reproject.reproject_interp(input_data, output_projection, shape_out=None, hdu_in=0, order='bilinear', independent_celestial_slices=False, output_array=None, return_footprint=True)[source]

Reproject data to a new projection using interpolation (this is typically the fastest way to reproject an image).


The input data to reproject. This can be:

  • The name of a FITS file

  • An HDUList object

  • An image HDU object such as a PrimaryHDU, ImageHDU, or CompImageHDU instance

  • A tuple where the first element is a ndarray and the second element is either a WCS or a Header object

  • An NDData object from which the .data and .wcs attributes will be used as the input data.

output_projectionWCS or Header

The output projection, which can be either a WCS or a Header instance.

shape_outtuple, optional

If output_projection is a WCS instance, the shape of the output data should be specified separately.

hdu_inint or str, optional

If input_data is a FITS file or an HDUList instance, specifies the HDU to use.

orderint or str, optional

The order of the interpolation. This can be any of the following strings:

  • ‘nearest-neighbor’

  • ‘bilinear’

  • ‘biquadratic’

  • ‘bicubic’

or an integer. A value of 0 indicates nearest neighbor interpolation.

output_arrayNone or ndarray

An array in which to store the reprojected data. This can be any numpy array including a memory map, which may be helpful when dealing with extremely large files.


Whether to return the footprint in addition to the output array.


The reprojected array


Footprint of the input array in the output array. Values of 0 indicate no coverage or valid values in the input image, while values of 1 indicate valid values.