def __init__(self, fitsfile=None, extdir=None):
if extdir is None:
extdir = os.path.join('$FERMIPY_DATA_DIR', 'catalogs',
'Extended_archive_2fhl_v00')
if fitsfile is None:
fitsfile = os.path.join(fermipy.PACKAGE_DATA, 'catalogs',
'gll_psch_v08.fit')
hdulist = fits.open(fitsfile)
table = Table(hdulist['2FHL Source Catalog'].data)
table_extsrc = Table(hdulist['Extended Sources'].data)
table_extsrc.meta.clear()
strip_columns(table)
strip_columns(table_extsrc)
table = join_tables(
table, table_extsrc, 'Source_Name', 'Source_Name', [
'Model_Form', 'Model_SemiMajor', 'Model_SemiMinor',
'Model_PosAng', 'Spatial_Filename'
])
table.sort('Source_Name')
super(Catalog2FHL, self).__init__(table, extdir)
self._table['Flux_Density'] = \
PowerLaw.eval_norm(50E3, -np.array(self.table['Spectral_Index']),
50E3, 2000E3,
np.array(self.table['Flux50']))
self._table['Pivot_Energy'] = 50E3
self._table['SpectrumType'] = 'PowerLaw'
def __init__(self, fitsfile=None, extdir=None):
if extdir is None:
extdir = os.path.join('$FERMIPY_DATA_DIR', 'catalogs',
'Extended_archive_2fhl_v00')
if fitsfile is None:
fitsfile = os.path.join(fermipy.PACKAGE_DATA, 'catalogs',
'gll_psch_v08.fit')
hdulist = fits.open(fitsfile)
table = Table(hdulist['2FHL Source Catalog'].data)
table_extsrc = Table(hdulist['Extended Sources'].data)
table_extsrc.meta.clear()
strip_columns(table)
strip_columns(table_extsrc)
table = join_tables(table, table_extsrc, 'Source_Name', 'Source_Name',
['Model_Form', 'Model_SemiMajor',
'Model_SemiMinor', 'Model_PosAng',
'Spatial_Filename'])
table.sort('Source_Name')
super(Catalog2FHL, self).__init__(table, extdir)
self._table['Flux_Density'] = \
PowerLaw.eval_norm(50E3, -np.array(self.table['Spectral_Index']),
50E3, 2000E3,
np.array(self.table['Flux50']))
self._table['Pivot_Energy'] = 50E3
self._table['SpectrumType'] = 'PowerLaw'
def calc_model_eff(self):
from fermipy.spectrum import PowerLaw
fn = PowerLaw([1E-13, -2.0])
skydir = SkyCoord(0.0,0.0,unit='deg')
exp = irfs.calc_wtd_exp(skydir, self._ltc, 'P8R2_SOURCE_V6', ['FRONT','BACK'],
self._energy_bins[10:13], self._ctheta_bins, fn)
return exp
def convert_tscube_old(infile, outfile):
"""Convert between old and new TSCube formats."""
inhdulist = fits.open(infile)
# If already in the new-style format just write and exit
if 'DLOGLIKE_SCAN' in inhdulist['SCANDATA'].columns.names:
if infile != outfile:
inhdulist.writeto(outfile, clobber=True)
return
# Get stuff out of the input file
nrows = inhdulist['SCANDATA']._nrows
nebins = inhdulist['EBOUNDS']._nrows
npts = inhdulist['SCANDATA'].data.field('NORMSCAN').shape[1] / nebins
emin = inhdulist['EBOUNDS'].data.field('e_min') / 1E3
emax = inhdulist['EBOUNDS'].data.field('e_max') / 1E3
eref = np.sqrt(emin * emax)
dnde_emin = inhdulist['EBOUNDS'].data.field('E_MIN_FL')
dnde_emax = inhdulist['EBOUNDS'].data.field('E_MAX_FL')
index = np.log(dnde_emin / dnde_emax) / np.log(emin / emax)
flux = PowerLaw.eval_flux(emin, emax, [dnde_emin, index], emin)
eflux = PowerLaw.eval_eflux(emin, emax, [dnde_emin, index], emin)
dnde = PowerLaw.eval_dnde(np.sqrt(emin * emax), [dnde_emin, index], emin)
ts_map = inhdulist['PRIMARY'].data.reshape((nrows))
ok_map = inhdulist['TSMAP_OK'].data.reshape((nrows))
n_map = inhdulist['N_MAP'].data.reshape((nrows))
errp_map = inhdulist['ERRP_MAP'].data.reshape((nrows))
errn_map = inhdulist['ERRN_MAP'].data.reshape((nrows))
err_map = np.ndarray((nrows))
m = errn_map > 0
err_map[m] = 0.5 * (errp_map[m] + errn_map[m])
err_map[~m] = errp_map[~m]
ul_map = n_map + 2.0 * errp_map
ncube = np.rollaxis(inhdulist['N_CUBE'].data, 0, 3).reshape(
(nrows, nebins))
errpcube = np.rollaxis(inhdulist['ERRPCUBE'].data, 0, 3).reshape(
(nrows, nebins))
errncube = np.rollaxis(inhdulist['ERRNCUBE'].data, 0, 3).reshape(
(nrows, nebins))
tscube = np.rollaxis(inhdulist['TSCUBE'].data, 0, 3).reshape(
(nrows, nebins))
nll_cube = np.rollaxis(inhdulist['NLL_CUBE'].data, 0, 3).reshape(
(nrows, nebins))
ok_cube = np.rollaxis(inhdulist['TSCUBE_OK'].data, 0, 3).reshape(
(nrows, nebins))
ul_cube = ncube + 2.0 * errpcube
m = errncube > 0
errcube = np.ndarray((nrows, nebins))
errcube[m] = 0.5 * (errpcube[m] + errncube[m])
errcube[~m] = errpcube[~m]
norm_scan = inhdulist['SCANDATA'].data.field('NORMSCAN').reshape(
(nrows, npts, nebins)).swapaxes(1, 2)
nll_scan = inhdulist['SCANDATA'].data.field('NLL_SCAN').reshape(
(nrows, npts, nebins)).swapaxes(1, 2)
# Adjust the "EBOUNDS" hdu
columns = inhdulist['EBOUNDS'].columns
columns.add_col(
fits.Column(name=str('e_ref'),
format='E',
array=eref * 1E3,
unit='keV'))
columns.add_col(
fits.Column(name=str('ref_flux'),
format='D',
array=flux,
unit='ph / (cm2 s)'))
columns.add_col(
fits.Column(name=str('ref_eflux'),
format='D',
array=eflux,
unit='MeV / (cm2 s)'))
columns.add_col(
fits.Column(name=str('ref_dnde'),
format='D',
array=dnde,
unit='ph / (MeV cm2 s)'))
columns.change_name('E_MIN_FL', str('ref_dnde_e_min'))
columns.change_unit('ref_dnde_e_min', 'ph / (MeV cm2 s)')
columns.change_name('E_MAX_FL', str('ref_dnde_e_max'))
columns.change_unit('ref_dnde_e_max', 'ph / (MeV cm2 s)')
columns.change_name('NPRED', str('ref_npred'))
hdu_e = fits.BinTableHDU.from_columns(columns, name='EBOUNDS')
# Make the "FITDATA" hdu
columns = fits.ColDefs([])
columns.add_col(fits.Column(name=str('fit_ts'), format='E', array=ts_map))
columns.add_col(
fits.Column(name=str('fit_status'), format='E', array=ok_map))
columns.add_col(fits.Column(name=str('fit_norm'), format='E', array=n_map))
columns.add_col(
fits.Column(name=str('fit_norm_err'), format='E', array=err_map))
columns.add_col(
fits.Column(name=str('fit_norm_errp'), format='E', array=errp_map))
columns.add_col(
fits.Column(name=str('fit_norm_errn'), format='E', array=errn_map))
hdu_f = fits.BinTableHDU.from_columns(columns, name='FITDATA')
# Make the "SCANDATA" hdu
columns = fits.ColDefs([])
columns.add_col(
fits.Column(name=str('ts'),
format='%iE' % nebins,
array=tscube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('bin_status'),
format='%iE' % nebins,
array=ok_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm'),
format='%iE' % nebins,
array=ncube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_ul'),
format='%iE' % nebins,
array=ul_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_err'),
format='%iE' % nebins,
array=errcube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_errp'),
format='%iE' % nebins,
array=errpcube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_errn'),
format='%iE' % nebins,
array=errncube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('loglike'),
format='%iE' % nebins,
array=nll_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_scan'),
format='%iE' % (nebins * npts),
array=norm_scan,
dim=str('(%i,%i)' % (npts, nebins))))
columns.add_col(
fits.Column(name=str('dloglike_scan'),
format='%iE' % (nebins * npts),
array=nll_scan,
dim=str('(%i,%i)' % (npts, nebins))))
hdu_s = fits.BinTableHDU.from_columns(columns, name='SCANDATA')
hdulist = fits.HDUList(
[inhdulist[0], hdu_s, hdu_f, inhdulist["BASELINE"], hdu_e])
hdulist['SCANDATA'].header['UL_CONF'] = 0.95
hdulist.writeto(outfile, clobber=True)
return hdulist
def convert_tscube_old(infile, outfile):
"""Convert between old and new TSCube formats."""
inhdulist = fits.open(infile)
# If already in the new-style format just write and exit
if 'DLOGLIKE_SCAN' in inhdulist['SCANDATA'].columns.names:
if infile != outfile:
inhdulist.writeto(outfile, clobber=True)
return
# Get stuff out of the input file
nrows = inhdulist['SCANDATA']._nrows
nebins = inhdulist['EBOUNDS']._nrows
npts = inhdulist['SCANDATA'].data.field('NORMSCAN').shape[1] / nebins
emin = inhdulist['EBOUNDS'].data.field('e_min') / 1E3
emax = inhdulist['EBOUNDS'].data.field('e_max') / 1E3
eref = np.sqrt(emin * emax)
dnde_emin = inhdulist['EBOUNDS'].data.field('E_MIN_FL')
dnde_emax = inhdulist['EBOUNDS'].data.field('E_MAX_FL')
index = np.log(dnde_emin / dnde_emax) / np.log(emin / emax)
flux = PowerLaw.eval_flux(emin, emax, [dnde_emin, index], emin)
eflux = PowerLaw.eval_eflux(emin, emax, [dnde_emin, index], emin)
dnde = PowerLaw.eval_dnde(np.sqrt(emin * emax), [dnde_emin, index], emin)
ts_map = inhdulist['PRIMARY'].data.reshape((nrows))
ok_map = inhdulist['TSMAP_OK'].data.reshape((nrows))
n_map = inhdulist['N_MAP'].data.reshape((nrows))
errp_map = inhdulist['ERRP_MAP'].data.reshape((nrows))
errn_map = inhdulist['ERRN_MAP'].data.reshape((nrows))
err_map = np.ndarray((nrows))
m = errn_map > 0
err_map[m] = 0.5 * (errp_map[m] + errn_map[m])
err_map[~m] = errp_map[~m]
ul_map = n_map + 2.0 * errp_map
ncube = np.rollaxis(inhdulist['N_CUBE'].data,
0, 3).reshape((nrows, nebins))
errpcube = np.rollaxis(
inhdulist['ERRPCUBE'].data, 0, 3).reshape((nrows, nebins))
errncube = np.rollaxis(
inhdulist['ERRNCUBE'].data, 0, 3).reshape((nrows, nebins))
tscube = np.rollaxis(inhdulist['TSCUBE'].data,
0, 3).reshape((nrows, nebins))
nll_cube = np.rollaxis(
inhdulist['NLL_CUBE'].data, 0, 3).reshape((nrows, nebins))
ok_cube = np.rollaxis(
inhdulist['TSCUBE_OK'].data, 0, 3).reshape((nrows, nebins))
ul_cube = ncube + 2.0 * errpcube
m = errncube > 0
errcube = np.ndarray((nrows, nebins))
errcube[m] = 0.5 * (errpcube[m] + errncube[m])
errcube[~m] = errpcube[~m]
norm_scan = inhdulist['SCANDATA'].data.field(
'NORMSCAN').reshape((nrows, npts, nebins)).swapaxes(1, 2)
nll_scan = inhdulist['SCANDATA'].data.field(
'NLL_SCAN').reshape((nrows, npts, nebins)).swapaxes(1, 2)
# Adjust the "EBOUNDS" hdu
columns = inhdulist['EBOUNDS'].columns
columns.add_col(fits.Column(name=str('e_ref'),
format='E', array=eref * 1E3,
unit='keV'))
columns.add_col(fits.Column(name=str('ref_flux'),
format='D', array=flux,
unit='ph / (cm2 s)'))
columns.add_col(fits.Column(name=str('ref_eflux'),
format='D', array=eflux,
unit='MeV / (cm2 s)'))
columns.add_col(fits.Column(name=str('ref_dnde'),
format='D', array=dnde,
unit='ph / (MeV cm2 s)'))
columns.change_name('E_MIN_FL', str('ref_dnde_e_min'))
columns.change_unit('ref_dnde_e_min', 'ph / (MeV cm2 s)')
columns.change_name('E_MAX_FL', str('ref_dnde_e_max'))
columns.change_unit('ref_dnde_e_max', 'ph / (MeV cm2 s)')
columns.change_name('NPRED', str('ref_npred'))
hdu_e = fits.BinTableHDU.from_columns(columns, name='EBOUNDS')
# Make the "FITDATA" hdu
columns = fits.ColDefs([])
columns.add_col(fits.Column(
name=str('fit_ts'), format='E', array=ts_map))
columns.add_col(fits.Column(
name=str('fit_status'), format='E', array=ok_map))
columns.add_col(fits.Column(
name=str('fit_norm'), format='E', array=n_map))
columns.add_col(fits.Column(
name=str('fit_norm_err'), format='E', array=err_map))
columns.add_col(fits.Column(
name=str('fit_norm_errp'), format='E', array=errp_map))
columns.add_col(fits.Column(
name=str('fit_norm_errn'), format='E', array=errn_map))
hdu_f = fits.BinTableHDU.from_columns(columns, name='FITDATA')
# Make the "SCANDATA" hdu
columns = fits.ColDefs([])
columns.add_col(fits.Column(name=str('ts'),
format='%iE' % nebins, array=tscube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('bin_status'),
format='%iE' % nebins, array=ok_cube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm'),
format='%iE' % nebins, array=ncube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm_ul'),
format='%iE' % nebins, array=ul_cube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm_err'),
format='%iE' % nebins, array=errcube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm_errp'),
format='%iE' % nebins, array=errpcube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm_errn'),
format='%iE' % nebins, array=errncube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('loglike'),
format='%iE' % nebins, array=nll_cube,
dim=str('(%i)' % nebins)))
columns.add_col(fits.Column(name=str('norm_scan'),
format='%iE' % (nebins * npts),
array=norm_scan,
dim=str('(%i,%i)' % (npts, nebins))))
columns.add_col(fits.Column(name=str('dloglike_scan'),
format='%iE' % (nebins * npts),
array=nll_scan,
dim=str('(%i,%i)' % (npts, nebins))))
hdu_s = fits.BinTableHDU.from_columns(columns, name='SCANDATA')
hdulist = fits.HDUList([inhdulist[0],
hdu_s,
hdu_f,
inhdulist["BASELINE"],
hdu_e])
hdulist['SCANDATA'].header['UL_CONF'] = 0.95
hdulist.writeto(outfile, clobber=True)
return hdulist
from fermipy.spectrum import PowerLaw import numpy as np from astropy.table import Table, Column np.random.seed(1) s = PowerLaw([1E-12, -2.2], scale=1E3) sref = PowerLaw([1E-12, -2.0], scale=1E3) ebins = np.linspace(2.0, 5.0, 3 * 8 + 1) emin = ebins[:-1] emax = ebins[1:] ectr = 0.5 * (emin + emax) flux = s.flux(10**emin, 10**emax) dnde = s.dnde(10**ectr) ref_flux = sref.flux(10**emin, 10**emax) ref_eflux = sref.eflux(10**emin, 10**emax) ref_dnde = sref.dnde(10**ectr) exp = 1.1E12 npred = exp * flux ref_npred = exp * ref_flux nmeas = np.random.poisson(npred).astype(float) meas_flux = nmeas / npred * flux meas_flux_err = nmeas**0.5 / npred * flux meas_flux_ul = 1.644853 * meas_flux_err + meas_flux