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| author | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-05 22:24:34 +0200 |
|---|---|---|
| committer | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-06 11:20:19 +0200 |
| commit | 773e3cad9a71cb9a7849d8e251fb8a99ab35d06b (patch) | |
| tree | 793dab9410558a21622d6e6b948d0491997cc54c /doc/source/f2py/python-usage.rst | |
| parent | adc155e12648256eea754d1d53e8322e3ac19549 (diff) | |
| download | numpy-773e3cad9a71cb9a7849d8e251fb8a99ab35d06b.tar.gz | |
change all non-code instances of Numpy to NumPy
Instances remain for NumpyVersion and Numpy.rec.fromarrays that are
references to code.
Release notes were left unchanged.
see issue #7986
Diffstat (limited to 'doc/source/f2py/python-usage.rst')
| -rw-r--r-- | doc/source/f2py/python-usage.rst | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/doc/source/f2py/python-usage.rst b/doc/source/f2py/python-usage.rst index c7c7e3fd0..f5f1d2304 100644 --- a/doc/source/f2py/python-usage.rst +++ b/doc/source/f2py/python-usage.rst @@ -66,7 +66,7 @@ String arguments F2PY generated wrapper functions accept (almost) any Python object as a string argument, ``str`` is applied for non-string objects. -Exceptions are Numpy arrays that must have type code ``'c'`` or +Exceptions are NumPy arrays that must have type code ``'c'`` or ``'1'`` when used as string arguments. A string can have arbitrary length when using it as a string argument @@ -95,7 +95,7 @@ Array arguments ================ In general, array arguments of F2PY generated wrapper functions accept -arbitrary sequences that can be transformed to Numpy array objects. +arbitrary sequences that can be transformed to NumPy array objects. An exception is ``intent(inout)`` array arguments that always must be proper-contiguous and have proper type, otherwise an exception is raised. Another exception is ``intent(inplace)`` array arguments that @@ -103,13 +103,13 @@ attributes will be changed in-situ if the argument has different type than expected (see ``intent(inplace)`` attribute for more information). -In general, if a Numpy array is proper-contiguous and has a proper +In general, if a NumPy array is proper-contiguous and has a proper type then it is directly passed to wrapped Fortran/C function. Otherwise, an element-wise copy of an input array is made and the copy, being proper-contiguous and with proper type, is used as an array argument. -There are two types of proper-contiguous Numpy arrays: +There are two types of proper-contiguous NumPy arrays: * Fortran-contiguous arrays when data is stored column-wise, i.e. indexing of data as stored in memory starts from the lowest @@ -130,7 +130,7 @@ and C-contiguous if the order is as follows:: A[0,0] A[0,1] A[1,0] A[1,1] To test whether an array is C-contiguous, use ``.iscontiguous()`` -method of Numpy arrays. To test for Fortran contiguity, all +method of NumPy arrays. To test for Fortran contiguity, all F2PY generated extension modules provide a function ``has_column_major_storage(<array>)``. This function is equivalent to ``<array>.flags.f_contiguous`` but more efficient. @@ -313,7 +313,7 @@ with the current extension module, but not to other extension modules (this restriction is due to how Python imports shared libraries). In Python, the F2PY wrappers to ``common`` blocks are ``fortran`` type objects that have (dynamic) attributes related to data members of -common blocks. When accessed, these attributes return as Numpy array +common blocks. When accessed, these attributes return as NumPy array objects (multidimensional arrays are Fortran-contiguous) that directly link to data members in common blocks. Data members can be changed by direct assignment or by in-place changes to the |