Installation as a user

It is recommended to use python 3.13 with a dedicated virtual environment for this package. Learn how to manage python versions and virtual environments.

There are many alternatives to managing virtual environments and package dependencies (requirements). We recommend using uv, an extremely fast manager Python package and project manager. In this tutorial, you will find paralleled descriptions, using either uv or a more classical approach using venv and pip. Using a conda environment is yet another alternative.

The library can be used on Windows, Mac, or Unix, provided a Python version is installed.

Setup

Start by creating a virtual environment, e.g., in a directory on your local computer:

uvvenvconda

uv is capable of creating a virtual environment and install the required Python version at the same time.

uv venv --python 3.13

Note that you will need to install the Python version manually beforehand.

python -m venv .venv
source .venv/bin/activate

Note that you will need to install the Python version manually beforehand.

conda create -n venv python=3.13
conda activate venv
conda install pip

That command creates a new virtual environment in a directory called .venv.

Installation

Install the latest stable version of this package from PyPI with

uvpipconda

uv pip install ifes_apt_tc_data_modeling[ipynb]

pip install ifes_apt_tc_data_modeling[ipynb]

python -m pip install ifes_apt_tc_data_modeling[ipynb]

This will install the module and jupyterlab whereby the notebook with examples become executable.

Start using ifes_apt_tc_data_modeling

The jupyterlab server is started with

jupyter-lab

The notebook to run is the following examples/ExamplesForUsersOrDevelopers.ipynb That's it! You can now use the library that you have installed!