PyQwt: A Beginner’s Guide to Scientific Plotting in PythonPyQwt is a set of Python bindings and widgets that extend the Qwt C++ library (Qt Widgets for Technical Applications) to provide high-quality, interactive plotting and data-visualization components inside Qt-based applications. Although newer plotting ecosystems (Matplotlib, Plotly, PyQtGraph) are more commonly used today, PyQwt remains relevant for developers maintaining legacy PyQt applications or needing Qt-integrated, widget-based plotting with extensive customization.
This guide introduces PyQwt’s core concepts, shows how to create common scientific plots, explains interactive features (zooming, panning, picking), and offers practical tips for styling, performance, and migrating to modern alternatives when appropriate.
What PyQwt is best for
- Embedding interactive plots directly inside Qt/PyQt applications where plots are widgets rather than separate figure windows.
- Highly customizable technical plots with fine control over axes, scales, markers, and custom item drawing.
- Applications that require fast redrawing of many small graphical elements using Qwt’s efficient drawing model.
- Integrating with existing Qt-based GUI workflows, signals/slots, and event handling.
Environment and installation notes
PyQwt historically binds to PyQt4 and PyQt5 depending on version. Because PyQwt is not as actively maintained as some alternatives, installation can require matching compatible versions of Qt, SIP, and PyQt. Common approaches:
- Use system packages or prebuilt wheels when available for your OS and Python version.
- Consider building from source against your installed Qt/PyQt if binary wheels are unavailable.
- If starting a new project, evaluate PyQtGraph, Matplotlib (with Qt backends), or VisPy, which have more modern support and easier installation.
Basic concepts and architecture
PyQwt follows Qwt’s object model. Important concepts:
- QwtPlot: main plotting widget that contains scales, canvas, and a legend.
- QwtPlotCurve / QwtPlotItem: represent data series you can add to a plot.
- QwtScaleDraw / QwtScaleEngine: control tick labeling, scale transformations, and formatting.
- QwtPlotMarker, QwtPlotSymbol: add markers, reference lines, and styled points.
- QwtPlotZoomer / QwtPlotPanner / QwtPicker: interactive tools for zooming, panning, and selecting points.
- Layered drawing: items are drawn in a painter-friendly sequence; you can subclass items for custom rendering.
Minimal example: a line plot
Below is a compact example showing how to create a basic line plot inside a PyQt application. (Adjust imports for your PyQt/PyQwt versions; this is representative code.)
# example.py import sys import numpy as np from PyQt5.QtWidgets import QApplication, QMainWindow from PyQt5.QtCore import Qt from PyQt5 import QtGui from qwt import QwtPlot, QwtPlotCurve, QwtSymbol class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("PyQwt Line Plot Example") self.resize(800, 600) plot = QwtPlot(self) self.setCentralWidget(plot) # generate data x = np.linspace(0, 2 * np.pi, 200) y = np.sin(x) # create a curve curve = QwtPlotCurve("Sine") curve.setData(x, y) symbol = QwtSymbol(QwtSymbol.Ellipse, QtGui.QBrush(Qt.blue), pen=None, size=6) curve.setSymbol(symbol) curve.attach(plot) plot.setAxisTitle(QwtPlot.xBottom, "x") plot.setAxisTitle(QwtPlot.yLeft, "sin(x)") if __name__ == "__main__": app = QApplication(sys.argv) w = MainWindow() w.show() sys.exit(app.exec_()) Notes:
- setData accepts sequences or numpy arrays for x and y.
- Attaching the curve to the plot makes it managed and displayed by QwtPlot.
- Symbols and pens can style points and lines.
Styling plots: axes, scales, and legends
- Axis titles: plot.setAxisTitle(axis, “label”)
- Axis scales and tick formatting: subclass QwtScaleDraw or set scale engine parameters to control logarithmic scales, step sizes, and precision.
- Gridlines: use QwtPlotGrid to add major/minor gridlines and style with pens/dash patterns.
- Legends: QwtPlotLegendItem or QwtLegend can be added and positioned; items typically show the curve titles.
Example: add a grid and legend (pseudo-code):
grid = QwtPlotGrid() grid.attach(plot) grid.setMajPen(QtGui.QPen(Qt.lightGray, 1, Qt.DotLine)) legend = QwtLegend() plot.insertLegend(legend, QwtPlot.RightLegend) Interactivity: zoom, pan, and pick
- QwtPlotZoomer: rubber-band zooming with configurable max/min scales.
- QwtPlotPanner: mouse-driven panning (typically middle-button drag).
- QwtPlotPicker: capture clicks and report coordinates; can be used to implement data point selection.
Typical pattern:
- Create a zoomer attached to the plot canvas and set its mouse button masks.
- Add a panner for middle-button dragging.
- Use a picker to connect to a slot that receives selected positions.
Example setup (conceptual):
zoomer = QwtPlotZoomer(plot.canvas()) zoomer.setMousePattern(QwtPlotZoomer.MouseSelection, Qt.LeftButton) panner = QwtPlotPanner(plot.canvas()) panner.setMouseButton(Qt.MiddleButton) picker = QwtPlotPicker(plot.canvas()) picker.setMousePattern(QwtPlotPicker.MouseSelect, Qt.RightButton) picker.selected.connect(self.on_pick) Multiple curves and overlays
You can attach as many QwtPlotCurve instances as needed. For overlays like error bars, use QwtPlotErrorBars or draw custom items by subclassing QwtPlotItem and implementing draw(). Layers and z-order are controllable when attaching items.
Performance tip: for many thousands of points prefer using QwtPlotDirectPainter or draw into an offscreen pixmap and blit; excessive per-point symbol rendering can be slow.
Custom items and annotations
Subclass QwtPlotItem for custom renderers. Implement item’s boundingRect() and draw(painter, xmap, ymap, canvasRect) to integrate properly with Qwt’s redraw logic.
Use QwtText and QwtTextLabel for formatted annotations, and QwtPlotMarker for reference lines/boxes.
Real-time plotting
For live data (e.g., instrument readouts), update curve data and call replot() minimally. Strategies:
- Update only changed curves or regions.
- Use a QTimer or background thread to collect data; push updates to the GUI thread via signals.
- For very high update rates, accumulate data and refresh at a lower UI rate (e.g., 20–60 Hz).
Example pattern:
curve.setData(x_new, y_new) plot.replot() If plotting thousands of points at high frequency, consider using QwtPlotDirectPainter or drawing to a QPixmap to avoid stutter.
Exporting figures
QwtPlot can render to image formats (PNG, JPEG) via QPixmap or QImage rendering of the plot canvas. For vector output (PDF/SVG), render via QPrinter or export to an SVG/QPdfWriter, depending on Qt bindings available.
Example:
pixmap = QPixmap(plot.size()) plot.render(pixmap) pixmap.save("plot.png") For publication-quality vector output, ensure fonts and line widths are set appropriately when exporting through QPainter-backed writers.
Troubleshooting common issues
- Import errors: ensure PyQt, SIP, and PyQwt versions are compatible.
- Crashes on draw: confirm that custom items implement required methods and respect Qwt’s painter state.
- Slow redraws: reduce symbol complexity, batch updates, or use direct painting.
- Inconsistent axis scaling: check autoscaling settings and ensure scale engines are configured before attaching curves.
When to migrate away from PyQwt
Consider migrating if you need:
- Easier installation and wider community support.
- Web/Notebook-friendly plotting (Plotly, Bokeh).
- Better performance for extremely large datasets or GPU acceleration (VisPy).
- Tight integration with non-Qt GUIs.
Migration path: export data and recreate plots in Matplotlib/PyQtGraph/Plotly. For Qt-based apps, PyQtGraph often has the most straightforward replacement because it provides Qt widgets with high-performance plotting and modern features.
Example: converting a simple PyQwt plot to PyQtGraph
PyQtGraph example (equivalent sine plot):
import sys, numpy as np from PyQt5.QtWidgets import QApplication, QMainWindow import pyqtgraph as pg app = QApplication(sys.argv) win = pg.GraphicsLayoutWidget(show=True, title="Sine Example") plot = win.addPlot(title="sin(x)") x = np.linspace(0, 2*np.pi, 200) y = np.sin(x) plot.plot(x, y, pen='b', symbol='o', symbolSize=6) if __name__ == '__main__': sys.exit(app.exec_()) PyQtGraph installs more easily and provides efficient real-time plotting.
Summary
PyQwt is a powerful option when you need Qt-integrated, widget-based plotting with deep customization. For new projects, evaluate modern alternatives for easier installation and broader support, but for maintaining existing Qt/PyQt applications PyQwt remains a capable solution. Use efficient drawing strategies, the built-in interactive tools (zoomer, panner, picker), and subclassing for custom visuals to build robust scientific plotting applications.
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