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Current state which was cloned to Zac's Workstation smargopolo

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Wayne Glettig
2022-01-27 20:51:51 +01:00
parent 74151b51b0
commit 6174b36f48
5 changed files with 389 additions and 390 deletions
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360
python_algorithms/Dominik/5_Compensation/LUT_Smargon_Error_corrected.csv
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@@ -1,181 +1,181 @@
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10.0,5.0,-0.00935140084186747,-0.06636178480722892,-0.015146505103915665
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30.0,5.0,0.00909531809992598,-0.12156959589711326,-0.05855558797187268
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30.0,215.0,0.011320623297029703,0.13504386136633667,-0.02276043713861386
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1 CHI OMEGA X_LUT Y_LUT Z_LUT
2 0.0 5.0 -5.138674665551837e-05 -4.325712485681787e-06 -8.530335618729096e-05 0.00011717850630011463 0.00019314879264214048 -7.926044100801827e-05
3 0.0 15.0 -8.495834999999997e-05 2.495818999999984e-05 -0.00012204545 5.339410000000008e-05 -0.0002976171599999999 -0.0004525307600000001
4 0.0 25.0 -2.5971199999999983e-05 0.00013904783999999992 -0.00032540676 -0.00012578527999999994 -0.00061287036 -0.00099165173
5 0.0 35.0 7.699177000000002e-05 0.0001875075399999998 -0.00062427341 -0.0002402365899999999 -0.0008834381600000001 -0.00166818015
6 0.0 45.0 0.00019925848514851485 0.00030298471999999974 -0.000981885702970297 -0.00048681302999999983 -0.000931275425742574 -0.00218575573
7 0.0 55.0 0.00032517811111111117 0.0003923894899999998 -0.0014270460909090908 -0.00084657281 -0.0007628025454545456 -0.0022908311
8 0.0 65.0 0.0004959868415841584 0.0005370019799999997 -0.0018770133465346535 -0.0014397464299999996 -0.0004275259207920791 -0.00210883281
9 0.0 75.0 0.0006551330000000001 0.0008217761799999997 -0.0023853337800000007 -0.0022080864799999995 5.956299000000001e-05 -0.0017757797300000002
10 0.0 85.0 0.0008455228712871287 0.0009660931299999997 -0.0027498195940594055 -0.0027761822799999996 0.0007618152277227724 -0.0013985611799999997
11 0.0 95.0 0.0010563698899999998 0.0011355574099999998 -0.0028766497399999994 -0.0030695184299999994 0.0014805365800000002 -0.00099341431
12 0.0 105.0 0.0012340906732673266 0.0013188249499999998 -0.002654638861386139 -0.00298698564 0.002344220247524752 -0.0004332729999999999
13 0.0 115.0 0.0014156316799999995 0.00148637805 -0.0021762277599999998 -0.0027095190200000004 0.003297186550000001 0.00038136671000000004
14 0.0 125.0 0.00159783097029703 0.00169679705 -0.0014218292277227728 -0.00238462997 0.004143532534653466 0.0013026769700000003
15 0.0 135.0 0.0018015395599999997 0.0018915138500000001 -0.0006341150399999998 -0.0019749771199999997 0.004762036659999999 0.00202914163
16 0.0 145.0 0.00190121637254902 0.0019710665858585857 0.00043710397058823537 -0.001381941404040404 0.0050844811274509795 0.0023180336565656566
17 0.0 155.0 0.0020385002500000002 0.0020352299099999998 0.0015446352299999999 -0.0005022113499999999 0.0050769497100000005 0.0021952841799999998
18 0.0 165.0 0.0021055600396039605 0.0020888758399999997 0.002803376277227722 0.00057392372 0.004799966217821782 0.0018958144200000005
19 0.0 175.0 0.0021564562970297027 0.0021549130792079207 0.0038972725445544556 0.001425277742574257 0.004270241118811881 0.00151921001980198
20 0.0 185.0 0.00216786449 0.0022226144242424237 0.004658729279999999 0.0020682191010101014 0.00349917367 0.0011217058282828281
21 0.0 195.0 0.0021488043762376238 0.00216610983 0.005000920801980198 0.0021235501099999997 0.002513818287128713 0.0004800172799999999
22 0.0 205.0 0.0020223415841584156 0.002127580131313131 0.005065030881188118 0.0018897974343434342 0.0013939663069306935 -0.0004181233333333333
23 0.0 215.0 0.0019064091100000002 0.002046960980392157 0.004964907349999999 0.0017692558333333335 0.00026727273 -0.001310397019607843
24 0.0 225.0 0.0017838834444444444 0.00194533898989899 0.004667684949494951 0.0014314653131313135 -0.0007263056868686869 -0.0021094586363636365
25 0.0 235.0 0.0016332711099999997 0.001818802801980198 0.00408572414 0.0010598457326732673 -0.00152622668 -0.002553115207920792
26 0.0 245.0 0.0014694333636363634 0.00165797475 0.003191180424242424 0.0004924397100000001 -0.0019696835050505047 -0.00268257693
27 0.0 255.0 0.0012194489595959597 0.0015026636767676764 0.002276311191919192 -0.0003993787474747474 -0.002222856808080808 -0.002586123919191919
28 0.0 265.0 0.001063185505050505 0.0013670229399999996 0.0013255774242424243 -0.0012610185599999997 -0.0023012036767676766 -0.00243077201
29 0.0 275.0 0.000861724484848485 0.0011801512599999998 0.0005748048080808082 -0.0018484936699999998 -0.0022089196060606064 -0.00226523091
30 0.0 285.0 0.0006669424444444446 0.0009941110396039603 4.0204474747474755e-05 -0.0021205569702970294 -0.0019003588989898986 -0.001853767277227723
31 0.0 295.0 0.0005083123333333334 0.0007895023699999998 -0.00034317525252525253 -0.00213720146 -0.0013755381919191914 -0.00117994229
32 0.0 305.0 0.0003796769696969697 0.0006207680699999997 -0.0006240890808080808 -0.00203627296 -0.0007696765757575759 -0.00041190393999999996
33 0.0 315.0 0.00026426251000000005 0.0004171644699999996 -0.0008052948900000001 -0.00192368271 -0.00025814168000000003 0.00022959791000000003
34 0.0 325.0 0.00015265357142857144 0.00027835924999999976 -0.0007404523571428572 -0.0016596393599999997 5.39861530612245e-05 0.00054483109
35 0.0 335.0 6.367042000000002e-05 0.00015133802999999977 -0.00048052641 -0.0012015537599999997 0.00025535339000000006 0.0005868683399999999
36 0.0 345.0 -1.6950579999999978e-05 0.00010085476999999978 -8.441774999999999e-05 -0.00039779183999999994 0.00034325351 0.0004195291200000001
37 0.0 355.0 -5.950503999999998e-05 1.0912875299760005e-05 0.00015681476 8.869291127098327e-05 0.0003605284800000001 -2.696869784172659e-05
38 10.0 5.0 -0.007909518542972699 -0.00935140084186747 -0.06340224958897876 -0.06636178480722892 -0.016512764000000003 -0.015146505103915665
39 10.0 15.0 -0.00785271062 -0.00922982147 -0.06567901545 -0.06849187008 -0.0004997710499999999 0.0020159402500000002
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171 40.0 255.0 0.028223615898989897 0.023165891089999998 0.11973055676767676 0.11898558582000003 -0.0847028082929293 -0.09431644404
172 40.0 265.0 0.028028510222222223 0.02335355736 0.1031246474949495 0.10143185168 -0.10391875401010099 -0.11213804346999998
173 40.0 275.0 0.027815942810000008 0.022779602269999998 0.08344452303000001 0.08033281176000001 -0.12008647397999997 -0.12762530594000002
174 40.0 285.0 0.027600816132653066 0.022231191089999998 0.061564377540816326 0.05698729188 -0.13243323651020403 -0.13968672782
175 40.0 295.0 0.027390502595959598 0.021993526170000003 0.03803991914141414 0.032320098789999994 -0.14067239923232322 -0.14683544041
176 40.0 305.0 0.027196058929292926 0.02192358714 0.013295207737373737 0.006466230349999999 -0.14483382835353534 -0.14938432937
177 40.0 315.0 0.027040669565656563 0.021588281039999994 -0.011753136070707071 -0.019400691379999998 -0.14475367135353537 -0.14757740621
178 40.0 325.0 0.026848007373737368 0.021381531386138613 -0.03643887398989898 -0.04488799233663366 -0.14054621435353531 -0.14150683053465346
179 40.0 335.0 0.026691012989898996 0.0211593852020202 -0.059905465454545455 -0.0686713566969697 -0.13220526314141415 -0.13145655375757576
180 40.0 345.0 0.026565955603960398 0.02104293209 -0.08167552412871287 -0.09001654646 -0.11977729784158415 -0.11737345136
181 40.0 355.0 0.026480377590000003 0.02108540218918919 -0.10112424737999999 -0.11464882664864866 -0.10365180097999999 -0.09353762782625481

10
python_algorithms/Dominik/5_Compensation/create_LUT_from_ErrorCSV.py
View File

@@ -21,17 +21,17 @@ from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
#Daten von Error Datei in Panda DataFrame
rawdf = read_csv('2021_12_10_09:43:19_measure_Smargon_Error_OXOY_CHI0_40.csv')
rawdf = read_csv('2022_01_19_21:24:31_measure_Smargon_Error_OXOY_CHI0_40.csv')
#Aufbereiten der Daten
rawdf['time']=rawdf['DMS_Secs']+rawdf['DMS_Nsecs']*1e-9
rawdf['OMEGA_Cont']=(rawdf['OMEGA_Cont'])
rawdf['X']=(rawdf['DMS_X']-0.000304032) #mit Offset zu 0 Referenz
rawdf['Y']=(rawdf['DMS_Y']+0.000483005) #mit Offset zu 0 Referenz
rawdf['Z']=(rawdf['DMS_Z']+0.000608233) #mit Offset zu 0 Referenz
rawdf['X']=(rawdf['DMS_X']-0.008198765) #mit Offset zu 0 Referenz
rawdf['Y']=(rawdf['DMS_Y']+0.003291426) #mit Offset zu 0 Referenz
rawdf['Z']=(rawdf['DMS_Z']+0.001798504) #mit Offset zu 0 Referenz
# Relevante Daten deklarieren
df = rawdf[1200:28100] # Bewegung startet ab Zeile 1200 (CHI= 0 und OMEGA = 0) und endet mit Zeile 28100 (CHI= 40 und OMEGA = 360)
df = rawdf[1200:28060] # Bewegung startet ab Zeile 1200 (CHI= 0 und OMEGA = 0) und endet mit Zeile 28100 (CHI= 40 und OMEGA = 360)
# Generiert LUT mit Header
lut = pd.DataFrame(columns=['CHI','OMEGA', 'X_LUT', 'Y_LUT', 'Z_LUT'])