Simplified HampelFilter

wafo/sg_filter.py

@@ -438,7 +438,7 @@ def smoothn(data, s=None, weight=None, robust=False, z0=None, tolz=1e-3,
     W = W * IsFinite
     if (W < 0).any():
         raise ValueError('Weights must all be >=0')
-    else:
+
     W = W / W.max()
 
     isweighted = (W < 1).any()  # Weighted or missing data?
@@ -1217,25 +1217,13 @@ class HampelFilter(object):
         self.adaptive = adaptive
         self.fulloutput = fulloutput
 
-    def __call__(self, y, x=None):
+    def _check(self, dx):
-        Y = np.atleast_1d(y).ravel()
-        if x is None:
-            x = range(len(Y))
-        X = np.atleast_1d(x).ravel()
-
-        dx = self.dx
-        if dx is None:
-            dx = 3 * np.median(np.diff(X))
         if not np.isscalar(dx):
             raise ValueError('DX must be a scalar.')
-        elif dx < 0:
+        if dx < 0:
             raise ValueError('DX must be larger than zero.')
 
-        YY = Y
+    @staticmethod
-        S0 = np.nan * np.zeros(YY.shape)
-        Y0 = np.nan * np.zeros(YY.shape)
-        ADX = dx * np.ones(Y.shape)
-
     def localwindow(X, Y, DX, i):
         mask = (X[i] - DX <= X) & (X <= X[i] + DX)
         Y0 = np.median(Y[mask])
@@ -1243,6 +1231,7 @@ class HampelFilter(object):
         S0 = 1.4826 * np.median(np.abs(Y[mask] - Y0))
         return Y0, S0
 
+    @staticmethod
     def smgauss(X, V, DX):
         Xj = X
         Xk = np.atleast_2d(X).T
@@ -1250,17 +1239,13 @@ class HampelFilter(object):
         G = np.dot(Wjk, V) / np.sum(Wjk, axis=0)
         return G
 
-        if len(X) > 1:
+    def _adaptive(self, Y, X, dx):
-            if self.adaptive is None:
+        localwindow = self.localwindow
-                for i in range(len(Y)):
+        Y0, S0, ADX = self._init(Y, dx)
-                    Y0[i], S0[i] = localwindow(X, Y, dx, i)
+        Y0Tmp = np.nan * np.zeros(Y.shape)
-            else:  # 'adaptive'
+        S0Tmp = np.nan * np.zeros(Y.shape)
-
-                Y0Tmp = np.nan * np.zeros(YY.shape)
-                S0Tmp = np.nan * np.zeros(YY.shape)
         DXTmp = np.arange(1, len(S0) + 1) * dx
         # Integer variation of Window Half Size
-
         # Calculate Initial Guess of Optimal Parameters Y0, S0, ADX
         for i in range(len(Y)):
             j = 0
@@ -1271,16 +1256,42 @@ class HampelFilter(object):
                     S0Rel = abs((S0Tmp[j - 1] - S0Tmp[j]) /
                                 (S0Tmp[j - 1] + S0Tmp[j]) / 2)
                 j += 1
+
             Y0[i] = Y0Tmp[j - 2]
             S0[i] = S0Tmp[j - 2]
             ADX[i] = DXTmp[j - 2] / dx
 
     # Gaussian smoothing of relevant parameters
         DX = 2 * np.median(np.diff(X))
-                ADX = smgauss(X, ADX, DX)
+        ADX = self.smgauss(X, ADX, DX)
-                S0 = smgauss(X, S0, DX)
+        S0 = self.smgauss(X, S0, DX)
-                Y0 = smgauss(X, Y0, DX)
+        Y0 = self.smgauss(X, Y0, DX)
+        return Y0, S0, ADX
+
+    def _init(self, Y, dx):
+        S0 = np.nan * np.zeros(Y.shape)
+        Y0 = np.nan * np.zeros(Y.shape)
+        ADX = dx * np.ones(Y.shape)
+        return Y0, S0, ADX
 
+    def __call__(self, y, x=None):
+        Y = np.atleast_1d(y).ravel()
+        if x is None:
+            x = range(len(Y))
+        X = np.atleast_1d(x).ravel()
+
+        dx = 3 * np.median(np.diff(X)) if self.dx is None else self.dx
+        self._check(dx)
+
+        if len(X) > 1:
+            if self.adaptive is None:
+                localwindow = self.localwindow
+                Y0, S0, ADX = self._init(Y, dx)
+                for i in range(len(Y)):
+                    Y0[i], S0[i] = localwindow(X, Y, dx, i)
+            else:  # 'adaptive'
+                Y0, S0, ADX = self._adaptive(Y, X, dx)
+        YY = Y.copy()
         T = self.t
         # Prepare Output
         self.UB = Y0 + T * S0