pyqtlabgraph 0.1.1

Reusable PySide6/PyQtGraph live plotting widget with toolbar, legend, and smart axis formatting.

PyQtLabGraph

A powerful, interactive, and premium live plotting library for PySide6/Qt6, based on PyQtGraph.

PyQtLabGraph is built for both interactive scientific data analysis and high-performance real-time visualization, providing an embeddable plot widget, dedicated toolbars, external legend layouts, smart axis formatting, layout persistence, and modern, explicit visual themes.

Previews

Light Theme (Default)	Dark Theme

Modeless Customize Dialog	Modular Widget Layout
	[1] Plot Widget • [2] External Legend Widget • [3] Plot Toolbar Widget

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Why PyQtLabGraph? (Aesthetic & Usability Philosophy)

In scientific and engineering environments, two plotting libraries dominate the Python ecosystem:

• Matplotlib: Unrivaled for generating static, publication-quality figures, but often heavy or sluggish when handling interactive real-time telemetry or rapid live data streams.
• PyQtGraph: An exceptionally fast, graphics-hardware-accelerated library built for high-performance plotting, but one that deliberately leaves user-interface chrome (like toolbars, legends, customize dialogs, and layout persistence) to be implemented by the developer from scratch.

PyQtLabGraph bridges this gap. It is designed as a high-productivity, instrument-grade plotting interface that brings the ease of use of traditional graphical programming environments to Python.

Many scientists, laboratory engineers, and researchers are familiar with the instant, out-of-the-box utility of instruments and classic engineering software platforms (such as LabVIEW), where plotting components come pre-packaged with zoom controls, custom legends, scaling tools, and runtime style editors. PyQtLabGraph delivers this exact workflow in a clean, pythonic PySide6 package:

• Familiar, Hardware-Like Controls: The interface mimics the look, feel, and rapid utility of physical lab instruments (e.g., oscilloscopes, analyzers) and classic instrumentation software.
• Instant Interactive Zooming & Panning: Features intuitive mouse bindings (wheel zoom, key-constrained zooms) and double-click axis inputs out-of-the-box.
• Rich Quality-of-Life (QoL) Features: Includes a dedicated toolbar (X/Y locked zoom, autoscaling, live rolling window), a modeless live-preview Customize dialog, and complete JSON layout persistence.
• Modern Aesthetic Themes: Sleek, built-in themes (light, dark, solarized) independent of OS-level dark-mode checks, adapting naturally to the host application's active Qt stylesheet.
• Modular Widget Architecture & Qt Designer Support: The plot canvas, toolbar, and legend are decoupled into three independent QWidgets. You can lay them out freely or drag and size placeholder containers inside Qt Designer, letting PyQtLabGraph mount itself automatically.

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Features

• Real-Time Plotting: High-performance rendering optimized for rapid updates, live sensor streams, or fast oscilloscope-style displays.
• Smart Axis Formatting (SmartAxisItem):
  • AUTO: Automatic SI-prefix scaling (e.g., scaling raw Hertz to kHz / MHz / GHz).
  • LINEAR: Explicit raw values with user-defined units, bypassing auto-scaling.
  • TIME: Adaptive relative time formatting displaying seconds formatted elegantly as d h min s depending on zoom level.
• Dedicated External Legend (PyQtLabGraphLegend):
  • Displays curve symbols, colors, and labels.
  • Interactive: Double-click a curve's legend item to open the Customize dialog immediately focused on that curve. Single-click to toggle curve visibility.
  • Configurable orientation: Can be placed vertically (default) or horizontally.
• Integrated Toolbar:
  • Action buttons for Show All, rectangle zoom, X-zoom, and Y-zoom.
  • Quick autoscale toggle for X and Y axes individually.
  • Rolling X-range display with custom size configuration.
  • Live PNG export and instant Customize dialog access.
• Modeless Customize Dialog:
  • Adjust titles, labels, units, and axis formatting modes.
  • Toggle grids, global anti-aliasing, downsampling, clip-to-view, and adaptive performance.
  • Manage individual curves: toggling visibility, line width, line colors, marker styles (circle, square, cross, diamond, etc.), size, and borders.
  • Live Preview: All configuration edits preview immediately in the plot in real-time, and revert instantly if the user clicks Cancel.
• Layout Persistence: Save/Load all layout configurations (visual properties, themes, active ranges, curve states) to a shared versioned JSON file.
• Adaptive Performance: Automatic visual simplification when rendering very dense datasets to avoid UI lag.

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Interactive Controls

PyQtLabGraph introduces advanced viewport mouse controls on top of the standard PyQtGraph mouse interactions:

• Mouse Drag (Left Click): Pans the view in the selected tool mode.
• Mouse Drag (Right Click): Zooms X and Y scale dynamically (drag left/right for X, up/down for Y).
• Mouse Wheel: Zooms both X and Y axes centered on the cursor position.
• Shift + Mouse Wheel: Zooms X-axis only, preserving the Y-axis range.
• Ctrl + Mouse Wheel: Zooms Y-axis only, preserving the X-axis range.
• Double Click Axis: Opens a quick manual range pop-up directly underneath the cursor for entering exact values.

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Installation

Install PyQtLabGraph from PyPI:

pip install pyqtlabgraph

Or install it directly from the repository source:

pip install .

For development installations, make sure you have the runtime dependencies installed:

pip install PySide6 pyqtgraph

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Quick Start

Here is a minimal working example of embedding the PyQtLabGraphWidget inside a basic Qt window:

import sys
from PySide6.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout
from pyqtlabgraph import PyQtLabGraphWidget

app = QApplication(sys.argv)
window = QMainWindow()
central = QWidget()
layout = QVBoxLayout(central)
window.setCentralWidget(central)

# Initialize the plot widget
plot = PyQtLabGraphWidget(
    plot_container=central,
    plot_identifier="quickstart_plot",
    show_toolbar=True
)

# Plot a simple sensor temperature curve
plot.plot(
    key="temp_sensor",
    x=[0, 1, 2, 3, 4, 5],
    y=[22.1, 22.4, 23.0, 22.8, 23.5, 24.1],
    label="Temperature",
)

# Set axis labels and units
plot.set_axis_labels(
    x_label="Time", x_units="s",
    y_label="Temperature", y_units="°C"
)

window.resize(800, 600)
window.show()
sys.exit(app.exec())

To see more complex features, run the bundled examples:

• Thermostat Simulation Demo:

  python examples/demo_thermostat.py
  

• Time Domain & FFT Demo:

  python examples/demo_time_fft.py
  

• Host Application Styling Comparison:

  python examples/demo_thermostat_qdarktheme.py
  

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Detailed Documentation

• 📖 API Reference: Complete details on classes, parameters, and methods.
• 🎨 Visual Styling & Themes: Built-in themes, plot styles, and integrating with host stylesheets.
• ⚡ Performance Optimization: Downsampling, clip-to-view, and adaptive rendering mechanics.

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Project Structure

├── pyqtlabgraph/            # Main library package
│   ├── __init__.py          # Public exports & versioning
│   ├── widget.py            # Main PyQtLabGraphWidget and API wrapping
│   ├── dialogs.py           # Modeless Customize dialog & popups
│   ├── layouts.py           # JSON Layout save/load mechanics
│   ├── toolbar.py           # Toolbar buttons, export, and mode controllers
│   ├── legend.py            # External interactive PyQtLabGraphLegend
│   ├── axis.py              # SmartAxisItem tick formatting implementation
│   ├── models.py            # Core dataclasses (CurveState, InteractionState)
│   ├── styles.py            # Curve style configurations and palettes
│   ├── themes.py            # Background themes and color registries
│   ├── qt_styles.py         # Standard fallback borders and QSS wrappers
│   └── assets/              # PNG icon assets used by the toolbar
├── docs/                    # Detailed user-facing documentation
├── tests/                   # Standalone smoke test suite
├── examples/                # Packaged demo and example files
└── pyproject.toml           # Build system and package metadata

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Development & Verification

Tests are located in the tests/ directory and can be executed via a unified test runner. Run this command after any code changes to verify syntax, assets, and UI state:

python3 tests/run_smoke_checks.py

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License

This project is licensed under the MIT License - see the LICENSE file for details.