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- Astropy v5.2.1 »
- N-Dimensional Datasets (`astropy.nddata`) »
- support_nddata

# support_nddata

astropy.nddata.support_nddata(_func=None, accepts=<class 'astropy.nddata.nddata.NDData'>, repack=False, returns=None, keeps=None, **attribute_argument_mapping)

  Decorator to wrap functions that could accept an NDData instance with its properties passed as function arguments.

  Parameters:
    _func `python:callable()`, `python:None`, optional

      The function to decorate or `None` if used as factory. The first positional argument should be `data` and take a numpy array. It is possible to overwrite the name, see `attribute_argument_mapping` argument. Default is `None`.

    accepts class, optional

      The class or subclass of `NDData` that should be unpacked before calling the function. Default is `NDData`

    repack bool, optional

      Should be `True` if the return should be converted to the input class again after the wrapped function call. Default is `False`.

      Note

      Must be `True` if either one of `returns` or `keeps` is specified.

    returns python:iterable, `python:None`, optional

      An iterable containing strings which returned value should be set on the class. For example if a function returns data and mask, this should be `['data', 'mask']`. If `None` assume the function only returns one argument: `'data'`. Default is `None`.

      Note

      Must be `None` if `repack=False`.

    keeps iterable. `python:None`, optional

      An iterable containing strings that indicate which values should be copied from the original input to the returned class. If `None` assume that no attributes are copied. Default is `None`.

      Note

      Must be `None` if `repack=False`.

    attribute_argument_mapping

      Keyword parameters that optionally indicate which function argument should be interpreted as which attribute on the input. By default it assumes the function takes a `data` argument as first argument, but if the first argument is called `input` one should pass `support_nddata(..., data='input')` to the function.

  Returns:
    decorator_factory or decorated_function `python:callable()`

      If `_func=None` this returns a decorator, otherwise it returns the decorated `_func`.

  Notes

  If properties of `NDData` are set but have no corresponding function argument a Warning is shown.

  If a property is set of the `NDData` are set and an explicit argument is given, the explicitly given argument is used and a Warning is shown.

  The supported properties are:

  - `mask`

  - `unit`

  - `wcs`

  - `meta`

  - `uncertainty`

  - `flags`

  Examples

  This function takes a Numpy array for the data, and some WCS information with the `wcs` keyword argument:

      def downsample(data, wcs=None):
          # downsample data and optionally WCS here
          pass

  However, you might have an NDData instance that has the `wcs` property set and you would like to be able to call the function with `downsample(my_nddata)` and have the WCS information, if present, automatically be passed to the `wcs` keyword argument.

  This decorator can be used to make this possible:

      @support_nddata
      def downsample(data, wcs=None):
          # downsample data and optionally WCS here
          pass

  This function can now either be called as before, specifying the data and WCS separately, or an NDData instance can be passed to the `data` argument.