You are reading an old version of the documentation (v2.0.2). For the latest version see https://matplotlib.org/stable/
pylab_examples example code: arrow_simple_demo.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/
statistics example code: histogram_demo_cumulative.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/ """ ========================================================== Demo of using histograms to plot a cumulative distribution ========================================================== This shows how to plot a cumulative, normalized histogram as a step function in order to visualize
pylab_examples example code: contourf_demo.py — Matplotlib 2.0.2 documentation
import numpy as np import matplotlib.pyplot as plt origin = 'lower' #origin = 'upper' delta = 0.025 x = y = np.arange(-3.0, 3.01, delta) X, Y = np.meshgrid(x, y) Z1 = plt.mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) Z2 = plt.mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) Z = 10 * (Z1 - Z2) nr, nc = Z.shape # put NaNs in one corner: Z[-nr//6:, -nc//6:] = np.nan # contourf will convert these to mask
pylab_examples example code: colorbar_tick_labelling_demo.py — Matplotlib 2.0.2 documentation
"""Produce custom labelling for a colorbar. Contributed by Scott Sinclair """ import matplotlib.pyplot as plt import numpy as np from matplotlib import cm from numpy.random import randn # Make plot with vertical (default) colorbar fig, ax = plt.subplots() data = np.clip(randn(250, 250), -1, 1) cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm) ax.set_title('Gaussian noise with vertic
api example code: colorbar_only.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/ ''' ==================== Customized colorbars ==================== This example shows how to build colorbars without an attached mappable. ''' import matplotlib.pyplot as plt import matplotlib as mpl # Make a figure and axes with dimensions as desired. fig = plt.figure(figsize=(8
Matplotlib Examples — 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/
color example code: colormaps_reference.py — Matplotlib 2.0.2 documentation
""" ================== Colormap reference ================== Reference for colormaps included with Matplotlib. This reference example shows all colormaps included with Matplotlib. Note that any colormap listed here can be reversed by appending "_r" (e.g., "pink_r"). These colormaps are divided into the following categories: Sequential: These colormaps are approximately monochromatic colormaps vary
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
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
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