我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用astropy.io.fits.ImageHDU()。
def save_fibmodel(self): ''' Save the fibers to fits file with two extensions The first is 3-d for trace, wavelength and fiber_to_fiber The second is which fibers are good and not dead ''' try: self.fibers[0].fibmodel except: self.log.warning('Trying to save fibermodel but none exist.') return None ylims = np.linspace(-1.*self.fsize, self.fsize, self.fibmodel_nbins) fibmodel = np.zeros((len(self.fibers), self.fibmodel_nbins, self.D)) for i,fiber in enumerate(self.fibers): fibmodel[i,:,:] = fiber.fibmodel s = fits.PrimaryHDU(np.array(fibmodel,dtype='float32')) t = fits.ImageHDU(np.array(ylims,dtype='float32')) hdu = fits.HDUList([s,t]) fn = op.join(self.path, 'fibermodel_%s_%s_%s_%s.fits' %(self.specid, self.ifuslot, self.ifuid, self.amp)) self.write_to_fits(hdu, fn)
def writeSVD(self, svdoutputfits='ZAP_SVD.fits'): """Write the SVD to an individual fits file.""" check_file_exists(svdoutputfits) header = fits.Header() header['ZAPvers'] = (__version__, 'ZAP version') header['ZAPzlvl'] = (self.run_zlevel, 'ZAP zero level correction') header['ZAPclean'] = (self.run_clean, 'ZAP NaN cleaning performed for calculation') header['ZAPcftyp'] = (self._cftype, 'ZAP continuum filter type') header['ZAPcfwid'] = (self._cfwidth, 'ZAP continuum filter size') header['ZAPmask'] = (self.maskfile, 'ZAP mask used to remove sources') nseg = len(self.pranges) header['ZAPnseg'] = (nseg, 'Number of segments used for ZAP SVD') hdu = fits.HDUList([fits.PrimaryHDU(self.zlsky)]) for i in range(len(self.pranges)): hdu.append(fits.ImageHDU(self.especeval[i][0])) # write for later use hdu.writeto(svdoutputfits) logger.info('SVD file saved to %s', svdoutputfits)
def _open(self, cache=False, **kwargs): if not _fits: load_lib() hdulist = _fits.open(self.request.get_file(), cache=cache, **kwargs) self._index = [] for n, hdu in zip(range(len(hdulist)), hdulist): if (isinstance(hdu, _fits.ImageHDU) or isinstance(hdu, _fits.PrimaryHDU)): # Ignore (primary) header units with no data (use '.size' # rather than '.data' to avoid actually loading the image): if hdu.size > 0: self._index.append(n) self._hdulist = hdulist
def to_fits(self): primary_hdu = fits.PrimaryHDU() image_hdu = fits.ImageHDU(self.data, self.header) hdulist = fits.HDUList([primary_hdu, image_hdu]) return hdulist
def create_mock_fits(): x = np.ones((5, 5)) prihdu = fits.PrimaryHDU(x) # Single extension FITS img = fits.ImageHDU(data=x) singlehdu = fits.HDUList([prihdu, img]) singlehdu.writeto('image.fits', clobber=True)
def load_data(self): hdulist = fits.open(self.filename) print("MuseCube: Loading the cube fluxes and variances...") # import pdb; pdb.set_trace() self.cube = hdulist[1].data self.stat = hdulist[2].data # for ivar weighting ; consider creating it in init ; takes long # self.flux_over_ivar = self.cube / self.stat self.header_1 = hdulist[1].header # Necesito el header para crear una buena copia del white. self.header_0 = hdulist[0].header if self.filename_white is None: print("MuseCube: No white image given, creating one.") w_data = self.create_white(save=False) w_header_0 = copy.deepcopy(self.header_0) w_header_1 = copy.deepcopy(self.header_1) # These loops remove the third dimension from the header's keywords. This is neccesary in order to # create the white image and preserve the cube astrometry for i in w_header_0.keys(): if '3' in i: del w_header_0[i] for i in w_header_1.keys(): if '3' in i: del w_header_1[i] # prepare the header hdu = fits.HDUList() hdu_0 = fits.PrimaryHDU(header=w_header_0) hdu_1 = fits.ImageHDU(data=w_data, header=w_header_1) hdu.append(hdu_0) hdu.append(hdu_1) hdu.writeto('new_white.fits', clobber=True) self.filename_white = 'new_white.fits' print("MuseCube: `new_white.fits` image saved to disk.")
def spec_to_redmonster_format(spec, fitsname, n_id=None, mag=None): """ Function used to create a spectrum in the REDMONSTER software format :param spec: XSpectrum1D object :param mag: List containing 2 elements, the first is the keyword in the header that will contain the magnitud saved in the second element :param fitsname: Name of the fitsfile that will be created :return: """ from scipy import interpolate wave = spec.wavelength.value wave_log = np.log10(wave) n = len(wave) spec.wavelength = wave_log * u.angstrom new_wave_log = np.arange(wave_log[1], wave_log[n - 2], 0.0001) spec_rebined = spec.rebin(new_wv=new_wave_log * u.angstrom) flux = spec_rebined.flux.value f = interpolate.interp1d(wave_log, spec.sig.value) sig = f(new_wave_log) inv_sig = 1. / np.array(sig) ** 2 inv_sig = np.where(np.isinf(inv_sig), 0, inv_sig) inv_sig = np.where(np.isnan(inv_sig), 0, inv_sig) hdu1 = fits.PrimaryHDU([flux]) hdu2 = fits.ImageHDU([inv_sig]) hdu1.header['COEFF0'] = new_wave_log[0] hdu1.header['COEFF1'] = new_wave_log[1] - new_wave_log[0] if n_id != None: hdu1.header['ID'] = n_id if mag != None: hdu1.header[mag[0]] = mag[1] hdulist_new = fits.HDUList([hdu1, hdu2]) hdulist_new.writeto(fitsname, clobber=True)
def save(self, image_list=[], spec_list=[]): ''' Save the entire amplifier include the list of fibers. This property is not used often as "amp*.pkl" is large and typically the fibers can be loaded and the other amplifier properties quickly recalculated. ''' self.log.info('Saving images/properties to %s' %self.path) fn = op.join(self.path, 'multi_%s_%s_%s_%s.fits' %(self.specid, self.ifuslot, self.ifuid, self.amp)) fits_list = [] for i,image in enumerate(image_list): if i==0: fits_list.append(fits.PrimaryHDU(np.array(getattr(self, image), dtype='float32'))) else: fits_list.append(fits.ImageHDU(np.array(getattr(self, image), dtype='float32'))) fits_list[-1].header['EXTNAME'] = image for i, spec in enumerate(spec_list): try: s = np.array([getattr(fiber, spec) for fiber in self.fibers], dtype='float32') fits_list.append(fits.ImageHDU(s)) fits_list[-1].header['EXTNAME'] = spec except AttributeError: self.log.warning('Attribute %s does not exist to save' %spec) if fits_list: fits_list[0] = self.write_header(fits_list[0]) hdu = fits.HDUList(fits_list) self.write_to_fits(hdu, fn)
def subtract_sky_from_sci(sci, sky_list, sky_model_list, wave, use_sci=False): sci.log.info('Subtracting sky from %s' %sci.basename) if use_sci: fn = op.join(sci.path, 'sky_model.txt') if True:#not op.exists(fn): F = fits.open('panacea/leoI_20131209.fits') G = get_fits(sci) get_master_sky(G['wavelength'].data, G['spectrum'].data - F[0].data*sci.exptime, G['fiber_to_fiber'].data, sci.path, sci.exptime) wave, sky_model = np.loadtxt(fn, unpack=True) else: weight, sorted_ind = get_interp_weights(sci, sky_list) arr_list = [] for i,w in enumerate(weight): if w > 0.0: arr_list.append(w * sky_model_list[sorted_ind[i]]) sky_model = np.sum(arr_list, axis=(0,)) SCI = get_fits(sci) sky_subtracted = np.zeros(SCI['spectrum'].data.shape) for i, spec in enumerate(SCI['spectrum'].data): sky_subtracted[i,:] = (spec - np.interp(SCI['wavelength'].data[i,:], wave, sky_model*sci.exptime) * SCI['fiber_to_fiber'].data[i,:]) s = fits.ImageHDU(sky_subtracted) erase = [] for i,S in enumerate(SCI): if S.header['EXTNAME'] == 'sky_subtracted': erase.append(i) for i in sorted(erase,reverse=True): del SCI[i] SCI.append(s) SCI[-1].header['EXTNAME'] = 'sky_subtracted' write_fits(SCI) return SCI['wavelength'].data, sky_subtracted, wave, sky_model
def load_data(self): hdulist = fits.open(self.filename) print("MuseCube: Loading the cube fluxes and variances...") # import pdb; pdb.set_trace() self.cube = ma.MaskedArray(hdulist[1].data) self.stat = ma.MaskedArray(hdulist[2].data) print("MuseCube: Defining master masks (this may take a while but it is for the greater good).") # masking self.mask_init = np.isnan(self.cube) | np.isnan(self.stat) self.cube.mask = self.mask_init self.stat.mask = self.mask_init # for ivar weighting ; consider creating it in init ; takes long # self.flux_over_ivar = self.cube / self.stat self.header_1 = hdulist[1].header # Necesito el header para crear una buena copia del white. self.header_0 = hdulist[0].header if self.filename_white is None: print("MuseCube: No white image given, creating one.") w_data = copy.deepcopy(self.create_white(save=False).data) w_header_0 = copy.deepcopy(self.header_0) w_header_1 = copy.deepcopy(self.header_1) # These loops remove the third dimension from the header's keywords. This is neccesary in order to # create the white image and preserve the cube astrometry for i in w_header_0.keys(): if '3' in i: del w_header_0[i] for i in w_header_1.keys(): if '3' in i: del w_header_1[i] # prepare the header hdu = fits.HDUList() hdu_0 = fits.PrimaryHDU(header=w_header_0) hdu_1 = fits.ImageHDU(data=w_data, header=w_header_1) hdu.append(hdu_0) hdu.append(hdu_1) hdu.writeto('new_white.fits', clobber=True) self.filename_white = 'new_white.fits' print("MuseCube: `new_white.fits` image saved to disk.")
