diff --git a/bilby/core/likelihood.py b/bilby/core/likelihood.py
index 910f6eb3226c14ea4f05e2124a4a024c6411a781..e1fbec22cdaa8dfe29523536b25f8103fe61b415 100644
--- a/bilby/core/likelihood.py
+++ b/bilby/core/likelihood.py
@@ -14,9 +14,11 @@ class Likelihood(object):
Parameters
----------
- parameters:
+ parameters: dict
+ A dictionary of the parameter names and associated values
"""
self.parameters = parameters
+ self._meta_data = None
def __repr__(self):
return self.__class__.__name__ + '(parameters={})'.format(self.parameters)
@@ -50,10 +52,7 @@ class Likelihood(object):
@property
def meta_data(self):
- try:
- return self._meta_data
- except AttributeError:
- return None
+ return self._meta_data
@meta_data.setter
def meta_data(self, meta_data):
@@ -84,8 +83,8 @@ class Analytical1DLikelihood(Likelihood):
Likelihood.__init__(self, dict.fromkeys(parameters))
self.x = x
self.y = y
- self.__func = func
- self.__function_keys = list(self.parameters.keys())
+ self._func = func
+ self._function_keys = list(self.parameters.keys())
def __repr__(self):
return self.__class__.__name__ + '(x={}, y={}, func={})'.format(self.x, self.y, self.func.__name__)
@@ -93,7 +92,7 @@ class Analytical1DLikelihood(Likelihood):
@property
def func(self):
""" Make func read-only """
- return self.__func
+ return self._func
@property
def model_parameters(self):
@@ -103,7 +102,7 @@ class Analytical1DLikelihood(Likelihood):
@property
def function_keys(self):
""" Makes function_keys read_only """
- return self.__function_keys
+ return self._function_keys
@property
def n(self):
@@ -113,24 +112,24 @@ class Analytical1DLikelihood(Likelihood):
@property
def x(self):
""" The independent variable. Setter assures that single numbers will be converted to arrays internally """
- return self.__x
+ return self._x
@x.setter
def x(self, x):
if isinstance(x, int) or isinstance(x, float):
x = np.array([x])
- self.__x = x
+ self._x = x
@property
def y(self):
""" The dependent variable. Setter assures that single numbers will be converted to arrays internally """
- return self.__y
+ return self._y
@y.setter
def y(self, y):
if isinstance(y, int) or isinstance(y, float):
y = np.array([y])
- self.__y = y
+ self._y = y
@property
def residual(self):
@@ -287,7 +286,7 @@ class ExponentialLikelihood(Analytical1DLikelihood):
@property
def y(self):
""" Property assures that y-value is positive. """
- return self.__y
+ return self._y
@y.setter
def y(self, y):
@@ -295,7 +294,7 @@ class ExponentialLikelihood(Analytical1DLikelihood):
y = np.array([y])
if np.any(y < 0):
raise ValueError("Data must be non-negative")
- self.__y = y
+ self._y = y
class StudentTLikelihood(Analytical1DLikelihood):
@@ -403,19 +402,19 @@ class JointLikelihood(Likelihood):
@property
def likelihoods(self):
""" The list of likelihoods """
- return self.__likelihoods
+ return self._likelihoods
@likelihoods.setter
def likelihoods(self, likelihoods):
likelihoods = copy.deepcopy(likelihoods)
if isinstance(likelihoods, tuple) or isinstance(likelihoods, list):
if all(isinstance(likelihood, Likelihood) for likelihood in likelihoods):
- self.__likelihoods = list(likelihoods)
+ self._likelihoods = list(likelihoods)
else:
raise ValueError('Try setting the JointLikelihood like this\n'
'JointLikelihood(first_likelihood, second_likelihood, ...)')
elif isinstance(likelihoods, Likelihood):
- self.__likelihoods = [likelihoods]
+ self._likelihoods = [likelihoods]
else:
raise ValueError('Input likelihood is not a list of tuple. You need to set multiple likelihoods.')
diff --git a/bilby/core/prior.py b/bilby/core/prior.py
index 853740c0e0823233ae82b9c5192d61bc9eae501d..8cfdf7b2060617d61ca7a0684fb7451d805807cd 100644
--- a/bilby/core/prior.py
+++ b/bilby/core/prior.py
@@ -23,9 +23,9 @@ class PriorDict(OrderedDict):
Parameters
----------
- dictionary: dict, None
+ dictionary: Union[dict, str, None]
If given, a dictionary to generate the prior set.
- filename: str, None
+ filename: Union[str, None]
If given, a file containing the prior to generate the prior set.
conversion_function: func
Function to convert between sampled parameters and constraints.
@@ -127,7 +127,7 @@ class PriorDict(OrderedDict):
val = prior
except (NameError, SyntaxError, TypeError):
logger.debug(
- "Failed to load dictionary value {} correctlty"
+ "Failed to load dictionary value {} correctly"
.format(key))
pass
self[key] = val
@@ -482,7 +482,7 @@ class Prior(object):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
A random number between 0 and 1
Returns
@@ -497,7 +497,7 @@ class Prior(object):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -511,7 +511,7 @@ class Prior(object):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -525,7 +525,7 @@ class Prior(object):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -540,7 +540,7 @@ class Prior(object):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Raises
-------
@@ -616,7 +616,7 @@ class Prior(object):
@property
def latex_label_with_unit(self):
- """ If a unit is specifed, returns a string of the latex label and unit """
+ """ If a unit is specified, returns a string of the latex label and unit """
if self.unit is not None:
return "{} [{}]".format(self.latex_label, self.unit)
else:
@@ -697,7 +697,7 @@ class DeltaFunction(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -711,11 +711,11 @@ class DeltaFunction(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: np.inf if val = peak, 0 otherwise
+ Union[float, array_like]: np.inf if val = peak, 0 otherwise
"""
at_peak = (val == self.peak)
@@ -758,12 +758,12 @@ class PowerLaw(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Uniform probability
Returns
-------
- float: Rescaled probability
+ Union[float, array_like]: Rescaled probability
"""
self.test_valid_for_rescaling(val)
if self.alpha == -1:
@@ -777,7 +777,7 @@ class PowerLaw(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -795,7 +795,7 @@ class PowerLaw(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -838,6 +838,20 @@ class Uniform(Prior):
periodic_boundary=periodic_boundary)
def rescale(self, val):
+ """
+ 'Rescale' a sample from the unit line element to the power-law prior.
+
+ This maps to the inverse CDF. This has been analytically solved for this case.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+ Uniform probability
+
+ Returns
+ -------
+ Union[float, array_like]: Rescaled probability
+ """
self.test_valid_for_rescaling(val)
return self.minimum + val * (self.maximum - self.minimum)
@@ -846,7 +860,7 @@ class Uniform(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -860,7 +874,7 @@ class Uniform(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -901,9 +915,9 @@ class SymmetricLogUniform(Prior):
def __init__(self, minimum, maximum, name=None, latex_label=None,
unit=None, periodic_boundary=False):
- """Symmetric Log-Uniform distribtions with bounds
+ """Symmetric Log-Uniform distributions with bounds
- This is identical to a Log-Uniform distribition, but mirrored about
+ This is identical to a Log-Uniform distribution, but mirrored about
the zero-axis and subsequently normalized. As such, the distribution
has support on the two regions [-maximum, -minimum] and [minimum,
maximum].
@@ -935,12 +949,12 @@ class SymmetricLogUniform(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Uniform probability
Returns
-------
- float: Rescaled probability
+ Union[float, array_like]: Rescaled probability
"""
self.test_valid_for_rescaling(val)
if val < 0.5:
@@ -955,7 +969,7 @@ class SymmetricLogUniform(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -970,7 +984,7 @@ class SymmetricLogUniform(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -1019,7 +1033,7 @@ class Cosine(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -1067,11 +1081,11 @@ class Sine(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return np.sin(val) / 2 * self.is_in_prior_range(val)
@@ -1104,6 +1118,10 @@ class Gaussian(Prior):
"""
'Rescale' a sample from the unit line element to the appropriate Gaussian prior.
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
This maps to the inverse CDF. This has been analytically solved for this case.
"""
self.test_valid_for_rescaling(val)
@@ -1114,15 +1132,26 @@ class Gaussian(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return np.exp(-(self.mu - val) ** 2 / (2 * self.sigma ** 2)) / (2 * np.pi) ** 0.5 / self.sigma
def ln_prob(self, val):
+ """Return the Log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return -0.5 * ((self.mu - val) ** 2 / self.sigma ** 2 + np.log(2 * np.pi * self.sigma ** 2))
@@ -1208,7 +1237,7 @@ class TruncatedGaussian(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -1332,20 +1361,31 @@ class LogNormal(Prior):
return scipy.stats.lognorm.ppf(val, self.sigma, scale=np.exp(self.mu))
def prob(self, val):
- """Return the prior probability of val.
+ """Returns the prior probability of val.
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.lognorm.pdf(val, self.sigma, scale=np.exp(self.mu))
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return scipy.stats.lognorm.logpdf(val, self.sigma, scale=np.exp(self.mu))
@@ -1409,16 +1449,27 @@ class Exponential(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.expon.pdf(val, scale=self.mu)
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return scipy.stats.expon.logpdf(val, scale=self.mu)
@@ -1472,15 +1523,26 @@ class StudentT(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.t.pdf(val, self.df, loc=self.mu, scale=self.scale)
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return scipy.stats.t.logpdf(val, self.df, loc=self.mu, scale=self.scale)
@@ -1540,11 +1602,11 @@ class Beta(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
spdf = self._dist.pdf(val)
@@ -1560,6 +1622,17 @@ class Beta(Prior):
return 0.
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
spdf = self._dist.logpdf(val)
if np.all(np.isfinite(spdf)):
return spdf
@@ -1572,7 +1645,6 @@ class Beta(Prior):
return -np.inf
def _set_dist(self):
- """Try/except to stop it falling over at instantiation"""
self._dist = scipy.stats.beta(
a=self.alpha, b=self.beta, loc=self.minimum,
scale=(self.maximum - self.minimum))
@@ -1659,15 +1731,26 @@ class Logistic(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.logistic.pdf(val, loc=self.mu, scale=self.scale)
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return scipy.stats.logistic.logpdf(val, loc=self.mu, scale=self.scale)
@@ -1716,15 +1799,25 @@ class Cauchy(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.cauchy.pdf(val, loc=self.alpha, scale=self.beta)
def ln_prob(self, val):
+ """Return the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Log prior probability of val
+ """
return scipy.stats.cauchy.logpdf(val, loc=self.alpha, scale=self.beta)
@@ -1799,16 +1892,27 @@ class Gamma(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return scipy.stats.gamma.pdf(val, self.k, loc=0., scale=self.theta)
def ln_prob(self, val):
+ """Returns the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Prior probability of val
+ """
+
return scipy.stats.gamma.logpdf(val, self.k, loc=0., scale=self.theta)
@@ -1873,7 +1977,7 @@ class Interped(Prior):
See superclass
Attributes
- -------
+ ----------
probability_density: scipy.interpolate.interp1d
Interpolated prior probability distribution
cumulative_distribution: scipy.interpolate.interp1d
@@ -1886,10 +1990,15 @@ class Interped(Prior):
"""
self.xx = xx
self.yy = yy
+ self.YY = None
+ self.probability_density = None
+ self.cumulative_distribution = None
+ self.inverse_cumulative_distribution = None
self.__all_interpolated = interp1d(x=xx, y=yy, bounds_error=False, fill_value=0)
+ minimum = float(np.nanmax(np.array((min(xx), minimum))))
+ maximum = float(np.nanmin(np.array((max(xx), maximum))))
Prior.__init__(self, name=name, latex_label=latex_label, unit=unit,
- minimum=np.nanmax(np.array((min(xx), minimum))),
- maximum=np.nanmin(np.array((max(xx), maximum))),
+ minimum=minimum, maximum=maximum,
periodic_boundary=periodic_boundary)
self._update_instance()
@@ -1905,11 +2014,11 @@ class Interped(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
- float: Prior probability of val
+ Union[float, array_like]: Prior probability of val
"""
return self.probability_density(val)
@@ -2003,11 +2112,6 @@ class FromFile(Interped):
periodic_boundary: bool
See superclass
- Attributes
- -------
- all_interpolated: scipy.interpolate.interp1d
- Interpolated prior function
-
"""
try:
self.id = file_name
@@ -2074,6 +2178,10 @@ class FermiDirac(Prior):
"""
'Rescale' a sample from the unit line element to the appropriate Fermi-Dirac prior.
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
This maps to the inverse CDF. This has been analytically solved for this case,
see Equation 24 of [1]_.
@@ -2097,7 +2205,7 @@ class FermiDirac(Prior):
return -self.sigma * np.log(inv)
else:
idx = inv >= 0.
- tmpinv = np.inf * np.ones(len(val))
+ tmpinv = np.inf * np.ones(len(np.atleast_1d(val)))
tmpinv[idx] = -self.sigma * np.log(inv[idx])
return tmpinv
@@ -2106,7 +2214,7 @@ class FermiDirac(Prior):
Parameters
----------
- val: float
+ val: Union[float, int, array_like]
Returns
-------
@@ -2115,6 +2223,17 @@ class FermiDirac(Prior):
return np.exp(self.ln_prob(val))
def ln_prob(self, val):
+ """Return the log prior probability of val.
+
+ Parameters
+ ----------
+ val: Union[float, int, array_like]
+
+ Returns
+ -------
+ Union[float, array_like]: Log prior probability of val
+ """
+
norm = -np.log(self.sigma * np.log(1. + np.exp(self.r)))
if isinstance(val, (float, int)):
if val < self.minimum:
@@ -2122,6 +2241,7 @@ class FermiDirac(Prior):
else:
return norm - np.logaddexp((val / self.sigma) - self.r, 0.)
else:
+ val = np.atleast_1d(val)
lnp = -np.inf * np.ones(len(val))
idx = val >= self.minimum
lnp[idx] = norm - np.logaddexp((val[idx] / self.sigma) - self.r, 0.)
diff --git a/bilby/core/sampler/base_sampler.py b/bilby/core/sampler/base_sampler.py
index ca2d5c4073ccaf286119b2fddc22e4d823cfce92..a120dda1920daf6dd74e12e22451665d38dbb7c2 100644
--- a/bilby/core/sampler/base_sampler.py
+++ b/bilby/core/sampler/base_sampler.py
@@ -104,7 +104,7 @@ class Sampler(object):
self._search_parameter_keys = list()
self._fixed_parameter_keys = list()
- self._constraint_keys = list()
+ self._constraint_parameter_keys = list()
self._initialise_parameters()
self._verify_parameters()
self._time_likelihood()
@@ -192,13 +192,13 @@ class Sampler(object):
and self.priors[key].is_fixed is False:
self._search_parameter_keys.append(key)
elif isinstance(self.priors[key], Constraint):
- self._constraint_keys.append(key)
+ self._constraint_parameter_keys.append(key)
elif isinstance(self.priors[key], DeltaFunction):
self.likelihood.parameters[key] = self.priors[key].sample()
self._fixed_parameter_keys.append(key)
logger.info("Search parameters:")
- for key in self._search_parameter_keys + self._constraint_keys:
+ for key in self._search_parameter_keys + self._constraint_parameter_keys:
logger.info(' {} = {}'.format(key, self.priors[key]))
for key in self._fixed_parameter_keys:
logger.info(' {} = {}'.format(key, self.priors[key].peak))
@@ -215,7 +215,7 @@ class Sampler(object):
sampler=self.__class__.__name__.lower(),
search_parameter_keys=self._search_parameter_keys,
fixed_parameter_keys=self._fixed_parameter_keys,
- constraint_parameter_keys=self._constraint_keys,
+ constraint_parameter_keys=self._constraint_parameter_keys,
priors=self.priors, meta_data=self.meta_data,
injection_parameters=self.injection_parameters,
sampler_kwargs=self.kwargs)
@@ -502,7 +502,7 @@ class NestedSampler(Sampler):
the prior constraint here.
Parameters
- theta: array-like
+ theta: array_like
Parameter values at which to evaluate likelihood
Returns
diff --git a/bilby/core/utils.py b/bilby/core/utils.py
index 5636a594ade7835d9dd691acdef6e7eda764ce6a..ef1bec99b20d7c3188af0ec732fe43ef15af4aae 100644
--- a/bilby/core/utils.py
+++ b/bilby/core/utils.py
@@ -692,7 +692,7 @@ def logtrapzexp(lnf, dx):
----------
lnf: array_like
A :class:`numpy.ndarray` of values that are the natural logarithm of a function
- dx: (array_like, float)
+ dx: Union[array_like, float]
A :class:`numpy.ndarray` of steps sizes between values in the function, or a
single step size value.
@@ -761,16 +761,31 @@ def run_commandline(cl, log_level=20, raise_error=True, return_output=True):
class UnsortedInterp2d(interp2d):
- """
- Wrapper to scipy.interpolate.interp2d which preserves the input ordering.
- See https://stackoverflow.com/questions/44941271/scipy-interp2d-returned-function-sorts-input-argument-automatically-and-undesira
- for the implementation details.
- """
+ def __call__(self, x, y, dx=0, dy=0, assume_sorted=False):
+ """ Wrapper to scipy.interpolate.interp2d which preserves the input ordering.
+
+ See https://stackoverflow.com/questions/44941271/scipy-interp2d-returned-function-sorts-input-argument-automatically-and-undesira
+ for the implementation details.
+
+
+ Parameters
+ ----------
+ x: See superclass
+ y: See superclass
+ dx: See superclass
+ dy: See superclass
+ assume_sorted: bool, optional
+ This is just a place holder to prevent a warning.
+ Overwriting this will not do anything
+
+ Returns
+ ----------
+ array_like: See superclass
- def __call__(self, x, y, dx=0, dy=0):
+ """
unsorted_idxs = np.argsort(np.argsort(x))
- return interp2d.__call__(self, x, y, dx=dx, dy=dy)[unsorted_idxs]
+ return interp2d.__call__(self, x, y, dx=dx, dy=dy, assume_sorted=False)[unsorted_idxs, :]
# Instantiate the default argument parser at runtime
@@ -804,7 +819,7 @@ class BilbyJsonEncoder(json.JSONEncoder):
return {'__array__': True, 'content': obj.tolist()}
if isinstance(obj, complex):
return {'__complex__': True, 'real': obj.real, 'imag': obj.imag}
- if isinstance(obj, pd.core.frame.DataFrame):
+ if isinstance(obj, pd.DataFrame):
return {'__dataframe__': True, 'content': obj.to_dict(orient='list')}
return json.JSONEncoder.default(self, obj)
diff --git a/test/prior_test.py b/test/prior_test.py
index b89699af9f7d906e0c771ec62f3e5572560340ee..888f607c8ea3754efe2d9fc6ad26d474af2fc260 100644
--- a/test/prior_test.py
+++ b/test/prior_test.py
@@ -192,6 +192,9 @@ class TestPriorClasses(unittest.TestCase):
bilby.core.prior.MultivariateNormal(mvg=mvn, name='testb', unit='unit')
]
+ def tearDown(self):
+ del self.priors
+
def test_minimum_rescaling(self):
"""Test the the rescaling works as expected."""
for prior in self.priors: