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FastPlot

Ultra-fast time series plotting for MATLAB and GNU Octave with dynamic downsampling, sensor monitoring, and dashboard layouts.

Key Metrics

Metric	Value
10M point zoom cycle	4.7 ms (212 FPS)
Point reduction	99.96% (10M to ~4K displayed)
GPU memory (10M pts)	0.06 MB vs 153 MB for plot()
Implementation	Pure MATLAB + optional C MEX (AVX2/NEON SIMD)

Library Components

FastPlot consists of five integrated libraries:

Library	Description
FastSense	Core plotting engine with dynamic downsampling, dashboard layouts (FastSenseGrid, FastSenseDock), interactive toolbar, themes, and disk-backed storage via FastSenseDataStore
Dashboard	Widget-based dashboard engine with 8 widget types, 24-column responsive grid, edit mode, and JSON persistence
SensorThreshold	Sensor data containers with state-dependent threshold rules, violation detection, and SensorRegistry catalog
EventDetection	Event detection from threshold violations, EventViewer with Gantt timeline, live pipeline with notifications
WebBridge	TCP server for web-based visualization with NDJSON protocol

Features

• Smart downsampling — per-pixel MinMax and LTTB algorithms, auto-selected per zoom level
• Pyramid cache — multi-resolution pre-computation for instant zoom-out on 50M+ datasets
• MEX acceleration — optional C with SIMD (AVX2/NEON), auto-fallback to pure MATLAB
• Dashboard layouts — tiled grids (FastSenseGrid) and tabbed containers (FastSenseDock)
• Interactive toolbar — data cursor, crosshair, grid/legend toggle, autoscale, PNG export
• 6 built-in themes — default, dark, light, industrial, scientific, ocean
• Linked axes — synchronized zoom/pan across subplots
• Sensor system — state-dependent thresholds with condition-based rules and violation markers
• Event detection — group violations into events with statistics, Gantt viewer, click-to-plot
• Live mode — file polling with auto-refresh (preserve/follow/reset view modes)
• Disk-backed storage — SQLite-backed chunked DataStore for 100M+ point datasets

Quick Start

install;

% Basic plot with 10M points
fp = FastSense('Theme', 'dark');
x = linspace(0, 100, 1e7);
y = sin(x) + 0.1 * randn(size(x));
fp.addLine(x, y, 'DisplayName', 'Sensor');
fp.addThreshold(0.8, 'Direction', 'upper', 'ShowViolations', true, 'Label', 'High');
fp.render();

% Dashboard with tiled layout
fig = FastSenseGrid(2, 2, 'Theme', 'dark');
fig.setTileSpan(1, [1 2]);

fp1 = fig.tile(1);
fp1.addLine(x, sin(x), 'DisplayName', 'Pressure');
fp1.addBand(0.8, 1.0, 'FaceColor', [1 0.3 0.3], 'FaceAlpha', 0.15, 'Label', 'Alarm');
fig.setTileTitle(1, 'Pressure Monitor');

fp2 = fig.tile(2);
fp2.addLine(x, cos(x), 'DisplayName', 'Temperature');
fig.setTileTitle(2, 'Temperature');

fig.renderAll();

% Sensor with state-dependent thresholds
s = Sensor('pressure', 'Name', 'Chamber Pressure');
s.X = linspace(0, 100, 1e6);
s.Y = randn(1, 1e6) * 10 + 50;

sc = StateChannel('machine');
sc.X = [0 30 60 80]; sc.Y = [0 1 2 1];
s.addStateChannel(sc);
s.addThresholdRule(struct('machine', 1), 70, 'Direction', 'upper', 'Label', 'Run HI');
s.resolve();

fp = FastSense('Theme', 'industrial');
fp.addSensor(s, 'ShowThresholds', true);
fp.render();

Requirements

• MATLAB R2020b+ or GNU Octave 7+
• C compiler (optional) for MEX acceleration
• No toolbox dependencies

Getting Started

Start with the Installation guide to set up FastPlot and compile MEX acceleration. Then follow the Getting Started tutorial for step-by-step examples covering basic plotting, dashboards, sensors, and live mode.

API Reference

Core Classes

• API Reference: FastPlot — main plotting engine with dynamic downsampling
• API Reference: Dashboard — FastSenseGrid, FastSenseDock, FastSenseToolbar
• API Reference: Sensors — Sensor, StateChannel, ThresholdRule, SensorRegistry
• API Reference: Event Detection — EventDetector, EventViewer, LiveEventPipeline
• API Reference: Themes — theme presets, customization, color palettes
• API Reference: Utilities — ConsoleProgressBar, FastSenseDefaults

Specialized Guides

• Live Mode Guide — file polling, view modes, live dashboards
• Dashboard Engine Guide — DashboardEngine with widget-based dashboards
• Datetime Guide — working with time series data
• Examples — 40+ categorized runnable examples