Overview — PlotPy 2.7 Manual (original) (raw)

Main features¶

plotpy is a Python module providing a set of high-level widgets and functions to ease plotting data with Python and Qt. It can also be seen as a handy toolbox to develop custom scientific or technical data visualization applications using Python and Qt.

plotpy is part of the PlotPyStack project, a software stack for scientific data visualization containing the following modules:

• guidata: a Python library generating graphical user interfaces (GUIs) for easy dataset editing and display. It also provides helpers and application development tools for creating Qt applications.
• qwt (PythonQwt): a Python reimplementation of the Qwt C++ library which provides a lot of plotting types and a flexible widget class (qwt.plot.QwtPlot) which may be used to create custom plotting widgets.

plotpy is also based on the following Python modules:

• numpy: a Python module providing efficient array manipulation facilities.
• scipy: a Python module providing scientific computing tools.

The plotpy library provides the following main features:

• Curve and image ready-to-use plotting widgets and dialog boxes
• Components for custom plotting widgets and Qt applications
• Curve fitting tools
• Interactive plotting tools
• And more…

Advanced features¶

The plotpy library provides the following advanced features.

Curve plotting:

• Curve fitting tools
• Interval selection tools for computing on selected area

Image plotting:

• Contrast adjustment panel
• X-axis and Y-axis cross-sections
• Powerful C++ image transform engine for real-time affine transform, interpolation (including antialiasing) or non-linear X and Y axes

Performance¶

The most popular Python module for data plotting for desktop applications ismatplotlib. It is a very powerful module providing a lot of plotting types and an API (the pylab interface) which is very close to MATLAB’s plotting interface.

Even if plotpy plotting features are quite limited in terms of plot types compared to matplotlib, the currently implemented plot types are much more efficient.

For example, the plotpy image showing features do not make any copy of the displayed data, hence allowing to show images that are much larger than what is possible with matplotlib. Moreover, the memory footprint ofplotpy is much smaller than matplotlib’s one. For example, a 30-MB image is displayed using 30-MB of memory with plotpy whereas matplotlibuses more than 600-MB of memory to display the same image. This is due to the fact that matplotlib makes multiple copies of the original data array: the original array is duplicated four times using 64-bits float data types.

Detailed features¶

The plotpy library provides the following features:

• pyplot: equivalent to matplotlib.pyplot, at least for the implemented functions
• Plot items: 1D histogram, curve, error bar curve, image, label, shapes (polygon, polyline, rectangle, circle, ellipse, segment, etc.), annotations (shapes with labels showing position and dimensions), markers, etc.
• Plot widgets:
  • ready-to-use plotting widgets (widget, dialog box, window)
  • multiple curve/image selection for moving curves or editing their properties through automatically generated dialog boxes
  • item list panel: move curves/images from foreground to background, show/hide curves/images, remove curves/images, …
  • specific curve features:
    * interval selection tools with labels showing results of computing on selected area
    * curve fitting tool with automatic fit, manual fit with sliders, …
  • specific image features:
    * contrast adjustment panel: select the LUT by moving a range selection object on the image levels histogram, eliminate outliers, …
    * X-axis and Y-axis cross-sections: support for multiple images, average cross-section tool on a rectangular area, …
    * apply any affine transform to displayed images in real-time (rotation, magnification, translation, horizontal/vertical flip, …)
    * ready-to-use tools (plot canvas export to image file, image snapshot, image rectangular filter, …)
• application development helpers:
  • ready-to-use curve and image plot widgets, dialog boxes and windows
  • graphical objects (curves, images, shapes) serialization/deserialization to/from .ini, .h5 or .json files
  • a lot of test scripts (see Examples)