def test_save_no_sum_stats(history: History):
"""
Test that what has been stored can be retrieved correctly
also when no sum stats are saved.
"""
particle_list = []
for _ in range(0, 6):
particle = Particle(
m=0,
parameter=Parameter({"th0": np.random.random()}),
weight=1.0 / 6,
sum_stat={"ss0": np.random.random(), "ss1": np.random.random()},
distance=np.random.random(),
)
particle_list.append(particle)
population = Population(particle_list)
# do not save sum stats
# use the attribute first to make sure we have no typo
print(history.stores_sum_stats)
history.stores_sum_stats = False
# test some basic routines
history.append_population(
t=0,
current_epsilon=42.97,
population=population,
nr_simulations=10,
model_names=[""],
)
# just call
history.get_distribution(0, 0)
# test whether weights and distances returned correctly
weighted_distances_h = history.get_weighted_distances()
weighted_distances = population.get_weighted_distances()
assert np.allclose(
weighted_distances_h[['distance', 'w']],
weighted_distances[['distance', 'w']],
)
weights, sum_stats = history.get_weighted_sum_stats(t=0)
# all particles should be contained nonetheless
assert len(weights) == len(particle_list)
for sum_stat in sum_stats:
# should be empty
assert not sum_stat
history.get_population_extended()
def animated_gif(db, output):
from pyabc import History
from pyabc.visualization import plot_kde_matrix
import matplotlib.pyplot as plt
import subprocess
import tempfile
tempdir = tempfile.mkdtemp()
print("tmpdir", tempdir)
import os
h_loaded = History("sqlite:///" + db)
limits = dict(log_division_rate=(-3, -1),
log_division_depth=(1, 3),
log_initial_spheroid_radius=(0, 1.2),
log_initial_quiescent_cell_fraction=(-5, 0),
log_ecm_production_rate=(-5, 0),
log_ecm_degradation_rate=(-5, 0),
log_ecm_division_threshold=(-5, 0))
for t in range(h_loaded.n_populations):
print("Plot population {t/h_loaded.n_populations}")
df, w = h_loaded.get_distribution(m=0, t=t)
plot_kde_matrix(df, w, limits=limits)
plt.savefig(os.path.join(tempdir, f"{t:0>2}.png"))
subprocess.run([
"convert", "-delay", "15",
os.path.join(tempdir, "%02d.png"), output + ".gif"
])
def abchpdi(dbfile, history_id):
"""
Diagnostic plots for examining how abc fitting worked
"""
db_path = 'sqlite:///' + dbfile
abc_history = History(db_path)
abc_history.id = history_id
# simtools.PARAMS = toml.load(paramfile)
parameters = ['s', 'c', 'w', 'n', 'm', 'r']
abc_data, __ = abc_history.get_distribution(m=0, t=abc_history.max_t)
data = {x: abc_data[x] for x in parameters}
# print(list(data['m']))
hpdis = {x: hpdi(list(data[x])) for x in data}
means = {x: np.mean(list(data[x])) for x in data}
stds = {x: np.std(list(data[x])) for x in data}
covs = {x: np.std(list(data[x])) / np.mean(list(data[x])) for x in data}
# t_quartile1, t_medians, t_quartile3 = np.percentile(
# data, [25, 50, 75], axis=1
# )
for k, v in hpdis.items():
print(k, v)
for k, v in means.items():
print(k, v)
for k, v in stds.items():
print(k, v)
for k, v in covs.items():
print("cov", k, np.round(v, 3))
def print_abcfit_rate_data(paramfile, dbfile, history_id):
"""
Plots showing off the fit from abc
"""
db_path = 'sqlite:///' + dbfile
abc_history = History(db_path)
abc_history.id = history_id
simtools.PARAMS = toml.load(paramfile)
### PLOT OF RATE###
abc_data, __ = abc_history.get_distribution(m=0, t=abc_history.max_t)
parameters = ['s', 'c', 'w', 'n', 'm', 'r']
params = {k: np.median(abc_data[k]) for k in parameters}
f_rate_1 = Rate(params['s'], params['c'], params['w'],
simtools.PARAMS['optimum_normal'], params['m'])
f_rate_2 = Rate(params['s'], params['c'], params['w'],
simtools.PARAMS['optimum_treatment'],
params['m'] * params['r'])
f_noise = Noise(params['n'])
x_axis = np.linspace(*simtools.PARAMS['parameter_range'],
simtools.PARAMS['parameter_points'])
print('x\trate1\trate2')
for x, r1, r2 in zip(x_axis, f_rate_1(x_axis), f_rate_2(x_axis)):
print(x, r1, r2, sep='\t')
def test_single_particle_save_load_np_int64(history: History):
# Test if np.int64 can also be used for indexing
# This is an important test!!!
m_list = [0, np.int64(0)]
t_list = [0, np.int64(0)]
particle_list = [
Particle(m=0,
parameter=Parameter({
"a": 23,
"b": 12
}),
weight=.2,
accepted_sum_stats=[{
"ss": .1
}],
accepted_distances=[.1])
]
history.append_population(0, 42, Population(particle_list), 2, [""])
for m in m_list:
for t in t_list:
df, w = history.get_distribution(m, t)
assert w[0] == 1
assert df.a.iloc[0] == 23
assert df.b.iloc[0] == 12
def test_single_particle_save_load(history: History):
particle_list = [
Particle(m=0,
parameter=Parameter({"a": 23, "b": 12}),
weight=.2,
accepted_sum_stats=[{"ss": .1}],
accepted_distances=[.1])
]
history.append_population(0, 42, Population(particle_list), 2, [""])
df, w = history.get_distribution(0, 0)
assert w[0] == 1
assert df.a.iloc[0] == 23
assert df.b.iloc[0] == 12
def test_single_particle_save_load(history: History):
particle_list = [
Particle(0, Parameter({
"a": 23,
"b": 12
}), .2, [.1], [{
"ss": .1
}], [], True)
]
history.append_population(0, 42, Population(particle_list), 2, [""])
df, w = history.get_distribution(0, 0)
assert w[0] == 1
assert df.a.iloc[0] == 23
assert df.b.iloc[0] == 12
def test_single_particle_save_load(history: History):
particle_list = [
Particle(
m=0,
parameter=Parameter({"a": 23, "b": 12}),
weight=1.0,
sum_stat={"ss": 0.1},
distance=0.1,
),
]
history.append_population(0, 42, Population(particle_list), 2, [""])
df, w = history.get_distribution(0, 0)
assert w[0] == 1
assert df.a.iloc[0] == 23
assert df.b.iloc[0] == 12
def test_single_particle_save_load_np_int64(history: History):
# Test if np.int64 can also be used for indexing
# This is an important test!!!
m_list = [0, np.int64(0)]
t_list = [0, np.int64(0)]
particle_population = [
ValidParticle(0, Parameter({
"a": 23,
"b": 12
}), .2, [.1], [{
"ss": .1
}])
]
history.append_population(0, 42, particle_population, 2, [""])
for m in m_list:
for t in t_list:
df, w = history.get_distribution(m, t)
assert w[0] == 1
assert df.a.iloc[0] == 23
assert df.b.iloc[0] == 12
def setup(modelfile: str,
*experiments: Experiment,
err_pars: List[str]=None,
pacevar: str='membrane.V',
tvar: str='phys.T',
prev_runs: List[str]=[],
logvars: List[str]=myokit.LOG_ALL,
log_interval: float=None,
normalise: bool=True
) -> Tuple[pd.DataFrame, Callable, Callable]:
"""Combine chosen experiments into inputs for ABC.
Args:
modelfile (str): Path to Myokit MMT file.
*experiments (Experiment): Any number of experiments to run in ABC.
err_pars (List[str]): Optional list of parameters representing model
discrepancy variance for each experiment.
pacevar (str): Optionally specify name of pacing variable in modelfile.
Defaults to `membrane.V` assuming voltage clamp protocol but could
also be set to stimulating current.
tvar (str): Optionally specify name of temperature in modelfile.
Defaults to `phys.T`.
prev_runs (List[str]): Path to previous pyABC runs containing samples
to randomly sample outside of ABC algorithm.
logvars (List[str]): Optionally specify variables to log in simulations.
Returns:
Tuple[pd.DataFrame, Callable, Callable]:
Observations combined from experiments.
Model function to run combined protocols from experiments.
Summary statistics function to convert 'raw' simulation output.
"""
# Create Myokit model instance
m = myokit.load_model(modelfile)
# Set pacing variable
pace = m.get(pacevar)
if pace.binding() != 'pace':
if pace.is_state():
pace.demote()
pace.set_rhs(0)
pace.set_binding('pace')
model_temperature = m.get(tvar).value()
# Initialise combined variables
observations = get_observations_df(list(experiments),
normalise=normalise,
temp_adjust=True,
model_temperature=model_temperature)
# Combine protocols into Myokit simulations
simulations, times = [], []
for exp in list(experiments):
s = myokit.Simulation(m, exp.protocol)
for ci, vi in exp.conditions.items():
s.set_constant(ci, vi)
simulations.append(s)
times.append(exp.protocol.characteristic_time())
# Get previous pyABC runs
# Note: defaults to latest run in database file
sample_df, sample_w = [], []
for run in prev_runs:
h = History(run)
df, w = h.get_distribution()
sample_df.append(df)
sample_w.append(w)
# Create model function
def simulate_model(**pars):
sim_output = []
# Pre-optimised parameters
for df, w in zip(sample_df, sample_w):
pars = dict([(key[4:], 10**value) if key.startswith("log")
else (key, value)
for key, value in df.sample(weights=w, replace=True).items()],
**pars)
for sim, time in zip(simulations, times):
for p, v in pars.items():
if err_pars is not None and p in err_pars:
continue
try:
sim.set_constant(p, v)
except:
warnings.warn("Could not set value of {}"
.format(p))
return None
sim.reset()
try:
sim_output.append(sim.run(time, log=logvars, log_interval=log_interval))
except:
del(sim_output)
return None
return sim_output
def model(x):
return log_transform(simulate_model)(**x)
# Combine summary statistic functions
normalise_factor = {}
for i, f in enumerate(observations.normalise_factor):
normalise_factor[i] = f
sum_stats_combined = combine_sum_stats(
*[e.sum_stats for e in list(experiments)]
)
def summary_statistics(data):
if data is None:
return {str(i): np.inf for i in range(len(observations))}
ss = {str(i): val/normalise_factor[i]
for i, val in enumerate(sum_stats_combined(data))}
return ss
return observations, model, summary_statistics
mean_dict.update({param: res_mean[0]})
return mean_dict
if __name__ == '__main__':
# load history
h_loaded = History("sqlite:///"
+ "hft_abm_smc_abc/resultsReal_Data_Small_Test - Smaller Test - eps1_negfix_pop6_pop301597579353.943031.db")
# check that the history is not empty
print(h_loaded.all_runs())
from pyabc.visualization import plot_kde_matrix
df, w = h_loaded.get_distribution(m=0, t=4)
plot_kde_matrix(df, w);
plt.show()
fig, axs = plt.subplots(2, 3)
plot_coonvergence(h_loaded, 'mu', MU_MIN, MU_MAX, MU_TRUE, ax=axs[0, 0])
plot_coonvergence(h_loaded, 'lambda0', LAMBDA0_MIN, LAMBDA0_MAX, LAMBDA0_TRUE, ax=axs[0, 1])
plot_coonvergence(h_loaded, 'delta', DELTA_MIN, DELTA_MAX, DELTA_TRUE, ax=axs[0, 2])
plot_coonvergence(h_loaded, 'delta_S', DELTAS_MIN, DELTAS_MAX, DELTA_S_TRUE, ax=axs[1, 0])
plot_coonvergence(h_loaded, 'alpha', ALPHA_MIN, ALPHA_MAX, ALPHA_TRUE, ax=axs[1, 1])
plot_coonvergence(h_loaded, 'C_lambda', C_LAMBDA_MIN, C_LAMBDA_MAX, C_LAMBDA_TRUE, ax=axs[1, 2])
plt.gcf().set_size_inches((12, 8))
plt.gcf().tight_layout()
plt.show()
_, arr_ax = plt.subplots(1, 2)
def __init__(self, abc_hist: History):
# Get the dataframe of particles (parameter point estimates) and associated weights
dist_df, dist_w = abc_hist.get_distribution(m=0, t=abc_history.max_t)
# Create a KDE using the particles
self.kde = MultivariateNormalTransition(scaling=1)
self.kde.fit(dist_df, dist_w)
def abc_info(paramfile, obsfile, dbfile, run_id, save):
"""
Plots for examining ABC fitting process
"""
db_path = 'sqlite:///' + dbfile
abc_history = History(db_path)
abc_history.id = run_id
observed = simtools.parse_observations(obsfile)
simtools.parse_params(paramfile, observed)
### PLOTS SHOWING MODEL PROBABILITIES ###
num_models = abc_history.nr_of_models_alive(0)
max_points_in_models = max([abc_history.get_distribution(m=x, t=0)[0].shape[1] for x in range(num_models)])
axs = abc_history.get_model_probabilities().plot.bar()
axs.set_ylabel("Probability")
axs.set_xlabel("Generation")
resolutions = list(range(simtools.PARAMS['abc_params']['resolution_limits'][0],
simtools.PARAMS['abc_params']['resolution_limits'][1] + 1))
axs.legend(resolutions,
title="Reconstruction resolution")
if save is not None:
# first time, construct the multipage pdf
pdf_out = PdfPages(save)
pdf_out.savefig()
else:
plt.show()
### ABC SIMULATION DIAGNOSTICS ###
fig, ax = plt.subplots(nrows=3, sharex=True)
t_axis = list(range(abc_history.max_t + 1))
populations = abc_history.get_all_populations()
populations = populations[populations.t >= 0]
ax[0].plot(t_axis, populations['particles'])
ax[1].plot(t_axis, populations['epsilon'])
ax[2].plot(t_axis, populations['samples'])
ax[0].set_title('ABC parameters per generation')
ax[0].set_ylabel('Particles')
ax[1].set_ylabel('Epsilon')
ax[2].set_ylabel('Samples')
ax[-1].set_xlabel('Generation (t)')
ax[0].xaxis.set_major_locator(MaxNLocator(integer=True))
fig.set_size_inches(8, 5)
if save is not None:
pdf_out.savefig()
else:
plt.show()
### PARAMETERS OVER TIME ###
fig, axs = plt.subplots(nrows=max_points_in_models, sharex=True, sharey=True)
t_axis = np.arange(abc_history.max_t + 1)
# print(t_axis)
# parameters = ['birthrate.s0.d', 'birthrate.s0.r0']
all_parameters = [list(abc_history.get_distribution(m=m, t=0)[0].columns)
for m in range(num_models)]
# abc_data, __ = abc_history.get_distribution(m=m, t=generation)
parameters = []
for x in all_parameters:
for y in x:
parameters.append(y)
parameters = list(set(parameters))
parameters = sorted(parameters, key=lambda x: x[-1])
# print(parameters)
for m in range(num_models):
qs1 = {param: [np.nan for __ in t_axis] for param in parameters}
medians = {param: [np.nan for __ in t_axis] for param in parameters}
qs3 = {param: [np.nan for __ in t_axis] for param in parameters}
for i, generation in enumerate(t_axis):
abc_data, __ = abc_history.get_distribution(m=m, t=generation)
data = {x: np.array(abc_data[x]) for x in parameters if x in abc_data}
for k, v in data.items():
t_q1, t_m, t_q3 = np.percentile(
v, [25, 50, 75]
)
qs1[k][i] = t_q1
medians[k][i] = t_m
qs3[k][i] = t_q3
for i, param in enumerate(parameters):
# if len(medians[param]) == 0:
if not medians[param]:
continue
# print(t_axis, medians[param])
axs[i].plot(t_axis, medians[param], color=COLORS[m])
axs[i].fill_between(t_axis, qs1[param], qs3[param], color=COLORS[m], alpha=0.2)
axs[i].set_ylabel(param[10:])
axs[-1].set_xlabel('Generation (t)')
if save is not None:
pdf_out.savefig()
else:
plt.show()
if save is not None:
pdf_out.close()
def tabulate_single(paramfile, obsfile, dbfile, csvfile, run_id):
"""
Table of results (appending to table)
"""
fieldnames = ['name', 'model_index', 'model_probability', 'rate_position', 'rate_mean', 'rate_stdev']
db_path = 'sqlite:///' + dbfile
abc_history = History(db_path)
abc_history.id = run_id
observed = simtools.parse_observations(obsfile)
# print(observed)
# id_str = next(iter(observed))
simtools.parse_params(paramfile, observed)
# violin plot of results
max_gen = abc_history.max_t
# num_models_total = abc_history.nr_of_models_alive(0)
num_models_total = simtools.PARAMS['abc_params']['resolution_limits'][1] - simtools.PARAMS['abc_params']['resolution_limits'][0] + 1
num_models_final = abc_history.nr_of_models_alive(max_gen)
max_point_in_models = max([abc_history.get_distribution(m=x, t=max_gen)[0].shape[1]
for x in range(num_models_final)])
# print(max_gen, num_models_total, num_models_final)
with open(csvfile, 'w') as csv_out:
wtr = csv.DictWriter(csv_out, fieldnames=fieldnames)
wtr.writeheader()
for j in range(num_models_total):
# print(abc_history.get_model_probabilities())
if j not in abc_history.get_model_probabilities():
continue
model_prob = abc_history.get_model_probabilities()[j][max_gen]
if model_prob == 0.0:
continue
# print(j + 1, model_prob)
df, w = abc_history.get_distribution(m=j, t=max_gen)
# print(df)
# print(df.columns)
# abc_data = [sorted(df['birthrate.b' + str(x)]) for x in range(df.shape[1])]
# for x in list(df.columns):
# print(x)
# print(df[x])
abc_data = [sorted(df[x]) for x in list(df.columns)]
# print(abc_data)
for i, d in enumerate(abc_data):
print('HPDI')
hpdi_interval = hpdi(d)
print(hpdi_interval)
print('MEAN')
mean = np.mean(d)
print(mean)
print('SIGMA')
sigma = np.std(d)
print(sigma)
row = {
'name': simtools.PARAMS['plot_params']['coupling_names'],
'model_index': j,
'model_probability': model_prob,
'rate_position': i,
'rate_mean': mean,
'rate_stdev': sigma,
}
wtr.writerow(row)
def result_single(paramfile, obsfile, dbfile, run_id, save):
"""
Plot the result of a single fitting
"""
db_path = 'sqlite:///' + dbfile
abc_history = History(db_path)
abc_history.id = run_id
observed = simtools.parse_observations(obsfile)
# print(observed)
id_str = next(iter(observed))
simtools.parse_params(paramfile, observed)
# violin plot of results
max_gen = abc_history.max_t
# num_models_total = abc_history.nr_of_models_alive(0)
num_models_total = simtools.PARAMS['abc_params']['resolution_limits'][1] - simtools.PARAMS['abc_params']['resolution_limits'][0] + 1
num_models_final = abc_history.nr_of_models_alive(max_gen)
max_point_in_models = max([abc_history.get_distribution(m=x, t=max_gen)[0].shape[1]
for x in range(num_models_final)])
# fig, axs = plt.subplots(ncols=num_models_final, sharey=True, sharex=True)
# fig.set_size_inches(num_models_final*3, 3)
if save is not None:
# first time, construct the multipage pdf
pdf_out = PdfPages(save)
for j in range(num_models_total):
if j not in abc_history.get_model_probabilities():
continue
model_prob = abc_history.get_model_probabilities()[j][max_gen]
# print(model_prob)
if model_prob == 0.0:
continue
fig, axs = plt.subplots()
fig.set_size_inches(4, 3)
end_time = simtools.PARAMS['end_time'][id_str]()
# print(end_time)
df, w = abc_history.get_distribution(m=j, t=max_gen)
# print(df)
# print(df.columns)
# abc_data = [sorted(df['birthrate.b' + str(x)]) for x in range(df.shape[1])]
time_axis = np.linspace(0, end_time, len(list(df.columns)))
# for x in list(df.columns):
# print(x)
# print(df[x])
abc_data = [sorted(df[x]) for x in list(df.columns)]
# print(abc_data)
violinparts = axs.violinplot(abc_data, positions=time_axis,
widths=end_time/(max_point_in_models + 1)*0.8,
showmeans=False, showmedians=False, showextrema=False)
for part in violinparts['bodies']:
part.set_facecolor('lightgrey')
part.set_alpha(1)
# from user Ruggero Turra https://stackoverflow.com/questions/29776114/half-violin-plot
m = np.mean(part.get_paths()[0].vertices[:, 0])
part.get_paths()[0].vertices[:, 0] = np.clip(
part.get_paths()[0].vertices[:, 0],
-np.inf,
m
)
part.set_facecolor('lightgrey')
part.set_color('lightgrey')
for t, d in zip(time_axis, abc_data):
axs.scatter(t + np.random.uniform(
0.1,
end_time/(max_point_in_models + 1)*0.4,
size=len(d)
), d, color='grey', marker='.', s=1.0, alpha = 0.8)
# print('HPDI')
hpdi_interval = hpdi(d)
axs.plot([t + 0.1, t + end_time/(max_point_in_models + 1)*0.4],
[hpdi_interval[0], hpdi_interval[0]],
linestyle='--', color='k', linewidth=1.0)
axs.plot([t + 0.1, t + end_time/(max_point_in_models + 1)*0.4],
[hpdi_interval[1], hpdi_interval[1]],
linestyle='--', color='k', linewidth=1.0)
# for b in v1['bodies']:
# m = np.mean(b.get_paths()[0].vertices[:, 0])
# b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf, m)
# b.set_color('r')
quartile1, medians, quartile3 = np.percentile(abc_data, [25, 50, 75], axis=1)
whiskers = np.array([
adjacent_values(sorted_array, q1, q3)
for sorted_array, q1, q3 in zip(abc_data, quartile1, quartile3)])
whiskers_min, whiskers_max = whiskers[:, 0], whiskers[:, 1]
axs.scatter(time_axis, medians, marker='.', color='white', s=30, zorder=3)
axs.vlines(time_axis, whiskers_min, whiskers_max, color='k', linestyle='-', lw=1)
axs.vlines(time_axis, quartile1, quartile3, color='k', linestyle='-', lw=5)
birthrate = [statistics.median(x) for x in abc_data]
axs.plot(time_axis, birthrate, color='k')
axs.set_xlabel('Time [days]')
axs.set_ylabel(r'Growth rate [divisions day$^{-1}$ cell$^{-1}$]')
title = simtools.PARAMS['plot_params']['coupling_names']
axs.set_title(title)
# axs.set_ylim(0, simtools.PARAMS['abc_params']['rate_limits'][1])
plt.tight_layout()
if save is not None:
pdf_out.savefig()
else:
plt.show()
# fit against timeline
for j in range(num_models_total):
if j not in abc_history.get_model_probabilities():
continue
model_prob = abc_history.get_model_probabilities()[j][max_gen]
if model_prob == 0.0:
continue
fig, axs = plt.subplots()
fig.set_size_inches(4, 3)
end_time = simtools.PARAMS['end_time'][id_str]()
df, w = abc_history.get_distribution(m=j, t=max_gen)
time_axis = np.linspace(0, end_time, len(list(df.columns)))
# samplings = [simtools.get_samplings_dilutions(observed[id_str], x)[0]
# for x, __ in enumerate(observed[id_str]['time'])]
# dilutions = [simtools.get_samplings_dilutions(observed[id_str], x)[1]
# for x, __ in enumerate(observed[id_str]['time'])]
# print(observed)
# print('main obs', simtools.OBSERVED)
# id_str = list(observed.keys())[j]
samplings, dilutions = simtools.get_samplings_dilutions(observed[id_str])
# samplings = list(zip(*samplings))
# dilutions = list(zip(*dilutions))
abc_data = [sorted(df[x]) for x in list(df.columns)]
for k, v in observed.items():
# print(k, v)
samplings, dilutions = simtools.get_samplings_dilutions(observed[k])
measured = np.array(v['count'])
for s in samplings.transpose():
# print(measured, s)
measured /= s
for d in dilutions.transpose():
measured *= d
axs.scatter(v['time'], measured, marker='.', color='k')
# print(samplings, dilutions)
simulations = None
time_axis = np.linspace(0, max(observed[id_str]['time']), 100)
i = 0
for index, row in df.iterrows():
# if i > 100:
# break
# print(index, row)
time, size, rate = simtools.simulate_timeline(
simtools.PARAMS['starting_population'][id_str](),
time_axis,
list(row),
simtools.PARAMS['simulation_params']['deathrate_interaction'],
# simtools.PARAMS['abc_params']['simulator'],
'bernoulli',
verbosity=1
)
if simulations is None:
simulations = np.zeros((len(size), len(df)))
simulations[:, i] = size
i += 1
qt1, qt2, qt3 = np.quantile(simulations, (0.05, 0.5, 0.95), axis=1)
# print(qt2)
# axs.plot(time, qt1)
axs.plot(time_axis, qt2, color='k')
# axs.plot(time, qt3)
axs.fill_between(time_axis, qt1, qt3, zorder=-1, color='lightgray')
axs.set_xlabel('Time [days]')
measurename = 'Population measure'
if 'population_measure' in simtools.PARAMS['plot_params']:
measurename = simtools.PARAMS['plot_params']['population_measure']
axs.set_ylabel(measurename)
# print(j, i, index)
# print(simtools.PARAMS['abc_params']['birthrate_coupling_sets'])
title = simtools.PARAMS['plot_params']['coupling_names']
axs.set_title(title)
plt.tight_layout()
if save is not None:
pdf_out.savefig()
else:
plt.show()
if save is not None:
pdf_out.close()
