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plot_posterior_corner.py
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plot_posterior_corner.py
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#! /usr/bin/env python
# plot_posterior_corner.py
#
# --posterior-file f1 --posterior-label n1 --posterior-file f2 --posterior-label n2 ...
# --parameter p1 --parameter p2 ...
#
# EXAMPLE
# python plot_posterior_corner.py --posterior-file downloads/TidalP4.dat --parameter lambda1 --parameter lambda2 --parameter mc
# python plot_posterior_corner.py --parameter mc --parameter eta --posterior-file G298048/production_C00_cleaned_TaylorT4/posterior-samples.dat --parameter lambdat
# plot_posterior_corner.py --posterior-file ejecta.dat --parameter mej_dyn --parameter mej_wind --parameter-log-scale mej_dyn --parameter-log-scale mej_wind --change-parameter-label "mej_dyn=m_{\rm ej,d}" --change-parameter-label "mej_wind=m_{\rm ej,w}"
#
# USAGE
# - hardcoded list of colors, used in order, for multiple plots
#
import RIFT.lalsimutils as lalsimutils
import RIFT.misc.samples_utils as samples_utils
from RIFT.misc.samples_utils import add_field, extract_combination_from_LI, standard_expand_samples
import lal
import numpy as np
import argparse
import numpy.lib.recfunctions as rfn
eos_param_names = ['logp1', 'gamma1','gamma2', 'gamma3', 'R1_km', 'R2_km']
try:
import matplotlib
print(" Matplotlib backend ", matplotlib.get_backend())
if matplotlib.get_backend() == 'agg':
fig_extension = '.png'
bNoInteractivePlots=True
else:
matplotlib.use('agg')
fig_extension = '.png'
bNoInteractivePlots =True
from matplotlib import pyplot as plt
bNoPlots=False
except:
print(" Error setting backend")
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.lines as mlines
import corner
import RIFT.misc.our_corner as our_corner
try:
import RIFT.misc.bounded_kde as bounded_kde
except:
print(" -No 1d kdes- ")
print(" WARNINGS : BoundedKDE class can oversmooth. Need to edit options for using this class! ")
def render_coord(x,logscale=False):
if x in lalsimutils.tex_dictionary.keys():
mystr= lalsimutils.tex_dictionary[x]
if logscale:
mystr=mystr.lstrip('$')
mystr = "$\log_{10}"+mystr
return mystr
else:
return mystr
if 'product(' in x:
a=x.replace(' ', '') # drop spaces
a = a[:len(a)-1] # drop last
a = a[8:]
terms = a.split(',')
exprs =list(map(render_coord, terms))
exprs = list(map( lambda x: x.replace('$', ''), exprs))
my_label = ' '.join(exprs)
return '$'+my_label+'$'
else:
if logscale:
return "log10 "+str(x)
return x
def render_coordinates(coord_names,logparams=[]):
print("log params ",logparams)
return list(map(lambda x: render_coord(x,logscale=(x in logparams)), coord_names))
remap_ILE_2_LI = samples_utils.remap_ILE_2_LI
remap_LI_to_ILE = samples_utils.remap_LI_to_ILE
# def extract_combination_from_LI(samples_LI, p):
# """
# extract_combination_from_LI
# - reads in known columns from posterior samples
# - for selected known combinations not always available, it will compute them from standard quantities
# Unike version in ConstructIntrinsicPosterior, this code does not rely on ChooseWaveformParams to perform coordinate changes...
# """
# if p in samples_LI.dtype.names: # e.g., we have precomputed it
# return samples_LI[p]
# if p in remap_ILE_2_LI.keys():
# if remap_ILE_2_LI[p] in samples_LI.dtype.names:
# return samples_LI[ remap_ILE_2_LI[p] ]
# # Return cartesian components of spin1, spin2. NOTE: I may already populate these quantities in 'Add important quantities'
# if (p == 'chi_eff' or p=='xi') and 'a1z' in samples_LI.dtype.names:
# m1 = samples_LI['m1']
# m2 = samples_LI['m2']
# a1z = samples_LI['a1z']
# a2z = samples_LI['a2z']
# return (m1 * a1z + m2*a2z)/(m1+m2)
# if p == 'chiz_plus':
# print(" Transforming ")
# if 'a1z' in samples_LI.dtype.names:
# return (samples_LI['a1z']+ samples_LI['a2z'])/2.
# if 'theta1' in samples_LI.dtype.names:
# return (samples_LI['a1']*np.cos(samples_LI['theta1']) + samples_LI['a2']*np.cos(samples_LI['theta2']) )/2.
# # return (samples_LI['a1']+ samples_LI['a2'])/2.
# if p == 'chiz_minus':
# print(" Transforming ")
# if 'a1z' in samples_LI.dtype.names:
# return (samples_LI['a1z']- samples_LI['a2z'])/2.
# if 'theta1' in samples_LI.dtype.names:
# return (samples_LI['a1']*np.cos(samples_LI['theta1']) - samples_LI['a2']*np.cos(samples_LI['theta2']) )/2.
# # return (samples_LI['a1']- samples_LI['a2'])/2.
# if 'theta1' in samples_LI.dtype.names:
# if p == 's1x':
# return samples_LI["a1"]*np.sin(samples_LI[ 'theta1']) * np.cos( samples_LI['phi1'])
# if p == 's1y' :
# return samples_LI["a1"]*np.sin(samples_LI[ 'theta1']) * np.sin( samples_LI['phi1'])
# if p == 's2x':
# return samples_LI["a2"]*np.sin(samples_LI[ 'theta2']) * np.cos( samples_LI['phi2'])
# if p == 's2y':
# return samples_LI["a2"]*np.sin(samples_LI[ 'theta2']) * np.sin( samples_LI['phi2'])
# if p == 'chi1_perp' :
# return samples_LI["a1"]*np.sin(samples_LI[ 'theta1'])
# if p == 'chi2_perp':
# return samples_LI["a2"]*np.sin(samples_LI[ 'theta2'])
# elif 'tilt1' in samples_LI.dtype.names:
# if p == 'chi1_perp' :
# return samples_LI["a1"]*np.sin(samples_LI[ 'tilt1'])
# if p == 'chi2_perp':
# return samples_LI["a2"]*np.sin(samples_LI[ 'tilt2'])
# else: # aligned
# if p == 'chi1_perp' :
# return np.zeros(len(samples_LI["m1"]))
# if p == 'chi2_perp':
# return np.zeros(len(samples_LI["m1"]))
# if 'lambdat' in samples_LI.dtype.names: # LI does sampling in these tidal coordinates
# lambda1, lambda2 = lalsimutils.tidal_lambda_from_tilde(samples_LI["m1"], samples_LI["m2"], samples_LI["lambdat"], samples_LI["dlambdat"])
# if p == "lambda1":
# return lambda1
# if p == "lambda2":
# return lambda2
# if p == 'delta' or p=='delta_mc':
# return (samples_LI['m1'] - samples_LI['m2'])/((samples_LI['m1'] + samples_LI['m2']))
# # Return cartesian components of Lhat
# if p == 'product(sin_beta,sin_phiJL)':
# return np.sin(samples_LI[ remap_ILE_2_LI['beta'] ]) * np.sin( samples_LI['phi_jl'])
# if p == 'product(sin_beta,cos_phiJL)':
# return np.sin(samples_LI[ remap_ILE_2_LI['beta'] ]) * np.cos( samples_LI['phi_jl'])
# if p == 'mc':
# m1v= samples_LI["m1"]
# m2v = samples_LI["m2"]
# return lalsimutils.mchirp(m1v,m2v)
# if p == 'eta':
# m1v= samples_LI["m1"]
# m2v = samples_LI["m2"]
# return lalsimutils.symRatio(m1v,m2v)
# if p == 'phi1':
# return np.angle(samples_LI['a1x']+1j*samples_LI['a1y'])
# if p == 'chi_pavg':
# samples = np.array([samples_LI["m1"], samples_LI["m2"], samples_LI["a1x"], samples_LI["a1y"], samples_LI["a1z"], samples_LI["a2x"], samples_LI["a2y"], samples_LI["a2z"]]).T
# with Pool(12) as pool:
# chipavg = np.array(pool.map(fchipavg, samples))
# return chipavg
# if p == 'chi_p':
# samples = np.array([samples_LI["m1"], samples_LI["m2"], samples_LI["a1x"], samples_LI["a1y"], samples_LI["a1z"], samples_LI["a2x"], samples_LI["a2y"], samples_LI["a2z"]]).T
# with Pool(12) as pool:
# chip = np.array(pool.map(fchip, samples))
# return chip
# # Backup : access lambdat if not present
# if (p == 'lambdat' or p=='dlambdat') and 'lambda1' in samples.dtype.names:
# Lt,dLt = lalsimutils.tidal_lambda_tilde(samples['m1'], samples['m2'], samples['lambda1'], samples['lambda2'])
# if p=='lambdat':
# return Lt
# if p=='dlambdat':
# return dLt
# if p == "q" and 'm1' in samples.dtype.names:
# return samples["m2"]/samples["m1"]
# print(" No access for parameter ", p, " in ", samples.dtype.names)
# return np.zeros(len(samples_LI['m1'])) # to avoid causing a hard failure
################################################
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("--posterior-file",action='append',help="filename of *.dat file [standard LI output]")
parser.add_argument("--truth-file",type=str, help="file containing the true parameters")
parser.add_argument("--posterior-distance-factor",action='append',help="Sequence of factors used to correct the distances")
parser.add_argument("--truth-event",type=int, default=0,help="file containing the true parameters")
parser.add_argument("--composite-file",action='append',help="filename of *.dat file [standard ILE intermediate]")
parser.add_argument("--composite-file-has-labels",action='store_true',help="Assume header for composite file")
parser.add_argument("--use-all-composite-but-grayscale",action='store_true',help="Composite")
parser.add_argument("--flag-tides-in-composite",action='store_true',help='Required, if you want to parse files with tidal parameters')
parser.add_argument("--flag-eos-index-in-composite",action='store_true',help='Required, if you want to parse files with EOS index in composite (and tides)')
parser.add_argument("--posterior-label",action='append',help="label for posterior file")
parser.add_argument("--posterior-color",action='append',help="color and linestyle for posterior. PREPENDED onto default list, so defaults exist")
parser.add_argument("--posterior-linestyle",action='append',help="color and linestyle for posterior. PREPENDED onto default list, so defaults exist")
parser.add_argument("--parameter", action='append',help="parameter name (ILE). Note source-frame masses are only natively supported for LI")
parser.add_argument("--parameter-log-scale",action='append',help="Put this parameter in log scale")
parser.add_argument("--change-parameter-label", action='append',help="format name=string. Will be wrapped in $...$")
parser.add_argument("--use-legend",action='store_true')
parser.add_argument("--use-title",default=None,type=str)
parser.add_argument("--use-smooth-1d",action='store_true')
parser.add_argument("--plot-1d-extra",action='store_true')
parser.add_argument("--pdf",action='store_true',help="Export PDF plots")
#option deprecated by bind-param and param-bound
#parser.add_argument("--mc-range",default=None,help='List for mc range. Default is None')
parser.add_argument("--bind-param",default=None,action="append",help="a parameter to impose a bound on, with corresponding --param-bound arg in respective order")
parser.add_argument("--param-bound",action="append",help="respective bounds for above params")
parser.add_argument("--ci-list",default=None,help='List for credible intervals. Default is 0.95,0.9,0.68')
parser.add_argument("--quantiles",default=None,help='List for 1d quantiles intervals. Default is 0.95,0.05')
parser.add_argument("--chi-max",default=1,type=float)
parser.add_argument("--lambda-plot-max",default=2000,type=float)
parser.add_argument("--lnL-cut",default=None,type=float)
parser.add_argument("--sigma-cut",default=0.4,type=float)
parser.add_argument("--eccentricity", action="store_true", help="Read sample files in format including eccentricity")
parser.add_argument("--matplotlib-block-defaults",action="store_true",help="Relies entirely on user to set plot options for plot styles from matplotlibrc")
parser.add_argument("--no-mod-psi",action="store_true",help="Default is to take psi mod pi. If present, does not do this")
parser.add_argument("--verbose",action='store_true',help='print matplotlibrc data')
opts= parser.parse_args()
plt.rc('axes',unicode_minus=False)
dpi_base=200
if not(opts.matplotlib_block_defaults):
legend_font_base=16
rc_params = {'backend': 'ps',
'axes.labelsize': 11,
'axes.titlesize': 10,
'font.size': 11,
'legend.fontsize': legend_font_base,
'xtick.labelsize': 11,
'ytick.labelsize': 11,
#'text.usetex': True,
'font.family': 'Times New Roman'}#,
#'font.sans-serif': ['Bitstream Vera Sans']}#,
plt.rcParams.update(rc_params)
if opts.verbose:
print(plt.rcParams)
if opts.posterior_file is None:
print(" No input files ")
import sys
sys.exit(0)
if opts.pdf:
fig_extension='.pdf'
truth_P_list = None
P_ref = None
if opts.truth_file:
print(" Loading true parameters from ", opts.truth_file)
truth_P_list =lalsimutils.xml_to_ChooseWaveformParams_array(opts.truth_file)
P_ref = truth_P_list[opts.truth_event]
# P_ref.print_params()
if opts.change_parameter_label:
for name, new_str in map( lambda c: c.split("="),opts.change_parameter_label):
if name in lalsimutils.tex_dictionary:
lalsimutils.tex_dictionary[name] = "$"+new_str+"$"
else:
print(" Assigning new variable string",name,new_str)
lalsimutils.tex_dictionary[name] = "$"+new_str+"$" # should be able to ASSIGN NEW NAMES, not restrict
special_param_ranges = {
'q':[0,1],
'eta':[0,0.25],
'a1z':[-opts.chi_max,opts.chi_max],
'a2z':[-opts.chi_max,opts.chi_max],
'chi_eff': [-opts.chi_max,opts.chi_max], # this can backfire for very narrow constraints
'lambda1':[0,4000],
'lambda2':[0,4000],
'chi_pavg':[0,2],
'chi_p':[0,1],
'lambdat':[0,4000],
'eccentricity':[0,1]
}
#mc_range deprecated by generic bind_param
#if opts.mc_range:
# special_param_ranges['mc'] = eval(opts.mc_range)
# print(" mc range ", special_param_ranges['mc'])
if opts.bind_param:
for i,par in enumerate(opts.bind_param):
special_param_ranges[par]=eval(opts.param_bound[i])
print(par +" range ",special_param_ranges[par])
# Parameters
param_list = opts.parameter
# Legend
color_list=['black', 'red', 'green', 'blue','yellow','C0','C1','C2','C3']
if opts.posterior_color:
color_list =opts.posterior_color + color_list
else:
color_list += len(opts.posterior_file)*['black']
linestyle_list = ['-' for k in color_list]
if opts.posterior_linestyle:
linestyle_list = opts.posterior_linestyle + linestyle_list
#linestyle_remap_contour = {":", 'dotted', '-'
posterior_distance_factors = np.ones(len(opts.posterior_file))
if opts.posterior_distance_factor:
for indx in np.arange(len(opts.posterior_file)):
posterior_distance_factors[indx] = float(opts.posterior_distance_factor[indx])
line_handles = []
corner_legend_location=None; corner_legend_prop=None
if opts.use_legend and opts.posterior_label:
n_elem = len(opts.posterior_file)
for indx in np.arange(n_elem):
my_line = mlines.Line2D([],[],color=color_list[indx],linestyle=linestyle_list[indx],label=opts.posterior_label[indx])
line_handles.append(my_line)
corner_legend_location=(0.7, 1.0)
corner_legend_prop = {'size':8}
# https://stackoverflow.com/questions/7125009/how-to-change-legend-size-with-matplotlib-pyplot
#params = {'legend.fontsize': 20, 'legend.linewidth': 2}
#plt.rcParams.update(params)
# Import
posterior_list = []
posteriorP_list = []
label_list = []
# Load posterior files
if opts.posterior_file:
for fname in opts.posterior_file:
samples = np.genfromtxt(fname,names=True,replace_space=None) # don't replace underscores in names
samples = standard_expand_samples(samples)
if not(opts.no_mod_psi) and 'psi' in samples.dtype.names:
samples['psi'] = np.mod(samples['psi'],np.pi)
# if not 'mtotal' in samples.dtype.names and 'mc' in samples.dtype.names: # raw LI samples use
# q_here = samples['q']
# eta_here = q_here/(1+q_here)
# mc_here = samples['mc']
# mtot_here = mc_here / np.power(eta_here, 3./5.)
# m1_here = mtot_here/(1+q_here)
# samples = add_field(samples, [('mtotal', float)]); samples['mtotal'] = mtot_here
# samples = add_field(samples, [('eta', float)]); samples['eta'] = eta_here
# samples = add_field(samples, [('m1', float)]); samples['m1'] = m1_here
# samples = add_field(samples, [('m2', float)]); samples['m2'] = mtot_here * q_here/(1+q_here)
# if (not 'theta1' in samples.dtype.names) and ('a1x' in samples.dtype.names): # probably does not have polar coordinates
# chiperp_here = np.sqrt( samples['a1x']**2+ samples['a1y']**2)
# chi1_here = np.sqrt( samples['a1z']**2 + chiperp_here**2)
# theta1_here = np.arctan( samples['a1z']/chiperp_here)
# phi1_here = np.angle(samples['a1x']+1j*samples['a1y'])
# samples = add_field(samples, [('chi1', float)]); samples['chi1'] = chi1_here
# samples = add_field(samples, [('theta1', float)]); samples['theta1'] = theta1_here
# samples = add_field(samples, [('phi1', float)]); samples['phi1'] = phi1_here
# # we almost certainly use standard
# chi1_perp = np.sqrt(samples['a1x']**2 + samples['a1y']**2)
# chi2_perp = np.sqrt(samples['a2x']**2 + samples['a2y']**2)
# samples = add_field(samples, [('chi1_perp',float)]); samples['chi1_perp'] = chi1_perp
# samples = add_field(samples, [('chi2_perp',float)]); samples['chi2_perp'] = chi2_perp
# elif "theta1" in samples.dtype.names:
# a1x_dat = samples["a1"]*np.sin(samples["theta1"])*np.cos(samples["phi1"])
# a1y_dat = samples["a1"]*np.sin(samples["theta1"])*np.sin(samples["phi1"])
# chi1_perp = samples["a1"]*np.sin(samples["theta1"])
# a2x_dat = samples["a2"]*np.sin(samples["theta2"])*np.cos(samples["phi2"])
# a2y_dat = samples["a2"]*np.sin(samples["theta2"])*np.sin(samples["phi2"])
# chi2_perp = samples["a2"]*np.sin(samples["theta2"])
# samples = add_field(samples, [('a1x', float)]); samples['a1x'] = a1x_dat
# samples = add_field(samples, [('a1y', float)]); samples['a1y'] = a1y_dat
# samples = add_field(samples, [('a2x', float)]); samples['a2x'] = a2x_dat
# samples = add_field(samples, [('a2y', float)]); samples['a2y'] = a2y_dat
# samples = add_field(samples, [('chi1_perp',float)]); samples['chi1_perp'] = chi1_perp
# samples = add_field(samples, [('chi2_perp',float)]); samples['chi2_perp'] = chi2_perp
# if not 'chi_eff' in samples.dtype.names:
# samples = add_field(samples, [('chi_eff',float)]); samples['chi_eff'] = (samples["m1"]*samples["a1z"]+samples["m2"]*samples["a2z"])/(samples["m1"]+samples["m2"])
# elif 'a1x' in samples.dtype.names:
# chi1_perp = np.sqrt(samples['a1x']**2 + samples['a1y']**2)
# chi2_perp = np.sqrt(samples['a2x']**2 + samples['a2y']**2)
# samples = add_field(samples, [('chi1_perp',float)]); samples['chi1_perp'] = chi1_perp
# samples = add_field(samples, [('chi2_perp',float)]); samples['chi2_perp'] = chi2_perp
# if 'lambda1' in samples.dtype.names and not ('lambdat' in samples.dtype.names):
# Lt,dLt = lalsimutils.tidal_lambda_tilde(samples['m1'], samples['m2'], samples['lambda1'], samples['lambda2'])
# samples = add_field(samples, [('lambdat', float)]); samples['lambdat'] = Lt
# samples = add_field(samples, [('dlambdat', float)]); samples['dlambdat'] = dLt
if 'chi1_perp' in samples.dtype.names:
# impose Kerr limit, if neede
npts = len(samples["m1"])
indx_ok =np.arange(npts)
chi1_squared = samples['chi1_perp']**2 + samples["a1z"]**2
chi2_squared = samples["chi2_perp"]**2 + samples["a2z"]**2
indx_ok = np.logical_and(chi1_squared < opts.chi_max ,chi2_squared < opts.chi_max)
npts_out = np.sum(indx_ok)
new_samples = np.recarray( (npts_out,), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][indx_ok]
samples = new_samples
# Save samples
posterior_list.append(samples)
# Continue ... rest not used at present
continue
# Populate a P_list with the samples, so I can perform efficient conversion for plots
# note only the DETECTOR frame properties are stored here
P_list = []
P = lalsimutils.ChooseWaveformParams()
for indx in np.arange(len(samples["m1"])):
P.m1 = samples["m1"][indx]*lal.MSUN_SI
P.m2 = samples["m2"][indx]*lal.MSUN_SI
P.s1x = samples["a1x"][indx]
P.s1y = samples["a1y"][indx]
P.s1z = samples["a1z"][indx]
P.s2x = samples["a2x"][indx]
P.s2y = samples["a2y"][indx]
P.s2z = samples["a2z"][indx]
if "lnL" in samples.keys():
P.lnL = samples["lnL"][indx] # creates a new field !
else:
P.lnL = -1
# Populate other parameters as needed ...
P_list.append(P)
posteriorP_list.append(P_list)
for indx in np.arange(len(posterior_list)):
samples = posterior_list[indx]
fac = posterior_distance_factors[indx]
if 'dist' in samples.dtype.names:
samples["dist"]*= fac
if 'distance' in samples.dtype.names:
samples["distance"]*= fac
# Import
composite_list = []
composite_full_list = []
field_names=("indx","m1", "m2", "a1x", "a1y", "a1z", "a2x", "a2y", "a2z","lnL", "sigmaOverL", "ntot", "neff")
if opts.flag_tides_in_composite:
if opts.flag_eos_index_in_composite:
print(" Reading composite file, assumingtide/eos-index-based format ")
field_names=("indx","m1", "m2", "a1x", "a1y", "a1z", "a2x", "a2y", "a2z","lambda1", "lambda2", "eos_table_index","lnL", "sigmaOverL", "ntot", "neff")
else:
print(" Reading composite file, assuming tide-based format ")
field_names=("indx","m1", "m2", "a1x", "a1y", "a1z", "a2x", "a2y", "a2z","lambda1", "lambda2", "lnL", "sigmaOverL", "ntot", "neff")
if opts.eccentricity:
print(" Reading composite file, assuming eccentricity-based format ")
field_names=("indx","m1", "m2", "a1x", "a1y", "a1z", "a2x", "a2y", "a2z","eccentricity", "lnL", "sigmaOverL", "ntot", "neff")
field_formats = [np.float32 for x in field_names]
composite_dtype = [ (x,float) for x in field_names] #np.dtype(names=field_names ,formats=field_formats)
# Load posterior files
if opts.composite_file:
print(opts.composite_file)
for fname in opts.composite_file[:1]: # Only load the first one!
print(" Loading ... ", fname)
if not(opts.composite_file_has_labels):
samples = np.loadtxt(fname,dtype=composite_dtype) # Names are not always available
else:
samples = np.genfromtxt(fname,names=True)
samples = rfn.rename_fields(samples, {'sigmalnL': 'sigmaOverL', 'sigma_lnL': 'sigmaOverL'}) # standardize names, some drift in labels
samples = samples[ ~np.isnan(samples["lnL"])] # remove nan likelihoods -- they can creep in with poor settings/overflows
name_ref = samples.dtype.names[0]
if opts.sigma_cut >0:
npts = len(samples[name_ref])
# strip NAN
sigma_vals = samples["sigmaOverL"]
good_sigma = sigma_vals < opts.sigma_cut
npts_out = np.sum(good_sigma)
if npts_out < npts:
new_samples = np.recarray( (npts_out,), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][good_sigma]
samples = new_samples
# samples = np.recarray(samples.T,names=field_names,dtype=field_formats) #,formats=field_formats)
# If no record names
# Add mtotal, q,
if 'm1' in samples.dtype.names:
samples=add_field(samples,[('mtotal',float)]); samples["mtotal"]= samples["m1"]+samples["m2"];
samples=add_field(samples,[('q',float)]); samples["q"]= samples["m2"]/samples["m1"];
samples=add_field(samples,[('mc',float)]); samples["mc"] = lalsimutils.mchirp(samples["m1"], samples["m2"])
samples=add_field(samples,[('eta',float)]); samples["eta"] = lalsimutils.symRatio(samples["m1"], samples["m2"])
samples=add_field(samples,[('chi_eff',float)]); samples["chi_eff"]= (samples["m1"]*samples["a1z"]+samples["m2"]*samples["a2z"])/(samples["mtotal"]);
chi1_perp = np.sqrt(samples['a1x']*samples["a1x"] + samples['a1y']**2)
chi2_perp = np.sqrt(samples['a2x']**2 + samples['a2y']**2)
samples = add_field(samples, [('chi1_perp',float)]); samples['chi1_perp'] = chi1_perp
samples = add_field(samples, [('chi2_perp',float)]); samples['chi2_perp'] = chi2_perp
if ('lambda1' in samples.dtype.names):
Lt,dLt = lalsimutils.tidal_lambda_tilde(samples['m1'], samples['m2'], samples['lambda1'], samples['lambda2'])
samples= add_field(samples, [('LambdaTilde',float), ('DeltaLambdaTilde',float),('lambdat',float),('dlambdat',float)])
samples['LambdaTilde'] = samples['lambdat']= Lt
samples['DeltaLambdaTilde'] = samples['dlambdat']= dLt
samples_orig = samples
if opts.lnL_cut:
npts = len(samples[name_ref])
# strip NAN
lnL_vals = samples["lnL"]
not_nan = np.logical_not(np.isnan(lnL_vals))
npts_out = np.sum(not_nan)
if npts_out < npts:
new_samples = np.recarray( (npts_out,), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][not_nan]
samples = new_samples
# apply cutoff
indx_ok =np.arange(npts)
lnL_max = np.max(samples["lnL"])
print(" lnL_max = ", lnL_max)
indx_ok = samples["lnL"]>lnL_max -opts.lnL_cut
npts_out = np.sum(indx_ok)
new_samples = np.recarray( (npts_out,), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][indx_ok]
samples = new_samples
print(" Loaded samples from ", fname , len(samples[name_ref]))
if 'm1' in samples.dtype.names:
# impose Kerr limit
npts = len(samples["m1"])
indx_ok =np.arange(npts)
chi1_squared = samples["chi1_perp"]**2 + samples["a1z"]**2
chi2_squared = samples["chi2_perp"]**2 + samples["a2z"]**2
indx_ok = np.logical_and(chi1_squared < opts.chi_max ,chi2_squared < opts.chi_max)
npts_out = np.sum(indx_ok)
if npts_out < npts:
print(" Ok systems ", npts_out)
new_samples = np.recarray( (npts_out,), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][indx_ok]
samples = new_samples
print(" Stripped samples from ", fname , len(samples["m1"]))
composite_list.append(samples)
composite_full_list.append(samples_orig)
continue
## Plot posterior files
CIs = [0.95,0.9, 0.68]
if opts.ci_list:
CIs = eval(opts.ci_list) # force creation
quantiles_1d = [0.05,0.95]
if opts.quantiles:
quantiles_1d=eval(opts.quantiles)
# Generate labels
if opts.parameter_log_scale is None:
opts.parameter_log_scale = []
labels_tex = render_coordinates(opts.parameter,logparams=opts.parameter_log_scale)#map(lambda x: tex_dictionary[x], coord_names)
fig_base= None
# Create figure workspace for 1d plots
fig_1d_list = []
fig_1d_list_cum = []
#fig_1d_list_ids = []
if opts.plot_1d_extra:
for indx in np.arange(len(opts.parameter))+5:
fig_1d_list.append(plt.figure(indx))
fig_1d_list_cum.append(plt.figure(indx+len(opts.parameter)))
# fig_1d_list_ids.append(indx)
plt.figure(1)
# Find parameter ranges
x_range = {}
range_list = []
if opts.posterior_file:
for param in opts.parameter:
xmax_list = []
xmin_list = []
for indx in np.arange(len(posterior_list)):
dat_here = None
samples = posterior_list[indx]
if param in samples.dtype.names:
dat_here = samples[param]
else:
dat_here = extract_combination_from_LI(samples, param)
if param in opts.parameter_log_scale:
indx_ok = dat_here > 0
dat_here= np.log10(dat_here[indx_ok])
if len(dat_here) < 1:
print(" Failed to etract data ", param, " from ", opts.posterior_file[indx])
# extend the limits, so we have *extremal* limits
xmax_list.append(np.max(dat_here))
xmin_list.append(np.min(dat_here))
x_range[param] = np.array([np.min(xmin_list), np.max(xmax_list)]) # give a small buffer
# if param == 'chi_eff':
# x_range[param] -= 0.1*np.sign([-1,1])*(x_range[param]+np.array([-1,1]))
if param in special_param_ranges:
x_range[param] = special_param_ranges[param]
if param in ['lambda1', 'lambda2', 'lambdat']:
x_range[param][1] = opts.lambda_plot_max
range_list.append(x_range[param])
print(param, x_range[param])
my_cmap_values=None
for pIndex in np.arange(len(posterior_list)):
samples = posterior_list[pIndex]
sample_names = samples.dtype.names; sample_ref_name = sample_names[0]
# Create data for corner plot
dat_mass = np.zeros( (len(list(samples[sample_ref_name])), len(list(labels_tex))) )
my_cmap_values = color_list[pIndex]
plot_range_list = []
smooth_list =[]
truths_here= None
if opts.truth_file:
truths_here = np.zeros(len(opts.parameter))
for indx in np.arange(len(opts.parameter)):
param = opts.parameter[indx]
if param in samples.dtype.names:
dat_mass[:,indx] = samples[param]
else:
dat_mass[:,indx] = extract_combination_from_LI(samples, param)
if param in opts.parameter_log_scale:
dat_mass[:,indx] = np.log10(dat_mass[:,indx])
# Parameter ranges (duplicate)
dat_here = np.array(dat_mass[:,indx]) # force copy ! I need to sort
weights = np.ones(len(dat_here))*1.0/len(dat_here)
if 'weights' in samples.dtype.names:
weights = samples['weights']
indx_sort= dat_here.argsort()
dat_here = dat_here[indx_sort]
weights =weights[indx_sort]
#
dat_here.sort() # sort it
xmin, xmax = x_range[param]
# xmin = np.min([np.min( posterior_list[x][param]) for x in np.arange(len(posterior_list)) ]) # loop over all
xmin = np.min([xmin, np.mean(dat_here) -4*np.std(dat_here)])
xmax = np.max([xmax, np.mean(dat_here) +4*np.std(dat_here)])
# xmax = np.max(dat_here)
if param in special_param_ranges:
xmin,xmax = special_param_ranges[param]
plot_range_list.append((xmin,xmax))
# smoothing list
smooth_list.append(np.std(dat_here)/np.power(len(dat_here), 1./3))
# truths
if opts.truth_file:
param_to_extract = param
if param in remap_LI_to_ILE.keys():
param_to_extract = remap_LI_to_ILE[param]
if param in eos_param_names:
continue
if param == 'time':
truths_here[indx] = P_ref.tref
continue
truths_here[indx] = P_ref.extract_param(param_to_extract)
if param in [ 'mc', 'm1', 'm2', 'mtotal']:
truths_here[indx] = truths_here[indx]/lal.MSUN_SI
if param in ['dist', 'distance']:
truths_here[indx] = truths_here[indx]/lal.PC_SI/1e6
# print param, truths_here[indx]
# if 1d plots needed, make them
if opts.plot_1d_extra:
range_here = range_list[indx]
# 1d PDF
# Set range based on observed results in ALL sets of samples, by default
fig =fig_1d_list[indx]
ax = fig.gca()
ax.set_xlabel(labels_tex[indx])
ax.set_ylabel('$dP/d'+labels_tex[indx].replace('$','')+"$")
try:
my_kde = bounded_kde.BoundedKDE(dat_here,low=xmin,high=xmax)
xvals = np.linspace(range_here[0],range_here[1],1000)
yvals = my_kde.evaluate(xvals)
ax.plot(xvals,yvals,color=my_cmap_values,linestyle= linestyle_list[pIndex])
if opts.truth_file:
ax.axvline(truths_here[indx], color='k',linestyle='dashed')
except:
print(" Failed to plot 1d KDE for ", labels_tex[indx])
# 1d CDF
fig =fig_1d_list_cum[indx]
ax = fig.gca()
ax.set_xlabel(labels_tex[indx])
ax.set_ylabel('$P(<'+labels_tex[indx].replace('$','')+")$")
xvals = dat_here
#yvals = np.arange(len(dat_here))*1.0/len(dat_here)
yvals = np.cumsum(weights)
yvals = yvals/yvals[-1]
ax.plot(xvals,yvals,color=my_cmap_values,linestyle= linestyle_list[pIndex] )
if opts.truth_file:
ax.axvline(truths_here[indx], color='k',linestyle='dashed')
ax.set_xlim(xmin,xmax)
# Add weight columns (unsorted) for overall unsorted plot
weights = np.ones(len(dat_mass))*1.0/len(dat_mass)
if 'weights' in samples.dtype.names:
weights= samples['weights']
weights = weights/np.sum(weights)
# plot corner
# smooth=smooth_list
smooth1d=None
# if opts.use_smooth_1d:
# smooth1d=smooth_list
# print smooth1d
fig_base = corner.corner(dat_mass,smooth1d=smooth1d, range=range_list,weights=weights, labels=labels_tex, quantiles=quantiles_1d, plot_datapoints=False, plot_density=False, no_fill_contours=True, contours=True, levels=CIs,fig=fig_base,color=my_cmap_values ,hist_kwargs={'linestyle': linestyle_list[pIndex]}, linestyle=linestyle_list[pIndex],contour_kwargs={'linestyles':linestyle_list[pIndex]},truths=truths_here)
if opts.plot_1d_extra:
for indx in np.arange(len(opts.parameter)):
fig = fig_1d_list[indx]
param = opts.parameter[indx]
ax = fig.gca()
# https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html
if opts.use_legend:
ax.legend(handles=line_handles, bbox_to_anchor=corner_legend_location, prop=corner_legend_prop,loc=2)
fig.savefig(param+fig_extension,dpi=dpi_base)
fig = fig_1d_list_cum[indx]
param = opts.parameter[indx]
ax = fig.gca()
# https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html
if opts.use_legend:
ax.legend(handles=line_handles, prop=corner_legend_prop, loc=4) # bbox_to_anchor=corner_legend_location,
fig.savefig(param+"_cum"+fig_extension,dpi=dpi_base)
if composite_list:
for pIndex in [0]: # np.arange(len(composite_list)): # should NEVER have more than one
samples = composite_list[pIndex]
samples_orig = composite_full_list[pIndex]
samples_ref_name = samples.dtype.names[0]
samples_orig_ref_name = samples_orig.dtype.names[0]
# Create data for corner plot
dat_mass = np.zeros( (len(samples[samples_ref_name]), len(labels_tex)) )
dat_mass_orig = np.zeros( (len(samples_orig[samples_orig_ref_name]), len(labels_tex)) )
lnL = samples["lnL"]
indx_sorted = lnL.argsort()
if len(lnL)<1:
print(" Failed to retrieve lnL for composite file ", composite_list[0])
cm = plt.cm.get_cmap('rainbow') #'RdYlBu_r')
y_span = lnL.max() - lnL.min()
print(" Composite file : lnL span ", y_span)
y_min = lnL.min()
cm2 = lambda x: cm( (x - y_min)/y_span)
my_cmap_values = cm( (lnL-y_min)/y_span)
# print my_cmap_values[:10]
truths_here=None
if opts.truth_file:
truths_here =np.zeros(len(opts.parameter))
for indx in np.arange(len(opts.parameter)):
param = opts.parameter[indx]
if param in field_names:
dat_mass[:,indx] = samples[param]
dat_mass_orig[:,indx] = samples_orig[param]
else:
print(" Trying alternative access for ", param)
dat_mass[:,indx] = extract_combination_from_LI(samples, param)
dat_mass_orig[:,indx] = extract_combination_from_LI(samples_orig, param)
# truths
if opts.truth_file:
param_to_extract = param
if param in remap_LI_to_ILE.keys():
param_to_extract = remap_LI_to_ILE[param]
truths_here[indx] = P_ref.extract_param(param_to_extract)
if param in [ 'mc', 'm1', 'm2', 'mtotal']:
truths_here[indx] = truths_here[indx]/lal.MSUN_SI
# print param, truths_here[indx]
print(" Truths here ", truths_here)
# fix ranges
if range_list == [] :
range_list=None
# reverse order ... make sure largest plotted last
dat_mass = dat_mass[indx_sorted] # Sort by lnL
my_cmap_values = my_cmap_values[indx_sorted]
# my_cmap_values = my_cmap_values[::-1]
# dat_mass = dat_mass[::-1]
# We will need to rewrite 'corner' to do what we want: see the source
# https://github.com/dfm/corner.py/blob/master/corner/corner.py
# Grayscale, using all points
if opts.use_all_composite_but_grayscale:
fig_base = our_corner.corner(dat_mass_orig,range=range_list, plot_datapoints=True,weights=np.ones(len(dat_mass_orig))*1.0/len(dat_mass_orig), plot_density=False, no_fill_contours=True, plot_contours=False,contours=False,levels=None,fig=fig_base,data_kwargs={'color':'0.5','s':1})
# Color scale with colored points
fig_base = our_corner.corner(dat_mass,range=range_list, plot_datapoints=True,weights=np.ones(len(dat_mass))*1.0/len(dat_mass), plot_density=False, no_fill_contours=True, plot_contours=False,contours=False,levels=None,fig=fig_base,data_kwargs={'color':my_cmap_values, 's':1}, truths=truths_here)
# Create colorbar mappable
# ax=plt.figure().gca()
# ax.contourf(lnL, cm)
if opts.use_legend and opts.posterior_label:
plt.legend(handles=line_handles, bbox_to_anchor=corner_legend_location, prop=corner_legend_prop,loc=4)
#plt.colorbar() # Will fail, because colors not applied
# title
if opts.use_title:
print(" Addding title ", opts.use_title)
plt.title(opts.use_title)
param_postfix = "_".join(opts.parameter)
res_base = len(opts.parameter)*dpi_base
if not(opts.matplotlib_block_defaults):
matplotlib.rcParams.update({'font.size': 11+int(len(opts.parameter)), 'legend.fontsize': legend_font_base+int(1.3*len(opts.parameter))}) # increase font size if I have more panels, to keep similar aspect
plt.savefig("corner_"+param_postfix+fig_extension,dpi=res_base) # use more resolution, to make sure each image remains of consistent quality