def __init__(self, pwndata, fitdir, webdir):
self.fitdir = expandvars(fitdir)
self.webdir = expandvars(webdir)
self.relpath = os.path.relpath(self.fitdir, self.webdir)
if not os.path.exists(self.webdir): os.makedirs(self.webdir)
self.loader = PWNResultsLoader(pwndata, self.fitdir)
self.pwnlist = self.loader.get_pwnlist()
self.formatter = TableFormatter(self.loader)
def auxiliary_table(pwndata,
phase_shift,
fitdir, filename, pwn_classification):
loader = PWNResultsLoader(
pwndata=pwndata,
fitdir=fitdir,
phase_shift=phase_shift
)
classifier = PWNManualClassifier(loader=loader, pwn_classification=pwn_classification)
pwnlist = loader.get_pwnlist()
npwn = len(pwnlist)
table=atpy.Table(name='Off_Peak')
def add_float(name, **kwargs):
table.add_empty_column(name, np.dtype('float32'), shape=npwn, **kwargs)
table[name][:]=np.nan
return name
def add_int(name, **kwargs):
table.add_empty_column(name, np.dtype('uint32'), shape=npwn, **kwargs)
table[name][:]=np.nan
return name
def add_vector_float(name, size, *args, **kwargs):
table.add_empty_column(name, np.dtype('float32'), shape=(npwn, size), **kwargs)
table[name][:]=np.nan
return name
def add_vector_int(name, size, *args, **kwargs):
table.add_empty_column(name, np.dtype('uint32'), shape=(npwn, size), **kwargs)
table[name][:]=np.nan
return name
def add_string(name, width, *args, **kwargs):
table.add_empty_column(name, np.dtype((str, width)), shape=npwn, *args, **kwargs)
return name
maxwidth=max([len(i) for i in pwnlist])
psr_name=add_string('PSR', maxwidth)
len_class = max(map(len,PWNClassifier.abbreviated_source_class_mapper.values()))
classification_name=add_string('Classification_OP', len_class)
# Phase Stuff
off_peak_min_name=add_float('Min_Phase_OP')
off_peak_max_name=add_float('Max_Phase_OP')
second_off_peak_min_name=add_float('Min_2_Phase_OP')
second_off_peak_max_name=add_float('Max_2_Phase_OP')
# Significance stuff
ts_point_name=add_float('TS_point_OP')
ts_ext_name=add_float('TS_ext_OP')
ts_cutoff_name=add_float('TS_cutoff_OP')
ts_altdiff_name = add_float(r'TS_altdiff_OP')
ts_var_name=add_float('TS_var_OP')
# Spectral Stuff
len_spectral = max(map(len,PWNClassifier.allowed_spectral_models))
spectral_model_name=add_string('Spectral_Model_OP', len_spectral)
energy_units = 'MeV'
energy_flux_units = 'erg/cm^2/s'
flux_units = 'ph/cm^2/s'
prefactor_units = 'ph/cm^2/s/erg'
flux_name = add_float('Flux_OP', unit=flux_units)
flux_err_name = add_float('Unc_Flux_OP', unit=flux_units)
energy_flux_name = add_float('EFlux_OP', unit=energy_flux_units)
energy_flux_err_name = add_float('Unc_EFlux_OP', unit=energy_flux_units)
prefactor_name = add_float('Prefactor_OP', unit=prefactor_units)
prefactor_err_name = add_float('Unc_Prefactor_OP', unit=prefactor_units)
normalization_name = add_float('Normalization_OP')
normalization_err_name = add_float('Unc_Normalization_OP')
scale_name = add_float('Scale_OP', unit=energy_units)
index_name = add_float('Index_OP')
index_err_name = add_float('Unc_Index_OP')
cutoff_name = add_float('Energy_Cutoff_OP', unit=energy_units)
cutoff_err_name = add_float('Unc_Energy_Cutoff_OP', unit=energy_units)
# Spatial Stuff
len_spatial = max(map(len,PWNClassifier.allowed_spatial_models))
spatial_model_name=add_string('Spatial_Model_OP',len_spatial)
ra_name = add_float('RAJ2000_OP', unit='deg')
dec_name = add_float('DECJ2000_OP', unit='deg')
glon_name = add_float('GLON_OP', unit='deg')
glat_name = add_float('GLAT_OP', unit='deg')
poserr_name = add_float('Unc_Position_OP', unit='deg')
extension_name = add_float('Extension_OP', unit='deg')
extension_err_name = add_float('Unc_Extension_OP', unit='deg')
powerlaw_flux_upper_limit_name = add_float('PowerLaw_Flux_UL_OP')
powerlaw_energy_flux_upper_limit_name =add_float('PowerLaw_EFlux_UL_OP')
cutoff_flux_upper_limit_name = add_float('Cutoff_Flux_UL_OP')
cutoff_energy_flux_upper_limit_name =add_float('Cutoff_EFlux_UL_OP')
sed_size=14
sed_lower_energy_name = add_vector_float('SED_Lower_Energy_OP', size=sed_size, unit=energy_units)
sed_upper_energy_name = add_vector_float('SED_Upper_Energy_OP', size=sed_size, unit=energy_units)
sed_middle_energy_name = add_vector_float('SED_Center_Energy_OP', size=sed_size, unit=energy_units)
sed_ts_name = add_vector_float('SED_TS_OP', size=sed_size)
sed_prefactor_name = add_vector_float('SED_Prefactor_OP', size=sed_size, unit='ph*cm**-2*s**-1*erg**-1')
sed_prefactor_lower_err_name = add_vector_float('SED_Neg_Unc_Prefactor_OP', size=sed_size, unit='ph*cm**-2*s**-1*erg**-1')
sed_prefactor_upper_err_name = add_vector_float('SED_Pos_Unc_Prefactor_OP', size=sed_size, unit='ph*cm**-2*s**-1*erg**-1')
sed_prefactor_upper_limit_name = add_vector_float('SED_Prefactor_UL_OP', size=sed_size, unit='ph*cm**-2*s**-1*erg**-1')
for i,pwn in enumerate(pwnlist):
print pwn
try:
r = classifier.get_results(pwn)
except PWNClassifierException:
print 'Skipping %s' % pwn
continue
phase=r['shifted_phase']
table[psr_name][i]=pwn.replace('PSRJ','J')
source_class = r['source_class']
table[classification_name][i] = r['abbreviated_source_class']
assert source_class in PWNClassifier.allowed_source_class
if phase.is_continuous():
a,b = phase.tolist(dense=True)
table[off_peak_min_name][i]=a
table[off_peak_max_name][i]=b
else:
ranges = phase.split_ranges()
assert len(ranges) == 2
a,b = ranges[0].tolist(dense=True)
table[off_peak_min_name][i]=a
table[off_peak_max_name][i]=b
a,b = ranges[1].tolist(dense=True)
table[second_off_peak_min_name][i]=a
table[second_off_peak_max_name][i]=b
# likelihood stuff
ts_point = r['ts_point']
table[ts_point_name][i]=ts_point
if source_class in ['Confused', 'Pulsar', 'Pulsar_Confused', 'PWN']:
table[ts_ext_name][i]=r['ts_ext']
table[ts_cutoff_name][i]=r['ts_cutoff']
elif source_class == 'Upper_Limit':
pass
else:
raise Exception("...")
if source_class in ['Pulsar','Pulsar_Confused']:
table[ts_altdiff_name][i]=r['ts_altdiff']
elif source_class in ['Confused', 'PWN', 'Upper_Limit']:
pass
else:
raise Exception("...")
table[ts_var_name][i]=r['ts_var']
# spectral stuff
spectral_model = r['spectral_model']
if not type(spectral_model) == str and np.isnan(spectral_model):
spectral_model = 'NULL'
table[spectral_model_name][i]= spectral_model
table[energy_flux_name][i] = r['energy_flux']
table[energy_flux_err_name][i] = r['energy_flux_err']
table[flux_name][i]=r['flux']
table[flux_err_name][i]=r['flux_err']
table[prefactor_name][i]=r['prefactor']
table[prefactor_err_name][i]=r['prefactor_err']
table[normalization_name][i]=r['normalization']
table[normalization_err_name][i]=r['normalization_err']
table[index_name][i]=r['index']
table[index_err_name][i]=r['index_err']
table[scale_name][i]=r['model_scale']
table[cutoff_name][i]=r['cutoff']
table[cutoff_err_name][i]=r['cutoff_err']
# spatial stuff
spatial_model = r['spatial_model']
if not type(spatial_model) == str and np.isnan(spatial_model):
spatial_model = 'NULL'
table[spatial_model_name][i] = spatial_model
table[ra_name][i] = r['ra']
table[dec_name][i] = r['dec']
table[glon_name][i] = r['glon']
table[glat_name][i] = r['glat']
table[poserr_name][i] = r['poserr']
table[extension_name][i] = r['extension']
table[extension_err_name][i] = r['extension_err']
# Add powerlaw upper limit
table[powerlaw_flux_upper_limit_name][i] = r['powerlaw_flux_upper_limit']
table[powerlaw_energy_flux_upper_limit_name][i] = r['powerlaw_energy_flux_upper_limit']
# Add cutoff upper limit
table[cutoff_flux_upper_limit_name][i] = r['cutoff_flux_upper_limit']
table[cutoff_energy_flux_upper_limit_name][i] = r['cutoff_energy_flux_upper_limit']
# Add SED results
table[sed_lower_energy_name] = r['sed_lower_energy']
table[sed_upper_energy_name] = r['sed_upper_energy']
table[sed_middle_energy_name] = r['sed_middle_energy']
table[sed_ts_name][i] = r['sed_ts']
table[sed_prefactor_name][i] = r['sed_prefactor']
table[sed_prefactor_lower_err_name][i] = r['sed_prefactor_lower_err']
table[sed_prefactor_upper_err_name][i] = r['sed_prefactor_upper_err']
table[sed_prefactor_upper_limit_name][i] = r['sed_prefactor_upper_limit']
table.write(expandvars(filename), overwrite=True)
