1. Installing Python with Anaconda

Python can be installed in many ways or may even already be installed on your machine (e.g., on some Unix distros). However, since phenopype is a scientific Python package, only the installation procedure using Anaconda, a scientific Python distribution, is described here. Anaconda, more specifically, its terminal interface conda, is both a Python package and environment manager. To avoid conflicts between package dependencies, and for a cleaner and more reproducible workflow, phenopype should always be installed inside a Python virtual environment that you create first (read about virtual envs here). This procedure is explained here.

1.1. Installing conda

Download and install Miniconda3 to create virtual environments using the conda manager. Miniconda is a scientific Python distribution that comes with some packages already built in. Follow the OS-specific installation instructions. WINDOWS USERS: install miniconda directly to the top level of your drive so you don’t run into privilege issues - e.g. C:\miniconda3. Test if conda was successfully installed:

conda --version

If this doesn’t show the current conda version, please refer to the references below for troubleshooting.

Troubleshooting references

Consult these references if you have trouble installing Miniconda (they are discussing Anaconda, but the same applies for Miniconda):

1.2. Installing mamba (optional)

This step is optional, but highly recommended! The mamba package manager replaces conda and is much faster. For detailed installation instructions and user guide refer to the documentation. In short, do the following:

conda install -c conda-forge mamba

Test the installation with

mamba --version

1.3. Creating a virtual environment with conda (or mamba)

Note

If you have installed mamba, use mamba instead of conda here (except when activating and environment: there you still need to use conda activate).

Use conda to create a new Python virtual environment (needs to be Python 3.7 for phenopype):

conda create -n <NAME> python=3.7  # <NAME> == chosen name, e.g. "pp-env"
conda activate <NAME>                      # activates the new environment

After successful installation and activation, you should see the name of the environment in the console - e.g.:

(pp-env) D:\projects>

Now all libraries installed into this environment will be isolated from those installed in other virtual environments.