def spherical_gNFW_gas(self):
print('--------------------------------------------------')
print('spherical gNFW + gas model')
# calculate gas mge profile
if model['Mgas'] is None:
raise RuntimeError('Gas mass must be provided')
gas = util_gas.gas_exp(model['Mgas'])
model['gas3d'] = gas.mge3d
model['lnprob'] = lnprob_spherical_gNFW_gas
model['type'] = 'spherical_gNFW_gas'
model['ndim'] = 6
model['JAMpars'] = ['cosinc', 'beta', 'ml', 'logrho_s', 'rs', 'gamma']
# initialize the JAM class and pass to the global parameter
model['JAM'] = \
pyjam.axi_rms.jam(model['lum2d'], model['pot2d'],
model['distance'],
model['xbin'], model['ybin'], mbh=model['bh'],
quiet=True, sigmapsf=model['sigmapsf'],
pixsize=model['pixsize'], nrad=model['nrad'],
shape=model['shape'])
printModelInfo(model)
print('Gas Mass: {:.4e}'.format(model['Mgas']))
printBoundaryPrior(model)
nwalkers = model['nwalkers']
threads = model['threads']
ndim = model['ndim']
JAMpars = model['JAMpars']
if model['p0'] == 'flat':
p0 = flat_initp(JAMpars, nwalkers)
elif model['p0'] == 'fit':
raise ValueError('Calculate maximum lnprob positon from '
'optimisiztion - not implemented yet')
else:
raise ValueError('p0 must be flat or fit, {} is '
'not supported'.format(model['p0']))
initSampler = \
emcee.EnsembleSampler(nwalkers, ndim, model['lnprob'],
kwargs={'model': model}, threads=threads)
sys.stdout.flush()
sampler = _runEmcee(initSampler, p0)
# pool.close()
print('--------------------------------------------------')
print('Finish! Total elapsed time: {:.2f}s'.format(time() -
self.startTime))
rst = analyzeRst(sampler, model)
sys.stdout.flush()
model['rst'] = rst
dump(model)
def massFollowLight(self):
print('--------------------------------------------------')
print('Mass follow light model')
model['lnprob'] = lnprob_massFollowLight
model['type'] = 'massFollowLight'
model['ndim'] = 3
model['JAMpars'] = ['cosinc', 'beta', 'ml']
# initialize the JAM class and pass to the global parameter
model['JAM'] = \
pyjam.axi_rms.jam(model['lum2d'], model['pot2d'],
model['distance'],
model['xbin'], model['ybin'], mbh=model['bh'],
quiet=True, sigmapsf=model['sigmapsf'],
pixsize=model['pixsize'], nrad=model['nrad'],
shape=model['shape'])
printModelInfo(model)
printBoundaryPrior(model)
nwalkers = model['nwalkers']
threads = model['threads']
ndim = model['ndim']
JAMpars = model['JAMpars']
if model['p0'] == 'flat':
p0 = flat_initp(JAMpars, nwalkers)
elif model['p0'] == 'fit':
raise ValueError('Calculate maximum lnprob positon from '
'optimisiztion - not implemented yet')
else:
raise ValueError('p0 must be flat or fit, {} is '
'not supported'.format(model['p0']))
# pool = MPIPool()
# if not pool.is_master():
# pool.wait()
# sys.exit(0)
# Initialize sampler
initSampler = \
emcee.EnsembleSampler(nwalkers, ndim, model['lnprob'],
kwargs={'model': model}, threads=threads)
sys.stdout.flush()
sampler = _runEmcee(initSampler, p0)
# pool.close()
print('--------------------------------------------------')
print('Finish! Total elapsed time: {:.2f}s'.format(time() -
self.startTime))
rst = analyzeRst(sampler, model)
sys.stdout.flush()
model['rst'] = rst
dump(model)