plt.close()
elif opts.doSpec:
names = opts.name.split(",")
filenames = []
legend_names = []
for name in names:
for ii, model in enumerate(models):
filename = '%s/%s/%s_spec.dat' % (outputDir, model, name)
if not os.path.isfile(filename):
continue
filenames.append(filename)
legend_names.append(models_ref[ii])
break
specs, names = lightcurve_utils.read_files_spec(filenames)
if opts.doEvent:
events = opts.event.split(",")
eventdata = {}
for event in events:
filename = "%s/%s.dat" % (spectraDir, event)
data_out = lightcurve_utils.loadEventSpec(filename)
eventdata[event] = data_out
maxhist = -1e10
colors = ["g", "r", "c", "y", "m"]
plotName = "%s/models_spec.pdf" % (plotDir)
plt.figure(figsize=(12, 10))
for ii, name in enumerate(names):
spec_d = specs[name]
def calc_svd_spectra(tini,
tmax,
dt,
lambdaini,
lambdamax,
dlambda,
n_coeff=100,
model="BaKa2016"):
print("Calculating SVD model of lightcurve spectra...")
if model == "BaKa2016":
fileDir = "../output/barnes_kilonova_spectra"
elif model == "Ka2017":
fileDir = "../output/kasen_kilonova_grid"
elif model == "RoFe2017":
fileDir = "../output/macronovae-rosswog_wind"
filenames = glob.glob('%s/*_spec.dat' % fileDir)
specs, names = lightcurve_utils.read_files_spec(filenames)
speckeys = specs.keys()
tt = np.arange(tini, tmax + dt, dt)
lambdas = np.arange(lambdaini, lambdamax + dlambda, dlambda)
for key in speckeys:
keySplit = key.split("_")
if keySplit[0] == "rpft":
mej0 = float("0." + keySplit[1].replace("m", ""))
vej0 = float("0." + keySplit[2].replace("v", ""))
specs[key]["mej"] = mej0
specs[key]["vej"] = vej0
elif keySplit[0] == "knova":
mej0 = float(keySplit[3].replace("m", ""))
vej0 = float(keySplit[4].replace("vk", ""))
if len(keySplit) == 6:
Xlan0 = 10**float(keySplit[5].replace("Xlan1e", ""))
elif len(keySplit) == 7:
#del specs[key]
#continue
if "Xlan1e" in keySplit[6]:
Xlan0 = 10**float(keySplit[6].replace("Xlan1e", ""))
elif "Xlan1e" in keySplit[5]:
Xlan0 = 10**float(keySplit[5].replace("Xlan1e", ""))
#if (mej0 == 0.05) and (vej0 == 0.2) and (Xlan0 == 1e-3):
# del specs[key]
# continue
specs[key]["mej"] = mej0
specs[key]["vej"] = vej0
specs[key]["Xlan"] = Xlan0
elif keySplit[0] == "SED":
specs[key]["mej"], specs[key]["vej"], specs[key][
"Ye"] = lightcurve_utils.get_macronovae_rosswog(key)
data = specs[key]["data"].T
data[data == 0.0] = 1e-20
f = interp.interp2d(specs[key]["t"],
specs[key]["lambda"],
np.log10(data),
kind='cubic')
#specs[key]["data"] = (10**(f(tt,lambdas))).T
specs[key]["data"] = f(tt, lambdas).T
speckeys = specs.keys()
param_array = []
for key in speckeys:
if model == "BaKa2016":
param_array.append(
[np.log10(specs[key]["mej"]), specs[key]["vej"]])
elif model == "Ka2017":
param_array.append([
np.log10(specs[key]["mej"]), specs[key]["vej"],
np.log10(specs[key]["Xlan"])
])
elif model == "RoFe2017":
param_array.append([
np.log10(specs[key]["mej"]), specs[key]["vej"],
specs[key]["Ye"]
])
param_array_postprocess = np.array(param_array)
param_mins, param_maxs = np.min(param_array_postprocess,
axis=0), np.max(param_array_postprocess,
axis=0)
for i in range(len(param_mins)):
param_array_postprocess[:, i] = (param_array_postprocess[:, i] -
param_mins[i]) / (param_maxs[i] -
param_mins[i])
svd_model = {}
for jj, lambda_d in enumerate(lambdas):
if np.mod(jj, 1) == 0:
print("%d / %d" % (jj, len(lambdas)))
spec_array = []
for key in speckeys:
spec_array.append(specs[key]["data"][:, jj])
spec_array_postprocess = np.array(spec_array)
mins, maxs = np.min(spec_array_postprocess,
axis=0), np.max(spec_array_postprocess, axis=0)
for i in range(len(mins)):
spec_array_postprocess[:, i] = (spec_array_postprocess[:, i] -
mins[i]) / (maxs[i] - mins[i])
spec_array_postprocess[np.isnan(spec_array_postprocess)] = 0.0
UA, sA, VA = np.linalg.svd(spec_array_postprocess, full_matrices=True)
VA = VA.T
n, n = UA.shape
m, m = VA.shape
cAmat = np.zeros((n_coeff, n))
cAvar = np.zeros((n_coeff, n))
ErrorLevel = 2
for i in range(n):
cAmat[:, i] = np.dot(spec_array_postprocess[i, :], VA[:, :n_coeff])
errors = ErrorLevel * spec_array_postprocess[i, :]
cAvar[:, i] = np.diag(
np.dot(VA[:, :n_coeff].T,
np.dot(np.diag(np.power(errors, 2.)), VA[:, :n_coeff])))
cAstd = np.sqrt(cAvar)
nsvds, nparams = param_array_postprocess.shape
kernel = 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1)
gps = []
for i in range(n_coeff):
gp = GaussianProcessRegressor(kernel=kernel,
n_restarts_optimizer=0)
gp.fit(param_array_postprocess, cAmat[i, :])
gps.append(gp)
svd_model[lambda_d] = {}
svd_model[lambda_d]["n_coeff"] = n_coeff
svd_model[lambda_d]["param_array"] = param_array
svd_model[lambda_d]["cAmat"] = cAmat
svd_model[lambda_d]["cAstd"] = cAstd
svd_model[lambda_d]["VA"] = VA
svd_model[lambda_d]["param_mins"] = param_mins
svd_model[lambda_d]["param_maxs"] = param_maxs
svd_model[lambda_d]["mins"] = mins
svd_model[lambda_d]["maxs"] = maxs
svd_model[lambda_d]["gps"] = gps
svd_model[lambda_d]["tt"] = tt
print("Finished calculating SVD model of lightcurve spectra...")
return svd_model
