Simple plots
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Visit this page for full documentation on simple plots using pyplot. |
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import numpy as np import matplotlib.pyplot as plt # Evenly sampled time from 0s to 10s at 200ms intervals t = np.arange(0.0, 10.0, 0.2) # Plotting t at x-axis and sin(t) at y-axis plt.plot(t, np.sin(t)) # Naming the title and both axis plt.title('Sinus function') plt.ylabel('sin(t)') plt.xlabel('t [s]') # Need to call the show() function at the end to display my figure plt.show() |
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import numpy as np import matplotlib.pyplot as plt # Evenly sampled time at 200ms intervals t = np.arange(0.0, 5.0, 0.2) # plot() can plot several lines in the same figure. To seperate the different lines # from eachother, we may change the line style and format strings. # See the plot() documentation for a complete list of line styles and format strings. # The following lines have red dashes, blue squares and green triangles plt.plot(t, t, 'r--', label='Linear line') plt.plot(t, t**2, color='blue', linestyle='none', marker='s', label='Second degree polynom') plt.plot(t, t**3, 'g^', label='Third degree polynom') # To describe our plot even more detailed we can draw the labels we previously gave our lines using legend. plt.legend(loc='upper left') # The function axis() sets the axis sizes, and takes the argument [xmin, xmax, ymin, ymax] plt.axis([0, 5, 0, 100]) plt.title('Mulitple polynoms') plt.show() |
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More in-depth plot() documentation and legend() documentation. |
Quiver plot
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More in-depth quiver documentation and functions. |
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import numpy as np
import matplotlib.pyplot as plt
# X and Y define the arrow locations
X = np.arange(-10, 10, 1)
Y = np.arange(-10, 10, 1)
# U and V define the arrow directions, respectively in x- and y-direction
# meshgrid() returns coordinate matrices from coordinate vectors
U, V = np.meshgrid(X, Y)
plt.quiver(X, Y, U, V)
plt.title('Simple quiver plot')
plt.show() |