Merge branch 'master' of https://github.com/rlabbe/filterpy

LICENSE

@@ -1,4 +1,4 @@
-he MIT License (MIT)
+The MIT License (MIT)
 
 Copyright (c) 2015 Roger R. Labbe Jr
 
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
+THE SOFTWARE.

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/EKF.py

@@ -86,7 +86,7 @@ def predict_update(self, z, HJacobian, Hx, args=(), hx_args=(), u=0):
             variable.
 
         hx_args : tuple, optional, default (,)
-            arguments to be passed into HJacobian after the required state
+            arguments to be passed into Hx after the required state
             variable.
 
         u : np.array or scalar

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)
 
     """