import numpy as np
from Trait_sim_in_branches_stat import traitsim
from ABC_MCMC import calibrication,MCMC_ABC
# Observation parameters [gamma,a]
par_obs = np.array([0.1,0.1])
# Observation generated
obs = traitsim(h = 1, num_iteration=1,num_species=10,gamma1=par_obs[0],gamma_K2=par_obs[0],a = par_obs[1],r = 1,
theta = 0,K = 5000 , mean_trait=0,dev_trait=20,mean_pop=50,dev_pop=20, num_time=2000,replicate=1)
# Calibriation step
cal_size = 20000
priorpar = [0.2,0.5,0.1,0.4]
collection = calibrication(samplesize = cal_size, priorpar = priorpar, obs = obs, mode='nor')
np.savetxt("/home/p274981/Python_p2/calibration2w_3chains.txt",collection)
#collection = np.loadtxt("/home/p274981/Python_p2/testcal.txt")
import matplotlib.pyplot as plt
from ABC_MCMC import calibrication, MCMC_ABC
from sklearn.neighbors import KernelDensity
import scipy.stats
import numpy as np
# Calibrication step
cal_size = 20000
# TEST1: Uniform prior distribution example
priorpar = [0.0001, 1, 0.0001, 1]
collection = calibrication(samplesize=cal_size, priorpar=priorpar, obs=obs)
# np.savetxt("c:/Liang/Googlebox/Research/Project2/python_p2/testcal.txt",collection)
# collection = np.loadtxt("c:/Liang/Googlebox/Research/Project2/python_p2/testcal.txt")
#TEST2: Normal prior distribution example
priorpar = [0.1, 0.2, 0.1, 0.3]
# collection = calibrication(samplesize = cal_size, priorpar = priorpar, obs = obs, mode = 'nor')
# np.savetxt("c:/Liang/Googlebox/Research/Project2/python_p2/testcal.txt",collection)
# collection = np.loadtxt("c:/Liang/Googlebox/Research/Project2/python_p2/priorresult/calibration2w.txt")
#TEST3: Normal prior distribution with 3 MCMCs
priorpar = [0.2, 0.5, 0.1, 0.4]
# collection = calibrication(samplesize = cal_size, priorpar = priorpar, obs = obs, mode = 'nor')
# np.savetxt("c:/Liang/Googlebox/Research/Project2/python_p2/testcal.txt",collection)
# collection = np.loadtxt("c:/Liang/Googlebox/Research/Project2/python_p2/MCMC3/calibration2w_3chains.txt")
cal_size = 100000
# TEST4: Uniform prior distribution example
priorpar = [0.0001, 1, 0.0001, 1]
collection = calibrication(samplesize=cal_size, priorpar=priorpar, obs=obs)
# np.savetxt("c:/Liang/Googlebox/Research/Project2/python_p2/testcal.txt",collection)