How can I create a heatmap with an uneven mesh size from a pandas dataset?
I want to create a plot that looks something like this which maps values to a user-defined mesh size to represent a physical geometry.
I was looking for a way to create heat maps and saw that seaborn has a function that creates heat maps. The code below makes the heat map. However, the mesh is equal size and does not represent the geometry that I want.
Here is the input.
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# NumPy arrays
IFE_x = np.array([7.11815 ,7.19875 ,7.34769 ,7.496 ,7.7413 ,
7.98354 , 8.22184 , 8.45534 , 8.77249 , 9.076 , 9.32338 ,
9.59487 , 9.84534 , 10.04127, 10.24567, 10.4217 , 10.54814,
10.63518, 10.70466, 10.74704, 10.77731])
IFE_x = np.repeat(IFE_x, 19)
IFE_y = np.array([24.90 ,24.40 , 23.40 , 22.40 , 21.00 ,16.00 , 11.00 ,6.00, 1.00,
-1.00 ,-6.00 ,-11.00,-16.00,-21.00,-22.40,-23.40,-24.40,-24.90,-25.40])
IFE_y = np.tile(IFE_y, 21)
IFE_ratio = np.array([1.0155, 0.9938, 0.9872, 0.9959, 1.0112, 0.9983,
1.0054, 0.9910, 1.0042, 0.9994, 1.0028, 1.0016, 1.0127, 1.0094, 1.0115,
0.9852, 0.9989, 0.9896, 0.9968, 1.0156, 0.9999, 0.9950, 1.0074, 0.9979,
0.9967, 1.0036, 0.9962, 1.0046, 0.9960, 1.0015, 1.0040, 1.0120, 1.0099,
1.0138, 1.0059, 1.0341, 0.9593, 1.0268, 0.9871, 0.9954, 0.9994, 1.0217,
0.9832, 1.0012, 1.0040, 1.0038, 0.9995, 0.9957, 0.9950, 1.0011, 1.0014,
0.9973, 0.9999, 1.0006, 1.0120, 1.0041, 1.0096, 1.0077, 0.9821, 0.9897,
0.9970, 0.9848, 1.0046, 0.9986, 0.9953, 0.9928, 0.9911, 1.0070, 0.9934,
1.0063, 1.0025, 0.9867, 1.0136, 1.0220, 1.0072, 1.0053, 1.0058, 0.9928,
0.9989, 0.9953, 0.9976, 1.0005, 1.0070, 0.9952, 0.9960, 0.9988, 0.9965,
1.0036, 1.0013, 0.9991, 0.9928, 0.9848, 1.0156, 0.9888, 0.9735, 0.9852,
1.0064, 1.0075, 0.9936, 1.0157, 0.9964, 1.0015, 1.0004, 0.9999, 1.0036,
1.0012, 0.9945, 1.0037, 0.9979, 0.9838, 0.9880, 1.0116, 1.0200, 1.0194,
0.9992, 0.9999, 0.9888, 0.9898, 0.9872, 0.9953, 1.0007, 1.0044, 0.9978,
0.9867,1.0073,1.0041,1.0048,1.0048, 0.9954,1.0056,
1.0090,1.0100,0.9965,1.0060,1.0008,1.0112,0.9984,1.0087,0.9985,1.0014,1.0083,
1.0037,0.9986,1.0043,1.0043,0.9990,1.0033,1.0049,0.9849,1.0026,1.0261,1.0203,0.9953,
0.9884,1.0120,0.9945,1.0032,1.0016,1.0031,1.0019,1.0016,0.9993,1.0037,0.9997,0.9977,1.0009,
1.0064,1.0026,1.0066,1.0020,1.0198,0.9736,0.9966,0.9930,1.0088,1.0036,1.0078,1.0072,
1.0012,1.0027,0.9968,0.9971,0.9969,0.9992,1.0021,0.9847,1.0125,0.9966,0.9927,
1.0105,0.9805,0.9927,0.9782,0.9937,1.0158,1.0032,0.9990,0.9968,1.0003,0.9919,1.0039,
1.0014,0.9996,0.9989,0.9990,0.9928,0.9855,1.0040,0.9954,0.9958,0.9974,1.0093,0.9886,
0.9969,0.9940,0.9989,0.9982,0.9997,0.9922,0.9941,0.9970,1.0017,0.9943,0.9930,0.9952,0.9945,
0.9659,0.9881,0.9847,1.0074,0.9939,0.9969,1.0119,0.9979,0.9974,1.0034,0.9989,1.0021,
0.9893,0.9976,1.0081,1.0012,0.9904,1.0096,1.0052,1.0037,0.9991,1.0073,1.0055,1.0130,
1.0042,1.0055,0.9923,0.9965,0.9953,1.0010,1.0086,0.9900,1.0050,1.0073,1.0017,0.9926,
0.9980,0.9993,0.9965,0.9987,0.9896,0.9833,1.0036,1.0025,0.9982,0.9962,0.9941,0.9904,
1.0061,1.0026,0.9871,1.0031,1.0042,0.9986,0.9848,1.0061,1.0061,0.9882,1.0033,1.0014,
0.9935,0.9885,0.9986,0.9979,1.0016,0.9999,0.9946,1.0033,0.9965,0.9934,0.9986,1.0020,
1.0026,0.9950,1.0035,1.0017,1.0030,0.9906,0.9970,1.0020,0.9969,0.9869,1.0037,0.9979,1.0019,
1.0000,0.9984,0.9985,0.9918,1.0029,1.0025,0.9989,1.0071,1.0110,1.0122,1.0183,0.9722,1.0052,
1.0038,1.0121,0.9916,0.9883,0.9993,1.0011,0.9963,1.0038,0.9918,0.9895,1.0007,0.9969,
0.9994,1.0028,0.9950,1.0053,1.0092,0.9779,0.9940,0.9921,0.9991,1.0036,0.9855,0.9951,1.0091,0.9943,
0.9988,0.9931,0.9891,0.9978,0.9965,0.9936,1.0048,0.9997,0.9882,0.9933,1.0087,0.9844,
1.0047,0.9975,0.9951,0.9946,0.9949,1.0035,0.9900,0.9967,0.9912,0.9883,0.9959,0.9898,
1.0009,0.9983,0.9957,1.0030,0.9900,1.0037,1.0078,0.9970,1.0008,0.9866,1.0003,0.9973,1.0057,
0.9929,0.9918,0.9871,0.9988,1.0040,0.9905,1.0012,0.9739,0.9985,1.0050,0.9901,1.0206])
# Pandas dataframe and plot creation
df = pd.DataFrame({'X': IFE_x, 'Y': IFE_y, 'Ratio': IFE_ratio})
table = df.pivot('Y', 'X', 'Ratio')
ax = sns.heatmap(table)
ax.invert_yaxis()
plt.show()
The output I get is the following image. However, I do not want equally spaced mesh. Is there a way to change the way the mesh is spaced on the plot to create something more similar to the first image in this post?
Solution
As suggested by JohanC in the comment to your question, you can use matplotlib.axes.Axes.pcolormesh. In order to do this, as explained in the documentation, you need:
- an array-like
Xfor x axis - an array-like
Yfor y axis - a matrix-like
Cof mapped values
You can use IFE_x and IFE_y for the first two, respectively, without any manipulation. Insted you need to reshape IFE_ratio as a matrix, with:
IFE_ratio = np.reshape(IFE_ratio, (len(IFE_x), len(IFE_y)))
now you can draw the plot:
fig, ax = plt.subplots()
ax.pcolormesh(IFE_x, IFE_y, IFE_ratio.T)
plt.show()
Note that you need to transpose IFE_ratio with .T method, in order to properly draw this plot.
Complete Code
import matplotlib.pyplot as plt
import numpy as np
IFE_x = np.array([7.11815 ,7.19875 ,7.34769 ,7.496 ,7.7413 ,
7.98354 , 8.22184 , 8.45534 , 8.77249 , 9.076 , 9.32338 ,
9.59487 , 9.84534 , 10.04127, 10.24567, 10.4217 , 10.54814,
10.63518, 10.70466, 10.74704, 10.77731])
IFE_y = np.array([24.90 ,24.40 , 23.40 , 22.40 , 21.00 ,16.00 , 11.00 ,6.00, 1.00,
-1.00 ,-6.00 ,-11.00,-16.00,-21.00,-22.40,-23.40,-24.40,-24.90,-25.40])
IFE_ratio = np.array([1.0155, 0.9938, 0.9872, 0.9959, 1.0112, 0.9983,
1.0054, 0.9910, 1.0042, 0.9994, 1.0028, 1.0016, 1.0127, 1.0094, 1.0115,
0.9852, 0.9989, 0.9896, 0.9968, 1.0156, 0.9999, 0.9950, 1.0074, 0.9979,
0.9967, 1.0036, 0.9962, 1.0046, 0.9960, 1.0015, 1.0040, 1.0120, 1.0099,
1.0138, 1.0059, 1.0341, 0.9593, 1.0268, 0.9871, 0.9954, 0.9994, 1.0217,
0.9832, 1.0012, 1.0040, 1.0038, 0.9995, 0.9957, 0.9950, 1.0011, 1.0014,
0.9973, 0.9999, 1.0006, 1.0120, 1.0041, 1.0096, 1.0077, 0.9821, 0.9897,
0.9970, 0.9848, 1.0046, 0.9986, 0.9953, 0.9928, 0.9911, 1.0070, 0.9934,
1.0063, 1.0025, 0.9867, 1.0136, 1.0220, 1.0072, 1.0053, 1.0058, 0.9928,
0.9989, 0.9953, 0.9976, 1.0005, 1.0070, 0.9952, 0.9960, 0.9988, 0.9965,
1.0036, 1.0013, 0.9991, 0.9928, 0.9848, 1.0156, 0.9888, 0.9735, 0.9852,
1.0064, 1.0075, 0.9936, 1.0157, 0.9964, 1.0015, 1.0004, 0.9999, 1.0036,
1.0012, 0.9945, 1.0037, 0.9979, 0.9838, 0.9880, 1.0116, 1.0200, 1.0194,
0.9992, 0.9999, 0.9888, 0.9898, 0.9872, 0.9953, 1.0007, 1.0044, 0.9978,
0.9867,1.0073,1.0041,1.0048,1.0048, 0.9954,1.0056,
1.0090,1.0100,0.9965,1.0060,1.0008,1.0112,0.9984,1.0087,0.9985,1.0014,1.0083,
1.0037,0.9986,1.0043,1.0043,0.9990,1.0033,1.0049,0.9849,1.0026,1.0261,1.0203,0.9953,
0.9884,1.0120,0.9945,1.0032,1.0016,1.0031,1.0019,1.0016,0.9993,1.0037,0.9997,0.9977,1.0009,
1.0064,1.0026,1.0066,1.0020,1.0198,0.9736,0.9966,0.9930,1.0088,1.0036,1.0078,1.0072,
1.0012,1.0027,0.9968,0.9971,0.9969,0.9992,1.0021,0.9847,1.0125,0.9966,0.9927,
1.0105,0.9805,0.9927,0.9782,0.9937,1.0158,1.0032,0.9990,0.9968,1.0003,0.9919,1.0039,
1.0014,0.9996,0.9989,0.9990,0.9928,0.9855,1.0040,0.9954,0.9958,0.9974,1.0093,0.9886,
0.9969,0.9940,0.9989,0.9982,0.9997,0.9922,0.9941,0.9970,1.0017,0.9943,0.9930,0.9952,0.9945,
0.9659,0.9881,0.9847,1.0074,0.9939,0.9969,1.0119,0.9979,0.9974,1.0034,0.9989,1.0021,
0.9893,0.9976,1.0081,1.0012,0.9904,1.0096,1.0052,1.0037,0.9991,1.0073,1.0055,1.0130,
1.0042,1.0055,0.9923,0.9965,0.9953,1.0010,1.0086,0.9900,1.0050,1.0073,1.0017,0.9926,
0.9980,0.9993,0.9965,0.9987,0.9896,0.9833,1.0036,1.0025,0.9982,0.9962,0.9941,0.9904,
1.0061,1.0026,0.9871,1.0031,1.0042,0.9986,0.9848,1.0061,1.0061,0.9882,1.0033,1.0014,
0.9935,0.9885,0.9986,0.9979,1.0016,0.9999,0.9946,1.0033,0.9965,0.9934,0.9986,1.0020,
1.0026,0.9950,1.0035,1.0017,1.0030,0.9906,0.9970,1.0020,0.9969,0.9869,1.0037,0.9979,1.0019,
1.0000,0.9984,0.9985,0.9918,1.0029,1.0025,0.9989,1.0071,1.0110,1.0122,1.0183,0.9722,1.0052,
1.0038,1.0121,0.9916,0.9883,0.9993,1.0011,0.9963,1.0038,0.9918,0.9895,1.0007,0.9969,
0.9994,1.0028,0.9950,1.0053,1.0092,0.9779,0.9940,0.9921,0.9991,1.0036,0.9855,0.9951,1.0091,0.9943,
0.9988,0.9931,0.9891,0.9978,0.9965,0.9936,1.0048,0.9997,0.9882,0.9933,1.0087,0.9844,
1.0047,0.9975,0.9951,0.9946,0.9949,1.0035,0.9900,0.9967,0.9912,0.9883,0.9959,0.9898,
1.0009,0.9983,0.9957,1.0030,0.9900,1.0037,1.0078,0.9970,1.0008,0.9866,1.0003,0.9973,1.0057,
0.9929,0.9918,0.9871,0.9988,1.0040,0.9905,1.0012,0.9739,0.9985,1.0050,0.9901,1.0206])
IFE_ratio = np.reshape(IFE_ratio, (len(IFE_x), len(IFE_y)))
fig, ax = plt.subplots()
ax.pcolormesh(IFE_x, IFE_y, IFE_ratio.T)
plt.show()
Plot
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