diff --git a/examples/other_examples/sine_gaussian_example.py b/examples/other_examples/sine_gaussian_example.py
index f7273415a2f2c0947252077fac336c2b9ae953af..66d04866394ffad615158e4cea17168dfeea742b 100644
--- a/examples/other_examples/sine_gaussian_example.py
+++ b/examples/other_examples/sine_gaussian_example.py
@@ -43,13 +43,16 @@ priors = dict()
# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
# not sample any parameter that has a delta-function prior.
-for key in ['hrss', 'psi', 'ra', 'dec', 'geocent_time']:
+for key in ['psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
# The above list does *not* include frequency and Q, which means those are the parameters
# that will be included in the sampler. If we do nothing, then the default priors get used.
-priors['Q'] = tupak.prior.create_default_prior(name='Q')
-priors['frequency'] = tupak.prior.create_default_prior(name='frequency')
+#priors['Q'] = tupak.prior.create_default_prior(name='Q')
+#priors['frequency'] = tupak.prior.create_default_prior(name='frequency')
+priors['Q'] = tupak.prior.Uniform(2, 50, 'Q')
+priors['frequency'] = tupak.prior.Uniform(30, 1000, 'frequency')
+priors['hrss'] = tupak.prior.Uniform(1e-23, 1e-21, 'hrss')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index d2b0a8751fddbc4cc886f91917f4514e13d437b0..a872830d1b928e947c83e6459fe22d6aade70e5e 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -23,7 +23,7 @@ np.random.seed(170801)
# We are going to inject a supernova waveform. We first establish a dictionary of parameters that
# includes all of the different waveform parameters. It will read in a signal to inject from a txt file.
-injection_parameters = dict(file_path = 'MuellerL15_example_inj.txt', luminosity_distance = 60.0, ra = 1.375,
+injection_parameters = dict(file_path = 'MuellerL15_example_inj.txt', luminosity_distance = 10.0, ra = 1.375,
dec = -1.2108, geocent_time = 1126259642.413, psi= 2.659)
# Create the waveform_generator using a supernova source function
@@ -44,8 +44,8 @@ realPCs = np.loadtxt('SupernovaRealPCs.txt')
imagPCs = np.loadtxt('SupernovaImagPCs.txt')
# now we have to do the waveform_generator again because the signal model is not the same as the injection in this case.
-simulation_parameters = dict(realPCs=realPCs, imagPCs=imagPCs, coeff1 = 0.1, coeff2 = 0.1,
- coeff3 = 0.1, coeff4 = 0.1, coeff5 = 0.1, luminosity_distance = 60.0,
+simulation_parameters = dict(realPCs=realPCs, imagPCs=imagPCs, pc_coeff1 = 0.1, pc_coeff2 = 0.1,
+ pc_coeff3 = 0.1, pc_coeff4 = 0.1, pc_coeff5 = 0.1, luminosity_distance = 10.0,
ra = 1.375, dec = -1.2108, geocent_time = 1126259642.413, psi=2.659)
waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
@@ -62,14 +62,13 @@ priors = dict()
for key in ['psi', 'geocent_time']:
priors[key] = injection_parameters[key]
-# The above list does *not* include frequency and Q, which means those are the parameters
-# that will be included in the sampler. If we do nothing, then the default priors get used.
-priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
-priors['coeff1'] = tupak.prior.create_default_prior(name='coeff1')
-priors['coeff2'] = tupak.prior.create_default_prior(name='coeff2')
-priors['coeff3'] = tupak.prior.create_default_prior(name='coeff3')
-priors['coeff4'] = tupak.prior.create_default_prior(name='coeff4')
-priors['coeff5'] = tupak.prior.create_default_prior(name='coeff5')
+# don't use default for luminosity distance because we want kpc not Mpc
+priors['luminosity_distance'] = tupak.prior.Uniform(2, 20, 'luminosity_distance')
+priors['pc_coeff1'] = tupak.prior.Uniform(-100, 100, 'pc_coeff1')
+priors['pc_coeff2'] = tupak.prior.Uniform(-100, 100, 'pc_coeff2')
+priors['pc_coeff3'] = tupak.prior.Uniform(-100, 100, 'pc_coeff3')
+priors['pc_coeff4'] = tupak.prior.Uniform(-100, 100, 'pc_coeff4')
+priors['pc_coeff5'] = tupak.prior.Uniform(-100, 100, 'pc_coeff5')
priors['ra'] = tupak.prior.create_default_prior(name='ra')
priors['dec'] = tupak.prior.create_default_prior(name='dec')
diff --git a/tupak/prior.py b/tupak/prior.py
index 9324442ecd8be9d4e4d7f2024b5ac0e3ba5e5282..d26f3f12e486c585fadc04db426b3d34962bfb5b 100644
--- a/tupak/prior.py
+++ b/tupak/prior.py
@@ -438,15 +438,7 @@ def create_default_prior(name):
'iota': Sine(name=name),
'cos_iota': Uniform(name=name, minimum=-1, maximum=1),
'psi': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'phase': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'hrss': Uniform(name=name, minimum=1e-23, maximum=1e-21),
- 'Q': Uniform(name=name, minimum=2.0, maximum=50.0),
- 'frequency': Uniform(name=name, minimum=30.0, maximum=2000.0),
- 'coeff1': Uniform(name=name, minimum=-1.0, maximum=1.0),
- 'coeff2': Uniform(name=name, minimum=-1.0, maximum=1.0),
- 'coeff3': Uniform(name=name, minimum=-1.0, maximum=1.0),
- 'coeff4': Uniform(name=name, minimum=-1.0, maximum=1.0),
- 'coeff5': Uniform(name=name, minimum=-1.0, maximum=1.0)
+ 'phase': Uniform(name=name, minimum=0, maximum=2 * np.pi)
}
if name in default_priors.keys():
prior = default_priors[name]
diff --git a/tupak/source.py b/tupak/source.py
index 5cdf3e43efd672de7a5f29e2b2aa52cf235e4842..8c203db4e0be0ab276a5eed5ef2f95e849184623 100644
--- a/tupak/source.py
+++ b/tupak/source.py
@@ -59,7 +59,7 @@ def sinegaussian(frequency_array, hrss, Q, frequency, ra, dec, geocent_time, psi
fm = frequency_array - frequency
fp = frequency_array + frequency
- h_plus = (hrss / np.sqrt(temp * (1+np.exp(-Q**2)))) * ((np.sqrt(np.pi)*tau)/2.0) * (np.exp(-fm**2 * np.pi**2 * tau**2) + np.exp(-fp**2 * pi**2 * tau**2))
+ h_plus = (hrss / np.sqrt(temp * (1+np.exp(-Q**2)))) * ((np.sqrt(np.pi)*tau)/2.0) * (np.exp(-fm**2 * np.pi**2 * tau**2) + np.exp(-fp**2 * np.pi**2 * tau**2))
h_cross = -1j*(hrss / np.sqrt(temp * (1-np.exp(-Q**2)))) * ((np.sqrt(np.pi)*tau)/2.0) * (np.exp(-fm**2 * np.pi**2 * tau**2) - np.exp(-fp**2 * np.pi**2 * tau**2))
@@ -71,13 +71,13 @@ def supernova(frequency_array, realPCs, imagPCs, file_path, luminosity_distance,
realhplus, imaghplus, realhcross, imaghcross = np.loadtxt(file_path, usecols = (0,1,2,3), unpack=True)
# waveform in file at 10kpc
- scaling = 1e-2 * (10.0 / luminosity_distance)
+ scaling = 1e-3 * (10.0 / luminosity_distance)
h_plus = scaling * (realhplus + 1.0j*imaghplus)
h_cross = scaling * (realhcross + 1.0j*imaghcross)
return {'plus': h_plus, 'cross': h_cross}
-def supernova_pca_model(frequency_array, realPCs, imagPCs, coeff1, coeff2, coeff3, coeff4, coeff5, luminosity_distance, ra, dec, geocent_time, psi):
+def supernova_pca_model(frequency_array, realPCs, imagPCs, pc_coeff1, pc_coeff2, pc_coeff3, pc_coeff4, pc_coeff5, luminosity_distance, ra, dec, geocent_time, psi):
""" Supernova signal model """
pc1 = realPCs[:,0] + 1.0j*imagPCs[:,0]
@@ -87,10 +87,10 @@ def supernova_pca_model(frequency_array, realPCs, imagPCs, coeff1, coeff2, coeff
pc5 = realPCs[:,4] + 1.0j*imagPCs[:,5]
# file at 10kpc
- scaling = 1e-22 * (10.0 / luminosity_distance)
+ scaling = 1e-23 * (10.0 / luminosity_distance)
- h_plus = scaling * (coeff1*pc1 + coeff2*pc2 + coeff3*pc3 + coeff4*pc4 + coeff5*pc5)
- h_cross = scaling * (coeff1*pc1 + coeff2*pc2 + coeff3*pc3 + coeff4*pc4 + coeff5*pc5)
+ h_plus = scaling * (pc_coeff1*pc1 + pc_coeff2*pc2 + pc_coeff3*pc3 + pc_coeff4*pc4 + pc_coeff5*pc5)
+ h_cross = scaling * (pc_coeff1*pc1 + pc_coeff2*pc2 + pc_coeff3*pc3 + pc_coeff4*pc4 + pc_coeff5*pc5)
return {'plus': h_plus, 'cross': h_cross}