matplotlib 具有极值的轮廓

Question

I have data I want to plot with extreme edge values, given below is a generic example:

import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np

Z = np.random.rand(100,100)

plt.contourf(Z, 100, cmap='RdGy', vmin=0, vmax=1)
plt.colorbar()
plt.show()

Using the above code I get this plot:

But if I change only one row of Z to contain extreme values it "dominates" the whole plot:

import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np

Z = np.random.rand(100,100)
Z[:1] *= 100

plt.contourf(Z, 100, cmap='RdGy', vmin=0, vmax=1)
plt.colorbar()
plt.show()

My question is so: In the second example, although I have extreme values, all the interesting things obviously happen in the range of 0 and 1, which is totally dominated by the multiplication I entered in the 1st row, even though I set vmin and vmax accordingly. How do I keep the data as is, while "focusing" on the 0-1 range? I don't really care what goes on in the first row, for all I care there can be a single color for the values 1-100.

Many Thanks.

Answer 1

This seems to be a known behavior, as reported in this GH issue .

A workaround (given in the issue comments) is to use an iterable for the levels arguments instead of relying on vmin and vmax .

Here is a code snippet to exhibit how vmin and vmax can be used with pcolormesh (as you said in the comment) but how to achieve a similar result with contourf .

import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np


def main():
    fig, axs = plt.subplots(2)
    Z = np.random.rand(100,100)
    Z[:1] *= 100

    cmap = plt.get_cmap("viridis")

    p1 = axs[0].pcolormesh(Z, vmin=0., vmax=1, cmap=cmap)
    fig.colorbar(p1, ax=axs[0])
    p2 = axs[1].contourf(Z, levels=np.linspace(0, 1, 100), cmap=cmap)
    fig.colorbar(p2, ax=axs[1], ticks=np.linspace(0, 1, 5))
    plt.show()

if __name__ == '__main__':
    main()