.. _getting_started: Getting Started =============== This guide will walk you through installing `mtflib` and running your first example. Installation ------------ You can install `mtflib` using `uv` (recommended) or `pip`. There are two installation options depending on your needs. Basic Installation ~~~~~~~~~~~~~~~~~~ For standard usage with a NumPy backend, you can install the library directly from PyPI: .. code-block:: bash uv pip install mtflib Optional: PyTorch Backend for GPU Acceleration ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you have a CUDA-enabled GPU and want to leverage it for significant performance improvements, you can install `mtflib` with the optional PyTorch dependency: .. code-block:: bash uv pip install mtflib[torch] This will install the necessary PyTorch libraries alongside `mtflib`, enabling the GPU-accelerated backend for `neval`. A Quick-Start Example --------------------- Here is a simple example to get you started. This script initializes the library, creates a two-variable function, and evaluates it at a point. .. code-block:: python from mtflib import mtf # 1. Initialize the library's global settings. This is a crucial first step. # We'll set a maximum order of 4 and 2 variables (dimensions). mtf.initialize_mtf(max_order=4, max_dimension=2) # 2. Create variables x and y. # var(1) corresponds to the first variable, var(2) to the second. x = mtf.var(1) y = mtf.var(2) # 3. Define a function, for example, f(x, y) = sin(x + y**2) f = mtf.sin(x + y**2) # 4. Print the function's Taylor series coefficients in a readable format. print("Taylor series for f(x, y) = sin(x + y^2):") print(f.get_tabular_dataframe()) # 5. Evaluate the function at the point (x=2, y=3). evaluation_point = [2, 3] result = f.eval(evaluation_point) print(f"\\nResult of f(2, 3): {result[0]}") This example demonstrates the basic workflow of defining a function and evaluating it. For more complex examples, see the :ref:`examples` page.