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| author | Matti Picus <matti.picus@gmail.com> | 2019-07-09 15:00:37 -0500 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-07-09 15:00:37 -0500 |
| commit | 27b59efd958313491d51bc45d5ffdf1173b8f903 (patch) | |
| tree | 404e11794aceb399189937e130c402b12a8c40e1 /numpy/doc/basics.py | |
| parent | ad34df696322068036c3e82ef5cd89a95e700479 (diff) | |
| parent | 830d5b5fd712d4c27c3695b4a1c91d64561b683a (diff) | |
| download | numpy-27b59efd958313491d51bc45d5ffdf1173b8f903.tar.gz | |
Merge pull request #13947 from johannfaouzi/fix_rst_rendering_dtype
DOC: Fix rst rendering in data types
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 7946c6432..1871512bf 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -314,8 +314,8 @@ 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 does not provide a dtype with more precision than C -``long double``\\s; in particular, the 128-bit IEEE quad precision +NumPy does not provide a dtype with more precision than C's +``long double``\\; 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 |