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| author | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-06 14:42:08 +0200 |
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| committer | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-06 14:42:08 +0200 |
| commit | 2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681 (patch) | |
| tree | 1a4a81faf9e59d0cabf9cbace9dc967bad60caa1 /numpy/doc/internals.py | |
| parent | 773e3cad9a71cb9a7849d8e251fb8a99ab35d06b (diff) | |
| download | numpy-2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681.tar.gz | |
DOC: change Numpy to NumPy in dosctrings and comments
The strings in error messages were left untouched
Diffstat (limited to 'numpy/doc/internals.py')
| -rw-r--r-- | numpy/doc/internals.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/numpy/doc/internals.py b/numpy/doc/internals.py index c25872bc0..a14fee7c2 100644 --- a/numpy/doc/internals.py +++ b/numpy/doc/internals.py @@ -6,13 +6,13 @@ Array Internals Internal organization of numpy arrays ===================================== -It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy". +It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to NumPy". -Numpy arrays consist of two major components, the raw array data (from now on, +NumPy arrays consist of two major components, the raw array data (from now on, referred to as the data buffer), and the information about the raw array data. The data buffer is typically what people think of as arrays in C or Fortran, a contiguous (and fixed) block of memory containing fixed sized data items. -Numpy also contains a significant set of data that describes how to interpret +NumPy also contains a significant set of data that describes how to interpret the data in the data buffer. This extra information contains (among other things): 1) The basic data element's size in bytes @@ -109,7 +109,7 @@ The situation with numpy makes this issue yet more complicated. The internal machinery of numpy arrays is flexible enough to accept any ordering of indices. One can simply reorder indices by manipulating the internal stride information for arrays -without reordering the data at all. Numpy will know how to map the new index +without reordering the data at all. NumPy will know how to map the new index order to the data without moving the data. So if this is true, why not choose |