Fix docstrings for loading with DocFileSuite.

6 files changed, 75 insertions, 60 deletions

diff --git a/numpy/core/numerictypes.py b/numpy/core/numerictypes.py
index 0836241cd..0ca208390 100644
--- a/numpy/core/numerictypes.py
+++ b/numpy/core/numerictypes.py
@@ -80,7 +80,7 @@ __all__ = ['sctypeDict', 'sctypeNA', 'typeDict', 'typeNA', 'sctypes',
            'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
            'maximum_sctype', 'issctype', 'typecodes']
 
-from multiarray import typeinfo, ndarray, array, empty, dtype
+from numpy.core.multiarray import typeinfo, ndarray, array, empty, dtype
 import types as _types
 
 # we don't export these for import *, but we do want them accessible
diff --git a/numpy/distutils/ccompiler.py b/numpy/distutils/ccompiler.py
index 3a9ee48dc..e850569f4 100644
--- a/numpy/distutils/ccompiler.py
+++ b/numpy/distutils/ccompiler.py
@@ -8,9 +8,9 @@ from distutils import ccompiler
 from distutils.sysconfig import customize_compiler
 from distutils.version import LooseVersion
 
-import log
-from exec_command import exec_command
-from misc_util import cyg2win32, is_sequence, mingw32
+from numpy.distutils import log
+from numpy.distutils.exec_command import exec_command
+from numpy.distutils.misc_util import cyg2win32, is_sequence, mingw32
 from distutils.spawn import _nt_quote_args
 
 # hack to set compiler optimizing options. Needs to integrated with something.
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 02e7f605e..14f02f797 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -143,6 +143,7 @@ def histogramdd(sample, bins=10, range=None, normed=False):
     H, edges = histogramdd(x, bins = (5, 6, 7))
 
     See also: histogram
+    
     """
 
     try:
@@ -263,6 +264,7 @@ def average(a, axis=None, weights=None, returned=False):
 
     Raises ZeroDivisionError if appropriate.  (The version in MA does
     not -- it returns masked values).
+    
     """
     if axis is None:
         a = array(a).ravel()
@@ -376,31 +378,32 @@ def piecewise(x, condlist, funclist, *args, **kw):
 
 def select(condlist, choicelist, default=0):
     """ Return an array composed of different elements of choicelist
-        depending on the list of conditions.
-
-        condlist is a list of condition arrays containing ones or zeros
-
-        choicelist is a list of choice arrays (of the "same" size as the
-        arrays in condlist).  The result array has the "same" size as the
-        arrays in choicelist.  If condlist is [c0, ..., cN-1] then choicelist
-        must be of length N.  The elements of the choicelist can then be
-        represented as [v0, ..., vN-1]. The default choice if none of the
-        conditions are met is given as the default argument.
-
-        The conditions are tested in order and the first one statisfied is
-        used to select the choice. In other words, the elements of the
-        output array are found from the following tree (notice the order of
-        the conditions matters):
-
-        if c0: v0
-        elif c1: v1
-        elif c2: v2
-        ...
-        elif cN-1: vN-1
-        else: default
-
-        Note that one of the condition arrays must be large enough to handle
-        the largest array in the choice list.
+    depending on the list of conditions.
+
+    condlist is a list of condition arrays containing ones or zeros
+
+    choicelist is a list of choice arrays (of the "same" size as the
+    arrays in condlist).  The result array has the "same" size as the
+    arrays in choicelist.  If condlist is [c0, ..., cN-1] then choicelist
+    must be of length N.  The elements of the choicelist can then be
+    represented as [v0, ..., vN-1]. The default choice if none of the
+    conditions are met is given as the default argument.
+
+    The conditions are tested in order and the first one statisfied is
+    used to select the choice. In other words, the elements of the
+    output array are found from the following tree (notice the order of
+    the conditions matters):
+
+    if c0: v0
+    elif c1: v1
+    elif c2: v2
+    ...
+    elif cN-1: vN-1
+    else: default
+
+    Note that one of the condition arrays must be large enough to handle
+    the largest array in the choice list.
+    
     """
     n = len(condlist)
     n2 = len(choicelist)
@@ -452,7 +455,8 @@ def gradient(f, *varargs):
     Outputs:
 
       N arrays of the same shape as f giving the derivative of f with respect
-       to each dimension.
+      to each dimension.
+       
     """
     N = len(f.shape)  # number of dimensions
     n = len(varargs)
@@ -542,6 +546,7 @@ bins is monotonically increasing, or bins [i-1] > x >= bins[i] if
 bins is monotonically decreasing.
 
 Beyond the bounds of the bins 0 or len(bins) is returned as appropriate.
+
 """)
 except RuntimeError:
     pass
@@ -558,6 +563,7 @@ is specified, every occurrence of i at a position p contributes
 weights[p] instead of 1.
 
 See also: histogram, digitize, unique.
+
 """)
 except RuntimeError:
     pass
@@ -570,6 +576,7 @@ Add a docstring to a built-in obj if possible.
 If the obj already has a docstring raise a RuntimeError
 If this routine does not know how to add a docstring to the object
 raise a TypeError
+
 """)
 except RuntimeError:
     pass
@@ -612,6 +619,7 @@ def sort_complex(a):
     the imaginary part if the real part is equal (the default sort order
     for complex arrays).  This function is a wrapper ensuring a complex
     return type.
+
     """
     b = array(a,copy=True)
     b.sort()
@@ -633,6 +641,7 @@ def trim_zeros(filt, trim='fb'):
         >>> a = array((0, 0, 0, 1, 2, 3, 2, 1, 0))
         >>> numpy.trim_zeros(a)
         array([1, 2, 3, 2, 1])
+        
     """
     first = 0
     trim = trim.upper()
@@ -657,6 +666,7 @@ def unique(x):
     Example:
     >>> unique([5,2,4,0,4,4,2,2,1])
     array([0,1,2,4,5])
+    
     """
     try:
         tmp = x.flatten()
@@ -682,6 +692,7 @@ def place(arr, mask, vals):
     """Similar to putmask arr[mask] = vals but the 1D array vals has the
     same number of elements as the non-zero values of mask. Inverse of
     extract.
+    
     """
     return _insert(arr, mask, vals)
 
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 8ac065c47..8f8e1b235 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -119,6 +119,7 @@ class nd_grid(object):
            >>> ogrid = nd_grid(sparse=True)
            >>> ogrid[0:5,0:5]
            [array([[0],[1],[2],[3],[4]]), array([[0, 1, 2, 3, 4]])]
+           
     """
     def __init__(self, sparse=False):
         self.sparse = sparse
@@ -312,6 +313,7 @@ class r_class(concatenator):
         For example:
         >>> r_[array([1,2,3]), 0, 0, array([4,5,6])]
         array([1, 2, 3, 0, 0, 4, 5, 6])
+        
     """
     def __init__(self):
         concatenator.__init__(self, 0)
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index f8bc1f693..ff429e3f8 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -50,13 +50,14 @@ def _lstsq(X, y, rcond):
 def poly(seq_of_zeros):
     """ Return a sequence representing a polynomial given a sequence of roots.
 
-        If the input is a matrix, return the characteristic polynomial.
-
-        Example:
-
-         >>> b = roots([1,3,1,5,6])
-         >>> poly(b)
-         array([1., 3., 1., 5., 6.])
+    If the input is a matrix, return the characteristic polynomial.
+
+    Example:
+        
+        >>> b = roots([1,3,1,5,6])
+        >>> poly(b)
+        array([1., 3., 1., 5., 6.])
+        
     """
     seq_of_zeros = atleast_1d(seq_of_zeros)
     sh = seq_of_zeros.shape
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index cb15a2760..1e9b55bb6 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -274,29 +274,30 @@ def column_stack(tup):
 def dstack(tup):
     """ Stack arrays in sequence depth wise (along third dimension)
 
-        Description:
-            Take a sequence of arrays and stack them along the third axis.
-            All arrays in the sequence must have the same shape along all
-            but the third axis.  This is a simple way to stack 2D arrays
-            (images) into a single 3D array for processing.
-            dstack will rebuild arrays divided by dsplit.
-        Arguments:
-            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))
-            array([       [[1, 2],
-                    [2, 3],
-                    [3, 4]]])
-            >>> a = array([[1],[2],[3]])
-            >>> b = array([[2],[3],[4]])
-            >>> numpy.dstack((a,b))
-            array([[        [1, 2]],
-                   [        [2, 3]],
-                   [        [3, 4]]])
+    Description:
+        Take a sequence of arrays and stack them along the third axis.
+        All arrays in the sequence must have the same shape along all
+        but the third axis.  This is a simple way to stack 2D arrays
+        (images) into a single 3D array for processing.
+        dstack will rebuild arrays divided by dsplit.
+    Arguments:
+        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))
+        array([       [[1, 2],
+                       [2, 3],
+                       [3, 4]]])
+        >>> a = array([[1],[2],[3]])
+        >>> b = array([[2],[3],[4]])
+        >>> numpy.dstack((a,b))
+        array([[        [1, 2]],
+        [        [2, 3]],
+        [        [3, 4]]])
+        
     """
     return _nx.concatenate(map(atleast_3d,tup),2)