| import numpy as np | |
| ## Tri Diagonal Matrix Algorithm(a.k.a Thomas algorithm) solver | |
| def TDMAsolver(a, b, c, d): | |
| ''' | |
| TDMA solver, a b c d can be NumPy array type or Python list type. | |
| refer to http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm | |
| and to http://www.cfd-online.com/Wiki/Tridiagonal_matrix_algorithm_-_TDMA_(Thomas_algorithm) | |
| ''' | |
| nf = len(d) # number of equations |
| def xplot(axisx, axisy, axisz): | |
| plt.scatter(axisx, axisy, c=axisz, s=5, cmap='gist_rainbow') | |
| plt.colorbar() |
| 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()) |
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.
| """ | |
| 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? |
| "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, |
| 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)" |
| 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) |