def linear_model(model_0, a, wl):
p_0 = np.array(model_0.getParams(), dtype=model_0.dtype)
wl_0 = p_0['wl']
p = np.zeros(len(wl), dtype=p_0.dtype)
p['wl'] = wl
for n in p_0.dtype.names:
p[n] = line(wl_0, p_0[n], a[n], wl)
return [BDModel.fromParamVector(k) for k in p]
def loadModel(self, model_file):
if not path.exists(model_file):
logger.warn('Initial model file not found (%s), guessing one. ' % model_file)
x0 = (self.flux.shape[1] / 2.0) + 1.0
y0 = (self.flux.shape[0] / 2.0) + 1.0
pa, ell = ellipse_params(self.flux, x0, y0)
r = distance(self.flux.shape, x0, y0, pa, ell)
r = np.ma.array(r, mask=self.flux.mask)
hlr = r50(self.flux, r)
I_e = self.flux.max() * 0.1
r_e = 0.5 * hlr
n = 2.0
I_0 = self.flux.max() * 0.1
h = 1.0 * hlr
model = BDModel()
model.wl = 5635.0
model.x0.setValue(x0)
model.x0.setLimitsRel(10, 10)
model.y0.setValue(y0)
model.y0.setLimitsRel(10, 10)
model.disk.I_0.setValue(I_0)
model.disk.I_0.setLimits(0.0, 10.0 * I_0)
model.disk.h.setValue(h)
model.disk.h.setLimits(0.0, 5.0 * hlr)
model.disk.PA.setValue(pa)
model.disk.PA.setLimits(0.0, 180.0)
model.disk.ell.setValue(ell)
model.disk.ell.setLimits(0.0, 1.0)
model.bulge.I_e.setValue(I_e)
model.bulge.I_e.setLimits(1e-33, 3.0 * I_e)
model.bulge.r_e.setValue(r_e)
model.bulge.r_e.setLimits(1e-33, 2.5 * r_e)
model.bulge.n.setValue(n, vmin=1.0, vmax=5.0)
model.bulge.PA.setValue(pa)
model.bulge.PA.setLimits(0.0, 180.0)
model.bulge.ell.setValue(ell)
model.bulge.ell.setLimits(0.0, 1.0)
return model
else:
return BDModel.load(model_file)
def get_model(model_file=None, with_default=False):
if model_file is not None:
try:
logger.info('Loading model from %s.' % model_file)
model = BDModel.load(model_file)
model.wl = 5635.0
return model
except:
raise Exception('Could not read model file %s.' % model_file)
elif not with_default:
logger.info('Using default model.')
return default_model()
else:
raise Exception('No model_file and with_default=False, what do you want?')
t_1p = c.firstPassParams
l_obs_1p = t_1p['wl'][:]
flag_bad_1p = (t_1p['flag'] > 0.0) | (t_1p['chi2'] > 20.0)
box_radius = c.attrs['box_radius']
target_vd = c.attrs['target_vd']
PSF_FWHM = c.attrs['PSF_FWHM']
galaxyName = c.attrs['object_name']
flux_unit = c.attrs['flux_unit']
flag_bad = (t['flag'] > 0.0) | (t['chi2'] > 20.0)
flag_ok = ~flag_bad
fit_l_obs = t['wl']
masked_wl = load_line_mask('data/starlight/Mask.mE', fit_l_obs)
initial_params = np.array(c.initialParams, dtype=t.dtype)
initial_model = BDModel.fromParamVector(initial_params)
psf = moffat_psf(PSF_FWHM, c.attrs['PSF_beta'], size=c.attrs['PSF_size'])
bulge_model_im, disk_model_im = create_model_images(initial_model, c.total.f_obs.shape[1:], psf)
bulge_model_im *= flux_unit
disk_model_im *= flux_unit
l1 = find_nearest_index(fit_l_obs, 5635.0)
x0 = t['x0'][0]
y0 = t['y0'][0]
total_im = c.total.f_obs[l1] * flux_unit
model_im = disk_model_im + bulge_model_im
residual_im = (total_im - model_im) / total_im
colnames = [
'I_e',
disk_ifs = g.disk_ifs[...]
disk_ifs_nopsf = g.disk_ifs_nopsf[...]
full_ifs = np.ma.array(g.full_ifs[...], mask=masked)
full_ifs_noise = np.ma.array(g.full_ifs_noise[...], mask=masked)
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([])
