transformations.py 文件源码

def zoom_fits(fitsfile, scalefactor, preserve_bad_pixels=True, **kwargs):

    """
    This function is used to zoom in on a FITS image by interpolating using scipy.ndimage.interpolation.zoom.
    It takes the following arguments:
    :param fitsfile: the FITS file name
    :param scalefactor: the zoom factor along all axes
    :param preserve_bad_pixels: try to set NaN pixels to NaN in the zoomed image. Otherwise, bad pixels will be set to
    zero.
    :param kwargs:
    :return:
    """

    # Get the data array and the header of the FITS file
    arr = pyfits.getdata(fitsfile)
    h = pyfits.getheader(fitsfile)

    h['CRPIX1'] = (h['CRPIX1']-1)*scalefactor + scalefactor/2. + 0.5
    h['CRPIX2'] = (h['CRPIX2']-1)*scalefactor + scalefactor/2. + 0.5
    if 'CD1_1' in h:
        for ii in (1,2):
            for jj in (1,2):
                k = "CD%i_%i" % (ii,jj)
                if k in h: # allow for CD1_1 but not CD1_2
                    h[k] = h[k]/scalefactor
    elif 'CDELT1' in h:
        h['CDELT1'] = h['CDELT1']/scalefactor
        h['CDELT2'] = h['CDELT2']/scalefactor

    bad_pixels = np.isnan(arr) + np.isinf(arr)

    arr[bad_pixels] = 0

    upscaled = scipy.ndimage.zoom(arr,scalefactor,**kwargs)

    if preserve_bad_pixels:
        bp_up = scipy.ndimage.zoom(bad_pixels,scalefactor,mode='constant',cval=np.nan,order=0)
        upscaled[bp_up] = np.nan

    up_hdu = pyfits.PrimaryHDU(data=upscaled, header=h)

    return up_hdu

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