import numpy as np
import time
import emcee
from multiprocessing import Pool
dir_repo = str(pathlib.Path(__file__).parent.absolute()) + '/../..'
dir_KLens = dir_repo + '/KLens'
sys.path.append(dir_KLens)
dir_binnedFit = dir_repo + '/BinnedFit'
sys.path.append(dir_binnedFit)
sys.path.append(dir_binnedFit + '/tests')
from get_pars import get_pars0
from gen_mocks import gen_mock_tfCube
from gamma import GammaInference
pars, _ = get_pars0()
dataInfo = gen_mock_tfCube(pars, 'Halpha', slits='both', noise_mode=0)
# ----------------------
GI = GammaInference(dataInfo,
active_par_key=[
'vcirc', 'sini', 'vscale', 'r_0', 'v_0', 'g1', 'g2',
'r_hl_image', 'theta_int', 'flux'
],
par_fix=None,
vTFR_mean=200.)
chainInfo = GI.run_MCMC(Nwalker=20,
Nsteps=4,
outfile_MCMC="./chain_Ha_noise0.pkl",
save_step_size=2)
# change dataInfo['spec'] = [2Darr_1, 2Darr_2] from a list of np.array to be a list of Spec2D objects
# making Spec2D objects
for j in range(len(dataInfo['spec'])):
dataInfo['spec'][j] = Spec2D(array=dataInfo['spec'][j],
array_var=dataInfo['spec_variance'][j],
spaceGrid=dataInfo['spaceGrid'],
lambdaGrid=dataInfo['lambdaGrid'],
line_species=line_species,
z=dataInfo['par_fid']['redshift'],
auto_cut=False)
# make Image object
dataInfo['image'] = Image(dataInfo['image'],
dataInfo['spaceGrid'],
array_var=dataInfo['image_variance'])
dataInfo['eint_thy'] = eint_thy
return dataInfo
if __name__ == '__main__':
sys.path.append(dir_binnedFit + '/tests')
from get_pars import get_pars0
pars, line_species = get_pars0()
dataInfo = gen_mock_tfCube(pars, line_species, slits='major', noise_mode=0)
#dataInfo['image'].display(xlim=[-2,2])