Merge branch 'default_prior_bounds' into 'master'

Default prior bounds

See merge request Monash/tupak!34

tupak/prior.py

@@ -10,11 +10,41 @@ import os
 
 
 class Prior(object):
-    """Prior class"""
+    """
+    Prior class
+
+    Methods
+    -------
+    __init__:
+        Instantiate a prior object.
+    __call__:
+        Draw a single sample from the prior.
+    __repr__:
+        Print prior type and parameters.
+    sample(size=None):
+        Draw samples of size size from the prior.
+    rescale(val):
+        Rescale samples from a uniform distribution on [0, 1] to samples from the prior.
+    test_valid_for_recaling(val):
+        Test whether val is in [0, 1] and hence valid for rescaling.
 
-    def __init__(self, name=None, latex_label=None):
+    Parameters
+    ----------
+    name: str
+        Name associated with prior.
+    latex_label: str
+        Latex label associated with prior, used for plotting.
+    minimum: float, optional
+        Minimum of the domain, default=-np.inf
+    maximum: float, optional
+        Maximum of the domain, default=np.inf
+    """
+
+    def __init__(self, name=None, latex_label=None, minimum=-np.inf, maximum=np.inf):
         self.name = name
         self.latex_label = latex_label
+        self.minimum = minimum
+        self.maximum = maximum
 
     def __call__(self):
         return self.sample()
@@ -93,9 +123,7 @@ class Uniform(Prior):
     """Uniform prior"""
 
     def __init__(self, minimum, maximum, name=None, latex_label=None):
-        Prior.__init__(self, name, latex_label)
-        self.minimum = minimum
-        self.maximum = maximum
+        Prior.__init__(self, name, latex_label, minimum, maximum)
         self.support = maximum - minimum
 
     def rescale(self, val):
@@ -104,7 +132,7 @@ class Uniform(Prior):
 
     def prob(self, val):
         """Return the prior probability of val"""
-        if (self.minimum < val) and (val < self.maximum):
+        if (self.minimum < val) & (val < self.maximum):
             return 1 / self.support
         else:
             return 0
@@ -114,7 +142,7 @@ class DeltaFunction(Prior):
     """Dirac delta function prior, this always returns peak."""
 
     def __init__(self, peak, name=None, latex_label=None):
-        Prior.__init__(self, name, latex_label)
+        Prior.__init__(self, name, latex_label, minimum=peak, maximum=peak)
         self.peak = peak
 
     def rescale(self, val):
@@ -135,10 +163,8 @@ class PowerLaw(Prior):
 
     def __init__(self, alpha, minimum, maximum, name=None, latex_label=None):
         """Power law with bounds and alpha, spectral index"""
-        Prior.__init__(self, name, latex_label)
+        Prior.__init__(self, name, latex_label, minimum, maximum)
         self.alpha = alpha
-        self.minimum = minimum
-        self.maximum = maximum
 
     def rescale(self, val):
         """
@@ -167,8 +193,8 @@ class PowerLaw(Prior):
 
 class Cosine(Prior):
 
-    def __init__(self, name=None, latex_label=None):
-        Prior.__init__(self, name, latex_label)
+    def __init__(self, name=None, latex_label=None, minimum=-np.pi / 2, maximum=np.pi / 2):
+        Prior.__init__(self, name, latex_label, minimum, maximum)
 
     def rescale(self, val):
         """
@@ -182,7 +208,7 @@ class Cosine(Prior):
     @staticmethod
     def prob(val):
         """Return the prior probability of val, defined over [-pi/2, pi/2]"""
-        if (val > -np.pi / 2) and (val < np.pi / 2):
+        if (val > np.minimum) and (val < np.maximum):
             return np.cos(val) / 2
         else:
             return 0
@@ -190,8 +216,8 @@ class Cosine(Prior):
 
 class Sine(Prior):
 
-    def __init__(self, name=None, latex_label=None):
-        Prior.__init__(self, name, latex_label)
+    def __init__(self, name=None, latex_label=None, minimum=0, maximum=np.pi):
+        Prior.__init__(self, name, latex_label, minimum, maximum)
 
     def rescale(self, val):
         """