What’s new in matplotlib — Matplotlib 2.0.0rc2 documentation
Improved color conversion API and RGBA support ¶ The colors gained a new color conversion API with full support for the alpha channel. The main public functions are is_color_like() , matplotlib.colors.to_rgba() , matplotlib.colors.to_rgba_array() and to_hex() . RGBA quadruplets are encoded in hex format as #rrggbbaa . A side benefit is that the Qt options editor now allows setting the alpha channe
images_contours_and_fields example code: interpolation_methods.py — Matplotlib 2.0.2 documentation
''' Show all different interpolation methods for imshow ''' import matplotlib.pyplot as plt import numpy as np # from the docs: # If interpolation is None, default to rc image.interpolation. See also # the filternorm and filterrad parameters. If interpolation is 'none', then # no interpolation is performed on the Agg, ps and pdf backends. Other # backends will fall back to 'nearest'. # # http://ma
Working with Matplotlib on OSX — Matplotlib 3.1.0 documentation
Working with Matplotlib on OSX¶ On OSX, two different types of Python builds exist: a regular build and a framework build. In order to interact correctly with OSX through the native GUI frameworks, you need a framework build of Python. At the time of writing the macosx and WXAgg backends require a framework build to function correctly. This can result in issues for a Python installation not build
shapes_and_collections example code: artist_reference.py — Matplotlib 2.0.2 documentation
You are reading an old version of the documentation (v2.0.2). For the latest version see https://matplotlib.org/stable/ """ Reference for matplotlib artists This example displays several of matplotlib's graphics primitives (artists) drawn using matplotlib API. A full list of artists and the documentation is available at http://matplotlib.org/api/artist_api.html. Copyright (c) 2010, Bartosz Telencz
pylab_examples example code: boxplot_demo.py — Matplotlib 2.0.2 documentation
import matplotlib.pyplot as plt import numpy as np # fake up some data spread = np.random.rand(50) * 100 center = np.ones(25) * 50 flier_high = np.random.rand(10) * 100 + 100 flier_low = np.random.rand(10) * -100 data = np.concatenate((spread, center, flier_high, flier_low), 0) # basic plot plt.boxplot(data) # notched plot plt.figure() plt.boxplot(data, 1) # change outlier point symbols plt.figure
axes_grid example code: demo_parasite_axes2.py — Matplotlib 2.0.2 documentation
""" Parasite axis demo The following code is an example of a parasite axis. It aims to show a user how to plot multiple different values onto one single plot. Notice how in this example, par1 and par2 are both calling twinx meaning both are tied directly to the x-axis. From there, each of those two axis can behave separately from the each other, meaning they can take on separate values from themse
Text introduction — Matplotlib 2.0.2 documentation
Text introduction¶ matplotlib has extensive text support, including support for mathematical expressions, truetype support for raster and vector outputs, newline separated text with arbitrary rotations, and unicode support. Because it embeds fonts directly in output documents, e.g., for postscript or PDF, what you see on the screen is what you get in the hardcopy. FreeType support produces very ni
matplotlib.axes.Axes.plot — Matplotlib 3.10.3 documentation
matplotlib.axes.Axes.plot# Axes.plot(*args, scalex=True, scaley=True, data=None, **kwargs)[source]# Plot y versus x as lines and/or markers. Call signatures: plot([x], y, [fmt], *, data=None, **kwargs) plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) The coordinates of the points or line nodes are given by x, y. The optional parameter fmt is a convenient way for defining basic formatting like
Usage — Matplotlib 2.0.2 documentation
General Concepts¶ matplotlib has an extensive codebase that can be daunting to many new users. However, most of matplotlib can be understood with a fairly simple conceptual framework and knowledge of a few important points. Plotting requires action on a range of levels, from the most general (e.g., ‘contour this 2-D array’) to the most specific (e.g., ‘color this screen pixel red’). The purpose of
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Examples — Matplotlib 3.10.3 documentation
https://matplotlib.org/stable/api/patches_api.html
https://matplotlib.org/stable/api/ticker_api.html
https://matplotlib.org/stable/api/legend_api.html