def build_roi(name, snrdata, latdata):
snrdata=loaddict(snrdata)
latdata=loaddict(latdata)
roi_dir = SkyDir(*snrdata[name]['cel'])
snrsize = snrdata[name]['size']
if isinstance(snrsize,list) and len(snrsize) == 2:
snrradius = math.sqrt(snrsize[0]*snrsize[1])/2.0
else:
snrradius = snrsize/2.0
ds = DataSpecification(**latdata['data'])
sa = SpectralAnalysis(ds,
binsperdec = 4,
emin = 1e4,
emax = 10**5.5,
irf = "P7SOURCE_V6",
roi_dir = roi_dir,
maxROI = 10,
minROI = 10,
event_class= 0)
diffuse_sources = get_default_diffuse(**latdata['diffuse'])
catalog = Catalog2FGL(**latdata['catalog'])
roi=sa.roi(point_sources=[],
diffuse_sources=diffuse_sources,
catalogs=catalog)
print 'bins',roi.bin_edges
for source in roi.get_sources():
if np.degrees(source.skydir.difference(roi_dir)) < snrradius + 0.5:
roi.del_source(source)
snr = ExtendedSource(
name = name,
model = PowerLaw(),
spatial_model = Disk(sigma=snrradius, center=roi_dir)
)
roi.add_source(snr)
return roi
def load_me_maybe(filename):
if require_all_exists and not exists(filename):
raise ResultsException("%s does not exist" % filename)
elif not exists(filename):
print '%s does not exist' % filename
return dict()
return loaddict(filename)
def load_me_maybe(filename):
if require_all_exists and not exists(filename):
raise ResultsException("%s does not exist" % filename)
elif not exists(filename):
print '%s does not exist' % filename
return dict()
return loaddict(filename)
def gtlike_followup(self, hypothesis):
name = self.name
roi = self.reload_roi(hypothesis)
cutoff = (not self.no_cutoff) and hypothesis in ['at_pulsar', 'point']
upper_limit = hypothesis=='at_pulsar'
if cutoff:
modify = import_module(self.modify)
cutoff_model=modify.get_gtlike_cutoff_model(name)
if cutoff_model is None:
pointlike_results = loaddict('results_%s_pointlike_%s.yaml' % (name,hypothesis))
cutoff_model=pointlike_results['test_cutoff']['hypothesis_1']['spectrum']
cutoff_model=pointlike_dict_to_spectrum(cutoff_model)
cutoff_model.set_default_limits(oomp_limits=True)
print 'override cutoff_model = ',cutoff_model
else:
cutoff_model=None
results=gtlike_analysis(roi, name=name,
cutoff_model = cutoff_model,
max_free = self.max_free,
seddir=self.seddir, datadir=self.datadir, plotdir=self.plotdir,
hypothesis=hypothesis,
upper_limit=upper_limit,
cutoff=cutoff,
do_bandfitter=True, do_sed=True,
)
savedict(results,'results_%s_gtlike_%s.yaml' % (name,hypothesis))
def __init__(self, results, **kwargs):
keyword_options.process(self, kwargs)
if isinstance(results,dict):
self.results = results
elif isinstance(results, str):
self.results = loaddict(results)
else:
raise Exception("Unrecognized results %s" % results)
def overlay(ax):
#results = loaddict('$simsrcdata/v12/merged.hdf5')
# flux = np.asarray(results['flux_gtlike'])
# flux_err = np.asarray(results['flux_gtlike_err'])
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_point_emin_100_emax_100000_time_2years/merged.hdf5')
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_point_emin_100_emax_100000_time_2fgl/merged.hdf5')
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_point_emin_100_emax_100000_time_2years/merged.hdf5')
#results = loaddict('$simsrcdata/v15/merged.hdf5')
results = loaddict('$simsrcdata/v15/merged.hdf5')
flux = np.asarray(results['flux_pointlike'])
flux_err = np.asarray(results['flux_pointlike_err'])
flux_mc = np.asarray(results['flux_mc'])
glon = np.asarray(results['glon'])
glat = np.asarray(results['glat'])
ra = np.asarray(results['ra'])
dec = np.asarray(results['dec'])
phibins = np.asarray(results['phibins'])
time = np.asarray(results['time'])
pull = (flux-flux_mc)/flux_err
perr = (flux-flux_mc)/flux_mc
cut = (phibins == nphi)&(time=='2fgl')
pull=pull[cut]
perr=perr[cut]
glon=glon[cut]
glat=glat[cut]
maxerr = 0.05
print 'max',maxerr
color_mapper=lambda perr: (min(perr/maxerr,1),0,0) if perr > 0 else (0,0,min(np.abs(perr)/maxerr,1))
for _glon, _glat, _perr in zip(glon, glat, perr):
ax['gal'].plot([_glon],[_glat], marker='o', color=color_mapper(_perr), markersize=10)
def gtlike_followup(self, hypothesis):
name = self.name
roi = self.reload_roi(hypothesis)
cutoff = (not self.no_cutoff) and hypothesis in ['at_pulsar', 'point']
upper_limit = hypothesis == 'at_pulsar'
if cutoff:
modify = import_module(self.modify)
cutoff_model = modify.get_gtlike_cutoff_model(name)
if cutoff_model is None:
pointlike_results = loaddict('results_%s_pointlike_%s.yaml' %
(name, hypothesis))
cutoff_model = pointlike_results['test_cutoff'][
'hypothesis_1']['spectrum']
cutoff_model = pointlike_dict_to_spectrum(cutoff_model)
cutoff_model.set_default_limits(oomp_limits=True)
print 'override cutoff_model = ', cutoff_model
else:
cutoff_model = None
results = gtlike_analysis(
roi,
name=name,
cutoff_model=cutoff_model,
max_free=self.max_free,
seddir=self.seddir,
datadir=self.datadir,
plotdir=self.plotdir,
hypothesis=hypothesis,
upper_limit=upper_limit,
cutoff=cutoff,
do_bandfitter=True,
do_sed=True,
)
savedict(results, 'results_%s_gtlike_%s.yaml' % (name, hypothesis))
def get_results(self,
pwn,
require_all_exists=True,
get_seds=True,
get_variability=True,
get_altdiff=True,
verbosity=None):
filename = join(self.fitdir, pwn, 'results_%s_general.yaml' % pwn)
if verbosity or (verbosity is None and self.verbosity):
print 'Getting results for %s' % pwn
if not exists(filename):
raise PWNResultsException("%s does not exist" % filename)
results = loaddict(filename)
for hypothesis in self.all_hypotheses:
results[hypothesis] = dict()
for code in ['gtlike', 'pointlike']:
for hypothesis in self.all_hypotheses:
filename = join(
self.fitdir, pwn,
'results_%s_%s_%s.yaml' % (pwn, code, hypothesis))
if exists(filename):
results[hypothesis][code] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException("%s does not exist" %
filename)
if get_variability:
for hypothesis in ['at_pulsar', 'point']:
filename = join(
self.fitdir, pwn,
'results_%s_variability_%s.yaml' % (pwn, hypothesis))
if exists(filename):
results[hypothesis]['variability'] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException('%s does not exist' %
filename)
if get_altdiff:
for hypothesis in ['point']:
results[hypothesis]['gtlike']['altdiff'] = dict()
for dist in ['SNR', 'Lorimer']:
for halo in [4, 10]:
for TS in [150, 100000]:
filename = join(
self.fitdir, pwn,
'results_%s_altdiff_dist_%s_halo_%s_TS_%s_%s.yaml'
% (pwn, dist, halo, TS, hypothesis))
if exists(filename):
results[hypothesis]['gtlike']['altdiff'][
dist, halo, TS] = loaddict(filename)
else:
results[hypothesis]['gtlike']['altdiff'][
dist, halo, TS] = None
if get_seds:
for hypothesis in self.all_hypotheses:
for code, all_binning in [['gtlike', ['1bpd', '2bpd', '4bpd']],
['pointlike', ['4bpd']]]:
results[hypothesis][code]['seds'] = dict()
for binning in all_binning:
filename = join(
self.fitdir, pwn, 'seds', 'sed_%s_%s_%s_%s.yaml' %
(code, binning, hypothesis, pwn))
if exists(filename):
results[hypothesis][code]['seds'][
binning] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException("%s does not exist" %
filename)
results['raw_phase'] = PhaseRange(results['phase'])
del results['phase'] # to avoid ambiguity
if self.phase_shift is not None:
import pickle
ps = pickle.load(open(self.phase_shift))
shift = float(ps[pwn.replace('PSRJ', 'J')]['shift'])
""" Convention taken from private correspondence with Matthew Kerr:
me: ok
so i am trying to implement phase shifts in my analysis
did you decide the right way to do it
Matthew: it seems to be +
me: so correct_phase = phase_in_file + shift
Matthew: yes """
results['shifted_phase'] = results['raw_phase'].offset(shift)
return results
def get_sed(self, pwn, binning, hypothesis):
sed = join(self.fitdir, pwn, 'seds',
'sed_gtlike_%s_%s_%s.yaml' % (binning, hypothesis, pwn))
return loaddict(sed)
def __init__(self, radiopsr_data, bigfile):
self.radiopsr_data = loaddict(radiopsr_data)
self.bigfile = pyfits.open(expandvars(bigfile))
import scipy.stats
from matplotlib.ticker import MaxNLocator
import numpy as np
import pylab as P
from lande.utilities.pubplot import set_latex_defaults
set_latex_defaults()
from lande.utilities.save import loaddict
name = expandvars(join('$fitdiffdata','v11','merged.hdf5'))
r = loaddict(name)
#plot_pull=True
plot_pull=False
#pointlike=True
pointlike=False
if pointlike:
print 'pointlike'
norm=np.asarray(r['pointlike_norm'])
norm_err=np.asarray(r['pointlike_norm_err'])
norm_mc=np.asarray(r['pointlike_norm_mc'])
else:
print 'gtlike'
norm=np.asarray(r['gtlike_norm'])
def __init__(self, gamma_quiet_psrs_data, analysisdir, verbosity=True):
self.gamma_quiet_psrs_data = loaddict(expandvars(gamma_quiet_psrs_data))
self.analysisdir = expandvars(analysisdir)
self.verbosity = verbosity
import pylab as P
import yaml
import h5py
import numpy as np
from scipy.stats import chi2
from lande.utilities.arrays import almost_equal
from lande.utilities.pubplot import set_latex_defaults, get_bw, save
from lande.utilities.save import loaddict
set_latex_defaults()
bw = get_bw()
r = loaddict('$tsext_plane_data/v12/merged.hdf5')
print r.keys()
flux_list = np.asarray(r['flux_mc'])
index_list = np.asarray(r['index_mc'])
ts_point_list = np.asarray(r['TS_point'])
ts_ext_list = np.asarray(r['TS_ext'])
ts_ext_list = np.where(ts_ext_list > 0, ts_ext_list, 0)
l_list = np.asarray(r['glon'])
b_list = np.asarray(r['glat'])
max_ts_ext = max(ts_ext_list)
fig = P.figure(None, figsize=(6,6))
axes = fig.add_subplot(111)
def __init__(self, radiopsr_data, bigfile):
self.radiopsr_data = loaddict(radiopsr_data)
self.bigfile = pyfits.open(expandvars(bigfile))
from lande.utilities.save import loaddict
import numpy as np
import pylab as P
from lande.utilities.arrays import almost_equal
#r = loaddict('$simsrcdata/v22/merged.hdf5')
#r = loaddict('$simsrcdata/v21/merged.hdf5')
r = loaddict('$simsrcdata/v19/merged.hdf5')
ax1=P.subplot(211)
ax2=P.subplot(212)
flux=np.asarray(r['flux_gtlike'])
flux_mc=np.asarray(r['flux_mc'])
flux_err=np.asarray(r['flux_gtlike_err'])
spatial=np.asarray(r['spatial'])
#binsz=np.asarray(r['binsz'])
#rfactor=np.asarray(r['rfactor'])
#for label,cut in [('binsz=0.1 & rfactor=2',(almost_equal(binsz,0.1)&almost_equal(rfactor,2))),
# ('binsz=0.05 & rfactor=4',(almost_equal(binsz,0.05)&almost_equal(rfactor,4))),
# ('binsz=0.025 & rfactor=8',(almost_equal(binsz,0.025)&almost_equal(rfactor,8)))]:
for label in ['?']:
cut = spatial == 'w44'
#P.hist((flux-flux_mc)/flux_err)
print label
ax1.hist((flux[cut]-flux_mc[cut])/flux_mc[cut], label=label, histtype='step')
#ax1.hist((flux[cut]-flux_mc[cut])/flux_err[cut], label=label, histtype='step')
def get_sed(self,pwn,binning,hypothesis):
sed=join(self.fitdir,pwn,'seds','sed_gtlike_%s_%s_%s.yaml' % (binning, hypothesis, pwn))
return loaddict(sed)
from lande.utilities.save import loaddict
from lande.utilities.jobtools import JobBuilder
superfile='$superfile/snrdata.yaml'
snrs=loaddict(superfile).keys()
params=dict(name=snrs,
snrdata='$superfile/snrdata.yaml',
latdata='$snrlimcode/snrlatdata.yaml')
b = JobBuilder(
savedir='$snrlimdata/v1',
params=params,
code='$snrlimcode/run.py',
short_folder_names=True)
b.build()
def get_results(self, pwn, require_all_exists=True, get_seds=True, get_variability=True, get_altdiff=True, verbosity=None):
filename = join(self.fitdir,pwn,'results_%s_general.yaml' % pwn)
if verbosity or (verbosity is None and self.verbosity):
print 'Getting results for %s' % pwn
if not exists(filename):
raise PWNResultsException("%s does not exist" % filename)
results = loaddict(filename)
for hypothesis in self.all_hypotheses:
results[hypothesis] = dict()
for code in ['gtlike','pointlike']:
for hypothesis in self.all_hypotheses:
filename=join(self.fitdir,pwn,'results_%s_%s_%s.yaml' % (pwn,code,hypothesis))
if exists(filename):
results[hypothesis][code] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException("%s does not exist" % filename)
if get_variability:
for hypothesis in ['at_pulsar','point']:
filename =join(self.fitdir,pwn,'results_%s_variability_%s.yaml' % (pwn,hypothesis))
if exists(filename):
results[hypothesis]['variability'] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException('%s does not exist' % filename)
if get_altdiff:
for hypothesis in ['point']:
results[hypothesis]['gtlike']['altdiff'] = dict()
for dist in ['SNR','Lorimer']:
for halo in [4,10]:
for TS in [150,100000]:
filename =join(self.fitdir,pwn,'results_%s_altdiff_dist_%s_halo_%s_TS_%s_%s.yaml' % (pwn,dist,halo,TS,hypothesis))
if exists(filename):
results[hypothesis]['gtlike']['altdiff'][dist,halo,TS] = loaddict(filename)
else:
results[hypothesis]['gtlike']['altdiff'][dist,halo,TS] = None
if get_seds:
for hypothesis in self.all_hypotheses:
for code,all_binning in [['gtlike',['1bpd','2bpd','4bpd']], ['pointlike',['4bpd']]]:
results[hypothesis][code]['seds'] = dict()
for binning in all_binning:
filename = join(self.fitdir,pwn,'seds','sed_%s_%s_%s_%s.yaml' % (code,binning,hypothesis,pwn))
if exists(filename):
results[hypothesis][code]['seds'][binning] = loaddict(filename)
else:
if require_all_exists:
raise PWNResultsException("%s does not exist" % filename)
results['raw_phase'] = PhaseRange(results['phase'])
del results['phase'] # to avoid ambiguity
if self.phase_shift is not None:
import pickle
ps=pickle.load(open(self.phase_shift))
shift = float(ps[pwn.replace('PSRJ','J')]['shift'])
""" Convention taken from private correspondence with Matthew Kerr:
me: ok
so i am trying to implement phase shifts in my analysis
did you decide the right way to do it
Matthew: it seems to be +
me: so correct_phase = phase_in_file + shift
Matthew: yes """
results['shifted_phase'] = results['raw_phase'].offset(shift)
return results
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_extended_emin_100_emax_100000_time_2fgl/merged.hdf5')
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_point_emin_100_emax_100000_time_2years/merged.hdf5')
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_extended_emin_100_emax_100000_time_2years/merged.hdf5')
#results = loaddict('$simsrcdata/v11/sim_flux_1e-06_position_allsky_phibins_0_spatial_point_emin_100_emax_100000_time_2fgl/merged.hdf5')
#results = loaddict('$simsrcdata/v12/merged.hdf5')
#results = loaddict('$simsrcdata/v14/merged.hdf5')
#results = loaddict('$simsrcdata/v17/merged.hdf5')
#results = loaddict('$simsrcdata/v18/merged.hdf5')
#results = loaddict('$simsrcdata/v19/merged.hdf5')
#results = loaddict('$simsrcdata/v20/merged.hdf5')
#results = loaddict('$simsrcdata/v21/merged.hdf5')
results = loaddict('$simsrcdata/v22/merged.hdf5')
print results.keys()
print len(results['flux_gtlike'])
flux_gtlike = np.asarray(results['flux_gtlike'])
flux_gtlike_err = np.asarray(results['flux_gtlike_err'])
flux_pointlike = np.asarray(results['flux_pointlike'])
flux_pointlike_err = np.asarray(results['flux_pointlike_err'])
flux_mc = np.asarray(results['flux_mc'])
glon = np.asarray(results['glon'])
glat = np.asarray(results['glat'])
ra = np.asarray(results['ra'])
dec = np.asarray(results['dec'])
i = np.asarray(results['i'])
def __init__(self, gamma_quiet_psrs_data, analysisdir, verbosity=True):
self.gamma_quiet_psrs_data = loaddict(
expandvars(gamma_quiet_psrs_data))
self.analysisdir = expandvars(analysisdir)
self.verbosity = verbosity