# Licensed under a 3-clause BSD style license - see LICENSE.rst # This module implements the base NDData class. from copy import deepcopy import numpy as np from astropy import log from astropy.units import Quantity, Unit from astropy.utils.masked import Masked, MaskedNDArray from astropy.utils.metadata import MetaData from astropy.wcs.wcsapi import SlicedLowLevelWCS # noqa: F401 from astropy.wcs.wcsapi import BaseHighLevelWCS, BaseLowLevelWCS, HighLevelWCSWrapper from .nddata_base import NDDataBase from .nduncertainty import NDUncertainty, UnknownUncertainty __all__ = ["NDData"] _meta_doc = """`dict`-like : Additional meta information about the dataset.""" 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: https://docs.astropy.org/en/stable/nddata/ 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 psf : `numpy.ndarray` or None, optional Image representation of the PSF. In order for convolution to be flux- preserving, this should generally be normalized to sum to unity. 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]) >>> nd.data 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, unit=u.cm) INFO: overwriting Quantity's current unit with specified unit. [astropy.nddata.nddata] >>> nd2.data # doctest: +FLOAT_CMP array([100., 200., 300., 400.]) >>> 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, psf=None, ): # 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 None and data.unit is not None: unit = data.unit elif unit is not None and data.unit is not None: log.info("overwriting NDData's current unit with specified unit.") if uncertainty is not None and data.uncertainty is not None: log.info( "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: log.info("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: log.info("overwriting NDData's current wcs with specified wcs.") elif data.wcs is not None: wcs = data.wcs if psf is not None and data.psf is not None: log.info("Overwriting NDData's current psf with specified psf.") elif data.psf is not None: psf = data.psf if meta is not None and data.meta is not None: log.info("overwriting NDData's current meta with specified meta.") elif data.meta is not None: meta = data.meta # get the data attribute as it is, and continue to process it: data = data.data # if the data is wrapped by astropy.utils.masked.Masked: if isinstance(data, Masked): # first get the mask if one is available: if hasattr(data, "mask"): if mask is not None: log.info( "overwriting Masked Quantity's current mask with specified mask." ) else: mask = data.mask if isinstance(data, MaskedNDArray): if unit is not None and hasattr(data, "unit") and data.unit != unit: log.info( "overwriting MaskedNDArray's current unit with specified unit." ) data = data.to(unit).value elif unit is None and hasattr(data, "unit"): unit = data.unit data = data.value # now get the unmasked ndarray: data = np.asarray(data) if isinstance(data, Quantity): # this is a Quantity: if unit is not None and data.unit != unit: log.info("overwriting Quantity's current unit with specified unit.") data = data.to(unit) elif unit is None and data.unit is not None: unit = data.unit data = data.value if isinstance(data, np.ma.masked_array): if mask is not None: log.info( "overwriting masked ndarray's current mask with specified mask." ) else: mask = data.mask data = data.data if isinstance(data, Quantity): # this is a Quantity: if unit is not None and data.unit != unit: log.info("overwriting Quantity's current unit with specified unit.") data = data.to(unit) elif unit is None and data.unit is not None: unit = data.unit data = data.value if isinstance(data, np.ndarray): # check for mask from np.ma.masked_ndarray if hasattr(data, "mask"): if mask is not None: log.info( "overwriting masked ndarray's current mask with specified mask." ) else: mask = data.mask # 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) psf = deepcopy(psf) 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 self.psf = psf def __str__(self): data = str(self.data) unit = f" {self.unit}" if self.unit is not None else "" return data + unit def __repr__(self): prefix = self.__class__.__name__ + "(" data = np.array2string(self.data, 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 psf(self): return self._psf @psf.setter def psf(self, value): self._psf = value @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"): log.info("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