diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 103 |
1 files changed, 43 insertions, 60 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 86ea4b8b6..73efdb6a9 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -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) @@ -2172,42 +2180,13 @@ add_newdoc('numpy.core', 'einsum', array([[10, 28, 46, 64], [13, 40, 67, 94]]) - """) - -add_newdoc('numpy.core', 'alterdot', - """ - 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 - called when Numpy is imported. - - When Numpy is built with an accelerated BLAS like ATLAS, these functions - are replaced to make use of the faster implementations. The faster - implementations only affect float32, float64, complex64, and complex128 - arrays. Furthermore, the BLAS API only includes matrix-matrix, - matrix-vector, and vector-vector products. Products of arrays with larger - dimensionalities use the built in functions and are not accelerated. - - See Also - -------- - restoredot : `restoredot` undoes the effects of `alterdot`. - - """) - -add_newdoc('numpy.core', 'restoredot', - """ - Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS - implementations. - - Typically, the user will only need to call this when troubleshooting and - installation problem, reproducing the conditions of a build without an - accelerated BLAS, or when being very careful about benchmarking linear - algebra operations. - - See Also - -------- - alterdot : `restoredot` undoes the effects of `alterdot`. + >>> # 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.]]) """) @@ -3834,7 +3813,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('put', add_newdoc('numpy.core.multiarray', 'copyto', """ - copyto(dst, src, casting='same_kind', where=None, preservena=False) + copyto(dst, src, casting='same_kind', where=None) Copies values from one array to another, broadcasting as necessary. @@ -3862,9 +3841,6 @@ add_newdoc('numpy.core.multiarray', 'copyto', A boolean array which is broadcasted to match the dimensions of `dst`, and selects elements to copy from `src` to `dst` wherever it contains the value True. - preservena : bool, optional - If set to True, leaves any NA values in `dst` untouched. This - is similar to the "hard mask" feature in numpy.ma. """) @@ -3879,11 +3855,6 @@ add_newdoc('numpy.core.multiarray', 'putmask', If `values` is not the same size as `a` and `mask` then it will repeat. This gives behavior different from ``a[mask] = values``. - .. note:: The `putmask` functionality is also provided by `copyto`, which - can be significantly faster and in addition is NA-aware - (`preservena` keyword). Replacing `putmask` with - ``np.copyto(a, values, where=mask)`` is recommended. - Parameters ---------- a : array_like @@ -4459,12 +4430,12 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist', tobytesdoc = """ - a.tostring(order='C') + a.{name}(order='C') - Construct a Python string containing the raw data bytes in the array. + Construct Python bytes containing the raw data bytes in the array. - Constructs a Python string showing a copy of the raw contents of - data memory. The string can be produced in either 'C' or 'Fortran', + Constructs Python bytes showing a copy of the raw contents of + data memory. The bytes object can be produced in either 'C' or 'Fortran', or 'Any' order (the default is 'C'-order). 'Any' order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means 'Fortran' order. @@ -4479,29 +4450,31 @@ tobytesdoc = """ Returns ------- - s : str - A Python string exhibiting a copy of `a`'s raw data. + s : bytes + Python bytes exhibiting a copy of `a`'s raw data. Examples -------- >>> x = np.array([[0, 1], [2, 3]]) >>> x.tobytes() - '\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00' + b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00' >>> x.tobytes('C') == x.tobytes() True >>> x.tobytes('F') - '\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00' + b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00' """ add_newdoc('numpy.core.multiarray', 'ndarray', - ('tostring', tobytesdoc.format(deprecated= + ('tostring', tobytesdoc.format(name='tostring', + deprecated= 'This function is a compatibility ' 'alias for tobytes. Despite its ' 'name it returns bytes not ' 'strings.'))) add_newdoc('numpy.core.multiarray', 'ndarray', - ('tobytes', tobytesdoc.format(deprecated='.. versionadded:: 1.9.0'))) + ('tobytes', tobytesdoc.format(name='tobytes', + deprecated='.. versionadded:: 1.9.0'))) add_newdoc('numpy.core.multiarray', 'ndarray', ('trace', """ @@ -4880,14 +4853,15 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', Parameters ---------- x : array_like - Input array to be binned. It has to be 1-dimensional. + Input array to be binned. Prior to Numpy 1.10.0, this array had to + be 1-dimensional, but can now have any shape. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. right : bool, optional Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval - does not include the right edge. The left bin and is open in this - case. Ie., bins[i-1] <= x < bins[i] is the default behavior for + does not include the right edge. The left bin end is open in this + case, i.e., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins. Returns @@ -4898,7 +4872,7 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', Raises ------ ValueError - If the input is not 1-dimensional, or if `bins` is not monotonic. + If `bins` is not monotonic. TypeError If the type of the input is complex. @@ -4912,6 +4886,13 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', attempting to index `bins` with the indices that `digitize` returns will result in an IndexError. + .. versionadded:: 1.10.0 + + `np.digitize` is implemented in terms of `np.searchsorted`. This means + that a binary search is used to bin the values, which scales much better + for larger number of bins than the previous linear search. It also removes + the requirement for the input array to be 1-dimensional. + Examples -------- >>> x = np.array([0.2, 6.4, 3.0, 1.6]) @@ -4928,7 +4909,7 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', 1.0 <= 1.6 < 2.5 >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.]) - >>> bins = np.array([0,5,10,15,20]) + >>> bins = np.array([0, 5, 10, 15, 20]) >>> np.digitize(x,bins,right=True) array([1, 2, 3, 4, 4]) >>> np.digitize(x,bins,right=False) @@ -5519,6 +5500,8 @@ add_newdoc('numpy.core', 'ufunc', ('reduce', in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. + .. versionadded:: 1.7.0 + Returns ------- r : ndarray |