A software tool that provides a Python interface to the GNUPlot program, allowing for data visualization and graphing capabilities.
If you are looking for a Python package that offers a simple but effective way to interface with gnuplot, then gnuplot-py is an excellent choice. This open-source plotting program is incredibly popular, and with the help of gnuplot-py, you can use gnuplot from within Python to plot arrays of data from memory, data files, or mathematical functions. If you use Python for computations or as a "glue" for numerical programs, then this package can be immensely useful in plotting data on the fly as they are computed.
One of the biggest advantages of using gnuplot-py is its ease of use when it comes to automation. You can create crude "animations" by plotting different datasets one after another, making it ideal for data visualization. Commands are communicated to gnuplot via a pipe, and data is sent either through the same pipe as "inline" data or through FIFOs (named pipes) or temporary files, depending on the circumstances.
This package has been developed under Unix and is compatible with various forms of Unix (including Linux), MS Windows, MacOS, and Mac OS X, making it accessible to a broad range of users. There is now preliminary support for running Gnuplot.py under Jython/Java, which adds even more versatility and flexibility to this already powerful package.
Gnuplot-py offers an object-oriented design that allows you to set plot options and run multiple gnuplot sessions concurrently. If you're feeling more ambitious, it's easy to add entirely new types of plottable items by deriving from the 'PlotItem' class, giving you even greater control over your plots.
In addition to its many other features, gnuplot-py includes a demonstration that you can run with a simple 'python demo.py' command. This demonstration gives you a taste of what the package can do and helps you get started with using it in your own projects. Overall, gnuplot-py is an excellent tool that should be part of any Python programmer's toolkit.
One of the biggest advantages of using gnuplot-py is its ease of use when it comes to automation. You can create crude "animations" by plotting different datasets one after another, making it ideal for data visualization. Commands are communicated to gnuplot via a pipe, and data is sent either through the same pipe as "inline" data or through FIFOs (named pipes) or temporary files, depending on the circumstances.
This package has been developed under Unix and is compatible with various forms of Unix (including Linux), MS Windows, MacOS, and Mac OS X, making it accessible to a broad range of users. There is now preliminary support for running Gnuplot.py under Jython/Java, which adds even more versatility and flexibility to this already powerful package.
Gnuplot-py offers an object-oriented design that allows you to set plot options and run multiple gnuplot sessions concurrently. If you're feeling more ambitious, it's easy to add entirely new types of plottable items by deriving from the 'PlotItem' class, giving you even greater control over your plots.
In addition to its many other features, gnuplot-py includes a demonstration that you can run with a simple 'python demo.py' command. This demonstration gives you a taste of what the package can do and helps you get started with using it in your own projects. Overall, gnuplot-py is an excellent tool that should be part of any Python programmer's toolkit.
What's New
Version 1.8: N/A