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()