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| author | Charles Harris <charlesr.harris@gmail.com> | 2017-08-12 12:54:03 -0500 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2017-08-12 12:54:03 -0500 |
| commit | 1f291b8eee4bd3d7a343f03617e5fac40d8dbb36 (patch) | |
| tree | ecac1ccc426bc2f77615372bb2c5a1cccd4e58c5 /numpy/doc/basics.py | |
| parent | 8ed1770a8ca968388eacd5d96b8068b272fb47b7 (diff) | |
| parent | 01d5886a5ad760e7a27f2f7d715177fafb3777cd (diff) | |
| download | numpy-1f291b8eee4bd3d7a343f03617e5fac40d8dbb36.tar.gz | |
Merge pull request #9422 from deniederhut/doc/types-formatting
DOC: correct formatting of basic.types.html
Diffstat (limited to 'numpy/doc/basics.py')
| -rw-r--r-- | numpy/doc/basics.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index 10d7248c1..9e0bee357 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. +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 |