3D surface plotting producing discontinuous polygonal faces instead of a smooth curve
I am trying to plot energy surfaces of triatomic molecules, A-B-C, in Python/Matplotlib. I have tabulated data of bond length (B1 and B2) combinations and computed molecular energies (MP3s) which I want to convert to a smooth 3D surface plot. A similar Excel Macro function output the following Excel surface output:
I implemented a similar graphical solution in Matplotlib (in Jupyter Notebook) using plot_surface:
import libraries:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
set up arrays and retrieve data from file:
N = [] #Not needed
B1 = [] #Bond 1 Distance
B2 = [] #Bond 2 Distance
SCF = [] #Not needed
MP2 = [] #Not needed
MP3 = [] #Energy
filename = #insert filepath here
f = open(filename, 'r')
for line in f:
columns = line.split()
N.append(float(columns[0]))
B1.append(float(columns[1])) #B1
B2.append(float(columns[2])) #B2
SCF.append(float(columns[3]))
MP2.append(float(columns[4]))
MP3.append(float(columns[5])) #Energy
f.close()
Remove duplicate values from bond distance arrays and convert bond distances and energies to 2D grids:
Xset = set(B1)
Xlist = list(Xset)
Xarray = np.array(Xlist)
Yset = set(B2)
Ylist = list(Yset)
Yarray = np.array(Ylist)
X, Y = np.meshgrid(Xarray, Yarray) #convert X and Y to 2D grids
Zarray = np.array(MP3)
Z = Zarray.reshape(len(Xset), len(Yset)) #convert Z to 2D grid
Plotting the data to output:
fig = plt.figure(figsize =(10, 10))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap="viridis")
fig.colorbar(surf, shrink=0.4)
plt.show()
However, the plot generated has large polygonal subsections and is not a smooth, continuous surface, and it has what appear to be inaccurate, smooth plotting artifacts resembling large planar faces. I was curious about the original version so I also decided to plot out a wireframe version and got a similarly confusing result, with what looked like large polygonal faces.
Plot_surface and wireframe versions:
I tested the original text file data with the scatter function and it perfectly reproduced the Excel results, and I then superimposed the results and found that they matched the coarse surface plot relatively well.
Scatter and Surface Plots compared:
I wanted to ensure that the scatter data was mirroring Excel so I rotated it and found additional matching geometry with a similar minimum (The direction of rotation is reversed vs. the Excel).
Head-on view of the scatter plot:
So, I am reasonably sure that the input file contains the right information to recreate an accurate surface plot, and the scatter plot matches Excel, but why doesn't the surface plot output a smooth surface? Is it to do with the low resolution of the data? I am only plotting 484 total points in these examples. Is it not working since the data are not from a self-defined equation but from outside data? Do I possibly need to apply something like a spline fitting to smooth the surface?
Supporting Information Here is a pastebin link to the text file I am using for input: https://pastebin.com/4fURFjMU Note: Only columns 2 (Bond distance 1), 3 (Bond distance 2), and 6 (Molecular Energy) of this file are used for any calculations/input, the other columns are unnecessary output artifacts of the computational program used.
1 0.7000 0.7000 -98.37390 -98.57110 -98.58410
2 0.8000 0.7000 -98.46298 -98.66474 -98.67770
3 0.9000 0.7000 -98.49247 -98.69853 -98.71140
4 1.0000 0.7000 -98.49104 -98.70128 -98.71400
5 1.1000 0.7000 -98.47388 -98.68824 -98.70077
6 1.2000 0.7000 -98.44935 -98.66785 -98.68011
7 1.3000 0.7000 -98.42241 -98.64533 -98.65713
8 1.4000 0.7000 -98.39649 -98.62526 -98.63616
9 1.5000 0.7000 -98.37423 -98.61235 -98.62225
10 1.6000 0.7000 -98.35668 -98.60720 -98.61857
11 1.7000 0.7000 -98.34331 -98.60679 -98.62365
12 1.8000 0.7000 -98.33310 -98.60857 -98.63398
13 1.9000 0.7000 -98.32513 -98.61123 -98.64673
14 2.0000 0.7000 -98.31873 -98.61426 -98.66026
15 2.1000 0.7000 -98.31342 -98.61750 -98.67377
16 2.2000 0.7000 -98.30890 -98.62091 -98.68698
17 2.3000 0.7000 -98.30495 -98.62448 -98.69982
18 2.4000 0.7000 -98.30144 -98.62823 -98.71235
19 2.5000 0.7000 -98.29826 -98.63216 -98.72463
20 2.6000 0.7000 -98.29535 -98.63628 -98.73676
21 2.7000 0.7000 -98.29267 -98.64059 -98.74882
22 2.8000 0.7000 -98.29017 -98.64508 -98.76086
23 0.7000 0.8000 -99.16190 -99.36656 -99.37968
24 0.8000 0.8000 -99.25060 -99.45989 -99.47287
25 0.9000 0.8000 -99.27908 -99.49270 -99.50547
26 1.0000 0.8000 -99.27631 -99.49407 -99.50656
27 1.1000 0.8000 -99.25767 -99.47943 -99.49155
28 1.2000 0.8000 -99.23153 -99.45716 -99.46884
29 1.3000 0.8000 -99.20265 -99.43195 -99.44309
30 1.4000 0.8000 -99.17395 -99.40705 -99.41745
31 1.5000 0.8000 -99.14855 -99.38995 -99.39814
32 1.6000 0.8000 -99.12939 -99.38736 -99.39451
33 1.7000 0.8000 -99.11589 -99.39252 -99.40563
34 1.8000 0.8000 -99.10640 -99.40007 -99.42466
35 1.9000 0.8000 -99.09959 -99.40781 -99.44615
36 2.0000 0.8000 -99.09451 -99.41507 -99.46723
37 2.1000 0.8000 -99.09058 -99.42173 -99.48680
38 2.2000 0.8000 -99.08738 -99.42785 -99.50465
39 2.3000 0.8000 -99.08468 -99.43356 -99.52093
40 2.4000 0.8000 -99.08232 -99.43898 -99.53594
41 2.5000 0.8000 -99.08019 -99.44420 -99.54995
42 2.6000 0.8000 -99.07823 -99.44929 -99.56324
43 2.7000 0.8000 -99.07640 -99.45434 -99.57601
44 2.8000 0.8000 -99.07467 -99.45937 -99.58843
45 0.7000 0.9000 -99.58864 -99.80002 -99.81317
46 0.8000 0.9000 -99.67767 -99.89382 -99.90673
47 0.9000 0.9000 -99.70605 -99.92665 -99.93924
48 1.0000 0.9000 -99.70298 -99.92786 -99.94003
49 1.1000 0.9000 -99.68402 -99.91305 -99.92471
50 1.2000 0.9000 -99.65762 -99.89070 -99.90176
51 1.3000 0.9000 -99.62853 -99.86562 -99.87598
52 1.4000 0.9000 -99.59946 -99.84093 -99.85046
53 1.5000 0.9000 -99.57256 -99.82211 -99.83006
54 1.6000 0.9000 -99.55109 -99.81828 -99.82283
55 1.7000 0.9000 -99.53576 -99.82431 -99.83076
56 1.8000 0.9000 -99.52503 -99.83345 -99.84900
57 1.9000 0.9000 -99.51739 -99.84272 -99.87136
58 2.0000 0.9000 -99.51180 -99.85116 -99.89396
59 2.1000 0.9000 -99.50752 -99.85866 -99.91512
60 2.2000 0.9000 -99.50411 -99.86533 -99.93437
61 2.3000 0.9000 -99.50128 -99.87138 -99.95181
62 2.4000 0.9000 -99.49882 -99.87697 -99.96775
63 2.5000 0.9000 -99.49663 -99.88227 -99.98253
64 2.6000 0.9000 -99.49463 -99.88739 -99.99645
65 2.7000 0.9000 -99.49278 -99.89242 -100.00975
66 2.8000 0.9000 -99.49103 -99.89742 -100.02263
67 0.7000 1.0000 -99.80989 -100.02698 -100.03998
68 0.8000 1.0000 -99.89942 -100.12138 -100.13404
69 0.9000 1.0000 -99.92799 -100.15456 -100.16677
70 1.0000 1.0000 -99.92504 -100.15607 -100.16773
71 1.1000 1.0000 -99.90625 -100.14167 -100.15268
72 1.2000 1.0000 -99.88019 -100.12002 -100.13027
73 1.3000 1.0000 -99.85169 -100.09617 -100.10552
74 1.4000 1.0000 -99.82366 -100.07370 -100.08189
75 1.5000 1.0000 -99.79815 -100.05657 -100.06278
76 1.6000 1.0000 -99.77677 -100.04861 -100.05181
77 1.7000 1.0000 -99.75999 -100.04909 -100.05118
78 1.8000 1.0000 -99.74713 -100.05428 -100.05986
79 1.9000 1.0000 -99.73726 -100.06130 -100.07460
80 2.0000 1.0000 -99.72953 -100.06863 -100.09226
81 2.1000 1.0000 -99.72334 -100.07570 -100.11074
82 2.2000 1.0000 -99.71826 -100.08232 -100.12894
83 2.3000 1.0000 -99.71397 -100.08852 -100.14640
84 2.4000 1.0000 -99.71027 -100.09439 -100.16299
85 2.5000 1.0000 -99.70702 -100.10001 -100.17879
86 2.6000 1.0000 -99.70412 -100.10547 -100.19390
87 2.7000 1.0000 -99.70150 -100.11085 -100.20847
88 2.8000 1.0000 -99.69911 -100.11620 -100.22262
89 0.7000 1.1000 -99.91553 -100.13738 -100.15002
90 0.8000 1.1000 -100.00550 -100.23231 -100.24451
91 0.9000 1.1000 -100.03434 -100.26589 -100.27752
92 1.0000 1.1000 -100.03165 -100.26783 -100.27878
93 1.1000 1.1000 -100.01319 -100.25398 -100.26414
94 1.2000 1.1000 -99.98755 -100.23305 -100.24230
95 1.3000 1.1000 -99.95960 -100.21012 -100.21831
96 1.4000 1.1000 -99.93214 -100.18851 -100.19538
97 1.5000 1.1000 -99.90689 -100.17090 -100.17590
98 1.6000 1.1000 -99.88487 -100.15941 -100.16188
99 1.7000 1.1000 -99.86645 -100.15450 -100.15474
100 1.8000 1.1000 -99.85139 -100.15474 -100.15479
101 1.9000 1.1000 -99.83915 -100.15815 -100.16104
102 2.0000 1.1000 -99.82916 -100.16323 -100.17176
103 2.1000 1.1000 -99.82090 -100.16902 -100.18526
104 2.2000 1.1000 -99.81399 -100.17505 -100.20023
105 2.3000 1.1000 -99.80812 -100.18108 -100.21584
106 2.4000 1.1000 -99.80306 -100.18703 -100.23157
107 2.5000 1.1000 -99.79866 -100.19289 -100.24718
108 2.6000 1.1000 -99.79477 -100.19868 -100.26254
109 2.7000 1.1000 -99.79130 -100.20443 -100.27762
110 2.8000 1.1000 -99.78819 -100.21016 -100.29244
111 0.7000 1.2000 -99.95657 -100.18246 -100.19456
112 0.8000 1.2000 -100.04688 -100.27779 -100.28932
113 0.9000 1.2000 -100.07598 -100.31172 -100.32258
114 1.0000 1.2000 -100.07358 -100.31407 -100.32414
115 1.1000 1.2000 -100.05546 -100.30071 -100.30987
116 1.2000 1.2000 -100.03022 -100.28034 -100.28848
117 1.3000 1.2000 -100.00264 -100.25794 -100.26491
118 1.4000 1.2000 -99.97543 -100.23652 -100.24214
119 1.5000 1.2000 -99.95006 -100.21808 -100.22203
120 1.6000 1.2000 -99.92732 -100.20394 -100.20586
121 1.7000 1.2000 -99.90750 -100.19468 -100.19444
122 1.8000 1.2000 -99.89053 -100.18996 -100.18819
123 1.9000 1.2000 -99.87614 -100.18889 -100.18701
124 2.0000 1.2000 -99.86397 -100.19043 -100.19033
125 2.1000 1.2000 -99.85365 -100.19370 -100.19729
126 2.2000 1.2000 -99.84486 -100.19807 -100.20697
127 2.3000 1.2000 -99.83732 -100.20311 -100.21857
128 2.4000 1.2000 -99.83081 -100.20854 -100.23142
129 2.5000 1.2000 -99.82513 -100.21422 -100.24506
130 2.6000 1.2000 -99.82016 -100.22005 -100.25916
131 2.7000 1.2000 -99.81575 -100.22597 -100.27350
132 2.8000 1.2000 -99.81184 -100.23197 -100.28794
133 0.7000 1.3000 -99.96204 -100.19141 -100.20281
134 0.8000 1.3000 -100.05258 -100.28701 -100.29775
135 0.9000 1.3000 -100.08192 -100.32123 -100.33118
136 1.0000 1.3000 -100.07982 -100.32393 -100.33299
137 1.1000 1.3000 -100.06204 -100.31098 -100.31905
138 1.2000 1.3000 -100.03715 -100.29101 -100.29799
139 1.3000 1.3000 -100.00985 -100.26890 -100.27467
140 1.4000 1.3000 -99.98275 -100.24743 -100.25187
141 1.5000 1.3000 -99.95720 -100.22824 -100.23121
142 1.6000 1.3000 -99.93387 -100.21227 -100.21360
143 1.7000 1.3000 -99.91299 -100.19994 -100.19955
144 1.8000 1.3000 -99.89456 -100.19127 -100.18932
145 1.9000 1.3000 -99.87844 -100.18593 -100.18296
146 2.0000 1.3000 -99.86442 -100.18343 -100.18034
147 2.1000 1.3000 -99.85225 -100.18322 -100.18113
148 2.2000 1.3000 -99.84169 -100.18477 -100.18493
149 2.3000 1.3000 -99.83252 -100.18766 -100.19124
150 2.4000 1.3000 -99.82452 -100.19154 -100.19958
151 2.5000 1.3000 -99.81752 -100.19615 -100.20952
152 2.6000 1.3000 -99.81137 -100.20130 -100.22065
153 2.7000 1.3000 -99.80594 -100.20683 -100.23268
154 2.8000 1.3000 -99.80113 -100.21266 -100.24534
155 0.7000 1.4000 -99.94865 -100.18100 -100.19161
156 0.8000 1.4000 -100.03937 -100.27679 -100.28663
157 0.9000 1.4000 -100.06893 -100.31124 -100.32021
158 1.0000 1.4000 -100.06712 -100.31424 -100.32224
159 1.1000 1.4000 -100.04968 -100.30160 -100.30856
160 1.2000 1.4000 -100.02510 -100.28192 -100.28775
161 1.3000 1.4000 -99.99804 -100.25996 -100.26459
162 1.4000 1.4000 -99.97103 -100.23837 -100.24175
163 1.5000 1.4000 -99.94534 -100.21860 -100.22069
164 1.6000 1.4000 -99.92159 -100.20139 -100.20216
165 1.7000 1.4000 -99.89998 -100.18706 -100.18653
166 1.8000 1.4000 -99.88054 -100.17567 -100.17397
167 1.9000 1.4000 -99.86319 -100.16711 -100.16449
168 2.0000 1.4000 -99.84778 -100.16114 -100.15804
169 2.1000 1.4000 -99.83415 -100.15747 -100.15446
170 2.2000 1.4000 -99.82212 -100.15579 -100.15356
171 2.3000 1.4000 -99.81152 -100.15579 -100.15511
172 2.4000 1.4000 -99.80216 -100.15719 -100.15884
173 2.5000 1.4000 -99.79390 -100.15973 -100.16446
174 2.6000 1.4000 -99.78659 -100.16321 -100.17171
175 2.7000 1.4000 -99.78012 -100.16745 -100.18031
176 2.8000 1.4000 -99.77436 -100.17227 -100.19002
177 0.7000 1.5000 -99.92602 -100.16084 -100.17061
178 0.8000 1.5000 -100.01689 -100.25677 -100.26568
179 0.9000 1.5000 -100.04668 -100.29142 -100.29939
180 1.0000 1.5000 -100.04516 -100.29466 -100.30161
181 1.1000 1.5000 -100.02802 -100.28228 -100.28815
182 1.2000 1.5000 -100.00374 -100.26279 -100.26755
183 1.3000 1.5000 -99.97691 -100.24090 -100.24452
184 1.4000 1.5000 -99.94999 -100.21918 -100.22166
185 1.5000 1.5000 -99.92425 -100.19896 -100.20034
186 1.6000 1.5000 -99.90025 -100.18092 -100.18126
187 1.7000 1.5000 -99.87819 -100.16530 -100.16473
188 1.8000 1.5000 -99.85812 -100.15217 -100.15085
189 1.9000 1.5000 -99.83997 -100.14147 -100.13964
190 2.0000 1.5000 -99.82365 -100.13307 -100.13103
191 2.1000 1.5000 -99.80902 -100.12678 -100.12491
192 2.2000 1.5000 -99.79594 -100.12241 -100.12115
193 2.3000 1.5000 -99.78426 -100.11975 -100.11959
194 2.4000 1.5000 -99.77385 -100.11860 -100.12005
195 2.5000 1.5000 -99.76456 -100.11878 -100.12236
196 2.6000 1.5000 -99.75628 -100.12010 -100.12633
197 2.7000 1.5000 -99.74888 -100.12240 -100.13178
198 2.8000 1.5000 -99.74227 -100.12554 -100.13853
199 0.7000 1.6000 -99.89961 -100.13637 -100.14528
200 0.8000 1.6000 -99.99065 -100.23241 -100.24041
201 0.9000 1.6000 -100.02066 -100.26724 -100.27424
202 1.0000 1.6000 -100.01939 -100.27068 -100.27662
203 1.1000 1.6000 -100.00254 -100.25848 -100.26334
204 1.2000 1.6000 -99.97853 -100.23913 -100.24291
205 1.3000 1.6000 -99.95192 -100.21726 -100.22000
206 1.4000 1.6000 -99.92515 -100.19540 -100.19716
207 1.5000 1.6000 -99.89944 -100.17485 -100.17573
208 1.6000 1.6000 -99.87534 -100.15621 -100.15637
209 1.7000 1.6000 -99.85307 -100.13974 -100.13936
210 1.8000 1.6000 -99.83266 -100.12550 -100.12480
211 1.9000 1.6000 -99.81407 -100.11344 -100.11270
212 2.0000 1.6000 -99.79722 -100.10347 -100.10300
213 2.1000 1.6000 -99.78199 -100.09546 -100.09560
214 2.2000 1.6000 -99.76827 -100.08926 -100.09039
215 2.3000 1.6000 -99.75592 -100.08472 -100.08723
216 2.4000 1.6000 -99.74482 -100.08167 -100.08599
217 2.5000 1.6000 -99.73485 -100.07997 -100.08650
218 2.6000 1.6000 -99.72589 -100.07948 -100.08864
219 2.7000 1.6000 -99.71783 -100.08005 -100.09226
220 2.8000 1.6000 -99.71059 -100.08157 -100.09720
221 0.7000 1.7000 -99.87244 -100.11054 -100.11865
222 0.8000 1.7000 -99.96364 -100.20672 -100.21385
223 0.9000 1.7000 -99.99385 -100.24170 -100.24778
224 1.0000 1.7000 -99.99282 -100.24530 -100.25031
225 1.1000 1.7000 -99.97622 -100.23323 -100.23719
226 1.2000 1.7000 -99.95247 -100.21396 -100.21691
227 1.3000 1.7000 -99.92609 -100.19209 -100.19413
228 1.4000 1.7000 -99.89950 -100.17010 -100.17136
229 1.5000 1.7000 -99.87390 -100.14927 -100.14992
230 1.6000 1.7000 -99.84985 -100.13020 -100.13046
231 1.7000 1.7000 -99.82754 -100.11313 -100.11325
232 1.8000 1.7000 -99.80702 -100.09813 -100.09839
233 1.9000 1.7000 -99.78826 -100.08517 -100.08590
234 2.0000 1.7000 -99.77118 -100.07419 -100.07573
235 2.1000 1.7000 -99.75569 -100.06507 -100.06778
236 2.2000 1.7000 -99.74166 -100.05769 -100.06196
237 2.3000 1.7000 -99.72897 -100.05192 -100.05815
238 2.4000 1.7000 -99.71752 -100.04763 -100.05621
239 2.5000 1.7000 -99.70718 -100.04469 -100.05601
240 2.6000 1.7000 -99.69785 -100.04298 -100.05744
241 2.7000 1.7000 -99.68942 -100.04238 -100.06036
242 2.8000 1.7000 -99.68181 -100.04278 -100.06463
243 0.7000 1.8000 -99.84606 -100.08495 -100.09231
244 0.8000 1.8000 -99.93742 -100.18127 -100.18760
245 0.9000 1.8000 -99.96781 -100.21639 -100.22164
246 1.0000 1.8000 -99.96698 -100.22011 -100.22428
247 1.1000 1.8000 -99.95062 -100.20815 -100.21130
248 1.2000 1.8000 -99.92711 -100.18892 -100.19118
249 1.3000 1.8000 -99.90097 -100.16702 -100.16853
250 1.4000 1.8000 -99.87461 -100.14491 -100.14588
251 1.5000 1.8000 -99.84921 -100.12384 -100.12452
252 1.6000 1.8000 -99.82531 -100.10442 -100.10508
253 1.7000 1.8000 -99.80311 -100.08689 -100.08785
254 1.8000 1.8000 -99.78266 -100.07133 -100.07292
255 1.9000 1.8000 -99.76393 -100.05773 -100.06032
256 2.0000 1.8000 -99.74684 -100.04603 -100.05001
257 2.1000 1.8000 -99.73131 -100.03614 -100.04189
258 2.2000 1.8000 -99.71722 -100.02795 -100.03588
259 2.3000 1.8000 -99.70446 -100.02136 -100.03186
260 2.4000 1.8000 -99.69290 -100.01623 -100.02972
261 2.5000 1.8000 -99.68244 -100.01247 -100.02932
262 2.6000 1.8000 -99.67298 -100.00994 -100.03057
263 2.7000 1.8000 -99.66441 -100.00856 -100.03333
264 2.8000 1.8000 -99.65665 -100.00821 -100.03749
265 0.7000 1.9000 -99.82122 -100.06039 -100.06708
266 0.8000 1.9000 -99.91273 -100.15686 -100.16247
267 0.9000 1.9000 -99.94326 -100.19210 -100.19659
268 1.0000 1.9000 -99.94261 -100.19592 -100.19935
269 1.1000 1.9000 -99.92645 -100.18403 -100.18650
270 1.2000 1.9000 -99.90318 -100.16483 -100.16652
271 1.3000 1.9000 -99.87731 -100.14289 -100.14404
272 1.4000 1.9000 -99.85121 -100.12066 -100.12154
273 1.5000 1.9000 -99.82607 -100.09938 -100.10032
274 1.6000 1.9000 -99.80241 -100.07966 -100.08099
275 1.7000 1.9000 -99.78043 -100.06175 -100.06384
276 1.8000 1.9000 -99.76018 -100.04574 -100.04898
277 1.9000 1.9000 -99.74163 -100.03163 -100.03642
278 2.0000 1.9000 -99.72470 -100.01938 -100.02611
279 2.1000 1.9000 -99.70930 -100.00890 -100.01799
280 2.2000 1.9000 -99.69533 -100.00012 -100.01196
281 2.3000 1.9000 -99.68266 -99.99291 -100.00791
282 2.4000 1.9000 -99.67118 -99.98717 -100.00573
283 2.5000 1.9000 -99.66078 -99.98280 -100.00530
284 2.6000 1.9000 -99.65135 -99.97970 -100.00650
285 2.7000 1.9000 -99.64281 -99.97775 -100.00924
286 2.8000 1.9000 -99.63506 -99.97686 -100.01339
287 0.7000 2.0000 -99.79818 -100.03723 -100.04333
288 0.8000 2.0000 -99.88982 -100.13384 -100.13881
289 0.9000 2.0000 -99.92047 -100.16919 -100.17301
290 1.0000 2.0000 -99.91995 -100.17309 -100.17585
291 1.1000 2.0000 -99.90398 -100.16125 -100.16313
292 1.2000 2.0000 -99.88095 -100.14206 -100.14331
293 1.3000 2.0000 -99.85536 -100.12008 -100.12101
294 1.4000 2.0000 -99.82956 -100.09772 -100.09870
295 1.5000 2.0000 -99.80473 -100.07625 -100.07766
296 1.6000 2.0000 -99.78139 -100.05627 -100.05850
297 1.7000 2.0000 -99.75972 -100.03803 -100.04150
298 1.8000 2.0000 -99.73976 -100.02164 -100.02676
299 1.9000 2.0000 -99.72149 -100.00711 -100.01430
300 2.0000 2.0000 -99.70484 -99.99441 -100.00408
301 2.1000 2.0000 -99.68969 -99.98348 -99.99601
302 2.2000 2.0000 -99.67595 -99.97423 -99.99001
303 2.3000 2.0000 -99.66350 -99.96656 -99.98597
304 2.4000 2.0000 -99.65221 -99.96038 -99.98377
305 2.5000 2.0000 -99.64199 -99.95556 -99.98331
306 2.6000 2.0000 -99.63272 -99.95203 -99.98447
307 2.7000 2.0000 -99.62431 -99.94967 -99.98715
308 2.8000 2.0000 -99.61668 -99.94839 -99.99124
309 0.7000 2.1000 -99.77698 -100.01560 -100.02118
310 0.8000 2.1000 -99.86870 -100.11234 -100.11672
311 0.9000 2.1000 -99.89943 -100.14777 -100.15097
312 1.0000 2.1000 -99.89903 -100.15174 -100.15390
313 1.1000 2.1000 -99.88325 -100.13995 -100.14130
314 1.2000 2.1000 -99.86046 -100.12076 -100.12166
315 1.3000 2.1000 -99.83516 -100.09872 -100.09955
316 1.4000 2.1000 -99.80969 -100.07624 -100.07746
317 1.5000 2.1000 -99.78521 -100.05459 -100.05663
318 1.6000 2.1000 -99.76223 -100.03437 -100.03768
319 1.7000 2.1000 -99.74092 -100.01584 -100.02086
320 1.8000 2.1000 -99.72133 -99.99913 -100.00628
321 1.9000 2.1000 -99.70342 -99.98427 -99.99395
322 2.0000 2.1000 -99.68711 -99.97123 -99.98383
323 2.1000 2.1000 -99.67230 -99.95996 -99.97584
324 2.2000 2.1000 -99.65888 -99.95038 -99.96988
325 2.3000 2.1000 -99.64672 -99.94241 -99.96585
326 2.4000 2.1000 -99.63572 -99.93592 -99.96364
327 2.5000 2.1000 -99.62575 -99.93083 -99.96313
328 2.6000 2.1000 -99.61671 -99.92704 -99.96422
329 2.7000 2.1000 -99.60851 -99.92443 -99.96681
330 2.8000 2.1000 -99.60107 -99.92291 -99.97079
331 0.7000 2.2000 -99.75750 -99.99553 -100.00061
332 0.8000 2.2000 -99.84927 -100.09238 -100.09618
333 0.9000 2.2000 -99.88005 -100.12788 -100.13047
334 1.0000 2.2000 -99.87977 -100.13191 -100.13348
335 1.1000 2.2000 -99.86416 -100.12014 -100.12102
336 1.2000 2.2000 -99.84162 -100.10095 -100.10155
337 1.3000 2.2000 -99.81662 -100.07884 -100.07967
338 1.4000 2.2000 -99.79149 -100.05624 -100.05781
339 1.5000 2.2000 -99.76739 -100.03441 -100.03722
340 1.6000 2.2000 -99.74480 -100.01397 -100.01849
341 1.7000 2.2000 -99.72391 -99.99520 -100.00187
342 1.8000 2.2000 -99.70473 -99.97824 -99.98747
343 1.9000 2.2000 -99.68722 -99.96313 -99.97529
344 2.0000 2.2000 -99.67131 -99.94987 -99.96529
345 2.1000 2.2000 -99.65689 -99.93841 -99.95739
346 2.2000 2.2000 -99.64384 -99.92867 -99.95149
347 2.3000 2.2000 -99.63204 -99.92055 -99.94749
348 2.4000 2.2000 -99.62136 -99.91395 -99.94528
349 2.5000 2.2000 -99.61169 -99.90876 -99.94475
350 2.6000 2.2000 -99.60293 -99.90487 -99.94579
351 2.7000 2.2000 -99.59498 -99.90218 -99.94831
352 2.8000 2.2000 -99.58777 -99.90059 -99.95219
353 0.7000 2.3000 -99.73961 -99.97697 -99.98154
354 0.8000 2.3000 -99.83140 -100.07392 -100.07713
355 0.9000 2.3000 -99.86223 -100.10949 -100.11145
356 1.0000 2.3000 -99.86204 -100.11357 -100.11453
357 1.1000 2.3000 -99.84661 -100.10183 -100.10221
358 1.2000 2.3000 -99.82431 -100.08261 -100.08294
359 1.3000 2.3000 -99.79962 -100.06041 -100.06129
360 1.4000 2.3000 -99.77485 -100.03768 -100.03968
361 1.5000 2.3000 -99.75114 -100.01568 -100.01934
362 1.6000 2.3000 -99.72897 -99.99504 -100.00084
363 1.7000 2.3000 -99.70850 -99.97608 -99.98444
364 1.8000 2.3000 -99.68977 -99.95895 -99.97023
365 1.9000 2.3000 -99.67270 -99.94372 -99.95821
366 2.0000 2.3000 -99.65722 -99.93038 -99.94835
367 2.1000 2.3000 -99.64322 -99.91889 -99.94058
368 2.2000 2.3000 -99.63057 -99.90916 -99.93478
369 2.3000 2.3000 -99.61915 -99.90108 -99.93087
370 2.4000 2.3000 -99.60882 -99.89455 -99.92872
371 2.5000 2.3000 -99.59948 -99.88943 -99.92823
372 2.6000 2.3000 -99.59102 -99.88562 -99.92929
373 2.7000 2.3000 -99.58335 -99.88301 -99.93179
374 2.8000 2.3000 -99.57639 -99.88148 -99.93564
375 0.7000 2.4000 -99.72316 -99.95991 -99.96389
Solution
I guess you shouldn't meshgrid your arrays but rather just reshape them
import matplotlib.pyplot as plt
from numpy import array
data_by_rows = array([[float(v)for v in line.split()]for line in open('data')]).T
x, y, z = data_by_rows[1], data_by_rows[2], data_by_rows[5]
x, y, z = [arr.reshape(22, 22) for arr in (x, y, z)]
fig, ax = plt.subplots(subplot_kw={'projection':'3d'})
ax.plot_surface(x, y, z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.view_init(azim=120)
plt.show()
- how to average in a specific dimension with numpy.mean?
- Removed Realm, but still getting this error: module importing failed: invalid token (rlm_lldb.py, line 37) File "temp.py", line 1,
- Bool object does not support item assignment
- Conda env vs venv / pyenv / virtualenv / poetry / docker, etc
- How to import values from excel with pandas into tkinter faster?
- Speed up concatenation of excel files with Pandas
- Faster way to read Excel files to pandas dataframe
- Create Azure TimerTrigger Durable Function in python
- Read same file from src and test in PyCharm
- How to use Cython to compile Python 3 into C
- Outputting variable from one thread to other
- How to POST a JSON having a single body parameter in FastAPI?
- touchscreen raspberry pi phantom touch (reset fixes)
- How to determine the encoding of text
- Specify extras_require with pip install -e
- Plotting a second scaled y axis in matplotlib from one set of data
- How can I call scikit-learn classifiers from Java?
- How does Pandas.Series.nbytes work for strings? Results don't seem to match expectations
- How to install ursina on pydroid?
- auditlog with Django and DRF
- Python: How to RECURSIVELY remove None values from a NESTED data structure (lists and dictionaries)?
- How to Mark Repeated Entries as True Starting from the Second Occurrence Using NumPy?
- Python3.5 send object over socket (Pygame cam image)
- Value based partial slicing with non-existing keys is now deprecated
- Camelot: DeprecationError: PdfFileReader is deprecated
- How can I partially fill a numpy array with a value given a range?
- Conda wont install pdfplumber
- tomli-w, adding arrays from a python script
- Override value in pydantic model with environment variable
- Polars-Python: Compare list columns