High-performance time series downsampling algorithms for visualization
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Updated
Jun 1, 2026 - Jupyter Notebook
High-performance time series downsampling algorithms for visualization
downsampling time series data algorithm in Go
C++ implementation of the Largest Triangle Three Buckets (LTTB) downsampling algorithm
Largest-Triangle-Three-Buckets (LTTB) downsampling algorithm in Python (C-Extension)
MinMax-preselection for Efficient Time Series Line Chart Visualization (using LTTB)
Dead simple line chart in webgl. https://codesandbox.io/embed/0pq5v6j1qp?fontsize=14&hidenavigation=1&theme=dark
MinMaxLTTB and clasical LTTB algorithms for downsampling of timeseries for visualization
ECG viewer for medical device data using D3.js
Downsampling algorithms for Python written in C
Data resampling for efficient Plotille terminal plots, with stride, min/max, LTTB and MinMaxLTTB.
Aplicação para downsampling de dados de forma otimizada, permitindo ao usuário escolher a densidade de pontos desejada facilitando pós-processamento e garantindo melhor gerenciamento de memória do computador em múltiplos contextos.
Google Maps for time-series data — level-of-detail charting that streams and decimates to the viewport. Framework-agnostic, uPlot-based.
Hardware-accelerated 2D data visualization and plotting engine for .NET 10. High-frequency 60/120 FPS rendering on 10M+ points with SIMD decimation.
High-density telemetry plots (10M+ pts), LTTB decimation, Candle, Heatmap, and Gantt charts for .NET.
High-performance, memory-efficient LTTB implementation, designed to downsample massive datasets with thousands, millions, or hundreds of millions of points while keeping memory usage to a minimum.
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