def calc_drm(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, nbin=64):
"""Calculate the detector response matrix."""
npts = int(np.ceil(128. / bins_per_dec(egy_bins)))
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
etrue_bins = 10**np.linspace(1.0, 6.5, nbin * 5.5 + 1)
egy = 10**utils.edge_to_center(np.log10(egy_bins))
egy_width = utils.edge_to_width(egy_bins)
etrue = 10**utils.edge_to_center(np.log10(etrue_bins))
edisp = create_avg_edisp(skydir, ltc, event_class, event_types,
egy, etrue, cth_bins)
edisp = edisp * egy_width[:, None, None]
edisp = sum_bins(edisp, 0, npts)
return edisp
def calc_drm(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, nbin=64):
"""Calculate the detector response matrix."""
npts = int(np.ceil(128. / bins_per_dec(egy_bins)))
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
etrue_bins = 10**np.linspace(1.0, 6.5, nbin * 5.5 + 1)
egy = 10**utils.edge_to_center(np.log10(egy_bins))
egy_width = utils.edge_to_width(egy_bins)
etrue = 10**utils.edge_to_center(np.log10(etrue_bins))
edisp = create_avg_edisp(skydir, ltc, event_class, event_types,
egy, etrue, cth_bins)
edisp = edisp * egy_width[:, None, None]
edisp = sum_bins(edisp,0,npts)
return edisp
def get_skydir_lthist(self, skydir, cth_bins):
"""Get the livetime distribution (observing profile) for a given sky
direction with binning in incidence angle defined by
``cth_bins``.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
Sky coordinate for which the observing profile will be
computed.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the incidence angle.
"""
ra = skydir.ra.deg
dec = skydir.dec.deg
npts = 1
bins = utils.split_bin_edges(cth_bins, npts)
center = edge_to_center(bins)
width = edge_to_width(bins)
ipix = hp.ang2pix(self.hpx.nside, np.pi / 2. - np.radians(dec),
np.radians(ra), nest=self.hpx.nest)
lt = np.histogram(self._cth_center,
weights=self.data[:, ipix], bins=bins)[0]
lt = np.sum(lt.reshape(-1, npts), axis=1)
return lt
def create_average_psf(skydir,ltc,event_class,event_types,dtheta,egy):
if isinstance(event_types,int):
event_types = bitmask_to_bits(event_types)
cth_edge = np.linspace(0.0,1.0,51)
cth = edge_to_center(cth_edge)
wpsf = np.zeros((len(dtheta),len(egy)))
exps = np.zeros(len(egy))
ltw = ltc.get_src_lthist(skydir,cth_edge)
# print cth_edge
# print ltw
#ltw = np.ones(len(cth))
for et in event_types:
psf = create_psf(event_class,et,dtheta,egy,cth)
aeff = create_exposure(event_class,et,egy,cth)
wpsf += np.sum(psf*aeff[np.newaxis,:,:]*
ltw[np.newaxis,np.newaxis,:],axis=2)
exps += np.sum(aeff*ltw[np.newaxis,:],axis=1)
wpsf /= exps[np.newaxis,:]
return wpsf
def calc_exp(skydir, ltc, event_class, event_types, egy, cth_bins, npts=None):
"""Calculate the exposure on a 2D grid of energy and incidence angle.
Parameters
----------
npts : int
Number of points by which to sample the response in each
incidence angle bin. If None then npts will be automatically
set such that incidence angle is sampled on intervals of <
0.05 in Cos(Theta).
Returns
-------
exp : `~numpy.ndarray`
2D Array of exposures vs. energy and incidence angle.
"""
if npts is None:
npts = int(np.ceil(np.max(cth_bins[1:] - cth_bins[:-1]) / 0.025))
exp = np.zeros((len(egy), len(cth_bins) - 1))
cth_bins = utils.split_bin_edges(cth_bins, npts)
cth = edge_to_center(cth_bins)
ltw = ltc.get_skydir_lthist(skydir, cth_bins).reshape(-1, npts)
for et in event_types:
aeff = create_aeff(event_class, et, egy, cth)
aeff = aeff.reshape(exp.shape + (npts, ))
exp += np.sum(aeff * ltw[np.newaxis, :, :], axis=-1)
return exp
def create_avg_rsp(rsp_fn, skydir, ltc, event_class, event_types, x,
egy, cth_bins, npts=None):
"""Calculate the weighted response function.
"""
if npts is None:
npts = int(np.ceil(np.max(cth_bins[1:] - cth_bins[:-1]) / 0.05))
wrsp = np.zeros((len(x), len(egy), len(cth_bins) - 1))
exps = np.zeros((len(egy), len(cth_bins) - 1))
cth_bins = utils.split_bin_edges(cth_bins, npts)
cth = edge_to_center(cth_bins)
ltw = ltc.get_skydir_lthist(skydir, cth_bins)
ltw = ltw.reshape(-1, npts)
for et in event_types:
rsp = rsp_fn(event_class, et, x, egy, cth)
aeff = create_aeff(event_class, et, egy, cth)
rsp = rsp.reshape(wrsp.shape + (npts,))
aeff = aeff.reshape(exps.shape + (npts,))
wrsp += np.sum(rsp * aeff[np.newaxis, :, :, :] *
ltw[np.newaxis, np.newaxis, :, :], axis=-1)
exps += np.sum(aeff * ltw[np.newaxis, :, :], axis=-1)
exps_inv = np.zeros_like(exps)
exps_inv[exps > 0] = 1./exps[exps>0]
wrsp *= exps_inv[np.newaxis, :, :]
return wrsp
def __init__(self, gdiff, iso, ltc, ebins, event_class, event_types=None,
gdiff_fit=None, iso_fit=None, spatial_model='PointSource',
spatial_size=None):
self._gdiff = gdiff
self._gdiff_fit = gdiff_fit
self._iso = iso
self._iso_fit = iso_fit
self._ltc = ltc
self._ebins = ebins
self._log_ebins = np.log10(ebins)
self._ectr = np.exp(utils.edge_to_center(np.log(self._ebins)))
self._event_class = event_class
self._spatial_model = spatial_model
self._spatial_size = spatial_size
if event_types is None:
self._event_types = [['FRONT'], ['BACK']]
else:
self._event_types = event_types
self._psf = []
self._exp = []
ebins = 10**np.linspace(1.0, 6.0, 5 * 8 + 1)
skydir = SkyCoord(0.0, 0.0, unit='deg')
for et in self._event_types:
self._psf += [irfs.PSFModel.create(skydir.icrs, self._ltc,
self._event_class, et,
ebins)]
self._exp += [irfs.ExposureMap.create(self._ltc,
self._event_class, et,
ebins)]
def interp_bin(self, egy_bins, dtheta, scale_fn=None):
"""Evaluate the bin-averaged PSF model over the energy bins ``egy_bins``.
Parameters
----------
egy_bins : array_like
Energy bin edges in MeV.
dtheta : array_like
Array of angular separations in degrees.
scale_fn : callable
Function that evaluates the PSF scaling function.
Argument is energy in MeV.
"""
npts = 4
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
egy = np.exp(utils.edge_to_center(np.log(egy_bins)))
log_energies = np.log10(egy)
vals = self.interp(egy[None, :], dtheta[:, None],
scale_fn=scale_fn)
wts = np.exp(self._wts_fn((log_energies,)))
wts = wts.reshape((1,) + wts.shape)
vals = np.sum(
(vals * wts).reshape((vals.shape[0], int(vals.shape[1] / npts), npts)), axis=2)
vals /= np.sum(wts.reshape(wts.shape[0],
int(wts.shape[1] / npts), npts), axis=2)
return vals
def calc_exp(skydir, ltc, event_class, event_types,
egy, cth_bins, npts=None):
"""Calculate the exposure on a 2D grid of energy and incidence angle.
Parameters
----------
npts : int
Number of points by which to sample the response in each
incidence angle bin. If None then npts will be automatically
set such that incidence angle is sampled on intervals of <
0.05 in Cos(Theta).
Returns
-------
exp : `~numpy.ndarray`
2D Array of exposures vs. energy and incidence angle.
"""
if npts is None:
npts = int(np.ceil(np.max(cth_bins[1:] - cth_bins[:-1]) / 0.025))
exp = np.zeros((len(egy), len(cth_bins) - 1))
cth_bins = utils.split_bin_edges(cth_bins, npts)
cth = edge_to_center(cth_bins)
ltw = ltc.get_skydir_lthist(skydir, cth_bins).reshape(-1, npts)
for et in event_types:
aeff = create_aeff(event_class, et, egy, cth)
aeff = aeff.reshape(exp.shape + (npts,))
exp += np.sum(aeff * ltw[np.newaxis, :, :], axis=-1)
return exp
def interp_bin(self, egy_bins, dtheta, scale_fn=None):
"""Evaluate the bin-averaged PSF model over the energy bins ``egy_bins``.
Parameters
----------
egy_bins : array_like
Energy bin edges in MeV.
dtheta : array_like
Array of angular separations in degrees.
scale_fn : callable
Function that evaluates the PSF scaling function.
Argument is energy in MeV.
"""
npts = 4
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
egy = np.exp(utils.edge_to_center(np.log(egy_bins)))
log_energies = np.log10(egy)
vals = self.interp(egy[None, :], dtheta[:, None], scale_fn=scale_fn)
wts = np.exp(self._wts_fn((log_energies, )))
wts = wts.reshape((1, ) + wts.shape)
vals = np.sum((vals * wts).reshape(
(vals.shape[0], int(vals.shape[1] / npts), npts)),
axis=2)
vals /= np.sum(wts.reshape(wts.shape[0], int(wts.shape[1] / npts),
npts),
axis=2)
return vals
def create_avg_rsp(rsp_fn,
skydir,
ltc,
event_class,
event_types,
x,
egy,
cth_bins,
npts=None):
if npts is None:
npts = int(np.ceil(np.max(cth_bins[1:] - cth_bins[:-1]) / 0.05))
wrsp = np.zeros((len(x), len(egy), len(cth_bins) - 1))
exps = np.zeros((len(egy), len(cth_bins) - 1))
cth_bins = utils.split_bin_edges(cth_bins, npts)
cth = edge_to_center(cth_bins)
ltw = ltc.get_skydir_lthist(skydir, cth_bins)
ltw = ltw.reshape(-1, npts)
for et in event_types:
rsp = rsp_fn(event_class, et, x, egy, cth)
aeff = create_aeff(event_class, et, egy, cth)
rsp = rsp.reshape(wrsp.shape + (npts, ))
aeff = aeff.reshape(exps.shape + (npts, ))
wrsp += np.sum(rsp * aeff[np.newaxis, :, :, :] *
ltw[np.newaxis, np.newaxis, :, :],
axis=-1)
exps += np.sum(aeff * ltw[np.newaxis, :, :], axis=-1)
wrsp /= exps[np.newaxis, :, :]
return wrsp
def get_skydir_lthist(self, skydir, cth_bins):
"""Get the livetime distribution (observing profile) for a given sky
direction with binning in incidence angle defined by
``cth_bins``.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
Sky coordinate for which the observing profile will be
computed.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the incidence angle.
"""
ra = skydir.ra.deg
dec = skydir.dec.deg
bins = np.linspace(cth_bins[0], cth_bins[-1],
(len(cth_bins) - 1) * 4 + 1)
center = edge_to_center(bins)
width = edge_to_width(bins)
ipix = hp.ang2pix(self.hpx.nside, np.pi / 2. - np.radians(dec),
np.radians(ra), nest=self.hpx.nest)
lt = np.interp(center, self._cth_center,
self.data[:, ipix] / self._cth_width) * width
lt = np.sum(lt.reshape(-1, 4), axis=1)
return lt
def get_skydir_lthist(self, skydir, cth_bins):
"""Get the livetime distribution (observing profile) for a given sky
direction with binning in incidence angle defined by
``cth_bins``.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
Sky coordinate for which the observing profile will be
computed.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the incidence angle.
"""
ra = skydir.ra.deg
dec = skydir.dec.deg
npts = 1
bins = utils.split_bin_edges(cth_bins, npts)
center = edge_to_center(bins)
width = edge_to_width(bins)
ipix = hp.ang2pix(self.hpx.nside, np.pi / 2. - np.radians(dec),
np.radians(ra), nest=self.hpx.nest)
lt = np.histogram(self._cth_center,
weights=self.data[:, ipix], bins=bins)[0]
lt = np.sum(lt.reshape(-1, npts), axis=1)
return lt
def compute_ps_counts(ebins, exp, psf, bkg, fn, egy_dim=0, spatial_model='PointSource',
spatial_size=1E-3):
"""Calculate the observed signal and background counts given models
for the exposure, background intensity, PSF, and source flux.
Parameters
----------
ebins : `~numpy.ndarray`
Array of energy bin edges.
exp : `~numpy.ndarray`
Model for exposure.
psf : `~fermipy.irfs.PSFModel`
Model for average PSF.
bkg : `~numpy.ndarray`
Array of background intensities.
fn : `~fermipy.spectrum.SpectralFunction`
egy_dim : int
Index of energy dimension in ``bkg`` and ``exp`` arrays.
"""
ewidth = utils.edge_to_width(ebins)
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
r68 = psf.containment_angle(ectr, fraction=0.68)
if spatial_model != 'PointSource':
r68[r68 < spatial_size] = spatial_size
# * np.ones((len(ectr), 31))
theta_edges = np.linspace(0.0, 3.0, 31)[np.newaxis, :]
theta_edges = theta_edges * r68[:, np.newaxis]
theta = 0.5 * (theta_edges[:, :-1] + theta_edges[:, 1:])
domega = np.pi * (theta_edges[:, 1:]**2 - theta_edges[:, :-1]**2)
if spatial_model == 'PointSource':
sig_pdf = domega * psf.interp(ectr[:, np.newaxis], theta)
elif spatial_model == 'RadialGaussian':
sig_pdf = domega * utils.convolve2d_gauss(lambda t: psf.interp(ectr[:, np.newaxis, np.newaxis], t),
theta, spatial_size / 1.5095921854516636, nstep=2000)
elif spatial_model == 'RadialDisk':
sig_pdf = domega * utils.convolve2d_disk(lambda t: psf.interp(ectr[:, np.newaxis, np.newaxis], t),
theta, spatial_size / 0.8246211251235321)
else:
raise ValueError('Invalid spatial model: {}'.format(spatial_model))
sig_pdf *= (np.pi / 180.)**2
sig_flux = fn.flux(ebins[:-1], ebins[1:])
# Background and signal counts
bkgc = bkg[..., np.newaxis] * domega * exp[..., np.newaxis] * \
ewidth[..., np.newaxis] * (np.pi / 180.)**2
sigc = sig_pdf * sig_flux[..., np.newaxis] * exp[..., np.newaxis]
return sigc, bkgc
def __init__(self, data, hpx, cth_edges, tstart=None, tstop=None):
HpxMap.__init__(self, data, hpx)
self._cth_edges = cth_edges
self._cth_center = edge_to_center(self._cth_edges)
self._cth_width = edge_to_width(self._cth_edges)
self._domega = (self._cth_edges[1:] -
self._cth_edges[:-1]) * 2 * np.pi
self._tstart = tstart
self._tstop = tstop
def create_wtd_psf(skydir, ltc, event_class, event_types, dtheta,
egy_bins, cth_bins, fn, nbin=64, npts=1):
"""Create an exposure- and dispersion-weighted PSF model for a source
with spectral parameterization ``fn``. The calculation performed
by this method accounts for the influence of energy dispersion on
the PSF.
Parameters
----------
dtheta : `~numpy.ndarray`
egy_bins : `~numpy.ndarray`
Bin edges in observed energy.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the true incidence angle.
nbin : int
Number of bins per decade in true energy.
npts : int
Number of points by which to oversample each energy bin.
"""
#npts = int(np.ceil(32. / bins_per_dec(egy_bins)))
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
etrue_bins = 10**np.linspace(1.0, 6.5, nbin * 5.5 + 1)
etrue = 10**utils.edge_to_center(np.log10(etrue_bins))
psf = create_avg_psf(skydir, ltc, event_class, event_types, dtheta,
etrue, cth_bins)
drm = calc_drm(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, nbin=nbin)
cnts = calc_counts(skydir, ltc, event_class, event_types,
etrue_bins, cth_bins, fn)
wts = drm * cnts[None, :, :]
wts_norm = np.sum(wts,axis=1)
wts_norm[wts_norm == 0] = 1.0
wts = wts / wts_norm[:, None, :]
wpsf = np.sum(wts[None, :, :, :] * psf[:, None, :, :], axis=2)
wts = np.sum(wts[None, :, :, :],axis=2)
if npts > 1:
shape = (wpsf.shape[0], int(wpsf.shape[1] / npts), npts, wpsf.shape[2])
wpsf = np.sum((wpsf * wts).reshape(shape), axis=2)
shape = (wts.shape[0], int(wts.shape[1] / npts), npts, wts.shape[2])
wpsf = wpsf / np.sum(wts.reshape(shape), axis=2)
return wpsf
def create_wtd_psf(skydir, ltc, event_class, event_types, dtheta,
egy_bins, cth_bins, fn, nbin=64, npts=1):
"""Create an exposure- and dispersion-weighted PSF model for a source
with spectral parameterization ``fn``. The calculation performed
by this method accounts for the influence of energy dispersion on
the PSF.
Parameters
----------
dtheta : `~numpy.ndarray`
egy_bins : `~numpy.ndarray`
Bin edges in observed energy.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the true incidence angle.
nbin : int
Number of bins per decade in true energy.
npts : int
Number of points by which to oversample each energy bin.
"""
#npts = int(np.ceil(32. / bins_per_dec(egy_bins)))
egy_bins = np.exp(utils.split_bin_edges(np.log(egy_bins), npts))
etrue_bins = 10**np.linspace(1.0, 6.5, nbin * 5.5 + 1)
etrue = 10**utils.edge_to_center(np.log10(etrue_bins))
psf = create_avg_psf(skydir, ltc, event_class, event_types, dtheta,
etrue, cth_bins)
drm = calc_drm(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, nbin=nbin)
cnts = calc_counts(skydir, ltc, event_class, event_types,
etrue_bins, cth_bins, fn)
wts = drm * cnts[None, :, :]
wts_norm = np.sum(wts, axis=1)
wts_norm[wts_norm == 0] = 1.0
wts = wts / wts_norm[:, None, :]
wpsf = np.sum(wts[None, :, :, :] * psf[:, None, :, :], axis=2)
wts = np.sum(wts[None, :, :, :], axis=2)
if npts > 1:
shape = (wpsf.shape[0], int(wpsf.shape[1] / npts), npts, wpsf.shape[2])
wpsf = np.sum((wpsf * wts).reshape(shape), axis=2)
shape = (wts.shape[0], int(wts.shape[1] / npts), npts, wts.shape[2])
wpsf = wpsf / np.sum(wts.reshape(shape), axis=2)
return wpsf
def compute_counts(self, skydir, fn, ebins=None):
"""Compute signal and background counts for a point source at
position ``skydir`` with spectral parameterization ``fn``.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
ebins : `~numpy.ndarray`
Returns
-------
sig : `~numpy.ndarray`
Signal counts array. Dimensions are energy, angular
separation, and event type.
bkg : `~numpy.ndarray`
Background counts array. Dimensions are energy, angular
separation, and event type.
"""
if ebins is None:
ebins = self.ebins
ectr = self.ectr
else:
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
skydir_cel = skydir.transform_to('icrs')
skydir_gal = skydir.transform_to('galactic')
sig = []
bkg = []
for psf, exp in zip(self._psf, self._exp):
expv = exp.interpolate(
skydir_cel.icrs.ra.deg, skydir_cel.icrs.dec.deg, ectr)
bkgv = self._gdiff.interpolate(
skydir_gal.l.deg, skydir_gal.b.deg, ectr)
isov = np.exp(np.interp(np.log(ectr), np.log(self._iso[0]),
np.log(self._iso[1])))
bkgv += isov
s, b = irfs.compute_ps_counts(ebins, expv, psf, bkgv, fn)
sig += [s]
bkg += [b]
sig = np.concatenate([np.expand_dims(t, -1) for t in sig])
bkg = np.concatenate([np.expand_dims(t, -1) for t in bkg])
return sig, bkg
def plot_projection(self,iaxis,**kwargs):
data = kwargs.pop('data',self._data)
noerror = kwargs.pop('noerror',False)
axes = wcs_to_axes(self._wcs,self._data.shape[::-1])
x = edge_to_center(axes[iaxis])
w = edge_to_width(axes[iaxis])
c = self.get_data_projection(data,axes,iaxis,erange=self._erange)
if noerror:
plt.errorbar(x,c,**kwargs)
else:
plt.errorbar(x,c,yerr=c**0.5,xerr=w/2.,**kwargs)
def __init__(self, dtheta, energies, cth_bins, exp, psf, wts):
"""Create a PSFModel.
Parameters
----------
dtheta : `~numpy.ndarray`
Array of angular offsets in degrees at which the PSF is
evaluated.
energies : `~numpy.ndarray`
Array of energies in MeV at which the PSF is evaluated.
cth_bins : `~numpy.ndarray`
Interval in cosine of the incidence angle for which this
model of the PSF was generated.
exp : `~numpy.ndarray`
Array of exposure vs. energy in cm^2 s.
psf : `~numpy.ndarray`
2D array of PSF values evaluated on an NxM grid of N
offset angles and M energies (defined by ``dtheta`` and
``energies``).
wts : `~numpy.ndarray`
Array of weights vs. energy. These are used to evaluate
the bin-averaged PSF model.
"""
self._dtheta = dtheta
self._log_energies = np.log10(energies)
self._energies = energies
self._cth_bins = cth_bins
self._cth = utils.edge_to_center(cth_bins)
self._scale_fn = None
self._exp = exp
self._psf = psf
self._wts = wts
self._psf_fn = RegularGridInterpolator(
(self._dtheta, self._log_energies),
np.log(self._psf),
bounds_error=False,
fill_value=None)
self._wts_fn = RegularGridInterpolator((self._log_energies, ),
np.log(self._wts),
bounds_error=False,
fill_value=None)
def get_src_lthist(self,skydir,cth_edges):
ra = skydir.ra.deg
dec = skydir.dec.deg
edges = np.linspace(cth_edges[0],cth_edges[-1],(len(cth_edges)-1)*4+1)
center = edge_to_center(edges)
width = edge_to_width(edges)
ipix = hp.ang2pix(64,np.pi/2. - np.radians(dec),
np.radians(ra),nest=True)
lt = np.interp(center,self._cth_center,
self._ltmap[ipix,::-1]/self._cth_width)*width
lt = np.sum(lt.reshape(-1,4),axis=1)
return lt
def __init__(self, dtheta, energies, cth_bins, exp, psf, wts):
"""Create a PSFModel.
Parameters
----------
dtheta : `~numpy.ndarray`
Array of angular offsets in degrees at which the PSF is
evaluated.
energies : `~numpy.ndarray`
Array of energies in MeV at which the PSF is evaluated.
cth_bins : `~numpy.ndarray`
Interval in cosine of the incidence angle for which this
model of the PSF was generated.
exp : `~numpy.ndarray`
Array of exposure vs. energy in cm^2 s.
psf : `~numpy.ndarray`
2D array of PSF values evaluated on an NxM grid of N
offset angles and M energies (defined by ``dtheta`` and
``energies``).
wts : `~numpy.ndarray`
Array of weights vs. energy. These are used to evaluate
the bin-averaged PSF model.
"""
self._dtheta = dtheta
self._log_energies = np.log10(energies)
self._energies = energies
self._cth_bins = cth_bins
self._cth = utils.edge_to_center(cth_bins)
self._scale_fn = None
self._exp = exp
self._psf = psf
self._wts = wts
self._psf_fn = RegularGridInterpolator((self._dtheta, self._log_energies),
np.log(self._psf),
bounds_error=False,
fill_value=None)
self._wts_fn = RegularGridInterpolator((self._log_energies,),
np.log(self._wts),
bounds_error=False,
fill_value=None)
def load_ltfile(self,ltfile):
hdulist = pyfits.open(ltfile)
if self._ltmap is None:
self._ltmap = hdulist[1].data.field(0)
self._tstart = hdulist[0].header['TSTART']
self._tstop = hdulist[0].header['TSTOP']
else:
self._ltmap += hdulist[1].data.field(0)
self._tstart = min(self._tstart,hdulist[0].header['TSTART'])
self._tstop = max(self._tstop,hdulist[0].header['TSTOP'])
cth_edges = np.array(hdulist[3].data.field(0))
cth_edges = np.concatenate(([1],cth_edges))
self._cth_edges = cth_edges[::-1]
self._cth_center = edge_to_center(self._cth_edges)
self._cth_width = edge_to_width(self._cth_edges)
def compute_counts(self, skydir, fn, ebins=None):
"""Compute signal and background counts for a point source at
position ``skydir`` with spectral parameterization ``fn``.
Parameters
----------
ebins : `~numpy.ndarray`
Returns
-------
sig : `~numpy.ndarray`
Signal counts array. Dimensions are energy, angular
separation, and event type.
bkg : `~numpy.ndarray`
Background counts array. Dimensions are energy, angular
separation, and event type.
"""
if ebins is None:
ebins = self.ebins
ectr = self.ectr
else:
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
sig = []
bkg = []
for psf, exp in zip(self._psf, self._exp):
expv = exp.interpolate(skydir.icrs.ra.deg, skydir.icrs.dec.deg,
ectr)
bkgv = self._gdiff.interpolate(skydir.l.deg, skydir.b.deg, ectr)
isov = np.exp(
np.interp(np.log(ectr), np.log(self._iso[0]),
np.log(self._iso[1])))
bkgv += isov
s, b = irfs.compute_ps_counts(ebins, expv, psf, bkgv, fn)
sig += [s]
bkg += [b]
sig = np.concatenate([np.expand_dims(t, -1) for t in sig])
bkg = np.concatenate([np.expand_dims(t, -1) for t in bkg])
return sig, bkg
def __init__(self,skydir,ltc,event_class,event_types,egy):
if isinstance(event_types,int):
event_types = bitmask_to_bits(event_types)
self._dtheta = np.logspace(-4,1.5,1000)
self._dtheta = np.insert(self._dtheta,0,[0])
self._egy = egy
self._exp = np.zeros(len(egy))
self._psf = self.create_average_psf(skydir,ltc,event_class,event_types,
self._dtheta,egy)
cth_edge = np.linspace(0.0,1.0,51)
cth = edge_to_center(cth_edge)
ltw = ltc.get_src_lthist(skydir,cth_edge)
for et in event_types:
aeff = create_exposure(event_class,et,egy,cth)
self._exp += np.sum(aeff*ltw[np.newaxis,:],axis=1)
def compute_ps_counts(ebins, exp, psf, bkg, fn, egy_dim=0):
"""Calculate the observed signal and background counts given models
for the exposure, background intensity, PSF, and source flux.
Parameters
----------
ebins : `~numpy.ndarray`
Array of energy bin edges.
exp : `~numpy.ndarray`
Model for exposure.
psf : `~fermipy.irfs.PSFModel`
Model for average PSF.
bkg : `~numpy.ndarray`
Array of background intensities.
fn : `~fermipy.spectrum.SpectralFunction`
egy_dim : int
Index of energy dimension in ``bkg`` and ``exp`` arrays.
"""
ewidth = utils.edge_to_width(ebins)
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
theta_edges = np.linspace(0.0, 3.0, 31)[
np.newaxis, :] * np.ones((len(ectr), 31))
theta_edges *= psf.containment_angle(ectr, fraction=0.68)[:, np.newaxis]
theta = 0.5 * (theta_edges[:, :-1] + theta_edges[:, 1:])
domega = np.pi * (theta_edges[:, 1:]**2 - theta_edges[:, :-1]**2)
sig_pdf = domega * \
psf.interp(ectr[:, np.newaxis], theta) * (np.pi / 180.)**2
sig_flux = fn.flux(ebins[:-1], ebins[1:])
# Background and signal counts
bkgc = bkg[:, np.newaxis] * domega * exp[:, np.newaxis] * \
ewidth[:, np.newaxis] * (np.pi / 180.)**2
sigc = sig_pdf * sig_flux[:, np.newaxis] * exp[:, np.newaxis]
return sigc, bkgc
def compute_ps_counts(ebins, exp, psf, bkg, fn, egy_dim=0):
"""Calculate the observed signal and background counts given models
for the exposure, background intensity, PSF, and source flux.
Parameters
----------
ebins : `~numpy.ndarray`
Array of energy bin edges.
exp : `~numpy.ndarray`
Model for exposure.
psf : `~fermipy.irfs.PSFModel`
Model for average PSF.
bkg : `~numpy.ndarray`
Array of background intensities.
fn : `~fermipy.spectrum.SpectralFunction`
egy_dim : int
Index of energy dimension in ``bkg`` and ``exp`` arrays.
"""
ewidth = utils.edge_to_width(ebins)
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
theta_edges = np.linspace(0.0, 3.0, 31)[
np.newaxis, :] * np.ones((len(ectr), 31))
theta_edges *= psf.containment_angle(ectr, fraction=0.68)[:, np.newaxis]
theta = 0.5 * (theta_edges[:, :-1] + theta_edges[:, 1:])
domega = np.pi * (theta_edges[:, 1:]**2 - theta_edges[:, :-1]**2)
sig_pdf = domega * \
psf.interp(ectr[:, np.newaxis], theta) * (np.pi / 180.)**2
sig_flux = fn.flux(ebins[:-1], ebins[1:])
# Background and signal counts
bkgc = bkg[:, np.newaxis] * domega * exp[:, np.newaxis] * \
ewidth[:, np.newaxis] * (np.pi / 180.)**2
sigc = sig_pdf * sig_flux[:, np.newaxis] * exp[:, np.newaxis]
return sigc, bkgc
def __init__(self, gdiff, iso, ltc, ebins, event_class, event_types):
"""
Parameters
----------
gdiff : `~fermipy.skymap.SkyMap`
Galactic diffuse map cube object.
iso : `~numpy.ndarray`
Array of background isotropic intensity vs. energy.
ltc : `~fermipy.irfs.LTCube`
event_class : str
event_types : list
"""
self._gdiff = gdiff
self._iso = iso
self._ltc = ltc
self._ebins = ebins
self._log_ebins = np.log10(ebins)
self._ectr = np.exp(utils.edge_to_center(np.log(self._ebins)))
self._event_class = event_class
self._event_types = event_types
self._psf = []
self._exp = []
ebins = 10**np.linspace(1.0, 6.0, 5 * 8 + 1)
skydir = SkyCoord(0.0, 0.0, unit='deg')
for et in self._event_types:
self._psf += [
irfs.PSFModel.create(skydir.icrs, self._ltc, self._event_class,
et, ebins)
]
self._exp += [
irfs.ExposureMap.create(self._ltc, self._event_class, et,
ebins)
]
def __init__(self, counts, wcs, ebins=None):
"""
Parameters
----------
counts : `~numpy.ndarray`
Counts array in row-wise ordering (LON is first dimension).
"""
Map_Base.__init__(self, counts)
self._wcs = wcs
self._npix = counts.shape[::-1]
if len(self._npix) == 3:
self._xindex = 2
self._yindex = 1
elif len(self._npix) == 2:
self._xindex = 1
self._yindex = 0
else:
raise Exception('Wrong number of dimensions for Map object.')
# if len(self._npix) != 3 and len(self._npix) != 2:
# raise Exception('Wrong number of dimensions for Map object.')
self._width = np.array([
np.abs(self.wcs.wcs.cdelt[0]) * self.npix[0],
np.abs(self.wcs.wcs.cdelt[1]) * self.npix[1]
])
self._pix_center = np.array([(self.npix[0] - 1.0) / 2.,
(self.npix[1] - 1.0) / 2.])
self._pix_size = np.array(
[np.abs(self.wcs.wcs.cdelt[0]),
np.abs(self.wcs.wcs.cdelt[1])])
self._skydir = SkyCoord.from_pixel(self._pix_center[0],
self._pix_center[1], self.wcs)
self._ebins = ebins
if ebins is not None:
self._ectr = np.exp(utils.edge_to_center(np.log(ebins)))
else:
self._ectr = None
def __init__(self, gdiff, iso, ltc, ebins, event_class, event_types):
"""
Parameters
----------
gdiff : `~fermipy.skymap.SkyMap`
Galactic diffuse map cube object.
iso : `~numpy.ndarray`
Array of background isotropic intensity vs. energy.
ltc : `~fermipy.ltcube.LTCube`
event_class : str
event_types : list
"""
self._gdiff = gdiff
self._iso = iso
self._ltc = ltc
self._ebins = ebins
self._log_ebins = np.log10(ebins)
self._ectr = np.exp(utils.edge_to_center(np.log(self._ebins)))
self._event_class = event_class
self._event_types = event_types
self._psf = []
self._exp = []
ebins = 10**np.linspace(1.0, 6.0, 5 * 8 + 1)
skydir = SkyCoord(0.0, 0.0, unit='deg')
for et in self._event_types:
self._psf += [irfs.PSFModel.create(skydir.icrs, self._ltc,
self._event_class, et,
ebins)]
self._exp += [irfs.ExposureMap.create(self._ltc,
self._event_class, et,
ebins)]
def __init__(self, data, hpx, cth_edges, **kwargs):
HpxMap.__init__(self, data, hpx)
self._cth_edges = cth_edges
self._cth_center = edge_to_center(self._cth_edges)
self._cth_width = edge_to_width(self._cth_edges)
self._domega = (self._cth_edges[1:] -
self._cth_edges[:-1]) * 2 * np.pi
self._tstart = kwargs.get('tstart', None)
self._tstop = kwargs.get('tstop', None)
self._zmin = kwargs.get('zmin', 0.0)
self._zmax = kwargs.get('zmax', 180.0)
self._tab_gti = kwargs.get('tab_gti', None)
self._header = kwargs.get('header', None)
self._data_wt = kwargs.get('data_wt', None)
if self._data_wt is None:
self._data_wt = np.zeros_like(self.data)
if self._tab_gti is None:
cols = [Column(name='START', dtype='f8', unit='s'),
Column(name='STOP', dtype='f8', unit='s')]
self._tab_gti = Table(cols)
def __init__(self, counts, wcs, ebins=None):
"""
Parameters
----------
counts : `~numpy.ndarray`
Counts array in row-wise ordering (LON is first dimension).
"""
Map_Base.__init__(self, counts)
self._wcs = wcs
self._npix = counts.shape[::-1]
if len(self._npix) == 3:
self._xindex = 2
self._yindex = 1
elif len(self._npix) == 2:
self._xindex = 1
self._yindex = 0
else:
raise Exception('Wrong number of dimensions for Map object.')
# if len(self._npix) != 3 and len(self._npix) != 2:
# raise Exception('Wrong number of dimensions for Map object.')
self._width = np.array([np.abs(self.wcs.wcs.cdelt[0]) * self.npix[0],
np.abs(self.wcs.wcs.cdelt[1]) * self.npix[1]])
self._pix_center = np.array([(self.npix[0] - 1.0) / 2.,
(self.npix[1] - 1.0) / 2.])
self._pix_size = np.array([np.abs(self.wcs.wcs.cdelt[0]),
np.abs(self.wcs.wcs.cdelt[1])])
self._skydir = SkyCoord.from_pixel(self._pix_center[0],
self._pix_center[1],
self.wcs)
self._ebins = ebins
if ebins is not None:
self._ectr = np.exp(utils.edge_to_center(np.log(ebins)))
else:
self._ectr = None
def __init__(self, data, hpx, cth_edges, **kwargs):
HpxMap.__init__(self, data, hpx)
self._cth_edges = cth_edges
self._cth_center = edge_to_center(self._cth_edges)
self._cth_width = edge_to_width(self._cth_edges)
self._domega = (self._cth_edges[1:] -
self._cth_edges[:-1]) * 2 * np.pi
self._tstart = kwargs.get('tstart', None)
self._tstop = kwargs.get('tstop', None)
self._zmin = kwargs.get('zmin', 0.0)
self._zmax = kwargs.get('zmax', 180.0)
self._tab_gti = kwargs.get('tab_gti', None)
self._header = kwargs.get('header', None)
self._data_wt = kwargs.get('data_wt', None)
if self._data_wt is None:
self._data_wt = np.zeros_like(self.data)
if self._tab_gti is None:
cols = [Column(name='START', dtype='f8', unit='s'),
Column(name='STOP', dtype='f8', unit='s')]
self._tab_gti = Table(cols)
def run_flux_sensitivity(**kwargs):
index = kwargs.get('index', 2.0)
sedshape = kwargs.get('sedshape', 'PowerLaw')
cutoff = kwargs.get('cutoff', 1e3)
curvindex = kwargs.get('curvindex', 1.0)
beta = kwargs.get('beta', 0.0)
dmmass = kwargs.get('DMmass', 100.0)
dmchannel = kwargs.get('DMchannel', 'bb')
emin = kwargs.get('emin', 10**1.5)
emax = kwargs.get('emax', 10**6.0)
nbin = kwargs.get('nbin', 18)
glon = kwargs.get('glon', 0.0)
glat = kwargs.get('glat', 0.0)
ltcube_filepath = kwargs.get('ltcube', None)
galdiff_filepath = kwargs.get('galdiff', None)
isodiff_filepath = kwargs.get('isodiff', None)
galdiff_fit_filepath = kwargs.get('galdiff_fit', None)
isodiff_fit_filepath = kwargs.get('isodiff_fit', None)
wcs_npix = kwargs.get('wcs_npix', 40)
wcs_cdelt = kwargs.get('wcs_cdelt', 0.5)
wcs_proj = kwargs.get('wcs_proj', 'AIT')
map_type = kwargs.get('map_type', None)
spatial_model = kwargs.get('spatial_model', 'PointSource')
spatial_size = kwargs.get('spatial_size', 1E-2)
obs_time_yr = kwargs.get('obs_time_yr', None)
event_class = kwargs.get('event_class', 'P8R2_SOURCE_V6')
min_counts = kwargs.get('min_counts', 3.0)
ts_thresh = kwargs.get('ts_thresh', 25.0)
nside = kwargs.get('hpx_nside', 16)
output = kwargs.get('output', None)
event_types = [['FRONT', 'BACK']]
if sedshape == 'PowerLaw':
fn = spectrum.PowerLaw([1E-13, -index], scale=1E3)
elif sedshape == 'PLSuperExpCutoff':
fn = spectrum.PLSuperExpCutoff(
[1E-13, -index, cutoff, curvindex], scale=1E3)
elif sedshape == 'LogParabola':
fn = spectrum.LogParabola([1E-13, -index, beta], scale=1E3)
elif sedshape == 'DM':
fn = spectrum.DMFitFunction([1E-26, dmmass], chan=dmchannel)
log_ebins = np.linspace(np.log10(emin),
np.log10(emax), nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(glon, glat, unit='deg', frame='galactic')
if ltcube_filepath is None:
if obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = LTCube.create_from_obs_time(obs_time_yr * 365 * 24 * 3600.)
else:
ltc = LTCube.create(ltcube_filepath)
if obs_time_yr is not None:
ltc._counts *= obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(galdiff_filepath)
gdiff_fit = None
if galdiff_fit_filepath is not None:
gdiff_fit = skymap.Map.create_from_fits(galdiff_fit_filepath)
if isodiff_filepath is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % event_class,
search_dirs=[os.path.join('$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = isodiff_filepath
iso = np.loadtxt(isodiff, unpack=True)
iso_fit = None
if isodiff_fit_filepath is not None:
iso_fit = np.loadtxt(isodiff_fit_filepath, unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins,
event_class, event_types, gdiff_fit=gdiff_fit,
iso_fit=iso_fit, spatial_model=spatial_model,
spatial_size=spatial_size)
# Compute Maps
map_diff_flux = None
map_diff_npred = None
map_int_flux = None
map_int_npred = None
map_nstep = 500
if map_type == 'hpx':
hpx = HPX(nside, True, 'GAL', ebins=ebins)
map_diff_flux = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_diff_npred = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_skydir = map_diff_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.diff_flux_threshold(
map_skydir[s], fn, ts_thresh, min_counts)
map_diff_flux.data[:, s] = o['flux'].T
map_diff_npred.data[:, s] = o['npred'].T
hpx = HPX(nside, True, 'GAL')
map_int_flux = HpxMap(np.zeros((hpx.npix)), hpx)
map_int_npred = HpxMap(np.zeros((hpx.npix)), hpx)
map_skydir = map_int_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.int_flux_threshold(
map_skydir[s], fn, ts_thresh, min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
elif map_type == 'wcs':
wcs_shape = [wcs_npix, wcs_npix]
wcs_size = wcs_npix * wcs_npix
map_diff_flux = Map.create(
c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj, ebins=ebins)
map_diff_npred = Map.create(
c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj, ebins=ebins)
map_skydir = map_diff_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(
np.arange(i, min(i + map_nstep, wcs_size)), wcs_shape)
s = (slice(None), idx[1], idx[0])
o = scalc.diff_flux_threshold(
map_skydir[slice(i, i + map_nstep)], fn, ts_thresh, min_counts)
map_diff_flux.data[s] = o['flux'].T
map_diff_npred.data[s] = o['npred'].T
map_int_flux = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_int_npred = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_skydir = map_int_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(
np.arange(i, min(i + map_nstep, wcs_size)), wcs_shape)
s = (idx[1], idx[0])
o = scalc.int_flux_threshold(
map_skydir[slice(i, i + map_nstep)], fn, ts_thresh, min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
o = scalc.diff_flux_threshold(c, fn, ts_thresh, min_counts)
cols = [Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o[
'flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o[
'eflux'], unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8',
data=o['e2dnde'], unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')]
tab_diff = Table(cols)
cols = [Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph'),
Column(name='ebin_e_min', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='ebin_e_ref', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='ebin_e_max', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='ebin_flux', dtype='f8',
unit='ph / (cm2 s)', shape=(len(ectr),)),
Column(name='ebin_eflux', dtype='f8',
unit='MeV / (cm2 s)', shape=(len(ectr),)),
Column(name='ebin_dnde', dtype='f8',
unit='ph / (MeV cm2 s)', shape=(len(ectr),)),
Column(name='ebin_e2dnde', dtype='f8',
unit='MeV / (cm2 s)', shape=(len(ectr),)),
Column(name='ebin_npred', dtype='f8', unit='ph', shape=(len(ectr),))]
cols_ebounds = [Column(name='E_MIN', dtype='f8',
unit='MeV', data=ebins[:-1]),
Column(name='E_MAX', dtype='f8',
unit='MeV', data=ebins[1:]), ]
tab_int = Table(cols)
tab_ebounds = Table(cols_ebounds)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if colname == 'index':
continue
if 'ebin' in colname:
row += [o['bins'][colname.replace('ebin_', '')]]
else:
row += [o[colname]]
tab_int.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_ebounds))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'EBOUNDS'
if map_type is not None:
hdu = map_diff_flux.create_image_hdu()
hdu.name = 'MAP_DIFF_FLUX'
hdulist.append(hdu)
hdu = map_diff_npred.create_image_hdu()
hdu.name = 'MAP_DIFF_NPRED'
hdulist.append(hdu)
hdu = map_int_flux.create_image_hdu()
hdu.name = 'MAP_INT_FLUX'
hdulist.append(hdu)
hdu = map_int_npred.create_image_hdu()
hdu.name = 'MAP_INT_NPRED'
hdulist.append(hdu)
hdulist.writeto(output, overwrite=True)
def compute_counts(self, skydir, fn, ebins=None):
"""Compute signal and background counts for a point source at
position ``skydir`` with spectral parameterization ``fn``.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
ebins : `~numpy.ndarray`
Returns
-------
sig : `~numpy.ndarray`
Signal counts array. Dimensions are energy, angular
separation, and event type.
bkg : `~numpy.ndarray`
Background counts array. Dimensions are energy, angular
separation, and event type.
"""
if ebins is None:
ebins = self.ebins
ectr = self.ectr
else:
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
skydir_cel = skydir.transform_to('icrs')
skydir_gal = skydir.transform_to('galactic')
sig = []
bkg = []
bkg_fit = None
if self._gdiff_fit is not None:
bkg_fit = []
for psf, exp in zip(self._psf, self._exp):
coords0 = np.meshgrid(*[skydir_cel.ra.deg, ectr], indexing='ij')
coords1 = np.meshgrid(*[skydir_cel.dec.deg, ectr], indexing='ij')
# expv = exp.interpolate(skydir_cel.icrs.ra.deg,
# skydir_cel.icrs.dec.deg,
# ectr)
expv = exp.interpolate(coords0[0], coords1[0], coords0[1])
coords0 = np.meshgrid(*[skydir_gal.l.deg, ectr], indexing='ij')
coords1 = np.meshgrid(*[skydir_gal.b.deg, ectr], indexing='ij')
bkgv = self._gdiff.interpolate(np.ravel(coords0[0]),
np.ravel(coords1[0]),
np.ravel(coords0[1]))
bkgv = bkgv.reshape(expv.shape)
# bkgv = self._gdiff.interpolate(
# skydir_gal.l.deg, skydir_gal.b.deg, ectr)
isov = np.exp(np.interp(np.log(ectr), np.log(self._iso[0]),
np.log(self._iso[1])))
bkgv += isov
s0, b0 = irfs.compute_ps_counts(ebins, expv, psf, bkgv, fn,
egy_dim=1,
spatial_model=self.spatial_model,
spatial_size=self.spatial_size)
sig += [s0]
bkg += [b0]
if self._iso_fit is not None:
isov_fit = np.exp(np.interp(np.log(ectr), np.log(self._iso_fit[0]),
np.log(self._iso_fit[1])))
else:
isov_fit = isov
if self._gdiff_fit is not None:
bkgv_fit = self._gdiff_fit.interpolate(np.ravel(coords0[0]),
np.ravel(coords1[0]),
np.ravel(coords0[1]))
bkgv_fit = bkgv_fit.reshape(expv.shape)
bkgv_fit += isov_fit
s0, b0 = irfs.compute_ps_counts(ebins, expv, psf,
bkgv_fit, fn, egy_dim=1,
spatial_model=self.spatial_model,
spatial_size=self.spatial_size)
bkg_fit += [b0]
sig = np.concatenate([np.expand_dims(t, -1) for t in sig])
bkg = np.concatenate([np.expand_dims(t, -1) for t in bkg])
if self._gdiff_fit is not None:
bkg_fit = np.concatenate([np.expand_dims(t, -1) for t in bkg_fit])
return sig, bkg, bkg_fit
def run_flux_sensitivity(**kwargs):
index = kwargs.get('index', 2.0)
emin = kwargs.get('emin', 10**1.5)
emax = kwargs.get('emax', 10**6.0)
nbin = kwargs.get('nbin', 18)
glon = kwargs.get('glon', 0.0)
glat = kwargs.get('glat', 0.0)
ltcube_filepath = kwargs.get('ltcube', None)
galdiff_filepath = kwargs.get('galdiff', None)
isodiff_filepath = kwargs.get('isodiff', None)
obs_time_yr = kwargs.get('obs_time_yr', None)
event_class = kwargs.get('event_class', 'P8R2_SOURCE_V6')
min_counts = kwargs.get('min_counts', 3.0)
ts_thresh = kwargs.get('ts_thresh', 25.0)
output = kwargs.get('output', None)
event_types = [['FRONT', 'BACK']]
fn = spectrum.PowerLaw([1E-13, -index], scale=1E3)
log_ebins = np.linspace(np.log10(emin),
np.log10(emax), nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(glon, glat, unit='deg', frame='galactic')
if ltcube_filepath is None:
if obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = LTCube.create_from_obs_time(obs_time_yr * 365 * 24 * 3600.)
else:
ltc = LTCube.create(ltcube_filepath)
if obs_time_yr is not None:
ltc._counts *= obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(galdiff_filepath)
if isodiff_filepath is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % event_class,
search_dirs=[os.path.join('$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = isodiff_filepath
iso = np.loadtxt(isodiff, unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins,
event_class, event_types)
o = scalc.diff_flux_threshold(c, fn, ts_thresh, min_counts)
cols = [Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o[
'flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o[
'eflux'], unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8',
data=o['e2dnde'], unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')]
tab_diff = Table(cols)
cols = [Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph')]
cols_ebin = [Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='e_ref', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='e_max', dtype='f8',
unit='MeV', shape=(len(ectr),)),
Column(name='flux', dtype='f8',
unit='ph / (cm2 s)', shape=(len(ectr),)),
Column(name='eflux', dtype='f8',
unit='MeV / (cm2 s)', shape=(len(ectr),)),
Column(name='dnde', dtype='f8',
unit='ph / (MeV cm2 s)', shape=(len(ectr),)),
Column(name='e2dnde', dtype='f8',
unit='MeV / (cm2 s)', shape=(len(ectr),)),
Column(name='npred', dtype='f8', unit='ph', shape=(len(ectr),))]
tab_int = Table(cols)
tab_int_ebin = Table(cols_ebin)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o[colname]]
tab_int.add_row(row)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o['bins'][colname]]
tab_int_ebin.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_int_ebin))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'INT_FLUX_EBIN'
hdulist.writeto(output, clobber=True)
def main():
usage = "usage: %(prog)s [options]"
description = "Calculate the LAT point-source flux sensitivity."
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('--ltcube',
default=None,
help='Set the path to the livetime cube.')
parser.add_argument('--galdiff',
default=None,
required=True,
help='Set the path to the galactic diffuse model.')
parser.add_argument(
'--isodiff',
default=None,
help='Set the path to the isotropic model. If none then the '
'default model will be used for the given event class.')
parser.add_argument('--ts_thresh',
default=25.0,
type=float,
help='Set the detection threshold.')
parser.add_argument('--min_counts',
default=3.0,
type=float,
help='Set the minimum number of counts.')
parser.add_argument(
'--joint',
default=False,
action='store_true',
help='Compute sensitivity using joint-likelihood of all event types.')
parser.add_argument('--event_class',
default='P8R2_SOURCE_V6',
help='Set the IRF name (e.g. P8R2_SOURCE_V6).')
parser.add_argument('--glon',
default=0.0,
type=float,
help='Galactic longitude.')
parser.add_argument('--glat',
default=0.0,
type=float,
help='Galactic latitude.')
parser.add_argument('--index',
default=2.0,
type=float,
help='Source power-law index.')
parser.add_argument('--emin',
default=10**1.5,
type=float,
help='Minimum energy in MeV.')
parser.add_argument('--emax',
default=10**6.0,
type=float,
help='Maximum energy in MeV.')
parser.add_argument(
'--nbin',
default=18,
type=int,
help='Number of energy bins for differential flux calculation.')
parser.add_argument('--output',
default='output.fits',
type=str,
help='Output filename.')
parser.add_argument(
'--obs_time_yr',
default=None,
type=float,
help=
'Rescale the livetime cube to this observation time in years. If none then the '
'calculation will use the intrinsic observation time of the livetime cube.'
)
args = parser.parse_args()
event_types = [['FRONT', 'BACK']]
fn = spectrum.PowerLaw([1E-13, -args.index], scale=1E3)
log_ebins = np.linspace(np.log10(args.emin), np.log10(args.emax),
args.nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(args.glon, args.glat, unit='deg', frame='galactic')
if args.ltcube is None:
if args.obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = irfs.LTCube.create_from_obs_time(args.obs_time_yr * 365 * 24 *
3600.)
else:
ltc = irfs.LTCube.create(args.ltcube)
if args.obs_time_yr is not None:
ltc._counts *= args.obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(args.galdiff)
if args.isodiff is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % args.event_class,
search_dirs=[
os.path.join(
'$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'
])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = args.isodiff
iso = np.loadtxt(isodiff, unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins, args.event_class,
event_types)
o = scalc.diff_flux_threshold(c, fn, args.ts_thresh, args.min_counts)
cols = [
Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o['flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o['eflux'],
unit='MeV / (cm2 s)'),
Column(name='dnde',
dtype='f8',
data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde',
dtype='f8',
data=o['e2dnde'],
unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')
]
tab_diff = Table(cols)
cols = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph')
]
cols_ebin = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='e_ref', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='e_max', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='flux',
dtype='f8',
unit='ph / (cm2 s)',
shape=(len(ectr), )),
Column(name='eflux',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='dnde',
dtype='f8',
unit='ph / (MeV cm2 s)',
shape=(len(ectr), )),
Column(name='e2dnde',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='npred', dtype='f8', unit='ph', shape=(len(ectr), ))
]
tab_int = Table(cols)
tab_int_ebin = Table(cols_ebin)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, args.ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o[colname]]
tab_int.add_row(row)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o['bins'][colname]]
tab_int_ebin.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_int_ebin))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'INT_FLUX_EBIN'
hdulist.writeto(args.output, clobber=True)
def run_flux_sensitivity(**kwargs):
index = kwargs.get('index', 2.0)
sedshape = kwargs.get('sedshape', 'PowerLaw')
cutoff = kwargs.get('cutoff', 1e3)
curvindex = kwargs.get('curvindex', 1.0)
beta = kwargs.get('beta', 0.0)
emin = kwargs.get('emin', 10**1.5)
emax = kwargs.get('emax', 10**6.0)
nbin = kwargs.get('nbin', 18)
glon = kwargs.get('glon', 0.0)
glat = kwargs.get('glat', 0.0)
ltcube_filepath = kwargs.get('ltcube', None)
galdiff_filepath = kwargs.get('galdiff', None)
isodiff_filepath = kwargs.get('isodiff', None)
galdiff_fit_filepath = kwargs.get('galdiff_fit', None)
isodiff_fit_filepath = kwargs.get('isodiff_fit', None)
wcs_npix = kwargs.get('wcs_npix', 40)
wcs_cdelt = kwargs.get('wcs_cdelt', 0.5)
wcs_proj = kwargs.get('wcs_proj', 'AIT')
map_type = kwargs.get('map_type', None)
spatial_model = kwargs.get('spatial_model', 'PointSource')
spatial_size = kwargs.get('spatial_size', 1E-2)
obs_time_yr = kwargs.get('obs_time_yr', None)
event_class = kwargs.get('event_class', 'P8R2_SOURCE_V6')
min_counts = kwargs.get('min_counts', 3.0)
ts_thresh = kwargs.get('ts_thresh', 25.0)
nside = kwargs.get('hpx_nside', 16)
output = kwargs.get('output', None)
event_types = [['FRONT', 'BACK']]
if sedshape == 'PowerLaw':
fn = spectrum.PowerLaw([1E-13, -index], scale=1E3)
elif sedshape == 'PLSuperExpCutoff':
fn = spectrum.PLSuperExpCutoff([1E-13, -index, cutoff, curvindex],
scale=1E3)
elif sedshape == 'LogParabola':
fn = spectrum.LogParabola([1E-13, -index, beta], scale=1E3)
log_ebins = np.linspace(np.log10(emin), np.log10(emax), nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(glon, glat, unit='deg', frame='galactic')
if ltcube_filepath is None:
if obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = LTCube.create_from_obs_time(obs_time_yr * 365 * 24 * 3600.)
else:
ltc = LTCube.create(ltcube_filepath)
if obs_time_yr is not None:
ltc._counts *= obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(galdiff_filepath)
gdiff_fit = None
if galdiff_fit_filepath is not None:
gdiff_fit = skymap.Map.create_from_fits(galdiff_fit_filepath)
if isodiff_filepath is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % event_class,
search_dirs=[
os.path.join(
'$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'
])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = isodiff_filepath
iso = np.loadtxt(isodiff, unpack=True)
iso_fit = None
if isodiff_fit_filepath is not None:
iso_fit = np.loadtxt(isodiff_fit_filepath, unpack=True)
scalc = SensitivityCalc(gdiff,
iso,
ltc,
ebins,
event_class,
event_types,
gdiff_fit=gdiff_fit,
iso_fit=iso_fit,
spatial_model=spatial_model,
spatial_size=spatial_size)
# Compute Maps
map_diff_flux = None
map_diff_npred = None
map_int_flux = None
map_int_npred = None
map_nstep = 500
if map_type == 'hpx':
hpx = HPX(nside, True, 'GAL', ebins=ebins)
map_diff_flux = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_diff_npred = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_skydir = map_diff_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.diff_flux_threshold(map_skydir[s], fn, ts_thresh,
min_counts)
map_diff_flux.data[:, s] = o['flux'].T
map_diff_npred.data[:, s] = o['npred'].T
hpx = HPX(nside, True, 'GAL')
map_int_flux = HpxMap(np.zeros((hpx.npix)), hpx)
map_int_npred = HpxMap(np.zeros((hpx.npix)), hpx)
map_skydir = map_int_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.int_flux_threshold(map_skydir[s], fn, ts_thresh,
min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
elif map_type == 'wcs':
wcs_shape = [wcs_npix, wcs_npix]
wcs_size = wcs_npix * wcs_npix
map_diff_flux = Map.create(c,
wcs_cdelt,
wcs_shape,
'GAL',
wcs_proj,
ebins=ebins)
map_diff_npred = Map.create(c,
wcs_cdelt,
wcs_shape,
'GAL',
wcs_proj,
ebins=ebins)
map_skydir = map_diff_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(np.arange(i, min(i + map_nstep, wcs_size)),
wcs_shape)
s = (slice(None), idx[1], idx[0])
o = scalc.diff_flux_threshold(map_skydir[slice(i, i + map_nstep)],
fn, ts_thresh, min_counts)
map_diff_flux.data[s] = o['flux'].T
map_diff_npred.data[s] = o['npred'].T
map_int_flux = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_int_npred = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_skydir = map_int_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(np.arange(i, min(i + map_nstep, wcs_size)),
wcs_shape)
s = (idx[1], idx[0])
o = scalc.int_flux_threshold(map_skydir[slice(i, i + map_nstep)],
fn, ts_thresh, min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
o = scalc.diff_flux_threshold(c, fn, ts_thresh, min_counts)
cols = [
Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o['flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o['eflux'],
unit='MeV / (cm2 s)'),
Column(name='dnde',
dtype='f8',
data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde',
dtype='f8',
data=o['e2dnde'],
unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')
]
tab_diff = Table(cols)
cols = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph'),
Column(name='ebin_e_min', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_e_ref', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_e_max', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_flux',
dtype='f8',
unit='ph / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_eflux',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_dnde',
dtype='f8',
unit='ph / (MeV cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_e2dnde',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_npred', dtype='f8', unit='ph', shape=(len(ectr), ))
]
cols_ebounds = [
Column(name='E_MIN', dtype='f8', unit='MeV', data=ebins[:-1]),
Column(name='E_MAX', dtype='f8', unit='MeV', data=ebins[1:]),
]
tab_int = Table(cols)
tab_ebounds = Table(cols_ebounds)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if colname == 'index':
continue
if 'ebin' in colname:
row += [o['bins'][colname.replace('ebin_', '')]]
else:
row += [o[colname]]
tab_int.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_ebounds))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'EBOUNDS'
if map_type is not None:
hdu = map_diff_flux.create_image_hdu()
hdu.name = 'MAP_DIFF_FLUX'
hdulist.append(hdu)
hdu = map_diff_npred.create_image_hdu()
hdu.name = 'MAP_DIFF_NPRED'
hdulist.append(hdu)
hdu = map_int_flux.create_image_hdu()
hdu.name = 'MAP_INT_FLUX'
hdulist.append(hdu)
hdu = map_int_npred.create_image_hdu()
hdu.name = 'MAP_INT_NPRED'
hdulist.append(hdu)
hdulist.writeto(output, clobber=True)
def compute_ps_counts(ebins,
exp,
psf,
bkg,
fn,
egy_dim=0,
spatial_model='PointSource',
spatial_size=1E-3):
"""Calculate the observed signal and background counts given models
for the exposure, background intensity, PSF, and source flux.
Parameters
----------
ebins : `~numpy.ndarray`
Array of energy bin edges.
exp : `~numpy.ndarray`
Model for exposure.
psf : `~fermipy.irfs.PSFModel`
Model for average PSF.
bkg : `~numpy.ndarray`
Array of background intensities.
fn : `~fermipy.spectrum.SpectralFunction`
egy_dim : int
Index of energy dimension in ``bkg`` and ``exp`` arrays.
"""
ewidth = utils.edge_to_width(ebins)
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
r68 = psf.containment_angle(ectr, fraction=0.68)
if spatial_model != 'PointSource':
r68[r68 < spatial_size] = spatial_size
# * np.ones((len(ectr), 31))
theta_edges = np.linspace(0.0, 3.0, 31)[np.newaxis, :]
theta_edges = theta_edges * r68[:, np.newaxis]
theta = 0.5 * (theta_edges[:, :-1] + theta_edges[:, 1:])
domega = np.pi * (theta_edges[:, 1:]**2 - theta_edges[:, :-1]**2)
if spatial_model == 'PointSource':
sig_pdf = domega * psf.interp(ectr[:, np.newaxis], theta)
elif spatial_model == 'RadialGaussian':
sig_pdf = domega * utils.convolve2d_gauss(
lambda t: psf.interp(ectr[:, np.newaxis, np.newaxis], t),
theta,
spatial_size / 1.5095921854516636,
nstep=2000)
elif spatial_model == 'RadialDisk':
sig_pdf = domega * utils.convolve2d_disk(
lambda t: psf.interp(ectr[:, np.newaxis, np.newaxis], t), theta,
spatial_size / 0.8246211251235321)
else:
raise ValueError('Invalid spatial model: {}'.format(spatial_model))
sig_pdf *= (np.pi / 180.)**2
sig_flux = fn.flux(ebins[:-1], ebins[1:])
# Background and signal counts
bkgc = bkg[..., np.newaxis] * domega * exp[..., np.newaxis] * \
ewidth[..., np.newaxis] * (np.pi / 180.)**2
sigc = sig_pdf * sig_flux[..., np.newaxis] * exp[..., np.newaxis]
return sigc, bkgc
