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- extract_array

# extract_array

`astropy.nddata.``extract_array`(array_large, shape, position, mode=u'partial', fill_value=nan, return_position=False)

  Extract a smaller array of the given shape and position from a larger array.

  Parameters:

  array_large : `ndarray`

  > The array from which to extract the small array.

  shape : tuple or int

  > The shape of the extracted array (for 1D arrays, this can be an `int`). See the `mode` keyword for additional details.

  position : tuple of numbers or number

  > The position of the small array’s center with respect to the large array. The pixel coordinates should be in the same order as the array shape. Integer positions are at the pixel centers (for 1D arrays, this can be a number).

  mode : {‘partial’, ‘trim’, ‘strict’}, optional

  > The mode used for extracting the small array. For the `'partial'` and `'trim'` modes, a partial overlap of the small array and the large array is sufficient. For the `'strict'` mode, the small array has to be fully contained within the large array, otherwise an `PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `NoOverlapError`. In `'partial'` mode, positions in the small array that do not overlap with the large array will be filled with `fill_value`. In `'trim'` mode only the overlapping elements are returned, thus the resulting small array may be smaller than the requested `shape`.

  fill_value : number, optional

  > If `mode='partial'`, the value to fill pixels in the extracted small array that do not overlap with the input `array_large`. `fill_value` must have the same `dtype` as the `array_large` array.

  return_position : boolean, optional

  > If `True`, return the coordinates of `position` in the coordinate system of the returned array.

  Returns:

  array_small : `ndarray`

  > The extracted array.

  new_position : tuple

  > If `return_position` is true, this tuple will contain the coordinates of the input `position` in the coordinate system of `array_small`. Note that for partially overlapping arrays, `new_position` might actually be outside of the `array_small`; `array_small[new_position]` might give wrong results if any element in `new_position` is negative.

  Examples

  We consider a large array with the shape 11x10, from which we extract a small array of shape 3x5:

      >>> import numpy as np
      >>> from astropy.nddata.utils import extract_array
      >>> large_array = np.arange(110).reshape((11, 10))
      >>> extract_array(large_array, (3, 5), (7, 7))
      array([[65, 66, 67, 68, 69],
             [75, 76, 77, 78, 79],
             [85, 86, 87, 88, 89]])