def main():
""" Main function for command line usage """
usage = "usage: %(prog)s [options] "
description = "Merge a set of Fermi-LAT files."
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-o', '--output', default=None, type=str,
help='Output file.')
parser.add_argument('--ccube', default=None, type=str,
help='Input counts cube file .')
parser.add_argument('--bexpcube', default=None, type=str,
help='Input binned exposure cube.')
parser.add_argument('--hpx_order', default=None, type=int,
help='Order of output map: default = counts map order')
parser.add_argument('--clobber', action='store_true',
help='Overwrite output file')
args = parser.parse_args()
ccube = HpxMap.create_from_fits(args.ccube, hdu='SKYMAP')
bexpcube = HpxMap.create_from_fits(args.bexpcube, hdu='HPXEXPOSURES')
if args.hpx_order:
hpx_order = args.hpx_order
else:
hpx_order = ccube.hpx.order
out_cube = intensity_cube(ccube, bexpcube, hpx_order)
out_cube.hpx.write_fits(out_cube.data, args.output, clobber=args.clobber)
def copy_ccube(ccube, outsrcmap, hpx_order):
"""Copy a counts cube into outsrcmap file
reducing the HEALPix order to hpx_order if needed.
"""
sys.stdout.write(" Copying counts cube from %s to %s\n" % (ccube, outsrcmap))
try:
hdulist_in = fits.open(ccube)
except IOError:
hdulist_in = fits.open("%s.gz" % ccube)
hpx_order_in = hdulist_in[1].header['ORDER']
if hpx_order_in > hpx_order:
hpxmap = HpxMap.create_from_hdulist(hdulist_in)
hpxmap_out = hpxmap.ud_grade(hpx_order, preserve_counts=True)
hpxlist_out = hdulist_in
#hpxlist_out['SKYMAP'] = hpxmap_out.create_image_hdu()
hpxlist_out[1] = hpxmap_out.create_image_hdu()
hpxlist_out[1].name = 'SKYMAP'
hpxlist_out.writeto(outsrcmap)
return hpx_order
else:
os.system('cp %s %s' % (ccube, outsrcmap))
#os.system('cp %s.gz %s.gz' % (ccube, outsrcmap))
#os.system('gunzip -f %s.gz' % (outsrcmap))
return None
def _compute_counts_from_model(model, bexpcube):
""" Make the counts maps from teh mdoe
"""
data = model.data * bexpcube.data
ebins = model.hpx.ebins
ratio = ebins[1:] / ebins[0:-1]
half_log_ratio = np.log(ratio) / 2.
int_map = ((data[0:-1].T * ebins[0:-1]) + (data[1:].T * ebins[1:])) * half_log_ratio
return HpxMap(int_map.T, model.hpx)
def _make_bright_pixel_mask(intensity_mean, mask_factor=5.0):
""" Make of mask of all the brightest pixels """
mask = np.zeros((intensity_mean.data.shape), bool)
nebins = len(intensity_mean.data)
sum_intensity = intensity_mean.data.sum(0)
mean_intensity = sum_intensity.mean()
for i in range(nebins):
mask[i, 0:] = sum_intensity > (mask_factor * mean_intensity)
return HpxMap(mask, intensity_mean.hpx)
def _smooth_hpx_map(hpx_map, sigma):
""" Smooth a healpix map using a Gaussian
"""
if hpx_map.hpx.ordering == "NESTED":
ring_map = hpx_map.swap_scheme()
else:
ring_map = hpx_map
ring_data = ring_map.data.copy()
nebins = len(hpx_map.data)
smoothed_data = np.zeros((hpx_map.data.shape))
for i in range(nebins):
smoothed_data[i] = healpy.sphtfunc.smoothing(
ring_data[i], sigma=np.radians(sigma), verbose=False)
smoothed_data.clip(0., 1e99)
smoothed_ring_map = HpxMap(smoothed_data, ring_map.hpx)
if hpx_map.hpx.ordering == "NESTED":
return smoothed_ring_map.swap_scheme()
return smoothed_ring_map
def _fill_masked_intensity_resid(intensity_resid, bright_pixel_mask):
""" Fill the pixels used to compute the effective area correction with the mean intensity
"""
filled_intensity = np.zeros((intensity_resid.data.shape))
nebins = len(intensity_resid.data)
for i in range(nebins):
masked = bright_pixel_mask.data[i]
unmasked = np.invert(masked)
mean_intensity = intensity_resid.data[i][unmasked].mean()
filled_intensity[i] = np.where(masked, mean_intensity, intensity_resid.data[i])
return HpxMap(filled_intensity, intensity_resid.hpx)
def _differential_to_integral(hpx_map):
""" Convert a differential map to an integral map
Here we are using log-log-quadrature to compute the integral quantities.
"""
ebins = hpx_map.hpx.ebins
ratio = ebins[1:] / ebins[0:-1]
half_log_ratio = np.log(ratio) / 2.
int_map = ((hpx_map.data[0:-1].T * ebins[0:-1]) +
(hpx_map.data[1:].T * ebins[1:])) * half_log_ratio
return HpxMap(int_map.T, hpx_map.hpx)
def create_from_gti(cls, skydir, tab_sc, tab_gti, zmax, **kwargs):
radius = kwargs.get('radius', 180.0)
cth_edges = kwargs.get('cth_edges', None)
if cth_edges is None:
cth_edges = 1.0 - np.linspace(0, 1.0, 41)**2
cth_edges = cth_edges[::-1]
hpx = HPX(2**4, True, 'CEL', ebins=cth_edges)
hpx_skydir = hpx.get_sky_dirs()
m = skydir.separation(hpx_skydir).deg < radius
map_lt = HpxMap(np.zeros((40, hpx.npix)), hpx)
map_lt_wt = HpxMap(np.zeros((40, hpx.npix)), hpx)
lt, lt_wt = fill_livetime_hist(
hpx_skydir[m], tab_sc, tab_gti, zmax, cth_edges)
map_lt.data[:, m] = lt
map_lt_wt.data[:, m] = lt_wt
hpx2 = HPX(2**6, True, 'CEL', ebins=cth_edges)
ltc = cls(np.zeros((len(cth_edges) - 1, hpx2.npix)), hpx2, cth_edges)
ltc_skydir = ltc.hpx.get_sky_dirs()
m = skydir.separation(ltc_skydir).deg < radius
ltc.data[:, m] = map_lt.interpolate(ltc_skydir[m].ra.deg,
ltc_skydir[m].dec.deg,
interp_log=False)
ltc.data_wt[:, m] = map_lt_wt.interpolate(ltc_skydir[m].ra.deg,
ltc_skydir[m].dec.deg,
interp_log=False)
return ltc
def main():
""" Main function for command line usage """
usage = "usage: %(prog)s [options] "
description = "Merge a set of Fermi-LAT files."
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-o',
'--output',
default=None,
type=str,
help='Output file.')
parser.add_argument('--ccube',
default=None,
type=str,
help='Input counts cube file .')
parser.add_argument('--bexpcube',
default=None,
type=str,
help='Input binned exposure cube.')
parser.add_argument('--hpx_order',
default=None,
type=int,
help='Order of output map: default = counts map order')
parser.add_argument('--clobber',
action='store_true',
help='Overwrite output file')
args = parser.parse_args()
ccube = HpxMap.create_from_fits(args.ccube, hdu='SKYMAP')
bexpcube = HpxMap.create_from_fits(args.bexpcube, hdu='HPXEXPOSURES')
if args.hpx_order:
hpx_order = args.hpx_order
else:
hpx_order = ccube.hpx.order
out_cube = intensity_cube(ccube, bexpcube, hpx_order)
out_cube.hpx.write_fits(out_cube.data, args.output, clobber=args.clobber)
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, 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 test_hpxmap(tmpdir):
n = np.ones((10, 192), 'd')
hpx = HPX(4, False, 'GAL')
filename = str(tmpdir / 'test_hpx.fits')
hpx.write_fits(n, filename, clobber=True)
ebins = np.logspace(2, 5, 8)
hpx_2 = HPX(1024, False, 'GAL', region='DISK(110.,75.,2.)', ebins=ebins)
npixels = hpx_2.npix
n2 = np.ndarray((8, npixels), 'd')
for i in range(8):
n2[i].flat = np.arange(npixels)
hpx_map = HpxMap(n2, hpx_2)
wcs, wcs_data = hpx_map.make_wcs_from_hpx(normalize=True)
wcs_out = hpx_2.make_wcs(3)
filename = str(tmpdir / 'test_hpx_2_wcs.fits')
write_fits_image(wcs_data, wcs_out.wcs, filename)
def stack_energy_planes_hpx(filelist, **kwargs):
"""
"""
from fermipy.skymap import HpxMap
from fermipy.hpx_utils import HPX
maplist = [HpxMap.create_from_fits(fname, **kwargs) for fname in filelist]
energies = np.log10(
np.hstack([amap.hpx.evals for amap in maplist])).squeeze()
counts = np.hstack([amap.counts.flat for amap in maplist])
counts = counts.reshape((len(energies), int(len(counts) / len(energies))))
template_map = maplist[0]
hpx = HPX.create_from_header(template_map.hpx.make_header(), energies)
return HpxMap(counts, hpx)
def append_hdus(hdulist, srcmap_file, source_names, hpx_order):
"""Append HEALPix maps to a list
Parameters
----------
hdulist : list
The list being appended to
srcmap_file : str
Path to the file containing the HDUs
source_names : list of str
Names of the sources to extract from srcmap_file
hpx_order : int
Maximum order for maps
"""
sys.stdout.write(" Extracting %i sources from %s" %
(len(source_names), srcmap_file))
try:
hdulist_in = fits.open(srcmap_file)
except IOError:
try:
hdulist_in = fits.open('%s.gz' % srcmap_file)
except IOError:
sys.stdout.write(" Missing file %s\n" % srcmap_file)
return
for source_name in source_names:
sys.stdout.write('.')
sys.stdout.flush()
if hpx_order is None:
hdulist.append(hdulist_in[source_name])
else:
try:
hpxmap = HpxMap.create_from_hdulist(hdulist_in,
hdu=source_name)
except IndexError:
print(" Index error on source %s in file %s" %
(source_name, srcmap_file))
continue
except KeyError:
print(" Key error on source %s in file %s" %
(source_name, srcmap_file))
continue
hpxmap_out = hpxmap.ud_grade(hpx_order, preserve_counts=True)
hdulist.append(hpxmap_out.create_image_hdu(name=source_name))
sys.stdout.write("\n")
hdulist.flush()
hdulist_in.close()
def intensity_cube(ccube, bexpcube, hpx_order):
"""
"""
if hpx_order == ccube.hpx.order:
ccube_at_order = ccube
else:
ccube_at_order = ccube.ud_grade(hpx_order, preserve_counts=True)
if hpx_order == bexpcube.hpx.order:
bexpcube_at_order = bexpcube
else:
bexpcube_at_order = bexpcube.ud_grade(hpx_order, preserve_counts=True)
bexpcube_data = np.sqrt(bexpcube_at_order.data[0:-1,0:]*bexpcube_at_order.data[1:,0:])
out_data = ccube_at_order.counts / bexpcube_data
return HpxMap(out_data, ccube_at_order.hpx)
def update_hpx_skymap_allsky(map_in, map_out):
""" 'Update' a HEALPix skymap
This checks map_out exists and creates it from map_in if it does not.
If map_out does exist, this adds the data in map_in to map_out
"""
if map_out is None:
in_hpx = map_in.hpx
out_hpx = HPX.create_hpx(in_hpx.nside, in_hpx.nest, in_hpx.coordsys,
None, in_hpx.ebins, None, in_hpx.conv, None)
data_out = map_in.expanded_counts_map()
print(data_out.shape, data_out.sum())
map_out = HpxMap(data_out, out_hpx)
else:
map_out.data += map_in.expanded_counts_map()
return map_out
def append_hdus(hdulist, srcmap_file, source_names, hpx_order):
"""Append HEALPix maps to a list
Parameters
----------
hdulist : list
The list being appended to
srcmap_file : str
Path to the file containing the HDUs
source_names : list of str
Names of the sources to extract from srcmap_file
hpx_order : int
Maximum order for maps
"""
sys.stdout.write(" Extracting %i sources from %s" % (len(source_names), srcmap_file))
try:
hdulist_in = fits.open(srcmap_file)
except IOError:
try:
hdulist_in = fits.open('%s.gz' % srcmap_file)
except IOError:
sys.stdout.write(" Missing file %s\n" % srcmap_file)
return
for source_name in source_names:
sys.stdout.write('.')
sys.stdout.flush()
if hpx_order is None:
hdulist.append(hdulist_in[source_name])
else:
try:
hpxmap = HpxMap.create_from_hdulist(hdulist_in, hdu=source_name)
except IndexError:
print(" Index error on source %s in file %s" % (source_name, srcmap_file))
continue
except KeyError:
print(" Key error on source %s in file %s" % (source_name, srcmap_file))
continue
hpxmap_out = hpxmap.ud_grade(hpx_order, preserve_counts=True)
hdulist.append(hpxmap_out.create_image_hdu(name=source_name))
sys.stdout.write("\n")
hdulist.flush()
hdulist_in.close()
def _intergral_to_differential(hpx_map, gamma=-2.0):
""" Convert integral quantity to differential quantity
Here we are assuming the spectrum is a powerlaw with index gamma and we
are using log-log-quadrature to compute the integral quantities.
"""
nebins = len(hpx_map.data)
diff_map = np.zeros((nebins + 1, hpx_map.hpx.npix))
ebins = hpx_map.hpx.ebins
ratio = ebins[1:] / ebins[0:-1]
half_log_ratio = np.log(ratio) / 2.
ratio_gamma = np.power(ratio, gamma)
#ratio_inv_gamma = np.power(ratio, -1. * gamma)
diff_map[0] = hpx_map.data[0] / ((ebins[0] + ratio_gamma[0] * ebins[1]) * half_log_ratio[0])
for i in range(nebins):
diff_map[i + 1] = (hpx_map.data[i] / (ebins[i + 1] *
half_log_ratio[i])) - (diff_map[i] / ratio[i])
return HpxMap(diff_map, hpx_map.hpx)
def main():
import sys
import argparse
# Argument defintion
usage = "usage: %(prog)s [options]"
description = "Collect all the new source"
parser = argparse.ArgumentParser(usage, description=__abstract__)
parser.add_argument("-i",
"--input",
type=argparse.FileType('r'),
required=True,
help="Input file")
parser.add_argument("-e",
"--extension",
type=str,
default="SKYMAP",
help="FITS HDU with HEALPix map")
parser.add_argument("--ebin",
type=str,
default=None,
help="Energy bin, integer or 'ALL'")
parser.add_argument("--zscale",
type=str,
default='log',
help="Scaling for color scale")
parser.add_argument("--zmin",
type=float,
default=None,
help="Minimum z-axis value")
parser.add_argument("--zmax",
type=float,
default=None,
help="Maximum z-axis value")
parser.add_argument("-o",
"--output",
type=argparse.FileType('w'),
help="Output file. Leave blank for interactive.")
# Parse the command line
args = parser.parse_args(sys.argv[1:])
# Get the model
f = pf.open(args.input.name)
# We need a better check
maptype = "None"
model_hdu = f[args.extension]
hpxmap = HpxMap.create_from_hdulist(f, hdu=args.extension)
outdata = []
if args.ebin == "ALL":
wcsproj = hpxmap.hpx.make_wcs(naxis=2,
proj='MOL',
energies=None,
oversample=2)
mapping = HpxToWcsMapping(hpxmap.hpx, wcsproj)
for i, data in enumerate(hpxmap.counts):
ip = ImagePlotter(data=data, proj=hpxmap.hpx, mapping=mapping)
fig = plt.figure(i)
im, ax = ip.plot(zscale=args.zscale,
vmin=args.zmin,
vmax=args.zmax)
outdata.append(fig)
elif args.ebin is None:
ip = ImagePlotter(data=hpxmap.counts, proj=hpxmap.hpx)
im, ax = ip.plot(zscale=args.zscale, vmin=args.zmin, vmax=args.zmax)
outdata.append((im, ax))
else:
try:
ibin = int(args.ebin)
ip = ImagePlotter(data=hpxmap.counts[ibin], proj=hpxmap.hpx)
im, ax = ip.plot(zscale=args.zscale,
vmin=args.zmin,
vmax=args.zmax)
outdata.append((im, ax))
except:
raise ValueError("--ebin argument must be an integer or 'ALL'")
if args.output is None:
plt.show()
else:
if len(outdata) == 1:
plt.savefig(args.output.name)
else:
base, ext = os.path.splitext(args.output.name)
for i, fig in enumerate(outdata):
fig.savefig("%s_%02i%s" % (base, i, ext))
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 main():
import sys
import argparse
# Argument defintion
usage = "usage: %(prog)s [options]"
description = "Collect all the new source"
parser = argparse.ArgumentParser(usage,description=__abstract__)
parser.add_argument("-i", "--input",type=argparse.FileType('r'),required=True,
help="Input file")
parser.add_argument("-e", "--extension",type=str,default="SKYMAP",
help="FITS HDU with HEALPix map")
parser.add_argument("--ebin",type=str,default=None,
help="Energy bin, integer or 'ALL'")
parser.add_argument("-o", "--output",type=argparse.FileType('w'),
help="Output file. Leave blank for interactive.")
# Parse the command line
args = parser.parse_args(sys.argv[1:])
# Get the model
f = pf.open(args.input.name)
# We need a better check
maptype = "None"
model_hdu = f[args.extension]
hpxmap = HpxMap.create_from_hdulist(f,extname=args.extension,ebounds="EBOUNDS")
outdata = []
if args.ebin == "ALL":
wcsproj = hpxmap.hpx.make_wcs(naxis=2,proj='AIT',energies=None,oversample=2)
mapping = HpxToWcsMapping(hpxmap.hpx,wcsproj)
for i,data in enumerate(hpxmap.counts):
ip = ImagePlotter(data=data,proj=hpxmap.hpx,mapping=mapping)
fig = plt.figure(i)
im,ax = ip.plot(zscale='log')
outdata.append(fig)
elif args.ebin is None:
ip = ImagePlotter(data=hpxmap.counts,proj=hpxmap.hpx)
im,ax = ip.plot(zscale='log')
outdata.append((im,ax))
else:
try:
ibin = int(args.ebin)
ip = ImagePlotter(data=hpxmap.counts[ibin],proj=hpxmap.hpx)
im,ax = ip.plot(zscale='log')
outdata.append((im,ax))
except:
print("--ebin argument must be an integer or 'ALL'")
if args.output is None:
plt.show()
else:
plt.savefig(args.output.name)
def merge_hpx_counts_cubes(filelist):
""" Merge all the files in filelist, assuming that they HEALPix counts cubes
"""
out_prim = None
out_skymap = None
out_ebounds = None
datalist_gti = []
exposure_sum = 0.
nfiles = len(filelist)
ngti = np.zeros(nfiles, int)
out_name = None
for i, filename in enumerate(filelist):
fin = fits.open(filename)
sys.stdout.write('.')
sys.stdout.flush()
if i == 0:
out_prim = update_null_primary(fin[0], out_prim)
out_name = fin[1].name
map_in = HpxMap.create_from_hdulist(fin)
out_skymap = update_hpx_skymap_allsky(map_in, out_skymap)
if i == 0:
try:
out_ebounds = update_ebounds(fin["EBOUNDS"], out_ebounds)
except KeyError:
out_ebounds = update_energies(fin["ENERGIES"], out_ebounds)
try:
(gti_data, exposure, tstop) = extract_gti_data(fin["GTI"])
datalist_gti.append(gti_data)
exposure_sum += exposure
ngti[i] = len(gti_data)
except KeyError:
pass
if i == 0:
first = fin
elif i == nfiles - 1:
try:
date_end = fin[0].header['DATE-END']
except KeyError:
date_end = None
else:
fin.close()
out_skymap_hdu = out_skymap.create_image_hdu("SKYMAP")
hdulist = [out_prim, out_skymap_hdu, out_ebounds]
if len(datalist_gti) > 0:
out_gti = merge_all_gti_data(datalist_gti, ngti, first['GTI'])
out_gti.header['EXPOSURE'] = exposure_sum
out_gti.header['TSTOP'] = tstop
hdulist.append(out_gti)
for hdu in hdulist:
if date_end:
hdu.header['DATE-END'] = date_end
out_prim.update_header()
sys.stdout.write("!\n")
return fits.HDUList(hdulist)
def run_analysis(self, argv):
"""Run this analysis"""
args = self._parser.parse_args(argv)
# Read the input maps
ccube_dirty = HpxMap.create_from_fits(args.ccube_dirty, hdu='SKYMAP')
bexpcube_dirty = HpxMap.create_from_fits(args.bexpcube_dirty, hdu='HPXEXPOSURES')
ccube_clean = HpxMap.create_from_fits(args.ccube_clean, hdu='SKYMAP')
bexpcube_clean = HpxMap.create_from_fits(args.bexpcube_clean, hdu='HPXEXPOSURES')
# Decide what HEALPix order to work at
if args.hpx_order:
hpx_order = args.hpx_order
else:
hpx_order = ccube_dirty.hpx.order
# Cast all the input maps to match ccube_clean
cube_dict = ResidualCR._match_cubes(ccube_clean, ccube_dirty,
bexpcube_clean, bexpcube_dirty, hpx_order)
# Intenstiy maps
intensity_clean = ResidualCR._compute_intensity(cube_dict['ccube_clean'],
cube_dict['bexpcube_clean'])
intensity_dirty = ResidualCR._compute_intensity(cube_dict['ccube_dirty'],
cube_dict['bexpcube_dirty'])
# Mean & ratio of intensity maps
intensity_mean = ResidualCR._compute_mean(intensity_dirty,
intensity_clean)
intensity_ratio = ResidualCR._compute_ratio(intensity_dirty,
intensity_clean)
# Selecting the bright pixels for Aeff correction and to mask when filling output map
bright_pixel_select = ResidualCR._make_bright_pixel_mask(intensity_mean,
args.select_factor)
bright_pixel_mask = ResidualCR._make_bright_pixel_mask(intensity_mean,
args.mask_factor)
# Compute thte Aeff corrections using the brightest pixels
aeff_corrections = ResidualCR._get_aeff_corrections(intensity_ratio,
bright_pixel_select)
# Apply the Aeff corrections and get the intensity residual
corrected_dirty = ResidualCR._apply_aeff_corrections(intensity_dirty,
aeff_corrections)
corrected_ratio = ResidualCR._compute_ratio(corrected_dirty,
intensity_clean)
intensity_resid = ResidualCR._compute_diff(corrected_dirty,
intensity_clean)
# Replace the masked pixels with the map mean to avoid features associates with sources
filled_resid = ResidualCR._fill_masked_intensity_resid(intensity_resid,
bright_pixel_mask)
# Smooth the map
smooth_resid = ResidualCR._smooth_hpx_map(filled_resid,
args.sigma)
# Convert to a differential map
out_model = ResidualCR._intergral_to_differential(smooth_resid)
# Make the ENERGIES HDU
out_energies = ccube_dirty.hpx.make_energies_hdu()
# Write the maps
cubes = dict(SKYMAP=out_model)
fits_utils.write_maps(None, cubes,
args.outfile, energy_hdu=out_energies)
if args.full_output:
# Some diagnostics
check = ResidualCR._differential_to_integral(out_model)
check_resid = ResidualCR._compute_diff(smooth_resid, check)
counts_resid =\
ResidualCR._compute_counts_from_intensity(intensity_resid,
cube_dict['bexpcube_dirty'])
pred_counts\
= ResidualCR._compute_counts_from_model(out_model,
cube_dict['bexpcube_dirty'])
pred_resid = ResidualCR._compute_diff(pred_counts, counts_resid)
out_ebounds = ccube_dirty.hpx.make_energy_bounds_hdu()
cubes = dict(INTENSITY_CLEAN=intensity_clean,
INTENSITY_DIRTY=intensity_dirty,
INTENSITY_RATIO=intensity_ratio,
CORRECTED_DIRTY=corrected_dirty,
CORRECTED_RATIO=corrected_ratio,
INTENSITY_RESID=intensity_resid,
PIXEL_SELECT=bright_pixel_select,
PIXEL_MASK=bright_pixel_mask,
FILLED_RESID=filled_resid,
SMOOTH_RESID=smooth_resid,
CHECK=check,
CHECK_RESID=check_resid,
COUNTS_RESID=counts_resid,
PRED_COUNTS=pred_counts,
PRED_RESID=pred_resid)
fits_utils.write_maps(None, cubes,
args.outfile.replace('.fits', '_full.fits'),
energy_hdu=out_ebounds)
def merge_hpx_counts_cubes(filelist):
""" Merge all the files in filelist, assuming that they HEALPix counts cubes
"""
out_prim = None
out_skymap = None
out_ebounds = None
datalist_gti = []
exposure_sum = 0.
nfiles = len(filelist)
ngti = np.zeros(nfiles, int)
out_name = None
for i, filename in enumerate(filelist):
fin = fits.open(filename)
sys.stdout.write('.')
sys.stdout.flush()
if i == 0:
out_prim = update_null_primary(fin[0], out_prim)
out_name = fin[1].name
map_in = HpxMap.create_from_hdulist(fin)
out_skymap = update_hpx_skymap_allsky(map_in, out_skymap)
if i == 0:
try:
out_ebounds = update_ebounds(fin["EBOUNDS"], out_ebounds)
except KeyError:
out_ebounds = update_energies(fin["ENERGIES"], out_ebounds)
try:
(gti_data, exposure, tstop) = extract_gti_data(fin["GTI"])
datalist_gti.append(gti_data)
exposure_sum += exposure
ngti[i] = len(gti_data)
except KeyError:
pass
if i == 0:
first = fin
elif i == nfiles - 1:
try:
date_end = fin[0].header['DATE-END']
except KeyError:
date_end = None
else:
fin.close()
out_skymap_hdu = out_skymap.create_image_hdu("SKYMAP")
hdulist = [out_prim, out_skymap_hdu, out_ebounds]
if len(datalist_gti) > 0:
out_gti = merge_all_gti_data(datalist_gti, ngti, first['GTI'])
out_gti.header['EXPOSURE'] = exposure_sum
out_gti.header['TSTOP'] = tstop
hdulist.append(out_gti)
for hdu in hdulist:
if date_end:
hdu.header['DATE-END'] = date_end
out_prim.update_header()
sys.stdout.write("!\n")
return fits.HDUList(hdulist)
def _compute_ratio(top, bot):
""" Make a map that is the ratio of two maps
"""
data = np.where(bot.data > 0, top.data / bot.data, 0.)
return HpxMap(data, top.hpx)
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 _apply_aeff_corrections(intensity_map, aeff_corrections):
""" Multipy a map by the effective area correction
"""
data = aeff_corrections * intensity_map.data.T
return HpxMap(data.T, intensity_map.hpx)
def _compute_diff(map1, map2):
""" Make a map that is the difference of two maps
"""
data = map1.data - map2.data
return HpxMap(data, map1.hpx)
def __init__(self, data, hpx):
HpxMap.__init__(self, data, hpx)
def _compute_product(map1, map2):
""" Make a map that is the product of two maps
"""
data = map1.data * map2.data
return HpxMap(data, map1.hpx)
def _compute_mean(map1, map2):
""" Make a map that is the mean of two maps
"""
data = (map1.data + map2.data) / 2.
return HpxMap(data, map1.hpx)
def __init__(self, data, hpx):
HpxMap.__init__(self, data, hpx)
def _compute_counts_from_intensity(intensity, bexpcube):
""" Make the counts map from the intensity
"""
data = intensity.data * np.sqrt(bexpcube.data[1:] * bexpcube.data[0:-1])
return HpxMap(data, intensity.hpx)