To install AMSIMP, you will need to utilise Python 3.7, or later. Preferably, you will also want to have an installation of either Anaconda, or Miniconda on your machine. For installation instructions, please visit Anaconda’s documentation page.
AMSIMP is distributed on Anaconda Cloud and can be installed using conda:
$ conda install -c amsimp amsimp
You can also install AMSIMP from the source code. First, clone the repository off of GitHub:
$ git clone https://github.com/amsimp/amsimp.git && cd amsimp
You will then need to install the software requirements, you can do this via:
$ conda env create -f environment.yml && conda activate amsimp
Further information about the required dependencies can be found here:
Python 3.7.x (https://www.python.org/)
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.
Matplotlib (https://matplotlib.org/) | Python package for 2D plotting. Python package required for any graphical capabilities.
Cartopy (https://scitools.org.uk/cartopy/docs/latest/index.html) | Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.
Iris (https://scitools.org.uk/iris/docs/latest/) | A powerful, format-agnostic, community-driven Python library for analysing and visualising Earth science data.
SciPy (https://www.scipy.org/) | A Python package for scientific computing.
scikit-learn (https://scikit-learn.org/stable/) | Machine Learning in Python
Tensorflow 0.2 or later (https://www.tensorflow.org) | TensorFlow is an end-to-end open source platform for machine learning.
Progress (http://github.com/verigak/progress/) | Easy progress reporting for Python.
Finally, you can the software via install:
$ python setup.py install