8000 Merge pull request #11643 from CAM-Gerlach/doc-spyder-link · numpy/numpy@9bb569c · GitHub
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Merge pull request #11643 from CAM-Gerlach/doc-spyder-link
DOC: Insert up to date link to Spyder website in Dev Env doc where Spyder is mentioned
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doc/source/dev/development_environment.rst

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@@ -59,7 +59,7 @@ For development, you can set up an in-place build so that changes made to
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This allows you to import the in-place built NumPy *from the repo base
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directory only*. If you want the in-place build to be visible outside that
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base dir, you need to point your ``PYTHONPATH`` environment variable to this
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directory. Some IDEs (Spyder for example) have utilities to manage
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directory. Some IDEs (`Spyder`_ for example) have utilities to manage
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``PYTHONPATH``. On Linux and OSX, you can run the command::
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$ export PYTHONPATH=$PWD
@@ -78,6 +78,8 @@ installs a ``.egg-link`` file into your site-packages as well as adjusts the
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``easy-install.pth`` there, so its a more permanent (and magical) operation.
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.. _Spyder: https://www.spyder-ide.org/
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Other build options
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-------------------
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----------------------------------------
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The best strategy to better understand the code base is to pick something you
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want to change and start reading the code to figure out how it works. When in
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want to change and start reading the code to figure out how it works. When in
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doubt, you can ask questions on the mailing list. It is perfectly okay if your
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pull requests aren't perfect, the community is always happy to help. As a
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volunteer project, things do sometimes get dropped and it's totally fine to
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pull requests aren't perfect, the community is always happy to help. As a
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volunteer project, things do sometimes get dropped and it's totally fine to
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ping us if something has sat without a response for about two to four weeks.
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So go ahead and pick something that annoys or confuses you about numpy,
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experiment with the code, hang around for discussions or go through the
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reference documents to try to fix it. Things will fall in place and soon
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So go ahead and pick something that annoys or confuses you about numpy,
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experiment with the code, hang around for discussions or go through the
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reference documents to try to fix it. Things will fall in place and soon
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you'll have a pretty good understanding of the project as a whole. Good Luck!

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