pyqtgraph/pyqtgraph
Pyqtgraph
PyQtGraph is a scientific plotting and graphics framework built for PyQt and PySide applications, providing fast, interactive 2D and 3D visualizations with GPU-accelerated rendering. It serves as both a real-time signal monitoring system for streaming time-series data and a toolkit for constructing interactive data dashboards with dockable panels, parameter trees, and custom widgets. The library also includes a node-based visual flowchart tool for building data processing pipelines and a scientific graphics export system that saves plots as PNG, SVG, or CSV and converts items to Matplotlib for publication output.
The framework distinguishes itself through its combination of high-performance rendering capabilities and flexible GUI construction tools. It renders 2D scientific plots using Qt's GraphicsView framework for responsive interaction, while 3D visualizations leverage OpenGL for real-time exploration of surfaces, volumes, and meshes. The library supports image and video display with color mapping and normalization, and provides interactive features such as mouse-based pan/zoom, data selection, and annotation with text labels, arrows, and region-of-interest selectors.
Beyond core plotting, PyQtGraph offers capabilities for building complete scientific interfaces, including dockable panel layouts, parameter tree controls, and custom widget embedding. It handles data processing through NumPy array operations, supports multi-dimensional data slicing, and enables export to HDF5 format. The library also integrates with Jupyter notebooks for exploratory analysis and provides SI unit conversion for readable axis labels.
Features
- Scientific Plotting Frameworks - Renders interactive 2D and 3D scientific visualizations using PyQt or PySide with GPU-accelerated graphics.
- Qt GraphicsView Plots - Displays interactive line, scatter, and image plots using Qt's GraphicsView framework for fast visualization.
- Quick Plot Functions - Provides single-function plot calls for quickly visualizing arrays and data sequences.
- Composable Panel Layouts - Organizes multiple resizable and movable panels within a window for flexible workspace layouts.
- GraphicsView 2D Plot Renderers - Renders 2D plots using Qt's GraphicsView framework for efficient item management and event handling.
- 3D Rendering Engines - Renders interactive 3D scenes using OpenGL for scientific and engineering data visualization.
- Interactive Scientific Plot Constructors - Creates interactive line, scatter, and image plots for real-time exploration of scientific and engineering data.
- OpenGL 3D Pipelines - Ships a GPU-accelerated 3D pipeline using OpenGL for interactive scientific visualization.
- Scientific - Displays interactive 3D surfaces, volume data, and meshes using OpenGL for real-time scientific exploration.
- Scientific Plot Items - Draws interactive plot elements like curves, scatter points, bars, images, and regions on a canvas.
- 3D Surface Visualizations - Renders interactive 3D surfaces, volumes, scatter plots, and meshes using OpenGL for multidimensional data exploration.
- Scientific 3D Items - Displays interactive 3D objects including surfaces, volumes, scatter plots, and meshes.
- 3D Surface and Volume Renderers - Uses OpenGL shaders and buffers for hardware-accelerated rendering of 3D scientific data.
- Real-Time Plot Rendering - Renders streaming time-series data with smooth scrolling for real-time monitoring applications.
- Interactive Plotting Frameworks - Creates fast, interactive 2D scientific plots using PyQt/PySide and NumPy arrays.
- Interactive Plot Exploration Widgets - Provides ready-made GUI components for plotting, image viewing, parameter editing, and data exploration.
- Desktop Plot Widgets - Creates responsive 2D plots within PyQt/PySide applications with pan, zoom, and real-time exploration.
- NumPy Array Integration - Performs numerical operations and data transformations directly on NumPy arrays for accelerated computation.
- Streaming Signal Monitors - Renders streaming time-series data with smooth scrolling for live monitoring applications.
- Composite Plot Types - Combines lines, scatter points, images, and regions on a single plot axis for composite visualizations.
- Time-Series Signal Monitors - Renders streaming time-series data with smooth scrolling for live monitoring applications.
- Plot Pan and Zoom Controls - Enables mouse-driven panning, zooming, and data inspection on plot canvases.
- Plot Mouse Handlers - Captures mouse clicks, drags, and hovers on graphical items for interactive plot exploration.
- Scientific 3D Interactions - Enables rotation, panning, and inspection of 3D surfaces, scatter points, and meshes interactively.
- GUI Widget Toolkits - Embeds plots and graphics into PyQt or PySide application interfaces using reusable widgets.
- Scientific Interface Toolkits - Embeds high-performance plots and graphics into PyQt or PySide applications with dockable panels and custom widgets.
- Docking Layout Systems - Provides a dock widget layout manager for arranging resizable and movable scientific GUI panels.
- Hierarchical Parameter Trees - Provides hierarchical parameter tree controls for organizing and editing configuration parameters.
- Flowchart Editors - Provides a visual flowchart editor for constructing data processing pipelines by connecting nodes.
- Visual Data Exports - Exports plots and graphics to PNG, SVG, and CSV formats for sharing and analysis.
- Notebook Plot Rendering - Renders interactive plots directly within Jupyter notebook cells for exploratory data analysis.
- Dataflow Engines - Implements a node-based dataflow engine for building visual data processing pipelines.
- Image Exports - Saves plot views as PNG, SVG, or raw data arrays for external use and sharing.
- Image and Video Viewports - Displays 2D image and video data with color mapping, normalization, and interactive inspection tools.
- Plot Annotations - Adds text labels, arrows, legends, and region-of-interest selectors directly onto plotted data.
- Scalar Color Mapping - Applies and customizes color lookup tables to map data values to visual colors.
- Scene Graphs - Organizes visual elements as hierarchical items in a scene graph for efficient rendering and hit-testing.
- Scientific Plot Exporters - Saves plots as PNG, SVG, or CSV and converts items to Matplotlib for publication output.
- Event-Driven Signal Systems - Handles user interactions and plot updates through Qt's event-driven signal-slot mechanism.
- Data Selection - Uses click-and-drag controls to highlight, measure, or extract subsets of plotted data.
- Visual Node Editors - Constructs data processing pipelines by connecting nodes in a visual flowchart interface.
- Visual Pipeline Builders - Builds node-based flowcharts to connect data processing steps and visualize results in a graphical interface.
- Data Visualization - High-performance GUI and plotting tools for scientific applications.
- Data Visualization - Fast visualization for scientific and engineering applications.
- Data Visualization and Analytics - Fast visualization and GUI tools for scientific applications.
