# reproject_interp¶

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

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

Parameters: input_data : str or HDUList or PrimaryHDU or ImageHDU or tuple 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 output_projection : WCS or Header The output projection, which can be either a WCS or a Header instance. shape_out : tuple, optional If output_projection is a WCS instance, the shape of the output data should be specified separately. hdu_in : int or str, optional If input_data is a FITS file or an HDUList instance, specifies the HDU to use. order : int 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. independent_celestial_slices : bool, optional This can be set to True for n-dimensional input in the following case (all conditions have to be fulfilled): The number of pixels in each non-celestial dimension is the same between the input and target header. The WCS coordinates along the non-celestial dimensions are the same between the input and target WCS. The celestial WCS component is independent from other WCS coordinates. In this special case, we can make things a little faster by reprojecting each celestial slice independently using the same transformation. array_new : ndarray The reprojected array footprint : ndarray 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.