Case Study on Oise Data Set¶

In [1]:
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
return false;
}

This case study reads information from the gstlearn repository.

In [2]:
ranges = [1000,5000]
oldMethod = False
In [3]:
import os
import matplotlib.pyplot as plt
import gstlearn as gl
import gstlearn.plot as gp
import numpy as np
import scipy as sp
from scipy import interpolate
In [4]:
!wget -q https://soft.minesparis.psl.eu/gstlearn/data/Oise/Oise_Thickness.csv
In [5]:
!wget -q https://soft.minesparis.psl.eu/gstlearn/data/Oise/Alluvial.csv
In [6]:
filename = "Oise_Thickness.csv"
In [7]:
csv = gl.CSVformat(True, 0, ";", ",", "9999")
data = gl.Db.createFromCSV(filename, csv, True)
data.setLocator("X",gl.ELoc.X,0)
data.setLocator("Y",gl.ELoc.X,1)
data.setLocator("Epaisseur",gl.ELoc.L,0)
data.setLocator("Epaisseur_1",gl.ELoc.U,0)
dbfmt = gl.DbStringFormat.createFromFlags(flag_stats=True)
data.display(dbfmt)

Column Name (1): X
 Column Name (2): Y
 Column Name (3): Epaisseur
 Column Name (4): Epaisseur_1
 Number of columns = 4
 Data table read (Oise_Thickness.csv) successfully
 - Number of columns = 4
 - Number of rows    = 1022
 
Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 5
Maximum Number of UIDs       = 5
Total number of samples      = 1022

Data Base Statistics
--------------------
1 - Name rank - Locator NA
 Nb of data          =       1022
 Nb of active values =       1022
 Minimum value       =      1.000
 Maximum value       =   1022.000
 Mean value          =    511.500
 Standard Deviation  =    295.026
 Variance            =  87040.250
2 - Name X - Locator x1
 Nb of data          =       1022
 Nb of active values =       1022
 Minimum value       = 629450.000
 Maximum value       = 741438.000
 Mean value          = 679467.745
 Standard Deviation  =  29334.862
 Variance            = 860534131.372
3 - Name Y - Locator x2
 Nb of data          =       1022
 Nb of active values =       1022
 Minimum value       = 6875872.000
 Maximum value       = 6981243.000
 Mean value          = 6918746.035
 Standard Deviation  =  23198.048
 Variance            = 538149412.703
4 - Name Epaisseur - Locator lower1
 Nb of data          =       1022
 Nb of active values =       1022
 Minimum value       =      0.400
 Maximum value       =     15.900
 Mean value          =      7.237
 Standard Deviation  =      2.892
 Variance            =      8.363
5 - Name Epaisseur_1 - Locator upper1
 Nb of data          =       1022
 Nb of active values =       1022
 Minimum value       =      0.400
 Maximum value       =     15.900
 Mean value          =      7.237
 Standard Deviation  =      2.892
 Variance            =      8.363

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = X - Locator = x1
Column = 2 - Name = Y - Locator = x2
Column = 3 - Name = Epaisseur - Locator = lower1
Column = 4 - Name = Epaisseur_1 - Locator = upper1
In [8]:
thickness = data.getWithinBounds(0)
In [9]:
err = data.addColumns(thickness,"Thickness",gl.ELoc.Z)
data
Out[9]:
Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 6
Maximum Number of UIDs       = 6
Total number of samples      = 1022

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = X - Locator = x1
Column = 2 - Name = Y - Locator = x2
Column = 3 - Name = Epaisseur - Locator = lower1
Column = 4 - Name = Epaisseur_1 - Locator = upper1
Column = 5 - Name = Thickness - Locator = z1
In [10]:
err = gl.db_duplicate(data)
data
Out[10]:
Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 7
Maximum Number of UIDs       = 7
Total number of samples      = 1022
Number of active samples     = 1007

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = X - Locator = x1
Column = 2 - Name = Y - Locator = x2
Column = 3 - Name = Epaisseur - Locator = lower1
Column = 4 - Name = Epaisseur_1 - Locator = upper1
Column = 5 - Name = Thickness - Locator = z1
Column = 6 - Name = Duplicate - Locator = sel
In [11]:
gp.setDefaultGeographic(dims=[10,10])
ax = data.plot(name_color="Thickness")
No description has been provided for this image
In [12]:
filename = "Alluvial.csv"
In [13]:
csv = gl.CSVformat(False, 0, ";", ",", "9999")
poly = gl.Polygons.createFromCSV(filename, csv, False)
In [14]:
ax = poly.plot()
No description has been provided for this image
In [15]:
grid = gl.DbGrid()
err = grid.reset([3300,400],[50,50],[630000,6865000],[40,0])
In [16]:
ax = grid.plot()
No description has been provided for this image
In [17]:
ax = grid.plot(flagLegendRaster=False,alpha=0.3)
ax = data.plot()
No description has been provided for this image
In [18]:
err = gl.db_polygon(grid,poly)
grid
Out[18]:
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Maximum Number of UIDs       = 4
Total number of samples      = 1320000
Number of active samples     = 138248

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = sel
In [19]:
##CenterLine + échantillonnage 
xc = [631880,631843,631796,631753,631697,631640,631606,631574,631523,631491,631266,631073,630862,630664,630465,630352,630306,630258,630323,630621,630991,631413,631618,631783,631954,632204,632540,632928,633140,633319,633525,633769,633891,634042,634095,634161,634242,634296,634449,634630,634845,635043,635183,635488,635647,635861,636066,636355,636590,636761,637228,637427,637645,637863,638098,638352,638735,639187,639421,639699,640020,640304,640528,640785,641173,641313,641515,641664,641824,642002,642172,642413,642591,642770,642974,643167,643344,643485,643735,643972,644189,644517,644846,645014,645205,645415,645637,646070,646282,646431,646725,647043,647461,647615,648004,648253,648483,648843,649214,649532,649953,650404,650671,651107,651610,651995,652416,652835,653212,653450,653669,653897,653900,653903,653830,653791,653850,653911,653952,654031,654150,654349,654487,654830,655178,655458,655764,656059,656358,656715,657117,657464,657784,657975,658159,658379,658525,658800,658946,659084,659232,659390,659547,659688,659845,660079,660244,660435,660531,660821,661116,661454,661839,662193,662496,663071,663382,663721,664032,664283,664588,664918,665237,665574,665869,666324,666647,667053,667450,668207,668575,669135,669546,670300,670797,671250,671653,672023,672390,672880,673290,673714,674149,674540,675014,675393,675792,676206,676552,676901,677202,677533,677766,678096,678420,679022,679376,679828,680229,680669,680997,681423,681675,681940,682208,682375,682527,682699,682843,682976,683147,683314,683463,683632,683731,683815,683950,684088,684246,684292,684373,684557,684786,684970,685146,685358,685707,685898,686115,686356,686677,687010,687252,687497,687745,688213,688601,689061,689451,689819,690100,690321,690504,690631,690799,690999,691164,691354,691524,691651,691792,691869,692231,692629,693197,693572,693847,694198,694424,694672,694926,695155,695465,695825,696081,696421,696590,696826,697075,697270,697398,697489,697612,697738,697868,697979,698062,698077,698089,698102,698120,698132,698250,698284,698482,698884,699368,699669,699838,700192,700595,700949,701235,701499,701800,702126,702472,702629,702922,703037,703267,703457,703679,703810,704068,704304,704526,704889,705251,705438,705723,705929,706196,706578,706900,707208,707509,707784,707998,708310,708519,708755,708946,709273,709672,709996,710213,710409,710665,711057,711411,711694,712030,712372,712597,712878,713079,713343,713630,713973,714135,714280,714396,714642,714884,715182,715376,715680,715920,716120,716366,716607,716820,717102,717319,717723,717990,718205,718509,718715,719184,719361,719703,720028,720392,720751,721123,721548,721801,722203,722533,722957,723333,723764,724076,724394,724771,725135,725590,725834,726029,726188,726338,726453,726498,726673,726836,726950,727135,727233,727289,727370,727293,727217,727131,726918,726748,726608,726452,726313,726316,726318,726309,726297,726290,726418,726532,726649,726712,726774,726828,726861,727096,727252,727394,727697,727882,727959,728056,728322,728423,728643,728975,729238,729588,729837,729999,730207,730434,730639,730803,730976,731222,731464,731701,731900,732100,732272,732477,732674,732846,732969,733141,733281,733402,733469,733532,733587,733724,733977,734137,734293,734455,734574,734617,734666,734780,734868,735117,735200,735286,735397,735486,735571,735756,736072,736342,736625,736919,737125,737393,737641,737968,738288,738668,738740,738798,738910,739013,739273,739604,739736,739890,740015,740247,740266,740335,740413,740527,740732,740910,740984,741172,741329,741467,741678,741809,741926,742113]
yc = [6876232,6876495,6876828,6877135,6877532,6877919,6878137,6878336,6878666,6878869,6879272,6879617,6879946,6880255,6880569,6880746,6880990,6881252,6881667,6881767,6881644,6881403,6881247,6881121,6880944,6880686,6880546,6880558,6880658,6880742,6881012,6881332,6881599,6881927,6882137,6882401,6882720,6882934,6883142,6883387,6883645,6883883,6884051,6884320,6884461,6884650,6884830,6885077,6885278,6885424,6885590,6885588,6885586,6885584,6885582,6885580,6885577,6885573,6885571,6885632,6885704,6885766,6885982,6886228,6886600,6886937,6887422,6887782,6888168,6888595,6889011,6889599,6890055,6890557,6890884,6891185,6891480,6891717,6892136,6892325,6892498,6892760,6892890,6892956,6893032,6893150,6893276,6893659,6893858,6893999,6894249,6894520,6894806,6894915,6895052,6895140,6895221,6895199,6895178,6895159,6895134,6895107,6895092,6895066,6895036,6895013,6894988,6894964,6894941,6894905,6895120,6895344,6895688,6896032,6896562,6896849,6897225,6897614,6897880,6898251,6898810,6899130,6899352,6899632,6899918,6900148,6900398,6900640,6900885,6901067,6901272,6901374,6901469,6901660,6901843,6902087,6902355,6902859,6903170,6903520,6903897,6904296,6904693,6905051,6905374,6905853,6906189,6906580,6906776,6906988,6907203,6907450,6907730,6907988,6908209,6908661,6908905,6909172,6909416,6909613,6909853,6910112,6910363,6910628,6910859,6911152,6911361,6911623,6911751,6911997,6912117,6912299,6912432,6912622,6912748,6912862,6912964,6913057,6913149,6913273,6913420,6913571,6913726,6913866,6914035,6914171,6914313,6914461,6914584,6914709,6914816,6914935,6914970,6915021,6915070,6915162,6915215,6915284,6915345,6915412,6915462,6915527,6915716,6915915,6916116,6916242,6916564,6916930,6917235,6917517,6917982,6918438,6918794,6919200,6919509,6919772,6920195,6920452,6920745,6920963,6921349,6921711,6922164,6922561,6922933,6923370,6923645,6923931,6924184,6924466,6924809,6925147,6925392,6925641,6925733,6925907,6926052,6926223,6926368,6926504,6926609,6926882,6927109,6927380,6927741,6928179,6928539,6928955,6929324,6929603,6929911,6930079,6930301,6930545,6930894,6931124,6931292,6931508,6931680,6931869,6932062,6932237,6932473,6932747,6932942,6933201,6933589,6934130,6934699,6935146,6935439,6935647,6935929,6936218,6936514,6936769,6936959,6937278,6937514,6937781,6938166,6938410,6938773,6939061,6939392,6939542,6939715,6939819,6939878,6939944,6940019,6939963,6939918,6939876,6939885,6939894,6940019,6940075,6940428,6940700,6940883,6941034,6941267,6941404,6941449,6941490,6941509,6941540,6941573,6941590,6941615,6941634,6941592,6941531,6941480,6941533,6941585,6941633,6941670,6941724,6941760,6941951,6942106,6942372,6942492,6942589,6942654,6942713,6942732,6942762,6942818,6942863,6942916,6942833,6942779,6942795,6942934,6943118,6943328,6943592,6943812,6944009,6944166,6944387,6944604,6944801,6944929,6945129,6945212,6945281,6945366,6945449,6945523,6945725,6945880,6946169,6946347,6946491,6946656,6946768,6947022,6947126,6947326,6947516,6947730,6947940,6948158,6948407,6948555,6948790,6948984,6949232,6949452,6949704,6949887,6950073,6950294,6950508,6950774,6951074,6951313,6951509,6951879,6952276,6952568,6952957,6953319,6953572,6953983,6954199,6954583,6955129,6955489,6955844,6956252,6956685,6957056,6957375,6957733,6958218,6958469,6958758,6958985,6959271,6959448,6959804,6960122,6960446,6960732,6961009,6961250,6961401,6961776,6962025,6962253,6962555,6962823,6963019,6963265,6963580,6963751,6963903,6964066,6964195,6964368,6964491,6964571,6964743,6964931,6965166,6965354,6965552,6965812,6966066,6966339,6966568,6966777,6966957,6967171,6967377,6967556,6967685,6967865,6968011,6968138,6968388,6968628,6968836,6969027,6969381,6969581,6969944,6970188,6970365,6970691,6971052,6971301,6971706,6972008,6972310,6972619,6972741,6973074,6973390,6973627,6973922,6974128,6974344,6974627,6974782,6974983,6975116,6975292,6975504,6975754,6975949,6976106,6976360,6976594,6976810,6977086,6977309,6977569,6977779,6978104,6978569,6978883,6979236,6979456,6979851,6980187,6980441,6980701,6980919,6981110,6981384,6981554,6981706,6981949]

x1=xc[1:499]
y1=yc[1:499] 
np.shape(x1)

## Données contours alluvial Plain 
xp=[631065,631083,631145,631200,631169,631186,631265,631292,631279,631252,631161,631011,630893,630791,630680,630572,630225,630048,629614,629381,629414,629460,629508,629652,629866,630054,630280,630496,630604,630748,630911,631009,631120,631149,631265,631459,631649,631883,632061,632336,632579,632797,632992,633127,633244,633461,633684,633832,633909,633995,634171,634517,634821,635145,635452,636176,636392,636645,637013,637314,637956,638332,638706,638837,639162,639481,639565,639836,640104,640403,640674,640835,640997,641246,641283,641298,641359,641401,641353,641420,641529,641621,641693,641781,641844,641990,642180,642241,642312,642372,642389,642487,642582,642688,643186,643461,643660,643998,644371,644472,644738,644944,645200,645412,645675,645976,646227,646761,647163,647304,648453,648603,649014,649279,649594,649794,650099,650505,651016,651452,651601,651876,652286,652431,652701,652906,652987,653087,653209,653315,653416,653473,653519,653632,653686,653727,653693,653694,653731,653748,653775,653719,653711,653778,653913,654072,654147,654368,654553,654708,654879,655026,655226,655316,655361,655395,655383,655357,655476,655571,655732,655952,656244,656526,656673,656848,656953,657064,657149,657299,657470,657640,657876,658102,658503,659035,659221,659248,659189,659025,658766,658636,658482,658453,658309,658204,657791,657467,657682,657927,658178,658403,658483,658668,658858,659408,659518,659684,659739,659770,659882,660008,660145,660289,660483,660714,660795,660901,661046,661187,661463,661603,661690,661801,661931,662027,661949,661777,661418,661063,660783,660598,660433,660214,660031,659876,659982,659943,659924,659906,660106,660120,660240,660404,660784,660949,661103,661761,661826,662911,663082,663388,663892,664231,664556,664807,665013,665554,665924,666513,666855,667095,667132,667838,667965,668159,668337,668492,668730,668966,669126,669275,669344,669199,669095,669110,669179,669404,669528,669684,669949,670190,670161,670203,670312,670468,670649,670753,670939,671043,671016,671129,671649,672091,672206,672296,672435,672652,673159,673280,673336,673632,673831,674193,674387,674524,674956,675067,675333,675533,675709,675973,676052,676268,676786,676921,676982,677276,677335,677395,677475,677582,677655,677789,677893,678012,678060,678166,678235,678370,678470,678625,678973,679082,679304,679434,679581,679651,679722,680066,680194,680379,680716,680849,680993,681077,681134,681183,681260,681341,681398,681494,681574,681646,681741,682090,682223,682344,682432,682519,682665,682736,683012,683189,683207,683360,683419,683497,683681,683722,683845,683858,683851,683853,683888,683939,683990,684053,684113,684218,684289,684338,684489,684528,684595,684758,684816,684885,684958,685036,685126,685185,685224,685290,685373,685485,685606,685658,685679,685716,685777,685817,685934,686087,686305,686637,686816,686960,687117,687395,687497,687695,687882,688019,688070,688223,688310,688319,688289,688202,688084,688012,687994,687985,688099,688137,688432,688677,688911,689003,689131,689292,689432,689624,689789,689890,690006,690118,690174,690188,690183,690199,690177,690129,690031,689971,689923,689856,689848,689867,689904,689955,690008,690115,690298,690489,690554,690635,690763,690868,690925,691024,691066,691114,691189,691262,691318,691436,691601,691919,692132,692320,692400,692498,692528,692546,692539,692505,692547,692619,692676,692743,692816,692855,692835,692837,692871,692884,692871,692805,692762,692855,692917,692976,693076,693219,693399,693434,693505,693571,693633,693657,693657,693658,693673,693821,693849,694013,694116,694211,694262,694325,694384,694435,694502,694551,694680,694749,694794,694859,694903,694965,695027,695098,695193,695334,695399,695461,695593,695714,695827,695914,696003,696070,696200,696278,696384,696473,696497,696525,696503,696439,696400,696440,696484,696595,696751,696772,696721,696589,696448,696456,696434,696319,696317,696249,696216,696249,696330,696308,696513,696770,696905,697406,697534,697622,697633,697691,697653,697546,697271,697184,697352,697555,697550,697494,697597,697649,697742,697808,698086,698444,698930,699281,699610,699762,699788,699678,699495,700034,700594,700483,700316,700503,700862,701125,701723,702378,703063,703686,704122,704879,705533,706204,707280,708166,709673,710445,710780,711258,711876,712812,713211,713029,712689,712731,713496,713963,714471,714886,715807,716514,718267,719095,719984,719988,720569,720706,720099,719555,719616,719577,720114,720506,720784,720963,720487,720782,721253,721908,722093,722657,723374,724631,725087,726415,726656,726562,726412,725932,725717,725572,725697,725549,725496,725691,725766,726109,727473,727666,727910,728195,728614,729115,729339,729535,729700,730122,730515,730790,731388,731967,732216,732750,732813,732908,733053,733162,733375,733630,733803,734147,734218,734239,734229,734415,734547,734930,735084,735219,735503,736256,736877,737536,737816,738578,738880,739536,740007,739985,740021,740292,740565,740795,741086,741488,741911,742345,742176,741805,741329,740723,740526,740348,739711,739185,738796,737289,736634,736256,735964,735904,735966,735860,735362,735133,735042,734824,734788,734237,733169,732455,731148,730173,729828,729406,729023,728684,728315,728179,727709,727340,727308,727391,726934,727475,727575,727542,727447,727447,727745,727750,727874,728570,728755,727954,727775,727335,726732,726596,726126,726097,726023,725840,725922,725640,725124,724983,724807,724488,723909,723300,722766,722159,721395,721129,720811,720505,720370,720044,719599,718193,717451,717113,716305,715347,714787,714515,714524,712753,712236,711721,711260,710716,710425,708927,706574,705573,705157,704576,703989,703906,703881,703522,703256,702936,701929,700122,699293,698935,698788,699381,699040,699007,698859,698691,698500,698285,698249,698370,698496,698317,697756,697766,697939,698258,698397,698549,698742,698830,698293,697911,697824,697607,697451,697233,696976,696675,696383,696139,696171,696086,695874,695664,695208,695252,695546,695830,695929,695831,695770,695621,695402,695131,694915,694743,694554,694429,694275,694105,693849,693685,693394,693122,693032,692659,692327,692058,691701,691548,691565,691460,691094,690896,691059,691699,691743,691379,690945,690564,690022,689716,689377,689212,688917,688394,688022,687418,687032,686278,685710,685644,685336,685105,685015,684803,684621,684699,684840,685304,685712,685775,685609,685458,685288,684942,684605,684449,684379,684349,684327,684230,684270,684339,684484,684671,684928,685213,685463,685582,685747,685852,686314,686458,686386,686197,685872,685613,685431,685172,684919,684529,684169,683859,683593,683376,683028,682867,682573,682422,682277,682089,682151,682299,682783,683098,683501,683733,684011,684148,683967,683904,683685,683712,683638,683486,683167,682709,682583,682207,681971,681594,681128,680617,680285,679723,679569,679234,679085,678852,678675,678462,678322,678128,677968,677789,677693,677609,677153,677099,677068,677017,676959,676832,676550,676247,675881,675313,674846,674613,674453,674262,673965,673766,673440,673002,672777,672494,672210,671937,671761,671528,671218,670442,670338,669878,669562,668753,668456,668265,668106,667825,667665,667437,667052,666534,666416,666312,666269,666291,666347,666341,666274,666217,666137,666038,665946,665811,665656,665400,665284,665248,665228,665101,665050,664831,664793,664748,664683,664614,664549,664487,664446,664396,664371,664286,664236,664181,664122,664035,663973,663739,663679,663535,663487,663452,663442,663441,663450,663435,663409,663343,663257,663152,663051,662960,662840,662713,662623,662512,662256,662136,662061,661996,661814,661709,661558,661413,660990,660874,660809,660748,660667,660561,660455,660399,660332,660321,660326,660380,660365,660274,660228,660026,659986,659955,659949,659923,659898,659793,659753,659722,659722,659746,659726,659472,659422,659421,659401,659401,659380,659339,659147,659031,658848,658731,658544,658518,658439,658351,658240,658129,658003,657961,657857,657767,657637,657491,657446,657356,657155,656979,656924,656673,656548,656498,656482,656501,656531,656570,656625,656679,656814,656889,656964,657014,657044,657103,657138,657158,657213,657258,657313,657408,657443,657479,657538,657615,657616,657777,657883,658003,658068,658158,658166,658137,658117,658020,657955,657904,657899,657903,657928,657937,657936,657916,657916,657850,657805,657744,657679,657629,657553,657435,657370,657280,657210,657009,656949,656924,656919,656938,656983,657043,657088,657113,657243,657328,657367,657372,657362,657331,657276,657195,657145,657065,656985,656846,656740,656270,655913,655668,655478,655303,655045,654813,654645,654533,654424,654363,654229,654228,654191,654160,654140,654144,654174,654258,654263,654230,654207,654241,654321,654556,654736,655368,655598,655807,656031,656450,657163,657393,657480,657373,657261,656415,656269,656144,655958,655546,654713,654037,653977,653811,653706,653570,653504,653439,653388,653349,653283,653214,653120,653049,652925,652844,652777,652686,652424,652345,652291,652181,652108,651995,651902,651828,651773,651656,651528,651416,651285,651266,651236,651183,651102,651004,650909,650782,650690,650626,650527,650367,650277,650197,650062,649887,649717,649603,649498,649378,649201,649088,648872,648822,648652,648592,648430,648319,648141,648006,647981,647839,647668,647538,647436,647138,647024,646962,646867,646807,645876,645800,645664,645654,645618,644743,644650,644480,644315,644163,644020,643821,643761,643531,643366,643270,643080,643000,642973,642931,642867,642784,642705,642489,642318,642109,642102,642038,641967,641925,641869,641824,641716,641617,641321,641244,641155,641018,640617,640360,640208,640038,639529,639196,638759,638613,638349,637993,637660,637326,637155,637042,636903,636665,636527,636436,636247,636008,635878,635716,635589,635556,635569,635611,635721,635761,635824,635868,635945,635723,635668,635598,635399,635220,635031,634889,634776,634681,634615,634565,634413,634347,634242,634089,633852,633765,633703,633631,633520,633444,633339,633248,633138,633069,632933,632799,632724,632546,632103,631686,631567,631421,631293,631112,631021,630961,630911,630856,630861,630945,631050,631174,631268,631355,631461,631641,631731,631810,631907,632059,632137,632192,632202,632195,632206,632205,632413]
yp=[6875650,6875930,6876080,6876700,6877010,6877130,6877380,6877550,6878240,6878410,6878710,6879040,6879350,6879600,6879790,6880040,6880360,6880560,6880890,6881540,6881960,6882180,6882270,6882400,6882560,6882690,6882820,6882930,6882940,6882920,6882850,6882780,6882600,6882420,6882040,6881730,6881600,6881550,6881380,6881150,6881010,6880960,6880980,6881020,6881100,6881530,6881980,6882440,6882830,6883180,6883450,6883970,6884210,6884610,6884890,6885500,6885680,6885730,6885890,6885940,6886020,6886000,6885910,6885920,6886000,6886080,6886070,6886100,6886180,6886320,6886520,6886610,6886810,6887130,6887240,6887530,6887800,6888050,6888290,6888410,6888600,6888660,6888860,6889370,6889560,6890020,6890350,6890620,6890800,6891030,6891370,6891830,6892050,6892140,6892430,6892770,6892870,6892990,6893400,6893470,6893670,6893860,6894030,6894270,6894590,6894690,6894870,6895570,6895590,6895640,6896020,6896040,6896150,6896120,6896030,6895990,6896020,6896080,6896180,6896170,6896140,6896070,6895930,6895900,6895890,6895940,6895980,6896060,6896220,6896380,6896480,6896610,6896870,6897340,6898100,6898650,6898790,6898930,6899180,6899550,6900140,6900300,6900680,6900900,6901170,6901360,6901410,6901500,6901460,6901500,6901590,6901700,6901830,6901800,6901750,6901610,6901390,6901210,6901090,6901100,6901130,6901190,6901360,6901620,6901740,6901870,6901890,6901950,6901980,6902000,6902000,6902020,6902100,6902250,6903040,6904300,6904680,6904970,6905130,6905310,6905500,6905560,6905700,6905780,6905930,6905950,6905860,6905820,6906460,6906540,6906570,6906450,6906470,6906490,6906450,6906310,6906330,6906420,6906490,6906580,6906780,6906950,6907260,6907450,6907590,6907650,6907750,6907930,6908060,6908120,6908130,6908170,6908390,6908470,6908510,6908680,6908860,6909060,6909340,6909580,6909670,6909660,6909690,6909900,6910080,6910180,6910260,6910440,6910590,6910820,6910820,6910700,6910590,6910540,6910510,6910420,6910280,6909860,6909840,6909760,6909820,6909990,6910410,6910640,6910860,6911090,6911350,6911400,6911460,6911700,6911960,6912310,6913090,6913570,6913610,6913750,6913900,6913910,6913960,6914160,6914240,6914220,6914060,6913940,6913810,6913580,6913480,6913350,6913310,6913300,6913360,6913500,6913710,6913770,6913860,6913950,6914060,6914090,6914100,6914330,6914520,6914790,6915380,6915880,6916010,6916040,6916080,6916130,6916130,6916170,6916160,6916090,6916160,6916170,6916110,6916120,6915970,6915960,6916060,6916070,6916110,6916250,6916260,6916270,6916270,6916250,6916250,6916340,6916340,6916340,6916310,6916280,6916290,6916310,6916360,6916450,6916440,6916410,6916400,6916430,6916450,6916450,6916400,6916400,6916450,6916500,6916580,6916600,6916590,6916440,6916440,6916390,6916420,6916440,6916520,6916610,6916740,6916790,6916920,6917090,6917220,6917440,6917580,6917670,6917810,6918100,6918210,6918300,6918450,6918570,6918840,6918950,6919310,6919640,6919670,6919920,6920080,6920240,6920580,6920740,6920950,6921080,6921150,6921250,6921390,6921560,6921690,6921810,6921900,6922040,6922210,6922270,6922570,6922620,6922690,6922900,6922980,6923120,6923240,6923330,6923440,6923550,6923670,6923760,6923860,6923910,6924020,6924080,6924180,6924290,6924360,6924430,6924740,6924890,6925080,6925320,6925490,6925550,6925670,6925960,6926040,6926110,6926250,6926320,6926410,6926560,6926730,6926820,6926950,6927110,6927250,6927350,6927390,6927580,6927630,6927630,6927630,6927320,6927090,6926960,6926850,6926780,6926780,6926800,6926860,6926920,6927030,6927220,6927440,6927620,6927730,6928010,6928130,6928240,6928410,6928520,6928630,6928880,6929010,6929150,6929330,6929500,6929620,6929760,6929940,6930090,6930130,6930210,6930420,6930680,6930770,6930880,6930900,6930900,6930870,6930810,6930790,6930790,6930850,6931030,6931160,6931300,6931380,6931530,6931590,6931750,6931830,6931890,6932020,6931960,6931980,6932070,6932220,6932430,6932600,6932650,6932840,6932980,6933030,6933120,6933150,6933170,6933190,6933240,6933360,6933450,6933590,6933560,6933550,6933560,6933580,6933620,6933730,6933800,6933900,6934010,6933960,6933950,6933970,6934000,6934040,6934130,6934230,6934280,6934320,6934340,6934370,6934410,6934440,6934470,6934480,6934460,6934430,6934420,6934430,6934520,6934530,6934520,6934470,6934390,6934350,6934350,6934370,6934360,6934390,6934360,6934330,6934300,6934260,6934440,6934520,6934600,6934630,6934730,6934760,6935030,6935180,6935300,6935440,6935700,6935910,6936080,6936150,6936280,6936330,6936400,6936810,6937110,6937860,6937930,6937940,6938240,6938410,6938500,6938630,6938860,6939090,6939430,6939540,6939530,6939450,6939460,6939890,6940040,6940200,6940470,6940570,6940480,6940230,6940180,6940100,6940200,6940450,6940740,6940930,6941200,6941740,6942100,6942310,6942460,6942340,6942160,6941790,6941440,6941380,6941210,6941290,6941250,6941550,6941970,6942270,6942430,6942470,6942470,6942110,6942290,6943510,6943860,6944180,6944280,6944160,6943970,6944090,6944290,6944750,6945120,6944950,6945430,6945830,6946120,6946350,6946900,6948070,6948580,6949510,6949910,6950100,6950390,6950570,6951160,6951380,6951750,6951750,6951740,6952400,6953530,6954680,6954730,6954100,6952960,6951700,6951060,6951030,6951540,6952260,6954760,6955340,6956030,6956340,6956830,6957350,6958120,6958760,6959250,6959540,6959940,6961160,6962190,6963750,6963950,6964480,6964680,6964650,6965120,6965250,6965500,6965590,6965610,6966160,6966790,6967160,6967840,6968100,6968650,6969070,6969200,6969430,6969890,6970050,6970150,6970420,6970770,6971000,6971300,6971750,6972110,6972610,6973030,6973730,6973920,6974300,6974720,6975110,6975640,6975760,6976320,6976950,6977580,6978070,6978730,6979160,6979790,6980170,6980970,6981240,6981590,6982160,6981850,6981560,6980790,6979950,6979150,6978260,6977910,6976710,6976360,6975310,6974440,6973540,6973330,6972970,6972570,6972390,6972020,6971410,6970920,6970080,6969590,6968460,6967740,6966840,6966090,6964720,6963760,6963410,6963380,6963320,6962900,6962360,6962220,6961760,6961130,6960620,6960280,6959070,6958520,6958180,6957560,6957140,6957140,6956590,6956200,6956010,6954780,6953990,6952990,6951810,6950870,6950430,6950130,6949630,6949400,6948980,6948620,6947950,6947720,6947350,6947230,6946790,6946760,6947080,6947010,6946640,6946740,6946280,6946470,6946520,6946620,6946170,6945800,6945770,6945030,6944480,6944360,6944200,6943500,6943210,6942800,6942440,6941570,6941790,6941790,6941250,6941370,6941690,6941350,6940970,6941090,6941060,6940840,6940960,6940640,6940190,6939730,6939420,6939020,6938660,6938950,6938910,6938810,6938310,6937760,6936570,6935920,6935880,6935890,6936010,6935850,6935690,6935380,6935190,6935120,6934510,6934410,6934380,6934410,6934530,6934610,6934540,6934290,6933640,6933380,6933120,6932880,6932580,6932440,6932410,6932310,6932170,6932080,6931810,6931640,6931690,6931640,6931250,6931030,6930790,6930700,6930560,6930390,6930140,6929830,6929940,6929890,6929860,6929780,6929460,6929300,6928940,6928680,6928720,6928840,6928980,6928950,6929100,6929100,6929220,6929120,6928630,6928180,6927760,6927600,6927200,6926890,6926460,6926340,6926070,6925500,6925030,6924760,6924610,6924600,6924760,6925130,6925320,6925340,6924940,6924670,6924240,6923430,6922990,6922470,6921650,6921150,6920510,6920320,6920030,6919360,6919250,6919230,6919400,6919210,6918760,6918630,6918800,6918930,6918980,6918890,6918590,6918230,6917740,6917430,6917100,6916930,6916810,6916820,6916930,6916970,6916990,6917120,6917280,6917230,6916890,6916600,6916470,6916390,6916180,6916130,6916000,6915990,6915960,6915760,6915570,6915450,6915550,6915560,6915330,6915020,6914900,6914780,6914310,6914090,6913750,6913690,6913660,6913800,6914190,6914400,6914490,6914420,6914200,6913970,6913670,6913270,6912180,6911980,6911970,6912050,6912070,6911990,6912040,6911940,6911910,6911950,6912050,6912210,6912240,6912250,6912150,6912120,6912170,6912270,6912300,6912260,6912250,6912290,6912300,6912280,6911960,6911860,6911840,6911840,6911860,6911910,6912070,6912150,6912080,6911810,6911460,6911470,6911480,6911590,6911590,6911750,6911770,6911600,6911660,6911620,6911600,6911530,6911570,6911610,6911410,6911150,6911170,6911160,6910890,6910770,6910820,6910920,6910960,6910910,6910930,6910950,6910830,6910540,6910440,6910330,6910240,6910160,6910070,6910040,6910020,6910030,6910040,6910040,6910030,6909980,6909900,6909690,6909610,6909560,6909510,6909400,6909290,6909050,6909070,6909090,6909080,6909030,6908990,6908960,6908940,6908920,6908920,6908950,6908960,6908940,6908900,6908850,6908820,6908710,6908680,6908560,6908510,6908430,6908390,6908270,6908200,6908110,6908040,6907940,6907850,6907760,6907630,6907500,6907390,6907300,6907250,6907210,6907130,6907120,6907100,6907070,6906910,6906840,6906780,6906740,6906410,6906340,6906280,6906190,6906090,6905920,6905780,6905660,6905360,6905260,6905220,6905150,6905100,6904970,6904890,6904650,6904570,6904490,6904350,6904290,6904270,6904250,6904210,6904130,6904090,6904010,6903940,6903520,6903420,6903400,6903310,6903280,6903190,6903110,6902850,6902640,6902250,6902050,6901760,6901710,6901570,6901450,6901280,6901150,6901040,6901010,6900950,6900910,6900850,6900810,6900800,6900770,6900700,6900650,6900640,6900580,6900530,6900480,6900410,6900310,6900220,6900150,6900070,6900030,6900000,6899990,6899980,6899950,6899900,6899740,6899710,6899710,6899790,6899810,6899800,6899770,6899770,6899790,6899890,6900030,6900050,6900220,6900290,6900340,6900350,6900330,6900030,6900020,6900020,6899920,6899870,6899790,6899750,6899710,6899620,6899540,6899430,6899350,6899340,6899240,6899200,6899180,6899180,6899200,6899250,6899310,6899320,6899320,6899320,6899290,6899260,6899230,6899190,6899140,6899090,6899070,6899060,6899060,6899040,6899020,6898970,6898930,6898860,6898790,6898720,6898680,6898670,6898670,6898690,6898770,6898780,6899000,6899220,6899320,6899350,6899280,6898990,6898730,6898560,6898440,6898250,6898090,6897480,6897410,6897080,6897030,6896950,6896910,6896860,6896750,6896700,6896620,6896500,6896440,6896410,6896390,6896410,6896530,6896510,6896400,6896230,6896040,6895650,6895600,6895220,6894860,6894630,6894600,6894520,6894490,6894370,6894130,6893860,6893830,6893810,6893780,6893730,6893650,6893590,6893510,6893420,6893390,6893360,6893350,6893330,6893300,6893260,6893240,6893250,6893250,6893190,6893200,6893210,6893260,6893280,6893310,6893330,6893360,6893410,6893450,6893490,6893500,6893510,6893570,6893620,6893670,6893720,6893790,6893830,6893970,6894100,6894160,6894210,6894290,6894330,6894350,6894360,6894370,6894410,6894480,6894510,6894530,6894530,6894530,6894490,6894490,6894540,6894550,6894500,6894470,6894400,6894310,6894270,6894150,6894050,6893990,6893910,6893660,6893420,6893370,6893310,6893170,6891780,6891750,6891600,6891470,6891420,6891200,6891130,6891070,6891050,6891000,6890830,6890520,6890480,6890360,6890210,6890050,6889800,6889650,6889480,6889390,6889330,6889190,6888970,6888570,6888270,6887660,6887530,6887190,6886920,6886820,6886590,6886520,6886410,6886180,6885940,6885840,6885680,6885620,6885410,6885260,6885220,6885230,6885250,6885210,6885270,6885260,6885280,6885260,6885170,6885160,6885150,6885130,6885070,6884950,6884900,6884830,6884660,6884420,6884300,6884180,6883990,6883890,6883730,6883630,6883530,6883410,6883280,6883210,6883140,6882980,6883020,6883140,6883180,6883220,6883260,6883180,6883110,6883010,6882850,6882700,6882040,6881750,6881500,6881310,6881100,6881040,6880970,6880850,6880640,6880570,6880480,6880410,6880340,6880340,6880300,6880240,6880240,6880170,6880290,6880600,6880780,6881170,6881300,6881310,6881270,6881140,6880980,6880760,6880630,6880350,6880170,6880010,6879900,6879810,6879710,6879550,6879430,6879300,6879150,6878950,6878820,6878660,6878600,6878410,6878200,6877830,6876610]
x2=xp[1:xp.index(max(xp))]
x3=xp[len(xp):xp.index(max(xp))+1:-1]
y2=yp[1:xp.index(max(xp))]
y3=yp[len(xp):xp.index(max(xp))+1:-1]

## Points de contrôle supplémentaires
# coordonnées points extrêmes
XA1=640000
YA1=6875000
XB1=740000
YB1=6955000

XA2=630000
YA2=6890000
XB2=735000
YB2=6980000

# ajouts de deux zones de vecteurs supplementaires => limites de l'interpolation
n=19
x4= np.zeros(n)
y4= np.zeros(n)
x5= np.zeros(n)
y5= np.zeros(n)
for i in range(0,len(x4)):
    x4[i]=(XA1+(XB1-XA1)/(n-1)*i)
    y4[i]=(YA1+(YB1-YA1)/(n-1)*i)
    x5[i]=(XA2+(XB2-XA2)/(n-1)*i)
    y5[i]=(YA2+(YB2-YA2)/(n-1)*i)

# Methode du gradient 
#  https://stackoverflow.com/questions/28269379/curve-curvature-in-numpy
dx_dt1 = np.gradient(x1)
dy_dt1 = np.gradient(y1)
velocity = np.array([ [dx_dt1[i], dy_dt1[i]] for i in range(dx_dt1.size)])
ds_dt1 = np.sqrt(dx_dt1 * dx_dt1 + dy_dt1 * dy_dt1)
tangent1 = np.array([1/ds_dt1] * 2).transpose() * velocity

dx_dt2 = np.gradient(x2)
dy_dt2 = np.gradient(y2)
velocity2 = np.array([ [dx_dt2[i], dy_dt2[i]] for i in range(dx_dt2.size)])
ds_dt2 = np.sqrt(dx_dt2 * dx_dt2 + dy_dt2 * dy_dt2)
tangent2 = np.array([1/ds_dt2] * 2).transpose() * velocity2

dx_dt3 = np.gradient(x3)
dy_dt3 = np.gradient(y3)
velocity3 = np.array([ [dx_dt3[i], dy_dt3[i]] for i in range(dx_dt3.size)])
ds_dt3 = np.sqrt(dx_dt3 * dx_dt3 + dy_dt3 * dy_dt3)
tangent3 = np.array([1/ds_dt3] * 2).transpose() * velocity3

dx_dt4 = np.gradient(x4)
dy_dt4 = np.gradient(y4)
velocity4 = np.array([ [dx_dt4[i], dy_dt4[i]] for i in range(dx_dt4.size)])
ds_dt4 = np.sqrt(dx_dt4 * dx_dt4 + dy_dt4 * dy_dt4)
tangent4 = np.array([1/ds_dt4] * 2).transpose() * velocity4

dx_dt5 = np.gradient(x5)
dy_dt5 = np.gradient(y5)
velocity5 = np.array([ [dx_dt5[i], dy_dt5[i]] for i in range(dx_dt5.size)])
ds_dt5 = np.sqrt(dx_dt5 * dx_dt5 + dy_dt5 * dy_dt5)
tangent5 = np.array([1/ds_dt5] * 2).transpose() * velocity5

tangent=np.concatenate((tangent1,tangent2,tangent3,tangent4,tangent5),axis=0) 

x0=np.concatenate((x1,x2,x3,x4,x5),axis=0) 
y0=np.concatenate((y1,y2,y3,y4,y5),axis=0) 
u0 = tangent[:, 0]
v0 = tangent[:, 1]
In [20]:
# Methode d'interpolation -> https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html

xx = np.linspace(629000, 742000, 200)
yy = np.linspace(6875000, 6982160, 200)
xx, yy = np.meshgrid(xx, yy)

u0.shape=(np.size(u0),)
np.shape(u0)
v0.shape=(np.size(v0),)
np.shape(v0)

points = np.transpose(np.vstack((x0, y0)))
u_interp = interpolate.griddata(points, u0, (xx, yy), method='linear')
v_interp = interpolate.griddata(points, v0, (xx, yy), method='linear')
In [21]:
plt.figure(figsize=(200,200))
plt.quiver(xx, yy, u_interp, v_interp, label='Field vectors')
l = plt.plot(x0,y0,'ro',markersize=20, label='Alluvial plain')
No description has been provided for this image
In [22]:
xx1 = np.concatenate(xx)
yy1 = np.concatenate(yy)
u_interp1 = np.concatenate(u_interp)
v_interp1 = np.concatenate(v_interp)
In [23]:
#Créer une Db point avec xx, yy, u_interp, v_interp
dbv = gl.Db()
dbv.addColumns(xx1,"xx",gl.ELoc.X, 0)
dbv.addColumns(yy1,"yy",gl.ELoc.X, 1)
dbv.addColumns(u_interp1,"u_interp",gl.ELoc.Z, 0)
dbv.addColumns(v_interp1,"v_interp",gl.ELoc.Z, 1)
dbv.display()
grid.setLocator("Pol*")
grid.display()

Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 4
Maximum Number of UIDs       = 4
Total number of samples      = 40000

Variables
---------
Column = 0 - Name = xx - Locator = x1
Column = 1 - Name = yy - Locator = x2
Column = 2 - Name = u_interp - Locator = z1
Column = 3 - Name = v_interp - Locator = z2
 
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Maximum Number of UIDs       = 4
Total number of samples      = 1320000

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = NA
In [24]:
#Tester la valeur retournée par migrate :
grid.deleteColumn("Migr*")
grid.display()
gl.migrate(dbv, grid, "u_interp")
gl.migrate(dbv, grid, "v_interp")
grid.setLocator("Migrate*", gl.ELoc.Z)
#grid.display(dbfmt)

Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Maximum Number of UIDs       = 4
Total number of samples      = 1320000

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = NA
In [25]:
grid.useSel = False
grid.setLocator("Polygon")
iuid = grid.addSelectionByLimit("*u_interp*", gl.Limits([-10], [10]), "vec_define")
In [26]:
xplt = grid.getColumnByLocator(gl.ELoc.X,0,useSel=True)
yplt = grid.getColumnByLocator(gl.ELoc.X,1,useSel=True)
uplt = grid.getColumnByLocator(gl.ELoc.Z,0,useSel=True)
vplt = grid.getColumnByLocator(gl.ELoc.Z,1,useSel=True)
In [27]:
plt.figure(figsize=(200,200))
plt.quiver(xplt, yplt, uplt, vplt, label='Field vectors')
l = plt.plot(x0,y0,'ro',markersize=20, label='Alluvial plain')
No description has been provided for this image
In [28]:
ax = grid.plot(flagLegendRaster=False,alpha=0.3,usesel=False)
ax = poly.plot()
ax = data.plot()
No description has been provided for this image
In [29]:
myVarioParamBidir = gl.VarioParam()
mydir = gl.DirParam(40,800.,0.5,45.,0,0,np.nan,np.nan,0.,[],[1,1])
myVarioParamBidir.addDir(mydir)
mydir = gl.DirParam(20,400.,0.5,45.,0,0,np.nan,np.nan,0.,[],[-1,1])
myVarioParamBidir.addDir(mydir)
myVarioBidir = gl.Vario(myVarioParamBidir,data)
err = myVarioBidir.compute(gl.ECalcVario.VARIOGRAM)
myVarioBidir.display()
axs = gp.varmod(myVarioBidir,idir=0)
axs.decoration(title="Bi-directional Variogram for thickness(45°)")
axs = gp.varmod(myVarioBidir,idir=1)
axs.decoration(title="Bi-directional Variogram for thickness(135°)")
plt.show()

Variogram characteristics
=========================
Number of variable(s)       = 1
Number of direction(s)      = 2
Space dimension             = 2
Variance-Covariance Matrix     8.308

Direction #1
------------
Number of lags              = 40
Direction coefficients      =      1.000     1.000
Direction angles (degrees)  =     45.000     0.000
Tolerance on direction      =     45.000 (degrees)
Calculation lag             =    800.000
Tolerance on distance       =     50.000 (Percent of the lag value)

For variable 1
      Rank    Npairs  Distance     Value
         0  1497.000   222.651     4.320
         1  4144.000   817.595     6.006
         2  4991.000  1597.901     5.768
         3  5136.000  2402.202     7.184
         4  4917.000  3203.355     7.062
         5  5376.000  3996.992     6.872
         6  5950.000  4805.500     6.823
         7  5977.000  5607.812     6.221
         8  5882.000  6391.725     6.132
         9  5773.000  7198.759     7.200
        10  5596.000  8003.985     6.710
        11  4791.000  8802.836     7.648
        12  5395.000  9590.811     6.759
        13  5080.000 10419.575     6.715
        14  5648.000 11189.883     6.848
        15  6131.000 12007.431     6.499
        16  5404.000 12773.473     6.806
        17  4858.000 13605.535     6.477
        18  4512.000 14393.349     6.468
        19  4757.000 15203.934     6.522
        20  4611.000 16000.736     5.907
        21  4441.000 16793.025     6.945
        22  4744.000 17616.342     7.342
        23  6278.000 18421.513     7.217
        24  5724.000 19187.978     7.032
        25  5358.000 19995.792     6.630
        26  5150.000 20792.670     7.825
        27  5452.000 21594.321     7.903
        28  4787.000 22407.797     7.569
        29  5381.000 23190.746     9.347
        30  4957.000 24010.196     7.407
        31  5197.000 24797.916     8.004
        32  5178.000 25608.936     7.778
        33  5400.000 26409.634     8.091
        34  4918.000 27190.981     7.408
        35  4704.000 27990.733     7.810
        36  4499.000 28793.542     7.391
        37  5276.000 29647.480     7.027
        38  5407.000 30398.366     7.499
        39  6072.000 31199.740     7.748

Direction #2
------------
Number of lags              = 20
Direction coefficients      =     -1.000     1.000
Direction angles (degrees)  =    135.000     0.000
Tolerance on direction      =     45.000 (degrees)
Calculation lag             =    400.000
Tolerance on distance       =     50.000 (Percent of the lag value)

For variable 1
      Rank    Npairs  Distance     Value
         0   608.000   118.768     2.770
         1  1411.000   393.556     4.525
         2  1451.000   794.496     6.282
         3  1231.000  1197.348     6.558
         4  1028.000  1597.631     7.271
         5   855.000  1982.172     7.292
         6   696.000  2405.045     7.805
         7   676.000  2795.556     7.764
         8   417.000  3190.162     8.728
         9   353.000  3598.353    11.266
        10   337.000  3995.587    10.021
        11   260.000  4401.091     7.744
        12   233.000  4792.091     7.633
        13   179.000  5181.753     7.729
        14   169.000  5587.892     7.010
        15   101.000  5976.535     9.957
        16    89.000  6390.457     9.290
        17    55.000  6800.051     5.859
        18    29.000  7192.062     9.570
        19    40.000  7634.709     4.950
No description has been provided for this image
In [30]:
model = gl.Model.createFromDb(data)

structs = [gl.ECov.NUGGET,gl.ECov.BESSEL_K]

#consNug = gl.ConsItem.define(gl.EConsElem.SILL,0, type = gl.EConsType.UPPER,value = 0.1)

cons1P = gl.ConsItem.define(gl.EConsElem.PARAM,1, type = gl.EConsType.EQUAL,value = 1)
#cons1Rm = gl.ConsItem.define(gl.EConsElem.RANGE,1, type = gl.EConsType.LOWER,value = 100)
#cons1RM = gl.ConsItem.define(gl.EConsElem.RANGE,1, type = gl.EConsType.UPPER,value = 350)

#cons2P = gl.ConsItem.define(gl.EConsElem.PARAM,2, type = gl.EConsType.EQUAL,value = 2)
#cons2Rm = gl.ConsItem.define(gl.EConsElem.RANGE,2, type = gl.EConsType.LOWER,value = 100)
#cons2RM = gl.ConsItem.define(gl.EConsElem.RANGE,2, type = gl.EConsType.UPPER,value = 400)

a = gl.Constraints()
#a.addItem(consNug)
a.addItem(cons1P)
#a.addItem(cons1Rm)
#a.addItem(cons1RM)
#a.addItem(cons2P)
#a.addItem(cons2Rm)
#a.addItem(cons2RM)

err = model.fit(myVarioBidir,structs,constraints=a)


#err = model.fit(myVarioBidir,[gl.ECov.NUGGET,gl.ECov.BESSEL_K,gl.ECov.BESSEL_K], False)

model.display()
axs = gp.varmod(myVarioBidir,model,idir=0)
axs.decoration(title="Bi-directional Variogram for thickness(45°)")
axs = gp.varmod(myVarioBidir,model,idir=1)
axs.decoration(title="Bi-directional Variogram for thickness(135°)")

Model characteristics
=====================
Space dimension              = 2
Number of variable(s)        = 1
Number of basic structure(s) = 2
Number of drift function(s)  = 0
Number of drift equation(s)  = 0

Covariance Part
---------------
Nugget Effect
- Sill         =      2.208
K-Bessel (Third Parameter = 1)
- Sill         =      4.699
- Ranges       =   1022.106  1497.510
- Theo. Ranges =    295.057   432.294
- Angles       =     45.000     0.000
- Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.707    -0.707
     [  1,]     0.707     0.707
Total Sill     =      6.907
No description has been provided for this image
In [31]:
if not oldMethod :
    model = gl.Model.createFromDb(data)
    covBessel = gl.CovAniso.createAnisotropic(type = gl.ECov.BESSEL_K,ranges=ranges,sill=4.7,param=1,ctxt=model.getContext())
    covNugget = gl.CovAniso.createIsotropic(type = gl.ECov.NUGGET,sill=2.2,ctxt=model.getContext(),range=1)
    model.addCov(covNugget)
    model.addCov(covBessel)
    
model.setDriftIRF(order = 0)
In [32]:
grid.setLocator("Poly*", gl.ELoc.SEL)
#grid.setLocator("Poly*",gl.ELoc.UNKNOWN)
In [33]:
grid
Out[33]:
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 7
Maximum Number of UIDs       = 7
Total number of samples      = 1320000
Number of active samples     = 138248

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = sel
Column = 4 - Name = Migrate.u_interp - Locator = z1
Column = 5 - Name = Migrate.v_interp - Locator = z2
Column = 6 - Name = vec_define - Locator = NA
In [34]:
u = grid["Migrate.u_interp"]
v = grid["Migrate.v_interp"]
res = 0. * u
for i in range(u.shape[0]):
    res[i] = gl.GH.rotationGetAngles((u[i], v[i]))[0]
In [35]:
grid.deleteColumn("angles*")
grid["angles1"]= res
grid.setLocator("angles*",gl.ELoc.NOSTAT)
nostat = gl.NoStatArray(["M2A"],grid)
if not oldMethod:
    model.addNoStat(nostat)
In [36]:
grid.deleteColumns(["spde*"])
In [37]:
spde = gl.SPDE(model,grid,data,gl.ESPDECalcMode.KRIGING)
spde.compute()
iuid = spde.query(grid)
In [38]:
gp.setDefaultGeographic(dims=[20,20])
ax = grid.plot(name_raster="spde*kriging",cmap="gnuplot2", flagLegendRaster= True)
ax = poly.plot()
No description has been provided for this image
In [39]:
#Kriging data to new grid
raster = gl.DbGrid()
raster.reset([2400,2600],[50,50],[625000,6870000],[0,0])
gl.migrate(grid, raster, "spde.Thickness.kriging",2,[100,100],True)

# Visualize
ax = raster.plot(flagLegendRaster=False,alpha=0.3,usesel=False)
raster
grid
Out[39]:
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 9
Maximum Number of UIDs       = 9
Total number of samples      = 1320000
Number of active samples     = 138248

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = sel
Column = 4 - Name = Migrate.u_interp - Locator = NA
Column = 5 - Name = Migrate.v_interp - Locator = NA
Column = 6 - Name = vec_define - Locator = NA
Column = 7 - Name = angles1 - Locator = nostat1
Column = 8 - Name = spde.Thickness.kriging - Locator = z1
No description has been provided for this image
In [40]:
# Write the contents of the file into an external file using ArcGis format

#aof = gl.GridArcGis("KrigSPDE.txt",raster)
#aof.setCol(3)
#if aof.isAuthorized():
#    aof.writeInFile()
In [41]:
# Export Kriging grid to Ascii for Raster format => raster.gridwrite.format("arcgis","filename.txt")
Kri=raster.getColumn("Migrate.spde.Thickness.kriging")
len(Kri)
KriG = np.array(Kri)
KriG = np.where(KriG > 1000, -999999, KriG)
KriG = np.where(KriG==0, -999999, KriG) 
np.savetxt("filename.txt", KriG)
In [ ]: