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generate_plots.py
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import numpy as np
import matplotlib.pyplot as plt
from numpy.core.numeric import True_
import pandas as pd
from tqdm import tqdm
from pathlib import Path
# from iminuit import Minuit
from collections import defaultdict
import joblib
from importlib import reload
from src.utils import utils
from src import plot
from src import file_loaders
from src import rc_params
from src import fits
from src import database
rc_params.set_rc_params(dpi=100)
num_cores_max = 30
make_1D_scan = True
force_rerun = True
verbose = True
make_fits = False
#%%
reload(plot)
reload(file_loaders)
abm_files = file_loaders.ABM_simulations(verbose=True)
N_files = len(abm_files)
#%%
reload(plot)
network_files = file_loaders.ABM_simulations(base_dir="Data/network", filetype="hdf5")
plot.plot_corona_type(
network_files,
force_rerun=force_rerun,
xlim=(15, 100),
N_max_runs=3,
reposition_x_axis=True,
normalize=False,
)
x = x
#%%
reload(plot)
# plot.plot_ABM_simulations(abm_files, force_rerun=force_rerun)
plot.plot_ABM_simulations(abm_files, force_rerun=force_rerun, xlim=(0, 150))
x = x
plot.plot_corona_type_ratio_plot(network_files, force_rerun=force_rerun, xlim=(10, 100))
# for cfg in network_files.iter_cfgs():
# break
#%%
reload(plot)
parameters_1D_scan = [
# dict(scan_parameter="event_size_max", non_default_parameters=dict(N_events=1)),
# dict(scan_parameter="event_size_max", non_default_parameters=dict(N_events=10)),
# dict(scan_parameter="event_size_max", non_default_parameters=dict(N_events=100)),
# dict(scan_parameter="event_size_max", non_default_parameters=dict(N_events=1_000)),
# dict(scan_parameter="event_size_max", non_default_parameters=dict(N_events=10_000)),
# dict(scan_parameter="mu"),
# dict(scan_parameter="beta", non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="beta"),
# dict(scan_parameter="beta", non_default_parameters=dict(sigma_beta=1)),
# dict(scan_parameter="beta", non_default_parameters=dict(sigma_beta=1, rho=0.1)),
# dict(scan_parameter="N_tot", do_log=True),
# dict(scan_parameter="N_tot", do_log=True, non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="N_init", do_log=True),
# dict(scan_parameter="N_init", do_log=True, non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="rho"),
# dict(scan_parameter="rho", non_default_parameters=dict(epsilon_rho=0)),
# dict(scan_parameter="rho", non_default_parameters=dict(epsilon_rho=0.02)),
# dict(scan_parameter="rho", non_default_parameters=dict(beta=0.007)),
# dict(scan_parameter="rho", non_default_parameters=dict(sigma_beta=1)),
# dict(scan_parameter="rho", non_default_parameters=dict(sigma_mu=1)),
# dict(scan_parameter="rho", non_default_parameters=dict(sigma_mu=1, sigma_beta=1)),
# dict(scan_parameter="rho", non_default_parameters=dict(algo=1)),
# dict(scan_parameter="rho", non_default_parameters=dict(N_tot=5_800_000)),
# dict(scan_parameter="epsilon_rho"),
# dict(scan_parameter="epsilon_rho", non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="epsilon_rho", non_default_parameters=dict(rho=0.1, algo=1)),
# dict(scan_parameter="sigma_beta"),
# dict(scan_parameter="sigma_beta", non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="sigma_beta", non_default_parameters=dict(sigma_mu=1)),
# dict(scan_parameter="sigma_beta", non_default_parameters=dict(rho=0.1, sigma_mu=1)),
# dict(scan_parameter="sigma_mu"),
# dict(scan_parameter="sigma_mu", non_default_parameters=dict(rho=0.1)),
# dict(scan_parameter="sigma_mu", non_default_parameters=dict(sigma_beta=1)),
# dict(scan_parameter="sigma_mu", non_default_parameters=dict(rho=0.1, sigma_beta=1)),
# dict(scan_parameter="lambda_E"),
# dict(scan_parameter="lambda_I"),
dict(
scan_parameter="beta_UK_multiplier",
non_default_parameters=dict(N_init=2_000, N_init_UK=200, beta=0.004),
),
]
# reload(plot)
if make_1D_scan:
for parameter_1D_scan in parameters_1D_scan:
plot.plot_1D_scan(**parameter_1D_scan)
#%%
if make_fits:
reload(fits)
num_cores = utils.get_num_cores(num_cores_max)
all_fits = fits.get_fit_results(abm_files, force_rerun=False, num_cores=num_cores, y_max=0.01)
reload(plot)
plot.plot_fits(all_fits, force_rerun=force_rerun, verbose=verbose)
#%%
reload(plot)
if make_1D_scan:
for parameter_1D_scan in parameters_1D_scan:
plot.plot_1D_scan_fit_results(all_fits, **parameter_1D_scan)
#%%
reload(plot)
# force_rerun=True
network_files = file_loaders.ABM_simulations(base_dir="Data/network", filetype="hdf5")
plot.plot_number_of_contacts(network_files, force_rerun=force_rerun)
# %%
reload(plot)
from matplotlib.backends.backend_pdf import PdfPages
d_query = utils.DotDict(
{
"beta": 0.004,
},
)
cfgs = utils.query_cfg(d_query)
cfgs.sort(key=lambda cfg: cfg["beta_UK_multiplier"])
pdf_name = Path(f"Figures/ABM_simulations_UK.pdf")
utils.make_sure_folder_exist(pdf_name)
with PdfPages(pdf_name) as pdf:
for cfg in tqdm(cfgs, desc="Plotting only events"):
filenames = utils.hash_to_filenames(cfg.hash)
fig, ax = plot.plot_single_ABM_simulation(cfg, abm_files)
pdf.savefig(fig, dpi=100)
plt.close("all")
#%%
if False:
d_query = utils.DotDict(
{
# "epsilon_rho": 0.02,
# "N_tot": 580_000,
# "rho": 0.0,
# "beta": 0.0108,
# "weighted_random_initial_infections": True,
# "results_delay_in_clicks": [30, 30, 30],
# "event_size_mean": 7.9997,
"hash": "1e04392284"
},
)
cfgs = utils.query_cfg(d_query)
for cfg in cfgs:
print(cfg)
# x = x
# cfgs.sort(key=lambda cfg: cfg["N_tot"])
# [cfg.hash for cfg in cfgs]
# plot.plot_single_ABM_simulation(cfgs[0], abm_files)
#%%
# R_eff for beta 1D-scan
if False:
cfgs, _ = utils.get_1D_scan_cfgs_all_filenames(
scan_parameter="beta",
non_default_parameters={},
# non_default_parameters=dict(weighted_random_initial_infections=True),
)
cfgs.sort(key=lambda cfg: cfg["beta"])
plot.plot_R_eff_beta_1D_scan(cfgs, abm_files)
# %%
reload(plot)
reload(database)
# x = x
# plot MCMC results
variable = "event_size_max"
variable = "results_delay_in_clicks"
reverse_order = True
extra_selections = {"tracking_rates": [1.0, 0.8, 0.0]}
# variable_subset = [
# [20, 20, 20],
# [30, 30, 30],
# ]
N_max_figures = 2
N_max_figures = None
plot.make_MCMC_plots(
variable="results_delay_in_clicks",
abm_files=abm_files,
extra_selections={"tracking_rates": [1.0, 0.8, 0.0]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=0,
reverse_order=True, # True since a higher value of results_delay_in_clicks is less intervention
# variable_subset=variable_subset,
)
plot.make_MCMC_plots(
variable="results_delay_in_clicks",
abm_files=abm_files,
extra_selections={"tracking_rates": [1.0, 0.8, 0.25]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=0,
reverse_order=True, # True since a higher value of results_delay_in_clicks is less intervention
# variable_subset=variable_subset,
)
plot.make_MCMC_plots(
variable="results_delay_in_clicks",
abm_files=abm_files,
extra_selections={"tracking_rates": [1.0, 0.8, 0.5]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=0,
reverse_order=True, # True since a higher value of results_delay_in_clicks is less intervention
# variable_subset=variable_subset,
)
plot.make_MCMC_plots(
variable="results_delay_in_clicks",
abm_files=abm_files,
extra_selections={"tracking_rates": [1.0, 0.8, 0.75]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=0,
reverse_order=True, # True since a higher value of results_delay_in_clicks is less intervention
# variable_subset=variable_subset,
)
# plot MCMC results
plot.make_MCMC_plots(
variable="tracking_rates",
abm_files=abm_files,
extra_selections={"results_delay_in_clicks": [30, 30, 30]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=-1,
reverse_order=False,
)
# plot MCMC results
plot.make_MCMC_plots(
variable="tracking_rates",
abm_files=abm_files,
extra_selections={"results_delay_in_clicks": [20, 20, 20]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=-1,
reverse_order=False,
)
# plot MCMC results
plot.make_MCMC_plots(
variable="tracking_rates",
abm_files=abm_files,
extra_selections={"results_delay_in_clicks": [10, 10, 10]},
N_max_figures=N_max_figures,
index_in_list_to_sortby=-1,
reverse_order=False,
)
# %%
reload(plot)
network_files = file_loaders.ABM_simulations(base_dir="Data/network", filetype="hdf5")
# for cfg in network_files.iter_cfgs():
# fig, axes = plot.plot_corona_type_single_plot(cfg, network_files)
plot.plot_corona_type_single_plot(network_files, force_rerun=False)
# %%