from lande.utilities import pubplot
from lande.fermi.pipeline.pwncat2.interp.bigfile import PulsarCatalogLoader
pubplot.set_latex_defaults()
bw = pubplot.get_bw()
fig = P.figure(None,(4,4))
axes = fig.add_subplot(111)
axes.set_xscale("log")
axes.set_yscale("log")
cat=PulsarCatalogLoader(
bigfile_filename='$lat2pc/BigFile/Pulsars_BigFile_v20121002103223.fits',
off_peak_auxiliary_filename='$lat2pc/OffPeak/tables/off_peak_auxiliary_table.fits')
bigfile_fits = cat.bigfile_fits
psrlist = cat.get_off_peak_psrlist()
Edot=np.empty_like(psrlist,dtype=float)
age=np.empty_like(psrlist,dtype=float)
classification=np.empty_like(psrlist,dtype=object)
has_distance=np.empty_like(psrlist,dtype=bool)
distance=np.empty_like(psrlist,dtype=float)
for i,psr in enumerate(psrlist):
import pylab as P
import numpy as np
from lande.utilities.plotting import plot_points
from lande.utilities import pubplot
from lande.fermi.pipeline.pwncat2.interp.bigfile import PulsarCatalogLoader
pubplot.set_latex_defaults()
bw = pubplot.get_bw()
cat=PulsarCatalogLoader(
bigfile_filename='$lat2pc/BigFile/Pulsars_BigFile_v20130214170325.fits',
off_peak_auxiliary_filename='$lat2pc/OffPeak/auxiliary/off_peak_auxiliary_table.fits')
psrlist = cat.get_off_peak_psrlist()
fig = P.figure(None,(6,6))
axes = fig.add_subplot(111)
axes.set_xscale("log")
axes.set_yscale("log")
classification=np.empty_like(psrlist,dtype=object)
Edot=np.empty_like(psrlist,dtype=float)
luminosity=np.empty_like(psrlist,dtype=float)
luminosity_error_statistical=np.empty_like(psrlist,dtype=float)
luminosity_lower_error_systematic=np.empty_like(psrlist,dtype=float)
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')
