def read_posterior_samples(f,injrow):
"""Returns a bppu posterior sample object
"""
peparser=bppu.PEOutputParser('common')
commonResultsObj=peparser.parse(open(f,'r'))
data = bppu.Posterior(commonResultsObj,SimInspiralTableEntry=injrow,injFref=100.0)
# add tilts, comp masses, tidal...
try:
data.extend_posterior()
except Exception as e:
pass
return data
def load_data(filename, header=None):
peparser = bppu.PEOutputParser('common')
commonObj = peparser.parse(open(filename, 'r'), info=[header, None])
pos = bppu.Posterior(commonObj)
return pos
def downsample_and_evidence(data_hdf5, deltaLogP=None, fixedBurnin=None, nDownsample=None, verbose=False):
# Remove burnin from beginning of MCMC-chains, downsample by the chain's respective autocorrelation length.
# Compute the evidence for the set of parallel tempered chains through a thermodynamic integral
if not data_hdf5.lower().endswith(('.hdf5', '.hdf', '.h5')):
print('cbcBayesMCMC2pos only suports hdf5 input, for older file formats plese revert to cbcBayesThermoInt and cbcBayesPosProc')
sys.exit(1)
peparser = bppu.PEOutputParser('hdf5')
ps, samps = peparser.parse(data_hdf5, deltaLogP=deltaLogP, fixedBurnins=fixedBurnin,
nDownsample=nDownsample, tablename=None)
posterior_samples = apt.Table(samps, names=ps)
highTchains = []
for i in range(1,int(posterior_samples['nTemps'][0])):
ps, samps = peparser.parse(data_hdf5, deltaLogP=deltaLogP, fixedBurnins=fixedBurnin,
nDownsample=nDownsample, tablename='chain_'+str('%02.f' %i))
highTchains.append(apt.Table(samps, names=ps))
if verbose: print('chain_'+str('%02.f' %i)+' at a temperature '+str(highTchains[i-1]['temperature'].mean()))
betas = np.zeros(len(highTchains)+1)
logls = np.zeros_like(betas)
betas[0] = 1./np.median(posterior_samples['temperature'])
logls[0] = np.median(posterior_samples['logl'])
for i in range(len(highTchains)):
betas[i+1] = 1./np.median(highTchains[i]['temperature'])
logls[i+1] = np.median(highTchains[i]['logl'])
inds = np.argsort(betas)[::-1]
betas = betas[inds]
logls = logls[inds]
# Now extend to infinite temperature by copying the last <log(L)>.
# This works as long as the chains have extended to high enough
# temperature to sample the prior.
# If infinite temperature is already included, this 'duplicate'
# will not change the final evidence.
ebetas = np.concatenate((betas, [0.0]))
elogls = np.concatenate((logls, [logls[-1]]))
ebetas2 = np.concatenate((betas[::2], [0.0]))
elogls2 = np.concatenate((logls[::2], [logls[::2][-1]]))
evidence = -si.trapz(elogls, ebetas)
evidence2 = -si.trapz(elogls2, ebetas2)
posterior_samples['chain_log_evidence'] = evidence
posterior_samples['chain_delta_log_evidence'] = np.absolute(evidence - evidence2)
posterior_samples['chain_log_noise_evidence'] = posterior_samples['nullLogL']
posterior_samples['chain_log_bayes_factor'] = posterior_samples['chain_log_evidence'] - posterior_samples['chain_log_noise_evidence']
if verbose:
print('logZ = '+str(posterior_samples['chain_log_evidence'][0])+'+-'+str(posterior_samples['chain_delta_log_evidence'][0]))
print('logB_SN = '+str(posterior_samples['chain_log_bayes_factor'][0]))
posterior_samples = reassign_metadata(posterior_samples, data_hdf5)
return posterior_samples
metavar="N")
parser.add_option("--bootstrap",
dest='bootstrap',
action='store',
default=1,
type='int',
metavar='N')
parser.add_option('--output',
dest='output',
action='store',
default=None,
metavar='FILE')
(opts, args) = parser.parse_args()
pos_parser = bp.PEOutputParser('common')
f = open(opts.data, "r")
try:
pos = bp.Posterior(pos_parser.parse(f))
finally:
f.close()
outfile = None
if opts.output:
outfile = open(opts.output, 'w')
try:
for i in range(opts.bootstrap):
if i == 0:
log_ev = pos.di_evidence(boxing=opts.Nboxing)
else:
def cbcBayesGraceDBinfo(gid=None,samples=None,skymap=None,analysis='LALInference', bcifile=None,bsnfile=None,email=None,message=None,server="https://gracedb.ligo.org/api/"):
if gid is None or (samples is None and skymap is None):
print("Must provide both a graceDB id and a posterior samples file or skymap file\n")
sys.exit(1)
import ligo.gracedb.rest
import os
if server is not None:
g=ligo.gracedb.rest.GraceDb(server)
else:
g=ligo.gracedb.rest.GraceDb()
if samples is not None:
samples=os.path.realpath(samples)
if '.hdf' in samples or '.h5' in samples:
peparser = bppu.PEOutputParser('hdf5')
commonResultsObj=peparser.parse(samples)
else:
peparser=bppu.PEOutputParser('common')
commonResultsObj=peparser.parse(open(samples,'r'))
try:
pos = bppu.BurstPosterior(commonResultsObj)
pars=['frequency','quality','hrss']
units={'frequency':'[Hz]','quality':'','hrss':'','loghrss':''}
except:
pos = bppu.Posterior(commonResultsObj)
pars=['mchirp','q','distance']
strs=[]
outstr='<table><tr><th colspan=2 align=center>%s PE summary</th></tr>'%analysis
for i in pars:
if i in pos.names:
_,which=pos._posMap()
if i=='hrss':
outstr+='<tr><td align=left>%s %s</td>'%(i,units[i])
outstr+='<td align=left>%.3e ± %.3e</td></tr>'%(pos[i].samples[which][0],pos[i].stdev)
else:
outstr+='<tr><td align=left>%s %s</td>'%(i,units[i])
outstr+='<td align=left>%.3f ± %.3f</td></tr>'%(pos[i].samples[which][0],pos[i].stdev)
if bcifile is not None and os.path.isfile(bcifile):
bci=np.loadtxt(bcifile)
else: bci=None
if bci is not None:
outstr+='<tr><td align=left>logBCI</td>'
outstr+='<td align=center>%.2f</td></tr>'%(bci)
bsn=None
if bsnfile is not None and os.path.isfile(bsnfile):
bsn=np.loadtxt(bsnfile)
bsn=bsn[0]
else:
try:
import h5py
with h5py.File(samples,'r') as h5grp:
tmp=h5grp['lalinference']['lalinference_nest'].attrs
bsn=tmp['log_bayes_factor']
except Exception as e:
print("Could not obtain BNS\n")
print(e)
if bsn is not None:
outstr+='<tr><td align=left>logBSN</td>'
outstr+='<td align=center>%.2f</td></tr>'%(bsn)
outstr+='</table>'
if email is not None and bci is not None:
import os
import smtplib
import subprocess
address=email.split(',')
SERVER="localhost"
USER=os.environ['USER']
import socket
HOST=socket.getfqdn()#socket.gethostbyaddr(socket.gethostname())[0]
pref=""
if bci>3 and bci<6:
pref="A promising"
elif bci>6 and bci<10:
pref="A very interesting"
elif bci>10:
pref="A SPECTACULAR"
FROM="salvatore.vitale@"+HOST
SUBJECT="%s LIB result page is ready at "%pref+HOST+" for graceID %s!"%(gid)
TEXT="LIB run for graceID %s is done on "%gid+HOST+".\nThe BCI is %lf\n"%bci
if bci>10:
TEXT+="RUN!!!!!!!!!!\n"
message="From: %s\nTo: %s\nSubject: %s\n\n%s"%(FROM,', '.join(address),SUBJECT,TEXT)
try:
import os
os.system('echo "%s" | mail -s "%s" "%s"'%(TEXT,SUBJECT,', '.join(address)))
server=smtplib.SMTP(SERVER)
server.sendmail(FROM,address,message)
server.quit()
except:
print("Cound not send email\n")
g.writeLog(gid,outstr,filename=None,tagname='pe')
elif skymap is not None:
if bcifile is not None and os.path.isfile(bcifile):
bci=np.loadtxt(bcifile)
else: bci=None
if bsnfile is not None and os.path.isfile(bsnfile):
bsn=np.loadtxt(bsnfile)
else: bsn=None
tag=['sky_loc']
"""
if bci is not None and bsn is not None:
if bsn>5. and bci>2.:
tag.append('lvem')
"""
g.writeLog(gid,message,filename=skymap,tagname=tag)
def lalinference_to_bilby_result(postfile):
"""
Convert LALInference-derived pulsar posterior samples file, as created by
``lalapps_pulsar_parameter_estimation_nested``, into a
:class:`bilby.core.result.Result` object.
Parameters
----------
postfile: str
The path to a posterior samples file.
Returns
-------
result:
The results as a :class:`bilby.core.result.Result` object
"""
import h5py
from bilby.core.result import Result
from lalinference import bayespputils as bppu
from pandas import DataFrame
try:
peparser = bppu.PEOutputParser("hdf5")
nsResultsObject = peparser.parse(postfile)
pos = bppu.Posterior(nsResultsObject, SimInspiralTableEntry=None)
except Exception as e:
raise IOError(
f"Could not import posterior samples from {postfile}: {e}")
# remove any unchanging variables and randomly shuffle the rest
pnames = pos.names
nsamps = len(pos[pnames[0]].samples)
permarr = np.arange(nsamps)
np.random.shuffle(permarr)
posdict = {}
for pname in pnames:
# ignore if all samples are the same
if not pos[pname].samples.tolist().count(pos[pname].samples[0]) == len(
pos[pname].samples):
# shuffle and store
posdict[pname] = pos[pname].samples[permarr, 0]
# get evidence values from HDF5 file
logZ = None
logZn = None
logbayes = None
try:
hdf = h5py.File(postfile, "r")
a = hdf["lalinference"]["lalinference_nest"]
logZ = a.attrs["log_evidence"]
logZn = a.attrs["log_noise_evidence"]
logbayes = logZ - logZn
hdf.close()
except KeyError:
pass
return Result(
posterior=DataFrame(posdict),
log_evidence=logZ,
log_noise_evidence=logZn,
log_bayes_factor=logbayes,
)
def compare_bayes(outdir,names_and_pos_folders,injection_path,eventnum,username,password,reload_flag,clf,ldg_flag,contour_figsize=(4.5,4.5),contour_dpi=250,contour_figposition=[0.15,0.15,0.5,0.75],fail_on_file_err=True,covarianceMatrices=None,meanVectors=None,Npixels2D=50):
injection=None
if injection_path is not None and os.path.exists(injection_path) and eventnum is not None:
eventnum=int(eventnum)
from glue.ligolw import ligolw, lsctables, utils
injections = lsctables.SimInspiralTable.get_table(
utils.load_filename(injection_path, contenthandler = lsctables.use_in(ligolw.LIGOLWContentHandler)))
if eventnum is not None:
if(len(injections)<eventnum):
print("Error: You asked for event %d, but %s contains only %d injections" %(eventnum,injection_path,len(injections)))
sys.exit(1)
else:
injection=injections[eventnum]
#Create analytic likelihood functions if covariance matrices and mean vectors were given
analyticLikelihood = None
if covarianceMatrices and meanVectors:
analyticLikelihood = bppu.AnalyticLikelihood(covarianceMatrices, meanVectors)
peparser=bppu.PEOutputParser('common')
pos_list={}
tp_list={}
common_params=None
working_folder=os.getcwd()
for name,pos_folder in names_and_pos_folders:
import urlparse
pos_folder_url=urlparse.urlparse(pos_folder)
pfu_scheme,pfu_netloc,pfu_path,pfu_params,pfu_query,pfu_fragment=pos_folder_url
if 'http' in pfu_scheme:
"""
Retrieve a file over http(s).
"""
downloads_folder=os.path.join(os.getcwd(),"downloads")
pos_folder_parse=urlparse.urlparse(pos_folder)
pfp_scheme,pfp_netloc,pfp_path,pfp_params,pfp_query,pfp_fragment=pos_folder_parse
head,tail=os.path.split(pfp_path)
if tail is 'posplots.html' or tail:
pos_file_part=head
else:
pos_file_part=pfp_path
pos_file_url=urlparse.urlunsplit((pfp_scheme,pfp_netloc,os.path.join(pos_file_part,'posterior_samples.dat'),'',''))
print(pos_file_url)
pos_file=os.path.join(os.getcwd(),downloads_folder,"%s.dat"%name)
if not os.path.exists(pos_file):
reload_flag=True
if reload_flag:
if os.path.exists(pos_file):
os.remove(pos_file)
if not os.path.exists(downloads_folder):
os.makedirs(downloads_folder)
open_url_curl(pos_file_url,args=["-o","%s"%pos_file])
elif pfu_scheme is '' or pfu_scheme is 'file':
pos_file=os.path.join(pos_folder,'%s.dat'%name)
# Try looking for posterior_samples.dat if name.dat doesn't exist
if not os.path.exists(pos_file):
print('%s does not exist, trying posterior_samples.dat'%(pos_file))
pos_file=os.path.join(pos_folder,'posterior_samples.dat')
else:
print("Unknown scheme for input data url: %s\nFull URL: %s"%(pfu_scheme,str(pos_folder_url)))
exit(0)
print("Reading posterior samples from %s ..."%pos_file)
try:
common_output_table_header,common_output_table_raw=peparser.parse(open(pos_file,'r'))
except:
print('Unable to read file '+pos_file)
continue
test_and_switch_param(common_output_table_header,'distance','dist')
test_and_switch_param(common_output_table_header,'chirpmass','mchirp')
test_and_switch_param(common_output_table_header,'mc','mchirp')
test_and_switch_param(common_output_table_header,'asym_massratio','q')
test_and_switch_param(common_output_table_header,'massratio', 'eta')
test_and_switch_param(common_output_table_header,'RA','ra')
test_and_switch_param(common_output_table_header,'rightascension','ra')
test_and_switch_param(common_output_table_header,'declination','dec')
test_and_switch_param(common_output_table_header,'tilt_spin1','tilt1')
test_and_switch_param(common_output_table_header,'tilt_spin2','tilt2')
if 'LI_MCMC' in name or 'FU_MCMC' in name:
try:
idx=common_output_table_header.index('iota')
print("Inverting iota!")
common_output_table_raw[:,idx]= np.pi*np.ones(len(common_output_table_raw[:,0])) - common_output_table_raw[:,idx]
except:
pass
# try:
# print "Converting phi_orb-> 2phi_orb"
# idx=common_output_table_header.index('phi_orb')
# common_output_table_header[idx]='2phi_orb'
# common_output_table_raw[:,idx]= 2*common_output_table_raw[:,idx]
# except:
# pass
try:
print("Converting iota-> cos(iota)")
idx=common_output_table_header.index('iota')
common_output_table_header[idx]='cos(iota)'
common_output_table_raw[:,idx]=np.cos(common_output_table_raw[:,idx])
except:
pass
#try:
# print "Converting tilt1 -> cos(tilt1)"
# idx=common_output_table_header.index('tilt1')
# common_output_table_header[idx]='cos(tilt1)'
# common_output_table_raw[:,idx]=np.cos(common_output_table_raw[:,idx])
#except:
# pass
#try:
# print "Converting tilt2 -> cos(tilt2)"
# idx=common_output_table_header.index('tilt2')
# common_output_table_header[idx]='cos(tilt2)'
# common_output_table_raw[:,idx]=np.cos(common_output_table_raw[:,idx])
#except:
# pass
try:
print("Converting thetas -> cos(thetas)")
idx=common_output_table_header.index('thetas')
common_output_table_header[idx]='cos(thetas)'
common_output_table_raw[:,idx]=np.cos(common_output_table_raw[:,idx])
except:
pass
try:
print("Converting beta -> cos(beta)")
idx=common_output_table_header.index('beta')
common_output_table_header[idx]='cos(beta)'
common_output_table_raw[:,idx]=np.cos(common_output_table_raw[:,idx])
except:
pass
try:
idx=common_output_table_header.index('f_ref')
injFrefs=np.unique(common_output_table_raw[:,idx])
if len(injFrefs) == 1:
injFref = injFrefs[0]
print("Using f_ref in results as injected value")
except:
injFref = None
pass
pos_temp=bppu.Posterior((common_output_table_header,common_output_table_raw),SimInspiralTableEntry=injection, injFref=injFref)
if 'a1' in pos_temp.names and min(pos_temp['a1'].samples)[0] < 0:
pos_temp.append_mapping('spin1', lambda a:a, 'a1')
pos_temp.pop('a1')
pos_temp.append_mapping('a1', lambda a:np.abs(a), 'spin1')
if 'a2' in pos_temp.names and min(pos_temp['a2'].samples)[0] < 0:
pos_temp.append_mapping('spin2', lambda a:a, 'a2')
pos_temp.pop('a2')
pos_temp.append_mapping('a2', lambda a:np.abs(a), 'spin2')
if 'm1' in pos_temp.names and 'm2' in pos_temp.names:
print("Calculating total mass")
pos_temp.append_mapping('mtotal', lambda m1,m2: m1+m2, ['m1','m2'])
if 'mass1' in pos_temp.names and 'mass2' in pos_temp.names:
print("Calculating total mass")
pos_temp.append_mapping('mtotal', lambda m1,m2: m1+m2, ['mass1','mass2'])
try:
idx=common_output_table_header.index('m1')
idx2=common_output_table_header.index('m2')
if pos_temp['m1'].mean<pos_temp['m2'].mean:
print("SWAPPING MASS PARAMS!")
common_output_table_header[idx]='x'
common_output_table_header[idx2]='m1'
common_output_table_header[idx]='m2'
pos_temp=bppu.Posterior((common_output_table_header,common_output_table_raw),SimInspiralTableEntry=injection)
except:
pass
pos_list[name]=pos_temp
if common_params is None:
common_params=pos_temp.names
else:
set_of_pars = set(pos_temp.names)
common_params=list(set_of_pars.intersection(common_params))
print("Common parameters are %s"%str(common_params))
if injection is None and injection_path is not None:
injections = SimInspiralUtils.ReadSimInspiralFromFiles([injection_path])
injection=bppu.get_inj_by_time(injections,pos_temp.means['time'])
if injection is not None:
for pos in pos_list.values():
pos.set_injection(injection)
set_of_pars = set(allowed_params)
common_params=list(set_of_pars.intersection(common_params))
print("Using parameters %s"%str(common_params))
if not os.path.exists(os.path.join(os.getcwd(),'results')):
os.makedirs('results')
if not os.path.exists(outdir):
os.makedirs(outdir)
pdfdir=os.path.join(outdir,'pdfs')
if not os.path.exists(pdfdir):
os.makedirs(pdfdir)
greedy2savepaths=[]
if common_params is not [] and common_params is not None: #If there are common parameters....
colorlst=bppu.__default_color_lst
if len(common_params)>1: #If there is more than one parameter...
temp=copy.copy(common_params)
#Plot two param contour plots
#Assign some colours to each different analysis result
color_by_name={}
hatches_by_name={}
my_cm=mpl_cm.Dark2
cmap_size=my_cm.N
color_idx=0
color_idx_max=len(names_and_pos_folders)
cmap_array=my_cm(np.array(range(cmap_size)))
#cmap_array=['r','g','b','c','m','k','0.5','#ffff00']
hatches=['/','\\','|','-','+','x','o','O','.','*']
ldg='auto'
if not ldg_flag:
ldg=None
for name,infolder in names_and_pos_folders:
#color_by_name=cmap_array[color_idx]
color_by_name[name]=cmap_array[int(floor(color_idx*cmap_size/color_idx_max)),:]
color_idx=(color_idx+1) % color_idx_max
hatches_by_name[name]=hatches[color_idx]
for i,j in all_pairs(temp):#Iterate over all unique pairs in the set of common parameters
pplst=[i,j]
rpplst=pplst[:]
rpplst.reverse()
pplst_cond=(pplst in twoDplots)
rpplst_cond=(rpplst in twoDplots)
if pplst_cond or rpplst_cond:#If this pair of parameters is in the plotting list...
try:
print('2d plots: building ',i,j)
greedy2Params={i:greedyBinSizes[i],j:greedyBinSizes[j]}
except KeyError:
continue
name_list=[]
cs_list=[]
slinestyles=['solid', 'dashed', 'dashdot', 'dotted']
fig=bppu.plot_two_param_kde_greedy_levels(pos_list,greedy2Params,TwoDconfidenceLevels,color_by_name,figsize=contour_figsize,dpi=contour_dpi,figposition=contour_figposition,legend=ldg,line_styles=slinestyles,hatches_by_name=hatches_by_name,Npixels=Npixels2D)
if fig is None: continue
#fig=bppu.plot_two_param_greedy_bins_contour(pos_list,greedy2Params,TwoDconfidenceLevels,color_by_name,figsize=contour_figsize,dpi=contour_dpi,figposition=contour_figposition)
greedy2savepaths.append('%s-%s.png'%(pplst[0],pplst[1]))
fig.savefig(os.path.join(outdir,'%s-%s.png'%(pplst[0],pplst[1])),bbox_inches='tight')
fig.savefig(os.path.join(pdfdir,'%s-%s.pdf'%(pplst[0],pplst[1])),bbox_inches='tight')
plt.clf()
oned_data={}
#confidence_levels={}
confidence_levels=[{},{},{},{}]
confidence_uncertainty={}
for param in common_params:
print("Plotting comparison for '%s'"%param)
cl_table_header='<table><th>Run</th>'
cl_table={}
save_paths=[]
cl_table_min_max_str='<tr><td> Min | Max </td>'
level_index=0
for confidence_level in OneDconfidenceLevels:
if analyticLikelihood:
pdf=analyticLikelihood.pdf(param)
cdf=analyticLikelihood.cdf(param)
else:
pdf=None
cdf=None
cl_table_header+='<th colspan="2">%i%% (Lower|Upper)</th>'%(int(100*confidence_level))
hist_fig,cl_intervals=compare_plots_one_param_line_hist(pos_list,param,confidence_level,color_by_name,cl_lines_flag=clf,legend=ldg,analyticPDF=pdf)
hist_fig2,cl_intervals=compare_plots_one_param_line_hist_cum(pos_list,param,confidence_level,color_by_name,cl_lines_flag=clf,analyticCDF=cdf,legend=ldg)
# Save confidence levels and uncertainty
#confidence_levels[param]=[]
confidence_levels[level_index][param]=[]
for name,pos in pos_list.items():
median=pos[param].median
low,high=cl_intervals[name]
#confidence_levels[param].append((name,low,median,high))
confidence_levels[level_index][param].append((name,low,median,high))
level_index=level_index+1
cl_bounds=[]
poses=[]
for name,pos in pos_list.items():
cl_bounds.append(cl_intervals[name])
poses.append(pos[param])
confidence_uncertainty[param]=bppu.confidence_interval_uncertainty(confidence_level, cl_bounds, poses)
save_path=''
if hist_fig is not None:
save_path=os.path.join(outdir,'%s_%i.png'%(param,int(100*confidence_level)))
save_path_pdf=os.path.join(pdfdir,'%s_%i.pdf'%(param,int(100*confidence_level)))
try:
plt.tight_layout(hist_fig)
plt.tight_layout(hist_fig2)
except:
pass
hist_fig.savefig(save_path,bbox_inches='tight')
hist_fig.savefig(save_path_pdf,bbox_inches='tight')
save_paths.append(save_path)
save_path=os.path.join(outdir,'%s_%i_cum.png'%(param,int(100*confidence_level)))
save_path_pdf=os.path.join(pdfdir,'%s_%i_cum.pdf'%(param,int(100*confidence_level)))
hist_fig2.savefig(save_path,bbox_inches='tight')
hist_fig2.savefig(save_path_pdf,bbox_inches='tight')
save_paths.append(save_path)
min_low,max_high=cl_intervals.values()[0]
for name,interval in cl_intervals.items():
low,high=interval
if low<min_low:
min_low=low
if high>max_high:
max_high=high
try:
cl_table[name]+='<td>%s</td><td>%s</td>'%(low,high)
except:
cl_table[name]='<td>%s</td><td>%s</td>'%(low,high)
cl_table_min_max_str+='<td>%s</td><td>%s</td>'%(min_low,max_high)
cl_table_str=cl_table_header
for name,row_contents in cl_table.items():
cl_table_str+='<tr><td>%s<font color="%s"></font></td>'%(name,str(mpl_colors.rgb2hex(color_by_name[name][0:3])))#,'·'.encode('utf-8'))
cl_table_str+=row_contents+'</tr>'
cl_table_str+=cl_table_min_max_str+'</tr>'
cl_table_str+='</table>'
cl_uncer_str='<table> <th>Confidence Relative Uncertainty</th> <th>Confidence Fractional Uncertainty</th> <th>Confidence Percentile Uncertainty</th>\n'
cl_uncer_str+='<tr> <td> %g </td> <td> %g </td> <td> %g </td> </tr> </table>'%(confidence_uncertainty[param][0], confidence_uncertainty[param][1], confidence_uncertainty[param][2])
ks_matrix=compute_ks_pvalue_matrix(pos_list, param)
N=ks_matrix.shape[0]+1
# Make up KS-test table
ks_table_str='<table><th colspan="%d"> K-S test p-value matrix </th>'%N
# Column headers
ks_table_str+='<tr> <td> -- </td> '
for name,pos in pos_list.items():
ks_table_str+='<td> %s </td>'%name
ks_table_str+='</tr>'
# Now plot rows of matrix
for i in range(len(pos_list)):
ks_table_str+='<tr> <td> %s </td>'%(pos_list.keys()[i])
for j in range(len(pos_list)):
if i == j:
ks_table_str+='<td> -- </td>'
elif ks_matrix[i,j] < 0.05:
# Failing at suspiciously low p-value
ks_table_str+='<td> <b> %g </b> </td>'%ks_matrix[i,j]
else:
ks_table_str+='<td> %g </td>'%ks_matrix[i,j]
ks_table_str+='</tr>'
ks_table_str+='</table>'
oned_data[param]=(save_paths,cl_table_str,ks_table_str,cl_uncer_str)
# Watch out---using private variable _logL
max_logls = [[name,max(pos._logL)] for name,pos in pos_list.items()]
dics = [pos.DIC for name, pos in pos_list.items()]
return greedy2savepaths,oned_data,confidence_uncertainty,confidence_levels,max_logls,dics
import sys
from optparse import OptionParser
from lalinference import bayespputils as bppu
parser = OptionParser(USAGE)
parser.add_option("-o",
"--outpath",
dest="outpath",
default=None,
help="make page and plots in DIR",
metavar="DIR")
parser.add_option(
"-d",
"--data",
dest="data",
help="Posteriors samples file (must be in common format)")
(opts, args) = parser.parse_args()
if opts.outpath is None:
opts.outpath = os.getcwd()
if not os.path.isfile(opts.data):
print("Cannot find posterior file %s\n" % opts.data)
sys.exit(1)
else:
peparser = bppu.PEOutputParser('common')
commonResultsObj = peparser.parse(open(opts.data, 'r'),
info=[None, None])
ps, samps = commonResultsObj
pos = bppu.Posterior(commonResultsObj)
make_disk_plot(pos, opts.outpath)
