Welcome to Helikite Data Processing’s Documentation!

Overview

This library supports Helikite campaigns by unifying field-collected data, generating quicklooks, and performing quality control on instrument recordings. It is available on PyPI and is designed to be used as a Python package within Jupyter notebooks.

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

  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

  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. - level0_DataProcessing: Level 0 processing tutorial. Level 0 synchronizes timestamps across instruments and merges their data into a unified structure. - level1_DataProcessing: Level 1 processing tutorial. Level 1 performs quality control, averages humidity and temperature measurements, calculates flight altitude

using the barometric equation, and applies instrument-specific processing.

  • level1_5_DataProcessing: Level 1.5 processing tutorial. Level 1.5 detects flags that indicate environmental or flight conditions, such as hovering, pollution exposure,

or cloud immersion.

  • level2_DataProcessing: Level 2 processing tutorial. Level 2 averages data to 10-second intervals and can merge flights into a final campaign dataset.

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.

Additional Resources

Indices and Tables