Merge pull request #120 from sblunt/mynameisjeff

Rename JeffreysPrior to LogUniformPrior

orbitize/priors.py

@@ -106,7 +106,7 @@ def compute_lnprob(self, element_array):
 
         return lnprob
 
-class JeffreysPrior(Prior):
+class LogUniformPrior(Prior):
     """
     This is the probability distribution :math:`p(x) \\propto 1/x`
 
@@ -128,7 +128,7 @@ def __init__(self, minval, maxval):
         self.logmax = np.log(maxval)
 
     def __repr__(self):
-        return "Jeffreys"
+        return "Log Uniform"
 
     def draw_samples(self, num_samples):
         """
@@ -150,7 +150,7 @@ def draw_samples(self, num_samples):
 
     def compute_lnprob(self, element_array):
         """
-        Compute the prior probability of each element given that its drawn from a Jeffreys prior
+        Compute the prior probability of each element given that its drawn from a Log-Uniofrm  prior
 
         Args:
             element_array (float or np.array of float): array of paramters to compute the prior probability of

orbitize/system.py

@@ -100,7 +100,7 @@ def __init__(self, num_secondary_bodies, data_table, system_mass,
 
         for body in np.arange(num_secondary_bodies):
             # Add semimajor axis prior
-            self.sys_priors.append(priors.JeffreysPrior(0.001, 1e7))
+            self.sys_priors.append(priors.LogUniformPrior(0.001, 1e7))
             self.labels.append('sma{}'.format(body+1))
 
             # Add eccentricity prior
@@ -135,7 +135,7 @@ def __init__(self, num_secondary_bodies, data_table, system_mass,
 
         if self.fit_secondary_mass:
             for body in np.arange(num_secondary_bodies):
-                    self.sys_priors.append(priors.JeffreysPrior(1e-6, 1)) # in Solar masses for onw
+                    self.sys_priors.append(priors.LogUniformPrior(1e-6, 1)) # in Solar masses for onw
 
         if mass_err > 0:
             self.sys_priors.append(priors.GaussianPrior(system_mass, mass_err))

tests/test_priors.py

@@ -8,31 +8,31 @@
 
 initialization_inputs = {
 	priors.GaussianPrior : [1000., 1.], 
-	priors.JeffreysPrior : [1., 2.], 
+	priors.LogUniformPrior : [1., 2.], 
 	priors.UniformPrior : [0., 1.], 
 	priors.SinPrior : [], 
 	priors.LinearPrior : [-2., 2.]
 }
 
 expected_means_mins_maxes = {
 	priors.GaussianPrior : (1000.,0.,np.inf), 
-	priors.JeffreysPrior : (1/np.log(2),1., 2.), 
+	priors.LogUniformPrior : (1/np.log(2),1., 2.), 
 	priors.UniformPrior : (0.5, 0., 1.), 
 	priors.SinPrior : (np.pi/2., 0., np.pi), 
 	priors.LinearPrior : (1./3.,0.,1.0)
 }
 
 lnprob_inputs = {
 	priors.GaussianPrior : np.array([-3.0, np.inf, 1000., 999.]),
-	priors.JeffreysPrior : np.array([-1., 0., 1., 1.5, 2., 2.5]),
+	priors.LogUniformPrior : np.array([-1., 0., 1., 1.5, 2., 2.5]),
 	priors.UniformPrior : np.array([0., 0.5, 1., -1., 2.]),
 	priors.SinPrior : np.array([0., np.pi/2., np.pi, 10., -1.]),
 	priors.LinearPrior : np.array([0., 0.5, 1., 2., -1.])
 }
 
 expected_probs = {
 	priors.GaussianPrior : np.array([0., 0., nm(1000.,1.).pdf(1000.), nm(1000.,1.).pdf(999.)]),
-	priors.JeffreysPrior : np.array([0., 0., 1., 2./3., 0.5, 0.])/np.log(2),
+	priors.LogUniformPrior : np.array([0., 0., 1., 2./3., 0.5, 0.])/np.log(2),
 	priors.UniformPrior : np.array([1., 1., 1., 0., 0.]),
 	priors.SinPrior : np.array([0., 0.5, 0., 0., 0.]),
 	priors.LinearPrior : np.array([2., 1., 0., 0., 0.])