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"
])
# 'delay': DiscreteJumpTransition(domain=delay_domain),
# 'n01': DiscreteJumpTransition(domain=n01_domain),
# 'n02': DiscreteJumpTransition(domain=n02_domain),
'k': DiscreteJumpTransition(domain=k_domain, p_stay=.8),
('n01', 'n02', 'log_p', 'p_inf'): GridSearchCV()
})
id = 'n=3e5_2'
db = "sqlite:///" + os.path.join(os.getcwd(), id + ".db")
abc = ABCSMC(
model,
prior,
distance,
transitions=transition,
population_size=200,
)
# abc.load(db, int(np.load('run_id_'+id+'.npy')))#, {'cases': data})
abc.load(db, 1)
# history = abc.new(db, {'cases': data})
np.save('run_id_' + id + '.npy', abc.history.id)
abc.run(max_nr_populations=3)
history = abc.history
print(history.max_t)
df, w = history.get_distribution()
# kde = GridSearchCV().fit(df, w)
# print(kde.rvs()['log_p'])
# df['p'] = 10 ** (-df['log_p'])
plot_kde_matrix(df, w)
plt.show()
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)
# Parameter inference using approximate Bayesian computation (pyABC)
limits = dict(Omega=(0, 0.3),
Probability=(0, 0.2),
Lambda=(0, 1.5),
Gamma=(0, 3))
parameter_prior = Distribution(
**{key: RV("uniform", a, b - a)
for key, (a, b) in limits.items()})
db_path = pyabc.create_sqlite_db_id(file_="glioblatomaLanModel_syn.db")
abc = ABCSMC(models = determineTestParameters, \
parameter_priors = parameter_prior, \
distance_function = DistanceAfterBinning, \
population_size = 160, \
sampler = sampler.MulticoreParticleParallelSampler(), \
transitions = transition.LocalTransition(k_fraction=0.3))
abc.new(db_path, expBarFreq)
h = abc.run(minimum_epsilon=0.1, max_nr_populations=10)
df, w = h.get_distribution(m=0)
plot_kde_matrix(df, w, limits=limits)
plt.savefig('infer_result.pdf')
plt.clf()
pickle_out = open('result', 'wb')
pickle.dump(h.get_distribution(), pickle_out)
pickle_out.close()
plot_epsilons(h)
plt.savefig('epsilon_plot.pdf')
plt.clf()
abc = ABCSMC(models = model, \
parameter_priors = parameter_prior, \
distance_function = DistanceAfterBinning, \
# population_size = 100, \
sampler = sampler.MulticoreParticleParallelSampler(), \
transitions = [transition.LocalTransition(k_fraction=0.3), transition.LocalTransition(k_fraction=0.3)])
abc.new(db_path, expBarFreq)
h = abc.run(minimum_epsilon=0.1, max_nr_populations=5)
pickle_out = open('model_probability', 'wb')
pickle.dump(h.get_model_probabilities(), pickle_out)
pickle_out.close()
df, w = h.get_distribution(m=0)
plot_kde_matrix(df, w, limits=LanPrior)
plt.savefig('infer_result_MS_SCH.pdf')
plt.clf()
df, w = h.get_distribution(m=1)
plot_kde_matrix(df, w, limits=QSCPrior)
plt.savefig('infer_result_MS_QSC.pdf')
plt.clf()
#pickle_out = open('history_10G','wb')
#pickle.dump(h, pickle_out)
#pickle_out.close()
#pickle_out = open('result_10G','wb')
#pickle.dump(h.get_distribution(), pickle_out)
#pickle_out.close()