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
initial_model.disk.I_0.value,
initial_model.disk.h.value,
args.modelPsfFWHM)
plt.suptitle(r'Initial model: $I_e = %.3f$, $r_e = %.3f$, $n = %.3f$, $I_0 = %.3f$, $h = %.3f$, $FWHM = %.2f$' % tmp)
gs.tight_layout(fig, rect=[0, 0, 1, 0.97])
pdf.savefig()
logger.info('Starting first pass modeling.')
t1 = time.time()
first_pass_models = decomp.fitSpectra(step=100, box_radius=50, initial_model=initial_model, mode='LM')
first_pass_params = np.array([m.getParams() for m in first_pass_models], dtype=first_pass_models[0].dtype)
first_pass_lambdas = decomp.wl[::100]
logger.info('Done first pass modeling, time: %.2f' % (time.time() - t1))
logger.info('Smoothing parameters with polynomial of degree %d.' % args.paramDegree)
smoothed_models = smooth_models(first_pass_models, decomp.wl, degree=args.paramDegree)
smoothed_params = np.array([m.getParams() for m in smoothed_models], dtype=smoothed_models[0].dtype)
logger.info('Starting second pass modeling...')
t1 = time.time()
fitted_models = decomp.fitSpectra(step=1, box_radius=0, initial_model=smoothed_models, mode='LM')
fitted_params = np.array([m.getParams() for m in fitted_models], dtype=fitted_models[0].dtype)
logger.info('Done second pass modeling, time: %.2f' % (time.time() - t1))
logger.info('Computing model spectra.')
fitted_bulge_ifs, fitted_disk_ifs = decomp.getModelSpectra(fitted_models)
fitted_bulge_ifs_nopsf, fitted_disk_ifs_nopsf = decomp.getModelSpectra(fitted_models, use_PSF=False)
logger.info('Average fit results:')
print_params = ('I_e', 'r_e', 'n', 'PA_b', 'ell_b', 'I_0', 'h', 'PA_d', 'ell_d', )
for p in fitted_params.dtype.names:
initial_model.disk.I_0.value,
initial_model.disk.h.value,
args.modelPsfFWHM)
plt.suptitle(r'Initial model: $I_e = %.3f$, $r_e = %.3f$, $n = %.3f$, $I_0 = %.3f$, $h = %.3f$, $FWHM = %.2f$' % tmp)
gs.tight_layout(fig, rect=[0, 0, 1, 0.97])
pdf.savefig()
logger.info('Starting first pass modeling.')
t1 = time.time()
first_pass_models = decomp.fitSpectra(step=100, box_radius=50, initial_model=initial_model, mode='NM')
first_pass_params = np.array([m.getParams() for m in first_pass_models], dtype=first_pass_models[0].dtype)
first_pass_lambdas = decomp.wl[::100]
logger.info('Done first pass modeling, time: %.2f' % (time.time() - t1))
logger.info('Smoothing parameters.')
smoothed_models = smooth_models(first_pass_models, decomp.wl, degree=1)
smoothed_params = np.array([m.getParams() for m in smoothed_models], dtype=smoothed_models[0].dtype)
logger.info('Starting second pass modeling...')
t1 = time.time()
fitted_models = decomp.fitSpectra(step=1, box_radius=0, initial_model=smoothed_models, mode='LM', insist=True)
fitted_params = np.array([m.getParams() for m in fitted_models], dtype=fitted_models[0].dtype)
logger.info('Done second pass modeling, time: %.2f' % (time.time() - t1))
logger.info('Computing model spectra.')
fitted_bulge_spectra, fitted_disk_spectra = decomp.getModelSpectra(fitted_models)
logger.info('Average fit results:')
original_params = np.array([m.getParams() for m in original_models], dtype=original_models[0].dtype)
print_params = ('I_e', 'r_e', 'n', 'PA_b', 'ell_b', 'I_0', 'h', 'PA_d', 'ell_d', )
for p in fitted_params.dtype.names:
