from pyABC_study.ODE import ODESolver, PriorLimits, para_true1, para_prior
print("\n\n\n Base\n Median eps, 2000 particles, 20 generations\n\n\n")
# %% Set path
db_path = "sqlite:///dbfiles/ib_wide.db"
# %% Generate synthetic data
# Using default time points
solver = ODESolver()
solver.time_point = solver.time_point_default
obs_data_raw = solver.ode_model1(para_true1)
print("Target data")
print(obs_data_raw)
# Set factors
# print("Factors applied: 75/25 factor")
# factors = {}
# time_length: int = len(solver.time_point) * 4
# # for i in range(0, time_length, 4):
# # factors[i] = 1/26.520
def result_data(history,
solver: ODESolver,
compare_data=exp_data_s,
nr_population=1,
sample_size=500,
savefig=False):
"""
Visualise the simulated trajectories from the inferred posterior
:param solver: ODE solver object
:param savefig: Decide to save the plot as file or not
:param history: abc.history object
:param compare_data: target data
:param nr_population: the id of population to be visualised
:param sample_size: sampling size of the selected population
"""
df, w = history.get_distribution(t=nr_population)
# if is_old:
# df.columns = ['i_beta_phi', 'k_phi_beta', 'k_n_beta', 'lambda_phi', 'lambda_n', 'mu_alpha', 'mu_beta', 'mu_phi',
# 'mu_n', 's_alpha_phi', 's_beta_n', 'v_n_phi']
df_sample = df.sample(sample_size, replace=False)
df_all_sim_data = pd.DataFrame(columns=['N', 'M', 'B', 'A'])
solver.time_point = solver.time_point_default
for ii in range(sample_size):
temp_dict = df_sample.iloc[ii].to_dict()
sim_data = solver.ode_model(temp_dict, flatten=False)
# print(sim_data)
sim_data = pd.DataFrame.from_dict(sim_data)
df_all_sim_data = pd.concat([df_all_sim_data, sim_data])
# plt.ylim(0, 2) # make lim a function parameter
# plt.show()
df_mean = pd.DataFrame(columns=['N', 'M', 'B', 'A'])
for jj in range(solver.time_point.__len__()):
# print(df_all_sim_data.loc[jj].mean())
df_mean = df_mean.append(df_all_sim_data.loc[jj].mean(),
ignore_index=True)
df_median = quantile_calculate(df_all_sim_data,
solver.time_point.__len__(), 0.5)
df_75 = quantile_calculate(df_all_sim_data, solver.time_point.__len__(),
0.75)
df_25 = quantile_calculate(df_all_sim_data, solver.time_point.__len__(),
0.25)
fig, axs = plt.subplots(4, 1, figsize=(4, 12))
# plt.subplots_adjust(hspace=0.5)
index_cov = ['N', 'M', 'B', 'A']
titles = ["N", "Φ", "β", "α"]
for kk in range(4):
seq_mask = np.isfinite(compare_data[index_cov[kk]])
# axs[kk].plot(solver.timePoint, df_25.iloc[:, kk], 'b--')
# axs[kk].plot(solver.timePoint, df_75.iloc[:, kk], 'b--')
axs[kk].fill_between(solver.time_point,
df_25.iloc[:, kk],
df_75.iloc[:, kk],
alpha=0.9,
color='lightgrey',
label='25% – 75% quantile range')
axs[kk].plot(solver.time_point,
df_mean.iloc[:, kk],
'b',
label="Mean",
alpha=0.6)
axs[kk].plot(solver.time_point_exp[seq_mask],
compare_data[index_cov[kk]][seq_mask],
alpha=0.7,
marker='^',
color='black',
label='Observed')
axs[kk].errorbar(solver.time_point_exp,
compare_data[index_cov[kk]],
yerr=[[0.5 * x for x in exp_data_SEM[index_cov[kk]]],
[0.5 * x for x in exp_data_SEM[index_cov[kk]]]],
fmt='none',
ecolor='grey',
elinewidth=2,
alpha=0.6)
# axs[kk].legend(['Mean', '25% – 75% quantile range', 'Observed'])
axs[kk].set_title(titles[kk])
# handles, labels = axs[0].get_legend_handles_labels()
# fig.legend(handles, labels, loc='upper center')
# fig.tight_layout(pad=5.0)
if savefig:
plt.savefig("resultCurve.png", dpi=200)
# plt.subplots_adjust(hspace=5.5)
plt.show()
from pyABC_study.ODE import ODESolver, PriorLimits, arr2d_to_dict, para_true1, para_prior
from pyABC_study.dataPlot import result_data_old, result_plot, result_data
# %% Settings
lim = PriorLimits(1e-6, 50)
prior_distribution = "uniform"
print(prior_distribution)
para_prior1 = para_prior(lim, prior_distribution, 1)
solver = ODESolver()
solver.time_point = solver.time_point_default
obs_data_raw_s = solver.ode_model1(para_true1, flatten=False, add_noise=False)
solver.time_point = solver.time_point_exp
obs_data_raw_s_less = solver.ode_model(para_true1, flatten=False, add_noise=False)
# print("Target data")
# print(obs_data_noisy_s)
# %% Load database
db_path = "sqlite:///db/abcsmc_test.db"
history = pyabc.History(db_path)
import pyabc
from pyABC_study.ODE import ODESolver, PriorLimits, para_true1, para_prior
print("\n\n\n Base\n Median eps, 2000 particles, 20 generations\n\n\n")
# %% Set path
db_path = "sqlite:///dbfiles/ib_less.db"
# %% Generate synthetic data
# Using default time points
solver = ODESolver()
solver.time_point = solver.time_point_exp
obs_data_raw = solver.ode_model1(para_true1)
print("Target data")
print(obs_data_raw)
# Set factors
# print("Factors applied: 75/25 factor")
# factors = {}
# time_length: int = len(solver.time_point) * 4
# # for i in range(0, time_length, 4):
# # factors[i] = 1/26.520