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
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|
| Jupyter notebook: | |
|
In the notebook, insert, at the beginning of the notebook the following magic: %matplotlib inline
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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
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
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
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
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
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
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
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
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
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
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
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figure.edgecolor
|
color of edge around the drawing background |
frameon
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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
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
|
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, "\n\n Display an image to current axes ",
horizontalalignment='left',
verticalalignment='top',
family='Lint McCree Intl BB',
size='large',
transform=plt.gca().transAxes)
plt.show()
Display the contours of a function
An example demoing how to plot the contours of a function, with additional layout tweeks.
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.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(())
# 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, " Contour Plot: plt.contour(..)\n",
horizontalalignment='left',
verticalalignment='top',
size='xx-large',
transform=plt.gca().transAxes)
plt.text(-0.05, 1.01, "\n\n Draw contour lines and filled contours ",
horizontalalignment='left',
verticalalignment='top',
size='large',
transform=plt.gca().transAxes)
plt.show()
Grid elaborate
An example displaying a grid on the axes and tweaking the layout.
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
fig = plt.figure(figsize=(8, 6), dpi=72, facecolor="white")
axes = plt.subplot(111)
axes.set_xlim(0, 4)
axes.set_ylim(0, 3)
axes.xaxis.set_major_locator(MultipleLocator(1.0))
axes.xaxis.set_minor_locator(MultipleLocator(0.1))
axes.yaxis.set_major_locator(MultipleLocator(1.0))
axes.yaxis.set_minor_locator(MultipleLocator(0.1))
axes.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
axes.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
axes.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
axes.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
axes.set_xticklabels([])
axes.set_yticklabels([])
# 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, " Grid: plt.grid(...)\n",
horizontalalignment='left',
verticalalignment='top',
size='xx-large',
transform=axes.transAxes)
plt.text(-0.05, 1.01, "\n\n Draw ticks and grid ",
horizontalalignment='left',
verticalalignment='top',
size='large',
transform=axes.transAxes)
Text printing decorated
An example showing text printing and decorating the resulting figure.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
plt.xticks(())
plt.yticks(())
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"$F_G = G\frac{m_1m_2}{r^2}$"))
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)
# 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, " Text: plt.text(...)\n",
horizontalalignment='left',
verticalalignment='top',
size='xx-large',
transform=plt.gca().transAxes)
plt.text(-0.05, 1.01, "\n\n Draw any kind of text ",
horizontalalignment='left',
verticalalignment='top',
size='large',
transform=plt.gca().transAxes)
plt.show()