From defd97647a329109d020329c321bb75d712d91d2 Mon Sep 17 00:00:00 2001
From: Roger Labbe <rlabbejr@gmail.com>
Date: Wed, 28 Jan 2015 13:12:05 -0800
Subject: [PATCH] Added documentation for functions in common.

---
 docs/common/common.rst   | 43 ++++++++++++++++++++++++++++++++++++++++
 filterpy/common/stats.py | 18 ++++++++---------
 2 files changed, 52 insertions(+), 9 deletions(-)

diff --git a/docs/common/common.rst b/docs/common/common.rst
index 04488a1..4abe9d4 100644
--- a/docs/common/common.rst
+++ b/docs/common/common.rst
@@ -23,6 +23,49 @@ will find useful when you build your filters.
 
 .. autofunction:: linear_ode_discretation
 
+
+-----
+
+.. autofunction:: runge_kutta4
+
+
+-----
+
+.. autofunction:: gaussian
+
+-----
+
+.. autofunction:: mul
+
+-----
+
+.. autofunction:: add
+
+-----
+
+.. autofunction:: multivariate_gaussian
+
+-----
+
+.. autofunction:: plot_gaussian
+
+
+-----
+
+.. autofunction:: covariance_ellipse
+
+-----
+
+.. autofunction:: plot_covariance_ellipse
+
+-----
+
+.. autofunction:: norm_cdf
+
+-----
+
+.. autofunction:: rand_student_t
+
 -----
 
 .. autofunction:: dot3
diff --git a/filterpy/common/stats.py b/filterpy/common/stats.py
index ff69e88..9793659 100644
--- a/filterpy/common/stats.py
+++ b/filterpy/common/stats.py
@@ -381,6 +381,15 @@ def plot_std_vs_var():
     plot_covariance_ellipse(x, P, variance=[1,2,3], facecolor='r', alpha=.5)
 
 
+def rand_student_t(df, mu=0, std=1):
+    """return random number distributed by student's t distribution with
+    `df` degrees of freedom with the specified mean and standard deviation.
+    """
+    x = random.gauss(0, std)
+    y = 2.0*random.gammavariate(0.5*df, 2.0)
+    return x / (math.sqrt(y/df)) + mu  
+
+
 if __name__ == '__main__':
     plot_std_vs_var()
     plt.figure()
@@ -411,12 +420,3 @@ def plot_std_vs_var():
     plt.show()
 
     print("all tests passed")
-
-
-def rand_student_t(df, mu=0, std=1):
-    """return random number distributed by student's t distribution with
-    `df` degrees of freedom with the specified mean and standard deviation.
-    """
-    x = random.gauss(0, std)
-    y = 2.0*random.gammavariate(0.5*df, 2.0)
-    return x / (math.sqrt(y/df)) + mu  
\ No newline at end of file