2.4. Matplotlib Scales

2.4.1. Rationale

• Liniowa

• Logarytmiczna

• Symmetrical log (można ustawić fragmentami liniowo linthreshx: int)

• Logit - odwrotność logistycznej

• Subtracting x.mean() is used to better highlight the function

2.4.2. Linear Scale

import matplotlib.pyplot as plt
import numpy as np


x = np.linspace(0, 10, 1000)
y = x - x.mean()

plt.yscale('linear')

plt.plot(x, y)
plt.show()

2.4.3. Logarithmic Scale

import matplotlib.pyplot as plt
import numpy as np


x = np.linspace(0, 10, 1000)
y = x - x.mean()

plt.yscale('log')

plt.plot(x, y)
plt.show()

2.4.4. Symmetrical Logarithmic Scale

import matplotlib.pyplot as plt
import numpy as np


x = np.linspace(0, 10, 1000)
y = x - x.mean()

plt.yscale('symlog', linthresh=0.01)

plt.plot(x, y)
plt.show()

2.4.5. Logit Scale

import matplotlib.pyplot as plt
import numpy as np


x = np.linspace(0, 10, 1000)
y = x - x.mean()

plt.yscale('logit')

plt.plot(x, y)
plt.show()