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More context for BitBucket archive in docs, mailing list link, regular apostrophes
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README.md

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@@ -59,7 +59,7 @@ Upon migrating the code base to GitHub and publishing distributions on PyPI in A
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### Profile directories
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The _labscript suite_ profile directory, containing application configurations, logs, and user-side code, is now located by default in the current users home directory, e.g. for a local user named `wkheisenberg` this is:
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The _labscript suite_ profile directory, containing application configurations, logs, and user-side code, is now located by default in the current user's home directory, e.g. for a local user named `wkheisenberg` this is:
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* `C:\Users\wkheisenberg\labscript-suite` on Windows.
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* `~/labscript-suite` or `/home/wkheisenberg/labscript-suite` on Linux and Mac OS X.
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The issue tracking on GitHub is very similar to BitBucket, with the added advantage that you can add inter-repository issue references, e.g. referring to [labscript-suite/runmanager#68](https://github.com/labscript-suite/runmanager/issues/68) in any issue or pull request will link to the corresponding issue. We have imported all issues from the BitBucket repositories into the GitHub repositories. This import is not perfect (as each comment is now posted by Phil Starkey) but the comments have been modified to contain the original author attribution. We have also updated all links to files, pull requests, issues, and commits so that they point to the equivalent GitHub location and/or the archived copy of the data (as discussed above).
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Please use the GitHub issue tracker (rather than the mailing list) for:
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Please use the issue tracker of the relevant GitHub repository for:
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* Reporting **bugs** (when something doesnt work or works ina way you didnt expect);
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* Reporting **bugs** (when something doesn't work or works in a way you didn't expect);
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* Suggesting **enhancements**: new features or requests;
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* Issues relating to **installation**, **performance**, or **documentation**.
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However, advice for how to do something with the existing functionality of the _labscript suite_ is better suited to the mailing list.
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For advice on _how_ to use the existing functionality of the _labscript suite_, please use our [mailing list](http://groups.google.com/group/labscriptsuite).
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### Request for developers

docs/source/archive.md

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## BitBucket archive
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Some repository metadata (such as pull request discussions) could not be migrated directly from BitBucket to GitHub. As such, we have created an archived copy of everything that was on BitBucket. This includes:
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In April–May 2020 the _labscript suite_ code base was migrated from BitBucket to GitHub. All commit history and issues was preserved, however some repository metadata (such as pull request discussions) could not be migrated directly. As such, we have created an archived copy of everything that was on BitBucket. This includes:
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* Issues (as they appear on BitBucket);
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* Pull requests discussions;

docs/source/contributing.md

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The issue tracking on GitHub is very similar to BitBucket, with the added advantage that you can add inter-repository issue references, e.g. referring to [labscript-suite/runmanager#68](https://github.com/labscript-suite/runmanager/issues/68) in any issue or pull request will link to the corresponding issue. We have imported all issues from the BitBucket repositories into the GitHub repositories. This import is not perfect (as each comment is now posted by Phil Starkey) but the comments have been modified to contain the original author attribution. We have also updated all links to files, pull requests, issues, and commits so that they point to the equivalent GitHub location and/or the archived copy of the data (as discussed above).
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Please use the GitHub issue tracker (rather than the mailing list) for:
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Please use the issue tracker of the relevant GitHub repository for:
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* Reporting **bugs** (when something doesnt work or works ina way you didnt expect);
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* Reporting **bugs** (when something doesn't work or works in a way you didn't expect);
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* Suggesting **enhancements**: new features or requests;
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* Issues relating to **installation**, **performance**, or **documentation**.
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However, advice for how to do something with the existing functionality of the _labscript suite_ is better suited to the mailing list.
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For advice on _how_ to use the existing functionality of the _labscript suite_, please use our [mailing list](http://groups.google.com/group/labscriptsuite).
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### Request for developers
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### Learning Git
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As our former development, installation, and upgrading practices involved Mercurial revision-control, some of you may not be familiar with Git. While you no longer need to use _any_ revision control system to use the _labscript suite_, those of you wanting to contribute to development who arent acquainted with Git may benefit from these resources:
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As our former development, installation, and upgrading practices involved Mercurial revision-control, some of you may not be familiar with Git. While you no longer need to use _any_ revision control system to use the _labscript suite_, those of you wanting to contribute to development who aren't acquainted with Git may benefit from these resources:
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* NumPy: [Getting started with Git development](https://numpy.org/doc/stable/dev/gitwash/development_setup.html)
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* [GitHub Guides](https://guides.github.com/): Very cogent information for beginners. We recommend starting with:

docs/source/installation.md

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### Virtual environments
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We recommend installing labscript (regular or developer mode) in a [virtual environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments). This helps sandbox the codebase without interfering with (or being interfered with) your system Python installation, or Python environments used for other purposes. There are a number of ways to configure a virtual environment. If you are unfamiliar with doing so, we recommend using the [venv module](https://docs.python.org/3/library/venv.html), part of the Python Standard Library. Heres an example (on Windows):
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We recommend installing labscript (regular or developer mode) in a [virtual environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments). This helps sandbox the codebase without interfering with (or being interfered with) your system Python installation, or Python environments used for other purposes. There are a number of ways to configure a virtual environment. If you are unfamiliar with doing so, we recommend using the [venv module](https://docs.python.org/3/library/venv.html), part of the Python Standard Library. Here's an example (on Windows):
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#### Quick start
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C:\labscript-suite> .venv\Scripts\activate
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```
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_Note:_ This step is OS specific, e.g. on Linux its `source .venv/bin/activate`.
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_Note:_ This step is OS specific, e.g. on Linux it's `source .venv/bin/activate`.
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4. Update the Python package installer and other installation packages of your virtual environment.
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#### Detailed instructions
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1. Activate the virtual environment (this step is OS specific, e.g. on Linux its `source .venv/bin/activate`).
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1. Activate the virtual environment (this step is OS specific, e.g. on Linux it's `source .venv/bin/activate`).
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```
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C:\labscript-suite> .venv\Scripts\activate

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