Source code for FITS_tools.match_images

import numpy as np
import as fits
import astropy.wcs as wcs
from .strip_headers import flatten_header
from .cube_regrid import regrid_cube_hdu
from .load_header import load_header

__all__ = ['match_fits', 'match_fits_cubes', 'project_to_header']

[docs]def project_to_header(fitsfile, header, use_montage=True, quiet=True, **kwargs): """ Light wrapper of montage with hcongrid as a backup kwargs will be passed to `~hcongrid.hcongrid` if ``use_montage==False`` Parameters ---------- fitsfile : string a FITS file name header : `` A fits Header instance with valid WCS to project to use_montage : bool Use montage or hcongrid (based on `~scipy.ndimage.interpolation.map_coordinates`) quiet : bool Silence Montage's output Returns ------- image : `~numpy.ndarray` image projected to header's coordinates """ try: import montage montageOK=True except ImportError: montageOK=False try: from hcongrid import hcongrid hcongridOK=True except ImportError: hcongridOK=False import tempfile if montageOK and use_montage: temp_headerfile = tempfile.NamedTemporaryFile() header.toTxtFile( outfile = tempfile.NamedTemporaryFile() montage.wrappers.reproject(fitsfile,,, exact_size=True, silent_cleanup=quiet) image = fits.getdata( outfile.close() temp_headerfile.close() elif hcongridOK: # only works for 2D images image = hcongrid(fits.getdata(fitsfile).squeeze(), flatten_header(fits.getheader(fitsfile)), header, **kwargs) return image
[docs]def match_fits(fitsfile1, fitsfile2, header=None, sigma_cut=False, return_header=False, **kwargs): """ Project one FITS file into another's coordinates. If ``sigma_cut`` is used, will try to find only regions that are significant in both images using the standard deviation, masking out other signal Parameters ---------- fitsfile1 : str Offset fits file name fitsfile2 : str Reference fits file name header : `` Optional - can pass a header that both input images will be projected to match sigma_cut : bool or int Perform a sigma-cut on the returned images at this level Returns ------- image1,image2,[header] : `~numpy.ndarray`, `~numpy.ndarray`, `` Two images projected into the same space, and optionally the header used to project them See Also -------- match_fits_cubes : this function, but for cubes """ if header is None: header = flatten_header(fits.getheader(fitsfile2)) image2 = fits.getdata(fitsfile2).squeeze() else: # project image 1 to input header coordinates image2 = project_to_header(fitsfile2, header, **kwargs) # project image 1 to image 2 coordinates image1_projected = project_to_header(fitsfile1, header, **kwargs) if image1_projected.shape != image2.shape: raise ValueError("Failed to reproject images to same shape.") if sigma_cut: corr_image1 = image1_projected*(image1_projected > image1_projected.std()*sigma_cut) corr_image2 = image2*(image2 > image2.std()*sigma_cut) OK = (corr_image1==corr_image1)*(corr_image2==corr_image2) if (corr_image1[OK]*corr_image2[OK]).sum() == 0: print("Could not use sigma_cut of %f because it excluded all valid data" % sigma_cut) corr_image1 = image1_projected corr_image2 = image2 else: corr_image1 = image1_projected corr_image2 = image2 returns = corr_image1, corr_image2 if return_header: returns = returns + (header,) return returns
[docs]def match_fits_cubes(fitsfile1, fitsfile2, header=None, sigma_cut=False, return_header=False, smooth=False, **kwargs): """ Project one FITS file representing a data cube into another's coordinates. Parameters ---------- fitsfile1 : str FITS file name to reproject fitsfile2 : str Reference FITS file name. If ``header`` is specified, smooth : bool Smooth the HDUs to match resolution? Kernel size is determined using `cube_regrid.smoothing_kernel_size` .. WARNING:: Smoothing is done in 3D to be maximally general. This can be exceedingly slow! header : `` Optional - can pass a header that both input images will be projected to match Raises ------ ValueError : Will raise an error if the axes are not consistent with a FITS cube, i.e. two spatial and one spectral axis. Returns ------- image1,image2,[header] : `~numpy.ndarray`, `~numpy.ndarray`, `` Two images projected into the same space, and optionally the header used to project them See Also -------- cube_regrid.regrid_fits_cube : regrid a single cube This function performs a similar purpose and does the underlying work for `match_fits_cubes`, but it has a different call specification and returns an HDU """ header1 = load_header(fitsfile1) header2 = load_header(fitsfile2) wcs1 = wcs.WCS(header1) wcs2 = wcs.WCS(header2) if wcs1.wcs.naxis != 3: raise ValueError("First input file is not a cube.") if wcs2.wcs.naxis != 3: raise ValueError("Second input file is not a cube.") if header is not None: wcs3 = wcs.WCS(header) if wcs3.wcs.naxis != 3: raise ValueError("Input header is not a cube header.") if wcs2 != wcs3: image2 = regrid_cube_hdu([0], outheader=header, smooth=smooth, **kwargs).data header2 = header else: image2 = fits.getdata(fitsfile2) image1 = regrid_cube_hdu([0], outheader=header2, smooth=smooth, **kwargs).data if return_header: return image1,image2,header2 else: return image1,image2