def optimize_amp(self):
light_ind_model = []
source_ind_model = []
xl, yl = pylens.getDeflections(self.lens_models, (self.X, self.Y))
lpix = []
spix = []
for light in self.light_sb_models:
light.amp = 1.
lpix.append(light.pixeval(self.X, self.Y))
for source in self.source_sb_models:
source.amp = 1.
spix.append(source.pixeval(xl, yl))
chi2sum = 0.
for band in self.bands:
modlist = []
for l in lpix:
modlist.append(
(convolve.convolve(l, self.convol_matrix[band], False)[0] /
self.err[band]).ravel()[self.mask_r])
for s in spix:
modlist.append(
(convolve.convolve(s, self.convol_matrix[band], False)[0] /
self.err[band]).ravel()[self.mask_r])
modarr = np.array(modlist).T
if np.isnan(modarr).any() or not np.isfinite(modarr).any():
amps = np.ones(self.nlight + self.nsource)
chi = 1e300
else:
amps, chi = nnls(modarr, (self.sci[band] /
self.err[band]).ravel()[self.mask_r])
chi2sum += chi**2
i = 0
for light, mags in zip(self.light_sb_models, self.light_mags):
if amps[i] > 0.:
light.amp = amps[i]
mags[band] = light.Mag(self.zp[band])
else:
mags[band] = 99.
i += 1
for source, mags in zip(self.source_sb_models, self.source_mags):
if amps[i] > 0.:
source.amp = amps[i]
mags[band] = source.Mag(self.zp[band])
else:
mags[band] = 99.
i += 1
self.logp = -0.5 * chi2sum
return chi2sum
def save(self, outname, config, make_rgb=True):
fitsname = outname + '.fits'
rgbname = outname + '_rgb.png'
hdr = pyfits.Header()
hdr['logp'] = self.logp
light_ind_model = []
source_ind_model = []
xl, yl = pylens.getDeflections(self.lens_models, (self.X, self.Y))
n = 0
for light, sed, mags in zip(self.light_sb_models,
self.light_sed_models, self.light_mags):
light_ind_dic = {}
if light.__class__.__name__ == 'PointSource':
for band in self.bands:
hdr['%s.mag_%s' % (light.name, band)] = mags[band]
scale = sed.scale(band, self.reference_band)
light_ind_dic[band] = scale * light.pixeval(
self.X, self.Y, band)
else:
lpix = light.pixeval(self.X, self.Y)
for band in self.bands:
hdr['%s.mag_%s' % (light.name, band)] = mags[band]
scale = sed.scale(band, self.reference_band)
light_ind_dic[band] = scale * convolve.convolve(
lpix, self.convol_matrix[band], False)[0]
light_ind_model.append(light_ind_dic)
n += 1
for source, sed, mags in zip(self.source_sb_models,
self.source_sed_models, self.source_mags):
source_ind_dic = {}
spix = source.pixeval(xl, yl)
for band in self.bands:
hdr['%s.mag_%s' % (source.name, band)] = mags[band]
scale = sed.scale(band, self.reference_band)
source_ind_here = scale * convolve.convolve(
spix, self.convol_matrix[band], False)[0]
source_ind_dic[band] = source_ind_here
source_ind_model.append(source_ind_dic)
n += 1
phdu = pyfits.PrimaryHDU(header=hdr)
hdulist = pyfits.HDUList([phdu])
for light, light_ind_dic in zip(self.light_sb_models, light_ind_model):
for band in self.bands:
hdu_here = pyfits.ImageHDU(data=light_ind_dic[band])
hdu_here.header['EXTNAME'] = '%s_%s' % (light.name, band)
hdulist.append(hdu_here)
for source, source_ind_dic in zip(self.source_sb_models,
source_ind_model):
for band in self.bands:
hdu_here = pyfits.ImageHDU(data=source_ind_dic[band])
hdu_here.header['EXTNAME'] = '%s_%s' % (source.name, band)
hdulist.append(hdu_here)
hdulist.writeto(fitsname, overwrite=True)
if make_rgb:
# makes model rgb
sci_list = []
model_list = []
for band in self.bands:
sci_list.append(self.sci[band])
comp_list = []
for light in light_ind_model:
comp_list.append(light[band])
for source in source_ind_model:
comp_list.append(source[band])
model_list.append(comp_list)
pyplz_rgbtools.make_full_rgb(sci_list,
model_list,
outname=rgbname,
scheme=config['rgbscheme'],
cuts=config['rgbcuts'],
scales=config['rgbscales'])
def optimize_amp(self):
light_ind_model = []
source_ind_model = []
xl, yl = pylens.getDeflections(self.lens_models, (self.X, self.Y))
modlist = []
for light, sed, mags in zip(self.light_sb_models,
self.light_sed_models, self.light_mags):
lmodel = 0. * self.scistack
light.amp = 1.
if light.__class__.__name__ == 'PointSource':
for i in range(self.nbands):
scale = sed.scale(self.bands[i], self.reference_band)
lmodel[i * self.ny:(i + 1) * self.ny, :] = light.pixeval(
self.X, self.Y, self.bands[i])
mags[self.bands[i]] = -2.5 * np.log10(scale) + self.zp[
self.bands[i]] - self.zp[self.reference_band]
else:
lpix = light.pixeval(self.X, self.Y)
for i in range(self.nbands):
scale = sed.scale(self.bands[i], self.reference_band)
lmodel[i * self.ny:(i + 1) *
self.ny, :] = scale * convolve.convolve(
lpix, self.convol_matrix[self.bands[i]],
False)[0]
mags[self.bands[i]] = -2.5 * np.log10(scale) + self.zp[
self.bands[i]] - self.zp[self.reference_band]
modlist.append((lmodel / self.errstack).ravel()[self.maskstack_r])
for source, sed, mags in zip(self.source_sb_models,
self.source_sed_models, self.source_mags):
smodel = 0. * self.scistack
source.amp = 1.
spix = source.pixeval(xl, yl)
for i in range(self.nbands):
scale = sed.scale(self.bands[i], self.reference_band)
smodel[i * self.ny:(i + 1) *
self.ny, :] = scale * convolve.convolve(
spix, self.convol_matrix[self.bands[i]], False)[0]
mags[self.bands[i]] = -2.5 * np.log10(scale) + self.zp[
self.bands[i]] - self.zp[self.reference_band]
modlist.append((smodel / self.errstack).ravel()[self.maskstack_r])
modarr = np.array(modlist).T
if np.isnan(modarr).any() or not np.isfinite(modarr).any():
amps = np.ones(self.nlight + self.nsource)
chi = 1e300
else:
amps, chi = nnls(modarr, (self.scistack /
self.errstack).ravel()[self.maskstack_r])
i = 0
for light, sed, mags in zip(self.light_sb_models,
self.light_sed_models, self.light_mags):
if amps[i] > 0.:
light.amp *= amps[i]
mainmag = light.Mag(self.zp[self.reference_band])
for band in self.bands:
mags[band] += mainmag
if sed.__class__.__name__ == 'SPS':
mags['mstar'] = 10.**(
-2. / 5. * (mainmag - sed.mags[self.reference_band]))
else:
for band in self.bands:
mags[band] = 99.
if sed.__class__.__name__ == 'SPS':
mags['mstar'] = 0.
i += 1
for source, mags in zip(self.source_sb_models, self.source_mags):
if amps[i] > 0.:
source.amp *= amps[i]
mainmag = source.Mag(self.zp[self.reference_band])
for band in self.bands:
mags[band] = mags[band] + mainmag
else:
for band in self.bands:
mags[band] = 99.
i += 1
self.logp = -0.5 * chi**2
return chi**2