minte9

learningjourney

Axes

 
"""
Simple line, subplots, bubbles
"""

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots() # Figure with single axes

# Lines
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.show()

# Subplots
fig, axs = plt.subplots(1, 2) # Figure with 2x2 grid of axes
plt.show()

# Bubbles
np.random.seed(100000000)
data = {
    'a': np.arange(50),
    'c': np.random.randint(0, 50, 50),
    'd': np.random.randn(50),
}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100

fix, ax = plt.subplots(figsize=(5, 2.7))
ax.scatter('a', 'b', c='c', s='d', data=data) # s=size, c=color
ax.set_xlabel('a')
ax.set_ylabel('b')
plt.show()

Styles

 
"""
There are two ways of using Matplotlib:
1) Explicitly create Figures and Axes, and call methods on them (OOP)
2) Rely on pyplot to create and manage Figures and Axes, end use functions
"""

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2, 100)

# OOP style
fix, ax = plt.subplots()
ax.plot(x, x, label='linear')
ax.plot(x, x**2, label='quadratic')
ax.plot(x, x**3, label='cubic')
ax.set_xlabel('x')
ax.set_ylabel('f(x)')
ax.legend()
plt.show()

# Pyplot style
plt.figure(figsize=(5, 2.7), layout='constrained')
plt.plot(x, x, label='linear')
plt.plot(x, x**2, label='quadratic')
plt.plot(x, x**3, label='cubic')
plt.xlabel('x')
plt.ylabel('f(x)')
plt.legend()
plt.show()

Annotation

 
"""
Annotate points on plot
"""

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0.0, 5.0, 0.01)
y = np.cos(2 * np.pi * x)

fig, ax = plt.subplots(figsize=(5, 2.7))
ax.plot(x, y, lw=2)

ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
    arrowprops=dict(facecolor='gray', shrink=0.05))
ax.set_ylim(-2, 2)
plt.show()

Annotations (plt)

 
""" Annotations (when using plt.scatter)
"""

import matplotlib.pyplot as plt

xa = [1, 2, 3, 4]
ya = [5, 6, 7, 8]

plt.scatter(xa, ya, color='r', marker='x')

for i, p in enumerate(zip(xa, ya)):
    plt.annotate(f"({p[0]}, {p[1]})", (p[0]+0.1, p[1]-.1))

plt.show()

Additional axis

 
"""
Plotting data of different magnitude in one chart 
may require an additional y-axis
"""

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

x = np.arange(0.0, 5.0, 0.01)
y = np.cos(2 * np.pi * x)

l1, = ax.plot(x, y)
ax2 = ax.twinx() # invisible x-ax
l2, = ax2.plot(x, range(len(x)), 'C1')

plt.show()

Colored map

 
"""
Third dimension in a plot represented by a colors in a colormap
"""

import matplotlib.pyplot as plt
import numpy as np

X, Y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

fig, ax = plt.subplots()

co = ax.contourf(X, Y, Z, levels=np.linspace(-1.25, 1.25, 11))
fig.colorbar(co)
ax.set_title('contourf()')
ax.set_xlabel('X')
ax.set_ylabel('Y')

plt.show()