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 __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)
import numpy as np
from lande.fermi.pipeline.pwncat2.interp.classify import PWNManualClassifier,PWNClassifierException
from lande.fermi.pipeline.pwncat2.interp.loader import PWNResultsLoader
loader = PWNResultsLoader(
pwndata="$pwndata/pwncat2_data_lande.yaml",
fitdir="$pwnpipeline/v36/analysis"
)
print loader.pwndata
print loader.fitdir
pwnlist = loader.get_pwnlist()
classifier=PWNManualClassifier(
loader=loader,
pwn_classification='$pwnclassify/manual_classifications.yaml'
)
npsr = 0
psrs = []
npsr_confused = 0
psrs_confused = []
npwn = 0
pwne = []
ndetect = 0
nconfused = 0
def spatial_spectral_table(pwndata,
phase_shift,
fitdir, savedir, pwn_classification, filebase, table_type,
bigfile_filename):
assert table_type == 'latex'
format=PWNFormatter(table_type=table_type, precision=2)
loader = PWNResultsLoader(
pwndata=pwndata,
fitdir=fitdir,
phase_shift=phase_shift
)
classifier = PWNManualClassifier(loader=loader, pwn_classification=pwn_classification)
table = OrderedDefaultDict(list)
psr_name='PSR'
classification_name = 'Type'
ts_point_name=r'$\tspoint$'
ts_ext_name=r'$\tsext$'
ts_cutoff_name = r'$\tscutoff$'
ts_altdiff_name = r'$\tsaltdiff$'
eflux_name = r'Energy Flux'
index_name = r'$\Gamma$'
cutoff_name = r'$\Ecutoff$'
pwnlist = loader.get_pwnlist()
#pwnlist = pwnlist[10:20]
pcl = PulsarCatalogLoader(bigfile_filename=bigfile_filename)
young = [ psr for psr in pwnlist if 'm' not in pcl.get_pulsar_classification(psr.replace('PSRJ','J'))]
msps = [ psr for psr in pwnlist if 'm' in pcl.get_pulsar_classification(psr.replace('PSRJ','J'))]
print 'young',young
print 'msps',msps
sorted_pwnlist = young + msps
first_msp_index = None
for pwn in sorted_pwnlist:
print pwn
try:
r = classifier.get_results(pwn)
if r['source_class'] == 'Upper_Limit':
continue
if first_msp_index is None and pwn in msps:
first_msp_index = len(table[psr_name])
table[psr_name].append(format.pwn(pwn))
table[classification_name].append(r['abbreviated_source_class'])
def david_format_ts(x):
if x >= 100:
return format.value(x,precision=0) + '.'
else:
return format.value(x,precision=1)
table[ts_point_name].append(david_format_ts(r['ts_point']))
table[ts_ext_name].append(david_format_ts(r['ts_ext']))
table[ts_cutoff_name].append(david_format_ts(r['ts_cutoff']))
table[ts_altdiff_name].append(david_format_ts(r['ts_altdiff']) if r['ts_altdiff'] is not None else format.nodata)
def david_format_flux(x, y):
if x >= 10:
return format.error(x,y, precision=1)
else:
return format.error(x,y, precision=2)
table[eflux_name].append(david_format_flux(r['energy_flux']/1e-11,r['energy_flux_err']/1e-11))
if r['spectral_model'] in ['PowerLaw','PLSuperExpCutoff']:
table[index_name].append(format.error(r['index'],r['index_err']))
elif pwn == 'PSRJ0534+2200':
table[index_name].append(r'\tablenotemark{a}')
elif pwn == 'PSRJ0835-4510':
table[index_name].append(r'\tablenotemark{b}')
else:
table[index_name].append(format.nodata)
if r['spectral_model'] == 'PLSuperExpCutoff':
table[cutoff_name].append(format.error(r['cutoff']/1e3,r['cutoff_err']/1e3, precision=2))
else:
table[cutoff_name].append(format.nodata)
except PWNClassifierException, ex:
print 'Skipping %s: %s' % (pwn,ex)
table[psr_name].append(format.pwn(pwn))
table[classification_name].append('None')
table[ts_point_name].append('None')
table[ts_ext_name].append('None')
table[ts_cutoff_name].append('None')
table[eflux_name].append('None')
table[index_name].append('None')
table[cutoff_name].append('None')
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)
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)
class WebsiteBuilder(object):
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 build(self):
self.build_main_website()
self.build_all_pages()
def build_all_pages(self):
for pwn in self.pwnlist:
self.build_each_page(pwn)
def build_main_website(self):
print 'Building Main Webpage'
index_t2t = []
index_t2t.append('PWNCatalog+\n\n')
t = self.formatter.format(self.pwnlist)
index_t2t.append(str(t))
t2t(index_t2t, join(self.webdir, 'index.t2t'))
def build_each_page(self, pwn):
print 'Building webpage for %s' % pwn
index_t2t = []
index_t2t.append(pwn + '\n\n')
index_t2t.append('([back index.html])')
t = self.formatter.format([pwn])
index_t2t.append(str(t))
index_t2t.append('')
index_t2t.append('[Analysis Folder %s/%s]\n' % (self.relpath, pwn))
index_t2t.append('[log (pointlike) %s/%s/log_run_%s.txt]\n' %
(self.relpath, pwn, pwn))
index_t2t.append(
'pointlike results: [(at_pulsar) %s/%s/results_%s_pointlike_at_pulsar.yaml] [(point) %s/%s/results_%s_pointlike_point.yaml] [(extended) %s/%s/results_%s_pointlike_extended.yaml] \n'
% (self.relpath, pwn, pwn, self.relpath, pwn, pwn, self.relpath,
pwn, pwn))
index_t2t.append(
'gtlike results: [(at_pulsar) %s/%s/results_%s_gtlike_at_pulsar.yaml] [(point) %s/%s/results_%s_gtlike_point.yaml] [(extended) %s/%s/results_%s_gtlike_extended.yaml] \n'
% (self.relpath, pwn, pwn, self.relpath, pwn, pwn, self.relpath,
pwn, pwn))
get_plot_table = lambda *args: index_t2t.append('|| ' + ' | '.join(
['[%s/%s/plots/%s]' % (self.relpath, pwn, i)
for i in args]) + ' |\n\n')
get_sed_table = lambda *args: index_t2t.append('|| ' + ' | '.join(
['[%s/%s/seds/%s]' % (self.relpath, pwn, i)
for i in args]) + ' |\n\n')
title = lambda i: index_t2t.append('\n\n== %s ==' % i)
title('Phase Info')
get_plot_table('phaseogram_%s.png' % (pwn),
'phase_vs_time_%s.png' % (pwn))
all = ['at_pulsar', 'point', 'extended']
title('Big Residual TS map')
get_plot_table(
*['tsmap_residual_%s_%s_10deg.png' % (i, pwn) for i in all])
index_t2t.append(
'[tsmap_residual_%s_%s_10deg.fits %s/%s/data/tsmap_residual_%s_%s_10deg.fits]'
% (pwn, 'at_pulsar', self.relpath, pwn, 'at_pulsar', pwn))
title('SED gtlike (4bpd)')
get_sed_table(*['sed_gtlike_4bpd_%s_%s.png' % (i, pwn) for i in all])
title('Combined gtlike')
get_plot_table(
*['combined_gtlike_spectra_%s_%s.png' % (i, pwn) for i in all])
title('Combined pointlike')
get_plot_table(
*['combined_pointlike_spectra_%s_%s.png' % (i, pwn) for i in all])
title('gtlike Cutoff test')
get_plot_table(*[
'test_cutoff_gtlike_%s_%s.png' % (i, pwn)
for i in ['at_pulsar', 'point']
])
title('pointlike Cutoff test')
get_plot_table(*[
'test_cutoff_pointlike_%s_%s.png' % (i, pwn)
for i in ['at_pulsar', 'point']
])
title('Source TS Maps')
get_plot_table(
*['tsmap_source_%s_%s_5deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_source_%s_%s_5deg.png' % (i, pwn) for i in all])
title('Residual TS Maps')
get_plot_table(
*['tsmap_residual_%s_%s_5deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_residual_%s_%s_5deg.png' % (i, pwn) for i in all])
title('New Source TS Maps')
get_plot_table(
*['tsmap_newsrc_%s_%s_5deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_newsrc_%s_%s_5deg.png' % (i, pwn) for i in all])
title('at_pulsar Smoothed Counts Diffuse Subtracted (0.1)')
get_plot_table(
*['sources_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_sources_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
title('Smoothed Counts BG Source Subtracted (0.1)')
get_plot_table(
*['source_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
title('Band Smoothed Counts BG Source Subtracted (0.1)')
get_plot_table(
*['band_source_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
title('gtlike SED')
get_sed_table(*['sed_gtlike_1bpd_%s_%s.png' % (i, pwn) for i in all])
get_sed_table(*['sed_gtlike_2bpd_%s_%s.png' % (i, pwn) for i in all])
title('Pointlike SEDs')
get_sed_table(
*['sed_pointlike_4bpd_%s_%s.png' % (i, pwn) for i in all])
title('Extra: Source TS Maps (10 deg)')
get_plot_table(
*['tsmap_source_%s_%s_10deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_source_%s_%s_10deg.png' % (i, pwn) for i in all])
title('Extra: Residual TS Maps')
get_plot_table(
*['tsmap_residual_%s_%s_10deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_residual_%s_%s_10deg.png' % (i, pwn) for i in all])
title('Extra: New Source TS Maps (10 deg)')
get_plot_table(
*['tsmap_newsrc_%s_%s_10deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_tsmap_newsrc_%s_%s_10deg.png' % (i, pwn) for i in all])
title('Extra: Smoothed Counts (0.25deg)')
get_plot_table(*['source_0.25_%s_%s_5deg.png' % (i, pwn) for i in all])
title('Extra: Smoothed Counts (0.25deg)')
get_plot_table(
*['sources_0.25_%s_%s_5deg.png' % (i, pwn) for i in all])
title('Extra: Band Smoothed Counts (0.25)')
get_plot_table(
*['band_source_%s_%s_5deg_0.25deg.png' % (i, pwn) for i in all])
get_plot_table(
*['band_sources_%s_%s_5deg_0.25deg.png' % (i, pwn) for i in all])
title('Counts (0.1)')
get_plot_table(
*['counts_residual_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
get_plot_table(
*['counts_source_%s_%s_5deg_0.1deg.png' % (i, pwn) for i in all])
title('Extra: Counts (0.25)')
get_plot_table(
*['counts_source_%s_%s_5deg_0.25deg.png' % (i, pwn) for i in all])
get_plot_table(
*
['counts_residual_%s_%s_5deg_0.25deg.png' % (i, pwn) for i in all])
t2t(index_t2t, join(self.webdir, '%s.t2t' % pwn))
class WebsiteBuilder(object):
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 build(self):
self.build_main_website()
self.build_all_pages()
def build_all_pages(self):
for pwn in self.pwnlist:
self.build_each_page(pwn)
def build_main_website(self):
print 'Building Main Webpage'
index_t2t = []
index_t2t.append('PWNCatalog+\n\n')
t=self.formatter.format(self.pwnlist)
index_t2t.append(str(t))
t2t(index_t2t, join(self.webdir,'index.t2t'))
def build_each_page(self,pwn):
print 'Building webpage for %s' % pwn
index_t2t = []
index_t2t.append(pwn+'\n\n')
index_t2t.append('([back index.html])')
t=self.formatter.format([pwn])
index_t2t.append(str(t))
index_t2t.append('')
index_t2t.append('[Analysis Folder %s/%s]\n' % (self.relpath,pwn))
index_t2t.append('[log (pointlike) %s/%s/log_run_%s.txt]\n' % (self.relpath,pwn,pwn))
index_t2t.append('pointlike results: [(at_pulsar) %s/%s/results_%s_pointlike_at_pulsar.yaml] [(point) %s/%s/results_%s_pointlike_point.yaml] [(extended) %s/%s/results_%s_pointlike_extended.yaml] \n' % (self.relpath,pwn,pwn,self.relpath,pwn,pwn,self.relpath,pwn,pwn))
index_t2t.append('gtlike results: [(at_pulsar) %s/%s/results_%s_gtlike_at_pulsar.yaml] [(point) %s/%s/results_%s_gtlike_point.yaml] [(extended) %s/%s/results_%s_gtlike_extended.yaml] \n' % (self.relpath,pwn,pwn,self.relpath,pwn,pwn,self.relpath,pwn,pwn))
get_plot_table = lambda *args: index_t2t.append('|| ' + ' | '.join(['[%s/%s/plots/%s]' % (self.relpath,pwn,i) for i in args]) + ' |\n\n')
get_sed_table = lambda *args: index_t2t.append('|| ' + ' | '.join(['[%s/%s/seds/%s]' % (self.relpath,pwn,i) for i in args]) + ' |\n\n')
title = lambda i: index_t2t.append('\n\n== %s ==' % i)
title('Phase Info')
get_plot_table('phaseogram_%s.png' % (pwn),'phase_vs_time_%s.png' % (pwn))
all = ['at_pulsar', 'point', 'extended']
title('Big Residual TS map')
get_plot_table(*['tsmap_residual_%s_%s_10deg.png' % (i,pwn) for i in all])
index_t2t.append('[tsmap_residual_%s_%s_10deg.fits %s/%s/data/tsmap_residual_%s_%s_10deg.fits]' % (pwn,'at_pulsar',self.relpath,pwn,'at_pulsar',pwn))
title('SED gtlike (4bpd)')
get_sed_table(*['sed_gtlike_4bpd_%s_%s.png' % (i,pwn) for i in all])
title('Combined gtlike')
get_plot_table(*['combined_gtlike_spectra_%s_%s.png' % (i,pwn) for i in all])
title('Combined pointlike')
get_plot_table(*['combined_pointlike_spectra_%s_%s.png' % (i,pwn) for i in all])
title('gtlike Cutoff test')
get_plot_table(*['test_cutoff_gtlike_%s_%s.png' % (i,pwn) for i in ['at_pulsar', 'point']])
title('pointlike Cutoff test')
get_plot_table(*['test_cutoff_pointlike_%s_%s.png' % (i,pwn) for i in ['at_pulsar', 'point']])
title('Source TS Maps')
get_plot_table(*['tsmap_source_%s_%s_5deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_source_%s_%s_5deg.png' % (i,pwn) for i in all])
title('Residual TS Maps')
get_plot_table(*['tsmap_residual_%s_%s_5deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_residual_%s_%s_5deg.png' % (i,pwn) for i in all])
title('New Source TS Maps')
get_plot_table(*['tsmap_newsrc_%s_%s_5deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_newsrc_%s_%s_5deg.png' % (i,pwn) for i in all])
title('at_pulsar Smoothed Counts Diffuse Subtracted (0.1)')
get_plot_table(*['sources_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_sources_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
title('Smoothed Counts BG Source Subtracted (0.1)')
get_plot_table(*['source_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
title('Band Smoothed Counts BG Source Subtracted (0.1)')
get_plot_table(*['band_source_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
title('gtlike SED')
get_sed_table(*['sed_gtlike_1bpd_%s_%s.png' % (i,pwn) for i in all])
get_sed_table(*['sed_gtlike_2bpd_%s_%s.png' % (i,pwn) for i in all])
title('Pointlike SEDs')
get_sed_table(*['sed_pointlike_4bpd_%s_%s.png' % (i,pwn) for i in all])
title('Extra: Source TS Maps (10 deg)')
get_plot_table(*['tsmap_source_%s_%s_10deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_source_%s_%s_10deg.png' % (i,pwn) for i in all])
title('Extra: Residual TS Maps')
get_plot_table(*['tsmap_residual_%s_%s_10deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_residual_%s_%s_10deg.png' % (i,pwn) for i in all])
title('Extra: New Source TS Maps (10 deg)')
get_plot_table(*['tsmap_newsrc_%s_%s_10deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_tsmap_newsrc_%s_%s_10deg.png' % (i,pwn) for i in all])
title('Extra: Smoothed Counts (0.25deg)')
get_plot_table(*['source_0.25_%s_%s_5deg.png' % (i,pwn) for i in all])
title('Extra: Smoothed Counts (0.25deg)')
get_plot_table(*['sources_0.25_%s_%s_5deg.png' % (i,pwn) for i in all])
title('Extra: Band Smoothed Counts (0.25)')
get_plot_table(*['band_source_%s_%s_5deg_0.25deg.png' % (i,pwn) for i in all])
get_plot_table(*['band_sources_%s_%s_5deg_0.25deg.png' % (i,pwn) for i in all])
title('Counts (0.1)')
get_plot_table(*['counts_residual_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
get_plot_table(*['counts_source_%s_%s_5deg_0.1deg.png' % (i,pwn) for i in all])
title('Extra: Counts (0.25)')
get_plot_table(*['counts_source_%s_%s_5deg_0.25deg.png' % (i,pwn) for i in all])
get_plot_table(*['counts_residual_%s_%s_5deg_0.25deg.png' % (i,pwn) for i in all])
t2t(index_t2t, join(self.webdir,'%s.t2t' % pwn))
