BaseSpectralCube¶
- class spectral_cube.spectral_cube.BaseSpectralCube(data, wcs, mask=None, meta=None, fill_value=nan, header=None, allow_huge_operations=False, wcs_tolerance=0.0)[source]¶
Bases:
spectral_cube.base_class.BaseNDClass
,spectral_cube.base_class.MaskableArrayMixinClass
,spectral_cube.base_class.SpectralAxisMixinClass
,spectral_cube.base_class.SpatialCoordMixinClass
,spectral_cube.base_class.HeaderMixinClass
Attributes Summary
The data type ‘base’ of the cube - useful for, e.g., joblib
The replacement value used by
filled_data
.Return a portion of the data array, with excluded mask values
HDU version of self
Dimensionality of the data
Length of cube along each axis
Number of elements in the cube
A
Quantity
array containing the central values of each channel along the spectral axis.The flux unit
Return a copy of self with unit set to None
Return a portion of the data array, with excluded mask values
Return a view of the subset of the underlying data,
The
equivalencies
that describes the spectral axisReturn a list of the world coordinates in a cube, projection, or a view
Methods Summary
apply_function
(function[, axis, weights, …])Apply a function to valid data along the specified axis or to the whole cube, optionally using a weight array that is the same shape (or at least can be sliced in the same way)
apply_function_parallel_spatial
(function[, …])Apply a function in parallel along the spatial dimension.
apply_function_parallel_spectral
(function[, …])Apply a function in parallel along the spectral dimension.
apply_numpy_function
(function[, fill, …])Apply a numpy function to the cube
argmax
([axis, how])Return the index of the maximum data value.
argmax_world
(axis, **kwargs)Return the spatial or spectral index of the maximum value along a line of sight.
argmin
([axis, how])Return the index of the minimum data value.
argmin_world
(axis, **kwargs)Return the spatial or spectral index of the minimum value along a line of sight.
chunked
([chunksize])Not Implemented.
closest_spectral_channel
(value)Find the index of the closest spectral channel to the specified spectral coordinate.
convolve_to
(beam[, convolve, update_function])Convolve each channel in the cube to a specified beam
downsample_axis
(factor, axis[, estimator, …])Downsample the cube by averaging over factor pixels along an axis.
filled
([fill_value])find_lines
([velocity_offset, …])Using astroquery’s splatalogue interface, search for lines within the spectral band.
flattened
([slice, weights])Return a slice of the cube giving only the valid data (i.e., removing bad values)
flattened_world
([view])Retrieve the world coordinates corresponding to the extracted flattened version of the cube
Convert the mask to a boolean numpy array
linewidth_fwhm
([how])Compute a (FWHM) linewidth map along the spectral axis.
linewidth_sigma
([how])Compute a (sigma) linewidth map along the spectral axis.
mad_std
([axis, how])Use astropy’s mad_std to computer the standard deviation
mask_channels
(goodchannels)Helper function to mask out channels.
max
([axis, how])Return the maximum data value of the cube, optionally over an axis.
mean
([axis, how])Return the mean of the cube, optionally over an axis.
median
([axis, iterate_rays])Compute the median of an array, optionally along an axis.
min
([axis, how])Return the minimum data value of the cube, optionally over an axis.
minimal_subcube
([spatial_only])Return the minimum enclosing subcube where the mask is valid
moment
([order, axis, how])Compute moments along the spectral axis.
moment0
([axis, how])Compute the zeroth moment along an axis.
moment1
([axis, how])Compute the 1st moment along an axis.
moment2
([axis, how])Compute the 2nd moment along an axis.
percentile
(q[, axis, iterate_rays])Return percentiles of the data.
plot_channel_maps
(nx, ny, channels[, …])Make channel maps from a spectral cube
reproject
(header[, order, use_memmap, filled])Spatially reproject the cube into a new header.
sigma_clip_spectrally
(threshold[, verbose, …])Run astropy’s sigma clipper along the spectral axis, converting all bad (excluded) values to NaN.
spatial_smooth
(kernel[, convolve])Smooth the image in each spatial-spatial plane of the cube.
spatial_smooth_median
(ksize[, update_function])Smooth the image in each spatial-spatial plane of the cube using a median filter.
spectral_interpolate
(spectral_grid[, …])Resample the cube spectrally onto a specific grid
spectral_slab
(lo, hi)Extract a new cube between two spectral coordinates
spectral_smooth
(kernel[, convolve, verbose, …])Smooth the cube along the spectral dimension
spectral_smooth_median
(ksize[, use_memmap, …])Smooth the cube along the spectral dimension
std
([axis, how, ddof])Return the standard deviation of the cube, optionally over an axis.
subcube
([xlo, xhi, ylo, yhi, zlo, zhi, …])Extract a sub-cube spatially and spectrally.
subcube_from_crtfregion
(crtf_region[, …])Extract a masked subcube from a CRTF region.
subcube_from_ds9region
(ds9_region[, allow_empty])Extract a masked subcube from a ds9 region (only functions on celestial dimensions)
subcube_from_mask
(region_mask)Given a mask, return the minimal subcube that encloses the mask
subcube_from_regions
(region_list[, allow_empty])Extract a masked subcube from a list of
regions.Region
object (only functions on celestial dimensions)subcube_slices_from_mask
(region_mask[, …])Given a mask, return the slices corresponding to the minimum subcube that encloses the mask
sum
([axis, how])Return the sum of the cube, optionally over an axis.
to
(unit[, equivalencies])Return the cube converted to the given unit (assuming it is equivalent).
to_ds9
([ds9id, newframe])Send the data to ds9 (this will create a copy in memory)
to_glue
([name, glue_app, dataset, start_gui])Send data to a new or existing Glue application
Open the cube in a quick viewer written in matplotlib that allows you to create PV extractions within the GUI
to_yt
([spectral_factor, nprocs])Convert a spectral cube to a yt object that can be further analyzed in yt.
Return a copy of the cube with no mask (i.e., all data included)
with_fill_value
(fill_value)Create a new object with a different
fill_value
.with_mask
(mask[, inherit_mask, wcs_tolerance])Return a new SpectralCube instance that contains a composite mask of the current SpectralCube and the new
mask
.with_spectral_unit
(unit[, …])Returns a new Cube with a different Spectral Axis unit
Returns a list of 1D arrays, for the world coordinates along each pixel axis.
Attributes Documentation
- base¶
The data type ‘base’ of the cube - useful for, e.g., joblib
- fill_value¶
The replacement value used by
filled_data
.fill_value is immutable; use
with_fill_value
to create a new cube with a different fill value.
- filled_data¶
Return a portion of the data array, with excluded mask values replaced by
fill_value
.- Returns
- dataQuantity
The masked data.
Notes
Supports efficient Numpy slice notation, like
filled_data[0:3, :, 2:4]
- hdu¶
HDU version of self
- hdulist¶
- header¶
- latitude_extrema¶
- longitude_extrema¶
- mask¶
- meta¶
- ndim¶
Dimensionality of the data
- read = <spectral_cube.io.core.SpectralCubeRead object>¶
- shape¶
Length of cube along each axis
- size¶
Number of elements in the cube
- spatial_coordinate_map¶
- spectral_axis¶
A
Quantity
array containing the central values of each channel along the spectral axis.
- spectral_extrema¶
- unit¶
The flux unit
- unitless¶
Return a copy of self with unit set to None
- unitless_filled_data¶
Return a portion of the data array, with excluded mask values replaced by
fill_value
.- Returns
- datanumpy.array
The masked data.
Notes
Supports efficient Numpy slice notation, like
unitless_filled_data[0:3, :, 2:4]
- unmasked_data¶
Return a view of the subset of the underlying data, ignoring the mask.
- Returns
- dataQuantity instance
The unmasked data
Notes
Supports efficient Numpy slice notation, like
unmasked_data[0:3, :, 2:4]
- velocity_convention¶
The
equivalencies
that describes the spectral axis
- wcs¶
- world¶
Return a list of the world coordinates in a cube, projection, or a view of it.
SpatialCoordMixinClass.world is called with bracket notation, like a NumPy array:
c.world[0:3, :, :]
- Returns
- [v, y, x]list of NumPy arrays
The 3 world coordinates at each pixel in the view. For a 2D image, the output is
[y, x]
.
Notes
Supports efficient Numpy slice notation, like
world[0:3, :, 2:4]
Examples
Extract the first 3 velocity channels of the cube:
>>> v, y, x = c.world[0:3]
Extract all the world coordinates:
>>> v, y, x = c.world[:, :, :]
Extract every other pixel along all axes:
>>> v, y, x = c.world[::2, ::2, ::2]
Extract all the world coordinates for a 2D image:
>>> y, x = c.world[:, :]
- world_extrema¶
- write = <spectral_cube.io.core.SpectralCubeWrite object>¶
Methods Documentation
- apply_function(function, axis=None, weights=None, unit=None, projection=False, progressbar=False, update_function=None, keep_shape=False, **kwargs)[source]¶
Apply a function to valid data along the specified axis or to the whole cube, optionally using a weight array that is the same shape (or at least can be sliced in the same way)
- Parameters
- functionfunction
A function that can be applied to a numpy array. Does not need to be nan-aware
- axis1, 2, 3, or None
The axis to operate along. If None, the return is scalar.
- weights(optional) np.ndarray
An array with the same shape (or slicing abilities/results) as the data cube
- unit(optional)
Unit
The unit of the output projection or value. Not all functions should return quantities with units.
- projectionbool
Return a projection if the resulting array is 2D?
- progressbarbool
Show a progressbar while iterating over the slices/rays through the cube?
- keep_shapebool
If
True
, the returned object will be the same dimensionality as the cube.- update_functionfunction
An alternative tracker for the progress of applying the function to the cube data. If
progressbar
isTrue
, this argument is ignored.
- Returns
- result
Projection
orQuantity
or float The result depends on the value of
axis
,projection
, andunit
. Ifaxis
is None, the return will be a scalar with or without units. If axis is an integer, the return will be aProjection
ifprojection
is set
- result
- apply_function_parallel_spatial(function, num_cores=None, verbose=0, use_memmap=True, parallel=True, **kwargs)[source]¶
Apply a function in parallel along the spatial dimension. The function will be performed on data with masked values replaced with the cube’s fill value.
- Parameters
- functionfunction
The function to apply in the spatial dimension. It must take two arguments: an array representing an image and a boolean array representing the mask. It may also accept
**kwargs
. The function must return an object with the same shape as the input spectrum.- num_coresint or None
The number of cores to use if running in parallel
- verboseint
Verbosity level to pass to joblib
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- parallelbool
If set to
False
, will force the use of a single core without usingjoblib
.- kwargsdict
Passed to
function
- apply_function_parallel_spectral(function, num_cores=None, verbose=0, use_memmap=True, parallel=True, **kwargs)[source]¶
Apply a function in parallel along the spectral dimension. The function will be performed on data with masked values replaced with the cube’s fill value.
- Parameters
- functionfunction
The function to apply in the spectral dimension. It must take two arguments: an array representing a spectrum and a boolean array representing the mask. It may also accept
**kwargs
. The function must return an object with the same shape as the input spectrum.- num_coresint or None
The number of cores to use if running in parallel
- verboseint
Verbosity level to pass to joblib
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- parallelbool
If set to
False
, will force the use of a single core without usingjoblib
.- kwargsdict
Passed to
function
- apply_numpy_function(function, fill=nan, reduce=True, how='auto', projection=False, unit=None, check_endian=False, progressbar=False, includemask=False, **kwargs)[source]¶
Apply a numpy function to the cube
- Parameters
- functionNumpy ufunc
A numpy ufunc to apply to the cube
- fillfloat
The fill value to use on the data
- reducebool
reduce indicates whether this is a reduce-like operation, that can be accumulated one slice at a time. sum/max/min are like this. argmax/argmin/stddev are not
- howcube | slice | ray | auto
How to compute the moment. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- projectionbool
Return a
Projection
if the resulting array is 2D or a OneDProjection if the resulting array is 1D and the sum is over both spatial axes?- unitNone or
astropy.units.Unit
The unit to include for the output array. For example,
SpectralCube.max
callsSpectralCube.apply_numpy_function(np.max, unit=self.unit)
, inheriting the unit from the original cube. However, for other numpy functions, e.g.numpy.argmax
, the return is an index and therefore unitless.- check_endianbool
A flag to check the endianness of the data before applying the function. This is only needed for optimized functions, e.g. those in the bottleneck package.
- progressbarbool
Show a progressbar while iterating over the slices through the cube?
- kwargsdict
Passed to the numpy function.
- Returns
- result
Projection
orQuantity
or float The result depends on the value of
axis
,projection
, andunit
. Ifaxis
is None, the return will be a scalar with or without units. If axis is an integer, the return will be aProjection
ifprojection
is set
- result
- argmax(axis=None, how='auto', **kwargs)[source]¶
Return the index of the maximum data value.
The return value is arbitrary if all pixels along
axis
are excluded from the mask.Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- argmax_world(axis, **kwargs)[source]¶
Return the spatial or spectral index of the maximum value along a line of sight.
- Parameters
- axisint
The axis to return the peak location along. e.g.,
axis=0
will return the value of the spectral axis at the peak value.- kwargsdict
Passed to
argmax
.
- argmin(axis=None, how='auto', **kwargs)[source]¶
Return the index of the minimum data value.
The return value is arbitrary if all pixels along
axis
are excluded from the maskIgnores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- argmin_world(axis, **kwargs)[source]¶
Return the spatial or spectral index of the minimum value along a line of sight.
- Parameters
- axisint
The axis to return the peak location along. e.g.,
axis=0
will return the value of the spectral axis at the peak value.- kwargsdict
Passed to
argmin
.
- closest_spectral_channel(value)[source]¶
Find the index of the closest spectral channel to the specified spectral coordinate.
- Parameters
- value
Quantity
The value of the spectral coordinate to search for.
- value
- convolve_to(beam, convolve=<function convolve_fft>, update_function=None, **kwargs)[source]¶
Convolve each channel in the cube to a specified beam
Warning
The current implementation of
convolve_to
creates an in-memory copy of the whole cube to store the convolved data. Issue #506 notes that this is a problem, and it is on our to-do list to fix.- Parameters
- beam
radio_beam.Beam
The beam to convolve to
- convolvefunction
The astropy convolution function to use, either
astropy.convolution.convolve
orastropy.convolution.convolve_fft
- update_functionmethod
Method that is called to update an external progressbar If provided, it disables the default
astropy.utils.console.ProgressBar
- kwargsdict
Keyword arguments to pass to the convolution function
- beam
- Returns
- cube
SpectralCube
A SpectralCube with a single
beam
- cube
- downsample_axis(factor, axis, estimator=<function nanmean>, truncate=False, use_memmap=True, progressbar=True)[source]¶
Downsample the cube by averaging over factor pixels along an axis. Crops right side if the shape is not a multiple of factor.
The WCS will be ‘downsampled’ by the specified factor as well. If the downsample factor is odd, there will be an offset in the WCS.
There is both an in-memory and a memory-mapped implementation; the default is to use the memory-mapped version. Technically, the ‘large data’ warning doesn’t apply when using the memory-mapped version, but the warning is still there anyway.
- Parameters
- myarr
ndarray
The array to downsample
- factorint
The factor to downsample by
- axisint
The axis to downsample along
- estimatorfunction
defaults to mean. You can downsample by summing or something else if you want a different estimator (e.g., downsampling error: you want to sum & divide by sqrt(n))
- truncatebool
Whether to truncate the last chunk or average over a smaller number. e.g., if you downsample [1,2,3,4] by a factor of 3, you could get either [2] or [2,4] if truncate is True or False, respectively.
- use_memmapbool
Use a memory map on disk to avoid loading the whole cube into memory (several times)? If set, the warning about large cubes can be ignored (though you still have to override the warning)
- progressbarbool
Include a progress bar? Only works with
use_memmap=True
- myarr
- filled(fill_value=None)¶
- find_lines(velocity_offset=None, velocity_convention=None, rest_value=None, **kwargs)[source]¶
Using astroquery’s splatalogue interface, search for lines within the spectral band. See
astroquery.splatalogue.Splatalogue
for information on keyword arguments- Parameters
- velocity_offsetu.km/u.s equivalent
An offset by which the spectral axis should be shifted before searching splatalogue. This value will be added to the velocity, so if you want to redshift a spectrum, make this value positive, and if you want to un-redshift it, make this value negative.
- velocity_convention‘radio’, ‘optical’, ‘relativistic’
The doppler convention to pass to
with_spectral_unit
- rest_valueu.GHz equivalent
The rest frequency (or wavelength or energy) to be passed to
with_spectral_unit
- flattened(slice=(), weights=None)[source]¶
Return a slice of the cube giving only the valid data (i.e., removing bad values)
- Parameters
- slice: 3-tuple
A length-3 tuple of view (or any equivalent valid slice of a cube)
- weights: (optional) np.ndarray
An array with the same shape (or slicing abilities/results) as the data cube
- flattened_world(view=())¶
Retrieve the world coordinates corresponding to the extracted flattened version of the cube
- linewidth_fwhm(how='auto')[source]¶
Compute a (FWHM) linewidth map along the spectral axis.
For an explanation of the
how
parameter, seemoment()
.
- linewidth_sigma(how='auto')[source]¶
Compute a (sigma) linewidth map along the spectral axis.
For an explanation of the
how
parameter, seemoment()
.
- mad_std(axis=None, how='cube', **kwargs)[source]¶
Use astropy’s mad_std to computer the standard deviation
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- mask_channels(goodchannels)[source]¶
Helper function to mask out channels. This function is equivalent to adding a mask with
cube[view]
whereview
is broadcastable to the cube shape, but it accepts 1D arrays that are not normally broadcastable.- Parameters
- goodchannelsarray
A 1D boolean array declaring which channels should be kept.
- Returns
- cube
SpectralCube
A cube with the specified channels masked
- cube
- max(axis=None, how='auto', **kwargs)[source]¶
Return the maximum data value of the cube, optionally over an axis.
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- mean(axis=None, how='cube', **kwargs)[source]¶
Return the mean of the cube, optionally over an axis.
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- median(axis=None, iterate_rays=False, **kwargs)[source]¶
Compute the median of an array, optionally along an axis.
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse
- iterate_raysbool
Iterate over individual rays? This mode is slower but can save RAM costs, which may be extreme for large cubes
- Returns
- medndarray
The median
- min(axis=None, how='auto', **kwargs)[source]¶
Return the minimum data value of the cube, optionally over an axis.
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- minimal_subcube(spatial_only=False)[source]¶
Return the minimum enclosing subcube where the mask is valid
- Parameters
- spatial_only: bool
Only compute the minimal subcube in the spatial dimensions
- moment(order=0, axis=0, how='auto')[source]¶
Compute moments along the spectral axis.
Moments are defined as follows, where \(I\) is the intensity in a channel and \(x\) is the spectral coordinate:
Moment 0:
\[M_0 \int I dx\]Moment 1:
\[M_1 = \frac{\int I x dx}{M_0}\]Moment N:
\[M_N = \frac{\int I (x - M_1)^N dx}{M_0}\]Warning
Note that these follow the mathematical definitions of moments, and therefore the second moment will return a variance map. To get linewidth maps, you can instead use the
linewidth_fwhm()
orlinewidth_sigma()
methods.- Parameters
- orderint
The order of the moment to take. Default=0
- axisint
The axis along which to compute the moment. Default=0
- howcube | slice | ray | auto
How to compute the moment. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- Returns
- map [, wcs]
The moment map (numpy array) and, if wcs=True, the WCS object describing the map
Notes
Generally, how=’cube’ is fastest for small cubes that easily fit into memory. how=’slice’ is best for most larger datasets. how=’ray’ is probably only a good idea for very large cubes whose data are contiguous over the axis of the moment map.
For the first moment, the result for axis=1, 2 is the angular offset relative to the cube face. For axis=0, it is the absolute velocity/frequency of the first moment.
- moment1(axis=0, how='auto')[source]¶
Compute the 1st moment along an axis.
For an explanation of the
axis
andhow
parameters, seemoment()
.
- moment2(axis=0, how='auto')[source]¶
Compute the 2nd moment along an axis.
For an explanation of the
axis
andhow
parameters, seemoment()
.
- percentile(q, axis=None, iterate_rays=False, **kwargs)[source]¶
Return percentiles of the data.
- Parameters
- qfloat
The percentile to compute
- axisint, or None
Which axis to compute percentiles over
- iterate_raysbool
Iterate over individual rays? This mode is slower but can save RAM costs, which may be extreme for large cubes
- plot_channel_maps(nx, ny, channels, contourkwargs={}, output_file=None, fig=None, fig_smallest_dim_inches=8, decimals=3, zoom=1, textcolor=None, cmap='gray_r', tighten=False, textxloc=0.5, textyloc=0.9, savefig_kwargs={}, **kwargs)[source]¶
Make channel maps from a spectral cube
- Parameters
- input_filestr
Name of the input spectral cube
- nx, nyint
Number of sub-plots in the x and y direction
- channelslist
List of channels to show
- cmapstr
The name of a colormap to use for the
imshow
colors- contourkwargsdict
Keyword arguments passed to
contour
- textcolorNone or str
Color of the label text to overlay. If
None
, will be determined automatically. If'notext'
, no text will be added.- textxlocfloat
- textylocfloat
Text label X,Y-location in axis fraction units
- output_filestr
Name of the matplotlib plot
- figmatplotlib figure
The figure object to plot onto. Will be overridden to enforce a specific aspect ratio.
- fig_smallest_dim_inchesfloat
The size of the smallest dimension (either width or height) of the figure in inches. The other dimension will be selected based on the aspect ratio of the data: it cannot be a free parameter.
- decimalsint, optional
Number of decimal places to show in spectral value
- zoomint, optional
How much to zoom in. In future versions of this function, the pointing center will be customizable.
- tightenbool
Call
plt.tight_layout()
after plotting?- savefig_kwargsdict
Keyword arguments to pass to
savefig
(e.g.,bbox_inches='tight'
)- kwargsdict
Passed to
imshow
- reproject(header, order='bilinear', use_memmap=False, filled=True)[source]¶
Spatially reproject the cube into a new header. Fills the data with the cube’s
fill_value
to replace bad values before reprojection.If you want to reproject a cube both spatially and spectrally, you need to use
spectral_interpolate
as well.Warning
The current implementation of
reproject
requires that the whole cube be loaded into memory. Issue #506 notes that this is a problem, and it is on our to-do list to fix.- Parameters
- header
astropy.io.fits.Header
A header specifying a cube in valid WCS
- orderint or str, optional
The order of the interpolation (if
mode
is set to'interpolation'
). This can be either one of the following strings:‘nearest-neighbor’
‘bilinear’
‘biquadratic’
‘bicubic’
or an integer. A value of
0
indicates nearest neighbor interpolation.- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- filledbool
Fill the masked values with the cube’s fill value before reprojection? Note that setting
filled=False
will use the raw data array, which can be a workaround that prevents loading large data into memory.
- header
- sigma_clip_spectrally(threshold, verbose=0, use_memmap=True, num_cores=None, **kwargs)[source]¶
Run astropy’s sigma clipper along the spectral axis, converting all bad (excluded) values to NaN.
- Parameters
- thresholdfloat
The
sigma
parameter inastropy.stats.sigma_clip
, which refers to the number of sigma above which to cut.- verboseint
Verbosity level to pass to joblib
- Other Parameters
- parallelbool
Use joblib to parallelize the operation. If set to
False
, will force the use of a single core without usingjoblib
.- num_coresint or None
The number of cores to use when applying this function in parallel across the cube.
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- spatial_smooth(kernel, convolve=<function convolve>, **kwargs)[source]¶
Smooth the image in each spatial-spatial plane of the cube.
- Parameters
- kernel
Kernel2D
A 2D kernel from astropy
- convolvefunction
The astropy convolution function to use, either
astropy.convolution.convolve
orastropy.convolution.convolve_fft
- kwargsdict
Passed to the convolve function
- kernel
- Other Parameters
- parallelbool
Use joblib to parallelize the operation. If set to
False
, will force the use of a single core without usingjoblib
.- num_coresint or None
The number of cores to use when applying this function in parallel across the cube.
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- spatial_smooth_median(ksize, update_function=None, **kwargs)[source]¶
Smooth the image in each spatial-spatial plane of the cube using a median filter.
- Parameters
- ksizeint
Size of the median filter (scipy.ndimage.filters.median_filter)
- update_functionmethod
Method that is called to update an external progressbar If provided, it disables the default
astropy.utils.console.ProgressBar
- kwargsdict
Passed to the convolve function
- Other Parameters
- parallelbool
Use joblib to parallelize the operation. If set to
False
, will force the use of a single core without usingjoblib
.- num_coresint or None
The number of cores to use when applying this function in parallel across the cube.
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- spectral_interpolate(spectral_grid, suppress_smooth_warning=False, fill_value=None, update_function=None)[source]¶
Resample the cube spectrally onto a specific grid
- Parameters
- spectral_gridarray
An array of the spectral positions to regrid onto
- suppress_smooth_warningbool
If disabled, a warning will be raised when interpolating onto a grid that does not nyquist sample the existing grid. Disable this if you have already appropriately smoothed the data.
- fill_valuefloat
Value for extrapolated spectral values that lie outside of the spectral range defined in the original data. The default is to use the nearest spectral channel in the cube.
- update_functionmethod
Method that is called to update an external progressbar If provided, it disables the default
astropy.utils.console.ProgressBar
- Returns
- cubeSpectralCube
- spectral_slab(lo, hi)[source]¶
Extract a new cube between two spectral coordinates
- Parameters
- lo, hi
Quantity
The lower and upper spectral coordinate for the slab range. The units should be compatible with the units of the spectral axis. If the spectral axis is in frequency-equivalent units and you want to select a range in velocity, or vice-versa, you should first use
with_spectral_unit()
to convert the units of the spectral axis.
- lo, hi
- spectral_smooth(kernel, convolve=<function convolve>, verbose=0, use_memmap=True, num_cores=None, **kwargs)[source]¶
Smooth the cube along the spectral dimension
Note that the mask is left unchanged in this operation.
- Parameters
- kernel
Kernel1D
A 1D kernel from astropy
- convolvefunction
The astropy convolution function to use, either
astropy.convolution.convolve
orastropy.convolution.convolve_fft
- verboseint
Verbosity level to pass to joblib
- kwargsdict
Passed to the convolve function
- kernel
- Other Parameters
- parallelbool
Use joblib to parallelize the operation. If set to
False
, will force the use of a single core without usingjoblib
.- num_coresint or None
The number of cores to use when applying this function in parallel across the cube.
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- spectral_smooth_median(ksize, use_memmap=True, verbose=0, num_cores=None, **kwargs)[source]¶
Smooth the cube along the spectral dimension
- Parameters
- ksizeint
Size of the median filter (scipy.ndimage.filters.median_filter)
- verboseint
Verbosity level to pass to joblib
- kwargsdict
Not used at the moment.
- Other Parameters
- parallelbool
Use joblib to parallelize the operation. If set to
False
, will force the use of a single core without usingjoblib
.- num_coresint or None
The number of cores to use when applying this function in parallel across the cube.
- use_memmapbool
If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory.
- std(axis=None, how='cube', ddof=0, **kwargs)[source]¶
Return the standard deviation of the cube, optionally over an axis.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- Other Parameters
- ddofint
Means Delta Degrees of Freedom. The divisor used in calculations is
N - ddof
, whereN
represents the number of elements. By defaultddof
is zero.- Ignores excluded mask elements.
- subcube(xlo='min', xhi='max', ylo='min', yhi='max', zlo='min', zhi='max', rest_value=None)[source]¶
Extract a sub-cube spatially and spectrally.
When spatial WCS dimensions are given as an
Quantity
, the spatial coordinates of the ‘lo’ and ‘hi’ corners are solved together. This minimizes WCS variations due to the sky curvature when slicing from a large (>1 deg) image.- Parameters
- [xyz]lo/[xyz]hiint or
Quantity
ormin
/max
The endpoints to extract. If given as a quantity, will be interpreted as World coordinates. If given as a string or int, will be interpreted as pixel coordinates.
- [xyz]lo/[xyz]hiint or
- subcube_from_crtfregion(crtf_region, allow_empty=False)[source]¶
Extract a masked subcube from a CRTF region.
- Parameters
- crtf_region: str
The CRTF region(s) string to extract
- allow_empty: bool
If this is False, an exception will be raised if the region contains no overlap with the cube
- subcube_from_ds9region(ds9_region, allow_empty=False)[source]¶
Extract a masked subcube from a ds9 region (only functions on celestial dimensions)
- Parameters
- ds9_region: str
The DS9 region(s) to extract
- allow_empty: bool
If this is False, an exception will be raised if the region contains no overlap with the cube
- subcube_from_mask(region_mask)[source]¶
Given a mask, return the minimal subcube that encloses the mask
- Parameters
- region_mask: `~spectral_cube.masks.MaskBase` or boolean `numpy.ndarray`
The mask with appropraite WCS or an ndarray with matched coordinates
- subcube_from_regions(region_list, allow_empty=False)[source]¶
Extract a masked subcube from a list of
regions.Region
object (only functions on celestial dimensions)- Parameters
- region_list: ``regions.Region`` list
The region(s) to extract
- allow_empty: bool, optional
If this is False, an exception will be raised if the region contains no overlap with the cube. Default is False.
- subcube_slices_from_mask(region_mask, spatial_only=False)[source]¶
Given a mask, return the slices corresponding to the minimum subcube that encloses the mask
- Parameters
- region_mask: `~spectral_cube.masks.MaskBase` or boolean `numpy.ndarray`
The mask with appropriate WCS or an ndarray with matched coordinates
- spatial_only: bool
Return only slices that affect the spatial dimensions; the spectral dimension will be left unchanged
- sum(axis=None, how='auto', **kwargs)[source]¶
Return the sum of the cube, optionally over an axis.
Ignores excluded mask elements.
- Parameters
- axisint (optional)
The axis to collapse, or None to perform a global aggregation
- howcube | slice | ray | auto
How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default=’auto’
- to(unit, equivalencies=())[source]¶
Return the cube converted to the given unit (assuming it is equivalent). If conversion was required, this will be a copy, otherwise it will
- to_ds9(ds9id=None, newframe=False)[source]¶
Send the data to ds9 (this will create a copy in memory)
- Parameters
- ds9id: None or string
The DS9 session ID. If ‘None’, a new one will be created. To find your ds9 session ID, open the ds9 menu option File:XPA:Information and look for the XPA_METHOD string, e.g.
XPA_METHOD: 86ab2314:60063
. You would then calll this function ascube.to_ds9('86ab2314:60063')
- newframe: bool
Send the cube to a new frame or to the current frame?
- to_glue(name=None, glue_app=None, dataset=None, start_gui=True)[source]¶
Send data to a new or existing Glue application
- Parameters
- namestr or None
The name of the dataset within Glue. If None, defaults to ‘SpectralCube’. If a dataset with the given name already exists, a new dataset with “_” appended will be added instead.
- glue_appGlueApplication or None
A glue application to send the data to. If this is not specified, a new glue application will be started if one does not already exist for this cube. Otherwise, the data will be sent to the existing glue application,
self._glue_app
.- datasetglue.core.Data or None
An existing Data object to add the cube to. This is a good way to compare cubes with the same dimensions. Supercedes
glue_app
- start_guibool
Start the GUI when this is run. Set to
False
for testing.
- to_pvextractor()[source]¶
Open the cube in a quick viewer written in matplotlib that allows you to create PV extractions within the GUI
- to_yt(spectral_factor=1.0, nprocs=None, **kwargs)[source]¶
Convert a spectral cube to a yt object that can be further analyzed in yt.
- Parameters
- spectral_factorfloat, optional
Factor by which to stretch the spectral axis. If set to 1, one pixel in spectral coordinates is equivalent to one pixel in spatial coordinates.
- If using yt 3.0 or later, additional keyword arguments will be passed
- onto yt’s ``FITSDataset`` constructor. See the yt documentation
- (http://yt-project.org/docs/3.0/examining/loading_data.html?#fits-data)
- for details on options for reading FITS data.
- with_fill_value(fill_value)¶
Create a new object with a different
fill_value
.Notes
This method is fast (it does not copy any data)
- with_mask(mask, inherit_mask=True, wcs_tolerance=None)[source]¶
Return a new SpectralCube instance that contains a composite mask of the current SpectralCube and the new
mask
. Values of the mask that areTrue
will be included (masks are analogous to numpy boolean index arrays, they are the inverse of the.mask
attribute of a numpy masked array).- Parameters
- mask
MaskBase
instance, or boolean numpy array The mask to apply. If a boolean array is supplied, it will be converted into a mask, assuming that
True
values indicate included elements.- inherit_maskbool (optional, default=True)
If True, combines the provided mask with the mask currently attached to the cube
- wcs_toleranceNone or float
The tolerance of difference in WCS parameters between the cube and the mask. Defaults to
self._wcs_tolerance
(which itself defaults to 0.0) if unspecified
- mask
- Returns
- new_cube
SpectralCube
A cube with the new mask applied.
- new_cube
Notes
This operation returns a view into the data, and not a copy.
- with_spectral_unit(unit, velocity_convention=None, rest_value=None)[source]¶
Returns a new Cube with a different Spectral Axis unit
- Parameters
- unit
Unit
Any valid spectral unit: velocity, (wave)length, or frequency. Only vacuum units are supported.
- velocity_convention‘relativistic’, ‘radio’, or ‘optical’
The velocity convention to use for the output velocity axis. Required if the output type is velocity. This can be either one of the above strings, or an
astropy.units
equivalency.- rest_value
Quantity
A rest wavelength or frequency with appropriate units. Required if output type is velocity. The cube’s WCS should include this already if the input type is velocity, but the WCS’s rest wavelength/frequency can be overridden with this parameter.
- unit
- world_spines()¶
Returns a list of 1D arrays, for the world coordinates along each pixel axis.
Raises error if this operation is ill-posed (e.g. rotated world coordinates, strong distortions)
This method is not currently implemented. Use
world
instead.