def single_trait_sim(par):
sim = traitsim(h=1,
num_iteration=1,
num_species=10,
gamma1=par[0],
gamma_K2=par[0],
a=par[1],
r=1,
theta=0,
K=5000,
mean_trait=0,
dev_trait=20,
mean_pop=50,
dev_pop=20,
num_time=2000,
replicate=0)
return sim
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")
a = 0.01
h = 1
# statistics for settings
for gamma1 in gamma_vec:
count2 = 1
gamma_K2 = gamma1
for a in a_vec:
for num_species in num_species_vec:
traitdata = traitsim(num_time=num_time,
num_species=num_species,
num_iteration=num_iteration,
gamma1=gamma1,
a=a,
r=r,
K=K,
theta=theta,
mean_trait=0,
dev_trait=10,
mean_pop=50,
dev_pop=10,
gamma_K2=gamma_K2,
h=h)
fig = drawplot(traitdata=traitdata)
par = (num_species, num_time, num_iteration, count1, count2) #
# detect the current dir
script_dir = os.path.dirname('__file__')
results_dir = os.path.join(script_dir, 'resultes/')
# file names
name = "species%d-time%d-sim%d-nat%d-com%d-DRvsDK" % par
file_name = "%s.pdf" % name
# if dir doesn't exist, create it
