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)
#!/usr/bin/env python from os.path import expandvars,join 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: