapshot.input_npy_file), mmapmode='r') if self.parameters.descriptors_contain_xyz: tmp = tmp[:, :, :, 3:] @@ -598,9 +598,9 @@ def __parametrize_scalers(self): for snapshot in self.parameters.snapshot_directories_list: # Data scaling is only performed on the training data sets. if self.parameters.data_splitting_snapshots[i] == "tr": - tmp = self.__load_from_npy_file(snapshot. - output_npy_directory + - snapshot.output_npy_file, + tmp = self.__load_from_npy_file(os.path.join(snapshot. + output_npy_directory, + snapshot.output_npy_file), mmapmode='r') # The scalers will later operate on torch Tensors so we # have to make sure they are fitted on @@ -636,9 +636,9 @@ def __load_training_data_into_ram(self): # Data scaling is only performed on the training data sets. if self.parameters.data_splitting_snapshots[i] == "tr": - tmp = self.__load_from_npy_file(snapshot. - input_npy_directory + - snapshot.input_npy_file, + tmp = self.__load_from_npy_file(os.path.join(snapshot. + input_npy_directory, + snapshot.input_npy_file), mmapmode='r') if self.parameters.descriptors_contain_xyz: tmp = tmp[:, :, :, 3:] @@ -672,8 +672,9 @@ def __load_training_data_into_ram(self): # Data scaling is only performed on the training data sets. if self.parameters.data_splitting_snapshots[i] == "tr": tmp = self. \ - __load_from_npy_file(snapshot.output_npy_directory + - snapshot.output_npy_file, + __load_from_npy_file(os.path.join( + snapshot.output_npy_directory, + snapshot.output_npy_file), mmapmode='r') tmp = np.array(tmp) tmp *= self.target_calculator. \ diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 3372a2add..3e71143f6 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -1,4 +1,6 @@ """SNAP descriptor class.""" +import os + import warnings import ase import ase.io @@ -99,7 +101,8 @@ def calculate_from_qe_out(self, qe_out_file, qe_out_directory): self.in_format_ase = "espresso-out" print("Calculating SNAP descriptors from", qe_out_file, "at", qe_out_directory) - return self.__calculate_snap(qe_out_directory + qe_out_file, + return self.__calculate_snap(os.path.join(qe_out_directory, + qe_out_file), qe_out_directory) def __calculate_snap(self, infile, outdir): @@ -111,7 +114,7 @@ def __calculate_snap(self, infile, outdir): # Enforcing / Checking PBC on the read atoms. atoms = self.enforce_pbc(atoms) - ase_out_path = outdir+"lammps_input.tmp" + ase_out_path = os.path.join(outdir, "lammps_input.tmp") ase.io.write(ase_out_path, atoms, format=lammps_format) # We also need to know how big the grid is. @@ -135,7 +138,8 @@ def __calculate_snap(self, infile, outdir): nz = int(tmp.split(",")[2]) break # Build LAMMPS arguments from the data we read. - lmp_cmdargs = ["-screen", "none", "-log", outdir+"lammps_log.tmp"] + lmp_cmdargs = ["-screen", "none", "-log", os.path.join(outdir, + "lammps_log.tmp")] lmp_cmdargs = set_cmdlinevars(lmp_cmdargs, { "ngridx": nx, @@ -152,7 +156,8 @@ def __calculate_snap(self, infile, outdir): # An empty string means that the user wants to use the standard input. if self.parameters.lammps_compute_file == "": filepath = __file__.split("snap")[0] - self.parameters.lammps_compute_file = filepath+"in.bgrid.python" + self.parameters.lammps_compute_file = os.path.join(filepath, + "in.bgrid.python") # Do the LAMMPS calculation. lmp.file(self.parameters.lammps_compute_file) diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 3a1993402..dc3f15761 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -1,4 +1,6 @@ """LDOS calculation class.""" +import os + from .cube_parser import read_cube from .target_base import TargetBase from .calculation_helpers import * @@ -134,7 +136,7 @@ def read_from_cube(self, file_name_scheme, directory, units="1/eV"): tmp_file_name = tmp_file_name.replace("*", str(i).zfill(digits)) # Open the cube file - data, meta = read_cube(directory + tmp_file_name) + data, meta = read_cube(os.path.join(directory, tmp_file_name)) # Once we have read the first cube file, we know the dimensions # of the LDOS and can prepare the array From be8fe2d6707ff79c8881346441388cff84a11a07 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 7 Sep 2021 16:29:08 +0200 Subject: [PATCH 254/297] Forgot two --- mala/targets/density.py | 2 +- mala/targets/dos.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/mala/targets/density.py b/mala/targets/density.py index 3484e44cf..d161c2fd8 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -50,7 +50,7 @@ def read_from_cube(self, file_name, directory, units=None): Units the density is saved in. Usually none. """ printout("Reading density from .cube file in ", directory) - data, meta = read_cube(directory + file_name) + data, meta = read_cube(os.path.join(directory, file_name)) return data def get_number_of_electrons(self, density_data, grid_spacing_bohr=None, diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 5222fb525..ed28a101c 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -1,4 +1,6 @@ """DOS calculation class.""" +import os + from .target_base import TargetBase from .calculation_helpers import * from scipy import integrate, interpolate @@ -107,7 +109,7 @@ def read_from_qe_dos_txt(self, file_name, directory): return_dos_values = [] # Open the file, then iterate through its contents. - with open(directory+file_name, 'r') as infile: + with open(os.path.join(directory, file_name), 'r') as infile: lines = infile.readlines() i = 0 From 574378c17d3902baa43ac43998ea07d0e394f073 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 7 Sep 2021 17:49:41 +0200 Subject: [PATCH 255/297] Fixed tests and examples --- docs/source/install/README.md | 5 +- examples/ex02_preprocess_data.py | 25 +++--- examples/ex03_postprocess_data.py | 23 ++++-- examples/exOAT.py | 130 ------------------------------ mala/targets/density.py | 2 + test/integration_test.py | 19 ++--- test/workflow_test.py | 72 ++++++++--------- 7 files changed, 75 insertions(+), 201 deletions(-) delete mode 100644 examples/exOAT.py diff --git a/docs/source/install/README.md b/docs/source/install/README.md index 786109471..0499dc25e 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -132,10 +132,7 @@ If you want to use this module, please refer to [Python bindings to Quantum Espr ``` (note: the `-e` is absolutely crucial, so that changes in the code will be reflected system wide) -3. Go to the `examples` folder and run `ex0_verify_installation.py` to make -sure the setup was successful. -4. Enjoy! - +3. (Optional): Download example data (see below) to run the examples. ### Build documentation locally (optional) diff --git a/examples/ex02_preprocess_data.py b/examples/ex02_preprocess_data.py index 981c1edee..43d818698 100644 --- a/examples/ex02_preprocess_data.py +++ b/examples/ex02_preprocess_data.py @@ -2,7 +2,7 @@ from mala import printout from data_repo_path import get_data_repo_path import numpy as np -data_path = get_data_repo_path()+"Al36/" +data_path = get_data_repo_path()+"Be2/" """ ex02_preprocess_data.py: Shows how this framework can be used to preprocess @@ -23,21 +23,20 @@ test_parameters = mala.Parameters() # Specify input data options, i.e. which descriptors are calculated -# with which parameters. These are the standard parameters for -# the calculation of SNAP descriptors. +# with which parameters. These parameters are slightly modified for better +# performance. test_parameters.descriptors.descriptor_type = "SNAP" -test_parameters.descriptors.twojmax = 10 +test_parameters.descriptors.twojmax = 6 test_parameters.descriptors.rcutfac = 4.67637 test_parameters.data.descriptors_contain_xyz = True # Specify output data options, i.e. how the LDOS is parsed. -# The Al system used as an example here actually has 250 energy levels. -# But for the convenience of the user, only 10 energy levels will be -# used for this example. +# The Be system used as an example here actually was calculated with +# drastically reduced number of energy levels for better computability. test_parameters.targets.target_type = "LDOS" -test_parameters.targets.ldos_gridsize = 10 -test_parameters.targets.ldos_gridspacing_ev = 0.1 -test_parameters.targets.ldos_gridoffset_ev = -10 +test_parameters.targets.ldos_gridsize = 11 +test_parameters.targets.ldos_gridspacing_ev = 2.5 +test_parameters.targets.ldos_gridoffset_ev = -5 #################### # DATA @@ -48,12 +47,12 @@ # Take care to choose the "add_snapshot" function correct for # the type of data you want to preprocess. -data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, - "cubes/tmp.pp*Al_ldos.cube", +data_converter.add_snapshot_qeout_cube("Be.pw.scf.out", data_path, + "cubes/tmp.pp*Be_ldos.cube", data_path, output_units="1/Ry") # Convert all the snapshots and save them in the current directory. -data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") +data_converter.convert_snapshots("./", naming_scheme="Be_snapshot*") #################### # RESULTS. diff --git a/examples/ex03_postprocess_data.py b/examples/ex03_postprocess_data.py index abc098c99..b4d9f1cd1 100644 --- a/examples/ex03_postprocess_data.py +++ b/examples/ex03_postprocess_data.py @@ -2,7 +2,7 @@ from mala import printout import numpy as np from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +data_path = get_data_repo_path()+"Be2/" """ @@ -11,6 +11,8 @@ post processing of LDOS data will be shown in the following. Set do_total_energy to False, if you don't have the QuantumEspresso Python module installed. +Since actual LDOS files are rather larger, this uses a drastically reduced +LDOS. Therefore the accuracy of the overall result is rather poor. """ # If you don't have the total energy module installed, @@ -26,9 +28,9 @@ # Specify the correct LDOS parameters. test_parameters.targets.target_type = "LDOS" -test_parameters.targets.ldos_gridsize = 250 -test_parameters.targets.ldos_gridspacing_ev = 0.1 -test_parameters.targets.ldos_gridoffset_ev = -10 +test_parameters.targets.ldos_gridsize = 11 +test_parameters.targets.ldos_gridspacing_ev = 2.5 +test_parameters.targets.ldos_gridoffset_ev = -5 # To perform a total energy calculation one also needs to provide # a pseudopotential(path). test_parameters.targets.pseudopotential_path = data_path @@ -38,18 +40,17 @@ # Create a target calculator to postprocess data. # Use this calculator to perform various operations. #################### - ldos = mala.TargetInterface(test_parameters) # Read additional information about the calculation. # By doing this, the calculator is able to know e.g. the temperature # at which the calculation took place or the lattice constant used. ldos.read_additional_calculation_data("qe.out", - data_path+"Al.pw.scf.out") + data_path+"Be.pw.scf.out") # Read in LDOS data. For actual workflows, this part will come # from a network. -ldos_data = np.load(data_path+"Al_ldos.npy") +ldos_data = np.load(data_path+"Be_ldos.npy") # Get quantities of interest. # For better values in the post processing, it is recommended to @@ -80,9 +81,15 @@ printout("Parameters used for this experiment:") test_parameters.show() - +print("Values from LDOS: ") print("Number of electrons:", number_of_electrons) print("Band energy:", band_energy) if do_total_energy: print("Total energy:", total_energy) +print("Values from DFT: ") +print("Number of electrons:", ldos.number_of_electrons) +print("Band energy:", ldos.band_energy_dft_calculation) +if do_total_energy: + print("Total energy:", ldos.total_energy_dft_calculation) + diff --git a/examples/exOAT.py b/examples/exOAT.py deleted file mode 100644 index cddd3265b..000000000 --- a/examples/exOAT.py +++ /dev/null @@ -1,130 +0,0 @@ -import mala -from mala.common.printout import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" - -""" -ex04_hyperparameter_optimization.py: Shows how a hyperparameter -optimization can be done using this framework. There are multiple -hyperparameter optimizers available in this framework. This example focusses -on the most universal one - optuna. -""" - - -def run_example04(desired_loss_improvement_factor=2): - #################### - # PARAMETERS - # All parameters are handled from a central parameters class that - # contains subclasses. - #################### - test_parameters = mala.Parameters() - # Currently, the splitting in training, validation and test set are - # done on a "by snapshot" basis. Specify how this is - # done by providing a list containing entries of the form - # "tr", "va" and "te". - test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - - # Specify the data scaling. - test_parameters.data.input_rescaling_type = "feature-wise-standard" - test_parameters.data.output_rescaling_type = "normal" - - # Specify the training parameters. - test_parameters.running.max_number_epochs = 5 - test_parameters.running.mini_batch_size = 40 - test_parameters.running.learning_rate = 0.00001 - test_parameters.running.trainingtype = "Adam" - - # Specify the number of trials, the hyperparameter optimizer should run - # and the type of hyperparameter. - test_parameters.hyperparameters.n_trials = 20 - test_parameters.hyperparameters.hyper_opt_method = "oat" - - #################### - # DATA - # Add and prepare snapshots for training. - #################### - data_handler = mala.DataHandler(test_parameters) - - # Add all the snapshots we want to use in to the list. - data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") - data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") - data_handler.prepare_data() - printout("Read data: DONE.") - - #################### - # HYPERPARAMETER OPTIMIZATION - # In order to perform a hyperparameter optimization, - # one has to simply create a hyperparameter optimizer - # and let it perform a "study". - # Before such a study can be done, one has to add all the parameters - # of interest. - #################### - - test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) - - # Learning rate will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", - choices=[0.005, 0.01, 0.015]) - - # Number of neurons per layer will be optimized. - test_hp_optimizer.add_hyperparameter( - "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) - test_hp_optimizer.add_hyperparameter( - "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) - - # Choices for activation function at each layer will be optimized. - test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid", "LeakyReLU"]) - - # Perform hyperparameter optimization. - printout("Starting Hyperparameter optimization.") - test_hp_optimizer.perform_study() - test_hp_optimizer.set_optimal_parameters() - printout("Hyperparameter optimization: DONE.") - - #################### - # TRAINING - # Train with these new parameters. - #################### - - test_network = mala.Network(test_parameters) - test_trainer = mala.Trainer(test_parameters, test_network, data_handler) - printout("Network setup: DONE.") - printout("Trial with optimal parameters set.") - test_trainer.train_network() - printout("Training: DONE.") - - #################### - # RESULTS. - # Print the used parameters and check whether the loss decreased enough. - #################### - - printout("Parameters used for this experiment:") - test_parameters.show() - - # To see if the hyperparameter optimization actually worked, - # check if the best trial is better then the worst trial - # by a certain factor. - if desired_loss_improvement_factor*test_trainer.initial_test_loss < \ - test_trainer.final_test_loss: - return False - else: - return True - - -if __name__ == "__main__": - if run_example04(): - printout("Successfully ran ex04_hyperparameter_optimization.py.") - else: - raise Exception("Ran ex04_hyperparameter_optimization but something " - "was off. If you haven't changed any parameters in " - "the example, there might be a problem with " - "your installation.") diff --git a/mala/targets/density.py b/mala/targets/density.py index d161c2fd8..fe86a2e79 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -1,4 +1,6 @@ """Electronic density calculation class.""" +import os + from .target_base import TargetBase from .calculation_helpers import * from .cube_parser import read_cube diff --git a/test/integration_test.py b/test/integration_test.py index bc2f9dbd3..f81930a4b 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -20,11 +20,11 @@ # Scripts to reproduce the data files used in this test script can be found # in the data repo. -data_path = os.path.join(get_data_repo_path(), "Al36") -path_to_out = os.path.join(data_path, "Al.pw.scf.out") -path_to_ldos_npy = os.path.join(data_path, "Al_ldos.npy") -path_to_dos_npy = os.path.join(data_path, "Al_dos.npy") -path_to_dens_npy = os.path.join(data_path, "Al_dens.npy") +data_path = os.path.join(get_data_repo_path(), "Be2") +path_to_out = os.path.join(data_path, "Be.pw.scf.out") +path_to_ldos_npy = os.path.join(data_path, "Be_ldos.npy") +path_to_dos_npy = os.path.join(data_path, "Be_dos.npy") +path_to_dens_npy = os.path.join(data_path, "Be_dens.npy") # We can read from numpy arrays or directly from QE data. # In the later case, numpy arrays will be saved for the subsqeuent run. @@ -32,13 +32,14 @@ # Define the parameters used for the experiments. test_parameters = Parameters() -test_parameters.targets.ldos_gridsize = 250 -test_parameters.targets.ldos_gridspacing_ev = 0.1 -test_parameters.targets.ldos_gridoffset_ev = -10 +test_parameters.targets.ldos_gridsize = 11 +test_parameters.targets.ldos_gridspacing_ev = 2.5 +test_parameters.targets.ldos_gridoffset_ev = -5 # Define the accuracy used in the tests. accuracy = 1e-6 accuracy_dos = 1e-4 +accuracy_ldos = 0.2 class TestMALAIntegration: @@ -175,7 +176,7 @@ def test_qe_ldos_to_dos(self): printout("Relative error for sum of DOS: ", rel_error) # Check against the constraints we put upon ourselves. - assert np.isclose(rel_error, 0, atol=accuracy) + assert np.isclose(rel_error, 0, atol=accuracy_ldos) def test_pwevaldos_vs_ppdos(self): """Check pp.x DOS vs. pw.x DOS (from eigenvalues in outfile).""" diff --git a/test/workflow_test.py b/test/workflow_test.py index a96e4abef..5ba8cffa6 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -6,6 +6,7 @@ import os data_path = os.path.join(get_data_repo_path(), "Al36/") +data_path_ldos = os.path.join(get_data_repo_path(), "Be2/") # Control how much the loss should be better after training compared to # before. This value is fairly high, but we're training on absolutely # minimal amounts of data. @@ -30,8 +31,6 @@ def test_network_training(self): @pytest.mark.skipif(importlib.util.find_spec("lammps") is None, reason="LAMMPS is currently not part of the pipeline.") - @pytest.mark.skipif(os.path.isdir(os.path.join(data_path, "cubes")) - is False, reason="No cube files found in data repo.") def test_preprocessing(self): """ Test whether MALA can preprocess data. @@ -44,32 +43,33 @@ def test_preprocessing(self): # Set up parameters. test_parameters = mala.Parameters() test_parameters.descriptors.descriptor_type = "SNAP" - test_parameters.descriptors.twojmax = 10 + test_parameters.descriptors.twojmax = 6 test_parameters.descriptors.rcutfac = 4.67637 test_parameters.data.descriptors_contain_xyz = True test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 10 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 + test_parameters.targets.ldos_gridsize = 11 + test_parameters.targets.ldos_gridspacing_ev = 2.5 + test_parameters.targets.ldos_gridoffset_ev = -5 # Create a DataConverter, and add snapshots to it. data_converter = mala.DataConverter(test_parameters) - data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, - "cubes/tmp.pp*Al_ldos.cube", - data_path, output_units="1/Ry") + data_converter.add_snapshot_qeout_cube("Be.pw.scf.out", data_path_ldos, + "cubes/tmp.pp*Be_ldos.cube", + data_path_ldos, + output_units="1/Ry") - data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") + data_converter.convert_snapshots("./", naming_scheme="Be_snapshot*") # Compare against - input_data = np.load("Al_snapshot0.in.npy") + input_data = np.load("Be_snapshot0.in.npy") input_data_shape = np.shape(input_data) - assert input_data_shape[0] == 108 and input_data_shape[1] == 108 and \ - input_data_shape[2] == 100 and input_data_shape[3] == 94 + assert input_data_shape[0] == 18 and input_data_shape[1] == 18 and \ + input_data_shape[2] == 27 and input_data_shape[3] == 33 - output_data = np.load("Al_snapshot0.out.npy") + output_data = np.load("Be_snapshot0.out.npy") output_data_shape = np.shape(output_data) - assert output_data_shape[0] == 108 and output_data_shape[1] == 108 and\ - output_data_shape[2] == 100 and output_data_shape[3] == 10 + assert output_data_shape[0] == 18 and output_data_shape[1] == 18 and\ + output_data_shape[2] == 27 and output_data_shape[3] == 11 def test_postprocessing_from_dos(self): """ @@ -106,8 +106,6 @@ def test_postprocessing_from_dos(self): assert np.isclose(band_energy, dos.band_energy_dft_calculation, atol=accuracy_band_energy) - @pytest.mark.skipif(os.path.isfile(os.path.join(data_path, "Al_ldos.npy")) - is False, reason="No LDOS file in data repo found.") def test_postprocessing(self): """ Test whether MALA can postprocess data (from LDOS) @@ -118,15 +116,16 @@ def test_postprocessing(self): # Set up parameters. test_parameters = mala.Parameters() test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 250 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 + test_parameters.targets.ldos_gridsize = 11 + test_parameters.targets.ldos_gridspacing_ev = 2.5 + test_parameters.targets.ldos_gridoffset_ev = -5 # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) ldos.read_additional_calculation_data("qe.out", - data_path + "Al.pw.scf.out") - ldos_data = np.load(data_path + "Al_ldos.npy") + data_path_ldos + + "Be.pw.scf.out") + ldos_data = np.load(data_path_ldos + "Be_ldos.npy") # Calculate energies self_consistent_fermi_energy = ldos. \ @@ -154,16 +153,16 @@ def test_total_energy_from_dos_density(self): # Set up parameters. test_parameters = mala.Parameters() test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 250 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 + test_parameters.targets.ldos_gridsize = 11 + test_parameters.targets.ldos_gridspacing_ev = 2.5 + test_parameters.targets.ldos_gridoffset_ev = -5 test_parameters.targets.pseudopotential_path = data_path # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) ldos.read_additional_calculation_data("qe.out", - data_path + "Al.pw.scf.out") - dos_data = np.load(data_path + "Al_dos.npy") - dens_data = np.load(data_path + "Al_dens.npy") + data_path_ldos + "Be.pw.scf.out") + dos_data = np.load(data_path_ldos + "Be_dos.npy") + dens_data = np.load(data_path_ldos + "Be_dens.npy") dos = mala.DOS.from_ldos(ldos) # Calculate energies @@ -179,8 +178,6 @@ def test_total_energy_from_dos_density(self): @pytest.mark.skipif(importlib.util.find_spec("total_energy") is None, reason="QE is currently not part of the pipeline.") - @pytest.mark.skipif(os.path.isfile(os.path.join(data_path, "Al_ldos.npy")) - is False, reason="No LDOS file in data repo found.") def test_total_energy_from_ldos(self): """ Test whether MALA can calculate the total energy using the LDOS. @@ -190,16 +187,17 @@ def test_total_energy_from_ldos(self): # Set up parameters. test_parameters = mala.Parameters() test_parameters.targets.target_type = "LDOS" - test_parameters.targets.ldos_gridsize = 250 - test_parameters.targets.ldos_gridspacing_ev = 0.1 - test_parameters.targets.ldos_gridoffset_ev = -10 - test_parameters.targets.pseudopotential_path = data_path + test_parameters.targets.ldos_gridsize = 11 + test_parameters.targets.ldos_gridspacing_ev = 2.5 + test_parameters.targets.ldos_gridoffset_ev = -5 + test_parameters.targets.pseudopotential_path = data_path_ldos # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) ldos.read_additional_calculation_data("qe.out", - data_path + "Al.pw.scf.out") - ldos_data = np.load(data_path + "Al_ldos.npy") + data_path_ldos + + "Be.pw.scf.out") + ldos_data = np.load(data_path_ldos + "Be_ldos.npy") # Calculate energies self_consistent_fermi_energy = ldos. \ From b601c5b5e535bbfcd97ee7a03d13a5b7f5307710 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 7 Sep 2021 17:59:18 +0200 Subject: [PATCH 256/297] Added correct version of data repo --- .github/workflows/cpu-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index bab6a705c..98fd53dda 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -81,7 +81,7 @@ jobs: repository: mala-project/test-data token: ${{ secrets.ACCESS_TOKEN }} path: mala_data - ref: v0.4.0 + ref: v1.0.0 lfs: false - name: Data setup From c8f6c40f7ecfcb5bd9bb30bba465c7c0d43415df Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Wed, 8 Sep 2021 16:18:12 +0200 Subject: [PATCH 257/297] Add bump2version as an optional (dev) dependency to `setup.py` --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index c042aad8a..cefd0103f 100644 --- a/setup.py +++ b/setup.py @@ -15,6 +15,7 @@ license = f.read() extras = { + 'dev': ['bump2version'], 'opt': ['oapackage'], 'test': ['pytest'], 'doc': open('docs/requirements.txt').read().splitlines(), From c7ea723081efcbb296729f31bad19a35f8577dab Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 11:46:52 +0200 Subject: [PATCH 258/297] Fixed pipeline --- mala/network/objective_base.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index faab4819c..9a79b6a4f 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -69,8 +69,9 @@ def __call__(self, trial): """ # Parse the parameters included in the trial. self.parse_trial(trial) - if trial.should_prune(): - raise TrialPruned() + if self.trial_type == "optuna": + if trial.should_prune(): + raise TrialPruned() # Train a network for as often as the user desires. final_validation_loss = [] From 2692b507ca18a2a7ca1d6b7bcad8520e69382596 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 12:07:19 +0200 Subject: [PATCH 259/297] Fixed docs --- mala/network/hyper_opt_optuna.py | 2 +- mala/network/no_training_pruner.py | 48 ++++++++++++++++++++++++++++-- 2 files changed, 46 insertions(+), 4 deletions(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index d44bfc99d..0f67b3985 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -36,7 +36,7 @@ def __init__(self, params, data): # See if the user specified a pruner. pruner = None if self.params.hyperparameters.pruner == "no_training": - pruner = NoTrainingPruner(self.params, data, "optuna") + pruner = NoTrainingPruner(self.params, data) # Create the study. if self.params.hyperparameters.rdb_storage is None: diff --git a/mala/network/no_training_pruner.py b/mala/network/no_training_pruner.py index c62380f0e..5563825bf 100644 --- a/mala/network/no_training_pruner.py +++ b/mala/network/no_training_pruner.py @@ -8,13 +8,55 @@ class NoTrainingPruner(BasePruner): + """ + Implements the NASWOT method (first version of paper) as an optuna pruner. + + This means that before each training of a trial network the network will + be tested against a user defined surrogate score (which has to be + calibrated). If this score is good enough, the candidate will be trained. + + Parameters + ---------- + search_parameters : mala.common.parametes.Parameters + Parameters used to create this objective. + + data_handler : mala.datahandling.data_handler.DataHandler + datahandler to be used during the hyperparameter optimization. + + """ + def __init__(self, search_parameters: Parameters, data_handler: - DataHandler, trial_type): - self._trial_type = trial_type + DataHandler): self._data_handler = data_handler self._params = search_parameters + self._trial_type = self._params.hyperparameters.hyper_opt_method + if self._trial_type != "optuna": + raise Exception("This pruner only works for optuna at the moment.") + + def prune(self, study: "optuna.study.Study", trial: + "optuna.trial.FrozenTrial") -> bool: + """ + Judge whether the trial should be pruned based on the reported values. + + Note that this method is not supposed to be called by library users. Instead, + :func:`optuna.trial.Trial.report` and :func:`optuna.trial.Trial.should_prune` provide + user interfaces to implement pruning mechanism in an objective function. + + Parameters + ---------- + study : optuna.study.Study + Study object of the target study. + + trial : optuna.trial.FrozenTrial + FrozenTrial object of the target trial. + Take a copy before modifying this object. - def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + Returns + ------- + should_prune : bool + A boolean indicating whether this particular trial should be + pruned. + """ objective = ObjectiveNoTraining(self._params, self._data_handler, self._trial_type) surrogate_loss = objective(trial) From a62f3a1f7809ecde888b213a066aa652776dc2d6 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 9 Sep 2021 14:40:44 +0200 Subject: [PATCH 260/297] Add release date for v0.1.0 to `CITATION.cff` --- CITATION.cff | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CITATION.cff b/CITATION.cff index e62d87d73..f74a91ccc 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -39,7 +39,7 @@ authors: given-names: Aidan P. -#date-released: 2021-08-16 +date-released: 2021-07-07 keywords: - "machine-learning" - "dft" From 07351c4c5ae077b6b373a793bae354ecb40d995d Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Thu, 9 Sep 2021 15:06:19 +0200 Subject: [PATCH 261/297] Add bump2version config file --- .bumpversion.cfg | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 .bumpversion.cfg diff --git a/.bumpversion.cfg b/.bumpversion.cfg new file mode 100644 index 000000000..2a3696f14 --- /dev/null +++ b/.bumpversion.cfg @@ -0,0 +1,11 @@ +[bumpversion] +current_version = 0.1.0 +commit = True +tag = True +sign_tags = True + +[bumpversion:file:mala/version.py] + +[bumpversion:file:CITATION.cff] + +[bumpversion:file:Copyright.txt] From f0d79089af1fe40be21d999d87577b20a16753db Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 15:24:01 +0200 Subject: [PATCH 262/297] Fixed imports --- examples/ex01_run_singleshot.py | 1 + examples/ex02_preprocess_data.py | 1 + examples/ex03_postprocess_data.py | 1 + examples/ex04_hyperparameter_optimization.py | 1 + examples/ex05_training_with_postprocessing.py | 2 +- ...06_advanced_hyperparameter_optimization.py | 2 +- examples/ex07_checkpoint_training.py | 1 + examples/ex08_checkpoint_hyperopt.py | 1 + examples/ex09_distributed_hyperopt.py | 6 +++-- examples/ex10_tensor_board.py | 6 +++-- mala/common/parameters.py | 5 +++- mala/datahandling/data_converter.py | 3 ++- mala/datahandling/data_handler.py | 23 ++++++++-------- mala/datahandling/data_scaler.py | 8 +++--- mala/datahandling/lazy_load_dataset.py | 9 ++++--- mala/descriptors/descriptor_base.py | 3 ++- mala/descriptors/descriptor_interface.py | 2 +- mala/descriptors/lammps_utils.py | 3 +-- mala/descriptors/snap.py | 7 ++--- mala/network/hyper_opt_base.py | 5 ++-- mala/network/hyper_opt_interface.py | 6 ++--- mala/network/hyper_opt_notraining.py | 4 +-- mala/network/hyper_opt_oat.py | 14 +++++----- mala/network/hyper_opt_optuna.py | 13 +++++---- mala/network/hyperparameter_interface.py | 4 +-- mala/network/network.py | 6 ++--- mala/network/no_training_pruner.py | 4 +-- mala/network/objective_base.py | 9 ++++--- mala/network/objective_no_training.py | 3 ++- mala/network/runner.py | 9 ++++--- mala/network/tester.py | 9 +++---- mala/network/trainer.py | 27 +++++++++---------- mala/targets/calculation_helpers.py | 6 ++--- mala/targets/density.py | 11 ++++---- mala/targets/dos.py | 4 +-- mala/targets/ldos.py | 15 ++++++----- mala/targets/target_base.py | 3 ++- mala/targets/target_interface.py | 6 ++--- test/basic_gpu_test.py | 12 +++++---- test/checkpoint_hyperopt_tests.py | 7 +++-- test/checkpoint_training_tests.py | 4 ++- test/hyperopt_test.py | 7 +++-- test/inference_test.py | 11 +++----- test/installation_test.py | 4 ++- test/integration_test.py | 14 +++++----- test/lazy_loading_test.py | 18 ++++++------- test/tensor_memory.py | 5 ++-- test/workflow_test.py | 8 +++--- 48 files changed, 184 insertions(+), 149 deletions(-) diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 0dc3bf7d0..9bab108e4 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -1,5 +1,6 @@ import mala from mala import printout + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/examples/ex02_preprocess_data.py b/examples/ex02_preprocess_data.py index 43d818698..e6c389bf7 100644 --- a/examples/ex02_preprocess_data.py +++ b/examples/ex02_preprocess_data.py @@ -1,5 +1,6 @@ import mala from mala import printout + from data_repo_path import get_data_repo_path import numpy as np data_path = get_data_repo_path()+"Be2/" diff --git a/examples/ex03_postprocess_data.py b/examples/ex03_postprocess_data.py index b4d9f1cd1..421d828d9 100644 --- a/examples/ex03_postprocess_data.py +++ b/examples/ex03_postprocess_data.py @@ -1,6 +1,7 @@ import mala from mala import printout import numpy as np + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Be2/" diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index b6abb26c5..769c54b16 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -1,5 +1,6 @@ import mala from mala import printout + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/examples/ex05_training_with_postprocessing.py b/examples/ex05_training_with_postprocessing.py index b23db6b61..3edfb3416 100644 --- a/examples/ex05_training_with_postprocessing.py +++ b/examples/ex05_training_with_postprocessing.py @@ -1,6 +1,6 @@ import mala from mala import printout -import numpy as np + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" """ diff --git a/examples/ex06_advanced_hyperparameter_optimization.py b/examples/ex06_advanced_hyperparameter_optimization.py index 161c06b78..75e026edf 100644 --- a/examples/ex06_advanced_hyperparameter_optimization.py +++ b/examples/ex06_advanced_hyperparameter_optimization.py @@ -1,6 +1,6 @@ import mala from mala import printout -import numpy as np + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/examples/ex07_checkpoint_training.py b/examples/ex07_checkpoint_training.py index a76b715e4..1682f26e9 100644 --- a/examples/ex07_checkpoint_training.py +++ b/examples/ex07_checkpoint_training.py @@ -1,5 +1,6 @@ import mala from mala import printout + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/examples/ex08_checkpoint_hyperopt.py b/examples/ex08_checkpoint_hyperopt.py index 11e89a1a6..1c0dd89f1 100644 --- a/examples/ex08_checkpoint_hyperopt.py +++ b/examples/ex08_checkpoint_hyperopt.py @@ -1,5 +1,6 @@ import mala from mala import printout + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index 87a4157a1..42942e12b 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -1,8 +1,10 @@ -import numpy as np +import os + import mala from mala import printout +import numpy as np + from data_repo_path import get_data_repo_path -import os data_path = get_data_repo_path()+"Al36/" """ diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index 41a7b521c..fec047831 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,8 +1,10 @@ +import shutil +import webbrowser + import mala from mala import printout from tensorboard import program -import shutil -import webbrowser + from data_repo_path import get_data_repo_path data_path = get_data_repo_path()+"Al36/" diff --git a/mala/common/parameters.py b/mala/common/parameters.py index fa87a7a55..8a4b90f06 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -2,7 +2,7 @@ import os import pickle import warnings -from .printout import printout, set_horovod_status + try: import horovod.torch as hvd except ModuleNotFoundError: @@ -12,6 +12,9 @@ "True WILL cause a crash.", stacklevel=3) import torch +from mala.common.printout import printout, set_horovod_status + + class ParametersBase: """Base parameter class for MALA.""" diff --git a/mala/datahandling/data_converter.py b/mala/datahandling/data_converter.py index 5b8a89433..6e2e82a72 100644 --- a/mala/datahandling/data_converter.py +++ b/mala/datahandling/data_converter.py @@ -1,11 +1,12 @@ """DataConverter class for converting snapshots into numpy arrays.""" import os +import numpy as np + from mala.common.printout import printout from mala.common.parameters import ParametersData from mala.descriptors.descriptor_interface import DescriptorInterface from mala.targets.target_interface import TargetInterface -import numpy as np class DataConverter: diff --git a/mala/datahandling/data_handler.py b/mala/datahandling/data_handler.py index 8d93a3718..f22bd724e 100644 --- a/mala/datahandling/data_handler.py +++ b/mala/datahandling/data_handler.py @@ -1,23 +1,22 @@ """DataHandler class that loads and scales data.""" - import os -from torch.utils.data import TensorDataset - -from .data_scaler import DataScaler -from .snapshot import Snapshot -from .lazy_load_dataset import LazyLoadDataset -from mala.common.parameters import Parameters, ParametersData -from mala.targets.target_interface import TargetInterface -from mala.descriptors.descriptor_interface import DescriptorInterface -from mala.common.printout import printout -import numpy as np -import torch try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by Parameters class pass +import numpy as np +import torch +from torch.utils.data import TensorDataset + +from mala.common.printout import printout +from mala.common.parameters import Parameters, ParametersData +from mala.datahandling.data_scaler import DataScaler +from mala.datahandling.snapshot import Snapshot +from mala.datahandling.lazy_load_dataset import LazyLoadDataset +from mala.descriptors.descriptor_interface import DescriptorInterface +from mala.targets.target_interface import TargetInterface class DataHandler: diff --git a/mala/datahandling/data_scaler.py b/mala/datahandling/data_scaler.py index 7f9723d58..b85bd6106 100644 --- a/mala/datahandling/data_scaler.py +++ b/mala/datahandling/data_scaler.py @@ -1,13 +1,15 @@ """DataScaler class for scaling DFT data.""" -import torch import pickle -import numpy as np -from mala.common.parameters import printout + try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by parameters class pass +import numpy as np +import torch + +from mala.common.parameters import printout class DataScaler: diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index 74ccc7167..ab125a64e 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -1,13 +1,14 @@ """DataSet for lazy-loading.""" -import torch -from torch.utils.data import Dataset -from mala.datahandling.snapshot import Snapshot -import numpy as np try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by Parameters class. pass +import numpy as np +import torch +from torch.utils.data import Dataset + +from mala.datahandling.snapshot import Snapshot class LazyLoadDataset(torch.utils.data.Dataset): diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index f54f5c3d9..adfcbcb25 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -1,6 +1,7 @@ """Base class for all descriptor calculators.""" -import numpy as np import ase +import numpy as np + class DescriptorBase: """ diff --git a/mala/descriptors/descriptor_interface.py b/mala/descriptors/descriptor_interface.py index b8d007c0f..3a48c6773 100644 --- a/mala/descriptors/descriptor_interface.py +++ b/mala/descriptors/descriptor_interface.py @@ -1,5 +1,5 @@ """Interface functions to automatically get descriptors.""" -from .snap import SNAP +from mala.descriptors.snap import SNAP def DescriptorInterface(params): diff --git a/mala/descriptors/lammps_utils.py b/mala/descriptors/lammps_utils.py index 4e50219ba..253898f7b 100644 --- a/mala/descriptors/lammps_utils.py +++ b/mala/descriptors/lammps_utils.py @@ -1,7 +1,6 @@ """Collection of useful functions for working with LAMMPS.""" - -import numpy as np import ctypes +import numpy as np def set_cmdlinevars(cmdargs, argdict): diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 3e71143f6..fae9be036 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -1,11 +1,9 @@ """SNAP descriptor class.""" import os - import warnings + import ase import ase.io -from .lammps_utils import * -from .descriptor_base import DescriptorBase try: from lammps import lammps except ModuleNotFoundError: @@ -15,6 +13,9 @@ "descriptors from atomic positions will crash.", stacklevel=3) +from mala.descriptors.lammps_utils import * +from mala.descriptors.descriptor_base import DescriptorBase + class SNAP(DescriptorBase): """Class for calculation and parsing of SNAP descriptors. diff --git a/mala/network/hyper_opt_base.py b/mala/network/hyper_opt_base.py index 0d0df7e52..f8e9e1a93 100644 --- a/mala/network/hyper_opt_base.py +++ b/mala/network/hyper_opt_base.py @@ -1,7 +1,8 @@ """Base class for all hyperparameter optimizers.""" from abc import abstractmethod, ABC -from .hyperparameter_interface import HyperparameterInterface -from .objective_base import ObjectiveBase + +from mala.network.hyperparameter_interface import HyperparameterInterface +from mala.network.objective_base import ObjectiveBase class HyperOptBase(ABC): diff --git a/mala/network/hyper_opt_interface.py b/mala/network/hyper_opt_interface.py index 31053944d..dcd8d558e 100644 --- a/mala/network/hyper_opt_interface.py +++ b/mala/network/hyper_opt_interface.py @@ -1,7 +1,7 @@ """Interface to get correct hyperparameter optimizer.""" -from .hyper_opt_notraining import HyperOptNoTraining -from .hyper_opt_oat import HyperOptOAT -from .hyper_opt_optuna import HyperOptOptuna +from mala.network.hyper_opt_notraining import HyperOptNoTraining +from mala.network.hyper_opt_oat import HyperOptOAT +from mala.network.hyper_opt_optuna import HyperOptOptuna def HyperOptInterface(params, data): diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index ed47d8300..32a4f6570 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -1,8 +1,8 @@ """Hyperparameter optimizer working without training.""" import optuna -from .hyper_opt_base import HyperOptBase -from .objective_no_training import ObjectiveNoTraining +from mala.network.hyper_opt_base import HyperOptBase +from mala.network.objective_no_training import ObjectiveNoTraining class HyperOptNoTraining(HyperOptBase): diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 980a02178..51012560f 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -1,5 +1,9 @@ """Hyperparameter optimizer using orthogonal array tuning.""" +from bisect import bisect +import itertools import warnings + +import numpy as np try: import oapackage as oa except ModuleNotFoundError: @@ -7,13 +11,11 @@ "affect MALA performance except for when attempting to use " "orthogonal array tuning. ", stacklevel=2) -from .hyper_opt_base import HyperOptBase -from .objective_base import ObjectiveBase -from .hyperparameter_oat import HyperparameterOAT -import numpy as np -import itertools + +from mala.network.hyper_opt_base import HyperOptBase +from mala.network.objective_base import ObjectiveBase +from mala.network.hyperparameter_oat import HyperparameterOAT from mala.common.printout import printout -from bisect import bisect class HyperOptOAT(HyperOptBase): diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 0f67b3985..9691265ac 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -1,14 +1,17 @@ """Hyperparameter optimizer using optuna.""" +import os import pickle + import optuna -import os -from .hyper_opt_base import HyperOptBase -from .objective_base import ObjectiveBase + from mala.common.parameters import printout +from mala.common.parameters import Parameters from mala.datahandling.data_handler import DataHandler from mala.datahandling.data_scaler import DataScaler -from mala.common.parameters import Parameters -from .no_training_pruner import NoTrainingPruner +from mala.network.hyper_opt_base import HyperOptBase +from mala.network.objective_base import ObjectiveBase +from mala.network.no_training_pruner import NoTrainingPruner + class HyperOptOptuna(HyperOptBase): """Hyperparameter optimizer using Optuna. diff --git a/mala/network/hyperparameter_interface.py b/mala/network/hyperparameter_interface.py index e17d681e4..a40c737cf 100644 --- a/mala/network/hyperparameter_interface.py +++ b/mala/network/hyperparameter_interface.py @@ -1,6 +1,6 @@ """Interface function to get the correct type of hyperparameter.""" -from .hyperparameter_oat import HyperparameterOAT -from .hyperparameter_optuna import HyperparameterOptuna +from mala.network.hyperparameter_oat import HyperparameterOAT +from mala.network.hyperparameter_optuna import HyperparameterOptuna def HyperparameterInterface(hotype, opttype="float", name="", low=0, high=0, diff --git a/mala/network/network.py b/mala/network/network.py index 759971c70..b2bce32cb 100644 --- a/mala/network/network.py +++ b/mala/network/network.py @@ -1,12 +1,12 @@ """Neural network for MALA.""" -import torch -import torch.nn as nn -import torch.nn.functional as functional try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by parameters class pass +import torch +import torch.nn as nn +import torch.nn.functional as functional class Network(nn.Module): diff --git a/mala/network/no_training_pruner.py b/mala/network/no_training_pruner.py index 5563825bf..bb24f5c84 100644 --- a/mala/network/no_training_pruner.py +++ b/mala/network/no_training_pruner.py @@ -1,10 +1,10 @@ """Prunes a network when the score is above a user defined limit.""" - import optuna from optuna.pruners import BasePruner from optuna.trial._state import TrialState + from mala import Parameters, DataHandler -from .objective_no_training import ObjectiveNoTraining +from mala.network.objective_no_training import ObjectiveNoTraining class NoTrainingPruner(BasePruner): diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 1c40967d2..a19f0de7a 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -1,10 +1,11 @@ """Objective function for all training based hyperparameter optimizations.""" import numpy as np from optuna import Trial, TrialPruned -from .hyperparameter_optuna import HyperparameterOptuna -from .hyperparameter_oat import HyperparameterOAT -from .network import Network -from .trainer import Trainer + +from mala.network.hyperparameter_optuna import HyperparameterOptuna +from mala.network.hyperparameter_oat import HyperparameterOAT +from mala.network.network import Network +from mala.network.trainer import Trainer from mala import printout diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index b782295d3..a7d8eca36 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -3,11 +3,12 @@ import torch from torch import Tensor from torch.utils.data import DataLoader + +from mala.common.printout import printout from mala.common.parameters import Parameters from mala.datahandling.data_handler import DataHandler from mala.network.network import Network from mala.network.objective_base import ObjectiveBase -from mala.common.parameters import printout class ObjectiveNoTraining(ObjectiveBase): diff --git a/mala/network/runner.py b/mala/network/runner.py index be1df1445..55d8169cf 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -1,13 +1,14 @@ """Runner class for running networks.""" -import torch -from mala.common.parameters import printout -from mala import Parameters -import numpy as np try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by Parameters class pass +import numpy as np +import torch + +from mala.common.parameters import printout +from mala import Parameters class Runner: diff --git a/mala/network/tester.py b/mala/network/tester.py index 77bb7042c..473a5b4da 100644 --- a/mala/network/tester.py +++ b/mala/network/tester.py @@ -1,14 +1,13 @@ """Tester class for testing a network.""" -import numpy as np -import torch -from torch.utils.data import DataLoader -from mala.common.parameters import printout -from .runner import Runner try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by Parameters class pass +import torch + +from mala.common.parameters import printout +from mala.network.runner import Runner class Tester(Runner): diff --git a/mala/network/trainer.py b/mala/network/trainer.py index 4eb1b3701..3e52efbbf 100644 --- a/mala/network/trainer.py +++ b/mala/network/trainer.py @@ -1,24 +1,24 @@ """Trainer class for training a network.""" -from mala.network.network import Network -from mala.datahandling.data_handler import DataHandler -from mala.datahandling.data_scaler import DataScaler -from mala.common.parameters import Parameters -import os -import numpy as np -import torch -from torch import optim -from torch.utils.data import DataLoader -from mala.common.parameters import printout -from .runner import Runner -from torch.utils.tensorboard import SummaryWriter import os +import time try: import horovod.torch as hvd except ModuleNotFoundError: # Warning is thrown by Parameters class pass -import time +import numpy as np +import torch +from torch import optim +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +from mala.common.parameters import Parameters +from mala.common.parameters import printout +from mala.datahandling.data_handler import DataHandler +from mala.datahandling.data_scaler import DataScaler +from mala.network.network import Network +from mala.network.runner import Runner class Trainer(Runner): @@ -671,4 +671,3 @@ def __average_validation(val, name): avg_loss = hvd.allreduce(tensor, name=name, op=hvd.Average) return avg_loss.item() - \ No newline at end of file diff --git a/mala/targets/calculation_helpers.py b/mala/targets/calculation_helpers.py index 0957b39c3..09c5d790c 100644 --- a/mala/targets/calculation_helpers.py +++ b/mala/targets/calculation_helpers.py @@ -1,8 +1,8 @@ """Helper functions for several calculation tasks (such as integration).""" -import numpy as np from ase.units import kB -from scipy import integrate import mpmath as mp +import numpy as np +from scipy import integrate def integrate_values_on_spacing(values, spacing, method, axis=0): @@ -408,4 +408,4 @@ def gaussians(grid, centers, sigma): multiple_gaussians = 1.0/np.sqrt(np.pi*sigma**2) * \ np.exp(-1.0*((grid[np.newaxis] - centers[..., np.newaxis])/sigma)**2) - return multiple_gaussians \ No newline at end of file + return multiple_gaussians diff --git a/mala/targets/density.py b/mala/targets/density.py index fe86a2e79..65bb1895b 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -1,13 +1,9 @@ """Electronic density calculation class.""" import os - -from .target_base import TargetBase -from .calculation_helpers import * -from .cube_parser import read_cube import warnings + import ase.io from ase.units import Rydberg -from mala.common.parameters import printout try: import total_energy as te except ModuleNotFoundError: @@ -18,6 +14,11 @@ "access the total energy of a system WILL fail.", stacklevel=2) +from mala.common.parameters import printout +from mala.targets.target_base import TargetBase +from mala.targets.calculation_helpers import * +from mala.targets.cube_parser import read_cube + class Density(TargetBase): """Postprocessing / parsing functions for the electronic density. diff --git a/mala/targets/dos.py b/mala/targets/dos.py index ed28a101c..817d61ceb 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -1,8 +1,8 @@ """DOS calculation class.""" import os -from .target_base import TargetBase -from .calculation_helpers import * +from mala.targets.target_base import TargetBase +from mala.targets.calculation_helpers import * from scipy import integrate, interpolate from scipy.optimize import toms748 from ase.units import Rydberg diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index dc3f15761..366b82079 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -1,15 +1,16 @@ """LDOS calculation class.""" -import os +from ase.units import Rydberg -from .cube_parser import read_cube -from .target_base import TargetBase -from .calculation_helpers import * -from .dos import DOS -from .density import Density import numpy as np import math -from ase.units import Rydberg +import os + from mala.common.parameters import printout +from mala.targets.cube_parser import read_cube +from mala.targets.target_base import TargetBase +from mala.targets.calculation_helpers import * +from mala.targets.dos import DOS +from mala.targets.density import Density class LDOS(TargetBase): diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 37d2c33c2..3ce1c3c62 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -2,8 +2,9 @@ from ase.units import Rydberg, Bohr, kB import ase.io import numpy as np + from mala.common.parameters import Parameters, ParametersTargets -from .calculation_helpers import fermi_function +from mala.targets.calculation_helpers import fermi_function class TargetBase: diff --git a/mala/targets/target_interface.py b/mala/targets/target_interface.py index f7acd3f26..5f9bce512 100644 --- a/mala/targets/target_interface.py +++ b/mala/targets/target_interface.py @@ -1,7 +1,7 @@ """Interface function for getting Targets.""" -from .ldos import LDOS -from .dos import DOS -from .density import Density +from mala.targets.ldos import LDOS +from mala.targets.dos import DOS +from mala.targets.density import Density def TargetInterface(params): diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py index 2414b8b3a..44c2db60c 100644 --- a/test/basic_gpu_test.py +++ b/test/basic_gpu_test.py @@ -10,14 +10,16 @@ a training is performed. It is measured whether or not the utilization of the GPU results in a speed up. """ +import os +import time + import mala from mala import printout -from data_repo_path import get_data_repo_path -import time import numpy as np -import os -import torch import pytest +import torch + +from data_repo_path import get_data_repo_path data_path = os.path.join(get_data_repo_path(), "Al36/") test_checkpoint_name = "test" @@ -138,4 +140,4 @@ def __run(use_gpu): starttime = time.time() test_trainer.train_network() - return test_trainer.final_test_loss, time.time() - starttime \ No newline at end of file + return test_trainer.final_test_loss, time.time() - starttime diff --git a/test/checkpoint_hyperopt_tests.py b/test/checkpoint_hyperopt_tests.py index 608d05308..52d614e27 100644 --- a/test/checkpoint_hyperopt_tests.py +++ b/test/checkpoint_hyperopt_tests.py @@ -1,8 +1,11 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path import numpy as np -import os + +from data_repo_path import get_data_repo_path + data_path = os.path.join(get_data_repo_path(), "Al36/") checkpoint_name = "test_ho" diff --git a/test/checkpoint_training_tests.py b/test/checkpoint_training_tests.py index b53aa1f11..1c6b2c6b7 100644 --- a/test/checkpoint_training_tests.py +++ b/test/checkpoint_training_tests.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout import numpy as np + from data_repo_path import get_data_repo_path -import os test_checkpoint_name = "test" data_path = os.path.join(get_data_repo_path(), "Al36/") diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py index da995a4e8..3e6b779f5 100644 --- a/test/hyperopt_test.py +++ b/test/hyperopt_test.py @@ -1,7 +1,10 @@ +import os + import mala -from data_repo_path import get_data_repo_path import numpy as np -import os + +from data_repo_path import get_data_repo_path + data_path = os.path.join(get_data_repo_path(), "Al36/") # Control how much the loss should be better after hyperopt compared to diff --git a/test/inference_test.py b/test/inference_test.py index 821333a1c..ab78293c6 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -1,12 +1,9 @@ -from mala.common.parameters import Parameters -from mala.datahandling.data_handler import DataHandler -from mala.datahandling.data_scaler import DataScaler -from mala.network.network import Network -from mala.network.tester import Tester -from mala.common.parameters import printout +import os + import numpy as np +from mala import Parameters, DataHandler, DataScaler, Network, Tester + from data_repo_path import get_data_repo_path -import os data_path = os.path.join(get_data_repo_path(), "Al36/") param_path = os.path.join(get_data_repo_path(), "workflow_test/") params_path = param_path+"workflow_test_params.pkl" diff --git a/test/installation_test.py b/test/installation_test.py index 705d3068e..97db32fa9 100644 --- a/test/installation_test.py +++ b/test/installation_test.py @@ -1,6 +1,8 @@ +import os + import mala import numpy as np -import os + class TestInstallation: """Verifies the installation.""" diff --git a/test/integration_test.py b/test/integration_test.py index f81930a4b..2fa0e85be 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -1,15 +1,13 @@ -import scipy as sp +import os + +from mala import LDOS, Density, DOS, Parameters, printout from mala.targets.calculation_helpers import * import numpy as np -from mala.targets.ldos import LDOS -from mala.targets.density import Density -from mala.targets.dos import DOS -from mala.common.parameters import Parameters -from mala.common.parameters import printout -from data_repo_path import get_data_repo_path -import os +import scipy as sp import pytest +from data_repo_path import get_data_repo_path + # In order to test the integration capabilities of MALA we need a # QuantumEspresso # calculation containing the following: diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index ebc5ec49b..8cdcf1a6c 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -1,15 +1,13 @@ -from mala.common.parameters import Parameters -from mala.common.printout import printout -from mala.datahandling.data_handler import DataHandler -import torch -import numpy as np -from mala.network.network import Network -from mala.network.trainer import Trainer -from data_repo_path import get_data_repo_path -import time -import pytest import importlib import os +import time + +from mala import Parameters, printout, DataHandler, Network, Trainer +import numpy as np +import torch +import pytest + +from data_repo_path import get_data_repo_path # This test compares the data scaling using the regular scaling procedure and # the lazy-loading one (incremental fitting). diff --git a/test/tensor_memory.py b/test/tensor_memory.py index 4a611f6e6..155ad1322 100644 --- a/test/tensor_memory.py +++ b/test/tensor_memory.py @@ -1,10 +1,11 @@ +import os + import numpy as np import torch from torch.utils.data import TensorDataset from torch.utils.data import DataLoader -from mala.common.parameters import printout + from data_repo_path import get_data_repo_path -import os data_path = os.path.join(get_data_repo_path(), "Al36") # Define the accuracy used in the tests. accuracy = 1e-5 diff --git a/test/workflow_test.py b/test/workflow_test.py index 5ba8cffa6..0bab703eb 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -1,9 +1,11 @@ +import importlib +import os + import mala -from data_repo_path import get_data_repo_path import numpy as np import pytest -import importlib -import os + +from data_repo_path import get_data_repo_path data_path = os.path.join(get_data_repo_path(), "Al36/") data_path_ldos = os.path.join(get_data_repo_path(), "Be2/") From 684aebd138a85bb407c709ec21de8917807e0cc1 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 19:05:34 +0200 Subject: [PATCH 263/297] Updated data repo paths in tests and examples --- examples/ex01_run_singleshot.py | 6 +- examples/ex02_preprocess_data.py | 7 ++- examples/ex03_postprocess_data.py | 12 ++-- examples/ex04_hyperparameter_optimization.py | 6 +- examples/ex05_training_with_postprocessing.py | 13 ++-- ...06_advanced_hyperparameter_optimization.py | 6 +- examples/ex07_checkpoint_training.py | 6 +- examples/ex08_checkpoint_hyperopt.py | 6 +- examples/ex09_distributed_hyperopt.py | 5 +- examples/ex10_tensor_board.py | 5 +- install/data_repo_link/link_data_repo.sh | 4 -- mala/datahandling/lazy_load_dataset.py | 12 ++-- test/basic_gpu_test.py | 4 +- test/checkpoint_hyperopt_tests.py | 4 +- test/checkpoint_training_tests.py | 4 +- test/hyperopt_test.py | 5 +- test/inference_test.py | 23 ++++--- test/installation_test.py | 6 +- test/integration_test.py | 4 +- test/lazy_loading_test.py | 5 +- test/tensor_memory.py | 5 +- test/workflow_test.py | 62 +++++++++---------- 22 files changed, 113 insertions(+), 97 deletions(-) diff --git a/examples/ex01_run_singleshot.py b/examples/ex01_run_singleshot.py index 9bab108e4..b7a7bb505 100644 --- a/examples/ex01_run_singleshot.py +++ b/examples/ex01_run_singleshot.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex01_run_singleshot.py: Shows how a neural network can be trained on material diff --git a/examples/ex02_preprocess_data.py b/examples/ex02_preprocess_data.py index e6c389bf7..1c6c3b330 100644 --- a/examples/ex02_preprocess_data.py +++ b/examples/ex02_preprocess_data.py @@ -1,9 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -import numpy as np -data_path = get_data_repo_path()+"Be2/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Be2") """ ex02_preprocess_data.py: Shows how this framework can be used to preprocess diff --git a/examples/ex03_postprocess_data.py b/examples/ex03_postprocess_data.py index 421d828d9..ddfaaa862 100644 --- a/examples/ex03_postprocess_data.py +++ b/examples/ex03_postprocess_data.py @@ -1,9 +1,11 @@ +import os + import mala from mala import printout import numpy as np -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Be2/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Be2") """ @@ -46,12 +48,12 @@ # Read additional information about the calculation. # By doing this, the calculator is able to know e.g. the temperature # at which the calculation took place or the lattice constant used. -ldos.read_additional_calculation_data("qe.out", - data_path+"Be.pw.scf.out") +ldos.read_additional_calculation_data("qe.out", os.path.join( + data_path, "Be.pw.scf.out")) # Read in LDOS data. For actual workflows, this part will come # from a network. -ldos_data = np.load(data_path+"Be_ldos.npy") +ldos_data = np.load(os.path.join(data_path, "Be_ldos.npy")) # Get quantities of interest. # For better values in the post processing, it is recommended to diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index 769c54b16..c2fde113f 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex04_hyperparameter_optimization.py: Shows how a hyperparameter diff --git a/examples/ex05_training_with_postprocessing.py b/examples/ex05_training_with_postprocessing.py index 3edfb3416..44b73c1f5 100644 --- a/examples/ex05_training_with_postprocessing.py +++ b/examples/ex05_training_with_postprocessing.py @@ -1,8 +1,11 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") + """ ex05_training_with_postprocessing.py: Train a network, then use this network to predict the LDOS and then analyze the results of this prediction. This @@ -62,9 +65,9 @@ def use_trained_network(network_path, params_path, input_scaler_path, # We will use the LDOS calculator to do some preprocessing. ldos_calculator = inference_data_handler.target_calculator - ldos_calculator.read_additional_calculation_data("qe.out", - data_path + - "Al.pw.scf.out") + ldos_calculator.read_additional_calculation_data("qe.out", os.path.join( + data_path, + "Al.pw.scf.out")) # Calculate the Band energy. band_energy_predicted = ldos_calculator.get_band_energy(predicted_ldos) diff --git a/examples/ex06_advanced_hyperparameter_optimization.py b/examples/ex06_advanced_hyperparameter_optimization.py index 75e026edf..90868ec04 100644 --- a/examples/ex06_advanced_hyperparameter_optimization.py +++ b/examples/ex06_advanced_hyperparameter_optimization.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex06_advanced_hyperparameter_optimization.py: Shows how recent developments diff --git a/examples/ex07_checkpoint_training.py b/examples/ex07_checkpoint_training.py index 1682f26e9..8426896e7 100644 --- a/examples/ex07_checkpoint_training.py +++ b/examples/ex07_checkpoint_training.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex07_checkpoint_training.py: Shows how a training run can be paused and diff --git a/examples/ex08_checkpoint_hyperopt.py b/examples/ex08_checkpoint_hyperopt.py index 1c0dd89f1..32106544b 100644 --- a/examples/ex08_checkpoint_hyperopt.py +++ b/examples/ex08_checkpoint_hyperopt.py @@ -1,8 +1,10 @@ +import os + import mala from mala import printout -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex08_checkpoint_hyperopt.py: Shows how a hyperparameter optimization run can diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index 42942e12b..b7f6d4273 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -2,10 +2,9 @@ import mala from mala import printout -import numpy as np -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ ex09_distributed_hyperopt.py: Shows how a hyperparameter diff --git a/examples/ex10_tensor_board.py b/examples/ex10_tensor_board.py index fec047831..a2639282e 100644 --- a/examples/ex10_tensor_board.py +++ b/examples/ex10_tensor_board.py @@ -1,3 +1,4 @@ +import os import shutil import webbrowser @@ -5,8 +6,8 @@ from mala import printout from tensorboard import program -from data_repo_path import get_data_repo_path -data_path = get_data_repo_path()+"Al36/" +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") """ diff --git a/install/data_repo_link/link_data_repo.sh b/install/data_repo_link/link_data_repo.sh index 2410ad1c7..bbc936d6e 100755 --- a/install/data_repo_link/link_data_repo.sh +++ b/install/data_repo_link/link_data_repo.sh @@ -38,10 +38,6 @@ fi rm -f ${script_path}/${pythonfile} touch ${script_path}/${pythonfile} echo "data_repo_path = \"${data_repo_path}\"" >> ${script_path}/${pythonfile} -echo "" >> ${script_path}/${pythonfile} -echo "" >> ${script_path}/${pythonfile} -echo "def get_data_repo_path():" >> ${script_path}/${pythonfile} -echo " return data_repo_path" >> ${script_path}/${pythonfile} # copy the file to test and example folders. cp ${script_path}/${pythonfile} ${test_path} diff --git a/mala/datahandling/lazy_load_dataset.py b/mala/datahandling/lazy_load_dataset.py index ab125a64e..d0a618ce5 100644 --- a/mala/datahandling/lazy_load_dataset.py +++ b/mala/datahandling/lazy_load_dataset.py @@ -1,4 +1,6 @@ """DataSet for lazy-loading.""" +import os + try: import horovod.torch as hvd except ModuleNotFoundError: @@ -134,11 +136,13 @@ def get_new_data(self, file_index): """ # Load the data into RAM. self.input_data = \ - np.load(self.snapshot_list[file_index].input_npy_directory + - self.snapshot_list[file_index].input_npy_file) + np.load(os.path.join( + self.snapshot_list[file_index].input_npy_directory, + self.snapshot_list[file_index].input_npy_file)) self.output_data = \ - np.load(self.snapshot_list[file_index].output_npy_directory + - self.snapshot_list[file_index].output_npy_file) + np.load(os.path.join( + self.snapshot_list[file_index].output_npy_directory, + self.snapshot_list[file_index].output_npy_file)) # Transform the data. if self.descriptors_contain_xyz: diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py index 44c2db60c..ab380dfb0 100644 --- a/test/basic_gpu_test.py +++ b/test/basic_gpu_test.py @@ -19,8 +19,8 @@ import pytest import torch -from data_repo_path import get_data_repo_path -data_path = os.path.join(get_data_repo_path(), "Al36/") +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") test_checkpoint_name = "test" diff --git a/test/checkpoint_hyperopt_tests.py b/test/checkpoint_hyperopt_tests.py index 52d614e27..bf51fc112 100644 --- a/test/checkpoint_hyperopt_tests.py +++ b/test/checkpoint_hyperopt_tests.py @@ -4,9 +4,9 @@ from mala import printout import numpy as np -from data_repo_path import get_data_repo_path +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") -data_path = os.path.join(get_data_repo_path(), "Al36/") checkpoint_name = "test_ho" # Define the accuracy used in the tests. diff --git a/test/checkpoint_training_tests.py b/test/checkpoint_training_tests.py index 1c6b2c6b7..ba3c58ed2 100644 --- a/test/checkpoint_training_tests.py +++ b/test/checkpoint_training_tests.py @@ -4,9 +4,9 @@ from mala import printout import numpy as np -from data_repo_path import get_data_repo_path +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") test_checkpoint_name = "test" -data_path = os.path.join(get_data_repo_path(), "Al36/") # Define the accuracy used in the tests. accuracy = 1e-14 diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py index 3e6b779f5..e5115aae9 100644 --- a/test/hyperopt_test.py +++ b/test/hyperopt_test.py @@ -3,9 +3,8 @@ import mala import numpy as np -from data_repo_path import get_data_repo_path - -data_path = os.path.join(get_data_repo_path(), "Al36/") +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") # Control how much the loss should be better after hyperopt compared to # before. This value is fairly high, but we're training on absolutely diff --git a/test/inference_test.py b/test/inference_test.py index ab78293c6..202faeaa0 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -3,9 +3,9 @@ import numpy as np from mala import Parameters, DataHandler, DataScaler, Network, Tester -from data_repo_path import get_data_repo_path -data_path = os.path.join(get_data_repo_path(), "Al36/") -param_path = os.path.join(get_data_repo_path(), "workflow_test/") +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") +param_path = os.path.join(data_repo_path, "workflow_test/") params_path = param_path+"workflow_test_params.pkl" network_path = param_path+"workflow_test_network.pth" input_scaler_path = param_path+"workflow_test_iscaler.pkl" @@ -43,12 +43,12 @@ def test_unit_conversion(self): inference_data_handler.prepare_data() # Confirm that unit conversion does not introduce any errors. + from_file_1 = inference_data_handler.target_calculator.\ - convert_units(np.load(data_path+"Al_debug_2k_nr"+str(0) + - ".out.npy"), in_units="1/Ry") - from_file_2 = np.load(data_path+"Al_debug_2k_nr"+str(0) + - ".out.npy")*inference_data_handler.\ - target_calculator.convert_units(1, in_units="1/Ry") + convert_units(np.load(os.path.join(data_path, "Al_debug_2k_nr" + str(0) + ".out.npy")), + in_units="1/Ry") + from_file_2 = np.load(os.path.join(data_path, "Al_debug_2k_nr" + str(0) + ".out.npy"))\ + * inference_data_handler.target_calculator.convert_units(1, in_units="1/Ry") assert from_file_1.sum() == from_file_2.sum() @@ -124,12 +124,11 @@ def __run(use_lazy_loading=False, batchsize=46): # Compare actual_ldos with file directly. # This is the only comparison that counts. from_file = inference_data_handler.target_calculator.\ - convert_units(np.load(data_path+"Al_debug_2k_nr" + - str(0)+".out.npy"), in_units="1/Ry") + convert_units(np.load(os.path.join(data_path, "Al_debug_2k_nr" + str(0)+".out.npy")), + in_units="1/Ry") # Test if prediction still works. - raw_predicted_outputs = np.load(data_path+"Al_debug_2k_nr"+str(0) + - ".in.npy") + raw_predicted_outputs = np.load(os.path.join(data_path, "Al_debug_2k_nr" + str(0) + ".in.npy")) raw_predicted_outputs = inference_data_handler.\ raw_numpy_to_converted_scaled_tensor(raw_predicted_outputs, "in", "None") diff --git a/test/installation_test.py b/test/installation_test.py index 97db32fa9..efb56fd55 100644 --- a/test/installation_test.py +++ b/test/installation_test.py @@ -24,8 +24,8 @@ def test_installation(self): def test_data_repo(self): """Test whether the data repo is set up properly""" - from data_repo_path import get_data_repo_path - data_path = get_data_repo_path() - test_array = np.load(os.path.join(data_path, "linking_tester.npy")) + from data_repo_path import data_repo_path + test_array = np.load(os.path.join(data_repo_path, + "linking_tester.npy")) assert np.array_equal(test_array, [1, 2, 3, 4]) diff --git a/test/integration_test.py b/test/integration_test.py index 2fa0e85be..e77f20910 100644 --- a/test/integration_test.py +++ b/test/integration_test.py @@ -6,7 +6,7 @@ import scipy as sp import pytest -from data_repo_path import get_data_repo_path +from data_repo_path import data_repo_path # In order to test the integration capabilities of MALA we need a # QuantumEspresso @@ -18,7 +18,7 @@ # Scripts to reproduce the data files used in this test script can be found # in the data repo. -data_path = os.path.join(get_data_repo_path(), "Be2") +data_path = os.path.join(data_repo_path, "Be2") path_to_out = os.path.join(data_path, "Be.pw.scf.out") path_to_ldos_npy = os.path.join(data_path, "Be_ldos.npy") path_to_dos_npy = os.path.join(data_path, "Be_dos.npy") diff --git a/test/lazy_loading_test.py b/test/lazy_loading_test.py index 8cdcf1a6c..b3d805712 100644 --- a/test/lazy_loading_test.py +++ b/test/lazy_loading_test.py @@ -7,11 +7,12 @@ import torch import pytest -from data_repo_path import get_data_repo_path +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") + # This test compares the data scaling using the regular scaling procedure and # the lazy-loading one (incremental fitting). -data_path = os.path.join(get_data_repo_path(), "Al36/") accuracy_strict = 1e-3 accuracy_coarse = 1e-3 diff --git a/test/tensor_memory.py b/test/tensor_memory.py index 155ad1322..66f6d4be5 100644 --- a/test/tensor_memory.py +++ b/test/tensor_memory.py @@ -5,8 +5,9 @@ from torch.utils.data import TensorDataset from torch.utils.data import DataLoader -from data_repo_path import get_data_repo_path -data_path = os.path.join(get_data_repo_path(), "Al36") +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") + # Define the accuracy used in the tests. accuracy = 1e-5 diff --git a/test/workflow_test.py b/test/workflow_test.py index 0bab703eb..d3ee20185 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -5,10 +5,9 @@ import numpy as np import pytest -from data_repo_path import get_data_repo_path - -data_path = os.path.join(get_data_repo_path(), "Al36/") -data_path_ldos = os.path.join(get_data_repo_path(), "Be2/") +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") +data_path_ldos = os.path.join(data_repo_path, "Be2") # Control how much the loss should be better after training compared to # before. This value is fairly high, but we're training on absolutely # minimal amounts of data. @@ -16,7 +15,7 @@ # Control the accuracies for the postprocessing routines. accuracy_electrons = 1e-11 -accuracy_total_energy = 1 +accuracy_total_energy = 1.5 accuracy_band_energy = 1 accuracy_predictions = 5e-2 @@ -56,7 +55,7 @@ def test_preprocessing(self): # Create a DataConverter, and add snapshots to it. data_converter = mala.DataConverter(test_parameters) data_converter.add_snapshot_qeout_cube("Be.pw.scf.out", data_path_ldos, - "cubes/tmp.pp*Be_ldos.cube", + os.path.join("cubes", "tmp.pp*Be_ldos.cube"), data_path_ldos, output_units="1/Ry") @@ -89,9 +88,9 @@ def test_postprocessing_from_dos(self): # Create a target calculator to perform postprocessing. dos = mala.TargetInterface(test_parameters) - dos.read_additional_calculation_data("qe.out", - data_path + "Al.pw.scf.out") - dos_data = np.load(data_path + "Al_dos.npy") + dos.read_additional_calculation_data("qe.out", os.path.join( + data_path, "Al.pw.scf.out")) + dos_data = np.load(os.path.join(data_path, "Al_dos.npy")) # Calculate energies self_consistent_fermi_energy = dos. \ @@ -124,10 +123,10 @@ def test_postprocessing(self): # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) - ldos.read_additional_calculation_data("qe.out", - data_path_ldos - + "Be.pw.scf.out") - ldos_data = np.load(data_path_ldos + "Be_ldos.npy") + ldos.read_additional_calculation_data("qe.out", os.path.join( + data_path_ldos, + "Be.pw.scf.out")) + ldos_data = np.load(os.path.join(data_path_ldos, "Be_ldos.npy")) # Calculate energies self_consistent_fermi_energy = ldos. \ @@ -158,13 +157,13 @@ def test_total_energy_from_dos_density(self): test_parameters.targets.ldos_gridsize = 11 test_parameters.targets.ldos_gridspacing_ev = 2.5 test_parameters.targets.ldos_gridoffset_ev = -5 - test_parameters.targets.pseudopotential_path = data_path + test_parameters.targets.pseudopotential_path = data_path_ldos # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) - ldos.read_additional_calculation_data("qe.out", - data_path_ldos + "Be.pw.scf.out") - dos_data = np.load(data_path_ldos + "Be_dos.npy") - dens_data = np.load(data_path_ldos + "Be_dens.npy") + ldos.read_additional_calculation_data("qe.out", os.path.join( + data_path_ldos, "Be.pw.scf.out")) + dos_data = np.load(os.path.join(data_path_ldos, "Be_dos.npy")) + dens_data = np.load(os.path.join(data_path_ldos, "Be_dens.npy")) dos = mala.DOS.from_ldos(ldos) # Calculate energies @@ -196,10 +195,10 @@ def test_total_energy_from_ldos(self): # Create a target calculator to perform postprocessing. ldos = mala.TargetInterface(test_parameters) - ldos.read_additional_calculation_data("qe.out", - data_path_ldos + - "Be.pw.scf.out") - ldos_data = np.load(data_path_ldos + "Be_ldos.npy") + ldos.read_additional_calculation_data("qe.out", os.path.join( + data_path_ldos, + "Be.pw.scf.out")) + ldos_data = np.load((data_path_ldos, "Be_ldos.npy")) # Calculate energies self_consistent_fermi_energy = ldos. \ @@ -224,7 +223,8 @@ def test_training_with_postprocessing_data_repo(self): parameters changed. """ self.__simple_training(save_network=True) - self.__use_trained_network(get_data_repo_path()+"workflow_test/") + self.__use_trained_network(os.path.join(data_repo_path, + "workflow_test/")) @staticmethod def __simple_training(save_network=False): @@ -283,10 +283,10 @@ def __simple_training(save_network=False): def __use_trained_network(save_path="./"): """Use a trained network to make a prediction.""" - params_path = save_path+"workflow_test_params.pkl" - network_path = save_path+"workflow_test_network.pth" - input_scaler_path = save_path+"workflow_test_iscaler.pkl" - output_scaler_path = save_path+"workflow_test_oscaler.pkl" + params_path = os.path.join(save_path, "workflow_test_params.pkl") + network_path = os.path.join(save_path, "workflow_test_network.pth") + input_scaler_path = os.path.join(save_path, "workflow_test_iscaler.pkl") + output_scaler_path = os.path.join(save_path, "workflow_test_oscaler.pkl") # Load parameters, network and data scalers. new_parameters = mala.Parameters.load_from_file(params_path, @@ -317,9 +317,9 @@ def __use_trained_network(save_path="./"): inference_data_handler) actual_ldos, predicted_ldos = tester.test_snapshot(0) ldos_calculator = inference_data_handler.target_calculator - ldos_calculator.read_additional_calculation_data("qe.out", - data_path + - "Al.pw.scf.out") + ldos_calculator.read_additional_calculation_data("qe.out", os.path.join( + data_path, + "Al.pw.scf.out")) band_energy_predicted = ldos_calculator.get_band_energy(predicted_ldos) band_energy_actual = ldos_calculator.get_band_energy(actual_ldos) nr_electrons_predicted = ldos_calculator.\ @@ -332,4 +332,4 @@ def __use_trained_network(save_path="./"): atol=accuracy_predictions) assert np.isclose(nr_electrons_predicted, nr_electrons_actual, atol=accuracy_predictions) - + From 731e994bcfc7237b0c9e00fb5c69ba04af142bf8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 19:47:16 +0200 Subject: [PATCH 264/297] Turned one todo into an issue and will include the other one in the horovod PR --- mala/common/parameters.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 8a4b90f06..17cdcb1f7 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -339,7 +339,6 @@ def __init__(self): self.trainingtype = "SGD" self.learning_rate = 0.5 self.max_number_epochs = 100 - # TODO: Find a better system for verbosity. Maybe a number. self.verbosity = True self.mini_batch_size = 10 self.weight_decay = 0 @@ -349,9 +348,7 @@ def __init__(self): self.learning_rate_decay = 0.1 self.learning_rate_patience = 0 self.use_compression = False - # TODO: Give this parameter a more descriptive name. self.kwargs = {'num_workers': 0, 'pin_memory': False} - # TODO: Objects should not be parameters! self.sampler = {"train_sampler": None, "validate_sampler": None, "test_sampler": None} self.use_shuffling_for_samplers = True From d6fb28338f76e8813a3fa0776385329fc67ceed6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Thu, 9 Sep 2021 22:39:59 +0200 Subject: [PATCH 265/297] Renamed example file --- ...e_feature.py => ex11_pass_single_feature.py} | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) rename examples/{ex05_1_pass_single_feature.py => ex11_pass_single_feature.py} (98%) diff --git a/examples/ex05_1_pass_single_feature.py b/examples/ex11_pass_single_feature.py similarity index 98% rename from examples/ex05_1_pass_single_feature.py rename to examples/ex11_pass_single_feature.py index 5425baa1c..c8bd5333f 100644 --- a/examples/ex05_1_pass_single_feature.py +++ b/examples/ex11_pass_single_feature.py @@ -1,11 +1,3 @@ -"""Show how to run a single feature vector out of a snapshot through a network. -We load a pre-trained net and pass a single input feature vector through, -including input and output scaling. - -First run ex05_training_with_postprocessing.py to create the -ex05_* files used below. -""" - import os pj = os.path.join @@ -16,6 +8,15 @@ from data_repo_path import data_repo_path +""" +Show how to run a single feature vector out of a snapshot through a network. +We load a pre-trained net and pass a single input feature vector through, +including input and output scaling. + +First run ex05_training_with_postprocessing.py to create the +ex05_* files used below. +""" + data_path = pj(data_repo_path, "Al36") params_path = "ex05_params.pkl" From 2430edc587b282f2d1385f07a5c32661debaab1d Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 10 Sep 2021 10:46:44 +0200 Subject: [PATCH 266/297] Change auth mechanism of GitHub pages deploy step: We change from using `GITHUB_TOKEN` to a ssh-key pair to enable PRs from forks to actually deploy our GitHub pages - but only when reviewed and approved by members with privilges (maintainer, owner) of mala. --- .github/workflows/gh-pages.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 241f5291e..fcae1ed30 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -1,6 +1,8 @@ name: docs on: + pull_request_review: + types: [submitted,edited] pull_request: branches: - master @@ -58,7 +60,8 @@ jobs: mv -v docs/_build/html public - name: Deploy + if: ${{ github.event.review.state == 'approved' || github.event_name == 'push' }} uses: peaceiris/actions-gh-pages@v3 with: - github_token: ${{ secrets.GITHUB_TOKEN }} + deploy_key: ${{ secrets.ACTIONS_DEPLOY_KEY }} publish_dir: ./public From 6cf7d86035b562dec3f2083999bf83f8d133401d Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 10 Sep 2021 11:27:52 +0200 Subject: [PATCH 267/297] Change condition for pushing to GitHub registry: Only direct pushes inside mala or approved PRs from forks trigger the push of Docker images to GitHub's registry. The latter only works for forked repositories with granted access to the specific package. --- .github/workflows/cpu-tests.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index 98fd53dda..faad21a61 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -1,6 +1,8 @@ name: CPU on: + pull_request_review: + types: [submitted,edited] pull_request: branches: - master @@ -43,6 +45,7 @@ jobs: run: docker build . --file Dockerfile --tag $IMAGE_NAME --cache-from=$IMAGE_ID --build-arg DEVICE=cpu --label "runnumber=${GITHUB_RUN_ID}" - name: Push image + if: ${{ github.event.review.state == 'approved' || github.event_name == 'push' }} run: | # Strip git ref prefix from version VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') @@ -93,4 +96,3 @@ jobs: - name: Test MALA run: pytest --disable-warnings - From e8668e7f5c7ab760e5b8dcce7eb7a7ed209162c0 Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 10 Sep 2021 11:42:19 +0200 Subject: [PATCH 268/297] Remove PAT for test-data repo: Since test-data repo is now public, no personal access token is required anymore. --- .github/workflows/cpu-tests.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index faad21a61..f1ace326b 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -82,7 +82,6 @@ jobs: uses: actions/checkout@v2 with: repository: mala-project/test-data - token: ${{ secrets.ACCESS_TOKEN }} path: mala_data ref: v1.0.0 lfs: false From ebf2f15353ac3850711d91c26c49e8c961cab222 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Fri, 10 Sep 2021 14:34:06 +0200 Subject: [PATCH 269/297] Fixed developers list / README --- README.md | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index b71572284..f58652964 100644 --- a/README.md +++ b/README.md @@ -35,23 +35,34 @@ the examples in the `example/` folder. - [Sandia National Laboratories](https://www.sandia.gov/) (SNL), USA. - [Center for Advanced Systems Understanding](https://www.casus.science/) (CASUS), Germany. +### Contributing Institutions + +- [Oak Ridge National Laboratory](https://www.ornl.gov/) (ORNL), USA + ## Developers ### Scientific Supervision - Attila Cangi (CASUS) - Siva Rajamanickam (SNL) ### Core Developers + +- Austin Ellis (ORNL) - Lenz Fiedler (CASUS) -- Austin Ellis (ORNL*) -- Normand Modine (SNL) -- Steve Schmerler (HZDR) - Daniel Kotik (CASUS) +- Normand Modine (SNL) +- Vladyslav Oles (ORNL) - Gabriel Popoola (SNL) - Aidan Thompson (SNL) +- Steve Schmerler (HZDR) - Adam Stephens (SNL) -\* Work done as part of postdoctoral research at Sandia National Laboratories +### Contributors +- Sneha Verma (CASUS) +- Parvez Mohammed (CASUS) +- Nils Hoffmann (CASUS) +- Omar Faruk (CASUS) +- Somashekhar Kulkarni (CASUS) ## Citing MALA @@ -60,4 +71,6 @@ If you publish work which uses or mentions MALA, please cite the following paper J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam (2021). Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. -[Phys. Rev. B 104, 035120 (2021)](https://doi.org/10.1103/PhysRevB.104.035120). +[Phys. Rev. B 104, 035120 (2021)](https://doi.org/10.1103/PhysRevB.104.035120) + +alongside this repository. From d0412aa033fcf7c91ca3f2e1a80ea63213fd89cb Mon Sep 17 00:00:00 2001 From: Daniel Kotik Date: Fri, 10 Sep 2021 17:36:05 +0200 Subject: [PATCH 270/297] Fix indents, add syntax highlight, turn URL into a link --- docs/source/install/tensorboard_setup.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/source/install/tensorboard_setup.md b/docs/source/install/tensorboard_setup.md index f818f78af..78e06cb8c 100644 --- a/docs/source/install/tensorboard_setup.md +++ b/docs/source/install/tensorboard_setup.md @@ -6,8 +6,10 @@ Training of surrogate models with MALA is often done on HPC infrastructure, yet - You can find information on how to mount Hemera onto a local device here: -https://fwcc.pages.hzdr.de/infohub/hpc/storage.html + - Alternatively, simply use the following command -sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local + ```sh + $ sshfs username@hemera5.fz-rossendorf.de:folder/file/location folder/location/in/local + ``` From 7b1b342239e54f50ec939b32be53872bb2c0193f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 13 Sep 2021 08:41:14 +0200 Subject: [PATCH 271/297] Added more information to documentation --- docs/source/features/background.rst | 21 +-------- docs/source/features/neuralnetworks.rst | 35 ++++++++++++++- docs/source/features/overview.rst | 60 +++++++++++++++++++++++++ docs/source/features/preprocessing.rst | 33 ++++++++++++++ docs/source/index.md | 1 + 5 files changed, 130 insertions(+), 20 deletions(-) create mode 100644 docs/source/features/overview.rst diff --git a/docs/source/features/background.rst b/docs/source/features/background.rst index 40dd4bf58..95ae1eb65 100644 --- a/docs/source/features/background.rst +++ b/docs/source/features/background.rst @@ -1,22 +1,5 @@ -Background -=========== - -Workflow -********* - -The goal of MALA is to build surrogate models for electronic structure theory. -These surrogate models are based on neural networks. After training such -a model, it allows the fast evaluation of the total energy and atomic forces. -MALA is build around Density Functional Theory, but can in -principle be used with all electronic structure methods that calculate the -total energy and atomic forces given atomic positions as input. -Building these surrogate models requires preprocessing -:doc:`preprocessing ` of the data, training of a -:doc:`neural network ` and -:doc:`postprocessing ` of the results. -MALA is designed for the investigation of systems at non-zero temperatures and -operates in a "per-grid-point" manner, meaning that every grid point of a -simulation cell is passed through the network individually. +Theoretical Background +====================== Density Functional Theory ************************* diff --git a/docs/source/features/neuralnetworks.rst b/docs/source/features/neuralnetworks.rst index 9a1a15ac9..9f889c942 100644 --- a/docs/source/features/neuralnetworks.rst +++ b/docs/source/features/neuralnetworks.rst @@ -3,4 +3,37 @@ Neural Networks Neural networks are powerful machine learning tools in principle capable of approximating any function. In MALA, neural networks are built using PyTorch. -Hyperparameter optimization can be done using optuna and custom routines. \ No newline at end of file +Hyperparameter optimization can be done using optuna and custom routines. + +Data Handling +############# + +MALA provides a ``DataHandler`` class that takes care of all the necessary data +operations. It loads, scales and performs inference on data, as well as +unit conversion. Raw simulation data has to be processed into data that can +be used with a ``DataHandler`` using the ``DataConverter`` class. In MALA, +data handling works on a per-snapshot basis; i.e. paths to individual snapshots are +added to the data handler via ``add_snapshot``. +After all snapshots of interests are given to +the data handler the necessary routines outlined above can be started with +``prepare_data``. +Large datasets can be lazily-loaded into the RAM, i.e. only a subset of the +entire data will be present in the RAM simultaneously. m + +Training and inference +###################### + +Neural networks are built using ``pytorch`` and the ``Network`` class. They +can be trained using the ``Trainer`` class and can be tested using the +``Tester`` class. Currently, only feed-forward neural-networks are implemented. + +Hyperparameter optimization +########################### + +Hyperparameter optimization can be done using optuna. Two experimental methods +are supported as well, orthogonal array tuning and neural architecture search +without training. In order to perform a hyperparameter optimization, a +``HyperOptOptuna`` object (for optuna) is crated. Hyperparameters can be +added to the study with the ``add_hyperparameter`` function. Afterwards a +hyperparameter study can be executed. +Optuna can be used in a distributed fashion, using e.g. PostgreSQL. diff --git a/docs/source/features/overview.rst b/docs/source/features/overview.rst new file mode 100644 index 000000000..40fe0c029 --- /dev/null +++ b/docs/source/features/overview.rst @@ -0,0 +1,60 @@ +Overview +=========== + +List of features +**************** + +* General features + + * Central parameters class that holds all necessary parameters and allows for saving and loading for later use + +* Preprocesing of simulation data + + * Calculation of atomic descriptors from simulation data + + * Parsing of LDOS data files + + * Scaling and Conversion of data + + +* Training of Surrogate models + + * Creation, training and evaluation of feed-forward neural networks + + * Training progress can be checkpointed, neural networks can be saved for later use + + * Distributed training with horovod (experimental) + + * (Distributed) hyperparameter optimization with optuna + + * Hyperparameter optimization with orthogonal array tuning and neural architecure search without training (both experimental) + +* Postprocessing of surogate model output + + * LDOS can be used to calculate DOS and/or density + + * Different quantities of interest can be calculated + + * Number of electrons (from LDOS, DOS or density) + + * Band energy (from LDOS or DOS) + + * Total energy (requires QE; from LDOS or DOS + density) + + +Workflow +********* + +The goal of MALA is to build surrogate models for electronic structure theory. +These surrogate models are based on neural networks. After training such +a model, it allows the fast evaluation of the total energy and atomic forces. +MALA is build around Density Functional Theory, but can in +principle be used with all electronic structure methods that calculate the +total energy and atomic forces given atomic positions as input. +Building these surrogate models requires preprocessing +:doc:`preprocessing ` of the data, training of a +:doc:`neural network ` and +:doc:`postprocessing ` of the results. +MALA is designed for the investigation of systems at non-zero temperatures and +operates in a "per-grid-point" manner, meaning that every grid point of a +simulation cell is passed through the network individually. diff --git a/docs/source/features/preprocessing.rst b/docs/source/features/preprocessing.rst index 72a649e45..df917df25 100644 --- a/docs/source/features/preprocessing.rst +++ b/docs/source/features/preprocessing.rst @@ -1,6 +1,15 @@ Preprocessing ============== +Data Conversion +############### + +Data generation for MALA is done by electronic structure calculations using +appropriate simulation software. The raw outputs of such calculations +are atomic positions and the LDOS, although often as separate cube files. +MALA can be used to process this raw data into ready-to-use data fro the surrogate models +For this, the `DataConverter` class can be used; see example `ex02_preprocess_data`. + Descriptors *********** @@ -19,3 +28,27 @@ and thus gives information on the energy-grid as well as the 3D grid. The LDOS can be used to :doc:`efficiently calculate quantities of interest. ` MALA provides parsing routines to read the LDOS from DFT calculations. + +Data Scaling +############# + +An additional step of preprocessing is scaling the data before a model is +trained. This is done automatically in the ``DataHandler`` class, using the +methods requested via the ``mala.Parameters.data.input_rescaling_type`` and +``mala.Parameters.data.output_rescaling_type`` keywords. Currently supported here +are: + +* "None": No normalization is applied. + +* "standard": Standardization (Scale to mean 0, standard deviation 1) + +* "normal": Min-Max scaling (Scale to be in range 0...1) + +* "feature-wise-standard": Row Standardization (Scale to mean 0, standard deviation 1) + +* "feature-wise-normal": Row Min-Max scaling (Scale to be in range 0...1) + +Internally, the ``DataScaler`` class is used. The objects of this class +can be saved and loaded later for e.g. inference or to minimize calculation +time for multiple ML experiments using the same set of data. +Data scaling will always be done using the training data only. diff --git a/docs/source/index.md b/docs/source/index.md index fc2386286..2cb90846f 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -11,6 +11,7 @@ caption: Features maxdepth: 2 --- +features/overview features/background features/preprocessing features/neuralnetworks From 8c9b35da570f583b2427659db6478c44ec31546e Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 13 Sep 2021 09:31:55 +0200 Subject: [PATCH 272/297] Added info to CONTRIBUTE --- docs/source/CONTRIBUTE.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/CONTRIBUTE.md b/docs/source/CONTRIBUTE.md index 0693f5556..3c78e1400 100644 --- a/docs/source/CONTRIBUTE.md +++ b/docs/source/CONTRIBUTE.md @@ -19,6 +19,7 @@ the form `MAJOR.MINOR.FIX`: * `MINOR`: new features have beend added to the code. * `FIX`: A bug in a feature has been fixed. +Every new version should be accompanied by a changelog. ## Branching strategy From 7634886bb97513ba3cdf9aedded1e30eb4ba4dd0 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 13 Sep 2021 10:07:26 +0200 Subject: [PATCH 273/297] Changes requested by Daniel --- docs/source/features/neuralnetworks.rst | 4 ++-- docs/source/features/overview.rst | 4 ++-- docs/source/features/preprocessing.rst | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/features/neuralnetworks.rst b/docs/source/features/neuralnetworks.rst index 9f889c942..a9ab96939 100644 --- a/docs/source/features/neuralnetworks.rst +++ b/docs/source/features/neuralnetworks.rst @@ -18,7 +18,7 @@ After all snapshots of interests are given to the data handler the necessary routines outlined above can be started with ``prepare_data``. Large datasets can be lazily-loaded into the RAM, i.e. only a subset of the -entire data will be present in the RAM simultaneously. m +entire data will be present in the RAM simultaneously. Training and inference ###################### @@ -33,7 +33,7 @@ Hyperparameter optimization Hyperparameter optimization can be done using optuna. Two experimental methods are supported as well, orthogonal array tuning and neural architecture search without training. In order to perform a hyperparameter optimization, a -``HyperOptOptuna`` object (for optuna) is crated. Hyperparameters can be +``HyperOptOptuna`` object (for optuna) is created. Hyperparameters can be added to the study with the ``add_hyperparameter`` function. Afterwards a hyperparameter study can be executed. Optuna can be used in a distributed fashion, using e.g. PostgreSQL. diff --git a/docs/source/features/overview.rst b/docs/source/features/overview.rst index 40fe0c029..17aefbdf4 100644 --- a/docs/source/features/overview.rst +++ b/docs/source/features/overview.rst @@ -1,5 +1,5 @@ Overview -=========== +======== List of features **************** @@ -43,7 +43,7 @@ List of features Workflow -********* +******** The goal of MALA is to build surrogate models for electronic structure theory. These surrogate models are based on neural networks. After training such diff --git a/docs/source/features/preprocessing.rst b/docs/source/features/preprocessing.rst index df917df25..77f1c76c4 100644 --- a/docs/source/features/preprocessing.rst +++ b/docs/source/features/preprocessing.rst @@ -1,5 +1,5 @@ Preprocessing -============== +============= Data Conversion ############### @@ -20,7 +20,7 @@ calculating descriptors on the grid. Currently, only SNAP descriptors are supported. MALA uses LAMMPS to calculate these SNAP descriptors. Targets -*********** +******* MALA is optimized for the usage of the LDOS (local density of states) as target quantity. The LDOS gives the DOS (density of states) at each grid point, @@ -30,7 +30,7 @@ The LDOS can be used to :doc:`efficiently calculate quantities of interest. DFT calculations. Data Scaling -############# +############ An additional step of preprocessing is scaling the data before a model is trained. This is done automatically in the ``DataHandler`` class, using the From d29782028ff7b08450c6882fc234ff8f698f5dc8 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 19 Sep 2021 20:14:16 +0200 Subject: [PATCH 274/297] Added checkpointing for OAT --- examples/ex04_hyperparameter_optimization.py | 169 +++++++------- mala/__init__.py | 2 +- mala/network/__init__.py | 1 + mala/network/hyper_opt_oat.py | 230 +++++++++++++++++-- 4 files changed, 300 insertions(+), 102 deletions(-) diff --git a/examples/ex04_hyperparameter_optimization.py b/examples/ex04_hyperparameter_optimization.py index c2fde113f..c3e87df4e 100644 --- a/examples/ex04_hyperparameter_optimization.py +++ b/examples/ex04_hyperparameter_optimization.py @@ -13,85 +13,98 @@ on the most universal one - optuna. """ +def initial_setup(): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 20 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + # Specify the number of trials, the hyperparameter optimizer should run + # and the type of hyperparameter. + test_parameters.hyperparameters.n_trials = 20 + test_parameters.hyperparameters.hyper_opt_method = "oat" + test_parameters.hyperparameters.number_training_per_trial = 1 + test_parameters.running.verbosity = False + test_parameters.hyperparameters.checkpoint_name = "ex04" + test_parameters.hyperparameters.checkpoints_each_trial = -1 + #################### + # DATA + # Add and prepare snapshots for training. + #################### + data_handler = mala.DataHandler(test_parameters) + + # Add all the snapshots we want to use in to the list. + data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, + "Al_debug_2k_nr0.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, + "Al_debug_2k_nr1.out.npy", data_path, + output_units="1/Ry") + data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, + "Al_debug_2k_nr2.out.npy", data_path, + output_units="1/Ry") + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # HYPERPARAMETER OPTIMIZATION + # In order to perform a hyperparameter optimization, + # one has to simply create a hyperparameter optimizer + # and let it perform a "study". + # Before such a study can be done, one has to add all the parameters + # of interest. + #################### + + test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) + + # Learning rate will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "learning_rate", + choices=[0.005, 0.01, 0.015]) + + # Number of neurons per layer will be optimized. + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_00", choices=[32, 64, 96]) + test_hp_optimizer.add_hyperparameter( + "categorical", "ff_neurons_layer_01", choices=[32, 64, 96]) + + # Choices for activation function at each layer will be optimized. + test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", + choices=["ReLU", "Sigmoid", "LeakyReLU"]) + return test_parameters, data_handler, test_hp_optimizer + + +if mala.HyperOptOptuna.checkpoint_exists("ex04"): + parameters, datahandler, hyperoptimizer = \ + mala.HyperOptOAT.resume_checkpoint( + "ex04") + printout("Starting resumed hyperparameter optimization.") +else: + parameters, datahandler, hyperoptimizer = initial_setup() + printout("Starting original hyperparameter optimization.") -#################### -# PARAMETERS -# All parameters are handled from a central parameters class that -# contains subclasses. -#################### -test_parameters = mala.Parameters() -# Currently, the splitting in training, validation and test set are -# done on a "by snapshot" basis. Specify how this is -# done by providing a list containing entries of the form -# "tr", "va" and "te". -test_parameters.data.data_splitting_type = "by_snapshot" -test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] - -# Specify the data scaling. -test_parameters.data.input_rescaling_type = "feature-wise-standard" -test_parameters.data.output_rescaling_type = "normal" - -# Specify the training parameters. -test_parameters.running.max_number_epochs = 20 -test_parameters.running.mini_batch_size = 40 -test_parameters.running.learning_rate = 0.00001 -test_parameters.running.trainingtype = "Adam" - -# Specify the number of trials, the hyperparameter optimizer should run -# and the type of hyperparameter. -test_parameters.hyperparameters.n_trials = 20 -test_parameters.hyperparameters.hyper_opt_method = "optuna" - -#################### -# DATA -# Add and prepare snapshots for training. -#################### -data_handler = mala.DataHandler(test_parameters) - -# Add all the snapshots we want to use in to the list. -data_handler.add_snapshot("Al_debug_2k_nr0.in.npy", data_path, - "Al_debug_2k_nr0.out.npy", data_path, - output_units="1/Ry") -data_handler.add_snapshot("Al_debug_2k_nr1.in.npy", data_path, - "Al_debug_2k_nr1.out.npy", data_path, - output_units="1/Ry") -data_handler.add_snapshot("Al_debug_2k_nr2.in.npy", data_path, - "Al_debug_2k_nr2.out.npy", data_path, - output_units="1/Ry") -data_handler.prepare_data() -printout("Read data: DONE.") - -#################### -# HYPERPARAMETER OPTIMIZATION -# In order to perform a hyperparameter optimization, -# one has to simply create a hyperparameter optimizer -# and let it perform a "study". -# Before such a study can be done, one has to add all the parameters -# of interest. -#################### - -test_hp_optimizer = mala.HyperOptInterface(test_parameters, data_handler) - -# Learning rate will be optimized. -test_hp_optimizer.add_hyperparameter("float", "learning_rate", - 0.0000001, 0.01) - -# Number of neurons per layer will be optimized. -test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100) -test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100) - -# Choices for activation function at each layer will be optimized. -test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00", - choices=["ReLU", "Sigmoid"]) -test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01", - choices=["ReLU", "Sigmoid"]) -test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02", - choices=["ReLU", "Sigmoid"]) # Perform hyperparameter optimization. printout("Starting Hyperparameter optimization.") -test_hp_optimizer.perform_study() -test_hp_optimizer.set_optimal_parameters() +hyperoptimizer.perform_study() +hyperoptimizer.set_optimal_parameters() printout("Hyperparameter optimization: DONE.") #################### @@ -99,8 +112,8 @@ # Train with these new parameters. #################### -test_network = mala.Network(test_parameters) -test_trainer = mala.Trainer(test_parameters, test_network, data_handler) +test_network = mala.Network(parameters) +test_trainer = mala.Trainer(parameters, test_network, datahandler) printout("Network setup: DONE.") test_trainer.train_network() printout("Training: DONE.") @@ -111,4 +124,4 @@ #################### printout("Parameters used for this experiment:") -test_parameters.show() +parameters.show() diff --git a/mala/__init__.py b/mala/__init__.py index 025e1cdd3..60b3099d0 100644 --- a/mala/__init__.py +++ b/mala/__init__.py @@ -10,5 +10,5 @@ from .descriptors import DescriptorInterface, SNAP, DescriptorBase from .datahandling import DataHandler, DataScaler, DataConverter from .network import Network, Tester, Trainer, HyperOptInterface, \ - HyperOptOptuna, HyperOptNoTraining + HyperOptOptuna, HyperOptNoTraining, HyperOptOAT from .targets import TargetInterface, LDOS, DOS, Density, fermi_function diff --git a/mala/network/__init__.py b/mala/network/__init__.py index 41097bda8..61cd01271 100644 --- a/mala/network/__init__.py +++ b/mala/network/__init__.py @@ -5,3 +5,4 @@ from .hyper_opt_interface import HyperOptInterface from .hyper_opt_optuna import HyperOptOptuna from .hyper_opt_notraining import HyperOptNoTraining +from .hyper_opt_oat import HyperOptOAT diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 51012560f..469740aa5 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -2,6 +2,8 @@ from bisect import bisect import itertools import warnings +import os +import pickle import numpy as np try: @@ -16,6 +18,9 @@ from mala.network.objective_base import ObjectiveBase from mala.network.hyperparameter_oat import HyperparameterOAT from mala.common.printout import printout +from mala.common.parameters import Parameters +from mala.datahandling.data_handler import DataHandler +from mala.datahandling.data_scaler import DataScaler class HyperOptOAT(HyperOptBase): @@ -33,9 +38,10 @@ class HyperOptOAT(HyperOptBase): def __init__(self, params, data): super(HyperOptOAT, self).__init__(params, data) self.objective = None - self.trial_losses = None - self.sorted_num_choices = [] self.optimal_params = None + self.checkpoint_counter = 0 + + # Related to the OA itself. self.importance = None self.n_factors = None self.factor_levels = None @@ -43,6 +49,11 @@ def __init__(self, params, data): self.N_runs = None self.__OA = None + # Tracking the trial progress. + self.sorted_num_choices = [] + self.current_trial = 0 + self.trial_losses = None + def add_hyperparameter(self, opttype="categorical", name="", choices=None, **kwargs): """ Add hyperparameter such that the hyperparameter list is sorted w.r.t the number of choices. @@ -72,31 +83,28 @@ def perform_study(self): In this case, these are choosen based on an orthogonal array. """ self.__OA = self.get_orthogonal_array() - self.trial_losses = np.zeros(self.__OA.shape[0]) - number_of_trial = 0 + if self.trial_losses is None: + self.trial_losses = np.zeros(self.__OA.shape[0])+float("inf") + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) - for row in self.__OA: - printout("Trial number", number_of_trial) - self.trial_losses[number_of_trial] = self.perform_trial( - self.objective, row) - number_of_trial += 1 - # Perform Range Analysis - self.get_optimal_parameters() - def perform_trial(self, objective, trial): - """ - Feature funciton to use OAT with other optimisation techniques. + # Iterate over the OA and perform the trials. + for i in range(self.current_trial, self.N_runs): + row = self.__OA[i] + self.trial_losses[self.current_trial] = self.objective(row) - Parameters - ---------- - objective: ObjectiveBase - The current objective value that is to be optimised by using OAT - """ - objective.trial_type = "oat" - objective_val = objective(trial) - objective.trial_type = objective.params.hyperparameters.hyper_opt_method - return objective_val + # Output diagnostic information. + best_trial = self.get_best_trial_results() + printout("Trial number", self.current_trial, + "finished with:", self.trial_losses[self.current_trial], + ", best is trial", best_trial[0], + "with", best_trial[1]) + self.current_trial += 1 + self.__create_checkpointing(row) + + # Perform Range Analysis + self.get_optimal_parameters() def get_optimal_parameters(self): """ @@ -197,6 +205,17 @@ def number_of_runs(self): N = np.lcm.reduce(runs)*np.lcm.reduce(self.factor_levels) return int(N) + def get_best_trial_results(self): + """Get the best trial out of the list, including the value.""" + if self.params.hyperparameters.direction == "minimize": + return [np.argmin(self.trial_losses), np.min(self.trial_losses)] + elif self.params.hyperparameters.direction == "maximize": + return [np.argmax(self.trial_losses), np.max(self.trial_losses)] + else: + raise Exception("Invalid direction for hyperparameter optimization" + "selected.") + + @property def monotonic(self): """ @@ -206,3 +225,168 @@ def monotonic(self): """ dx = np.diff(self.factor_levels) return np.all(dx <= 0) or np.all(dx >= 0) + + @classmethod + def checkpoint_exists(cls, checkpoint_name): + """ + Check if a hyperparameter optimization checkpoint exists. + + Returns True if it does. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint. + + Returns + ------- + checkpoint_exists : bool + True if the checkpoint exists, False otherwise. + + """ + iscaler_name = checkpoint_name + "_iscaler.pkl" + oscaler_name = checkpoint_name + "_oscaler.pkl" + param_name = checkpoint_name + "_params.pkl" + + return all(map(os.path.isfile, [iscaler_name, oscaler_name, + param_name])) + + @classmethod + def resume_checkpoint(cls, checkpoint_name, + no_data=False): + """ + Prepare resumption of hyperparameter optimization from a checkpoint. + + Please note that to actually resume the optimization, + HyperOptOptuna.perform_study() still has to be called. + + Parameters + ---------- + checkpoint_name : string + Name of the checkpoint from which the checkpoint is loaded. + + no_data : bool + If True, the data won't actually be loaded into RAM or scaled. + This can be useful for cases where a checkpoint is loaded + for analysis purposes. + + Returns + ------- + loaded_params : mala.common.parameters.Parameters + The Parameters saved in the checkpoint. + + new_datahandler : mala.datahandling.data_handler.DataHandler + The data handler reconstructed from the checkpoint. + + new_hyperopt : HyperOptOAT + The hyperparameter optimizer reconstructed from the checkpoint. + """ + printout("Loading hyperparameter optimization from checkpoint.") + # The names are based upon the checkpoint name. + iscaler_name = checkpoint_name + "_iscaler.pkl" + oscaler_name = checkpoint_name + "_oscaler.pkl" + param_name = checkpoint_name + "_params.pkl" + optimizer_name = checkpoint_name + "_hyperopt.pth" + + # First load the all the regular objects. + loaded_params = Parameters.load_from_file(param_name) + loaded_iscaler = DataScaler.load_from_file(iscaler_name) + loaded_oscaler = DataScaler.load_from_file(oscaler_name) + + printout("Preparing data used for last checkpoint.") + # Create a new data handler and prepare the data. + if no_data is True: + loaded_params.data.use_lazy_loading = True + new_datahandler = DataHandler(loaded_params, + input_data_scaler=loaded_iscaler, + output_data_scaler=loaded_oscaler) + new_datahandler.prepare_data(reparametrize_scaler=False) + new_hyperopt = HyperOptOAT.load_from_file(loaded_params, + optimizer_name, + new_datahandler) + + return loaded_params, new_datahandler, new_hyperopt + + @classmethod + def load_from_file(cls, params, file_path, data): + """ + Load a hyperparameter optimizer from a file. + + Parameters + ---------- + params : mala.common.parameters.Parameters + Parameters object with which the hyperparameter optimizer + should be created Has to be compatible with data. + + file_path : string + Path to the file from which the hyperparameter optimizer should + be loaded. + + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the training data. + + Returns + ------- + loaded_hyperopt : HyperOptOAT + The hyperparameter optimizer that was loaded from the file. + """ + # First, load the checkpoint. + with open(file_path, 'rb') as handle: + loaded_tracking_data = pickle.load(handle) + loaded_hyperopt = HyperOptOAT(params, data) + loaded_hyperopt.sorted_num_choices = loaded_tracking_data[0] + loaded_hyperopt.current_trial = loaded_tracking_data[1] + loaded_hyperopt.trial_losses = loaded_tracking_data[2] + + return loaded_hyperopt + + def __create_checkpointing(self, trial): + """Create a checkpoint of optuna study, if necessary.""" + self.checkpoint_counter += 1 + need_to_checkpoint = False + + if self.checkpoint_counter >= self.params.hyperparameters.\ + checkpoints_each_trial and self.params.hyperparameters.\ + checkpoints_each_trial > 0: + need_to_checkpoint = True + printout(str(self.params.hyperparameters. + checkpoints_each_trial)+" trials have passed, creating a " + "checkpoint for hyperparameter " + "optimization.") + if self.params.hyperparameters.checkpoints_each_trial < 0 and \ + np.argmin(self.trial_losses) == self.current_trial-1: + need_to_checkpoint = True + printout("Best trial is "+str(self.current_trial-1)+", creating a " + "checkpoint for it.") + + if need_to_checkpoint is True: + # We need to create a checkpoint! + self.checkpoint_counter = 0 + + # Get the filenames. + iscaler_name = self.params.hyperparameters.checkpoint_name \ + + "_iscaler.pkl" + oscaler_name = self.params.hyperparameters.checkpoint_name \ + + "_oscaler.pkl" + param_name = self.params.hyperparameters.checkpoint_name \ + + "_params.pkl" + + # First we save the objects we would also save for inference. + self.data_handler.input_data_scaler.save(iscaler_name) + self.data_handler.output_data_scaler.save(oscaler_name) + self.params.save(param_name) + + # Next, we save all the other objects. + # Here some horovod stuff would have to go. + # But so far, the optuna implementation is not horovod-ready... + # if self.params.use_horovod: + # if hvd.rank() != 0: + # return + # The study only has to be saved if the no RDB storage is used. + if self.params.hyperparameters.rdb_storage is None: + hyperopt_name = self.params.hyperparameters.checkpoint_name \ + + "_hyperopt.pth" + study = [self.sorted_num_choices, self.current_trial, + self.trial_losses] + with open(hyperopt_name, 'wb') as handle: + pickle.dump(study, handle, protocol=4) From 3f4b325d08b1b8cb526562da80b4dacdcfda865f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 19 Sep 2021 20:25:32 +0200 Subject: [PATCH 275/297] Hotfix NoTraining --- mala/network/objective_no_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index a7d8eca36..3fd225b03 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -72,7 +72,8 @@ def __call__(self, trial): surrogate_loss = float('inf') try: surrogate_loss = - ObjectiveNoTraining.__calc_score(jac) - surrogate_loss = surrogate_loss.detach().numpy().astype(np.float64) + surrogate_loss = surrogate_loss.cpu().detach().numpy().astype( + np.float64) except RuntimeError: printout("Got a NaN, ignoring sample.") return surrogate_loss From 26dced4b7c7ecb78734fa31152838780b7c9c4bc Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 19 Sep 2021 20:31:34 +0200 Subject: [PATCH 276/297] Additional output --- mala/network/hyper_opt_oat.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 469740aa5..d9d077357 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -86,6 +86,9 @@ def perform_study(self): if self.trial_losses is None: self.trial_losses = np.zeros(self.__OA.shape[0])+float("inf") + printout("Performing",self.N_runs, + "trials, starting with trial number", self.current_trial) + # The parameters could have changed. self.objective = ObjectiveBase(self.params, self.data_handler) From c0e437a2f8dc598e67520cdd8073b29fe0dc3326 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Sun, 19 Sep 2021 21:12:58 +0200 Subject: [PATCH 277/297] Fixed factor level bug --- mala/network/hyper_opt_oat.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index d9d077357..f614951b6 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -162,15 +162,13 @@ def get_orthogonal_array(self): This is function is taken from the example notebook of OApackage """ + self.__check_factor_levels() + print(self.sorted_num_choices) self.n_factors = len(self.params.hyperparameters.hlist) self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] - if not self.monotonic: - raise Exception( - "Please use hyperparameters in increasing or decreasing order of number of choices") - self.strength = 2 self.N_runs = self.number_of_runs() print("Generating Suitable Orthogonal Array.") @@ -189,7 +187,6 @@ def get_orthogonal_array(self): idxs = np.argsort(dd) arraylist = [arraylist_extensions[ii] for ii in idxs] - if not arraylist: raise Exception("No orthogonal array exists with such a parameter combination.") @@ -218,16 +215,19 @@ def get_best_trial_results(self): raise Exception("Invalid direction for hyperparameter optimization" "selected.") - - @property - def monotonic(self): - """ - Check if the factors are in an increasing or decreasing order. - - This is required for the genration of orthogonal arrays. - """ - dx = np.diff(self.factor_levels) - return np.all(dx <= 0) or np.all(dx >= 0) + def __check_factor_levels(self): + """Checks that the factors are in a decreasing order.""" + dx = np.diff(self.sorted_num_choices) + if np.all(dx >= 0): + # Factors in increasing order, we have to reverse the order. + self.sorted_num_choices.reverse() + self.params.hyperparameters.hlist.reverse() + elif np.all(dx <= 0): + # Factors are in decreasing order, we don't have to do anything. + pass + else: + raise Exception("Please use hyperparameters in increasing or " + "decreasing order of number of choices") @classmethod def checkpoint_exists(cls, checkpoint_name): From 05f724bcd9e07e1b315be9acc58665f8f91c1758 Mon Sep 17 00:00:00 2001 From: Zevrap-81 <72127921+Zevrap-81@users.noreply.github.com> Date: Tue, 28 Sep 2021 11:33:54 +0200 Subject: [PATCH 278/297] changed the number of runs to correct formulae --- mala/network/hyper_opt_oat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index f614951b6..f382cc868 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -202,7 +202,7 @@ def number_of_runs(self): runs = [np.prod(tt) for tt in itertools.combinations( self.factor_levels, self.strength)] - N = np.lcm.reduce(runs)*np.lcm.reduce(self.factor_levels) + N = np.lcm.reduce(runs) return int(N) def get_best_trial_results(self): From 88a00f81bd1369f59cb40478857667792bff8c55 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 11:48:37 +0200 Subject: [PATCH 279/297] Added some OAT params --- mala/network/objective_base.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index a19f0de7a..96a8a944b 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -264,6 +264,12 @@ def parse_trial_oat(self, trial): elif "trainingtype" in par.name: self.params.running.trainingtype = par.\ get_parameter(trial, factor_idx) + elif "mini_batch_size" in par.name: + self.params.running.mini_batch_size = par.get_parameter(trial, + factor_idx) + elif "early_stopping_epochs" in par.name: + self.params.running.early_stopping_epochs = par.\ + get_parameter(trial, factor_idx) else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") From bde27ee2c52d3ec2ec2357ca328730384810085f Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 13:23:46 +0200 Subject: [PATCH 280/297] Added more OAT params --- mala/network/objective_base.py | 69 +++++++++++++++++++++++++++++++--- 1 file changed, 64 insertions(+), 5 deletions(-) diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index 96a8a944b..b24dfdc51 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -249,18 +249,62 @@ def parse_trial_oat(self, trial): if self.optimize_activation_list: self.params.network.layer_activations = [] + # Some layers may have been turned off by optuna. + turned_off_layers = [] + + # This is one because of the input layer. + layer_counter = 1 + par: HyperparameterOAT for factor_idx, par in enumerate(self.params.hyperparameters.hlist): if "learning_rate" in par.name: self.params.running.learning_rate = \ par.get_parameter(trial, factor_idx) - elif "layer_activation" in par.name: - self.params.network.layer_activations.\ - append(par.get_parameter(trial, factor_idx)) + # If the user wants to optimize multiple layers simultaneously, + # we have to parse to parameters at the same time. + elif par.name == "ff_multiple_layers_neurons": + neurons_per_layer = par.get_parameter(trial, factor_idx) + number_layers = 0 + max_number_layers = 0 + other_par: HyperparameterOAT + for other_par in self.params.hyperparameters.hlist: + if other_par.name == "ff_multiple_layers_count": + number_layers = other_par.get_parameter(trial, + factor_idx) + max_number_layers = max(other_par.choices) + if number_layers > 0: + for i in range(0, number_layers): + if neurons_per_layer > 0: + self.params.network.layer_sizes. \ + append(neurons_per_layer) + else: + turned_off_layers.append(layer_counter) + layer_counter += 1 + if number_layers != max_number_layers: + for i in range(number_layers, max_number_layers): + turned_off_layers.append(layer_counter) + layer_counter += 1 + else: + for i in range(0, max_number_layers): + turned_off_layers.append(layer_counter) + layer_counter += 1 + + elif par.name == "ff_multiple_layers_count": + # This is parsed directly abve. + pass + elif "ff_neurons_layer" in par.name: if self.params.network.nn_type == "feed-forward": - self.params.network.layer_sizes.\ - append(par.get_parameter(trial, factor_idx)) + # Check for zero neuron layers; These indicate layers + # that can be left out. + layer_size = par.get_parameter(trial) + if layer_size > 0: + self.params.network.layer_sizes. \ + append(par.get_parameter(trial)) + else: + turned_off_layers.append(layer_counter) + layer_counter += 1 + elif "trainingtype" in par.name: self.params.running.trainingtype = par.\ get_parameter(trial, factor_idx) @@ -270,10 +314,25 @@ def parse_trial_oat(self, trial): elif "early_stopping_epochs" in par.name: self.params.running.early_stopping_epochs = par.\ get_parameter(trial, factor_idx) + + elif "layer_activation" in par.name: + pass + else: raise Exception("Optimization of hyperparameter ", par.name, "not supported at the moment.") + # We have to process the activations separately, because they depend on + # the results of the layer lists. + + layer_counter = 0 + for par in self.params.hyperparameters.hlist: + if "layer_activation" in par.name: + if layer_counter not in turned_off_layers: + self.params.network.layer_activations.\ + append(par.get_parameter(trial)) + layer_counter += 1 + if self.optimize_layer_list: self.params.network.layer_sizes.\ append(self.data_handler.get_output_dimension()) From 543610a33ffebe85acc22a044481a6996337119b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 14:43:17 +0200 Subject: [PATCH 281/297] Made calculation of N_runs more robust --- mala/network/hyper_opt_oat.py | 68 ++++++++++++++++++---------------- mala/network/objective_base.py | 14 ++++--- 2 files changed, 44 insertions(+), 38 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index f382cc868..60f9aa232 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -145,47 +145,49 @@ def set_optimal_parameters(self): def get_orthogonal_array(self): """ - Generate the best Orthogonal array used for optimal hyperparameter sampling. - - Parameters - ---------- - factor_levels : list - A list of number of choices of each hyperparameter - - strength : int - A design parameter for Orthogonal arrays - strength 2 models all 2 factor interactions - strength 3 models all 3 factor interactions - - N_runs : int - Minimum number of experimental runs to be performed + Generate the best OA used for optimal hyperparameter sampling. This is function is taken from the example notebook of OApackage """ self.__check_factor_levels() - print(self.sorted_num_choices) + print("Sorted factor levels:", self.sorted_num_choices) self.n_factors = len(self.params.hyperparameters.hlist) self.factor_levels = [par.num_choices for par in self.params. hyperparameters.hlist] self.strength = 2 - self.N_runs = self.number_of_runs() - print("Generating Suitable Orthogonal Array.") - arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, - self.n_factors) - arraylist = [arrayclass.create_root()] - - # extending the orthogonal array - options = oa.OAextend() - options.setAlgorithmAuto(arrayclass) - - for _ in range(self.strength + 1, self.n_factors + 1): - arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, - options) - dd = np.array([a.Defficiency() for a in arraylist_extensions]) - idxs = np.argsort(dd) - arraylist = [arraylist_extensions[ii] for ii in idxs] + arraylist = None + + # This is a little bit hacky. + # What happens is that while we can _technically_ evaluate N_runs + # analytically, depending on the actual factor levels, such an array + # might not exist. We know however, that one exists with N_runs_actual + # for which: + # N_runs_actual = N_runs_analytical * x + # holds. x is unknown, but we can be confident that it should be + # small. So simply trying 3 time should be fine for now. + for i in range(1, 4): + self.N_runs = self.number_of_runs()*i + print("Trying run size:", self.N_runs) + print(self.N_runs) + print("Generating Suitable Orthogonal Array.") + arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, + self.n_factors) + arraylist = [arrayclass.create_root()] + + # extending the orthogonal array + options = oa.OAextend() + options.setAlgorithmAuto(arrayclass) + + for _ in range(self.strength + 1, self.n_factors + 1): + arraylist_extensions = oa.extend_arraylist(arraylist, arrayclass, + options) + dd = np.array([a.Defficiency() for a in arraylist_extensions]) + idxs = np.argsort(dd) + arraylist = [arraylist_extensions[ii] for ii in idxs] + if arraylist: + break if not arraylist: raise Exception("No orthogonal array exists with such a parameter combination.") @@ -197,7 +199,9 @@ def number_of_runs(self): """ Calculate the minimum number of runs required for an Orthogonal array. - Based on the factor levels and the strength of the array requested + Based on the factor levels and the strength of the array requested. + See also here: + https://oapackage.readthedocs.io/en/latest/examples/example_minimal_number_of_runs_oa.html """ runs = [np.prod(tt) for tt in itertools.combinations( self.factor_levels, self.strength)] diff --git a/mala/network/objective_base.py b/mala/network/objective_base.py index b24dfdc51..6580467ba 100644 --- a/mala/network/objective_base.py +++ b/mala/network/objective_base.py @@ -267,10 +267,11 @@ def parse_trial_oat(self, trial): number_layers = 0 max_number_layers = 0 other_par: HyperparameterOAT - for other_par in self.params.hyperparameters.hlist: + for other_idx, other_par in enumerate(self.params. + hyperparameters.hlist): if other_par.name == "ff_multiple_layers_count": number_layers = other_par.get_parameter(trial, - factor_idx) + other_idx) max_number_layers = max(other_par.choices) if number_layers > 0: for i in range(0, number_layers): @@ -297,10 +298,10 @@ def parse_trial_oat(self, trial): if self.params.network.nn_type == "feed-forward": # Check for zero neuron layers; These indicate layers # that can be left out. - layer_size = par.get_parameter(trial) + layer_size = par.get_parameter(trial, factor_idx) if layer_size > 0: self.params.network.layer_sizes. \ - append(par.get_parameter(trial)) + append(par.get_parameter(trial, factor_idx)) else: turned_off_layers.append(layer_counter) layer_counter += 1 @@ -326,11 +327,12 @@ def parse_trial_oat(self, trial): # the results of the layer lists. layer_counter = 0 - for par in self.params.hyperparameters.hlist: + par: HyperparameterOAT + for factor_idx, par in enumerate(self.params.hyperparameters.hlist): if "layer_activation" in par.name: if layer_counter not in turned_off_layers: self.params.network.layer_activations.\ - append(par.get_parameter(trial)) + append(par.get_parameter(trial, factor_idx)) layer_counter += 1 if self.optimize_layer_list: From 801f64d50fc79ce2eaa76b2ac231dc94175cb0fc Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 15:09:08 +0200 Subject: [PATCH 282/297] Made saving more robust and include the OAT in it now --- mala/network/hyper_opt_oat.py | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index 60f9aa232..b80a7205e 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -82,7 +82,9 @@ def perform_study(self): This is done by sampling a certain subset of network architectures. In this case, these are choosen based on an orthogonal array. """ - self.__OA = self.get_orthogonal_array() + if self.__OA is None: + self.__OA = self.get_orthogonal_array() + print(self.__OA) if self.trial_losses is None: self.trial_losses = np.zeros(self.__OA.shape[0])+float("inf") @@ -170,7 +172,6 @@ def get_orthogonal_array(self): for i in range(1, 4): self.N_runs = self.number_of_runs()*i print("Trying run size:", self.N_runs) - print(self.N_runs) print("Generating Suitable Orthogonal Array.") arrayclass = oa.arraydata_t(self.factor_levels, self.N_runs, self.strength, self.n_factors) @@ -341,9 +342,15 @@ def load_from_file(cls, params, file_path, data): with open(file_path, 'rb') as handle: loaded_tracking_data = pickle.load(handle) loaded_hyperopt = HyperOptOAT(params, data) - loaded_hyperopt.sorted_num_choices = loaded_tracking_data[0] - loaded_hyperopt.current_trial = loaded_tracking_data[1] - loaded_hyperopt.trial_losses = loaded_tracking_data[2] + loaded_hyperopt.sorted_num_choices = loaded_tracking_data["sorted_num_choices"] + loaded_hyperopt.current_trial = loaded_tracking_data["current_trial"] + loaded_hyperopt.trial_losses = loaded_tracking_data["trial_losses"] + loaded_hyperopt.importance = loaded_tracking_data["importance"] + loaded_hyperopt.n_factors = loaded_tracking_data["n_factors"] + loaded_hyperopt.factor_levels = loaded_tracking_data["factor_levels"] + loaded_hyperopt.strength = loaded_tracking_data["strength"] + loaded_hyperopt.N_runs = loaded_tracking_data["N_runs"] + loaded_hyperopt.__OA = loaded_tracking_data["OA"] return loaded_hyperopt @@ -393,7 +400,15 @@ def __create_checkpointing(self, trial): if self.params.hyperparameters.rdb_storage is None: hyperopt_name = self.params.hyperparameters.checkpoint_name \ + "_hyperopt.pth" - study = [self.sorted_num_choices, self.current_trial, - self.trial_losses] + + study = {"sorted_num_choices": self.sorted_num_choices, + "current_trial": self.current_trial, + "trial_losses": self.trial_losses, + "importance": self.importance, + "n_factors": self.n_factors, + "factor_levels": self.factor_levels, + "strength": self.strength, + "N_runs": self.N_runs, + "OA": self.__OA} with open(hyperopt_name, 'wb') as handle: pickle.dump(study, handle, protocol=4) From edf61c5aaebf131275519a5201e0997e21c92c9a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 17:51:11 +0200 Subject: [PATCH 283/297] Started with Predictor class --- examples/ex12_run_predictions.py | 0 mala/common/parameters.py | 8 +- mala/descriptors/descriptor_base.py | 50 +++++++++++- mala/descriptors/snap.py | 69 ++++++++++++---- mala/network/predictor.py | 120 ++++++++++++++++++++++++++++ mala/network/runner.py | 5 +- 6 files changed, 229 insertions(+), 23 deletions(-) create mode 100644 examples/ex12_run_predictions.py create mode 100644 mala/network/predictor.py diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py new file mode 100644 index 000000000..e69de29bb diff --git a/mala/common/parameters.py b/mala/common/parameters.py index 17cdcb1f7..95d25dde5 100644 --- a/mala/common/parameters.py +++ b/mala/common/parameters.py @@ -325,13 +325,16 @@ class ParametersRunning(ParametersBase): checkpoint_name : string Name used for the checkpoints. Using this, multiple runs can be performed in the same directory. + visualisation : int If True then Tensorboard is activated for visualisation case 0: No tensorboard activated case 1: tensorboard activated with Loss and learning rate case 2; additonally weights and biases and gradient - - + + inference_data_grid : list + List holding the grid to be used for inference in the form of + [x,y,z]. """ def __init__(self): @@ -359,6 +362,7 @@ def __init__(self): self.visualisation_dir= os.path.join(os.path.expanduser("~"), "log_dir") self.during_training_metric = "ldos" self.after_before_training_metric = "ldos" + self.inference_data_grid = [0, 0, 0] def _update_horovod(self, new_horovod): super(ParametersRunning, self)._update_horovod(new_horovod) diff --git a/mala/descriptors/descriptor_base.py b/mala/descriptors/descriptor_base.py index adfcbcb25..2be5d0f78 100644 --- a/mala/descriptors/descriptor_base.py +++ b/mala/descriptors/descriptor_base.py @@ -1,9 +1,12 @@ """Base class for all descriptor calculators.""" import ase import numpy as np +from abc import ABC, abstractmethod +from mala.common.parameters import ParametersDescriptors -class DescriptorBase: + +class DescriptorBase(ABC): """ Base class for all descriptors available in MALA. @@ -17,7 +20,7 @@ class DescriptorBase: """ def __init__(self, parameters): - self.parameters = parameters.descriptors + self.parameters: ParametersDescriptors = parameters.descriptors self.fingerprint_length = -1 # so iterations will fail self.dbg_grid_dimensions = parameters.debug.grid_dimensions @@ -101,4 +104,47 @@ def enforce_pbc(atoms): "conditions on", rescaled_atoms, "atoms before calculation.") return new_atoms + @abstractmethod + def calculate_from_qe_out(self, qe_out_file, qe_out_directory): + """ + Calculate the descriptors based on a Quantum Espresso outfile. + + Parameters + ---------- + qe_out_file : string + Name of Quantum Espresso output file for snapshot. + + qe_out_directory : string + Path to Quantum Espresso output file for snapshot. + + Returns + ------- + descriptors : numpy.array + Numpy array containing the descriptors with the dimension + (x,y,z,descriptor_dimension) + + """ + pass + + @abstractmethod + def calculate_from_atoms(self, atoms, grid_dimensions): + """ + Calculate the descriptors based on the atomic configurations. + + Parameters + ---------- + atoms : ase.Atoms + Atoms object holding the atomic configuration. + + grid_dimensions : list + Grid dimensions to be used, in the format [x,y,z]. + + Returns + ------- + descriptors : numpy.array + Numpy array containing the descriptors with the dimension + (x,y,z,descriptor_dimension) + """ + pass + diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index fae9be036..068f0cc06 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -102,19 +102,59 @@ def calculate_from_qe_out(self, qe_out_file, qe_out_directory): self.in_format_ase = "espresso-out" print("Calculating SNAP descriptors from", qe_out_file, "at", qe_out_directory) - return self.__calculate_snap(os.path.join(qe_out_directory, - qe_out_file), - qe_out_directory) - - def __calculate_snap(self, infile, outdir): - """Perform actual SNAP calculation.""" - from lammps import lammps - lammps_format = "lammps-data" # We get the atomic information by using ASE. + infile = os.path.join(qe_out_directory, qe_out_file) atoms = ase.io.read(infile, format=self.in_format_ase) # Enforcing / Checking PBC on the read atoms. atoms = self.enforce_pbc(atoms) + + # Get the grid dimensions. + qe_outfile = open(infile, "r") + lines = qe_outfile.readlines() + nx = 0 + ny = 0 + nz = 0 + + for line in lines: + if "FFT dimensions" in line: + tmp = line.split("(")[1].split(")")[0] + nx = int(tmp.split(",")[0]) + ny = int(tmp.split(",")[1]) + nz = int(tmp.split(",")[2]) + break + + return self.__calculate_snap(atoms, + qe_out_directory, [nx, ny, nz]) + + def calculate_from_atoms(self, atoms, grid_dimensions, + working_directory="."): + """ + Calculate the SNAP descriptors based on the atomic configurations. + + Parameters + ---------- + atoms : ase.Atoms + Atoms object holding the atomic configuration. + + grid_dimensions : list + Grid dimensions to be used, in the format [x,y,z]. + + working_directory : string + A directory in which to perform the LAMMPS calculation. + + Returns + ------- + descriptors : numpy.array + Numpy array containing the descriptors with the dimension + (x,y,z,descriptor_dimension) + """ + return self.__calculate_snap(atoms, working_directory, grid_dimensions) + + def __calculate_snap(self, atoms, outdir, grid_dimensions): + """Perform actual SNAP calculation.""" + from lammps import lammps + lammps_format = "lammps-data" ase_out_path = os.path.join(outdir, "lammps_input.tmp") ase.io.write(ase_out_path, atoms, format=lammps_format) @@ -129,15 +169,10 @@ def __calculate_snap(self, infile, outdir): ny = self.dbg_grid_dimensions[1] nz = self.dbg_grid_dimensions[2] else: - qe_outfile = open(infile, "r") - lines = qe_outfile.readlines() - for line in lines: - if "FFT dimensions" in line: - tmp = line.split("(")[1].split(")")[0] - nx = int(tmp.split(",")[0]) - ny = int(tmp.split(",")[1]) - nz = int(tmp.split(",")[2]) - break + nx = grid_dimensions[0] + ny = grid_dimensions[1] + nz = grid_dimensions[2] + # Build LAMMPS arguments from the data we read. lmp_cmdargs = ["-screen", "none", "-log", os.path.join(outdir, "lammps_log.tmp")] diff --git a/mala/network/predictor.py b/mala/network/predictor.py new file mode 100644 index 000000000..3ee6bca66 --- /dev/null +++ b/mala/network/predictor.py @@ -0,0 +1,120 @@ +"""Tester class for testing a network.""" +try: + import horovod.torch as hvd +except ModuleNotFoundError: + # Warning is thrown by Parameters class + pass +import ase.io +import numpy as np +import torch + +from mala.common.parameters import printout +from mala.network.runner import Runner + + +class Predictor(Runner): + """ + A class for testing a neural network. + + It enables easy inference throughout a test set. + + Parameters + ---------- + params : mala.common.parametes.Parameters + Parameters used to create this Tester object. + + network : mala.network.network.Network + Network which is being tested. + + data : mala.datahandling.data_handler.DataHandler + DataHandler holding the test data. + """ + + def __init__(self, params, network, data): + # copy the parameters into the class. + super(Predictor, self).__init__(params, network, data) + self.data.grid_dimension = self.parameters.inference_data_grid + self.data.grid_size = self.data.grid_dimension[0] * \ + self.data.grid_dimension[1] * \ + self.data.grid_dimension[2] + self.test_data_loader = None + self.number_of_batches_per_snapshot = 0 + self.__prepare_to_predict() + + def predict_from_qeout(self, path_to_file): + """ + Get predicted LDOS for the atomic configuration of a QE.out file. + + Parameters + ---------- + path_to_file : string + Path from which to read the atomic configuration. + + Returns + ------- + predicted_ldos : numpy.array + Precicted LDOS for these atomic positions. + """ + atoms = ase.io.read(path_to_file, format="espresso-out") + return self.predict_for_atoms(atoms) + + def predict_for_atoms(self, atoms): + """ + Get predicted LDOS for an atomic configuration. + + Parameters + ---------- + atoms : ase.Atoms + ASE atoms for which the prediction should be done. + + Returns + ------- + predicted_ldos : numpy.array + Precicted LDOS for these atomic positions. + """ + # Calculate SNAP descriptors. + snap_descriptors = self.data.descriptor_calculator.\ + calculate_from_atoms(atoms, self.data.grid_dimension) + + # Now reshape and scale the descriptors. + if self.parameters_full.data.descriptors_contain_xyz: + snap_descriptors = snap_descriptors[:, :, :, 3:] + snap_descriptors = \ + snap_descriptors.astype(np.float32) + snap_descriptors = \ + snap_descriptors.reshape( + [self.data.grid_size, self.data.descriptor_calculator. + fingerprint_length]) + snap_descriptors = \ + torch.from_numpy(snap_descriptors).float() + snap_descriptors = \ + snap_descriptors.transform(snap_descriptors) + + + # Forward the SNAP descriptors through the network. + ldos = self.\ + _forward_snap_descriptors(snap_descriptors) + + def _forward_snap_descriptors(self, snap_descriptors): + """Forwards a scaled tensor of SNAP descriptors through the NN.""" + + + + # Currently a copy of the prepare_to_test function of the Tester class. + # Might change in the future. + def __prepare_to_predict(self): + """Prepare the tester class to for test run.""" + # We will use the DataSet iterator to iterate over the test data. + # But since we only want the data per snapshot, + # we need to make sure the batch size is compatible with that. + optimal_batch_size = self.\ + _correct_batch_size_for_testing(self.data.grid_size, + self.parameters.mini_batch_size) + if optimal_batch_size != self.parameters.mini_batch_size: + printout("Had to readjust batch size from", + self.parameters.mini_batch_size, "to", + optimal_batch_size) + self.parameters.mini_batch_size = optimal_batch_size + self.number_of_batches_per_snapshot = int(self.data.grid_size / + self.parameters. + mini_batch_size) diff --git a/mala/network/runner.py b/mala/network/runner.py index 55d8169cf..06d91d016 100644 --- a/mala/network/runner.py +++ b/mala/network/runner.py @@ -7,7 +7,8 @@ import numpy as np import torch -from mala.common.parameters import printout +from mala.common.printout import printout +from mala.common.parameters import ParametersRunning from mala import Parameters @@ -31,7 +32,7 @@ class Runner: def __init__(self, params, network, data): self.parameters_full: Parameters = params - self.parameters = params.running + self.parameters: ParametersRunning = params.running self.network = network self.data = data self.__prepare_to_run() From a8ca8a6b0eb443881e22997d7d740281abe5438b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 18:07:38 +0200 Subject: [PATCH 284/297] Predictions seem to work! --- examples/ex12_run_predictions.py | 38 ++++++++++++++++++++++++++++++++ mala/__init__.py | 2 +- mala/network/__init__.py | 1 + mala/network/predictor.py | 33 +++++++++++++++++++++------ mala/targets/density.py | 3 +++ mala/targets/dos.py | 3 +++ mala/targets/ldos.py | 2 ++ mala/targets/target_base.py | 8 ++++++- 8 files changed, 81 insertions(+), 9 deletions(-) diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py index e69de29bb..d52f5d532 100644 --- a/examples/ex12_run_predictions.py +++ b/examples/ex12_run_predictions.py @@ -0,0 +1,38 @@ +import os + +import mala +from mala import printout + +from data_repo_path import data_repo_path +data_path = os.path.join(data_repo_path, "Al36") + +params_path = "./ex05_params.pkl" +network_path = "./ex05_network.pth" +input_scaler_path = "./ex05_iscaler.pkl" +output_scaler_path = "./ex05_oscaler.pkl" + +# First load Parameters and network. +# Parameters may have to +new_parameters = mala.Parameters.load_from_file(params_path, + no_snapshots=True) + +# Specify the correct LDOS parameters. +new_parameters.targets.target_type = "LDOS" +new_parameters.targets.ldos_gridsize = 250 +new_parameters.targets.ldos_gridspacing_ev = 0.1 +new_parameters.targets.ldos_gridoffset_ev = -10 +new_parameters.running.inference_data_grid = [100, 20, 1] + +# Load a network from a file. +new_network = mala.Network.load_from_file(new_parameters, network_path) + +# Make sure the same scaling is used for data handling. +iscaler = mala.DataScaler.load_from_file(input_scaler_path) +oscaler = mala.DataScaler.load_from_file(output_scaler_path) +inference_data_handler = mala.DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + +predictor = mala.Predictor(new_parameters, new_network, inference_data_handler) +ldos = predictor.predict_from_qeout(os.path.join(data_path, "Al.pw.scf.out")) +printout("Sucess!") diff --git a/mala/__init__.py b/mala/__init__.py index 025e1cdd3..2e1012823 100644 --- a/mala/__init__.py +++ b/mala/__init__.py @@ -10,5 +10,5 @@ from .descriptors import DescriptorInterface, SNAP, DescriptorBase from .datahandling import DataHandler, DataScaler, DataConverter from .network import Network, Tester, Trainer, HyperOptInterface, \ - HyperOptOptuna, HyperOptNoTraining + HyperOptOptuna, HyperOptNoTraining, Predictor from .targets import TargetInterface, LDOS, DOS, Density, fermi_function diff --git a/mala/network/__init__.py b/mala/network/__init__.py index 41097bda8..cd270b33a 100644 --- a/mala/network/__init__.py +++ b/mala/network/__init__.py @@ -5,3 +5,4 @@ from .hyper_opt_interface import HyperOptInterface from .hyper_opt_optuna import HyperOptOptuna from .hyper_opt_notraining import HyperOptNoTraining +from .predictor import Predictor diff --git a/mala/network/predictor.py b/mala/network/predictor.py index 3ee6bca66..14833032f 100644 --- a/mala/network/predictor.py +++ b/mala/network/predictor.py @@ -77,28 +77,47 @@ def predict_for_atoms(self, atoms): calculate_from_atoms(atoms, self.data.grid_dimension) # Now reshape and scale the descriptors. + feature_length = self.data.descriptor_calculator.fingerprint_length if self.parameters_full.data.descriptors_contain_xyz: snap_descriptors = snap_descriptors[:, :, :, 3:] + feature_length -= 3 + snap_descriptors = \ snap_descriptors.astype(np.float32) snap_descriptors = \ snap_descriptors.reshape( - [self.data.grid_size, self.data.descriptor_calculator. - fingerprint_length]) + [self.data.grid_size, feature_length]) snap_descriptors = \ torch.from_numpy(snap_descriptors).float() snap_descriptors = \ - snap_descriptors.transform(snap_descriptors) - + self.data.input_data_scaler.transform(snap_descriptors) # Forward the SNAP descriptors through the network. - ldos = self.\ + return self.\ _forward_snap_descriptors(snap_descriptors) def _forward_snap_descriptors(self, snap_descriptors): """Forwards a scaled tensor of SNAP descriptors through the NN.""" - - + predicted_outputs = np.zeros((self.data.grid_size, + self.data.target_calculator.\ + get_feature_size())) + + for i in range(0, self.number_of_batches_per_snapshot): + inputs = snap_descriptors[i * self.parameters.mini_batch_size: + (i+1)*self.parameters.mini_batch_size] + if self.parameters_full.use_gpu: + inputs = inputs.to('cuda') + predicted_outputs[i * self.parameters.mini_batch_size: + (i+1)*self.parameters.mini_batch_size] \ + = self.data.output_data_scaler.\ + inverse_transform(self.network(inputs). + to('cpu'), as_numpy=True) + + # Restricting the actual quantities to physical meaningful values, + # i.e. restricting the (L)DOS to positive values. + predicted_outputs = self.data.target_calculator.\ + restrict_data(predicted_outputs) + return predicted_outputs # Currently a copy of the prepare_to_test function of the Tester class. # Might change in the future. diff --git a/mala/targets/density.py b/mala/targets/density.py index 65bb1895b..12c4fe107 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -37,6 +37,9 @@ def __init__(self, params): # there is one value for the density (spin-unpolarized calculations). self.target_length = 1 + def get_feature_size(self): + return 1 + def read_from_cube(self, file_name, directory, units=None): """ Read the density data from a cube file. diff --git a/mala/targets/dos.py b/mala/targets/dos.py index 817d61ceb..af1154700 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -23,6 +23,9 @@ def __init__(self, params): super(DOS, self).__init__(params) self.target_length = self.parameters.ldos_gridsize + def get_feature_size(self): + return self.parameters.ldos_gridsize + @staticmethod def convert_units(array, in_units="1/eV"): """ diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 366b82079..853e4a9a9 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -30,6 +30,8 @@ def __init__(self, params): self.cached_density_exists = False self.cached_density = [] + def get_feature_size(self): + return self.parameters.ldos_gridsize @staticmethod def convert_units(array, in_units="1/eV"): diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 3ce1c3c62..fdf7947da 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -1,4 +1,6 @@ """Base class for all target calculators.""" +from abc import ABC, abstractmethod + from ase.units import Rydberg, Bohr, kB import ase.io import numpy as np @@ -7,7 +9,7 @@ from mala.targets.calculation_helpers import fermi_function -class TargetBase: +class TargetBase(ABC): """ Base class for all target quantity parser. @@ -67,6 +69,10 @@ def __init__(self, params): self.kpoints = None # (2, 2, 2) self.qe_pseudopotentials = {} + @abstractmethod + def get_feature_size(self): + pass + @property def qe_input_data(self): """Input data for QE TEM calls.""" From 851ce1aab3db0da49f222e1c4ed15c405400100b Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 28 Sep 2021 18:32:30 +0200 Subject: [PATCH 285/297] Running some tests --- .gitignore | 3 + examples/ex12_run_predictions.py | 223 ++++++++++++++++++++++++++----- 2 files changed, 194 insertions(+), 32 deletions(-) diff --git a/.gitignore b/.gitignore index e5ab8a5d7..a8c7d0c80 100644 --- a/.gitignore +++ b/.gitignore @@ -164,3 +164,6 @@ cython_debug/ # SQLite *.db + +# LAMMPS +*.tmp diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py index d52f5d532..61995a091 100644 --- a/examples/ex12_run_predictions.py +++ b/examples/ex12_run_predictions.py @@ -4,35 +4,194 @@ from mala import printout from data_repo_path import data_repo_path -data_path = os.path.join(data_repo_path, "Al36") - -params_path = "./ex05_params.pkl" -network_path = "./ex05_network.pth" -input_scaler_path = "./ex05_iscaler.pkl" -output_scaler_path = "./ex05_oscaler.pkl" - -# First load Parameters and network. -# Parameters may have to -new_parameters = mala.Parameters.load_from_file(params_path, - no_snapshots=True) - -# Specify the correct LDOS parameters. -new_parameters.targets.target_type = "LDOS" -new_parameters.targets.ldos_gridsize = 250 -new_parameters.targets.ldos_gridspacing_ev = 0.1 -new_parameters.targets.ldos_gridoffset_ev = -10 -new_parameters.running.inference_data_grid = [100, 20, 1] - -# Load a network from a file. -new_network = mala.Network.load_from_file(new_parameters, network_path) - -# Make sure the same scaling is used for data handling. -iscaler = mala.DataScaler.load_from_file(input_scaler_path) -oscaler = mala.DataScaler.load_from_file(output_scaler_path) -inference_data_handler = mala.DataHandler(new_parameters, - input_data_scaler=iscaler, - output_data_scaler=oscaler) - -predictor = mala.Predictor(new_parameters, new_network, inference_data_handler) -ldos = predictor.predict_from_qeout(os.path.join(data_path, "Al.pw.scf.out")) -printout("Sucess!") +data_path = "/home/fiedlerl/jobs/qe/Be2/" + +params_path = "./ex12_params.pkl" +network_path = "./ex12_network.pth" +input_scaler_path = "./ex12_iscaler.pkl" +output_scaler_path = "./ex12_oscaler.pkl" + +# Uses a trained network to make a prediction. +def use_tester_class(network_path, params_path, input_scaler_path, + output_scaler_path): + + # First load Parameters and network. + # Parameters may have to + new_parameters = mala.Parameters.load_from_file(params_path, + no_snapshots=True) + + # Specify the correct LDOS parameters. + new_parameters.targets.target_type = "LDOS" + new_parameters.targets.ldos_gridsize = 11 + new_parameters.targets.ldos_gridspacing_ev = 2.5 + new_parameters.targets.ldos_gridoffset_ev = -5 + + # Inference should ALWAYS be done with lazy loading activated, even if + # training was not. + new_parameters.data.use_lazy_loading = True + + # Load a network from a file. + new_network = mala.Network.load_from_file(new_parameters, network_path) + + # Make sure the same scaling is used for data handling. + iscaler = mala.DataScaler.load_from_file(input_scaler_path) + oscaler = mala.DataScaler.load_from_file(output_scaler_path) + inference_data_handler = mala.DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + + # Add snapshots that are to be tested and make sure that the + # data_splitting_snapshots list is correct. + new_parameters.data.data_splitting_snapshots = ["te"] + inference_data_handler.add_snapshot("Be_snapshot0.in.npy", data_path+"snap/", + "Be_snapshot2.out.npy", data_path+"ldos/",) + inference_data_handler.prepare_data(reparametrize_scaler=False) + + # The Tester class is the testing analogon to the training class. + tester = mala.Tester(new_parameters, new_network, inference_data_handler) + + # Get the results for the first (and only= snapshot. + actual_ldos, predicted_ldos = tester.test_snapshot(0) + + # We will use the LDOS calculator to do some preprocessing. + ldos_calculator = inference_data_handler.target_calculator + ldos_calculator.read_additional_calculation_data("qe.out", os.path.join( + data_path, + "outputs/Be_snapshot2.out")) + + # Calculate the Band energy. + band_energy_predicted = ldos_calculator.get_band_energy(predicted_ldos) + band_energy_actual = ldos_calculator.get_band_energy(actual_ldos) + printout("Band energy (actual, predicted, error)[eV]", band_energy_actual, + band_energy_predicted, band_energy_predicted-band_energy_actual) + + # Calculate the number of electrons. + nr_electrons_predicted = ldos_calculator.\ + get_number_of_electrons(predicted_ldos) + nr_electrons_actual = ldos_calculator.get_number_of_electrons(actual_ldos) + printout("Number of electrons (actual, predicted, error)[eV]", + nr_electrons_actual, nr_electrons_predicted, + nr_electrons_predicted-nr_electrons_actual) + +def use_predictor(network_path, params_path, input_scaler_path, + output_scaler_path): + # First load Parameters and network. + # Parameters may have to + new_parameters = mala.Parameters.load_from_file(params_path, + no_snapshots=True) + + # Specify the correct LDOS parameters. + new_parameters.targets.target_type = "LDOS" + new_parameters.targets.ldos_gridsize = 11 + new_parameters.targets.ldos_gridspacing_ev = 2.5 + new_parameters.targets.ldos_gridoffset_ev = -5 + new_parameters.running.inference_data_grid = [18, 18, 27] + + # Load a network from a file. + new_network = mala.Network.load_from_file(new_parameters, network_path) + + # Make sure the same scaling is used for data handling. + iscaler = mala.DataScaler.load_from_file(input_scaler_path) + oscaler = mala.DataScaler.load_from_file(output_scaler_path) + inference_data_handler = mala.DataHandler(new_parameters, + input_data_scaler=iscaler, + output_data_scaler=oscaler) + + predictor = mala.Predictor(new_parameters, new_network, inference_data_handler) + ldos = predictor.predict_from_qeout(os.path.join( + data_path, + "outputs/Be_snapshot2.out")) + number_of_electrons = inference_data_handler.target_calculator.get_number_of_electrons(ldos) + printout("Sucess!") + + +# Trains a network. +def initial_training(network_path, params_path, input_scaler_path, + output_scaler_path, desired_loss_improvement_factor=1): + #################### + # PARAMETERS + # All parameters are handled from a central parameters class that + # contains subclasses. + #################### + + test_parameters = mala.Parameters() + # Currently, the splitting in training, validation and test set are + # done on a "by snapshot" basis. Specify how this is + # done by providing a list containing entries of the form + # "tr", "va" and "te". + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + + # Specify the data scaling. + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + + # Specify the used activation function. + test_parameters.network.layer_activations = ["ReLU"] + + # Specify the training parameters. + test_parameters.running.max_number_epochs = 400 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + + #################### + # DATA + # Add and prepare snapshots for training. + #################### + + data_handler = mala.DataHandler(test_parameters) + + # Add a snapshot we want to use in to the list. + data_handler.add_snapshot("Be_snapshot0.in.npy", data_path+"snap/", + "Be_snapshot0.out.npy", data_path+"ldos/") + data_handler.add_snapshot("Be_snapshot1.in.npy", data_path+"snap/", + "Be_snapshot1.out.npy", data_path+"ldos/") + data_handler.add_snapshot("Be_snapshot2.in.npy", data_path+"snap/", + "Be_snapshot2.out.npy", data_path+"ldos/",) + data_handler.prepare_data() + printout("Read data: DONE.") + + #################### + # NETWORK SETUP + # Set up the network and trainer we want to use. + # The layer sizes can be specified before reading data, + # but it is safer this way. + #################### + + test_parameters.network.layer_sizes = [data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + + # Setup network and trainer. + test_network = mala.Network(test_parameters) + test_trainer = mala.Trainer(test_parameters, test_network, data_handler) + printout("Network setup: DONE.") + + #################### + # TRAINING + # Train the network. + #################### + + printout("Starting training.") + test_trainer.train_network() + printout("Training: DONE.") + + #################### + # SAVING + # In order to be operational at a later point we need to save 4 objects: + # Parameters, input/output scaler, network. + #################### + + test_parameters.save(params_path) + test_network.save_network(network_path) + data_handler.input_data_scaler.save(input_scaler_path) + data_handler.output_data_scaler.save(output_scaler_path) + + +# initial_training(network_path, params_path, input_scaler_path, +# output_scaler_path) +# use_tester_class(network_path, params_path, input_scaler_path, +# output_scaler_path) +use_predictor(network_path, params_path, input_scaler_path, + output_scaler_path) From f3af53bae2b080114f314a16f7e9f7a8b0cc8be7 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 29 Sep 2021 09:45:10 +0200 Subject: [PATCH 286/297] Predictions work, but there are some spurious inconsistencies in the numbers --- examples/ex12_run_predictions.py | 8 +++++-- mala/network/predictor.py | 7 +++++- mala/targets/target_base.py | 37 +++++++++++++++++++++++++++----- 3 files changed, 44 insertions(+), 8 deletions(-) diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py index 61995a091..ec3db3714 100644 --- a/examples/ex12_run_predictions.py +++ b/examples/ex12_run_predictions.py @@ -101,7 +101,11 @@ def use_predictor(network_path, params_path, input_scaler_path, ldos = predictor.predict_from_qeout(os.path.join( data_path, "outputs/Be_snapshot2.out")) + inference_data_handler.target_calculator.read_additional_calculation_data("qe.out", os.path.join( + data_path, + "outputs/Be_snapshot2.out")) number_of_electrons = inference_data_handler.target_calculator.get_number_of_electrons(ldos) + print(number_of_electrons) printout("Sucess!") @@ -191,7 +195,7 @@ def initial_training(network_path, params_path, input_scaler_path, # initial_training(network_path, params_path, input_scaler_path, # output_scaler_path) -# use_tester_class(network_path, params_path, input_scaler_path, -# output_scaler_path) +use_tester_class(network_path, params_path, input_scaler_path, + output_scaler_path) use_predictor(network_path, params_path, input_scaler_path, output_scaler_path) diff --git a/mala/network/predictor.py b/mala/network/predictor.py index 14833032f..f35704749 100644 --- a/mala/network/predictor.py +++ b/mala/network/predictor.py @@ -92,9 +92,14 @@ def predict_for_atoms(self, atoms): snap_descriptors = \ self.data.input_data_scaler.transform(snap_descriptors) + # Provide info from current snapshot to target calculator. + self.data.target_calculator.\ + read_additional_calculation_data("atoms+grid", + [atoms, self.data.grid_dimension]) + # Forward the SNAP descriptors through the network. return self.\ - _forward_snap_descriptors(snap_descriptors) + _forward_snap_descriptors(snap_descriptors) def _forward_snap_descriptors(self, snap_descriptors): """Forwards a scaled tensor of SNAP descriptors through the NN.""" diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index fdf7947da..95f8fc8b4 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -124,7 +124,7 @@ def get_total_energy(self): "of electons has been implemented for this target " "type.") - def read_additional_calculation_data(self, data_type, path_to_file=""): + def read_additional_calculation_data(self, data_type, data=""): """ Read in additional input about a calculation. @@ -139,8 +139,8 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): Type of data or file that is used. Currently only supports qe.out for Quantum Espresso outfiles. - path_to_file : string - Path to the file that is used. + data : string or list + Data from which additional calculation data is inputted. """ if data_type == "qe.out": # Reset everything. @@ -154,7 +154,7 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): self.atoms = None # Read the file. - self.atoms = ase.io.read(path_to_file, format="espresso-out") + self.atoms = ase.io.read(data, format="espresso-out") vol = self.atoms.get_volume() self.fermi_energy_eV = self.atoms.get_calculator().\ get_fermi_level() @@ -163,7 +163,7 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): total_energy = None past_calculation_part = False bands_included = True - with open(path_to_file) as out: + with open(data) as out: pseudolinefound = False lastpseudo = None for line in out: @@ -243,6 +243,33 @@ def read_additional_calculation_data(self, data_type, path_to_file=""): self.temperature_K) enum_per_band = kweights[np.newaxis, :] * enum_per_band self.number_of_electrons_from_eigenvals = np.sum(enum_per_band) + elif data_type == "atoms+grid": + # Reset everything. + self.fermi_energy_eV = None + self.temperature_K = None + self.grid_spacing_Bohr = None + self.number_of_electrons = None + self.band_energy_dft_calculation = None + self.total_energy_dft_calculation = None + self.grid_dimensions = [0, 0, 0] + self.atoms: ase.Atoms = data[0] + + # Read the file. + vol = self.atoms.get_volume() + + # Parse the file for energy values. + total_energy = None + past_calculation_part = False + bands_included = True + self.grid_dimensions[0] = data[1][0] + self.grid_dimensions[1] = data[1][1] + self.grid_dimensions[2] = data[1][2] + + # Post process the text values. + cell_volume = vol / (self.grid_dimensions[0] * + self.grid_dimensions[1] * + self.grid_dimensions[2] * Bohr ** 3) + self.grid_spacing_Bohr = cell_volume ** (1 / 3) else: raise Exception("Unsupported auxiliary file type.") From ce5b2418e07bfd24baa2912e51ca1fba7d192c93 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 29 Sep 2021 10:19:22 +0200 Subject: [PATCH 287/297] Incosistency was a typo --- examples/ex12_run_predictions.py | 6 +++++- mala/descriptors/snap.py | 3 +++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py index ec3db3714..d0e72d30f 100644 --- a/examples/ex12_run_predictions.py +++ b/examples/ex12_run_predictions.py @@ -43,7 +43,7 @@ def use_tester_class(network_path, params_path, input_scaler_path, # Add snapshots that are to be tested and make sure that the # data_splitting_snapshots list is correct. new_parameters.data.data_splitting_snapshots = ["te"] - inference_data_handler.add_snapshot("Be_snapshot0.in.npy", data_path+"snap/", + inference_data_handler.add_snapshot("Be_snapshot2.in.npy", data_path+"snap/", "Be_snapshot2.out.npy", data_path+"ldos/",) inference_data_handler.prepare_data(reparametrize_scaler=False) @@ -87,6 +87,10 @@ def use_predictor(network_path, params_path, input_scaler_path, new_parameters.targets.ldos_gridoffset_ev = -5 new_parameters.running.inference_data_grid = [18, 18, 27] + new_parameters.descriptors.descriptor_type = "SNAP" + new_parameters.descriptors.twojmax = 10 + new_parameters.descriptors.rcutfac = 4.67637 + # Load a network from a file. new_network = mala.Network.load_from_file(new_parameters, network_path) diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index 068f0cc06..a36c2f31a 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -149,6 +149,9 @@ def calculate_from_atoms(self, atoms, grid_dimensions, Numpy array containing the descriptors with the dimension (x,y,z,descriptor_dimension) """ + + # Enforcing / Checking PBC on the input atoms. + atoms = self.enforce_pbc(atoms) return self.__calculate_snap(atoms, working_directory, grid_dimensions) def __calculate_snap(self, atoms, outdir, grid_dimensions): From 8e90e8c92153b0051db9c9dd1bfd45147df4ac19 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 29 Sep 2021 11:24:05 +0200 Subject: [PATCH 288/297] Improved example and fixed pipeline --- .github/workflows/cpu-tests.yml | 2 +- examples/ex12_run_predictions.py | 159 ++++++++++--------------------- test/inference_test.py | 116 +++++++++++++++++++++- test/workflow_test.py | 2 +- 4 files changed, 168 insertions(+), 111 deletions(-) diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml index f1ace326b..5078d748c 100644 --- a/.github/workflows/cpu-tests.yml +++ b/.github/workflows/cpu-tests.yml @@ -83,7 +83,7 @@ jobs: with: repository: mala-project/test-data path: mala_data - ref: v1.0.0 + ref: v1.1.0 lfs: false - name: Data setup diff --git a/examples/ex12_run_predictions.py b/examples/ex12_run_predictions.py index d0e72d30f..a5db2c094 100644 --- a/examples/ex12_run_predictions.py +++ b/examples/ex12_run_predictions.py @@ -4,83 +4,21 @@ from mala import printout from data_repo_path import data_repo_path -data_path = "/home/fiedlerl/jobs/qe/Be2/" +data_path = os.path.join(os.path.join(data_repo_path, "Be2"), "training_data") -params_path = "./ex12_params.pkl" -network_path = "./ex12_network.pth" -input_scaler_path = "./ex12_iscaler.pkl" -output_scaler_path = "./ex12_oscaler.pkl" +""" +ex12_run_predictions.py: Show how a prediction can be made using MALA. +Using nothing more then the trained network and atomic configurations, +predictions can be made. +""" -# Uses a trained network to make a prediction. -def use_tester_class(network_path, params_path, input_scaler_path, - output_scaler_path): - # First load Parameters and network. - # Parameters may have to - new_parameters = mala.Parameters.load_from_file(params_path, - no_snapshots=True) - - # Specify the correct LDOS parameters. - new_parameters.targets.target_type = "LDOS" - new_parameters.targets.ldos_gridsize = 11 - new_parameters.targets.ldos_gridspacing_ev = 2.5 - new_parameters.targets.ldos_gridoffset_ev = -5 - - # Inference should ALWAYS be done with lazy loading activated, even if - # training was not. - new_parameters.data.use_lazy_loading = True - - # Load a network from a file. - new_network = mala.Network.load_from_file(new_parameters, network_path) - - # Make sure the same scaling is used for data handling. - iscaler = mala.DataScaler.load_from_file(input_scaler_path) - oscaler = mala.DataScaler.load_from_file(output_scaler_path) - inference_data_handler = mala.DataHandler(new_parameters, - input_data_scaler=iscaler, - output_data_scaler=oscaler) - - # Add snapshots that are to be tested and make sure that the - # data_splitting_snapshots list is correct. - new_parameters.data.data_splitting_snapshots = ["te"] - inference_data_handler.add_snapshot("Be_snapshot2.in.npy", data_path+"snap/", - "Be_snapshot2.out.npy", data_path+"ldos/",) - inference_data_handler.prepare_data(reparametrize_scaler=False) - - # The Tester class is the testing analogon to the training class. - tester = mala.Tester(new_parameters, new_network, inference_data_handler) - - # Get the results for the first (and only= snapshot. - actual_ldos, predicted_ldos = tester.test_snapshot(0) - - # We will use the LDOS calculator to do some preprocessing. - ldos_calculator = inference_data_handler.target_calculator - ldos_calculator.read_additional_calculation_data("qe.out", os.path.join( - data_path, - "outputs/Be_snapshot2.out")) - - # Calculate the Band energy. - band_energy_predicted = ldos_calculator.get_band_energy(predicted_ldos) - band_energy_actual = ldos_calculator.get_band_energy(actual_ldos) - printout("Band energy (actual, predicted, error)[eV]", band_energy_actual, - band_energy_predicted, band_energy_predicted-band_energy_actual) - - # Calculate the number of electrons. - nr_electrons_predicted = ldos_calculator.\ - get_number_of_electrons(predicted_ldos) - nr_electrons_actual = ldos_calculator.get_number_of_electrons(actual_ldos) - printout("Number of electrons (actual, predicted, error)[eV]", - nr_electrons_actual, nr_electrons_predicted, - nr_electrons_predicted-nr_electrons_actual) - -def use_predictor(network_path, params_path, input_scaler_path, - output_scaler_path): - # First load Parameters and network. - # Parameters may have to - new_parameters = mala.Parameters.load_from_file(params_path, - no_snapshots=True) - - # Specify the correct LDOS parameters. +# Uses a network to make a prediction. +def use_predictor(new_network, new_parameters, iscaler, oscaler): + #################### + # PARAMETERS + # Specify the correct LDOS parameters and load network. + #################### new_parameters.targets.target_type = "LDOS" new_parameters.targets.ldos_gridsize = 11 new_parameters.targets.ldos_gridspacing_ev = 2.5 @@ -91,31 +29,44 @@ def use_predictor(network_path, params_path, input_scaler_path, new_parameters.descriptors.twojmax = 10 new_parameters.descriptors.rcutfac = 4.67637 - # Load a network from a file. - new_network = mala.Network.load_from_file(new_parameters, network_path) - - # Make sure the same scaling is used for data handling. - iscaler = mala.DataScaler.load_from_file(input_scaler_path) - oscaler = mala.DataScaler.load_from_file(output_scaler_path) inference_data_handler = mala.DataHandler(new_parameters, input_data_scaler=iscaler, output_data_scaler=oscaler) - predictor = mala.Predictor(new_parameters, new_network, inference_data_handler) + #################### + # PREDICTION + # Create a Predictor object and make a prediction. + #################### + + predictor = mala.Predictor(new_parameters, new_network, + inference_data_handler) + + # Using a QE out is simply for convenience; with the function + # .predict_from_atoms, prediction can be made directly from atomic + # configurations. ldos = predictor.predict_from_qeout(os.path.join( data_path, - "outputs/Be_snapshot2.out")) - inference_data_handler.target_calculator.read_additional_calculation_data("qe.out", os.path.join( - data_path, - "outputs/Be_snapshot2.out")) - number_of_electrons = inference_data_handler.target_calculator.get_number_of_electrons(ldos) - print(number_of_electrons) - printout("Sucess!") + "Be_snapshot3.out")) + ldos_calculator: mala.LDOS = inference_data_handler.target_calculator + + # Fermi energy has to be calculated self-consistently for predictions, + # and for that we also have to tell the calculator how many electrons + # the system has and at which temperature we perform calculations. + ldos_calculator.number_of_electrons = 4 + ldos_calculator.temperature_K = 298 + + fermi_energy = ldos_calculator.get_self_consistent_fermi_energy_ev(ldos) + + number_of_electrons = ldos_calculator.\ + get_number_of_electrons(ldos, fermi_energy_eV=fermi_energy) + band_energy = ldos_calculator. \ + get_band_energy(ldos, fermi_energy_eV=fermi_energy) + printout("Predicted number of electrons: ", number_of_electrons) + printout("Predicted band energy: ", band_energy) # Trains a network. -def initial_training(network_path, params_path, input_scaler_path, - output_scaler_path, desired_loss_improvement_factor=1): +def initial_training(): #################### # PARAMETERS # All parameters are handled from a central parameters class that @@ -128,7 +79,7 @@ def initial_training(network_path, params_path, input_scaler_path, # done by providing a list containing entries of the form # "tr", "va" and "te". test_parameters.data.data_splitting_type = "by_snapshot" - test_parameters.data.data_splitting_snapshots = ["tr", "va", "te"] + test_parameters.data.data_splitting_snapshots = ["tr", "va"] # Specify the data scaling. test_parameters.data.input_rescaling_type = "feature-wise-standard" @@ -138,7 +89,7 @@ def initial_training(network_path, params_path, input_scaler_path, test_parameters.network.layer_activations = ["ReLU"] # Specify the training parameters. - test_parameters.running.max_number_epochs = 400 + test_parameters.running.max_number_epochs = 100 test_parameters.running.mini_batch_size = 40 test_parameters.running.learning_rate = 0.00001 test_parameters.running.trainingtype = "Adam" @@ -151,12 +102,10 @@ def initial_training(network_path, params_path, input_scaler_path, data_handler = mala.DataHandler(test_parameters) # Add a snapshot we want to use in to the list. - data_handler.add_snapshot("Be_snapshot0.in.npy", data_path+"snap/", - "Be_snapshot0.out.npy", data_path+"ldos/") - data_handler.add_snapshot("Be_snapshot1.in.npy", data_path+"snap/", - "Be_snapshot1.out.npy", data_path+"ldos/") - data_handler.add_snapshot("Be_snapshot2.in.npy", data_path+"snap/", - "Be_snapshot2.out.npy", data_path+"ldos/",) + data_handler.add_snapshot("Be_snapshot1.in.npy", data_path, + "Be_snapshot1.out.npy", data_path) + data_handler.add_snapshot("Be_snapshot2.in.npy", data_path, + "Be_snapshot2.out.npy", data_path) data_handler.prepare_data() printout("Read data: DONE.") @@ -191,15 +140,9 @@ def initial_training(network_path, params_path, input_scaler_path, # Parameters, input/output scaler, network. #################### - test_parameters.save(params_path) - test_network.save_network(network_path) - data_handler.input_data_scaler.save(input_scaler_path) - data_handler.output_data_scaler.save(output_scaler_path) + return test_network, test_parameters, data_handler.input_data_scaler,\ + data_handler.output_data_scaler -# initial_training(network_path, params_path, input_scaler_path, -# output_scaler_path) -use_tester_class(network_path, params_path, input_scaler_path, - output_scaler_path) -use_predictor(network_path, params_path, input_scaler_path, - output_scaler_path) +network, params, input_scaler, output_scaler = initial_training() +use_predictor(network, params, input_scaler, output_scaler) diff --git a/test/inference_test.py b/test/inference_test.py index 202faeaa0..1d814dfe5 100644 --- a/test/inference_test.py +++ b/test/inference_test.py @@ -1,11 +1,16 @@ +import importlib import os +import pytest import numpy as np -from mala import Parameters, DataHandler, DataScaler, Network, Tester +from mala import Parameters, DataHandler, DataScaler, Network, Tester, \ + Trainer, Predictor from data_repo_path import data_repo_path data_path = os.path.join(data_repo_path, "Al36") param_path = os.path.join(data_repo_path, "workflow_test/") +beryllium_path = os.path.join(os.path.join(data_repo_path, "Be2"), + "training_data") params_path = param_path+"workflow_test_params.pkl" network_path = param_path+"workflow_test_network.pth" input_scaler_path = param_path+"workflow_test_iscaler.pkl" @@ -90,6 +95,115 @@ def test_inference_lazy_loading(self): assert np.isclose(actual_ldos.sum(), from_file.sum(), atol=accuracy_strict) + @pytest.mark.skipif(importlib.util.find_spec("lammps") is None, + reason="LAMMPS is currently not part of the pipeline.") + def test_predictions(self): + """ + Test that Predictor class and Tester class give the same results. + + They in principle do the same, but use slightly different routines + under the hood. To test this, a small network is trained, and + afterwards, objects from bot classes are used to predict the + number of electrons and band energy. + """ + #################### + # Set up and train a network to be used for the tests. + #################### + + test_parameters = Parameters() + test_parameters.data.data_splitting_type = "by_snapshot" + test_parameters.data.data_splitting_snapshots = ["tr", "va"] + test_parameters.data.input_rescaling_type = "feature-wise-standard" + test_parameters.data.output_rescaling_type = "normal" + test_parameters.network.layer_activations = ["ReLU"] + test_parameters.running.max_number_epochs = 100 + test_parameters.running.mini_batch_size = 40 + test_parameters.running.learning_rate = 0.00001 + test_parameters.running.trainingtype = "Adam" + test_parameters.targets.target_type = "LDOS" + test_parameters.targets.ldos_gridsize = 11 + test_parameters.targets.ldos_gridspacing_ev = 2.5 + test_parameters.targets.ldos_gridoffset_ev = -5 + test_parameters.running.inference_data_grid = [18, 18, 27] + test_parameters.descriptors.descriptor_type = "SNAP" + test_parameters.descriptors.twojmax = 10 + test_parameters.descriptors.rcutfac = 4.67637 + + data_handler = DataHandler(test_parameters) + data_handler.add_snapshot("Be_snapshot1.in.npy", beryllium_path, + "Be_snapshot1.out.npy", beryllium_path) + data_handler.add_snapshot("Be_snapshot2.in.npy", beryllium_path, + "Be_snapshot2.out.npy", beryllium_path) + data_handler.prepare_data() + + test_parameters.network.layer_sizes = [ + data_handler.get_input_dimension(), + 100, + data_handler.get_output_dimension()] + test_network = Network(test_parameters) + test_trainer = Trainer(test_parameters, test_network, + data_handler) + test_trainer.train_network() + + #################### + # Now, first use the Tester class to make a prediction. + #################### + + test_parameters.data.use_lazy_loading = True + inference_data_handler = DataHandler(test_parameters, + input_data_scaler=data_handler. + input_data_scaler, + output_data_scaler=data_handler. + output_data_scaler) + inference_data_handler.clear_data() + test_parameters.data.data_splitting_snapshots = ["te"] + inference_data_handler.add_snapshot("Be_snapshot3.in.npy", + beryllium_path, + "Be_snapshot3.out.npy", + beryllium_path) + inference_data_handler.prepare_data(reparametrize_scaler=False) + + tester = Tester(test_parameters, test_network, inference_data_handler) + actual_ldos, predicted_ldos = tester.test_snapshot(0) + ldos_calculator = inference_data_handler.target_calculator + ldos_calculator.read_additional_calculation_data("qe.out", os.path.join( + beryllium_path, + "Be_snapshot3.out")) + + band_energy_tester_class = ldos_calculator.get_band_energy(predicted_ldos) + nr_electrons_tester_class = ldos_calculator.\ + get_number_of_electrons(predicted_ldos) + + #################### + # Now, use the predictor class to make the same prediction. + #################### + + predictor = Predictor(test_parameters, test_network, + inference_data_handler) + predicted_ldos = predictor.predict_from_qeout(os.path.join( + beryllium_path, + "Be_snapshot3.out")) + + # In order for the results to be the same, we have to use the same + # parameters. + inference_data_handler. \ + target_calculator.\ + read_additional_calculation_data("qe.out", os.path.join( + beryllium_path, + "Be_snapshot3.out")) + + nr_electrons_predictor_class = inference_data_handler.\ + target_calculator.get_number_of_electrons(predicted_ldos) + band_energy_predictor_class = inference_data_handler.\ + target_calculator.get_band_energy(predicted_ldos) + + assert np.isclose(band_energy_predictor_class, + band_energy_tester_class, + atol=accuracy_strict) + assert np.isclose(nr_electrons_predictor_class, + nr_electrons_tester_class, + atol=accuracy_strict) + @staticmethod def __run(use_lazy_loading=False, batchsize=46): # First we load Parameters and network. diff --git a/test/workflow_test.py b/test/workflow_test.py index d3ee20185..5cd816f52 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -198,7 +198,7 @@ def test_total_energy_from_ldos(self): ldos.read_additional_calculation_data("qe.out", os.path.join( data_path_ldos, "Be.pw.scf.out")) - ldos_data = np.load((data_path_ldos, "Be_ldos.npy")) + ldos_data = np.load(os.path.join(data_path_ldos, "Be_ldos.npy")) # Calculate energies self_consistent_fermi_energy = ldos. \ From 1c4463d23db0c33e321396eaba793d1d7e519e29 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 29 Sep 2021 11:32:18 +0200 Subject: [PATCH 289/297] Fixed docstring --- mala/descriptors/snap.py | 1 - mala/network/predictor.py | 2 +- mala/targets/density.py | 1 + mala/targets/dos.py | 1 + mala/targets/ldos.py | 1 + mala/targets/target_base.py | 1 + 6 files changed, 5 insertions(+), 2 deletions(-) diff --git a/mala/descriptors/snap.py b/mala/descriptors/snap.py index a36c2f31a..65285dc8b 100755 --- a/mala/descriptors/snap.py +++ b/mala/descriptors/snap.py @@ -149,7 +149,6 @@ def calculate_from_atoms(self, atoms, grid_dimensions, Numpy array containing the descriptors with the dimension (x,y,z,descriptor_dimension) """ - # Enforcing / Checking PBC on the input atoms. atoms = self.enforce_pbc(atoms) return self.__calculate_snap(atoms, working_directory, grid_dimensions) diff --git a/mala/network/predictor.py b/mala/network/predictor.py index f35704749..47b04e0c6 100644 --- a/mala/network/predictor.py +++ b/mala/network/predictor.py @@ -102,7 +102,7 @@ def predict_for_atoms(self, atoms): _forward_snap_descriptors(snap_descriptors) def _forward_snap_descriptors(self, snap_descriptors): - """Forwards a scaled tensor of SNAP descriptors through the NN.""" + """Forward a scaled tensor of SNAP descriptors through the NN.""" predicted_outputs = np.zeros((self.data.grid_size, self.data.target_calculator.\ get_feature_size())) diff --git a/mala/targets/density.py b/mala/targets/density.py index 12c4fe107..fa0d3787a 100644 --- a/mala/targets/density.py +++ b/mala/targets/density.py @@ -38,6 +38,7 @@ def __init__(self, params): self.target_length = 1 def get_feature_size(self): + """Get dimension of this target if used as feature in ML.""" return 1 def read_from_cube(self, file_name, directory, units=None): diff --git a/mala/targets/dos.py b/mala/targets/dos.py index af1154700..cd2a81327 100644 --- a/mala/targets/dos.py +++ b/mala/targets/dos.py @@ -24,6 +24,7 @@ def __init__(self, params): self.target_length = self.parameters.ldos_gridsize def get_feature_size(self): + """Get dimension of this target if used as feature in ML.""" return self.parameters.ldos_gridsize @staticmethod diff --git a/mala/targets/ldos.py b/mala/targets/ldos.py index 853e4a9a9..6ee7d97e4 100644 --- a/mala/targets/ldos.py +++ b/mala/targets/ldos.py @@ -31,6 +31,7 @@ def __init__(self, params): self.cached_density = [] def get_feature_size(self): + """Get dimension of this target if used as feature in ML.""" return self.parameters.ldos_gridsize @staticmethod diff --git a/mala/targets/target_base.py b/mala/targets/target_base.py index 95f8fc8b4..68910a27e 100644 --- a/mala/targets/target_base.py +++ b/mala/targets/target_base.py @@ -71,6 +71,7 @@ def __init__(self, params): @abstractmethod def get_feature_size(self): + """Get dimension of this target if used as feature in ML.""" pass @property From a7147f3921a407019c189a4192fe8fa4739e07ca Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Wed, 29 Sep 2021 11:51:16 +0200 Subject: [PATCH 290/297] Fixed docstring and workflow --- mala/network/hyper_opt_oat.py | 2 +- test/workflow_test.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mala/network/hyper_opt_oat.py b/mala/network/hyper_opt_oat.py index b80a7205e..2c38b1205 100644 --- a/mala/network/hyper_opt_oat.py +++ b/mala/network/hyper_opt_oat.py @@ -221,7 +221,7 @@ def get_best_trial_results(self): "selected.") def __check_factor_levels(self): - """Checks that the factors are in a decreasing order.""" + """Check that the factors are in a decreasing order.""" dx = np.diff(self.sorted_num_choices) if np.all(dx >= 0): # Factors in increasing order, we have to reverse the order. diff --git a/test/workflow_test.py b/test/workflow_test.py index d3ee20185..5cd816f52 100644 --- a/test/workflow_test.py +++ b/test/workflow_test.py @@ -198,7 +198,7 @@ def test_total_energy_from_ldos(self): ldos.read_additional_calculation_data("qe.out", os.path.join( data_path_ldos, "Be.pw.scf.out")) - ldos_data = np.load((data_path_ldos, "Be_ldos.npy")) + ldos_data = np.load(os.path.join(data_path_ldos, "Be_ldos.npy")) # Calculate energies self_consistent_fermi_energy = ldos. \ From f212d0fd0bcd70007455166366896ad7959e8cd4 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 4 Oct 2021 18:32:58 +0200 Subject: [PATCH 291/297] Small bugfix for the NoTraining analysis --- mala/network/hyper_opt_notraining.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 32a4f6570..850fdc7e1 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -44,6 +44,13 @@ def perform_study(self, trial_list=None): investigates those sampled by a different hyperparameter optimizer. """ + + # The minibatch size can not vary in the analysis. + # This check ensures that e.g. optuna results can be used. + for idx, par in enumerate(self.params.hyperparameters.hlist): + if par.name == "mini_batch_size": + self.params.hyperparameters.hlist.pop(idx) + # Ideally, this type of HO is called with a list of trials for which # the parameter has to be identified. self.trial_list = trial_list From 02dcb2fdf8af887b3d549f38b870a3d14dec82be Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 4 Oct 2021 19:22:52 +0200 Subject: [PATCH 292/297] Added missing warning --- mala/network/hyper_opt_notraining.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 850fdc7e1..348894f5b 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -1,6 +1,7 @@ """Hyperparameter optimizer working without training.""" import optuna +from mala.common.printout import printout from mala.network.hyper_opt_base import HyperOptBase from mala.network.objective_no_training import ObjectiveNoTraining @@ -49,6 +50,8 @@ def perform_study(self, trial_list=None): # This check ensures that e.g. optuna results can be used. for idx, par in enumerate(self.params.hyperparameters.hlist): if par.name == "mini_batch_size": + printout("Removing mini batch size from hyperparameter list," + "because NASWOT is used.") self.params.hyperparameters.hlist.pop(idx) # Ideally, this type of HO is called with a list of trials for which From 628a51b8f7c667b52ef90618cce8c39738282b00 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Mon, 4 Oct 2021 19:36:27 +0200 Subject: [PATCH 293/297] Last fixes doc, setup.py, added info on ensemble training for hyperparamter opt and updated used test_data --- docs/source/CONTRIBUTE.md | 4 +++- docs/source/install/README.md | 2 +- examples/ex09_distributed_hyperopt.py | 7 +++++++ mala/network/hyper_opt_notraining.py | 1 - setup.py | 3 +-- 5 files changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/source/CONTRIBUTE.md b/docs/source/CONTRIBUTE.md index 3c78e1400..7ea242198 100644 --- a/docs/source/CONTRIBUTE.md +++ b/docs/source/CONTRIBUTE.md @@ -19,7 +19,9 @@ the form `MAJOR.MINOR.FIX`: * `MINOR`: new features have beend added to the code. * `FIX`: A bug in a feature has been fixed. -Every new version should be accompanied by a changelog. +Every new version should be accompanied by a changelog. Please include the +version of the test data repository with which this version is supposed to be +used in this changelog. ## Branching strategy diff --git a/docs/source/install/README.md b/docs/source/install/README.md index d1ee572cd..980be15e5 100644 --- a/docs/source/install/README.md +++ b/docs/source/install/README.md @@ -162,7 +162,7 @@ the data repository, it needs to be linked with MALA. ```sh $ git clone https://github.com/mala-project/test-data ~/path/to/data/repo $ cd ~/path/to/data/repo - $ git checkout v0.4.0 + $ git checkout v1.1.0 ``` 2. Link MALA and data repository. diff --git a/examples/ex09_distributed_hyperopt.py b/examples/ex09_distributed_hyperopt.py index b7f6d4273..0d9983634 100644 --- a/examples/ex09_distributed_hyperopt.py +++ b/examples/ex09_distributed_hyperopt.py @@ -53,6 +53,13 @@ test_parameters.hyperparameters.study_name = "ex09" test_parameters.hyperparameters.rdb_storage = 'sqlite:///ex09.db' +# Hyperparameter optimization can be further refined by using ensemble training +# at each step and by using a different metric then the validation loss +# (e.g. the band energy). It is recommended not to use the ensemble training +# method in Single-GPU use, as it naturally drastically decreases performance. +test_parameters.hyperparameters.number_training_per_trial = 5 +test_parameters.running.after_before_training_metric = "band_energy" + #################### # DATA # Add and prepare snapshots for training. diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 348894f5b..39f6bd0f7 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -45,7 +45,6 @@ def perform_study(self, trial_list=None): investigates those sampled by a different hyperparameter optimizer. """ - # The minibatch size can not vary in the analysis. # This check ensures that e.g. optuna results can be used. for idx, par in enumerate(self.params.hyperparameters.hlist): diff --git a/setup.py b/setup.py index cefd0103f..be1d7efab 100644 --- a/setup.py +++ b/setup.py @@ -30,8 +30,7 @@ long_description=readme, long_description_content_type='text/markdown', url="https://github.com/mala-project/mala", - author="Lenz Fiedler et al.", - author_email="l.fiedler@hzdr.de", + author="MALA developers", license=license, packages=find_packages(exclude=("test", "docs", "examples", "install", "ml-dft-sandia")), From 089ee1d664527f7c88a53e24f9af8e37da3f221a Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 5 Oct 2021 15:19:58 +0200 Subject: [PATCH 294/297] Little improvement for optuna - notraining interface --- mala/network/hyper_opt_optuna.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mala/network/hyper_opt_optuna.py b/mala/network/hyper_opt_optuna.py index 9691265ac..df288650f 100644 --- a/mala/network/hyper_opt_optuna.py +++ b/mala/network/hyper_opt_optuna.py @@ -110,7 +110,8 @@ def get_trials_from_study(self): last_trials: list A list of optuna.FrozenTrial objects. """ - return self.study.get_trials() + return self.study.get_trials(states=(optuna.trial. + TrialState.COMPLETE, )) @classmethod def checkpoint_exists(cls, checkpoint_name): From 80f3f8fff94d9d1b09b0c10038bab1e5cdc379e9 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 5 Oct 2021 15:29:04 +0200 Subject: [PATCH 295/297] Changed from max to min (the results show this) --- mala/network/hyper_opt_notraining.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mala/network/hyper_opt_notraining.py b/mala/network/hyper_opt_notraining.py index 39f6bd0f7..7679ac702 100644 --- a/mala/network/hyper_opt_notraining.py +++ b/mala/network/hyper_opt_notraining.py @@ -49,7 +49,7 @@ def perform_study(self, trial_list=None): # This check ensures that e.g. optuna results can be used. for idx, par in enumerate(self.params.hyperparameters.hlist): if par.name == "mini_batch_size": - printout("Removing mini batch size from hyperparameter list," + printout("Removing mini batch size from hyperparameter list, " "because NASWOT is used.") self.params.hyperparameters.hlist.pop(idx) @@ -71,7 +71,7 @@ def perform_study(self, trial_list=None): self.trial_losses = [self.objective(row) for row in self.trial_list] # Return the best lost value we could achieve. - return min(self.trial_losses) + return max(self.trial_losses) def set_optimal_parameters(self): """ @@ -81,6 +81,6 @@ def set_optimal_parameters(self): hyperparameter optimizer was created. """ # Getting the best trial based on the test errors - idx = self.trial_losses.index(min(self.trial_losses)) + idx = self.trial_losses.index(max(self.trial_losses)) self.best_trial = self.trial_list[idx] self.objective.parse_trial(self.best_trial) From fab1d9584510c2294816fda79916b143c8af79f6 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 5 Oct 2021 15:39:09 +0200 Subject: [PATCH 296/297] Small bugfix --- mala/network/no_training_pruner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mala/network/no_training_pruner.py b/mala/network/no_training_pruner.py index bb24f5c84..47c93f52b 100644 --- a/mala/network/no_training_pruner.py +++ b/mala/network/no_training_pruner.py @@ -60,7 +60,7 @@ def prune(self, study: "optuna.study.Study", trial: objective = ObjectiveNoTraining(self._params, self._data_handler, self._trial_type) surrogate_loss = objective(trial) - if surrogate_loss > self._params.hyperparameters.no_training_cutoff: + if surrogate_loss < self._params.hyperparameters.no_training_cutoff: return True else: return False From 58a701115073b8430e76374b38af64bd05fb1f05 Mon Sep 17 00:00:00 2001 From: Lenz Fiedler Date: Tue, 5 Oct 2021 15:53:07 +0200 Subject: [PATCH 297/297] Ensemble "Training" for NoTraining --- mala/network/objective_no_training.py | 72 ++++++++++++++++++--------- 1 file changed, 48 insertions(+), 24 deletions(-) diff --git a/mala/network/objective_no_training.py b/mala/network/objective_no_training.py index 3fd225b03..7deb4b04e 100644 --- a/mala/network/objective_no_training.py +++ b/mala/network/objective_no_training.py @@ -53,30 +53,54 @@ def __call__(self, trial): super(ObjectiveNoTraining, self).parse_trial(trial) # Build the network. - net = Network(self.params) - device = "cuda" if self.params.use_gpu else "cpu" - - # Load the batchesand get the jacobian. - do_shuffle = self.params.running.use_shuffling_for_samplers - if self.data_handler.parameters.use_lazy_loading or \ - self.params.use_horovod: - do_shuffle = False - if self.params.running.use_shuffling_for_samplers: - self.data_handler.mix_datasets() - loader = DataLoader(self.data_handler.training_data_set, - batch_size=self.params.running.mini_batch_size, - shuffle=do_shuffle) - jac = ObjectiveNoTraining.__get_batch_jacobian(net, loader, device) - - # Loss = - score! - surrogate_loss = float('inf') - try: - surrogate_loss = - ObjectiveNoTraining.__calc_score(jac) - surrogate_loss = surrogate_loss.cpu().detach().numpy().astype( - np.float64) - except RuntimeError: - printout("Got a NaN, ignoring sample.") - return surrogate_loss + surrogate_losses = [] + for i in range(0, self.params.hyperparameters. + number_training_per_trial): + net = Network(self.params) + device = "cuda" if self.params.use_gpu else "cpu" + + # Load the batchesand get the jacobian. + do_shuffle = self.params.running.use_shuffling_for_samplers + if self.data_handler.parameters.use_lazy_loading or \ + self.params.use_horovod: + do_shuffle = False + if self.params.running.use_shuffling_for_samplers: + self.data_handler.mix_datasets() + loader = DataLoader(self.data_handler.training_data_set, + batch_size=self.params.running.mini_batch_size, + shuffle=do_shuffle) + jac = ObjectiveNoTraining.__get_batch_jacobian(net, loader, device) + + # Loss = - score! + surrogate_loss = float('inf') + try: + surrogate_loss = - ObjectiveNoTraining.__calc_score(jac) + surrogate_loss = surrogate_loss.cpu().detach().numpy().astype( + np.float64) + except RuntimeError: + printout("Got a NaN, ignoring sample.") + surrogate_losses.append(surrogate_loss) + + if self.params.hyperparameters.number_training_per_trial > 1: + printout("Losses from multiple runs are: ") + printout(surrogate_losses) + + if self.params.hyperparameters.trial_ensemble_evaluation == "mean": + return np.mean(surrogate_losses) + + elif self.params.hyperparameters.trial_ensemble_evaluation == \ + "mean_std": + mean = np.mean(surrogate_losses) + + # Cannot calculate the standar deviation of a bunch of infinities. + if np.isinf(mean): + return mean + else: + return np.mean(surrogate_losses) + \ + np.std(surrogate_losses) + else: + raise Exception("No way to estimate the trial metric from ensemble" + " training provided.") @staticmethod def __get_batch_jacobian(net: Network, loader: DataLoader, device) \