Soundless Plots

This repository contains three Jupyter Notebooks that work together to process raw data and produce insightful visualizations about noise levels and train schedules.

Notebooks

1. process_data.ipynb

Objective:
Read all the raw data from the dataset, perform the necessary calculations, and store the final results in a file called data.pkl.

Key Steps:

• Data extraction and filtering.
• Calculation of metrics (e.g., average dB levels, heart rate metrics, sleep incidents).
• Saving the processed data for plotting.

2. plot_data.ipynb

Objective:
Read the processed data from data.pkl and generate the corresponding plots.

Key Steps:

• Loading the processed data.
• Generating visualizations for noise levels, heart rate and sleep data during different scenarios.
• Customized plotting using matplotlib.

3. adif_data.ipynb

Objective:
Analyze and detect the hours when the most trains depart from Tarragona's port.

Key Steps:

• Reading and preprocessing Adif train schedule data.
• Filtering data to focus on trains departing to Tarragona.
• Plotting the number of trains by the hour.

Data

The data used in these notebooks is stored in the ./soundless/data/ and ./soundless/history/ directories. The datasets include measurements of noise levels, heart rate, sleep stages, and train schedules.

Usage

To run the notebooks, you need to have Jupyter Notebook installed along with the required Python packages. You can install the dependencies with: