Gaussian filter truncation

scipy/ndimage/filters.py

@@ -168,7 +168,7 @@ def convolve1d(input, weights, axis = -1, output = None, mode = "reflect",
 
 @docfiller
 def gaussian_filter1d(input, sigma, axis = -1, order = 0, output = None,
-                      mode = "reflect", cval = 0.0):
+                      mode = "reflect", cval = 0.0, truncate = 4.0):
     """One-dimensional Gaussian filter.
 
     Parameters
@@ -185,13 +185,15 @@ def gaussian_filter1d(input, sigma, axis = -1, order = 0, output = None,
     %(output)s
     %(mode)s
     %(cval)s
+    truncate : float
+        Truncate the filter at this many standard deviations.
+        Default is 4.0.
     """
     if order not in range(4):
         raise ValueError('Order outside 0..3 not implemented')
     sd = float(sigma)
-    # make the length of the filter equal to 4 times the standard
-    # deviations:
-    lw = int(4.0 * sd + 0.5)
+    # make the radius of the filter equal to truncate standard deviations
+    lw = int(truncate * sd + 0.5)
     weights = [0.0] * (2 * lw + 1)
     weights[lw] = 1.0
     sum = 1.0
@@ -232,7 +234,7 @@ def gaussian_filter1d(input, sigma, axis = -1, order = 0, output = None,
 
 @docfiller
 def gaussian_filter(input, sigma, order = 0, output = None,
-                  mode = "reflect", cval = 0.0):
+                  mode = "reflect", cval = 0.0, truncate = 4.0):
     """Multi-dimensional Gaussian filter.
 
     Parameters
@@ -253,6 +255,9 @@ def gaussian_filter(input, sigma, order = 0, output = None,
     %(output)s
     %(mode)s
     %(cval)s
+    truncate : float
+        Truncate the filter at this many standard deviations.
+        Default is 4.0.
 
     Notes
     -----
@@ -275,7 +280,7 @@ def gaussian_filter(input, sigma, order = 0, output = None,
     if len(axes) > 0:
         for axis, sigma, order in axes:
             gaussian_filter1d(input, sigma, axis, order, output,
-                              mode, cval)
+                              mode, cval, truncate)
             input = output
     else:
         output[...] = input[...]
@@ -386,7 +391,7 @@ def derivative2(input, axis, output, mode, cval):
 
 @docfiller
 def gaussian_laplace(input, sigma, output = None, mode = "reflect",
-                     cval = 0.0):
+                     cval = 0.0, **kwargs):
     """Calculate a multidimensional laplace filter using gaussian
     second derivatives.
 
@@ -400,14 +405,17 @@ def gaussian_laplace(input, sigma, output = None, mode = "reflect",
     %(output)s
     %(mode)s
     %(cval)s
+    Extra keyword arguments will be passed to gaussian_filter().
     """
     input = numpy.asarray(input)
-    def derivative2(input, axis, output, mode, cval, sigma):
+    def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
         order = [0] * input.ndim
         order[axis] = 2
-        return gaussian_filter(input, sigma, order, output, mode, cval)
+        return gaussian_filter(input, sigma, order, output, mode, cval,
+                               **kwargs)
     return generic_laplace(input, derivative2, output, mode, cval,
-                           extra_arguments = (sigma,))
+                           extra_arguments = (sigma,),
+                           extra_keywords = kwargs)
 
 
 @docfiller
@@ -462,7 +470,7 @@ def generic_gradient_magnitude(input, derivative, output = None,
 
 @docfiller
 def gaussian_gradient_magnitude(input, sigma, output = None,
-                mode = "reflect", cval = 0.0):
+                mode = "reflect", cval = 0.0, **kwargs):
     """Calculate a multidimensional gradient magnitude using gaussian
     derivatives.
 
@@ -476,14 +484,17 @@ def gaussian_gradient_magnitude(input, sigma, output = None,
     %(output)s
     %(mode)s
     %(cval)s
+    Extra keyword arguments will be passed to gaussian_filter().
     """
     input = numpy.asarray(input)
-    def derivative(input, axis, output, mode, cval, sigma):
+    def derivative(input, axis, output, mode, cval, sigma, **kwargs):
         order = [0] * input.ndim
         order[axis] = 1
-        return gaussian_filter(input, sigma, order, output, mode, cval)
+        return gaussian_filter(input, sigma, order, output, mode,
+                               cval, **kwargs)
     return generic_gradient_magnitude(input, derivative, output, mode,
-                            cval, extra_arguments = (sigma,))
+                                      cval, extra_arguments = (sigma,),
+                                      extra_keywords = kwargs)
 
 
 def _correlate_or_convolve(input, weights, output, mode, cval, origin,

scipy/ndimage/tests/test_filters.py

@@ -52,3 +52,11 @@ def test_valid_origins():
         # Just check this raises an error instead of silently accepting or
         # segfaulting.
         assert_raises(ValueError, filter, data, 3, origin=2)
+
+def test_gaussian_truncate():
+    """Test that Gaussian filters can be truncated at different widths."""
+    arr = np.zeros((100, 100), np.float)
+    arr[50, 50] = 1
+    num_zeros_2 = (sndi.gaussian_filter(arr, 5, truncate=2) > 0).sum()
+    num_zeros_5 = (sndi.gaussian_filter(arr, 5, truncate=5) > 0).sum()
+    assert (num_zeros_5 > num_zeros_2 * 2)