使用 Matplotlib 的颜色条刻度不匹配

Question

我正在绘制一个形状为(220,220)的数组Pe 。 使用红色圆圈突出显示的颜色条的某些刻度不匹配。 基本上，我希望颜色条上的每种颜色都有一个明确定义的最终值。 我该如何做到这一点？

import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
from matplotlib.colors import Normalize
from matplotlib import cm
import math
from  numpy import nan

fig,aPe = plt.subplots(1)
n=11
arMax=[]
arMin=[]

N=2*n*(n-1)

J = np.array([[]])

Pe=np.array([[397.9245283 , 431.37280501, 423.07492578, 448.72320111,
       415.76282885, 403.98056072, 406.32617702, 439.50026536,
       398.74902582, 402.08146852, 416.21242203, 427.5394595 ,
       427.06507633, 413.08553581, 412.20075077, 437.50197698,
       437.75076871, 398.33635041, 401.03411236, 436.50963158,
       414.19687294, 439.2494816 , 413.7516213 , 401.87155987,
       430.88988181, 422.61039289, 420.07358693, 411.75977806,
       448.46178462, 403.76866504, 425.41299332, 436.26224822,
       402.92330055, 399.36964466, 437.00524093, 430.16752099,
       414.86655013, 426.59174471, 401.66187026, 418.70266927,
       438.74877169, 416.66298861, 409.56899181, 408.04926082,
       399.9921984 , 447.93986435, 429.20813236, 432.34190614,
       426.8282793 , 407.83307657, 417.34067053, 417.11453175,
       429.68729115, 438.49884457, 423.54048103, 421.91550311,
       441.01100224, 402.71251224, 427.77704651, 441.26380205,
       444.06384971, 406.9706254 , 442.78670822, 431.13120817,
       429.92727196, 435.27551245, 444.3201626 , 447.6793595 ,
       404.40501999, 413.30732589, 407.40139454, 404.83037214,
       408.69919091, 445.3483799 , 428.49139455, 434.53838488,
       434.04835195, 427.30213625, 440.75849192, 412.64266901,
       406.11181421, 412.42159151, 430.64882546, 400.20014785,
       433.07159431, 405.68376641, 411.10008524, 437.25346787,
       419.61561798, 428.2530136 , 439.75133565, 440.25433778,
       405.25662001, 433.80374971, 422.14687898, 432.8280914 ,
       442.02394342, 424.70886414, 423.77364309, 403.34553985,
       425.64822218, 424.4746721 , 405.04338393, 404.19267892,
       408.9162946 , 421.45351141, 398.54258129, 443.55211004,
       447.41915748, 410.22377598, 434.29323018, 442.27790619,
       445.60617823, 446.6403631 , 432.09922288, 420.53255663,
       436.01514511, 412.86398365, 440.00269296, 420.30294648,
       436.75729566, 418.47505247, 400.61669609, 425.17802432,
       411.53964533, 444.57677156, 438.24920203, 408.48231763,
       406.75558225, 423.3075754 , 419.38700777, 415.31420594,
       417.56705465, 428.73004103, 418.24768301, 441.77027214,
       443.29668225, 407.61712126, 414.41985832, 417.7936845 ,
       406.54076625, 424.007062  , 403.13430964, 413.9741274 ,
       401.45239937, 432.58486216, 415.09025734, 411.31974785,
       446.12267131, 398.13033286, 415.53839631, 400.40831363,
       444.83367708, 422.84253175, 422.37850877, 426.11946115,
       409.35119469, 445.86427519, 416.43758344, 429.4475781 ,
       428.01489773, 430.40803867, 440.5062706 , 402.29159657,
       446.89965981, 408.26567439, 410.44250286, 415.98750396,
       443.04154848, 416.88863791, 421.68438072, 448.20067254,
       398.95568434, 399.78446494, 399.16255717, 405.47008071,
       433.559423  , 428.96895348, 419.84447757, 411.98014641,
       405.89767746, 409.78702083, 410.66146312, 431.61467276,
       434.78381651, 410.88065712, 414.64308392, 426.35547214,
       442.53216095, 441.51689185, 410.00528209, 418.02056049,
       409.13362907, 419.15864652, 447.15925776, 420.76241778,
       400.82529555, 431.8568119 , 421.22289476, 420.99253035,
       433.31537135, 402.50194436, 446.3813671 , 438.99898387,
       418.93053382, 445.09087969, 407.18589604, 443.80783236,
       435.76832176, 435.52177769, 399.57694714, 425.88371131,
       424.94331474, 404.61758428, 437.99984357, 403.55699154,
       413.52935426, 435.02952554, 424.2407382 , 397.9245283 ]])

C1 = nan
for i in J[0]:
    Pe = np.insert(Pe, i, [C1], axis=1)
print("Pe =", [Pe])

for i in range(0,len(Pe)):
    Max=max(max(Pe[i]), max(Pe[i]))
    Min=min(min(Pe[i]), min(Pe[i]))
    
    arMax.append(Max)
    Max=np.array(arMax)
    
    arMin.append(Min)
    Min=np.array(arMin)

a=min(Min)
b=max(Max)

print("a =",a)
print("b =",b)


Amax= math.ceil(b)
Amin= math.floor(a)
print(Amax, Amin)

color = cm.get_cmap('Dark2')
norm = Normalize(vmin=Amin, vmax=Amax)
color_list = []
for i in range(len(Pe[0])):   
    color_list.append(color(((Pe[0,i])-Amin)/(Amax-Amin)))
    
id = 0
for j in range(0, n):
    for k in range(n-1):
        aPe.hlines(200+200*(n-j-1)+5*n, 200*(k+1)+5*n, 200*(k+2)+5*n, zorder=0, linewidth=5.0,colors=color_list[id])
        id += 1

    for i in range(0, n):
        rect = mpl.patches.Rectangle((200+200*i, 200+200*j), 10*n, 10*n, linewidth=1.0, edgecolor='black', facecolor='black')
        aPe.add_patch(rect)
        if j < n-1:
            aPe.vlines(200+200*i+5*n, 200*(n-1-j)+5*n, 200*(n-j)+5*n, zorder=0,linewidth=5.0, colors=color_list[id])
            id += 1

cb = fig.colorbar(cm.ScalarMappable(cmap=color, norm=norm), ticks=np.arange(Amin, Amax+len(color.colors), len(color.colors)))
cb.set_ticks(np.arange(Amin, Amax+1, (Amax-Amin)/8).astype(np.int64))
cb.set_label("Entry pressure (N/m$^{2}$)")

aPe.set_xlim(left = 0, right = 220*n)
aPe.set_ylim(bottom = 0, top = 220*n)
plt.axis('off')
plt.title("Time = 5",fontsize=20)
plt.show()

Answer 1

目前，您首先设置ticks ：

ticsk=np.arange(Amin, Amax+len(color.colors), len(color.colors))
# array([397, 405, 413, 421, 429, 437, 445, 453])

这仍然会产生更严重的不匹配（请注意，此数组的长度为8 ，而您正在寻找 9 个刻度标签，如下面的 arrays 中所示）。

但是你用以下内容覆盖它：

cb.set_ticks(np.arange(Amin, Amax+1, (Amax-Amin)/8).astype(np.int64))
# array([397, 403, 410, 416, 423, 429, 436, 442, 449], dtype=int64)

正如@r-beginners 的评论中所提到的，在这里您通过将浮点数转换为整数来引入不匹配。 原来的花车是：

np.arange(Amin, Amax+1, (Amax-Amin)/8)
array([397. , 403.5, 410. , 416.5, 423. , 429.5, 436. , 442.5, 449. ])

您想要做的是首先将实际浮点值传递给刻度，然后简单地调整它们的格式。 例如：

tks = np.arange(Amin, Amax+1, (Amax-Amin)/8)

cb = fig.colorbar(cm.ScalarMappable(cmap=color, norm=norm), ticks=tks)
cb.set_ticklabels(['{:.0f}'.format(t) for t in tks])

# or: cb.set_ticklabels([str(int(t)) for t in tks])

结果：