diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 21 |
1 files changed, 16 insertions, 5 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index ab4dc5681..dd48191b6 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -1406,7 +1406,7 @@ add_newdoc('numpy.core.multiarray', 'arange', (in other words, the interval including `start` but excluding `stop`). For integer arguments the function is equivalent to the Python built-in `range <http://docs.python.org/lib/built-in-funcs.html>`_ function, - but returns a ndarray rather than a list. + but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use ``linspace`` for these cases. @@ -1432,7 +1432,7 @@ add_newdoc('numpy.core.multiarray', 'arange', Returns ------- - out : ndarray + arange : ndarray Array of evenly spaced values. For floating point arguments, the length of the result is @@ -1443,8 +1443,8 @@ add_newdoc('numpy.core.multiarray', 'arange', See Also -------- linspace : Evenly spaced numbers with careful handling of endpoints. - ogrid: Arrays of evenly spaced numbers in N-dimensions - mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions + ogrid: Arrays of evenly spaced numbers in N-dimensions. + mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions. Examples -------- @@ -1584,6 +1584,17 @@ add_newdoc('numpy.core.multiarray', 'where', [ 3., 4., -1.], [-1., -1., -1.]]) + Find the indices of elements of `x` that are in `goodvalues`. + + >>> goodvalues = [3, 4, 7] + >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape) + >>> ix + array([[False, False, False], + [ True, True, False], + [False, True, False]], dtype=bool) + >>> np.where(ix) + (array([1, 1, 2]), array([0, 1, 1])) + """) @@ -2236,7 +2247,7 @@ add_newdoc('numpy.core', 'einsum', add_newdoc('numpy.core', 'alterdot', """ - Change `dot`, `vdot`, and `innerproduct` to use accelerated BLAS functions. + Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions. Typically, as a user of Numpy, you do not explicitly call this function. If Numpy is built with an accelerated BLAS, this function is automatically |