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This macro was designed to hep automate your STAR-CCM post-processes. This repo contains a macro which exports all scenes and plots to a separate folder. Once completed, run the supplied python file to complete post processing by generating a presentation with all scenes and plots labelled.

A focused resource for mastering Seaborn, featuring practice problems, code examples, and interview-oriented data visualization techniques in Python. Covers creating advanced statistical plots and customizing visualizations for data science interviews.

seaborn_objects_recipes is a Python package that extends the functionality of the Seaborn library, providing custom recipes for enhanced data visualization. This package includes below features to augment your Seaborn plots with additional capabilities.

Streamlit + Python pipeline that benchmarks gym workout efficiency (kcal/min) using present sessions only. Generates sortable workout-type benchmarks, distribution plots, fairness-aware gap analysis with uncertainty/low-sample flags, and a data-quality report to prevent misleading comparisons.

Latent space refers to an abstract multi-dimensional space containing feature values that we cannot interpret directly, but which encodes a meaningful internal representation of externally observed events. The latent space is simply a representation of compressed data in which similar data points... Read more

Fbref is a popular football stats/metrics site which collections information from Ted Knutson's StatsBomb. You can save this data in a csv or excel format. This is a Python module which collects any raw csv file from Fbref and plot the data based on whichever two axes you want it to. You can also... Read more