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Overview
========
Main features
-------------
:mod:`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.
:mod:`plotpy` is part of the `PlotPyStack`_ project, a software stack for
scientific data visualization containing the following modules:
* :mod:`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.
* :mod:`qwt` (PythonQwt): a Python reimplementation of the Qwt C++ library which
provides a lot of plotting types and a flexible widget class
(:py:class:`qwt.plot.QwtPlot`) which may be used to create custom plotting
widgets.
:mod:`plotpy` is also based on the following Python modules:
* :mod:`numpy`: a Python module providing efficient array manipulation
facilities.
* :mod:`scipy`: a Python module providing scientific computing tools.
The :mod:`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 :mod:`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 is
`matplotlib`. 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 :mod:`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 :mod:`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 of
:mod:`plotpy` is much smaller than `matplotlib`'s one. For example, a 30-MB
image is displayed using 30-MB of memory with `plotpy` whereas `matplotlib`
uses 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 :mod:`plotpy` library provides the following features:
* :mod:`pyplot`: equivalent to :py:mod:`matplotlib.pyplot`, at least for
the implemented functions
* :ref:`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.
* :ref:`plot`:
- 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 :ref:`examples`)
.. _PlotPyStack: https://github.com/PlotPyStack
|
