| Depth,LAGMWT,Time,EditFlag,TORQUE,MOTOR_RPM,STRATESUM,MWOUT,LAGMWDIFF,MWIN,BIT_RPM,DXC,MUDRETDEPTH,PUMP,LAGMTEMP,RigActivityCode,MRIN,FLOWOUT,ROP_AVG,LAGMRES,TOTGAS,MROUT,MTIN,LAGMRDIFF,FLOWIN,WOB,ONBOTTOM_TIME,ECDBIT,MTOUT,BIT_DIST,SURF_RPM,LAGMTDIFF,LITH | |
| 3300.0,1280.0,2013-11-28 12:54:29,0.0,3768.1461,0.0,0.30077287,1280.0,0.0,1280.0,0.26709381,0.98767405,3299.0,3112134.6,0.0,111.0,0.0,0.016037011,0.0050745891,0.0,0.00010829921,0.0,0.0,0.0,0.006662119100000001,-19762.743,194.0,1293.6605,0.0,0.99521129,0.26709381,0.0,claystone | |
| 3305.0,1280.0,2013-11-28 16:48:21,0.0,20991.543,0.0,1.6745582,1306.2254,26.225434000000003,1306.2254,1.3141475,0.98767405,3301.7245,17932214.0,0.0,111.0,0.0,0.038980972,0.004088054,0.0,0.00018335271,0.0,0.0,0.0,0.037091464,26217.864,1617.0,1360.874,0.0,5.995622,1.3147203,0.0,marl | |
| 3310.0,1315.2258,2013-11-28 17:01:59,0.0,22610.383,0.0,1.7667643,1320.0146,4.7887933,1320.0146,1.3287059,0.98767405,3301.7245,20878002.0,0.0,111.0,0.0,0.041079254,0.005159383,0.0,0.00017616453000000005,0.0,0.0 |
| import idm | |
| # Combine trajectory and drilling data | |
| df_data, df_new = traj_df, drill_df # trajectory and realtime drilling data | |
| xdata, xnew = "md", "Depth" | |
| ydata = ["surfNs", "surfEw", "TVD_calc"] | |
| merge1_df = idm.merge_data_interpolation(df_data, df_new, xdata, ydata, xnew) |
| import statviz | |
| # Choose lithology | |
| lith = 'marl' # sandstone, claystone, marl | |
| # Two variables to plot against | |
| x, y = 'TORQUE', 'BPHI' | |
| # Bounds of each variable to evaluate probabilities | |
| xbound, ybound = (20e3, 22.5e3), (20, 30) |
| name,id,nametype,recclass,mass,fall,year,reclat,reclong,GeoLocation | |
| Aachen,1,Valid,L5,21,Fell,1880,50.775000,6.083330,"(50.775000, 6.083330)" | |
| Aarhus,2,Valid,H6,720,Fell,1951,56.183330,10.233330,"(56.183330, 10.233330)" | |
| Abee,6,Valid,EH4,107000,Fell,1952,54.216670,-113.000000,"(54.216670, -113.000000)" | |
| Acapulco,10,Valid,Acapulcoite,1914,Fell,1976,16.883330,-99.900000,"(16.883330, -99.900000)" | |
| Achiras,370,Valid,L6,780,Fell,1902,-33.166670,-64.950000,"(-33.166670, -64.950000)" | |
| Adhi Kot,379,Valid,EH4,4239,Fell,1919,32.100000,71.800000,"(32.100000, 71.800000)" | |
| Adzhi-Bogdo (stone),390,Valid,LL3-6,910,Fell,1949,44.833330,95.166670,"(44.833330, 95.166670)" | |
| Agen,392,Valid,H5,30000,Fell,1814,44.216670,0.616670,"(44.216670, 0.616670)" | |
| Aguada,398,Valid,L6,1620,Fell,1930,-31.600000,-65.233330,"(-31.600000, -65.233330)" |
| "Package","Item","Title","Rows","Cols","n_binary","n_character","n_factor","n_logical","n_numeric","CSV","Doc" | |
| "AER","Affairs","Fair's Extramarital Affairs Data",601,9,2,0,2,0,7,"https://vincentarelbundock.github.io/Rdatasets/csv/AER/Affairs.csv","https://vincentarelbundock.github.io/Rdatasets/doc/AER/Affairs.html" | |
| "AER","ArgentinaCPI","Consumer Price Index in Argentina",80,2,0,0,0,0,2,"https://vincentarelbundock.github.io/Rdatasets/csv/AER/ArgentinaCPI.csv","https://vincentarelbundock.github.io/Rdatasets/doc/AER/ArgentinaCPI.html" | |
| "AER","BankWages","Bank Wages",474,4,2,0,3,0,1,"https://vincentarelbundock.github.io/Rdatasets/csv/AER/BankWages.csv","https://vincentarelbundock.github.io/Rdatasets/doc/AER/BankWages.html" | |
| "AER","BenderlyZwick","Benderly and Zwick Data: Inflation, Growth and Stock Returns",31,5,0,0,0,0,5,"https://vincentarelbundock.github.io/Rdatasets/csv/AER/BenderlyZwick.csv","https://vincentarelbundock.github.io/Rdatasets/doc/AER/BenderlyZwick.html" | |
| "AER","BondYield","Bond Yield Data",60,2,0,0, |
| """ | |
| Tensor rotation in Tilted Transverse Isotropy (TTI) for the 36 stiffness elements | |
| Discussion which I started here: https://www.researchgate.net/post/How_to_do_coordinate_transformation_of_a_6x6_stiffness_matrix_of_tilted_transverse_isotropic_medium | |
| @author: Yohanes Nuwara | |
| @email: ign.nuwara97@gmail.com | |
| Possible extension of this work (may need collaboration): | |
| * Verify that the rotation is right by calculating its tensor invariants | |
| * Test in real lab: Does the theoretically rotated tensor represent the true stiffness of the TTI rock? |
A lot of GitHub projects need to have pretty math formulas in READMEs, wikis or other markdown pages. The desired approach would be to just write inline LaTeX-style formulas like this:
$e^{i \pi} = -1$Unfortunately, GitHub does not support inline formulas. The issue is tracked here.
| import pandas as pd | |
| # create dataframe from data | |
| data = pd.DataFrame({"Column_1": numpy_1, "Column_2": numpy_2, "Column_3": numpy_3}) | |
| # print original size of data before NaN values removal | |
| print("Original size of data:", len(data), "rows") | |
| # look up the number of rows with NaN data | |
| print("How many data with NaN values?") | |
| print(data.isnull().sum()) |
| def xplot(axisx, axisy, axisz): | |
| plt.scatter(axisx, axisy, c=axisz, s=5, cmap='gist_rainbow') | |
| plt.colorbar() |