Source code for astropy.nddata.compat

# Licensed under a 3-clause BSD style license - see LICENSE.rst
# This module contains a class equivalent to pre-1.0 NDData.


import numpy as np

from astropy.units import UnitsError, UnitConversionError, Unit
from astropy import log

from .nddata import NDData
from .nduncertainty import NDUncertainty

from .mixins.ndslicing import NDSlicingMixin
from .mixins.ndarithmetic import NDArithmeticMixin
from .mixins.ndio import NDIOMixin

from .flag_collection import FlagCollection

__all__ = ['NDDataArray']


[docs]class NDDataArray(NDArithmeticMixin, NDSlicingMixin, NDIOMixin, NDData): """ An ``NDData`` object with arithmetic. This class is functionally equivalent to ``NDData`` in astropy versions prior to 1.0. The key distinction from raw numpy arrays is the presence of additional metadata such as uncertainties, a mask, units, flags, and/or a coordinate system. See also: https://docs.astropy.org/en/stable/nddata/ Parameters ----------- data : `~numpy.ndarray` or `NDData` The actual data contained in this `NDData` object. Not that this will always be copies by *reference* , so you should make copy the ``data`` before passing it in if that's the desired behavior. uncertainty : `~astropy.nddata.NDUncertainty`, optional Uncertainties on the data. mask : `~numpy.ndarray`-like, optional Mask for the data, given as a boolean Numpy array or any object that can be converted to a boolean Numpy array with a shape matching that of the data. The values must be ``False`` where the data is *valid* and ``True`` when it is not (like Numpy masked arrays). If ``data`` is a numpy masked array, providing ``mask`` here will causes the mask from the masked array to be ignored. flags : `~numpy.ndarray`-like or `~astropy.nddata.FlagCollection`, optional Flags giving information about each pixel. These can be specified either as a Numpy array of any type (or an object which can be converted to a Numpy array) with a shape matching that of the data, or as a `~astropy.nddata.FlagCollection` instance which has a shape matching that of the data. wcs : undefined, optional WCS-object containing the world coordinate system for the data. .. warning:: This is not yet defined because the discussion of how best to represent this class's WCS system generically is still under consideration. For now just leave it as None meta : `dict`-like object, optional Metadata for this object. "Metadata" here means all information that is included with this object but not part of any other attribute of this particular object. e.g., creation date, unique identifier, simulation parameters, exposure time, telescope name, etc. unit : `~astropy.units.UnitBase` instance or str, optional The units of the data. Raises ------ ValueError : If the `uncertainty` or `mask` inputs cannot be broadcast (e.g., match shape) onto ``data``. """ def __init__(self, data, *args, flags=None, **kwargs): # Initialize with the parent... super().__init__(data, *args, **kwargs) # ...then reset uncertainty to force it to go through the # setter logic below. In base NDData all that is done is to # set self._uncertainty to whatever uncertainty is passed in. self.uncertainty = self._uncertainty # Same thing for mask. self.mask = self._mask # Initial flags because it is no longer handled in NDData # or NDDataBase. if isinstance(data, NDDataArray): if flags is None: flags = data.flags else: log.info("Overwriting NDDataArrays's current " "flags with specified flags") self.flags = flags # Implement uncertainty as NDUncertainty to support propagation of # uncertainties in arithmetic operations @property def uncertainty(self): return self._uncertainty @uncertainty.setter def uncertainty(self, value): if value is not None: if isinstance(value, NDUncertainty): class_name = self.__class__.__name__ if not self.unit and value._unit: # Raise an error if uncertainty has unit and data does not raise ValueError("Cannot assign an uncertainty with unit " "to {} without " "a unit".format(class_name)) self._uncertainty = value self._uncertainty.parent_nddata = self else: raise TypeError("Uncertainty must be an instance of " "a NDUncertainty object") else: self._uncertainty = value # Override unit so that we can add a setter. @property def unit(self): return self._unit @unit.setter def unit(self, value): from . import conf try: if self._unit is not None and conf.warn_setting_unit_directly: log.info('Setting the unit directly changes the unit without ' 'updating the data or uncertainty. Use the ' '.convert_unit_to() method to change the unit and ' 'scale values appropriately.') except AttributeError: # raised if self._unit has not been set yet, in which case the # warning is irrelevant pass if value is None: self._unit = None else: self._unit = Unit(value) # Implement mask in a way that converts nicely to a numpy masked array @property def mask(self): if self._mask is np.ma.nomask: return None else: return self._mask @mask.setter def mask(self, value): # Check that value is not either type of null mask. if (value is not None) and (value is not np.ma.nomask): mask = np.array(value, dtype=np.bool_, copy=False) if mask.shape != self.data.shape: raise ValueError("dimensions of mask do not match data") else: self._mask = mask else: # internal representation should be one numpy understands self._mask = np.ma.nomask @property def shape(self): """ shape tuple of this object's data. """ return self.data.shape @property def size(self): """ integer size of this object's data. """ return self.data.size @property def dtype(self): """ `numpy.dtype` of this object's data. """ return self.data.dtype @property def ndim(self): """ integer dimensions of this object's data """ return self.data.ndim @property def flags(self): return self._flags @flags.setter def flags(self, value): if value is not None: if isinstance(value, FlagCollection): if value.shape != self.shape: raise ValueError("dimensions of FlagCollection does not match data") else: self._flags = value else: flags = np.array(value, copy=False) if flags.shape != self.shape: raise ValueError("dimensions of flags do not match data") else: self._flags = flags else: self._flags = value def __array__(self): """ This allows code that requests a Numpy array to use an NDData object as a Numpy array. """ if self.mask is not None: return np.ma.masked_array(self.data, self.mask) else: return np.array(self.data) def __array_prepare__(self, array, context=None): """ This ensures that a masked array is returned if self is masked. """ if self.mask is not None: return np.ma.masked_array(array, self.mask) else: return array
[docs] def convert_unit_to(self, unit, equivalencies=[]): """ Returns a new `NDData` object whose values have been converted to a new unit. Parameters ---------- unit : `astropy.units.UnitBase` instance or str The unit to convert to. equivalencies : list of equivalence pairs, optional A list of equivalence pairs to try if the units are not directly convertible. See :ref:`unit_equivalencies`. Returns ------- result : `~astropy.nddata.NDData` The resulting dataset Raises ------ UnitsError If units are inconsistent. """ if self.unit is None: raise ValueError("No unit specified on source data") data = self.unit.to(unit, self.data, equivalencies=equivalencies) if self.uncertainty is not None: uncertainty_values = self.unit.to(unit, self.uncertainty.array, equivalencies=equivalencies) # should work for any uncertainty class uncertainty = self.uncertainty.__class__(uncertainty_values) else: uncertainty = None if self.mask is not None: new_mask = self.mask.copy() else: new_mask = None # Call __class__ in case we are dealing with an inherited type result = self.__class__(data, uncertainty=uncertainty, mask=new_mask, wcs=self.wcs, meta=self.meta, unit=unit) return result