mtflib 1.4.3

A library for working with Multivariate Taylor Functions.

mtflib: Multivariate Taylor Function Library

A Python library for creating, manipulating, and composing Multivariate Taylor Functions, with a C++ backend for performance-critical applications.

Installation

The recommended way to install mtflib is from PyPI:

pip install mtflib

Installation from Source

Alternatively, you can install mtflib directly from the source repository using pip. Ensure you have a C++17 compliant compiler (e.g., GCC, Clang, MSVC) for building the backend extensions.

pip install .

Quick Start

Here's a simple example to get you started with mtflib:

import numpy as np
from mtflib import MultivariateTaylorFunction, Var, sin_taylor

# 1. Initialize global settings (must be done once)
# This sets the max order of the Taylor series and the number of variables.
MultivariateTaylorFunction.initialize_mtf(max_order=5, max_dimension=2)

# 2. Define symbolic variables
# Var(1) corresponds to x, Var(2) to y
x = Var(1)
y = Var(2)

# 3. Create a Taylor series expression
# This creates a Taylor series for sin(x) + y^2
f = sin_taylor(x) + y**2

# 4. Evaluate the result at a point
# Let's evaluate f at (x=0.5, y=2.0)
eval_point = np.array([0.5, 2.0])
result = f.eval(eval_point)

print(f"f(x, y) = sin(x) + y^2")
print(f"Result of f(0.5, 2.0): {result[0]}")

# For comparison, the exact value is sin(0.5) + 2.0^2
exact_value = np.sin(0.5) + 4.0
print(f"Exact value: {exact_value}")

# You can also view the Taylor series coefficients
print("\\nTaylor Series Representation:")
print(f)

Running Tests

The project uses pytest for testing. First, install the test dependencies:

pip install -e .[test]

Then, run the test suite from the root of the repository:

pytest