If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and some of the other necessary packages to successfully install and run the software.

AMSIMP can be installed with conda, or with pip. For more detailed instructions, consult our Python and AMSIMP installation guide below.

conda

Before installation via this particular method, TensorFlow needs to be installed through the Python Package Index (PyPI) using the following command:

pip install tensorflow

AMSIMP is distributed on Anaconda Cloud and can be installed using conda:

conda install -c amsimp amsimp

If you run into any issues, you can either create an issue on GitHub or contact us by email.

pip

If you use pip, you can install it with:

pip install amsimp

Python and AMSIMP installation guide

Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations.

Recommendations

On all of Windows, macOS, and Linux:

• Install Anaconda (it installs all packages you need and all other tools mentioned below).
• For writing and executing code, use notebooks in JupyterLab for exploratory and interactive computing, and Spyder or Visual Studio Code for writing scripts and packages.
• Use Anaconda Navigator to manage your packages and start JupyterLab, Spyder, or Visual Studio Code.

Alternative if you prefer pip/PyPI

For users who know, from personal preference, they prefer a pip/PyPI-based solution, we recommend:

• Install Python from, for example, python.org, Homebrew, or your Linux package manager.
• Use Poetry as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does.
• If you are installing the software via this method, please ensure you have all of the required dependencies prior to installation. You will also need to have a C compiler installed on your machine if you are choosing this method.

Further information about the required dependencies can be found here:

• Python 3.7.x (https://www.python.org/)
• tqdm (https://tqdm.github.io/) | A Fast, Extensible Progress Bar for Python and CLI.
• NumPy (https://numpy.org/) | Python package for scientific computing including a powerful N-dimensional array object.
• Astropy (https://www.astropy.org) | A Community Python Library for Astronomy.
• Iris (https://scitools.org.uk/iris/docs/latest/) | A powerful, format-agnostic, community-driven Python library for analysing and visualising Earth science data.
• Tensorflow 2.0 or later (https://www.tensorflow.org) | TensorFlow is an end-to-end open source platform for machine learning.