/usr/lib/python2.7/dist-packages/photutils/psf/matching/fourier.py is in python-photutils 0.4-1.
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"""
Tools for matching PSFs using Fourier methods.
"""
from __future__ import division
import numpy as np
from numpy.fft import fft2, ifft2, fftshift, ifftshift
__all__ = ['resize_psf', 'create_matching_kernel']
def resize_psf(psf, input_pixel_scale, output_pixel_scale, order=3):
"""
Resize a PSF using spline interpolation of the requested order.
Parameters
----------
psf : 2D `~numpy.ndarray`
The 2D data array of the PSF.
input_pixel_scale : float
The pixel scale of the input ``psf``. The units must
match ``output_pixel_scale``.
output_pixel_scale : float
The pixel scale of the output ``psf``. The units must
match ``input_pixel_scale``.
order : float, optional
The order of the spline interpolation (0-5). The default is 3.
Returns
-------
result : 2D `~numpy.ndarray`
The resampled/interpolated 2D data array.
"""
from scipy.ndimage import zoom
ratio = input_pixel_scale / output_pixel_scale
return zoom(psf, ratio, order=order) / ratio**2
def create_matching_kernel(source_psf, target_psf, window=None):
"""
Create a kernel to match 2D point spread functions (PSF) using the
ratio of Fourier transforms.
Parameters
----------
source_psf : 2D `~numpy.ndarray`
The source PSF. The source PSF should have higher resolution
(i.e. narrower) than the target PSF. ``source_psf`` and
``target_psf`` must have the same shape and pixel scale.
target_psf : 2D `~numpy.ndarray`
The target PSF. The target PSF should have lower resolution
(i.e. broader) than the source PSF. ``source_psf`` and
``target_psf`` must have the same shape and pixel scale.
window : callable, optional
The window (or taper) function or callable class instance used
to remove high frequency noise from the PSF matching kernel.
Some examples include:
* `~photutils.psf.matching.HanningWindow`
* `~photutils.psf.matching.TukeyWindow`
* `~photutils.psf.matching.CosineBellWindow`
* `~photutils.psf.matching.SplitCosineBellWindow`
* `~photutils.psf.matching.TopHatWindow`
For more information on window functions and example usage, see
:ref:`psf_matching`.
Returns
-------
kernel : 2D `~numpy.ndarray`
The matching kernel to go from ``source_psf`` to ``target_psf``.
The output matching kernel is normalized such that it sums to 1.
"""
source_psf = np.asanyarray(source_psf)
target_psf = np.asanyarray(target_psf)
if source_psf.shape != target_psf.shape:
raise ValueError('source_psf and target_psf must have the same shape '
'(i.e. registered with the same pixel scale).')
# ensure input PSFs are normalized
source_psf /= source_psf.sum()
target_psf /= target_psf.sum()
source_otf = fftshift(fft2(source_psf))
target_otf = fftshift(fft2(target_psf))
ratio = target_otf / source_otf
# apply a window function in frequency space
if window is not None:
ratio *= window(target_psf.shape)
kernel = np.real(fftshift((ifft2(ifftshift(ratio)))))
return kernel / kernel.sum()
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