ran reindent in preparation for the 1.1 release

10 files changed, 137 insertions, 138 deletions

diff --git a/numpy/lib/financial.py b/numpy/lib/financial.py
index 5bb4a3af7..2a751281f 100644
--- a/numpy/lib/financial.py
+++ b/numpy/lib/financial.py
@@ -2,12 +2,12 @@
 #  patterned after spreadsheet computations.
 
 # There is some complexity in each function
-#  so that the functions behave like ufuncs with 
+#  so that the functions behave like ufuncs with
 #  broadcasting and being able to be called with scalars
-#  or arrays (or other sequences). 
+#  or arrays (or other sequences).
 import numpy as np
 
-__all__ = ['fv', 'pmt', 'nper', 'ipmt', 'ppmt', 'pv', 'rate', 
+__all__ = ['fv', 'pmt', 'nper', 'ipmt', 'ppmt', 'pv', 'rate',
            'irr', 'npv', 'mirr']
 
 _when_to_num = {'end':0, 'begin':1,
@@ -19,7 +19,7 @@ _when_to_num = {'end':0, 'begin':1,
 
 eqstr = """
 
-                  nper       / (1 + rate*when) \   /        nper   \  
+                  nper       / (1 + rate*when) \   /        nper   \
   fv + pv*(1+rate)    + pmt*|-------------------|*| (1+rate)    - 1 | = 0
                              \     rate        /   \               /
 
@@ -28,23 +28,23 @@ eqstr = """
 where (all can be scalars or sequences)
 
     Parameters
-    ---------- 
-    rate : 
+    ----------
+    rate :
         Rate of interest (per period)
-    nper : 
+    nper :
         Number of compounding periods
-    pmt : 
-        Payment 
+    pmt :
+        Payment
     pv :
         Present value
     fv :
-        Future value 
-    when : 
+        Future value
+    when :
         When payments are due ('begin' (1) or 'end' (0))
-                                                                   
+
 """
 
-def _convert_when(when):    
+def _convert_when(when):
     try:
         return _when_to_num[when]
     except KeyError:
@@ -85,19 +85,19 @@ def pmt(rate, nper, pv, fv=0, when='end'):
     temp = (1+rate)**nper
     miter = np.broadcast(rate, nper, pv, fv, when)
     zer = np.zeros(miter.shape)
-    fact = np.where(rate==zer, nper+zer, (1+rate*when)*(temp-1)/rate+zer) 
+    fact = np.where(rate==zer, nper+zer, (1+rate*when)*(temp-1)/rate+zer)
     return -(fv + pv*temp) / fact
 pmt.__doc__ += eqstr + """
 Example
 -------
 
-What would the monthly payment need to be to pay off a $200,000 loan in 15 
+What would the monthly payment need to be to pay off a $200,000 loan in 15
   years at an annual interest rate of 7.5%?
 
 >>> pmt(0.075/12, 12*15, 200000)
 -1854.0247200054619
 
-In order to pay-off (i.e. have a future-value of 0) the $200,000 obtained 
+In order to pay-off (i.e. have a future-value of 0) the $200,000 obtained
   today, a monthly payment of $1,854.02 would be required.
 """
 
@@ -160,19 +160,19 @@ def pv(rate, nper, pmt, fv=0.0, when='end'):
 pv.__doc__ += eqstr
 
 # Computed with Sage
-#  (y + (r + 1)^n*x + p*((r + 1)^n - 1)*(r*w + 1)/r)/(n*(r + 1)^(n - 1)*x - p*((r + 1)^n - 1)*(r*w + 1)/r^2 + n*p*(r + 1)^(n - 1)*(r*w + 1)/r + p*((r + 1)^n - 1)*w/r)    
+#  (y + (r + 1)^n*x + p*((r + 1)^n - 1)*(r*w + 1)/r)/(n*(r + 1)^(n - 1)*x - p*((r + 1)^n - 1)*(r*w + 1)/r^2 + n*p*(r + 1)^(n - 1)*(r*w + 1)/r + p*((r + 1)^n - 1)*w/r)
 
 def _g_div_gp(r, n, p, x, y, w):
     t1 = (r+1)**n
     t2 = (r+1)**(n-1)
     return (y + t1*x + p*(t1 - 1)*(r*w + 1)/r)/(n*t2*x - p*(t1 - 1)*(r*w + 1)/(r**2) + n*p*t2*(r*w + 1)/r + p*(t1 - 1)*w/r)
 
-# Use Newton's iteration until the change is less than 1e-6 
+# Use Newton's iteration until the change is less than 1e-6
 #  for all values or a maximum of 100 iterations is reached.
-#  Newton's rule is 
-#  r_{n+1} = r_{n} - g(r_n)/g'(r_n) 
+#  Newton's rule is
+#  r_{n+1} = r_{n} - g(r_n)/g'(r_n)
 #     where
-#  g(r) is the formula 
+#  g(r) is the formula
 #  g'(r) is the derivative with respect to r.
 def rate(nper, pmt, pv, fv, when='end', guess=0.10, tol=1e-6, maxiter=100):
     """Number of periods found by solving the equation
@@ -194,7 +194,7 @@ def rate(nper, pmt, pv, fv, when='end', guess=0.10, tol=1e-6, maxiter=100):
     else:
         return rn
 rate.__doc__ += eqstr
-    
+
 def irr(values):
     """Internal Rate of Return
 
@@ -212,7 +212,7 @@ def irr(values):
     if rate.size == 1:
         rate = rate.item()
     return rate
-    
+
 def npv(rate, values):
     """Net Present Value
 
@@ -223,15 +223,15 @@ def npv(rate, values):
 
 def mirr(values, finance_rate, reinvest_rate):
     """Modified internal rate of return
-    
+
     Parameters
     ----------
     values:
         Cash flows (must contain at least one positive and one negative value)
         or nan is returned.
-    finance_rate : 
+    finance_rate :
         Interest rate paid on the cash flows
-    reinvest_rate : 
+    reinvest_rate :
         Interest rate received on the cash flows upon reinvestment
     """
 
@@ -240,7 +240,7 @@ def mirr(values, finance_rate, reinvest_rate):
     neg = values < 0
     if not (pos.size > 0 and neg.size > 0):
         return np.nan
-    
+
     n = pos.size + neg.size
     numer = -npv(reinvest_rate, values[pos])*((1+reinvest_rate)**n)
     denom = npv(finance_rate, values[neg])*(1+finance_rate)
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index e165c2672..4c02d7c7a 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -328,49 +328,49 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
 
 def average(a, axis=None, weights=None, returned=False):
     """Return the weighted average of array a over the given axis.
-       
-    
+
+
     Parameters
     ----------
     a : array_like
         Data to be averaged.
     axis : {None, integer}, optional
-        Axis along which to average a. If None, averaging is done over the 
-        entire array irrespective of its shape. 
+        Axis along which to average a. If None, averaging is done over the
+        entire array irrespective of its shape.
     weights : {None, array_like}, optional
-        The importance each datum has in the computation of the 
-        average. The weights array can either be 1D, in which case  its length 
-        must be the size of a along the given axis, or of the same shape as a. 
-        If weights=None, all data are assumed to have weight equal to one. 
+        The importance each datum has in the computation of the
+        average. The weights array can either be 1D, in which case  its length
+        must be the size of a along the given axis, or of the same shape as a.
+        If weights=None, all data are assumed to have weight equal to one.
     returned :{False, boolean}, optional
         If True, the tuple (average, sum_of_weights) is returned,
-        otherwise only the average is returmed. Note that if weights=None, then 
+        otherwise only the average is returmed. Note that if weights=None, then
         the sum of the weights is also the number of elements averaged over.
 
     Returns
     -------
     average, [sum_of_weights] : {array_type, double}
-        Return the average along the specified axis. When returned is True, 
-        return a tuple with the average as the first element and the sum 
-        of the weights as the second element. The return type is Float if a is 
+        Return the average along the specified axis. When returned is True,
+        return a tuple with the average as the first element and the sum
+        of the weights as the second element. The return type is Float if a is
         of integer type, otherwise it is of the same type as a.
         sum_of_weights is has the same type as the average.
 
-    
+
     Example
     -------
       >>> average(range(1,11), weights=range(10,0,-1))
       4.0
-    
+
     Exceptions
     ----------
     ZeroDivisionError
-        Raised when all weights along axis are zero. See numpy.ma.average for a 
-        version robust to this type of error. 
+        Raised when all weights along axis are zero. See numpy.ma.average for a
+        version robust to this type of error.
     TypeError
-        Raised when the length of 1D weights is not the same as the shape of a 
-        along axis. 
-    
+        Raised when the length of 1D weights is not the same as the shape of a
+        along axis.
+
     """
     if not isinstance(a, np.matrix) :
         a = np.asarray(a)
@@ -390,7 +390,7 @@ def average(a, axis=None, weights=None, returned=False):
                 raise TypeError, "1D weights expected when shapes of a and weights differ."
             if wgt.shape[0] != a.shape[axis] :
                 raise ValueError, "Length of weights not compatible with specified axis."
-    
+
             # setup wgt to broadcast along axis
             wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1,axis)
 
@@ -681,20 +681,20 @@ except RuntimeError:
 def interp(x, xp, fp, left=None, right=None):
     """Return the value of a piecewise-linear function at each value in x.
 
-    The piecewise-linear function, f, is defined by the known data-points 
-    fp=f(xp). The xp points must be sorted in increasing order but this is 
+    The piecewise-linear function, f, is defined by the known data-points
+    fp=f(xp). The xp points must be sorted in increasing order but this is
     not checked.
-    
-    For values of x < xp[0] return the value given by left.  If left is None, 
+
+    For values of x < xp[0] return the value given by left.  If left is None,
     then return fp[0].
-    For values of x > xp[-1] return the value given by right. If right is 
+    For values of x > xp[-1] return the value given by right. If right is
     None, then return fp[-1].
     """
     if isinstance(x, (float, int, number)):
         return compiled_interp([x], xp, fp, left, right).item()
     else:
         return compiled_interp(x, xp, fp, left, right)
-    
+
 
 def angle(z, deg=0):
     """Return the angle of the complex argument z.
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 22b8ef388..2039d5b5e 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -294,7 +294,7 @@ class AxisConcatenator(object):
             objs.append(newobj)
             if not scalar and isinstance(newobj, _nx.ndarray):
                 arraytypes.append(newobj.dtype)
-                
+
         #  Esure that scalars won't up-cast unless warranted
         final_dtype = find_common_type(arraytypes, scalartypes)
         if final_dtype is not None:
diff --git a/numpy/lib/io.py b/numpy/lib/io.py
index 9e61ab2f8..4c7180245 100644
--- a/numpy/lib/io.py
+++ b/numpy/lib/io.py
@@ -232,37 +232,37 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None,
 
     Parameters
     ----------
-    fname : filename or a file handle.  
+    fname : filename or a file handle.
       Support for gzipped files is automatic, if the filename ends in .gz
 
-    dtype : data-type 
-      Data type of the resulting array.  If this is a record data-type, the 
-      resulting array will be 1-d and each row will be interpreted as an 
-      element of the array. The number of columns used must match the number 
+    dtype : data-type
+      Data type of the resulting array.  If this is a record data-type, the
+      resulting array will be 1-d and each row will be interpreted as an
+      element of the array. The number of columns used must match the number
       of fields in the data-type in this case.
 
-    comments : str 
+    comments : str
       The character used to indicate the start of a comment in the file.
 
     delimiter : str
-      A string-like character used to separate values in the file. If delimiter 
+      A string-like character used to separate values in the file. If delimiter
       is unspecified or none, any whitespace string is a separator.
 
     converters : {}
-      A dictionary mapping column number to a function that will convert that 
-      column to a float.  Eg, if column 0 is a date string: 
-      converters={0:datestr2num}. Converters can also be used to provide 
-      a default value for missing data: converters={3:lambda s: float(s or 0)}. 
-    
+      A dictionary mapping column number to a function that will convert that
+      column to a float.  Eg, if column 0 is a date string:
+      converters={0:datestr2num}. Converters can also be used to provide
+      a default value for missing data: converters={3:lambda s: float(s or 0)}.
+
     skiprows : int
       The number of rows from the top to skip.
 
     usecols : sequence
-      A sequence of integer column indexes to extract where 0 is the first 
+      A sequence of integer column indexes to extract where 0 is the first
       column, eg. usecols=(1,4,5) will extract the 2nd, 5th and 6th columns.
 
     unpack : bool
-      If True, will transpose the matrix allowing you to unpack into named 
+      If True, will transpose the matrix allowing you to unpack into named
       arguments on the left hand side.
 
     Examples
@@ -271,8 +271,8 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None,
       >>> x,y,z = load('somefile.dat', usecols=(3,5,7), unpack=True)
       >>> r = np.loadtxt('record.dat', dtype={'names':('gender','age','weight'),
                 'formats': ('S1','i4', 'f4')})
-    
-    SeeAlso: scipy.io.loadmat to read and write matfiles.            
+
+    SeeAlso: scipy.io.loadmat to read and write matfiles.
     """
 
     if _string_like(fname):
@@ -332,23 +332,23 @@ def savetxt(fname, X, fmt='%.18e',delimiter=' '):
     Parameters
     ----------
     fname : filename or a file handle
-      If the filename ends in .gz, the file is automatically saved in 
-      compressed gzip format.  The load() command understands gzipped files 
+      If the filename ends in .gz, the file is automatically saved in
+      compressed gzip format.  The load() command understands gzipped files
       transparently.
     X : array or sequence
       Data to write to file.
-    fmt : string 
-      A format string %[flags][width][.precision]specifier. See notes below for 
+    fmt : string
+      A format string %[flags][width][.precision]specifier. See notes below for
       a description of some common flags and specifiers.
     delimiter : str
       Character separating columns.
-  
+
     Examples
     --------
       >>> savetxt('test.out', x, delimiter=',')         # X is an array
       >>> savetxt('test.out', (x,y,z))     # x,y,z equal sized 1D arrays
-      >>> savetxt('test.out', x, fmt='%1.4e')  # use exponential notation      
-    
+      >>> savetxt('test.out', x, fmt='%1.4e')  # use exponential notation
+
     Notes on fmt
     ------------
     flags:
@@ -362,19 +362,19 @@ def savetxt(fname, X, fmt='%.18e',delimiter=' '):
       For e, E and f specifiers, the number of digits to print after the decimal
       point.
       For g and G, the maximum number of significant digits.
-      For s, the maximum number of characters. 
+      For s, the maximum number of characters.
     specifiers:
       c : character
       d or i : signed decimal integer
-      e or E : scientific notation with e or E. 
+      e or E : scientific notation with e or E.
       f : decimal floating point
       g,G : use the shorter of e,E or f
       o : signed octal
       s : string of characters
       u : unsigned decimal integer
       x,X : unsigned hexadecimal integer
-      
-    This is not an exhaustive specification. 
+
+    This is not an exhaustive specification.
     """
 
     if _string_like(fname):
@@ -403,7 +403,7 @@ def savetxt(fname, X, fmt='%.18e',delimiter=' '):
 import re
 def fromregex(file, regexp, dtype):
     """Construct a record array from a text file, using regular-expressions parsing.
-    
+
     Array is constructed from all matches of the regular expression
     in the file. Groups in the regular expression are converted to fields.
 
@@ -423,7 +423,7 @@ def fromregex(file, regexp, dtype):
     >>> f.write("1312 foo\n1534  bar\n 444   qux")
     >>> f.close()
     >>> np.fromregex('test.dat', r"(\d+)\s+(...)", [('num', np.int64), ('key', 'S3')])
-    array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')], 
+    array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
           dtype=[('num', '<i8'), ('key', '|S3')])
 
     """
@@ -433,7 +433,7 @@ def fromregex(file, regexp, dtype):
         regexp = re.compile(regexp)
     if not isinstance(dtype, np.dtype):
         dtype = np.dtype(dtype)
-    
+
     seq = regexp.findall(file.read())
     if seq and not isinstance(seq[0], tuple):
         # make sure np.array doesn't interpret strings as binary data
@@ -441,4 +441,3 @@ def fromregex(file, regexp, dtype):
         seq = [(x,) for x in seq]
     output = np.array(seq, dtype=dtype)
     return output
-
diff --git a/numpy/lib/tests/test__datasource.py b/numpy/lib/tests/test__datasource.py
index ad3635490..93e77d28c 100644
--- a/numpy/lib/tests/test__datasource.py
+++ b/numpy/lib/tests/test__datasource.py
@@ -200,7 +200,7 @@ class TestDataSourceAbspath(NumpyTestCase):
         tmpfilename = os.path.split(tmpfile)[-1]
 
         tmp_path = lambda x: os.path.abspath(self.ds.abspath(x))
-        
+
         assert tmp_path(valid_httpurl()).startswith(self.tmpdir)
         assert tmp_path(invalid_httpurl()).startswith(self.tmpdir)
         assert tmp_path(tmpfile).startswith(self.tmpdir)
@@ -208,7 +208,7 @@ class TestDataSourceAbspath(NumpyTestCase):
         for fn in malicious_files:
             assert tmp_path(http_path+fn).startswith(self.tmpdir)
             assert tmp_path(fn).startswith(self.tmpdir)
-    
+
     def test_windows_os_sep(self):
         orig_os_sep = os.sep
         try:
@@ -244,7 +244,7 @@ class TestRepositoryAbspath(NumpyTestCase):
         for fn in malicious_files:
             assert tmp_path(http_path+fn).startswith(self.tmpdir)
             assert tmp_path(fn).startswith(self.tmpdir)
-        
+
     def test_windows_os_sep(self):
         orig_os_sep = os.sep
         try:
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index d1786969d..52c8cb4d0 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -58,36 +58,36 @@ class TestAverage(NumpyTestCase):
         assert_almost_equal(y5.mean(1), average(y5, 1))
 
         y6 = matrix(rand(5,5))
-        assert_array_equal(y6.mean(0), average(y6,0)) 
-               
+        assert_array_equal(y6.mean(0), average(y6,0))
+
     def check_weights(self):
         y = arange(10)
         w = arange(10)
         assert_almost_equal(average(y, weights=w), (arange(10)**2).sum()*1./arange(10).sum())
-    
+
         y1 = array([[1,2,3],[4,5,6]])
         w0 = [1,2]
         actual = average(y1,weights=w0,axis=0)
         desired = array([3.,4.,5.])
         assert_almost_equal(actual, desired)
-        
-        
+
+
         w1 = [0,0,1]
         desired = array([3., 6.])
         assert_almost_equal(average(y1, weights=w1, axis=1), desired)
 
         # This should raise an error. Can we test for that ?
         # assert_equal(average(y1, weights=w1), 9./2.)
-        
-    
+
+
         # 2D Case
         w2 = [[0,0,1],[0,0,2]]
         desired = array([3., 6.])
         assert_array_equal(average(y1, weights=w2, axis=1), desired)
-        
+
         assert_equal(average(y1, weights=w2), 5.)
-        
-        
+
+
     def check_returned(self):
         y = array([[1,2,3],[4,5,6]])
 
@@ -97,23 +97,23 @@ class TestAverage(NumpyTestCase):
 
         avg, scl = average(y, 0, returned=True)
         assert_array_equal(scl, array([2.,2.,2.]))
-        
+
         avg, scl = average(y, 1, returned=True)
         assert_array_equal(scl, array([3.,3.]))
-        
+
         # With weights
         w0 = [1,2]
         avg, scl = average(y, weights=w0, axis=0, returned=True)
         assert_array_equal(scl, array([3., 3., 3.]))
-        
+
         w1 = [1,2,3]
         avg, scl = average(y, weights=w1, axis=1, returned=True)
         assert_array_equal(scl, array([6., 6.]))
-        
+
         w2 = [[0,0,1],[1,2,3]]
         avg, scl = average(y, weights=w2, axis=1, returned=True)
         assert_array_equal(scl, array([1.,6.]))
-    
+
 
 class TestSelect(NumpyTestCase):
     def _select(self,cond,values,default=0):
@@ -433,7 +433,7 @@ class TestHistogram(NumpyTestCase):
         (a,b)=histogram(v)
         #check if the sum of the bins equals the number of samples
         assert(sum(a,axis=0)==n)
-        #check that the bin counts are evenly spaced when the data is from a 
+        #check that the bin counts are evenly spaced when the data is from a
         # linear function
         (a,b)=histogram(linspace(0,10,100))
         assert(all(a==10))
@@ -443,7 +443,7 @@ class TestHistogramdd(NumpyTestCase):
         x = array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \
         [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
         H, edges = histogramdd(x, (2,3,3), range = [[-1,1], [0,3], [0,3]])
-        answer = asarray([[[0,1,0], [0,0,1], [1,0,0]], [[0,1,0], [0,0,1], 
+        answer = asarray([[[0,1,0], [0,0,1], [1,0,0]], [[0,1,0], [0,0,1],
             [0,0,1]]])
         assert_array_equal(H,answer)
         # Check normalization
@@ -451,12 +451,12 @@ class TestHistogramdd(NumpyTestCase):
         H, edges = histogramdd(x, bins = ed, normed = True)
         assert(all(H == answer/12.))
         # Check that H has the correct shape.
-        H, edges = histogramdd(x, (2,3,4), range = [[-1,1], [0,3], [0,4]], 
+        H, edges = histogramdd(x, (2,3,4), range = [[-1,1], [0,3], [0,4]],
             normed=True)
-        answer = asarray([[[0,1,0,0], [0,0,1,0], [1,0,0,0]], [[0,1,0,0], 
+        answer = asarray([[[0,1,0,0], [0,0,1,0], [1,0,0,0]], [[0,1,0,0],
             [0,0,1,0], [0,0,1,0]]])
         assert_array_almost_equal(H, answer/6., 4)
-        # Check that a sequence of arrays is accepted and H has the correct 
+        # 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]])
@@ -473,7 +473,7 @@ class TestHistogramdd(NumpyTestCase):
 
     def check_shape_3d(self):
         # All possible permutations for bins of different lengths in 3D.
-        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4), 
+        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
             (4, 5, 6))
         r = rand(10,3)
         for b in bins:
@@ -482,11 +482,11 @@ class TestHistogramdd(NumpyTestCase):
 
     def check_shape_4d(self):
         # All possible permutations for bins of different lengths in 4D.
-        bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4), 
-            (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6), 
-            (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7), 
-            (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5), 
-            (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5), 
+        bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
+            (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
+            (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
+            (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
+            (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
             (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))
 
         r = rand(10,4)
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 9e398cf23..31eceb7f6 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -38,8 +38,8 @@ class TestSaveTxt(NumpyTestCase):
         np.savetxt(c, a, delimiter=',', fmt='%d')
         c.seek(0)
         assert_equal(c.readlines(), ['1,2\n', '3,4\n'])
-        
-        
+
+
 ##    def test_format(self):
 ##        a = np.array([(1, 2), (3, 4)])
 ##        c = StringIO.StringIO()
@@ -47,21 +47,21 @@ class TestSaveTxt(NumpyTestCase):
 ##        np.savetxt(c, a, fmt=['%02d', '%3.1f'])
 ##        c.seek(0)
 ##        assert_equal(c.readlines(), ['01 2.0\n', '03 4.0\n'])
-##        
+##
 ##        # A single multiformat string
 ##        c = StringIO.StringIO()
 ##        np.savetxt(c, a, fmt='%02d : %3.1f')
 ##        c.seek(0)
 ##        lines = c.readlines()
 ##        assert_equal(lines, ['01 : 2.0\n', '03 : 4.0\n'])
-##        
+##
 ##        # Specify delimiter, should be overiden
 ##        c = StringIO.StringIO()
 ##        np.savetxt(c, a, fmt='%02d : %3.1f', delimiter=',')
 ##        c.seek(0)
 ##        lines = c.readlines()
 ##        assert_equal(lines, ['01 : 2.0\n', '03 : 4.0\n'])
-        
+
 
 class TestLoadTxt(NumpyTestCase):
     def test_record(self):
@@ -122,7 +122,7 @@ class TestLoadTxt(NumpyTestCase):
             converters={3:lambda s: int(s or -999)})
         a = np.array([1,2,3,-999,5], int)
         assert_array_equal(x, a)
-        
+
     def test_comments(self):
         c = StringIO.StringIO()
         c.write('# comment\n1,2,3,5\n')
@@ -131,7 +131,7 @@ class TestLoadTxt(NumpyTestCase):
             comments='#')
         a = np.array([1,2,3,5], int)
         assert_array_equal(x, a)
-        
+
     def test_skiprows(self):
         c = StringIO.StringIO()
         c.write('comment\n1,2,3,5\n')
@@ -140,7 +140,7 @@ class TestLoadTxt(NumpyTestCase):
             skiprows=1)
         a = np.array([1,2,3,5], int)
         assert_array_equal(x, a)
-        
+
         c = StringIO.StringIO()
         c.write('# comment\n1,2,3,5\n')
         c.seek(0)
@@ -148,7 +148,7 @@ class TestLoadTxt(NumpyTestCase):
             skiprows=1)
         a = np.array([1,2,3,5], int)
         assert_array_equal(x, a)
-        
+
     def test_usecols(self):
         a =np.array( [[1,2],[3,4]], float)
         c = StringIO.StringIO()
@@ -156,14 +156,14 @@ class TestLoadTxt(NumpyTestCase):
         c.seek(0)
         x = np.loadtxt(c, dtype=float, usecols=(1,))
         assert_array_equal(x, a[:,1])
-        
+
         a =np.array( [[1,2,3],[3,4,5]], float)
         c = StringIO.StringIO()
         np.savetxt(c, a)
         c.seek(0)
         x = np.loadtxt(c, dtype=float, usecols=(1,2))
         assert_array_equal(x, a[:,1:])
-        
+
 
 class Testfromregex(NumpyTestCase):
     def test_record(self):
diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py
index 2e0b3cfb0..868184b81 100644
--- a/numpy/lib/tests/test_regression.py
+++ b/numpy/lib/tests/test_regression.py
@@ -28,6 +28,6 @@ class TestRegression(NumpyTestCase):
         tested = np.polyfit(x, y, 4)
         assert_array_almost_equal(ref, tested)
 
-        
+
 if __name__ == "__main__":
     NumpyTest().run()
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index a66e94d65..44082521c 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -83,9 +83,9 @@ def diag(v, k=0):
         raise ValueError, "Input must be 1- or 2-d."
 
 def diagflat(v,k=0):
-    """Return a 2D array whose k'th diagonal is a flattened v and all other 
-    elements are zero. 
-    
+    """Return a 2D array whose k'th diagonal is a flattened v and all other
+    elements are zero.
+
     Examples
     --------
       >>> diagflat([[1,2],[3,4]]])
@@ -93,12 +93,12 @@ def diagflat(v,k=0):
              [0, 2, 0, 0],
              [0, 0, 3, 0],
              [0, 0, 0, 4]])
-      
+
       >>> diagflat([1,2], 1)
       array([[0, 1, 0],
-             [0, 0, 2], 
+             [0, 0, 2],
              [0, 0, 0]])
-    """ 
+    """
     try:
         wrap = v.__array_wrap__
     except AttributeError:
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index b02ba540f..3f7d22092 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -512,7 +512,7 @@ def lookfor(what, module=None, import_modules=True, regenerate=False):
     if not whats: return
 
     for name, (docstring, kind, index) in cache.iteritems():
-        if kind in ('module', 'object'): 
+        if kind in ('module', 'object'):
             # don't show modules or objects
             continue
         ok = True
@@ -528,7 +528,7 @@ def lookfor(what, module=None, import_modules=True, regenerate=False):
     # XXX: this is full Harrison-Stetson heuristics now,
     # XXX: it probably could be improved
 
-    kind_relevance = {'func': 1000, 'class': 1000, 
+    kind_relevance = {'func': 1000, 'class': 1000,
                       'module': -1000, 'object': -1000}
 
     def relevance(name, docstr, kind, index):
@@ -597,7 +597,7 @@ def _lookfor_generate_cache(module, import_modules, regenerate):
     cache : dict {obj_full_name: (docstring, kind, index), ...}
         Docstring cache for the module, either cached one (regenerate=False)
         or newly generated.
-        
+
     """
     global _lookfor_caches
 
@@ -623,7 +623,7 @@ def _lookfor_generate_cache(module, import_modules, regenerate):
 
         index += 1
         kind = "object"
-       
+
         if inspect.ismodule(item):
             kind = "module"
             try:
@@ -649,13 +649,13 @@ def _lookfor_generate_cache(module, import_modules, regenerate):
                 stack.append(("%s.%s" % (name, n), v))
         elif callable(item):
             kind = "func"
-        
+
         doc = inspect.getdoc(item)
         if doc is not None:
             cache[name] = (doc, kind, index)
 
     return cache
- 
+
 #-----------------------------------------------------------------------------
 
 # The following SafeEval class and company are adapted from Michael Spencer's