Development environment

The development environment is an installation of ChemPlot on your local computer which can be used for testing existing features or developing new ones in order to contribute to the library.

Prerequisites

Start by making sure you have the following installed on your system:

Git: Download Git
Python (version 3.9 or higher): Download Python
Conda (optional, if you choose the Conda setup): Install Conda

1. Clone the ChemPlot Repository

First, clone your forked GitHub repository of ChemPlot to your local machine using either HTTPS or SSH.

Using HTTPS:

~$ git clone https://github.com/<your-username>/ChemPlot.git

Using SSH:

~$ git clone git@github.com:<your-username>/ChemPlot.git

Navigate to the cloned repository:

~$ cd ChemPlot

2. Choose Your Environment Setup Method

You have two options to set up your development environment:

Using Conda Environment
Using Python’s Built-in Virtual Environment (`venv`)

Choose the method that best fits your workflow.

Option 1: Using Conda Environment

Conda is recommended for managing more complex environments.

~/<PATH-TO-CLONE>/ChemPlot$ conda create -n chemplot

When conda finishes creating the environment, activate it by running:

~/<PATH-TO-CLONE>/ChemPlot$ conda activate chemplot

Option 2: Using Python’s Built-in Virtual Environment (venv)

If you prefer not to use Conda, Python’s built-in venv provides a lightweight alternative for creating isolated environments.

~/<PATH-TO-CLONE>/ChemPlot$ python -m venv venv

You can then activate the enviroment

On Linux/macOS:

~/<PATH-TO-CLONE>/ChemPlot$ source venv/bin/activate

On Windows:

~/<PATH-TO-CLONE>/ChemPlot$ venv/Scripts/activate

3. Install ChemPlot for Development

You can now install ChemPlot in editable mode. Editable mode will allow your code changes to be propagated through the library code without having to reinstall.

~/<PATH-TO-CLONE>/ChemPlot$ pip install -e .

You are now ready to develop and test ChemPlot!

4. Testing

To run the unit tests for ChemPlot use this command:

~$ pip install .[test]
~$ python -m pytest --pyargs chemplot

On your cloned version of the ChemPlot repository you have two more tests, used to check performance of the library on your machine and to check the figures ChemPlot can generate. You can find these tests inside the performance_tests folder:

ChemPlot
├── ...
├── performance_tests/
│   ├── performanceTest.py
│   └── visualplotsTest.py
└── ...

You can run these tests by navigating to the performance_test library:

~/ChemPlot$ cd performance_tests
~/ChemPlot/performance_tests$ python performanceTest.py
~/ChemPlot/performance_tests$ python visualplotsTest.py

If it doesn’t work you might have to change python with python3 in the command. performanceTest.py will generate a .csv file containing all the times taken by ChemPlot to run all the dimensionality reduction methods on your machine. It will use the sample datasets provided with the library. visualplotsTest.py will instead create a multipage .pdf file containing different figures illustrating all plotting options for ChemPlot. These method as well will use the sample datasets included in the library.