Author: Ronan Lamy <[email protected]>
Branch: ufunc-reduce
Changeset: r78748:adf035e65bc8
Date: 2015-08-02 18:18 +0100
http://bitbucket.org/pypy/pypy/changeset/adf035e65bc8/
Log: Remove wrong docstring
diff --git a/pypy/module/micronumpy/ufuncs.py b/pypy/module/micronumpy/ufuncs.py
--- a/pypy/module/micronumpy/ufuncs.py
+++ b/pypy/module/micronumpy/ufuncs.py
@@ -168,58 +168,6 @@
@unwrap_spec(keepdims=bool)
def descr_reduce(self, space, w_obj, w_axis=None, w_dtype=None,
w_out=None, keepdims=False):
- """reduce(...)
- reduce(a, axis=0)
-
- Reduces `a`'s dimension by one, by applying ufunc along one axis.
-
- Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
- :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]`
=
- the result of iterating `j` over :math:`range(N_i)`, cumulatively
applying
- ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
- For a one-dimensional array, reduce produces results equivalent to:
- ::
-
- r = op.identity # op = ufunc
- for i in xrange(len(A)):
- r = op(r, A[i])
- return r
-
- For example, add.reduce() is equivalent to sum().
-
- Parameters
- ----------
- a : array_like
- The array to act on.
- axis : int, optional
- The axis along which to apply the reduction.
-
- Examples
- --------
- >>> np.multiply.reduce([2,3,5])
- 30
-
- A multi-dimensional array example:
-
- >>> X = np.arange(8).reshape((2,2,2))
- >>> X
- array([[[0, 1],
- [2, 3]],
- [[4, 5],
- [6, 7]]])
- >>> np.add.reduce(X, 0)
- array([[ 4, 6],
- [ 8, 10]])
- >>> np.add.reduce(X) # confirm: default axis value is 0
- array([[ 4, 6],
- [ 8, 10]])
- >>> np.add.reduce(X, 1)
- array([[ 2, 4],
- [10, 12]])
- >>> np.add.reduce(X, 2)
- array([[ 1, 5],
- [ 9, 13]])
- """
from pypy.module.micronumpy.ndarray import W_NDimArray
if w_axis is None:
w_axis = space.wrap(0)
@@ -257,8 +205,6 @@
if x < 0 or x >= shapelen:
raise oefmt(space.w_ValueError, "'axis' entry is out of
bounds")
axes[i] = x
-
-
else:
if space.isinstance_w(w_axis, space.w_tuple) and
space.len_w(w_axis) == 1:
w_axis = space.getitem(w_axis, space.wrap(0))
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