def onFit(self, event): wx.BusyCursor() model = event.model fit_model, converged, chi2 = fit_image(self.flux, self.noise, model, self.psf, mode='NM') logger.debug('Fit converged: %s, chi2 = %f' % (converged, chi2)) self.controlPanel.resetParams(fit_model, reset_plot=False) self.updatePlots(fit_model) self.controlPanel.Enable()
def onSaveButton(self, event): self.originalModel = deepcopy(self.model) with open(self.modelFile, 'w') as f: logger.debug('Saving model file %s.' % self.modelFile) try: f.write(str(self.originalModel)) except: logger.warn('Could not write cache model file %s' % self.modelFile)
def load_line_mask(line_file, wl): import atpy import pystarlight.io # @UnusedImport t = atpy.Table(line_file, type='starlight_mask') masked_wl = np.zeros(wl.shape, dtype='bool') for i in xrange(len(t)): l_low, l_upp, line_w, line_name = t[i] if line_w > 0.0: continue logger.debug('Masking region: %s' % line_name) masked_wl |= (wl > l_low) & (wl < l_upp) return masked_wl
wl = g.wl[...] true_psf = np.ma.array(g.psf[...]) tau_image = g.tau_image[...] age_base = g.age_base[...] flux_unit = g.full_ifs.attrs.fluxUnit true_psf_FWHM = g.full_ifs.attrs.psfFWHM norm_params = g.full_ifs.attrs.model norm_model = BDModel.fromParamVector(norm_params) norm_x0 = norm_params['x0'] norm_y0 = norm_params['y0'] db.close() logger.debug('Plotting original model.') #index_norm = find_nearest_index(l_ssp, 5635.0) vmin = np.log10(full_image.min()) vmax = np.log10(full_image.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(full_image), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(r'Total') ax = plt.subplot(gs[0,1]) ax.imshow(np.log10(bulge_image), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([])
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
help='PSF beta parameter for Moffat profile. If not set, use Gaussian.') parser.add_argument('--psf-size', dest='psfSize', type=int, default=15, help='PSF size, in pixels. Must be an odd number.') parser.add_argument('--overwrite', dest='overwrite', action='store_true', help='Overwrite data.') parser.add_argument('--nproc', dest='nproc', type=int, default=None, help='Number of processors to use.') return parser.parse_args() ################################################################################ ################################################################################ if __name__ =='__main__': args = parse_args() if args.verbose: logger.setLevel(-1) logger.debug('Verbose output enabled.') sample = load_sample(args.sample) sampleId = path.basename(args.sample) for gal in sample: cube = gal['cube'] try: c = decomp(cube, sampleId, args) except Exception as e: logger.error('Error decomposing cube %s' % cube) logger.error('Exception: %s.' % str(e)) logger.warn('Skipping to next galaxy.')
disk_ifs = disk_spec[..., np.newaxis, np.newaxis] * (flux_unit * disk_image) full_ifs = bulge_ifs + disk_ifs full_ifs_noise = full_ifs * noise # FIXME: How to add gaussian noise to spectra? logger.info("Adding gaussian noise to IFS.") tmp_noise = np.zeros(ifsshape) for i in xrange(ifsshape[0]): for j in xrange(ifsshape[1]): for k in xrange(ifsshape[2]): if flagged[j, k]: continue tmp_noise[i, j, k] = np.random.normal(0.0, full_ifs_noise.data[i, j, k]) full_ifs += tmp_noise logger.debug("Plotting original model.") # index_norm = find_nearest_index(l_ssp, 5635.0) vmin = np.log10(full_image.min()) vmax = np.log10(full_image.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(full_image), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(r"Total") ax = plt.subplot(gs[0, 1]) ax.imshow(np.log10(bulge_image), vmin=vmin, vmax=vmax) ax.set_xticks([]) ax.set_yticks([])