NDArithmeticMixin#

class astropy.nddata.NDArithmeticMixin[source]#

Bases: object

Mixin class to add arithmetic to an NDData object.

When subclassing, be sure to list the superclasses in the correct order so that the subclass sees NDData as the main superclass. See NDDataArray for an example.

Notes

This class only aims at covering the most common cases so there are certain restrictions on the saved attributes:

- ``uncertainty`` : has to be something that has a `NDUncertainty`-like
  interface for uncertainty propagation
- ``mask`` : has to be something that can be used by a bitwise ``or``
  operation.
- ``wcs`` : has to implement a way of comparing with ``=`` to allow
  the operation.

But there is a workaround that allows to disable handling a specific attribute and to simply set the results attribute to None or to copy the existing attribute (and neglecting the other). For example for uncertainties not representing an NDUncertainty-like interface you can alter the propagate_uncertainties parameter in NDArithmeticMixin.add(). None means that the result will have no uncertainty, False means it takes the uncertainty of the first operand (if this does not exist from the second operand) as the result’s uncertainty. This behavior is also explained in the docstring for the different arithmetic operations.

Decomposing the units is not attempted, mainly due to the internal mechanics of Quantity, so the resulting data might have units like km/m if you divided for example 100km by 5m. So this Mixin has adopted this behavior.

Examples

Using this Mixin with NDData:

>>> from astropy.nddata import NDData, NDArithmeticMixin
>>> class NDDataWithMath(NDArithmeticMixin, NDData):
...     pass

Using it with one operand on an instance:

>>> ndd = NDDataWithMath(100)
>>> ndd.add(20)
NDDataWithMath(120)

Using it with two operand on an instance:

>>> ndd = NDDataWithMath(-4)
>>> ndd.divide(1, ndd)
NDDataWithMath(-0.25)

Using it as classmethod requires two operands:

>>> NDDataWithMath.subtract(5, 4)
NDDataWithMath(1)

Methods Summary

`add`(operand[, operand2])	Performs addition by evaluating `self` + `operand`.
`divide`(operand[, operand2])	Performs division by evaluating `self` / `operand`.
`max`(**kwargs)
`mean`(**kwargs)
`min`(**kwargs)
`multiply`(operand[, operand2])	Performs multiplication by evaluating `self` * `operand`.
`subtract`(operand[, operand2])	Performs subtraction by evaluating `self` - `operand`.
`sum`(**kwargs)

Methods Documentation

classmethod add(operand, operand2=None, **kwargs)[source]#

Performs addition by evaluating self + operand.

Parameters:

operand, operand2NDDataastropy:-like instance: If operand2 is None or not given it will perform the operation self + operand. If operand2 is given it will perform operand + operand2. If the method was called on a class rather than on the instance operand2 must be given.
propagate_uncertaintiesbool or None, optional: If None the result will have no uncertainty. If False the result will have a copied version of the first operand that has an uncertainty. If True the result will have a correctly propagated uncertainty from the uncertainties of the operands but this assumes that the uncertainties are NDUncertainty-like. Default is True.

Changed in version 1.2: This parameter must be given as keyword-parameter. Using it as positional parameter is deprecated. None was added as valid parameter value.
handle_maskcallable(), 'first_found' or None, optional: If None the result will have no mask. If 'first_found' the result will have a copied version of the first operand that has a mask). If it is a callable then the specified callable must create the results mask and if necessary provide a copy. Default is numpy.logical_or.

New in version 1.2.
handle_metacallable(), 'first_found' or None, optional: If None the result will have no meta. If 'first_found' the result will have a copied version of the first operand that has a (not empty) meta. If it is a callable then the specified callable must create the results meta and if necessary provide a copy. Default is None.

New in version 1.2.
compare_wcscallable(), 'first_found' or None, optional: If None the result will have no wcs and no comparison between the wcs of the operands is made. If 'first_found' the result will have a copied version of the first operand that has a wcs. If it is a callable then the specified callable must compare the wcs. The resulting wcs will be like if False was given otherwise it raises a ValueError if the comparison was not successful. Default is 'first_found'.

New in version 1.2.
uncertainty_correlationnumber or ndarray, optional: The correlation between the two operands is used for correct error propagation for correlated data as given in: https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulas Default is 0.

New in version 1.2.
kwargs: Any other parameter that should be passed to the callables used.

Returns:

resultNDDataastropy:-like: The resulting dataset

Notes

If a callable is used for mask, wcs or meta the callable must accept the corresponding attributes as first two parameters. If the callable also needs additional parameters these can be defined as kwargs and must start with "wcs_" (for wcs callable) or "meta_" (for meta callable). This startstring is removed before the callable is called.

"first_found" can also be abbreviated with "ff".

classmethod divide(operand, operand2=None, **kwargs)[source]#

Performs division by evaluating self / operand.

Parameters:

operand, operand2NDDataastropy:-like instance: If operand2 is None or not given it will perform the operation self / operand. If operand2 is given it will perform operand / operand2. If the method was called on a class rather than on the instance operand2 must be given.
propagate_uncertaintiesbool or None, optional: If None the result will have no uncertainty. If False the result will have a copied version of the first operand that has an uncertainty. If True the result will have a correctly propagated uncertainty from the uncertainties of the operands but this assumes that the uncertainties are NDUncertainty-like. Default is True.

Changed in version 1.2: This parameter must be given as keyword-parameter. Using it as positional parameter is deprecated. None was added as valid parameter value.
handle_maskcallable(), 'first_found' or None, optional: If None the result will have no mask. If 'first_found' the result will have a copied version of the first operand that has a mask). If it is a callable then the specified callable must create the results mask and if necessary provide a copy. Default is numpy.logical_or.

New in version 1.2.
handle_metacallable(), 'first_found' or None, optional: If None the result will have no meta. If 'first_found' the result will have a copied version of the first operand that has a (not empty) meta. If it is a callable then the specified callable must create the results meta and if necessary provide a copy. Default is None.

New in version 1.2.
compare_wcscallable(), 'first_found' or None, optional: If None the result will have no wcs and no comparison between the wcs of the operands is made. If 'first_found' the result will have a copied version of the first operand that has a wcs. If it is a callable then the specified callable must compare the wcs. The resulting wcs will be like if False was given otherwise it raises a ValueError if the comparison was not successful. Default is 'first_found'.

New in version 1.2.
uncertainty_correlationnumber or ndarray, optional: The correlation between the two operands is used for correct error propagation for correlated data as given in: https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulas Default is 0.

New in version 1.2.
kwargs: Any other parameter that should be passed to the callables used.

Returns:

resultNDDataastropy:-like: The resulting dataset

Notes

"first_found" can also be abbreviated with "ff".

classmethod max(**kwargs)[source]#

classmethod mean(**kwargs)[source]#

classmethod min(**kwargs)[source]#

classmethod multiply(operand, operand2=None, **kwargs)[source]#

Performs multiplication by evaluating self * operand.

Parameters:

operand, operand2NDDataastropy:-like instance: If operand2 is None or not given it will perform the operation self * operand. If operand2 is given it will perform operand * operand2. If the method was called on a class rather than on the instance operand2 must be given.
propagate_uncertaintiesbool or None, optional: If None the result will have no uncertainty. If False the result will have a copied version of the first operand that has an uncertainty. If True the result will have a correctly propagated uncertainty from the uncertainties of the operands but this assumes that the uncertainties are NDUncertainty-like. Default is True.

Changed in version 1.2: This parameter must be given as keyword-parameter. Using it as positional parameter is deprecated. None was added as valid parameter value.
handle_maskcallable(), 'first_found' or None, optional: If None the result will have no mask. If 'first_found' the result will have a copied version of the first operand that has a mask). If it is a callable then the specified callable must create the results mask and if necessary provide a copy. Default is numpy.logical_or.

New in version 1.2.
handle_metacallable(), 'first_found' or None, optional: If None the result will have no meta. If 'first_found' the result will have a copied version of the first operand that has a (not empty) meta. If it is a callable then the specified callable must create the results meta and if necessary provide a copy. Default is None.

New in version 1.2.
compare_wcscallable(), 'first_found' or None, optional: If None the result will have no wcs and no comparison between the wcs of the operands is made. If 'first_found' the result will have a copied version of the first operand that has a wcs. If it is a callable then the specified callable must compare the wcs. The resulting wcs will be like if False was given otherwise it raises a ValueError if the comparison was not successful. Default is 'first_found'.

New in version 1.2.
uncertainty_correlationnumber or ndarray, optional: The correlation between the two operands is used for correct error propagation for correlated data as given in: https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulas Default is 0.

New in version 1.2.
kwargs: Any other parameter that should be passed to the callables used.

Returns:

resultNDDataastropy:-like: The resulting dataset

Notes

"first_found" can also be abbreviated with "ff".

classmethod subtract(operand, operand2=None, **kwargs)[source]#

Performs subtraction by evaluating self - operand.

Parameters:

operand, operand2NDDataastropy:-like instance: If operand2 is None or not given it will perform the operation self - operand. If operand2 is given it will perform operand - operand2. If the method was called on a class rather than on the instance operand2 must be given.
propagate_uncertaintiesbool or None, optional: If None the result will have no uncertainty. If False the result will have a copied version of the first operand that has an uncertainty. If True the result will have a correctly propagated uncertainty from the uncertainties of the operands but this assumes that the uncertainties are NDUncertainty-like. Default is True.

Changed in version 1.2: This parameter must be given as keyword-parameter. Using it as positional parameter is deprecated. None was added as valid parameter value.
handle_maskcallable(), 'first_found' or None, optional: If None the result will have no mask. If 'first_found' the result will have a copied version of the first operand that has a mask). If it is a callable then the specified callable must create the results mask and if necessary provide a copy. Default is numpy.logical_or.

New in version 1.2.
handle_metacallable(), 'first_found' or None, optional: If None the result will have no meta. If 'first_found' the result will have a copied version of the first operand that has a (not empty) meta. If it is a callable then the specified callable must create the results meta and if necessary provide a copy. Default is None.

New in version 1.2.
compare_wcscallable(), 'first_found' or None, optional: If None the result will have no wcs and no comparison between the wcs of the operands is made. If 'first_found' the result will have a copied version of the first operand that has a wcs. If it is a callable then the specified callable must compare the wcs. The resulting wcs will be like if False was given otherwise it raises a ValueError if the comparison was not successful. Default is 'first_found'.

New in version 1.2.
uncertainty_correlationnumber or ndarray, optional: The correlation between the two operands is used for correct error propagation for correlated data as given in: https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulas Default is 0.

New in version 1.2.
kwargs: Any other parameter that should be passed to the callables used.

Returns:

resultNDDataastropy:-like: The resulting dataset

Notes

"first_found" can also be abbreviated with "ff".

classmethod sum(**kwargs)[source]#