def create_obs_galaxies(IDs, **kwargs):
"""
Create a list of galaxy object from the observation
Iterate through IDs and will pass the kwargs to the
bagpipes.galaxy
INPUT
======
IDs (an array of IDs) : A list of IDs
kwargs : arguments to be passed to the
bagpipes.galaxy
OUTPUT
======
a list of galaxy object from Bagpipes
"""
gal_list = []
for idx in IDs:
galaxy = pipes.galaxy(ID=idx, **kwargs)
gal_list.append(galaxy)
return gal_list
def fitmodel(ID, saveplot=False):
filt_list = np.loadtxt("filters/goodss_filt_list.txt", dtype="str")
galaxy = pipes.galaxy(ID,
load_goodss,
spectrum_exists=False,
filt_list=filt_list)
exp = {}
exp["age"] = (0.1, 15.)
exp["tau"] = (0.3, 12.)
exp["massformed"] = (2., 15.)
exp["metallicity"] = (0., 2.5)
dust = {}
dust["type"] = "Cardelli"
dust["Av"] = (0., 2.)
fit_instructions = {}
fit_instructions["redshift"] = (0., 10.)
fit_instructions["exponential"] = exp
fit_instructions["dust"] = dust
fit = pipes.fit(galaxy, fit_instructions)
fit.fit(verbose=False)
if saveplot == True:
fig = fit.plot_spectrum_posterior(
save=True, show=False) #####doesnt work, unclear why
fig = fit.plot_sfh_posterior(save=True, show=False)
fig = fit.plot_corner(save=True, show=False)
else:
fig = fit.plot_spectrum_posterior(
save=False, show=True) #####doesnt work, unclear why
fig = fit.plot_sfh_posterior(save=False, show=True)
fig = fit.plot_corner(save=False, show=True)
lognormal["massformed"] = (5.0, 12.5)
lognormal["metallicity"] = (0.0, 2.5)
dust = {}
dust["type"] = "Calzetti"
dust["Av"] = (0.0, 2.0)
nebular = {}
nebular["logU"] = (-4.0,-2.0)
chi_squ_vals = {}
for redshift, filename in zip(redshifts, datafiles):
# Get the galaxy object and a priori model compenents dictionary.
galaxy = pipes.galaxy(filename, load_xshooter, photometry_exists=False)
# Calculate redshift constraints.
z_low, z_high = redshift - 0.001, redshift + 0.001
try:
f = open("pipes/posterior/exponential_burst_r4/"+filename+".h5")
f.close()
fit_instructions = {
"redshift" : (z_low, z_high), # z varies tight_layout around z_obs.
"t_bc" : (0.013, 0.021), # Constraints from Murray 2011.
"veldisp" : (50.0, 450.0), # Constrained by Faber-Jackson. TODO: Lookup Minkowski 1962!
"exponential1" : exponential1, # Add the exp SFH component.
"exponential2" : exponential2, # Add the burst component.
"dust" : dust,
"nebular" : nebular
}
def main(segid_to_test):
print("Running BAGPIPES on galaxy SegID:", segid_to_test)
galaxy = pipes.galaxy(str(segid_to_test),
load_data,
photometry_exists=False)
galaxy.plot()
dblplaw = {}
dblplaw["tau"] = (0., 15.)
dblplaw["alpha"] = (0.01, 1000.)
dblplaw["beta"] = (0.01, 1000.)
dblplaw["alpha_prior"] = "log_10"
dblplaw["beta_prior"] = "log_10"
dblplaw["massformed"] = (1., 15.)
dblplaw["metallicity"] = (0.1, 2.)
dblplaw["metallicity_prior"] = "log_10"
nebular = {}
nebular["logU"] = -3.
dust = {}
dust["type"] = "CF00"
dust["eta"] = 2.
dust["Av"] = (0., 2.0)
dust["n"] = (0.3, 2.5)
dust["n_prior"] = "Gaussian"
dust["n_prior_mu"] = 0.7
dust["n_prior_sigma"] = 0.3
fit_instructions = {}
fit_instructions["redshift"] = (0.1, 2.5)
fit_instructions["t_bc"] = 0.01
#fit_instructions["redshift_prior"] = "Gaussian"
#fit_instructions["redshift_prior_mu"] = 0.5
#fit_instructions["redshift_prior_sigma"] = 0.2
fit_instructions["dblplaw"] = dblplaw
fit_instructions["nebular"] = nebular
fit_instructions["dust"] = dust
fit_instructions["veldisp"] = (1., 1000.) #km/s
fit_instructions["veldisp_prior"] = "log_10"
calib = {}
calib["type"] = "polynomial_bayesian"
calib["0"] = (0.5, 1.5)
# Zero order is centred on 1,
# at which point there is no change to the spectrum.
calib["0_prior"] = "Gaussian"
calib["0_prior_mu"] = 1.0
calib["0_prior_sigma"] = 0.25
calib["1"] = (-0.5, 0.5) # Subsequent orders are centred on zero.
calib["1_prior"] = "Gaussian"
calib["1_prior_mu"] = 0.
calib["1_prior_sigma"] = 0.25
calib["2"] = (-0.5, 0.5)
calib["2_prior"] = "Gaussian"
calib["2_prior_mu"] = 0.
calib["2_prior_sigma"] = 0.25
fit_instructions["calib"] = calib
noise = {}
noise["type"] = "white_scaled"
noise["scaling"] = (1., 10.)
noise["scaling_prior"] = "log_10"
fit_instructions["noise"] = noise
fit = pipes.fit(galaxy, fit_instructions, run="spectroscopy")
fit.fit(verbose=False)
fig = fit.plot_spectrum_posterior(save=True, show=True)
fig = fit.plot_calibration(save=True, show=True)
fig = fit.plot_sfh_posterior(save=True, show=True)
fig = fit.plot_corner(save=True, show=True)
print("\a \a \a")
return None
from loaddata import load_goodss
from loaddata import load_vandels_spec
import bagpipes as pipes
import numpy as np
filt_list = np.loadtxt("filters/goodss_filt_list.txt", dtype="str")
galaxy = pipes.galaxy(14697,load_goodss,spectrum_exists=False,filt_list=filt_list)
dust = {}
dust["type"] = "Cardelli"
dust["Av"] = (0.,2.)
power = {}
power["tau"] = (0., 15.)
power["alpha"] = (.1, 1000.)
power["beta"] = (.1, 1000.)
power["alpha_prior"] = "log_10"
power["beta_prior"] = "log_10"
power["massformed"] = (2., 15.)
power["metallicity"] = (0., 2.5)
secondfit = {}
secondfit["redshift"] = (0.,10.)
secondfit["dblplaw"] = power
secondfit["dust"] = dust
fit = pipes.fit(galaxy, secondfit, run="dblplaw_sfh")
fit.fit(verbose=False)
fig = fit.plot_sfh_posterior(save=False, show=True)
fig = fit.plot_corner(save=False, show=True)
def plot_tde_posterior_quantities(filename, redshift):
fit_instructions = {}
chi_squ_vals = {}
# Get the galaxy object and a priori model compenents dictionary.
galaxy = pipes.galaxy(filename, load_xshooter, photometry_exists=False)
# Calculate redshift constraints.
z_low, z_high = redshift - 0.001, redshift + 0.001
fit = pipes.fit(galaxy, fit_instructions, run="r4_exponential_burst")
fit.fit(verbose=False)
chi_squ_vals = {"r4_exponential_burst" : chi_squared(galaxy, fit)}
fit = pipes.fit(galaxy, fit_instructions, run="r4_dblplaw_burst")
fit.fit(verbose=True)
chi_squ_vals["r4_dblplaw_burst"] = chi_squared(galaxy, fit)
fit = pipes.fit(galaxy, fit_instructions, run="r4_delayed_burst")
fit.fit(verbose=False)
chi_squ_vals["r4_delayed_burst"] = chi_squared(galaxy, fit)
fit = pipes.fit(galaxy, fit_instructions, run="r4_lognormal_burst")
fit.fit(verbose=False)
chi_squ_vals["r4_lognormal_burst"] = chi_squared(galaxy, fit)
# Get the functional form with the lowest chi-squared value.
best_func = min(chi_squ_vals, key=lambda k: chi_squ_vals[k])
fit = pipes.fit(galaxy, fit_instructions, run=best_func)
# Select the fit with lowest chi-squared value and plot it.
print(filename)
print(best_func)
for key in chi_squ_vals.keys():
print(key, ": ", chi_squ_vals[key])
if best_func == "r4_exponential_burst":
labels = ["exponential2:age", "exponential2:massformed", "exponential1:age", "exponential1:massformed"]
if best_func == "r4_delayed_burst":
labels = ["exponential2:age", "exponential2:massformed", "delayed:age", "delayed:massformed"]
if best_func == "r4_lognormal_burst":
labels = ["exponential2:age", "exponential2:massformed", "lognormal:tmax", "lognormal:massformed"]
if best_func == "r4_dblplaw_burst":
labels = ["exponential2:age", "exponential2:massformed", "dblplaw:tau", "dblplaw:massformed"]
post_quantities = dict(zip(labels, [fit.posterior.samples[l] for l in labels]))
tex_on = True
update_rcParams()
fig, axes = plt.subplots(1, 4)
for i in range(4):
hist1d(post_quantities[labels[i]], axes[i], smooth=True, label=labels[i])
fixed_aspect_ratio(1,axes[i])
fig.set_figwidth(6.75)
plt.savefig("report/img/"+filename+"_posterior.pdf", bbox_inches = 'tight', pad_inches = 0)
plt.clf()
fig, ax = fit.plot_sfh_posterior(save=False, show=False)
# fig.set_figwidth(6.75)
plt.savefig("report/img/"+filename+"_sfh.pdf", bbox_inches = 'tight', pad_inches = 0)
plt.clf()
fig, ax = fit.plot_spectrum_posterior(save=False, show=False)
# fig.set_figwidth(6.75)
plt.savefig("report/img/"+filename+"_spec.pdf", bbox_inches = 'tight', pad_inches = 0)
print_posterior(fit)