def fillLivetime(self, ltc, src_type):
self._tau = Histogram(self._cth_axis)
if src_type == 'iso':
self._tau += self._cth_axis.width
elif src_type == 'isodec':
sinlat = np.linspace(-1, 1, 48)
m = ltc._ltmap[:, i]
self._tau[i] = 0
for s in sinlat:
lat = np.arcsin(s)
th = np.pi / 2. - lat
ipix = healpy.ang2pix(64, th, 0, nest=True)
self._tau[i] += m[ipix]
else:
if not isinstance(src_type, list):
src_type = [src_type]
for s in src_type:
cat = Catalog.get()
src = cat.get_source_by_name(s)
lonlat = (src['RAJ2000'], src['DEJ2000'])
self._tau += ltc.get_src_lthist(lonlat[0],
lonlat[1],
cth_axis=self._cth_axis)
def loadsrclist(self, fname):
self.srcs = []
self.src_names = []
self.src_redshifts = []
self.src_ra_deg = []
self.src_dec_deg = []
self.src_radec = []
# if not fname is None:
# src_names = np.genfromtxt(fname,unpack=True,dtype=None)
# else:
# src_names = np.array(srcs.split(','))
if isinstance(fname, list):
src_names = np.array(fname)
if src_names.ndim == 0: src_names = src_names.reshape(1)
cat = Catalog.get()
for name in src_names:
src = cat.get_source_by_name(name)
name = src['Source_Name']
ra = src['RAJ2000']
dec = src['DEJ2000']
# self._photon_data._srcs.append(src)
self.src_names.append(name)
self.src_ra_deg.append(ra)
self.src_dec_deg.append(dec)
self.src_radec.append((np.radians(ra), np.radians(dec)))
def fillLivetime(self,ltc,src_type):
self._tau = Histogram(self._cth_axis)
if src_type == 'iso':
self._tau += self._cth_axis.width
elif src_type == 'isodec':
sinlat = np.linspace(-1,1,48)
m = ltc._ltmap[:,i]
self._tau[i] = 0
for s in sinlat:
lat = np.arcsin(s)
th = np.pi/2. - lat
ipix = healpy.ang2pix(64,th,0,nest=True)
self._tau[i] += m[ipix]
else:
if not isinstance(src_type,list):
src_type = [src_type]
for s in src_type:
cat = Catalog.get()
src = cat.get_source_by_name(s)
lonlat = (src['RAJ2000'], src['DEJ2000'])
self._tau += ltc.get_src_lthist(lonlat[0],
lonlat[1],
cth_axis=self._cth_axis)
def loadsrclist(self,fname):
self.srcs = []
self.src_names = []
self.src_redshifts = []
self.src_ra_deg = []
self.src_dec_deg = []
self.src_radec = []
# if not fname is None:
# src_names = np.genfromtxt(fname,unpack=True,dtype=None)
# else:
# src_names = np.array(srcs.split(','))
if isinstance(fname,list):
src_names = np.array(fname)
if src_names.ndim == 0: src_names = src_names.reshape(1)
cat = Catalog.get()
for name in src_names:
src = cat.get_source_by_name(name)
name = src['Source_Name']
ra = src['RAJ2000']
dec = src['DEJ2000']
# self._photon_data._srcs.append(src)
self.src_names.append(name)
self.src_ra_deg.append(ra)
self.src_dec_deg.append(dec)
self.src_radec.append((np.radians(ra),np.radians(dec)))
def update_image(self):
self.scf()
bin_range = [self.slice,self.slice+self.nbin]
self._cm = []
for im in self._im:
if isinstance(im,SkyCube):
self._cm.append(im.marginalize(2,bin_range=bin_range))
else:
self._cm.append(im)
for i in range(len(self._cm)):
if self.smooth: self._cm[i] = self._cm[i].smooth(0.1)
if len(self._cm) == 0: return
cm = self._cm
cat = Catalog.get('3fgl')
if len(self._axim) == 0:
axim = cm[0].plot(**self._style[0])
cat.plot(cm[0],src_color='w')
self._axim.append(axim)
self._ax = plt.gca()
plt.colorbar(axim,orientation='horizontal',shrink=0.7,pad=0.15,
fraction=0.05)
return
self._axim[0].set_data(cm[0].counts.T)
self._axim[0].autoscale()
if self._style[0]['logz']:
self._axim[0].set_norm(LogNorm())
else:
self._axim[0].set_norm(Normalize())
self.canvas.draw()
def __init__(self,
irf,
nbin=600,
ebins_per_decade=16,
src_type='iso',
spectrum='powerlaw',
spectrum_pars=[2.0],
build_model=True,
ltfile=None,
edisp_table=None):
PSFModel.__init__(self, model=spectrum, sp_param=spectrum_pars)
self._src_type = src_type
self._nbin_dtheta = nbin
self._irf = irf
self._edisp_table = edisp_table
self._lonlat = (0, 0)
if src_type != 'iso' and src_type != 'isodec':
cat = Catalog.get()
src = cat.get_source_by_name(src_type)
self._lonlat = (src['RAJ2000'], src['DEJ2000'])
loge_step = 1. / float(ebins_per_decade)
emin = 1.0 + loge_step / 2.
emax = 6.0 - loge_step / 2.
nbin = int((emax - emin) / loge_step) + 1
self._loge_axis = Axis.create(emin, emax, nbin)
self._energy = np.power(10, self._loge_axis.center)
self._exps = np.zeros(self._loge_axis.nbins)
self._psf = np.zeros((self._loge_axis.nbins, self._nbin_dtheta))
self._dtheta = np.array([
self._dtheta_max_deg * (float(i) / float(self._nbin_dtheta))**2
for i in range(self._nbin_dtheta)
])
self._dtheta_axis = Axis(self._dtheta)
self._ctheta_axis = Axis.create(0.2, 1.0, 40)
self._tau = np.zeros(self._ctheta_axis.nbins)
self.loadLTCube(ltfile)
self.fillLivetime()
if build_model: self.buildModel()
def __init__(self,irf,nbin=600,
ebins_per_decade=16,
src_type='iso',
spectrum='powerlaw',
spectrum_pars=[2.0],
build_model=True,
ltfile=None,
edisp_table=None):
PSFModel.__init__(self,model=spectrum,sp_param=spectrum_pars)
self._src_type = src_type
self._nbin_dtheta = nbin
self._irf = irf
self._edisp_table = edisp_table
self._lonlat = (0, 0)
if src_type != 'iso' and src_type != 'isodec':
cat = Catalog.get()
src = cat.get_source_by_name(src_type)
self._lonlat = (src['RAJ2000'], src['DEJ2000'])
loge_step = 1./float(ebins_per_decade)
emin = 1.0+loge_step/2.
emax = 6.0-loge_step/2.
nbin = int((emax-emin)/loge_step)+1
self._loge_axis = Axis.create(emin,emax,nbin)
self._energy = np.power(10,self._loge_axis.center)
self._exps = np.zeros(self._loge_axis.nbins)
self._psf = np.zeros((self._loge_axis.nbins,self._nbin_dtheta))
self._dtheta = np.array([self._dtheta_max_deg*
(float(i)/float(self._nbin_dtheta))**2
for i in range(self._nbin_dtheta)])
self._dtheta_axis = Axis(self._dtheta)
self._ctheta_axis = Axis.create(0.2,1.0,40)
self._tau = np.zeros(self._ctheta_axis.nbins)
self.loadLTCube(ltfile)
self.fillLivetime()
if build_model: self.buildModel()
def loadsrclist(self,fname,srcs):
self.srcs = []
self.src_names = []
self.src_redshifts = []
self.src_ra_deg = []
self.src_dec_deg = []
self.src_radec = []
if not fname is None:
src_names = np.genfromtxt(fname,unpack=True,dtype=None)
else:
src_names = np.array(srcs.split(','))
if src_names.ndim == 0: src_names = src_names.reshape(1)
cat = Catalog.get()
for name in src_names:
src = cat.get_source_by_name(name)
name = src['Source_Name']
ra = src['RAJ2000']
dec = src['DEJ2000']
print 'Loading ', name
self._photon_data._srcs.append(src)
sd = skymaps.SkyDir(ra,dec)
self.srcs.append(sd)
self.src_names.append(name)
self.src_ra_deg.append(ra)
self.src_dec_deg.append(dec)
self.src_radec.append((np.radians(ra),np.radians(dec)))
self.src_redshifts.append(0.)
# Integrate over 3rd (energy) dimension
im = im.marginalize(2)
plt.figure()
im.plot(cmap='ds9_b')
plt.figure()
# Smooth by 0.2 deg
im.smooth(0.2).plot(cmap='ds9_b')
# Draw an arbitrary contour in Galactic Coordinates
phi = np.linspace(0, 2 * np.pi, 10000)
r = np.sqrt(2 * np.cos(2 * phi))
x = im.lon + r * np.cos(phi)
y = im.lat + r * np.sin(phi)
im.ax()['gal'].plot(x, y, color='w')
cat = Catalog.get('2fgl')
cat.plot(im, ax=plt.gca(), label_threshold=5, src_color='w')
# Make 1D projection on LON axis
plt.figure()
pim = im.project(0, offset_coord=True)
pim.plot()
plt.gca().grid(True)
plt.show()
def make_image_plot(self,subplot,h,fig,fig2,title,rpsf68,rpsf95,
smooth=False,
resid_type=None,mc_resid=None,**kwargs):
plt.figure(fig.get_label())
cb_label='Counts'
if resid_type == 'significance':
kwargs['vmin'] = -5
kwargs['vmax'] = 5
kwargs['levels'] = [-5.0,-3.0,3.0,5.0]
cb_label = 'Significance [$\sigma$]'
elif resid_type == 'fractional':
kwargs['vmin'] = -1.0
kwargs['vmax'] = 1.0
kwargs['levels'] = [-1.0,-0.5,0.5,1.0]
cb_label = 'Fractional Residual'
if smooth:
kwargs['beam_size'] = [self._rsmooth,self._rsmooth,0.0,4]
axim = h.plot(subplot=subplot,cmap='ds9_b',**kwargs)
h.plot_circle(rpsf68,color='w',lw=1.5)
h.plot_circle(rpsf95,color='w',linestyle='--',lw=1.5)
h.plot_marker(marker='+',color='w',linestyle='--')
ax = h.ax()
ax.set_title(title)
cb = plt.colorbar(axim,orientation='horizontal',
shrink=0.9,pad=0.15,
fraction=0.05)
cb.set_label(cb_label)
cat = Catalog.get('3fgl')
cat.plot(h,ax=ax,src_color='w',label_threshold=5.0)
if resid_type is None: return
plt.figure(fig2.get_label())
ax2 = fig2.add_subplot(subplot)
z = h.counts[10:-10,10:-10]
if resid_type == 'significance':
zproj_axis = Axis.create(-6,6,120)
elif resid_type == 'fractional':
zproj_axis = Axis.create(-1.0,1.0,120)
else:
zproj_axis = Axis.create(-10,10,120)
hz = Histogram(zproj_axis)
hz.fill(np.ravel(z))
nbin = np.prod(z.shape)
hz_mc = Histogram(zproj_axis)
if mc_resid:
for mch in mc_resid:
z = mch.counts[10:-10,10:-10]
hz_mc.fill(np.ravel(z))
hz_mc /= float(len(mc_resid))
fn = lambda t : 1./np.sqrt(2*np.pi)*np.exp(-t**2/2.)
hz.plot(label='Data',linestyle='None')
if resid_type == 'significance':
plt.plot(hz.axis().center,
fn(hz.axis().center)*hz.axis().width*nbin,
color='k',label='Gaussian ($\sigma = 1$)')
hz_mc.plot(label='MC',hist_style='line')
plt.gca().grid(True)
plt.gca().set_yscale('log')
plt.gca().set_ylim(0.5)
ax2.legend(loc='upper right',prop= {'size' : 10 })
data_stats = 'Mean = %.2f\nRMS = %.2f'%(hz.mean(),hz.stddev())
mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f'%(hz_mc.mean(),
hz_mc.stddev())
ax2.set_xlabel(cb_label)
ax2.set_title(title)
ax2.text(0.05,0.95,
'%s\n%s'%(data_stats,mc_stats),
verticalalignment='top',
transform=ax2.transAxes,fontsize=10)
def make_image_plot(self,
subplot,
h,
fig,
fig2,
title,
rpsf68,
rpsf95,
smooth=False,
resid_type=None,
mc_resid=None,
**kwargs):
plt.figure(fig.get_label())
cb_label = 'Counts'
if resid_type == 'significance':
kwargs['vmin'] = -5
kwargs['vmax'] = 5
kwargs['levels'] = [-5.0, -3.0, 3.0, 5.0]
cb_label = 'Significance [$\sigma$]'
elif resid_type == 'fractional':
kwargs['vmin'] = -1.0
kwargs['vmax'] = 1.0
kwargs['levels'] = [-1.0, -0.5, 0.5, 1.0]
cb_label = 'Fractional Residual'
if smooth:
kwargs['beam_size'] = [self._rsmooth, self._rsmooth, 0.0, 4]
axim = h.plot(subplot=subplot, cmap='ds9_b', **kwargs)
h.plot_circle(rpsf68, color='w', lw=1.5)
h.plot_circle(rpsf95, color='w', linestyle='--', lw=1.5)
h.plot_marker(marker='x', color='w', linestyle='--')
ax = h.ax()
ax.set_title(title)
cb = plt.colorbar(axim,
orientation='horizontal',
shrink=0.9,
pad=0.15,
fraction=0.05)
if kwargs.get('zscale', None) is not None:
import matplotlib.ticker
cb.locator = matplotlib.ticker.MaxNLocator(nbins=5)
cb.update_ticks()
cb.set_label(cb_label)
cat = Catalog.get('3fgl')
cat.plot(h,
ax=ax,
src_color='w',
label_threshold=self._srclabels_thresh)
if resid_type is None: return
plt.figure(fig2.get_label())
ax2 = fig2.add_subplot(subplot)
z = h.counts[10:-10, 10:-10]
if resid_type == 'significance':
zproj_axis = Axis.create(-6, 6, 120)
elif resid_type == 'fractional':
zproj_axis = Axis.create(-1.0, 1.0, 120)
else:
zproj_axis = Axis.create(-10, 10, 120)
hz = Histogram(zproj_axis)
hz.fill(np.ravel(z))
nbin = np.prod(z.shape)
hz_mc = Histogram(zproj_axis)
if mc_resid:
for mch in mc_resid:
z = mch.counts[10:-10, 10:-10]
hz_mc.fill(np.ravel(z))
hz_mc /= float(len(mc_resid))
fn = lambda t: 1. / np.sqrt(2 * np.pi) * np.exp(-t**2 / 2.)
hz.plot(label='Data', linestyle='None')
if resid_type == 'significance':
plt.plot(hz.axis().center,
fn(hz.axis().center) * hz.axis().width * nbin,
color='k',
label='Gaussian ($\sigma = 1$)')
hz_mc.plot(label='MC', hist_style='line')
plt.gca().grid(True)
plt.gca().set_yscale('log')
plt.gca().set_ylim(0.5)
ax2.legend(loc='upper right', prop={'size': 10})
data_stats = 'Mean = %.2f\nRMS = %.2f' % (hz.mean(), hz.stddev())
mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f' % (hz_mc.mean(),
hz_mc.stddev())
ax2.set_xlabel(cb_label)
ax2.set_title(title)
ax2.text(0.05,
0.95,
'%s\n%s' % (data_stats, mc_stats),
verticalalignment='top',
transform=ax2.transAxes,
fontsize=10)
def main(self, *argv):
usage = "usage: %(prog)s [options]"
description = """Generates PSF model."""
parser = argparse.ArgumentParser(usage=usage, description=description)
IRFManager.configure(parser)
parser.add_argument('--ltfile',
default=None,
help='Set the livetime cube which will be used '
'to generate the exposure-weighted PSF model.')
parser.add_argument('--src', default='Vela', help='')
parser.add_argument('--irf',
default=None,
help='Set the names of one or more IRF models.')
parser.add_argument('--output_dir',
default=None,
help='Set the output directory name.')
parser.add_argument('--cth_bin_edge',
default='0.4,1.0',
help='Edges of cos(theta) bins '
'(e.g. 0.2,0.5,1.0).')
parser.add_argument('--egy_bin_edge',
default=None,
help='Edges of energy bins.')
parser.add_argument('--egy_bin',
default='1.25/5.0/0.25',
help='Set min/max energy.')
parser.add_argument('--quantiles',
default='0.34,0.68,0.90,0.95',
help='Define the set of quantiles to compute.')
parser.add_argument('--conversion_type',
default='front',
help='Draw plots.')
parser.add_argument('--spectrum',
default='powerlaw/2',
help='Draw plots.')
parser.add_argument('--edisp',
default=None,
help='Set the energy dispersion lookup table.')
parser.add_argument('-o',
'--output',
default=None,
help='Set the output file.')
parser.add_argument('--load_from_file',
default=False,
action='store_true',
help='Load IRFs from FITS.')
opts = parser.parse_args(list(argv))
irfs = opts.irf.split(',')
[elo, ehi, ebin] = [float(t) for t in opts.egy_bin.split('/')]
egy_bin_edge = np.linspace(elo, ehi, 1 + int((ehi - elo) / ebin))
cth_bin_edge = [float(t) for t in opts.cth_bin_edge.split(',')]
quantiles = [float(t) for t in opts.quantiles.split(',')]
for irf in irfs:
if opts.output is None:
output_file = re.sub('\:\:', r'_', irf)
output_file += '_%03.f%03.f' % (100 * cth_bin_edge[0],
100 * cth_bin_edge[1])
output_file += '_psfdata.P'
else:
output_file = opts.output
irfm = IRFManager.create(irf, opts.load_from_file, opts.irf_dir)
lonlat = (0, 0)
if opts.src != 'iso' and opts.src != 'iso2':
cat = Catalog()
src = cat.get_source_by_name(opts.src)
lonlat = (src['RAJ2000'], src['DEJ2000'])
m = PSFModelLT(opts.ltfile,
irfm,
nbin=400,
cth_range=cth_bin_edge,
psf_type=opts.src,
lonlat=lonlat,
edisp_table=opts.edisp)
spectrum = opts.spectrum.split('/')
pars = [float(t) for t in spectrum[1].split(',')]
m.set_spectrum(spectrum[0], pars)
# m.set_spectrum('powerlaw_exp',(1.607,3508.6))
psf_data = PSFData(egy_bin_edge, cth_bin_edge, 'model')
# f = open(opts.o,'w')
for i in range(len(psf_data.quantiles)):
ql = psf_data.quantile_labels[i]
q = psf_data.quantiles[i]
for iegy in range(len(egy_bin_edge) - 1):
elo = egy_bin_edge[iegy]
ehi = egy_bin_edge[iegy + 1]
radius = m.quantile(10**elo, 10**ehi, q)
# print elo, ehi, radius
psf_data.qdata[i].set(iegy, 0, radius)
# line = '%6.3f '%(q)
# line += '%6.3f %6.3f '%(cth_range[0],cth_range[1])
# line += '%6.3f %6.3f %8.4f %8.4f'%(elo,ehi,radius,0.0)
# f.write(line + '\n')
# m.set_spectrum('powerlaw_exp',(1.607,3508.6))
# m.set_spectrum('powerlaw',(2.0))
# psf_data.print_quantiles('test')
psf_data.save(output_file)
def main(self,*argv):
usage = "usage: %(prog)s [options]"
description = """Generates PSF model."""
parser = argparse.ArgumentParser(usage=usage,description=description)
IRFManager.configure(parser)
parser.add_argument('--ltfile', default = None,
help = 'Set the livetime cube which will be used '
'to generate the exposure-weighted PSF model.')
parser.add_argument('--src', default = 'Vela',
help = '')
parser.add_argument('--irf', default = None,
help = 'Set the names of one or more IRF models.')
parser.add_argument('--output_dir', default = None,
help = 'Set the output directory name.')
parser.add_argument('--cth_bin_edge', default = '0.4,1.0',
help = 'Edges of cos(theta) bins '
'(e.g. 0.2,0.5,1.0).')
parser.add_argument('--egy_bin_edge', default = None,
help = 'Edges of energy bins.')
parser.add_argument('--egy_bin', default = '1.25/5.0/0.25',
help = 'Set min/max energy.')
parser.add_argument('--quantiles', default = '0.34,0.68,0.90,0.95',
help = 'Define the set of quantiles to compute.')
parser.add_argument('--conversion_type', default = 'front',
help = 'Draw plots.')
parser.add_argument('--spectrum', default = 'powerlaw/2',
help = 'Draw plots.')
parser.add_argument('--edisp', default = None,
help = 'Set the energy dispersion lookup table.')
parser.add_argument('-o', '--output', default = None,
help = 'Set the output file.')
parser.add_argument('--load_from_file', default = False,
action='store_true',
help = 'Load IRFs from FITS.')
opts = parser.parse_args(list(argv))
irfs = opts.irf.split(',')
[elo, ehi, ebin] = [float(t) for t in opts.egy_bin.split('/')]
egy_bin_edge = np.linspace(elo, ehi, 1 + int((ehi - elo) / ebin))
cth_bin_edge = [float(t) for t in opts.cth_bin_edge.split(',')]
quantiles = [float(t) for t in opts.quantiles.split(',')]
for irf in irfs:
if opts.output is None:
output_file = re.sub('\:\:',r'_',irf)
output_file += '_%03.f%03.f'%(100*cth_bin_edge[0],
100*cth_bin_edge[1])
output_file += '_psfdata.P'
else:
output_file = opts.output
irfm = IRFManager.create(irf,opts.load_from_file,opts.irf_dir)
lonlat = (0,0)
if opts.src != 'iso' and opts.src != 'iso2':
cat = Catalog()
src = cat.get_source_by_name(opts.src)
lonlat = (src['RAJ2000'], src['DEJ2000'])
m = PSFModelLT(opts.ltfile, irfm,
nbin=400,
cth_range=cth_bin_edge,
psf_type=opts.src,
lonlat=lonlat,
edisp_table=opts.edisp)
spectrum = opts.spectrum.split('/')
pars = [ float(t) for t in spectrum[1].split(',')]
m.set_spectrum(spectrum[0],pars)
# m.set_spectrum('powerlaw_exp',(1.607,3508.6))
psf_data = PSFData(egy_bin_edge,cth_bin_edge,'model')
# f = open(opts.o,'w')
for i in range(len(psf_data.quantiles)):
ql = psf_data.quantile_labels[i]
q = psf_data.quantiles[i]
for iegy in range(len(egy_bin_edge)-1):
elo = egy_bin_edge[iegy]
ehi = egy_bin_edge[iegy+1]
radius = m.quantile(10**elo,10**ehi,q)
# print elo, ehi, radius
psf_data.qdata[i].set(iegy,0,radius)
# line = '%6.3f '%(q)
# line += '%6.3f %6.3f '%(cth_range[0],cth_range[1])
# line += '%6.3f %6.3f %8.4f %8.4f'%(elo,ehi,radius,0.0)
# f.write(line + '\n')
# m.set_spectrum('powerlaw_exp',(1.607,3508.6))
# m.set_spectrum('powerlaw',(2.0))
# psf_data.print_quantiles('test')
psf_data.save(output_file)
# Integrate over 3rd (energy) dimension
im = im.marginalize(2)
plt.figure()
im.plot(cmap='ds9_b')
plt.figure()
# Smooth by 0.2 deg
im.smooth(0.2).plot(cmap='ds9_b')
# Draw an arbitrary contour in Galactic Coordinates
phi = np.linspace(0,2*np.pi,10000)
r = np.sqrt(2*np.cos(2*phi))
x = im.lon + r*np.cos(phi)
y = im.lat + r*np.sin(phi)
im.ax()['gal'].plot(x,y,color='w')
cat = Catalog.get('2fgl')
cat.plot(im,ax=plt.gca(),label_threshold=5,src_color='w')
# Make 1D projection on LON axis
plt.figure()
pim = im.project(0,offset_coord=True)
pim.plot()
plt.gca().grid(True)
plt.show()
