Documentation
/ User's manual in Python
: 
| XIV.13. Macros UncertModeler | ||
|---|---|---|
 | Chapter XIV. Use-cases in python |  |
The objective of the macro is to pass the 3 tests of fit based on Empirical Distribution Function (EDF) statistics
(Kolmogorov-Smirnov (D), Cramer-VonMises (W2)
and Anderson-Darling (A2)) on the attribute "E" in the
"youngsmodulus" dataset. The tested law is the "normal"
distribution when both the mean (30576) and variance (1450) are set or when both are defined either from the sample.
"""
Example of distribution testing with quality criteria on data
"""
from rootlogon import ROOT, DataServer, UncertModeler
tds = DataServer.TDataServer()
tds.fileDataRead("youngsmodulus.dat")
c = ROOT.TCanvas("c1", "Test on youngsmodulus dataset", 13, 38, 1210, 1874)
pad = ROOT.TPad("pad", "pad", 0, 0.03, 1, 1)
pad.Draw()
pad.Divide(1, 3)
tks = UncertModeler.TTestKolmogorovSmirnov(tds, "E")
pad.cd(1)
tks.computeScore("normal:normal(30576,1450)")
tcvm = UncertModeler.TTestCramerVonMises(tds, "E")
pad.cd(2)
tcvm.computeScore("normal:normal(30576,1450)")
tad = UncertModeler.TTestAndersonDarling(tds, "E")
pad.cd(3)
tad.computeScore("normal:normal(30576,1450)")
The objective of the macro uncertModelerCirce is to apply the Circe method on the dataset
"jdd_circe_summerschool2006_dataserver.dat", which contains 150 patterns described by 4
attributes ("code","exp" and the derivative from the two parameters
"sens1" and "sens2" of the study).
#COLUMN_NAMES: code | exp | sens1 | sens2
0.853828 0.720995 1.280695 0.426961
1.420676 1.467705 2.130798 0.710554
1.986837 1.277730 2.979664 0.994010
2.552036 1.991193 3.826800 1.277273
3.116001 2.036849 4.671714 1.560289
3.678459 3.445518 5.513915 1.843002
4.239138 4.735902 6.352916 2.125359
4.797768 3.381548 7.188232 2.407304
5.354081 5.383797 8.019378 2.688784
5.907808 5.001590 8.845874 2.969742
6.458685 5.330333 9.667245 3.250125
7.006447 7.952286 10.483015 3.529879
7.550833 4.561176 11.292717 3.808950
8.091583 7.968353 12.095884 4.087283
8.628440 8.644601 12.892057 4.364824
9.161150 7.772117 13.680780 4.641520
9.689460 11.946291 14.461602 4.917317
10.213121 9.110840 15.234080 5.192162
10.731888 9.179312 15.997775 5.466002
11.245518 12.044400 16.752254 5.738783
11.753772 9.955391 17.497091 6.010453
12.256413 8.516376 18.231867 6.280959
12.753210 11.832538 18.956171 6.550249
13.243933 15.764511 19.669597 6.818270
13.728360 13.636206 20.371749 7.084971
14.206269 15.828666 21.062237 7.350300
14.677444 15.371526 21.740682 7.614206
15.141674 17.624294 22.406711 7.876637
15.598751 16.365027 23.059960 8.137543
16.048474 13.622278 23.700075 8.396873
16.490644 16.654472 24.326711 8.654577
16.925069 18.445503 24.939533 8.910605
17.351561 21.030571 25.538214 9.164908
17.769937 17.357715 26.122438 9.417436
18.180020 11.956423 26.691900 9.668140
18.581638 19.157803 27.246304 9.916972
18.974624 16.126637 27.785365 10.163884
19.358818 18.922050 28.308810 10.408827
19.734065 20.848071 28.816374 10.651755
20.100213 18.048485 29.307807 10.892620
20.457121 8.858695 29.782866 11.131376
20.804650 17.677597 30.241324 11.367976
21.142669 22.920734 30.682962 11.602375
21.471051 18.370473 31.107575 11.834528
21.789678 16.656787 31.514968 12.064388
22.098436 19.602911 31.904959 12.291912
22.397218 27.669603 32.277379 12.517056
22.685923 22.298174 32.632070 12.739777
22.964458 21.384184 32.968887 12.960030
23.232734 30.519337 33.287695 13.177773
23.490671 15.001831 33.588376 13.392965
23.738192 25.366767 33.870822 13.605563
23.975231 23.949371 34.134936 13.815527
24.201726 13.391711 34.380637 14.022815
24.417621 21.553269 34.607854 14.227387
24.622868 26.531200 34.816530 14.429205
24.817425 20.392323 35.006621 14.628229
25.001258 32.189940 35.178096 14.824419
25.174337 20.032718 35.330935 15.017740
25.336642 26.195740 35.465133 15.208152
25.488157 30.414972 35.580695 15.395619
25.628873 29.119988 35.677642 15.580104
25.758789 32.045293 35.756006 15.761573
25.877910 21.195366 35.815830 15.939990
25.986247 28.350611 35.857174 16.115319
26.083817 38.814487 35.880105 16.287529
26.170646 31.542012 35.884708 16.456584
26.246764 26.275016 35.871075 16.622452
26.312208 38.313217 35.839315 16.785102
26.367023 28.568492 35.789546 16.944501
26.411259 31.091609 35.721899 17.100619
26.444972 16.263460 35.636518 17.253425
26.468224 21.124831 35.533558 17.402891
26.481085 33.708891 35.413184 17.548986
26.483629 24.250273 35.275576 17.691683
26.475938 35.046525 35.120922 17.830954
26.458098 24.052985 34.949423 17.966773
26.430201 34.135678 34.761291 18.099112
26.392347 27.700029 34.556749 18.227946
26.344640 32.737134 34.336029 18.353251
26.287189 28.450629 34.099376 18.475001
26.220109 23.829647 33.847044 18.593174
26.143522 27.528282 33.579297 18.707747
26.057552 36.671849 33.296408 18.818696
25.962331 26.930743 32.998661 18.926002
25.857996 32.444448 32.686349 19.029643
25.744687 28.236267 32.359775 19.129598
25.622549 22.031751 32.019249 19.225849
25.491734 21.495018 31.665091 19.318378
25.352397 21.132186 31.297629 19.407165
25.204696 19.217357 30.917198 19.492194
25.048797 26.490158 30.524145 19.573449
24.884866 21.316733 30.118819 19.650913
24.713076 25.592418 29.701580 19.724572
24.533603 19.577027 29.272793 19.794412
24.346625 34.922599 28.832833 19.860418
24.152327 35.735641 28.382076 19.922578
23.950895 19.302306 27.920910 19.980881
23.742519 22.246825 27.449724 20.035314
23.527392 27.026746 26.968916 20.085868
23.305709 22.656735 26.478887 20.132532
23.077671 15.588268 25.980044 20.175297
22.843477 26.051802 25.472798 20.214156
22.603332 26.182246 24.957565 20.249100
22.357443 38.381341 24.434763 20.280123
22.106018 22.768312 23.904816 20.307219
21.849267 26.030200 23.368151 20.330382
21.587402 15.635641 22.825196 20.349609
21.320639 18.753400 22.276382 20.364895
21.049191 20.947923 21.722145 20.376237
20.773276 24.642890 21.162919 20.383634
20.493113 14.828323 20.599142 20.387083
20.208919 17.455233 20.031254 20.386585
19.920916 16.168686 19.459693 20.382139
19.629322 15.843812 18.884899 20.373745
19.334360 18.983964 18.307313 20.361407
19.036251 20.541221 17.727376 20.345126
18.735215 14.704089 17.145526 20.324904
18.431475 27.724483 16.562203 20.300747
18.125252 20.854847 15.977844 20.272659
17.816766 15.890789 15.392886 20.240646
17.506238 16.750030 14.807764 20.204712
17.193888 10.107342 14.222909 20.164866
16.879934 15.763192 13.638752 20.121116
16.564594 27.922521 13.055719 20.073469
16.248084 14.705787 12.474234 20.021934
15.930621 9.417969 11.894719 19.966523
15.612417 13.446139 11.317589 19.907245
15.293685 13.532392 10.743257 19.844112
14.974634 20.769703 10.172131 19.777137
14.655473 17.227635 9.604615 19.706331
14.336409 12.008431 9.041108 19.631710
14.017644 17.118440 8.482001 19.553287
13.699381 14.424335 7.927684 19.471078
13.381817 14.105580 7.378537 19.385098
13.065150 8.072573 6.834935 19.295364
12.749572 10.533065 6.297249 19.201894
12.435272 11.022880 5.765839 19.104706
12.122439 12.865744 5.241061 19.003818
11.811257 10.789248 4.723263 18.899250
11.501904 5.134292 4.212786 18.791022
11.194558 11.284947 3.709961 18.679155
10.889392 10.426379 3.215113 18.563671
10.586576 8.555081 2.728559 18.444593
10.286274 10.517881 2.250605 18.321943
9.988648 12.657006 1.781552 18.195744
9.693856 9.822359 1.321689 18.066023
9.402049 9.082523 0.871296 17.932802
9.113378 12.983850 0.430646 17.796110
8.827985 6.205202 -2.89e-15 17.655971
"""
Example of Circe method application
"""
from rootlogon import DataServer, UncertModeler
tds = DataServer.TDataServer()
tds.fileDataRead("jdd_circe_summerschool2006_dataserver.dat")
# tds.addAttribute("uexp", "0.05*exp")
# tds.addAttribute("sens3", "sens1*sens2")
# Create the TCirce object from the TDS and specify Experimental attribute
# but also Code attribute and sensitivity attributes
tc = UncertModeler.TCirce(tds, "exp", "code", "sens1,sens2")
# tc.setTolerance(1e-5)
# tc.setYStarSigma("uexp")
# tc.setNCMatrix(5)
# TMatrixD initCMat(2,2)
# initCMat.Zero(); initCMat(0,0) = 0.042737; initCMat(1,1) = 0.525673
# tc.setCMatrixInitial(initCMat)
# TVectorD initBVec(2)
# initBVec(0) = -1.436394; initBVec(1) = -1.501561
# tc.setBVectorInitial(initBVec)
tc.estimate()
# Post-treatment
vBiais = tc.getBVector()
print(" ** vBiais rows["+str(vBiais.GetNrows())+"]")
vBiais.Print()
matC = tc.getCMatrix()
print(" ** matC rows["+str(matC.GetNrows())+"] col ["+str(matC.GetNcols())+"]")
matC.Print()
Processing uncertModelerCirce.py...
--- Uranie v0.0/0 --- Developed with ROOT (6.32.02)
Copyright (C) 2013-2024 CEA/DES
Contact: support-uranie@cea.fr
Date: Tue Jan 09, 2024
*********
** addData from an another TDS [jdd_circe_summerschool2006_dataserver]
** YStar[exp] YStarSigma[]YHat[code]
** Sensitivity Attributes[sens1 sens2]
** nparameter [sens1 sens2] size[2]
** List Of TDS size[1]
Collection name='TList', class='TList', size=1
OBJ: URANIE::DataServer::TDataServer jdd_circe_summerschool2006_dataserver _title_
** List Of Informations size[3]
Collection name='TList', class='TList', size=3
OBJ: TNamed __Circe_YStar_jdd_circe_summerschool2006_dataserver_1__ exp
OBJ: TNamed __Circe_YHat_jdd_circe_summerschool2006_dataserver_1__ code
OBJ: TNamed __Circe_Sensitivity_jdd_circe_summerschool2006_dataserver_1__ sens1,sens2
** End Of addData from an another TDS [jdd_circe_summerschool2006_dataserver]
*********
**********************************
** Begin Of Initial Matrix C [1/1]
** CIRCE HAS CONVERGED
** iter[90] ** Likelihood[-2.729559333111159]
***** Selected :: iter[0] Likelihood[-2.729559333111159]
** matrix C1
2x2 matrix is as follows
| 0 | 1 |
-------------------------------
0 | 0.01612 0
1 | 0 0.03616
** vector XM1
Vector (2) is as follows
| 1 |
------------------
0 |-0.0131705
1 |0.0106155
** End Of Initial Matrix C [1/1]
**********************************
** Residual :: Mean [-0.007054035043120376] Std[1.003324966600461]
** vBiais rows[2]
Vector (2) is as follows
| 1 |
------------------
0 |-0.0131705
1 |0.0106155
** matC rows[2] col [2]
2x2 matrix is as follows
| 0 | 1 |
-------------------------------
0 | 0.01612 0
1 | 0 0.03616
| |  | |
| XIV.12. Macros Calibration |  | XIV.14. Macros Reliability |