Source code for astropy.nddata.nddata

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

import numpy as np
from copy import deepcopy

from .nddata_base import NDDataBase
from .nduncertainty import NDUncertainty, UnknownUncertainty
from astropy import log
from astropy.units import Unit, Quantity
from astropy.utils.metadata import MetaData
from astropy.wcs.wcsapi import (BaseLowLevelWCS, BaseHighLevelWCS,
                                SlicedLowLevelWCS, HighLevelWCSWrapper)

__all__ = ['NDData']

_meta_doc = """`dict`-like : Additional meta information about the dataset."""

[docs]class NDData(NDDataBase): """ A container for `numpy.ndarray`-based datasets, using the `~astropy.nddata.NDDataBase` interface. The key distinction from raw `numpy.ndarray` is the presence of additional metadata such as uncertainty, mask, unit, a coordinate system and/or a dictionary containing further meta information. This class *only* provides a container for *storing* such datasets. For further functionality take a look at the ``See also`` section. See also: Parameters ---------- data : `numpy.ndarray`-like or `NDData`-like The dataset. uncertainty : any type, optional Uncertainty in the dataset. Should have an attribute ``uncertainty_type`` that defines what kind of uncertainty is stored, for example ``"std"`` for standard deviation or ``"var"`` for variance. A metaclass defining such an interface is `NDUncertainty` - but isn't mandatory. If the uncertainty has no such attribute the uncertainty is stored as `UnknownUncertainty`. Defaults to ``None``. mask : any type, optional Mask for the dataset. Masks should follow the ``numpy`` convention that **valid** data points are marked by ``False`` and **invalid** ones with ``True``. Defaults to ``None``. wcs : any type, optional World coordinate system (WCS) for the dataset. Default is ``None``. meta : `dict`-like object, optional Additional meta information about the dataset. If no meta is provided an empty `collections.OrderedDict` is created. Default is ``None``. unit : unit-like, optional Unit for the dataset. Strings that can be converted to a `~astropy.units.Unit` are allowed. Default is ``None``. copy : `bool`, optional Indicates whether to save the arguments as copy. ``True`` copies every attribute before saving it while ``False`` tries to save every parameter as reference. Note however that it is not always possible to save the input as reference. Default is ``False``. .. versionadded:: 1.2 Raises ------ TypeError In case ``data`` or ``meta`` don't meet the restrictions. Notes ----- Each attribute can be accessed through the homonymous instance attribute: ``data`` in a `NDData` object can be accessed through the `data` attribute:: >>> from astropy.nddata import NDData >>> nd = NDData([1,2,3]) >>> array([1, 2, 3]) Given a conflicting implicit and an explicit parameter during initialization, for example the ``data`` is a `~astropy.units.Quantity` and the unit parameter is not ``None``, then the implicit parameter is replaced (without conversion) by the explicit one and a warning is issued:: >>> import numpy as np >>> import astropy.units as u >>> q = np.array([1,2,3,4]) * u.m >>> nd2 = NDData(q, INFO: overwriting Quantity's current unit with specified unit. [astropy.nddata.nddata] >>> # doctest: +FLOAT_CMP array([1., 2., 3., 4.]) >>> nd2.unit Unit("cm") See also -------- NDDataRef NDDataArray """ # Instead of a custom property use the MetaData descriptor also used for # Tables. It will check if the meta is dict-like or raise an exception. meta = MetaData(doc=_meta_doc, copy=False) def __init__(self, data, uncertainty=None, mask=None, wcs=None, meta=None, unit=None, copy=False): # Rather pointless since the NDDataBase does not implement any setting # but before the NDDataBase did call the uncertainty # setter. But if anyone wants to alter this behavior again the call # to the superclass NDDataBase should be in here. super().__init__() # Check if data is any type from which to collect some implicitly # passed parameters. if isinstance(data, NDData): # don't use self.__class__ (issue #4137) # Of course we need to check the data because subclasses with other # init-logic might be passed in here. We could skip these # tests if we compared for self.__class__ but that has other # drawbacks. # Comparing if there is an explicit and an implicit unit parameter. # If that is the case use the explicit one and issue a warning # that there might be a conflict. In case there is no explicit # unit just overwrite the unit parameter with the NDData.unit # and proceed as if that one was given as parameter. Same for the # other parameters. if (unit is not None and data.unit is not None and unit != data.unit):"overwriting NDData's current " "unit with specified unit.") elif data.unit is not None: unit = data.unit if uncertainty is not None and data.uncertainty is not None:"overwriting NDData's current " "uncertainty with specified uncertainty.") elif data.uncertainty is not None: uncertainty = data.uncertainty if mask is not None and data.mask is not None:"overwriting NDData's current " "mask with specified mask.") elif data.mask is not None: mask = data.mask if wcs is not None and data.wcs is not None:"overwriting NDData's current " "wcs with specified wcs.") elif data.wcs is not None: wcs = data.wcs if meta is not None and data.meta is not None:"overwriting NDData's current " "meta with specified meta.") elif data.meta is not None: meta = data.meta data = else: if hasattr(data, 'mask') and hasattr(data, 'data'): # Separating data and mask if mask is not None:"overwriting Masked Objects's current " "mask with specified mask.") else: mask = data.mask # Just save the data for further processing, we could be given # a masked Quantity or something else entirely. Better to check # it first. data = if isinstance(data, Quantity): if unit is not None and unit != data.unit:"overwriting Quantity's current " "unit with specified unit.") else: unit = data.unit data = data.value # Quick check on the parameters if they match the requirements. if (not hasattr(data, 'shape') or not hasattr(data, '__getitem__') or not hasattr(data, '__array__')): # Data doesn't look like a numpy array, try converting it to # one. data = np.array(data, subok=True, copy=False) # Another quick check to see if what we got looks like an array # rather than an object (since numpy will convert a # non-numerical/non-string inputs to an array of objects). if data.dtype == 'O': raise TypeError("could not convert data to numpy array.") if unit is not None: unit = Unit(unit) if copy: # Data might have been copied before but no way of validating # without another variable. data = deepcopy(data) mask = deepcopy(mask) wcs = deepcopy(wcs) meta = deepcopy(meta) uncertainty = deepcopy(uncertainty) # Actually - copying the unit is unnecessary but better safe # than sorry :-) unit = deepcopy(unit) # Store the attributes self._data = data self.mask = mask self._wcs = None if wcs is not None: # Validate the wcs self.wcs = wcs self.meta = meta # TODO: Make this call the setter sometime self._unit = unit # Call the setter for uncertainty to further check the uncertainty self.uncertainty = uncertainty def __str__(self): data = str( unit = f" {self.unit}" if self.unit is not None else '' return data + unit def __repr__(self): prefix = self.__class__.__name__ + '(' data = np.array2string(, separator=', ', prefix=prefix) unit = f", unit='{self.unit}'" if self.unit is not None else '' return ''.join((prefix, data, unit, ')')) @property def data(self): """ `~numpy.ndarray`-like : The stored dataset. """ return self._data @property def mask(self): """ any type : Mask for the dataset, if any. Masks should follow the ``numpy`` convention that valid data points are marked by ``False`` and invalid ones with ``True``. """ return self._mask @mask.setter def mask(self, value): self._mask = value @property def unit(self): """ `~astropy.units.Unit` : Unit for the dataset, if any. """ return self._unit @property def wcs(self): """ any type : A world coordinate system (WCS) for the dataset, if any. """ return self._wcs @wcs.setter def wcs(self, wcs): if self._wcs is not None and wcs is not None: raise ValueError("You can only set the wcs attribute with a WCS if no WCS is present.") if wcs is None or isinstance(wcs, BaseHighLevelWCS): self._wcs = wcs elif isinstance(wcs, BaseLowLevelWCS): self._wcs = HighLevelWCSWrapper(wcs) else: raise TypeError("The wcs argument must implement either the high or" " low level WCS API.") @property def uncertainty(self): """ any type : Uncertainty in the dataset, if any. Should have an attribute ``uncertainty_type`` that defines what kind of uncertainty is stored, such as ``'std'`` for standard deviation or ``'var'`` for variance. A metaclass defining such an interface is `~astropy.nddata.NDUncertainty` but isn't mandatory. """ return self._uncertainty @uncertainty.setter def uncertainty(self, value): if value is not None: # There is one requirements on the uncertainty: That # it has an attribute 'uncertainty_type'. # If it does not match this requirement convert it to an unknown # uncertainty. if not hasattr(value, 'uncertainty_type'):'uncertainty should have attribute uncertainty_type.') value = UnknownUncertainty(value, copy=False) # If it is a subclass of NDUncertainty we must set the # parent_nddata attribute. (#4152) if isinstance(value, NDUncertainty): # In case the uncertainty already has a parent create a new # instance because we need to assume that we don't want to # steal the uncertainty from another NDData object if value._parent_nddata is not None: value = value.__class__(value, copy=False) # Then link it to this NDData instance (internally this needs # to be saved as weakref but that's done by NDUncertainty # setter). value.parent_nddata = self self._uncertainty = value