hdx-cli-toolkit 2024.4.5

HDX CLI tool kit for commandline interaction with HDX

HDX CLI Toolkit

Overview

This toolkit provides a commandline interface to the Humanitarian Data Exchange (HDX) to allow for bulk modification operations and other administrative activities such as getting id values for users and organization. It is useful for those managing HDX and developers building data pipelines for HDX. The currently supported commands are as follows:

  configuration              Print configuration information to terminal
  download                   Download dataset resources from HDX
  get_organization_metadata  Get an organization id and other metadata
  get_user_metadata          Get user id and other metadata
  list                       List datasets in HDX
  print                      Print datasets in HDX to the terminal
  quickcharts                Upload QuickChart JSON description to HDX
  showcase                   Upload showcase to HDX
  update                     Update datasets in HDX
  update_resource            Update a resource in HDX

It is a thin wrapper to the hdx-python-api library written by Mike Rans.

The library requires some configuration, described below, to authenticate to the HDX instance.

Installation

hdx-cli-toolkit is a Python application published to the PyPI package repository, therefore it can be installed easily with:

pip install hdx_cli_toolkit

Users may prefer to make a global, isolated installation using pipx which will make the hdx-toolkit commands available across their projects:

pipx install hdx_cli_toolkit

hdx-cli-toolkit uses the hdx-python-api library, this requires the following to be added to a file called .hdx_configuration.yaml in the user's home directory.

hdx_key_stage: "[hdx_key from the staging HDX site]"
hdx_key: "[hdx_key from the prod HDX site]"

A user agent (hdx_cli_toolkit_*) is specified in the ~/.useragents.yaml file with the * replaced with the users initials.

hdx-cli-toolkit:
    preprefix: [YOUR_ORGANIZATION]
    user_agent: hdx_cli_toolkit_ih

Usage

The hdx-toolkit is built using the Python click library. Details of the currently implemented commands can be revealed by running hdx-toolkit --help:

$ hdx-toolkit --help
Usage: hdx-toolkit [OPTIONS] COMMAND [ARGS]...

  Tools for Commandline interactions with HDX

Options:
  --help  Show this message and exit.

Commands:
  configuration              Print configuration information to terminal
  download                   Download dataset resources from HDX
  get_organization_metadata  Get an organization id and other metadata
  get_user_metadata          Get user id and other metadata
  list                       List datasets in HDX
  print                      Print datasets in HDX to the terminal
  quickcharts                Upload QuickChart JSON description to HDX
  showcase                   Upload showcase to HDX
  update                     Update datasets in HDX
  update_resource            Update a resource in HDX

And details of the arguments for a command can be found using:

hdx-toolkit [COMMAND] --help

A detailed walk through of commands can be found in the DEMO.md file

Contributions

For developers the code should be cloned installed from the GitHub repo, and a virtual enviroment created:

python -m venv venv
source venv/Scripts/activate

And then an editable installation created:

pip install -e .

The library is then configured, as described above.

This project uses a GitHub Action to run tests and linting. It requires the following environment variables/secrets to be set in the test environment:

HDX_KEY - secret. Value: fake secret
HDX_KEY_STAGE - secret. Value: a live API key for the stage server
HDX_SITE - environment variable. Value: stage
USER_AGENT - environment variable. Value: hdx_cli_toolkit_gha
PREPREFIX - - environment variable. Value: [YOUR_organization]

Most tests use mocking in place of HDX, although the test_integration.py suite runs against the stage server.

New features should be developed against a GitHub issue on a separate branch with a name starting GH[issue number]_. PULL_REQUEST_TEMPLATE.md should be used in preparing pull requests. Versioning is updated manually in pyproject.toml and is described in the template, in brief it is CalVer YYYY.MM.Micro.

Publication

Publication to PyPI is done automatically when a release is created.