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])