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([])
