Source code for reproject.adaptive.high_level

# Licensed under a 3-clause BSD style license - see LICENSE.rst

from ..utils import parse_input_data, parse_output_projection
from .core import _reproject_adaptive_2d

__all__ = ['reproject_adaptive']

ORDER = {}
ORDER['nearest-neighbor'] = 0
ORDER['bilinear'] = 1

[docs]def reproject_adaptive(input_data, output_projection, shape_out=None, hdu_in=0, order='bilinear', return_footprint=True): """ Reproject celestial slices from an 2d array from one WCS to another using the DeForest (2004) adaptive resampling algorithm. Parameters ---------- input_data The input data to reproject. This can be: * The name of a FITS file * An `` object * An image HDU object such as a ``, ``, or `` instance * A tuple where the first element is a `~numpy.ndarray` and the second element is either a `~astropy.wcs.WCS` or a `` object * An `~astropy.nddata.NDData` object from which the ``.data`` and ``.wcs`` attributes will be used as the input data. output_projection : `~astropy.wcs.WCS` or `` The output projection, which can be either a `~astropy.wcs.WCS` or a `` instance. shape_out : tuple, optional If ``output_projection`` is a `~astropy.wcs.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 `` 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' or an integer. A value of ``0`` indicates nearest neighbor interpolation. return_footprint : bool Whether to return the footprint in addition to the output array. Returns ------- array_new : `~numpy.ndarray` The reprojected array footprint : `~numpy.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. """ # TODO: add support for output_array array_in, wcs_in = parse_input_data(input_data, hdu_in=hdu_in) wcs_out, shape_out = parse_output_projection(output_projection, shape_out=shape_out) if isinstance(order, str): order = ORDER[order] return _reproject_adaptive_2d(array_in, wcs_in, wcs_out, shape_out, order=order, return_footprint=return_footprint)