diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/add_newdocs.py | 27 | ||||
| -rw-r--r-- | numpy/core/fromnumeric.py | 49 | ||||
| -rw-r--r-- | numpy/core/memmap.py | 8 | ||||
| -rw-r--r-- | numpy/core/numeric.py | 15 | ||||
| -rw-r--r-- | numpy/core/records.py | 5 | ||||
| -rw-r--r-- | numpy/doc/indexing.py | 14 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 8 | ||||
| -rw-r--r-- | numpy/matlib.py | 8 |
8 files changed, 70 insertions, 64 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 11a2688e5..75f88a85f 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -28,9 +28,9 @@ add_newdoc('numpy.core', 'flatiter', It allows iterating over the array as if it were a 1-D array, either in a for-loop or by calling its `next` method. - Iteration is done in C-contiguous style, with the last index varying the - fastest. The iterator can also be indexed using basic slicing or - advanced indexing. + Iteration is done in row-major, C-style order (the last + index varying the fastest). The iterator can also be indexed using + basic slicing or advanced indexing. See Also -------- @@ -745,8 +745,9 @@ add_newdoc('numpy.core.multiarray', 'empty', dtype : data-type, optional Desired output data-type. order : {'C', 'F'}, optional - Whether to store multi-dimensional data in C (row-major) or - Fortran (column-major) order in memory. + Whether to store multi-dimensional data in row-major + (C-style) or column-major (Fortran-style) order in + memory. Returns ------- @@ -2419,7 +2420,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', strides : tuple of ints, optional Strides of data in memory. order : {'C', 'F'}, optional - Row-major or column-major order. + Row-major (C-style) or column-major (Fortran-style) order. Attributes ---------- @@ -3564,9 +3565,9 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten', Parameters ---------- order : {'C', 'F', 'A'}, optional - Whether to flatten in C (row-major), Fortran (column-major) order, - or preserve the C/Fortran ordering from `a`. - The default is 'C'. + Whether to flatten in row-major (C-style) or + column-major (Fortran-style) order or preserve the + C/Fortran ordering from `a`. The default is 'C'. Returns ------- @@ -5144,8 +5145,9 @@ add_newdoc('numpy.core.multiarray', 'ravel_multi_index', In 'clip' mode, a negative index which would normally wrap will clip to 0 instead. order : {'C', 'F'}, optional - Determines whether the multi-index should be viewed as indexing in - C (row-major) order or FORTRAN (column-major) order. + Determines whether the multi-index should be viewed as + indexing in row-major (C-style) or column-major + (Fortran-style) order. Returns ------- @@ -5194,9 +5196,8 @@ add_newdoc('numpy.core.multiarray', 'unravel_index', The shape of the array to use for unraveling ``indices``. order : {'C', 'F'}, optional .. versionadded:: 1.6.0 - Determines whether the indices should be viewed as indexing in - C (row-major) order or FORTRAN (column-major) order. + row-major (C-style) or column-major (Fortran-style) order. Returns ------- diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 5f96b1d61..b6a28ec9b 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -1370,8 +1370,7 @@ def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None): def ravel(a, order='C'): - """ - Return a flattened array. + """Return a flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. @@ -1386,18 +1385,21 @@ def ravel(a, order='C'): Input array. The elements in `a` are read in the order specified by `order`, and packed as a 1-D array. order : {'C','F', 'A', 'K'}, optional - The elements of `a` are read using this index order. 'C' means to - index the elements in C-like order, with the last axis index changing - fastest, back to the first axis index changing slowest. 'F' means to - index the elements in Fortran-like index order, with the first index - changing fastest, and the last index changing slowest. Note that the - 'C' and 'F' options take no account of the memory layout of the - underlying array, and only refer to the order of axis indexing. - 'A' means to read the elements in Fortran-like index order if `a` is - Fortran *contiguous* in memory, C-like order otherwise. 'K' means to - read the elements in the order they occur in memory, except for - reversing the data when strides are negative. By default, 'C' index - order is used. + + The elements of `a` are read using this index order. 'C' means + to index the elements in row-major, C-style order, + with the last axis index changing fastest, back to the first + axis index changing slowest. 'F' means to index the elements + in column-major, Fortran-style order, with the + first index changing fastest, and the last index changing + slowest. Note that the 'C' and 'F' options take no account of + the memory layout of the underlying array, and only refer to + the order of axis indexing. 'A' means to read the elements in + Fortran-like index order if `a` is Fortran *contiguous* in + memory, C-like order otherwise. 'K' means to read the + elements in the order they occur in memory, except for + reversing the data when strides are negative. By default, 'C' + index order is used. Returns ------- @@ -1415,11 +1417,12 @@ def ravel(a, order='C'): Notes ----- - In C-like (row-major) order, in two dimensions, the row index varies the - slowest, and the column index the quickest. This can be generalized to - multiple dimensions, where row-major order implies that the index along the - first axis varies slowest, and the index along the last quickest. The - opposite holds for Fortran-like, or column-major, index ordering. + In row-major, C-style order, in two dimensions, the row index + varies the slowest, and the column index the quickest. This can + be generalized to multiple dimensions, where row-major order + implies that the index along the first axis varies slowest, and + the index along the last quickest. The opposite holds for + column-major, Fortran-style index ordering. Examples -------- @@ -1473,9 +1476,11 @@ def nonzero(a): """ Return the indices of the elements that are non-zero. - Returns a tuple of arrays, one for each dimension of `a`, containing - the indices of the non-zero elements in that dimension. The - corresponding non-zero values can be obtained with:: + Returns a tuple of arrays, one for each dimension of `a`, + containing the indices of the non-zero elements in that + dimension. The values in `a` are always tested and returned in + row-major, C-style order. The corresponding non-zero + values can be obtained with:: a[nonzero(a)] diff --git a/numpy/core/memmap.py b/numpy/core/memmap.py index 4b10f361c..6397e8939 100644 --- a/numpy/core/memmap.py +++ b/numpy/core/memmap.py @@ -21,8 +21,7 @@ mode_equivalents = { } class memmap(ndarray): - """ - Create a memory-map to an array stored in a *binary* file on disk. + """Create a memory-map to an array stored in a *binary* file on disk. Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. Numpy's @@ -79,8 +78,9 @@ class memmap(ndarray): will be 1-D with the number of elements determined by file size and data-type. order : {'C', 'F'}, optional - Specify the order of the ndarray memory layout: C (row-major) or - Fortran (column-major). This only has an effect if the shape is + Specify the order of the ndarray memory layout: + :term:`row-major`, C-style or :term:`column-major`, + Fortran-style. This only has an effect if the shape is greater than 1-D. The default order is 'C'. Attributes diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 24d92f16f..eb92d7f52 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -398,8 +398,7 @@ matmul = multiarray.matmul def asarray(a, dtype=None, order=None): - """ - Convert the input to an array. + """Convert the input to an array. Parameters ---------- @@ -410,8 +409,9 @@ def asarray(a, dtype=None, order=None): dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional - Whether to use row-major ('C') or column-major ('F' for FORTRAN) - memory representation. Defaults to 'C'. + Whether to use row-major (C-style) or + column-major (Fortran-style) memory representation. + Defaults to 'C'. Returns ------- @@ -468,8 +468,7 @@ def asarray(a, dtype=None, order=None): return array(a, dtype, copy=False, order=order) def asanyarray(a, dtype=None, order=None): - """ - Convert the input to an ndarray, but pass ndarray subclasses through. + """Convert the input to an ndarray, but pass ndarray subclasses through. Parameters ---------- @@ -480,8 +479,8 @@ def asanyarray(a, dtype=None, order=None): dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional - Whether to use row-major ('C') or column-major ('F') memory - representation. Defaults to 'C'. + Whether to use row-major (C-style) or column-major + (Fortran-style) memory representation. Defaults to 'C'. Returns ------- diff --git a/numpy/core/records.py b/numpy/core/records.py index 1b3d75db6..9c6d8347a 100644 --- a/numpy/core/records.py +++ b/numpy/core/records.py @@ -295,8 +295,7 @@ class record(nt.void): # the fields (and any subfields) class recarray(ndarray): - """ - Construct an ndarray that allows field access using attributes. + """Construct an ndarray that allows field access using attributes. Arrays may have a data-types containing fields, analogous to columns in a spread sheet. An example is ``[(x, int), (y, float)]``, @@ -345,7 +344,7 @@ class recarray(ndarray): offset : int, optional Start reading buffer (`buf`) from this offset onwards. order : {'C', 'F'}, optional - Row-major or column-major order. + Row-major (C-style) or column-major (Fortran-style) order. Returns ------- diff --git a/numpy/doc/indexing.py b/numpy/doc/indexing.py index 0891e7c8d..9e9f0a10c 100644 --- a/numpy/doc/indexing.py +++ b/numpy/doc/indexing.py @@ -1,5 +1,4 @@ -""" -============== +"""============== Array indexing ============== @@ -229,10 +228,13 @@ most straightforward case, the boolean array has the same shape: :: >>> y[b] array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]) -The result is a 1-D array containing all the elements in the indexed -array corresponding to all the true elements in the boolean array. As -with index arrays, what is returned is a copy of the data, not a view -as one gets with slices. +Unlike in the case of integer index arrays, in the boolean case, the +result is a 1-D array containing all the elements in the indexed array +corresponding to all the true elements in the boolean array. The +elements in the indexed array are always iterated and returned in +:term:`row-major` (C-style) order. The result is also identical to +``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy +of the data, not a view as one gets with slices. The result will be multidimensional if y has more dimensions than b. For example: :: diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 3826715e1..26d25cd6d 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -547,8 +547,7 @@ def average(a, axis=None, weights=None, returned=False): def asarray_chkfinite(a, dtype=None, order=None): - """ - Convert the input to an array, checking for NaNs or Infs. + """Convert the input to an array, checking for NaNs or Infs. Parameters ---------- @@ -559,8 +558,9 @@ def asarray_chkfinite(a, dtype=None, order=None): dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional - Whether to use row-major ('C') or column-major ('FORTRAN') memory - representation. Defaults to 'C'. + Whether to use row-major (C-style) or + column-major (Fortran-style) memory representation. + Defaults to 'C'. Returns ------- diff --git a/numpy/matlib.py b/numpy/matlib.py index 677400367..656ca3458 100644 --- a/numpy/matlib.py +++ b/numpy/matlib.py @@ -11,8 +11,7 @@ __all__ = np.__all__[:] # copy numpy namespace __all__ += ['rand', 'randn', 'repmat'] def empty(shape, dtype=None, order='C'): - """ - Return a new matrix of given shape and type, without initializing entries. + """Return a new matrix of given shape and type, without initializing entries. Parameters ---------- @@ -21,8 +20,9 @@ def empty(shape, dtype=None, order='C'): dtype : data-type, optional Desired output data-type. order : {'C', 'F'}, optional - Whether to store multi-dimensional data in C (row-major) or - Fortran (column-major) order in memory. + Whether to store multi-dimensional data in row-major + (C-style) or column-major (Fortran-style) order in + memory. See Also -------- |