def _scan_position(self, name, **kwargs):
saved_state = LikelihoodState(self.like)
skydir = kwargs.pop('skydir', self.roi[name].skydir)
scan_cdelt = kwargs.pop('scan_cdelt', 0.02)
nstep = kwargs.pop('nstep', 5)
use_cache = kwargs.get('use_cache', True)
use_pylike = kwargs.get('use_pylike', False)
optimizer = kwargs.get('optimizer', {})
# Fit without source
self.zero_source(name, loglevel=logging.DEBUG)
fit_output_nosrc = self._fit(loglevel=logging.DEBUG,
**optimizer)
self.unzero_source(name, loglevel=logging.DEBUG)
saved_state.restore()
self.free_norm(name, loglevel=logging.DEBUG)
lnlmap = Map.create(skydir, scan_cdelt, (nstep, nstep),
coordsys=wcs_utils.get_coordsys(self._skywcs))
src = self.roi.copy_source(name)
if use_cache and not use_pylike:
self._create_srcmap_cache(src.name, src)
scan_skydir = lnlmap.get_pixel_skydirs().transform_to('icrs')
loglike = []
for ra, dec in zip(scan_skydir.ra.deg, scan_skydir.dec.deg):
spatial_pars = {'ra': ra, 'dec': dec}
self.set_source_morphology(name,
spatial_pars=spatial_pars,
use_pylike=use_pylike)
fit_output = self._fit(loglevel=logging.DEBUG,
**optimizer)
loglike += [fit_output['loglike']]
self.set_source_morphology(name, spatial_pars=src.spatial_pars,
use_pylike=use_pylike)
saved_state.restore()
lnlmap.data = np.array(loglike).reshape((nstep, nstep)).T
lnlmap.data -= fit_output_nosrc['loglike']
tsmap = Map(2.0 * lnlmap.data, lnlmap.wcs)
self._clear_srcmap_cache()
return tsmap, fit_output_nosrc['loglike']
def _scan_position(self, name, **kwargs):
saved_state = LikelihoodState(self.like)
skydir = kwargs.pop('skydir', self.roi[name].skydir)
scan_cdelt = kwargs.pop('scan_cdelt', 0.02)
nstep = kwargs.pop('nstep', 5)
use_cache = kwargs.get('use_cache', True)
use_pylike = kwargs.get('use_pylike', False)
optimizer = kwargs.get('optimizer', {})
# Fit without source
self.zero_source(name, loglevel=logging.DEBUG)
fit_output_nosrc = self._fit(loglevel=logging.DEBUG, **optimizer)
self.unzero_source(name, loglevel=logging.DEBUG)
saved_state.restore()
self.free_norm(name, loglevel=logging.DEBUG)
lnlmap = Map.create(skydir,
scan_cdelt, (nstep, nstep),
coordsys=wcs_utils.get_coordsys(self._skywcs))
src = self.roi.copy_source(name)
if use_cache and not use_pylike:
self._create_srcmap_cache(src.name, src)
scan_skydir = lnlmap.get_pixel_skydirs().transform_to('icrs')
loglike = []
for ra, dec in zip(scan_skydir.ra.deg, scan_skydir.dec.deg):
spatial_pars = {'ra': ra, 'dec': dec}
self.set_source_morphology(name,
spatial_pars=spatial_pars,
use_pylike=use_pylike)
fit_output = self._fit(loglevel=logging.DEBUG, **optimizer)
loglike += [fit_output['loglike']]
self.set_source_morphology(name,
spatial_pars=src.spatial_pars,
use_pylike=use_pylike)
saved_state.restore()
lnlmap.data = np.array(loglike).reshape((nstep, nstep)).T
lnlmap.data -= fit_output_nosrc['loglike']
tsmap = Map(2.0 * lnlmap.data, lnlmap.wcs)
self._clear_srcmap_cache()
return tsmap, fit_output_nosrc['loglike']
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 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 localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
performed in two steps. First a TS map is computed centered
on the source with half-width set by ``dtheta_max``. A fit is
then performed to the maximum TS peak in this map. The source
position is then further refined by scanning the likelihood in
the vicinity of the peak found in the first step. The size of
the scan region is set to encompass the 99% positional
uncertainty contour as determined from the peak fit.
Parameters
----------
name : str
Source name.
dtheta_max : float
Maximum offset in RA/DEC in deg from the nominal source
position that will be used to define the boundaries of the
TS map search region.
nstep : int
Number of steps in longitude/latitude that will be taken
when refining the source position. The bounds of the scan
range are set to the 99% positional uncertainty as
determined from the TS map peak fit. The total number of
sampling points will be nstep**2.
fix_background : bool
Fix background parameters when fitting the source position.
update : bool
Update the model for this source with the best-fit
position. If newname=None this will overwrite the
existing source map of this source with one corresponding
to its new location.
newname : str
Name that will be assigned to the relocalized source
when update=True. If newname is None then the existing
source name will be used.
make_plots : bool
Generate plots.
write_fits : bool
Write the output to a FITS file.
write_npy : bool
Write the output dictionary to a numpy file.
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis. This dictionary is also saved to the
dictionary of this source in 'localize'.
"""
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('make_plots', False)
schema.add_option('write_fits', True)
schema.add_option('write_npy', True)
schema.add_option('newname', name)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
nstep = config['nstep']
dtheta_max = config['dtheta_max']
update = config['update']
newname = config['newname']
prefix = config['prefix']
self.logger.info('Running localization for %s' % name)
saved_state = LikelihoodState(self.like)
if config['fix_background']:
self.free_sources(free=False, loglevel=logging.DEBUG)
src = self.roi.copy_source(name)
skydir = src.skydir
skywcs = self._skywcs
src_pix = skydir.to_pixel(skywcs)
tsmap_fit, tsmap = self._localize_tsmap(name,
prefix=prefix,
dtheta_max=dtheta_max)
self.logger.debug(
'Completed localization with TS Map.\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)', tsmap_fit['ra'], tsmap_fit['dec'],
tsmap_fit['glon'], tsmap_fit['glat'])
# Fit baseline (point-source) model
self.free_norm(name)
fit_output = self._fit(loglevel=logging.DEBUG, **config['optimizer'])
# Save likelihood value for baseline fit
loglike0 = fit_output['loglike']
self.logger.debug('Baseline Model Likelihood: %f', loglike0)
self.zero_source(name)
o = {
'name': name,
'config': config,
'fit_success': True,
'loglike_base': loglike0,
'loglike_loc': np.nan,
'dloglike_loc': np.nan
}
cdelt0 = np.abs(skywcs.wcs.cdelt[0])
cdelt1 = np.abs(skywcs.wcs.cdelt[1])
scan_step = 2.0 * tsmap_fit['r95'] / (nstep - 1.0)
self.logger.debug(
'Refining localization search to '
'region of width: %.4f deg', tsmap_fit['r95'])
scan_map = Map.create(SkyCoord(tsmap_fit['ra'],
tsmap_fit['dec'],
unit='deg'),
scan_step, (nstep, nstep),
coordsys=wcs_utils.get_coordsys(skywcs))
scan_skydir = scan_map.get_pixel_skydirs()
lnlscan = dict(wcs=scan_map.wcs.to_header().items(),
loglike=np.zeros((nstep, nstep)),
dloglike=np.zeros((nstep, nstep)),
dloglike_fit=np.zeros((nstep, nstep)))
for i, t in enumerate(scan_skydir):
model_name = '%s_localize' % (name.replace(' ', '').lower())
src.set_name(model_name)
src.set_position(t)
self.add_source(model_name,
src,
free=True,
init_source=False,
save_source_maps=False,
loglevel=logging.DEBUG)
fit_output = self._fit(loglevel=logging.DEBUG,
**config['optimizer'])
loglike1 = fit_output['loglike']
lnlscan['loglike'].flat[i] = loglike1
self.delete_source(model_name, loglevel=logging.DEBUG)
lnlscan['dloglike'] = lnlscan['loglike'] - np.max(lnlscan['loglike'])
scan_tsmap = Map(2.0 * lnlscan['dloglike'].T, scan_map.wcs)
self.unzero_source(name)
saved_state.restore()
self._sync_params(name)
self._update_roi()
scan_fit, new_skydir = fit_error_ellipse(scan_tsmap, dpix=3)
o.update(scan_fit)
o['loglike_loc'] = np.max(
lnlscan['loglike']) + 0.5 * scan_fit['offset']
o['dloglike_loc'] = o['loglike_loc'] - o['loglike_base']
# lnlscan['dloglike_fit'] = \
# utils.parabola(np.linspace(0,nstep-1.0,nstep)[:,np.newaxis],
# np.linspace(0,nstep-1.0,nstep)[np.newaxis,:],
# *scan_fit['popt']).reshape((nstep,nstep))
o['lnlscan'] = lnlscan
# Best fit position and uncertainty from fit to TS map
o['tsmap_fit'] = tsmap_fit
# Best fit position and uncertainty from pylike scan
o['scan_fit'] = scan_fit
pix = new_skydir.to_pixel(skywcs)
o['xpix'] = float(pix[0])
o['ypix'] = float(pix[1])
o['deltax'] = (o['xpix'] - src_pix[0]) * cdelt0
o['deltay'] = (o['ypix'] - src_pix[1]) * cdelt1
o['offset'] = skydir.separation(new_skydir).deg
if o['offset'] > dtheta_max:
o['fit_success'] = False
if not o['fit_success']:
self.logger.error(
'Localization failed.\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)\n'
'offset = %8.4f deltax = %8.4f '
'deltay = %8.4f', o['ra'], o['dec'], o['glon'], o['glat'],
o['offset'], o['deltax'], o['deltay'])
else:
self.logger.info(
'Localization succeeded with '
'coordinates:\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)\n'
'offset = %8.4f r68 = %8.4f', o['ra'], o['dec'], o['glon'],
o['glat'], o['offset'], o['r68'])
self.roi[name]['localize'] = copy.deepcopy(o)
if config['make_plots']:
self._plotter.make_localization_plots(o,
tsmap,
self.roi,
prefix=prefix,
skydir=scan_skydir)
if update and o['fit_success']:
self.logger.info('Updating source %s '
'to localized position.', name)
src = self.delete_source(name)
src.set_position(new_skydir)
src.set_name(newname, names=src.names)
self.add_source(newname, src, free=True)
fit_output = self.fit(loglevel=logging.DEBUG)
o['loglike_loc'] = fit_output['loglike']
o['dloglike_loc'] = o['loglike_loc'] - o['loglike_base']
src = self.roi.get_source_by_name(newname)
self.roi[newname]['localize'] = copy.deepcopy(o)
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f',
o['loglike_loc'], o['dloglike_loc'])
if o['fit_success']:
src = self.roi.get_source_by_name(newname)
src['pos_sigma'] = o['sigma']
src['pos_sigma_semimajor'] = o['sigma_semimajor']
src['pos_sigma_semiminor'] = o['sigma_semiminor']
src['pos_r68'] = o['r68']
src['pos_r95'] = o['r95']
src['pos_r99'] = o['r99']
src['pos_angle'] = np.degrees(o['theta'])
self.logger.info('Finished localization.')
return o