def get_Bu2019rp_model(table, **kwargs):
if 'LoadModel' in kwargs:
LoadModel = kwargs['LoadModel']
else:
LoadModel = False
if 'SaveModel' in kwargs:
SaveModel = kwargs['SaveModel']
else:
SaveModel = False
if 'ModelPath' in kwargs:
ModelPath = kwargs['ModelPath']
if 'doAB' in kwargs:
doAB = kwargs['doAB']
else:
doAB = True
if 'doSpec' in kwargs:
doSpec = kwargs['doSpec']
else:
doSpec = False
if not 'n_coeff' in table.colnames:
if doAB:
table['n_coeff'] = 43
#table['n_coeff'] = 2
elif doSpec:
table['n_coeff'] = 21
if not 'gptype' in table.colnames:
table['gptype'] = sklearn
if doAB:
if not Global.svd_mag_model == 0:
svd_mag_model = Global.svd_mag_model
else:
if LoadModel:
#if True:
if table['gptype'] == "sklearn":
modelfile = os.path.join(ModelPath, 'Bu2019rp_mag.pkl')
elif table['gptype'] == "gpytorch":
modelfile = os.path.join(ModelPath, 'Bu2019rp_mag_gpy.pkl')
with open(modelfile, 'rb') as handle:
svd_mag_model = pickle.load(handle)
else:
svd_mag_model = svd_utils.calc_svd_mag(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019rp",
n_coeff=table['n_coeff'][0],
gptype=table['gptype'])
if table['gptype'] == "sklearn":
modelfile = os.path.join(ModelPath, 'Bu2019rp_mag.pkl')
elif table['gptype'] == "gpytorch":
modelfile = os.path.join(ModelPath, 'Bu2019rp_mag_gpy.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_mag_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_mag_model = svd_mag_model
if not Global.svd_lbol_model == 0:
svd_lbol_model = Global.svd_lbol_model
else:
if LoadModel:
#if True:
if table['gptype'] == "sklearn":
modelfile = os.path.join(ModelPath, 'Bu2019rp_lbol.pkl')
elif table['gptype'] == "gpytorch":
modelfile = os.path.join(ModelPath,
'Bu2019rp_lbol_gpy.pkl')
with open(modelfile, 'rb') as handle:
svd_lbol_model = pickle.load(handle)
else:
svd_lbol_model = svd_utils.calc_svd_lbol(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019rp",
n_coeff=table['n_coeff'][0],
gptype=table['gptype'])
if table['gptype'] == "sklearn":
modelfile = os.path.join(ModelPath, 'Bu2019rp_lbol.pkl')
elif table['gptype'] == "gpytorch":
modelfile = os.path.join(ModelPath,
'Bu2019rp_lbol_gpy.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_lbol_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_lbol_model = svd_lbol_model
elif doSpec:
if not Global.svd_spec_model == 0:
svd_spec_model = Global.svd_spec_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath, 'Bu2019rp_spec.pkl')
with open(modelfile, 'rb') as handle:
svd_spec_model = pickle.load(handle)
else:
svd_spec_model = svd_utils.calc_svd_spectra(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
table['lambdaini'][0],
table['lambdamax'][0],
table['dlambda'][0],
model="Bu2019rp",
n_coeff=table['n_coeff'][0])
modelfile = os.path.join(ModelPath, 'Bu2019rp_spec.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_spec_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_spec_model = svd_spec_model
# Throw out smaples where the mass ejecta is less than zero.
mask = (table['mej_1'] > 0)
table = table[mask]
if len(table) == 0: return table
timeseries = np.arange(table['tini'][0], table['tmax'][0] + table['dt'][0],
table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
if doAB:
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [np.zeros([9, timeseries.size])]
elif doSpec:
lambdas = np.arange(table['lambdaini'][0],
table['lambdamax'][0] + table['dlambda'][0],
table['dlambda'][0])
table['lambda'] = [np.zeros(lambdas.size)]
table['spec'] = [np.zeros([lambdas.size, timeseries.size])]
# calc lightcurve for each sample
for isample in range(len(table)):
print('Generating sample %d/%d' % (isample, len(table)))
if doAB:
table['t'][isample], table['lbol'][isample], table['mag'][
isample] = svd_utils.calc_lc(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample], [
np.log10(table['mej_1'][isample]),
np.log10(
table['mej_2'][isample]), table['phi'][isample],
table['theta'][isample], table['a'][isample]
],
svd_mag_model=svd_mag_model,
svd_lbol_model=svd_lbol_model,
model="Bu2019rp",
gptype=table['gptype'])
elif doSpec:
table['t'][isample], table['lambda'][isample], table['spec'][
isample] = svd_utils.calc_spectra(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample],
table['lambdaini'][isample],
table['lambdamax'][isample] + table['dlambda'][isample],
table['dlambda'][isample], [
np.log10(table['mej'][isample]), table['phi'][isample],
table['theta'][isample]
],
svd_spec_model=svd_spec_model,
model="Bu2019rp",
gptype=table['gptype'])
return table
def get_Bu2019lr_model(table, **kwargs):
if 'LoadModel' in kwargs:
LoadModel = kwargs['LoadModel']
else:
LoadModel = False
if 'SaveModel' in kwargs:
SaveModel = kwargs['SaveModel']
else:
SaveModel = False
if 'ModelPath' in kwargs:
ModelPath = kwargs['ModelPath']
if 'doAB' in kwargs:
doAB = kwargs['doAB']
else:
doAB = True
if 'doSpec' in kwargs:
doSpec = kwargs['doSpec']
else:
doSpec = False
if not 'n_coeff' in table.colnames:
if doAB:
table['n_coeff'] = 43
#table['n_coeff'] = 2
elif doSpec:
table['n_coeff'] = 21
if doAB:
if not Global.svd_mag_model == 0:
svd_mag_model = Global.svd_mag_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath, 'Bu2019lr_mag.pkl')
with open(modelfile, 'rb') as handle:
svd_mag_model = pickle.load(handle)
else:
svd_mag_model = svd_utils.calc_svd_mag(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019lr",
n_coeff=table['n_coeff'][0])
modelfile = os.path.join(ModelPath, 'Bu2019lr_mag.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_mag_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_mag_model = svd_mag_model
if not Global.svd_lbol_model == 0:
svd_lbol_model = Global.svd_lbol_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath, 'Bu2019lr_lbol.pkl')
with open(modelfile, 'rb') as handle:
svd_lbol_model = pickle.load(handle)
else:
svd_lbol_model = svd_utils.calc_svd_lbol(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019lr",
n_coeff=table['n_coeff'][0])
modelfile = os.path.join(ModelPath, 'Bu2019lr_lbol.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_lbol_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_lbol_model = svd_lbol_model
elif doSpec:
if not Global.svd_spec_model == 0:
svd_spec_model = Global.svd_spec_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath, 'Bu2019lr_spec.pkl')
with open(modelfile, 'rb') as handle:
svd_spec_model = pickle.load(handle)
else:
svd_spec_model = svd_utils.calc_svd_spectra(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
table['lambdaini'][0],
table['lambdamax'][0],
table['dlambda'][0],
model="Bu2019lr",
n_coeff=table['n_coeff'][0])
modelfile = os.path.join(ModelPath, 'Bu2019lr_spec.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_spec_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_spec_model = svd_spec_model
if not 'mej_dyn' in table.colnames:
# calc the mass of ejecta
table['mej_dyn'] = calc_meje(table['m1'], table['mb1'], table['c1'],
table['m2'], table['mb2'], table['c2'])
# calc the velocity of ejecta
table['vej'] = calc_vej(table['m1'], table['c1'], table['m2'],
table['c2'])
# Throw out smaples where the mass ejecta is less than zero.
mask = (table['mej_dyn'] > 0)
table = table[mask]
if len(table) == 0: return table
# Log mass ejecta
table['mej_dyn10'] = np.log10(table['mej_dyn'])
table['mej_wind10'] = np.log10(table['mej_wind'])
# Initialize lightcurve values in table
timeseries = np.arange(table['tini'][0], table['tmax'][0] + table['dt'][0],
table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
if doAB:
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [np.zeros([9, timeseries.size])]
elif doSpec:
lambdas = np.arange(table['lambdaini'][0],
table['lambdamax'][0] + table['dlambda'][0],
table['dlambda'][0])
table['lambda'] = [np.zeros(lambdas.size)]
table['spec'] = [np.zeros([lambdas.size, timeseries.size])]
# calc lightcurve for each sample
for isample in range(len(table)):
print('Generating sample %d/%d' % (isample, len(table)))
if doAB:
table['t'][isample], table['lbol'][isample], table['mag'][
isample] = svd_utils.calc_lc(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample], [
np.log10(table['mej_dyn'][isample]),
np.log10(table['mej_wind'][isample]),
table['phi'][isample], table['theta'][isample]
],
svd_mag_model=svd_mag_model,
svd_lbol_model=svd_lbol_model,
model="Bu2019lr")
elif doSpec:
table['t'][isample], table['lambda'][isample], table['spec'][
isample] = svd_utils.calc_spectra(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample],
table['lambdaini'][isample],
table['lambdamax'][isample] + table['dlambda'][isample],
table['dlambda'][isample], [
np.log10(table['mej_dyn'][isample]),
np.log10(table['mej_wind'][isample]),
table['phi'][isample], table['theta'][isample]
],
svd_spec_model=svd_spec_model,
model="Bu2019lr")
return table
def get_Bu2019ops_model(table, **kwargs):
if 'LoadModel' in kwargs:
LoadModel = kwargs['LoadModel']
else:
LoadModel = False
if 'SaveModel' in kwargs:
SaveModel = kwargs['SaveModel']
else:
SaveModel = False
if 'ModelPath' in kwargs:
ModelPath = kwargs['ModelPath']
if 'doAB' in kwargs:
doAB = kwargs['doAB']
else:
doAB = True
if 'doSpec' in kwargs:
doSpec = kwargs['doSpec']
else:
doSpec = False
if not 'n_coeff' in table.colnames:
if doAB:
table['n_coeff'] = 43
#table['n_coeff'] = 2
elif doSpec:
table['n_coeff'] = 21
if doAB:
if not Global.svd_mag_model == 0:
svd_mag_model = Global.svd_mag_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath,'Bu2019ops_mag.pkl')
with open(modelfile, 'rb') as handle:
svd_mag_model = pickle.load(handle)
else:
svd_mag_model = svd_utils.calc_svd_mag(table['tini'][0], table['tmax'][0], table['dt'][0], model = "Bu2019ops", n_coeff = table['n_coeff'][0])
modelfile = os.path.join(ModelPath,'Bu2019ops_mag.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_mag_model, handle, protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_mag_model = svd_mag_model
if not Global.svd_lbol_model == 0:
svd_lbol_model = Global.svd_lbol_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath,'Bu2019ops_lbol.pkl')
with open(modelfile, 'rb') as handle:
svd_lbol_model = pickle.load(handle)
else:
svd_lbol_model = svd_utils.calc_svd_lbol(table['tini'][0], table['tmax'][0], table['dt'][0], model = "Bu2019ops", n_coeff = table['n_coeff'][0])
modelfile = os.path.join(ModelPath,'Bu2019ops_lbol.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_lbol_model, handle, protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_lbol_model = svd_lbol_model
elif doSpec:
if not Global.svd_spec_model == 0:
svd_spec_model = Global.svd_spec_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath,'Bu2019ops_spec.pkl')
with open(modelfile, 'rb') as handle:
svd_spec_model = pickle.load(handle)
else:
svd_spec_model = svd_utils.calc_svd_spectra(table['tini'][0], table['tmax'][0], table['dt'][0], table['lambdaini'][0], table['lambdamax'][0], table['dlambda'][0], model = "Bu2019ops", n_coeff = table['n_coeff'][0])
modelfile = os.path.join(ModelPath,'Bu2019ops_spec.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_spec_model, handle, protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_spec_model = svd_spec_model
timeseries = np.arange(table['tini'][0], table['tmax'][0]+table['dt'][0], table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
if doAB:
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [np.zeros([9, timeseries.size])]
elif doSpec:
lambdas = np.arange(table['lambdaini'][0], table['lambdamax'][0]+table['dlambda'][0], table['dlambda'][0])
table['lambda'] = [np.zeros(lambdas.size)]
table['spec'] = [np.zeros([lambdas.size, timeseries.size])]
# calc lightcurve for each sample
for isample in range(len(table)):
print('Generating sample %d/%d' % (isample, len(table)))
if doAB:
table['t'][isample], table['lbol'][isample], table['mag'][isample] = svd_utils.calc_lc(table['tini'][isample], table['tmax'][isample],table['dt'][isample], [np.log10(table['kappaLF'][isample]),np.log10(table['kappaLR'][isample]),table['gammaLR'][isample]],svd_mag_model = svd_mag_model, svd_lbol_model = svd_lbol_model, model = "Bu2019ops")
elif doSpec:
table['t'][isample], table['lambda'][isample], table['spec'][isample] = svd_utils.calc_spectra(table['tini'][isample], table['tmax'][isample],table['dt'][isample], table['lambdaini'][isample], table['lambdamax'][isample]+table['dlambda'][isample], table['dlambda'][isample], [np.log10(table['kappaLF'][isample]),np.log10(table['kappaLR'][isample]),table['gammaLR'][isample]],svd_spec_model = svd_spec_model, model = "Bu2019ops")
return table
def get_Bu2019lm_model(table, **kwargs):
if 'LoadModel' in kwargs:
LoadModel = kwargs['LoadModel']
else:
LoadModel = False
if 'SaveModel' in kwargs:
SaveModel = kwargs['SaveModel']
else:
SaveModel = False
if 'ModelPath' in kwargs:
ModelPath = kwargs['ModelPath']
if 'doAB' in kwargs:
doAB = kwargs['doAB']
else:
doAB = True
if 'doSpec' in kwargs:
doSpec = kwargs['doSpec']
else:
doSpec = False
if not 'n_coeff' in table.colnames:
if doAB:
#table['n_coeff'] = 43
table['n_coeff'] = 10
elif doSpec:
table['n_coeff'] = 21
if not 'gptype' in table.colnames:
table['gptype'] = 'sklearn'
if doAB:
if not Global.svd_mag_model == 0:
svd_mag_model = Global.svd_mag_model
else:
if np.all(table['gptype'] == "sklearn"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_mag.pkl')
elif np.all(table['gptype'] == "gpytorch"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_mag_gpy.pkl')
elif np.all(table['gptype'] == "gp_api"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_mag_gpapi.pkl')
elif np.all(table['gptype'] == "tensorflow"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_mag_tf.pkl')
if LoadModel:
#if True:
with open(modelfile, 'rb') as handle:
svd_mag_model = pickle.load(handle)
if np.all(table['gptype'] == "tensorflow"):
outdir = modelfile.replace(".pkl", "")
for filt in svd_mag_model.keys():
outfile = os.path.join(outdir, f'{filt}.h5')
svd_mag_model[filt]['model'] = load_model(outfile)
else:
svd_mag_model = svd_utils.calc_svd_mag(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019lm",
n_coeff=table['n_coeff'][0],
gptype=table['gptype'])
if np.all(table['gptype'] == "tensorflow"):
outdir = modelfile.replace(".pkl", "")
if not os.path.isdir(outdir):
os.makedirs(outdir)
for filt in svd_mag_model.keys():
outfile = os.path.join(outdir, f'{filt}.h5')
svd_mag_model[filt]['model'].save(outfile)
del svd_mag_model[filt]['model']
with open(modelfile, 'wb') as handle:
pickle.dump(svd_mag_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
if np.all(table['gptype'] == "tensorflow"):
for filt in svd_mag_model.keys():
outfile = os.path.join(outdir, f'{filt}.h5')
svd_mag_model[filt]['model'] = load_model(outfile)
if np.all(table['gptype'] == "gp_api"):
for filt in svd_mag_model.keys():
for ii in range(len(svd_mag_model[filt]["gps"])):
svd_mag_model[filt]["gps"][ii] = svd_utils.load_gpapi(
svd_mag_model[filt]["gps"][ii])
Global.svd_mag_model = svd_mag_model
if not Global.svd_lbol_model == 0:
svd_lbol_model = Global.svd_lbol_model
else:
if np.all(table['gptype'] == "sklearn"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_lbol.pkl')
elif np.all(table['gptype'] == "gpytorch"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_lbol_gpy.pkl')
elif np.all(table['gptype'] == "gp_api"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_lbol_gpapi.pkl')
elif np.all(table['gptype'] == "tensorflow"):
modelfile = os.path.join(ModelPath, 'Bu2019lm_lbol_tf.pkl')
if LoadModel:
#if True:
with open(modelfile, 'rb') as handle:
svd_lbol_model = pickle.load(handle)
if np.all(table['gptype'] == "tensorflow"):
outdir = modelfile.replace(".pkl", "")
outfile = os.path.join(outdir, 'model.h5')
svd_lbol_model['model'] = load_model(outfile)
else:
svd_lbol_model = svd_utils.calc_svd_lbol(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="Bu2019lm",
n_coeff=table['n_coeff'][0],
gptype=table['gptype'])
if np.all(table['gptype'] == "tensorflow"):
outdir = modelfile.replace(".pkl", "")
if not os.path.isdir(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, 'model.h5')
svd_lbol_model['model'].save(outfile)
del svd_lbol_model['model']
with open(modelfile, 'wb') as handle:
pickle.dump(svd_lbol_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
if np.all(table['gptype'] == "tensorflow"):
svd_lbol_model['model'] = load_model(outfile)
if np.all(table['gptype'] == "gp_api"):
for ii in range(len(svd_lbol_model["gps"])):
svd_lbol_model["gps"][ii] = svd_utils.load_gpapi(
svd_lbol_model["gps"][ii])
Global.svd_lbol_model = svd_lbol_model
elif doSpec:
if not Global.svd_spec_model == 0:
svd_spec_model = Global.svd_spec_model
else:
if LoadModel:
#if True:
modelfile = os.path.join(ModelPath, 'Bu2019lm_spec.pkl')
with open(modelfile, 'rb') as handle:
svd_spec_model = pickle.load(handle)
else:
svd_spec_model = svd_utils.calc_svd_spectra(
table['tini'][0],
table['tmax'][0],
table['dt'][0],
table['lambdaini'][0],
table['lambdamax'][0],
table['dlambda'][0],
model="Bu2019lm",
n_coeff=table['n_coeff'][0])
modelfile = os.path.join(ModelPath, 'Bu2019lm_spec.pkl')
with open(modelfile, 'wb') as handle:
pickle.dump(svd_spec_model,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
Global.svd_spec_model = svd_spec_model
if not 'mej_dyn' in table.colnames:
# calc the mass of ejecta
table['mej_dyn'] = calc_meje(table['m1'], table['mb1'], table['c1'],
table['m2'], table['mb2'], table['c2'])
# calc the velocity of ejecta
table['vej'] = calc_vej(table['m1'], table['c1'], table['m2'],
table['c2'])
# Throw out smaples where the mass ejecta is less than zero.
mask = (table['mej_dyn'] > 0)
table = table[mask]
if len(table) == 0: return table
# Log mass ejecta
table['mej_dyn10'] = np.log10(table['mej_dyn'])
table['mej_wind10'] = np.log10(table['mej_wind'])
# Initialize lightcurve values in table
timeseries = np.arange(table['tini'][0], table['tmax'][0] + table['dt'][0],
table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
if doAB:
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [np.zeros([9, timeseries.size])]
elif doSpec:
lambdas = np.arange(table['lambdaini'][0],
table['lambdamax'][0] + table['dlambda'][0],
table['dlambda'][0])
table['lambda'] = [np.zeros(lambdas.size)]
table['spec'] = [np.zeros([lambdas.size, timeseries.size])]
# calc lightcurve for each sample
for isample in range(len(table)):
print(np.log10(table['mej_dyn'][isample]),
np.log10(table['mej_wind'][isample]), table['phi'][isample],
table['theta'][isample])
print('Generating sample %d/%d' % (isample, len(table)))
if doAB:
table['t'][isample], table['lbol'][isample], table['mag'][
isample] = svd_utils.calc_lc(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample], [
np.log10(table['mej_dyn'][isample]),
np.log10(table['mej_wind'][isample]),
table['phi'][isample], table['theta'][isample]
],
svd_mag_model=svd_mag_model,
svd_lbol_model=svd_lbol_model,
model="Bu2019lm",
gptype=table['gptype'][0])
elif doSpec:
table['t'][isample], table['lambda'][isample], table['spec'][
isample] = svd_utils.calc_spectra(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample],
table['lambdaini'][isample],
table['lambdamax'][isample] + table['dlambda'][isample],
table['dlambda'][isample], [
np.log10(table['mej_dyn'][isample]),
np.log10(table['mej_wind'][isample]),
table['phi'][isample], table['theta'][isample]
],
svd_spec_model=svd_spec_model,
model="Bu2019lm")
return table
def get_RoFe2017_model(table, **kwargs):
if not 'n_coeff' in table.colnames:
table['n_coeff'] = 100
if not Global.svd_mag_model == 0:
svd_mag_model = Global.svd_mag_model
else:
svd_mag_model = svd_utils.calc_svd_mag(table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="RoFe2017",
n_coeff=table['n_coeff'][0])
Global.svd_mag_model = svd_mag_model
if not Global.svd_lbol_model == 0:
svd_lbol_model = Global.svd_lbol_model
else:
svd_lbol_model = svd_utils.calc_svd_lbol(table['tini'][0],
table['tmax'][0],
table['dt'][0],
model="RoFe2017",
n_coeff=table['n_coeff'][0])
Global.svd_lbol_model = svd_lbol_model
if not 'mej' in table.colnames:
# calc the mass of ejecta
table['mej'] = calc_meje(table['m1'], table['mb1'], table['c1'],
table['m2'], table['mb2'], table['c2'])
# calc the velocity of ejecta
table['vej'] = calc_vej(table['m1'], table['c1'], table['m2'],
table['c2'])
# Throw out smaples where the mass ejecta is less than zero.
mask = (table['mej'] > 0)
table = table[mask]
if len(table) == 0: return table
# Log mass ejecta
table['mej10'] = np.log10(table['mej'])
# Initialize lightcurve values in table
timeseries = np.arange(table['tini'][0], table['tmax'][0] + table['dt'][0],
table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [np.zeros([9, timeseries.size])]
# calc lightcurve for each sample
for isample in range(len(table)):
table['t'][isample], table['lbol'][isample], table['mag'][
isample] = svd_utils.calc_lc(
table['tini'][isample],
table['tmax'][isample],
table['dt'][isample], [
np.log10(table['mej'][isample]), table['vej'][isample],
table['Ye'][isample]
],
svd_mag_model=svd_mag_model,
svd_lbol_model=svd_lbol_model,
model="RoFe2017")
return table
