picterra 2.0.5

Picterra API client

Picterra Python API Client

Easily integrate state-of-the-art machine learning models in your app

from picterra import APIClient

# Replace this with the id of one of your detectors
detector_id = 'd552605b-6972-4a68-8d51-91e6cb531c24'

# Set the PICTERRA_API_KEY environment variable to define your API key
client = APIClient()
print('Uploading raster...')
raster_id = client.upload_raster('data/raster1.tif', name='a nice raster')
print('Upload finished, starting detector...')
result_id = client.run_detector(detector_id, raster_id)
client.download_result_to_feature_collection(result_id, 'result.geojson')
print('Detection finished, results are in result.geojson')

Installation

pip install picterra

See the examples folder for examples.

API Reference and User Guide available on Read the Docs

Development

Setup

Make sure you have Python and pip in your OS and create a virtual environment in the root folder, eg

python3 -m venv .venv
source .venv/bin/activate 

Running

pip install --editable '.[lint,test]'

would allow to run test and linting locally, and also avoid re-installing the library every time you change the code.

Running tests

In order to test locally, run:

pytest

Release process

1. Bump the version number in setup.py
2. Manually run the publish to testpypi workflow
3. Check the publication result on testpypi
4. Create a release through github 4.1. Make sure you create a new tag vX.Y.Z through the release UI 4.2. Click the "generate release notes" button in the UI to get release notes
5. The 'publish to pypi' workflow should automatically run 5.1. Note this will not work if you create the release first as a draft - you have to create it immediatly
6. Updated package should be available on pypi