extract_array#
- astropy.nddata.extract_array(array_large, shape, position, mode='partial', fill_value=nan, return_position=False)[source]#
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
int
ortuple
thereof The shape of the extracted array (for 1D arrays, this can be an
int
). See themode
keyword for additional details.- positionnumber or
tuple
thereof 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 anPartialOverlapError
is raised. In all modes, non-overlapping arrays will raise aNoOverlapError
. In'partial'
mode, positions in the small array that do not overlap with the large array will be filled withfill_value
. In'trim'
mode only the overlapping elements are returned, thus the resulting small array may be smaller than the requestedshape
.- fill_valuenumber, optional
If
mode='partial'
, the value to fill pixels in the extracted small array that do not overlap with the inputarray_large
.fill_value
will be changed to have the samedtype
as thearray_large
array, with one exception. Ifarray_large
has integer type andfill_value
isnp.nan
, then aValueError
will be raised.- return_positionbool, optional
If
True
, return the coordinates ofposition
in the coordinate system of the returned array.
- array_large
- Returns:
- array_small
ndarray
The extracted array.
- new_position
tuple
If
return_position
is true, this tuple will contain the coordinates of the inputposition
in the coordinate system ofarray_small
. Note that for partially overlapping arrays,new_position
might actually be outside of thearray_small
;array_small[new_position]
might give wrong results if any element innew_position
is negative.
- array_small
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]])