sys.excepthook = info
# -----------------------------------------------------------------
n_walkers = 30
n_iterations = 2000
show_plots = False
# ----------------
# Read in spectrum
# ----------------
# ------------
# Initialize model
# ------------
model = Model()
model.print_parameters = False
# -----------------
# Initialize components
# -----------------
if True:
fe_comp = FeComponent()
#datafile = "../Data/FakeData/for_gisella/fake_host_spectrum.dat"
#datafile = "../Data/FakeData/Iron_comp/fakeFe1_deg.dat"
datafile = "../Fe_templates/FeSimdata_BevWills_0p05.dat"
wavelengths, flux, flux_err = np.loadtxt(datafile, unpack=True)
spectrum = Spectrum()
spectrum.wavelengths = wavelengths
spectrum.flux = flux
spectrum.flux_error = flux_err
def perform_test(components, datafile=None, comp_params=None):
"""
Args:
components : dictionary
A dictionary with at least one component to model, e.g. {"FE": True}
datafile : str
Pathname of the datafile to be used as input.
comp_params : dictionary
If None, the component parameters (at minimum- wavelength, flux, flux error)
will be determined from the datafile. If defined, comp_params *MUST* contain:
- wl
- flux
- err
- pname (can be None)
- broken_pl (if component=PL)
"""
# eventually need to update params to work with multiple components i.e. params["PL"]...
redshift = False
template_data = False
change_wl = False
for c in [
"PL", "FE", "HOST", "BC", "BpC", "Calzetti_ext", "SMC_ext",
"MW_ext", "AGN_ext", "LMC_ext"
]:
if c not in components:
components[c] = False
if "n_walkers" not in components:
components["n_walkers"] = 30
if "n_iterations" not in components:
components["n_iterations"] = 500
if comp_params is None:
keys = list(components.keys())
i = 0
while datafile is None:
if components[keys[i]] is True:
datafile = component_data[keys[i]]
print("Using data file {0}".format(datafile))
i += 1
try:
wavelengths, flux, flux_err = np.loadtxt(datafile, unpack=True)
except ValueError:
wavelengths, flux = np.loadtxt(datafile, unpack=True)
flux_err = flux * 0.05
finally:
pname = None
flux = np.where(flux < 0, 1e-19, flux)
else:
wavelengths = comp_params["wl"]
flux = comp_params["flux"]
flux_err = comp_params["err"]
pname = comp_params["pname"]
if redshift:
print("Accounting for redshift")
wavelengths /= 1.5
if template_data:
print("Scaling flux and error")
flux *= 0.5
flux_err *= 0.5
if change_wl:
print("Changing the wavelength range")
idx = len(flux) // 2
wavelengths = wavelengths[:idx]
flux = flux[:idx]
flux_err = flux_err[:idx]
# mask = Mask(wavelengths=wavelengths,maskType=maskType)
# spectrum.mask=mask
spectrum = Spectrum.from_array(flux, uncertainty=flux_err)
spectrum.dispersion = wavelengths #*units.angstrom
spectrum.flux_error = flux_err
# ------------
# Initialize model
# ------------
model = Model()
model.print_parameters = False #True#False#
# -----------------
# Initialize components
# -----------------
if components["PL"]:
if comp_params:
nuclear_comp = NuclearContinuumComponent(comp_params["broken_pl"])
else:
nuclear_comp = NuclearContinuumComponent()
model.components.append(nuclear_comp)
if components["FE"]:
fe_comp = FeComponent()
model.components.append(fe_comp)
if components["HOST"]:
host_galaxy_comp = HostGalaxyComponent()
model.components.append(host_galaxy_comp)
if components["BC"] or components["BpC"]:
balmer_comp = BalmerCombined(BalmerContinuum=BC,
BalmerPseudocContinuum=BpC)
model.components.append(balmer_comp)
if components["Calzetti_ext"] or components["SMC_ext"] or components[
"MW_ext"] or components["AGN_ext"] or components["LMC_ext"]:
ext_comp = Extinction(MW=MW_ext,
AGN=AGN_ext,
LMC=LMC_ext,
SMC=SMC_ext,
Calzetti=Calzetti_ext)
model.components.append(ext_comp)
if not comp_params:
comp_params = {}
comp_params["datafile"] = datafile
model.data_spectrum = spectrum # add data
# ------------
# Run MCMC
# ------------
model.run_mcmc(n_walkers=int(components["n_walkers"]),
n_iterations=int(components["n_iterations"]))
print("Mean acceptance fraction: {0:.3f}".format(
np.mean(model.sampler.acceptance_fraction)))
# -------------
# save chains & model
# ------------
p_data = {"model": model, "comp_params": comp_params}
if pname is None:
now = datetime.datetime.now()
pname = "model_{0}.pickle.gz".format(now.strftime("%Y%m%d_%M%S"))
with gzip.open(pname, "wb") as model_output:
model_output.write(pickle.dumps(p_data))
print("Saved pickle file {0}".format(pname))
make_chain_plots(pname)
wavelengths, flux, flux_err = np.loadtxt(datafile, unpack=True)
mask = Mask(wavelengths=wavelengths, maskType=maskType)
spectrum = Spectrum.from_array(flux, uncertainty=flux_err, mask=mask)
#spectrum = Spectrum(maskType="Emission lines reduced")#"Cont+Fe")#
spectrum.mask = mask
spectrum.dispersion = wavelengths #*units.angstrom
spectrum.flux_error = flux_err
pl.plot(spectrum.wavelengths, spectrum.flux)
pl.show()
#exit()
# ------------
# Initialize model
# ------------
model = Model()
model.print_parameters = False #True#False#
# -----------------
# Initialize components
# -----------------
if PL:
nuclear_comp = NuclearContinuumComponent()
model.components.append(nuclear_comp)
if FE:
fe_comp = FeComponent()
model.components.append(fe_comp)
if HOST:
host_galaxy_comp = HostGalaxyComponent()
model.components.append(host_galaxy_comp)
def spamm_wlflux(components,
wl,
flux,
flux_err=None,
n_walkers=30,
n_iterations=500,
pname=None,
comp_params=None):
"""
Args:
components (dictionary): A dictionary with at least one component to
model, e.g. {"FE": True}. Accepted key values are:
- PL
- FE
- HOST
- BC
- BpC
- Calzetti_ext
- SMC_ext
- MW_ext
- AGN_ext
- LMC_ext
wl (array-like): Wavelength of input data spectrum.
flux (array-like): Flux of input data spectrum.
flux_err (array-like): Error on flux measurement.
n_walkers (int): Number of walkers, or chains, to use in emcee.
n_iterations (int): Number of iterations for each walker/chain.
pname (str): Name of output pickle file. If None, name will be
determined based on current run time.
comp_params : dictionary
Contains the known values of component parameters, with keys
defined in each of the individual run scripts (run_XX.py).
If None, the actual values of parameters will not be plotted.
"""
t1 = datetime.datetime.now()
for c in [
"PL", "FE", "HOST", "BC", "BpC", "Calzetti_ext", "SMC_ext",
"MW_ext", "AGN_ext", "LMC_ext"
]:
if c not in components:
components[c] = False
if flux_err is None:
flux_err = flux * 0.05
spectrum = Spectrum.from_array(flux, uncertainty=flux_err)
spectrum.dispersion = wl #*units.angstrom
spectrum.flux_error = flux_err
if comp_params is None:
comp_params = {}
for k, v in zip(("wl", "flux", "err", "components"),
(wl, flux, flux_err, components)):
if k not in comp_params:
comp_params[k] = v
# ------------
# Initialize model
# ------------
model = Model()
model.print_parameters = False
# -----------------
# Initialize components
# -----------------
if components["PL"]:
try:
if comp_params["broken_pl"] is True:
brokenPL = True
else:
brokenPL = False
except:
brokenPL = False
finally:
nuclear_comp = NuclearContinuumComponent(broken=brokenPL)
model.components.append(nuclear_comp)
if components["FE"]:
fe_comp = FeComponent()
model.components.append(fe_comp)
if components["HOST"]:
host_galaxy_comp = HostGalaxyComponent()
model.components.append(host_galaxy_comp)
if components["BC"] or components["BpC"]:
balmer_comp = BalmerCombined(BalmerContinuum=components["BC"],
BalmerPseudocContinuum=components["BpC"])
model.components.append(balmer_comp)
if components["Calzetti_ext"] or components["SMC_ext"] or components[
"MW_ext"] or components["AGN_ext"] or components["LMC_ext"]:
ext_comp = Extinction(MW=MW_ext,
AGN=AGN_ext,
LMC=LMC_ext,
SMC=SMC_ext,
Calzetti=Calzetti_ext)
model.components.append(ext_comp)
model.data_spectrum = spectrum # add data
# ------------
# Run MCMC
# ------------
model.run_mcmc(n_walkers=n_walkers, n_iterations=n_iterations)
print("Mean acceptance fraction: {0:.3f}".format(
np.mean(model.sampler.acceptance_fraction)))
# -------------
# save chains & model
# ------------
p_data = {"model": model, "comp_params": comp_params}
if pname is None:
now = datetime.datetime.now()
pname = "model_{0}.pickle.gz".format(now.strftime("%Y%m%d_%M%S"))
with gzip.open(pname, "wb") as model_output:
model_output.write(pickle.dumps(p_data))
print("Saved pickle file {0}".format(pname))
make_chain_plots(pname)
t2 = datetime.datetime.now()
print("executed in {}".format(t2 - t1))
def perform_test(components, datafile=None, params=None):
if datafile:
try:
wavelengths, flux, flux_err = np.loadtxt(datafile, unpack=True)
except ValueError:
wavelengths, flux = np.loadtxt(datafile, unpack=True)
flux_err = flux * 0.05
finally:
pname = None
else:
wavelengths = params["wl"]
flux = params["flux"]
flux_err = params["err"]
pname = params["pname"]
# mask = Mask(wavelengths=wavelengths,maskType=maskType)
spectrum = Spectrum.from_array(flux, uncertainty=flux_err)
# spectrum.mask=mask
spectrum.dispersion = wavelengths #*units.angstrom
spectrum.flux_error = flux_err
# ------------
# Initialize model
# ------------
model = Model()
model.print_parameters = False #True#False#
# -----------------
# Initialize components
# -----------------
if PL:
nuclear_comp = NuclearContinuumComponent(params["broken_pl"])
model.components.append(nuclear_comp)
if FE:
fe_comp = FeComponent()
model.components.append(fe_comp)
if HOST:
host_galaxy_comp = HostGalaxyComponent()
model.components.append(host_galaxy_comp)
if BC or BpC:
balmer_comp = BalmerCombined(BalmerContinuum=BC,
BalmerPseudocContinuum=BpC)
model.components.append(balmer_comp)
if Calzetti_ext or SMC_ext or MW_ext or AGN_ext or LMC_ext:
ext_comp = Extinction(MW=MW_ext,
AGN=AGN_ext,
LMC=LMC_ext,
SMC=SMC_ext,
Calzetti=Calzetti_ext)
model.components.append(ext_comp)
model.data_spectrum = spectrum # add data
# ------------
# Run MCMC
# ------------
model.run_mcmc(n_walkers=n_walkers, n_iterations=n_iterations)
print("Mean acceptance fraction: {0:.3f}".format(
np.mean(model.sampler.acceptance_fraction)))
# -------------
# save chains & model
# ------------
p_data = {"model": model, "params": params}
if pname is None:
now = datetime.datetime.now()
pname = "model_{0}.pickle.gz".format(now.strftime("%Y%m%d_%M%S"))
with gzip.open(pname, "wb") as model_output:
model_output.write(pickle.dumps(p_data))
print("Saved pickle file {0}".format(pname))
make_chain_plots(pname)