Tutorial 2: Matplotlib¶

import numpy as np
import matplotlib.pyplot as plt

# data generation
x = np.linspace(-np.pi, np.pi, 100)
y = np.sin(x)

# display
plt.figure()
plt.plot(x, y)
plt.show()

# store figure
plt.savefig('fig.png')

# axis labels
plt.xlabel('--> x')
plt.ylabel('--> y')

# axis borders
plt.xlim(-3.15, 3.15)
plt.ylim(-1, 1)

# title inc. LaTeX syntax
plt.title(r'y = $\sin$(x)')

# graph
plt.grid('on')
plt.plot(x, y, color='r')
plt.show()

# graphs in single plot
z = np.cos(x)
plt.plot(x, y, label=r'$\sin$(x)')
plt.plot(x, z, label=r'$\cos$(x)')
plt.legend()
plt.show()

# subplots
plt.subplot(211)
plt.plot(x, y)
plt.title(r'$\sin$(x)')

plt.subplot(212)
plt.plot(x, z)
plt.title(r'$\cos$(x)')

plt.show()

x = np.linspace(-np.pi, np.pi, 20)
y = np.sin(x)

# stem graph
plt.subplot(211)
plt.stem(x, y)
plt.title('stem graph')

# bar graph
plt.subplot(212)
plt.bar(x, y)
plt.title('bar graph')

# scatter, non-correlated data
x = np.random.uniform(-10, 10, 100) # uniform distribution (-10,10)
y = np.random.uniform(-10, 10, 100)
plt.subplot(211)
plt.scatter(x, y)
plt.title('non-correlated data')

# scatter, correlated data
x = np.linspace(-10, 10, 100)
y = x + np.random.normal(2, 2, 100) # normal distribution, mean=2, standard deviation=2
plt.subplot(212)
plt.scatter(x, y)
plt.title('correlated data')

# histogram
plt.subplot(211)
plt.hist(y, 50) # 50 buckets
plt.title('histogram')

# boxplot
plt.subplot(212)
plt.boxplot(y)
plt.title('boxplot')

# pie chart
x = np.array([10,30,40,15,5,10]) # percentage
labels = ['item 1', 'item 2', 'item 3', 'item 4', 'item 5', 'item 6']
plt.pie(x, autopct='%1.1f%%', labels=labels, explode=[0.1]*6, shadow=True)

# polar coordinates
phi = np.arange(0, 2*np.pi, 2*np.pi/120) # 120 samples
rho = np.linspace(0, 1, len(phi))        # spiral
plt.polar(phi, rho)
plt.title('polar')

# contour
x = np.arange(-1.5, 1.5, 0.1)
y = np.arange(-1.5, 1.5, 0.1)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2
plt.contour(Z, N=np.arange(-1, 1.5, 0.3))
plt.title('contour')

from mpl_toolkits.mplot3d import Axes3D

# surface
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z)
plt.title('surface')

# wireframe
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_wireframe(x, y, z)
plt.title('wireframe')