diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 45 |
1 files changed, 31 insertions, 14 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 457c04803..7dd8c5649 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -885,7 +885,7 @@ add_newdoc('numpy.core.multiarray', 'zeros', >>> np.zeros(5) array([ 0., 0., 0., 0., 0.]) - >>> np.zeros((5,), dtype=numpy.int) + >>> np.zeros((5,), dtype=np.int) array([0, 0, 0, 0, 0]) >>> np.zeros((2, 1)) @@ -2063,6 +2063,14 @@ add_newdoc('numpy.core', 'einsum', ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples below have corresponding `einsum` calls with the two parameter methods. + .. versionadded:: 1.10.0 + + Views returned from einsum are now writeable whenever the input array + is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now + have the same effect as ``np.swapaxes(a, 0, 2)`` and + ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal + of a 2D array. + Examples -------- >>> a = np.arange(25).reshape(5,5) @@ -2172,6 +2180,14 @@ add_newdoc('numpy.core', 'einsum', array([[10, 28, 46, 64], [13, 40, 67, 94]]) + >>> # since version 1.10.0 + >>> a = np.zeros((3, 3)) + >>> np.einsum('ii->i', a)[:] = 1 + >>> a + array([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + """) add_newdoc('numpy.core', 'vdot', @@ -4613,7 +4629,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('view', >>> print x [(1, 20) (3, 4)] - Using a view to convert an array to a record array: + Using a view to convert an array to a recarray: >>> z = x.view(np.recarray) >>> z.a @@ -4816,11 +4832,11 @@ add_newdoc('numpy.core.umath', 'seterrobj', ############################################################################## # -# lib._compiled_base functions +# compiled_base functions # ############################################################################## -add_newdoc('numpy.lib._compiled_base', 'digitize', +add_newdoc('numpy.core.multiarray', 'digitize', """ digitize(x, bins, right=False) @@ -4900,7 +4916,7 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', array([1, 3, 3, 4, 5]) """) -add_newdoc('numpy.lib._compiled_base', 'bincount', +add_newdoc('numpy.core.multiarray', 'bincount', """ bincount(x, weights=None, minlength=None) @@ -4973,7 +4989,7 @@ add_newdoc('numpy.lib._compiled_base', 'bincount', """) -add_newdoc('numpy.lib._compiled_base', 'ravel_multi_index', +add_newdoc('numpy.core.multiarray', 'ravel_multi_index', """ ravel_multi_index(multi_index, dims, mode='raise', order='C') @@ -5030,7 +5046,7 @@ add_newdoc('numpy.lib._compiled_base', 'ravel_multi_index', 1621 """) -add_newdoc('numpy.lib._compiled_base', 'unravel_index', +add_newdoc('numpy.core.multiarray', 'unravel_index', """ unravel_index(indices, dims, order='C') @@ -5073,7 +5089,7 @@ add_newdoc('numpy.lib._compiled_base', 'unravel_index', """) -add_newdoc('numpy.lib._compiled_base', 'add_docstring', +add_newdoc('numpy.core.multiarray', 'add_docstring', """ add_docstring(obj, docstring) @@ -5083,7 +5099,7 @@ add_newdoc('numpy.lib._compiled_base', 'add_docstring', raise a TypeError """) -add_newdoc('numpy.lib._compiled_base', 'add_newdoc_ufunc', +add_newdoc('numpy.core.umath', '_add_newdoc_ufunc', """ add_ufunc_docstring(ufunc, new_docstring) @@ -5109,7 +5125,7 @@ add_newdoc('numpy.lib._compiled_base', 'add_newdoc_ufunc', and then throwing away the ufunc. """) -add_newdoc('numpy.lib._compiled_base', 'packbits', +add_newdoc('numpy.core.multiarray', 'packbits', """ packbits(myarray, axis=None) @@ -5153,7 +5169,7 @@ add_newdoc('numpy.lib._compiled_base', 'packbits', """) -add_newdoc('numpy.lib._compiled_base', 'unpackbits', +add_newdoc('numpy.core.multiarray', 'unpackbits', """ unpackbits(myarray, axis=None) @@ -5859,17 +5875,18 @@ add_newdoc('numpy.core.multiarray', 'dtype', >>> np.dtype(np.int16) dtype('int16') - Record, one field name 'f1', containing int16: + Structured type, one field name 'f1', containing int16: >>> np.dtype([('f1', np.int16)]) dtype([('f1', '<i2')]) - Record, one field named 'f1', in itself containing a record with one field: + Structured type, one field named 'f1', in itself containing a structured + type with one field: >>> np.dtype([('f1', [('f1', np.int16)])]) dtype([('f1', [('f1', '<i2')])]) - Record, two fields: the first field contains an unsigned int, the + Structured type, two fields: the first field contains an unsigned int, the second an int32: >>> np.dtype([('f1', np.uint), ('f2', np.int32)]) |