Python3-Matplotlib

参考：

1、http://www.scipy-lectures.org/intro/matplotlib/index.html

2、http://matplotlib.org/tutorials/index.html

1.4. Matplotlib: plotting

Chapter contents

• Introduction
• Simple plot
• Figures, Subplots, Axes and Ticks
• Other Types of Plots: examples and exercises
• Beyond this tutorial
• Quick references
• Full code examples

1.4.1. Introduction

For interactive matplotlib sessions, turn on the matplotlib mode

IPython console:
	When using the IPython console, use: In [1]: %matplotlib
Jupyter notebook:
	In the notebook, insert, at the beginning of the notebook the following magic: %matplotlib inline

1.4.1.2. pyplot

from matplotlib import pyplot as plt

1.4.2. Simple plot

import numpy as np

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

1.4.2.1. Plotting with default settings

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

plt.plot(X, C)
plt.plot(X, S)

plt.show()

Hint

 

Documentation

• plot tutorial
• plot() command

1.4.2.2. Instantiating defaults

import numpy as np
import matplotlib.pyplot as plt

# Create a figure of size 8x6 inches, 80 dots per inch
plt.figure(figsize=(8, 6), dpi=80)

# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

# Plot cosine with a blue continuous line of width 1 (pixels)
plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# Plot sine with a green continuous line of width 1 (pixels)
plt.plot(X, S, color="green", linewidth=1.0, linestyle="-")

# Set x limits
plt.xlim(-4.0, 4.0)

# Set x ticks
plt.xticks(np.linspace(-4, 4, 9, endpoint=True))

# Set y limits
plt.ylim(-1.0, 1.0)

# Set y ticks
plt.yticks(np.linspace(-1, 1, 5, endpoint=True))

# Save figure using 72 dots per inch
# plt.savefig("exercise_2.png", dpi=72)

# Show result on screen
plt.show()

Hint

 

Documentation

• Customizing matplotlib

1.4.2.3. Changing colors and line widths

...
plt.figure(figsize=(10, 6), dpi=80)
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-")
...

Hint

 

Documentation

• Controlling line properties
• Line API

1.4.2.4. Setting limits

...
plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.ylim(C.min() * 1.1, C.max() * 1.1)
...

Hint

 

Documentation

• xlim() command
• ylim() command

1.4.2.5. Setting ticks

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
plt.yticks([-1, 0, +1])
...

Hint

 

Documentation

• xticks() command
• yticks() command
• Tick container
• Tick locating and formatting

1.4.2.6. Setting tick labels

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.yticks([-1, 0, +1],
          [r'$-1$', r'$0$', r'$+1$'])
...

Hint

 

Documentation

• Working with text
• xticks() command
• yticks() command
• set_xticklabels()
• set_yticklabels()

1.4.2.7. Moving spines

...
ax = plt.gca()  # gca stands for 'get current axis'
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
...

Hint

 

Documentation

• Spines
• Axis container
• Transformations tutorial

1.4.2.8. Adding a legend

...
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")

plt.legend(loc='upper left')
...

Hint

 

Documentation

• Legend guide
• legend() command
• Legend API

1.4.2.9. Annotate some points

...

t = 2 * np.pi / 3
plt.plot([t, t], [0, np.cos(t)], color='blue', linewidth=2.5, linestyle="--")
plt.scatter([t, ], [np.cos(t), ], 50, color='blue')

plt.annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$',
             xy=(t, np.cos(t)), xycoords='data',
             xytext=(-90, -50), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t, t],[0, np.sin(t)], color='red', linewidth=2.5, linestyle="--")
plt.scatter([t, ],[np.sin(t), ], 50, color='red')

plt.annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
             xy=(t, np.sin(t)), xycoords='data',
             xytext=(+10, +30), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))
...

Hint

 

Documentation

• Annotating axis
• annotate() command

1.4.2.10. Devil is in the details

...
for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))
...

Hint

 

Documentation

• Artists
• BBox

1.4.3. Figures, Subplots, Axes and Ticks

1.4.3.1. Figures

. There are several parameters that determine what the figure looks like:

Argument	Default	Description
num	1	number of figure
figsize	figure.figsize	figure size in inches (width, height)
dpi	figure.dpi	resolution in dots per inch
facecolor	figure.facecolor	color of the drawing background
edgecolor	figure.edgecolor	color of edge around the drawing background
frameon	True	draw figure frame or not

plt.close(1)     # Closes figure 1

1.4.3.2. Subplots

With subplot you can arrange plots in a regular grid. You need to specify the number of rows and columns and the number of the plot. Note that thegridspec command is a more powerful alternative.

     

1.4.3.3. Axes

 

1.4.3.4. Ticks

Tick Locators

Tick locators control the positions of the ticks. They are set as follows:

ax = plt.gca()
ax.xaxis.set_major_locator(eval(locator))

There are several locators for different kind of requirements:

All of these locators derive from the base classmatplotlib.ticker.Locator. You can make your own locator deriving from it. Handling dates as ticks can be especially tricky. Therefore, matplotlib provides special locators in matplotlib.dates.

1.4.4. Other Types of Plots: examples and exercises

1.4.4.1. Regular Plots

n = 256
X = np.linspace(-np.pi, np.pi, n, endpoint=True)
Y = np.sin(2 * X)

plt.plot(X, Y + 1, color='blue', alpha=1.00)
plt.plot(X, Y - 1, color='blue', alpha=1.00)

Hint

 

You need to use the fill_between command.

1.4.4.2. Scatter Plots

n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)

plt.scatter(X,Y)

Hint

 

Color is given by angle of (X,Y).

1.4.4.3. Bar Plots

n = 12
X = np.arange(n)
Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)

plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x, y in zip(X, Y1):
    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')

plt.ylim(-1.25, +1.25)

Hint

 

You need to take care of text alignment.

1.4.4.4. Contour Plots

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 -y ** 2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X, Y = np.meshgrid(x, y)

plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap='jet')
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)

Hint

 

You need to use the clabelcommand.

1.4.4.5. Imshow

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)

n = 10
x = np.linspace(-3, 3, 4 * n)
y = np.linspace(-3, 3, 3 * n)
X, Y = np.meshgrid(x, y)
plt.imshow(f(X, Y))

Hint

 

You need to take care of the origin of the image in the imshow command and use a colorbar

1.4.4.6. Pie Charts

Z = np.random.uniform(0, 1, 20)
plt.pie(Z)

Hint

 

You need to modify Z.

1.4.4.7. Quiver Plots

n = 8
X, Y = np.mgrid[0:n, 0:n]
plt.quiver(X, Y)

Hint

 

You need to draw arrows twice.

1.4.4.8. Grids

axes = plt.gca()
axes.set_xlim(0, 4)
axes.set_ylim(0, 3)
axes.set_xticklabels([])
axes.set_yticklabels([])

1.4.4.9. Multi Plots

plt.subplot(2, 2, 1)
plt.subplot(2, 2, 3)
plt.subplot(2, 2, 4)

Hint

 

You can use several subplots with different partition.

1.4.4.10. Polar Axis

plt.axes([0, 0, 1, 1])

N = 20
theta = np.arange(0., 2 * np.pi, 2 * np.pi / N)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r, bar in zip(radii, bars):
    bar.set_facecolor(cm.jet(r / 10.))
    bar.set_alpha(0.5)

Hint

 

You only need to modify theaxes line

1.4.4.11. 3D Plots

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')

Hint

 

You need to use contourf

See also

 

3D plotting with Mayavi

1.4.4.12. Text

Hint

 

Have a look at thematplotlib logo.

Quick read

If you want to do a first quick pass through the Scipy lectures to learn the ecosystem, you can directly skip to the next chapter: Scipy : high-level scientific computing.

The remainder of this chapter is not necessary to follow the rest of the intro part. But be sure to come back and finish this chapter later.

1.4.5. Beyond this tutorial

1.4.5.1. Tutorials

• Pyplot tutorial
  • Introduction
  • Controlling line properties
  • Working with multiple figures and axes
  • Working with text
• Image tutorial
  • Startup commands
  • Importing image data into Numpy arrays
  • Plotting numpy arrays as images
• Text tutorial
  • Text introduction
  • Basic text commands
  • Text properties and layout
  • Writing mathematical expressions
  • Text rendering With LaTeX
  • Annotating text

• Artist tutorial
  • Introduction
  • Customizing your objects
  • Object containers
  • Figure container
  • Axes container
  • Axis containers
  • Tick containers
• Path tutorial
  • Introduction
  • Bézier example
  • Compound paths
• Transforms tutorial
  • Introduction
  • Data coordinates
  • Axes coordinates
  • Blended transformations
  • Using offset transforms to create a shadow effect
  • The transformation pipeline

1.4.5.2. Matplotlib documentation

User guide FAQ Installation Usage How-To Troubleshooting Environment Variables

1.4.5.3. Code documentation

import matplotlib.pyplot as plt
>>> help(plt.plot)    
Help on function plot in module matplotlib.pyplot:

plot(*args, **kwargs)
   Plot lines and/or markers to the
   :class:`~matplotlib.axes.Axes`.  *args* is a variable length
   argument, allowing for multiple *x*, *y* pairs with an
   optional format string.  For example, each of the following is
   legal::

       plot(x, y)         # plot x and y using default line style and color
       plot(x, y, 'bo')   # plot x and y using blue circle markers
       plot(y)            # plot y using x as index array 0..N-1
       plot(y, 'r+')      # ditto, but with red plusses

   If *x* and/or *y* is 2-dimensional, then the corresponding columns
   will be plotted.
...

1.4.5.4. Galleries

The matplotlib gallery is also incredibly useful when you search how to render a given graphic. Each example comes with its source.

1.4.5.5. Mailing lists

Finally, there is a user mailing list where you can ask for help and adevelopers mailing list that is more technical.

1.4.6. Quick references

1.4.6.1. Line properties

Property	Description	Appearance
alpha (or a)	alpha transparency on 0-1 scale
antialiased	True or False - use antialised rendering
color (or c)	matplotlib color arg
linestyle (or ls)	see Line properties
linewidth (or lw)	float, the line width in points
solid_capstyle	Cap style for solid lines
solid_joinstyle	Join style for solid lines
dash_capstyle	Cap style for dashes
dash_joinstyle	Join style for dashes
marker	see Markers
markeredgewidth (mew)	line width around the marker symbol
markeredgecolor (mec)	edge color if a marker is used
markerfacecolor (mfc)	face color if a marker is used
markersize (ms)	size of the marker in points

1.4.6.2. Line styles

1.4.6.3. Markers

1.4.6.4. Colormaps

1.4.7. Full code examples

1.4.7.1. Code samples for Matplotlib

Pie chart

A simple pie chart example with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 20
Z = np.ones(n)
Z[-1] *= 2

plt.axes([0.025, 0.025, 0.95, 0.95])

plt.pie(Z, explode=Z*.05, colors = ['%f' % (i/float(n)) for i in range(n)])
plt.axis('equal')
plt.xticks(())
plt.yticks()

plt.show()

Plotting a scatter of points

A simple example showing how to plot a scatter of points with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 1024
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)
T = np.arctan2(Y, X)

plt.axes([0.025, 0.025, 0.95, 0.95])
plt.scatter(X, Y, s=75, c=T, alpha=.5)

plt.xlim(-1.5, 1.5)
plt.xticks(())
plt.ylim(-1.5, 1.5)
plt.yticks(())

plt.show()

A simple, good-looking plot

Demoing some simple features of matplotlib

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(5, 4), dpi=72)
axes = fig.add_axes([0.01, 0.01, .98, 0.98])
X = np.linspace(0, 2, 200, endpoint=True)
Y = np.sin(2*np.pi*X)
plt.plot(X, Y, lw=2)
plt.ylim(-1.1, 1.1)
plt.grid()

plt.show()

Subplots

Show multiple subplots in matplotlib.

import matplotlib.pyplot as plt

fig = plt.figure()
fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)

plt.subplot(2, 1, 1)
plt.xticks(()), plt.yticks(())

plt.subplot(2, 3, 4)
plt.xticks(())
plt.yticks(())

plt.subplot(2, 3, 5)
plt.xticks(())
plt.yticks(())

plt.subplot(2, 3, 6)
plt.xticks(())
plt.yticks(())

plt.show()

Simple axes example

This example shows a couple of simple usage of axes.

import matplotlib.pyplot as plt

plt.axes([.1, .1, .8, .8])
plt.xticks(())
plt.yticks(())
plt.text(.6, .6, 'axes([0.1, 0.1, .8, .8])', ha='center', va='center',
        size=20, alpha=.5)

plt.axes([.2, .2, .3, .3])
plt.xticks(())
plt.yticks(())
plt.text(.5, .5, 'axes([0.2, 0.2, .3, .3])', ha='center', va='center',
        size=16, alpha=.5)

plt.show()

A simple plotting example

A plotting example with a few simple tweaks

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(5, 4), dpi=72)
axes = fig.add_axes([0.01, 0.01, .98, 0.98])
x = np.linspace(0, 2, 200, endpoint=True)
y = np.sin(2 * np.pi * x)
plt.plot(x, y, lw=.25, c='k')
plt.xticks(np.arange(0.0, 2.0, 0.1))
plt.yticks(np.arange(-1.0, 1.0, 0.1))
plt.grid()
plt.show()

Horizontal arrangement of subplots

An example showing horizontal arrangement of subplots with matplotlib.

import matplotlib.pyplot as plt

plt.figure(figsize=(6, 4))
plt.subplot(2, 1, 1)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(2,1,1)', ha='center', va='center',
        size=24, alpha=.5)

plt.subplot(2, 1, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(2,1,2)', ha='center', va='center',
        size=24, alpha=.5)

plt.tight_layout()
plt.show()

Subplot plot arrangement vertical

An example showing vertical arrangement of subplots with matplotlib.

import matplotlib.pyplot as plt


plt.figure(figsize=(6, 4))
plt.subplot(1, 2, 1)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(1,2,1)', ha='center', va='center',
        size=24, alpha=.5)

plt.subplot(1, 2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(1,2,2)', ha='center', va='center',
        size=24, alpha=.5)

plt.tight_layout()
plt.show()

3D plotting

A simple example of 3D plotting.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
ax.set_zlim(-2, 2)

plt.show()

Imshow elaborate

An example demoing imshow and styling the figure.

import numpy as np
import matplotlib.pyplot as plt

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3 ) * np.exp(-x ** 2 - y ** 2)

n = 10
x = np.linspace(-3, 3, 3.5 * n)
y = np.linspace(-3, 3, 3.0 * n)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

plt.axes([0.025, 0.025, 0.95, 0.95])
plt.imshow(Z, interpolation='nearest', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)

plt.xticks(())
plt.yticks(())
plt.show()

Plotting a vector field: quiver

A simple example showing how to plot a vector field (quiver) with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 8
X, Y = np.mgrid[0:n, 0:n]
T = np.arctan2(Y - n / 2., X - n/2.)
R = 10 + np.sqrt((Y - n / 2.0) ** 2 + (X - n / 2.0) ** 2)
U, V = R * np.cos(T), R * np.sin(T)

plt.axes([0.025, 0.025, 0.95, 0.95])
plt.quiver(X, Y, U, V, R, alpha=.5)
plt.quiver(X, Y, U, V, edgecolor='k', facecolor='None', linewidth=.5)

plt.xlim(-1, n)
plt.xticks(())
plt.ylim(-1, n)
plt.yticks(())

plt.show()

Plotting in polar coordinnates

A simple example showing how to plot in polar coordinnates with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes([0.025, 0.025, 0.95, 0.95], polar=True)

N = 20
theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / N)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r,bar in zip(radii, bars):
    bar.set_facecolor(plt.cm.jet(r/10.))
    bar.set_alpha(0.5)

ax.set_xticklabels([])
ax.set_yticklabels([])
plt.show()

Displaying the contours of a function

An example showing how to display the contours of a function with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

def f(x,y):
    return (1 - x / 2 + x**5 + y**3) * np.exp(-x**2 -y**2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X,Y = np.meshgrid(x, y)

plt.axes([0.025, 0.025, 0.95, 0.95])

plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap=plt.cm.hot)
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)
plt.clabel(C, inline=1, fontsize=10)

plt.xticks(())
plt.yticks(())
plt.show()

A example of plotting not quite right

An “ugly” example of plotting.

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

matplotlib.rc('grid', color='black', linestyle='-', linewidth=1)

fig = plt.figure(figsize=(5,4),dpi=72)
axes = fig.add_axes([0.01, 0.01, .98, 0.98], axisbg='.75')
X = np.linspace(0, 2, 40, endpoint=True)
Y = np.sin(2 * np.pi * X)
plt.plot(X, Y, lw=.05, c='b', antialiased=False)

plt.xticks(())
plt.yticks(np.arange(-1., 1., 0.2))
plt.grid()
ax = plt.gca()

plt.show()

Plot and filled plots

Simple example of plots and filling between them with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 256
X = np.linspace(-np.pi, np.pi, n, endpoint=True)
Y = np.sin(2 * X)

plt.axes([0.025, 0.025, 0.95, 0.95])

plt.plot(X, Y + 1, color='blue', alpha=1.00)
plt.fill_between(X, 1, Y + 1, color='blue', alpha=.25)

plt.plot(X, Y - 1, color='blue', alpha=1.00)
plt.fill_between(X, -1, Y - 1, (Y - 1) > -1, color='blue', alpha=.25)
plt.fill_between(X, -1, Y - 1, (Y - 1) < -1, color='red',  alpha=.25)

plt.xlim(-np.pi, np.pi)
plt.xticks(())
plt.ylim(-2.5, 2.5)
plt.yticks(())

plt.show()

Bar plots

An example of bar plots with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 12
X = np.arange(n)
Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)

plt.axes([0.025, 0.025, 0.95, 0.95])
plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x, y in zip(X, Y1):
    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va= 'bottom')

for x, y in zip(X, Y2):
    plt.text(x + 0.4, -y - 0.05, '%.2f' % y, ha='center', va= 'top')

plt.xlim(-.5, n)
plt.xticks(())
plt.ylim(-1.25, 1.25)
plt.yticks(())

plt.show()

Subplot grid

An example showing the subplot grid in matplotlib.

import matplotlib.pyplot as plt

plt.figure(figsize=(6, 4))
plt.subplot(2, 2, 1)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(2,2,1)', ha='center', va='center',
        size=20, alpha=.5)

plt.subplot(2, 2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(2,2,2)', ha='center', va='center',
        size=20, alpha=.5)

plt.subplot(2, 2, 3)
plt.xticks(())
plt.yticks(())

plt.text(0.5, 0.5, 'subplot(2,2,3)', ha='center', va='center',
        size=20, alpha=.5)

plt.subplot(2, 2, 4)
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'subplot(2,2,4)', ha='center', va='center',
        size=20, alpha=.5)

plt.tight_layout()
plt.show()

Axes

This example shows various axes command to position matplotlib axes.

import matplotlib.pyplot as plt

plt.axes([.1, .1, .5, .5])
plt.xticks(())
plt.yticks(())
plt.text(0.1, 0.1, 'axes([0.1, 0.1, .8, .8])', ha='left', va='center',
        size=16, alpha=.5)

plt.axes([.2, .2, .5, .5])
plt.xticks(())
plt.yticks(())
plt.text(0.1, 0.1, 'axes([0.2, 0.2, .5, .5])', ha='left', va='center',
        size=16, alpha=.5)

plt.axes([0.3, 0.3, .5, .5])
plt.xticks(())
plt.yticks(())
plt.text(0.1, 0.1, 'axes([0.3, 0.3, .5, .5])', ha='left', va='center',
        size=16, alpha=.5)

plt.axes([.4, .4, .5, .5])
plt.xticks(())
plt.yticks(())
plt.text(0.1, 0.1, 'axes([0.4, 0.4, .5, .5])', ha='left', va='center',
        size=16, alpha=.5)

plt.show()

Grid

Displaying a grid on the axes in matploblib.

import matplotlib.pyplot as plt

ax = plt.axes([0.025, 0.025, 0.95, 0.95])

ax.set_xlim(0,4)
ax.set_ylim(0,3)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
ax.set_xticklabels([])
ax.set_yticklabels([])

plt.show()

3D plotting

Demo 3D plotting with matplotlib and style the figure.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d

ax = plt.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
cset = ax.contourf(X, Y, Z)
ax.clabel(cset, fontsize=9, inline=1)

plt.xticks(())
plt.yticks(())
ax.set_zticks(())


ax.text2D(-0.05, 1.05, " 3D plots             \n",
          horizontalalignment='left',
          verticalalignment='top',
          bbox=dict(facecolor='white', alpha=1.0),
          family='Lint McCree Intl BB',
          size='x-large',
          transform=plt.gca().transAxes)

ax.text2D(-0.05, .975, " Plot 2D or 3D data",
          horizontalalignment='left',
          verticalalignment='top',
          family='Lint McCree Intl BB',
          size='medium',
          transform=plt.gca().transAxes)

plt.show()

GridSpec

An example demoing gridspec

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

plt.figure(figsize=(6, 4))
G = gridspec.GridSpec(3, 3)

axes_1 = plt.subplot(G[0, :])
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'Axes 1', ha='center', va='center', size=24, alpha=.5)

axes_2 = plt.subplot(G[1, :-1])
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'Axes 2', ha='center', va='center', size=24, alpha=.5)

axes_3 = plt.subplot(G[1:, -1])
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'Axes 3', ha='center', va='center', size=24, alpha=.5)

axes_4 = plt.subplot(G[-1, 0])
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'Axes 4', ha='center', va='center', size=24, alpha=.5)

axes_5 = plt.subplot(G[-1, -2])
plt.xticks(())
plt.yticks(())
plt.text(0.5, 0.5, 'Axes 5', ha='center', va='center', size=24, alpha=.5)

plt.tight_layout()
plt.show()

Demo text printing

A example showing off elaborate text printing with matplotlib.

import numpy as np
import matplotlib.pyplot as plt


eqs = []
eqs.append((r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$"))
eqs.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} = -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$"))
eqs.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$"))
eqs.append((r"$E = mc^2 = \sqrt{{m_0}^2c^4 + p^2c^2}$"))
eqs.append((r"$F_G = G\frac{m_1m_2}{r^2}$"))

plt.axes([0.025, 0.025, 0.95, 0.95])

for i in range(24):
    index = np.random.randint(0, len(eqs))
    eq = eqs[index]
    size = np.random.uniform(12, 32)
    x,y = np.random.uniform(0, 1, 2)
    alpha = np.random.uniform(0.25, .75)
    plt.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=alpha,
         transform=plt.gca().transAxes, fontsize=size, clip_on=True)
plt.xticks(())
plt.yticks(())

plt.show()

1.4.7.2. Code for the chapter’s exercises

Excercise 1

Solution of the excercise 1 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

n = 256
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C,S = np.cos(X), np.sin(X)
plt.plot(X, C)
plt.plot(X,S)

plt.show()

Exercise 4

Exercise 4 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
S = np.sin(X)
C = np.cos(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-")

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.ylim(C.min() * 1.1, C.max() * 1.1)

plt.show()

Exercise 3

Exercise 3 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-")

plt.xlim(-4.0, 4.0)
plt.xticks(np.linspace(-4, 4, 9, endpoint=True))

plt.ylim(-1.0, 1.0)
plt.yticks(np.linspace(-1, 1, 5, endpoint=True))

plt.show()

Exercise 5

Exercise 5 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
S = np.sin(X)
C = np.cos(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-")

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, 0, +1])

plt.show()

Exercise 6

Exercise 6 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C = np.cos(X)
S = np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-")

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, 0, +1],
          [r'$-1$', r'$0$', r'$+1$'])

plt.show()

Exercise 2

Exercise 2 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

# Create a new figure of size 8x6 points, using 100 dots per inch
plt.figure(figsize=(8, 6), dpi=80)

# Create a new subplot from a grid of 1x1
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

# Plot cosine using blue color with a continuous line of width 1 (pixels)
plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# Plot sine using green color with a continuous line of width 1 (pixels)
plt.plot(X, S, color="green", linewidth=1.0, linestyle="-")

# Set x limits
plt.xlim(-4., 4.)

# Set x ticks
plt.xticks(np.linspace(-4, 4, 9, endpoint=True))

# Set y limits
plt.ylim(-1.0, 1.0)

# Set y ticks
plt.yticks(np.linspace(-1, 1, 5, endpoint=True))

# Show result on screen
plt.show()

Exercise 7

Exercise 7 with matplotlib

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8,5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C = np.cos(X)
S = np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-")

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, 0, +1],
          [r'$-1$', r'$0$', r'$+1$'])

plt.show()

Exercise 8

Exercise 8 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8,5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C = np.cos(X)
S = np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-",  label="sine")

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, +1],
          [r'$-1$', r'$+1$'])

plt.legend(loc='upper left')

plt.show()

Exercise 9

Exercise 9 with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C = np.cos(X)
S = np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-",  label="sine")

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, +1],
          [r'$-1$', r'$+1$'])

t = 2*np.pi/3
plt.plot([t, t], [0, np.cos(t)],
        color='blue', linewidth=1.5, linestyle="--")
plt.scatter([t, ], [np.cos(t), ], 50, color='blue')
plt.annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
            xy=(t, np.sin(t)), xycoords='data',
            xytext=(+10, +30), textcoords='offset points', fontsize=16,
            arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t, t], [0, np.sin(t)],
        color='red', linewidth=1.5, linestyle="--")
plt.scatter([t, ], [np.sin(t), ], 50, color='red')
plt.annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$', xy=(t, np.cos(t)),
            xycoords='data', xytext=(-90, -50), textcoords='offset points',
            fontsize=16,
            arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.legend(loc='upper left')

plt.show()

Exercise

Exercises with matplotlib.

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5), dpi=80)
plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red", linewidth=2.5, linestyle="-",  label="sine")

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))

plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.ylim(C.min() * 1.1, C.max() * 1.1)
plt.yticks([-1, 1],
          [r'$-1$', r'$+1$'])

plt.legend(loc='upper left')

t = 2*np.pi/3
plt.plot([t, t], [0, np.cos(t)],
        color='blue', linewidth=1.5, linestyle="--")
plt.scatter([t, ], [np.cos(t), ], 50, color='blue')
plt.annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
            xy=(t, np.sin(t)), xycoords='data',
            xytext=(10, 30), textcoords='offset points', fontsize=16,
            arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t, t], [0, np.sin(t)],
        color='red', linewidth=1.5, linestyle="--")
plt.scatter([t, ], [np.sin(t), ], 50, color ='red')
plt.annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$', xy=(t, np.cos(t)),
            xycoords='data', xytext=(-90, -50),
            textcoords='offset points', fontsize=16,
            arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))

plt.show()

1.4.7.3. Example demoing choices for an option

The colors matplotlib line plots

An example demoing the various colors taken by matplotlib’s plot.

import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0.1, 1, .8], frameon=False)

for i in range(1,11):
    plt.plot([i, i], [0, 1], lw=1.5)

plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())
plt.show()

Linewidth

Plot various linewidth with matplotlib.

import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, .1, 1, .8], frameon=False)

for i in range(1, 11):
    plt.plot([i, i], [0, 1], color='b', lw=i/2.)

plt.xlim(0, 11)
plt.ylim(0, 1)
plt.xticks(())
plt.yticks(())

plt.show()

Alpha: transparency

This example demonstrates using alpha for transparency.

import matplotlib.pyplot as plt

size = 256,16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0.1, 1, .8], frameon=False)

for i in range(1, 11):
    plt.axvline(i, linewidth=1, color='blue', alpha= .25 + .75 * i / 10.)

plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())
plt.show()

Aliased versus anti-aliased

This example demonstrates aliased versus anti-aliased text.

import matplotlib.pyplot as plt

size = 128, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)

plt.axes([0, 0, 1, 1], frameon=False)

plt.rcParams['text.antialiased'] = False
plt.text(0.5, 0.5, "Aliased", ha='center', va='center')

plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xticks(())
plt.yticks(())

plt.show()

Aliased versus anti-aliased

The example shows aliased versus anti-aliased text.

import matplotlib.pyplot as plt

size = 128, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

plt.rcParams['text.antialiased'] = True
plt.text(0.5, 0.5, "Anti-aliased", ha='center', va='center')

plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xticks(())
plt.yticks(())

plt.show()

Marker size

Demo the marker size control in matplotlib.

import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

for i in range(1, 11):
    plt.plot([i, ], [1, ], 's', markersize=i, markerfacecolor='w',
         markeredgewidth=.5, markeredgecolor='k')

plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())

plt.show()

Marker edge width

Demo the marker edge widths of matplotlib’s markers.

import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

for i in range(1,11):
    plt.plot([i, ], [1, ], 's', markersize=5,
         markeredgewidth=1 + i/10., markeredgecolor='k', markerfacecolor='w')
plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())

plt.show()

Marker edge color

Demo the marker edge color of matplotlib’s markers.

import numpy as np
import matplotlib.pyplot as plt

size = 256,16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

for i in range(1, 11):
    r, g, b = np.random.uniform(0, 1, 3)
    plt.plot([i, ], [1, ], 's', markersize=5, markerfacecolor='w',
             markeredgewidth=1.5, markeredgecolor=(r, g, b, 1))

plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())

plt.show()

Marker face color

Demo the marker face color of matplotlib’s markers.

import numpy as np
import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

for i in range(1, 11):
    r, g, b = np.random.uniform(0, 1, 3)
    plt.plot([i, ], [1, ], 's', markersize=8, markerfacecolor=(r, g, b, 1),
             markeredgewidth=.1,  markeredgecolor=(0, 0, 0, .5))
plt.xlim(0, 11)
plt.xticks(())
plt.yticks(())
plt.show()

Colormaps

An example plotting the matplotlib colormaps.

import numpy as np
import matplotlib.pyplot as plt

plt.rc('text', usetex=False)
a = np.outer(np.arange(0, 1, 0.01), np.ones(10))

plt.figure(figsize=(10, 5))
plt.subplots_adjust(top=0.8, bottom=0.05, left=0.01, right=0.99)
maps = [m for m in plt.cm.datad if not m.endswith("_r")]
maps.sort()
l = len(maps) + 1

for i, m in enumerate(maps):
    plt.subplot(1, l, i+1)
    plt.axis("off")
    plt.imshow(a, aspect='auto', cmap=plt.get_cmap(m), origin="lower")
    plt.title(m, rotation=90, fontsize=10, va='bottom')

plt.show()

Solid cap style

An example demoing the solide cap style in matplotlib.

import numpy as np
import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

plt.plot(np.arange(4), np.ones(4), color="blue", linewidth=8,
        solid_capstyle='butt')

plt.plot(5 + np.arange(4), np.ones(4), color="blue", linewidth=8,
        solid_capstyle='round')

plt.plot(10 + np.arange(4), np.ones(4), color="blue", linewidth=8,
        solid_capstyle='projecting')

plt.xlim(0, 14)
plt.xticks(())
plt.yticks(())

plt.show()

Solid joint style

An example showing the differen solid joint styles in matplotlib.

import numpy as np
import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

plt.plot(np.arange(3), [0, 1, 0], color="blue", linewidth=8,
        solid_joinstyle='miter')
plt.plot(4 + np.arange(3), [0, 1, 0], color="blue", linewidth=8,
        solid_joinstyle='bevel')
plt.plot(8 + np.arange(3), [0, 1, 0], color="blue", linewidth=8,
        solid_joinstyle='round')

plt.xlim(0, 12)
plt.ylim(-1, 2)
plt.xticks(())
plt.yticks(())

plt.show()

Dash capstyle

An example demoing the dash capstyle.

import numpy as np
import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

plt.plot(np.arange(4), np.ones(4), color="blue", dashes=[15, 15],
        linewidth=8, dash_capstyle='butt')

plt.plot(5 + np.arange(4), np.ones(4), color="blue", dashes=[15, 15],
        linewidth=8, dash_capstyle='round')

plt.plot(10 + np.arange(4), np.ones(4), color="blue", dashes=[15, 15],
        linewidth=8, dash_capstyle='projecting')

plt.xlim(0, 14)
plt.xticks(())
plt.yticks(())

plt.show()

Dash join style

Example demoing the dash join style.

import numpy as np
import matplotlib.pyplot as plt

size = 256, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
plt.axes([0, 0, 1, 1], frameon=False)

plt.plot(np.arange(3), [0, 1, 0], color="blue", dashes=[12, 5], linewidth=8,
        dash_joinstyle='miter')
plt.plot(4 + np.arange(3), [0, 1, 0], color="blue", dashes=[12, 5],
        linewidth=8, dash_joinstyle='bevel')
plt.plot(8 + np.arange(3), [0, 1, 0], color="blue", dashes=[12, 5],
        linewidth=8, dash_joinstyle='round')

plt.xlim(0, 12)
plt.ylim(-1, 2)
plt.xticks(())
plt.yticks(())

plt.show()

Linestyles

Plot the different line styles.

import numpy as np
import matplotlib.pyplot as plt

def linestyle(ls, i):
    X = i * .5 * np.ones(11)
    Y = np.arange(11)
    plt.plot(X, Y, ls, color=(.0, .0, 1, 1), lw=3, ms=8,
            mfc=(.75, .75, 1, 1), mec=(0, 0, 1, 1))
    plt.text(.5 * i, 10.25, ls, rotation=90, fontsize=15, va='bottom')

linestyles = ['-', '--', ':', '-.', '.', ',', 'o', '^', 'v', '<', '>', 's',
              '+', 'x', 'd', '1', '2', '3', '4', 'h', 'p', '|', '_', 'D', 'H']
n_lines = len(linestyles)

size = 20 * n_lines, 300
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
plt.axes([0, 0.01, 1, .9], frameon=False)

for i, ls in enumerate(linestyles):
    linestyle(ls, i)

plt.xlim(-.2, .2 + .5*n_lines)
plt.xticks(())
plt.yticks(())

plt.show()

Markers

Show the different markers of matplotlib.

import numpy as np
import matplotlib.pyplot as plt

def marker(m, i):
    X = i * .5 * np.ones(11)
    Y = np.arange(11)

    plt.plot(X, Y, lw=1, marker=m, ms=10, mfc=(.75, .75, 1, 1),
            mec=(0, 0, 1, 1))
    plt.text(.5 * i, 10.25, repr(m), rotation=90, fontsize=15, va='bottom')

markers = [0, 1, 2, 3, 4, 5, 6, 7, 'o', 'h', '_', '1', '2', '3', '4',
          '8', 'p', '^', 'v', '<', '>', '|', 'd', ',', '+', 's', '*',
          '|', 'x', 'D', 'H', '.']

n_markers = len(markers)

size = 20 * n_markers, 300
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
plt.axes([0, 0.01, 1, .9], frameon=False)

for i, m in enumerate(markers):
    marker(m, i)

plt.xlim(-.2, .2 + .5 * n_markers)
plt.xticks(())
plt.yticks(())

plt.show()

Locators for tick on axis

An example demoing different locators to position ticks on axis for matplotlib.

import numpy as np
import matplotlib.pyplot as plt


def tickline():
    plt.xlim(0, 10), plt.ylim(-1, 1), plt.yticks([])
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['left'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.xaxis.set_ticks_position('bottom')
    ax.spines['bottom'].set_position(('data',0))
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
    ax.plot(np.arange(11), np.zeros(11))
    return ax

locators = [
                'plt.NullLocator()',
                'plt.MultipleLocator(1.0)',
                'plt.FixedLocator([0, 2, 8, 9, 10])',
                'plt.IndexLocator(3, 1)',
                'plt.LinearLocator(5)',
                'plt.LogLocator(2, [1.0])',
                'plt.AutoLocator()',
            ]

n_locators = len(locators)

size = 512, 40 * n_locators
dpi = 72.0
figsize = size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)


for i, locator in enumerate(locators):
    plt.subplot(n_locators, 1, i + 1)
    ax = tickline()
    ax.xaxis.set_major_locator(eval(locator))
    plt.text(5, 0.3, locator[3:], ha='center')

plt.subplots_adjust(bottom=.01, top=.99, left=.01, right=.99)
plt.show()

1.4.7.4. Code generating the summary figures with a title

Plotting in polar, decorated

An example showing how to plot in polar coordinnate, and some decorations.

import numpy as np
import matplotlib.pyplot as plt

plt.subplot(1, 1, 1, polar=True)

N = 20
theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / N)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)
for r, bar in zip(radii, bars):
    bar.set_facecolor(plt.cm.jet(r / 10.))
    bar.set_alpha(0.5)
plt.gca().set_xticklabels([])
plt.gca().set_yticklabels([])


plt.text(-0.2, 1.02, " Polar Axis                  \n",
      horizontalalignment='left',
      verticalalignment='top',
      size='xx-large',
      bbox=dict(facecolor='white', alpha=1.0),
      transform=plt.gca().transAxes)

plt.text(-0.2, 1.01, "\n\n Plot anything using polar axis ",
      horizontalalignment='left',
      verticalalignment='top',
      size='large',
      transform=plt.gca().transAxes)

plt.show()

3D plotting vignette

Demo 3D plotting with matplotlib and decorate the figure.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
ax.set_zlim(-2, 2)
plt.xticks(())
plt.yticks(())
ax.set_zticks(())

ax.text2D(0.05, .93, " 3D plots             \n",
          horizontalalignment='left',
          verticalalignment='top',
          size='xx-large',
          bbox=dict(facecolor='white', alpha=1.0),
          transform=plt.gca().transAxes)

ax.text2D(0.05, .87, " Plot 2D or 3D data",
          horizontalalignment='left',
          verticalalignment='top',
          size='large',
          transform=plt.gca().transAxes)

plt.show()

Plot example vignette

An example of plots with matplotlib, and added annotations.

import numpy as np
import matplotlib.pyplot as plt

n = 256
X = np.linspace(0, 2, n)
Y = np.sin(2 * np.pi * X)

plt.plot (X, Y, lw=2, color='violet')
plt.xlim(-0.2, 2.2)
plt.xticks(())
plt.ylim(-1.2, 1.2)
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Regular Plot:      plt.plot(...)\n",
        horizontalalignment='left',
        verticalalignment='top',
        size='xx-large',
        transform=plt.gca().transAxes)

plt.text(-0.05, 1.01, "\n\n   Plot lines and/or markers ",
        horizontalalignment='left',
        verticalalignment='top',
        size='large',
        transform=plt.gca().transAxes)

plt.show()

Multiple plots vignette

Demo multiple plots and style the figure.

import matplotlib.pyplot as plt

ax = plt.subplot(2, 1, 1)
ax.set_xticklabels([])
ax.set_yticklabels([])


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .72),
                            width=.66, height=.34, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Multiplot:     plt.subplot(...)\n",
      horizontalalignment='left',
      verticalalignment='top',
      size='xx-large',
      transform=ax.transAxes)
plt.text(-0.05, 1.01, "\n\n    Plot several plots at once ",
      horizontalalignment='left',
      verticalalignment='top',
      size='large',
      transform=ax.transAxes)

ax = plt.subplot(2, 2, 3)
ax.set_xticklabels([])
ax.set_yticklabels([])

ax = plt.subplot(2, 2, 4)
ax.set_xticklabels([])
ax.set_yticklabels([])

plt.show()

Boxplot with matplotlib

An example of doing box plots with matplotlib

import numpy as np
import matplotlib.pyplot as plt


fig = plt.figure(figsize=(8, 5))
axes = plt.subplot(111)

n = 5
Z = np.zeros((n, 4))
X = np.linspace(0, 2, n, endpoint=True)
Y = np.random.random((n, 4))
plt.boxplot(Y)

plt.xticks(())
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Box Plot:   plt.boxplot(...)\n ",
        horizontalalignment='left',
        verticalalignment='top',
        size='xx-large',
        transform=axes.transAxes)

plt.text(-0.04, .98, "\n Make a box and whisker plot ",
        horizontalalignment='left',
        verticalalignment='top',
        size='large',
        transform=axes.transAxes)

plt.show()

Plot scatter decorated

An example showing the scatter function, with decorations.

import numpy as np
import matplotlib.pyplot as plt

n = 1024
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)

T = np.arctan2(Y,X)

plt.scatter(X, Y, s=75, c=T, alpha=.5)
plt.xlim(-1.5, 1.5)
plt.xticks(())
plt.ylim(-1.5, 1.5)
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Scatter Plot:  plt.scatter(...)\n",
      horizontalalignment='left',
      verticalalignment='top',
      size='xx-large',
      transform=plt.gca().transAxes)

plt.text(-0.05, 1.01, "\n\n   Make a scatter plot of x versus y ",
      horizontalalignment='left',
      verticalalignment='top',
      size='large',
      transform=plt.gca().transAxes)

plt.show()

Pie chart vignette

Demo pie chart with matplotlib and style the figure.

import numpy as np
import matplotlib.pyplot as plt

n = 20
X = np.ones(n)
X[-1] *= 2
plt.pie(X, explode=X*.05, colors = ['%f' % (i/float(n)) for i in range(n)])

fig = plt.gcf()
w, h = fig.get_figwidth(), fig.get_figheight()
r = h / float(w)

plt.xlim(-1.5, 1.5)
plt.ylim(-1.5 * r, 1.5 * r)
plt.xticks(())
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Pie Chart:           plt.pie(...)\n",
        horizontalalignment='left',
        verticalalignment='top',
        size='xx-large',
        transform=plt.gca().transAxes)

plt.text(-0.05, 1.01, "\n\n   Make a pie chart of an array ",
        horizontalalignment='left',
        verticalalignment='top',
        size='large',
        transform=plt.gca().transAxes)

plt.show()

Bar plot advanced

An more elaborate bar plot example

import numpy as np
import matplotlib.pyplot as plt

n = 16
X = np.arange(n)
Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
plt.bar(X, Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
plt.xlim(-.5, n)
plt.xticks(())
plt.ylim(-1, 1)
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Bar Plot:              plt.bar(...)\n",
      horizontalalignment='left',
      verticalalignment='top',
      size='xx-large',
      transform=plt.gca().transAxes)

plt.text(-0.05, 1.01, "\n\n   Make a bar plot with rectangles ",
      horizontalalignment='left',
      verticalalignment='top',
      size='large',
      transform=plt.gca().transAxes)

plt.show()

Plotting quiver decorated

An example showing quiver with decorations.

import numpy as np
import matplotlib.pyplot as plt

n = 8
X, Y = np.mgrid[0:n, 0:n]
T = np.arctan2(Y - n/ 2., X - n / 2.)
R = 10 + np.sqrt((Y - n / 2.) ** 2 + (X - n / 2.) ** 2)
U, V = R * np.cos(T), R * np.sin(T)

plt.quiver(X, Y, U, V, R, alpha=.5)
plt.quiver(X, Y, U, V, edgecolor='k', facecolor='None', linewidth=.5)

plt.xlim(-1, n)
plt.xticks(())
plt.ylim(-1, n)
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Quiver Plot:    plt.quiver(...)\n",
      horizontalalignment='left',
      verticalalignment='top',
      size='xx-large',
      transform=plt.gca().transAxes)

plt.text(-0.05, 1.01, "\n\n    Plot a 2-D field of arrows ",
      horizontalalignment='left',
      verticalalignment='top',
      size='large',
      transform=plt.gca().transAxes)


plt.show()

Imshow demo

Demoing imshow

import numpy as np
import matplotlib.pyplot as plt

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)

n = 10
x = np.linspace(-3, 3, 8 * n)
y = np.linspace(-3, 3, 6 * n)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
plt.imshow(Z, interpolation='nearest', cmap='bone', origin='lower')
plt.xticks(())
plt.yticks(())


# Add a title and a box around it
from matplotlib.patches import FancyBboxPatch
ax = plt.gca()
ax.add_patch(FancyBboxPatch((-0.05, .87),
                            width=.66, height=.165, clip_on=False,
                            boxstyle="square,pad=0", zorder=3,
                            facecolor='white', alpha=1.0,
                            transform=plt.gca().transAxes))

plt.text(-0.05, 1.02, " Imshow:       plt.imshow(...)\n",
        horizontalalignment='left',
        verticalalignment='top',
        size='xx-large',
        transform=plt.gca().transAxes)

plt.text(-0.05, 1.01