# %% Plot
# obs_data_plot(solver.timePoint, obs_data_noisy_s, obs_data_raw_s)
# %% Define prior distribution of parameters
# Be careful that RV("uniform", -10, 15) means uniform distribution in [-10, 5], '15' here is the interval length
# Set prior
lim = PriorLimits(1e-6, 20)
prior_distribution = "loguniform"
print(prior_distribution)
para_prior1 = para_prior(lim, prior_distribution, 1)
para_prior2 = para_prior(lim, prior_distribution, 2)
para_prior3 = para_prior(lim, prior_distribution, 3)
para_prior4 = para_prior(lim, prior_distribution, 4)
para_prior5 = para_prior(lim, prior_distribution, 5)
# %% Define ABC-SMC model
distanceP2 = pyabc.PNormDistance(p=2) # , factors=factors)
eps0 = pyabc.MedianEpsilon(60)
# eps_fixed = pyabc.epsilon.ListEpsilon([50, 46, 43, 40, 37, 34, 31, 29, 27, 25,
# 23, 21, 19, 17, 15, 14, 13, 12, 11, 10])
# transition0 = pyabc.transition.LocalTransition(k=50, k_fraction=None)
# set number of cores
import numpy as np
import pandas as pd
import pyabc
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"
