diff --git a/peyote/__init__.py b/peyote/__init__.py
index f1acb80d74cbb674b5f65ca2a7aab5ad99ecc371..285c2c448bc9fb13387568a6d68915e664e8dcb4 100644
--- a/peyote/__init__.py
+++ b/peyote/__init__.py
@@ -4,7 +4,6 @@ from __future__ import print_function, division
from . import utils
from . import detector
from . import prior
-from . import parameter
from . import source
from . import likelihood
from . import waveform_generator
diff --git a/peyote/parameter.py b/peyote/parameter.py
deleted file mode 100644
index f99aaf0bbb20d318f732ad8d8276e3d53928708c..0000000000000000000000000000000000000000
--- a/peyote/parameter.py
+++ /dev/null
@@ -1,159 +0,0 @@
-from __future__ import division, print_function, absolute_import
-import numpy as np
-from . import prior
-
-
-class Parameter(object):
-
- def __init__(self, name, prior=None, latex_label=None):
- self.name = name
-
- self.prior = prior
- self.latex_label = latex_label
-
- @property
- def prior(self):
- return self.__prior
-
- @property
- def latex_label(self):
- return self.__latex_label
-
- @property
- def is_fixed(self):
- return isinstance(self.__prior, prior.DeltaFunction)
-
- @prior.setter
- def prior(self, prior=None):
- if prior is None:
- self.set_default_prior()
- else:
- self.__prior = prior
-
- @latex_label.setter
- def latex_label(self, latex_label=None):
- if latex_label is None:
- self.set_default_latex_label()
- else:
- self.__latex_label = latex_label
-
- def fix(self, value=None):
- """
- Specify parameter as fixed, this will not be sampled.
- """
- if value is None or np.isnan(value):
- raise ValueError("You can't fix the value to be np.nan. You need to assign it a legal value")
- self.prior = prior.DeltaFunction(value)
-
- def set_default_prior(self):
-
- if self.name == 'mass_1':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(5, 100))
- elif self.name == 'mass_2':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(5, 100))
- elif self.name == 'mchirp':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(5, 100))
- elif self.name == 'q':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 1))
- elif self.name == 'a1':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 1))
- elif self.name == 'a2':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 1))
- elif self.name == 'tilt1':
- self.__prior = prior.Sine()
- elif self.name == 'tilt2':
- self.__prior = prior.Sine()
- elif self.name == 'phi1':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
- elif self.name == 'phi2':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
- elif self.name == 'luminosity_distance':
- self.__prior = prior.PowerLaw(alpha=2, bounds=(1e2, 5e3))
- elif self.name == 'dec':
- self.__prior = prior.Cosine()
- elif self.name == 'ra':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
- elif self.name == 'iota':
- self.__prior = prior.Sine()
- elif self.name == 'psi':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
- elif self.name == 'phase':
- self.__prior = prior.PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
- else:
- self.__prior = None
-
- def set_default_values(self):
- # spins
- if self.name == 'a1':
- self.__prior = prior.DeltaFunction(0)
- elif self.name == 'a2':
- self.__prior = prior.DeltaFunction(0)
- elif self.name == 'tilt1':
- self.__prior = prior.DeltaFunction(0)
- elif self.name == 'tilt2':
- self.__prior = prior.DeltaFunction(0)
- elif self.name == 'phi1':
- self.__prior = prior.DeltaFunction(0)
- elif self.name == 'phi2':
- self.__prior = prior.DeltaFunction(0)
-
- def set_default_latex_label(self):
- if self.name == 'mass_1':
- self.__latex_label = '$m_1$'
- elif self.name == 'mass_2':
- self.__latex_label = '$m_2$'
- elif self.name == 'mchirp':
- self.__latex_label = '$\mathcal{M}$'
- elif self.name == 'q':
- self.__latex_label = 'q'
- elif self.name == 'a1':
- self.__latex_label = 'a_1'
- elif self.name == 'a2':
- self.__latex_label = 'a_2'
- elif self.name == 'tilt1':
- self.__latex_label = 'tilt_1'
- elif self.name == 'tilt2':
- self.__latex_label = 'tilt_2'
- elif self.name == 'phi1':
- self.__latex_label = '\phi_1'
- elif self.name == 'phi2':
- self.__latex_label = '\phi_2'
- elif self.name == 'luminosity_distance':
- self.__latex_label = 'd_L'
- elif self.name == 'dec':
- self.__latex_label = '\mathrm{DEC}'
- elif self.name == 'ra':
- self.__latex_label = '\mathrm{RA}'
- elif self.name == 'iota':
- self.__latex_label = '\iota'
- elif self.name == 'psi':
- self.__latex_label = '\psi'
- elif self.name == 'phase':
- self.__latex_label = '\phi'
- elif self.name == 'tc':
- self.__latex_label = 't_c'
- else:
- self.__latex_label = self.name
-
- @staticmethod
- def parse_floats_to_parameters(old_parameters):
- parameters = old_parameters.copy()
- for key in parameters:
- if type(parameters[key]) is not float and type(parameters[key]) is not int \
- and type(parameters[key]) is not Parameter:
- print("Expected parameter " + str(key) + " to be a float or int but was " + str(type(parameters[key]))
- + " instead. Will not be converted.")
- continue
- elif type(parameters[key]) is Parameter:
- continue
-
- parameters[key] = Parameter(key)
- parameters[key].fix(old_parameters[key])
- return parameters
-
- @staticmethod
- def parse_keys_to_parameters(keys):
- parameters = {}
- for key in keys:
- parameters[key] = Parameter(key)
- return parameters
\ No newline at end of file
diff --git a/peyote/prior.py b/peyote/prior.py
index b39d3a1798f8c3762e099389576f2147e0f16a81..8a85f7854d7cf4ac70f7f213049ef6c742c796e7 100644
--- a/peyote/prior.py
+++ b/peyote/prior.py
@@ -8,8 +8,9 @@ from scipy.integrate import cumtrapz
class Prior(object):
"""Prior class"""
- def __init__(self, **kwargs):
- return
+ def __init__(self, name=None, latex_label=None):
+ self.name = name
+ self.latex_label = latex_label
def __call__(self):
return self.sample(1)
@@ -32,12 +33,66 @@ class Prior(object):
['{}={}'.format(k, v) for k, v in self.__dict__.items()])
return "{}({})".format(prior_name, prior_args)
+ @property
+ def is_fixed(self):
+ return isinstance(self, DeltaFunction)
+
+ @property
+ def latex_label(self):
+ return self.__latex_label
+
+ @latex_label.setter
+ def latex_label(self, latex_label=None):
+ if latex_label is None:
+ self.__latex_label = self.__default_latex_label
+ else:
+ self.__latex_label = latex_label
+
+ @property
+ def __default_latex_label(self):
+ if self.name == 'mass_1':
+ return '$m_1$'
+ elif self.name == 'mass_2':
+ return '$m_2$'
+ elif self.name == 'mchirp':
+ return '$\mathcal{M}$'
+ elif self.name == 'q':
+ return 'q'
+ elif self.name == 'a1':
+ return 'a_1'
+ elif self.name == 'a2':
+ return 'a_2'
+ elif self.name == 'tilt1':
+ return 'tilt_1'
+ elif self.name == 'tilt2':
+ return 'tilt_2'
+ elif self.name == 'phi1':
+ return '\phi_1'
+ elif self.name == 'phi2':
+ return '\phi_2'
+ elif self.name == 'luminosity_distance':
+ return 'd_L'
+ elif self.name == 'dec':
+ return '\mathrm{DEC}'
+ elif self.name == 'ra':
+ return '\mathrm{RA}'
+ elif self.name == 'iota':
+ return '\iota'
+ elif self.name == 'psi':
+ return '\psi'
+ elif self.name == 'phase':
+ return '\phi'
+ elif self.name == 'tc':
+ return 't_c'
+ else:
+ return self.name
+
class Uniform(Prior):
"""Uniform prior"""
- def __init__(self, lower, upper):
- Prior.__init__(self)
+ def __init__(self, lower, upper, name=None, latex_label=None):
+ Prior.__init__(self, name, latex_label)
self.lower = lower
self.upper = upper
self.support = upper - lower
@@ -56,8 +111,8 @@ class Uniform(Prior):
class DeltaFunction(Prior):
"""Dirac delta function prior, this always returns peak."""
- def __init__(self, peak):
- Prior.__init__(self)
+ def __init__(self, peak, name=None, latex_label=None):
+ Prior.__init__(self, name, latex_label)
self.peak = peak
def rescale(self, val):
@@ -75,9 +130,9 @@ class DeltaFunction(Prior):
class PowerLaw(Prior):
"""Power law prior distribution"""
- def __init__(self, alpha, bounds):
+ def __init__(self, alpha, bounds, name=None, latex_label=None):
"""Power law with bounds and alpha, spectral index"""
- Prior.__init__(self)
+ Prior.__init__(self, name, latex_label)
self.alpha = alpha
self.low, self.high = bounds
@@ -101,8 +156,8 @@ class PowerLaw(Prior):
class Cosine(Prior):
- def __init__(self):
- Prior.__init__(self)
+ def __init__(self, name=None, latex_label=None):
+ Prior.__init__(self, name, latex_label)
def rescale(self, val):
"""
@@ -120,8 +175,8 @@ class Cosine(Prior):
class Sine(Prior):
- def __init__(self):
- Prior.__init__(self)
+ def __init__(self, name=None, latex_label=None):
+ Prior.__init__(self, name, latex_label)
def rescale(self, val):
"""
@@ -139,9 +194,9 @@ class Sine(Prior):
class Interped(Prior):
- def __init__(self, xx, yy):
+ def __init__(self, xx, yy, name=None, latex_label=None):
"""Initialise object from arrays of x and y=p(x)"""
- Prior.__init__(self)
+ Prior.__init__(self, name, latex_label)
self.xx = xx
self.low = min(self.xx)
self.high = max(self.xx)
@@ -179,3 +234,71 @@ class FromFile(Interped):
print("Can't load {}.".format(file_name))
print("Format should be:")
print(r"x\tp(x)")
+
+
+def fix(prior, value=None):
+ if value is None or np.isnan(value):
+ raise ValueError("You can't fix the value to be np.nan. You need to assign it a legal value")
+ prior = DeltaFunction(name=prior.name,
+ latex_label=prior.latex_label,
+ peak=value)
+ return prior
+
+
+def create_default_prior(name):
+ if name == 'mass_1':
+ prior = PowerLaw(alpha=0, bounds=(5, 100))
+ elif name == 'mass_2':
+ prior = PowerLaw(alpha=0, bounds=(5, 100))
+ elif name == 'mchirp':
+ prior = PowerLaw(alpha=0, bounds=(5, 100))
+ elif name == 'q':
+ prior = PowerLaw(alpha=0, bounds=(0, 1))
+ elif name == 'a1':
+ prior = PowerLaw(alpha=0, bounds=(0, 1))
+ elif name == 'a2':
+ prior = PowerLaw(alpha=0, bounds=(0, 1))
+ elif name == 'tilt1':
+ prior = Sine()
+ elif name == 'tilt2':
+ prior = Sine()
+ elif name == 'phi1':
+ prior = PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
+ elif name == 'phi2':
+ prior = PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
+ elif name == 'luminosity_distance':
+ prior = PowerLaw(alpha=2, bounds=(1e2, 5e3))
+ elif name == 'dec':
+ prior = Cosine()
+ elif name == 'ra':
+ prior = PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
+ elif name == 'iota':
+ prior = Sine()
+ elif name == 'psi':
+ prior = PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
+ elif name == 'phase':
+ prior = PowerLaw(alpha=0, bounds=(0, 2 * np.pi))
+ else:
+ prior = None
+ return prior
+
+
+def parse_floats_to_fixed_priors(old_parameters):
+ parameters = old_parameters.copy()
+ for key in parameters:
+ if type(parameters[key]) is not float and type(parameters[key]) is not int \
+ and type(parameters[key]) is not Prior:
+ print("Expected parameter " + str(key) + " to be a float or int but was " + str(type(parameters[key]))
+ + " instead. Will not be converted.")
+ continue
+ elif type(parameters[key]) is Prior:
+ continue
+ parameters[key] = DeltaFunction(name=key, latex_label=None, peak=old_parameters[key])
+ return parameters
+
+
+def parse_keys_to_parameters(keys):
+ parameters = {}
+ for key in keys:
+ parameters[key] = create_default_prior(key)
+ return parameters
diff --git a/peyote/sampler.py b/peyote/sampler.py
index 7916482c59913c0784097cdb1caf02d5b95bd73a..de3c851b485cc29631fefc7fd8c78d465e450131 100644
--- a/peyote/sampler.py
+++ b/peyote/sampler.py
@@ -8,7 +8,7 @@ import sys
import numpy as np
from .result import Result
-from .parameter import Parameter
+from peyote import prior
class Sampler(object):
@@ -98,18 +98,17 @@ class Sampler(object):
def initialise_parameters(self):
for key in self.priors:
- if isinstance(self.priors[key], Parameter) \
- and self.priors[key].prior is not None \
+ if isinstance(self.priors[key], prior.Prior) is True \
and self.priors[key].is_fixed is False:
self.__search_parameter_keys.append(key)
- elif isinstance(self.priors[key], Parameter) \
+ elif isinstance(self.priors[key], prior.Prior) \
and self.priors[key].is_fixed is True:
self.likelihood.waveform_generator.parameters[key] = \
self.priors[key].prior.sample()
logging.info("Search parameters:")
for key in self.__search_parameter_keys:
- logging.info(' {} ~ {}'.format(key, self.priors[key].prior))
+ logging.info(' {} ~ {}'.format(key, self.priors[key]))
def verify_parameters(self):
required_keys = self.priors
@@ -119,7 +118,7 @@ class Sampler(object):
"Source model does not contain keys {}".format(unmatched_keys))
def prior_transform(self, theta):
- return [self.priors[key].prior.rescale(t) for key, t in zip(self.__search_parameter_keys, theta)]
+ return [self.priors[key].rescale(t) for key, t in zip(self.__search_parameter_keys, theta)]
def log_likelihood(self, theta):
for i, k in enumerate(self.__search_parameter_keys):
diff --git a/peyote/waveform_generator.py b/peyote/waveform_generator.py
index f920dd6f17d7118aa565c41359a02e62f57e2da9..ab50226a902466ffbd3090a4efd9a242b15402cb 100644
--- a/peyote/waveform_generator.py
+++ b/peyote/waveform_generator.py
@@ -1,7 +1,7 @@
import inspect
from . import utils
-from . import parameter
+
class WaveformGenerator(object):
""" A waveform generator
diff --git a/test/parameter_tests.py b/test/parameter_tests.py
index 4c9129fb891f7d0f0e65d06098a341ecc5274d8f..022b00aefef3db490145580096b8fe1de96c41ef 100644
--- a/test/parameter_tests.py
+++ b/test/parameter_tests.py
@@ -7,7 +7,7 @@ class TestParameterInstantiationWithoutOptionalParameters(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -32,7 +32,7 @@ class TestParameterName(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -46,7 +46,7 @@ class TestParameterPrior(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -66,7 +66,7 @@ class TestParameterPrior(unittest.TestCase):
class TestParameterValue(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -89,7 +89,7 @@ class TestParameterValue(unittest.TestCase):
class TestParameterLatexLabel(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -111,7 +111,7 @@ class TestParameterLatexLabel(unittest.TestCase):
class TestParameterIsFixed(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
@@ -128,7 +128,7 @@ class TestFixMethod(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.parameter = peyote.parameter.Parameter(self.test_name)
+ self.parameter = peyote.parameter.PriorFactory(self.test_name)
def tearDown(self):
del self.parameter
diff --git a/test/tests.py b/test/tests.py
index ad3301d344956acaae8e2e94d1031deb6e784e93..76ba225441da2e70f3e9412b155dc3f0b0326b5e 100644
--- a/test/tests.py
+++ b/test/tests.py
@@ -50,8 +50,8 @@ class Test(unittest.TestCase):
[self.msd['IFO']], self.msd['waveform_generator'])
dL = self.msd['simulation_parameters']['luminosity_distance']
- priors = {'luminosity_distance' : peyote.parameter.Parameter('luminosity_distance',
- prior=peyote.prior.Uniform(lower=dL - 10, upper=dL + 10))
+ priors = {'luminosity_distance' : peyote.parameter.PriorFactory('luminosity_distance',
+ prior=peyote.prior.Uniform(lower=dL - 10, upper=dL + 10))
}
result = peyote.sampler.run_sampler(likelihood, priors, sampler='nestle',
diff --git a/tutorials/BasicTutorial.py b/tutorials/BasicTutorial.py
index 642384efcbf6f9cb00864a77c927ed5c3c32f7e6..7aec0b2eb30d58d79246106d92715b4165ef70b2 100644
--- a/tutorials/BasicTutorial.py
+++ b/tutorials/BasicTutorial.py
@@ -3,7 +3,7 @@ import pylab as plt
import dynesty.plotting as dyplot
import corner
-import peyote
+import peyote.prior
peyote.utils.setup_logger()
@@ -38,11 +38,9 @@ waveform_generator = peyote.waveform_generator.WaveformGenerator(
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
-sampling_parameters = peyote.parameter.Parameter.\
- parse_floats_to_parameters(injection_parameters)
+#sampling_parameters = peyote.prior.parse_floats_to_fixed_priors(injection_parameters)
-# sampling_parameters = peyote.parameter.Parameter.\
-# parse_keys_to_parameters(simulation_parameters.keys())
+sampling_parameters = peyote.prior.parse_keys_to_parameters(injection_parameters.keys())
# Simulate the data in H1
@@ -81,8 +79,8 @@ fig.savefig('data')
likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
# New way way of doing it, still not perfect
-sampling_parameters['mass_1'].prior = peyote.prior.Uniform(lower=35, upper=37)
-sampling_parameters['luminosity_distance'].prior = peyote.prior.Uniform(lower=30, upper=200)
+sampling_parameters['mass_1'] = peyote.prior.Uniform(lower=35, upper=37, name='mass1')
+sampling_parameters['luminosity_distance'] = peyote.prior.Uniform(lower=30, upper=200, name='luminosity_distance')
#sampling_parameters["geocent_time"].prior = peyote.prior.Uniform(lower=injection_parameters["geocent_time"] - 0.1,
# upper=injection_parameters["geocent_time"]+0.1)
diff --git a/tutorials/GW150914.py b/tutorials/GW150914.py
index 5ed131a9a2a567562e088855b8ec3b80176f73c7..c94d92c0cefed2fa2d8bfea9209c441d38b01966 100644
--- a/tutorials/GW150914.py
+++ b/tutorials/GW150914.py
@@ -108,14 +108,14 @@ prior = dict(spin11=0, spin12=0, spin13=0, spin21=0, spin22=0, spin23=0,
waveform_approximant='IMRPhenomPv2', reference_frequency=50.,
ra=1.375, dec=-1.2108, geocent_time=time_of_event, psi=2.659,
mass_1=36, mass_2=29)
-prior = peyote.parameter.Parameter.parse_floats_to_parameters(prior)
-prior['mass_1'] = peyote.parameter.Parameter(
+prior = peyote.parameter.PriorFactory.parse_floats_to_parameters(prior)
+prior['mass_1'] = peyote.parameter.PriorFactory(
'mass_1', prior=peyote.prior.Uniform(lower=35, upper=41),
latex_label='$m_1$')
-prior['mass_2'] = peyote.parameter.Parameter(
+prior['mass_2'] = peyote.parameter.PriorFactory(
'mass_2', prior=peyote.prior.Uniform(lower=20, upper=35),
latex_label='$m_2$')
-prior['geocent_time'] = peyote.parameter.Parameter(
+prior['geocent_time'] = peyote.parameter.PriorFactory(
'mass_2', prior=peyote.prior.Uniform(
lower=time_of_event-0.1, upper=time_of_event+0.1))
diff --git a/tutorials/Injection.py b/tutorials/Injection.py
index d5db982174b07d70571f0de2b90fc09564d0b122..080bdace58d11f0630e17d77a95a8b117b547cd0 100644
--- a/tutorials/Injection.py
+++ b/tutorials/Injection.py
@@ -32,10 +32,10 @@ print(likelihood.log_likelihood())
print(likelihood.log_likelihood_ratio())
prior = source.copy()
-prior.mass_1 = peyote.parameter.Parameter('mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
- latex_label='$m_1$')
-prior.mass_2 = peyote.parameter.Parameter('mass_2', prior=peyote.prior.Uniform(lower=28, upper=30),
- latex_label='$m_2$')
+prior.mass_1 = peyote.parameter.PriorFactory('mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
+ latex_label='$m_1$')
+prior.mass_2 = peyote.parameter.PriorFactory('mass_2', prior=peyote.prior.Uniform(lower=28, upper=30),
+ latex_label='$m_2$')
# result = peyote.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=100, print_progress=True)
diff --git a/tutorials/custom_source_tutorial.py b/tutorials/custom_source_tutorial.py
index 0a62fe44d2ed2ac5e355702a234ee0a1949c17ee..4d7aded9e2c8abcd37895674537575680e6029d4 100644
--- a/tutorials/custom_source_tutorial.py
+++ b/tutorials/custom_source_tutorial.py
@@ -71,7 +71,7 @@ simulation_parameters = dict(amplitude=1e-21,
dec=-1.2108,
geocent_time=1126259642.413,
psi=2.659)
-sampling_parameters = peyote.parameter.Parameter.parse_floats_to_parameters(simulation_parameters)
+sampling_parameters = peyote.parameter.PriorFactory.parse_floats_to_parameters(simulation_parameters)
wg = peyote.waveform_generator.WaveformGenerator(
source_model=gaussian_frequency_domain_strain,