"""
A class to represent a 3-d position-position-velocity spectral cube.
"""
from __future__ import print_function, absolute_import, division
import warnings
from functools import wraps
import operator
import re
import itertools
import copy
import tempfile
import textwrap
from pathlib import PosixPath
import six
from six.moves import zip, range
import dask.array as da
import astropy.wcs
from astropy import units as u
from astropy.io.fits import PrimaryHDU, BinTableHDU, Header, Card, HDUList
from astropy.utils.console import ProgressBar
from astropy import log
from astropy import wcs
from astropy import convolution
from astropy import stats
from astropy.constants import si
from astropy.io.registry import UnifiedReadWriteMethod
import numpy as np
from radio_beam import Beam, Beams
from . import cube_utils
from . import wcs_utils
from . import spectral_axis
from .masks import (LazyMask, LazyComparisonMask, BooleanArrayMask, MaskBase,
is_broadcastable_and_smaller)
from .ytcube import ytCube
from .lower_dimensional_structures import (Projection, Slice, OneDSpectrum,
LowerDimensionalObject,
VaryingResolutionOneDSpectrum
)
from .base_class import (BaseNDClass, SpectralAxisMixinClass,
DOPPLER_CONVENTIONS, SpatialCoordMixinClass,
MaskableArrayMixinClass, MultiBeamMixinClass,
HeaderMixinClass, BeamMixinClass,
)
from .utils import (cached, warn_slow, VarianceWarning, BeamWarning,
UnsupportedIterationStrategyWarning, WCSMismatchWarning,
NotImplementedWarning, SliceWarning, SmoothingWarning,
StokesWarning, ExperimentalImplementationWarning,
BeamAverageWarning, NonFiniteBeamsWarning, BeamWarning,
WCSCelestialError, BeamUnitsError)
from .spectral_axis import (determine_vconv_from_ctype, get_rest_value_from_wcs,
doppler_beta, doppler_gamma, doppler_z)
from .io.core import SpectralCubeRead, SpectralCubeWrite
from packaging.version import Version, parse
__all__ = ['BaseSpectralCube', 'SpectralCube', 'VaryingResolutionSpectralCube']
# apply_everywhere, world: do not have a valid cube to test on
__doctest_skip__ = ['BaseSpectralCube._apply_everywhere']
try:
from scipy import ndimage
scipyOK = True
except ImportError:
scipyOK = False
warnings.filterwarnings('ignore', category=wcs.FITSFixedWarning, append=True)
SIGMA2FWHM = 2. * np.sqrt(2. * np.log(2.))
# convenience structures to keep track of the reversed index
# conventions between WCS and numpy
np2wcs = {2: 0, 1: 1, 0: 2}
_NP_DOC = """
Ignores excluded mask elements.
Parameters
----------
axis : int (optional)
The axis to collapse, or None to perform a global aggregation
how : cube | 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'
""".replace('\n', '\n ')
def aggregation_docstring(func):
@wraps(func)
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
wrapper.__doc__ += _NP_DOC
return wrapper
_PARALLEL_DOC = """
Other Parameters
----------------
parallel : bool
Use joblib to parallelize the operation.
If set to ``False``, will force the use of a single core without
using ``joblib``.
num_cores : int or None
The number of cores to use when applying this function in parallel
across the cube.
use_memmap : bool
If specified, a memory mapped temporary file on disk will be
written to rather than storing the intermediate spectra in memory.
"""
def parallel_docstring(func):
@wraps(func)
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
line1 = wrapper.__doc__.split("\n")[1]
indentation = " "*(len(line1) - len(line1.lstrip()))
try:
wrapper.__doc__ += textwrap.indent(_PARALLEL_DOC, indentation)
except AttributeError:
# python2.7
wrapper.__doc__ = textwrap.dedent(wrapper.__doc__) + _PARALLEL_DOC
return wrapper
def _apply_spectral_function(arguments, outcube, function, **kwargs):
"""
Helper function to apply a function to a spectrum.
Needs to be declared toward the top of the code to allow pickling by
joblib.
"""
(spec, includemask, ii, jj) = arguments
if np.any(includemask):
outcube[:,jj,ii] = function(spec, **kwargs)
else:
outcube[:,jj,ii] = spec
def _apply_spatial_function(arguments, outcube, function, **kwargs):
"""
Helper function to apply a function to an image.
Needs to be declared toward the top of the code to allow pickling by
joblib.
"""
(img, includemask, ii) = arguments
if np.any(includemask):
outcube[ii, :, :] = function(img, **kwargs)
else:
outcube[ii, :, :] = img
[docs]
class BaseSpectralCube(BaseNDClass, MaskableArrayMixinClass,
SpectralAxisMixinClass, SpatialCoordMixinClass,
HeaderMixinClass):
def __init__(self, data, wcs, mask=None, meta=None, fill_value=np.nan,
header=None, allow_huge_operations=False, wcs_tolerance=0.0):
# Deal with metadata first because it can affect data reading
self._meta = meta or {}
# must extract unit from data before stripping it
if 'BUNIT' in self._meta:
self._unit = cube_utils.convert_bunit(self._meta["BUNIT"])
elif hasattr(data, 'unit'):
self._unit = data.unit
else:
self._unit = None
# data must not be a quantity when stored in self._data
if hasattr(data, 'unit'):
# strip the unit so that it can be treated as cube metadata
data = data.value
# TODO: mask should be oriented? Or should we assume correctly oriented here?
self._data, self._wcs = cube_utils._orient(data, wcs)
self._wcs_tolerance = wcs_tolerance
self._spectral_axis = None
self._mask = mask # specifies which elements to Nan/blank/ignore
# object or array-like object, given that WCS needs
# to be consistent with data?
#assert mask._wcs == self._wcs
self._fill_value = fill_value
self._header = Header() if header is None else header
if not isinstance(self._header, Header):
raise TypeError("If a header is given, it must be a fits.Header")
# We don't pass the spectral unit via the initializer since the user
# should be using ``with_spectral_unit`` if they want to set it.
# However, we do want to keep track of what units the spectral axis
# should be returned in, otherwise astropy's WCS can change the units,
# e.g. km/s -> m/s.
# This can be overridden with Header below
self._spectral_unit = u.Unit(self._wcs.wcs.cunit[2])
# This operation is kind of expensive?
header_specaxnum = astropy.wcs.WCS(header).wcs.spec
header_specaxunit = spectral_axis.unit_from_header(self._header,
spectral_axis_number=header_specaxnum+1)
# Allow the original header spectral axis unit to override the default
# unit
if header_specaxunit is not None:
self._spectral_unit = header_specaxunit
self._spectral_scale = spectral_axis.wcs_unit_scale(self._spectral_unit)
self.allow_huge_operations = allow_huge_operations
self._cache = {}
@property
def _is_huge(self):
return cube_utils.is_huge(self)
@property
def _new_thing_with(self):
return self._new_cube_with
def _new_cube_with(self, data=None, wcs=None, mask=None, meta=None,
fill_value=None, spectral_unit=None, unit=None,
wcs_tolerance=None, **kwargs):
data = self._data if data is None else data
if unit is None and hasattr(data, 'unit'):
if data.unit != self.unit:
raise u.UnitsError("New data unit '{0}' does not"
" match cube unit '{1}'. You can"
" override this by specifying the"
" `unit` keyword."
.format(data.unit, self.unit))
unit = data.unit
elif unit is not None:
# convert string units to Units
if not isinstance(unit, u.Unit):
unit = u.Unit(unit)
if hasattr(data, 'unit'):
if u.Unit(unit) != data.unit:
raise u.UnitsError("The specified new cube unit '{0}' "
"does not match the input unit '{1}'."
.format(unit, data.unit))
elif self._unit is not None:
unit = self.unit
wcs = self._wcs if wcs is None else wcs
mask = self._mask if mask is None else mask
if meta is None:
meta = {}
meta.update(self._meta)
if unit is not None:
meta['BUNIT'] = unit.to_string(format='FITS')
fill_value = self._fill_value if fill_value is None else fill_value
spectral_unit = self._spectral_unit if spectral_unit is None else u.Unit(spectral_unit)
cube = self.__class__(data=data, wcs=wcs, mask=mask, meta=meta,
fill_value=fill_value, header=self._header,
allow_huge_operations=self.allow_huge_operations,
wcs_tolerance=wcs_tolerance or self._wcs_tolerance,
**kwargs)
cube._spectral_unit = spectral_unit
cube._spectral_scale = spectral_axis.wcs_unit_scale(spectral_unit)
return cube
read = UnifiedReadWriteMethod(SpectralCubeRead)
write = UnifiedReadWriteMethod(SpectralCubeWrite)
@property
def unit(self):
""" The flux unit """
if self._unit:
return self._unit
else:
return u.one
@property
def shape(self):
""" Length of cube along each axis """
return self._data.shape
@property
def size(self):
""" Number of elements in the cube """
return self._data.size
@property
def base(self):
""" The data type 'base' of the cube - useful for, e.g., joblib """
return self._data.base
def __len__(self):
return self.shape[0]
@property
def ndim(self):
""" Dimensionality of the data """
return self._data.ndim
def __repr__(self):
s = "{1} with shape={0}".format(self.shape, self.__class__.__name__)
if self.unit is u.one:
s += ":\n"
else:
s += " and unit={0}:\n".format(self.unit)
s += (" n_x: {0:6d} type_x: {1:8s} unit_x: {2:5s}"
" range: {3:12.6f}:{4:12.6f}\n".format(self.shape[2],
self.wcs.wcs.ctype[0],
self.wcs.wcs.cunit[0],
self.longitude_extrema[0],
self.longitude_extrema[1],))
s += (" n_y: {0:6d} type_y: {1:8s} unit_y: {2:5s}"
" range: {3:12.6f}:{4:12.6f}\n".format(self.shape[1],
self.wcs.wcs.ctype[1],
self.wcs.wcs.cunit[1],
self.latitude_extrema[0],
self.latitude_extrema[1],
))
s += (" n_s: {0:6d} type_s: {1:8s} unit_s: {2:5s}"
" range: {3:12.3f}:{4:12.3f}".format(self.shape[0],
self.wcs.wcs.ctype[2],
self._spectral_unit,
self.spectral_extrema[0],
self.spectral_extrema[1],
))
return s
@property
@cached
def spectral_extrema(self):
_spectral_min = self.spectral_axis.min()
_spectral_max = self.spectral_axis.max()
return u.Quantity((_spectral_min, _spectral_max))
[docs]
def apply_numpy_function(self, function, fill=np.nan,
reduce=True, how='auto',
projection=False,
unit=None,
check_endian=False,
progressbar=False,
includemask=False,
**kwargs):
"""
Apply a numpy function to the cube
Parameters
----------
function : Numpy ufunc
A numpy ufunc to apply to the cube
fill : float
The fill value to use on the data
reduce : bool
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
how : cube | 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'
projection : bool
Return a :class:`~spectral_cube.lower_dimensional_structures.Projection` if the resulting array is 2D or a
OneDProjection if the resulting array is 1D and the sum is over both
spatial axes?
unit : None or `astropy.units.Unit`
The unit to include for the output array. For example,
`SpectralCube.max` calls
``SpectralCube.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_endian : bool
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 <https://pypi.python.org/pypi/Bottleneck>`_ package.
progressbar : bool
Show a progressbar while iterating over the slices through the
cube?
kwargs : dict
Passed to the numpy function.
Returns
-------
result : :class:`~spectral_cube.lower_dimensional_structures.Projection` or `~astropy.units.Quantity` or float
The result depends on the value of ``axis``, ``projection``, and
``unit``. If ``axis`` is None, the return will be a scalar with or
without units. If axis is an integer, the return will be a
:class:`~spectral_cube.lower_dimensional_structures.Projection` if ``projection`` is set
"""
# leave axis in kwargs to avoid overriding numpy defaults, e.g. if the
# default is axis=-1, we don't want to force it to be axis=None by
# specifying that in the function definition
axis = kwargs.get('axis', None)
if how == 'auto':
strategy = cube_utils.iterator_strategy(self, axis)
else:
strategy = how
out = None
log.debug("applying numpy function {0} with strategy {1}"
.format(function, strategy))
if strategy == 'slice' and reduce:
out = self._reduce_slicewise(function, fill, check_endian,
includemask=includemask,
progressbar=progressbar, **kwargs)
elif how == 'ray':
out = self.apply_function(function, **kwargs)
elif how not in ['auto', 'cube']:
warnings.warn("Cannot use how=%s. Using how=cube" % how,
UnsupportedIterationStrategyWarning)
if out is None:
out = function(self._get_filled_data(fill=fill,
check_endian=check_endian),
**kwargs)
if axis is None:
# return is scalar
if unit is not None:
return u.Quantity(out, unit=unit)
else:
return out
elif projection and reduce:
meta = {'collapse_axis': axis}
meta.update(self._meta)
if hasattr(axis, '__len__') and len(axis) == 2:
# if operation is over two spatial dims
if set(axis) == set((1,2)):
new_wcs = self._wcs.sub([wcs.WCSSUB_SPECTRAL])
header = self._nowcs_header
if cube_utils._has_beam(self):
bmarg = {'beam': self.beam}
elif cube_utils._has_beams(self):
bmarg = {'beams': self.unmasked_beams}
else:
bmarg = {}
return self._oned_spectrum(value=out,
wcs=new_wcs,
copy=False,
unit=unit,
header=header,
meta=meta,
spectral_unit=self._spectral_unit,
**bmarg
)
else:
warnings.warn("Averaging over a spatial and a spectral "
"dimension cannot produce a Projection "
"quantity (no units or WCS are preserved).",
SliceWarning
)
return out
else:
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
header = self._nowcs_header
return Projection(out, copy=False, wcs=new_wcs, meta=meta,
unit=unit, header=header)
else:
return out
def _reduce_slicewise(self, function, fill, check_endian,
includemask=False, progressbar=False, **kwargs):
"""
Compute a numpy aggregation by grabbing one slice at a time
"""
ax = kwargs.pop('axis', None)
full_reduce = ax is None
ax = ax or 0
if isinstance(ax, tuple):
assert len(ax) == 2 # we only work with cubes...
iterax = [x for x in range(3) if x not in ax][0]
else:
iterax = ax
log.debug("reducing slicewise with axis = {0}".format(ax))
if includemask:
planes = self._iter_mask_slices(iterax)
else:
planes = self._iter_slices(iterax, fill=fill, check_endian=check_endian)
result = next(planes)
if progressbar:
progressbar = ProgressBar(self.shape[iterax])
pbu = progressbar.update
else:
pbu = lambda: True
if isinstance(ax, tuple):
# have to make a result a list of itself, since we already "got"
# the first plane above
result = [function(result, axis=(0,1), **kwargs)]
for plane in planes:
# apply to axes 0 and 1, because we're fully reducing the plane
# to a number if we're applying over two axes
result.append(function(plane, axis=(0,1), **kwargs))
pbu()
result = np.array(result)
else:
for plane in planes:
# axis = 2 means we're stacking two planes, the previously
# computed one and the current one
result = function(np.dstack((result, plane)), axis=2, **kwargs)
pbu()
if full_reduce:
result = function(result)
return result
[docs]
def get_mask_array(self):
"""
Convert the mask to a boolean numpy array
"""
return self._mask.include(data=self._data, wcs=self._wcs,
wcs_tolerance=self._wcs_tolerance)
def _naxes_dropped(self, view):
"""
Determine how many axes are being selected given a view.
(1,2) -> 2
None -> 3
1 -> 1
2 -> 1
"""
if hasattr(view,'__len__'):
return len(view)
elif view is None:
return 3
else:
return 1
[docs]
@aggregation_docstring
@warn_slow
def sum(self, axis=None, how='auto', **kwargs):
"""
Return the sum of the cube, optionally over an axis.
"""
from .np_compat import allbadtonan
projection = self._naxes_dropped(axis) in (1,2)
return self.apply_numpy_function(allbadtonan(np.nansum), fill=np.nan,
how=how, axis=axis, unit=self.unit,
projection=projection, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def mean(self, axis=None, how='cube', **kwargs):
"""
Return the mean of the cube, optionally over an axis.
"""
projection = self._naxes_dropped(axis) in (1,2)
if how == 'slice':
# two-pass approach: first total the # of points,
# then total the value of the points, then divide
# (a one-pass approach is possible but requires
# more sophisticated bookkeeping)
counts = self._count_nonzero_slicewise(axis=axis,
progressbar=kwargs.get('progressbar'))
ttl = self.apply_numpy_function(np.nansum, fill=np.nan, how=how,
axis=axis, unit=None,
projection=False, **kwargs)
out = ttl / counts
if projection:
if self._naxes_dropped(axis) == 1:
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
meta = {'collapse_axis': axis}
meta.update(self._meta)
return Projection(out, copy=False, wcs=new_wcs,
meta=meta,
unit=self.unit, header=self._nowcs_header)
elif axis == (1,2):
newwcs = self._wcs.sub([wcs.WCSSUB_SPECTRAL])
if cube_utils._has_beam(self):
bmarg = {'beam': self.beam}
elif cube_utils._has_beams(self):
bmarg = {'beams': self.unmasked_beams}
else:
bmarg = {}
return self._oned_spectrum(value=out,
wcs=newwcs,
copy=False,
unit=self.unit,
spectral_unit=self._spectral_unit,
meta=self.meta,
**bmarg
)
else:
# this is a weird case, but even if projection is
# specified, we can't return a Quantity here because of WCS
# issues. `apply_numpy_function` already does this
# silently, which is unfortunate.
warnings.warn("Averaging over a spatial and a spectral "
"dimension cannot produce a Projection "
"quantity (no units or WCS are preserved).",
SliceWarning
)
return out
else:
return out
return self.apply_numpy_function(np.nanmean, fill=np.nan, how=how,
axis=axis, unit=self.unit,
projection=projection, **kwargs)
def _count_nonzero_slicewise(self, axis=None, progressbar=False):
"""
Count the number of finite pixels along an axis slicewise. This is a
helper function for the mean and std deviation slicewise iterators.
"""
counts = self.apply_numpy_function(np.sum, fill=np.nan,
how='slice', axis=axis,
unit=None,
projection=False,
progressbar=progressbar,
includemask=True)
return counts
[docs]
@aggregation_docstring
@warn_slow
def std(self, axis=None, how='cube', ddof=0, **kwargs):
"""
Return the standard deviation of the cube, optionally over an axis.
Other Parameters
----------------
ddof : int
Means Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements. By
default ``ddof`` is zero.
"""
projection = self._naxes_dropped(axis) in (1,2)
if how == 'slice':
if axis is None:
raise NotImplementedError("The overall standard deviation "
"cannot be computed in a slicewise "
"manner. Please use a "
"different strategy.")
if hasattr(axis, '__len__') and len(axis) == 2:
return self.apply_numpy_function(np.nanstd,
axis=axis,
how='slice',
projection=projection,
unit=self.unit,
**kwargs)
else:
counts = self._count_nonzero_slicewise(axis=axis)
ttl = self.apply_numpy_function(np.nansum, fill=np.nan, how='slice',
axis=axis, unit=None,
projection=False, **kwargs)
# Equivalent, but with more overhead:
# ttl = self.sum(axis=axis, how='slice').value
mean = ttl/counts
planes = self._iter_slices(axis, fill=np.nan, check_endian=False)
result = (next(planes)-mean)**2
for plane in planes:
result = np.nansum(np.dstack((result, (plane-mean)**2)), axis=2)
out = (result/(counts-ddof))**0.5
if projection:
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
meta = {'collapse_axis': axis}
meta.update(self._meta)
return Projection(out, copy=False, wcs=new_wcs,
meta=meta,
unit=self.unit, header=self._nowcs_header)
else:
return out
# standard deviation cannot be computed as a trivial step-by-step
# process. There IS a one-pass algorithm for std dev, but it is not
# implemented, so we must force cube here. We could and should also
# implement raywise reduction
return self.apply_numpy_function(np.nanstd, fill=np.nan, how=how,
axis=axis, unit=self.unit,
projection=projection, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def mad_std(self, axis=None, how='cube', **kwargs):
"""
Use astropy's mad_std to computer the standard deviation
"""
if int(astropy.__version__[0]) < 2:
raise NotImplementedError("mad_std requires astropy >= 2")
projection = self._naxes_dropped(axis) in (1,2)
if how == 'ray' and not hasattr(axis, '__len__'):
# no need for fill here; masked-out data are simply not included
return self.apply_numpy_function(stats.mad_std,
axis=axis,
how='ray',
unit=self.unit,
projection=projection,
ignore_nan=True,
)
elif how == 'slice' and hasattr(axis, '__len__') and len(axis) == 2:
return self.apply_numpy_function(stats.mad_std,
axis=axis,
how='slice',
projection=projection,
unit=self.unit,
fill=np.nan,
ignore_nan=True,
**kwargs)
elif how in ('ray', 'slice'):
raise NotImplementedError('Cannot run mad_std slicewise or raywise '
'unless the dimensionality is also reduced in the same direction.')
else:
return self.apply_numpy_function(stats.mad_std,
fill=np.nan,
axis=axis,
unit=self.unit,
ignore_nan=True,
how=how,
projection=projection, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def max(self, axis=None, how='auto', **kwargs):
"""
Return the maximum data value of the cube, optionally over an axis.
"""
projection = self._naxes_dropped(axis) in (1,2)
return self.apply_numpy_function(np.nanmax, fill=np.nan, how=how,
axis=axis, unit=self.unit,
projection=projection, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def min(self, axis=None, how='auto', **kwargs):
"""
Return the minimum data value of the cube, optionally over an axis.
"""
projection = self._naxes_dropped(axis) in (1,2)
return self.apply_numpy_function(np.nanmin, fill=np.nan, how=how,
axis=axis, unit=self.unit,
projection=projection, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def argmax(self, axis=None, how='auto', **kwargs):
"""
Return the index of the maximum data value.
The return value is arbitrary if all pixels along ``axis`` are
excluded from the mask.
"""
return self.apply_numpy_function(np.nanargmax, fill=-np.inf,
reduce=False, projection=False,
how=how, axis=axis, **kwargs)
[docs]
@aggregation_docstring
@warn_slow
def argmin(self, axis=None, how='auto', **kwargs):
"""
Return the index of the minimum data value.
The return value is arbitrary if all pixels along ``axis`` are
excluded from the mask
"""
return self.apply_numpy_function(np.nanargmin, fill=np.inf,
reduce=False, projection=False,
how=how, axis=axis, **kwargs)
def _argmaxmin_world(self, axis, method, **kwargs):
'''
Return the spatial or spectral index of the maximum or minimum value.
Use `argmax_world` and `argmin_world` directly.
'''
operation_name = '{}_world'.format(method)
if wcs_utils.is_pixel_axis_to_wcs_correlated(self.wcs, axis):
raise WCSCelestialError("{} requires the celestial axes"
" to be aligned along image axes."
.format(operation_name))
if method == 'argmin':
arg_pixel_plane = self.argmin(axis=axis, **kwargs)
elif method == 'argmax':
arg_pixel_plane = self.argmax(axis=axis, **kwargs)
else:
raise ValueError("`method` must be 'argmin' or 'argmax'")
# Convert to WCS coordinates.
out = cube_utils.world_take_along_axis(self, arg_pixel_plane, axis)
# Compute whether the mask has any valid data along `axis`
collapsed_mask = self.mask.include().any(axis=axis)
out[~collapsed_mask] = np.NaN
# Return a Projection.
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
meta = {'collapse_axis': axis}
meta.update(self._meta)
return Projection(out, copy=False, wcs=new_wcs, meta=meta,
unit=out.unit, header=self._nowcs_header)
[docs]
@warn_slow
def argmax_world(self, axis, **kwargs):
'''
Return the spatial or spectral index of the maximum value
along a line of sight.
Parameters
----------
axis : int
The axis to return the peak location along. e.g., `axis=0`
will return the value of the spectral axis at the peak value.
kwargs : dict
Passed to `~SpectralCube.argmax`.
'''
return self._argmaxmin_world(axis, 'argmax', **kwargs)
[docs]
@warn_slow
def argmin_world(self, axis, **kwargs):
'''
Return the spatial or spectral index of the minimum value
along a line of sight.
Parameters
----------
axis : int
The axis to return the peak location along. e.g., `axis=0`
will return the value of the spectral axis at the peak value.
kwargs : dict
Passed to `~SpectralCube.argmin`.
'''
return self._argmaxmin_world(axis, 'argmin', **kwargs)
[docs]
def chunked(self, chunksize=1000):
"""
Not Implemented.
Iterate over chunks of valid data
"""
raise NotImplementedError()
def _get_flat_shape(self, axis):
"""
Get the shape of the array after flattening along an axis
"""
iteraxes = [0, 1, 2]
iteraxes.remove(axis)
# x,y are defined as first,second dim to iterate over
# (not x,y in pixel space...)
nx = self.shape[iteraxes[0]]
ny = self.shape[iteraxes[1]]
return nx, ny
@warn_slow
def _apply_everywhere(self, function, *args, check_units=True):
"""
Return a new cube with ``function`` applied to all pixels
Private because this doesn't have an obvious and easy-to-use API
Parameters
----------
function : function
An operator that takes the data (self) and any number of additional
arguments
check_units : bool
When doing the initial test before running the full operation,
should units be included on the 'fake' test quantity? This is
specifically added as an option to enable using the subtraction and
addition operators without checking unit compatibility here because
they _already_ enforce unit compatibility.
Examples
--------
>>> newcube = cube.apply_everywhere(np.add, 0.5*u.Jy)
"""
try:
if check_units:
test_result = function(np.ones([1,1,1])*self.unit, *args)
new_unit = test_result.unit
else:
test_result = function(np.ones([1,1,1]), *args)
new_unit = self.unit
# First, check that function returns same # of dims?
assert test_result.ndim == 3,"Output is not 3-dimensional"
except Exception as ex:
raise AssertionError("Function could not be applied to a simple "
"cube. The error was: {0}".format(ex))
# We don't need to convert to a quantity here because the shape check
data_in = self._get_filled_data(fill=self._fill_value)
data = function(data_in, *args)
# strip the unit because data_in does not have a unit
# (we calculate the appropriate unit above and pass it on below)
if hasattr(data, 'unit'):
data = data.value
return self._new_cube_with(data=data, unit=new_unit)
@warn_slow
def _cube_on_cube_operation(self, function, cube, equivalencies=[], **kwargs):
"""
Apply an operation between two cubes. Inherits the metadata of the
left cube.
Parameters
----------
function : function
A function to apply to the cubes
cube : SpectralCube
Another cube to put into the function
equivalencies : list
A list of astropy equivalencies
kwargs : dict
Passed to np.testing.assert_almost_equal
"""
assert cube.shape == self.shape
if not self.unit.is_equivalent(cube.unit, equivalencies=equivalencies):
raise u.UnitsError("{0} is not equivalent to {1}"
.format(self.unit, cube.unit))
if not wcs_utils.check_equality(self.wcs, cube.wcs, warn_missing=True,
**kwargs):
warnings.warn("Cube WCSs do not match, but their shapes do",
WCSMismatchWarning)
try:
test_result = function(np.ones([1,1,1])*self.unit,
np.ones([1,1,1])*self.unit)
# First, check that function returns same # of dims?
assert test_result.shape == (1,1,1)
except Exception as ex:
raise AssertionError("Function {1} could not be applied to a "
"pair of simple "
"cube. The error was: {0}".format(ex,
function))
cube = cube.to(self.unit)
data = function(self._data, cube._data)
try:
# multiplication, division, etc. are valid inter-unit operations
unit = function(self.unit, cube.unit)
except TypeError:
# addition, subtraction are not
unit = self.unit
return self._new_cube_with(data=data, unit=unit)
[docs]
def apply_function(self, function, axis=None, weights=None, unit=None,
projection=False, progressbar=False,
update_function=None, keep_shape=False, **kwargs):
"""
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
----------
function : function
A function that can be applied to a numpy array. Does not need to
be nan-aware
axis : 1, 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) `~astropy.units.Unit`
The unit of the output projection or value. Not all functions
should return quantities with units.
projection : bool
Return a projection if the resulting array is 2D?
progressbar : bool
Show a progressbar while iterating over the slices/rays through the
cube?
keep_shape : bool
If `True`, the returned object will be the same dimensionality as
the cube.
update_function : function
An alternative tracker for the progress of applying the function
to the cube data. If ``progressbar`` is ``True``, this argument is
ignored.
Returns
-------
result : :class:`~spectral_cube.lower_dimensional_structures.Projection` or `~astropy.units.Quantity` or float
The result depends on the value of ``axis``, ``projection``, and
``unit``. If ``axis`` is None, the return will be a scalar with or
without units. If axis is an integer, the return will be a
:class:`~spectral_cube.lower_dimensional_structures.Projection` if ``projection`` is set
"""
if axis is None:
out = function(self.flattened(), **kwargs)
if unit is not None:
# return is scalar
return u.Quantity(out, unit=unit)
else:
return out
if hasattr(axis, '__len__'):
raise NotImplementedError("`apply_function` does not support "
"function application across multiple "
"axes. Try `apply_numpy_function`.")
# determine the output array shape
nx, ny = self._get_flat_shape(axis)
nz = self.shape[axis] if keep_shape else 1
# allocate memory for output array
out = np.empty([nz, nx, ny]) * np.nan
if progressbar:
progressbar = ProgressBar(nx*ny)
pbu = progressbar.update
elif update_function is not None:
pbu = update_function
else:
pbu = lambda: True
# iterate over "lines of sight" through the cube
for y, x, slc in self._iter_rays(axis):
# acquire the flattened, valid data for the slice
data = self.flattened(slc, weights=weights)
if len(data) != 0:
result = function(data, **kwargs)
if hasattr(result, 'value'):
# store result in array
out[:, y, x] = result.value
else:
out[:, y, x] = result
pbu()
if not keep_shape:
out = out[0, :, :]
if projection and axis in (0, 1, 2):
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
meta = {'collapse_axis': axis}
meta.update(self._meta)
return Projection(out, copy=False, wcs=new_wcs, meta=meta,
unit=unit, header=self._nowcs_header)
else:
return out
def _iter_rays(self, axis=None):
"""
Iterate over view corresponding to lines-of-sight through a cube
along the specified axis
"""
ny, nx = self._get_flat_shape(axis)
for y in range(ny):
for x in range(nx):
# create length-1 view for each position
slc = [slice(y, y + 1), slice(x, x + 1), ]
# create a length-N slice (all-inclusive) along the selected axis
slc.insert(axis, slice(None))
yield y, x, tuple(slc)
def _iter_slices(self, axis, fill=np.nan, check_endian=False):
"""
Iterate over the cube one slice at a time,
replacing masked elements with fill
"""
view = [slice(None)] * 3
for x in range(self.shape[axis]):
view[axis] = x
yield self._get_filled_data(view=tuple(view), fill=fill,
check_endian=check_endian)
def _iter_mask_slices(self, axis):
"""
Iterate over the cube one slice at a time,
replacing masked elements with fill
"""
view = [slice(None)] * 3
for x in range(self.shape[axis]):
view[axis] = x
yield self._mask.include(data=self._data,
view=tuple(view),
wcs=self._wcs,
wcs_tolerance=self._wcs_tolerance,
)
[docs]
def flattened(self, slice=(), weights=None):
"""
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
"""
data = self._mask._flattened(data=self._data, wcs=self._wcs, view=slice)
if isinstance(data, da.Array):
# Quantity does not work well with lazily evaluated data with an
# unkonwn shape (which is the case when doing boolean indexing of arrays)
data = self._compute(data)
if weights is not None:
weights = self._mask._flattened(data=weights, wcs=self._wcs, view=slice)
return u.Quantity(data * weights, self.unit, copy=False)
else:
return u.Quantity(data, self.unit, copy=False)
[docs]
def percentile(self, q, axis=None, iterate_rays=False, **kwargs):
"""
Return percentiles of the data.
Parameters
----------
q : float
The percentile to compute
axis : int, or None
Which axis to compute percentiles over
iterate_rays : bool
Iterate over individual rays? This mode is slower but can save RAM
costs, which may be extreme for large cubes
"""
if hasattr(np, 'nanpercentile') and not iterate_rays:
result = self.apply_numpy_function(np.nanpercentile, q=q,
axis=axis, projection=True,
unit=self.unit, how='cube',
**kwargs)
else:
result = self.apply_function(np.percentile, q=q, axis=axis,
projection=True, unit=self.unit,
**kwargs)
return result
[docs]
def with_mask(self, mask, inherit_mask=True, wcs_tolerance=None):
"""
Return a new SpectralCube instance that contains a composite mask of
the current SpectralCube and the new ``mask``. Values of the mask that
are ``True`` 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 : :class:`~spectral_cube.masks.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_mask : bool (optional, default=True)
If True, combines the provided mask with the
mask currently attached to the cube
wcs_tolerance : None 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
Returns
-------
new_cube : :class:`SpectralCube`
A cube with the new mask applied.
Notes
-----
This operation returns a view into the data, and not a copy.
"""
if isinstance(mask, np.ndarray):
if not is_broadcastable_and_smaller(mask.shape, self._data.shape):
raise ValueError("Mask shape is not broadcastable to data shape: "
"%s vs %s" % (mask.shape, self._data.shape))
mask = BooleanArrayMask(mask, self._wcs, shape=self._data.shape)
if self._mask is not None and inherit_mask:
new_mask = np.bitwise_and(self._mask, mask)
else:
new_mask = mask
new_mask._validate_wcs(new_data=self._data, new_wcs=self._wcs,
wcs_tolerance=wcs_tolerance or self._wcs_tolerance)
return self._new_cube_with(mask=new_mask, wcs_tolerance=wcs_tolerance)
def __getitem__(self, view):
# Need to allow self[:], self[:,:]
if isinstance(view, (slice,int,np.int64)):
view = (view, slice(None), slice(None))
elif len(view) == 2:
view = view + (slice(None),)
elif len(view) > 3:
raise IndexError("Too many indices")
meta = {}
meta.update(self._meta)
slice_data = [(s.start, s.stop, s.step)
if hasattr(s,'start') else s
for s in view]
if 'slice' in meta:
meta['slice'].append(slice_data)
else:
meta['slice'] = [slice_data]
intslices = [2-ii for ii,s in enumerate(view) if not hasattr(s,'start')]
if intslices:
if len(intslices) > 1:
if 2 in intslices:
raise NotImplementedError("1D slices along non-spectral "
"axes are not yet implemented.")
newwcs = self._wcs.sub([a
for a in (1,2,3)
if a not in [x+1 for x in intslices]])
if cube_utils._has_beam(self):
bmarg = {'beam': self.beam}
elif cube_utils._has_beams(self):
bmarg = {'beams': self.beams}
else:
bmarg = {}
return self._oned_spectrum(value=self._data[view],
wcs=newwcs,
copy=False,
unit=self.unit,
spectral_unit=self._spectral_unit,
mask=self.mask[view] if self.mask is not None else None,
meta=meta,
**bmarg
)
# only one element, so drop an axis
newwcs = wcs_utils.drop_axis(self._wcs, intslices[0])
header = self._nowcs_header
if intslices[0] == 0:
# celestial: can report the wavelength/frequency of the axis
header['CRVAL3'] = self.spectral_axis[intslices[0]].value
header['CDELT3'] = self.wcs.sub([wcs.WCSSUB_SPECTRAL]).wcs.cdelt[0]
header['CUNIT3'] = self._spectral_unit.to_string(format='FITS')
return Slice(value=self.filled_data[view],
mask=self.mask[view] if self.mask is not None else None,
wcs=newwcs,
copy=False,
unit=self.unit,
header=header,
meta=meta)
newmask = self._mask[view] if self._mask is not None else None
newwcs = wcs_utils.slice_wcs(self._wcs, view, shape=self.shape)
return self._new_cube_with(data=self._data[view],
wcs=newwcs,
mask=newmask,
meta=meta)
@property
def unitless(self):
"""Return a copy of self with unit set to None"""
newcube = self._new_cube_with()
newcube._unit = None
return newcube
[docs]
def with_spectral_unit(self, unit, velocity_convention=None,
rest_value=None):
"""
Returns a new Cube with a different Spectral Axis unit
Parameters
----------
unit : :class:`~astropy.units.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 : :class:`~astropy.units.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.
.. note: This must be the rest frequency/wavelength *in vacuum*,
even if your cube has air wavelength units
"""
newwcs,newmeta = self._new_spectral_wcs(unit=unit,
velocity_convention=velocity_convention,
rest_value=rest_value)
if self._mask is not None:
newmask = self._mask.with_spectral_unit(unit,
velocity_convention=velocity_convention,
rest_value=rest_value)
newmask._wcs = newwcs
else:
newmask = None
cube = self._new_cube_with(wcs=newwcs, mask=newmask, meta=newmeta,
spectral_unit=unit)
return cube
@cube_utils.slice_syntax
def unmasked_data(self, view):
"""
Return a view of the subset of the underlying data,
ignoring the mask.
Returns
-------
data : Quantity instance
The unmasked data
"""
values = self._data[view]
# Astropy Quantities don't play well with dask arrays with shape ()
if isinstance(values, da.Array) and values.shape == ():
values = values.compute()
return u.Quantity(values, self.unit, copy=False)
[docs]
def unmasked_copy(self):
"""
Return a copy of the cube with no mask (i.e., all data included)
"""
newcube = self._new_cube_with()
newcube._mask = None
return newcube
@cached
def _pix_cen(self):
"""
Offset of every pixel from the origin, along each direction
Returns
-------
tuple of spectral_offset, y_offset, x_offset, each 3D arrays
describing the distance from the origin
Notes
-----
These arrays are broadcast, and are not memory intensive
Each array is in the units of the corresponding wcs.cunit, but
this is implicit (e.g., they are not astropy Quantity arrays)
"""
# Start off by extracting the world coordinates of the pixels
_, lat, lon = self.world[0, :, :]
spectral, _, _ = self.world[:, 0, 0]
spectral -= spectral[0] # offset from first pixel
# Convert to radians
lon = np.radians(lon)
lat = np.radians(lat)
# Find the dx and dy arrays
from astropy.coordinates import angular_separation
dx = angular_separation(lon[:, :-1], lat[:, :-1],
lon[:, 1:], lat[:, :-1])
dy = angular_separation(lon[:-1, :], lat[:-1, :],
lon[1:, :], lat[1:, :])
# Find the cumulative offset - need to add a zero at the start
x = np.zeros(self._data.shape[1:])
y = np.zeros(self._data.shape[1:])
x[:, 1:] = np.cumsum(np.degrees(dx), axis=1)
y[1:, :] = np.cumsum(np.degrees(dy), axis=0)
if isinstance(self._data, da.Array):
x, y, spectral = da.broadcast_arrays(x[None,:,:], y[None,:,:], spectral[:,None,None])
# NOTE: we need to rechunk these to the actual data size, otherwise
# the resulting arrays have a single chunk which can cause issues with
# da.store (which writes data out in chunks)
return (spectral.rechunk(self._data.chunksize),
y.rechunk(self._data.chunksize),
x.rechunk(self._data.chunksize))
else:
x, y, spectral = np.broadcast_arrays(x[None,:,:], y[None,:,:], spectral[:,None,None])
return spectral, y, x
@cached
def _pix_size_slice(self, axis):
"""
Return the size of each pixel along any given direction. Assumes
pixels have equal size. Also assumes that the spectral and spatial
directions are separable, which is enforced throughout this code.
Parameters
----------
axis : 0, 1, or 2
The axis along which to compute the pixel size
Returns
-------
Pixel size in units of either degrees or the appropriate spectral unit
"""
if axis == 0:
# note that self._spectral_scale is required here because wcs
# forces into units of m, m/s, or Hz
return np.abs(self.wcs.pixel_scale_matrix[2,2]) * self._spectral_scale
elif axis in (1,2):
# the pixel size is a projection. I think the pixel_scale_matrix
# must be symmetric, such that psm[axis,:]**2 == psm[:,axis]**2
return np.sum(self.wcs.pixel_scale_matrix[2-axis,:]**2)**0.5
else:
raise ValueError("Cubes have 3 axes.")
@cached
def _pix_size(self):
"""
Return the size of each pixel along each direction, in world units
Returns
-------
dv, dy, dx : tuple of 3D arrays
The extent of each pixel along each direction
Notes
-----
These arrays are broadcast, and are not memory intensive
Each array is in the units of the corresponding wcs.cunit, but
this is implicit (e.g., they are not astropy Quantity arrays)
"""
# First, scale along x direction
xpix = np.linspace(-0.5, self._data.shape[2] - 0.5, self._data.shape[2] + 1)
ypix = np.linspace(0., self._data.shape[1] - 1, self._data.shape[1])
xpix, ypix = np.meshgrid(xpix, ypix)
zpix = np.zeros(xpix.shape)
lon, lat, _ = self._wcs.all_pix2world(xpix, ypix, zpix, 0)
# Convert to radians
lon = np.radians(lon)
lat = np.radians(lat)
# Find the dx and dy arrays
from astropy.coordinates import angular_separation
dx = angular_separation(lon[:, :-1], lat[:, :-1],
lon[:, 1:], lat[:, :-1])
# Next, scale along y direction
xpix = np.linspace(0., self._data.shape[2] - 1, self._data.shape[2])
ypix = np.linspace(-0.5,
self._data.shape[1] - 0.5,
self._data.shape[1] + 1)
xpix, ypix = np.meshgrid(xpix, ypix)
zpix = np.zeros(xpix.shape)
lon, lat, _ = self._wcs.all_pix2world(xpix, ypix, zpix, 0)
# Convert to radians
lon = np.radians(lon)
lat = np.radians(lat)
# Find the dx and dy arrays
from astropy.coordinates import angular_separation
dy = angular_separation(lon[:-1, :], lat[:-1, :],
lon[1:, :], lat[1:, :])
# Next, spectral coordinates
zpix = np.linspace(-0.5, self._data.shape[0] - 0.5,
self._data.shape[0] + 1)
xpix = np.zeros(zpix.shape)
ypix = np.zeros(zpix.shape)
_, _, spectral = self._wcs.all_pix2world(xpix, ypix, zpix, 0)
# Take spectral units into account
# order of operations here is crucial! If this is done after
# broadcasting, the full array size is allocated, which is bad!
dspectral = np.diff(spectral) * self._spectral_scale
dx = np.abs(np.degrees(dx.reshape(1, dx.shape[0], dx.shape[1])))
dy = np.abs(np.degrees(dy.reshape(1, dy.shape[0], dy.shape[1])))
dspectral = np.abs(dspectral.reshape(-1, 1, 1))
dx, dy, dspectral = np.broadcast_arrays(dx, dy, dspectral)
return dspectral, dy, dx
[docs]
def moment(self, order=0, axis=0, how='auto'):
"""
Compute moments along the spectral axis.
Moments are defined as follows, where :math:`I` is the intensity in a
channel and :math:`x` is the spectral coordinate:
Moment 0:
.. math:: M_0 \\int I dx
Moment 1:
.. math:: M_1 = \\frac{\\int I x dx}{M_0}
Moment N:
.. math:: 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 :meth:`~SpectralCube.linewidth_fwhm` or
:meth:`~SpectralCube.linewidth_sigma` methods.
Parameters
----------
order : int
The order of the moment to take. Default=0
axis : int
The axis along which to compute the moment. Default=0
how : cube | 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.
"""
if axis == 0 and order == 2:
warnings.warn("Note that the second moment returned will be a "
"variance map. To get a linewidth map, use the "
"SpectralCube.linewidth_fwhm() or "
"SpectralCube.linewidth_sigma() methods instead.",
VarianceWarning)
from ._moments import (moment_slicewise, moment_cubewise,
moment_raywise, moment_auto)
dispatch = dict(slice=moment_slicewise,
cube=moment_cubewise,
ray=moment_raywise,
auto=moment_auto)
if how not in dispatch:
return ValueError("Invalid how. Must be in %s" %
sorted(list(dispatch.keys())))
out = dispatch[how](self, order, axis)
# apply units
if order == 0:
if axis == 0 and self._spectral_unit is not None:
axunit = unit = self._spectral_unit
else:
axunit = unit = u.Unit(self._wcs.wcs.cunit[np2wcs[axis]])
out = u.Quantity(out, self.unit * axunit, copy=False)
else:
if axis == 0 and self._spectral_unit is not None:
unit = self._spectral_unit ** max(order, 1)
else:
unit = u.Unit(self._wcs.wcs.cunit[np2wcs[axis]]) ** max(order, 1)
out = u.Quantity(out, unit, copy=False)
# special case: for order=1, axis=0, you usually want
# the absolute velocity and not the offset
if order == 1 and axis == 0:
out += self.world[0, :, :][0]
new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis])
meta = {'moment_order': order,
'moment_axis': axis,
'moment_method': how}
meta.update(self._meta)
return Projection(out, copy=False, wcs=new_wcs, meta=meta,
header=self._nowcs_header)
[docs]
def moment0(self, axis=0, how='auto'):
"""
Compute the zeroth moment along an axis.
See :meth:`moment`.
"""
return self.moment(axis=axis, order=0, how=how)
[docs]
def moment1(self, axis=0, how='auto'):
"""
Compute the 1st moment along an axis.
For an explanation of the ``axis`` and ``how`` parameters, see :meth:`moment`.
"""
return self.moment(axis=axis, order=1, how=how)
[docs]
def moment2(self, axis=0, how='auto'):
"""
Compute the 2nd moment along an axis.
For an explanation of the ``axis`` and ``how`` parameters, see :meth:`moment`.
"""
return self.moment(axis=axis, order=2, how=how)
[docs]
def linewidth_sigma(self, how='auto'):
"""
Compute a (sigma) linewidth map along the spectral axis.
For an explanation of the ``how`` parameter, see :meth:`moment`.
"""
with np.errstate(invalid='ignore'):
with warnings.catch_warnings():
warnings.simplefilter("ignore", VarianceWarning)
return np.sqrt(self.moment2(how=how))
[docs]
def linewidth_fwhm(self, how='auto'):
"""
Compute a (FWHM) linewidth map along the spectral axis.
For an explanation of the ``how`` parameter, see :meth:`moment`.
"""
return self.linewidth_sigma() * SIGMA2FWHM
@property
def spectral_axis(self):
"""
A `~astropy.units.Quantity` array containing the central values of
each channel along the spectral axis.
"""
return self.world[:, 0, 0][0].ravel()
@property
def velocity_convention(self):
"""
The `~astropy.units.equivalencies` that describes the spectral axis
"""
return spectral_axis.determine_vconv_from_ctype(self.wcs.wcs.ctype[self.wcs.wcs.spec])
[docs]
def closest_spectral_channel(self, value):
"""
Find the index of the closest spectral channel to the specified
spectral coordinate.
Parameters
----------
value : :class:`~astropy.units.Quantity`
The value of the spectral coordinate to search for.
"""
# TODO: we have to not compute this every time
spectral_axis = self.spectral_axis
try:
value = value.to(spectral_axis.unit, equivalencies=u.spectral())
except u.UnitsError:
if value.unit.is_equivalent(u.Hz, equivalencies=u.spectral()):
if spectral_axis.unit.is_equivalent(u.m / u.s):
raise u.UnitsError("Spectral axis is in velocity units and "
"'value' is in frequency-equivalent units "
"- use SpectralCube.with_spectral_unit "
"first to convert the cube to frequency-"
"equivalent units, or search for a "
"velocity instead")
else:
raise u.UnitsError("Unexpected spectral axis units: {0}".format(spectral_axis.unit))
elif value.unit.is_equivalent(u.m / u.s):
if spectral_axis.unit.is_equivalent(u.Hz, equivalencies=u.spectral()):
raise u.UnitsError("Spectral axis is in frequency-equivalent "
"units and 'value' is in velocity units "
"- use SpectralCube.with_spectral_unit "
"first to convert the cube to frequency-"
"equivalent units, or search for a "
"velocity instead")
else:
raise u.UnitsError("Unexpected spectral axis units: {0}".format(spectral_axis.unit))
else:
raise u.UnitsError("'value' should be in frequency equivalent or velocity units (got {0})".format(value.unit))
# TODO: optimize the next line - just brute force for now
return np.argmin(np.abs(spectral_axis - value))
[docs]
def spectral_slab(self, lo, hi):
"""
Extract a new cube between two spectral coordinates
Parameters
----------
lo, hi : :class:`~astropy.units.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 :meth:`~spectral_cube.SpectralCube.with_spectral_unit`
to convert the units of the spectral axis.
"""
# Find range of values for spectral axis
ilo = self.closest_spectral_channel(lo)
ihi = self.closest_spectral_channel(hi)
if ilo == ihi:
warnings.warn("The maxmimum and minimum spectral channel in the spectral"
"slab are identical; this indicates that one or both are "
"likely incorrect and/or out of range.",
SliceWarning)
if ilo > ihi:
ilo, ihi = ihi, ilo
ihi += 1
# Create WCS slab
wcs_slab = self._wcs.deepcopy()
wcs_slab.wcs.crpix[2] -= ilo
# Create mask slab
if self._mask is None:
mask_slab = None
else:
try:
mask_slab = self._mask[ilo:ihi, :, :]
except NotImplementedError:
warnings.warn("Mask slicing not implemented for "
"{0} - dropping mask".
format(self._mask.__class__.__name__),
NotImplementedWarning
)
mask_slab = None
# Create new spectral cube
slab = self._new_cube_with(data=self._data[ilo:ihi], wcs=wcs_slab,
mask=mask_slab)
# TODO: we could change the WCS to give a spectral axis in the
# correct units as requested - so if the initial cube is in Hz and we
# request a range in km/s, we could adjust the WCS to be in km/s
# instead
return slab
[docs]
def minimal_subcube(self, spatial_only=False):
"""
Return the minimum enclosing subcube where the mask is valid
Parameters
----------
spatial_only: bool
Only compute the minimal subcube in the spatial dimensions
"""
if self._mask is not None:
return self[self.subcube_slices_from_mask(self._mask,
spatial_only=spatial_only)]
else:
return self[:]
[docs]
def subcube_from_mask(self, region_mask):
"""
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
"""
return self[self.subcube_slices_from_mask(region_mask)]
[docs]
def subcube_slices_from_mask(self, region_mask, spatial_only=False):
"""
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
"""
if not scipyOK:
raise ImportError("Scipy could not be imported: this function won't work.")
if isinstance(region_mask, np.ndarray):
if is_broadcastable_and_smaller(region_mask.shape, self.shape):
region_mask = BooleanArrayMask(region_mask, self._wcs)
else:
raise ValueError("Mask shape does not match cube shape.")
include = region_mask.include(self._data, self._wcs,
wcs_tolerance=self._wcs_tolerance)
if not include.any():
return (slice(0),)*3
slices = ndimage.find_objects(np.broadcast_arrays(include,
self._data)[0])[0]
if spatial_only:
slices = (slice(None), slices[1], slices[2])
return tuple(slices)
[docs]
def subcube(self, xlo='min', xhi='max', ylo='min', yhi='max', zlo='min',
zhi='max', rest_value=None):
"""
Extract a sub-cube spatially and spectrally.
When spatial WCS dimensions are given as an `~astropy.units.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]hi : int or :class:`~astropy.units.Quantity` or ``min``/``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.
"""
dims = {'x': 2,
'y': 1,
'z': 0}
limit_dict = {}
limit_dict['zlo'] = 0 if zlo == 'min' else zlo
limit_dict['zhi'] = self.shape[0] if zhi == 'max' else zhi
# Specific warning for slicing a frequency axis with a velocity or
# vice/versa
if ((hasattr(zlo, 'unit') and not
zlo.unit.is_equivalent(self.spectral_axis.unit)) or
(hasattr(zhi, 'unit') and not
zhi.unit.is_equivalent(self.spectral_axis.unit))):
raise u.UnitsError("Spectral units are not equivalent to the "
"spectral slice. Use `.with_spectral_unit` "
"to convert to equivalent units first")
# Solve for the spatial pixel indices together
limit_dict_spat = wcs_utils.find_spatial_pixel_index(self, xlo, xhi, ylo, yhi)
limit_dict.update(limit_dict_spat)
# Handle the z (spectral) axis. This shouldn't change
# much spacially, so solve one at a time
# Track if the z axis values had units. Will need to make a +1 correction below
united = []
for lim in limit_dict:
if 'z' not in lim:
continue
limval = limit_dict[lim]
if hasattr(limval, 'unit'):
united.append(lim)
dim = dims[lim[0]]
sl = [slice(0,1)]*2
sl.insert(dim, slice(None))
sl = tuple(sl)
spine = self.world[sl][dim]
val = np.argmin(np.abs(limval-spine))
if limval > spine.max() or limval < spine.min():
log.warning("The limit {0} is out of bounds."
" Using min/max instead.".format(lim))
limit_dict[lim] = val
# Check spectral axis ordering.
hi,lo = limit_dict['zhi'], limit_dict['zlo']
if hi < lo:
# must have high > low
limit_dict['zhi'], limit_dict['zlo'] = lo, hi
if 'zhi' in united:
# End-inclusive indexing: need to add one for the high slice
# Only do this for converted values, not for pixel values
# (i.e., if the xlo/ylo/zlo value had units)
limit_dict['zhi'] += 1
for xx in 'zyx':
if limit_dict[xx+'hi'] == limit_dict[xx+'lo']:
# I think this should be unreachable now
raise ValueError("The slice in the {0} direction will remove "
"all elements. If you want a single-channel "
"slice, you need a different approach."
.format(xx))
slices = [slice(limit_dict[xx+'lo'], limit_dict[xx+'hi'])
for xx in 'zyx']
slices = tuple(slices)
log.debug('slices: {0}'.format(slices))
return self[slices]
[docs]
def subcube_from_ds9region(self, ds9_region, allow_empty=False):
"""
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
"""
import regions
if isinstance(ds9_region, six.string_types):
if hasattr(regions, 'DS9Parser'):
region_list = regions.DS9Parser(ds9_region).shapes.to_regions()
else:
region_list = regions.Regions.parse(ds9_region, format="ds9")
else:
raise TypeError("{0} should be a DS9 string".format(ds9_region))
return self.subcube_from_regions(region_list, allow_empty)
[docs]
def subcube_from_crtfregion(self, crtf_region, allow_empty=False):
"""
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
"""
import regions
if isinstance(crtf_region, six.string_types):
region_list = regions.CRTFParser(crtf_region).shapes.to_regions()
else:
raise TypeError("{0} should be a CRTF string".format(crtf_region))
return self.subcube_from_regions(region_list, allow_empty)
[docs]
def subcube_from_regions(self, region_list, allow_empty=False,
minimize=True):
"""
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.
minimize : bool
Run :meth:`~SpectralCube.minimal_subcube`. This is mostly redundant, since the
bounding box of the region is already used, but it will sometimes
slice off a one-pixel rind depending on the details of the region
shape. If minimize is disabled, there will potentially be a ring
of NaN values around the outside.
"""
import regions
# Convert every region to a `regions.PixelRegion` object.
regs = []
for x in region_list:
if isinstance(x, regions.SkyRegion):
regs.append(x.to_pixel(self.wcs.celestial))
elif isinstance(x, regions.PixelRegion):
regs.append(x)
else:
raise TypeError("'{}' should be `regions.Region` object".format(x))
# List of regions are converted to a `regions.CompoundPixelRegion` object.
compound_region = _regionlist_to_single_region(regs)
# Compound mask of all the regions.
mask = compound_region.to_mask()
# Collecting frequency/velocity range, velocity type and rest frequency
# of each region.
ranges = [x.meta.get('range', None) for x in regs]
veltypes = [x.meta.get('veltype', None) for x in regs]
restfreqs = [x.meta.get('restfreq', None) for x in regs]
xlo, xhi, ylo, yhi = mask.bbox.ixmin, mask.bbox.ixmax, mask.bbox.iymin, mask.bbox.iymax
# Negative indices will do bad things, like wrap around the cube
# If xhi/yhi are negative, there is not overlap
if (xhi < 0) or (yhi < 0):
raise ValueError("Region is outside of cube.")
if xlo < 0:
xlo = 0
if ylo < 0:
ylo = 0
# If None, then the whole spectral range of the cube is selected.
if None in ranges:
subcube = self.subcube(xlo=xlo, ylo=ylo, xhi=xhi, yhi=yhi)
else:
ranges = self._velocity_freq_conversion_regions(ranges, veltypes, restfreqs)
zlo = min([x[0] for x in ranges])
zhi = max([x[1] for x in ranges])
slab = self.spectral_slab(zlo, zhi)
subcube = slab.subcube(xlo=xlo, ylo=ylo, xhi=xhi, yhi=yhi)
if any(dim == 0 for dim in subcube.shape):
if allow_empty:
warnings.warn("The derived subset is empty: the region does not"
" overlap with the cube (but allow_empty=True).")
else:
raise ValueError("The derived subset is empty: the region does not"
" overlap with the cube.")
shp = self.shape[1:]
_, slices_small = mask.get_overlap_slices(shp)
maskarray = np.zeros(subcube.shape[1:], dtype='bool')
maskarray[:] = mask.data[slices_small]
BAM = BooleanArrayMask(maskarray, subcube.wcs, shape=subcube.shape)
masked_subcube = subcube.with_mask(BAM)
# by using ceil / floor above, we potentially introduced a NaN buffer
# that we can now crop out
if minimize:
return masked_subcube.minimal_subcube(spatial_only=True)
else:
return masked_subcube
def _velocity_freq_conversion_regions(self, ranges, veltypes, restfreqs):
"""
Makes the spectral range of the regions compatible with the spectral
convention of the cube.
ranges: `~astropy.units.Quantity` object
List of range(a list of max and min limits on the spectral axis) of
each ``regions.Region`` object.
veltypes: List of `str`
It contains list of velocity convention that each region is following.
The string should be a combination of the following elements:
{'RADIO' | 'OPTICAL' | 'Z' | 'BETA' | 'GAMMA' | 'RELATIVISTIC' | None}
An element can be `None` if veltype of the region is unknown and is
assumed to take that of the cube.
restfreqs: List of `~astropy.units.Quantity`
It contains the rest frequency of each region.
"""
header = self.wcs.to_header()
# Obtaining rest frequency of the cube in GHz.
restfreq_cube = get_rest_value_from_wcs(self.wcs).to("GHz",
equivalencies=u.spectral())
CTYPE3 = header['CTYPE3']
veltype_cube = determine_vconv_from_ctype(CTYPE3)
veltype_equivalencies = dict(RADIO=u.doppler_radio,
OPTICAL=u.doppler_optical,
Z=doppler_z,
BETA=doppler_beta,
GAMMA=doppler_gamma,
RELATIVISTIC=u.doppler_relativistic
)
final_ranges = []
for range, veltype, restfreq in zip(ranges, veltypes, restfreqs):
if restfreq is None:
restfreq = restfreq_cube
restfreq = restfreq.to("GHz", equivalencies=u.spectral())
if veltype not in veltype_equivalencies and veltype is not None:
raise ValueError("Spectral Cube doesn't support {} this type of"
"velocity".format(veltype))
veltype = veltype_equivalencies.get(veltype, veltype_cube)
# Because there is chance that the veltype and rest frequency
# of the region may not be the same as that of cube, we convert it
# to frequency and then convert to the spectral unit of the cube.
freq_range = (u.Quantity(range).to("GHz",
equivalencies=veltype(restfreq)))
final_ranges.append(freq_range.to(header['CUNIT3'],
equivalencies=veltype_cube(restfreq_cube)))
return final_ranges
def _val_to_own_unit(self, value, operation='compare', tofrom='to',
keepunit=False):
"""
Given a value, check if it has a unit. If it does, convert to the
cube's unit. If it doesn't, raise an exception.
"""
if isinstance(value, BaseSpectralCube):
if self.unit.is_equivalent(value.unit):
return value
else:
return value.to(self.unit)
elif hasattr(value, 'unit'):
if keepunit:
return value.to(self.unit)
else:
return value.to(self.unit).value
elif self.unit.is_equivalent(u.dimensionless_unscaled):
# if the value is a numpy array or scalar, and the cube has no
# unit, no additional conversion is needed
return value
else:
raise ValueError("Can only {operation} cube objects {tofrom}"
" SpectralCubes or Quantities with "
"a unit attribute."
.format(operation=operation, tofrom=tofrom))
def __gt__(self, value):
"""
Return a LazyMask representing the inequality
Parameters
----------
value : number
The threshold
"""
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.gt, value, data=self._data, wcs=self._wcs)
def __ge__(self, value):
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.ge, value, data=self._data, wcs=self._wcs)
def __le__(self, value):
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.le, value, data=self._data, wcs=self._wcs)
def __lt__(self, value):
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.lt, value, data=self._data, wcs=self._wcs)
def __eq__(self, value):
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.eq, value, data=self._data, wcs=self._wcs)
def __hash__(self):
return id(self)
def __ne__(self, value):
value = self._val_to_own_unit(value)
return LazyComparisonMask(operator.ne, value, data=self._data, wcs=self._wcs)
def __add__(self, value):
if isinstance(value, BaseSpectralCube):
return self._cube_on_cube_operation(operator.add, value)
else:
value = self._val_to_own_unit(value, operation='add', tofrom='from',
keepunit=False)
return self._apply_everywhere(operator.add, value, check_units=False)
def __sub__(self, value):
if isinstance(value, BaseSpectralCube):
return self._cube_on_cube_operation(operator.sub, value)
else:
value = self._val_to_own_unit(value, operation='subtract',
tofrom='from', keepunit=False)
return self._apply_everywhere(operator.sub, value, check_units=False)
def __mul__(self, value):
if isinstance(value, BaseSpectralCube):
return self._cube_on_cube_operation(operator.mul, value)
else:
return self._apply_everywhere(operator.mul, value)
def __truediv__(self, value):
return self.__div__(value)
def __div__(self, value):
if isinstance(value, BaseSpectralCube):
return self._cube_on_cube_operation(operator.truediv, value)
else:
return self._apply_everywhere(operator.truediv, value)
def __floordiv__(self, value):
raise NotImplementedError("Floor-division (division with truncation) "
"is not supported.")
#if isinstance(value, BaseSpectralCube):
# # (Pdb) operator.floordiv(u.K, u.K)
# # *** TypeError: unsupported operand type(s) for //: 'IrreducibleUnit' and 'IrreducibleUnit'
# return self._cube_on_cube_operation(operator.floordiv, value)
#else:
# # only cube-on-cube division allowed
# #
# # we don't support this:
# # (Pdb) np.array([5,5,5])*u.K // (2*u.K)
# # <Quantity [2., 2., 2.]>
# # astropy doesn't support this:
# # >>> np.array([5,5,5])*u.K // (2*u.Jy)
# # astropy.units.core.UnitConversionError: Can only apply 'floor_divide' function to quantities with compatible dimensions
# # >>> np.array([5,5,5])*u.K // (np.array([2])*u.Jy)
# # astropy.units.core.UnitConversionError: Can only apply 'floor_divide' function to quantities with compatible dimensions
# raise NotImplementedError("Floor-division (division with truncation) "
# "is not supported.")
def __pow__(self, value):
if isinstance(value, BaseSpectralCube):
return self._cube_on_cube_operation(operator.pow, value)
else:
return self._apply_everywhere(operator.pow, value)
[docs]
def to_yt(self, spectral_factor=1.0, nprocs=None, **kwargs):
"""
Convert a spectral cube to a yt object that can be further analyzed in
yt.
Parameters
----------
spectral_factor : float, 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/doc/examining/loading_data.html?#fits-data)
for details on options for reading FITS data.
"""
import yt
if (('dev' in yt.__version__) or
(parse(yt.__version__) >= Version('3.0'))):
# yt has updated their FITS data set so that only the SpectralCube
# variant takes spectral_factor
try:
from yt.frontends.fits.api import SpectralCubeFITSDataset as FITSDataset
except ImportError:
from yt.frontends.fits.api import FITSDataset
from yt.units.unit_object import UnitParseError
data = self._get_filled_data(fill=0.)
if isinstance(data, da.Array):
# Note that >f8 can cause issues with yt, and for visualization
# we don't really need the full 64-bit of floating point
# precision, so we cast to float32.
data = data.astype(np.float32).compute()
hdu = PrimaryHDU(data, header=self.wcs.to_header())
units = str(self.unit.to_string())
hdu.header["BUNIT"] = units
hdu.header["BTYPE"] = "flux"
ds = FITSDataset(hdu, nprocs=nprocs,
spectral_factor=spectral_factor, **kwargs)
# Check to make sure the units are legit
try:
ds.quan(1.0,units)
except UnitParseError:
raise RuntimeError("The unit %s was not parsed by yt. " % units+
"Check to make sure it is correct.")
else:
from yt import load_uniform_grid
data = {'flux': self._get_filled_data(fill=0.).transpose()}
nz, ny, nx = self.shape
if nprocs is None:
nprocs = 1
bbox = np.array([[0.5,float(nx)+0.5],
[0.5,float(ny)+0.5],
[0.5,spectral_factor*float(nz)+0.5]])
ds = load_uniform_grid(data, [nx,ny,nz], 1., bbox=bbox,
nprocs=nprocs, periodicity=(False, False,
False))
return ytCube(self, ds, spectral_factor=spectral_factor)
[docs]
def to_glue(self, name=None, glue_app=None, dataset=None, start_gui=True):
"""
Send data to a new or existing Glue application
Parameters
----------
name : str 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_app : GlueApplication 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`.
dataset : glue.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_gui : bool
Start the GUI when this is run. Set to `False` for testing.
"""
if name is None:
name = 'SpectralCube'
from glue.app.qt import GlueApplication
from glue.core import DataCollection, Data
from glue.core.coordinates import coordinates_from_header
try:
from glue.viewers.image.qt.data_viewer import ImageViewer
except ImportError:
from glue.viewers.image.qt.viewer_widget import ImageWidget as ImageViewer
if dataset is not None:
if name in [d.label for d in dataset.components]:
name = name+"_"
dataset[name] = self
else:
result = Data(label=name)
result.coords = coordinates_from_header(self.header)
result.add_component(self, name)
if glue_app is None:
if hasattr(self,'_glue_app'):
glue_app = self._glue_app
else:
# Start a new glue session. This will quit when done.
# I don't think the return statement is ever reached, based on
# past attempts [@ChrisBeaumont - chime in here if you'd like]
dc = DataCollection([result])
#start Glue
ga = self._glue_app = GlueApplication(dc)
self._glue_viewer = ga.new_data_viewer(ImageViewer,
data=result)
if start_gui:
self._glue_app.start()
return self._glue_app
glue_app.add_datasets(self._glue_app.data_collection, result)
[docs]
def to_ds9(self, ds9id=None, newframe=False):
"""
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 as ``cube.to_ds9('86ab2314:60063')``
newframe: bool
Send the cube to a new frame or to the current frame?
"""
try:
import ds9
except ImportError:
import pyds9 as ds9
if ds9id is None:
dd = ds9.DS9(start=True)
else:
dd = ds9.DS9(target=ds9id, start=False)
if newframe:
dd.set('frame new')
dd.set_pyfits(self.hdulist)
return dd
@property
def header(self):
log.debug("Creating header")
header = super(BaseSpectralCube, self).header
# Preserve the cube's spectral units
# (if CUNIT3 is not in the header, it is whatever that type's default unit is)
if 'CUNIT3' in header and self._spectral_unit != u.Unit(header['CUNIT3']):
header['CDELT3'] *= self._spectral_scale
header['CRVAL3'] *= self._spectral_scale
header['CUNIT3'] = self._spectral_unit.to_string(format='FITS')
return header
@property
def hdu(self):
"""
HDU version of self
"""
log.debug("Creating HDU")
hdu = PrimaryHDU(self.unitless_filled_data[:], header=self.header)
return hdu
@property
def hdulist(self):
return HDUList(self.hdu)
[docs]
@warn_slow
def to(self, unit, equivalencies=()):
"""
Return the cube converted to the given unit (assuming it is equivalent).
If conversion was required, this will be a copy, otherwise it will
"""
if not isinstance(unit, u.Unit):
unit = u.Unit(unit)
if unit == self.unit:
# No copying
return self
# Create the tuple of unit conversions needed.
factor = cube_utils.bunit_converters(self, unit, equivalencies=equivalencies)
# special case: array in equivalencies
# (I don't think this should have to be special cased, but I don't know
# how to manipulate broadcasting rules any other way)
if hasattr(factor, '__len__') and len(factor) == len(self):
return self._new_cube_with(data=self._data*factor[:,None,None],
unit=unit)
else:
return self._new_cube_with(data=self._data*factor,
unit=unit)
[docs]
def find_lines(self, velocity_offset=None, velocity_convention=None,
rest_value=None, **kwargs):
"""
Using astroquery's splatalogue interface, search for lines within the
spectral band. See `astroquery.splatalogue.Splatalogue` for
information on keyword arguments
Parameters
----------
velocity_offset : u.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_value : u.GHz equivalent
The rest frequency (or wavelength or energy) to be passed to
`with_spectral_unit`
"""
warnings.warn("The line-finding routine is experimental. Please "
"report bugs on the Issues page: "
"https://github.com/radio-astro-tools/spectral-cube/issues",
ExperimentalImplementationWarning
)
from astroquery.splatalogue import Splatalogue
if velocity_convention in DOPPLER_CONVENTIONS:
velocity_convention = DOPPLER_CONVENTIONS[velocity_convention]
if velocity_offset is not None:
newspecaxis = self.with_spectral_unit(u.km/u.s,
velocity_convention=velocity_convention,
rest_value=rest_value).spectral_axis
spectral_axis = (newspecaxis + velocity_offset).to(u.GHz,
velocity_convention(rest_value))
else:
spectral_axis = self.spectral_axis.to(u.GHz)
numin,numax = spectral_axis.min(), spectral_axis.max()
log.log(19, "Min/max frequency: {0},{1}".format(numin, numax))
result = Splatalogue.query_lines(numin, numax, **kwargs)
return result
[docs]
@warn_slow
def reproject(self, header, order='bilinear', use_memmap=False,
filled=True, **kwargs):
"""
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
order : int 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_memmap : bool
If specified, a memory mapped temporary file on disk will be
written to rather than storing the intermediate spectra in memory.
filled : bool
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.
kwargs : dict
Passed to `reproject.reproject_interp`.
"""
try:
from reproject.version import version
except ImportError:
raise ImportError("Requires the reproject package to be"
" installed.")
reproj_kwargs = kwargs
# Need version > 0.2 to work with cubes, >= 0.5 for memmap
if parse(version) < Version("0.5"):
raise Warning("Requires version >=0.5 of reproject. The current "
"version is: {}".format(version))
elif parse(version) >= Version("0.6"):
pass # no additional kwargs, no warning either
else:
reproj_kwargs['independent_celestial_slices'] = True
from reproject import reproject_interp
# TODO: Find the minimal subcube that contains the header and only reproject that
# (see FITS_tools.regrid_cube for a guide on how to do this)
newwcs = wcs.WCS(header)
shape_out = tuple([header['NAXIS{0}'.format(i + 1)] for i in
range(header['NAXIS'])][::-1])
if filled:
data = self.unitless_filled_data[:]
else:
data = self._data
if use_memmap:
if data.dtype.itemsize not in (4,8):
raise ValueError("Data must be float32 or float64 to be "
"reprojected. Other data types need some "
"kind of additional memory handling.")
# note: requires reproject from December 2018 or later
outarray = np.memmap(filename='output.np', mode='w+',
shape=tuple(shape_out),
dtype='float64' if data.dtype.itemsize == 8 else 'float32')
else:
outarray = None
newcube, newcube_valid = reproject_interp((data,
self.header),
newwcs,
output_array=outarray,
shape_out=shape_out,
order=order,
**reproj_kwargs)
if np.all(np.isnan(newcube)):
raise ValueError("All values in reprojected cube are nan. This can be caused"
" by an error in which coordinates do not 'round-trip'. Try "
"setting ``roundtrip_coords=False``. You might also check "
"whether the WCS transformation produces valid pixel->world "
"and world->pixel coordinates in each axis."
)
return self._new_cube_with(data=newcube,
wcs=newwcs,
mask=BooleanArrayMask(newcube_valid.astype('bool'),
newwcs),
meta=self.meta,
)
[docs]
@parallel_docstring
def spatial_filter(self, ksize, filter, update_function=None, raise_error_jybm=True, **kwargs):
"""
Smooth the image in each spatial-spatial plane of the cube using a scipy.ndimage filter.
Parameters
----------
ksize : int
Size of the filter in pixels (scipy.ndimage.*_filter).
filter : function
A filter from `scipy.ndimage <https://docs.scipy.org/doc/scipy/reference/ndimage.html#filters>`_.
update_function : method
Method that is called to update an external progressbar
If provided, it disables the default `astropy.utils.console.ProgressBar`
raise_error_jybm : bool, optional
Raises a `~spectral_cube.utils.BeamUnitsError` when smoothing a cube in Jy/beam units,
since the brightness is dependent on the spatial resolution.
kwargs : dict
Passed to the convolve function
"""
if not scipyOK:
raise ImportError("Scipy could not be imported: this function won't work.")
self.check_jybeam_smoothing(raise_error_jybm=raise_error_jybm)
def _msmooth_image(im, **kwargs):
return filter(im, size=ksize, **kwargs)
newcube = self.apply_function_parallel_spatial(_msmooth_image,
**kwargs)
return newcube
[docs]
@parallel_docstring
def spatial_smooth(self, kernel,
convolve=convolution.convolve,
raise_error_jybm=True,
**kwargs):
"""
Smooth the image in each spatial-spatial plane of the cube.
Parameters
----------
kernel : `~astropy.convolution.Kernel2D`
A 2D kernel from astropy
convolve : function
The astropy convolution function to use, either
`astropy.convolution.convolve` or
`astropy.convolution.convolve_fft`
raise_error_jybm : bool, optional
Raises a `~spectral_cube.utils.BeamUnitsError` when smoothing a cube in Jy/beam units,
since the brightness is dependent on the spatial resolution.
kwargs : dict
Passed to the convolve function
"""
self.check_jybeam_smoothing(raise_error_jybm=raise_error_jybm)
def _gsmooth_image(img, **kwargs):
"""
Helper function to smooth an image
"""
return convolve(img, kernel, normalize_kernel=True, **kwargs)
newcube = self.apply_function_parallel_spatial(_gsmooth_image,
**kwargs)
return newcube
[docs]
@parallel_docstring
def spectral_filter(self, ksize, filter, use_memmap=True, verbose=0,
num_cores=None, **kwargs):
"""
Smooth the cube along the spectral dimension using a scipy.ndimage filter.
Parameters
----------
ksize : int
Size of the filter in spectral channels.
filter : function
A filter from `scipy.ndimage <https://docs.scipy.org/doc/scipy/reference/ndimage.html#filters>`_.
"""
# note: same body as spectral_smooth_median right now, but `filter`
# is a required kwarg
if not scipyOK:
raise ImportError("Scipy could not be imported: this function won't work.")
return self.apply_function_parallel_spectral(function=filter,
size=ksize,
verbose=verbose,
num_cores=num_cores,
use_memmap=use_memmap,
**kwargs)
def _apply_function_parallel_base(self,
iteration_data,
function,
applicator,
num_cores=None,
verbose=0,
use_memmap=True,
parallel=False,
memmap_dir=None,
update_function=None,
**kwargs
):
"""
Apply a function in parallel using the ``applicator`` function. The
function will be performed on data with masked values replaced with the
cube's fill value.
Parameters
----------
iteration_data : generator
The data to be iterated over in the format expected by ``applicator``
function : function
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.
applicator : function
Either ``_apply_spatial_function`` or ``_apply_spectral_function``,
a tool to handle the iteration data and send it to the ``function``
appropriately.
num_cores : int or None
The number of cores to use if running in parallel. Should be >1 if
``parallel==True`` and cannot be >1 if ``parallel==False``
verbose : int
Verbosity level to pass to joblib
use_memmap : bool
If specified, a memory mapped temporary file on disk will be
written to rather than storing the intermediate spectra in memory.
parallel : bool
If set to ``False``, will force the use of a single thread instead
of using ``joblib``.
update_function : function
A callback function to call on each iteration of the application.
It should not accept any arguments. For example, this can be
``Progressbar.update`` or some function that prints a status
report. The function *must* be picklable if ``parallel==True``.
kwargs : dict
Passed to ``function``
"""
if use_memmap:
ntf = tempfile.NamedTemporaryFile(dir=memmap_dir)
outcube = np.memmap(ntf, mode='w+', shape=self.shape, dtype=float)
else:
if self._is_huge and not self.allow_huge_operations:
raise ValueError("Applying a function without ``use_memmap`` "
"requires loading the whole array into "
"memory *twice*, which can overload the "
"machine's memory for large cubes. Either "
"set ``use_memmap=True`` or set "
"``cube.allow_huge_operations=True`` to "
"override this restriction.")
outcube = np.empty(shape=self.shape, dtype=float)
if num_cores == 1 and parallel:
warnings.warn("parallel=True was specified but num_cores=1. "
"Joblib will be used to run the task with a "
"single thread.")
elif num_cores is not None and num_cores > 1 and not parallel:
raise ValueError("parallel execution was not requested, but "
"multiple cores were: these are incompatible "
"options. Either specify num_cores=1 or "
"parallel=True")
if parallel and use_memmap:
# it is not possible to run joblib parallelization without memmap
try:
import joblib
from joblib._parallel_backends import MultiprocessingBackend
from joblib import register_parallel_backend, parallel_backend
from joblib import Parallel, delayed
if update_function is not None:
# https://stackoverflow.com/questions/38483874/intermediate-results-from-joblib
class MultiCallback:
def __init__(self, *callbacks):
self.callbacks = [cb for cb in callbacks if cb]
def __call__(self, out):
for cb in self.callbacks:
cb(out)
class Callback_Backend(MultiprocessingBackend):
def callback(self, result):
update_function()
# Overload apply_async and set callback=self.callback
def apply_async(self, func, callback=None):
cbs = MultiCallback(callback, self.callback)
return super().apply_async(func, cbs)
joblib.register_parallel_backend('custom',
Callback_Backend,
make_default=True)
Parallel(n_jobs=num_cores,
verbose=verbose,
max_nbytes=None)(delayed(applicator)(arg, outcube,
function,
**kwargs)
for arg in iteration_data)
except ImportError:
if num_cores is not None and num_cores > 1:
warnings.warn("Could not import joblib. Will run in serial.",
warnings.ImportWarning)
parallel = False
# this isn't an else statement because we want to catch the case where
# the above clause fails on ImportError
if not parallel or not use_memmap:
if update_function is not None:
pbu = update_function
elif verbose > 0:
progressbar = ProgressBar(self.shape[1]*self.shape[2])
pbu = progressbar.update
else:
pbu = object
for arg in iteration_data:
applicator(arg, outcube, function, **kwargs)
pbu()
# TODO: do something about the mask?
newcube = self._new_cube_with(data=outcube, wcs=self.wcs,
mask=self.mask, meta=self.meta,
fill_value=self.fill_value)
return newcube
[docs]
def apply_function_parallel_spatial(self,
function,
num_cores=None,
verbose=0,
use_memmap=True,
parallel=True,
**kwargs
):
"""
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
----------
function : function
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_cores : int or None
The number of cores to use if running in parallel
verbose : int
Verbosity level to pass to joblib
use_memmap : bool
If specified, a memory mapped temporary file on disk will be
written to rather than storing the intermediate spectra in memory.
parallel : bool
If set to ``False``, will force the use of a single core without
using ``joblib``.
kwargs : dict
Passed to ``function``
"""
shape = self.shape
data = self.unitless_filled_data
# 'images' is a generator
# the boolean check will skip the function for bad spectra
images = ((data[ii,:,:],
self.mask.include(view=(ii, slice(None), slice(None))),
ii,
)
for ii in range(shape[0]))
return self._apply_function_parallel_base(images, function,
applicator=_apply_spatial_function,
verbose=verbose,
parallel=parallel,
num_cores=num_cores,
use_memmap=use_memmap,
**kwargs)
[docs]
def apply_function_parallel_spectral(self,
function,
num_cores=None,
verbose=0,
use_memmap=True,
parallel=True,
**kwargs
):
"""
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
----------
function : function
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_cores : int or None
The number of cores to use if running in parallel
verbose : int
Verbosity level to pass to joblib
use_memmap : bool
If specified, a memory mapped temporary file on disk will be
written to rather than storing the intermediate spectra in memory.
parallel : bool
If set to ``False``, will force the use of a single core without
using ``joblib``.
kwargs : dict
Passed to ``function``
"""
shape = self.shape
data = self.unitless_filled_data
# 'spectra' is a generator
# the boolean check will skip the function for bad spectra
# TODO: should spatial good/bad be cached?
spectra = ((data[:,jj,ii],
self.mask.include(view=(slice(None), jj, ii)),
ii, jj,
)
for jj in range(shape[1])
for ii in range(shape[2]))
return self._apply_function_parallel_base(iteration_data=spectra,
function=function,
applicator=_apply_spectral_function,
use_memmap=use_memmap,
parallel=parallel,
verbose=verbose,
num_cores=num_cores,
**kwargs
)
[docs]
@parallel_docstring
def sigma_clip_spectrally(self, threshold, verbose=0, use_memmap=True,
num_cores=None, **kwargs):
"""
Run astropy's sigma clipper along the spectral axis, converting all bad
(excluded) values to NaN.
Parameters
----------
threshold : float
The ``sigma`` parameter in `astropy.stats.sigma_clip`, which refers
to the number of sigma above which to cut.
verbose : int
Verbosity level to pass to joblib
"""
return self.apply_function_parallel_spectral(stats.sigma_clip,
sigma=threshold,
axis=0, # changes behavior of sigmaclip
num_cores=num_cores,
use_memmap=use_memmap,
verbose=verbose,
**kwargs)
[docs]
@parallel_docstring
def spectral_smooth(self, kernel,
convolve=convolution.convolve,
verbose=0,
use_memmap=True,
num_cores=None,
**kwargs):
"""
Smooth the cube along the spectral dimension
Note that the mask is left unchanged in this operation.
Parameters
----------
kernel : `~astropy.convolution.Kernel1D`
A 1D kernel from astropy
convolve : function
The astropy convolution function to use, either
`astropy.convolution.convolve` or
`astropy.convolution.convolve_fft`
verbose : int
Verbosity level to pass to joblib
kwargs : dict
Passed to the convolve function
"""
if isinstance(kernel.array, u.Quantity):
raise u.UnitsError("The convolution kernel should be defined "
"without a unit.")
return self.apply_function_parallel_spectral(convolve,
kernel=kernel,
normalize_kernel=True,
num_cores=num_cores,
use_memmap=use_memmap,
verbose=verbose,
**kwargs)
[docs]
def spectral_interpolate(self, spectral_grid,
suppress_smooth_warning=False,
fill_value=None,
update_function=None):
"""Resample the cube spectrally onto a specific grid
Parameters
----------
spectral_grid : array
An array of the spectral positions to regrid onto
suppress_smooth_warning : bool
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_value : float
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_function : method
Method that is called to update an external progressbar
If provided, it disables the default `astropy.utils.console.ProgressBar`
Returns
-------
cube : SpectralCube
"""
inaxis = self.spectral_axis.to(spectral_grid.unit)
indiff = np.mean(np.diff(inaxis))
outdiff = np.mean(np.diff(spectral_grid))
# account for reversed axes
if outdiff < 0:
spectral_grid = spectral_grid[::-1]
outdiff = np.mean(np.diff(spectral_grid))
outslice = slice(None, None, -1)
else:
outslice = slice(None, None, 1)
cubedata = self.filled_data
specslice = slice(None) if indiff >= 0 else slice(None, None, -1)
inaxis = inaxis[specslice]
indiff = np.mean(np.diff(inaxis))
# insanity checks
if indiff < 0 or outdiff < 0:
raise ValueError("impossible.")
assert np.all(np.diff(spectral_grid) > 0)
assert np.all(np.diff(inaxis) > 0)
np.testing.assert_allclose(np.diff(spectral_grid), outdiff,
err_msg="Output grid must be linear")
if outdiff > 2 * indiff and not suppress_smooth_warning:
warnings.warn("Input grid has too small a spacing. The data should "
"be smoothed prior to resampling.",
SmoothingWarning
)
newcube = np.empty([spectral_grid.size, self.shape[1], self.shape[2]],
dtype=cubedata[:1, 0, 0].dtype)
newmask = np.empty([spectral_grid.size, self.shape[1], self.shape[2]],
dtype='bool')
yy,xx = np.indices(self.shape[1:])
if update_function is None:
pb = ProgressBar(xx.size)
update_function = pb.update
for ix, iy in (zip(xx.flat, yy.flat)):
mask = self.mask.include(view=(specslice, iy, ix))
if any(mask):
newcube[outslice,iy,ix] = \
np.interp(spectral_grid.value, inaxis.value,
cubedata[specslice,iy,ix].value,
left=fill_value, right=fill_value)
if all(mask):
newmask[:,iy,ix] = True
else:
interped = np.interp(spectral_grid.value,
inaxis.value, mask) > 0
newmask[outslice,iy,ix] = interped
else:
newmask[:, iy, ix] = False
newcube[:, iy, ix] = np.NaN
update_function()
newwcs = self.wcs.deepcopy()
newwcs.wcs.crpix[2] = 1
newwcs.wcs.crval[2] = spectral_grid[0].value if outslice.step > 0 \
else spectral_grid[-1].value
newwcs.wcs.cunit[2] = spectral_grid.unit.to_string('FITS')
newwcs.wcs.cdelt[2] = outdiff.value if outslice.step > 0 \
else -outdiff.value
newwcs.wcs.set()
newbmask = BooleanArrayMask(newmask, wcs=newwcs)
newcube = self._new_cube_with(data=newcube, wcs=newwcs, mask=newbmask,
meta=self.meta,
fill_value=self.fill_value)
return newcube
[docs]
@warn_slow
def convolve_to(self, beam, convolve=convolution.convolve_fft, update_function=None, **kwargs):
"""
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
convolve : function
The astropy convolution function to use, either
`astropy.convolution.convolve` or
`astropy.convolution.convolve_fft`
update_function : method
Method that is called to update an external progressbar
If provided, it disables the default `astropy.utils.console.ProgressBar`
kwargs : dict
Keyword arguments to pass to the convolution function
Returns
-------
cube : `SpectralCube`
A SpectralCube with a single ``beam``
"""
# Check if the beams are the same.
if beam == self.beam:
warnings.warn("The given beam is identical to the current beam. "
"Skipping convolution.")
return self
pixscale = wcs.utils.proj_plane_pixel_area(self.wcs.celestial)**0.5*u.deg
convolution_kernel = beam.deconvolve(self.beam).as_kernel(pixscale)
# Scale Jy/beam units by the change in beam size
if self.unit.is_equivalent(u.Jy / u.beam):
beam_ratio_factor = (beam.sr / self.beam.sr).value
else:
beam_ratio_factor = 1.
# See #631: kwargs get passed within self.apply_function_parallel_spatial
def convfunc(img, **kwargs):
return convolve(img, convolution_kernel, normalize_kernel=True,
**kwargs) * beam_ratio_factor
if convolve is convolution.convolve_fft and 'allow_huge' not in kwargs:
kwargs['allow_huge'] = self.allow_huge_operations
newcube = self.apply_function_parallel_spatial(convfunc,
**kwargs).with_beam(beam, raise_error_jybm=False)
return newcube
[docs]
def mask_channels(self, goodchannels):
"""
Helper function to mask out channels. This function is equivalent to
adding a mask with ``cube[view]`` where ``view`` is broadcastable to
the cube shape, but it accepts 1D arrays that are not normally
broadcastable.
Parameters
----------
goodchannels : array
A 1D boolean array declaring which channels should be kept.
Returns
-------
cube : `SpectralCube`
A cube with the specified channels masked
"""
goodchannels = np.asarray(goodchannels, dtype='bool')
if goodchannels.ndim != 1:
raise ValueError("goodchannels mask must be one-dimensional")
if goodchannels.size != self.shape[0]:
raise ValueError("goodchannels must have a length equal to the "
"cube's spectral dimension.")
return self.with_mask(goodchannels[:,None,None])
[docs]
@warn_slow
def downsample_axis(self, factor, axis, estimator=np.nanmean,
truncate=False, use_memmap=True, progressbar=True):
"""
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 : `~numpy.ndarray`
The array to downsample
factor : int
The factor to downsample by
axis : int
The axis to downsample along
estimator : function
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))
truncate : bool
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_memmap : bool
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)
progressbar : bool
Include a progress bar? Only works with ``use_memmap=True``
"""
def makeslice(startpoint,axis=axis,step=factor):
# make empty slices
view = [slice(None) for ii in range(self.ndim)]
# then fill the appropriate slice
view[axis] = slice(startpoint,None,step)
return tuple(view)
# size of the dimension of interest
xs = self.shape[axis]
if not use_memmap:
if xs % int(factor) != 0:
if truncate:
view = [slice(None) for ii in range(self.ndim)]
view[axis] = slice(None,xs-(xs % int(factor)))
view = tuple(view)
crarr = self.unitless_filled_data[view]
mask = self.mask[view].include()
else:
extension_shape = list(self.shape)
extension_shape[axis] = (factor - xs % int(factor))
extension = np.empty(extension_shape) * np.nan
crarr = np.concatenate((self.unitless_filled_data[:],
extension), axis=axis)
extension[:] = 0
mask = np.concatenate((self.mask.include(), extension), axis=axis)
else:
crarr = self.unitless_filled_data[:]
mask = self.mask.include()
# The extra braces here are crucial: We're adding an extra dimension so we
# can average across it
stacked_array = np.concatenate([[crarr[makeslice(ii)]]
for ii in range(factor)])
dsarr = estimator(stacked_array, axis=0)
if not isinstance(mask, np.ndarray):
raise TypeError("Mask is of wrong data type")
stacked_mask = np.concatenate([[mask[makeslice(ii)]] for ii in
range(factor)])
mask = np.any(stacked_mask, axis=0)
else:
def makeslice_local(startpoint, axis=axis, nsteps=factor):
# make empty slices
view = [slice(None) for ii in range(self.ndim)]
# then fill the appropriate slice
view[axis] = slice(startpoint,startpoint+nsteps,1)
return tuple(view)
newshape = list(self.shape)
newshape[axis] = (newshape[axis]//factor +
((1-int(truncate)) * (xs % int(factor) != 0)))
newshape = tuple(newshape)
if progressbar:
progressbar = ProgressBar
else:
progressbar = lambda x: x
# Create a view that will add a blank newaxis at the right spot
view_newaxis = [slice(None) for ii in range(self.ndim)]
view_newaxis[axis] = None
view_newaxis = tuple(view_newaxis)
ntf = tempfile.NamedTemporaryFile()
dsarr = np.memmap(ntf, mode='w+', shape=newshape, dtype=float)
ntf2 = tempfile.NamedTemporaryFile()
mask = np.memmap(ntf2, mode='w+', shape=newshape, dtype=bool)
for ii in progressbar(range(newshape[axis])):
view_fulldata = makeslice_local(ii*factor)
view_newdata = makeslice_local(ii, nsteps=1)
to_average = self.unitless_filled_data[view_fulldata]
to_anyfy = self.mask[view_fulldata].include()
dsarr[view_newdata] = estimator(to_average, axis)[view_newaxis]
mask[view_newdata] = np.any(to_anyfy, axis).astype('bool')[view_newaxis]
# the slice should just start at zero; we had factor//2 here earlier,
# and that was an error that probably half-compensated for an error in
# wcs_utils
view = makeslice(0)
newwcs = wcs_utils.slice_wcs(self.wcs, view, shape=self.shape)
newwcs._naxis = list(self.shape)
# this is an assertion to ensure that the WCS produced is valid
# (this is basically a regression test for #442)
assert newwcs[:, slice(None), slice(None)]
assert len(newwcs._naxis) == 3
return self._new_cube_with(data=dsarr, wcs=newwcs,
mask=BooleanArrayMask(mask, wcs=newwcs))
[docs]
def plot_channel_maps(self, 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):
"""
Make channel maps from a spectral cube
Parameters
----------
input_file : str
Name of the input spectral cube
nx, ny : int
Number of sub-plots in the x and y direction
channels : list
List of channels to show
cmap : str
The name of a colormap to use for the ``imshow`` colors
contourkwargs : dict
Keyword arguments passed to ``contour``
textcolor : None or str
Color of the label text to overlay. If ``None``, will be
determined automatically. If ``'notext'``, no text will be added.
textxloc : float
textyloc : float
Text label X,Y-location in axis fraction units
output_file : str
Name of the matplotlib plot
fig : matplotlib figure
The figure object to plot onto. Will be overridden to enforce a
specific aspect ratio.
fig_smallest_dim_inches : float
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.
decimals : int, optional
Number of decimal places to show in spectral value
zoom : int, optional
How much to zoom in. In future versions of this function, the
pointing center will be customizable.
tighten : bool
Call ``plt.tight_layout()`` after plotting?
savefig_kwargs : dict
Keyword arguments to pass to ``savefig`` (e.g.,
``bbox_inches='tight'``)
kwargs : dict
Passed to ``imshow``
"""
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
cmap = getattr(plt.cm, cmap)
if len(channels) != nx * ny:
raise ValueError("Number of channels should be equal to nx * ny")
# Read in spectral cube and get spectral axis
spectral_axis = self.spectral_axis
sizey, sizex = self.shape[1:]
cenx = sizex / 2.
ceny = sizey / 2.
aspect_ratio = self.shape[2]/float(self.shape[1])
gridratio = ny / float(nx) * aspect_ratio
if gridratio > 1:
ysize = fig_smallest_dim_inches*gridratio
xsize = fig_smallest_dim_inches
else:
xsize = fig_smallest_dim_inches*gridratio
ysize = fig_smallest_dim_inches
if fig is None:
fig = plt.figure(figsize=(xsize, ysize))
else:
fig.set_figheight(ysize)
fig.set_figwidth(xsize)
# unclear if needed
#fig.subplots_adjust(margin,margin,1.-margin,1.-margin,0.,0.)
axis_list = []
gs = GridSpec(ny, nx, figure=fig, hspace=0, wspace=0)
for ichannel, channel in enumerate(channels):
slc = self[channel,:,:]
ax = plt.subplot(gs[ichannel], projection=slc.wcs)
im = ax.imshow(slc.value, origin='lower', cmap=cmap, **kwargs)
if contourkwargs:
ax.contour(slc.value, **contourkwargs)
ax.set_xlim(cenx - cenx / zoom, cenx + cenx / zoom)
ax.set_ylim(ceny - ceny / zoom, ceny + ceny / zoom)
if textcolor != 'notext':
if textcolor is None:
# determine average image color and set textcolor to opposite
# (this is a bit hacky and there is _definitely_ a better way
# to do this)
avgcolor = im.cmap(im.norm(im.get_array())).mean(axis=(0,1))
totalcolor = avgcolor[:3].sum()
if totalcolor > 0.5:
textcolor = 'w'
else:
textcolor = 'k'
ax.tick_params(color=textcolor)
ax.set_title(("{0:." + str(decimals) + "f}").format(spectral_axis[channel]),
x=textxloc, y=textyloc, color=textcolor)
# only label bottom-left panel with locations
if (ichannel != nx*(ny-1)):
ax.coords[0].set_ticklabel_position('')
ax.coords[1].set_ticklabel_position('')
ax.tick_params(direction='in')
axis_list.append(ax)
if tighten:
plt.tight_layout()
if output_file is not None:
fig.savefig(output_file, **savefig_kwargs)
return axis_list
[docs]
class SpectralCube(BaseSpectralCube, BeamMixinClass):
__name__ = "SpectralCube"
_oned_spectrum = OneDSpectrum
def __new__(cls, *args, **kwargs):
if kwargs.pop('use_dask', False):
from .dask_spectral_cube import DaskSpectralCube
return super().__new__(DaskSpectralCube)
else:
return super().__new__(cls)
def __init__(self, data, wcs, mask=None, meta=None, fill_value=np.nan,
header=None, allow_huge_operations=False, beam=None,
wcs_tolerance=0.0, use_dask=False, **kwargs):
super(SpectralCube, self).__init__(data=data, wcs=wcs, mask=mask,
meta=meta, fill_value=fill_value,
header=header,
allow_huge_operations=allow_huge_operations,
wcs_tolerance=wcs_tolerance,
**kwargs)
# Beam loading must happen *after* WCS is read
if beam is None:
beam = cube_utils.try_load_beam(self.header)
else:
if not isinstance(beam, Beam):
raise TypeError("beam must be a radio_beam.Beam object.")
# Allow setting the beam attribute even if there is no beam defined
# Accessing `SpectralCube.beam` without a beam defined raises a
# `NoBeamError` with an informative message.
self.beam = beam
if beam is not None:
self._meta['beam'] = beam
self._header.update(beam.to_header_keywords())
def _new_cube_with(self, **kwargs):
beam = kwargs.pop('beam', None)
if 'beam' in self._meta and beam is None:
beam = self._beam
newcube = super(SpectralCube, self)._new_cube_with(beam=beam, **kwargs)
return newcube
_new_cube_with.__doc__ = BaseSpectralCube._new_cube_with.__doc__
[docs]
def with_beam(self, beam, raise_error_jybm=True):
'''
Attach a beam object to the `~SpectralCube`.
Parameters
----------
beam : `~radio_beam.Beam`
`Beam` object defining the resolution element of the
`~SpectralCube`.
'''
if not isinstance(beam, Beam):
raise TypeError("beam must be a radio_beam.Beam object.")
self.check_jybeam_smoothing(raise_error_jybm=raise_error_jybm)
meta = self._meta.copy()
meta['beam'] = beam
header = self._header.copy()
header.update(beam.to_header_keywords())
newcube = self._new_cube_with(meta=self.meta, beam=beam)
return newcube
[docs]
class VaryingResolutionSpectralCube(BaseSpectralCube, MultiBeamMixinClass):
"""
A variant of the SpectralCube class that has PSF (beam) information on a
per-channel basis.
"""
__name__ = "VaryingResolutionSpectralCube"
_oned_spectrum = VaryingResolutionOneDSpectrum
def __new__(cls, *args, **kwargs):
if kwargs.pop('use_dask', False):
from .dask_spectral_cube import DaskVaryingResolutionSpectralCube
return super().__new__(DaskVaryingResolutionSpectralCube)
else:
return super().__new__(cls)
def __init__(self, *args, major_unit=u.arcsec, minor_unit=u.arcsec, **kwargs):
"""
Create a SpectralCube with an associated beam table. The new
VaryingResolutionSpectralCube will have a ``beams`` attribute and a
``beam_threshold`` attribute as described below. It will perform some
additional checks when trying to perform analysis across image frames.
Three new keyword arguments are accepted:
Other Parameters
----------------
beam_table : `numpy.recarray`
A table of beam major and minor axes in arcseconds and position
angles, with labels BMAJ, BMIN, BPA
beams : list
A list of `radio_beam.Beam` objects
beam_threshold : float or dict
The fractional threshold above which beams are considered
different. A dictionary may be used with entries 'area', 'major',
'minor', 'pa' so that you can specify a different fractional
threshold for each of these. For example, if you want to check
only that the areas are the same, and not worry about the shape
(which might be a bad idea...), you could set
``beam_threshold={'area':0.01, 'major':1.5, 'minor':1.5,
'pa':5.0}``
"""
# these types of cube are undefined without the radio_beam package
beam_table = kwargs.pop('beam_table', None)
beams = kwargs.pop('beams', None)
beam_threshold = kwargs.pop('beam_threshold', 0.01)
if (beam_table is None and beams is None):
raise ValueError(
"Must give either a beam table or a list of beams to "
"initialize a VaryingResolutionSpectralCube")
super(VaryingResolutionSpectralCube, self).__init__(*args, **kwargs)
if isinstance(beam_table, BinTableHDU):
beam_data_table = beam_table.data
else:
beam_data_table = beam_table
if beam_table is not None:
# CASA beam tables are in arcsec, and that's what we support
beams = Beams(major=u.Quantity(beam_data_table['BMAJ'], major_unit),
minor=u.Quantity(beam_data_table['BMIN'], minor_unit),
pa=u.Quantity(beam_data_table['BPA'], u.deg),
meta=[{key: row[key] for key in beam_data_table.names
if key not in ('BMAJ','BPA', 'BMIN')}
for row in beam_data_table],
)
goodbeams = beams.isfinite
# track which, if any, beams are masked for later use
self.goodbeams_mask = goodbeams
if not all(goodbeams):
warnings.warn("There were {0} non-finite beams; layers with "
"non-finite beams will be masked out.".format(
np.count_nonzero(np.logical_not(goodbeams))),
NonFiniteBeamsWarning
)
beam_mask = BooleanArrayMask(goodbeams[:,None,None],
wcs=self._wcs,
shape=self.shape,
)
if not is_broadcastable_and_smaller(beam_mask.shape,
self._data.shape):
# this should never be allowed to happen
raise ValueError("Beam mask shape is not broadcastable to data shape: "
"%s vs %s" % (beam_mask.shape, self._data.shape))
assert beam_mask.shape == self.shape
new_mask = np.bitwise_and(self._mask, beam_mask)
new_mask._validate_wcs(new_data=self._data, new_wcs=self._wcs)
self._mask = new_mask
if (len(beams) != self.shape[0]):
raise ValueError("Beam list must have same size as spectral "
"dimension")
self.beams = beams
self.beam_threshold = beam_threshold
def __getitem__(self, view):
# Need to allow self[:], self[:,:]
if isinstance(view, (slice,int,np.int64)):
view = (view, slice(None), slice(None))
elif len(view) == 2:
view = view + (slice(None),)
elif len(view) > 3:
raise IndexError("Too many indices")
meta = {}
meta.update(self._meta)
slice_data = [(s.start, s.stop, s.step)
if hasattr(s,'start') else s
for s in view]
if 'slice' in meta:
meta['slice'].append(slice_data)
else:
meta['slice'] = [slice_data]
# intslices identifies the slices that are given by integers, i.e.
# indices. Other slices are slice objects, e.g. obj[5:10], and have
# 'start' attributes.
intslices = [2-ii for ii,s in enumerate(view) if not hasattr(s,'start')]
# for beams, we care only about the first slice, independent of its
# type
specslice = view[0]
if intslices:
if len(intslices) > 1:
if 2 in intslices:
raise NotImplementedError("1D slices along non-spectral "
"axes are not yet implemented.")
newwcs = self._wcs.sub([a
for a in (1,2,3)
if a not in [x+1 for x in intslices]])
if cube_utils._has_beam(self):
bmarg = {'beam': self.beam}
elif cube_utils._has_beams(self):
bmarg = {'beams': self.unmasked_beams[specslice]}
else:
bmarg = {}
return self._oned_spectrum(value=self._data[view],
wcs=newwcs,
copy=False,
unit=self.unit,
spectral_unit=self._spectral_unit,
mask=self.mask[view],
meta=meta,
goodbeams_mask=self.goodbeams_mask[specslice]
if hasattr(self, '_goodbeams_mask')
else None,
**bmarg
)
# only one element, so drop an axis
newwcs = wcs_utils.drop_axis(self._wcs, intslices[0])
header = self._nowcs_header
# Slice objects know how to parse Beam objects stored in the
# metadata
# A 2D slice with a VRSC should not be allowed along a
# position-spectral axis
if not isinstance(self.unmasked_beams[specslice], Beam):
raise AttributeError("2D slices along a spectral axis are not "
"allowed for "
"VaryingResolutionSpectralCubes. Convolve"
" to a common resolution with "
"`convolve_to` before attempting "
"position-spectral slicing.")
meta['beam'] = self.unmasked_beams[specslice]
return Slice(value=self.filled_data[view],
wcs=newwcs,
copy=False,
unit=self.unit,
header=header,
meta=meta)
newmask = self._mask[view] if self._mask is not None else None
newwcs = wcs_utils.slice_wcs(self._wcs, view, shape=self.shape)
newwcs._naxis = list(self.shape)
# this is an assertion to ensure that the WCS produced is valid
# (this is basically a regression test for #442)
assert newwcs[:, slice(None), slice(None)]
assert len(newwcs._naxis) == 3
return self._new_cube_with(data=self._data[view],
wcs=newwcs,
mask=newmask,
beams=self.unmasked_beams[specslice],
meta=meta)
[docs]
def spectral_slab(self, lo, hi):
"""
Extract a new cube between two spectral coordinates
Parameters
----------
lo, hi : :class:`~astropy.units.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 :meth:`~spectral_cube.SpectralCube.with_spectral_unit`
to convert the units of the spectral axis.
"""
# Find range of values for spectral axis
ilo = self.closest_spectral_channel(lo)
ihi = self.closest_spectral_channel(hi)
if ilo > ihi:
ilo, ihi = ihi, ilo
ihi += 1
# Create WCS slab
wcs_slab = self._wcs.deepcopy()
wcs_slab.wcs.crpix[2] -= ilo
# Create mask slab
if self._mask is None:
mask_slab = None
else:
try:
mask_slab = self._mask[ilo:ihi, :, :]
except NotImplementedError:
warnings.warn("Mask slicing not implemented for "
"{0} - dropping mask".
format(self._mask.__class__.__name__),
NotImplementedWarning
)
mask_slab = None
# Create new spectral cube
slab = self._new_cube_with(data=self._data[ilo:ihi], wcs=wcs_slab,
beams=self.unmasked_beams[ilo:ihi],
mask=mask_slab)
return slab
def _new_cube_with(self, goodbeams_mask=None, **kwargs):
beams = kwargs.pop('beams', self.unmasked_beams)
beam_threshold = kwargs.pop('beam_threshold', self.beam_threshold)
VRSC = VaryingResolutionSpectralCube
newcube = super(VRSC, self)._new_cube_with(beams=beams,
beam_threshold=beam_threshold,
**kwargs)
if goodbeams_mask is not None:
newcube.goodbeams_mask = goodbeams_mask
assert hasattr(newcube, '_goodbeams_mask')
else:
newcube.goodbeams_mask = np.isfinite(newcube.beams)
assert hasattr(newcube, '_goodbeams_mask')
return newcube
_new_cube_with.__doc__ = BaseSpectralCube._new_cube_with.__doc__
def _check_beam_areas(self, threshold, mean_beam, mask=None):
"""
Check that the beam areas are the same to within some threshold
"""
if mask is not None:
assert len(mask) == len(self.unmasked_beams)
mask = np.array(mask, dtype='bool')
else:
mask = np.ones(len(self.unmasked_beams), dtype='bool')
qtys = dict(sr=self.unmasked_beams.sr,
major=self.unmasked_beams.major.to(u.deg),
minor=self.unmasked_beams.minor.to(u.deg),
# position angles are not really comparable
#pa=u.Quantity([bm.pa for bm in self.unmasked_beams], u.deg),
)
errormessage = ""
for (qtyname, qty) in (qtys.items()):
minv = qty[mask].min()
maxv = qty[mask].max()
mn = getattr(mean_beam, qtyname)
maxdiff = (np.max(np.abs(u.Quantity((maxv-mn, minv-mn))))/mn).decompose()
if isinstance(threshold, dict):
th = threshold[qtyname]
else:
th = threshold
if maxdiff > th:
errormessage += ("Beam {2}s differ by up to {0}x, which is greater"
" than the threshold {1}\n".format(maxdiff,
threshold,
qtyname
))
if errormessage != "":
raise ValueError(errormessage)
def __getattribute__(self, attrname):
"""
For any functions that operate over the spectral axis, perform beam
sameness checks before performing the operation to avoid unexpected
results
"""
# short name to avoid long lines below
VRSC = VaryingResolutionSpectralCube
# what about apply_numpy_function, apply_function? since they're
# called by some of these, maybe *only* those should be wrapped to
# avoid redundant calls
if attrname in ('moment', 'apply_numpy_function', 'apply_function',
'apply_function_parallel_spectral'):
origfunc = super(VRSC, self).__getattribute__(attrname)
return self._handle_beam_areas_wrapper(origfunc)
else:
return super(VRSC, self).__getattribute__(attrname)
@property
def header(self):
header = super(VaryingResolutionSpectralCube, self).header
# this indicates to CASA that there is a beam table
header['CASAMBM'] = True
return header
@property
def hdu(self):
raise ValueError("For VaryingResolutionSpectralCube's, use hdulist "
"instead of hdu.")
@property
def hdulist(self):
"""
HDUList version of self
"""
hdu = PrimaryHDU(self.filled_data[:].value, header=self.header)
from .cube_utils import beams_to_bintable
# use unmasked beams because, even if the beam is masked out, we should
# write it
bmhdu = beams_to_bintable(self.unmasked_beams)
return HDUList([hdu, bmhdu])
[docs]
@warn_slow
def convolve_to(self, beam, allow_smaller=False,
convolve=convolution.convolve_fft,
update_function=None,
**kwargs):
"""
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.
.. warning::
Note that if there is any misaligment between the cube's spatial
pixel axes and the WCS's spatial axes *and* the beams are not
round, the convolution kernels used here may be incorrect. Be wary
in such cases!
Parameters
----------
beam : `radio_beam.Beam`
The beam to convolve to
allow_smaller : bool
If the specified target beam is smaller than the beam in a channel
in any dimension and this is ``False``, it will raise an exception.
convolve : function
The astropy convolution function to use, either
`astropy.convolution.convolve` or
`astropy.convolution.convolve_fft`
update_function : method
Method that is called to update an external progressbar
If provided, it disables the default `astropy.utils.console.ProgressBar`
kwargs : dict
Keyword arguments to pass to the convolution function
Returns
-------
cube : `SpectralCube`
A SpectralCube with a single ``beam``
"""
if ((self.wcs.celestial.wcs.get_pc()[0,1] != 0 or
self.wcs.celestial.wcs.get_pc()[1,0] != 0)):
warnings.warn("The beams will produce convolution kernels "
"that are not aware of any misaligment "
"between pixel and world coordinates, "
"and there are off-diagonal elements of the "
"WCS spatial transformation matrix. "
"Unexpected results are likely.",
BeamWarning
)
pixscale = wcs.utils.proj_plane_pixel_area(self.wcs.celestial)**0.5*u.deg
convolution_kernels = []
beam_ratio_factors = []
for bm,valid in zip(self.unmasked_beams, self.goodbeams_mask):
if not valid:
# just skip masked-out beams
convolution_kernels.append(None)
beam_ratio_factors.append(1.)
continue
elif beam == bm:
# Point response when beams are equal, don't convolve.
convolution_kernels.append(None)
beam_ratio_factors.append(1.)
continue
try:
cb = beam.deconvolve(bm)
ck = cb.as_kernel(pixscale)
convolution_kernels.append(ck)
beam_ratio_factors.append((beam.sr / bm.sr))
except ValueError:
if allow_smaller:
convolution_kernels.append(None)
beam_ratio_factors.append(1.)
else:
raise
# Only use the beam ratios when convolving in Jy/beam
if not self.unit.is_equivalent(u.Jy / u.beam):
beam_ratio_factors = [1.] * len(convolution_kernels)
if update_function is None:
pb = ProgressBar(self.shape[0])
update_function = pb.update
newdata = np.empty(self.shape)
for ii,kernel in enumerate(convolution_kernels):
# load each image from a slice to avoid loading whole cube into
# memory
img = self[ii,:,:].filled_data[:]
# Kernel can only be None when `allow_smaller` is True,
# or if the beams are equal. Only the latter is really valid.
if kernel is None:
newdata[ii, :, :] = img
else:
# See #631: kwargs get passed within self.apply_function_parallel_spatial
newdata[ii, :, :] = convolve(img, kernel,
normalize_kernel=True,
**kwargs) * beam_ratio_factors[ii]
update_function()
newcube = SpectralCube(data=newdata, wcs=self.wcs, mask=self.mask,
meta=self.meta, fill_value=self.fill_value,
header=self.header,
allow_huge_operations=self.allow_huge_operations,
beam=beam,
wcs_tolerance=self._wcs_tolerance)
return newcube
[docs]
@warn_slow
def to(self, unit, equivalencies=()):
"""
Return the cube converted to the given unit (assuming it is equivalent).
If conversion was required, this will be a copy, otherwise it will
"""
if not isinstance(unit, u.Unit):
unit = u.Unit(unit)
if unit == self.unit:
# No copying
return self
# Create the tuple of unit conversions needed.
factor = cube_utils.bunit_converters(self, unit, equivalencies=equivalencies)
factor = np.array(factor)
# special case: array in equivalencies
# (I don't think this should have to be special cased, but I don't know
# how to manipulate broadcasting rules any other way)
if hasattr(factor, '__len__') and len(factor) == len(self):
return self._new_cube_with(data=self._data*factor[:,None,None],
unit=unit)
else:
return self._new_cube_with(data=self._data*factor,
unit=unit)
[docs]
def mask_channels(self, goodchannels):
"""
Helper function to mask out channels. This function is equivalent to
adding a mask with ``cube[view]`` where ``view`` is broadcastable to
the cube shape, but it accepts 1D arrays that are not normally
broadcastable. Additionally, for `VaryingResolutionSpectralCube` s,
the beams in the bad channels will not be checked when averaging,
convolving, and doing other operations that are multibeam-aware.
Parameters
----------
goodchannels : array
A 1D boolean array declaring which channels should be kept.
Returns
-------
cube : `SpectralCube`
A cube with the specified channels masked
"""
goodchannels = np.asarray(goodchannels, dtype='bool')
if goodchannels.ndim != 1:
raise ValueError("goodchannels mask must be one-dimensional")
if goodchannels.size != self.shape[0]:
raise ValueError("goodchannels must have a length equal to the "
"cube's spectral dimension.")
cube = self.with_mask(goodchannels[:,None,None])
cube.goodbeams_mask = np.logical_and(goodchannels, self.goodbeams_mask)
return cube
[docs]
def spectral_interpolate(self, *args, **kwargs):
raise AttributeError("VaryingResolutionSpectralCubes can't be "
"spectrally interpolated. Convolve to a "
"common resolution with `convolve_to` before "
"attempting spectral interpolation.")
[docs]
def spectral_smooth(self, *args, **kwargs):
raise AttributeError("VaryingResolutionSpectralCubes can't be "
"spectrally smoothed. Convolve to a "
"common resolution with `convolve_to` before "
"attempting spectral smoothed.")
def _regionlist_to_single_region(region_list):
"""
Recursively merge a region list into a single compound region
"""
import regions
if len(region_list) == 1:
return region_list[0]
left = _regionlist_to_single_region(region_list[:len(region_list)//2])
right = _regionlist_to_single_region(region_list[len(region_list)//2:])
return regions.CompoundPixelRegion(left, right, operator.or_)