def eval_matrix(config, n_taylor=10):
filters, filter_lambdas = load_filters(config)
print 'Calculating Taylor coefficients matrix for n_taylor = %i.' % n_taylor
bt = StarlightBase(config['base_file'], config['base_path'])
z = np.arange(config['z_ini'], config['z_fin'] + config['z_delta'],
config['z_delta'])
m, a, f = taylor_matrix_ssp(config['taylor_file'], bt, n_taylor, z,
filters)
config['filters'] = ','.join(sorted(config['filters'].keys()))
f.attrs.update(config)
f.close()
def test_matrix_time():
print 'Test time to calculate all the matrix over a range of redshifts of (0, 7, 0.001)'
bt = StarlightBase(
'/Users/william/BasesDir/Base.bc03.Padova1994.chab.All.hdf5',
'Base.bc03.Padova1994.chab.All')
z = np.arange(0.001, 1, 0.001)
for n_taylor in range(1, 10):
t0 = time.time()
aux_fname = '/Users/william/tmp_out/m_av.hdf5'
os.unlink(aux_fname)
m, a, f = taylor_matrix_ssp(aux_fname, bt, n_taylor, z, filters)
print 'n_taylor, t:', n_taylor, time.time() - t0
f.close()
def test_magnitudes():
bt = StarlightBase(
'/Users/william/BasesDir/Base.bc03.Padova1994.chab.All.hdf5',
'Base.bc03.Padova1994.chab.All')
i_met = 0
i_z = 0
z = np.array([0.5, 1.0, 1.5])
# Generate random parameters to test:
z_age_limit = cosmo.age(0.01).to('yr').value
t0_young = np.log10(6e6) + np.random.rand() * (np.log10(5e9) -
np.log10(6e6))
tau_young = np.log10(.001) + np.random.rand() * (np.log10(.001) -
np.log10(1000))
t0_old = np.log10(1e9) + np.random.rand() * (np.log10(z_age_limit) -
np.log10(1e9))
tau_old = np.log10(.001) + np.random.rand() * (np.log10(.001) -
np.log10(1000))
frac_young = np.random.rand()
a_v = 2 * np.random.rand()
print 'Parameters: ', z_age_limit, t0_young, tau_young, t0_old, tau_old, frac_young, a_v
# Create the CSP with it
csp_model = n_component(bt.ageBase)
csp_model.add_exp(t0_young, tau_young, frac_young)
csp_model.add_exp(t0_old, tau_old, 1 - frac_young)
# Calculate the analytic integral
spec = np.sum(bt.f_ssp[i_met] * csp_model.get_sfh()[:, np.newaxis],
axis=0) # Base spectra [??units??]
spec *= 10**(-0.4 * (Cardelli_RedLaw(bt.l_ssp) * a_v))
mags_analytic = mag_in_z(bt.l_ssp, spec, z[i_z], filters)
# Calculate the Taylor Approximation
for n_taylor in range(1, 10):
t0 = time.time()
m, a, f = taylor_matrix_ssp(bt, n_taylor, z, filters)
mags_taylor = mag_in_z_taylor(csp_model.get_sfh(), a_v, i_z, i_met, m,
a)
print 'n_taylor, t, max |delta_m| = ', n_taylor, time.time(
) - t0, np.max(np.abs(mags_taylor - mags_analytic))
def __init__(self,
template_magnitudes,
filters,
z,
base_file,
base_path,
taylor_file=None,
magerr=1e-14,
is_single=False):
# Template magnitudes
self.template_magnitudes = template_magnitudes
# self.chi2_constant = np.average(template_magnitudes) # This will be summed to chi2, avoiding to get them ~ 0.
self.filters = filters
self.z = z
self.z_age_limit = cosmo.age(z).to('yr').value
# Base
self.bt = StarlightBase(base_file, base_path)
## Metallicity
aux = np.log10(self.bt.metBase)
aux2 = (aux[1:] - aux[0:-1]) / 2
self.met_min = aux - np.append(aux2[0], aux2)
self.met_max = aux + np.append(aux2, aux2[-1])
self.met_low = min(self.met_min)
self.met_upp = max(self.met_max)
# Extinction Law
self.q = Cardelli_RedLaw(self.bt.l_ssp)
# If Taylor expansion:
if taylor_file:
self.m, self.a, self.tz, self.int_f = load_taylor(config, filters)
# Magnitude error
self.magerr = magerr
file_fit = h5py.File('%s/%s' % (save_dir, config['fit_bpz_outfile']), 'r')
# Doing this copy due bug: https://github.com/h5py/h5py/issues/480
params = np.copy(file_fit.get('/model_parameters').value)
n_z, n_t, n_models, n_parameters = params.shape
file_fit_processed = h5py.File('%s/%s' % (save_dir, config['fit_bpz_post']), 'w')
post_parameters_data = np.empty(
dtype=np.dtype([('at_mass', np.float), ('at_flux', np.float), ('metallicity', np.float), ('a_v', np.float),
('m2l', np.float)]),
shape=(n_z, n_t, n_models))
# m2l_spec = np.zeros((n_z, n_t, n_models, len(config["m2l_lambdas"])))
bt = StarlightBase(config['base_file'], config['base_path'])
bt.ageBase[bt.ageBase == 0] = 1e5 # Avoid NaN on log10(age)
i_norm = np.argmin((bt.l_ssp - config['lambda_norm']) ** 2)
# i_met = int(np.argwhere(bt.metBase == metallicity))
l2m_norm_ssp = np.empty((len(bt.metBase), len(bt.ageBase)))
for i_met in range(len(bt.metBase)):
for i_age in range(len(bt.ageBase)):
l2m_norm_ssp[i_met, i_age] = bt.f_ssp[i_met, i_age, i_norm]
# from pystarlight, eval l2m on filter
filter_m2l = readfilterfile(config['filter_m2l'], ).data
aux_l = np.arange(filter_m2l['lambda'].min(), filter_m2l['lambda'].max())
filter_m2l_new = np.empty(len(aux_l), dtype=filter_m2l.dtype)
filter_m2l_new['lambda'] = aux_l
filter_m2l_new['transm'] = np.interp(aux_l, filter_m2l['lambda'], filter_m2l['transm'])
filter_m2l = filter_m2l_new
# For the A_V Arrow:
e_c1 = Cardelli_RedLaw([4580])[0] - Cardelli_RedLaw([7680])[0]
e_c2 = Cardelli_RedLaw([3650])[0] - Cardelli_RedLaw([6440])[0]
# M/L calculations
## Filter
filter_m2l = readfilterfile(config['filter_m2l'], norm=False).data
aux_l = np.arange(filter_m2l['lambda'].min(), filter_m2l['lambda'].max())
filter_m2l_new = np.empty(len(aux_l), dtype=filter_m2l.dtype)
filter_m2l_new['lambda'] = aux_l
filter_m2l_new['transm'] = np.interp(aux_l, filter_m2l['lambda'], filter_m2l['transm'])
filter_m2l = filter_m2l_new
LsunFilter = calc_LsunFilter(filter_m2l)
## Base
bt = StarlightBase(config['base_file'], config['base_path'])
m2l_ssp = np.empty((len(bt.metBase), len(bt.ageBase)))
i_norm = np.argmin((bt.l_ssp - config['lambda_norm']) ** 2)
l2m_norm_ssp = np.empty((len(bt.metBase), len(bt.ageBase)))
for i_met in range(len(bt.metBase)):
for i_age in range(len(bt.ageBase)):
aux = np.interp(filter_m2l['lambda'], bt.l_ssp, bt.f_ssp[i_met, i_age])
m2l_ssp[i_met, i_age] = bt.Mstars[i_met, i_age] * LsunFilter / np.trapz(aux * filter_m2l['transm'],
filter_m2l['lambda'])
l2m_norm_ssp[i_met, i_age] = bt.f_ssp[i_met, i_age, i_norm]
# Open BPZ magnitudes
templates = np.loadtxt(config['bpz_library'], np.str)
aux = templates[:, 0] if len(templates.shape) > 1 else templates
templates_data = [
np.loadtxt('%s/%s' % (config['bpz_library_dir'], t), dtype=np.dtype([('lambda', np.float), ('flux', np.float)]))
# guess = lib_guess()
# for key in guess.keys():
# lib_param[key] = guess[key]
# print lib_param
#
# spec = lib.get_model_spectrum(0)[0]
# # plt.clf()
# cut = np.bitwise_and(spec['wl'] > 3000, spec['wl'] < 9000)
# plt.plot(spec['wl'][cut], spec['flux'][cut])
# # raw_input('next...')
f = FilterSet('/Users/william/doutorado/photo_filters/Alhambra_24.hdf5')
f.load('Alhambra_24', 1)
n_filters = len(f.filter_wls)
aux_base = StarlightBase(base_file, base_path)
base_maxage = np.max(aux_base.ageBase)
min_z_age = cosmo.age(0.001).to('yr').value
if base_maxage > min_z_age:
base_maxage = min_z_age
allow_overwrite_bpz = True
allow_overwrite_lib = True
bpz_lib_file = '/Users/william/tmp_out/pzT_parametric_noelines.hdf5'
parametric_lib = '/Users/william/tmp_out/zT_weights_noelines.hdf5'
if allow_overwrite_bpz:
try:
os.unlink(bpz_lib_file)
except OSError:
pass
################################################################################
################################################################################
##########
########## Population model setup
##########
################################################################################
logger.setLevel(-1)
args = parse_args()
logger.info("Loading base %s", path.basename(args.baseFile))
t1 = time.clock()
base = StarlightBase(args.baseFile, args.baseDir)
l_ssp = np.arange(3650.0, 6850.0, 2.0)
f_ssp = base.f_sspResam(l_ssp)
logger.info("Took %.2f seconds to read the base (%d files)" % (time.clock() - t1, base.sspfile.size))
wl_norm_window = (l_ssp < 5680.0) & (l_ssp > 5590.0)
################################################################################
##########
########## Morphology model setup
##########
################################################################################
logger.info("Creating original B-D model.")
norm_model = get_model(args.trueModel, with_default=True)
norm_params = np.array(norm_model.getParams(), dtype=norm_model.dtype)
logger.info("Original model at normalization window:\n%s\n" % str(norm_model))
norm_h = norm_model.disk.h.value
plt.suptitle(r'Modelo original (sem convoluir com PSF)')
gs.tight_layout(fig, rect=[0, 0, 1, 0.95])
plt.savefig('plots/tese/simulation_initmodel.pdf')
################################################################################
##########
########## Plot original population model
##########
################################################################################
logger.debug('Plotting original population model.')
#index_norm = find_nearest_index(l_ssp, 5635.0)
logger.info('Loading base %s', path.basename(args.baseFile))
t1 = time.clock()
base = StarlightBase(args.baseFile, args.baseDir)
wl = np.arange(3650.0, 6850.0, 2.0)
f_ssp = base.f_sspResam(wl)
logger.info('Took %.2f seconds to read the base (%d files)' % (time.clock() - t1, base.sspfile.size))
wl_norm_window = (wl < 5680.0) & (wl > 5590.0)
t0 = base.ageBase.max()
r = np.arange(30)
tau = tau_r(args.tau0, args.dtau_dr, r)
print tau
bulge_sfh_tau0 = get_synth_sfh(t0, args.tau0, base.ageBase)
bulge_sfh_tau1 = get_synth_sfh(t0, 4e9, base.ageBase)
bulge_sfh_tau2 = get_synth_sfh(t0, 8e9, base.ageBase)
disk_sfh_tauInf = get_synth_sfh(t0, 1e12, base.ageBase)
def main_CSP(argv):
from pystarlight.util.base import StarlightBase
from pystarlight.util.redenninglaws import calc_redlaw
base_select = 'Padova2000.salp'
base_file = '/Users/lacerda/LOCAL/data/Base.bc03.h5'
base = StarlightBase(base_file, base_select, hdf5=True)
M = 1e10
tauV = np.linspace(0, 1, 10)
t0 = np.linspace(0, 20e9, 11)
tau = np.logspace(9, 11, 12)
q = calc_redlaw(base.l_ssp, R_V=3.1, redlaw='CCM')
t = base.ageBase.max() - base.ageBase[::-1]
steps = (t[1:] - t[:-1]) / 2.0
t_edges = np.empty((len(t) + 1))
t_edges[1:-1] = t[1:] - steps
t_edges[0] = t[0] - steps[0]
if t_edges[0] < 0:
t_edges[0] = 0.
t_edges[-1] = t[-1] + steps[-1]
if t_edges[-1] > t[-1]:
t_edges[-1] = t[-1]
# shape_model = (len(t0), len(tau), len(tauV))
M__t0tautauVl = np.ndarray((len(t0), len(tau), len(tauV), len(base.l_ssp)),
dtype='float')
dM__t0taut = np.ndarray((len(t0), len(tau), base.nAges), dtype='float')
for it0, _t0 in enumerate(t0):
for itau, _tau in enumerate(tau):
dM = []
for i in range(len(t)):
l, r = t_edges[i], t_edges[i + 1]
dM.append(deltaM_integral(M, t.max(), _t0, _tau, l, r))
# print 't0:', _t0,' tau:', _tau, ' dM:', dM[-1]
dM = np.array(dM)
dM__t0taut[it0, itau, :] = dM
for itauV, _tV in enumerate(tauV):
norm_lambda = 5635.0
spec_window = np.bitwise_and(
np.greater(base.l_ssp, norm_lambda - 45.0),
np.less(base.l_ssp, norm_lambda + 45.0))
# spec_window = (base.l_ssp > norm_lambda - 45.0) & (base.l_ssp < norm_lambda + 45.0)
spec = np.tensordot(
base.f_ssp.sum(axis=0) * np.exp(_tV * q), dM[::-1], (0, 0))
spec_norm = np.median(spec[spec_window])
M__t0tautauVl[it0, itau, itauV, :] = spec / spec_norm
# Save FITS
from astropy.io import fits
hdu = fits.HDUList()
header = fits.Header()
header['NL'] = len(base.l_ssp)
header['NBURSTINI'] = len(t0)
header['NBURSTLENGTH'] = len(tau)
header['NTAUV'] = len(tauV)
hdu.append(fits.PrimaryHDU(header=header))
hdu.append(fits.ImageHDU(data=t, name='ages'))
hdu.append(fits.ImageHDU(data=t_edges, name='ages_edges'))
hdu.append(fits.ImageHDU(data=t0, name='burst_ini'))
hdu.append(fits.ImageHDU(data=tau, name='burst_length'))
hdu.append(fits.ImageHDU(data=tauV, name='tau_V'))
tmp = fits.ImageHDU(data=dM__t0taut, name='dM')
tmp.header['COMMENT'] = 'Dimension [BURSTINI,BURSTLENGTH,AGES]'
hdu.append(tmp)
tmp = fits.ImageHDU(data=M__t0tautauVl, name='F_CSP')
tmp.header['COMMENT'] = 'Dimension [BURSTINI,BURSTLENGTH,TAUV,LAMBDA]'
hdu.append(tmp)
hdu.append(fits.ImageHDU(data=base.l_ssp, name='L_CSP'))
hdu.writeto('CSPModels.fits')
