def decomp(cube, sampleId, args): galaxyId = califa_id_from_cube(cube) c = DecompContainer() if not args.overwrite: logger.info('Checking if the decomposition is already done for %s ...' % galaxyId) try: c.loadHDF5(args.db, sampleId, galaxyId) logger.warn('Previous data found, skipping decomposition.') return c except Exception as e: print e logger.info('No previous data found, continuing decomposition.') logger.info('Starting fit for %s...' % galaxyId) dec = CALIFADecomposer(cube, grating=args.grating, nproc=args.nproc) npix = dec.K.qMask.sum() dec.minNPix = npix / 2 logger.info('Minimum number of pixels for fitting: %d' % dec.minNPix) dec.useEstimatedVariance = args.estVar dec.setSynthPSF(FWHM=args.psfFWHM, beta=args.psfBeta, size=args.psfSize) logger.warn('Computing initial model using DE algorithm (takes a LOT of time).') t1 = time.time() if not path.exists(args.maskFile): logger.error('Mask file %s not found.' % args.maskFile) exit(1) logger.info('Using mask file %s.' % args.maskFile) masked_wl = load_line_mask(args.maskFile, dec.wl) l1 = find_nearest_index(dec.wl, 4500.0) l2 = dec.Nl_obs cache_file = cube + '.initmodel' if not path.exists(cache_file): logger.info('Creating gray image for initial model.') gray_image, gray_noise, _ = dec.getSpectraSlice(l1, l2, masked_wl) else: gray_image = None gray_noise = None initial_model = bd_initial_model(gray_image, gray_noise, dec.PSF, quiet=False, nproc=args.nproc, cache_model_file=cache_file) logger.debug('Refined initial model:\n%s\n' % initial_model) logger.warn('Initial model time: %.2f\n' % (time.time() - t1)) t1 = time.time() c.zones = np.ma.array(dec.K.qZones, mask=dec.K.qZones < 0) c.initialParams = initial_model.getParams() c.attrs = dict(PSF_FWHM=args.psfFWHM, PSF_beta=args.psfBeta, PSF_size=args.psfSize, box_step=args.boxStep, box_radius=args.boxRadius, orig_file=cube, mask_file=args.maskFile, object_name=dec.K.galaxyName, flux_unit=dec.flux_unit, distance_Mpc=dec.K.distance_Mpc, x0=dec.K.x0, y0=dec.K.y0, target_vd=dec.targetVd, wl_FWHM=dec.wlFWHM) models = dec.fitSpectra(step=50*args.boxStep, box_radius=25*args.boxStep, initial_model=initial_model, mode=args.fitAlgorithm, masked_wl=masked_wl) c.firstPassParams = np.array([m.getParams() for m in models], dtype=models[0].dtype) logger.info('Done first pass modeling, time: %.2f' % (time.time() - t1)) t1 = time.time() logger.info('Smoothing parameters.') models = smooth_models(models, dec.wl, degree=1) logger.info('Starting second pass modeling...') models = dec.fitSpectra(step=args.boxStep, box_radius=args.boxRadius, initial_model=models, mode=args.fitAlgorithm, insist=False, masked_wl=masked_wl) logger.info('Done second pass modeling, time: %.2f' % (time.time() - t1)) t1 = time.time() logger.info('Computing model spectra...') c.total.f_obs = dec.flux[::args.boxStep] c.total.f_err = dec.error[::args.boxStep] c.total.f_flag = dec.flags[::args.boxStep] c.total.mask = dec.K.qMask c.total.wl = dec.wl[::args.boxStep] c.bulge.f_obs, c.disk.f_obs = dec.getModelSpectra(models, args.nproc) c.bulge.mask = dec.K.qMask c.bulge.wl = dec.wl[::args.boxStep] c.disk.mask = dec.K.qMask c.disk.wl = dec.wl[::args.boxStep] # TODO: better array and dtype handling. c.fitParams = np.array([m.getParams() for m in models], dtype=models[0].dtype) flag_bad_fit = c.fitParams['flag'][:, np.newaxis, np.newaxis] > 0.0 c.updateErrorsFlags(flag_bad_fit) c.updateIntegratedSpec() logger.info('Saving qbick planes...') fname = path.join(args.zoneFileDir, '%s_%s-planes.fits' % (galaxyId, sampleId)) save_qbick_images(c.total, dec, fname, overwrite=args.overwrite) fname = path.join(args.zoneFileDir, '%s_%s-bulge-planes.fits' % (galaxyId, sampleId)) save_qbick_images(c.bulge, dec, fname, overwrite=args.overwrite) fname = path.join(args.zoneFileDir, '%s_%s-disk-planes.fits' % (galaxyId, sampleId)) save_qbick_images(c.disk, dec, fname, overwrite=args.overwrite) logger.info('Saving to storage...') c.writeHDF5(args.db, sampleId, galaxyId, args.overwrite) logger.info('Storage complete, time: %.2f' % (time.time() - t1)) return c
################################################################################ logger.info('Beginning decomposition.') decomp = IFSDecomposer() logger.info('Model using PSF FWHM = %.2f ", beta = %.2f.' % (args.modelPsfFWHM, args.modelPsfBeta)) decomp.setSynthPSF(FWHM=args.modelPsfFWHM, beta=args.modelPsfBeta, size=15) decomp.loadData(wl, full_ifs / flux_unit, full_ifs_noise / flux_unit, np.zeros_like(full_ifs, dtype='bool')) swll, swlu = 5590.0, 5680.0 sl1 = find_nearest_index(decomp.wl, swll) sl2 = find_nearest_index(decomp.wl, swlu) qSignal, qNoise, qWl = decomp.getSpectraSlice(sl1, sl2) logger.warn('Computing initial model (takes a LOT of time).') t1 = time.time() initial_model = bd_initial_model(qSignal, qNoise, decomp.PSF, quiet=False, cache_model_file=args.cacheModel) bulge_image, disk_image = create_model_images(initial_model, qSignal.shape, decomp.PSF) logger.warn('Initial model time: %.2f\n' % (time.time() - t1)) logger.debug('Plotting guessed initial model.') vmin = np.log10(qSignal.min()) vmax = np.log10(qSignal.max()) fig = plt.figure(figsize=(8, 6)) gs = plt.GridSpec(2, 3, height_ratios=[2.0, 3.0]) ax = plt.subplot(gs[0,0]) ax.imshow(np.log10(qSignal), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(r'Total') ax = plt.subplot(gs[0,1])
################################################################################ logger.info('Beginning decomposition.') decomp = IFSDecomposer() logger.info('Model using PSF FWHM = %.2f ".' % args.modelPsfFWHM) decomp.setSynthPSF(FWHM=args.modelPsfFWHM, size=9) decomp.loadData(l_ssp, full_spectra, full_noise, np.zeros_like(full_spectra, dtype='bool')) swll, swlu = 5590.0, 5680.0 sl1 = find_nearest_index(decomp.wl, swll) sl2 = find_nearest_index(decomp.wl, swlu) qSignal, qNoise, qWl = decomp.getSpectraSlice(sl1, sl2) logger.warn('Computing initial model (takes a LOT of time).') t1 = time.time() initial_model = bd_initial_model(qSignal, qNoise, decomp.PSF, quiet=False) bulge_image, disk_image = create_model_images(initial_model, qSignal.shape, decomp.PSF) logger.warn('Initial model time: %.2f\n' % (time.time() - t1)) logger.debug('Plotting guessed initial model.') vmin = np.log10(qSignal.min()) vmax = np.log10(qSignal.max()) fig = plt.figure(figsize=(8, 6)) gs = plt.GridSpec(2, 3, height_ratios=[2.0, 3.0]) ax = plt.subplot(gs[0,0]) ax.imshow(np.log10(model_image[index_norm]), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(r'Total') ax = plt.subplot(gs[0,1])