def test_update_nr_samples(history: History):
history.store_initial_data(None, {}, {}, {}, ["m0"], "", "", "")
pops = history.get_all_populations()
assert 0 == pops[pops['t'] == History.PRE_TIME]['samples'].values
history.update_nr_samples(History.PRE_TIME, 43)
pops = history.get_all_populations()
assert 43 == pops[pops['t'] == History.PRE_TIME]['samples'].values
def test_update_after_calibration(history: History):
history.store_initial_data(None, {}, {}, {}, ["m0"], "", "", "")
pops = history.get_all_populations()
assert 0 == pops[pops['t'] == History.PRE_TIME]['samples'].values
time = datetime.datetime.now()
history.update_after_calibration(43, end_time=time)
pops = history.get_all_populations()
assert 43 == pops[pops['t'] == History.PRE_TIME]['samples'].values
assert pops.population_end_time[0] == time
def test_population_retrieval(history: History):
history.append_population(
1, .23, Population(rand_pop_list(0)), 234, ["m1"])
history.append_population(
2, .123, Population(rand_pop_list(0)), 345, ["m1"])
history.append_population(
2, .1235, Population(rand_pop_list(5)), 20345, ["m1"] * 6)
history.append_population(
3, .12330, Population(rand_pop_list(30)), 30345, ["m1"] * 31)
df = history.get_all_populations()
assert df[df.t == 1].epsilon.iloc[0] == .23
assert df[df.t == 2].epsilon.iloc[0] == .123
assert df[df.t == 2].epsilon.iloc[1] == .1235
assert df[df.t == 3].epsilon.iloc[0] == .12330
assert df[df.t == 1].samples.iloc[0] == 234
assert df[df.t == 2].samples.iloc[0] == 345
assert df[df.t == 2].samples.iloc[1] == 20345
assert df[df.t == 3].samples.iloc[0] == 30345
assert history.alive_models(1) == [0]
assert history.alive_models(2) == [0, 5]
assert history.alive_models(3) == [30]
print("ID", history.id)
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()
