guess function when using built-in defined models in lmfit

guess function when using built-in defined models in lmfit - python

I am having a problem with the guess function of lmfit. I am trying to fit some experimental data and I want to use different built in models of lmfit, but I cannot run the built in modules, only if I define the function directly.
The following code does not work, but if I comment the guess function it works.
P.S. It would be more interesting for me that the index is the first column because I will put this in a loop that will use all the same first column of the data and therefore i could put each new second column of the data as a new column in the DataFrame.
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from lmfit import Model
from lmfit.models import GaussianModel
minvalue = 3.25
maxvalue = 3.45
rawdata = pd.read_csv('datafile.txt', delim_whitespace = True, names=['XX','YY'])
#Section the data
data = rawdata[(rawdata['XX']>minvalue) & (rawdata['XX'] < maxvalue)]
#Create a DataFrame with the data
dataDataframe = pd.DataFrame()
dataDataframe[0] = data['YY']
dataDataframe = dataDataframe.set_index(data['XX'])
# Gaussian curve
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(np.sqrt(2*np.pi)*wid)) * np.exp(-(x-cen)**2 /(2*wid**2))
result_gaussian = Model(gaussian).fit(dataDataframe[0], x=dataDataframe.index.values, amp=5, cen=5, wid=1)
mod = GaussianModel()
pars = mod.guess(dataDataframe[0], x = np.float32(dataDataframe.index.values))
out = mod.fit(dataDataframe[0], pars , x = np.float32(dataDataframe.index.values))
plt.plot(dataDataframe.index.values, dataDataframe[0],'bo')
plt.plot(dataDataframe.index.values, result_gaussian.best_fit, 'r-', label = 'Gaussian')
plt.plot(dataDataframe.index.values, out.best_fit, 'b-', label = 'Gaussian2')
plt.legend()
plt.show()
Error message I am having if I uncomment the built in modules:
File "/Users/johndoe/anaconda2/lib/python2.7/site-packages/lmfit/models.py", line 52, in guess_from_peak
cen = x[imaxy]
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
I have tried to run the guess_from_peak from models.py and i did not have a problem it resulted in an integer.
Raw data:
1.1661899e+000 7.3414581e+002
1.1730889e+000 7.4060590e+002
1.1799880e+000 7.3778076e+002
1.1868871e+000 7.2950366e+002
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3.9810147e+000 3.6295535e+002
3.9879138e+000 3.6387720e+002
3.9948130e+000 3.6416183e+002
4.0017118e+000 3.6089911e+002
4.0086112e+000 3.6826599e+002
4.0155101e+000 3.7570581e+002
4.0224090e+000 3.6361679e+002
4.0293083e+000 3.6003177e+002
4.0362072e+000 3.7528265e+002
4.0431066e+000 3.7368362e+002
4.0500054e+000 3.8174683e+002
4.0569048e+000 4.0386084e+002
4.0638037e+000 4.2738324e+002
4.0707026e+000 4.4587668e+002
4.0776019e+000 4.5433987e+002
4.0845008e+000 4.4404083e+002
4.0914001e+000 4.2589066e+002
4.0982990e+000 3.9662262e+002
4.1051979e+000 3.7311325e+002
4.1120973e+000 3.5790594e+002
4.1189961e+000 3.4554794e+002
4.1258955e+000 3.5435367e+002
4.1327944e+000 3.7766489e+002
4.1396937e+000 3.7425708e+002
4.1465926e+000 3.5805182e+002
4.1534915e+000 3.5078519e+002
4.1603909e+000 3.5888739e+002
4.1672897e+000 3.7242688e+002
4.1741891e+000 3.7792575e+002
4.1810880e+000 3.7338031e+002
4.1879873e+000 3.6538324e+002
4.1948862e+000 3.5872525e+002
4.2017851e+000 3.4688391e+002
4.2086844e+000 3.4881918e+002
4.2155833e+000 3.4818274e+002
4.2224827e+000 3.4055273e+002
4.2293816e+000 3.3977536e+002
4.2362804e+000 3.3322891e+002
4.2431798e+000 3.3594962e+002
4.2500787e+000 3.4658536e+002
4.2569780e+000 3.4479083e+002
4.2638769e+000 3.4267456e+002
4.2707763e+000 3.4828876e+002
4.2776752e+000 3.4845041e+002
4.2845740e+000 3.3986469e+002
4.2914734e+000 3.3093433e+002
4.2983723e+000 3.3255331e+002
4.3052716e+000 3.4089511e+002
4.3121705e+000 3.4742932e+002
4.3190699e+000 3.3570422e+002
4.3259687e+000 3.2636673e+002
4.3328676e+000 3.3228806e+002
4.3397670e+000 3.5141977e+002
4.3466659e+000 3.5683167e+002
4.3535652e+000 3.4719943e+002
4.3604641e+000 3.4054718e+002
4.3673630e+000 3.2842471e+002
4.3742623e+000 3.2503146e+002
4.3811612e+000 3.3431540e+002
4.3880606e+000 3.3462808e+002
4.3949594e+000 3.3529224e+002
4.4018588e+000 3.3313510e+002
4.4087577e+000 3.4015598e+002
4.4156566e+000 3.3703552e+002
4.4225559e+000 3.3024448e+002
4.4294548e+000 3.2974786e+002

As I suggested in the comment above, coercing the pandas Series into an ndarray will fix the problem:
mod = GaussianModel()
ydata = np.array(dataDataframe[0])
xdata = np.array(dataDataframe.index.values)
pars = mod.guess(ydata, x=xdata)
out = mod.fit(ydata, pars, x=xdata)

This example works for me:
#!/usr/bin/env python
from lmfit.models import LorentzianModel
import matplotlib.pyplot as plt
import pandas as pd
dframe = pd.read_csv('peak.csv')
model = LorentzianModel()
params = model.guess(dframe['y'], x=dframe['x'])
result = model.fit(dframe['y'], params, x=dframe['x'])
print(result.fit_report())
result.plot_fit()
plt.show()
with peaks.csv of
x,y
0.000000, 0.021654
0.200000, 0.385367
0.400000, 0.193304
0.600000, 0.103481
0.800000, 0.404041
1.000000, 0.212585
1.200000, 0.253212
1.400000, -0.037306
1.600000, 0.271415
1.800000, 0.025614
2.000000, 0.066419
2.200000, -0.034347
2.400000, 0.153702
2.600000, 0.161341
2.800000, -0.097676
3.000000, -0.061880
3.200000, 0.085341
3.400000, 0.083674
3.600000, 0.190944
3.800000, 0.222168
4.000000, 0.214417
4.200000, 0.341221
4.400000, 0.634501
4.600000, 0.302566
4.800000, 0.101096
5.000000, -0.106441
5.200000, 0.567396
5.400000, 0.531899
5.600000, 0.459800
5.800000, 0.646655
6.000000, 0.662228
6.200000, 0.820844
6.400000, 0.947696
6.600000, 1.541353
6.800000, 1.763981
7.000000, 1.846081
7.200000, 2.986333
7.400000, 3.182907
7.600000, 3.786487
7.800000, 4.822287
8.000000, 5.739122
8.200000, 6.744448
8.400000, 7.295213
8.600000, 8.737766
8.800000, 9.693782
9.000000, 9.894218
9.200000, 10.193956
9.400000, 10.091519
9.600000, 9.652392
9.800000, 8.670938
10.000000, 8.004205
10.200000, 6.773599
10.400000, 6.076502
10.600000, 5.127315
10.800000, 4.303762
11.000000, 3.426006
11.200000, 2.416431
11.400000, 2.311363
11.600000, 1.748020
11.800000, 1.135594
12.000000, 0.888514
12.200000, 1.030794
12.400000, 0.543024
12.600000, 0.767751
12.800000, 0.657551
13.000000, 0.495730
13.200000, 0.447520
13.400000, 0.173839
13.600000, 0.256758
13.800000, 0.596106
14.000000, 0.065328
14.200000, 0.197267
14.400000, 0.260038
14.600000, 0.460880
14.800000, 0.335248
15.000000, 0.295977
15.200000, -0.010228
15.400000, 0.138670
15.600000, 0.192113
15.800000, 0.304371
16.000000, 0.442517
16.200000, 0.164944
16.400000, 0.001907
16.600000, 0.207504
16.800000, 0.012640
17.000000, 0.090878
17.200000, -0.222967
17.400000, 0.391717
17.600000, 0.180295
17.800000, 0.206875
18.000000, 0.240595
18.200000, -0.037437
18.400000, 0.139918
18.600000, 0.012560
18.800000, -0.053009
19.000000, 0.226069
19.200000, 0.076879
19.400000, 0.078599
19.600000, 0.016125
19.800000, -0.071217
20.000000, -0.091474

Related

Smooth the stair-like surface plot in matplotlib

how can I smooth / extrapolate surface plot to avoid stair-like jumps in the envelope?
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import griddata
import pandas as pd
df = pd.read_csv("data.csv", header=None, delimiter=";",
na_values=[None], names=["X", "Y", "Z"])
df['Z'] = df['Z'].astype(float)
X = df["X"].unique()
Y = df["Y"].unique()
XX, YY = np.meshgrid(X, Y)
ZZ = df["Z"].to_numpy().reshape(Y.size, X.size)
fig, ax = plt.subplots(1, 1)
ax.contourf(XX, YY, ZZ, 25, cmap='jet')
plt.show()
Here is the data:
0;0;0
100;0;0
200;0;0
300;0;0
400;0;0
500;0;0
600;0;0
700;0;0
800;0;0
900;0;0
1000;0;0
1100;0;0
1200;0;0
1300;0;0
1400;0;0
1500;0;0
1600;0;0
1700;0;0
1800;0;0
1900;0;0
2000;0;0
2100;0;0
2200;0;0
2300;0;0
2400;0;0
2500;0;0
2600;0;0
2700;0;0
2800;0;0
2900;0;0
3000;0;0
3100;0;0
3200;0;0
3300;0;0
3400;0;0
3500;0;0
3600;0;0
3700;0;0
3800;0;0
3900;0;0
4000;0;0
4100;0;0
4200;0;0
4300;0;0
4400;0;0
4500;0;0
4600;0;0
4700;0;0
4800;0;0
4900;0;0
5000;0;0
5100;0;0
5200;0;0
5300;0;0
5400;0;0
5500;0;0
5600;0;0
5700;0;0
5800;0;0
5900;0;0
6000;0;0
6100;0;0
6200;0;0
6300;0;0
6400;0;0
6500;0;0
6600;0;0
6700;0;0
6800;0;0
6900;0;0
7000;0;0
7100;0;0
7200;0;0
7300;0;0
7400;0;0
7500;0;0
7600;0;0
7700;0;0
7800;0;0
7900;0;0
8000;0;0
8100;0;0
8200;0;0
8300;0;0
8400;0;0
8500;0;0
8600;0;0
8700;0;0
8800;0;0
8900;0;0
9000;0;0
9100;0;0
9200;0;0
9300;0;0
9400;0;0
9500;0;0
9600;0;0
9700;0;0
9800;0;0
9900;0;0
10000;0;0
0;5;0
100;5;43.775
200;5;60.729
300;5;69.712
400;5;75.267
500;5;79.037
600;5;81.759
700;5;83.815
800;5;85.42
900;5;86.706
1000;5;87.759
1100;5;88.636
1200;5;89.376
1300;5;90.009
1400;5;90.555
1500;5;91.031
1600;5;91.448
1700;5;91.817
1800;5;92.146
1900;5;92.439
2000;5;92.702
2100;5;92.94
2200;5;93.155
2300;5;93.35
2400;5;93.528
2500;5;93.691
2600;5;93.839
2700;5;93.976
2800;5;94.102
2900;5;94.218
3000;5;94.325
3100;5;94.424
3200;5;94.516
3300;5;94.601
3400;5;94.679
3500;5;94.753
3600;5;94.821
3700;5;94.884
3800;5;94.943
3900;5;94.998
4000;5;95.05
4100;5;95.098
4200;5;95.142
4300;5;95.184
4400;5;95.223
4500;5;95.259
4600;5;95.293
4700;5;95.324
4800;5;95.354
4900;5;95.381
5000;5;95.407
5100;5;95.43
5200;5;95.452
5300;5;95.473
5400;5;95.492
5500;5;95.509
5600;5;95.526
5700;5;95.541
5800;5;95.554
5900;5;95.567
6000;5;95.579
6100;5;95.589
6200;5;95.599
6300;5;95.602
6400;5;95.573
6500;5;95.511
6600;5;95.428
6700;5;95.325
6800;5;95.208
6900;5;95.074
7000;5;94.925
7100;5;94.779
7200;5;94.619
7300;5;94.443
7400;5;94.278
7500;5;94.103
7600;5;93.929
7700;5;93.748
7800;5;93.577
7900;5;93.365
8000;5;93.212
8100;5;93.044
8200;5;92.832
8300;5;92.678
8400;5;92.511
8500;5;92.365
8600;5;92.23
8700;5;92.025
8800;5;91.908
8900;5;91.668
9000;5;91.529
9100;5;91.461
9200;5;91.308
9300;5;91.147
9400;5;90.975
9500;5;90.885
9600;5;90.695
9700;5;90.594
9800;5;90.383
9900;5;90.27
10000;5;90.221
0;10;0
100;10;28.723
200;10;44.58
300;10;54.629
400;10;61.562
500;10;66.634
600;10;70.502
700;10;73.55
800;10;76.012
900;10;78.041
1000;10;79.743
1100;10;81.189
1200;10;82.433
1300;10;83.515
1400;10;84.463
1500;10;85.301
1600;10;86.047
1700;10;86.715
1800;10;87.316
1900;10;87.859
2000;10;88.353
2100;10;88.804
2200;10;89.217
2300;10;89.597
2400;10;89.946
2500;10;90.27
2600;10;90.569
2700;10;90.848
2800;10;91.107
2900;10;91.349
3000;10;91.576
3100;10;91.788
3200;10;91.987
3300;10;92.174
3400;10;92.35
3500;10;92.516
3600;10;92.673
3700;10;92.821
3800;10;92.961
3900;10;93.094
4000;10;93.22
4100;10;93.34
4200;10;93.454
4300;10;93.562
4400;10;93.665
4500;10;93.763
4600;10;93.857
4700;10;93.946
4800;10;94.032
4900;10;94.113
5000;10;94.191
5100;10;94.266
5200;10;94.338
5300;10;94.406
5400;10;94.472
5500;10;94.535
5600;10;94.595
5700;10;94.653
5800;10;94.709
5900;10;94.755
6000;10;94.774
6100;10;94.767
6200;10;94.735
6300;10;94.69
6400;10;94.636
6500;10;94.562
6600;10;94.475
6700;10;94.377
6800;10;94.297
6900;10;94.193
7000;10;94.084
7100;10;93.976
7200;10;93.867
7300;10;93.753
7400;10;93.641
7500;10;93.518
7600;10;93.428
7700;10;93.308
7800;10;93.198
7900;10;93.056
8000;10;92.983
8100;10;92.88
8200;10;92.768
8300;10;92.648
8400;10;92.519
8500;10;92.459
8600;10;92.313
8700;10;92.295
8800;10;92.168
8900;10;92.053
9000;10;91.968
9100;10;91.878
9200;10;91.782
9300;10;91.639
9400;10;91.64
9500;10;91.529
9600;10;91.483
9700;10;91.358
9800;10;91.303
9900;10;91.245
10000;10;91.1
0;15;0
100;15;21.556
200;15;35.446
300;15;45.139
400;15;52.286
500;15;57.772
600;15;62.115
700;15;65.639
800;15;68.554
900;15;71.005
1000;15;73.095
1100;15;74.897
1200;15;76.467
1300;15;77.848
1400;15;79.07
1500;15;80.16
1600;15;81.137
1700;15;82.019
1800;15;82.819
1900;15;83.547
2000;15;84.212
2100;15;84.823
2200;15;85.385
2300;15;85.904
2400;15;86.385
2500;15;86.831
2600;15;87.247
2700;15;87.635
2800;15;87.998
2900;15;88.338
3000;15;88.658
3100;15;88.958
3200;15;89.241
3300;15;89.508
3400;15;89.76
3500;15;89.999
3600;15;90.225
3700;15;90.44
3800;15;90.644
3900;15;90.838
4000;15;91.022
4100;15;91.198
4200;15;91.366
4300;15;91.526
4400;15;91.68
4500;15;91.826
4600;15;91.966
4700;15;92.101
4800;15;92.229
4900;15;92.353
5000;15;92.472
5100;15;92.586
5200;15;92.695
5300;15;92.801
5400;15;92.902
5500;15;92.997
5600;15;93.055
5700;15;93.075
5800;15;93.077
5900;15;93.061
6000;15;93.007
6100;15;92.952
6200;15;92.878
6300;15;92.809
6400;15;92.724
6500;15;92.641
6600;15;92.542
6700;15;92.458
6800;15;92.316
6900;15;92.252
7000;15;92.124
7100;15;92.014
7200;15;91.915
7300;15;91.781
7400;15;91.701
7500;15;91.57
7600;15;91.47
7700;15;91.355
7800;15;91.229
7900;15;91.127
8000;15;91.038
8100;15;90.884
8200;15;90.817
8300;15;90.642
8400;15;90.562
8500;15;90.475
8600;15;90.379
8700;15;90.23
8800;15;90.119
8900;15;90.001
9000;15;89.876
9100;15;89.83
9200;15;89.692
9300;15;89.545
9400;15;89.485
9500;15;89.324
9600;15;89.247
9700;15;89.053
9800;15;88.962
9900;15;88.748
10000;15;88.641
0;20;0
100;20;17.143
200;20;29.257
300;20;38.271
400;20;45.238
500;20;50.783
600;20;55.302
700;20;59.054
800;20;62.22
900;20;64.925
1000;20;67.265
1100;20;69.307
1200;20;71.106
1300;20;72.701
1400;20;74.127
1500;20;75.407
1600;20;76.564
1700;20;77.614
1800;20;78.572
1900;20;79.448
2000;20;80.253
2100;20;80.995
2200;20;81.681
2300;20;82.317
2400;20;82.908
2500;20;83.46
2600;20;83.975
2700;20;84.457
2800;20;84.909
2900;20;85.334
3000;20;85.735
3100;20;86.112
3200;20;86.469
3300;20;86.806
3400;20;87.126
3500;20;87.429
3600;20;87.717
3700;20;87.99
3800;20;88.251
3900;20;88.5
4000;20;88.737
4100;20;88.963
4200;20;89.179
4300;20;89.387
4400;20;89.585
4500;20;89.775
4600;20;89.957
4700;20;90.132
4800;20;90.3
4900;20;90.462
5000;20;90.617
5100;20;90.767
5200;20;90.906
5300;20;90.993
5400;20;91.04
5500;20;91.041
5600;20;91.013
5700;20;90.958
5800;20;90.904
5900;20;90.8
6000;20;90.7
6100;20;90.594
6200;20;90.513
6300;20;90.36
6400;20;90.236
6500;20;90.093
6600;20;89.929
6700;20;89.818
6800;20;89.661
6900;20;89.517
7000;20;89.297
7100;20;89.158
7200;20;88.937
7300;20;88.768
7400;20;88.581
7500;20;88.372
7600;20;88.198
7700;20;87.954
7800;20;87.785
7900;20;87.402
8000;20;87.261
8100;20;86.931
8200;20;86.571
8300;20;86.149
8400;20;85.651
8500;20;84.934
8600;20;NaN
8700;20;NaN
8800;20;NaN
8900;20;NaN
9000;20;NaN
9100;20;NaN
9200;20;NaN
9300;20;NaN
9400;20;NaN
9500;20;NaN
9600;20;NaN
9700;20;NaN
9800;20;NaN
9900;20;NaN
10000;20;NaN
0;25;0
100;25;14.542
200;25;24.631
300;25;32.887
400;25;39.506
500;25;44.932
600;25;49.46
700;25;53.295
800;25;56.585
900;25;59.439
1000;25;61.937
1100;25;64.142
1200;25;66.103
1300;25;67.858
1400;25;69.437
1500;25;70.866
1600;25;72.165
1700;25;73.351
1800;25;74.438
1900;25;75.438
2000;25;76.361
2100;25;77.215
2200;25;78.008
2300;25;78.746
2400;25;79.434
2500;25;80.078
2600;25;80.681
2700;25;81.247
2800;25;81.78
2900;25;82.282
3000;25;82.756
3100;25;83.204
3200;25;83.628
3300;25;84.031
3400;25;84.412
3500;25;84.775
3600;25;85.121
3700;25;85.45
3800;25;85.763
3900;25;86.063
4000;25;86.35
4100;25;86.623
4200;25;86.886
4300;25;87.137
4400;25;87.378
4500;25;87.61
4600;25;87.832
4700;25;88.046
4800;25;88.251
4900;25;88.449
5000;25;88.597
5100;25;88.662
5200;25;88.669
5300;25;88.641
5400;25;88.564
5500;25;88.454
5600;25;88.343
5700;25;88.173
5800;25;87.978
5900;25;87.779
6000;25;87.619
6100;25;87.334
6200;25;87.094
6300;25;86.786
6400;25;86.432
6500;25;86.029
6600;25;85.728
6700;25;85.226
6800;25;84.582
6900;25;83.788
7000;25;82.493
7100;25;NaN
7200;25;NaN
7300;25;NaN
7400;25;NaN
7500;25;NaN
7600;25;NaN
7700;25;NaN
7800;25;NaN
7900;25;NaN
8000;25;NaN
8100;25;NaN
8200;25;NaN
8300;25;NaN
8400;25;NaN
8500;25;NaN
8600;25;NaN
8700;25;NaN
8800;25;NaN
8900;25;NaN
9000;25;NaN
9100;25;NaN
9200;25;NaN
9300;25;NaN
9400;25;NaN
9500;25;NaN
9600;25;NaN
9700;25;NaN
9800;25;NaN
9900;25;NaN
10000;25;NaN
0;30;0
100;30;11.622
200;30;20.82
300;30;28.279
400;30;34.45
500;30;39.64
600;30;44.065
700;30;47.882
800;30;51.209
900;30;54.134
1000;30;56.725
1100;30;59.037
1200;30;61.112
1300;30;62.985
1400;30;64.684
1500;30;66.232
1600;30;67.648
1700;30;68.949
1800;30;70.147
1900;30;71.255
2000;30;72.282
2100;30;73.236
2200;30;74.126
2300;30;74.957
2400;30;75.736
2500;30;76.466
2600;30;77.152
2700;30;77.798
2800;30;78.408
2900;30;78.984
3000;30;79.529
3100;30;80.045
3200;30;80.536
3300;30;81.001
3400;30;81.444
3500;30;81.866
3600;30;82.268
3700;30;82.653
3800;30;83.019
3900;30;83.37
4000;30;83.706
4100;30;84.028
4200;30;84.337
4300;30;84.633
4400;30;84.918
4500;30;85.191
4600;30;85.454
4700;30;85.684
4800;30;85.875
4900;30;85.931
5000;30;85.873
5100;30;85.747
5200;30;85.589
5300;30;85.309
5400;30;84.994
5500;30;84.567
5600;30;84.204
5700;30;83.5
5800;30;82.652
5900;30;81.491
6000;30;NaN
6100;30;NaN
6200;30;NaN
6300;30;NaN
6400;30;NaN
6500;30;NaN
6600;30;NaN
6700;30;NaN
6800;30;NaN
6900;30;NaN
7000;30;NaN
7100;30;NaN
7200;30;NaN
7300;30;NaN
7400;30;NaN
7500;30;NaN
7600;30;NaN
7700;30;NaN
7800;30;NaN
7900;30;NaN
8000;30;NaN
8100;30;NaN
8200;30;NaN
8300;30;NaN
8400;30;NaN
8500;30;NaN
8600;30;NaN
8700;30;NaN
8800;30;NaN
8900;30;NaN
9000;30;NaN
9100;30;NaN
9200;30;NaN
9300;30;NaN
9400;30;NaN
9500;30;NaN
9600;30;NaN
9700;30;NaN
9800;30;NaN
9900;30;NaN
10000;30;NaN
0;35;0
100;35;96.527
200;35;17.603
300;35;24.265
400;35;29.928
500;35;34.8
600;35;39.036
700;35;42.754
800;35;46.042
900;35;48.971
1000;35;51.596
1100;35;53.963
1200;35;56.107
1300;35;58.059
1400;35;59.843
1500;35;61.481
1600;35;62.988
1700;35;64.381
1800;35;65.671
1900;35;66.871
2000;35;67.987
2100;35;69.03
2200;35;70.006
2300;35;70.922
2400;35;71.782
2500;35;72.592
2600;35;73.355
2700;35;74.077
2800;35;74.759
2900;35;75.406
3000;35;76.019
3100;35;76.602
3200;35;77.156
3300;35;77.684
3400;35;78.188
3500;35;78.668
3600;35;79.127
3700;35;79.566
3800;35;79.986
3900;35;80.389
4000;35;80.775
4100;35;81.146
4200;35;81.502
4300;35;81.844
4400;35;82.173
4500;35;82.49
4600;35;82.636
4700;35;82.522
4800;35;82.302
4900;35;81.917
5000;35;81.253
5100;35;80.269
5200;35;78.316
5300;35;NaN
5400;35;NaN
5500;35;NaN
5600;35;NaN
5700;35;NaN
5800;35;NaN
5900;35;NaN
6000;35;NaN
6100;35;NaN
6200;35;NaN
6300;35;NaN
6400;35;NaN
6500;35;NaN
6600;35;NaN
6700;35;NaN
6800;35;NaN
6900;35;NaN
7000;35;NaN
7100;35;NaN
7200;35;NaN
7300;35;NaN
7400;35;NaN
7500;35;NaN
7600;35;NaN
7700;35;NaN
7800;35;NaN
7900;35;NaN
8000;35;NaN
8100;35;NaN
8200;35;NaN
8300;35;NaN
8400;35;NaN
8500;35;NaN
8600;35;NaN
8700;35;NaN
8800;35;NaN
8900;35;NaN
9000;35;NaN
9100;35;NaN
9200;35;NaN
9300;35;NaN
9400;35;NaN
9500;35;NaN
9600;35;NaN
9700;35;NaN
9800;35;NaN
9900;35;NaN
10000;35;NaN
0;40;0
100;40;80.252
200;40;14.856
300;40;20.741
400;40;25.863
500;40;30.362
600;40;34.344
700;40;37.893
800;40;41.077
900;40;43.949
1000;40;46.553
1100;40;48.924
1200;40;51.092
1300;40;53.083
1400;40;54.917
1500;40;56.611
1600;40;58.182
1700;40;59.642
1800;40;61.002
1900;40;62.273
2000;40;63.462
2100;40;64.578
2200;40;65.627
2300;40;66.615
2400;40;67.546
2500;40;68.427
2600;40;69.26
2700;40;70.049
2800;40;70.798
2900;40;71.51
3000;40;72.187
3100;40;72.832
3200;40;73.447
3300;40;74.035
3400;40;74.596
3500;40;75.133
3600;40;75.647
3700;40;76.139
3800;40;76.612
3900;40;77.065
4000;40;77.501
4100;40;77.92
4200;40;78.323
4300;40;78.496
4400;40;78.403
4500;40;NaN
4600;40;NaN
4700;40;NaN
4800;40;NaN
4900;40;NaN
5000;40;NaN
5100;40;NaN
5200;40;NaN
5300;40;NaN
5400;40;NaN
5500;40;NaN
5600;40;NaN
5700;40;NaN
5800;40;NaN
5900;40;NaN
6000;40;NaN
6100;40;NaN
6200;40;NaN
6300;40;NaN
6400;40;NaN
6500;40;NaN
6600;40;NaN
6700;40;NaN
6800;40;NaN
6900;40;NaN
7000;40;NaN
7100;40;NaN
7200;40;NaN
7300;40;NaN
7400;40;NaN
7500;40;NaN
7600;40;NaN
7700;40;NaN
7800;40;NaN
7900;40;NaN
8000;40;NaN
8100;40;NaN
8200;40;NaN
8300;40;NaN
8400;40;NaN
8500;40;NaN
8600;40;NaN
8700;40;NaN
8800;40;NaN
8900;40;NaN
9000;40;NaN
9100;40;NaN
9200;40;NaN
9300;40;NaN
9400;40;NaN
9500;40;NaN
9600;40;NaN
9700;40;NaN
9800;40;NaN
9900;40;NaN
10000;40;NaN

Use SmoothBivariateSpline to interpolate NAN values.
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import SmoothBivariateSpline
import pandas as pd
df = pd.read_csv("data.csv", header=None, delimiter=";",
na_values=[None], names=["X", "Y", "Z"])
df['Z'] = df['Z'].astype(float)
x = df["X"]
y = df["Y"]
z = df["Z"]
xnew = np.unique(x)
ynew = np.unique(y)
mask = ~np.isnan(z)
x=(x[mask])
y=(y[mask])
z=(z[mask])
f = SmoothBivariateSpline(x,y,z,kx=1,ky=1)
znew=np.transpose(f(xnew, ynew))
fig, ax = plt.subplots(1, 1)
ax.contourf(xnew, ynew, znew, 25, cmap='jet')
plt.show()
You get the figure :

TypeError when fitting Statsmodels OLS with standard errors clustered 2 ways

Context
Building on top of How to run Panel OLS regressions with 3+ fixed-effect and errors clustering? and notably Josef's third comment, I am trying to adapt the OLS Coefficients and Standard Errors Clustered by Firm and Year section of this example notebook below:
cluster_2ways_ols = sm.ols(formula='y ~ x', data=df).fit(cov_type='cluster',
cov_kwds={'groups': np.array(df[['firmid', 'year']])},
use_t=True)
to my own example dataset.
Note that I am able to reproduce this example (and it works). I can also add fixed-effects, by using 'y ~ x + C(firmid) + C(year)' as formula instead.
Problem
However, trying to port the same command to my example dataset (see code below), I'm getting the following error:
>>> model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
>>> result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country', 'year_publication']])},
use_t=True
)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 343, in fit
lfit = OLSResults(
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 1607, in __init__
self.get_robustcov_results(cov_type=cov_type, use_self=True,
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 2568, in get_robustcov_results
res.cov_params_default = sw.cov_cluster_2groups(
File "/path/venv/lib64/python3.10/site-packages/statsmodels/stats/sandwich_covariance.py", line 591, in cov_cluster_2groups
combine_indices(group)[0],
File "/path/venv/lib64/python3.10/site-packages/statsmodels/tools/grouputils.py", line 55, in combine_indices
groups_ = groups.view([('', groups.dtype)] * groups.shape[1])
File "/path/venv/lib64/python3.10/site-packages/numpy/core/_internal.py", line 549, in _view_is_safe
raise TypeError("Cannot change data-type for object array.")
TypeError: Cannot change data-type for object array.
I have tried to manually cast the year_publication to string/object using np.array(df[['country', 'year_publication']].astype("str")), but it doesn't solve the issue.
Questions
What is the cause of the TypeError()?
How to adapt the example command to my dataset?
Minimal Working Example
from io import StringIO
import numpy as np
import pandas as pd
import statsmodels.api as sm
DATA = """
"continent","country","source","year_publication","year_data","population","gdp"
"Africa","Angola","OECD",2020,2018,972,52.69
"Africa","Angola","OECD",2020,2019,986,802.7
"Africa","Angola","OECD",2020,2020,641,568.74
"Africa","Angola","OECD",2021,2018,438,168.83
"Africa","Angola","OECD",2021,2019,958,310.57
"Africa","Angola","OECD",2021,2020,270,144.02
"Africa","Angola","OECD",2022,2018,528,359.71
"Africa","Angola","OECD",2022,2019,974,582.98
"Africa","Angola","OECD",2022,2020,835,820.49
"Africa","Angola","IMF",2020,2018,168,148.85
"Africa","Angola","IMF",2020,2019,460,236.21
"Africa","Angola","IMF",2020,2020,360,297.15
"Africa","Angola","IMF",2021,2018,381,249.13
"Africa","Angola","IMF",2021,2019,648,128.05
"Africa","Angola","IMF",2021,2020,206,179.05
"Africa","Angola","IMF",2022,2018,282,150.29
"Africa","Angola","IMF",2022,2019,125,23.42
"Africa","Angola","IMF",2022,2020,410,247.35
"Africa","Angola","WorldBank",2020,2018,553,182.06
"Africa","Angola","WorldBank",2020,2019,847,698.87
"Africa","Angola","WorldBank",2020,2020,844,126.61
"Africa","Angola","WorldBank",2021,2018,307,239.76
"Africa","Angola","WorldBank",2021,2019,659,510.73
"Africa","Angola","WorldBank",2021,2020,548,331.89
"Africa","Angola","WorldBank",2022,2018,448,122.76
"Africa","Angola","WorldBank",2022,2019,768,761.41
"Africa","Angola","WorldBank",2022,2020,324,163.57
"Africa","Benin","OECD",2020,2018,513,196.9
"Africa","Benin","OECD",2020,2019,590,83.7
"Africa","Benin","OECD",2020,2020,791,511.09
"Africa","Benin","OECD",2021,2018,799,474.43
"Africa","Benin","OECD",2021,2019,455,234.21
"Africa","Benin","OECD",2021,2020,549,238.83
"Africa","Benin","OECD",2022,2018,235,229.33
"Africa","Benin","OECD",2022,2019,347,46.51
"Africa","Benin","OECD",2022,2020,532,392.13
"Africa","Benin","IMF",2020,2018,138,137.05
"Africa","Benin","IMF",2020,2019,978,239.82
"Africa","Benin","IMF",2020,2020,821,33.41
"Africa","Benin","IMF",2021,2018,453,291.93
"Africa","Benin","IMF",2021,2019,526,381.88
"Africa","Benin","IMF",2021,2020,467,313.57
"Africa","Benin","IMF",2022,2018,948,555.23
"Africa","Benin","IMF",2022,2019,323,289.91
"Africa","Benin","IMF",2022,2020,421,62.35
"Africa","Benin","WorldBank",2020,2018,983,271.69
"Africa","Benin","WorldBank",2020,2019,138,23.55
"Africa","Benin","WorldBank",2020,2020,636,623.65
"Africa","Benin","WorldBank",2021,2018,653,534.99
"Africa","Benin","WorldBank",2021,2019,564,368.8
"Africa","Benin","WorldBank",2021,2020,741,312.02
"Africa","Benin","WorldBank",2022,2018,328,292.11
"Africa","Benin","WorldBank",2022,2019,653,429.21
"Africa","Benin","WorldBank",2022,2020,951,242.73
"Africa","Chad","OECD",2020,2018,176,95.06
"Africa","Chad","OECD",2020,2019,783,425.34
"Africa","Chad","OECD",2020,2020,885,461.6
"Africa","Chad","OECD",2021,2018,673,15.87
"Africa","Chad","OECD",2021,2019,131,74.46
"Africa","Chad","OECD",2021,2020,430,61.58
"Africa","Chad","OECD",2022,2018,593,211.34
"Africa","Chad","OECD",2022,2019,647,550.37
"Africa","Chad","OECD",2022,2020,154,105.65
"Africa","Chad","IMF",2020,2018,160,32.41
"Africa","Chad","IMF",2020,2019,654,27.84
"Africa","Chad","IMF",2020,2020,616,468.92
"Africa","Chad","IMF",2021,2018,996,22.4
"Africa","Chad","IMF",2021,2019,126,93.18
"Africa","Chad","IMF",2021,2020,879,547.87
"Africa","Chad","IMF",2022,2018,663,520
"Africa","Chad","IMF",2022,2019,681,544.76
"Africa","Chad","IMF",2022,2020,101,55.6
"Africa","Chad","WorldBank",2020,2018,786,757.22
"Africa","Chad","WorldBank",2020,2019,599,593.69
"Africa","Chad","WorldBank",2020,2020,641,529.84
"Africa","Chad","WorldBank",2021,2018,343,287.89
"Africa","Chad","WorldBank",2021,2019,438,340.83
"Africa","Chad","WorldBank",2021,2020,762,594.67
"Africa","Chad","WorldBank",2022,2018,430,128.69
"Africa","Chad","WorldBank",2022,2019,260,242.59
"Africa","Chad","WorldBank",2022,2020,607,216.1
"Europe","Denmark","OECD",2020,2018,114,86.75
"Europe","Denmark","OECD",2020,2019,937,373.29
"Europe","Denmark","OECD",2020,2020,866,392.93
"Europe","Denmark","OECD",2021,2018,296,41.04
"Europe","Denmark","OECD",2021,2019,402,32.67
"Europe","Denmark","OECD",2021,2020,306,7.88
"Europe","Denmark","OECD",2022,2018,540,379.51
"Europe","Denmark","OECD",2022,2019,108,26.72
"Europe","Denmark","OECD",2022,2020,752,307.2
"Europe","Denmark","IMF",2020,2018,157,24.24
"Europe","Denmark","IMF",2020,2019,303,79.04
"Europe","Denmark","IMF",2020,2020,286,122.36
"Europe","Denmark","IMF",2021,2018,569,69.32
"Europe","Denmark","IMF",2021,2019,808,642.67
"Europe","Denmark","IMF",2021,2020,157,5.58
"Europe","Denmark","IMF",2022,2018,147,112.21
"Europe","Denmark","IMF",2022,2019,414,311.16
"Europe","Denmark","IMF",2022,2020,774,230.46
"Europe","Denmark","WorldBank",2020,2018,695,350.03
"Europe","Denmark","WorldBank",2020,2019,511,209.84
"Europe","Denmark","WorldBank",2020,2020,181,29.27
"Europe","Denmark","WorldBank",2021,2018,503,176.89
"Europe","Denmark","WorldBank",2021,2019,710,609.02
"Europe","Denmark","WorldBank",2021,2020,264,165.78
"Europe","Denmark","WorldBank",2022,2018,670,638.99
"Europe","Denmark","WorldBank",2022,2019,651,354.6
"Europe","Denmark","WorldBank",2022,2020,632,623.94
"Europe","Estonia","OECD",2020,2018,838,263.67
"Europe","Estonia","OECD",2020,2019,638,533.95
"Europe","Estonia","OECD",2020,2020,898,638.73
"Europe","Estonia","OECD",2021,2018,262,98.16
"Europe","Estonia","OECD",2021,2019,569,552.54
"Europe","Estonia","OECD",2021,2020,868,252.48
"Europe","Estonia","OECD",2022,2018,927,264.65
"Europe","Estonia","OECD",2022,2019,205,150.6
"Europe","Estonia","OECD",2022,2020,828,752.61
"Europe","Estonia","IMF",2020,2018,841,176.31
"Europe","Estonia","IMF",2020,2019,614,230.55
"Europe","Estonia","IMF",2020,2020,500,41.19
"Europe","Estonia","IMF",2021,2018,510,169.68
"Europe","Estonia","IMF",2021,2019,765,401.85
"Europe","Estonia","IMF",2021,2020,751,319.6
"Europe","Estonia","IMF",2022,2018,314,58.81
"Europe","Estonia","IMF",2022,2019,155,2.24
"Europe","Estonia","IMF",2022,2020,734,187.6
"Europe","Estonia","WorldBank",2020,2018,332,160.17
"Europe","Estonia","WorldBank",2020,2019,466,385.33
"Europe","Estonia","WorldBank",2020,2020,487,435.06
"Europe","Estonia","WorldBank",2021,2018,461,249.19
"Europe","Estonia","WorldBank",2021,2019,932,763.38
"Europe","Estonia","WorldBank",2021,2020,650,463.91
"Europe","Estonia","WorldBank",2022,2018,570,549.97
"Europe","Estonia","WorldBank",2022,2019,909,80.48
"Europe","Estonia","WorldBank",2022,2020,523,242.22
"Europe","Finland","OECD",2020,2018,565,561.64
"Europe","Finland","OECD",2020,2019,646,161.62
"Europe","Finland","OECD",2020,2020,194,133.69
"Europe","Finland","OECD",2021,2018,529,39.76
"Europe","Finland","OECD",2021,2019,800,680.12
"Europe","Finland","OECD",2021,2020,418,399.19
"Europe","Finland","OECD",2022,2018,591,253.12
"Europe","Finland","OECD",2022,2019,457,272.58
"Europe","Finland","OECD",2022,2020,157,105.1
"Europe","Finland","IMF",2020,2018,860,445.03
"Europe","Finland","IMF",2020,2019,108,47.72
"Europe","Finland","IMF",2020,2020,523,500.58
"Europe","Finland","IMF",2021,2018,560,81.47
"Europe","Finland","IMF",2021,2019,830,664.64
"Europe","Finland","IMF",2021,2020,903,762.62
"Europe","Finland","IMF",2022,2018,179,167.73
"Europe","Finland","IMF",2022,2019,137,98.98
"Europe","Finland","IMF",2022,2020,666,524.86
"Europe","Finland","WorldBank",2020,2018,319,146.01
"Europe","Finland","WorldBank",2020,2019,401,219.56
"Europe","Finland","WorldBank",2020,2020,711,45.35
"Europe","Finland","WorldBank",2021,2018,828,20.97
"Europe","Finland","WorldBank",2021,2019,180,66.3
"Europe","Finland","WorldBank",2021,2020,682,92.57
"Europe","Finland","WorldBank",2022,2018,254,81.2
"Europe","Finland","WorldBank",2022,2019,619,159.08
"Europe","Finland","WorldBank",2022,2020,191,184.4
"""
df = pd.read_csv(StringIO(DATA))
model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country', 'year_publication']])},
use_t=True
)
print(result.summary())

I have realized that the groups must be an array of integers rather than of objects/strings.
Thus, label encoding the string column as follows:
df["country"] = df["country"].astype("category")
df["country_id"] = df.country.cat.codes
and using country_id to cluster the standard errors solves the issue:
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country_id', 'year_publication']])},
use_t=True
)
Fully working example:
from io import StringIO
import numpy as np
import pandas as pd
import statsmodels.api as sm
DATA = """
"continent","country","source","year_publication","year_data","population","gdp"
"Africa","Angola","OECD",2020,2018,972,52.69
"Africa","Angola","OECD",2020,2019,986,802.7
"Africa","Angola","OECD",2020,2020,641,568.74
"Africa","Angola","OECD",2021,2018,438,168.83
"Africa","Angola","OECD",2021,2019,958,310.57
"Africa","Angola","OECD",2021,2020,270,144.02
"Africa","Angola","OECD",2022,2018,528,359.71
"Africa","Angola","OECD",2022,2019,974,582.98
"Africa","Angola","OECD",2022,2020,835,820.49
"Africa","Angola","IMF",2020,2018,168,148.85
"Africa","Angola","IMF",2020,2019,460,236.21
"Africa","Angola","IMF",2020,2020,360,297.15
"Africa","Angola","IMF",2021,2018,381,249.13
"Africa","Angola","IMF",2021,2019,648,128.05
"Africa","Angola","IMF",2021,2020,206,179.05
"Africa","Angola","IMF",2022,2018,282,150.29
"Africa","Angola","IMF",2022,2019,125,23.42
"Africa","Angola","IMF",2022,2020,410,247.35
"Africa","Angola","WorldBank",2020,2018,553,182.06
"Africa","Angola","WorldBank",2020,2019,847,698.87
"Africa","Angola","WorldBank",2020,2020,844,126.61
"Africa","Angola","WorldBank",2021,2018,307,239.76
"Africa","Angola","WorldBank",2021,2019,659,510.73
"Africa","Angola","WorldBank",2021,2020,548,331.89
"Africa","Angola","WorldBank",2022,2018,448,122.76
"Africa","Angola","WorldBank",2022,2019,768,761.41
"Africa","Angola","WorldBank",2022,2020,324,163.57
"Africa","Benin","OECD",2020,2018,513,196.9
"Africa","Benin","OECD",2020,2019,590,83.7
"Africa","Benin","OECD",2020,2020,791,511.09
"Africa","Benin","OECD",2021,2018,799,474.43
"Africa","Benin","OECD",2021,2019,455,234.21
"Africa","Benin","OECD",2021,2020,549,238.83
"Africa","Benin","OECD",2022,2018,235,229.33
"Africa","Benin","OECD",2022,2019,347,46.51
"Africa","Benin","OECD",2022,2020,532,392.13
"Africa","Benin","IMF",2020,2018,138,137.05
"Africa","Benin","IMF",2020,2019,978,239.82
"Africa","Benin","IMF",2020,2020,821,33.41
"Africa","Benin","IMF",2021,2018,453,291.93
"Africa","Benin","IMF",2021,2019,526,381.88
"Africa","Benin","IMF",2021,2020,467,313.57
"Africa","Benin","IMF",2022,2018,948,555.23
"Africa","Benin","IMF",2022,2019,323,289.91
"Africa","Benin","IMF",2022,2020,421,62.35
"Africa","Benin","WorldBank",2020,2018,983,271.69
"Africa","Benin","WorldBank",2020,2019,138,23.55
"Africa","Benin","WorldBank",2020,2020,636,623.65
"Africa","Benin","WorldBank",2021,2018,653,534.99
"Africa","Benin","WorldBank",2021,2019,564,368.8
"Africa","Benin","WorldBank",2021,2020,741,312.02
"Africa","Benin","WorldBank",2022,2018,328,292.11
"Africa","Benin","WorldBank",2022,2019,653,429.21
"Africa","Benin","WorldBank",2022,2020,951,242.73
"Africa","Chad","OECD",2020,2018,176,95.06
"Africa","Chad","OECD",2020,2019,783,425.34
"Africa","Chad","OECD",2020,2020,885,461.6
"Africa","Chad","OECD",2021,2018,673,15.87
"Africa","Chad","OECD",2021,2019,131,74.46
"Africa","Chad","OECD",2021,2020,430,61.58
"Africa","Chad","OECD",2022,2018,593,211.34
"Africa","Chad","OECD",2022,2019,647,550.37
"Africa","Chad","OECD",2022,2020,154,105.65
"Africa","Chad","IMF",2020,2018,160,32.41
"Africa","Chad","IMF",2020,2019,654,27.84
"Africa","Chad","IMF",2020,2020,616,468.92
"Africa","Chad","IMF",2021,2018,996,22.4
"Africa","Chad","IMF",2021,2019,126,93.18
"Africa","Chad","IMF",2021,2020,879,547.87
"Africa","Chad","IMF",2022,2018,663,520
"Africa","Chad","IMF",2022,2019,681,544.76
"Africa","Chad","IMF",2022,2020,101,55.6
"Africa","Chad","WorldBank",2020,2018,786,757.22
"Africa","Chad","WorldBank",2020,2019,599,593.69
"Africa","Chad","WorldBank",2020,2020,641,529.84
"Africa","Chad","WorldBank",2021,2018,343,287.89
"Africa","Chad","WorldBank",2021,2019,438,340.83
"Africa","Chad","WorldBank",2021,2020,762,594.67
"Africa","Chad","WorldBank",2022,2018,430,128.69
"Africa","Chad","WorldBank",2022,2019,260,242.59
"Africa","Chad","WorldBank",2022,2020,607,216.1
"Europe","Denmark","OECD",2020,2018,114,86.75
"Europe","Denmark","OECD",2020,2019,937,373.29
"Europe","Denmark","OECD",2020,2020,866,392.93
"Europe","Denmark","OECD",2021,2018,296,41.04
"Europe","Denmark","OECD",2021,2019,402,32.67
"Europe","Denmark","OECD",2021,2020,306,7.88
"Europe","Denmark","OECD",2022,2018,540,379.51
"Europe","Denmark","OECD",2022,2019,108,26.72
"Europe","Denmark","OECD",2022,2020,752,307.2
"Europe","Denmark","IMF",2020,2018,157,24.24
"Europe","Denmark","IMF",2020,2019,303,79.04
"Europe","Denmark","IMF",2020,2020,286,122.36
"Europe","Denmark","IMF",2021,2018,569,69.32
"Europe","Denmark","IMF",2021,2019,808,642.67
"Europe","Denmark","IMF",2021,2020,157,5.58
"Europe","Denmark","IMF",2022,2018,147,112.21
"Europe","Denmark","IMF",2022,2019,414,311.16
"Europe","Denmark","IMF",2022,2020,774,230.46
"Europe","Denmark","WorldBank",2020,2018,695,350.03
"Europe","Denmark","WorldBank",2020,2019,511,209.84
"Europe","Denmark","WorldBank",2020,2020,181,29.27
"Europe","Denmark","WorldBank",2021,2018,503,176.89
"Europe","Denmark","WorldBank",2021,2019,710,609.02
"Europe","Denmark","WorldBank",2021,2020,264,165.78
"Europe","Denmark","WorldBank",2022,2018,670,638.99
"Europe","Denmark","WorldBank",2022,2019,651,354.6
"Europe","Denmark","WorldBank",2022,2020,632,623.94
"Europe","Estonia","OECD",2020,2018,838,263.67
"Europe","Estonia","OECD",2020,2019,638,533.95
"Europe","Estonia","OECD",2020,2020,898,638.73
"Europe","Estonia","OECD",2021,2018,262,98.16
"Europe","Estonia","OECD",2021,2019,569,552.54
"Europe","Estonia","OECD",2021,2020,868,252.48
"Europe","Estonia","OECD",2022,2018,927,264.65
"Europe","Estonia","OECD",2022,2019,205,150.6
"Europe","Estonia","OECD",2022,2020,828,752.61
"Europe","Estonia","IMF",2020,2018,841,176.31
"Europe","Estonia","IMF",2020,2019,614,230.55
"Europe","Estonia","IMF",2020,2020,500,41.19
"Europe","Estonia","IMF",2021,2018,510,169.68
"Europe","Estonia","IMF",2021,2019,765,401.85
"Europe","Estonia","IMF",2021,2020,751,319.6
"Europe","Estonia","IMF",2022,2018,314,58.81
"Europe","Estonia","IMF",2022,2019,155,2.24
"Europe","Estonia","IMF",2022,2020,734,187.6
"Europe","Estonia","WorldBank",2020,2018,332,160.17
"Europe","Estonia","WorldBank",2020,2019,466,385.33
"Europe","Estonia","WorldBank",2020,2020,487,435.06
"Europe","Estonia","WorldBank",2021,2018,461,249.19
"Europe","Estonia","WorldBank",2021,2019,932,763.38
"Europe","Estonia","WorldBank",2021,2020,650,463.91
"Europe","Estonia","WorldBank",2022,2018,570,549.97
"Europe","Estonia","WorldBank",2022,2019,909,80.48
"Europe","Estonia","WorldBank",2022,2020,523,242.22
"Europe","Finland","OECD",2020,2018,565,561.64
"Europe","Finland","OECD",2020,2019,646,161.62
"Europe","Finland","OECD",2020,2020,194,133.69
"Europe","Finland","OECD",2021,2018,529,39.76
"Europe","Finland","OECD",2021,2019,800,680.12
"Europe","Finland","OECD",2021,2020,418,399.19
"Europe","Finland","OECD",2022,2018,591,253.12
"Europe","Finland","OECD",2022,2019,457,272.58
"Europe","Finland","OECD",2022,2020,157,105.1
"Europe","Finland","IMF",2020,2018,860,445.03
"Europe","Finland","IMF",2020,2019,108,47.72
"Europe","Finland","IMF",2020,2020,523,500.58
"Europe","Finland","IMF",2021,2018,560,81.47
"Europe","Finland","IMF",2021,2019,830,664.64
"Europe","Finland","IMF",2021,2020,903,762.62
"Europe","Finland","IMF",2022,2018,179,167.73
"Europe","Finland","IMF",2022,2019,137,98.98
"Europe","Finland","IMF",2022,2020,666,524.86
"Europe","Finland","WorldBank",2020,2018,319,146.01
"Europe","Finland","WorldBank",2020,2019,401,219.56
"Europe","Finland","WorldBank",2020,2020,711,45.35
"Europe","Finland","WorldBank",2021,2018,828,20.97
"Europe","Finland","WorldBank",2021,2019,180,66.3
"Europe","Finland","WorldBank",2021,2020,682,92.57
"Europe","Finland","WorldBank",2022,2018,254,81.2
"Europe","Finland","WorldBank",2022,2019,619,159.08
"Europe","Finland","WorldBank",2022,2020,191,184.4
"""
df = pd.read_csv(StringIO(DATA))
df["country"] = df["country"].astype("category")
df["country_id"] = df.country.cat.codes
model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country_id', 'year_publication']])},
use_t=True
)
print(result.summary())

Plot wont display in python

I'm using the following bit of code to plot two arrays of the same length -
import matplotlib.pyplot as plt
from scipy import stats
from scipy.stats import linregress
G_mag_values = [11.436, 11.341, 11.822, 11.646, 11.924, 12.057, 11.884, 11.805, 12.347, 12.662, 12.362, 12.555, 12.794, 12.819, 12.945, 12.733, 12.789, 12.878, 12.963, 13.094, 13.031, 12.962, 13.018, 12.906, 13.016, 13.088, 13.04, 13.035, 13.094, 13.032, 13.216, 13.062, 13.083, 13.126, 13.101, 13.089, 13.073, 13.182, 13.116, 13.145, 13.235, 13.161, 13.154, 13.383, 13.315, 13.429, 13.461, 13.287, 13.494, 13.459, 13.478, 13.534, 13.536, 13.536, 13.483, 13.544, 13.564, 13.544, 13.608, 13.655, 13.665, 13.668, 13.697, 13.649, 13.742, 13.756, 13.671, 13.701, 13.788, 13.723, 13.697, 13.713, 13.708, 13.765, 13.847, 13.992, 13.706, 13.79, 13.783, 13.844, 13.945, 13.928, 13.936, 13.956, 13.898, 14.059, 13.945, 14.039, 13.999, 14.087, 14.05, 14.083, 14.136, 14.124, 14.189, 14.149, 14.182, 14.281, 14.177, 14.297, 14.268, 14.454, 14.295]
G_cal_values = [-8.553610547563503, -8.085853602272588, -7.98491731861732, -7.852060056526794, -7.550944423333883, -7.569289687795749, -7.547088847268468, -7.544445036682168, -7.480698829329534, -7.184407435874912, -7.382606680295108, -7.2231275160942054, -7.093385973539046, -7.0473097125206685, -6.775012624594927, -6.814667514017907, -6.719898703328146, -6.741699011193633, -6.483121454948265, -6.320533066162438, -6.216044707275117, -6.037365656714626, -6.058593802250578, -6.0203190345840865, -6.036176430437363, -5.817887798572345, -5.838439347527171, -5.864922270102037, -5.755152671040021, -5.7709095683554725, -5.729226240967218, -5.606533007538604, -5.5817719334376905, -5.578993138005095, -5.62616747769538, -5.648413591916503, -5.611676700504294, -5.557722166623976, -5.5584623064502825, -5.425878164810264, -5.582204334985258, -5.529395790688368, -5.560750195967957, -5.433224654816512, -5.4751198268734385, -5.4592032005417215, -5.514591770369543, -5.580278698184566, -5.520695348050357, -5.501615700174841, -5.578645415877418, -5.692203332547151, -5.569497861450115, -5.335209902666812, -5.470963349023013, -5.44265375533589, -5.538541653702721, -5.355732832969871, -5.318164588926453, -5.376154615199398, -5.372133774215322, -5.361689907587619, -5.37608154921679, -5.412657572197508, -5.454613589602333, -5.339384591430104, -5.367511403407703, -5.258069473329993, -5.347580031901464, -4.9905279263992, -5.445096880253789, -5.192885553786512, -5.2983352094538505, -5.3930571447307365, -5.057910469318639, -5.32585105504838, -5.238649399637653, -5.122431894813153, -5.084559296025157, -5.139042420486851, -4.9919273140342915, -5.103619454431522, -5.017946144298159, -4.98136832081144, -5.084355565584671, -5.048634391386494, -4.887073481359435, -5.074683293771264, -5.050703776716202, -5.104772289705188, -4.9597601680524415, -4.971489935988787, -4.895283369236485, -4.9859511256778974, -4.840717539517584, -4.815665714699117, -4.937635861879118, -4.887219819695687, -4.813729758415283, -4.82667464608015, -4.865176481168528, -4.885105289124561, -4.887072278243732]
fig, ax = plt.subplots()
plt.scatter(G_mag_values,G_cal_values)
ax.minorticks_on()
ax.grid(which='major', linestyle='-', linewidth='0.5')
ax.grid(which='minor', linestyle='-', linewidth='0.5')
fig.set_size_inches(10,7)
best_fit_Y_G = []
slope_G, intercept_G, r_value_G, p_value_G, std_err_G = stats.linregress(G_mag_values,G_cal_values)
for value_G in G_mag_values:
best_fit_Y_G.append(intercept_G + slope_G*value_G)
plt.plot(G_mag_values, best_fit_Y_G, 'r', label = 'Best fit')
plt.title('M67 Calibration graph for G filter')
plt.xlabel('Real magnitude')
plt.ylabel('Measured magnitude')
plt.show()
curve_G = np.polyfit(G_mag_values,G_cal_values,1)
print('G filter polyfit line: slope {}; intercept = {}'.format(curve_G[0],curve_G[1]))
print('G filter linregress: slope {}; intercept = {}'.format(slope_G,intercept_G))
When I run this, it prints the values for slope and intercept from the best_fit_Y_G and curve_G, but it doesnt display the plot at all. Where am I going wrong?

I copy/pasted and run your code.
curve_G = np.polyfit(G_mag_values,G_cal_values,1)
That line gives me error. Then I imported numpy as np and problem solved.
output figure

Python - Fit gaussian to noisy data with lmfit

I'm trying to fit a gaussian to this data
x = [4170.177259096838, 4170.377258006199, 4170.577256915561, 4170.777255824922, 4170.977254734283, 4171.177253643645, 4171.377252553006, 4171.577251462368, 4171.777250371729, 4171.977249281091, 4172.177248190453, 4172.377247099814, 4172.577246009175, 4172.777244918537, 4172.977243827898, 4173.17724273726, 4173.377241646621, 4173.577240555983, 4173.777239465344, 4173.977238374706, 4174.177237284067, 4174.377236193429, 4174.57723510279, 4174.777234012152, 4174.977232921513, 4175.177231830875, 4175.377230740236, 4175.577229649598, 4175.777228558959, 4175.977227468321, 4176.177226377682, 4176.377225287044, 4176.577224196405, 4176.777223105767, 4176.977222015128, 4177.17722092449, 4177.377219833851, 4177.577218743213, 4177.777217652574, 4177.977216561936, 4178.177215471297, 4178.377214380659, 4178.57721329002, 4178.777212199382, 4178.977211108743, 4179.177210018105, 4179.377208927466, 4179.577207836828, 4179.777206746189, 4179.977205655551, 4180.177204564912, 4180.377203474274, 4180.577202383635, 4180.777201292997, 4180.977200202357, 4181.17719911172, 4181.377198021081, 4181.577196930443, 4181.777195839804, 4181.977194749166, 4182.177193658527, 4182.377192567888, 4182.5771914772495, 4182.777190386612, 4182.9771892959725, 4183.177188205335, 4183.377187114696, 4183.577186024058, 4183.777184933419, 4183.9771838427805, 4184.177182752143, 4184.3771816615035, 4184.5771805708655, 4184.777179480228, 4184.977178389589, 4185.1771772989505, 4185.3771762083115, 4185.5771751176735, 4185.777174027035, 4185.977172936397, 4186.1771718457585, 4186.3771707551205, 4186.5771696644815, 4186.777168573843, 4186.977167483204, 4187.177166392566, 4187.377165301927, 4187.577164211289, 4187.77716312065, 4187.977162030013, 4188.177160939374, 4188.377159848735, 4188.577158758096, 4188.777157667458, 4188.977156576819, 4189.177155486181, 4189.377154395542, 4189.577153304904, 4189.777152214265, 4189.977151123627, 4190.177150032989, 4190.37714894235, 4190.577147851711, 4190.777146761073, 4190.977145670434, 4191.177144579796, 4191.377143489157, 4191.577142398519, 4191.77714130788, 4191.977140217242, 4192.177139126603, 4192.377138035965, 4192.577136945326, 4192.777135854688, 4192.977134764049, 4193.177133673411, 4193.377132582772, 4193.577131492134, 4193.777130401495, 4193.977129310857, 4194.177128220218, 4194.377127129579, 4194.577126038941, 4194.777124948303, 4194.977123857664, 4195.177122767026, 4195.377121676387, 4195.577120585749, 4195.77711949511, 4195.977118404472, 4196.177117313833, 4196.377116223195, 4196.577115132556, 4196.777114041918, 4196.977112951279, 4197.177111860641, 4197.377110770002, 4197.577109679364, 4197.777108588725, 4197.977107498087, 4198.177106407448, 4198.37710531681, 4198.577104226171, 4198.777103135533, 4198.977102044893, 4199.177100954256, 4199.377099863617, 4199.577098772979, 4199.77709768234, 4199.977096591702, 4200.177095501063, 4200.377094410424, 4200.5770933197855, 4200.777092229148, 4200.9770911385085, 4201.177090047871, 4201.377088957232, 4201.577087866594, 4201.7770867759555, 4201.9770856853165, 4202.177084594679, 4202.377083504041, 4202.5770824134015, 4202.777081322764, 4202.977080232125, 4203.1770791414865, 4203.377078050848, 4203.5770769602095, 4203.777075869571, 4203.9770747789335, 4204.1770736882945, 4204.3770725976565, 4204.5770715070175, 4204.777070416379, 4204.97706932574, 4205.177068235102, 4205.377067144463, 4205.577066053825, 4205.777064963186, 4205.977063872549, 4206.17706278191, 4206.377061691271, 4206.577060600632, 4206.777059509994, 4206.977058419355, 4207.177057328717, 4207.377056238078, 4207.57705514744, 4207.777054056801, 4207.977052966163, 4208.177051875525, 4208.377050784886, 4208.577049694247, 4208.777048603609, 4208.977047512971, 4209.177046422332, 4209.377045331693, 4209.577044241055, 4209.777043150416, 4209.977042059778, 4210.177040969139, 4210.377039878501, 4210.577038787862, 4210.777037697224, 4210.977036606585, 4211.177035515947, 4211.377034425308, 4211.57703333467, 4211.777032244031, 4211.977031153393, 4212.177030062754, 4212.377028972116, 4212.577027881477, 4212.777026790839, 4212.9770257002, 4213.177024609562, 4213.377023518923, 4213.577022428285, 4213.777021337646, 4213.977020247008, 4214.177019156369, 4214.377018065731, 4214.577016975092, 4214.777015884454, 4214.977014793814, 4215.177013703177, 4215.377012612538, 4215.5770115219, 4215.777010431261, 4215.977009340623, 4216.177008249984, 4216.377007159345, 4216.577006068707, 4216.777004978069, 4216.977003887429, 4217.177002796792, 4217.377001706153, 4217.577000615515, 4217.776999524876, 4217.976998434238, 4218.176997343599, 4218.37699625296, 4218.5769951623215, 4218.776994071684, 4218.9769929810445, 4219.176991890407, 4219.376990799769, 4219.5769897091295, 4219.7769886184915, 4219.9769875278525, 4220.176986437215, 4220.376985346577, 4220.5769842559375, 4220.7769831653, 4220.9769820746615, 4221.1769809840225, 4221.376979893384, 4221.5769788027455, 4221.776977712107, 4221.9769766214695, 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y = [1.0063203573226929, 0.9789621233940125, 0.9998905658721924, 0.9947001934051514, 1.023498773574829, 1.0001505613327026, 0.9659610986709596, 1.0141736268997192, 0.9910064339637756, 0.961456060409546, 0.9808377623558044, 0.9717124700546264, 1.0020164251327517, 0.9276596307754515, 1.0044682025909424, 0.9898168444633484, 1.0139398574829102, 1.016809344291687, 0.9985541105270386, 1.0404949188232422, 1.0104306936264038, 1.0101377964019775, 1.0228283405303955, 1.014385461807251, 0.9949180483818054, 0.9398794174194336, 1.0047662258148191, 1.0185784101486206, 0.9942153096199036, 1.0496678352355957, 0.929694890975952, 1.0259612798690796, 1.0174839496612549, 0.9557819366455078, 1.009858012199402, 1.0258405208587646, 1.0318727493286133, 0.9781686067581176, 0.9566296339035034, 0.9626089930534364, 1.040783166885376, 0.9469046592712402, 0.9732370972633362, 1.0082777738571167, 1.0438332557678225, 1.067220687866211, 1.0809389352798462, 1.0122139453887942, 0.995375156402588, 1.025692343711853, 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e = [3.865531107294373e-05, 3.866014958475717e-05, 3.866496626869776e-05, 3.8669764762744314e-05, 3.867453415296041e-05, 3.8679270801367245e-05, 3.8683978345943615e-05, 3.868864223477431e-05, 3.8693269743816934e-05, 3.8697849959135056e-05, 3.870237924274989e-05, 3.8706857594661415e-05, 3.871127773891203e-05, 3.871564331348054e-05, 3.871994340443053e-05, 3.872417437378317e-05, 3.8728336221538484e-05, 3.8732425309717655e-05, 3.8736438000341884e-05, 3.874037065543234e-05, 3.8744219637010247e-05, 3.874798130709678e-05, 3.8751652027713135e-05, 3.875523543683812e-05, 3.8758716982556514e-05, 3.876210394082591e-05, 3.8765389035688706e-05, 3.8768568629166105e-05, 3.87716390832793e-05, 3.877460039802827e-05, 3.877745257341303e-05, 3.878018469549716e-05, 3.8782800402259454e-05, 3.878529605572112e-05, 3.8787664379924536e-05, 3.878991265082732e-05, 3.8792029954493046e-05, 3.8794016290921725e-05, 3.879586802213453e-05, 3.8797588786110275e-05, 3.879916766891256e-05, 3.8800608308520175e-05, 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3.5899327485822134e-05, 3.589421248761937e-05, 3.588925756048411e-05]
I have tried the examples given in
Python gaussian fit on simulated gaussian noisy data, and Fitting (a gaussian) with Scipy vs. ROOT et al without luck.
I'm looking to do this with lmfit because it has several advantages. This attempt was done following lmfit documentation, here is the code and plot
from numpy import sqrt, pi, exp
from lmfit import Model
import matplotlib.pyplot as plt
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(sqrt(2*pi)*wid)) * exp(-(x-cen)**2 /(2*wid**2))
gmodel = Model(gaussian)
result = gmodel.fit(y, x=x, amp=-0.5, cen=4200, wid=2)
plt.plot(x, y,'ro', ms=6)
plt.plot(x, result.init_fit, 'g--', lw=2)
plt.plot(x, result.best_fit, 'b-', lw=2)
So in green is the fit with the initial parameters, and in blue is what should be the best fit, and as you can see I get a gaussian shifted from my points and a straight line.
Also, the third row of my data are the errors in the y axis. How can I take the errors into account when fitting the data with lmfit?

The easiest way to do this is probably to make use of the built-in models and combine the GaussianModel and ConstantModel. You can use the errors in the fitting using the keyword 'weights' as described here.
You'll probably want to do something like this:
import numpy as np
from lmfit import Model
from lmfit.models import GaussianModel, ConstantModel
import matplotlib.pyplot as plt
xval = np.array(x)
yval = np.array(y)
err = np.array(e)
peak = GaussianModel()
offset = ConstantModel()
model = peak + offset
pars = offset.make_params(c=np.median(y))
pars += peak.guess(yval, x=xval, amplitude=-0.5)
result = model.fit(yval, pars, x=xval, weights=1/err)
print(result.fit_report())
plt.plot(xval, yval, 'ro', ms=6)
plt.plot(xval, result.best_fit, 'b--')

Duplicate Labels & No Line Appearing in Plot in Python

I'm plotting two lines: a set of experimental data points and a mathematical model. I'm getting the experimental data to plot as expected, but the mathematical model will not plot a line (only the symbol). Additionally, I'm getting duplicate legends and despite trying some of the suggestion I've seen on the site I'm not getting to work (probably I'm not executing it correctly).
import numpy as np
from sympy import *
from sympy import Matrix
import matplotlib.pyplot as plt
Stretch = [0.998122066, 1.0157277, 1.034507042, 1.052112676, 1.06971831, 1.088497653, 1.106103286, 1.12370892, 1.143661972, 1.160093897, 1.178873239, 1.196478873, 1.214084507, 1.23286385, 1.249295775, 1.266901408, 1.28685446, 1.303286385, 1.322065728, 1.339671362, 1.357276995, 1.374882629, 1.393661972, 1.411267606, 1.430046948, 1.447652582, 1.464084507, 1.48286385, 1.500469484, 1.518075117, 1.535680751, 1.554460094, 1.572065728, 1.59084507, 1.608450704, 1.626056338, 1.643661972, 1.661267606, 1.680046948, 1.697652582, 1.715258216, 1.734037559, 1.751643192, 1.770422535, 1.78685446, 1.805633803, 1.824413146, 1.844366197, 1.860798122, 1.878403756, 1.894835681, 1.912441315, 1.930046948, 1.948826291, 1.967605634, 1.985211268, 2.00399061, 2.021596244, 2.038028169, 2.057981221, 2.075586854, 2.092018779, 2.110798122, 2.128403756, 2.147183099, 2.165962441, 2.183568075, 2.201173709, 2.218779343, 2.237558685, 2.255164319, 2.272769953, 2.291549296, 2.307981221, 2.326760563, 2.344366197, 2.361971831, 2.380751174, 2.398356808, 2.415962441, 2.434741784, 2.452347418, 2.469953052, 2.488732394, 2.505164319]
Stress = [0.010526316, 0.010549481, 0.01188998, 0.011913146, 0.012594206, 0.012618915, 0.013299975, 0.013323141, 0.014665184, 0.0153447, 0.016027304, 0.016708364, 0.017389424, 0.018729923, 0.018751544, 0.019432604, 0.019458858, 0.019480479, 0.020163084, 0.020844144, 0.020867309, 0.021548369, 0.022230974, 0.022254139, 0.022278849, 0.023617803, 0.024297319, 0.024979923, 0.025660983, 0.026999938, 0.027023104, 0.027705708, 0.029044663, 0.029069372, 0.030408327, 0.031747282, 0.033086237, 0.034425191, 0.035107796, 0.036446751, 0.037785705, 0.039784099, 0.041123054, 0.042463553, 0.044458858, 0.046457252, 0.048455646, 0.051113479, 0.053108784, 0.055763529, 0.059074623, 0.061729367, 0.065042006, 0.069014085, 0.072986163, 0.077614591, 0.081586669, 0.086872992, 0.092815666, 0.099420867, 0.106680875, 0.114597233, 0.123174574, 0.132408265, 0.142301396, 0.152852422, 0.164059797, 0.177240857, 0.191079812, 0.206236101, 0.22073295, 0.238519274, 0.256307141, 0.273434025, 0.293195577, 0.314929269, 0.335347171, 0.357740301, 0.382105572, 0.406470843, 0.434785026, 0.461123981, 0.488778725, 0.516435014, 0.544088213]
c= [6.11739377e+00, 4.78409591e-04]
plt.show()
for o, d in zip (Stress, Stretch):
d1 = d2 = d
d3 = 1/(d1*d2)
d3 = 1/(d1*d2)
C11 = d1**2
C22 = d2**2
C33 = d3**2
p = 4*(c[1]/c[0])*(d3**(c[0]-2))
S11 = -p/C11 + 4*C11*(c[1]/c[0])*(d1**(c[0]-2))
S22 = -p/C22 + 4*C22*(c[1]/c[0])*(d2**(c[0]-2))
T112 = (d1*S11)/(d2*d3)
T222 = (d2*S22)/(d1*d3)
plt.plot(d, T112, 'g--^', label = 'Model')
plt.plot(Stretch, Stress, 'b-o', label = 'Experimental')
plt.subplots_adjust(left=0.15)
plt.grid(True)
plt.ylabel('Stress')
plt.xlabel('Applied Stretch')
plt.title('Stress as a Function of Applied Stretch')
plt.legend()
plt.show()

plt.plot should not be included in a loop over the individual data because it work with 2 lists or 2 arrays.As a first step, i've created 2 lists for your model data, then i was able to plot it :
import matplotlib.pyplot as plt
Stretch = [0.998122066, 1.0157277, 1.034507042, 1.052112676, 1.06971831, 1.088497653, 1.106103286, 1.12370892, 1.143661972, 1.160093897, 1.178873239, 1.196478873, 1.214084507, 1.23286385, 1.249295775, 1.266901408, 1.28685446, 1.303286385, 1.322065728, 1.339671362, 1.357276995, 1.374882629, 1.393661972, 1.411267606, 1.430046948, 1.447652582, 1.464084507, 1.48286385, 1.500469484, 1.518075117, 1.535680751, 1.554460094, 1.572065728, 1.59084507, 1.608450704, 1.626056338, 1.643661972, 1.661267606, 1.680046948, 1.697652582, 1.715258216, 1.734037559, 1.751643192, 1.770422535, 1.78685446, 1.805633803, 1.824413146, 1.844366197, 1.860798122, 1.878403756, 1.894835681, 1.912441315, 1.930046948, 1.948826291, 1.967605634, 1.985211268, 2.00399061, 2.021596244, 2.038028169, 2.057981221, 2.075586854, 2.092018779, 2.110798122, 2.128403756, 2.147183099, 2.165962441, 2.183568075, 2.201173709, 2.218779343, 2.237558685, 2.255164319, 2.272769953, 2.291549296, 2.307981221, 2.326760563, 2.344366197, 2.361971831, 2.380751174, 2.398356808, 2.415962441, 2.434741784, 2.452347418, 2.469953052, 2.488732394, 2.505164319]
Stress = [0.010526316, 0.010549481, 0.01188998, 0.011913146, 0.012594206, 0.012618915, 0.013299975, 0.013323141, 0.014665184, 0.0153447, 0.016027304, 0.016708364, 0.017389424, 0.018729923, 0.018751544, 0.019432604, 0.019458858, 0.019480479, 0.020163084, 0.020844144, 0.020867309, 0.021548369, 0.022230974, 0.022254139, 0.022278849, 0.023617803, 0.024297319, 0.024979923, 0.025660983, 0.026999938, 0.027023104, 0.027705708, 0.029044663, 0.029069372, 0.030408327, 0.031747282, 0.033086237, 0.034425191, 0.035107796, 0.036446751, 0.037785705, 0.039784099, 0.041123054, 0.042463553, 0.044458858, 0.046457252, 0.048455646, 0.051113479, 0.053108784, 0.055763529, 0.059074623, 0.061729367, 0.065042006, 0.069014085, 0.072986163, 0.077614591, 0.081586669, 0.086872992, 0.092815666, 0.099420867, 0.106680875, 0.114597233, 0.123174574, 0.132408265, 0.142301396, 0.152852422, 0.164059797, 0.177240857, 0.191079812, 0.206236101, 0.22073295, 0.238519274, 0.256307141, 0.273434025, 0.293195577, 0.314929269, 0.335347171, 0.357740301, 0.382105572, 0.406470843, 0.434785026, 0.461123981, 0.488778725, 0.516435014, 0.544088213]
c= [6.11739377e+00, 4.78409591e-04]
Stretch_mod=[]
Stress_mod=[]
for o, d in zip (Stress, Stretch):
d1 = d2 = d
d3 = 1/(d1*d2)
d3 = 1/(d1*d2)
C11 = d1**2
C22 = d2**2
C33 = d3**2
p = 4*(c[1]/c[0])*(d3**(c[0]-2))
S11 = -p/C11 + 4*C11*(c[1]/c[0])*(d1**(c[0]-2))
S22 = -p/C22 + 4*C22*(c[1]/c[0])*(d2**(c[0]-2))
T112 = (d1*S11)/(d2*d3)
T222 = (d2*S22)/(d1*d3)
Stretch_mod.append(d)
Stress_mod.append(T112)
plt.plot(Stretch_mod, Stress_mod, 'g--^', label = 'Model')
plt.plot(Stretch, Stress, 'b-o', label = 'Experimental')
plt.subplots_adjust(left=0.15)
plt.grid(True)
plt.ylabel('Stress')
plt.xlabel('Applied Stretch')
plt.title('Stress as a Function of Applied Stretch')
plt.legend()
plt.show()

Develop Reference

Python is a programming language that lets you work quickly and integrate systems more effectively.

guess function when using built-in defined models in lmfit - python

As I suggested in the comment above, coercing the pandas Series into an ndarray will fix the problem: mod = GaussianModel() ydata = np.array(dataDataframe[0]) xdata = np.array(dataDataframe.index.values) pars = mod.guess(ydata, x=xdata) out = mod.fit(ydata, pars, x=xdata)

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