Matplotlib Cheat Sheet
Matplotlib Cheat Sheet
EPFL CS 328 Numerical Methods for Visual Computing
(Version 1)
Importing Matplotlib Packages
Common magic commands for Jupyter:
# Enable inline backend (plots within notebooks). %matplotlib inline # Enable interactive inline backend. %matplotlib notebook
Common import statements:
# Package used for state-machine usage of Matplotlib from matplotloib import pyplot as plt # Reading images. import matplotlib.image # Plotting 3D plots. import mpl_toolkits.mplot3d
Plotting Lines Pipeline
Preparing the data:
x = np.linspace(-2.0 * np.pi, 2.0 * np.pi, 1000) y = np.sin(x)
Limiting displayed axes ranges:
plt.xlim([-3.0, 3.0]) # [from, to] plt.ylim([-1.5, 1.5])
Adding title, axes labels, grid:
plt.title('My wonderful plot') plt.xlabel('T') plt.ylabel('amplitude') plt.grid()
Plotting:
plt.plot(x, y, label='sine wave') # Linear axes. plt.semilogx(x, y) # Logarithmic X axis. plt.semilogy(x, y) # Logarithmic Y axis. plt.loglog(x, y) # # Logarithmic both axes.
Legends:
# Legend uses labels set in plotting statements. plt.legend(loc='lower right') # loc={'best', 'upper center', ...}
Saving plotted image to file:
# File format inferred from extension (.pdf,.png, ...). plt.savefig('file.png', dpi=200) # 'dpi' can be used to set custom resolution.
Cheat-sheet by T. Zeltner and J. Bednarik ([tizian.zeltner|jan.bednarik]@epfl.ch). LATEX template by Michelle Cristina de Sousa Baltazar.
Line Styles
Using (optional) plotting parameters:
linestyle, linewidth, color, marker, markersize, label
plt.plot(x, y, color='b', linewidth=1, linestyle='--', label='b--')
plt.plot(x, y, color='r', linewidth=4, linestyle='-.', label='r-.')
plt.plot(x, y, color='g', linestyle='', marker='+', markersize=8, label='g+')
plt.plot(x, y, color='m', linewidth=10, label='m')
40
20
0
20
40
60
b-"r-."
"g+"
80
"m"
3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0
Histograms Draw the histogram of an array x :
plt.hist(x)
Returns three arrays of..
n bins patches
histogram values x-positions of bin edges patches/rectangle objects drawn in the figure
Common optional parameters:
bins normed
cumulative orientation color log
number of bins to use if True, mimics a probability density. (The histogram will integrate to 1.) if True, mimics a cumulative distribution. {'vertical', 'horizontal'} specifies color to be used for bars. if True, histogram axis is set to log scale.
Images Read image from disk:
img = matplotlib.image.imread('path/to/image.png')
Plot the image:
plt.imshow(img)
Optionally show colorbar:
plt.colorbar()
Common optional parameters:
cmap
colormap used for grayscale images.
{'gray', 'hot', 'plasma', ...}
interpolation {'nearest', 'bilinear', ...}
Subplots
Three plots in a 1 x 3 matrix with shared y-axis:
fig, ax = plt.subplots(1,3, figsize=(9,3), sharey=True) ax[0].plot(x, y, color='r') ax[1].plot(x, y, color='g') ax[2].plot(x, y, color='b')
1.0 0.5 0.0 0.5 1.0
0246
0246
0246
3D Plots
Create 3D figure:
fig = plt.figure() ax = fig.gca(projection='3d')
Preparing the data:
X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) Z = np.sin(np.sqrt(X**2 + Y**2))))
Plotting the surface:
ax.plot_surface(X, Y, Z)
Other common plot styles:
ax.plot(..) ax.scatter(..) ax.plot trisurf(..)
3D Line plot 3D Scatter plot Triangulated mesh data
Interactivity
Easily add interactivity with sliders in Jupyter notebooks:
from ipywidgets import interact
@interact(omega=(0, 10, 1)) # min, max, step def plotSin(omega = 1):
x = np.linspace(0.0, 2*np.pi, 1000) y = np.sin(omega * x) plt.plot(x, y)
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