def MIPS_24(MIPS_24, chains=4, iter=1000):
"""
Fit the MIPS 24 band
:param MIPS_24: xidplus.prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
#input data into a dictionary
XID_data = {
'nsrc': MIPS_24.nsrc,
'f_low_lim': [MIPS_24.prior_flux_lower],
'f_up_lim': [MIPS_24.prior_flux_upper],
'bkg_prior': [MIPS_24.bkg[0]],
'bkg_prior_sig': [MIPS_24.bkg[1]],
'npix_psw': MIPS_24.snpix,
'nnz_psw': MIPS_24.amat_data.size,
'db_psw': MIPS_24.sim,
'sigma_psw': MIPS_24.snim,
'Val_psw': MIPS_24.amat_data,
'Row_psw': MIPS_24.amat_row.astype(long),
'Col_psw': MIPS_24.amat_col.astype(long)
}
#see if model has already been compiled. If not, compile and save it
model_file = '/XID+MIPS'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data, iter=iter, chains=chains, verbose=True)
#return fit data
return fit
def MIPS_24(MIPS_24,chains=4,iter=1000):
"""
Fit the MIPS 24 band
:param MIPS_24: xidplus.prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
#input data into a dictionary
XID_data={'nsrc':MIPS_24.nsrc,
'f_low_lim':[MIPS_24.prior_flux_lower],
'f_up_lim':[MIPS_24.prior_flux_upper],
'bkg_prior':[MIPS_24.bkg[0]],
'bkg_prior_sig':[MIPS_24.bkg[1]],
'npix_psw':MIPS_24.snpix,
'nnz_psw':MIPS_24.amat_data.size,
'db_psw':MIPS_24.sim,
'sigma_psw':MIPS_24.snim,
'Val_psw':MIPS_24.amat_data,
'Row_psw': MIPS_24.amat_row.astype(long),
'Col_psw': MIPS_24.amat_col.astype(long)}
#see if model has already been compiled. If not, compile and save it
model_file= '/XID+MIPS'
from xidplus.stan_fit import get_stancode
sm= get_stancode(model_file)
fit = sm.sampling(data=XID_data,iter=iter,chains=chains,verbose=True)
#return fit data
return fit
def all_bands_log10(SPIRE_250,SPIRE_350,SPIRE_500,chains=4,iter=1000):
"""
Fit the three SPIRE bands
:param SPIRE_250: xidplus.prior class
:param SPIRE_350: xidplus.prior class
:param SPIRE_500: xidplus.prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
#input data into a dictionary
XID_data={'nsrc':SPIRE_250.nsrc,
'f_low_lim':[SPIRE_250.prior_flux_lower,SPIRE_350.prior_flux_lower,SPIRE_500.prior_flux_lower],
'f_up_lim':[SPIRE_250.prior_flux_upper,SPIRE_350.prior_flux_upper,SPIRE_500.prior_flux_upper],
'bkg_prior':[SPIRE_250.bkg[0],SPIRE_350.bkg[0],SPIRE_500.bkg[0]],
'bkg_prior_sig':[SPIRE_250.bkg[1],SPIRE_350.bkg[1],SPIRE_500.bkg[1]],
'npix_psw':SPIRE_250.snpix,
'nnz_psw':SPIRE_250.amat_data.size,
'db_psw':SPIRE_250.sim,
'sigma_psw':SPIRE_250.snim,
'Val_psw':SPIRE_250.amat_data,
'Row_psw': SPIRE_250.amat_row.astype(long),
'Col_psw': SPIRE_250.amat_col.astype(long),
'npix_pmw':SPIRE_350.snpix,
'nnz_pmw':SPIRE_350.amat_data.size,
'db_pmw':SPIRE_350.sim,
'sigma_pmw':SPIRE_350.snim,
'Val_pmw':SPIRE_350.amat_data,
'Row_pmw': SPIRE_350.amat_row.astype(long),
'Col_pmw': SPIRE_350.amat_col.astype(long),
'npix_plw':SPIRE_500.snpix,
'nnz_plw':SPIRE_500.amat_data.size,
'db_plw':SPIRE_500.sim,
'sigma_plw':SPIRE_500.snim,
'Val_plw':SPIRE_500.amat_data,
'Row_plw': SPIRE_500.amat_row.astype(long),
'Col_plw': SPIRE_500.amat_col.astype(long)}
#see if model has already been compiled. If not, compile and save it
model_file='/XID+logSPIRE'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data,iter=iter,chains=chains,verbose=True)
#return fit data
return fit
def MIPS_SPIRE_gen(phot_priors,sed_prior_model,chains=4,seed=5363,iter=1000,max_treedepth=10,adapt_delta=0.8):
"""
Fit the three SPIRE bands
:param priors: list of xidplus.prior class objects. Order (MIPS,PACS100,PACS160,SPIRE250,SPIRE350,SPIRE500)
:param sed_prior: xidplus.sed.sed_prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
prior24=phot_priors[0]
prior250=phot_priors[1]
prior350=phot_priors[2]
prior500=phot_priors[3]
#input data into a dictionary
XID_data = {
'nsrc': prior250.nsrc,
'bkg_prior': [prior24.bkg[0],prior250.bkg[0], prior350.bkg[0], prior500.bkg[0]],
'bkg_prior_sig': [prior24.bkg[1],prior250.bkg[1], prior350.bkg[1], prior500.bkg[1]],
'conf_prior_sig': [0.0001, 0.1, 0.1, 0.1],
'z_median': prior24.z_median,
'z_sig': prior24.z_sig,
'npix_psw': prior250.snpix,
'nnz_psw': prior250.amat_data.size,
'db_psw': prior250.sim,
'sigma_psw': prior250.snim,
'Val_psw': prior250.amat_data,
'Row_psw': prior250.amat_row.astype(np.long),
'Col_psw': prior250.amat_col.astype(np.long),
'npix_pmw': prior350.snpix,
'nnz_pmw': prior350.amat_data.size,
'db_pmw': prior350.sim,
'sigma_pmw': prior350.snim,
'Val_pmw': prior350.amat_data,
'Row_pmw': prior350.amat_row.astype(np.long),
'Col_pmw': prior350.amat_col.astype(np.long),
'npix_plw': prior500.snpix,
'nnz_plw': prior500.amat_data.size,
'db_plw': prior500.sim,
'sigma_plw': prior500.snim,
'Val_plw': prior500.amat_data,
'Row_plw': prior500.amat_row.astype(np.long),
'Col_plw': prior500.amat_col.astype(np.long),
'npix_mips24': prior24.snpix,
'nnz_mips24': prior24.amat_data.size,
'db_mips24': prior24.sim,
'sigma_mips24': prior24.snim,
'Val_mips24': prior24.amat_data,
'Row_mips24': prior24.amat_row.astype(np.long),
'Col_mips24': prior24.amat_col.astype(np.long),
'nTemp': sed_prior_model.shape[0],
'nz': sed_prior_model.shape[2],
'nband': sed_prior_model.shape[1],
'SEDs': sed_prior_model,
}
#see if model has already been compiled. If not, compile and save it
model_file='/XID+MIPS_SPIRE_SED_gen'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data,iter=iter,chains=chains,seed=seed,verbose=True,control=dict(max_treedepth=max_treedepth,adapt_delta=adapt_delta))
#return fit data
return fit
def all_bands_log10(SPIRE_250, SPIRE_350, SPIRE_500, chains=4, iter=1000):
"""
Fit the three SPIRE bands
:param SPIRE_250: xidplus.prior class
:param SPIRE_350: xidplus.prior class
:param SPIRE_500: xidplus.prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
#input data into a dictionary
XID_data = {
'nsrc':
SPIRE_250.nsrc,
'f_low_lim': [
SPIRE_250.prior_flux_lower, SPIRE_350.prior_flux_lower,
SPIRE_500.prior_flux_lower
],
'f_up_lim': [
SPIRE_250.prior_flux_upper, SPIRE_350.prior_flux_upper,
SPIRE_500.prior_flux_upper
],
'bkg_prior': [SPIRE_250.bkg[0], SPIRE_350.bkg[0], SPIRE_500.bkg[0]],
'bkg_prior_sig':
[SPIRE_250.bkg[1], SPIRE_350.bkg[1], SPIRE_500.bkg[1]],
'npix_psw':
SPIRE_250.snpix,
'nnz_psw':
SPIRE_250.amat_data.size,
'db_psw':
SPIRE_250.sim,
'sigma_psw':
SPIRE_250.snim,
'Val_psw':
SPIRE_250.amat_data,
'Row_psw':
SPIRE_250.amat_row.astype(long),
'Col_psw':
SPIRE_250.amat_col.astype(long),
'npix_pmw':
SPIRE_350.snpix,
'nnz_pmw':
SPIRE_350.amat_data.size,
'db_pmw':
SPIRE_350.sim,
'sigma_pmw':
SPIRE_350.snim,
'Val_pmw':
SPIRE_350.amat_data,
'Row_pmw':
SPIRE_350.amat_row.astype(long),
'Col_pmw':
SPIRE_350.amat_col.astype(long),
'npix_plw':
SPIRE_500.snpix,
'nnz_plw':
SPIRE_500.amat_data.size,
'db_plw':
SPIRE_500.sim,
'sigma_plw':
SPIRE_500.snim,
'Val_plw':
SPIRE_500.amat_data,
'Row_plw':
SPIRE_500.amat_row.astype(long),
'Col_plw':
SPIRE_500.amat_col.astype(long)
}
#see if model has already been compiled. If not, compile and save it
model_file = '/XID+logSPIRE'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data, iter=iter, chains=chains, verbose=True)
#return fit data
return fit
def all_bands(PACS_100,PACS_160,chains=4,iter=1000):
"""
Fit the two PACS bands
:param PACS_100: xidplus.prior class
:param PACS_160: xidplus.prior class
:param chains: number of chains
:param iter: number of samples
:return: pystan fit object
"""
#input data into a dictionary
XID_data={'nsrc':PACS_100.nsrc,
'npix_psw':PACS_100.snpix,
'nnz_psw':PACS_100.amat_data.size,
'db_psw':PACS_100.sim,
'sigma_psw':PACS_100.snim,
'bkg_prior_psw':PACS_100.bkg[0],
'bkg_prior_sig_psw':PACS_100.bkg[1],
'Val_psw':PACS_100.amat_data,
'Row_psw': PACS_100.amat_row.astype(np.long),
'Col_psw': PACS_100.amat_col.astype(np.long),
'f_low_lim_psw': PACS_100.prior_flux_lower,
'f_up_lim_psw': PACS_100.prior_flux_upper,
'npix_pmw':PACS_160.snpix,
'nnz_pmw':PACS_160.amat_data.size,
'db_pmw':PACS_160.sim,
'sigma_pmw':PACS_160.snim,
'bkg_prior_pmw':PACS_160.bkg[0],
'bkg_prior_sig_pmw':PACS_160.bkg[1],
'Val_pmw':PACS_160.amat_data,
'Row_pmw': PACS_160.amat_row.astype(np.long),
'Col_pmw': PACS_160.amat_col.astype(np.long),
'f_low_lim_pmw': PACS_160.prior_flux_lower,
'f_up_lim_pmw': PACS_160.prior_flux_upper}
XID_data = {'nsrc': PACS_100.nsrc,
'f_low_lim': [PACS_100.prior_flux_lower, PACS_160.prior_flux_lower],
'f_up_lim': [PACS_100.prior_flux_upper, PACS_160.prior_flux_upper],
'bkg_prior': [PACS_100.bkg[0], PACS_160.bkg[0]],
'bkg_prior_sig': [PACS_100.bkg[1], PACS_160.bkg[1]],
'npix_psw': PACS_100.snpix,
'nnz_psw': PACS_100.amat_data.size,
'db_psw': PACS_100.sim,
'sigma_psw': PACS_100.snim,
'Val_psw': PACS_100.amat_data,
'Row_psw': PACS_100.amat_row.astype(long),
'Col_psw': PACS_100.amat_col.astype(long),
'npix_pmw': PACS_160.snpix,
'nnz_pmw': PACS_160.amat_data.size,
'db_pmw': PACS_160.sim,
'sigma_pmw': PACS_160.snim,
'Val_pmw': PACS_160.amat_data,
'Row_pmw': PACS_160.amat_row.astype(long),
'Col_pmw': PACS_160.amat_col.astype(long)}
#see if model has already been compiled. If not, compile and save it
model_file='/XID+PACS'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data,iter=iter,chains=chains,verbose=True,init='random')
#return fit data
return fit
def MIPS_SPIRE_gen(phot_priors,
sed_prior_model,
chains=4,
seed=5363,
iter=1000,
max_treedepth=10,
adapt_delta=0.8):
"""
Fit the three SPIRE bands
:param priors: list of xidplus.prior class objects. Order (MIPS,PACS100,PACS160,SPIRE250,SPIRE350,SPIRE500)
:param sed_prior: xidplus.sed.sed_prior class
:param chains: number of chains
:param iter: number of iterations
:return: pystan fit object
"""
prior24 = phot_priors[0]
prior250 = phot_priors[1]
prior350 = phot_priors[2]
prior500 = phot_priors[3]
#input data into a dictionary
XID_data = {
'nsrc':
prior250.nsrc,
'bkg_prior':
[prior24.bkg[0], prior250.bkg[0], prior350.bkg[0], prior500.bkg[0]],
'bkg_prior_sig':
[prior24.bkg[1], prior250.bkg[1], prior350.bkg[1], prior500.bkg[1]],
'conf_prior_sig': [0.0001, 0.1, 0.1, 0.1],
'z_median':
prior24.z_median,
'z_sig':
prior24.z_sig,
'npix_psw':
prior250.snpix,
'nnz_psw':
prior250.amat_data.size,
'db_psw':
prior250.sim,
'sigma_psw':
prior250.snim,
'Val_psw':
prior250.amat_data,
'Row_psw':
prior250.amat_row.astype(np.long),
'Col_psw':
prior250.amat_col.astype(np.long),
'npix_pmw':
prior350.snpix,
'nnz_pmw':
prior350.amat_data.size,
'db_pmw':
prior350.sim,
'sigma_pmw':
prior350.snim,
'Val_pmw':
prior350.amat_data,
'Row_pmw':
prior350.amat_row.astype(np.long),
'Col_pmw':
prior350.amat_col.astype(np.long),
'npix_plw':
prior500.snpix,
'nnz_plw':
prior500.amat_data.size,
'db_plw':
prior500.sim,
'sigma_plw':
prior500.snim,
'Val_plw':
prior500.amat_data,
'Row_plw':
prior500.amat_row.astype(np.long),
'Col_plw':
prior500.amat_col.astype(np.long),
'npix_mips24':
prior24.snpix,
'nnz_mips24':
prior24.amat_data.size,
'db_mips24':
prior24.sim,
'sigma_mips24':
prior24.snim,
'Val_mips24':
prior24.amat_data,
'Row_mips24':
prior24.amat_row.astype(np.long),
'Col_mips24':
prior24.amat_col.astype(np.long),
'nTemp':
sed_prior_model.shape[0],
'nz':
sed_prior_model.shape[2],
'nband':
sed_prior_model.shape[1],
'SEDs':
sed_prior_model,
}
#see if model has already been compiled. If not, compile and save it
model_file = '/XID+MIPS_SPIRE_SED_gen'
from xidplus.stan_fit import get_stancode
sm = get_stancode(model_file)
fit = sm.sampling(data=XID_data,
iter=iter,
chains=chains,
seed=seed,
verbose=True,
control=dict(max_treedepth=max_treedepth,
adapt_delta=adapt_delta))
#return fit data
return fit