Whitespace cleanup.

18 files changed, 99 insertions, 109 deletions

diff --git a/numpy/lib/__init__.py b/numpy/lib/__init__.py
index 47e3f9d14..e17a0a726 100644
--- a/numpy/lib/__init__.py
+++ b/numpy/lib/__init__.py
@@ -1,4 +1,3 @@
-
 from info import __doc__
 from numpy.version import version as __version__
 
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index ca0269772..fe08912e7 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -66,11 +66,11 @@ def ediff1d(ary, to_end = None, to_begin = None):
         # We'll save ourselves a copy of a potentially large array in the common
         # case where neither to_begin or to_end was given.
         ed = nm.hstack(arrays)
-       
+
     return ed
 
 def unique1d(ar1, return_index=False):
-    """Find the unique elements of 1D array. 
+    """Find the unique elements of 1D array.
 
     Most of the other array set operations operate on the unique arrays
     generated by this function.
@@ -96,13 +96,13 @@ def unique1d(ar1, return_index=False):
     if ar.size == 0:
         if return_index: return nm.empty(0, nm.bool), ar
         else: return ar
-    
+
     if return_index:
         perm = ar.argsort()
         aux = ar[perm]
         flag = nm.concatenate( ([True], aux[1:] != aux[:-1]) )
         return perm[flag], aux[flag]
-    
+
     else:
         ar.sort()
         flag = nm.concatenate( ([True], ar[1:] != ar[:-1]) )
@@ -138,7 +138,7 @@ def intersect1d_nu( ar1, ar2 ):
     :Parameters:
       - `ar1` : array
       - `ar2` : array
-    
+
     :Returns:
       - `intersection` : array
 
@@ -172,7 +172,7 @@ def setxor1d( ar1, ar2 ):
     aux = nm.concatenate((ar1, ar2))
     if aux.size == 0:
         return aux
-    
+
     aux.sort()
 #    flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
     flag = nm.concatenate( ([True], aux[1:] != aux[:-1], [True] ) )
diff --git a/numpy/lib/convdtype.py b/numpy/lib/convdtype.py
index 17ed56102..ffef92e1c 100644
--- a/numpy/lib/convdtype.py
+++ b/numpy/lib/convdtype.py
@@ -63,4 +63,3 @@ def _test():
 
 if __name__ == "__main__":
     _test()
-        
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 6e44bc205..1b79afc25 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -109,39 +109,39 @@ def histogram(a, bins=10, range=None, normed=False):
 
 def histogramdd(sample, bins=10, range=None, normed=False):
     """histogramdd(sample, bins = 10, range = None, normed = False) -> H, edges
-    
+
     Return the D-dimensional histogram computed from sample.
-    
+
     Parameters
     ----------
-    sample: A sequence of D arrays, or an NxD array. 
-    bins:   A sequence of edge arrays, or a sequence of the number of bins. 
+    sample: A sequence of D arrays, or an NxD array.
+    bins:   A sequence of edge arrays, or a sequence of the number of bins.
             If a scalar is given, it is assumed to be the number of bins
-            for all dimensions. 
+            for all dimensions.
     range:  A sequence of lower and upper bin edges (default: [min, max]).
-    normed: If False, returns the number of samples in each bin. 
+    normed: If False, returns the number of samples in each bin.
             If True, returns the frequency distribution.
-    
-    
+
+
     Output
     ------
     H:      Histogram array.
-    edges:  List of arrays defining the bin edges. 
-    
+    edges:  List of arrays defining the bin edges.
+
     Example:
     x = random.randn(100,3)
     H, edges = histogramdd(x, bins = (5, 6, 7))
-    
+
     See also: histogram
     """
-    
+
     try:
         N, D = sample.shape
     except (AttributeError, ValueError):
         ss = atleast_2d(sample)
         sample = ss.transpose()
         N, D = sample.shape
-    
+
     nbin = empty(D, int)
     edges = D*[None]
     dedges = D*[None]
@@ -152,7 +152,7 @@ def histogramdd(sample, bins=10, range=None, normed=False):
             raise AttributeError, 'The dimension of bins must be a equal to the dimension of the sample x.'
     except TypeError:
         bins = D*[bins]
-        
+
     if range is None:
         smin = atleast_1d(sample.min(0))
         smax = atleast_1d(sample.max(0))
@@ -161,7 +161,7 @@ def histogramdd(sample, bins=10, range=None, normed=False):
         smax = zeros(D)
         for i in arange(D):
             smin[i], smax[i] = range[i]
-    
+
     for i in arange(D):
         if isscalar(bins[i]):
             nbin[i] = bins[i]
@@ -169,18 +169,18 @@ def histogramdd(sample, bins=10, range=None, normed=False):
         else:
             edges[i] = asarray(bins[i], float)
             nbin[i] = len(edges[i])-1
-            
+
 
 
     Ncount = {}
     nbin =  asarray(nbin)
 
     for i in arange(D):
-        Ncount[i] = digitize(sample[:,i], edges[i]) 
+        Ncount[i] = digitize(sample[:,i], edges[i])
         dedges[i] = diff(edges[i])
     # Remove values falling outside of bins
     # Values that fall on an edge are put in the right bin.
-    # For the rightmost bin, we want values equal to the right 
+    # For the rightmost bin, we want values equal to the right
     # edge to be counted in the last bin, and not as an outlier.
     outliers = zeros(N, int)
     for i in arange(D):
@@ -192,23 +192,23 @@ def histogramdd(sample, bins=10, range=None, normed=False):
     for i in arange(D):
         Ncount[i] = Ncount[i][indices] - 1
     N = len(indices)
-    
+
     # Flattened histogram matrix (1D)
     hist = zeros(nbin.prod(), int)
-    
+
     # Compute the sample indices in the flattened histogram matrix.
     ni = nbin.argsort()
     shape = []
     xy = zeros(N, int)
     for i in arange(0, D-1):
         xy += Ncount[ni[i]] * nbin[ni[i+1:]].prod()
-        
+
     xy += Ncount[ni[-1]]
 
-    # Compute the number of repetitions in xy and assign it to the flattened histmat. 
+    # Compute the number of repetitions in xy and assign it to the flattened histmat.
     if len(xy) == 0:
         return zeros(nbin, int)
-        
+
     flatcount = bincount(xy)
     a = arange(len(flatcount))
     hist[a] = flatcount
@@ -219,7 +219,7 @@ def histogramdd(sample, bins=10, range=None, normed=False):
         hist = hist.swapaxes(i,j)
         if (hist.shape == nbin).all():
             break
-    
+
     if normed:
         s = hist.sum()
         for i in arange(D):
@@ -227,9 +227,9 @@ def histogramdd(sample, bins=10, range=None, normed=False):
             shape[i] = nbin[i]
             hist = hist / dedges[i].reshape(shape)
         hist /= s
-        
-    return hist, edges 
-    
+
+    return hist, edges
+
 
 def average(a, axis=None, weights=None, returned=False):
     """average(a, axis=None weights=None, returned=False)
@@ -777,7 +777,7 @@ class vectorize(object):
     with the first element of the input.  This can be avoided by specifying
     the otypes argument as either a string of typecode characters or a list
     of data-types specifiers.  There should be one data-type specifier for
-    each output. 
+    each output.
 
   Input:
 
@@ -1054,7 +1054,7 @@ def i0(x):
 
 def kaiser(M,beta):
     """kaiser(M, beta) returns a Kaiser window of length M with shape parameter
-    beta. 
+    beta.
     """
     from numpy.dual import i0
     n = arange(0,M)
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 02f78bec5..e64d5dcd4 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -34,7 +34,7 @@ def unravel_index(x,dims):
     """
     if x > _nx.prod(dims)-1 or x < 0:
         raise ValueError("Invalid index, must be 0 <= x <= number of elements.")
-        
+
     idx = _nx.empty_like(dims)
 
     # Take dimensions
@@ -275,7 +275,7 @@ class concatenator(object):
                     newobj = array(newobj, copy=False, subok=True,
                                    ndmin=ndmin)
                     if trans1d != -1 and tempobj.ndim < ndmin:
-                        k2 = ndmin-tempobj.ndim                        
+                        k2 = ndmin-tempobj.ndim
                         if (trans1d < 0):
                             trans1d += k2 + 1
                         defaxes = range(ndmin)
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 93bbc1f6d..9aea4ef12 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -650,4 +650,3 @@ class poly1d(object):
 # Stuff to do on module import
 
 warnings.simplefilter('always',RankWarning)
-
diff --git a/numpy/lib/setup.py b/numpy/lib/setup.py
index f2ca221b2..0bd3c6ceb 100644
--- a/numpy/lib/setup.py
+++ b/numpy/lib/setup.py
@@ -1,4 +1,3 @@
-
 import imp
 import os
 from os.path import join
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index 32c47ede5..250525f76 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -1,6 +1,6 @@
 __all__ = ['atleast_1d','atleast_2d','atleast_3d','vstack','hstack',
            'column_stack','row_stack', 'dstack','array_split','split','hsplit',
-           'vsplit','dsplit','apply_over_axes','expand_dims', 
+           'vsplit','dsplit','apply_over_axes','expand_dims',
            'apply_along_axis', 'kron', 'tile', 'get_array_wrap']
 
 import numpy.core.numeric as _nx
@@ -249,7 +249,7 @@ def column_stack(tup):
             must have the same first dimension.  2D arrays are
             stacked as-is, just like with hstack.  1D arrays are turned
             into 2D columns first.
-            
+
         Arguments:
             tup -- sequence of 1D or 2D arrays.  All arrays must have the same
                    first dimension.
@@ -532,12 +532,12 @@ def get_array_wrap(*args):
     In case of ties, leftmost wins. If no wrapper is found, return None
     """
     wrappers = [(getattr(x, '__array_priority__', 0), -i,
-                 x.__array_wrap__) for i, x in enumerate(args) 
+                 x.__array_wrap__) for i, x in enumerate(args)
                                    if hasattr(x, '__array_wrap__')]
     wrappers.sort()
     if wrappers:
         return wrappers[-1][-1]
-    return None 
+    return None
 
 def kron(a,b):
     """kronecker product of a and b
@@ -548,7 +548,7 @@ def kron(a,b):
      [ a[m-1,0]*b, a[m-1,1]*b, ... , a[m-1,n-1]*b  ]]
     """
     wrapper = get_array_wrap(a, b)
-    b = asanyarray(b)    
+    b = asanyarray(b)
     a = array(a,copy=False,subok=True,ndmin=b.ndim)
     ndb, nda = b.ndim, a.ndim
     if (nda == 0 or ndb == 0):
@@ -565,7 +565,7 @@ def kron(a,b):
             as_ = (1,)*(ndb-nda) + as_
         else:
             bs = (1,)*(nda-ndb) + bs
-            nd = nda        
+            nd = nda
     result = outer(a,b).reshape(as_+bs)
     axis = nd-1
     for k in xrange(nd):
@@ -626,4 +626,3 @@ def tile(A, reps):
         shape[i] = dim_out
         n /= dim_in
     return c.reshape(shape)
-
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index 36b53c8e6..ccdcc7556 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -21,7 +21,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [1, 2, 5, 7] )
         c = unique1d( a )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], unique1d([]))
 
     ##
@@ -34,7 +34,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [1, 2, 5] )
         c = intersect1d( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], intersect1d([],[]))
 
     ##
@@ -47,7 +47,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [1, 2, 5] )
         c = intersect1d_nu( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], intersect1d_nu([],[]))
 
     ##
@@ -74,14 +74,14 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [1, 2, 3, 4, 5, 6] )
         c = setxor1d( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], setxor1d([],[]))
 
     def check_ediff1d(self):
         zero_elem = numpy.array([])
         one_elem = numpy.array([1])
         two_elem = numpy.array([1,2])
-        
+
         assert_array_equal([],ediff1d(zero_elem))
         assert_array_equal([0],ediff1d(zero_elem,to_begin=0))
         assert_array_equal([0],ediff1d(zero_elem,to_end=0))
@@ -109,7 +109,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [True, False, True, False] )
         c = setmember1d( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], setmember1d([],[]))
 
     ##
@@ -122,7 +122,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [1, 2, 3, 4, 5, 7] )
         c = union1d( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], union1d([],[]))
 
     ##
@@ -142,7 +142,7 @@ class test_aso(NumpyTestCase):
         ec = numpy.array( [19, 20] )
         c = setdiff1d( a, b )
         assert_array_equal( c, ec )
-        
+
         assert_array_equal([], setdiff1d([],[]))
 
 
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index c3eaa9dd2..58f73ec5f 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -1,4 +1,3 @@
-
 import sys
 
 from numpy.testing import *
@@ -93,7 +92,7 @@ class test_linspace(NumpyTestCase):
         t3 = linspace(0,1,2).dtype
         assert_equal(t1, t2)
         assert_equal(t2, t3)
-        
+
 class test_insert(NumpyTestCase):
     def check_basic(self):
         a = [1,2,3]
@@ -276,23 +275,23 @@ class test_vectorize(NumpyTestCase):
         f = vectorize(addsubtract)
         r = f([0,3,6,9],5)
         assert_array_equal(r,[5,8,1,4])
-    def check_large(self): 
-        x = linspace(-3,2,10000) 
-        f = vectorize(lambda x: x) 
-        y = f(x) 
-        assert_array_equal(y, x) 
+    def check_large(self):
+        x = linspace(-3,2,10000)
+        f = vectorize(lambda x: x)
+        y = f(x)
+        assert_array_equal(y, x)
 
 class test_digitize(NumpyTestCase):
     def check_forward(self):
 	x = arange(-6,5)
 	bins = arange(-5,5)
 	assert_array_equal(digitize(x,bins),arange(11))
-	
+
     def check_reverse(self):
 	x = arange(5,-6,-1)
 	bins = arange(5,-5,-1)
 	assert_array_equal(digitize(x,bins),arange(11))
-	
+
     def check_random(self):
 	x = rand(10)
 	bin = linspace(x.min(), x.max(), 10)
@@ -378,12 +377,12 @@ class test_histogramdd(NumpyTestCase):
         # Check that a sequence of arrays is accepted and H has the correct shape.
         z = [squeeze(y) for y in split(x,3,axis=1)]
         H, edges = histogramdd(z, bins=(4,3,2),range=[[-2,2], [0,3], [0,2]])
-        answer = asarray([[[0,0],[0,0],[0,0]], 
-                          [[0,1], [0,0], [1,0]], 
-                          [[0,1], [0,0],[0,0]], 
+        answer = asarray([[[0,0],[0,0],[0,0]],
+                          [[0,1], [0,0], [1,0]],
+                          [[0,1], [0,0],[0,0]],
                           [[0,0],[0,0],[0,0]]])
         assert_array_equal(H, answer)
-                
+
 
 class test_unique(NumpyTestCase):
     def check_simple(self):
diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py
index f7797396f..5d4f540b2 100644
--- a/numpy/lib/tests/test_index_tricks.py
+++ b/numpy/lib/tests/test_index_tricks.py
@@ -1,4 +1,3 @@
-
 from numpy.testing import *
 set_package_path()
 from numpy import array, ones, r_, mgrid
diff --git a/numpy/lib/tests/test_polynomial.py b/numpy/lib/tests/test_polynomial.py
index e2afc72ae..f3a8720d9 100644
--- a/numpy/lib/tests/test_polynomial.py
+++ b/numpy/lib/tests/test_polynomial.py
@@ -78,7 +78,7 @@ import numpy as N
 
 class test_docs(NumpyTestCase):
     def check_doctests(self): return self.rundocs()
-    
+
     def check_roots(self):
         assert_array_equal(N.roots([1,0,0]), [0,0])
 
diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py
index 416c2644f..a96b4fc2c 100644
--- a/numpy/lib/tests/test_shape_base.py
+++ b/numpy/lib/tests/test_shape_base.py
@@ -1,4 +1,3 @@
-
 from numpy.testing import *
 set_package_path()
 import numpy.lib;
@@ -360,17 +359,17 @@ class test_kron(NumpyTestCase):
     def check_return_type(self):
         a = ones([2,2])
         m = asmatrix(a)
-        assert_equal(type(kron(a,a)), ndarray) 
-        assert_equal(type(kron(m,m)), matrix) 
-        assert_equal(type(kron(a,m)), matrix) 
-        assert_equal(type(kron(m,a)), matrix) 
-        class myarray(ndarray): 
+        assert_equal(type(kron(a,a)), ndarray)
+        assert_equal(type(kron(m,m)), matrix)
+        assert_equal(type(kron(a,m)), matrix)
+        assert_equal(type(kron(m,a)), matrix)
+        class myarray(ndarray):
             __array_priority__ = 0.0
         ma = myarray(a.shape, a.dtype, a.data)
-        assert_equal(type(kron(a,a)), ndarray) 
-        assert_equal(type(kron(ma,ma)), myarray) 
-        assert_equal(type(kron(a,ma)), ndarray) 
-        assert_equal(type(kron(ma,a)), myarray) 
+        assert_equal(type(kron(a,a)), ndarray)
+        assert_equal(type(kron(ma,ma)), myarray)
+        assert_equal(type(kron(a,ma)), ndarray)
+        assert_equal(type(kron(ma,a)), myarray)
 
 
 class test_tile(NumpyTestCase):
diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py
index 3c6edfd24..7ef8a94e9 100644
--- a/numpy/lib/tests/test_twodim_base.py
+++ b/numpy/lib/tests/test_twodim_base.py
@@ -162,12 +162,12 @@ class test_histogram2d(NumpyTestCase):
         assert_array_almost_equal(H, answer/8., 3)
     def check_norm(self):
         x = array([1,2,3,1,2,3,1,2,3])
-        y = array([1,1,1,2,2,2,3,3,3]) 
+        y = array([1,1,1,2,2,2,3,3,3])
         H, xed, yed = histogram2d(x,y,[[1,2,3,5], [1,2,3,5]], normed=True)
         answer=array([[1,1,.5],
                      [1,1,.5],
                      [.5,.5,.25]])/9.
         assert_array_almost_equal(H, answer, 3)
-        
+
 if __name__ == "__main__":
     NumpyTest().run()
diff --git a/numpy/lib/tests/test_type_check.py b/numpy/lib/tests/test_type_check.py
index dd9678302..8b990c57e 100644
--- a/numpy/lib/tests/test_type_check.py
+++ b/numpy/lib/tests/test_type_check.py
@@ -1,4 +1,3 @@
-
 import sys
 
 from numpy.testing import *
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index d79fd03c6..fd2ff586a 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -50,7 +50,7 @@ def eye(N, M=None, k=0, dtype=float):
         return m.astype(dtype)
 
 def diag(v, k=0):
-    """ returns a copy of the the k-th diagonal if v is a 2-d array 
+    """ returns a copy of the the k-th diagonal if v is a 2-d array
         or returns a 2-d array with v as the k-th diagonal if v is a
         1-d array.
     """
@@ -100,7 +100,7 @@ def diagflat(v,k=0):
     if not wrap:
         return res
     return wrap(res)
-    
+
 def tri(N, M=None, k=0, dtype=float):
     """ returns a N-by-M array where all the diagonals starting from
         lower left corner up to the k-th are all ones.
@@ -108,7 +108,7 @@ def tri(N, M=None, k=0, dtype=float):
     if M is None: M = N
     m = greater_equal(subtract.outer(arange(N), arange(M)),-k)
     if m.dtype != dtype:
-        return m.astype(dtype)    
+        return m.astype(dtype)
 
 def tril(m, k=0):
     """ returns the elements on and below the k-th diagonal of m.  k=0 is the
@@ -145,20 +145,20 @@ def vander(x, N=None):
 
 def histogram2d(x,y, bins=10, range=None, normed=False):
     """histogram2d(x,y, bins=10, range=None, normed=False) -> H, xedges, yedges
-    
-    Compute the 2D histogram from samples x,y. 
+
+    Compute the 2D histogram from samples x,y.
 
     Parameters
     ----------
     x,y: 1D data series. Both arrays must have the same length.
-    bins: Number of bins -or- [nbin x, nbin y] -or- 
+    bins: Number of bins -or- [nbin x, nbin y] -or-
          [bin edges] -or- [x bin edges, y bin edges].
     range:  A sequence of lower and upper bin edges (default: [min, max]).
-    normed: True or False. 
-    
-    The histogram array is a count of the number of samples in each 
-    two dimensional bin. 
-    Setting normed to True returns a density rather than a bin count. 
+    normed: True or False.
+
+    The histogram array is a count of the number of samples in each
+    two dimensional bin.
+    Setting normed to True returns a density rather than a bin count.
     """
     import numpy as np
     try:
@@ -196,19 +196,19 @@ def histogram2d(x,y, bins=10, range=None, normed=False):
         xedges = yedges.copy()
         ynbin = len(yedges)-1
         xnbin = len(xedges)-1
-    
+
     dxedges = np.diff(xedges)
     dyedges = np.diff(yedges)
-    
+
     # Flattened histogram matrix (1D)
     hist = np.zeros((xnbin)*(ynbin), int)
 
     # Count the number of sample in each bin (1D)
-    xbin = np.digitize(x,xedges) 
-    ybin = np.digitize(y,yedges) 
- 
+    xbin = np.digitize(x,xedges)
+    ybin = np.digitize(y,yedges)
+
     # Values that fall on an edge are put in the right bin.
-    # For the rightmost bin, we want values equal to the right 
+    # For the rightmost bin, we want values equal to the right
     # edge to be counted in the last bin, and not as an outlier.
     xdecimal = int(-np.log10(dxedges.min()))+6
     ydecimal = int(-np.log10(dyedges.min()))+6
@@ -220,15 +220,15 @@ def histogram2d(x,y, bins=10, range=None, normed=False):
     outliers = (xbin==0) | (xbin==xnbin+1) | (ybin==0) | (ybin == ynbin+1)
     xbin = xbin[outliers==False] - 1
     ybin = ybin[outliers==False] - 1
-    
+
     # Compute the sample indices in the flattened histogram matrix.
     if xnbin >= ynbin:
         xy = ybin*(xnbin) + xbin
-        
+
     else:
         xy = xbin*(ynbin) + ybin
-        
-       
+
+
     # Compute the number of repetitions in xy and assign it to the flattened
     #  histogram matrix.
 
diff --git a/numpy/lib/user_array.py b/numpy/lib/user_array.py
index d20593f45..43e9da3f2 100644
--- a/numpy/lib/user_array.py
+++ b/numpy/lib/user_array.py
@@ -196,7 +196,7 @@ class container(object):
         return self.array.__getattribute__(attr)
 
 #############################################################
-# Test of class container 
+# Test of class container
 #############################################################
 if __name__ == '__main__':
     temp=reshape(arange(10000),(100,100))
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index 981ec6de5..e50679eaa 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -7,7 +7,7 @@ from numpy.core import product, ndarray
 
 __all__ = ['issubclass_', 'get_numpy_include', 'issubsctype',
            'issubdtype', 'deprecate', 'get_numarray_include',
-	   'get_include', 'info', 'source', 'who']
+           'get_include', 'info', 'source', 'who']
 
 def issubclass_(arg1, arg2):
     try:
@@ -195,7 +195,7 @@ _dictlist = None
 # Traverse all module directories underneath globals
 # to see if something is defined
 def _makenamedict(module='numpy'):
-    module = __import__(module, globals(), locals(), []) 
+    module = __import__(module, globals(), locals(), [])
     thedict = {module.__name__:module.__dict__}
     dictlist = [module.__name__]
     totraverse = [module.__dict__]
@@ -373,4 +373,3 @@ def source(object, output=sys.stdout):
         print >> output,  inspect.getsource(object)
     except:
         print >> output,  "Not available for this object."
-