Overview

This repository contains the Light Logger project, which involves the acquisition of calibration data with an emphasis on spectral and directional analysis. You will find both the raw sensor and spectrometer data within this repository. The provided code is intended for data visualization and analysis, and is accessible under the MIT license (see LICENSE.md file).

If you have any comments or queries, please reach out to us at [email protected] and [email protected].

Dependency Management

This Python project utilizes pip for package management. Use pip install -r requirements.txt to download the correct package versions (the given version or higher) and ensure computational reproducibility. When running the Python code files, make sure to use a virtual environment (e.g., created with venv or conda) to avoid issues with package dependencies and working directories.

Workflow description

Here we explain the Python script processing for analyzing

• Characterization of UV channels
• Spectral sensitivity
• Directional dependance of the novel light logger and
• Diffuser testing.

Each step includes the corresponding raw data, along with the data preparation and calculations necessary for visualization.

Step 1: Characterization of UV channels

Spectrometer

Folder: 01_UV
Input: file1 - file12.csv
Output: UV_spectrometer_code.ipynb
Code: UV_spectrometer_code.ipynb

For the calibration of the UV channels, the corresponding data from the OceanOptics spectrometer can be found in this folder 01_UV. The data file containing all acquired spectrometer data is in 01_UV/spectrometer_data. Hereby, the CSV files in the filtered folder were used, which contain only the relevant information obtained from the raw data.

Note: We have also included the raw data with all acquired data from the OceanOptics spectrometer, which can be found in the folder rawdata.

Sensor

Folder: 01_UV
Input: sensor_parameter.csv
Output: UV_sensor_code.ipynb
Code: UV_sensor_code.ipynb

In order to develop an algorithm that automatically adjusts the parameters to changing light conditions, data with varying parameters were tested. The corresponding data can be found in this folder: sensor_data.

Algorithm

Folder: 01_UV
Input: sensor_algorithm.csv
Output: UV_algorithm_code.ipynb
Code: UV_algorithm_code.ipynb

After developing the algorithm, sensor data was collected (see folder sensor_data).

Ste 2: Spectral sensitivity

Folder: 02_Spectral
Input (sensor): spectral_sensor_10% - 100%.csv
Input (spectrometer): spectral_Jeti_10% - 100%.csv, spectral_Jeti_all intensities.csv, total_irradiance.csv
Output: spectral_code.ipynb
Code: spectral_code.ipynb

The data required to run the code is organized into separate folders (sensor_data and jeti_data) containing sensor data and spectrometer data.

Step 3: Directional dependance

Folder: 03_Directional
Input (sensor): direc_sensor_450-550nm.csv, normalized_sensor_450-550nm.csv
Input (spectrometer): direc_450-550nm.csv
Output: directional_code.ipynb
Code: directional_code.ipynb

The data required to run the code is organized into separate folders (sensor_data and jeti_data) containing sensor data and spectrometer data.

Step 3: Diffuser testing

Folder: 03_Directional
Input: directional_pure_sensor+truebungskon0,5%-2%.csv, trübungskon0,5%-2%_normalized.csv
Output: directional_code.ipynb
Code: directional_code.ipynb

Additional test measurements with the diffusers were conducted, and the data can be retrieved from the folder diffuser.