Use the implicit "import numpy as np" made available to all doctests instead

of explicit imports or dependency on the local scope where the doctest is 
defined..

7 files changed, 93 insertions, 99 deletions

diff --git a/numpy/lib/financial.py b/numpy/lib/financial.py
index a3552ebc0..9c5d2753a 100644
--- a/numpy/lib/financial.py
+++ b/numpy/lib/financial.py
@@ -69,7 +69,7 @@ What is the future value after 10 years of saving $100 now, with
   an additional monthly savings of $100.  Assume the interest rate is
   5% (annually) compounded monthly?
 
->>> fv(0.05/12, 10*12, -100, -100)
+>>> np.fv(0.05/12, 10*12, -100, -100)
 15692.928894335748
 
 By convention, the negative sign represents cash flow out (i.e. money not
@@ -94,7 +94,7 @@ Examples
 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)
+>>> np.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
@@ -122,7 +122,7 @@ Examples
 If you only had $150 to spend as payment, how long would it take to pay-off
   a loan of $8,000 at 7% annual interest?
 
->>> nper(0.07/12, -150, 8000)
+>>> np.nper(0.07/12, -150, 8000)
 64.073348770661852
 
 So, over 64 months would be required to pay off the loan.
@@ -130,7 +130,7 @@ So, over 64 months would be required to pay off the loan.
 The same analysis could be done with several different interest rates and/or
     payments and/or total amounts to produce an entire table.
 
->>> nper(*(ogrid[0.06/12:0.071/12:0.01/12, -200:-99:100, 6000:7001:1000]))
+>>> np.nper(*(np.ogrid[0.06/12:0.071/12:0.01/12, -200:-99:100, 6000:7001:1000]))
 array([[[ 32.58497782,  38.57048452],
         [ 71.51317802,  86.37179563]],
 
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index fe6e67903..e8df0b439 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -471,7 +471,7 @@ def average(a, axis=None, weights=None, returned=False):
 
     Examples
     --------
-      >>> average(range(1,11), weights=range(10,0,-1))
+      >>> np.average(range(1,11), weights=range(10,0,-1))
       4.0
 
     Raises
@@ -893,7 +893,7 @@ def trim_zeros(filt, trim='fb'):
 
     Examples
     --------
-    >>> a = array((0, 0, 0, 1, 2, 3, 2, 1, 0))
+    >>> a = np.array((0, 0, 0, 1, 2, 3, 2, 1, 0))
     >>> np.trim_zeros(a)
     array([1, 2, 3, 2, 1])
 
@@ -1069,7 +1069,7 @@ class vectorize(object):
     ...    else:
     ...        return a+b
 
-    >>> vfunc = vectorize(myfunc)
+    >>> vfunc = np.vectorize(myfunc)
 
     >>> vfunc([1, 2, 3, 4], 2)
     array([3, 4, 1, 2])
@@ -1486,29 +1486,28 @@ def median(a, axis=0, out=None, overwrite_input=False):
 
     Examples
     --------
-    >>> from numpy import median
     >>> a = np.array([[10, 7, 4], [3, 2, 1]])
     >>> a
     array([[10,  7,  4],
            [ 3,  2,  1]])
-    >>> median(a)
+    >>> np.median(a)
     array([ 6.5,  4.5,  2.5])
-    >>> median(a, axis=None)
+    >>> np.median(a, axis=None)
     3.5
-    >>> median(a, axis=1)
+    >>> np.median(a, axis=1)
     array([ 7.,  2.])
-    >>> m = median(a)
+    >>> m = np.median(a)
     >>> out = np.zeros_like(m)
-    >>> median(a, out=m)
+    >>> np.median(a, out=m)
     array([ 6.5,  4.5,  2.5])
     >>> m
     array([ 6.5,  4.5,  2.5])
     >>> b = a.copy()
-    >>> median(b, axis=1, overwrite_input=True)
+    >>> np.median(b, axis=1, overwrite_input=True)
     array([ 7.,  2.])
     >>> assert not np.all(a==b)
     >>> b = a.copy()
-    >>> median(b, axis=None, overwrite_input=True)
+    >>> np.median(b, axis=None, overwrite_input=True)
     3.5
     >>> assert not np.all(a==b)
     """
@@ -1632,11 +1631,11 @@ def delete(arr, obj, axis=None):
     ...       [1,2,3],
     ...       [6,7,8]]
 
-    >>> delete(arr, 1, 1)
+    >>> np.delete(arr, 1, 1)
     array([[3, 5],
            [1, 3],
            [6, 8]])
-    >>> delete(arr, 1, 0)
+    >>> np.delete(arr, 1, 0)
     array([[3, 4, 5],
            [6, 7, 8]])
     """
@@ -1732,11 +1731,11 @@ def insert(arr, obj, values, axis=None):
 
     Examples
     --------
-    >>> a = array([[1,2,3],
-    ...            [4,5,6],
-    ...            [7,8,9]])
+    >>> a = np.array([[1,2,3],
+    ...               [4,5,6],
+    ...               [7,8,9]])
 
-    >>> insert(a, [1,2], [[4],[5]], axis=0)
+    >>> np.insert(a, [1,2], [[4],[5]], axis=0)
     array([[1, 2, 3],
            [4, 4, 4],
            [4, 5, 6],
diff --git a/numpy/lib/io.py b/numpy/lib/io.py
index db4e73358..36723f1d8 100644
--- a/numpy/lib/io.py
+++ b/numpy/lib/io.py
@@ -344,9 +344,9 @@ def savetxt(fname, X, fmt='%.18e',delimiter=' '):
 
     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
+    >>> np.savetxt('test.out', x, delimiter=',') # X is an array
+    >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
+    >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation
 
     Notes on fmt
     ------------
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 303cdb13c..8fb0337dc 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -52,8 +52,8 @@ def poly(seq_of_zeros):
 
     Example:
 
-        >>> b = roots([1,3,1,5,6])
-        >>> poly(b)
+        >>> b = np.roots([1,3,1,5,6])
+        >>> np.poly(b)
         array([ 1.,  3.,  1.,  5.,  6.])
 
     """
diff --git a/numpy/lib/scimath.py b/numpy/lib/scimath.py
index d3965668d..2a951135a 100644
--- a/numpy/lib/scimath.py
+++ b/numpy/lib/scimath.py
@@ -50,8 +50,8 @@ def _tocomplex(arr):
 
     >>> a = np.array([1,2,3],np.short)
 
-    >>> ac = _tocomplex(a); ac
-    array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
+    >>> ac = np.lib.scimath._tocomplex(a); ac
+    array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=np.complex64)
 
     >>> ac.dtype
     dtype('complex64')
@@ -61,7 +61,7 @@ def _tocomplex(arr):
 
     >>> b = np.array([1,2,3],np.double)
 
-    >>> bc = _tocomplex(b); bc
+    >>> bc = np.lib.scimath._tocomplex(b); bc
     array([ 1.+0.j,  2.+0.j,  3.+0.j])
 
     >>> bc.dtype
@@ -72,7 +72,7 @@ def _tocomplex(arr):
 
     >>> c = np.array([1,2,3],np.csingle)
 
-    >>> cc = _tocomplex(c); cc
+    >>> cc = np.lib.scimath._tocomplex(c); cc
     array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
 
     >>> c *= 2; c
@@ -102,10 +102,10 @@ def _fix_real_lt_zero(x):
 
     Examples
     --------
-    >>> _fix_real_lt_zero([1,2])
+    >>> np.lib.scimath._fix_real_lt_zero([1,2])
     array([1, 2])
 
-    >>> _fix_real_lt_zero([-1,2])
+    >>> np.lib.scimath._fix_real_lt_zero([-1,2])
     array([-1.+0.j,  2.+0.j])
     """
     x = asarray(x)
@@ -128,10 +128,10 @@ def _fix_int_lt_zero(x):
 
     Examples
     --------
-    >>> _fix_int_lt_zero([1,2])
+    >>> np.lib.scimath._fix_int_lt_zero([1,2])
     array([1, 2])
 
-    >>> _fix_int_lt_zero([-1,2])
+    >>> np.lib.scimath._fix_int_lt_zero([-1,2])
     array([-1.,  2.])
     """
     x = asarray(x)
@@ -154,10 +154,10 @@ def _fix_real_abs_gt_1(x):
 
     Examples
     --------
-    >>> _fix_real_abs_gt_1([0,1])
+    >>> np.lib.scimath._fix_real_abs_gt_1([0,1])
     array([0, 1])
 
-    >>> _fix_real_abs_gt_1([0,2])
+    >>> np.lib.scimath._fix_real_abs_gt_1([0,2])
     array([ 0.+0.j,  2.+0.j])
     """
     x = asarray(x)
@@ -180,17 +180,17 @@ def sqrt(x):
     --------
 
     For real, non-negative inputs this works just like numpy.sqrt():
-    >>> sqrt(1)
+    >>> np.lib.scimath.sqrt(1)
     1.0
 
-    >>> sqrt([1,4])
+    >>> np.lib.scimath.sqrt([1,4])
     array([ 1.,  2.])
 
     But it automatically handles negative inputs:
-    >>> sqrt(-1)
+    >>> np.lib.scimath.sqrt(-1)
     (0.0+1.0j)
 
-    >>> sqrt([-1,4])
+    >>> np.lib.scimath.sqrt([-1,4])
     array([ 0.+1.j,  2.+0.j])
     """
     x = _fix_real_lt_zero(x)
@@ -213,14 +213,13 @@ def log(x):
     Examples
     --------
     >>> import math
-
-    >>> log(math.exp(1))
+    >>> np.lib.scimath.log(math.exp(1))
     1.0
 
     Negative arguments are correctly handled (recall that for negative
     arguments, the identity exp(log(z))==z does not hold anymore):
 
-    >>> log(-math.exp(1)) == (1+1j*math.pi)
+    >>> np.lib.scimath.log(-math.exp(1)) == (1+1j*math.pi)
     True
     """
     x = _fix_real_lt_zero(x)
@@ -246,11 +245,11 @@ def log10(x):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> log10([10**1,10**2])
+    >>> np.lib.scimath.log10([10**1,10**2])
     array([ 1.,  2.])
 
 
-    >>> log10([-10**1,-10**2,10**2])
+    >>> np.lib.scimath.log10([-10**1,-10**2,10**2])
     array([ 1.+1.3644j,  2.+1.3644j,  2.+0.j    ])
     """
     x = _fix_real_lt_zero(x)
@@ -276,10 +275,10 @@ def logn(n, x):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> logn(2,[4,8])
+    >>> np.lib.scimath.logn(2,[4,8])
     array([ 2.,  3.])
 
-    >>> logn(2,[-4,-8,8])
+    >>> np.lib.scimath.logn(2,[-4,-8,8])
     array([ 2.+4.5324j,  3.+4.5324j,  3.+0.j    ])
     """
     x = _fix_real_lt_zero(x)
@@ -306,10 +305,10 @@ def log2(x):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> log2([4,8])
+    >>> np.lib.scimath.log2([4,8])
     array([ 2.,  3.])
 
-    >>> log2([-4,-8,8])
+    >>> np.lib.scimath.log2([-4,-8,8])
     array([ 2.+4.5324j,  3.+4.5324j,  3.+0.j    ])
     """
     x = _fix_real_lt_zero(x)
@@ -336,13 +335,13 @@ def power(x, p):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> power([2,4],2)
+    >>> np.lib.scimath.power([2,4],2)
     array([ 4, 16])
 
-    >>> power([2,4],-2)
+    >>> np.lib.scimath.power([2,4],-2)
     array([ 0.25  ,  0.0625])
 
-    >>> power([-2,4],2)
+    >>> np.lib.scimath.power([-2,4],2)
     array([  4.+0.j,  16.+0.j])
     """
     x = _fix_real_lt_zero(x)
@@ -368,10 +367,10 @@ def arccos(x):
     --------
     >>> np.set_printoptions(precision=4)
 
-    >>> arccos(1)
+    >>> np.lib.scimath.arccos(1)
     0.0
 
-    >>> arccos([1,2])
+    >>> np.lib.scimath.arccos([1,2])
     array([ 0.-0.j   ,  0.+1.317j])
     """
     x = _fix_real_abs_gt_1(x)
@@ -397,10 +396,10 @@ def arcsin(x):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> arcsin(0)
+    >>> np.lib.scimath.arcsin(0)
     0.0
 
-    >>> arcsin([0,1])
+    >>> np.lib.scimath.arcsin([0,1])
     array([ 0.    ,  1.5708])
     """
     x = _fix_real_abs_gt_1(x)
@@ -426,10 +425,10 @@ def arctanh(x):
     (We set the printing precision so the example can be auto-tested)
     >>> np.set_printoptions(precision=4)
 
-    >>> arctanh(0)
+    >>> np.lib.scimath.arctanh(0)
     0.0
 
-    >>> arctanh([0,2])
+    >>> np.lib.scimath.arctanh([0,2])
     array([ 0.0000+0.j    ,  0.5493-1.5708j])
     """
     x = _fix_real_abs_gt_1(x)
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index 77f158eb3..afdb879e4 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -192,13 +192,13 @@ def vstack(tup):
             tup -- sequence of arrays.  All arrays must have the same
                    shape.
         Examples:
-            >>> a = array((1,2,3))
-            >>> b = array((2,3,4))
+            >>> a = np.array((1,2,3))
+            >>> b = np.array((2,3,4))
             >>> np.vstack((a,b))
             array([[1, 2, 3],
                    [2, 3, 4]])
-            >>> a = array([[1],[2],[3]])
-            >>> b = array([[2],[3],[4]])
+            >>> a = np.array([[1],[2],[3]])
+            >>> b = np.array([[2],[3],[4]])
             >>> np.vstack((a,b))
             array([[1],
                    [2],
@@ -222,14 +222,13 @@ def hstack(tup):
             tup -- sequence of arrays.  All arrays must have the same
                    shape.
         Examples:
-            >>> import numpy
-            >>> a = array((1,2,3))
-            >>> b = array((2,3,4))
-            >>> numpy.hstack((a,b))
+            >>> a = np.array((1,2,3))
+            >>> b = np.array((2,3,4))
+            >>> np.hstack((a,b))
             array([1, 2, 3, 2, 3, 4])
-            >>> a = array([[1],[2],[3]])
-            >>> b = array([[2],[3],[4]])
-            >>> numpy.hstack((a,b))
+            >>> a = np.array([[1],[2],[3]])
+            >>> b = np.array([[2],[3],[4]])
+            >>> np.hstack((a,b))
             array([[1, 2],
                    [2, 3],
                    [3, 4]])
@@ -253,10 +252,9 @@ def column_stack(tup):
             tup -- sequence of 1D or 2D arrays.  All arrays must have the same
                    first dimension.
         Examples:
-            >>> import numpy
-            >>> a = array((1,2,3))
-            >>> b = array((2,3,4))
-            >>> numpy.column_stack((a,b))
+            >>> a = np.array((1,2,3))
+            >>> b = np.array((2,3,4))
+            >>> np.column_stack((a,b))
             array([[1, 2],
                    [2, 3],
                    [3, 4]])
@@ -283,16 +281,15 @@ def dstack(tup):
         tup -- sequence of arrays.  All arrays must have the same
                shape.
     Examples:
-        >>> import numpy
-        >>> a = array((1,2,3))
-        >>> b = array((2,3,4))
-        >>> numpy.dstack((a,b))
+        >>> a = np.array((1,2,3))
+        >>> b = np.array((2,3,4))
+        >>> np.dstack((a,b))
         array([[[1, 2],
                 [2, 3],
                 [3, 4]]])
-        >>> a = array([[1],[2],[3]])
-        >>> b = array([[2],[3],[4]])
-        >>> numpy.dstack((a,b))
+        >>> a = np.array([[1],[2],[3]])
+        >>> b = np.array([[2],[3],[4]])
+        >>> np.dstack((a,b))
         array([[[1, 2]],
         <BLANKLINE>
                [[2, 3]],
@@ -432,12 +429,11 @@ def hsplit(ary,indices_or_sections):
         Related:
             hstack, split, array_split, vsplit, dsplit.
         Examples:
-            >>> import numpy
-            >>> a= array((1,2,3,4))
-            >>> numpy.hsplit(a,2)
+            >>> a= np.array((1,2,3,4))
+            >>> np.hsplit(a,2)
             [array([1, 2]), array([3, 4])]
-            >>> a = array([[1,2,3,4],[1,2,3,4]])
-            >>> hsplit(a,2)
+            >>> a = np.array([[1,2,3,4],[1,2,3,4]])
+            >>> np.hsplit(a,2)
             [array([[1, 2],
                    [1, 2]]), array([[3, 4],
                    [3, 4]])]
@@ -482,9 +478,9 @@ def vsplit(ary,indices_or_sections):
             vstack, split, array_split, hsplit, dsplit.
         Examples:
             import numpy
-            >>> a = array([[1,2,3,4],
-            ...            [1,2,3,4]])
-            >>> numpy.vsplit(a,2)
+            >>> a = np.array([[1,2,3,4],
+            ...              [1,2,3,4]])
+            >>> np.vsplit(a,2)
             [array([[1, 2, 3, 4]]), array([[1, 2, 3, 4]])]
 
     """
@@ -519,8 +515,8 @@ def dsplit(ary,indices_or_sections):
         Related:
             dstack, split, array_split, hsplit, vsplit.
         Examples:
-            >>> a = array([[[1,2,3,4],[1,2,3,4]]])
-            >>> dsplit(a,2)
+            >>> a = np.array([[[1,2,3,4],[1,2,3,4]]])
+            >>> np.dsplit(a,2)
             [array([[[1, 2],
                     [1, 2]]]), array([[[3, 4],
                     [3, 4]]])]
@@ -596,15 +592,15 @@ def tile(A, reps):
 
 
     Examples:
-    >>> a = array([0,1,2])
-    >>> tile(a,2)
+    >>> a = np.array([0,1,2])
+    >>> np.tile(a,2)
     array([0, 1, 2, 0, 1, 2])
-    >>> tile(a,(1,2))
+    >>> np.tile(a,(1,2))
     array([[0, 1, 2, 0, 1, 2]])
-    >>> tile(a,(2,2))
+    >>> np.tile(a,(2,2))
     array([[0, 1, 2, 0, 1, 2],
            [0, 1, 2, 0, 1, 2]])
-    >>> tile(a,(2,1,2))
+    >>> np.tile(a,(2,1,2))
     array([[[0, 1, 2, 0, 1, 2]],
     <BLANKLINE>
            [[0, 1, 2, 0, 1, 2]]])
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index 44082521c..ab1e5fcf0 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -88,13 +88,13 @@ def diagflat(v,k=0):
 
     Examples
     --------
-      >>> diagflat([[1,2],[3,4]]])
+      >>> np.diagflat([[1,2],[3,4]])
       array([[1, 0, 0, 0],
              [0, 2, 0, 0],
              [0, 0, 3, 0],
              [0, 0, 0, 4]])
 
-      >>> diagflat([1,2], 1)
+      >>> np.diagflat([1,2], 1)
       array([[0, 1, 0],
              [0, 0, 2],
              [0, 0, 0]])
@@ -180,8 +180,8 @@ def histogram2d(x,y, bins=10, range=None, normed=False, weights=None):
       - `xedges, yedges` : Arrays defining the bin edges.
 
     Example:
-      >>> x = random.randn(100,2)
-      >>> hist2d, xedges, yedges = histogram2d(x, bins = (6, 7))
+      >>> x = np.random.randn(100,2)
+      >>> hist2d, xedges, yedges = np.lib.histogram2d(x, bins = (6, 7))
 
     :SeeAlso: histogramdd
     """