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authormelissawm <melissawm@gmail.com>2020-02-27 14:47:56 -0300
committermelissawm <melissawm@gmail.com>2020-02-27 14:47:56 -0300
commit4999107b74d686a5e9c5f09b6bff205a3c000083 (patch)
tree76c7c194b49bd743dc2a1ec362e5da186dc8abf5 /doc
parentc64cda0ddd7dee1aa846e5c3b57e497887e5653a (diff)
downloadnumpy-4999107b74d686a5e9c5f09b6bff205a3c000083.tar.gz
Added intersphinx mappings for skimage and imageio; added proper links to external docs.
Diffstat (limited to 'doc')
-rw-r--r--doc/source/conf.py4
-rw-r--r--doc/source/user/tutorial-svd.rst27
2 files changed, 10 insertions, 21 deletions
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 7e3a145f5..86662688e 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -236,7 +236,9 @@ texinfo_documents = [
intersphinx_mapping = {
'python': ('https://docs.python.org/dev', None),
'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
- 'matplotlib': ('https://matplotlib.org', None)
+ 'matplotlib': ('https://matplotlib.org', None),
+ 'imageio': ('https://imageio.readthedocs.io/en/stable', None),
+ 'skimage': ('https://scikit-image.org/docs/stable', None)
}
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst
index 8197465de..94f923cb0 100644
--- a/doc/source/user/tutorial-svd.rst
+++ b/doc/source/user/tutorial-svd.rst
@@ -12,8 +12,7 @@ Tutorial: Linear algebra on n-dimensional arrays
**Prerequisites**
Before reading this tutorial, you should know a bit of Python. If you
-would like to refresh your memory, take a look at the `Python
-tutorial <https://docs.python.org/tutorial/>`__.
+would like to refresh your memory, take a look at the :doc:`Python tutorial <python:tutorial/index>`.
If you want to be able to run the examples in this tutorial, you should also have
`matplotlib <https://matplotlib.org/>`_ and `SciPy <https://scipy.org>`_ installed on your computer.
@@ -46,16 +45,7 @@ generate a compressed approximation of an image. We'll use the ``face`` image fr
.. note::
- If you prefer, you can use your own image as you work through this tutorial. In
- order to transform your image into a NumPy array that can be manipulated, you can
- use the ``imread`` function from the `matplotlib.pyplot` submodule. Alternatively,
- you can use the ``imread`` function from the `imageio library
- <https://imageio.readthedocs.io/en/stable/userapi.html#imageio.imread>`_. Be aware
- that if you use your own image, you'll likely need to adapt the steps below. For more
- information on how images are treated when converted to NumPy arrays, see `A crash
- course on NumPy for images
- <https://scikit-image.org/docs/dev/user_guide/numpy_images.html>`_ from the
- ``scikit-image`` documentation.
+ If you prefer, you can use your own image as you work through this tutorial. In order to transform your image into a NumPy array that can be manipulated, you can use the ``imread`` function from the `matplotlib.pyplot` submodule. Alternatively, you can use the :func:`imageio.imread` function from the ``imageio`` library. Be aware that if you use your own image, you'll likely need to adapt the steps below. For more information on how images are treated when converted to NumPy arrays, see :std:doc:`user_guide/numpy_images` from the ``scikit-image`` documentation.
Now, ``img`` is a NumPy array, as we can see when using the ``type`` function::
@@ -137,8 +127,7 @@ matrices to represent the RGB values. We can do that by setting
This operation, dividing an array by a scalar, works because of NumPy's `broadcasting`
rules (see :ref:`array-broadcasting-in-numpy`). (Note that in real-world
-applications, it would be better to use, for example, the `img_as_float
-<https://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.img_as_float>`_
+applications, it would be better to use, for example, the :func:`img_as_float <skimage.img_as_float>`
utility function from ``scikit-image``).
You can check that the above works by doing some tests; for example, inquiring about
@@ -174,9 +163,7 @@ retaining some of its features.
users are encouraged to use the `scipy` module for real-world
applications. However, it is currently not possible to apply linear
algebra operations to n-dimensional arrays using the `scipy.linalg`
- module. For more information on this, check the
- `scipy.linalg Reference
- <https://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html>`_.
+ module. For more information on this, check the :doc:`scipy.linalg Reference<scipy:tutorial/linalg>`.
To proceed, import the linear algebra submodule from NumPy::
@@ -487,8 +474,8 @@ Press, 1985*.
**Further reading**
-- `Python tutorial <https://docs.python.org/tutorial/>`__
+- :doc:`Python tutorial <python:tutorial/index>`
- :ref:`reference`
-- `SciPy Tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/index.html>`__
+- :doc:`SciPy Tutorial <scipy:tutorial/index>`
- `SciPy Lecture Notes <https://scipy-lectures.org>`__
-- A `matlab, R, IDL, NumPy/SciPy dictionary <http://mathesaurus.sf.net/>`__
+- `A matlab, R, IDL, NumPy/SciPy dictionary <http://mathesaurus.sf.net/>`__