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Description:

panoramicauth 1.0.0

Panoramic Auth SDK
Getting Started
This SDK handles the access token generation using client_credentials flow against Panoramic. To install the SDK in your app, add it to your requirements.txt:
panoramic-auth==1.0.0

All you then need to do is subclass the OAuth2Client Python class from the SDK, pass your client_id and client_secret, and use the session attribute from your subclass when making HTTP requests:
# Import the class as usual
from panoramic.auth import OAuth2Client

# Note subclassing OAuth2Client below
class PanoramicApiClient(OAuth2Client):
def __init__(self, base_url: str, client_id: str, client_secret: str):
super().__init__(client_id, client_secret)
self.base_url = base_url

def async_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
url = urljoin('https://platform.panoramichq.com/api/example')
# Note self.session below
response = self.session.post(url=url, json=data)
response.raise_for_status()
return response.json()

Development
This repository does not have a dedicated docker image. At the moment, we create python virtual environment using command (in directory .venv inside current directory):
> python3 -m venv .venv

If you use pyenv and pyenv-virtualenv, you can create it using:
> pyenv virtualenv panoramic-auth

Then, you can switch to it from command-line using following command:
> source .venv/bin/activate

Or if using pyenv-virtualenv:
pyenv local panoramic-auth

Lastly, use following command to install dependencies (make sure you have correct python environment active):
> make install

Install pre-commit - useful to avoid commiting code that doesn't pass the linter:
> make pre-commit-install

This installs git hooks that run pre-commit.
Tests
Use following command to run all tests:
> make tests

Release process
To release a new version of the library, follow these steps:

In your PR, update version in setup.py and add entry to CHANGELOG.md
After merge, tag the commit with version number from setup.py. For example git tag v0.1.1. You can also do this by creating a new release.
This triggers a Travis pipeline which runs tests, linters and uploads the package to PyPI