Improved Sphinx documentation.

Reduced number of errors generated by Sphinx. Added SimplexSigmaPoints.

docs/conf.py

@@ -49,6 +49,7 @@
 # ones.
 extensions = [
     'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
     'sphinx.ext.doctest',
     'numpydoc',
     'sphinx.ext.intersphinx',
@@ -57,6 +58,7 @@
     'sphinx.ext.autodoc',
     'numpydoc'
 ]
+numpydoc_show_class_members = False
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']

docs/kalman/UnscentedKalmanFilter.rst

@@ -36,3 +36,10 @@ This implements the unscented Kalman filter.
     .. automethod:: __init__
 
 
+--------
+
+
+.. autoclass:: SimplexSigmaPoints
+    :members:
+
+    .. automethod:: __init__

filterpy/__init__.py

@@ -14,4 +14,4 @@
 for more information.
 """
 
-__version__ = "0.1.3"
+__version__ = "0.1.4"

filterpy/changelog.txt

@@ -1,13 +1,11 @@
-Version 0.1.5
-=============
-
-* Bug in Q_continuous_white_noise(). The first term in the matrix should be (dt**3)/3, not (dt**4)/3. 
-
 Version 0.1.4
 =============
 
 * Added Cubature Kalman filter.
-
+* Bug in Q_continuous_white_noise(). The first term in the matrix should be (dt**3)/3, not (dt**4)/3. 
+* Added log-likelihood computation to UKF.
+* Added simplex points for UKF
+* fixed bug in KF matrix size check
 
 Version 0.1.3
 =============

filterpy/common/discretization.py

@@ -106,8 +106,8 @@ def van_loan_discretization(F, G, dt):
 
         Given y'' + y = 2u(t), we create the continuous state model of
 
-        x' = | 0 1| * x + |0|*u(t)
-             |-1 0|       |2|
+        x' = [ 0 1] * x + [0]*u(t)
+             [-1 0]       [2]
 
         and a time step of 0.1:
 

filterpy/hinfinity/hinfinity_filter.py

@@ -250,7 +250,7 @@ def measurement_of_state(self, x):
 
     @property
     def x(self):
-        """ state vector property"""
+        """ state vector"""
         return self._x
 
 
@@ -271,7 +271,7 @@ def G(self, value):
 
     @property
     def P(self):
-        """ covariance matrix property"""
+        """ covariance matrix"""
         return self._P
 
 
@@ -282,6 +282,7 @@ def P(self, value):
 
     @property
     def F(self):
+        """ State transition matrix"""
         return self._F
 
 

filterpy/kalman/EKF.py

@@ -224,23 +224,25 @@ def predict(self, u=0):
 
     @property
     def Q(self):
-        """ Process uncertainty"""
+        """ Process uncertainty matrix"""
         return self._Q
 
 
     @Q.setter
     def Q(self, value):
+        """ Process uncertainty matrix"""
         self._Q = setter_scalar(value, self.dim_x)
 
 
     @property
     def P(self):
-        """ covariance matrix"""
+        """ state covariance matrix"""
         return self._P
 
 
     @P.setter
     def P(self, value):
+        """ state covariance matrix"""
         self._P = setter_scalar(value, self.dim_x)
 
 
@@ -252,21 +254,25 @@ def R(self):
 
     @R.setter
     def R(self, value):
+        """ measurement uncertainty"""
         self._R = setter_scalar(value, self.dim_z)
 
 
     @property
     def F(self):
+        """State Transition matrix"""
         return self._F
 
 
     @F.setter
     def F(self, value):
+        """State Transition matrix"""
         self._F = setter(value, self.dim_x, self.dim_x)
 
 
     @property
     def B(self):
+        """ control transition matrix"""
         return self._B
 
 
@@ -278,10 +284,12 @@ def B(self, value):
 
     @property
     def x(self):
+        """ state estimate vector """
         return self._x
 
     @x.setter
     def x(self, value):
+        """ state estimate vector """
         self._x = setter_1d(value, self.dim_x)
 
     @property

filterpy/kalman/fading_memory.py

@@ -60,7 +60,7 @@ def __init__(self, alpha, dim_x, dim_z, dim_u=0):
         You will have to assign reasonable values to all of these before
         running the filter. All must have dtype of float
 
-        X : ndarray (dim_x, 1), default = [0,0,0...0]
+        x : ndarray (dim_x, 1), default = [0,0,0...0]
             state of the filter
 
         P : ndarray (dim_x, dim_x), default identity matrix
@@ -93,7 +93,7 @@ def __init__(self, alpha, dim_x, dim_z, dim_u=0):
         self.dim_z = dim_z
         self.dim_u = dim_u
 
-        self._X = zeros((dim_x,1)) # state
+        self._x = zeros((dim_x,1)) # state
         self._P = eye(dim_x)       # uncertainty covariance
         self._Q = eye(dim_x)       # process uncertainty
         self._B = 0                # control transition matrix
@@ -139,7 +139,7 @@ def update(self, z, R=None):
         # rename for readability and a tiny extra bit of speed
         H = self._H
         P = self._P
-        x = self._X
+        x = self._x
 
         # y = z - Hx
         # error (residual) between measurement and prediction
@@ -155,7 +155,7 @@ def update(self, z, R=None):
 
         # x = x + Ky
         # predict new x with residual scaled by the kalman gain
-        self._X = x + dot(K, self._y)
+        self._x = x + dot(K, self._y)
 
         # P = (I-KH)P(I-KH)' + KRK'
         I_KH = self._I - dot(K, H)
@@ -177,7 +177,7 @@ def predict(self, u=0):
         """
 
         # x = Fx + Bu
-        self._X = dot(self._F, self._X) + dot(self._B, u)
+        self._x = dot(self._F, self._x) + dot(self._B, u)
 
         # P = FPF' + Q
         self._P = self.alpha_sq*dot3(self._F, self._P, self._F.T) + self._Q
@@ -239,20 +239,20 @@ def batch_filter(self, zs, Rs=None, update_first=False):
         if update_first:
             for i,(z,r) in enumerate(zip(zs,Rs)):
                 self.update(z,r)
-                means[i,:]         = self._X
+                means[i,:]         = self._x
                 covariances[i,:,:] = self._P
 
                 self.predict()
-                means_p[i,:]         = self._X
+                means_p[i,:]         = self._x
                 covariances_p[i,:,:] = self._P
         else:
             for i,(z,r) in enumerate(zip(zs,Rs)):
                 self.predict()
-                means_p[i,:]         = self._X
+                means_p[i,:]         = self._x
                 covariances_p[i,:,:] = self._P
 
                 self.update(z,r)
-                means[i,:]         = self._X
+                means[i,:]         = self._x
                 covariances[i,:,:] = self._P
 
         return (means, covariances, means_p, covariances_p)
@@ -275,7 +275,7 @@ def get_prediction(self, u=0):
             State vector and covariance array of the prediction.
         """
 
-        x = dot(self._F, self._X) + dot(self._B, u)
+        x = dot(self._F, self._x) + dot(self._B, u)
         P = self.alpha_sq*dot3(self._F, self._P, self._F.T) + self.Q
         return (x, P)
 
@@ -284,7 +284,7 @@ def residual_of(self, z):
         """ returns the residual for the given measurement (z). Does not alter
         the state of the filter.
         """
-        return z - dot(self._H, self._X)
+        return z - dot(self._H, self._x)
 
 
     def measurement_of_state(self, x):
@@ -369,19 +369,9 @@ def B(self, value):
         self._B = setter (value, self.dim_x, self.dim_u)
 
 
-    @property
-    def X(self):
-        """ filter state vector."""
-        return self._X
-
-
-    @X.setter
-    def X(self, value):
-        self._X = setter(value, self.dim_x, 1)
-
-
     @property
     def x(self):
+        """ state vector."""
         assert False
 
     @x.setter

filterpy/kalman/information_filter.py

@@ -284,7 +284,7 @@ def measurement_of_state(self, x):
 
     @property
     def Q(self):
-        """ Process uncertainty"""
+        """Process uncertainty"""
         return self._Q
 
 
@@ -305,7 +305,7 @@ def P_inv(self, value):
 
     @property
     def R_inv(self):
-        """ measurement uncertainty"""
+        """inverse measurement uncertainty"""
         return self._R_inv
 
 
@@ -315,6 +315,7 @@ def R_inv(self, value):
 
     @property
     def H(self):
+        """Measurement function"""
         return self._H
 
 
@@ -325,6 +326,7 @@ def H(self, value):
 
     @property
     def F(self):
+        """State Transition matrix"""
         return self._F
 
 
@@ -347,6 +349,7 @@ def B(self, value):
 
     @property
     def x(self):
+        """ State estimate vector """
         return self._x
 
 

filterpy/kalman/kalman_filter.py

@@ -700,6 +700,7 @@ def alpha(self):
 
     @property
     def likelihood(self):
+        """ likelihood of measurement"""
         return math.exp(self.log_likelihood)
 
 
@@ -712,22 +713,24 @@ def alpha(self, value):
 
     @property
     def Q(self):
-        """ Process uncertainty"""
+        """ Process uncertainty matrix"""
         return self._Q
 
 
     @Q.setter
     def Q(self, value):
+        """ Process uncertainty matrix"""
         self._Q = setter_scalar(value, self.dim_x)
 
     @property
     def P(self):
-        """ covariance matrix"""
+        """ state covariance matrix"""
         return self._P
 
 
     @P.setter
     def P(self, value):
+        """ state covariance matrix"""
         self._P = setter_scalar(value, self.dim_x)
 
 
@@ -739,6 +742,7 @@ def F(self):
 
     @F.setter
     def F(self, value):
+        """ state transition matrix"""
         self._F = setter(value, self.dim_x, self.dim_x)
 
     @property
@@ -749,7 +753,6 @@ def B(self):
 
     @B.setter
     def B(self, value):
-        self._B = value
         """ control transition matrix"""
         if np.isscalar(value):
             self._B = value
@@ -759,12 +762,13 @@ def B(self, value):
 
     @property
     def x(self):
-        """ filter state vector."""
+        """ state vector."""
         return self._x
 
 
     @x.setter
     def x(self, value):
+        """ state vector."""
         self._x = setter_1d(value, self.dim_x)