来自值数组的 Сontour 图

Question

我尝试使用二维数组中的值绘制二维等高线图：

[[495.9999999999999, 500.01606425702806, 500.00902029566515, 500.00712377560103, 500.00625978090756, `500.00576830636106, 500.0054518650384, 500.00523132254364], [495.01098934032973, 499.01702347018585, 499.0099971011282, 499.00810531762613, 499.00724348076085, 499.00675323367284, 499.00643758266295, 499.0062175909734], [494.0239547252757, 498.0199787681673, 498.0129699562654, 498.0110828998616, 498.0102232165132, 498.00973419443125, 498.009419332155, 498.0091998901702], [493.0388922066979, 497.024926162792, 497.01793487296663, 497.01605253421644, 497.0151950000822, 497.0147072005587, 497.0143931254402, 497.01417423206186], [492.05579784434497, 496.03186167384825, 496.0248878710904, 496.023010240568, 496.02215485135366, 496.021668271946, 496.0213549824126, 496.02113663654455], [491.07466770583807, 495.0407813290769, 495.0338249784473, 495.0319520467456, 495.03109879816566, 495.030613436436, 495.03030093091803, 495.03008313146637], [490.0954978666555, 494.05168116415524, 494.0447422307845, 494.04287398851517, 494.0420228762928, 494.04153872980817, 494.0412270067391, 494.0410097526121], [489.11828441011653, 493.0645572226812, 493.0576356717696, 493.05577210956324, 493.05492312942977, 493.0544401957622, 493.0541292535789, 493.0539125436869], [488.143023427366, 492.07940555615767, 492.072501352975, 492.0706424614809, 492.06979560917654, 492.0693138859027, 492.0690037230449, 492.06878755630066]]`

水平和垂直尺寸为r, d。在 2D 区域，我创建了一个有限元网格，我想要 将每个网格元素与二维数组中的每个数字连接起来，示意图如下所示：

我尝试使用matplotlib.figure，但x-axis和y-axis显示数组的数字，但不显示r和d的值：

import matplotlib.pyplot as plt
import pylab as pl
from matplotlib import cm
import numpy as np
from scipy import interpolate
from scipy import ndimage

d = 1
r = 1

I_ij = [[495.9999999999999, 500.01606425702806, 500.00902029566515, 500.00712377560103, 500.00625978090756, 500.00576830636106, 500.0054518650384, 500.00523132254364], [495.01098934032973, 499.01702347018585, 499.0099971011282, 499.00810531762613, 499.00724348076085, 499.00675323367284, 499.00643758266295, 499.0062175909734], [494.0239547252757, 498.0199787681673, 498.0129699562654, 498.0110828998616, 498.0102232165132, 498.00973419443125, 498.009419332155, 498.0091998901702], [493.0388922066979, 497.024926162792, 497.01793487296663, 497.01605253421644, 497.0151950000822, 497.0147072005587, 497.0143931254402, 497.01417423206186], [492.05579784434497, 496.03186167384825, 496.0248878710904, 496.023010240568, 496.02215485135366, 496.021668271946, 496.0213549824126, 496.02113663654455], [491.07466770583807, 495.0407813290769, 495.0338249784473, 495.0319520467456, 495.03109879816566, 495.030613436436, 495.03030093091803, 495.03008313146637], [490.0954978666555, 494.05168116415524, 494.0447422307845, 494.04287398851517, 494.0420228762928, 494.04153872980817, 494.0412270067391, 494.0410097526121], [489.11828441011653, 493.0645572226812, 493.0576356717696, 493.05577210956324, 493.05492312942977, 493.0544401957622, 493.0541292535789, 493.0539125436869], [488.143023427366, 492.07940555615767, 492.072501352975, 492.0706424614809, 492.06979560917654, 492.0693138859027, 492.0690037230449, 492.06878755630066]]


fig = plt.figure()
fig.suptitle('Test Title')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')
plt.imshow(I_ij, cmap='hot')
plt.colorbar()
plt.show()
   

Answer 1

您可以将索引d 创建为数组[1, 2, ..., 9]。和r 作为[1, 2, ..., 8]。然后您可以将它们用作等高线图的位置信息。 等高线图查找给定值之间的等值线。

同样，您也可以创建热图来显示二维数组的信息。

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

I_ij = [[495.9999999999999, 500.01606425702806, 500.00902029566515, 500.00712377560103, 500.00625978090756, 500.00576830636106, 500.0054518650384, 500.00523132254364], [495.01098934032973, 499.01702347018585, 499.0099971011282, 499.00810531762613, 499.00724348076085, 499.00675323367284, 499.00643758266295, 499.0062175909734], [494.0239547252757, 498.0199787681673, 498.0129699562654, 498.0110828998616, 498.0102232165132, 498.00973419443125, 498.009419332155, 498.0091998901702], [493.0388922066979, 497.024926162792, 497.01793487296663, 497.01605253421644, 497.0151950000822, 497.0147072005587, 497.0143931254402, 497.01417423206186], [492.05579784434497, 496.03186167384825, 496.0248878710904, 496.023010240568, 496.02215485135366, 496.021668271946, 496.0213549824126, 496.02113663654455], [491.07466770583807, 495.0407813290769, 495.0338249784473, 495.0319520467456, 495.03109879816566, 495.030613436436, 495.03030093091803, 495.03008313146637], [490.0954978666555, 494.05168116415524, 494.0447422307845, 494.04287398851517, 494.0420228762928, 494.04153872980817, 494.0412270067391, 494.0410097526121], [489.11828441011653, 493.0645572226812, 493.0576356717696, 493.05577210956324, 493.05492312942977, 493.0544401957622, 493.0541292535789, 493.0539125436869], [488.143023427366, 492.07940555615767, 492.072501352975, 492.0706424614809, 492.06979560917654, 492.0693138859027, 492.0690037230449, 492.06878755630066]]
I_ij = np.array(I_ij)
d = np.arange(I_ij.shape[0]) + 1
r = np.arange(I_ij.shape[1]) + 1

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 5))

cnt = ax1.contourf(r, d, I_ij, cmap='hot')
ax1.invert_yaxis()
plt.colorbar(cnt, ax=ax1)

sns.heatmap(I_ij, xticklabels=r, yticklabels=d, annot=True, fmt='.2f', cmap='hot')

plt.tight_layout()
plt.show()