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fermiPy/fermipy | fermipy/skymap.py | Map.get_pixel_indices | def get_pixel_indices(self, lons, lats, ibin=None):
"""Return the indices in the flat array corresponding to a set of coordinates
Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
ibin... | python | def get_pixel_indices(self, lons, lats, ibin=None):
"""Return the indices in the flat array corresponding to a set of coordinates
Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
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fermiPy/fermipy | fermipy/skymap.py | Map.get_map_values | def get_map_values(self, lons, lats, ibin=None):
"""Return the map values corresponding to a set of coordinates.
Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
ibin : int or array-l... | python | def get_map_values(self, lons, lats, ibin=None):
"""Return the map values corresponding to a set of coordinates.
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.create_from_hdu | def create_from_hdu(cls, hdu, ebins):
""" Creates and returns an HpxMap object from a FITS HDU.
hdu : The FITS
ebins : Energy bin edges [optional]
"""
hpx = HPX.create_from_hdu(hdu, ebins)
colnames = hdu.columns.names
cnames = []
if hpx.conv.convname ... | python | def create_from_hdu(cls, hdu, ebins):
""" Creates and returns an HpxMap object from a FITS HDU.
hdu : The FITS
ebins : Energy bin edges [optional]
"""
hpx = HPX.create_from_hdu(hdu, ebins)
colnames = hdu.columns.names
cnames = []
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.create_from_hdulist | def create_from_hdulist(cls, hdulist, **kwargs):
""" Creates and returns an HpxMap object from a FITS HDUList
extname : The name of the HDU with the map data
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"""
extname = kwargs.get('hdu', hdulist[1].name)
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""" Creates and returns an HpxMap object from a FITS HDUList
extname : The name of the HDU with the map data
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.make_wcs_from_hpx | def make_wcs_from_hpx(self, sum_ebins=False, proj='CAR', oversample=2,
normalize=True):
"""Make a WCS object and convert HEALPix data into WCS projection
NOTE: this re-calculates the mapping, if you have already
calculated the mapping it is much faster to use
c... | python | def make_wcs_from_hpx(self, sum_ebins=False, proj='CAR', oversample=2,
normalize=True):
"""Make a WCS object and convert HEALPix data into WCS projection
NOTE: this re-calculates the mapping, if you have already
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.convert_to_cached_wcs | def convert_to_cached_wcs(self, hpx_in, sum_ebins=False, normalize=True):
""" Make a WCS object and convert HEALPix data into WCS projection
Parameters
----------
hpx_in : `~numpy.ndarray`
HEALPix input data
sum_ebins : bool
sum energy bins over energy... | python | def convert_to_cached_wcs(self, hpx_in, sum_ebins=False, normalize=True):
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.get_pixel_skydirs | def get_pixel_skydirs(self):
"""Get a list of sky coordinates for the centers of every pixel. """
sky_coords = self._hpx.get_sky_coords()
if self.hpx.coordsys == 'GAL':
return SkyCoord(l=sky_coords.T[0], b=sky_coords.T[1], unit='deg', frame='galactic')
else:
retur... | python | def get_pixel_skydirs(self):
"""Get a list of sky coordinates for the centers of every pixel. """
sky_coords = self._hpx.get_sky_coords()
if self.hpx.coordsys == 'GAL':
return SkyCoord(l=sky_coords.T[0], b=sky_coords.T[1], unit='deg', frame='galactic')
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.sum_over_energy | def sum_over_energy(self):
""" Reduce a counts cube to a counts map """
# We sum over axis 0 in the array, and drop the energy binning in the
# hpx object
return HpxMap(np.sum(self.counts, axis=0), self.hpx.copy_and_drop_energy()) | python | def sum_over_energy(self):
""" Reduce a counts cube to a counts map """
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return HpxMap(np.sum(self.counts, axis=0), self.hpx.copy_and_drop_energy()) | [
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.get_map_values | def get_map_values(self, lons, lats, ibin=None):
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Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
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lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.interpolate | def interpolate(self, lon, lat, egy=None, interp_log=True):
"""Interpolate map values.
Parameters
----------
interp_log : bool
Interpolate the z-coordinate in logspace.
"""
if self.data.ndim == 1:
theta = np.pi / 2. - np.radians(lat)
... | python | def interpolate(self, lon, lat, egy=None, interp_log=True):
"""Interpolate map values.
Parameters
----------
interp_log : bool
Interpolate the z-coordinate in logspace.
"""
if self.data.ndim == 1:
theta = np.pi / 2. - np.radians(lat)
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fermiPy/fermipy | fermipy/skymap.py | HpxMap._interpolate_cube | def _interpolate_cube(self, lon, lat, egy=None, interp_log=True):
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then interpolation will be performed on the existing energy
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"""
shape = np.broadcast(lon, lat, egy).shape
lon = lon * np.ones(shape)
... | python | def _interpolate_cube(self, lon, lat, egy=None, interp_log=True):
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"""
shape = np.broadcast(lon, lat, egy).shape
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.expanded_counts_map | def expanded_counts_map(self):
""" return the full counts map """
if self.hpx._ipix is None:
return self.counts
output = np.zeros(
(self.counts.shape[0], self.hpx._maxpix), self.counts.dtype)
for i in range(self.counts.shape[0]):
output[i][self.hpx._i... | python | def expanded_counts_map(self):
""" return the full counts map """
if self.hpx._ipix is None:
return self.counts
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.explicit_counts_map | def explicit_counts_map(self, pixels=None):
""" return a counts map with explicit index scheme
Parameters
----------
pixels : `np.ndarray` or None
If set, grab only those pixels.
If none, grab only non-zero pixels
"""
# No pixel index, so build ... | python | def explicit_counts_map(self, pixels=None):
""" return a counts map with explicit index scheme
Parameters
----------
pixels : `np.ndarray` or None
If set, grab only those pixels.
If none, grab only non-zero pixels
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fermiPy/fermipy | fermipy/skymap.py | HpxMap.sparse_counts_map | def sparse_counts_map(self):
""" return a counts map with sparse index scheme
"""
if self.hpx._ipix is None:
flatarray = self.data.flattern()
else:
flatarray = self.expanded_counts_map()
nz = flatarray.nonzero()[0]
data_out = flatarray[nz]
... | python | def sparse_counts_map(self):
""" return a counts map with sparse index scheme
"""
if self.hpx._ipix is None:
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fermiPy/fermipy | fermipy/sensitivity.py | SensitivityCalc.compute_counts | 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
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"""Compute signal and background counts for a point source at
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fermiPy/fermipy | fermipy/sensitivity.py | SensitivityCalc.diff_flux_threshold | def diff_flux_threshold(self, skydir, fn, ts_thresh, min_counts):
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position ``skydir`` with spectral parameterization ``fn``.
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skydir : `~astropy.coordinates.SkyCoord`
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fermiPy/fermipy | fermipy/sensitivity.py | SensitivityCalc.int_flux_threshold | def int_flux_threshold(self, skydir, fn, ts_thresh, min_counts):
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fermiPy/fermipy | fermipy/hpx_utils.py | coords_to_vec | def coords_to_vec(lon, lat):
""" Converts longitute and latitude coordinates to a unit 3-vector
return array(3,n) with v_x[i],v_y[i],v_z[i] = directional cosines
"""
phi = np.radians(lon)
theta = (np.pi / 2) - np.radians(lat)
sin_t = np.sin(theta)
cos_t = np.cos(theta)
xVals = sin_t * ... | python | def coords_to_vec(lon, lat):
""" Converts longitute and latitude coordinates to a unit 3-vector
return array(3,n) with v_x[i],v_y[i],v_z[i] = directional cosines
"""
phi = np.radians(lon)
theta = (np.pi / 2) - np.radians(lat)
sin_t = np.sin(theta)
cos_t = np.cos(theta)
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fermiPy/fermipy | fermipy/hpx_utils.py | get_pixel_size_from_nside | def get_pixel_size_from_nside(nside):
""" Returns an estimate of the pixel size from the HEALPix nside coordinate
This just uses a lookup table to provide a nice round number for each
HEALPix order.
"""
order = int(np.log2(nside))
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raise ValueError('HEALPix o... | python | def get_pixel_size_from_nside(nside):
""" Returns an estimate of the pixel size from the HEALPix nside coordinate
This just uses a lookup table to provide a nice round number for each
HEALPix order.
"""
order = int(np.log2(nside))
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fermiPy/fermipy | fermipy/hpx_utils.py | hpx_to_axes | def hpx_to_axes(h, npix):
""" Generate a sequence of bin edge vectors corresponding to the
axes of a HPX object."""
x = h.ebins
z = np.arange(npix[-1] + 1)
return x, z | python | def hpx_to_axes(h, npix):
""" Generate a sequence of bin edge vectors corresponding to the
axes of a HPX object."""
x = h.ebins
z = np.arange(npix[-1] + 1)
return x, z | [
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fermiPy/fermipy | fermipy/hpx_utils.py | hpx_to_coords | def hpx_to_coords(h, shape):
""" Generate an N x D list of pixel center coordinates where N is
the number of pixels and D is the dimensionality of the map."""
x, z = hpx_to_axes(h, shape)
x = np.sqrt(x[0:-1] * x[1:])
z = z[:-1] + 0.5
x = np.ravel(np.ones(shape) * x[:, np.newaxis])
z = np.... | python | def hpx_to_coords(h, shape):
""" Generate an N x D list of pixel center coordinates where N is
the number of pixels and D is the dimensionality of the map."""
x, z = hpx_to_axes(h, shape)
x = np.sqrt(x[0:-1] * x[1:])
z = z[:-1] + 0.5
x = np.ravel(np.ones(shape) * x[:, np.newaxis])
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fermiPy/fermipy | fermipy/hpx_utils.py | make_hpx_to_wcs_mapping_centers | def make_hpx_to_wcs_mapping_centers(hpx, wcs):
""" Make the mapping data needed to from from HPX pixelization to a
WCS-based array
Parameters
----------
hpx : `~fermipy.hpx_utils.HPX`
The healpix mapping (an HPX object)
wcs : `~astropy.wcs.WCS`
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""" Make the mapping data needed to from from HPX pixelization to a
WCS-based array
Parameters
----------
hpx : `~fermipy.hpx_utils.HPX`
The healpix mapping (an HPX object)
wcs : `~astropy.wcs.WCS`
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fermiPy/fermipy | fermipy/hpx_utils.py | make_hpx_to_wcs_mapping | def make_hpx_to_wcs_mapping(hpx, wcs):
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hpx : `~fermipy.hpx_utils.HPX`
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hpx : `~fermipy.hpx_utils.HPX`
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fermiPy/fermipy | fermipy/hpx_utils.py | parse_hpxregion | def parse_hpxregion(region):
"""Parse the HPX_REG header keyword into a list of tokens."""
m = re.match(r'([A-Za-z\_]*?)\((.*?)\)', region)
if m is None:
raise Exception('Failed to parse hpx region string.')
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"""Parse the HPX_REG header keyword into a list of tokens."""
m = re.match(r'([A-Za-z\_]*?)\((.*?)\)', region)
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fermiPy/fermipy | fermipy/hpx_utils.py | upix_to_pix | def upix_to_pix(upix):
"""Get the nside from a unique pixel number."""
nside = np.power(2, np.floor(np.log2(upix / 4)) / 2).astype(int)
pix = upix - 4 * np.power(nside, 2)
return pix, nside | python | def upix_to_pix(upix):
"""Get the nside from a unique pixel number."""
nside = np.power(2, np.floor(np.log2(upix / 4)) / 2).astype(int)
pix = upix - 4 * np.power(nside, 2)
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.create_hpx | def create_hpx(cls, nside, nest, coordsys='CEL', order=-1, ebins=None,
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"""Create a HPX object.
Parameters
----------
nside : int
HEALPix nside paramter
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.identify_HPX_convention | def identify_HPX_convention(header):
""" Identify the convention used to write this file """
# Hopefully the file contains the HPX_CONV keyword specifying
# the convention used
try:
return header['HPX_CONV']
except KeyError:
pass
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""" Identify the convention used to write this file """
# Hopefully the file contains the HPX_CONV keyword specifying
# the convention used
try:
return header['HPX_CONV']
except KeyError:
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.create_from_header | def create_from_header(cls, header, ebins=None, pixels=None):
""" Creates an HPX object from a FITS header.
header : The FITS header
ebins : Energy bin edges [optional]
"""
convname = HPX.identify_HPX_convention(header)
conv = HPX_FITS_CONVENTIONS[convname]
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""" Creates an HPX object from a FITS header.
header : The FITS header
ebins : Energy bin edges [optional]
"""
convname = HPX.identify_HPX_convention(header)
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.create_from_hdu | def create_from_hdu(cls, hdu, ebins=None):
""" Creates an HPX object from a FITS header.
hdu : The FITS hdu
ebins : Energy bin edges [optional]
"""
convname = HPX.identify_HPX_convention(hdu.header)
conv = HPX_FITS_CONVENTIONS[convname]
try:
pixel... | python | def create_from_hdu(cls, hdu, ebins=None):
""" Creates an HPX object from a FITS header.
hdu : The FITS hdu
ebins : Energy bin edges [optional]
"""
convname = HPX.identify_HPX_convention(hdu.header)
conv = HPX_FITS_CONVENTIONS[convname]
try:
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.make_header | def make_header(self):
""" Builds and returns FITS header for this HEALPix map """
cards = [fits.Card("TELESCOP", "GLAST"),
fits.Card("INSTRUME", "LAT"),
fits.Card(self._conv.coordsys, self._coordsys),
fits.Card("PIXTYPE", "HEALPIX"),
f... | python | def make_header(self):
""" Builds and returns FITS header for this HEALPix map """
cards = [fits.Card("TELESCOP", "GLAST"),
fits.Card("INSTRUME", "LAT"),
fits.Card(self._conv.coordsys, self._coordsys),
fits.Card("PIXTYPE", "HEALPIX"),
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.make_hdu | def make_hdu(self, data, **kwargs):
""" Builds and returns a FITs HDU with input data
data : The data begin stored
Keyword arguments
-------------------
extname : The HDU extension name
colbase : The prefix for column names
"""
shape = d... | python | def make_hdu(self, data, **kwargs):
""" Builds and returns a FITs HDU with input data
data : The data begin stored
Keyword arguments
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extname : The HDU extension name
colbase : The prefix for column names
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.make_energy_bounds_hdu | def make_energy_bounds_hdu(self, extname="EBOUNDS"):
""" Builds and returns a FITs HDU with the energy bin boundries
extname : The HDU extension name
"""
if self._ebins is None:
return None
cols = [fits.Column("CHANNEL", "I", array=np.arange(1, len(self... | python | def make_energy_bounds_hdu(self, extname="EBOUNDS"):
""" Builds and returns a FITs HDU with the energy bin boundries
extname : The HDU extension name
"""
if self._ebins is None:
return None
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.make_energies_hdu | def make_energies_hdu(self, extname="ENERGIES"):
""" Builds and returns a FITs HDU with the energy bin boundries
extname : The HDU extension name
"""
if self._evals is None:
return None
cols = [fits.Column("ENERGY", "1E", unit='MeV',
... | python | def make_energies_hdu(self, extname="ENERGIES"):
""" Builds and returns a FITs HDU with the energy bin boundries
extname : The HDU extension name
"""
if self._evals is None:
return None
cols = [fits.Column("ENERGY", "1E", unit='MeV',
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.write_fits | def write_fits(self, data, outfile, extname="SKYMAP", clobber=True):
""" Write input data to a FITS file
data : The data begin stored
outfile : The name of the output file
extname : The HDU extension name
clobber : True -> overwrite existing files
"""
... | python | def write_fits(self, data, outfile, extname="SKYMAP", clobber=True):
""" Write input data to a FITS file
data : The data begin stored
outfile : The name of the output file
extname : The HDU extension name
clobber : True -> overwrite existing files
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.get_index_list | def get_index_list(nside, nest, region):
""" Returns the list of pixels indices for all the pixels in a region
nside : HEALPix nside parameter
nest : True for 'NESTED', False = 'RING'
region : HEALPix region string
"""
tokens = parse_hpxregion(region)
i... | python | def get_index_list(nside, nest, region):
""" Returns the list of pixels indices for all the pixels in a region
nside : HEALPix nside parameter
nest : True for 'NESTED', False = 'RING'
region : HEALPix region string
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tokens = parse_hpxregion(region)
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.get_ref_dir | def get_ref_dir(region, coordsys):
""" Finds and returns the reference direction for a given
HEALPix region string.
region : a string describing a HEALPix region
coordsys : coordinate system, GAL | CEL
"""
if region is None:
if coordsys == "GAL":
... | python | def get_ref_dir(region, coordsys):
""" Finds and returns the reference direction for a given
HEALPix region string.
region : a string describing a HEALPix region
coordsys : coordinate system, GAL | CEL
"""
if region is None:
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.get_region_size | def get_region_size(region):
""" Finds and returns the approximate size of region (in degrees)
from a HEALPix region string.
"""
if region is None:
return 180.
tokens = parse_hpxregion(region)
if tokens[0] in ['DISK', 'DISK_INC']:
return float... | python | def get_region_size(region):
""" Finds and returns the approximate size of region (in degrees)
from a HEALPix region string.
"""
if region is None:
return 180.
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.make_wcs | def make_wcs(self, naxis=2, proj='CAR', energies=None, oversample=2):
""" Make a WCS projection appropirate for this HPX pixelization
"""
w = WCS(naxis=naxis)
skydir = self.get_ref_dir(self._region, self.coordsys)
if self.coordsys == 'CEL':
w.wcs.ctype[0] = 'RA---%s'... | python | def make_wcs(self, naxis=2, proj='CAR', energies=None, oversample=2):
""" Make a WCS projection appropirate for this HPX pixelization
"""
w = WCS(naxis=naxis)
skydir = self.get_ref_dir(self._region, self.coordsys)
if self.coordsys == 'CEL':
w.wcs.ctype[0] = 'RA---%s'... | [
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.get_sky_coords | def get_sky_coords(self):
""" Get the sky coordinates of all the pixels in this pixelization """
if self._ipix is None:
theta, phi = hp.pix2ang(
self._nside, list(range(self._npix)), self._nest)
else:
theta, phi = hp.pix2ang(self._nside, self._ipix, self._... | python | def get_sky_coords(self):
""" Get the sky coordinates of all the pixels in this pixelization """
if self._ipix is None:
theta, phi = hp.pix2ang(
self._nside, list(range(self._npix)), self._nest)
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.get_pixel_indices | def get_pixel_indices(self, lats, lons):
""" "Return the indices in the flat array corresponding to a set of coordinates """
theta = np.radians(90. - lats)
phi = np.radians(lons)
return hp.ang2pix(self.nside, theta, phi, self.nest) | python | def get_pixel_indices(self, lats, lons):
""" "Return the indices in the flat array corresponding to a set of coordinates """
theta = np.radians(90. - lats)
phi = np.radians(lons)
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fermiPy/fermipy | fermipy/hpx_utils.py | HPX.skydir_to_pixel | def skydir_to_pixel(self, skydir):
"""Return the pixel index of a SkyCoord object."""
if self.coordsys in ['CEL', 'EQU']:
skydir = skydir.transform_to('icrs')
lon = skydir.ra.deg
lat = skydir.dec.deg
else:
skydir = skydir.transform_to('galactic')
... | python | def skydir_to_pixel(self, skydir):
"""Return the pixel index of a SkyCoord object."""
if self.coordsys in ['CEL', 'EQU']:
skydir = skydir.transform_to('icrs')
lon = skydir.ra.deg
lat = skydir.dec.deg
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skydir = skydir.transform_to('galactic')
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fermiPy/fermipy | fermipy/hpx_utils.py | HpxToWcsMapping.write_to_fitsfile | def write_to_fitsfile(self, fitsfile, clobber=True):
"""Write this mapping to a FITS file, to avoid having to recompute it
"""
from fermipy.skymap import Map
hpx_header = self._hpx.make_header()
index_map = Map(self.ipixs, self.wcs)
mult_map = Map(self.mult_val, self.wcs)... | python | def write_to_fitsfile(self, fitsfile, clobber=True):
"""Write this mapping to a FITS file, to avoid having to recompute it
"""
from fermipy.skymap import Map
hpx_header = self._hpx.make_header()
index_map = Map(self.ipixs, self.wcs)
mult_map = Map(self.mult_val, self.wcs)... | [
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fermiPy/fermipy | fermipy/hpx_utils.py | HpxToWcsMapping.create_from_fitsfile | def create_from_fitsfile(cls, fitsfile):
""" Read a fits file and use it to make a mapping
"""
from fermipy.skymap import Map
index_map = Map.create_from_fits(fitsfile)
mult_map = Map.create_from_fits(fitsfile, hdu=1)
ff = fits.open(fitsfile)
hpx = HPX.create_from... | python | def create_from_fitsfile(cls, fitsfile):
""" Read a fits file and use it to make a mapping
"""
from fermipy.skymap import Map
index_map = Map.create_from_fits(fitsfile)
mult_map = Map.create_from_fits(fitsfile, hdu=1)
ff = fits.open(fitsfile)
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fermiPy/fermipy | fermipy/hpx_utils.py | HpxToWcsMapping.fill_wcs_map_from_hpx_data | def fill_wcs_map_from_hpx_data(self, hpx_data, wcs_data, normalize=True):
"""Fills the wcs map from the hpx data using the pre-calculated
mappings
hpx_data : the input HEALPix data
wcs_data : the data array being filled
normalize : True -> perserve integral by splitting HEALPi... | python | def fill_wcs_map_from_hpx_data(self, hpx_data, wcs_data, normalize=True):
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fermiPy/fermipy | fermipy/jobs/target_collect.py | _get_enum_bins | def _get_enum_bins(configfile):
"""Get the number of energy bin in the SED
Parameters
----------
configfile : str
Fermipy configuration file.
Returns
-------
nbins : int
The number of energy bins
"""
config = yaml.safe_load(open(configfile))
emin = config['s... | python | def _get_enum_bins(configfile):
"""Get the number of energy bin in the SED
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configfile : str
Fermipy configuration file.
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nbins : int
The number of energy bins
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fermiPy/fermipy | fermipy/jobs/target_collect.py | fill_output_table | def fill_output_table(filelist, hdu, collist, nbins):
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Parameters
----------
filelist : list
List of the files to get data from.
hdu : str
Name of the HDU containing the table with the input data.
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List of the files to get data from.
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fermiPy/fermipy | fermipy/jobs/target_collect.py | vstack_tables | def vstack_tables(filelist, hdus):
"""vstack a set of HDUs from a set of files
Parameters
----------
filelist : list
List of the files to get data from.
hdus : list
Names of the HDU containing the table with the input data.
Returns
-------
out_tables : list
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"""vstack a set of HDUs from a set of files
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filelist : list
List of the files to get data from.
hdus : list
Names of the HDU containing the table with the input data.
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fermiPy/fermipy | fermipy/jobs/target_collect.py | collect_summary_stats | def collect_summary_stats(data):
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This creates a dictionry of output arrays of summary
statistics, with the input array dimension reducted by one.
Parameters
----------
data : `numpy.ndarray`
Array with the collected input data
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"""Collect summary statisitics from an array
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fermiPy/fermipy | fermipy/jobs/target_collect.py | add_summary_stats_to_table | def add_summary_stats_to_table(table_in, table_out, colnames):
"""Collect summary statisitics from an input table and add them to an output table
Parameters
----------
table_in : `astropy.table.Table`
Table with the input data.
table_out : `astropy.table.Table`
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fermiPy/fermipy | fermipy/jobs/target_collect.py | summarize_sed_results | def summarize_sed_results(sed_table):
"""Build a stats summary table for a table that has all the SED results """
del_cols = ['dnde', 'dnde_err', 'dnde_errp', 'dnde_errn', 'dnde_ul',
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"""Build a stats summary table for a table that has all the SED results """
del_cols = ['dnde', 'dnde_err', 'dnde_errp', 'dnde_errn', 'dnde_ul',
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fermiPy/fermipy | fermipy/jobs/target_collect.py | CollectSED.run_analysis | def run_analysis(self, argv):
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sedfile = args.sed_file
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"""Run this analysis"""
args = self._parser.parse_args(argv)
sedfile = args.sed_file
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fermiPy/fermipy | fermipy/jobs/target_collect.py | CollectSED_SG.build_job_configs | def build_job_configs(self, args):
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job_configs = {}
ttype = args['ttype']
(targets_yaml, sim) = NAME_FACTORY.resolve_targetfile(
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if targets_yaml is None:
return job_configs
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"""
job_configs = {}
ttype = args['ttype']
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fermiPy/fermipy | fermipy/jobs/name_policy.py | NameFactory.update_base_dict | def update_base_dict(self, yamlfile):
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fermiPy/fermipy | fermipy/jobs/name_policy.py | NameFactory._format_from_dict | def _format_from_dict(self, format_string, **kwargs):
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kwargs_copy = self.base_dict.copy()
kwargs_copy.update(**kwargs)
localpath = format_string.format(**kwargs_copy)
if kwargs.get('fullpath', False):
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fermiPy/fermipy | fermipy/jobs/name_policy.py | NameFactory.sim_sedfile | def sim_sedfile(self, **kwargs):
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"""
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kwargs['seed'] = 'SEED'
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fermiPy/fermipy | fermipy/jobs/name_policy.py | NameFactory.resolve_randconfig | def resolve_randconfig(self, args):
"""Get the name of the specturm file based on the job arguments"""
ttype = args.get('ttype')
if is_null(ttype):
sys.stderr.write('Target type must be specified')
return None
name_keys = dict(target_type=ttype,
... | python | def resolve_randconfig(self, args):
"""Get the name of the specturm file based on the job arguments"""
ttype = args.get('ttype')
if is_null(ttype):
sys.stderr.write('Target type must be specified')
return None
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fermiPy/fermipy | fermipy/scripts/make_ltcube.py | main | def main():
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description = "Run gtselect and gtmktime on one or more FT1 files. "
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usage = "usage: %(prog)s [options] "
description = "Run gtselect and gtmktime on one or more FT1 files. "
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fermiPy/fermipy | fermipy/castro.py | convert_sed_cols | def convert_sed_cols(tab):
"""Cast SED column names to lowercase."""
# Update Column names
for colname in list(tab.columns.keys()):
newname = colname.lower()
newname = newname.replace('dfde', 'dnde')
if tab.columns[colname].name == newname:
continue
tab.columns... | python | def convert_sed_cols(tab):
"""Cast SED column names to lowercase."""
# Update Column names
for colname in list(tab.columns.keys()):
newname = colname.lower()
newname = newname.replace('dfde', 'dnde')
if tab.columns[colname].name == newname:
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fermiPy/fermipy | fermipy/castro.py | Interpolator.derivative | def derivative(self, x, der=1):
""" return the derivative a an array of input values
x : the inputs
der : the order of derivative
"""
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""" return the derivative a an array of input values
x : the inputs
der : the order of derivative
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fermiPy/fermipy | fermipy/castro.py | LnLFn._compute_mle | def _compute_mle(self):
"""Compute the maximum likelihood estimate.
Calls `scipy.optimize.brentq` to find the roots of the derivative.
"""
min_y = np.min(self._interp.y)
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self._mle = self._interp.x[0]
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Calls `scipy.optimize.brentq` to find the roots of the derivative.
"""
min_y = np.min(self._interp.y)
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fermiPy/fermipy | fermipy/castro.py | LnLFn.getDeltaLogLike | def getDeltaLogLike(self, dlnl, upper=True):
"""Find the point at which the log-likelihood changes by a
given value with respect to its value at the MLE."""
mle_val = self.mle()
# A little bit of paranoia to avoid zeros
if mle_val <= 0.:
mle_val = self._interp.xmin
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"""Find the point at which the log-likelihood changes by a
given value with respect to its value at the MLE."""
mle_val = self.mle()
# A little bit of paranoia to avoid zeros
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fermiPy/fermipy | fermipy/castro.py | LnLFn.getLimit | def getLimit(self, alpha, upper=True):
""" Evaluate the limits corresponding to a C.L. of (1-alpha)%.
Parameters
----------
alpha : limit confidence level.
upper : upper or lower limits.
"""
dlnl = onesided_cl_to_dlnl(1.0 - alpha)
return self.getDeltaLo... | python | def getLimit(self, alpha, upper=True):
""" Evaluate the limits corresponding to a C.L. of (1-alpha)%.
Parameters
----------
alpha : limit confidence level.
upper : upper or lower limits.
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fermiPy/fermipy | fermipy/castro.py | LnLFn.getInterval | def getInterval(self, alpha):
""" Evaluate the interval corresponding to a C.L. of (1-alpha)%.
Parameters
----------
alpha : limit confidence level.
"""
dlnl = twosided_cl_to_dlnl(1.0 - alpha)
lo_lim = self.getDeltaLogLike(dlnl, upper=False)
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""" Evaluate the interval corresponding to a C.L. of (1-alpha)%.
Parameters
----------
alpha : limit confidence level.
"""
dlnl = twosided_cl_to_dlnl(1.0 - alpha)
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fermiPy/fermipy | fermipy/castro.py | ReferenceSpec.create_from_table | def create_from_table(cls, tab_e):
"""
Parameters
----------
tab_e : `~astropy.table.Table`
EBOUNDS table.
"""
convert_sed_cols(tab_e)
try:
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emax = np.array(tab_e['e_max'].to(u.M... | python | def create_from_table(cls, tab_e):
"""
Parameters
----------
tab_e : `~astropy.table.Table`
EBOUNDS table.
"""
convert_sed_cols(tab_e)
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.derivative | def derivative(self, x, der=1):
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bins
Parameters
----------
x : `~numpy.ndarray`
Array of N x M values
der : int
Order of the derivate
Returns
-------
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Order of the derivate
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.mles | def mles(self):
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"""
mle_vals = np.ndarray((self._nx))
for i in range(self._nx):
mle_vals[i] = self._loglikes[i].mle()
return mle_vals | python | def mles(self):
""" return the maximum likelihood estimates for each of the energy bins
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.ts_vals | def ts_vals(self):
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for i in range(self._nx):
ts_vals[i] = self._loglikes[i].TS()
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""" returns test statistic values for each energy bin
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.chi2_vals | def chi2_vals(self, x):
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----------
x :... | python | def chi2_vals(self, x):
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.getLimits | def getLimits(self, alpha, upper=True):
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Parameters
----------
alpha : float
limit confidence level.
upper : bool
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alpha : float
limit confidence level.
upper : bool
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.getIntervals | def getIntervals(self, alpha):
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alpha : float
limit confidence level.
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limit_vals_hi : `~numpy.ndarray`
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""" Evaluate the two-sided intervals corresponding to a C.L. of
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limit confidence level.
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.fitNormalization | def fitNormalization(self, specVals, xlims):
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This version is faster, and solves for the root of the derivatvie
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.fitNorm_v2 | def fitNorm_v2(self, specVals):
"""Fit the normalization given a set of spectral values
that define a spectral shape.
This version uses `scipy.optimize.fmin`.
Parameters
----------
specVals : an array of (nebin values that define a spectral shape
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"""Fit the normalization given a set of spectral values
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.fit_spectrum | def fit_spectrum(self, specFunc, initPars, freePars=None):
""" Fit for the free parameters of a spectral function
Parameters
----------
specFunc : `~fermipy.spectrum.SpectralFunction`
The Spectral Function
initPars : `~numpy.ndarray`
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""" Fit for the free parameters of a spectral function
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.build_scandata_table | def build_scandata_table(self):
"""Build an `astropy.table.Table` object from these data.
"""
shape = self._norm_vals.shape
col_norm = Column(name="norm", dtype=float)
col_normv = Column(name="norm_scan", dtype=float,
shape=shape)
col_dll = Colu... | python | def build_scandata_table(self):
"""Build an `astropy.table.Table` object from these data.
"""
shape = self._norm_vals.shape
col_norm = Column(name="norm", dtype=float)
col_normv = Column(name="norm_scan", dtype=float,
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fermiPy/fermipy | fermipy/castro.py | CastroData_Base.stack_nll | def stack_nll(shape, components, ylims, weights=None):
"""Combine the log-likelihoods from a number of components.
Parameters
----------
shape : tuple
The shape of the return array
components : `~fermipy.castro.CastroData_Base`
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shape : tuple
The shape of the return array
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_yamlfile | def create_from_yamlfile(cls, yamlfile):
"""Create a Castro data object from a yaml file contains
the likelihood data."""
data = load_yaml(yamlfile)
nebins = len(data)
emin = np.array([data[i]['emin'] for i in range(nebins)])
emax = np.array([data[i]['emax'] for i in rang... | python | def create_from_yamlfile(cls, yamlfile):
"""Create a Castro data object from a yaml file contains
the likelihood data."""
data = load_yaml(yamlfile)
nebins = len(data)
emin = np.array([data[i]['emin'] for i in range(nebins)])
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_flux_points | def create_from_flux_points(cls, txtfile):
"""Create a Castro data object from a text file containing a
sequence of differential flux points."""
tab = Table.read(txtfile, format='ascii.ecsv')
dnde_unit = u.ph / (u.MeV * u.cm ** 2 * u.s)
loge = np.log10(np.array(tab['e_ref'].to(u... | python | def create_from_flux_points(cls, txtfile):
"""Create a Castro data object from a text file containing a
sequence of differential flux points."""
tab = Table.read(txtfile, format='ascii.ecsv')
dnde_unit = u.ph / (u.MeV * u.cm ** 2 * u.s)
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_tables | def create_from_tables(cls, norm_type='eflux',
tab_s="SCANDATA",
tab_e="EBOUNDS"):
"""Create a CastroData object from two tables
Parameters
----------
norm_type : str
Type of normalization to use. Valid options are:
... | python | def create_from_tables(cls, norm_type='eflux',
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tab_e="EBOUNDS"):
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Parameters
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norm_type : str
Type of normalization to use. Valid options are:
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_fits | def create_from_fits(cls, fitsfile, norm_type='eflux',
hdu_scan="SCANDATA",
hdu_energies="EBOUNDS",
irow=None):
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Parameters
----------
fitsfile : str
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"""Create a CastroData object from a tscube FITS file.
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fitsfile : str
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_sedfile | def create_from_sedfile(cls, fitsfile, norm_type='eflux'):
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----------
fitsfile : str
Name of the fits file
norm_type : str
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fitsfile : str
Name of the fits file
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_from_stack | def create_from_stack(cls, shape, components, ylims, weights=None):
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Parameters
----------
shape : tuple
The shape of the return array
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shape : tuple
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fermiPy/fermipy | fermipy/castro.py | CastroData.spectrum_loglike | def spectrum_loglike(self, specType, params, scale=1E3):
""" return the log-likelihood for a particular spectrum
Parameters
----------
specTypes : str
The type of spectrum to try
params : array-like
The spectral parameters
scale : float
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""" return the log-likelihood for a particular spectrum
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specTypes : str
The type of spectrum to try
params : array-like
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fermiPy/fermipy | fermipy/castro.py | CastroData.create_functor | def create_functor(self, specType, initPars=None, scale=1E3):
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----------
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fermiPy/fermipy | fermipy/castro.py | TSCube.create_from_fits | def create_from_fits(cls, fitsfile, norm_type='flux'):
"""Build a TSCube object from a fits file created by gttscube
Parameters
----------
fitsfile : str
Path to the tscube FITS file.
norm_type : str
String specifying the quantity used for the normalization... | python | def create_from_fits(cls, fitsfile, norm_type='flux'):
"""Build a TSCube object from a fits file created by gttscube
Parameters
----------
fitsfile : str
Path to the tscube FITS file.
norm_type : str
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fermiPy/fermipy | fermipy/castro.py | TSCube.castroData_from_ipix | def castroData_from_ipix(self, ipix, colwise=False):
""" Build a CastroData object for a particular pixel """
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if colwise:
ipix = self._tsmap.ipix_swap_axes(ipix, colwise)
norm_d = self._norm_vals[ipix]
nll_d = self._nll_vals[ipix]
r... | python | def castroData_from_ipix(self, ipix, colwise=False):
""" Build a CastroData object for a particular pixel """
# pix = utils.skydir_to_pix
if colwise:
ipix = self._tsmap.ipix_swap_axes(ipix, colwise)
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fermiPy/fermipy | fermipy/castro.py | TSCube.castroData_from_pix_xy | def castroData_from_pix_xy(self, xy, colwise=False):
""" Build a CastroData object for a particular pixel """
ipix = self._tsmap.xy_pix_to_ipix(xy, colwise)
return self.castroData_from_ipix(ipix) | python | def castroData_from_pix_xy(self, xy, colwise=False):
""" Build a CastroData object for a particular pixel """
ipix = self._tsmap.xy_pix_to_ipix(xy, colwise)
return self.castroData_from_ipix(ipix) | [
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fermiPy/fermipy | fermipy/castro.py | TSCube.find_and_refine_peaks | def find_and_refine_peaks(self, threshold, min_separation=1.0,
use_cumul=False):
"""Run a simple peak-finding algorithm, and fit the peaks to
paraboloids to extract their positions and error ellipses.
Parameters
----------
threshold : float
... | python | def find_and_refine_peaks(self, threshold, min_separation=1.0,
use_cumul=False):
"""Run a simple peak-finding algorithm, and fit the peaks to
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threshold : float
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | make_lat_lons | def make_lat_lons(cvects):
""" Convert from directional cosines to latitidue and longitude
Parameters
----------
cvects : directional cosine (i.e., x,y,z component) values
returns (np.ndarray(2,nsrc)) with the directional cosine (i.e., x,y,z component) values
"""
lats = np.degrees(np.arcsi... | python | def make_lat_lons(cvects):
""" Convert from directional cosines to latitidue and longitude
Parameters
----------
cvects : directional cosine (i.e., x,y,z component) values
returns (np.ndarray(2,nsrc)) with the directional cosine (i.e., x,y,z component) values
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | make_cos_vects | def make_cos_vects(lon_vect, lat_vect):
""" Convert from longitude (RA or GLON) and latitude (DEC or GLAT) values to directional cosines
Parameters
----------
lon_vect,lat_vect : np.ndarray(nsrc)
Input values
returns (np.ndarray(3,nsrc)) with the directional cosine (i.e., x,y,z component)... | python | def make_cos_vects(lon_vect, lat_vect):
""" Convert from longitude (RA or GLON) and latitude (DEC or GLAT) values to directional cosines
Parameters
----------
lon_vect,lat_vect : np.ndarray(nsrc)
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | find_matches_by_distance | def find_matches_by_distance(cos_vects, cut_dist):
"""Find all the pairs of sources within a given distance of each
other.
Parameters
----------
cos_vects : np.ndarray(e,nsrc)
Directional cosines (i.e., x,y,z component) values of all the
sources
cut_dist : float
... | python | def find_matches_by_distance(cos_vects, cut_dist):
"""Find all the pairs of sources within a given distance of each
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Parameters
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cos_vects : np.ndarray(e,nsrc)
Directional cosines (i.e., x,y,z component) values of all the
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cut_dist : float
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | find_matches_by_sigma | def find_matches_by_sigma(cos_vects, unc_vect, cut_sigma):
"""Find all the pairs of sources within a given distance of each
other.
Parameters
----------
cos_vects : np.ndarray(3,nsrc)
Directional cosines (i.e., x,y,z component) values of all the sources
unc_vect : np.ndarray(nsrc)
... | python | def find_matches_by_sigma(cos_vects, unc_vect, cut_sigma):
"""Find all the pairs of sources within a given distance of each
other.
Parameters
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cos_vects : np.ndarray(3,nsrc)
Directional cosines (i.e., x,y,z component) values of all the sources
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | fill_edge_matrix | def fill_edge_matrix(nsrcs, match_dict):
""" Create and fill a matrix with the graph 'edges' between sources.
Parameters
----------
nsrcs : int
number of sources (used to allocate the size of the matrix)
match_dict : dict((int,int):float)
Each entry gives a pair of source in... | python | def fill_edge_matrix(nsrcs, match_dict):
""" Create and fill a matrix with the graph 'edges' between sources.
Parameters
----------
nsrcs : int
number of sources (used to allocate the size of the matrix)
match_dict : dict((int,int):float)
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | make_rev_dict_unique | def make_rev_dict_unique(cdict):
""" Make a reverse dictionary
Parameters
----------
in_dict : dict(int:dict(int:True))
A dictionary of clusters. Each cluster is a source index and
the dictionary of other sources in the cluster.
Returns
-------
rev_dict : dict(int:dict(i... | python | def make_rev_dict_unique(cdict):
""" Make a reverse dictionary
Parameters
----------
in_dict : dict(int:dict(int:True))
A dictionary of clusters. Each cluster is a source index and
the dictionary of other sources in the cluster.
Returns
-------
rev_dict : dict(int:dict(i... | [
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | make_clusters | def make_clusters(span_tree, cut_value):
""" Find clusters from the spanning tree
Parameters
----------
span_tree : a sparse nsrcs x nsrcs array
Filled with zeros except for the active edges, which are filled with the
edge measures (either distances or sigmas
cut_value : float
... | python | def make_clusters(span_tree, cut_value):
""" Find clusters from the spanning tree
Parameters
----------
span_tree : a sparse nsrcs x nsrcs array
Filled with zeros except for the active edges, which are filled with the
edge measures (either distances or sigmas
cut_value : float
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | select_from_cluster | def select_from_cluster(idx_key, idx_list, measure_vect):
""" Select a single source from a cluster and make it the new cluster key
Parameters
----------
idx_key : int
index of the current key for a cluster
idx_list : [int,...]
list of the other source indices in the cluster
measu... | python | def select_from_cluster(idx_key, idx_list, measure_vect):
""" Select a single source from a cluster and make it the new cluster key
Parameters
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idx_key : int
index of the current key for a cluster
idx_list : [int,...]
list of the other source indices in the cluster
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fermiPy/fermipy | fermipy/scripts/cluster_sources.py | find_centroid | def find_centroid(cvects, idx_list, weights=None):
""" Find the centroid for a set of vectors
Parameters
----------
cvects : ~numpy.ndarray(3,nsrc) with directional cosine (i.e., x,y,z component) values
idx_list : [int,...]
list of the source indices in the cluster
weights : ~numpy.ndar... | python | def find_centroid(cvects, idx_list, weights=None):
""" Find the centroid for a set of vectors
Parameters
----------
cvects : ~numpy.ndarray(3,nsrc) with directional cosine (i.e., x,y,z component) values
idx_list : [int,...]
list of the source indices in the cluster
weights : ~numpy.ndar... | [
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