Fixes rlabbe#14 and rlabbe#35.

numpydoc module is included in the list of sphinx extensions which
fixes rlabbe#14 and including scipy.ndimage in the list of mock modules fixes
rlabbe#35.

docs/conf.py

@@ -17,13 +17,13 @@
 import os
 
 import mock
- 
+
 MOCK_MODULES = ['numpy', 'scipy', 'matplotlib', 'matplotlib.pyplot',
                 'scipy.linalg', 'numpy.linalg', 'matplotlib.pyplot',
                 'numpy.random', 'scipy.sparse', 'scipy.sparse.linalg',
                 'scipy.stats', 'matplotlib.patches', 'scipy.ndimage.filters',
-                'scipy.ndimage.interpolation']
-                
+                'scipy.ndimage.interpolation', 'scipy.ndimage']
+
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock()
 
@@ -32,7 +32,8 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #sys.path.insert(0, os.path.abspath('.'))
-sys.path.insert(0, os.path.abspath('../filterpy'))
+#sys.path.insert(0, os.path.abspath('../filterpy'))
+sys.path.insert(0, os.path.abspath('../'))
 
 from filterpy import *
 import filterpy
@@ -53,7 +54,8 @@
     'sphinx.ext.intersphinx',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
-    'sphinx.ext.autodoc'
+    'sphinx.ext.autodoc',
+    'numpydoc'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -357,4 +359,4 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'http://docs.python.org/': None}
 
-autodoc_member_order = 'bysource'
+autodoc_member_order = 'bysource'

docs/discrete_bayes/discrete_bayes.rst

@@ -16,5 +16,3 @@ words go here
 -----
 
 .. autofunction:: predict
-
------

filterpy/kalman/kalman_filter.py

@@ -468,21 +468,21 @@ def batch_filter(self, zs, Fs=None, Qs=None, Hs=None, Rs=None, Bs=None, us=None,
         Returns
         -------
 
-        means: np.array((n,dim_x,1))
+        means : np.array((n,dim_x,1))
             array of the state for each time step after the update. Each entry
             is an np.array. In other words `means[k,:]` is the state at step
             `k`.
 
-        covariance: np.array((n,dim_x,dim_x))
+        covariance : np.array((n,dim_x,dim_x))
             array of the covariances for each time step after the update.
             In other words `covariance[k,:,:]` is the covariance at step `k`.
 
-        means_predictions: np.array((n,dim_x,1))
+        means_predictions : np.array((n,dim_x,1))
             array of the state for each time step after the predictions. Each
             entry is an np.array. In other words `means[k,:]` is the state at
             step `k`.
 
-        covariance_predictions: np.array((n,dim_x,dim_x))
+        covariance_predictions : np.array((n,dim_x,dim_x))
             array of the covariances for each time step after the prediction.
             In other words `covariance[k,:,:]` is the covariance at step `k`.
 
@@ -645,7 +645,7 @@ def get_prediction(self, u=0):
         Returns
         -------
 
-        (x, P)
+        (x, P) : tuple
             State vector and covariance array of the prediction.
         """
 
@@ -1006,34 +1006,36 @@ def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, update_first=False)
     Returns
     -------
 
-    means: np.array((n,dim_x,1))
+    means : np.array((n,dim_x,1))
         array of the state for each time step after the update. Each entry
         is an np.array. In other words `means[k,:]` is the state at step
         `k`.
 
-    covariance: np.array((n,dim_x,dim_x))
+    covariance : np.array((n,dim_x,dim_x))
         array of the covariances for each time step after the update.
         In other words `covariance[k,:,:]` is the covariance at step `k`.
 
-    means_predictions: np.array((n,dim_x,1))
+    means_predictions : np.array((n,dim_x,1))
         array of the state for each time step after the predictions. Each
         entry is an np.array. In other words `means[k,:]` is the state at
         step `k`.
 
-    covariance_predictions: np.array((n,dim_x,dim_x))
+    covariance_predictions : np.array((n,dim_x,dim_x))
         array of the covariances for each time step after the prediction.
         In other words `covariance[k,:,:]` is the covariance at step `k`.
 
     Examples
     --------
 
-    zs = [t + random.randn()*4 for t in range (40)]
-    Fs = [kf.F for t in range (40)]
-    Hs = [kf.H for t in range (40)]
+    .. code-block:: Python
+
+        zs = [t + random.randn()*4 for t in range (40)]
+        Fs = [kf.F for t in range (40)]
+        Hs = [kf.H for t in range (40)]
 
-    (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,
-                                      Bs=None, us=None, update_first=False)
-    (xs, Ps, Ks) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)
+        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,
+                                          Bs=None, us=None, update_first=False)
+        (xs, Ps, Ks) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)
 
     """