def BinByLabel(DataCube,
LabelMap,
CentroidMap=None,
CentroidAggregator=np.nanmean,
BackgroundLabels=[0]):
"""
Bin a data cube by a label mask, aligning the data to a common centroid.
Parameters
----------
DataCube : SpectralCube
The original spectral cube with spatial dimensions Nx, Ny and spectral dimension Nv
LabelMap : 2D numpy.ndarray
A 2D map containing integer labels for each pixel into objects defining the stacking.
CentroidMap : 2D numpy.ndarray
A 2D map of the centroid velocities for the lines to stack of dimensions Nx, Ny.
Note that DataCube and Centroid map must have the same spectral units (e.g., km/s)
CentroidAggregator : numpy.ufunc
Operates on a vector of centroid data and returns the value summarizing that object.
BackgroundLabels : list
List of values in the label map that correspond to background objects and should not
be processed with the stacking.
Returns
-------
OutputCube : SpectralCube
A SpectralCube instance matching the input but with spectra aligned
in velocity and averaged.
"""
UniqLabels = np.unique(LabelMap)
ChannelWidth = np.median(DataCube.spectral_axis -
np.roll(DataCube.spectral_axis, 1))
RawData = DataCube.unmasked_data[:].value
for ThisLabel in UniqLabels:
if ThisLabel not in BackgroundLabels:
y, x = np.where(ThisLabel == LabelMap)
AccumSpec = np.zeros(DataCube.shape[0])
if CentroidMap is None:
for ThisX, ThisY in zip(x, y):
AccumSpec += DataCube[:, ThisY, ThisX].value
else:
CentroidValue = CentroidAggregator(CentroidMap[y, x])
for ThisX, ThisY in zip(x, y):
DeltaV = CentroidMap[ThisY, ThisX] - CentroidValue
# Note this assumes the units of the centroid map
# are in same units as the spectral axis of the cube.
DeltaChan = DeltaV / ChannelWidth.value
AccumSpec += channelShift(DataCube[:, ThisY, ThisX].value,
-DeltaChan)
AccumSpec /= x.size
AccumSpec.shape = AccumSpec.shape + (1, )
RawData[:, y, x] = AccumSpec
return (SpectralCube(data=RawData, wcs=DataCube.wcs))
def BinByMask(DataCube,
Mask=None,
CentroidMap=None,
CentroidAggregator=np.nanmean,
x=None,
y=None):
"""
Bin a data cube by a label mask, aligning the data to a common centroid. Returns an array.
Parameters
----------
DataCube : SpectralCube
The original spectral cube with spatial dimensions Nx, Ny and spectral dimension Nv
Mask : 2D numpy.ndarray
A 2D map containing boolean values with True indicate where the spectra should be aggregated.
CentroidMap : 2D numpy.ndarray
A 2D map of the centroid velocities for the lines to stack of dimensions Nx, Ny.
Note that DataCube and Centroid map must have the same spectral units (e.g., km/s)
CentroidAggregator : numpy.ufunc
Operates on a vector of centroid data and returns the value summarizing that object.
BackgroundLabels : list
List of values in the label map that correspond to background objects and should not
be processed with the stacking.
Returns
-------
Spectrum : np.array
Spectrum of average over mask.
"""
ChannelWidth = (np.median(DataCube.spectral_axis -
np.roll(DataCube.spectral_axis, 1)))
RawData = DataCube.unmasked_data[:].value
if Mask:
y, x = np.where(Mask)
# Trap y,x not set HERE!
if CentroidMap is None:
AccumSpec = np.nanmean(DataCube.filled_data[:, y, x].value, axis=1)
else:
CentroidValue = CentroidAggregator(CentroidMap[y, x])
DeltaV = CentroidValue - CentroidMap[y, x]
DeltaChan = DeltaV / ChannelWidth.value
AccumSpec = np.zeros(DataCube.spectral_axis.shape + y.shape)
for idx, (ThisX, ThisY) in enumerate(zip(x, y)):
# Note this assumes the units of the centroid map
# are in same units as the spectral axis of the cube.
AccumSpec[:, idx] = channelShift(DataCube[:, ThisY, ThisX].value,
-DeltaChan[idx])
AccumSpec = np.nanmean(AccumSpec, axis=1)
OffsetVelocity = DataCube.spectral_axis.value - CentroidValue
return AccumSpec, OffsetVelocity
def BinByLabel(DataCube, LabelMap, CentroidMap = None,
CentroidAggregator = np.nanmean, BackgroundLabels = [0]):
"""
Bin a data cube by a label mask, aligning the data to a common centroid.
Parameters
----------
DataCube : SpectralCube
The original spectral cube with spatial dimensions Nx, Ny and spectral dimension Nv
LabelMap : 2D numpy.ndarray
A 2D map containing integer labels for each pixel into objects defining the stacking.
CentroidMap : 2D numpy.ndarray
A 2D map of the centroid velocities for the lines to stack of dimensions Nx, Ny.
Note that DataCube and Centroid map must have the same spectral units (e.g., km/s)
CentroidAggregator : numpy.ufunc
Operates on a vector of centroid data and returns the value summarizing that object.
BackgroundLabels : list
List of values in the label map that correspond to background objects and should not
be processed with the stacking.
Returns
-------
OutputCube : SpectralCube
A SpectralCube instance matching the input but with spectra aligned
in velocity and averaged.
"""
UniqLabels = np.unique(LabelMap)
ChannelWidth = np.median(DataCube.spectral_axis-
np.roll(DataCube.spectral_axis,1))
RawData = DataCube.unmasked_data[:].value
for ThisLabel in UniqLabels:
if ThisLabel not in BackgroundLabels:
y,x= np.where(ThisLabel == LabelMap)
AccumSpec = np.zeros(DataCube.shape[0])
if CentroidMap is None:
for ThisX,ThisY in zip(x,y):
AccumSpec += DataCube[:,ThisY,ThisX].value
else:
CentroidValue = CentroidAggregator(CentroidMap[y,x])
for ThisX,ThisY in zip(x,y):
DeltaV = CentroidMap[ThisY,ThisX] - CentroidValue
# Note this assumes the units of the centroid map
# are in same units as the spectral axis of the cube.
DeltaChan = DeltaV/ChannelWidth.value
AccumSpec += channelShift(DataCube[:,ThisY,ThisX].value,
-DeltaChan)
AccumSpec /= x.size
AccumSpec.shape = AccumSpec.shape+ (1,)
RawData[:,y,x] = AccumSpec
return (SpectralCube(data = RawData,wcs = DataCube.wcs))
def BinByMask(DataCube, Mask = None, CentroidMap = None,
CentroidAggregator = np.nanmean, x = None, y = None):
"""
Bin a data cube by a label mask, aligning the data to a common centroid. Returns an array.
Parameters
----------
DataCube : SpectralCube
The original spectral cube with spatial dimensions Nx, Ny and spectral dimension Nv
Mask : 2D numpy.ndarray
A 2D map containing boolean values with True indicate where the spectra should be aggregated.
CentroidMap : 2D numpy.ndarray
A 2D map of the centroid velocities for the lines to stack of dimensions Nx, Ny.
Note that DataCube and Centroid map must have the same spectral units (e.g., km/s)
CentroidAggregator : numpy.ufunc
Operates on a vector of centroid data and returns the value summarizing that object.
BackgroundLabels : list
List of values in the label map that correspond to background objects and should not
be processed with the stacking.
Returns
-------
Spectrum : np.array
Spectrum of average over mask.
"""
ChannelWidth = (np.median(DataCube.spectral_axis-
np.roll(DataCube.spectral_axis,1)))
RawData = DataCube.unmasked_data[:].value
if Mask:
y,x= np.where(Mask)
# Trap y,x not set HERE!
if CentroidMap is None:
AccumSpec = np.nanmean(DataCube.filled_data[:,y,x].value,axis=1)
else:
CentroidValue = CentroidAggregator(CentroidMap[y,x])
DeltaV = CentroidValue-CentroidMap[y,x]
DeltaChan = DeltaV/ChannelWidth.value
AccumSpec = np.zeros(DataCube.spectral_axis.shape+y.shape)
for idx,(ThisX,ThisY) in enumerate(zip(x,y)):
# Note this assumes the units of the centroid map
# are in same units as the spectral axis of the cube.
AccumSpec[:,idx] = channelShift(DataCube[:,ThisY,ThisX].value,
-DeltaChan[idx])
AccumSpec = np.nanmean(AccumSpec,axis=1)
OffsetVelocity = DataCube.spectral_axis.value-CentroidValue
return AccumSpec,OffsetVelocity
