def get_DiUj2017_model(table, **kwargs):
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 columns
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'] = [{}]
# Loop over samples
for isample in range(len(table)):
table['t'][isample], table['lbol'][isample], table['mag'][
isample] = calc_lc(table['tini'][isample], table['tmax'][isample],
table['dt'][isample], table['mej'][isample],
table['vej'][isample], table['vmin'][isample],
table['th'][isample], table['ph'][isample],
table['kappa'][isample], table['eps'][isample],
table['alp'][isample], table['eth'][isample],
table['flgbct'][isample])
return table
def lightcurve(tini, tmax, dt, beta, kappa_r, m1, mb1, c1, m2, mb2, c2):
mej = calc_meje(m1, mb1, c1, m2, mb2, c2)
vej = calc_vej(m1, c1, m2, c2)
t, lbol, mag, Tobs = calc_lc(tini, tmax, dt, mej, vej, beta, kappa_r)
return t, lbol, mag, Tobs
def lightcurve_break(tini,tmax,dt,slope_r,kappa_r,t_break,slope_break,m1,mb1,c1,m2,mb2,c2):
mej = calc_meje(m1,mb1,c1,m2,mb2,c2)
vej = calc_vej(m1,c1,m2,c2)
t, lbol, mag, Tobs = calc_lc_break(tini,tmax,dt,mej,vej,slope_r,kappa_r,t_break,slope_break)
return t, lbol, mag, Tobs
def get_SmCh2017_model(table, **kwargs):
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])]
table['Tobs'] = [np.zeros(timeseries.size)]
# calc lightcurve for each sample
for isample in range(len(table)):
table['t'][isample], table['lbol'][isample], table['mag'][isample], table['Tobs'][isample] = calc_lc(table['tini'][isample], table['tmax'][isample],
table['dt'][isample], table['mej'][isample],
table['vej'][isample], table['slope_r'][isample], table['kappa_r'][isample])
return table
def get_Me2017_model(table, **kwargs):
"""
.. py:function:: get_Me2017_model(table, **kwargs)
:param table table: a table which must at least have columns of solar masses of objects the baryonic masses of the objects and the compactness of the object. The table except m1, mb1, c1, m2, mb2, c2, mej and vej as column names
:return: The lbol, mag and sampling times of the KN Metzger 2017
:rtype: table
"""
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])]
table['Tobs'] = [np.zeros(timeseries.size)]
# calc lightcurve for each sample
for isample in range(len(table)):
# Calc lightcurve
table['t'][isample], table['lbol'][isample], table['mag'][
isample], table['Tobs'][isample] = calc_lc(
table['tini'][isample], table['tmax'][isample],
table['dt'][isample], table['mej'][isample],
table['vej'][isample], table['beta'][isample],
table['kappa_r'][isample])
return table
samples = samples.downsample(Nsamples=100)
# Calc lambdas
samples = samples.calc_tidal_lambda(remove_negative_lambda=True)
# Calc compactness
samples = samples.calc_compactness(fit=True)
# Calc baryonic mass
samples = samples.calc_baryonic_mass(EOS=None, TOV=None, fit=True)
if (not 'mej' in samples.colnames) and (not 'vej' in samples.colnames):
from gwemlightcurves.EjectaFits.DiUj2017 import calc_meje, calc_vej
# calc the mass of ejecta
samples['mej'] = calc_meje(samples['m1'], samples['mb1'],
samples['c1'], samples['m2'],
samples['mb2'], samples['c2'])
# calc the velocity of ejecta
samples['vej'] = calc_vej(samples['m1'], samples['c1'], samples['m2'],
samples['c2'])
# Add draw from a gaussian in the log of ejecta mass with 1-sigma size of 70%
erroropt = 'none'
if erroropt == 'none':
print "Not applying an error to mass ejecta"
elif erroropt == 'log':
samples['mej'] = np.power(
10., np.random.normal(np.log10(samples['mej']), 0.236))
elif erroropt == 'lin':
samples['mej'] = np.random.normal(samples['mej'],
0.72 * samples['mej'])
elif erroropt == 'loggauss':
samples['mej'] = np.power(
10., np.random.normal(np.log10(samples['mej']), 0.312))
idx = np.where(samples['mej'] > 0)[0]
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_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
c1, c2 = 0.147, 0.147
mb1, mb2 = lightcurve_utils.EOSfit(m1, c1), lightcurve_utils.EOSfit(
m2, c2)
th = 0.2
ph = 3.14
if m1 > 3:
from gwemlightcurves.EjectaFits.KaKy2016 import calc_meje, calc_vave
mej = calc_meje(q, chi_eff, c2, mb2, m2)
vej = calc_vave(q)
else:
from gwemlightcurves.EjectaFits.DiUj2017 import calc_meje, calc_vej
mej = calc_meje(m1, mb1, c1, m2, mb2, c2)
vej = calc_vej(m1, c1, m2, c2)
filename = os.path.join(plotDir, 'truth_mej_vej.dat')
fid = open(filename, 'w+')
fid.write('%.5f %.5f\n' % (mej, vej))
fid.close()
if m1 > 3:
filename = os.path.join(plotDir, 'truth.dat')
fid = open(filename, 'w+')
fid.write('%.5f %.5f %.5f %.5f %.5f\n' % (q, chi_eff, c2, mb2, m2))
fid.close()
t, lbol, mag = KaKy2016_model(q, chi_eff, m2, mb2, c2, th, ph)
else:
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
