VISION

VISualize your ambitION

I. Introduction

VISION is a python package is designed for quick visualization of scientific data. It is based on the popular matplotlib library, but with a more user-friendly interface.

II. Environment Set-up

i. Pre-requisites

Recommended python version: 3.12 (which is used by myself). Anyway, in theory it should work with any python version >= 3.0.

Highly recommending using anaconda to manage your python environment. It is a free and open-source distribution of python and R programming languages for scientific computing, that aims to simplify package management and deployment.

Required packages:

• Data acquisition: Pandas & Xlrd
• Scientific calculus: Numpy & Scipy
• Machine learning & statistical analysis: Scikit-learn
• Visualization: Matplotlib & Seaborn
• Notebook interface for python: JupyterLab

ii. Managing python environment via anaconda

Using anaconda, we can easily manage our python environment and packages through the command line interface (CLI).

(1) Creating new anaconda environment

conda create --name yourENV python=3.12

where yourEnv is the name of your environment.

(2) Activating the environment

conda activate yourENV

(3) Installing packages

Part of the required packages can be quickly installed by calling the requirements.txt file, which is located in the root directory of this repository. The installation can be done via conda by the following command:

conda install --yes --file requirements.txt

or pip by the following command:

pip install -r requirements.txt

(Reminding: When setting up virtual environments by anaconda, using conda install before pip install is usually beneficial for environment management.)

III. Installation