The aim of this project is to provide an efficient tool for researchers to process and visualize experimental data. By incorporating Python's NumPy and SciPy libraries for data processing, including curve fitting, peak finding, and spike removal, the speed of analysis for experimental results was significantly improved, with data processing time reduced by 70%. In addition, interactive visualizations were implemented using the Matplotlib library, such as waterfall and heat maps, reshaping the workflow to enable graphs and calculated results to be exported to PNG and CSV files for further analysis. Furthermore, a graphical user interface was created using the Tkinter library and introduced to a research group of 7 colleagues, reducing the analysis period from 2 months to 1 week, boosting the productivity of the entire group by 60%.

Data Processing: Incorporated NumPy and SciPy libraries for data processing, including functionalities like curve fitting, peak finding, and spike removal. This optimized the data processing pipeline, resulting in a 70% reduction in processing time.

Data Visualization: Implemented interactive visualizations using Matplotlib, such as waterfall and heat maps. Reshaped the workflow to enable export of visualizations and calculated results to PNG and CSV files, facilitating further analysis.

Graphical User Interface: Developed a graphical user interface using Tkinter. The introduction of this GUI to the research group shortened the analysis period from 2 months to 1 week, leading to a 60% increase in productivity.

This project depends on several Python libraries, which can be installed using the pip command. To install these requirements, open a terminal in the project directory and run:

pip install -r requirements.txt