STY: Giant comma spacing fixup.

Run the 2to3 ws_comma fixer on *.py files. Some lines are now too long
and will need to be broken at some point. OTOH, some lines were already
too long and need to be broken at some point. Now seems as good a time
as any to do this with open PRs at a minimum.

1 files changed, 26 insertions, 26 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 4285bf793..91eace2ff 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -351,7 +351,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
     # Compute the bin number each sample falls into.
     Ncount = {}
     for i in arange(D):
-        Ncount[i] = digitize(sample[:,i], edges[i])
+        Ncount[i] = digitize(sample[:, i], edges[i])
 
     # Using digitize, values that fall on an edge are put in the right bin.
     # For the rightmost bin, we want values equal to the right
@@ -362,7 +362,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
         if not np.isinf(mindiff):
             decimal = int(-log10(mindiff)) + 6
             # Find which points are on the rightmost edge.
-            on_edge = where(around(sample[:,i], decimal) == around(edges[i][-1],
+            on_edge = where(around(sample[:, i], decimal) == around(edges[i][-1],
                                                                    decimal))[0]
             # Shift these points one bin to the left.
             Ncount[i][on_edge] -= 1
@@ -392,11 +392,11 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
     hist = hist.reshape(sort(nbin))
     for i in arange(nbin.size):
         j = ni.argsort()[i]
-        hist = hist.swapaxes(i,j)
-        ni[i],ni[j] = ni[j],ni[i]
+        hist = hist.swapaxes(i, j)
+        ni[i], ni[j] = ni[j], ni[i]
 
     # Remove outliers (indices 0 and -1 for each dimension).
-    core = D*[slice(1,-1)]
+    core = D*[slice(1, -1)]
     hist = hist[core]
 
     # Normalize if normed is True
@@ -1196,7 +1196,7 @@ def sort_complex(a):
     array([ 1.+2.j,  2.-1.j,  3.-3.j,  3.-2.j,  3.+5.j])
 
     """
-    b = array(a,copy=True)
+    b = array(a, copy=True)
     b.sort()
     if not issubclass(b.dtype.type, _nx.complexfloating):
         if b.dtype.char in 'bhBH':
@@ -1269,7 +1269,7 @@ def unique(x):
         if tmp.size == 0:
             return tmp
         tmp.sort()
-        idx = concatenate(([True],tmp[1:]!=tmp[:-1]))
+        idx = concatenate(([True], tmp[1:]!=tmp[:-1]))
         return tmp[idx]
     except AttributeError:
         items = sorted(set(x))
@@ -1736,7 +1736,7 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
         rowvar = 1
     if rowvar:
         axis = 0
-        tup = (slice(None),newaxis)
+        tup = (slice(None), newaxis)
     else:
         axis = 1
         tup = (newaxis, slice(None))
@@ -1744,7 +1744,7 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
 
     if y is not None:
         y = array(y, copy=False, ndmin=2, dtype=float)
-        X = concatenate((X,y), axis)
+        X = concatenate((X, y), axis)
 
     X -= X.mean(axis=1-axis)[tup]
     if rowvar:
@@ -1820,7 +1820,7 @@ def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None):
         d = diag(c)
     except ValueError: # scalar covariance
         return 1
-    return c/sqrt(multiply.outer(d,d))
+    return c/sqrt(multiply.outer(d, d))
 
 def blackman(M):
     """
@@ -1916,7 +1916,7 @@ def blackman(M):
         return array([])
     if M == 1:
         return ones(1, float)
-    n = arange(0,M)
+    n = arange(0, M)
     return 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1))
 
 def bartlett(M):
@@ -2022,8 +2022,8 @@ def bartlett(M):
         return array([])
     if M == 1:
         return ones(1, float)
-    n = arange(0,M)
-    return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))
+    n = arange(0, M)
+    return where(less_equal(n, (M-1)/2.0), 2.0*n/(M-1), 2.0-2.0*n/(M-1))
 
 def hanning(M):
     """
@@ -2120,7 +2120,7 @@ def hanning(M):
         return array([])
     if M == 1:
         return ones(1, float)
-    n = arange(0,M)
+    n = arange(0, M)
     return 0.5-0.5*cos(2.0*pi*n/(M-1))
 
 def hamming(M):
@@ -2216,8 +2216,8 @@ def hamming(M):
     if M < 1:
         return array([])
     if M == 1:
-        return ones(1,float)
-    n = arange(0,M)
+        return ones(1, float)
+    n = arange(0, M)
     return 0.54-0.46*cos(2.0*pi*n/(M-1))
 
 ## Code from cephes for i0
@@ -2285,7 +2285,7 @@ def _chbevl(x, vals):
     b0 = vals[0]
     b1 = 0.0
 
-    for i in range(1,len(vals)):
+    for i in range(1, len(vals)):
         b2 = b1
         b1 = b0
         b0 = x*b1 - b2 + vals[i]
@@ -2364,7 +2364,7 @@ def i0(x):
 
 ## End of cephes code for i0
 
-def kaiser(M,beta):
+def kaiser(M, beta):
     """
     Return the Kaiser window.
 
@@ -2487,7 +2487,7 @@ def kaiser(M,beta):
     from numpy.dual import i0
     if M == 1:
         return np.array([1.])
-    n = arange(0,M)
+    n = arange(0, M)
     alpha = (M-1)/2.0
     return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(float(beta))
 
@@ -2592,7 +2592,7 @@ def msort(a):
     ``np.msort(a)`` is equivalent to  ``np.sort(a, axis=0)``.
 
     """
-    b = array(a,subok=True,copy=True)
+    b = array(a, subok=True, copy=True)
     b.sort(0)
     return b
 
@@ -2841,7 +2841,7 @@ def _compute_qth_percentile(sorted, q, axis, out):
     else:
         indexer[axis] = slice(i, i+2)
         j = i + 1
-        weights = array([(j - index), (index - i)],float)
+        weights = array([(j - index), (index - i)], float)
         wshape = [1]*sorted.ndim
         wshape[axis] = 2
         weights.shape = wshape
@@ -2926,8 +2926,8 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     nd = len(y.shape)
     slice1 = [slice(None)]*nd
     slice2 = [slice(None)]*nd
-    slice1[axis] = slice(1,None)
-    slice2[axis] = slice(None,-1)
+    slice1[axis] = slice(1, None)
+    slice2[axis] = slice(None, -1)
     try:
         ret = (d * (y[slice1] +y [slice2]) / 2.0).sum(axis)
     except ValueError: # Operations didn't work, cast to ndarray
@@ -3212,7 +3212,7 @@ def delete(arr, obj, axis=None):
         if stop == N:
             pass
         else:
-            slobj[axis] = slice(stop-numtodel,None)
+            slobj[axis] = slice(stop-numtodel, None)
             slobj2 = [slice(None)]*ndim
             slobj2[axis] = slice(stop, None)
             new[slobj] = arr[slobj2]
@@ -3253,9 +3253,9 @@ def delete(arr, obj, axis=None):
         new = empty(newshape, arr.dtype, arr.flags.fnc)
         slobj[axis] = slice(None, obj)
         new[slobj] = arr[slobj]
-        slobj[axis] = slice(obj,None)
+        slobj[axis] = slice(obj, None)
         slobj2 = [slice(None)]*ndim
-        slobj2[axis] = slice(obj+1,None)
+        slobj2[axis] = slice(obj+1, None)
         new[slobj] = arr[slobj2]
     else:
         if obj.size == 0 and not isinstance(_obj, np.ndarray):