def __init__(self, t, abcrun=None, prior_name=None):
''' A wrapper class for Inherit results with ABC best-fit parameters
'''
if abcrun is None:
raise ValueError('specify abcrun')
if prior_name is None:
raise ValueError('specify prior_name')
self.t = t
self.abcrun = abcrun
self.prior_name = prior_name
# Read in the theta values of the ABC run with 'prior_name' priors
# at time step t
theta_file = ''.join([
Util.code_dir(), 'dat/pmc_abc/', 'CenQue_theta_t',
str(self.t), '_', self.abcrun, '.dat'
])
w_file = ''.join([
Util.code_dir(), 'dat/pmc_abc/', 'CenQue_w_t',
str(t), '_', abcrun, '.dat'
])
self.theta = np.loadtxt(theta_file) # theta values
self.w = np.loadtxt(w_file) # w values
self.med_theta = [
np.median(self.theta[:, i]) for i in range(len(self.theta[0]))
]
self.sim_kwargs = self._ABCrun_kwargs()
def __init__(self, t, abcrun=None, prior_name=None):
''' A wrapper class for Inherit results with ABC best-fit parameters
'''
if abcrun is None:
raise ValueError('specify abcrun')
if prior_name is None:
raise ValueError('specify prior_name')
self.t = t
self.abcrun= abcrun
self.prior_name = prior_name
# Read in the theta values of the ABC run with 'prior_name' priors
# at time step t
theta_file = ''.join([Util.code_dir(),
'dat/pmc_abc/',
'CenQue_theta_t', str(self.t), '_', self.abcrun, '.dat'])
w_file = ''.join([Util.code_dir(),
'dat/pmc_abc/',
'CenQue_w_t', str(t), '_', abcrun, '.dat'])
self.theta = np.loadtxt(theta_file) # theta values
self.w = np.loadtxt(w_file) # w values
self.med_theta = [np.median(self.theta[:,i]) for i in range(len(self.theta[0]))]
self.sim_kwargs = self._ABCrun_kwargs()
def PickleFile(self, snapshots):
''' Name of the pickle file where the inherited snapshots are saved
'''
if isinstance(snapshots, float):
snapshots = [snapshots]
elif isinstance(snapshots, list):
pass
elif isinstance(snapshots, np.ndarray):
pass
elif isinstance(snapshots, str):
if snapshots == 'all':
snapshots = range(1, self.sim_kwargs['nsnap_ancestor'])
else:
raise ValueError
else:
raise ValueError
self.snapshots = snapshots
if snapshots == range(1, self.sim_kwargs['nsnap_ancestor']):
snap_str = '_all'
else:
snap_str = '_'.join([str(snap) for snap in self.snapshots])
abcinh_file = ''.join([Util.code_dir(), 'dat/pmc_abc/pickle/',
'Inherit',
'.t', str(self.t),
'.snapshots', snap_str,
'.ancestor', str(self.sim_kwargs['nsnap_ancestor']),
'.abcrun_', self.abcrun, '.p'])
return abcinh_file
def _ReadABCrun(self):
''' Read file that specifies all the choices of parameters and ABC settings.
'''
abcrun_file = ''.join([
Util.code_dir(), 'dat/pmc_abc/run/', 'abcrun_', self.abcrun, '.p'
])
return pickle.load(open(abcrun_file, 'rb'))
def _ReadGroupCat_GalData(self):
'''
'''
h = 0.7
galdata_file = ''.join([
code_dir(), 'dat/observations/',
'clf_groups_M', str(self.Mrcut), '_', str(self.masscut), '_D360.',
'galdata_corr.fits'])
gal_data = mrdfits(galdata_file)
#print gal_data.__dict__.keys()
for column in gal_data.__dict__.keys():
column_data = getattr(gal_data, column)
if column == 'stellmass':
# stellmass is in units of Msol/h^2
column_data = column_data / h**2
# convert to log Mass
setattr(gal_data, 'mass', np.log10(column_data))
elif column == 'ssfr':
column_data = column_data + np.log10(h**2)
# convert to log Mass
setattr(gal_data, 'ssfr', column_data)
elif column == 'cz':
# convert to z else:
setattr(gal_data, 'z', column_data/299792.458)
else:
pass
setattr(gal_data, 'sfr', gal_data.mass + gal_data.ssfr) # get sfr values
return gal_data
def PickleFile(self, snapshots):
''' Name of the pickle file where the inherited snapshots are saved
'''
if isinstance(snapshots, float):
snapshots = [snapshots]
elif isinstance(snapshots, list):
pass
elif isinstance(snapshots, np.ndarray):
pass
elif isinstance(snapshots, str):
if snapshots == 'all':
snapshots = range(1, self.sim_kwargs['nsnap_ancestor'])
else:
raise ValueError
else:
raise ValueError
self.snapshots = snapshots
if snapshots == range(1, self.sim_kwargs['nsnap_ancestor']):
snap_str = '_all'
else:
snap_str = '_'.join([str(snap) for snap in self.snapshots])
abcinh_file = ''.join([
Util.code_dir(), 'dat/pmc_abc/pickle/', 'Inherit', '.t',
str(self.t), '.snapshots', snap_str, '.ancestor',
str(self.sim_kwargs['nsnap_ancestor']), '.abcrun_', self.abcrun,
'.p'
])
return abcinh_file
def _iSEDfitMatch(self):
''' Match the GroupCat galaxies with iSEDfit galaxy properties from
John Moustakas's MFData objects. The matching is done using PyDL's
spherematch
'''
# import SDSS MFdata catalog
mfdata = mrdfits(code_dir(), 'dat/observations/mfdata_all_supergrid01_sdss.fits.gz')
spherematch_time = time.time()
match1, match2, d_match = spherematch(
self.ra, self.dec, mfdata.ra, mfdata.dec, 0.001)
print 'spherematch took ', time.time() - spherematch_time
iSEDfit_mass = np.repeat(-999., len(self.ra))
iSEDfit_SFR = np.repeat(-999., len(self.ra))
iSEDfit_SSFR = np.repeat(-999., len(self.ra))
if np.max(self.z[match1] - mfdata.z[match2]) > 0.1:
raise ValueError
#wrong = np.argmax(self.z[match1] - mfdata.z[match2])
#print self.ra[match1[wrong]], self.dec[match1[wrong]], self.z[match1[wrong]]
#print mfdata.ra[match2[wrong]], mfdata.dec[match2[wrong]], mfdata.z[match2[wrong]]
iSEDfit_mass[match1] = mfdata.mass[match2]
iSEDfit_SFR[match1] = mfdata.sfr[match2]
iSEDfit_SSFR[match1] = iSEDfit_SFR[match1]-iSEDfit_mass[match1]
setattr(self, 'iSEDfit_mass', iSEDfit_mass)
setattr(self, 'iSEDfit_sfr', iSEDfit_SFR)
setattr(self, 'iSEDfit_ssfr', iSEDfit_SSFR)
return None
def PlotABC_InfallCondition(tf, cen_tf=7, cen_abcrun=None, cen_prior_name=None):
'''
'''
# Simulator
cen_assigned_sat_file = ''.join(['/data1/hahn/pmc_abc/pickle/',
'satellite', '.cenassign',
'.', cen_abcrun, '_ABC',
'.', cen_prior_name, '_prior', '.p'])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.theta_t', str(pewl), '_', abcrun_flag, '.dat'])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:,i]) for i in range(len(theta[0]))]
abc_tqdelay_dict = {'name': 'explin', 'm': med_theta[0], 'b': med_theta[1]}
sg_obj = EvolveSatSFR(sat_cen, tqdelay_dict=abc_tqdelay_dict)
#infall = np.where(
# (sg_obj.first_infall_mass > 0.) &
# (sg_obj.first_infall_sfr > -999.) & (sg_obj.first_infall_t > 5.7))
infallmass0 = np.where(sg_obj.first_infall_mass == 0.)
infallmassG0 = np.where(sg_obj.first_infall_mass > 0.)
infall_nosfr = np.where(sg_obj.first_infall_sfr == -999.)
infall_longago = np.where(sg_obj.first_infall_t < 5.7)
print sg_obj.mass[infall_longago].min(), sg_obj.mass[infall_longago].max()
print sg_obj.sfr[infall_longago].min(), sg_obj.sfr[infall_longago].max()
print sg_obj.first_infall_sfr[infall_longago].min(), sg_obj.first_infall_sfr[infall_longago].max()
m_arr = np.arange(9.5, 11.6, 0.1)
m_mid = 0.5 * (m_arr[:-1] + m_arr[1:])
n_all, dum = np.histogram(sg_obj.mass, bins=m_arr)
n_mass0, dum = np.histogram(sg_obj.mass[infallmass0], bins=m_arr)
n_nosfr, dum = np.histogram(sg_obj.mass[infall_nosfr], bins=m_arr)
n_longago, dum = np.histogram(sg_obj.mass[infall_longago], bins=m_arr)
plt.plot(m_mid, n_mass0.astype('float')/n_all.astype('float'))
plt.plot(m_mid, n_nosfr.astype('float')/n_all.astype('float'))
plt.plot(m_mid, n_longago.astype('float')/n_all.astype('float'))
plt.show()
plt.close()
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infallmassG0], ssfr=sg_obj.ssfr[infallmassG0], line_color=5)
ssfr_plot.plot(mass=sg_obj.mass, ssfr=sg_obj.ssfr, line_color='k', line_style='--')
ssfr_plot.set_axes()
plt.show()
return None
def _ReadGroupCat_Prob(self):
''' wrapper for reading in probability galaxy data
'''
file = ''.join([
code_dir(), 'dat/observations/',
'clf_groups_M', str(self.Mrcut), '_', str(self.masscut), '_D360.',
'prob.fits'])
prob_data = mrdfits(file) # import probability file
return prob_data
def ReadABCrun(abcrun, restart=False):
''' Read text file that specifies all the choices of parameters and ABC settings.
'''
if abcrun is None:
abcrun = ''.join(['RENAME_ME_', str(datetime.today().date())])
restart_str = ''
if restart:
restart_str = '.restart'
pickle_name = ''.join([code_dir(), 'dat/pmc_abc/run/', 'abcrun_', abcrun, restart_str, '.p'])
return pickle.load(open(pickle_name, 'rb')), abcrun
def ReadABCrun(abcrun, restart=False):
''' Read text file that specifies all the choices of parameters and ABC settings.
'''
if abcrun is None:
abcrun = ''.join(['RENAME_ME_', str(datetime.today().date())])
restart_str = ''
if restart:
restart_str = '.restart'
pickle_name = ''.join([code_dir(), 'dat/pmc_abc/run/', 'abcrun_', abcrun, restart_str, '.p'])
return pickle.load(open(pickle_name, 'rb')), abcrun
def File(self, **spec_kwargs):
''' File name of output file given snapshot
'''
# write to hdf5 file
subsham_file = ''.join([
code_dir(), 'dat/wetzel_tree/', 'subhalo_sham', '.satellite',
'.snapshot',
str(spec_kwargs['snapshot']),
self._file_spec(**spec_kwargs), '.hdf5'
])
return subsham_file
def File(self):
''' GroupCat File
'''
output_file = ''.join([
code_dir(), 'dat/observations/',
'GroupCat.',
'Mr', str(self.Mrcut),
'.Mass', str(self.masscut),
'.D360.',
self.position,
'.hdf5'])
return output_file
def InheritSF_file(nsnap_descendant, nsnap_ancestor=20, tau='abc', abc_step=None,
subhalo_prop={'scatter': 0.2, 'source': 'li-march'},
sfr_prop={'fq': {'name': 'wetzelsmooth'}, 'sfr': {'name': 'average'}},
evol_prop={'sfr': {'dutycycle': {'name': 'notperiodic'}}, 'mass': {'name': 'sham'}},
flag=None):
'''
'''
if not isinstance(nsnap_descendant, list):
nsnap_descendant = [nsnap_descendant]
tau_str = ''
if abc_step is not None:
tau_str = '.tau_ABC'+str(abc_step)
else:
tau_str = evol_prop['tau']['name']
# SFMS string
sfms_str = '.SFMS'+sfr_prop['sfms']['name'].lower()
if sfr_prop['sfms']['name'] == 'linear':
sfms_str += ''.join([
'_z', str(round(sfr_prop['sfms']['zslope'],2))])
elif sfr_prop['sfms']['name'] == 'kinked':
sfms_str += ''.join([
'_Mlow', str(round(sfr_prop['sfms']['mslope_lowmass'],2)),
'z', str(round(sfr_prop['sfms']['zslope'],2))])
# SFR assigned based on subhalo growth
if 'subhalogrowth' in sfr_prop.keys():
sfms_str += '.halogrowth_SFRassign'
# mass evolution string
if evol_prop['mass']['name'] == 'sham':
mass_str = '.Mevo_SHAM'
elif evol_prop['mass']['name'] == 'integrated':
if evol_prop['mass']['type'] == 'euler':
mass_str = '.Mevo_EulerInt'
elif evol_prop['mass']['type'] == 'rk4':
mass_str = '.Mevo_RK4Int'
flag_str = ''
if flag is not None:
flag_str = '.'+flag
hdf5_file = ''.join([
code_dir(), 'dat/InheritSF/'
'InheritSF',
'.Snap', ''.join([str(nsnap) for nsnap in nsnap_descendant]), '_', str(nsnap_ancestor),
'.subhalo_sig', str(round(subhalo_prop['scatter'], 2)), '_', subhalo_prop['source'],
'.fq_', sfr_prop['fq']['name'],
sfms_str,
'.', evol_prop['type'], '_evol',
'.SF_Duty_', evol_prop['sfr']['dutycycle']['name'],
mass_str,
tau_str,
flag_str, '.hdf5'])
return hdf5_file
def File(self, **spec_kwargs):
''' File name of output file given snapshot
'''
# write to hdf5 file
subsham_cen_file = ''.join([
code_dir(), 'dat/wetzel_tree/',
'subhalo_sham',
'.central',
'.snapshot',
str(spec_kwargs['snapshot']),
self._file_spec(**spec_kwargs),
'.hdf5'])
return subsham_cen_file
def Read(self):
'''
'''
sfr_class = []
for sfq in ['star-forming', 'quiescent']:
file_dir = code_dir()+'dat/observations/envcount/'
if sfq == 'star-forming':
sfq_str = 'active'
else:
sfq_str = sfq
sdss_file = ''.join([file_dir,
'envcount_cylr2.5h35_thresh75_sdss_', sfq_str, '_z0.05_0.12_primuszerr.fits'])
primus_file = ''.join([file_dir,
'envcount_cylr2.5h35_thresh75_', sfq_str, '_z0.2_1.0_lit.fits'])
# redshift determines SDSS or PRIMUS sample
if self.redshift_bin < 0.2:
galdata = mrdfits(sdss_file)
else:
galdata = mrdfits(primus_file)
# environment cuts
if self.environment == 'no':
envcuts = (galdata.envcount == 0.)
else:
raise NotImplementedError
# redshift cuts
zlow = [0., 0.2, 0.4, 0.6, 0.8]
zhigh = [0.2, 0.4, 0.6, 0.8, 1.0]
i_z = int(np.floor(self.redshift_bin/0.2))
zcuts = (galdata.redshift >= zlow[i_z]) & (galdata.redshift < zhigh[i_z])
all_cuts = np.where(
envcuts & zcuts &
(galdata.mass > galdata.masslimit) &
(galdata.edgecut == 1)
)
for key in galdata.__dict__.keys():
try:
setattr(self, key,
np.concatenate([getattr(self,key), getattr(galdata, key)[all_cuts]]))
except AttributeError:
setattr(self, key, getattr(galdata, key)[all_cuts])
sfr_class_tmp = np.chararray(len(all_cuts[0]), itemsize=16)
sfr_class_tmp[:] = sfq
sfr_class.append(sfr_class_tmp)
setattr(self, 'sfr_class', np.concatenate(sfr_class))
return None
def __init__(self, t, abcrun=None, prior_name='try0'):
'''
'''
if abcrun is None:
raise ValueError('specify ABC run string')
self.t = t
self.abcrun = abcrun
theta_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_theta_t', str(t), '_', abcrun, '.dat'])
w_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_w_t', str(t), '_', abcrun, '.dat'])
self.theta = np.loadtxt(theta_file) # theta values
self.w = np.loadtxt(w_file) # w values
self.med_theta = [np.median(self.theta[:,i]) for i in range(len(self.theta[0]))]
# prior range
prior_min, prior_max = PriorRange(prior_name)
self.prior_range = [(prior_min[i], prior_max[i]) for i in range(len(prior_min))]
self.descendant = None
def Corner(self):
''' Wrapper to generate the corner plot of the ABC particle pool. The plot parameter
ranges are the prior ranges. The median of the parameters are marked in the plots.
'''
# list of parameters
params = [
'slope_gv',
'offset_gv',
'slope_fudge',
'offset_fudge',
'slope_tau',
'offset_tau'
]
fig_name = ''.join([code_dir(),
'figure/', 'abc_step', str(self.t), '_', self.abcrun, '_weighted.png'])
self._thetas(self.theta, w=self.w, truths=self.med_theta, plot_range=self.prior_range,
parameters=params,
fig_name=fig_name)
fig_name = ''.join([code_dir(),
'figure/', 'abc_step', str(self.t), '_', self.abcrun, '_unweighted.png'])
self._thetas(self.theta, truths=self.med_theta, plot_range=self.prior_range,
parameters=params,
fig_name=fig_name)
return None
def abc_posterior_median(n_step):
'''
Median theat from ABC posterior
'''
# read in ABC posterior file
posterior_file = ''.join(
[code_dir(), 'dat/pmc_abc/', 'theta_t',
str(n_step), '.dat'])
theta = np.loadtxt(posterior_file, unpack=True)
med_theta = []
for i_param in xrange(len(theta)):
med_theta.append(np.median(theta[i_param]))
return med_theta
def File(self):
''' File name of Lineage object. The name specifies the properties
of the subhalo catalog.
'''
# properties that are specified for SF assignment
if 'subhalo_prop' not in self.__dict__.keys():
raise ValueError
file_spec_str = self._file_spec(subhalo_prop = self.subhalo_prop)
lineage_filename = ''.join([
Util.code_dir(), 'dat/lineage/',
'lineage',
'.ancestor', str(self.nsnap_ancestor),
file_spec_str,
'.hdf5'])
return lineage_filename
def File(self):
''' File name of CGPop object. The file name acts as a tool to
label the properties of the object.
'''
if self.nsnap is None:
raise ValueError()
if self.subhalo_prop is None:
raise ValueError()
# Cenque Object with assigned starformation properties
file_spec_str = self._file_spec(subhalo_prop=self.subhalo_prop)
cgpop_file = ''.join([
Util.code_dir(), 'dat/galpop/', 'sgpop'
'.snapshot',
str(self.nsnap), file_spec_str, '.hdf5'
])
return cgpop_file
def QAplot(self, nsnap_descendant=1):
''' Quality assurance plots
'''
sfinherit_kwargs, abcrun_flag = ReadABCrun(self.abcrun)
gv_slope = self.med_theta[0]
gv_offset = self.med_theta[1]
fudge_slope = self.med_theta[2]
fudge_offset = self.med_theta[3]
tau_slope = self.med_theta[4]
tau_offset = self.med_theta[5]
# tau slopes and offsets of random particles
tau_slopes = self.theta[:,4]
tau_offsets = self.theta[:,5]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {
'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {
'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {
'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
if self.descendant is not None and self.descendant.nsnap == nsnap_descendant:
descendant = self.descendant
else:
bloodline = InheritSF(
nsnap_descendant,
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
# descendant
descendant = getattr(bloodline, 'descendant_snapshot'+str(nsnap_descendant))
self.descendant = descendant
fig_name = ''.join([code_dir(),
'figure/QAPlot',
'.nsnap', str(nsnap_descendant),
'.abc_step', str(self.t), '_', self.abcrun, '.png'])
QAplot(descendant, sim_kwargs, fig_name=fig_name, taus=[tau_slopes, tau_offsets])
return None
def PlotABC_Corner(tf, cen_abcrun=None):
'''
'''
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.theta_t', str(pewl), '_', abcrun_flag, '.dat'])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:,i]) for i in range(len(theta[0]))]
params = [ 'tqdelay_slope', 'tqdelay_offset']
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
#prior_min = [-12.0, 3.]
#prior_max = [-11.0, 5.]
prior_range = [(prior_min[i], prior_max[i]) for i in range(len(prior_min))]
fig = corner.corner(
theta,
truths=med_theta,
truth_color='#ee6a50',
labels=['$t_{Q,\;delay}$ Slope', '$t_{Q,\;delay}$ offset'],
label_kwargs={'fontsize': 15},
range=prior_range,
quantiles=[0.16,0.5,0.84],
show_titles=True,
title_args={"fontsize": 12},
plot_datapoints=True,
fill_contours=True,
levels=[0.68, 0.95],
color='b',
bins=20,
smooth=1.0)
fig_file = ''.join(['figure/',
'Satellite',
'.Corner',
'.tQdelayABC',
'.', cen_abcrun, '_central.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotABC_Corner(tf, cen_abcrun=None):
'''
'''
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:, i]) for i in range(len(theta[0]))]
params = ['tqdelay_slope', 'tqdelay_offset']
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
#prior_min = [-12.0, 3.]
#prior_max = [-11.0, 5.]
prior_range = [(prior_min[i], prior_max[i]) for i in range(len(prior_min))]
fig = corner.corner(
theta,
truths=med_theta,
truth_color='#ee6a50',
labels=['$t_{Q,\;delay}$ Slope', '$t_{Q,\;delay}$ offset'],
label_kwargs={'fontsize': 15},
range=prior_range,
quantiles=[0.16, 0.5, 0.84],
show_titles=True,
title_args={"fontsize": 12},
plot_datapoints=True,
fill_contours=True,
levels=[0.68, 0.95],
color='b',
bins=20,
smooth=1.0)
fig_file = ''.join([
'figure/', 'Satellite', '.Corner', '.tQdelayABC', '.', cen_abcrun,
'_central.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotABC_tQdelay(tf, cen_abcrun=None):
'''
'''
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.theta_t', str(pewl), '_', abcrun_flag, '.dat'])
theta = np.loadtxt(theta_file(tf))
m_arr = np.arange(7.5, 12.0, 0.1)
tdels = []
for i in xrange(len(theta)):
i_tqdelay_dict = {'name': 'explin', 'm': theta[i][0], 'b': theta[i][1]}
tdels.append(tDelay(m_arr, **i_tqdelay_dict))
tdels = np.array(tdels)
a, b, c, d, e = np.percentile(tdels, [2.5, 16, 50, 84, 97.5], axis=0)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(7,7))
sub = fig.add_subplot(111)
mstar = np.array([1.56781e+11, 1.01089e+11, 6.27548e+10, 3.98107e+10, 2.49831e+10, 1.57633e+10, 9.89223e+9, 6.37831e+9])
tqdel_high = np.array([1.36456e+0 , 2.53148e+0 , 3.06686e+0 , 3.52693e+0 , 3.62625e+0 , 3.99574e+0, 3.99915e+0 ,3.99915e+0])
tqdel_low = np.array([8.15728e-1, 1.98257e+0, 2.29240e+0, 2.82772e+0, 2.99466e+0, 2.72548e+0, 2.99016e+0, 2.68333e+0])
sub.fill_between(mstar, tqdel_low, tqdel_high, color=pretty_colors[0], edgecolor="none", label='Roughly Wetzel+(2013)')
#sub.plot(10**m_arr, tDelay(m_arr, **abc_tqdelay_dict), c=pretty_colors[3], label='ABC Best-fit')
sub.fill_between(10**m_arr, a, e, color=pretty_colors[3], alpha=0.5, edgecolor="none",
label='ABC Best-fit')
sub.fill_between(10**m_arr, b, d, color=pretty_colors[3], alpha=1., edgecolor="none")
sub.plot(10**m_arr, tDelay(m_arr, **{'name': 'hacked'}))
sub.set_ylim([0.0, 4.0])
sub.set_ylabel(r'$\mathtt{t_{Q, delay}}$', fontsize=25)
sub.set_xlim([5*10**9, 2*10**11.])
sub.set_xscale("log")
sub.set_xlabel(r'$\mathtt{M}_*$', fontsize=25)
sub.legend(loc='lower left')
fig.savefig('figure/sallite_tqdelay_'+cen_abcrun+'.png', bbox_inches='tight')
return None
def Corner(self, filename=None):
''' Wrapper to generate the corner plot of the ABC particle pool. The plot parameter
ranges are the prior ranges. The median of the parameters are marked in the plots.
'''
# list of parameters
if not self.satellite_run:
params = [
'slope_gv',
'offset_gv',
'slope_fudge',
'offset_fudge',
'slope_tau',
'offset_tau'
]
else:
params = [
'slope_gv',
'offset_gv',
'slope_fudge',
'offset_fudge'
]
if filename is None:
fig_name = ''.join([code_dir(),
'figure/', 'abc_step', str(self.t), '_', self.abcrun, '_weighted.png'])
else:
fig_name = filename
if 'weighted' not in fig_name:
fig_name = '_weighted.pn'.join(fig_name.split('.pn'))
self._thetas(self.theta, w=self.w, truths=self.med_theta, plot_range=self.prior_range,
parameters=params,
fig_name=fig_name)
fig_name = 'unweighted'.join(fig_name.split('weighted'))
# fig_name = ''.join([code_dir(),
# 'figure/', 'abc_step', str(self.t), '_', self.abcrun, '_unweighted.png'])
self._thetas(self.theta, truths=self.med_theta, plot_range=self.prior_range,
parameters=params,
fig_name=fig_name)
return fig_name
def QAplot(self, nsnap_descendant=1):
''' Quality assurance plots
'''
sfinherit_kwargs, abcrun_flag = ReadABCrun(self.abcrun)
gv_slope = self.med_theta[0]
gv_offset = self.med_theta[1]
fudge_slope = self.med_theta[2]
fudge_offset = self.med_theta[3]
if not self.satellite_run:
tau_slope = self.med_theta[4]
tau_offset = self.med_theta[5]
# tau slopes and offsets of random particles
tau_slopes = self.theta[:,4]
tau_offsets = self.theta[:,5]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {
'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {
'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
if not self.satellite_run:
sim_kwargs['evol_prop']['tau'] = {
'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
else:
sim_kwargs['evol_prop']['tau'] = {'name': 'satellite'}
inh = Inherit(nsnap_descendant,
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
des_dict = inh()
for nd in nsnap_descendant:
fig_name = ''.join([code_dir(),
'figure/QAPlot',
'.nsnap', str(nd),
'.abc_step', str(self.t), '_', self.abcrun, '.png'])
QAplot(des_dict[str(nd)], sim_kwargs, fig_name=fig_name)#, taus=[tau_slopes, tau_offsets])
return None
def __init__(self, t, abcrun=None, prior_name='try0'):
'''
'''
if abcrun is None:
raise ValueError('specify ABC run string')
self.t = t
self.abcrun = abcrun
if prior_name == 'satellite':
theta_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_theta_t', str(t), '_', abcrun, '.satellite.dat'])
w_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_w_t', str(t), '_', abcrun, '.satellite.dat'])
self.satellite_run = True
elif prior_name == 'longtau':
theta_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_theta_t', str(t), '_', abcrun,
'.fixedtau.long.dat'])
w_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_w_t', str(t), '_', abcrun,
'.fixedtau.long.dat'])
self.satellite_run = True
else:
theta_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_theta_t', str(t), '_', abcrun, '.dat'])
w_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_w_t', str(t), '_', abcrun, '.dat'])
dist_file = ''.join([
code_dir(), 'dat/pmc_abc/', 'CenQue_dist_t', str(t), '_', abcrun, '.dat'])
self.satellite_run = False
self.theta = np.loadtxt(theta_file) # theta values
self.w = np.loadtxt(w_file) # w values
#self.dist = np.loadtxt(dist_file)
self.med_theta = [np.median(self.theta[:,i]) for i in range(len(self.theta[0]))]
# prior range
prior_min, prior_max = PriorRange(prior_name)
self.prior_range = [(prior_min[i], prior_max[i]) for i in range(len(prior_min))]
self.descendant = None
def File(self):
''' File name of CGPop object. The file name acts as a tool to
label the properties of the object.
'''
if self.nsnap is None:
raise ValueError()
if self.subhalo_prop is None:
raise ValueError()
# Cenque Object with assigned starformation properties
file_spec_str = self._file_spec(
subhalo_prop=self.subhalo_prop
)
cgpop_file = ''.join([Util.code_dir(),
'dat/galpop/',
'sgpop'
'.snapshot', str(self.nsnap),
file_spec_str,
'.hdf5'
])
return cgpop_file
def abc_posterior_median(n_step):
'''
Median theat from ABC posterior
'''
# read in ABC posterior file
posterior_file = ''.join([
code_dir(), 'dat/pmc_abc/',
'theta_t',
str(n_step),
'.dat'])
theta = np.loadtxt(
posterior_file,
unpack = True
)
med_theta = []
for i_param in xrange(len(theta)):
med_theta.append(
np.median(theta[i_param])
)
return med_theta
def _ReadABCrun(self):
''' Read file that specifies all the choices of parameters and ABC settings.
'''
abcrun_file = ''.join([Util.code_dir(),
'dat/pmc_abc/run/', 'abcrun_', self.abcrun, '.p'])
return pickle.load(open(abcrun_file, 'rb'))
def ABC(T, eps_input, Npart=1000, prior_name='try0', observables=['ssfr'], abcrun=None,
restart=False, t_restart=None, eps_restart=None):
''' ABC-PMC implementation.
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
if isinstance(eps_input, list):
if len(eps_input) != len(observables):
raise ValueError
if len(observables) > 1 and isinstance(eps_input, float):
raise ValueError
# output abc run details
sfinherit_kwargs, abcrun_flag = MakeABCrun(
abcrun=abcrun, Niter=T, Npart=Npart, prior_name=prior_name,
eps_val=eps_input, restart=restart)
# Data
data_sum = DataSummary(observables=observables)
# Priors
prior_min, prior_max = PriorRange(prior_name)
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
## Read in ABC run file
#sfinherit_kwargs, abcrun_flag = ReadABCrun(abcrun, restart=restart)
def Simz(tt): # Simulator (forward model)
gv_slope = tt[0]
gv_offset = tt[1]
fudge_slope = tt[2]
fudge_offset = tt[3]
tau_slope = tt[4]
tau_offset = tt[5]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
return SimSummary(observables=observables, **sim_kwargs)
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_theta_t', str(pewl), '_', abcrun_flag, '.dat'])
w_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_w_t', str(pewl), '_', abcrun_flag, '.dat'])
dist_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_dist_t', str(pewl), '_', abcrun_flag, '.dat'])
eps_file = ''.join([code_dir(),
'dat/pmc_abc/epsilon_', abcrun_flag, '.dat'])
if observables == ['ssfr']:
distfn = RhoSSFR
elif observables == ['ssfr', 'fqz03']:
distfn = RhoSSFR_Fq
if restart:
if t_restart is None:
raise ValueError
last_thetas = np.loadtxt(theta_file(t_restart))
last_ws = np.loadtxt(w_file(t_restart))
last_dist = np.loadtxt(dist_file(t_restart))
init_pool = abcpmc.PoolSpec(t_restart, None, None, last_thetas, last_dist, last_ws)
else:
init_pool = None
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
if init_pool is None:
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=init_pool):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = '\t'+str(pool.eps)+'\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t), pool.thetas,
header='gv_slope, gv_offset, fudge_slope, fudge_offset, tau_slope, tau_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
if len(observables) == 1:
eps.eps = np.median(pool.dists)
else:
#print pool.dists
print np.median(np.atleast_2d(pool.dists), axis = 0)
eps.eps = np.median(np.atleast_2d(pool.dists), axis = 0)
print '----------------------------------------'
pools.append(pool)
return pools
def Ssfr(self, subsample_thetas=False):
''' Plot the SSFR distribution of the median paramater values of the
ABC particle pool.
'''
sfinherit_kwargs, abcrun_flag = ReadABCrun(self.abcrun)
ssfr_plot = None
if subsample_thetas:
N_theta = len(self.theta)
i_subs = np.random.choice(N_theta, size=20)
for i_sub in i_subs:
gv_slope, gv_offset, fudge_slope, fudge_offset, tau_slope, tau_offset = self.theta[i_sub]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
bloodline = InheritSF(
1,
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
# descendant
desc = getattr(bloodline, 'descendant_snapshot1')
ssfr_plot = desc.plotSsfr(line_color='red', lw=2, alpha=0.25, ssfr_plot=ssfr_plot)
# plot the median 'best-fit' theta
gv_slope = self.med_theta[0]
gv_offset = self.med_theta[1]
fudge_slope = self.med_theta[2]
fudge_offset = self.med_theta[3]
tau_slope = self.med_theta[4]
tau_offset = self.med_theta[5]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
if self.descendant is None:
bloodline = InheritSF(
1,
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
# descendant
descendant = getattr(bloodline, 'descendant_snapshot1')
self.descendant = descendant
else:
descendant = self.descendant
fig_name = ''.join([code_dir(),
'figure/SSFR.abc_step', str(self.t), '_', self.abcrun, '.png'])
descendant.plotSsfr(line_color='red', line_width=4,
sfms_prop=sim_kwargs['sfr_prop']['sfms'], z=descendant.zsnap,
groupcat=True, ssfr_plot=ssfr_plot, savefig=fig_name)
plt.close()
return None
def ABC(T,
eps_input,
Npart=1000,
cen_tf=None,
cen_prior_name=None,
cen_abcrun=None):
''' ABC-PMC implementation.
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
abcinh = ABCInherit(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
# Data (Group Catalog Satellite fQ)
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
qfrac = Fq()
m_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (m_bin[:-1] + m_bin[1:])
sfq = qfrac.Classify(grpcat.mass,
grpcat.sfr,
np.median(grpcat.z),
sfms_prop=abcinh.sim_kwargs['sfr_prop']['sfms'])
ngal, dum = np.histogram(grpcat.mass, bins=m_bin)
ngal_q, dum = np.histogram(grpcat.mass[sfq == 'quiescent'], bins=m_bin)
data_sum = [M_mid, ngal_q.astype('float') / ngal.astype('float')]
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
def Simz(tt): # Simulator (forward model)
tqdel_dict = {'name': 'explin', 'm': tt[0], 'b': tt[1]}
sat_evol = EvolveSatSFR(sat_cen, tqdelay_dict=tqdel_dict)
sfq_sim = qfrac.Classify(sat_evol.mass,
sat_evol.sfr,
sat_evol.zsnap,
sfms_prop=sat_evol.sfms_prop)
ngal_sim, dum = np.histogram(sat_evol.mass, bins=m_bin)
ngal_q_sim, dum = np.histogram(sat_evol.mass[sfq_sim == 'quiescent'],
bins=m_bin)
sim_sum = [
M_mid,
ngal_q_sim.astype('float') / ngal_sim.astype('float')
]
return sim_sum
# Priors
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def rho(simum, datum):
datum_dist = datum[1]
simum_dist = simum[1]
drho = np.sum((datum_dist - simum_dist)**2)
return drho
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
w_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.w_t',
str(pewl), '_', abcrun_flag, '.dat'
])
dist_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.dist_t',
str(pewl), '_', abcrun_flag, '.dat'
])
eps_file = ''.join([
code_dir(), 'dat/pmc_abc/Satellite.tQdelay.epsilon_', abcrun_flag,
'.dat'
])
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=None):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = '\t' + str(pool.eps) + '\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t),
pool.thetas,
header='tQdelay_slope, tQdelay_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
eps.eps = np.median(pool.dists)
pools.append(pool)
return pools
def MakeABCrun(abcrun=None, nsnap_start=15, subhalo=None, fq=None, sfms=None, dutycycle=None, mass_evol=None,
Niter=None, Npart=None, prior_name=None, eps_val=None, restart=False):
'''Pickle file that specifies all the choices of parameters and ABC settings
for record keeping purposes.
'''
if abcrun is None:
abcrun = ''.join(['RENAME_ME_', str(datetime.today().date())])
restart_str = ''
if restart:
restart_str = '.restart'
file_name = ''.join([code_dir(), 'dat/pmc_abc/run/', 'abcrun_', abcrun, restart_str, '.txt'])
pickle_name = file_name.replace('.txt', '.p')
f = open(file_name, 'w')
# ABC properities
f.write('# ABC Run Properties \n')
f.write('N_iter = '+str(Niter)+'\n')
f.write('N_particles = '+str(Npart)+'\n')
f.write('Prior Name = '+prior_name+'\n')
f.write('Epsilon_0 = '+str(eps_val)+'\n')
if restart:
f.write('Restarting')
f.write('\n')
# Subhalo properties
f.write('# Subhalo Properties \n')
if subhalo is None:
subhalo_dict = {'scatter': 0.2, 'source': 'li-march'}
elif isinstance(subhalo, str):
subhalo_dict = {'scatter': 0.2, 'source': subhalo}
f.write(''.join(['scatter = ', str(subhalo_dict['scatter']), '\n']))
f.write(''.join(['source = ', subhalo_dict['source'], '\n']))
f.write('\n')
# Quiescent fraction properties
f.write('# Quiescent Fraction Properties \n')
if fq is None: # quiescent fraction
#fq = {'name': 'wetzel'}
fq = {'name': 'cosmos_tinker'}
f.write(''.join(['name = ', fq['name'], '\n']))
f.write('\n')
# Starforming Main Sequence
f.write('# Starforming Main Sequence Properties \n')
if sfms is None: # SFMS
sfms = {'name': 'linear', 'zslope': 1.14}
f.write(''.join(['name = ', sfms['name'], '\n']))
f.write(''.join(['zslope = ', str(sfms['zslope']), '\n']))
f.write('\n')
# SF dutycycle properties
f.write('# SF Dutycycle Properties \n')
if dutycycle is None:
dutycycle = {'name': 'notperiodic'}
#dutycycle = {'name': 'newamp_squarewave', 'freq_range': [2.*np.pi, 20.*np.pi], 'sigma': 0.3}
f.write(''.join(['name = ', dutycycle['name'], '\n']))
if dutycycle['name'] == 'newamp_squarewave':
f.write(''.join(['frequency range = ', str(dutycycle['freq_range'][0]), ',', str(dutycycle['freq_range'][1]), '\n']))
f.write(''.join(['sigma = ', str(dutycycle['sigma']), '\n']))
f.write('\n')
# Mass Evolution properties
f.write('# Mass Evolution Properties \n')
if mass_evol is None:
mass_evol = {'name': 'sham'}
if mass_evol['name'] == 'sham':
f.write(''.join(['name = ', mass_evol['name'], '\n']))
f.close()
evol_dict = {
'type': 'simult',
'sfr': {'dutycycle': dutycycle},
'mass': mass_evol
}
kwargs = {
'nsnap_ancestor': nsnap_start,
'subhalo_prop': subhalo_dict,
'sfr_prop': {'fq': fq, 'sfms': sfms},
'evol_prop': evol_dict
}
pickle.dump(kwargs, open(pickle_name, 'wb'))
return kwargs, abcrun
def FixedTauABC(T, eps_input, fixtau='satellite', Npart=1000, prior_name='try0',
observables=['fqz_multi'], abcrun=None,
restart=False, t_restart=None, eps_restart=None, **sim_kwargs):
''' Run ABC-PMC analysis for central galaxy SFH model with *FIXED* quenching
timescale
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
if isinstance(eps_input, list):
if len(eps_input) != len(observables):
raise ValueError
if len(observables) > 1 and isinstance(eps_input, float):
raise ValueError
# output abc run details
sfinherit_kwargs, abcrun_flag = MakeABCrun(
abcrun=abcrun, Niter=T, Npart=Npart, prior_name=prior_name,
eps_val=eps_input, restart=restart, **sim_kwargs)
# Data
data_sum = DataSummary(observables=observables)
# Priors
prior_min, prior_max = PriorRange(prior_name)
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def Simz(tt): # Simulator (forward model)
gv_slope = tt[0]
gv_offset = tt[1]
fudge_slope = tt[2]
fudge_offset = tt[3]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {'name': fixtau}
sim_output = SimSummary(observables=observables, **sim_kwargs)
return sim_output
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_theta_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
w_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_w_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
dist_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_dist_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
eps_file = ''.join([code_dir(),
'dat/pmc_abc/epsilon_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
distfn = RhoFq
if restart:
if t_restart is None:
raise ValueError
last_thetas = np.loadtxt(theta_file(t_restart))
last_ws = np.loadtxt(w_file(t_restart))
last_dist = np.loadtxt(dist_file(t_restart))
init_pool = abcpmc.PoolSpec(t_restart, None, None, last_thetas, last_dist, last_ws)
else:
init_pool = None
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
if init_pool is None:
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=init_pool):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = str(pool.eps)+'\t'+str(pool.ratio)+'\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t), pool.thetas,
header='gv_slope, gv_offset, fudge_slope, fudge_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
if len(observables) == 1:
eps.eps = np.median(pool.dists)
else:
#print pool.dists
print np.median(np.atleast_2d(pool.dists), axis = 0)
eps.eps = np.median(np.atleast_2d(pool.dists), axis = 0)
print '----------------------------------------'
pools.append(pool)
return pools
def PlotABC_InfallCondition(tf,
cen_tf=7,
cen_abcrun=None,
cen_prior_name=None):
'''
'''
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:, i]) for i in range(len(theta[0]))]
abc_tqdelay_dict = {'name': 'explin', 'm': med_theta[0], 'b': med_theta[1]}
sg_obj = EvolveSatSFR(sat_cen, tqdelay_dict=abc_tqdelay_dict)
#infall = np.where(
# (sg_obj.first_infall_mass > 0.) &
# (sg_obj.first_infall_sfr > -999.) & (sg_obj.first_infall_t > 5.7))
infallmass0 = np.where(sg_obj.first_infall_mass == 0.)
infallmassG0 = np.where(sg_obj.first_infall_mass > 0.)
infall_nosfr = np.where(sg_obj.first_infall_sfr == -999.)
infall_longago = np.where(sg_obj.first_infall_t < 5.7)
print sg_obj.mass[infall_longago].min(), sg_obj.mass[infall_longago].max()
print sg_obj.sfr[infall_longago].min(), sg_obj.sfr[infall_longago].max()
print sg_obj.first_infall_sfr[infall_longago].min(
), sg_obj.first_infall_sfr[infall_longago].max()
m_arr = np.arange(9.5, 11.6, 0.1)
m_mid = 0.5 * (m_arr[:-1] + m_arr[1:])
n_all, dum = np.histogram(sg_obj.mass, bins=m_arr)
n_mass0, dum = np.histogram(sg_obj.mass[infallmass0], bins=m_arr)
n_nosfr, dum = np.histogram(sg_obj.mass[infall_nosfr], bins=m_arr)
n_longago, dum = np.histogram(sg_obj.mass[infall_longago], bins=m_arr)
plt.plot(m_mid, n_mass0.astype('float') / n_all.astype('float'))
plt.plot(m_mid, n_nosfr.astype('float') / n_all.astype('float'))
plt.plot(m_mid, n_longago.astype('float') / n_all.astype('float'))
plt.show()
plt.close()
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infallmassG0],
ssfr=sg_obj.ssfr[infallmassG0],
line_color=5)
ssfr_plot.plot(mass=sg_obj.mass,
ssfr=sg_obj.ssfr,
line_color='k',
line_style='--')
ssfr_plot.set_axes()
plt.show()
return None
def PlotABC_EvolvedSat(tf, cen_tf=7, cen_abcrun=None, cen_prior_name=None):
''' Plot stuff for the evolved satellite population
'''
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:, i]) for i in range(len(theta[0]))]
abc_tqdelay_dict = {'name': 'explin', 'm': med_theta[0], 'b': med_theta[1]}
sg_obj = EvolveSatSFR(sat_cen, tqdelay_dict=abc_tqdelay_dict)
#infall = np.arange(len(sg_obj.first_infall_mass))
infall = np.where(
(sg_obj.first_infall_mass > 0.) &
(sg_obj.first_infall_sfr > -999.)) # & (sg_obj.first_infall_t > 5.7))
print len(sg_obj.first_infall_mass)
print len(infall[0])
# SSFR plot
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infall],
ssfr=sg_obj.ssfr[infall],
line_color=3)
ssfr_plot.GroupCat(position='satellite')
ssfr_plot.set_axes()
ssfr_fig_name = ''.join([
'figure/', 'SSFR.Satellite', '.tQdelayABC', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
ssfr_plot.save_fig(ssfr_fig_name)
plt.close()
# Quiescent fraction plot
fq_plot = PlotFq()
fq_plot.plot(mass=sg_obj.mass[infall],
sfr=sg_obj.sfr[infall],
z=sg_obj.zsnap,
line_color='r',
sfms_prop=sg_obj.sfms_prop,
label='SHAM Sat. Simulation')
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
fq_plot.plot(mass=grpcat.mass,
sfr=grpcat.sfr,
z=np.median(grpcat.z),
line_color='k',
line_style='--',
sfms_prop=sg_obj.sfms_prop,
label='Satellite Group Catalog')
fq_plot.set_axes()
fq_fig_name = ''.join([
'figure/', 'Fq.Satellite', '.tQdelayABC', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
fq_plot.save_fig(fq_fig_name)
plt.close()
return None
def PlotABC_tQdelay(tf, cen_abcrun=None):
'''
'''
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
theta = np.loadtxt(theta_file(tf))
m_arr = np.arange(7.5, 12.0, 0.1)
tdels = []
for i in xrange(len(theta)):
i_tqdelay_dict = {'name': 'explin', 'm': theta[i][0], 'b': theta[i][1]}
tdels.append(tDelay(m_arr, **i_tqdelay_dict))
tdels = np.array(tdels)
a, b, c, d, e = np.percentile(tdels, [2.5, 16, 50, 84, 97.5], axis=0)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(7, 7))
sub = fig.add_subplot(111)
mstar = np.array([
1.56781e+11, 1.01089e+11, 6.27548e+10, 3.98107e+10, 2.49831e+10,
1.57633e+10, 9.89223e+9, 6.37831e+9
])
tqdel_high = np.array([
1.36456e+0, 2.53148e+0, 3.06686e+0, 3.52693e+0, 3.62625e+0, 3.99574e+0,
3.99915e+0, 3.99915e+0
])
tqdel_low = np.array([
8.15728e-1, 1.98257e+0, 2.29240e+0, 2.82772e+0, 2.99466e+0, 2.72548e+0,
2.99016e+0, 2.68333e+0
])
sub.fill_between(mstar,
tqdel_low,
tqdel_high,
color=pretty_colors[0],
edgecolor="none",
label='Roughly Wetzel+(2013)')
#sub.plot(10**m_arr, tDelay(m_arr, **abc_tqdelay_dict), c=pretty_colors[3], label='ABC Best-fit')
sub.fill_between(10**m_arr,
a,
e,
color=pretty_colors[3],
alpha=0.5,
edgecolor="none",
label='ABC Best-fit')
sub.fill_between(10**m_arr,
b,
d,
color=pretty_colors[3],
alpha=1.,
edgecolor="none")
sub.plot(10**m_arr, tDelay(m_arr, **{'name': 'hacked'}))
sub.set_ylim([0.0, 4.0])
sub.set_ylabel(r'$\mathtt{t_{Q, delay}}$', fontsize=25)
sub.set_xlim([5 * 10**9, 2 * 10**11.])
sub.set_xscale("log")
sub.set_xlabel(r'$\mathtt{M}_*$', fontsize=25)
sub.legend(loc='lower left')
fig.savefig('figure/sallite_tqdelay_' + cen_abcrun + '.png',
bbox_inches='tight')
return None
def Ssfr(self, subsample_thetas=False, filename=None ):
''' Plot the SSFR distribution of the median paramater values of the
ABC particle pool.
'''
sfinherit_kwargs, abcrun_flag = ReadABCrun(self.abcrun)
ssfr_plot = None
if subsample_thetas:
N_theta = len(self.theta)
i_subs = np.random.choice(N_theta, size=20)
for i_sub in i_subs:
if not self.satellite_run:
gv_slope, gv_offset, fudge_slope, fudge_offset, tau_slope, tau_offset = self.theta[i_sub]
else:
gv_slope, gv_offset, fudge_slope, fudge_offset = self.theta[i_sub]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
if not self.satellite_run:
sim_kwargs['evol_prop']['tau'] = {'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
else:
sim_kwargs['evol_prop']['tau'] = {'name': 'satellite'}
inh = Inherit([1],
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
des_dict = inh()
# descendant
desc = des_dict['1']
ssfr_plot = desc.plotSsfr(line_color='red', lw=2, alpha=0.25, ssfr_plot=ssfr_plot)
# plot the median 'best-fit' theta
gv_slope = self.med_theta[0]
gv_offset = self.med_theta[1]
fudge_slope = self.med_theta[2]
fudge_offset = self.med_theta[3]
if not self.satellite_run:
tau_slope = self.med_theta[4]
tau_offset = self.med_theta[5]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
if not self.satellite_run:
sim_kwargs['evol_prop']['tau'] = {'name': 'line', 'slope': tau_slope, 'fid_mass': 11.1, 'yint': tau_offset}
else:
sim_kwargs['evol_prop']['tau'] = {'name': 'satellite'}
inh = Inherit([1],
nsnap_ancestor=sim_kwargs['nsnap_ancestor'],
subhalo_prop=sim_kwargs['subhalo_prop'],
sfr_prop=sim_kwargs['sfr_prop'],
evol_prop=sim_kwargs['evol_prop'])
des_dict = inh()
descendant = des_dict['1']
self.descendant = descendant
if filename is None:
fig_name = ''.join([code_dir(),
'figure/SSFR.abc_step', str(self.t), '_', self.abcrun, '.png'])
else:
fig_name = filename
descendant.plotSsfr(line_color='red', line_width=4,
sfms_prop=sim_kwargs['sfr_prop']['sfms'], z=descendant.zsnap,
groupcat=True, ssfr_plot=ssfr_plot, savefig=fig_name)
plt.close()
return None
def InheritSF_file(nsnap_descendant,
nsnap_ancestor=20,
tau='abc',
abc_step=None,
subhalo_prop={
'scatter': 0.2,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
},
evol_prop={
'sfr': {
'dutycycle': {
'name': 'notperiodic'
}
},
'mass': {
'name': 'sham'
}
},
flag=None):
'''
'''
if not isinstance(nsnap_descendant, list):
nsnap_descendant = [nsnap_descendant]
tau_str = ''
if abc_step is not None:
tau_str = '.tau_ABC' + str(abc_step)
else:
tau_str = evol_prop['tau']['name']
# SFMS string
sfms_str = '.SFMS' + sfr_prop['sfms']['name'].lower()
if sfr_prop['sfms']['name'] == 'linear':
sfms_str += ''.join(['_z', str(round(sfr_prop['sfms']['zslope'], 2))])
elif sfr_prop['sfms']['name'] == 'kinked':
sfms_str += ''.join([
'_Mlow',
str(round(sfr_prop['sfms']['mslope_lowmass'], 2)), 'z',
str(round(sfr_prop['sfms']['zslope'], 2))
])
# SFR assigned based on subhalo growth
if 'subhalogrowth' in sfr_prop.keys():
sfms_str += '.halogrowth_SFRassign'
# mass evolution string
if evol_prop['mass']['name'] == 'sham':
mass_str = '.Mevo_SHAM'
elif evol_prop['mass']['name'] == 'integrated':
if evol_prop['mass']['type'] == 'euler':
mass_str = '.Mevo_EulerInt'
elif evol_prop['mass']['type'] == 'rk4':
mass_str = '.Mevo_RK4Int'
flag_str = ''
if flag is not None:
flag_str = '.' + flag
hdf5_file = ''.join([
code_dir(), 'dat/InheritSF/'
'InheritSF', '.Snap',
''.join([str(nsnap) for nsnap in nsnap_descendant]), '_',
str(nsnap_ancestor), '.subhalo_sig',
str(round(subhalo_prop['scatter'], 2)), '_', subhalo_prop['source'],
'.fq_', sfr_prop['fq']['name'], sfms_str, '.', evol_prop['type'],
'_evol', '.SF_Duty_', evol_prop['sfr']['dutycycle']['name'], mass_str,
tau_str, flag_str, '.hdf5'
])
return hdf5_file
def PlotABC_EvolvedSat(tf, cen_tf=7, cen_abcrun=None, cen_prior_name=None):
''' Plot stuff for the evolved satellite population
'''
# Simulator
cen_assigned_sat_file = ''.join(['/data1/hahn/pmc_abc/pickle/',
'satellite', '.cenassign',
'.', cen_abcrun, '_ABC',
'.', cen_prior_name, '_prior', '.p'])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.theta_t', str(pewl), '_', abcrun_flag, '.dat'])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:,i]) for i in range(len(theta[0]))]
abc_tqdelay_dict = {'name': 'explin', 'm': med_theta[0], 'b': med_theta[1]}
sg_obj = EvolveSatSFR(sat_cen, tqdelay_dict=abc_tqdelay_dict)
#infall = np.arange(len(sg_obj.first_infall_mass))
infall = np.where(
(sg_obj.first_infall_mass > 0.) &
(sg_obj.first_infall_sfr > -999.))# & (sg_obj.first_infall_t > 5.7))
print len(sg_obj.first_infall_mass)
print len(infall[0])
# SSFR plot
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infall], ssfr=sg_obj.ssfr[infall], line_color=3)
ssfr_plot.GroupCat(position='satellite')
ssfr_plot.set_axes()
ssfr_fig_name = ''.join(['figure/',
'SSFR.Satellite',
'.tQdelayABC',
'.', sg_obj.abcrun, '.', sg_obj.prior_name, '_prior',
'.png'])
ssfr_plot.save_fig(ssfr_fig_name)
plt.close()
# Quiescent fraction plot
fq_plot = PlotFq()
fq_plot.plot(mass=sg_obj.mass[infall], sfr=sg_obj.sfr[infall], z=sg_obj.zsnap, line_color='r',
sfms_prop=sg_obj.sfms_prop, label='SHAM Sat. Simulation')
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
fq_plot.plot(mass=grpcat.mass, sfr=grpcat.sfr, z=np.median(grpcat.z), line_color='k', line_style='--',
sfms_prop=sg_obj.sfms_prop, label='Satellite Group Catalog')
fq_plot.set_axes()
fq_fig_name = ''.join(['figure/',
'Fq.Satellite',
'.tQdelayABC',
'.', sg_obj.abcrun, '.', sg_obj.prior_name, '_prior',
'.png'])
fq_plot.save_fig(fq_fig_name)
plt.close()
return None
def fgas_comparison():
'''
Compare f_gas (gas fraction) from different literature
'''
m_arr = np.arange(9.0, 12.1, 0.1) # mass array
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(15, 7))
fig1 = plt.figure(figsize=(15, 7))
fig2 = plt.figure(figsize=(15, 7))
for iz, z in enumerate([0.1, 0.4, 0.8]):
sub = fig.add_subplot(1, 3, iz + 1)
sub1 = fig1.add_subplot(1, 3, iz + 1)
sub2 = fig2.add_subplot(1, 3, iz + 1)
# Stewart+2009
f_stargas = 0.04 * ((10.**m_arr) / (4.5 * 10.**11.))**(-0.59 *
(1. + z)**0.45)
f_gas_Stewart = 1. - 1. / (1. + f_stargas)
# Santini+2014
if z < 0.2:
dat_file = util.code_dir().split(
'CenQue')[0] + 'dat/santini_fgas_z0.1.dat'
elif (z >= 0.2) & (z < 0.6):
dat_file = util.code_dir().split(
'CenQue')[0] + 'dat/santini_fgas_z0.4.dat'
elif (z >= 0.6) & (z < 1.):
dat_file = util.code_dir().split(
'CenQue')[0] + 'dat/santini_fgas_z0.8.dat'
else:
raise ValueError
m_dat, fgas_dat = np.loadtxt(dat_file,
delimiter=',',
unpack=True,
usecols=[0, 1])
f_gas_Santini = fgas_dat
# Boselli+2014
f_gas_Boselli = 1. - 1. / (1. + 10**(-0.69 * m_arr + 6.63))
M_gas_Stewart = np.log10(1. / (1. / f_gas_Stewart - 1.) * 10**m_arr)
M_gas_Santini = np.log10(1. / (1. / f_gas_Santini - 1.) * 10**m_dat)
M_gas_Boselli = np.log10(1. / (1. / f_gas_Boselli - 1.) * 10**m_arr)
t_gas_stewart = 10**M_gas_Stewart / 10**AverageLogSFR_sfms(
m_arr, z, sfms_prop={
'name': 'linear',
'zslope': 1.14
}) / 10**9
t_gas_santini = 10**M_gas_Santini / 10**AverageLogSFR_sfms(
m_dat, z, sfms_prop={
'name': 'linear',
'zslope': 1.14
}) / 10**9
t_gas_boselli = 10**M_gas_Boselli / 10**AverageLogSFR_sfms(
m_arr, z, sfms_prop={
'name': 'linear',
'zslope': 1.14
}) / 10**9
sub.plot(m_arr,
f_gas_Stewart,
lw=3,
c=pretty_colors[1],
label='Stewart+2009')
sub.plot(m_dat,
f_gas_Santini,
lw=3,
c=pretty_colors[3],
label='Santini+2014')
if z < 0.2:
sub.plot(m_arr,
f_gas_Boselli,
lw=3,
c=pretty_colors[5],
label='Boselli+2014')
sub.text(10.0, 0.05, r"$\mathtt{z = " + str(z) + "}$", fontsize=25)
sub.set_xlim([9.7, 11.5])
if iz == 1:
sub.set_xlabel(r'Stellar Mass', fontsize=25)
sub.set_ylim([0., 0.5])
if iz == 0:
sub.set_ylabel(
r'$\mathtt{f_{gas}} = \mathtt{M_{gas}/(M_{gas} + M_{star})}$',
fontsize=25)
sub.legend(loc='upper right')
else:
sub.set_yticklabels([])
sub1.plot(m_arr,
M_gas_Stewart,
lw=3,
c=pretty_colors[1],
label='Stewart+2009')
sub1.plot(m_dat,
M_gas_Santini,
lw=3,
c=pretty_colors[3],
label='Santini+2014')
if z < 0.2:
sub1.plot(m_arr,
M_gas_Boselli,
lw=3,
c=pretty_colors[5],
label='Boselli+2014')
sub1.text(10.0, 9.25, r"$\mathtt{z = " + str(z) + "}$", fontsize=25)
sub1.set_xlim([9.7, 11.5])
if iz == 1:
sub1.set_xlabel(r'Stellar Mass', fontsize=25)
sub1.set_ylim([9., 10.5])
if iz == 0:
sub1.set_ylabel(r'$\mathtt{M_{gas}}$', fontsize=25)
sub1.legend(loc='upper right')
else:
sub1.set_yticklabels([])
sub2.plot(m_arr,
t_gas_stewart,
lw=3,
c=pretty_colors[1],
label='Stewart+2009')
sub2.plot(m_dat,
t_gas_santini,
lw=3,
c=pretty_colors[3],
label='Santini+2014')
if z < 0.2:
sub2.plot(m_arr,
t_gas_boselli,
lw=3,
c=pretty_colors[5],
label='Boselli+2014, z=0')
sub2.text(10.0, 0.05, r"$\mathtt{z = " + str(z) + "}$", fontsize=25)
sub2.set_xlim([9.7, 11.5])
if iz == 1:
sub2.set_xlabel(r'Stellar Mass', fontsize=25)
sub2.set_ylim([0., 10.])
if iz == 0:
sub2.set_ylabel(r'$\mathtt{M_{gas}(z)/SFR^{SFMS}(z)}$ [Gyr]',
fontsize=25)
sub2.legend(loc='upper right')
else:
sub2.set_yticklabels([])
fig.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/', 'f_gas_comparison', '.png'])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
fig1.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/', 'M_gas_comparison', '.png'])
fig1.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
fig2.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/', 't_gas_comparison', '.png'])
fig2.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
return None
def fgas_comparison():
'''
Compare f_gas (gas fraction) from different literature
'''
m_arr = np.arange(9.0, 12.1, 0.1) # mass array
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(15,7))
fig1 = plt.figure(figsize=(15,7))
fig2 = plt.figure(figsize=(15,7))
for iz, z in enumerate([0.1, 0.4, 0.8]):
sub = fig.add_subplot(1, 3, iz+1)
sub1 = fig1.add_subplot(1, 3, iz+1)
sub2 = fig2.add_subplot(1, 3, iz+1)
# Stewart+2009
f_stargas = 0.04 * ((10.**m_arr)/(4.5*10.**11.))**(-0.59 * (1. + z)**0.45)
f_gas_Stewart = 1. - 1./(1. + f_stargas)
# Santini+2014
if z < 0.2:
dat_file = util.code_dir().split('CenQue')[0]+'dat/santini_fgas_z0.1.dat'
elif (z >= 0.2) & (z < 0.6):
dat_file = util.code_dir().split('CenQue')[0]+'dat/santini_fgas_z0.4.dat'
elif (z >= 0.6) & (z < 1.):
dat_file = util.code_dir().split('CenQue')[0]+'dat/santini_fgas_z0.8.dat'
else:
raise ValueError
m_dat, fgas_dat = np.loadtxt(dat_file, delimiter=',', unpack=True, usecols=[0,1])
f_gas_Santini = fgas_dat
# Boselli+2014
f_gas_Boselli = 1. - 1./(1. + 10**(-0.69 * m_arr + 6.63))
M_gas_Stewart = np.log10(1./(1./f_gas_Stewart-1.) * 10**m_arr)
M_gas_Santini = np.log10(1./(1./f_gas_Santini-1.) * 10**m_dat)
M_gas_Boselli = np.log10(1./(1./f_gas_Boselli-1.) * 10**m_arr)
t_gas_stewart = 10**M_gas_Stewart / 10**AverageLogSFR_sfms(m_arr, z, sfms_prop={'name': 'linear', 'zslope':1.14})/10**9
t_gas_santini = 10**M_gas_Santini / 10**AverageLogSFR_sfms(m_dat, z, sfms_prop={'name': 'linear', 'zslope':1.14})/10**9
t_gas_boselli = 10**M_gas_Boselli / 10**AverageLogSFR_sfms(m_arr, z, sfms_prop={'name': 'linear', 'zslope':1.14})/10**9
sub.plot(m_arr, f_gas_Stewart, lw=3, c=pretty_colors[1], label='Stewart+2009')
sub.plot(m_dat, f_gas_Santini, lw=3, c=pretty_colors[3], label='Santini+2014')
if z < 0.2:
sub.plot(m_arr, f_gas_Boselli, lw=3, c=pretty_colors[5], label='Boselli+2014')
sub.text(10.0, 0.05, r"$\mathtt{z = "+str(z)+"}$", fontsize=25)
sub.set_xlim([9.7, 11.5])
if iz == 1:
sub.set_xlabel(r'Stellar Mass', fontsize=25)
sub.set_ylim([0., 0.5])
if iz == 0:
sub.set_ylabel(r'$\mathtt{f_{gas}} = \mathtt{M_{gas}/(M_{gas} + M_{star})}$', fontsize=25)
sub.legend(loc='upper right')
else:
sub.set_yticklabels([])
sub1.plot(m_arr, M_gas_Stewart, lw=3, c=pretty_colors[1], label='Stewart+2009')
sub1.plot(m_dat, M_gas_Santini, lw=3, c=pretty_colors[3], label='Santini+2014')
if z < 0.2:
sub1.plot(m_arr, M_gas_Boselli, lw=3, c=pretty_colors[5], label='Boselli+2014')
sub1.text(10.0, 9.25, r"$\mathtt{z = "+str(z)+"}$", fontsize=25)
sub1.set_xlim([9.7, 11.5])
if iz == 1:
sub1.set_xlabel(r'Stellar Mass', fontsize=25)
sub1.set_ylim([9., 10.5])
if iz == 0:
sub1.set_ylabel(r'$\mathtt{M_{gas}}$', fontsize=25)
sub1.legend(loc='upper right')
else:
sub1.set_yticklabels([])
sub2.plot(m_arr, t_gas_stewart, lw=3, c=pretty_colors[1], label='Stewart+2009')
sub2.plot(m_dat, t_gas_santini, lw=3, c=pretty_colors[3], label='Santini+2014')
if z < 0.2:
sub2.plot(m_arr, t_gas_boselli, lw=3, c=pretty_colors[5], label='Boselli+2014, z=0')
sub2.text(10.0, 0.05, r"$\mathtt{z = "+str(z)+"}$", fontsize=25)
sub2.set_xlim([9.7, 11.5])
if iz == 1:
sub2.set_xlabel(r'Stellar Mass', fontsize=25)
sub2.set_ylim([0., 10.])
if iz == 0:
sub2.set_ylabel(r'$\mathtt{M_{gas}(z)/SFR^{SFMS}(z)}$ [Gyr]', fontsize=25)
sub2.legend(loc='upper right')
else:
sub2.set_yticklabels([])
fig.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/',
'f_gas_comparison',
'.png'])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
fig1.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/',
'M_gas_comparison',
'.png'])
fig1.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
fig2.subplots_adjust(wspace=0.0, hspace=0.0)
fig_file = ''.join(['figure/',
't_gas_comparison',
'.png'])
fig2.savefig(fig_file, bbox_inches='tight', dpi=150)
plt.close()
return None
def ABC(T, eps_input, Npart=1000, cen_tf=None, cen_prior_name=None, cen_abcrun=None):
''' ABC-PMC implementation.
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
abcinh = ABCInherit(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
# Data (Group Catalog Satellite fQ)
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
qfrac = Fq()
m_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (m_bin[:-1] + m_bin[1:])
sfq = qfrac.Classify(grpcat.mass, grpcat.sfr, np.median(grpcat.z),
sfms_prop=abcinh.sim_kwargs['sfr_prop']['sfms'])
ngal, dum = np.histogram(grpcat.mass, bins=m_bin)
ngal_q, dum = np.histogram(grpcat.mass[sfq == 'quiescent'], bins=m_bin)
data_sum = [M_mid, ngal_q.astype('float')/ngal.astype('float')]
# Simulator
cen_assigned_sat_file = ''.join(['/data1/hahn/pmc_abc/pickle/',
'satellite', '.cenassign', '.', cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
def Simz(tt): # Simulator (forward model)
tqdel_dict = {'name': 'explin', 'm': tt[0] , 'b': tt[1]}
sat_evol = EvolveSatSFR(sat_cen, tqdelay_dict=tqdel_dict)
sfq_sim = qfrac.Classify(sat_evol.mass, sat_evol.sfr, sat_evol.zsnap,
sfms_prop=sat_evol.sfms_prop)
ngal_sim, dum = np.histogram(sat_evol.mass, bins=m_bin)
ngal_q_sim, dum = np.histogram(sat_evol.mass[sfq_sim == 'quiescent'], bins=m_bin)
sim_sum = [M_mid, ngal_q_sim.astype('float')/ngal_sim.astype('float')]
return sim_sum
# Priors
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def rho(simum, datum):
datum_dist = datum[1]
simum_dist = simum[1]
drho = np.sum((datum_dist - simum_dist)**2)
return drho
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.theta_t', str(pewl), '_', abcrun_flag, '.dat'])
w_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.w_t', str(pewl), '_', abcrun_flag, '.dat'])
dist_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'Satellite.tQdelay.dist_t', str(pewl), '_', abcrun_flag, '.dat'])
eps_file = ''.join([code_dir(),
'dat/pmc_abc/Satellite.tQdelay.epsilon_', abcrun_flag, '.dat'])
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=None):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = '\t'+str(pool.eps)+'\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t), pool.thetas,
header='tQdelay_slope, tQdelay_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
eps.eps = np.median(pool.dists)
pools.append(pool)
return pools
def MakeABCrun(abcrun=None, nsnap_start=15, subhalo=None, fq=None, sfms=None, dutycycle=None, mass_evol=None,
Niter=None, Npart=None, prior_name=None, eps_val=None, restart=False):
'''Pickle file that specifies all the choices of parameters and ABC settings
for record keeping purposes.
'''
if abcrun is None:
abcrun = ''.join(['RENAME_ME_', str(datetime.today().date())])
restart_str = ''
if restart:
restart_str = '.restart'
file_name = ''.join([code_dir(), 'dat/pmc_abc/run/', 'abcrun_', abcrun, restart_str, '.txt'])
pickle_name = file_name.replace('.txt', '.p')
f = open(file_name, 'w')
# ABC properities
f.write('# ABC Run Properties \n')
f.write('N_iter = '+str(Niter)+'\n')
f.write('N_particles = '+str(Npart)+'\n')
f.write('Prior Name = '+prior_name+'\n')
f.write('Epsilon_0 = '+str(eps_val)+'\n')
if restart:
f.write('Restarting')
f.write('\n')
# Subhalo properties
f.write('# Subhalo Properties \n')
if subhalo is None:
subhalo = {'scatter': 0.2, 'source': 'li-march'}
f.write(''.join(['scatter = ', str(subhalo['scatter']), '\n']))
f.write(''.join(['source = ', subhalo['source'], '\n']))
f.write('\n')
# Quiescent fraction properties
f.write('# Quiescent Fraction Properties \n')
if fq is None: # quiescent fraction
fq = {'name': 'wetzel'}
f.write(''.join(['name = ', fq['name'], '\n']))
f.write('\n')
# Starforming Main Sequence
f.write('# Starforming Main Sequence Properties \n')
if sfms is None: # SFMS
sfms = {'name': 'linear', 'zslope': 1.14}
f.write(''.join(['name = ', sfms['name'], '\n']))
f.write(''.join(['zslope = ', str(sfms['zslope']), '\n']))
f.write('\n')
# SF dutycycle properties
f.write('# SF Dutycycle Properties \n')
if dutycycle is None:
dutycycle = {'name': 'newamp_squarewave', 'freq_range': [2.*np.pi, 20.*np.pi], 'sigma': 0.3}
f.write(''.join(['name = ', dutycycle['name'], '\n']))
f.write(''.join(['frequency range = ', str(dutycycle['freq_range'][0]), ',', str(dutycycle['freq_range'][1]), '\n']))
f.write(''.join(['sigma = ', str(dutycycle['sigma']), '\n']))
f.write('\n')
# Mass Evolution properties
f.write('# Mass Evolution Properties \n')
if mass_evol is None:
mass_evol = {'name': 'sham'}
if mass_evol['name'] == 'sham':
f.write(''.join(['name = ', mass_evol['name'], '\n']))
f.close()
evol_dict = {
'type': 'simult',
'sfr': {'dutycycle': dutycycle},
'mass': mass_evol
}
kwargs = {
'nsnap_ancestor': nsnap_start,
'subhalo_prop': subhalo,
'sfr_prop': {'fq': fq, 'sfms': sfms},
'evol_prop': evol_dict
}
pickle.dump(kwargs, open(pickle_name, 'wb'))
return kwargs, abcrun
