Install pySIPFENN

Installing pySIPFENN is straightforward using the PyPI package repository, the conda-forge package repository, or by cloning from GitHub directly. While not required, it is recommended to first set up a virtual environment using venv or Conda. This ensures that (a) one of the required versions of Python (3.9+) is used and (b) there are no dependency conflicts. If you have Conda installed on your system (see miniconda install instructions), you can create a new environment with a simple:

conda create -n pysipfenn python=3.10 jupyter numpy 
conda activate pysipfenn

If you are managing a large set of dependencies in your project, you may consider using mamba in place of conda. It is a less mature, but much faster drop-in replacement compatible with existing environments. See micromamba install instructions.

Standard

If your main goal is to run pySIPFENN models, provided by us or any other vendor, you need only a subset of the capabilities of our code. Simply install pySIPFENN:

from PyPI with pip:
```
pip install pysipfenn
```
from conda-forge with conda:
```
conda install -c conda-forge pysipfenn
```

from conda-forge with micromamba:

micromamba install -c conda-forge pysipfenn

from source, by cloning. To get a stable version, you can specify a version tag after the URL with --branch <tag_name> --single-branch, or omit it to get the development version (which may have bugs!):
```
git clone https://github.com/PhasesResearchLab/pySIPFENN.git
```
then move to pySIPFENN directory and install in editable (-e) mode.
```
cd pySIPFENN
pip install -e .
```

Developer Install

If you want to utilize pySIPFENN beyond its core functionalities, for instance, to train new models on custom datasets or to export models in different formats or precisions, you need to install several other dependencies. This can be done by following the from source instructions above but appending the last instruction with dev extras marker.

pip install -e ".[dev]"

Note: pip install "pysipfenn[dev]" will also work, but will be less convenient for model modifications (which you likely want to do), as all persisted files will be located outside your working directory. You can quickly find where, by calling import pysipfenn; c = pysipfenn.Calculator(); print(c) and Calculator will tell you (amongst other things) where they are.