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Python and Matplotlib are cross-platform, and are therefore available for Windows, OS X, and the Unix-like operating systems like Linux and FreeBSD. Matplotlib can create plots in a variety of output formats, such as PNG and SVG. Matplotlib mainly does 2-D plots (such as line, contour, bar, scatter, etc.), but 3-D functionality is also available.
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
It's a free compiler, though it also has commercial add-ons (e.g. for hiding source code). Numba is used from Python, as a tool (enabled by adding a decorator to relevant Python code), a JIT compiler that translates a subset of Python and NumPy code into fast machine code. Pythran compiles a subset of Python 3 to C++ . [165]
Core Python Programming is a textbook on the Python programming language, written by Wesley J. Chun. The first edition of the book was released on December 14, 2000. [1] The second edition was released several years later on September 18, 2006. [2] Core Python Programming is mainly targeted at higher education students and IT professionals. [3]
Hunter initially developed Matplotlib during his postdoctoral research in neurobiology to visualize electrocorticography (ECoG) data of epilepsy patients. [4] The open-source tool emerged as the most widely used plotting library for the Python programming language and a core component of the scientific Python stack, along with NumPy, SciPy and IPython. [6]
The code intelligence features speed up editing, facilitated navigation through code, and inspected code for errors. These features rely both on static analysis of Python code found in the project and on the Python Path and runtime analysis of code whenever the debugger is active or the code is active in the integrated Python Shell.
The data shown is a random sample of 10,000 points from a normal distribution with a mean of 0 and a standard deviation of 1. The data used to construct a histogram are generated via a function m i that counts the number of observations that fall into each of the disjoint categories (known as bins ).
Michael Abrash's Graphics Programming Black Book Special Edition: Chapter 35: Bresenham Is Fast, and Fast Is Good; The Bresenham Line-Drawing Algorithm by Colin Flanagan; National Institute of Standards and Technology page on Bresenham's algorithm; Calcomp 563 Incremental Plotter Information; Bresenham Algorithm in several programming languages