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| author | Dillon Niederhut <dniederhut@enthought.com> | 2017-07-14 05:37:39 -0500 |
|---|---|---|
| committer | Dillon Niederhut <dniederhut@enthought.com> | 2017-07-26 23:08:25 -0600 |
| commit | ec3710f9b5f9957e57cf11cadac34a8c3e69cc79 (patch) | |
| tree | e8430ab6a65aa66beefbe6ff287acc66b65c71d0 /numpy/doc/basics.py | |
| parent | f3e4a44c43dd5b1adc9f003071badca7f294eceb (diff) | |
| download | numpy-ec3710f9b5f9957e57cf11cadac34a8c3e69cc79.tar.gz | |
DOC: correct formatting of basic.types.html
In the documentation for types allowed in numpy, missing spaces around
the backticks for fixed-width formatting cause code examples to appear
as plain text, or are causing plain text to appear as code. This commit
fixes back tick spacing in the 'Extended Precision' section of the
'Data Types' page.
Diffstat (limited to 'numpy/doc/basics.py')
| -rw-r--r-- | numpy/doc/basics.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index 083d55a84..40fb0501e 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -155,11 +155,11 @@ with 80-bit precision, and while most C compilers provide this as their ``long double`` identical to ``double`` (64 bits). NumPy makes the compiler's ``long double`` available as ``np.longdouble`` (and ``np.clongdouble`` for the complex numbers). You can find out what your -numpy provides with``np.finfo(np.longdouble)``. +numpy provides with ``np.finfo(np.longdouble)``. NumPy does not provide a dtype with more precision than C -``long double``s; in particular, the 128-bit IEEE quad precision -data type (FORTRAN's ``REAL*16``) is not available. +``long double`` s; in particular, the 128-bit IEEE quad precision +data type (FORTRAN's ``REAL*16`` ) is not available. For efficient memory alignment, ``np.longdouble`` is usually stored padded with zero bits, either to 96 or 128 bits. Which is more efficient |