def downsample(self,
factor,
preserve_counts=True,
axis_name="energy",
weights=None):
if axis_name is None:
return self.copy()
geom = self.geom.downsample(factor=factor, axis_name=axis_name)
block_size = [1] * self.data.ndim
idx = self.geom.axes.index_data(axis_name)
block_size[idx] = factor
if weights is None:
weights = 1
else:
weights = weights.data
func = np.nansum if preserve_counts else np.nanmean
if self.data.dtype == bool:
func = np.all
data = block_reduce(self.data * weights, tuple(block_size), func=func)
return self._init_copy(geom=geom, data=data)
def downsample(self,
factor,
preserve_counts=True,
axis_name=None,
weights=None):
if factor == 1 or factor is None:
return self
geom = self.geom.downsample(factor, axis_name=axis_name)
if axis_name is None:
block_size = (1, ) * len(self.geom.axes) + (factor, factor)
else:
block_size = [1] * self.data.ndim
idx = self.geom.axes.index_data(axis_name)
block_size[idx] = factor
func = np.nansum if preserve_counts else np.nanmean
if weights is None:
weights = 1
else:
weights = weights.data
data = block_reduce(self.data * weights, tuple(block_size), func=func)
return self._init_copy(geom=geom, data=data.astype(self.data.dtype))
def downsample(self,
factor,
preserve_counts=True,
axis=None,
weights=None):
geom = self.geom.downsample(factor, axis=axis)
if axis is None:
block_size = (factor, factor) + (1, ) * len(self.geom.axes)
else:
block_size = [1] * self.data.ndim
idx = self.geom.get_axis_index_by_name(axis)
block_size[idx + 2] = factor
func = np.nansum if preserve_counts else np.nanmean
if weights is None:
weights = 1
else:
weights = weights.data
data = block_reduce(self.data * weights,
tuple(block_size[::-1]),
func=func)
return self._init_copy(geom=geom, data=data.astype(self.data.dtype))
def downsample(self, factor, preserve_counts=True, axis="energy"):
geom = self.geom.downsample(factor=factor, axis=axis)
block_size = [1] * self.data.ndim
idx = self.geom.get_axis_index_by_name(axis)
block_size[-(idx + 1)] = factor
func = np.nansum if preserve_counts else np.nanmean
data = block_reduce(self.data, tuple(block_size[::-1]), func=func)
return self._init_copy(geom=geom, data=data)
def downsample(self, factor, preserve_counts=True, axis_name="energy"):
geom = self.geom.downsample(factor=factor, axis_name=axis_name)
block_size = [1] * self.data.ndim
idx = self.geom.axes.index_data(axis_name)
block_size[idx] = factor
func = np.nansum if preserve_counts else np.nanmean
data = block_reduce(self.data, tuple(block_size), func=func)
return self._init_copy(geom=geom, data=data)
def downsample(self,
factor,
preserve_counts=True,
axis_name="energy",
weights=None):
"""Downsample the non-spatial dimension by a given factor.
Parameters
----------
factor : int
Downsampling factor.
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity).
axis_name : str
Which axis to downsample. Default is "energy".
weights : `RegionNDMap`
Contains the weights to apply to the axis to reduce. Default
is just weighs of one.
Returns
-------
map : `RegionNDMap`
Downsampled region map.
"""
if axis_name is None:
return self.copy()
geom = self.geom.downsample(factor=factor, axis_name=axis_name)
block_size = [1] * self.data.ndim
idx = self.geom.axes.index_data(axis_name)
block_size[idx] = factor
if weights is None:
weights = 1
else:
weights = weights.data
func = np.nansum if preserve_counts else np.nanmean
if self.is_mask:
func = np.all
data = block_reduce(self.data * weights, tuple(block_size), func=func)
return self._init_copy(geom=geom, data=data)
def downsample(self, factor):
"""Downsample spectrum.
Parameters
----------
factor : int
Downsampling factor.
Returns
-------
spectrum : `~gammapy.spectrum.CountsSpectrum`
Downsampled spectrum.
"""
from gammapy.extern.skimage import block_reduce
data = block_reduce(self.data, block_size=(factor, ))
energy = self.energy.downsample(factor).edges
return self.__class__(energy_lo=energy[:-1],
energy_hi=energy[1:],
data=data)
