Feature/support masked array

filterpy/hinfinity/hinfinity_filter.py

@@ -101,7 +101,7 @@ def update(self, z):
             measurement for this update.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             return
 
         # rename for readability and a tiny extra bit of speed

filterpy/kalman/CubatureKalmanFilter.py

@@ -345,7 +345,7 @@ def update(self, z, R=None, hx_args=()):
             variable.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()

filterpy/kalman/EKF.py

@@ -294,7 +294,7 @@ def update(self, z, HJacobian, Hx, R=None, args=(), hx_args=(),
             example, if they are angles)
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()

filterpy/kalman/UKF.py

@@ -439,7 +439,7 @@ def update(self, z, R=None, UT=None, hx=None, **hx_args):
             arguments to be passed into h(x) after x -> h(x, **hx_args)
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self._dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()

filterpy/kalman/ensemble_kalman_filter.py

@@ -231,7 +231,7 @@ def update(self, z, R=None):
             one call, otherwise self.R will be used.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()

filterpy/kalman/fading_memory.py

@@ -208,7 +208,7 @@ def update(self, z, R=None):
             one call, otherwise  self.R will be used.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()

filterpy/kalman/information_filter.py

@@ -191,7 +191,7 @@ def update(self, z, R_inv=None):
             one call, otherwise  self.R will be used.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = None
             self.x_post = self.x.copy()
             self.P_inv_post = self.P_inv.copy()

filterpy/kalman/kalman_filter.py

@@ -200,7 +200,7 @@ class KalmanFilter(object):
 
         f.H = np.array([[1., 0.]])
 
-    Define the state's covariance matrix P. 
+    Define the state's covariance matrix P.
 
     .. code::
 
@@ -512,7 +512,7 @@ def update(self, z, R=None, H=None):
         self._likelihood = None
         self._mahalanobis = None
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()
@@ -641,7 +641,7 @@ def update_steadystate(self, z):
         self._likelihood = None
         self._mahalanobis = None
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()
@@ -702,7 +702,7 @@ def update_correlated(self, z, R=None, H=None):
         self._likelihood = None
         self._mahalanobis = None
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self.P_post = self.P.copy()
@@ -1070,7 +1070,7 @@ def get_update(self, z=None):
             State vector and covariance array of the update.
        """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             return self.x, self.P
         z = reshape_z(z, self.dim_z, self.x.ndim)
 
@@ -1183,7 +1183,7 @@ def log_likelihood_of(self, z):
         after a call to update(). Calling after predict() will yield an
         incorrect result."""
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             return log(sys.float_info.min)
         return logpdf(z, dot(self.H, self.x), self.S)
 
@@ -1387,7 +1387,7 @@ def update(x, P, z, R, H=None, return_all=False):
 
     #pylint: disable=bare-except
 
-    if z is None:
+    if z is None or z is np.ma.masked:
         if return_all:
             return x, P, None, None, None, None
         return x, P
@@ -1476,8 +1476,7 @@ def update_steadystate(x, z, K, H=None):
     >>> update_steadystate(x, P, z, H)
     """
 
-
-    if z is None:
+    if z is None or z is np.ma.masked:
         return x
 
     if H is None:

filterpy/kalman/square_root.py

@@ -188,7 +188,7 @@ def update(self, z, R2=None):
             be used.
         """
 
-        if z is None:
+        if z is None or z is np.ma.masked:
             self.z = np.array([[None]*self.dim_z]).T
             self.x_post = self.x.copy()
             self._P1_2_post = np.copy(self._P1_2)

filterpy/kalman/tests/test_kf.py

@@ -268,10 +268,15 @@ def test_batch_filter():
     f.R = 5                       # state uncertainty
     f.Q = 0.0001                  # process uncertainty
 
+    # Test with None for missing measurments
     zs = [None, 1., 2.]
     m, c, _, _ = f.batch_filter(zs, update_first=False)
     m, c, _, _ = f.batch_filter(zs, update_first=True)
 
+    # Test with masked array for missing measurments
+    zs = np.ma.masked_invalid([np.nan, 1., 2.])
+    m, c, _, _ = f.batch_filter(zs, update_first=False)
+    m, c, _, _ = f.batch_filter(zs, update_first=True)
 
 def test_univariate():
     f = KalmanFilter(dim_x=1, dim_z=1, dim_u=1)

filterpy/kalman/tests/test_ukf.py

@@ -430,7 +430,11 @@ def hx(x):
 
 
 def test_batch_missing_data():
-    """ batch filter should accept missing data with None in the measurements """
+    """
+    Batch filter should accept missing data with None in the measurements
+
+    Alternatively, a masked array can be used.
+    """
 
     def fx(x, dt):
         F = np.array([[1, dt, 0, 0],
@@ -456,11 +460,17 @@ def hx(x):
         z = np.array([i + randn()*0.1, i + randn()*0.1])
         zs.append(z)
 
+    # Test with None for missing measurments
     zs[2] = None
     Rs = [1]*len(zs)
     Rs[2] = None
     Ms, Ps = kf.batch_filter(zs)
 
+    # Test with masked array for missing measurments
+    zs[2] = np.nan
+    zs = np.ma.masked_invalid([np.nan, 1., 2.])
+    Ms, Ps = kf.batch_filter(zs)
+
 
 def test_rts():
     def fx(x, dt):