Potiron - Normalize, Index and Visualize Network Capture

Automatic knowledge graph generation for Obsidian.md

Kernel density estimation on a sphere

Solr Relevance Ranking Analysis and Visualization Tool

[PacificVis 2025] Multi-Criteria Exploratory Dashboard

该程序使用 Python 的 `PyAudio` 和 `Matplotlib` 库实现了一个实时音频示波器，并能够实时显示音频的频率和响度。程序会从麦克风采集音频数据，进行 FFT 变换来计算音频的频率，同时显示音频信号的波形和响度。

A visualization of tourism's economic impact, comparing countries like the Maldives and Fiji to highlight dependency and sustainability.

Visualizations of Crimean War death causes, inspired by Florence Nightingale's work. Built with Altair and vega_datasets to analyze and share the story behind the data.

This project visualizes 17 years of tuition trends at AAU public universities, highlighting affordability changes through Python Altair charts.

Visualize socioeconomic disadvantage and health outcomes in Seattle, using Python Altair interactive charts.

Explore how Chicago’s pothole data reflects social and demographic inequalities, using Python Altair visualizations.

Implemented the process of extrapolating from Gaia stellar data, to 3D visualizations, to three-views, to three-view signals, to three-view audio of signals, and even their inversions. This project proves the feasibility of the Logic (Luoji)'s “spell” from “The Three Body Problem” trilogy.

Miniature reader of Wikipedia articles.

Graphical is a Python library that adds graphs and visuals to Rich and Textual.

A collection of Python scripts to explore and visualize the B-Matrix of a graph network. Part of CPSC 547 (Information Visualization) 2022 at UBC.

Scrape credit card information from www.finder.com.au.

Semestrální práce KIV/VI - Vizualizace Informací

From OrariSTP project, an open-souce telegram bot.

The Census-Stub invariant descriptor is a data structure that captures meaningful structural hallmarks of graph topology, especially when traditional visualizations like node-link views are insufficient. The Census-Stub approach provides a framework for analyzing complex networks, supporting tasks such as network comparison and classification.

This project aims to explore the effectiveness of different machine learning algorithms for classification, clustering, and regression tasks. By applying preprocessing techniques and careful evaluation, we will identify the most suitable algorithms for each type of problem.