Source code for spectral_cube.base_class

from astropy import units as u
from astropy import log
import numpy as np
import warnings
import abc

import astropy
from astropy.io.fits import Card
from radio_beam import Beam, Beams
import dask.array as da

from . import wcs_utils
from . import cube_utils
from .utils import BeamWarning, cached, WCSCelestialError, BeamAverageWarning, NoBeamError, BeamUnitsError
from .masks import BooleanArrayMask


__doctest_skip__ = ['SpatialCoordMixinClass.world']
__all__ = ['BaseNDClass', 'BeamMixinClass',
           'HeaderMixinClass', 'MaskableArrayMixinClass',
           'MultiBeamMixinClass', 'SpatialCoordMixinClass',
           'SpectralAxisMixinClass',
          ]

DOPPLER_CONVENTIONS = {}
DOPPLER_CONVENTIONS['radio'] = u.doppler_radio
DOPPLER_CONVENTIONS['optical'] = u.doppler_optical
DOPPLER_CONVENTIONS['relativistic'] = u.doppler_relativistic


[docs]class BaseNDClass(object): _cache = {} @property def _nowcs_header(self): """ Return a copy of the header with no WCS information attached """ log.debug("Stripping WCS from header") return wcs_utils.strip_wcs_from_header(self._header) @property def wcs(self): return self._wcs @property def meta(self): return self._meta @property def mask(self): return self._mask
[docs]class HeaderMixinClass(object): """ A mixin class to provide header updating from WCS objects. The parent object must have a WCS. """
[docs] def wcs(self): raise TypeError("Classes inheriting from HeaderMixin must define a " "wcs method")
@property def header(self): header = self._nowcs_header wcsheader = self.wcs.to_header() if self.wcs is not None else {} # When preserving metadata, copy over keywords before doing the WCS # keyword copying, since those have specific formatting requirements # and will overwrite these in many cases (e.g., BMAJ) for key in self.meta: if key.upper() not in wcsheader: if isinstance(key, str) and len(key) <= 8: try: header[key.upper()] = str(self.meta[key]) except ValueError as ex: # need a silenced-by-default warning here? # log.warn("Skipped key {0} because {1}".format(key, ex)) pass elif isinstance(key, str) and len(key) > 8: header['COMMENT'] = "{0}={1}".format(key, self.meta[key]) # Preserve non-WCS information from previous header iteration header.update(wcsheader) if self.unit == u.one and 'BUNIT' in self._meta: # preserve the BUNIT even though it's not technically valid # (Jy/Beam) header['BUNIT'] = self._meta['BUNIT'] else: header['BUNIT'] = self.unit.to_string(format='FITS') if 'beam' in self._meta: header = self._meta['beam'].attach_to_header(header) with warnings.catch_warnings(): warnings.simplefilter("ignore") header.insert(2, Card(keyword='NAXIS', value=self.ndim)) for ind,sh in enumerate(self.shape[::-1]): header.insert(3+ind, Card(keyword='NAXIS{0:1d}'.format(ind+1), value=sh)) return header
[docs] def check_jybeam_smoothing(self, raise_error_jybm=True): ''' This runs for spatial resolution operations (e.g. `spatial_smooth`) and either an error or warning when smoothing will affect brightness in Jy/beam operations. This is also true for using the `with_beam` and `with_beams` methods, including 1D spectra with Jy/beam units. Parameters ---------- raise_error_jybeam : bool, optional Raises a `~spectral_cube.utils.BeamUnitsError` when True (default). When False, it triggers a `~spectral_cube.utils.BeamWarning`. .. note: This is a reminder to expose raise_error_jybm to top-level functions. ''' if self.unit.is_equivalent(u.Jy / u.beam) and raise_error_jybm: if raise_error_jybm: raise BeamUnitsError("Attempting to change the spatial resolution of a cube with Jy/beam units." " To ignore this error, set `raise_error_jybm=False`.") else: warnings.warn("Changing the spatial resolution of a cube with Jy/beam units." " The brightness units may be wrong!", BeamWarning)
[docs]class SpatialCoordMixinClass(object): @property def _has_wcs_celestial(self): return self.wcs.has_celestial def _raise_wcs_no_celestial(self): if not self._has_wcs_celestial: raise WCSCelestialError("WCS does not contain two spatial axes.") def _celestial_axes(self): ''' Return the spatial axes in the data from the WCS object. The order of the spatial axes returned is [y, x]. ''' self._raise_wcs_no_celestial() # This works for astropy >v3 # wcs_cel_axis = [self.wcs.world_axis_physical_types.index(axtype) # for axtype in # self.wcs.celestial.world_axis_physical_types] # This works for all LTS releases wcs_cel_axis = [ax for ax, ax_type in enumerate(self.wcs.get_axis_types()) if ax_type['coordinate_type'] == 'celestial'] # Swap to numpy ordering # Since we're mapping backwards to get the numpy convention, we need to # reverse the order at the end. # 0 is the y spatial axis and 1 is the x spatial axis np_order_cel_axis = [self.ndim - 1 - ind for ind in wcs_cel_axis][::-1] return np_order_cel_axis @cube_utils.slice_syntax def world(self, view): """ Return a list of the world coordinates in a cube, projection, or a view of it. SpatialCoordMixinClass.world is called with *bracket notation*, like a NumPy array:: c.world[0:3, :, :] Returns ------- [v, y, x] : list of NumPy arrays The 3 world coordinates at each pixel in the view. For a 2D image, the output is ``[y, x]``. Examples -------- Extract the first 3 velocity channels of the cube: >>> v, y, x = c.world[0:3] Extract all the world coordinates: >>> v, y, x = c.world[:, :, :] Extract every other pixel along all axes: >>> v, y, x = c.world[::2, ::2, ::2] Extract all the world coordinates for a 2D image: >>> y, x = c.world[:, :] """ self._raise_wcs_no_celestial() # the next 3 lines are equivalent to (but more efficient than) # inds = np.indices(self._data.shape) # inds = [i[view] for i in inds] inds = np.ogrid[[slice(0, s) for s in self.shape]] inds = np.broadcast_arrays(*inds) inds = [i[view] for i in inds[::-1]] # numpy -> wcs order shp = inds[0].shape inds = np.column_stack([i.ravel() for i in inds]) world = self._wcs.all_pix2world(inds, 0).T world = [w.reshape(shp) for w in world] # 1D->3D # apply units world = [w * u.Unit(self._wcs.wcs.cunit[i]) for i, w in enumerate(world)] # convert spectral unit if needed if hasattr(self, "_spectral_unit"): if self._spectral_unit is not None: specind = self.wcs.wcs.spec world[specind] = world[specind].to(self._spectral_unit) return world[::-1] # reverse WCS -> numpy order
[docs] def flattened_world(self, view=()): """ Retrieve the world coordinates corresponding to the extracted flattened version of the cube """ self._raise_wcs_no_celestial() return [wd_dim.ravel() for wd_dim in self.world[view]]
[docs] def world_spines(self): """ Returns a list of 1D arrays, for the world coordinates along each pixel axis. Raises error if this operation is ill-posed (e.g. rotated world coordinates, strong distortions) This method is not currently implemented. Use ``world`` instead. """ raise NotImplementedError()
@property def spatial_coordinate_map(self): view = tuple([0 for ii in range(self.ndim - 2)] + [slice(None)] * 2) return self.world[view][self.ndim - 2:] @property @cached def world_extrema(self): y_ax, x_ax = self._celestial_axes() corners = [(0, self.shape[x_ax]-1), (self.shape[y_ax]-1, 0), (self.shape[y_ax]-1, self.shape[x_ax]-1), (0,0)] if len(self.shape) == 2: latlon_corners = [self.world[y, x] for y,x in corners] else: latlon_corners = [self.world[0, y, x][1:] for y,x in corners] lon = u.Quantity([x for y,x in latlon_corners]) lat = u.Quantity([y for y,x in latlon_corners]) _lon_min = lon.min() _lon_max = lon.max() _lat_min = lat.min() _lat_max = lat.max() return u.Quantity(((_lon_min.to(u.deg).value, _lon_max.to(u.deg).value), (_lat_min.to(u.deg).value, _lat_max.to(u.deg).value)), u.deg) @property @cached def longitude_extrema(self): return self.world_extrema[0] @property @cached def latitude_extrema(self): return self.world_extrema[1]
[docs]class SpectralAxisMixinClass(object): def _new_spectral_wcs(self, unit, velocity_convention=None, rest_value=None): """ Returns a new WCS 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 """ from .spectral_axis import (convert_spectral_axis, determine_ctype_from_vconv) # Allow string specification of units, for example if not isinstance(unit, u.Unit): unit = u.Unit(unit) # Velocity conventions: required for frq <-> velo # convert_spectral_axis will handle the case of no velocity # convention specified & one is required if velocity_convention in DOPPLER_CONVENTIONS: velocity_convention = DOPPLER_CONVENTIONS[velocity_convention] elif (velocity_convention is not None and velocity_convention not in DOPPLER_CONVENTIONS.values()): raise ValueError("Velocity convention must be radio, optical, " "or relativistic.") # If rest value is specified, it must be a quantity if (rest_value is not None and (not hasattr(rest_value, 'unit') or not rest_value.unit.is_equivalent(u.m, u.spectral()))): raise ValueError("Rest value must be specified as an astropy " "quantity with spectral equivalence.") # Shorter versions to keep lines under 80 ctype_from_vconv = determine_ctype_from_vconv meta = self._meta.copy() if 'Original Unit' not in self._meta: meta['Original Unit'] = self._wcs.wcs.cunit[self._wcs.wcs.spec] meta['Original Type'] = self._wcs.wcs.ctype[self._wcs.wcs.spec] out_ctype = ctype_from_vconv(self._wcs.wcs.ctype[self._wcs.wcs.spec], unit, velocity_convention=velocity_convention) newwcs = convert_spectral_axis(self._wcs, unit, out_ctype, rest_value=rest_value) newwcs.wcs.set() return newwcs, meta @property def spectral_axis(self): # spectral objects should be forced to implement this raise NotImplementedError
[docs]class MaskableArrayMixinClass(object): """ Mixin class for maskable arrays """ def _get_filled_data(self, view=(), fill=np.nan, check_endian=False, use_memmap=None): """ Return the underlying data as a numpy array. Always returns the spectral axis as the 0th axis Sets masked values to *fill* """ if check_endian: if not self._data.dtype.isnative: kind = str(self._data.dtype.kind) sz = str(self._data.dtype.itemsize) dt = '=' + kind + sz data = self._data.astype(dt) else: data = self._data else: data = self._data if self._mask is None: return data[view] if use_memmap is None and hasattr(self, '_is_huge'): use_memmap = self._is_huge return self._mask._filled(data=data, wcs=self._wcs, fill=fill, view=view, wcs_tolerance=self._wcs_tolerance, use_memmap=use_memmap ) @cube_utils.slice_syntax def filled_data(self, view): """ Return a portion of the data array, with excluded mask values replaced by ``fill_value``. Returns ------- data : Quantity The masked data. """ return u.Quantity(self._get_filled_data(view, fill=self._fill_value), self.unit, copy=False)
[docs] def filled(self, fill_value=None): if fill_value is not None: return u.Quantity(self._get_filled_data(fill=fill_value), self.unit, copy=False) return self.filled_data[:]
@cube_utils.slice_syntax def unitless_filled_data(self, view): """ Return a portion of the data array, with excluded mask values replaced by ``fill_value``. Returns ------- data : numpy.array The masked data. """ return self._get_filled_data(view, fill=self._fill_value) @property def fill_value(self): """ The replacement value used by `~spectral_cube.base_class.MaskableArrayMixinClass.filled_data`. fill_value is immutable; use `~spectral_cube.base_class.MaskableArrayMixinClass.with_fill_value` to create a new cube with a different fill value. """ return self._fill_value
[docs] def with_fill_value(self, fill_value): """ Create a new object with a different ``fill_value``. Notes ----- This method is fast (it does not copy any data) """ return self._new_thing_with(fill_value=fill_value)
@abc.abstractmethod def _new_thing_with(self): raise NotImplementedError
[docs]class MultiBeamMixinClass(object): """ A mixin class to handle multibeam objects. To be used by VaryingResolutionSpectralCube's and OneDSpectrum's """
[docs] def jtok_factors(self, equivalencies=()): """ Compute an array of multiplicative factors that will convert from Jy/beam to K """ factors = [] for bm,frq in zip(self.beams, self.with_spectral_unit(u.Hz).spectral_axis): # create a beam equivalency for brightness temperature bmequiv = bm.jtok_equiv(frq) factor = (u.Jy).to(u.K, equivalencies=bmequiv+list(equivalencies)) factors.append(factor) factor = np.array(factors) return factor
@property def beams(self): return self._beams[self.goodbeams_mask] @beams.setter def beams(self, obj): if not isinstance(obj, Beams): raise TypeError("beam must be a radio_beam.Beams object.") if not obj.size == self.shape[0]: raise ValueError("The Beams object must have the same size as the " "data. Found a size of {0} and the data have a " "size of {1}".format(obj.size, self.size)) self._beams = obj @property @cached def pixels_per_beam(self): pixels_per_beam = [(beam.sr / (astropy.wcs.utils.proj_plane_pixel_area(self.wcs) * u.deg**2)).to(u.one).value for beam in self.beams] return pixels_per_beam @property def unmasked_beams(self): return self._beams @property def goodbeams_mask(self): if hasattr(self, '_goodbeams_mask'): return self._goodbeams_mask else: return self.unmasked_beams.isfinite @goodbeams_mask.setter def goodbeams_mask(self, value): if value.size != self.shape[0]: raise ValueError("The 'good beams' mask must have the same size " "as the cube's spectral dimension") self._goodbeams_mask = value
[docs] def identify_bad_beams(self, threshold, reference_beam=None, criteria=['sr','major','minor'], mid_value=np.nanmedian): """ Mask out any layers in the cube that have beams that differ from the central value of the beam by more than the specified threshold. Parameters ---------- threshold : float Fractional threshold reference_beam : Beam A beam to use as the reference. If unspecified, ``mid_value`` will be used to select a middle beam criteria : list A list of criteria to compare. Can include 'sr','major','minor','pa' or any subset of those. mid_value : function The function used to determine the 'mid' value to compare to. This will identify the middle-valued beam area/major/minor/pa. Returns ------- includemask : np.array A boolean array where ``True`` indicates the good beams """ includemask = np.ones(self.unmasked_beams.size, dtype='bool') all_criteria = {'sr','major','minor','pa'} if not set.issubset(set(criteria), set(all_criteria)): raise ValueError("Criteria must be one of the allowed options: " "{0}".format(all_criteria)) props = {prop: u.Quantity([getattr(beam, prop) for beam in self.unmasked_beams]) for prop in all_criteria} if reference_beam is None: reference_beam = Beam(major=mid_value(props['major']), minor=mid_value(props['minor']), pa=mid_value(props['pa']) ) for prop in criteria: val = props[prop] mid = getattr(reference_beam, prop) diff = np.abs((val-mid)/mid) assert diff.shape == includemask.shape includemask[diff > threshold] = False return includemask
[docs] def average_beams(self, threshold, mask='compute', warn=False): """ Average the beams. Note that this operation only makes sense in limited contexts! Generally one would want to convolve all the beams to a common shape, but this method is meant to handle the "simple" case when all your beams are the same to within some small factor and can therefore be arithmetically averaged. Parameters ---------- threshold : float The fractional difference between beam major, minor, and pa to permit mask : 'compute', None, or boolean array The mask to apply to the beams. Useful for excluding bad channels and edge beams. warn : bool Warn if successful? Returns ------- new_beam : radio_beam.Beam A new radio beam object that is the average of the unmasked beams """ use_dask = isinstance(self._data, da.Array) if mask == 'compute': if use_dask: # If we are dealing with dask arrays, we compute the beam # mask once and for all since it is used multiple times in its # entirety in the remainder of this method. beam_mask = da.any(da.logical_and(self._mask_include, self.goodbeams_mask[:, None, None]), axis=(1, 2)) # da.any appears to return an object dtype instead of a bool beam_mask = self._compute(beam_mask).astype('bool') elif self.mask is not None: beam_mask = np.any(np.logical_and(self.mask.include(), self.goodbeams_mask[:, None, None]), axis=(1, 2)) else: beam_mask = self.goodbeams_mask else: if mask.ndim > 1: beam_mask = np.logical_and(mask, self.goodbeams_mask[:, None, None]) else: beam_mask = np.logical_and(mask, self.goodbeams_mask) # use private _beams here because the public one excludes the bad beams # by default new_beam = self._beams.average_beam(includemask=beam_mask) if np.isnan(new_beam): raise ValueError("Beam was not finite after averaging. " "This either indicates that there was a problem " "with the include mask, one of the beam's values, " "or a bug.") self._check_beam_areas(threshold, mean_beam=new_beam, mask=beam_mask) if warn: warnings.warn("Arithmetic beam averaging is being performed. This is " "not a mathematically robust operation, but is being " "permitted because the beams differ by " "<{0}".format(threshold), BeamAverageWarning ) return new_beam
def _handle_beam_areas_wrapper(self, function, beam_threshold=None): """ Wrapper: if the function takes "axis" and is operating over axis 0 (the spectral axis), check that the beam threshold is not exceeded before performing the operation Also, if the operation *is* valid, average the beam appropriately to get the output """ # deferred import to avoid a circular import problem from .lower_dimensional_structures import LowerDimensionalObject if beam_threshold is None: beam_threshold = self.beam_threshold def newfunc(*args, **kwargs): """ Wrapper function around the standard operations to handle beams when creating projections """ # check that the spectral axis is being operated over. If it is, # we need to average beams # moments are a special case b/c they default to axis=0 need_to_handle_beams = (('axis' in kwargs and ((kwargs['axis']==0) or (hasattr(kwargs['axis'], '__len__') and 0 in kwargs['axis']))) or ('axis' not in kwargs and 'moment' in function.__name__)) if need_to_handle_beams: # do this check *first* so we don't do an expensive operation # and crash afterward avg_beam = self.average_beams(beam_threshold, warn=True) result = function(*args, **kwargs) if not isinstance(result, LowerDimensionalObject): # numpy arrays are sometimes returned; these have no metadata return result elif need_to_handle_beams: result.meta['beam'] = avg_beam result._beam = avg_beam return result return newfunc 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)
[docs] def mask_out_bad_beams(self, threshold, reference_beam=None, criteria=['sr','major','minor'], mid_value=np.nanmedian): """ See `identify_bad_beams`. This function returns a masked cube Returns ------- newcube : VaryingResolutionSpectralCube The cube with bad beams masked out """ goodbeams = self.identify_bad_beams(threshold=threshold, reference_beam=reference_beam, criteria=criteria, mid_value=mid_value) includemask = BooleanArrayMask(goodbeams[:, None, None], self._wcs, shape=self._data.shape) use_dask = isinstance(self._data, da.Array) if use_dask: newmask = da.logical_and(self._mask_include, includemask) elif self.mask is None: newmask = includemask else: newmask = np.bitwise_and(self.mask, includemask) return self._new_thing_with(mask=newmask, beam_threshold=threshold, goodbeams_mask=np.bitwise_and(self.goodbeams_mask, goodbeams), )
[docs] def with_beams(self, beams, goodbeams_mask=None, raise_error_jybm=True): ''' Attach a new beams object to the VaryingResolutionSpectralCube. Parameters ---------- beams : `~radio_beam.Beams` A new beams object. ''' # Catch cases with units in Jy/beam where new beams will alter the units. self.check_jybeam_smoothing(raise_error_jybm=raise_error_jybm) meta = self.meta.copy() meta['beams'] = beams return self._new_thing_with(beams=beams, meta=meta)
@abc.abstractmethod def _new_thing_with(self): # since the above two methods require this method, it's an ABC of this # mixin as well raise NotImplementedError
[docs]class BeamMixinClass(object): """ Functionality for objects with a single beam. Specific objects (cubes, LDOs) still need to define their own ``with_beam`` methods. """ @property def beam(self): if self._beam is None: raise NoBeamError("No beam is defined for this SpectralCube or the" " beam information could not be parsed from the" " header. A `~radio_beam.Beam` object can be" " added using `cube.with_beam`.") return self._beam @beam.setter def beam(self, obj): if not isinstance(obj, Beam) and obj is not None: raise TypeError("beam must be a radio_beam.Beam object.") self._beam = obj @property @cached def pixels_per_beam(self): return (self.beam.sr / (astropy.wcs.utils.proj_plane_pixel_area(self.wcs) * u.deg**2)).to(u.one).value