gram is a Python module designed for computing the Controllability Gramian of discrete linear dynamical systems. This tool extends the functionality of the python-control library, specifically tailored to handle discrete systems with stability checks. It provides an efficient and user-friendly way to analyze control energy in linear dynamical systems, leveraging the Slycot library's sb03md function for robust computations.
- Calculation of the Controllability Gramian for discrete linear systems.
- Stability check for the system before computing the Gramian.
- Integration with pandas DataFrames for batch processing of systems.
- Additional functions for analyzing eigenvalues of the Gramian.
To use gram, you need to have Python installed along with the numpy, control, and slycot packages. You can install these dependencies using pip:
git clone https://github.com/PsyControLab/PyC_Gramian.git
cd Gramian
pip install numpy control slycotHere's a basic example of how to use PyC_gram:
import pandas as pd
import numpy as np
import control as ct
from Gramian import PyC_compute_gramians
# Example DataFrame with 'A_Norm' and 'B_matrice' columns
# df = ...
df_with_grams = compute_gramians(df)
df_with_grams.head(2)This project is built upon the python-control library and uses the Slycot library's sb03md function for computations.
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