GraphModelParser

GraphModelParser is a Python library for representing and simulating dynamical graph models from a string description. The library supports various probability distributions such as normal, poisson, expon, binom, and uniform from the scipy.stats module.

Installation

You can install GraphModelParser from PyPI by running:

pip install graph-model-parser

Usage

from graph_model_parser import GraphModelParser
import numpy as np

# Set seed for reproducibility
np.random.seed(42)

model_description = '''
            a_{t} = a_{t-1} + 1
            b_{t} = b_{t-1} + 2*b_{t-2} + a_0
            c_{t} = c_{t-1} + normal(1, 2)
            '''
# Always enclose the dynamic time index in curly braces e.g. {t-1}  
initial_values = {'a_0': 0, 'a_1': 1, 'b_0': 0, 'b_1': 1, 'c_0': 0}
model = GraphModelParser(model_description, initial_values)
print(model(t=20))

In this example, we define a simple dynamical graph model with three variables (a_{t}, b_{t}, and c_{t}) and their relationships over time. The c_{t} variable is also affected by a random normal distribution with a mean of 1 and a standard deviation of 2.

Features

• Define dynamical graph models using a simple string-based syntax.
• Support for various probability distributions from scipy.stats, including normal, poisson, expon, binom, and uniform.
• Easily compute the values of variables at specific time points.
• Define initial values for variables and model their evolution over time.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT