# 2.4. Matplotlib Scales¶

• Linear

• Logarithmic

• Symmetrical log (partially linear linthreshx: int)

• Logit - reversed logarithmic

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

## 2.4.1. 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()  # doctest: +SKIP


## 2.4.2. 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()  # doctest: +SKIP


## 2.4.3. 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()  # doctest: +SKIP


## 2.4.4. 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()  # doctest: +SKIP