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This article mainly introduces matplotlib how to achieve scale calibration, the article is very detailed, has a certain reference value, interested friends must read it!

1.5. Logarithm or other nonlinear axes

Use plt.xscal () to change the scale of the axis

Import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.ticker import NullFormatter # useful for `logit` scale# Fixing random state for reproducibilitynp.random.seed (19680801) # make up some data in the interval] 0,1 [y = np.random.normal (loc=0.5, scale=0.4, size=1000) y = y [(y > 0) & (y < 1)] y.sort () x = np.arange (len (y)) # plot with various axes scalesplt.figure (1) # linearplt.subplot (221) plt.plot (x Y) plt.yscale ('linear') plt.title (' linear') plt.grid (True) # logplt.subplot plt.plot (x, y) plt.yscale ('log') plt.title (' log') plt.grid (True) # symmetric logplt.subplot (223) plt.plot (x, y-y.mean () plt.yscale ('symlog', linthreshy=0.01) plt.title (' symlog') plt.grid (True) # logitplt.subplot (224) plt.plot (x Y) plt.yscale ('logit') plt.title (' logit') plt.grid (True) # Format the minor tick labels of the y-axis into empty strings with# `NullFormatter`, to avoid cumbering the axis with too many labels.plt.gca (). Yaxis.set_minor_formatter (NullFormatter ()) # Adjust the subplot layout, because the logit one may take more space# than usual, due to y-tick labels like "1-10 ^ {- 3}" plt.subplots_adjust (top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25 Wspace=0.35) plt.show ()

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