pySCSA is a Python implementation of the one-dimensional Semi-Classical Signal Analysis method proposed in [1]. The primary concept behind SCSA is to consider any pulse-shaped signal as Schrödinger's operator potential and utilize its discrete spectrum to analyze the signal. For more information regarding the mathematical principles and the algorithm, please refer to [1].

In this repository, you will find the following scripts:

1. scsa.py A Python class that implements the basic one-dimensional SCSA algorithm
2. example_sech.py An example of how to use the SCSA class on a sech function
3. example_gaussian.py An example of how to use the SCSA class on a Gaussian function
4. example_neural_spike.py An example of how to apply SCSA on a neural spike

The user can run the demos/examples provided here by typing the following commands in a terminal:

$ python3 example_sech.py

$ python3 example_gaussian.py

The SCSA class and the example scripts require the following Python packages:

• Numpy >= 1.26.4
• Scipy >= 1.13.0
• Matplotlib >= 3.5.1
• Scikit-learn >= 1.4.1.post1

The software available in this repository has been tested on the following platforms:

• Ubuntu 22.04.4 LTS
• Python 3.10.12
• GCC 11.4.0
• x86_64

1. Laleg-Kirati, Taous-Meriem, Emmanuelle Crépeau, and Michel Sorine. "Semi-classical signal analysis." Mathematics of Control, signals, and Systems 25 (2013): 37-61.