DOC: rewrap docstring lines and remove unused import

skimage/filter/lpi_filter.py

@@ -7,7 +7,7 @@ __all__ = ['inverse', 'wiener', 'LPIFilter2D']
 __docformat__ = 'restructuredtext en'
 
 import numpy as np
-from scipy.fftpack import fftshift, ifftshift
+from scipy.fftpack import ifftshift
 
 eps = np.finfo(float).eps
 
@@ -50,13 +50,12 @@ class LPIFilter2D(object):
         Parameters
         ----------
         impulse_response : callable `f(r, c, **filter_params)`
-            Function that yields the impulse response.  `r` and
-            `c` are 1-dimensional vectors that represent row and
-            column positions, in other words coordinates are
-            (r[0],c[0]),(r[0],c[1]) etc.  `**filter_params` are
-            passed through.
+            Function that yields the impulse response.  `r` and `c` are
+            1-dimensional vectors that represent row and column positions, in
+            other words coordinates are (r[0],c[0]),(r[0],c[1]) etc.
+            `**filter_params` are passed through.
 
-            In other words, example would be called like this:
+            In other words, `impulse_response` would be called like this:
 
             >>> def impulse_response(r, c, **filter_params):
             ...     pass
@@ -116,8 +115,9 @@ class LPIFilter2D(object):
     def __call__(self, data):
         """Apply the filter to the given data.
 
-        *Parameters*:
-            data : (M,N) ndarray
+        Parameters
+        ----------
+        data : (M,N) ndarray
 
         """
         F, G = self._prepare(data)
@@ -142,9 +142,8 @@ def forward(data, impulse_response=None, filter_params={},
     Other Parameters
     ----------------
     predefined_filter : LPIFilter2D
-        If you need to apply the same filter multiple times over
-        different images, construct the LPIFilter2D and specify
-        it here.
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
 
     Examples
     --------
@@ -176,17 +175,15 @@ def inverse(data, impulse_response=None, filter_params={}, max_gain=2,
     filter_params : dict
         Additional keyword parameters to the impulse_response function.
     max_gain : float
-        Limit the filter gain.  Often, the filter contains
-        zeros, which would cause the inverse filter to have
-        infinite gain.  High gain causes amplification of
-        artefacts, so a conservative limit is recommended.
+        Limit the filter gain.  Often, the filter contains zeros, which would
+        cause the inverse filter to have infinite gain.  High gain causes
+        amplification of artefacts, so a conservative limit is recommended.
 
     Other Parameters
     ----------------
     predefined_filter : LPIFilter2D
-        If you need to apply the same filter multiple times over
-        different images, construct the LPIFilter2D and specify
-        it here.
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
 
     """
     if predefined_filter is None:
@@ -223,9 +220,8 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
     Other Parameters
     ----------------
     predefined_filter : LPIFilter2D
-        If you need to apply the same filter multiple times over
-        different images, construct the LPIFilter2D and specify
-        it here.
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
 
     """
     if predefined_filter is None: