astropy:docs

Source code for astropy.nddata.nddata

# Licensed under a 3-clause BSD style license - see LICENSE.rst
# This module implements the base NDData class.

from __future__ import absolute_import, division, print_function, unicode_literals

import collections
from copy import deepcopy

import numpy as np

from ..units import Unit
from .. import log

from .flag_collection import FlagCollection
from .nduncertainty import IncompatibleUncertaintiesException, NDUncertainty
from ..utils.compat.odict import OrderedDict
from ..io import registry as io_registry
from ..config import ConfigurationItem
from ..utils.metadata import MetaData

__all__ = ['NDData']


__doctest_skip__ = ['NDData']


WARN_UNSUPPORTED_CORRELATED = ConfigurationItem(
    'warn_unsupported_correlated', True,
    'Whether to issue a warning if NDData arithmetic is performed with '
    'uncertainties and the uncertainties do not support the propagation '
    'of correlated uncertainties.'
)


[docs]class NDData(object): """A Superclass for array-based data in Astropy. 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. Parameters ----------- data : `~numpy.ndarray` or `~astropy.nddata.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`, optional Mask for the data, given as 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` or `~astropy.nddata.FlagCollection`, optional Flags giving information about each pixel. These can be specified either as a Numpy array of any type 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 defind 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`. Notes ----- `NDData` objects can be easily converted to a regular Numpy array using `numpy.asarray` For example:: >>> from astropy.nddata import NDData >>> import numpy as np >>> x = NDData([1,2,3]) >>> np.asarray(x) array([1, 2, 3]) If the `NDData` object has a `mask`, `numpy.asarray` will return a Numpy masked array. This is useful, for example, when plotting a 2D image using matplotlib:: >>> from astropy.nddata import NDData >>> from matplotlib import pyplot as plt >>> x = NDData([[1,2,3], [4,5,6]]) >>> plt.imshow(x) """ meta = MetaData() def __init__(self, data, uncertainty=None, mask=None, flags=None, wcs=None, meta=None, unit=None): if isinstance(data, self.__class__): self.data = np.array(data.data, subok=True, copy=False) if uncertainty is not None: self.uncertainty = uncertainty log.info("Overwriting NDData's current uncertainty being" " overwritten with specified uncertainty") if mask is not None: self.mask = mask log.info("Overwriting NDData's current mask with specified mask") if flags is not None: self.flags = flags log.info("Overwriting NDData's current flags with specified flag") if wcs is not None: self.wcs = wcs log.info("Overwriting NDData's current wcs with specified wcs") if meta is not None: self.meta = meta log.info("Overwriting NDData's current meta with specified meta") if unit is not None: raise ValueError('To convert to different unit please use .to') else: if hasattr(data, 'mask'): self.data = np.array(data.data, subok=True, copy=False) if mask is not None: self.mask = mask log.info("NDData was created with a masked array, and a " "mask was explictly provided to NDData. The explicitly " "passed-in mask will be used and the masked array's " "mask will be ignored.") else: self.mask = data.mask else: self.data = np.array(data, subok=True, copy=False) self.mask = mask self.uncertainty = uncertainty self.flags = flags self.wcs = wcs self.meta = meta self.unit = unit def __str__(self): return str(self.data) def __repr__(self): prefix = self.__class__.__name__ + '(' body = np.array2string(self.data, separator=', ', prefix=prefix) return ''.join([prefix, body, ')']) @property def mask(self): return self._mask @mask.setter
[docs] def mask(self, value): if value is not None: if isinstance(value, np.ndarray): if value.dtype != np.bool_: raise TypeError("mask must be a boolean Numpy array") else: if value.shape != self.shape: raise ValueError("dimensions of mask do not match data") else: self._mask = value else: raise TypeError("mask must be a Numpy array") else: self._mask = value
@property def flags(self): return self._flags @flags.setter
[docs] def flags(self, value): if value is not None: if isinstance(value, np.ndarray): if value.shape != self.shape: raise ValueError("dimensions of flags do not match data") else: self._flags = value elif isinstance(value, FlagCollection): if value.shape != self.shape: raise ValueError("dimensions of FlagCollection does not match data") else: self._flags = value else: raise TypeError("flags should be a Numpy array or a FlagCollection instance") else: self._flags = value
@property def uncertainty(self): return self._uncertainty @uncertainty.setter
[docs] def uncertainty(self, value): if value is not None: if isinstance(value, NDUncertainty): self._uncertainty = value self._uncertainty.parent_nddata = self else: raise TypeError("Uncertainty must be an instance of a NDUncertainty object") else: self._uncertainty = value
@property def unit(self): return self._unit @unit.setter
[docs] def unit(self, value): if value is None: self._unit = None else: self._unit = Unit(value)
@property
[docs] def shape(self): """ shape tuple of this object's data. """ return self.data.shape
@property
[docs] def size(self): """ integer size of this object's data. """ return self.data.size
@property
[docs] def dtype(self): """ `numpy.dtype` of this object's data. """ return self.data.dtype
@property
[docs] def ndim(self): """ integer dimensions of this object's data """ return self.data.ndim
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 self.data def __getitem__(self, item): new_data = self.data[item] if self.uncertainty is not None: new_uncertainty = self.uncertainty[item] else: new_uncertainty = None if self.mask is not None: new_mask = self.mask[item] else: new_mask = None if self.flags is not None: if isinstance(self.flags, np.ndarray): new_flags = self.flags[item] elif isinstance(self.flags, FlagCollection): raise NotImplementedError('Slicing complex Flags is currently not implemented') else: new_flags = None if self.wcs is not None: raise NotImplementedError('Slicing for WCS is not currently implemented') else: new_wcs = None return self.__class__(new_data, uncertainty=new_uncertainty, mask=new_mask, flags=new_flags, wcs=new_wcs, meta=self.meta, unit=self.unit) def _arithmetic(self, operand, propagate_uncertainties, name, operation): """ {name} another dataset (`operand`) to this dataset. Parameters ---------- operand : `~astropy.nddata.NDData` The second operand in the operation a {operator} b propagate_uncertainties : bool Whether to propagate uncertainties following the propagation rules defined by the class used for the `uncertainty` attribute. Returns ------- result : `~astropy.nddata.NDData` The resulting dataset Notes ----- This method requires the datasets to have identical WCS properties, equivalent units, and identical shapes. Flags and meta-data get set to None in the resulting dataset. The unit in the result is the same as the unit in `self`. Uncertainties are propagated, although correlated errors are not supported by any of the built-in uncertainty classes. If uncertainties are assumed to be correlated, a warning is issued by default (though this can be disabled via the `WARN_UNSUPPORTED_CORRELATED` configuration item). Values masked in either dataset before the operation are masked in the resulting dataset. """ if self.wcs != operand.wcs: raise ValueError("WCS properties do not match") if not (self.unit is None and operand.unit is None): if (self.unit is None or operand.unit is None or not self.unit.is_equivalent(operand.unit)): raise ValueError("operand units do not match") if self.shape != operand.shape: raise ValueError("operand shapes do not match") if self.unit is not None: operand_data = operand.unit.to(self.unit, operand.data) else: operand_data = operand.data data = operation(self.data, operand_data) result = self.__class__(data) # in case we are dealing with an inherited type if propagate_uncertainties is None: result.uncertainty = None elif self.uncertainty is None and operand.uncertainty is None: result.uncertainty = None elif self.uncertainty is None: result.uncertainty = operand.uncertainty elif operand.uncertainty is None: result.uncertainty = self.uncertainty else: # both self and operand have uncertainties if (WARN_UNSUPPORTED_CORRELATED() and (not self.uncertainty.support_correlated or not operand.uncertainty.support_correlated)): log.info("The uncertainty classes used do not support the " "propagation of correlated errors, so uncertainties" " will be propagated assuming they are uncorrelated") try: method = getattr(self.uncertainty, propagate_uncertainties) result.uncertainty = method(operand, result.data) except IncompatibleUncertaintiesException: raise IncompatibleUncertaintiesException( "Cannot propagate uncertainties of type {0:s} with uncertainties of " "type {1:s} for {2:s}".format( self.uncertainty.__class__.__name__, operand.uncertainty.__class__.__name__, name)) if self.mask is None and operand.mask is None: result.mask = None elif self.mask is None: result.mask = operand.mask elif operand.mask is None: result.mask = self.mask else: # combine masks as for Numpy masked arrays result.mask = self.mask & operand.mask result.flags = None result.wcs = self.wcs result.meta = None result.unit = self.unit return result
[docs] def add(self, operand, propagate_uncertainties=True): if propagate_uncertainties: propagate_uncertainties = "propagate_add" else: propagate_uncertainties = None return self._arithmetic( operand, propagate_uncertainties, "addition", np.add)
add.__doc__ = _arithmetic.__doc__.format(name="Add", operator="+")
[docs] def subtract(self, operand, propagate_uncertainties=True): if propagate_uncertainties: propagate_uncertainties = "propagate_subtract" else: propagate_uncertainties = None return self._arithmetic( operand, propagate_uncertainties, "subtraction", np.subtract)
subtract.__doc__ = _arithmetic.__doc__.format(name="Subtract", operator="-")
[docs] def multiply(self, operand, propagate_uncertainties=True): if propagate_uncertainties: propagate_uncertainties = "propagate_multiply" else: propagate_uncertainties = None return self._arithmetic( operand, propagate_uncertainties, "multiplication", np.multiply)
multiply.__doc__ = _arithmetic.__doc__.format(name="Multiply", operator="*")
[docs] def divide(self, operand, propagate_uncertainties=True): if propagate_uncertainties: propagate_uncertainties = "propagate_divide" else: propagate_uncertainties = None return self._arithmetic( operand, propagate_uncertainties, "division", np.divide)
divide.__doc__ = _arithmetic.__doc__.format(name="Divide", operator="/")
[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) result = self.__class__(data) # in case we are dealing with an inherited type result.uncertainty = self.uncertainty result.mask = self.mask result.flags = None result.wcs = self.wcs result.meta = self.meta result.unit = unit return result
read = classmethod(io_registry.read) write = io_registry.write

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