Welcome to Helikite Data Processing’s Documentation!

Overview

Helikite Data Processing is a Python library designed to support Helikite campaigns by unifying field-collected data, generating quicklooks, and performing quality control on instrument data. Whether you’re a non-programmer who simply needs to run the provided command-line interface (CLI) or a developer looking to integrate its powerful API into your own workflows, this documentation will guide you every step of the way.

Installation & Environment Setup

There are several ways to install and run Helikite Data Processing:

  1. Pip Installation

Helikite is published on PyPI: https://pypi.org/project/helikite-data-processing/. To install via pip, run:

pip install helikite-data-processing

After installation, the CLI is available as a system command:

helikite --help

  1. Setting Up a Poetry Environment

For an isolated development environment or if you prefer Poetry for dependency management:

  • Clone the repository:

    git clone https://github.com/EERL-EPFL/helikite-data-processing.git
cd helikite-data-processing

  • Install dependencies with Poetry:

    poetry install

  • Run the CLI within Poetry:

    poetry run helikite --help

  1. Using Jupyter Notebooks

Helikite includes several Jupyter notebooks demonstrating various processing workflows. To work with these notebooks:

  • Start Jupyter Lab within your Poetry environment:

    poetry run jupyter lab

  • Open the notebooks from the notebooks/ folder. Notable examples include: - level0.ipynb or level0_tutorial.ipynb: An introductory tutorial covering basic processing. - OutlierRemoval.ipynb: Demonstrates techniques for identifying and removing outliers. - FeatureFlagging.ipynb: Shows how to apply feature flags to control processing features. - metadata.ipynb: Provides examples for handling metadata.

Using the Library

Once installed, you can import the library into your own Python scripts. The library is designed so that most functions are accessible by simply importing it with:

import helikite

For example, you can access core processing functions, instrument classes, and data cleaning utilities. API Reference ————- Below is the auto-generated API reference documentation that covers all modules, classes, and functions available in Helikite Data Processing.

API Reference

Notebooks & Tutorials

A collection of Jupyter notebooks in the notebooks/ folder provides practical, step-by-step examples of common workflows. These include:

  • Level 0 Tutorial: An introductory guide covering basic data processing steps.

  • Outlier Removal: Detailed techniques for outlier detection and removal.

  • Feature Flagging: How to enable and apply feature flags within your processing pipeline.

  • Metadata Handling: Examples for processing and utilizing metadata.

Additional Resources

Indices and Tables