贡献
============
Contributing
.. _new-issues:
新问题
----------
New Issues
.. tab:: 中文
如果你遇到的问题是关于以下内容的 bug 报告或功能请求：
* **某个特定 conda 软件包**：请在 https://github.com/ContinuumIO/anaconda-issues/issues 提交
* **anaconda.org**：请在 https://github.com/Anaconda-Platform/support/issues 提交
* **repo.anaconda.com**：请在 https://github.com/ContinuumIO/anaconda-issues/issues 提交
* ``conda build`` **下的命令**：请在 https://github.com/conda/conda-build/issues 提交
* ``conda env`` **下的命令**：请在 https://github.com/conda/conda/issues 提交
* **其他所有 conda 命令**：请在 https://github.com/conda/conda/issues 提交
.. tab:: 英文
If your issue is a bug report or feature request for:
* **a specific conda package**: please file it at https://github.com/ContinuumIO/anaconda-issues/issues
* **anaconda.org**: please file it at https://github.com/Anaconda-Platform/support/issues
* **repo.anaconda.com**: please file it at https://github.com/ContinuumIO/anaconda-issues/issues
* **commands under** ``conda build``: please file it at https://github.com/conda/conda-build/issues
* **commands under** ``conda env``: please file it at https://github.com/conda/conda/issues
* **all other conda commands**: please file it at https://github.com/conda/conda/issues
开发环境，Bash
-----------------------------
Development Environment, Bash
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要搭建本地开发环境以参与 conda 的开发，我们推荐如下步骤：
1. Fork `conda/conda` 仓库，并克隆到本地任意目录（一个隔离的 miniconda 环境会在该目录下自动创建），
然后设置 ``git remote`` 指向上游仓库和你的 fork。详见下文的详细操作说明。
1a. 选择你希望放置项目的目录位置（不是已有 conda 的位置）
.. code-block:: console
CONDA_PROJECT_ROOT="$HOME/conda"
1b. 克隆项目，设置 ``upstream`` 为主仓库。在 GitHub 页面点击 ``Fork`` 按钮，创建你自己的副本。
.. code-block:: console
GITHUB_USERNAME=kalefranz
git clone git@github.com:$GITHUB_USERNAME/conda "$CONDA_PROJECT_ROOT"
cd "$CONDA_PROJECT_ROOT"
git remote add upstream git@github.com:conda/conda
2. 创建本地开发环境，并激活该环境：
.. code-block:: console
. dev/start
此命令将在 ``./devenv`` 下创建一个特定于该项目的基础环境。
如果该环境已存在，命令将直接快速激活已存在的 ``./devenv`` 环境。
要确保当前解释器使用的是项目目录下的 conda 代码，
可以通过 ``conda info --all`` 命令输出中的 ``conda location:`` 字段来确认。
3. 使用 GNU make 运行 conda 的单元测试：
.. code-block:: console
make unit
或者，也可以使用 pytest：
.. code-block:: console
py.test -m "not integration and not installed" conda tests
如果你只想运行某个特定测试，也可以这样使用 pytest：
.. code-block:: console
py.test tests/test_create.py -k create_install_update_remove_smoketest
.. tab:: 英文
To set up an environment to start developing on conda code, we recommend the following steps:
1. Fork the conda/conda repository, clone it locally anywhere you choose (an isolation miniconda
will be set up within the clone directory), and set up ``git remote`` to point to upstream
and fork. For detailed directions, see below.
1a. Choose where you want the repository located (not location of existing conda)
.. code-block :: console
CONDA_PROJECT_ROOT="$HOME/conda"
1b. Clone the project, with ``upstream`` being the main repository. Make sure to click the ``Fork``
button above so you have your own copy of this repo.
.. code-block :: console
GITHUB_USERNAME=kalefranz
git clone git@github.com:$GITHUB_USERNAME/conda "$CONDA_PROJECT_ROOT"
cd "$CONDA_PROJECT_ROOT"
git remote add upstream git@github.com:conda/conda
2. Create a local development environment, and activate that environment
.. code-block :: console
. dev/start
This command will create a project-specific base environment at ``./devenv``. If
the environment already exists, this command will just quickly activate the
already-created ``./devenv`` environment.
To be sure that the conda code being interpreted is the code in the project directory,
look at the value of ``conda location:`` in the output of ``conda info --all``.
3. Run conda's unit tests using GNU make
.. code-block :: console
make unit
or alternately with pytest
.. code-block :: console
py.test -m "not integration and not installed" conda tests
or you can use pytest to focus on one specific test
.. code-block :: console
py.test tests/test_create.py -k create_install_update_remove_smoketest
开发环境，Windows cmd.exe shell
----------------------------------------------
Development Environment, Windows cmd.exe shell
.. tab:: 中文
上述步骤假设你已安装好 ``git`` 并已加入 ``PATH``。
1. 选择你希望放置项目的目录位置：
.. code-block :: console
set "CONDA_PROJECT_ROOT=%HOMEPATH%\conda"
2. 克隆项目，设置 ``origin`` 为主仓库。请确保点击页面上的 ``Fork`` 按钮，创建你自己的副本：
.. code-block :: console
set GITHUB_USERNAME=kalefranz
git clone git@github.com:conda/conda "%CONDA_PROJECT_ROOT%"
cd "%CONDA_PROJECT_ROOT%"
git remote add %GITHUB_USERNAME% git@github.com:%GITHUB_USERNAME%/conda
要确保当前解释器使用的是项目目录下的 conda 代码，
可通过 ``conda info --all`` 输出中的 ``conda location:`` 字段确认。
3. 创建本地开发环境并激活：
.. code-block :: console
.\dev\start
此命令将在 ``.\devenv`` 下创建一个特定于该项目的基础环境。
如果该环境已存在，命令将直接激活已存在的 ``.\devenv`` 环境。
.. tab:: 英文
In these steps, we assume ``git`` is installed and available on ``PATH``.
1. Choose where you want the project located
.. code-block :: console
set "CONDA_PROJECT_ROOT=%HOMEPATH%\conda"
2. Clone the project, with ``origin`` being the main repository. Make sure to click the ``Fork``
button above so you have your own copy of this repo.
.. code-block :: console
set GITHUB_USERNAME=kalefranz
git clone git@github.com:conda/conda "%CONDA_PROJECT_ROOT%"
cd "%CONDA_PROJECT_ROOT%"
git remote add %GITHUB_USERNAME% git@github.com:%GITHUB_USERNAME%/conda
To be sure that the conda code being interpreted is the code in the project directory,
look at the value of ``conda location:`` in the output of ``conda info --all``.
3. Create a local development environment, and activate that environment
.. code-block :: console
.\dev\start
This command will create a project-specific base environment at ``.\devenv``. If
the environment already exists, this command will just quickly activate the
already-created ``.\devenv`` environment.
Conda 贡献者许可协议
-----------------------------------
Conda Contributor License Agreement
.. tab:: 中文
如果你是首次接触 CLA（贡献者许可协议），这在大型开源项目中是非常常见的流程。
`Django <https://www.djangoproject.com/foundation/cla/>`_ 和
`Python <https://www.python.org/psf/contrib/contrib-form/>`_ 项目都采用了类似机制。
> CLA 协议的批准最终由人工完成，流程并非完全自动化，
> 因此你的 PR 上的 CLA 检查可能需要一些时间才能通过。
.. tab:: 英文
In case you're new to CLAs, this is rather standard procedure for larger projects.
`Django <https://www.djangoproject.com/foundation/cla/>`_ and even
`Python <https://www.python.org/psf/contrib/contrib-form/>`_ itself both use something similar.
> CLA agreements are ultimately approved by a person and are not fully automatic, so it
> may take some time for the CLA checks on your PRs to run successfully.
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