Installation - leahghartman/GaussBean GitHub Wiki

Before you try to install GaussBean, make sure that you have Anaconda (or some fork of Anaconda, like Miniconda or Mamba) installed. Instructions on how to install each of those are given at the links below:

Anaconda: https://docs.anaconda.com/free/anaconda/install/index.html
Miniconda: https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html
Mamba: https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html

If those links don't work, you should be able to just Google "____ installation" and find out how to install any of those distributions.

There are dependencies needed for GaussBean, but when creating the environment itself, all of the necessary dependencies should automatically install if they aren't already on your system.

GaussBean was originally built using Python, so once you have the Anaconda (or forked) distribution installed (as mentioned above), go to your terminal and use Git to clone GaussBean from this repository (if you have access to the CU-PWFA organization) :

git clone https://github.com/CU-PWFA/GaussBean.git

Alternatively, you can use the following command, which should work even if you are not a part of the CU-PWFA organization:

git clone https://github.com/leahghartman/GaussBean

Now, from your terminal, navigate to the top level of the directory where GaussBean was cloned (if you run the command "ls -a," you should see a file named "gaussbean.yml"; if you don't see this file, you are not at the top level of the directory) and run the following command:

conda env create -f gaussbean.yml

This command should create a conda (or mamba) environment named "gaussbean". If the environment won't build, you can create your own conda environment and manually install all of the dependencies listed in the "gaussbean.yml" file.

Now, enter the environment that we just created using the command below in the terminal:

conda activate gaussbean

Finally, install the gaussbean package using pip (note: this is a force-install because issues were occurring with the package not updating even after updating the PyPi package itself):

pip install --no-cache-dir --upgrade gaussbean

Once you're done with this step, you can pretty much delete the top level of the directory related to this package. Every time you want to do any data analysis with this package, you can just activate the conda/mamba environment, open a Jupyter Notebook, select the "gaussbean" kernel, import gaussbean, and go to town!

And that's it! Have fun! :)

If you are using JupyterLab, a single Jupyter Notebook, or an application like Spyder, you can use the following command in your terminal to use ipykernel, which makes it easier to use our new environment in any of the previously-listed applications:

python -m ipykernel install --user --name=gaussbean

Now, when you open (or create) a Jupyter Notebook, it should give you the option to select/use the "gaussbean" kernel.

If you want to upgrade GaussBean using Pip, run the following command in the "gaussbean" environment you previously created (in conda, mamba, etc.).

pip install --upgrade gaussbean