Merge branch 'JeschkeLab:main' into main

.github/workflows/ci_PR.yml

@@ -6,12 +6,13 @@ on:
 
 jobs:
   tests:
+    name: python test
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9]
+        python-version: ['3.10','3.11']
 
     steps:
       - uses: actions/checkout@v3

.github/workflows/ci_scheduled.yml

@@ -12,7 +12,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
 

.github/workflows/deploy_ghpages.yml

@@ -12,10 +12,10 @@ jobs:
     steps:
     - name: Checkout
       uses: actions/checkout@v1
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: '3.10'
     - uses: actions/cache@v2
       with:
         path: |

.github/workflows/docs_PR.yml

@@ -16,10 +16,10 @@ jobs:
     steps:
     - name: Checkout
       uses: actions/checkout@v1
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: '3.10'
     - uses: actions/cache@v2
       with:
         path: |

.github/workflows/examples_PR.yml

@@ -16,10 +16,10 @@ jobs:
     steps:
     - name: Checkout
       uses: actions/checkout@v1
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: '3.10'
     - uses: actions/cache@v2
       with:
         path: |

README.md

@@ -16,7 +16,7 @@ The early versions of DeerLab (up to version 0.9.2) are written in MATLAB. The o
 
 ## Requirements
 
-DeerLab is available for Windows, Mac and Linux systems and requires **Python 3.8**, **3.9**, or  **3.10**.
+DeerLab is available for Windows, Mac and Linux systems and requires **Python 3.8**, **3.9**, **3.10**, or  **3.11**.
 
 All additional dependencies are automatically downloaded and installed during the setup.
 

VERSION

@@ -1 +1 @@
-v1.0.0
+v1.0.2

deerlab/model.py

@@ -1569,11 +1569,11 @@ def fit(model_, y, *constants, par0=None, penalties=None, bootstrap=0, noiselvl=
     nnlsSolver : string, optional
         Solver used to solve a non-negative least-squares problem (if applicable):
 
-        * ``'qp'`` - Optimization of the NNLS problem using the ``quadprog`` package.
+        * ``'qp'`` - Optimization of the NNLS problem using the ``quadprog`` package. Only Python <= 3.10.
         * ``'cvx'`` - Optimization of the NNLS problem using the ``cvxopt`` package.
         * ``'fnnls'`` - Optimization using the fast NNLS algorithm.
         
-        The default is ``'qp'``.
+        The default is ``'cvx'``.
 
     verbose : scalar integer, optional
         Level of verbosity during the analysis:
@@ -1754,7 +1754,7 @@ def bootstrap_fcn(ysim):
     # Dictionary of parameter names and fit uncertainties
     FitResult_paramuq = {f'{key}Uncert': model._getparamuq(fitresults.paramUncert,idx) for key,idx in zip(keys,param_idx)}
     # Dictionary of other fit quantities of interest
-    FitResult_dict = {key: getattr(fitresults,key) for key in ['param','paramUncert','model','cost','plot','residuals','stats','regparam']}
+    FitResult_dict = {key: getattr(fitresults,key) for key in ['param','paramUncert','model','cost','plot','residuals','stats','regparam','regparam_stats']}
     _paramlist = model._parameter_list('vector')
 
     # Prepare the propagate() and evaluate() methods

deerlab/selregparam.py

@@ -99,7 +99,7 @@ def selregparam(y, A, solver, method='aic', algorithm='brent', noiselvl=None,
     residuals : ndarray
         Values of the residual norms evaluated during the search. 
         Returned if full_output is True.
-    residuals : ndarray
+    penalties : ndarray
         Values of the penalty norms evaluated during the search. 
         Returned if full_output is True.
     """

deerlab/solvers.py

@@ -14,7 +14,6 @@
 import time 
 from functools import partial
 from copy import deepcopy
-from quadprog import solve_qp
 
 
 def timestamp():
@@ -136,7 +135,6 @@ def _plot(ys,yfits,yuqs,noiselvls,axis=None,xlabel=None,gof=False,fontsize=13):
     # Adjust fontsize
     for ax in axs:
         for label in (ax.get_xticklabels() + ax.get_yticklabels()):
-            label.set_fontname('Calibri')
             label.set_fontsize(fontsize)
 
     return fig
@@ -388,7 +386,7 @@ def _insertfrozen(parfit,parfrozen,frozen):
 # ===========================================================================================
 
 
-def snlls(y, Amodel, par0=None, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver='qp', reg='auto', weights=None, verbose=0,
+def snlls(y, Amodel, par0=None, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver='cvx', reg='auto', weights=None, verbose=0,
           regparam='aic', regparamrange=None, multistart=1, regop=None, alphareopt=1e-3, extrapenalty=None, subsets=None,
           ftol=1e-8, xtol=1e-8, max_nfev=1e8, lin_tol=1e-15, lin_maxiter=1e4, noiselvl=None, lin_frozen=None, mask=None,
           nonlin_frozen=None, uq=True, modeluq=False):
@@ -482,11 +480,11 @@ def snlls(y, Amodel, par0=None, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver
     nnlsSolver : string, optional
         Solver used to solve a non-negative least-squares problem (if applicable):
 
-        * ``'qp'`` - Optimization of the NNLS problem using the ``quadprog`` package.
+        * ``'qp'`` - Optimization of the NNLS problem using the ``quadprog`` package. Only Python <= 3.10.
         * ``'cvx'`` - Optimization of the NNLS problem using the ``cvxopt`` package.
         * ``'fnnls'`` - Optimization using the fast NNLS algorithm.
         
-        The default is ``'qp'``.
+        The default is ``'cvx'``.
 
     noiselvl : array_like, optional
         Noise standard deviation of the input signal(s), if not specified it is estimated automatically. 
@@ -714,10 +712,18 @@ def linear_problem(y,A,optimize_alpha,alpha):
         yfrozen = (A[:,lin_frozen]@lin_parfrozen[lin_frozen]).astype(float)
 
         # Optimiza the regularization parameter only if needed
+
+
         if optimize_alpha:
-            alpha = dl.selregparam((y-yfrozen)[mask], Ared[mask,:], linSolver, regparam, 
+            output = dl.selregparam((y-yfrozen)[mask], Ared[mask,:], linSolver, regparam, 
                                         weights=weights[mask], regop=L, candidates=regparamrange, 
-                                        noiselvl=noiselvl,searchrange=regparamrange)
+                                        noiselvl=noiselvl,searchrange=regparamrange,full_output=True)
+            alpha = output[0]
+            alpha_stats['alphas_evaled'] = output[1]
+            alpha_stats['functional'] = output[2]
+            alpha_stats['residuals'] = output[3]
+            alpha_stats['penalties'] = output[4]
+
 
         # Components for linear least-squares
         AtA, Aty = _lsqcomponents((y-yfrozen)[mask], Ared[mask,:], L, alpha, weights=weights[mask])
@@ -732,8 +738,8 @@ def linear_problem(y,A,optimize_alpha,alpha):
         # Insert back the frozen linear parameters
         xfit = _insertfrozen(xfit,lin_parfrozen,lin_frozen)
 
-
         return xfit, alpha, Ndof
+
     #===========================================================================
 
     def ResidualsFcn(p):
@@ -745,8 +751,10 @@ def ResidualsFcn(p):
         non-linear least-squares solver. 
         """
 
-        nonlocal par_prev, check, regparam_prev, xfit, alpha, Ndof, Ndof_lin
-
+        nonlocal par_prev, check, regparam_prev, xfit, alpha, alpha_stats, Ndof, Ndof_lin
+
+
+
         # Non-linear model evaluation
         A = Amodel(p)
 
@@ -803,6 +811,8 @@ def ResidualsFcn(p):
         return res
     #===========================================================================
 
+    alpha_stats = {'alphas_evaled':[],'functional':[],'residuals':[],'penalties':[]}
+
     # -------------------------------------------------------------------
     #  Only linear parameters
     # -------------------------------------------------------------------
@@ -981,7 +991,7 @@ def ymodel(n):
 
     return FitResult(nonlin=nonlinfit, lin=linfit, param=parfit, model=modelfit, nonlinUncert=paramuq_nonlin,
                      linUncert=paramuq_lin, paramUncert=paramuq, modelUncert=modelfituq, regparam=alpha, plot=plotfcn,
-                     stats=stats, cost=fvals, residuals=res, noiselvl=noiselvl)
+                     stats=stats, cost=fvals, residuals=res, noiselvl=noiselvl,regparam_stats=alpha_stats)
 # ===========================================================================================
 
 
@@ -1232,6 +1242,15 @@ def qpnnls(AtA, Atb):
             Mathematical Programming, 27, 1-33.
 
     """
+
+    try:
+        from quadprog import solve_qp
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError(
+            'The quadprog package is required for this function.'+
+            'Install it with "pip install quadprog".')
+
+
     N = np.shape(AtA)[1]
     I = np.eye(N)
     lb = np.zeros(N)

docsrc/source/changelog.rst

@@ -14,6 +14,35 @@ Release Notes
 - |fix| : Something which was not working as expected or leading to errors has been fixed.
 - |api| : This will require changes in your scripts or code.
 
+Release ``v1.0.2`` - July 2023
+------------------------------------------
+- |fix| : Fixes errors in documentation (:pr:`429`).
+
+  * Changes the file name of figures 'modelling*.png` to `modeling*.png`. To keep all spelling consistent with american english.
+  * Adds a missing `)` in `fitting_guide`
+  * Corrects the time axis in `echo_crossing` example.
+
+- |fix| : Fixes an errors in tests (:pr:`429`).
+
+  * The test `test_algorithms` had an incorrect search range.
+
+- |fix| : Removes the default font from the `fit` function due to conflicts on some systems (:pr:`429`).
+
+Release ``v1.0.1`` - March 2023
+------------------------------------------
+- |fix| : Fixes some minor bugs in the documentation. 
+
+  * The file modelling_guide.rst is renamed to modeling_guide.rst to keep spelling consistency. 
+  * The "Simulating a two-pathway 5-pulse DEER signal" and "Simulating a three-pathway 4-pulse DEER signal" examples now run correctly.
+  * 
+- |fix| : Fixes issues with CVXOPT in tests. 
+
+  * The testing will now use quadprog as the default solver. To account for the change tested values are now generated using the grid method
+
+- |fix| : Updates GitHub actions to use latest packages.
+- |api| : Hardcodes out Python 3.11 support. This will remain until quadprog is fixed.
+- |api| : Removes hard-wired RNG seeding
+
 Release ``v1.0.0`` - December 2022
 ------------------------------------------
 

docsrc/source/fitting_guide.rst

@@ -66,7 +66,7 @@ Fitting multi-dataset models
 Models merged using the ``merge`` function (see :ref:`here <modeling_merging>` for details) can describe multiple datasets with a single model object and a common parameter set. To fit such a merged model to multiple datasets, the ``fit`` function can be used as above by passing a list of datasets ``[y1,y2,...,yN]`` instead of a single dataset  ::
 
     # Fit the model to multiple datasets
-    result = dl.fit(model, [y1,y2,y3]
+    result = dl.fit(model, [y1,y2,y3])
 
 The number of datasets must match the number of responses returned by the model. Additionally, the ordering in the list of datasets must match the order of responses from the model, i.e. ``response1`` of ``model`` will be fitted to ``y1``, and so on. 
 

docsrc/source/installation.rst

@@ -8,7 +8,7 @@ Requirements
 
 To install DeerLab, first install Python on your computer. Python can be downloaded from the `official Python distribution <https://www.python.org/>`_. There are
 many online tutorials to guide you through the installation and setup (see `here <https://realpython.com/installing-python/>`_ for example). Make sure you install
-one of the Python versions compatible with DeerLab, either **Python 3.8**, **3.9**, or  **3.10**.  
+one of the Python versions compatible with DeerLab, either **Python 3.8**, **3.9**, **3.10**, or  **3.11**.  
 
 .. rubric:: Windows systems
 

examples/intermediate/ex_crossing_echoes_masking.py

@@ -27,6 +27,8 @@
 # Load the experimental data
 t,Vexp = dl.deerload('../data/example_4pdeer_5.DTA')
 
+t *= 1e3 # convert from ms to us
+
 # Experimental parameters
 tau1 = 0.5  # First inter-pulse time delay, μs
 tau2 = 4.5  # Second inter-pulse time delay, μs