librariesio/conda-parser repository overview

⁠Conda parser

A tiny python web service for parsing dependency information from environment.yml files for Libraries.io⁠.

⁠Parsing

We use Conda.models.MatchSpec to do version matching, as such, environment.yml dependencies should match one of the following formats in order to parse properly.

• numpy
• numpy 1.8.*
• numpy 1.8*
• numpy 1.8.1
• numpy >=1.8
• numpy ==1.8.1
• numpy 1.8|1.8*
• numpy >=1.8,<2
• numpy >=1.8,<2|1.9
• numpy 1.8.1 py27_0
• numpy=1.8.1=py27_0

⁠Building and running options.

⁠Docker

You can use Docker to run conda-parser

First, install Docker. If you run macOS or Windows, Docker for Mac/Windows makes this really easy. (If you have Windows Home Edition, you'll need to download and run Docker Toolbox.)

Then, run:

$ docker pull librariesio/conda-parser
$ docker run -it -e PORT=5000 -p 5000:5000 librariesio/conda-parser

conda-parser will be running on http://localhost:5000⁠.

To build the docker image locally:

$ docker build -t librariesio/conda-parser .

Note: The Dockerfile has ./gunicorn_start.sh as it's CMD so this can be overridden with /bin/bash if you'd like to poke around:

$ docker run -it librariesio/conda-parser /bin/bash

⁠Docker Compose

Docker Compose makes this a lot easier, but there's one minor setup if using Docker for MacOS. You must add the directory your cloned this to in the Docker Desktop -> Preferences -> File Sharing -> [+] and Add the directory.

Then you can run

$ docker-compose build
$ docker-compose up

The server will be running, and any time you want to make a change to the code, you can just quit the docker-compose up process and re-run it. The new code will be reloaded. Because the Dockerfile and gunicorn_start.sh run Gunicorn, it won't auto reload when files are changed.

⁠Consuming the API

You can test that it works by running one of these curl commands:

$ curl -X POST -F "[email protected]" http://localhost:5000/parse # Post multipart

$ curl -X POST -F "file=<environment.yml;filename=environment.yml" http://localhost:5000/parse # Post urlencoded

To POST from something not curl (for example ruby typhoeus) please post a body with a file and a filename as such:

# Post urlencoded
Typhoeus.post("http://localhost:5000/parse", body: {file: file_string, filename: 'environment.yml'})

# post multipart
Typhoeus.post("http://localhost:5000/parse", body: {file: File.open(filename, "r")})

(Both multipart/form-data and application/x-www-form-urlencoded are supported)

⁠Development

Most of the logic is in conda_parser/parse.py⁠, the rest of the files are Flask/Tests/Gunicorn support. This file is a good place to start looking at the code.

Source hosted at GitHub⁠. Report issues/feature requests on GitHub Issues⁠. Follow us on Twitter @librariesio⁠. We also hangout on Slack⁠.

To get started, install Conda⁠ or Miniconda, and then run:

$ conda env create -f environment.yml
$ conda activate conda-parser

This will create you a conda-parser environment with all the packages installed from conda, (if you wish to not use conda for some reason, a requirements.txt file is also provided to pip install). To run the code, run either of the following lines:

$ python flask_start.py
$ FLASK_APP=conda_parser flask run

⁠Testing

This application uses pytest and coverage.py, to run tests, activate the conda environment and run one of the following lines:

$ pytest
$ pytest --cov=conda_parser
$ pytest --cov=conda_parser --cov-report html  # To get a pretty html report

⁠Code Style

We use black for formatting.

$ black .

⁠Note on Patches/Pull Requests

• Fork the project.
• Make your feature addition or bug fix.
• Add tests if adding code.
• Add documentation if necessary.
• Make sure you run black . before submitting a pull request.
• Send a pull request. Bonus points for topic branches.

⁠Copyright

Copyright (c) 2019 Tidelift. See LICENSE⁠ for details.