Thank you for considering contributing to VerticaPyLab and helping to make it even better than it is today!
This document will guide you through the contribution process. There are a number of ways you can help:
If you find a bug, submit an issue with a complete and reproducible bug report. If the issue can't be reproduced, it will be closed. If you opened an issue, but figured out the answer later on your own, comment on the issue to let people know, then close the issue.
Feel free to share your ideas for how to improve VerticaPyLab. We’re always open to suggestions. You can open an issue with details describing what feature(s) you'd like to be added or changed. For example: a new extension that could improve VerticaPyLab experience, a better way to organise extensions, etc...
If you would like to implement the feature yourself, open an issue to ask before working on it. Once approved, please refer to the Code Contributions section.
Fork the project on Github and check out your copy locally.
git clone git@github.com:YOURUSERNAME/VerticaPyLab.git
cd VerticaPyLabYour GitHub repository YOURUSERNAME/VerticaPyLab will be called "origin" in Git. You should also setup vertica/VerticaPyLab as an "upstream" remote.
git remote add upstream git@github.com:vertica/VerticaPyLab.git
git fetch upstreamMake sure git knows your name and email address:
git config --global user.name "John Smith"
git config --global user.email "email@example.com"Create a new branch for the work with a descriptive name:
git checkout -b my-fix-branchSkip this step if your changes do not involve adding/changing an extension.
Start by installing miniconda, following Conda’s installation documentation.
Next create a conda environment that includes: the latest release of JupyterLab, cookiecutter, NodeJS.
It’s a best practice to leave the root conda environment (i.e., the environment created by the miniconda installer) untouched and install your project-specific dependencies in a named conda environment. Run this command to create a new environment named jupyterlab-ext.
conda create -n jupyterlab-ext --override-channels --strict-channel-priority -c conda-forge -c nodefaults jupyterlab=3 cookiecutter nodejs jupyter-packaging
Now activate the new environment so that all further commands you run work out of that environment.
conda activate jupyterlab-ext
Skip this step if your changes do not involve adding/changing an extension.
You should complete Step 3 before.
Go the extensions directory and use cookiecutter to create a new project for your extension
cd docker-verticapy/extensions
cookiecutter https://github.com/jupyterlab/extension-cookiecutter-ts
When prompted, enter values like the following for all of the cookiecutter prompts
Select kind:
1 - frontend
2 - server
3 - theme
Choose from 1, 2, 3 [1]: 1
author_name []: Your Name
author_email []: your@name.org
labextension_name [myextension]: ext_name
python_name [myextension]: ext_name
project_short_description [A JupyterLab extension.]: This is a short description of this new extension.
has_settings [n]: n
has_binder [n]: n
repository [https://github.com/github_username/myextension]:
You can find more details in CookieCutter Documention.
Change to the directory the cookiecutter created and list the files.
cd ext_name
ls
You should see a list like the following.
CHANGELOG.md install.json jupyterlab_apod LICENSE MANIFEST.in package.json
pyproject.toml README.md RELEASE.md setup.py src style tsconfig.json
Now you can make your changes
Skip this step if your changes do not involve adding/changing an extension. Anytime you want to test new changes, run the following from your extension folder.
jlpm run build
If you get a tsc not found error you need to install typescript with this command.
npm install typescript@latest -g
tsc --version
If you get Cannot find module '@module_name' errors you will need to install these dependencies. Run the following commands in the extension root folder to install the dependencies and save them to your package.json:
jlpm add @module_name ## for each missing module
Build a local image containing your new extension. Update the Dockerfile with your extension.
open a second terminal, go the repository root folder and run the following to build an image and run a verticapylab container with your new extension:
make verticapylab-build ## build the image
make verticapylab-start ## start the container
The steps are almost the same, you just do not need to create a new folder.
You are now ready to create your first contribution!
Make some changes on your branch, then stage and commit as often as necessary:
git add .
git commit -m 'Show a random commit message'When writing the commit message, try to describe precisely what the commit does.
You can publish your work on GitHub just by doing:
git push origin my-fix-branchWhen you go to your GitHub page, you will notice commits made on your local branch is pushed to the remote repository.
When upstream (vertica/VerticaPyLab) has changed, you should rebase your work. The rebase command creates a linear history by moving your local commits onto the tip of the upstream commits.
You can rebase your branch locally and force-push to your GitHub repository by doing:
git checkout my-fix-branch
git fetch upstream
git rebase upstream/main
git push -f origin my-fix-branchWhen you think your work is ready to be pulled into VerticaPyLab, you should create a pull request(PR) at GitHub.
A good pull request means:
- commits with one logical change in each
- well-formed messages for each commit
- documentation and tests, if needed
Go to https://github.com/YOURUSERNAME/VerticaPyLab and make a Pull Request to VerticaPyLab:main.
Pull requests are usually reviewed within a few days. If there are comments to address, apply your changes in new commits, rebase your branch and force-push to the same branch. In order to produce a clean commit history, our maintainers would do squash merging once your PR is approved, which means combining all commits of your PR into a single commit in the master branch.
That's it! Thank you for your code contribution!
After your pull request is merged, you can safely delete your branch and pull the changes from the upstream repository.
One part of the verticapylab are the lessons that have been created for users to learn data science concepts using VerticaPy. You are welcome to create lessons on a topic of your interest. You can follow the instructions in the read me for content creation. After you have created it, you can follow the steps of code contribution above. After review, your lesson will be added to the package.
You can also propose lessons of interest by opening an issue. Please explain in detail what you would like covered and our content creators will have a look at it. After approval, the content will be added to the list of lessons/modules.