def _interpolate_cube(self, lon, lat, egy=None, interp_log=True):
"""Perform interpolation on a healpix cube. If egy is None
then interpolation will be performed on the existing energy
planes.
"""
shape = np.broadcast(lon, lat, egy).shape
lon = lon * np.ones(shape)
lat = lat * np.ones(shape)
theta = np.pi / 2. - np.radians(lat)
phi = np.radians(lon)
vals = []
for i, _ in enumerate(self.hpx.evals):
v = hp.pixelfunc.get_interp_val(self.counts[i], theta,
phi, nest=self.hpx.nest)
vals += [np.expand_dims(np.array(v, ndmin=1), -1)]
vals = np.concatenate(vals, axis=-1)
if egy is None:
return vals.T
egy = egy * np.ones(shape)
if interp_log:
xvals = utils.val_to_pix(np.log(self.hpx.evals), np.log(egy))
else:
xvals = utils.val_to_pix(self.hpx.evals, egy)
vals = vals.reshape((-1, vals.shape[-1]))
xvals = np.ravel(xvals)
v = map_coordinates(vals, [np.arange(vals.shape[0]), xvals],
order=1)
return v.reshape(shape)
def _interpolate_cube(self, lon, lat, egy=None, interp_log=True):
"""Perform interpolation on a healpix cube. If egy is None
then interpolation will be performed on the existing energy
planes.
"""
shape = np.broadcast(lon, lat, egy).shape
lon = lon * np.ones(shape)
lat = lat * np.ones(shape)
theta = np.pi / 2. - np.radians(lat)
phi = np.radians(lon)
vals = []
for i, _ in enumerate(self.hpx.evals):
v = hp.pixelfunc.get_interp_val(self.counts[i], theta,
phi, nest=self.hpx.nest)
vals += [np.expand_dims(np.array(v, ndmin=1), -1)]
vals = np.concatenate(vals, axis=-1)
if egy is None:
return vals.T
egy = egy * np.ones(shape)
if interp_log:
xvals = utils.val_to_pix(np.log(self.hpx.evals), np.log(egy))
else:
xvals = utils.val_to_pix(self.hpx.evals, egy)
vals = vals.reshape((-1, vals.shape[-1]))
xvals = np.ravel(xvals)
v = map_coordinates(vals, [np.arange(vals.shape[0]), xvals],
order=1)
return v.reshape(shape)
def interpolate(self, lon, lat, egy=None):
"""Interpolate map values.
"""
if self.data.ndim == 1:
theta = np.pi / 2. - np.radians(lat)
phi = np.radians(lon)
return hp.pixelfunc.get_interp_val(self.counts, theta,
phi, nest=self.hpx.nest)
else:
shape = np.broadcast(lon, lat, egy).shape
lon *= np.ones(shape)
lat *= np.ones(shape)
egy *= np.ones(shape)
theta = np.pi / 2. - np.radians(lat)
phi = np.radians(lon)
vals = []
for i, _ in enumerate(self.hpx.evals):
v = hp.pixelfunc.get_interp_val(self.counts[i], theta,
phi, nest=self.hpx.nest)
vals += [np.expand_dims(np.array(v, ndmin=1), -1)]
vals = np.concatenate(vals, axis=-1)
xvals = utils.val_to_pix(np.log(self.hpx.evals), np.log(egy))
return map_coordinates(vals, [np.arange(shape[0]), xvals], order=1)
def interpolate(self, lon, lat, egy=None):
if len(self.npix) == 2:
pixcrd = self.wcs.wcs_world2pix(lon, lat, 0)
else:
if egy is None:
egy = self._ectr
pixcrd = self.wcs.wcs_world2pix(lon, lat, egy, 0)
pixcrd[2] = np.array(utils.val_to_pix(np.log(self._ectr),
np.log(egy)), ndmin=1)
points = []
for npix in self.npix:
points += [np.linspace(0, npix - 1., npix)]
data = self.counts
fn = RegularGridInterpolator(points, data.T,
bounds_error=False,
fill_value=None)
return fn(np.column_stack(pixcrd))
def interpolate(self, lon, lat, egy=None):
if len(self.npix) == 2:
pixcrd = self.wcs.wcs_world2pix(lon, lat, 0)
else:
if egy is None:
egy = self._ectr
pixcrd = self.wcs.wcs_world2pix(lon, lat, egy, 0)
pixcrd[2] = np.array(utils.val_to_pix(np.log(self._ectr),
np.log(egy)), ndmin=1)
points = []
for npix in self.npix:
points += [np.linspace(0, npix - 1., npix)]
data = self.counts
fn = RegularGridInterpolator(points, data.T,
bounds_error=False,
fill_value=None)
return fn(np.column_stack(pixcrd))