In [1]:
#general imports
import pygslib
In [2]:
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')
In [4]:
# This is a 2D file, in this GSLIB version we require 3D data and drillhole name or domain code
# so, we are adding constant elevation = 0 and a dummy BHID = 1
mydata['Zlocation']=0.
mydata['bhid']=1.
# printing to verify results
print (' \n **** 5 first rows in my datafile \n\n ', mydata.head(n=5))
**** 5 first rows in my datafile
Xlocation Ylocation Primary Secondary Declustering Weight Zlocation \
0 39.5 18.5 0.06 0.22 1.619 0.0
1 5.5 1.5 0.06 0.27 1.619 0.0
2 38.5 5.5 0.08 0.40 1.416 0.0
3 20.5 1.5 0.09 0.39 1.821 0.0
4 27.5 14.5 0.09 0.24 1.349 0.0
bhid
0 1.0
1 1.0
2 1.0
3 1.0
4 1.0
In [5]:
# these are the parameters we need. Note that at difference of GSLIB this dictionary also stores
# the actual data (ex, X, Y, etc.).
#important! python is case sensitive 'bhid' is not equal to 'BHID'
parameters = {
'x' : mydata['Xlocation'].values,
'y' : mydata['Ylocation'].values,
'z' : mydata['Zlocation'].values,
'bhid' : mydata['bhid'].values,
'vr' : mydata['Primary'].values,
'tmin' : -1.0e21,
'tmax' : 1.0e21,
'nlag' : 10,
'xlag' : 1,
'ndir' : 10,
'ndip' : 10,
'orgdir': 0.,
'orgdip': 0.,
'isill' : 1,
'sills' : [mydata['Primary'].var()],
'ivtail' : [1],
'ivhead' : [1],
'ivtype' : [1]
}
In [6]:
#Now we are ready to calculate the veriogram
np, dis, gam, hm, tm, hv, tv = pygslib.gslib.gamv3D(parameters)
In [7]:
np
Out[7]:
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In [9]:
# create structured grid with data
import vtk
import vtk.util.numpy_support as vtknumpy
import math
import numpy as np
XYZPts = vtk.vtkPoints()
XYZPts.SetNumberOfPoints(parameters['ndip']*parameters['nlag']*parameters['ndir']*2)
angdir = (math.pi/180.)*180./(parameters['ndir'])
angdip = (math.pi/180.)*90./(parameters['ndip'])
orgdir = parameters['orgdir'] * math.pi/180.
orgdip = parameters['orgdip'] * math.pi/180.
id=-1
for k in range(-parameters['ndip']+1,parameters['ndip']+1):
for j in range(parameters['nlag']):
for i in range(parameters['ndir']):
id+=1
x= parameters['xlag']*(j+1)*math.cos(angdir*i-orgdir)*math.cos(angdip*k-orgdip)
y= parameters['xlag']*(j+1)*math.sin(angdir*i-orgdir)*math.cos(angdip*k-orgdip)
z= parameters['xlag']*(j+1)* math.sin(angdip*k-orgdip)
print (id, i,j,k, angdir*i*(180/math.pi), angdip*k*(180/math.pi),x,y,z)
#print math.cos(angdip*k-orgdip)
XYZPts.SetPoint(id,x,y,z)
XYZGrid = vtk.vtkStructuredGrid()
XYZGrid.SetDimensions(parameters['ndir'],parameters['nlag'],parameters['ndip']*2-1)
XYZGrid.SetPoints(XYZPts)
ptid = np.arange(2*parameters['ndip']*parameters['nlag']*(parameters['ndir']))
cscalars = vtknumpy.numpy_to_vtk(ptid)
cscalars.SetName('PointID|')
XYZGrid.GetPointData().AddArray(cscalars)
#Write file
writer = vtk.vtkXMLStructuredGridWriter()
writer.SetFileName("output.vts")
writer.SetInputData(XYZGrid)
writer.Write()
0 0 0 -9 0.0 -81.0 0.15643446504023092 0.0 -0.9876883405951378
1 1 0 -9 18.0 -81.0 0.148778017349658 0.04834090820338494 -0.9876883405951378
2 2 0 -9 36.0 -81.0 0.1265581407235004 0.0919498715009102 -0.9876883405951378
3 3 0 -9 54.0 -81.0 0.0919498715009102 0.1265581407235004 -0.9876883405951378
4 4 0 -9 72.0 -81.0 0.04834090820338496 0.148778017349658 -0.9876883405951378
5 5 0 -9 90.0 -81.0 9.578848344392366e-18 0.15643446504023092 -0.9876883405951378
6 6 0 -9 108.0 -81.0 -0.048340908203384936 0.14877801734965804 -0.9876883405951378
7 7 0 -9 126.0 -81.0 -0.09194987150091019 0.1265581407235004 -0.9876883405951378
8 8 0 -9 144.0 -81.0 -0.1265581407235004 0.09194987150091022 -0.9876883405951378
9 9 0 -9 162.0 -81.0 -0.148778017349658 0.048340908203384964 -0.9876883405951378
10 0 1 -9 0.0 -81.0 0.31286893008046185 0.0 -1.9753766811902755
11 1 1 -9 18.0 -81.0 0.297556034699316 0.09668181640676989 -1.9753766811902755
12 2 1 -9 36.0 -81.0 0.2531162814470008 0.1838997430018204 -1.9753766811902755
13 3 1 -9 54.0 -81.0 0.1838997430018204 0.2531162814470008 -1.9753766811902755
14 4 1 -9 72.0 -81.0 0.09668181640676991 0.297556034699316 -1.9753766811902755
15 5 1 -9 90.0 -81.0 1.9157696688784732e-17 0.31286893008046185 -1.9753766811902755
16 6 1 -9 108.0 -81.0 -0.09668181640676987 0.2975560346993161 -1.9753766811902755
17 7 1 -9 126.0 -81.0 -0.18389974300182038 0.2531162814470008 -1.9753766811902755
18 8 1 -9 144.0 -81.0 -0.2531162814470008 0.18389974300182044 -1.9753766811902755
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1095 5 9 1 90.0 9.0 6.047846824324981e-16 9.876883405951379 1.5643446504023086
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1804 4 0 9 72.0 81.0 0.04834090820338496 0.148778017349658 0.9876883405951378
1805 5 0 9 90.0 81.0 9.578848344392366e-18 0.15643446504023092 0.9876883405951378
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1813 3 1 9 54.0 81.0 0.1838997430018204 0.2531162814470008 1.9753766811902755
1814 4 1 9 72.0 81.0 0.09668181640676991 0.297556034699316 1.9753766811902755
1815 5 1 9 90.0 81.0 1.9157696688784732e-17 0.31286893008046185 1.9753766811902755
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1817 7 1 9 126.0 81.0 -0.18389974300182038 0.2531162814470008 1.9753766811902755
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1824 4 2 9 72.0 81.0 0.14502272461015486 0.44633405204897403 2.9630650217854133
1825 5 2 9 90.0 81.0 2.87365450331771e-17 0.46930339512069275 2.9630650217854133
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1827 7 2 9 126.0 81.0 -0.2758496145027306 0.37967442217050124 2.9630650217854133
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1829 9 2 9 162.0 81.0 -0.44633405204897403 0.1450227246101549 2.9630650217854133
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1834 4 3 9 72.0 81.0 0.19336363281353983 0.595112069398632 3.950753362380551
1835 5 3 9 90.0 81.0 3.8315393377569464e-17 0.6257378601609237 3.950753362380551
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1845 5 4 9 90.0 81.0 4.7894241721961836e-17 0.7821723252011547 4.938441702975689
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1864 4 6 9 72.0 81.0 0.33838635742369466 1.041446121447606 6.913818384165964
1865 5 6 9 90.0 81.0 6.705193841074657e-17 1.0950412552816164 6.913818384165964
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1867 7 6 9 126.0 81.0 -0.6436491005063713 0.8859069850645029 6.913818384165964
1868 8 6 9 144.0 81.0 -0.8859069850645027 0.6436491005063715 6.913818384165964
1869 9 6 9 162.0 81.0 -1.041446121447606 0.3383863574236947 6.913818384165964
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1871 1 7 9 18.0 81.0 1.190224138797264 0.38672726562707954 7.901506724761102
1872 2 7 9 36.0 81.0 1.0124651257880033 0.7355989720072816 7.901506724761102
1873 3 7 9 54.0 81.0 0.7355989720072816 1.0124651257880033 7.901506724761102
1874 4 7 9 72.0 81.0 0.38672726562707965 1.190224138797264 7.901506724761102
1875 5 7 9 90.0 81.0 7.663078675513893e-17 1.2514757203218474 7.901506724761102
1876 6 7 9 108.0 81.0 -0.3867272656270795 1.1902241387972643 7.901506724761102
1877 7 7 9 126.0 81.0 -0.7355989720072815 1.0124651257880033 7.901506724761102
1878 8 7 9 144.0 81.0 -1.0124651257880033 0.7355989720072817 7.901506724761102
1879 9 7 9 162.0 81.0 -1.190224138797264 0.3867272656270797 7.901506724761102
1880 0 8 9 0.0 81.0 1.4079101853620783 0.0 8.88919506535624
1881 1 8 9 18.0 81.0 1.3390021561469219 0.43506817383046453 8.88919506535624
1882 2 8 9 36.0 81.0 1.1390232665115037 0.8275488435081919 8.88919506535624
1883 3 8 9 54.0 81.0 0.8275488435081919 1.1390232665115037 8.88919506535624
1884 4 8 9 72.0 81.0 0.4350681738304646 1.3390021561469219 8.88919506535624
1885 5 8 9 90.0 81.0 8.62096350995313e-17 1.4079101853620783 8.88919506535624
1886 6 8 9 108.0 81.0 -0.4350681738304644 1.3390021561469223 8.88919506535624
1887 7 8 9 126.0 81.0 -0.8275488435081917 1.1390232665115037 8.88919506535624
1888 8 8 9 144.0 81.0 -1.1390232665115037 0.827548843508192 8.88919506535624
1889 9 8 9 162.0 81.0 -1.3390021561469219 0.43506817383046464 8.88919506535624
1890 0 9 9 0.0 81.0 1.5643446504023093 0.0 9.876883405951379
1891 1 9 9 18.0 81.0 1.4877801734965799 0.48340908203384947 9.876883405951379
1892 2 9 9 36.0 81.0 1.2655814072350042 0.9194987150091021 9.876883405951379
1893 3 9 9 54.0 81.0 0.9194987150091021 1.2655814072350042 9.876883405951379
1894 4 9 9 72.0 81.0 0.4834090820338495 1.4877801734965799 9.876883405951379
1895 5 9 9 90.0 81.0 9.578848344392367e-17 1.5643446504023093 9.876883405951379
1896 6 9 9 108.0 81.0 -0.4834090820338494 1.4877801734965803 9.876883405951379
1897 7 9 9 126.0 81.0 -0.9194987150091019 1.2655814072350042 9.876883405951379
1898 8 9 9 144.0 81.0 -1.2655814072350038 0.9194987150091022 9.876883405951379
1899 9 9 9 162.0 81.0 -1.4877801734965799 0.4834090820338496 9.876883405951379
1900 0 0 10 0.0 90.0 6.123233995736766e-17 0.0 1.0
1901 1 0 10 18.0 90.0 5.823541592445462e-17 1.892183365217075e-17 1.0
1902 2 0 10 36.0 90.0 4.953800363085458e-17 3.599146639029983e-17 1.0
1903 3 0 10 54.0 90.0 3.599146639029983e-17 4.953800363085458e-17 1.0
1904 4 0 10 72.0 90.0 1.8921833652170753e-17 5.823541592445462e-17 1.0
1905 5 0 10 90.0 90.0 3.749399456654644e-33 6.123233995736766e-17 1.0
1906 6 0 10 108.0 90.0 -1.8921833652170747e-17 5.823541592445463e-17 1.0
1907 7 0 10 126.0 90.0 -3.5991466390299825e-17 4.953800363085458e-17 1.0
1908 8 0 10 144.0 90.0 -4.9538003630854574e-17 3.599146639029984e-17 1.0
1909 9 0 10 162.0 90.0 -5.823541592445462e-17 1.8921833652170756e-17 1.0
1910 0 1 10 0.0 90.0 1.2246467991473532e-16 0.0 2.0
1911 1 1 10 18.0 90.0 1.1647083184890923e-16 3.78436673043415e-17 2.0
1912 2 1 10 36.0 90.0 9.907600726170916e-17 7.198293278059966e-17 2.0
1913 3 1 10 54.0 90.0 7.198293278059966e-17 9.907600726170916e-17 2.0
1914 4 1 10 72.0 90.0 3.7843667304341506e-17 1.1647083184890923e-16 2.0
1915 5 1 10 90.0 90.0 7.498798913309288e-33 1.2246467991473532e-16 2.0
1916 6 1 10 108.0 90.0 -3.7843667304341493e-17 1.1647083184890926e-16 2.0
1917 7 1 10 126.0 90.0 -7.198293278059965e-17 9.907600726170916e-17 2.0
1918 8 1 10 144.0 90.0 -9.907600726170915e-17 7.198293278059968e-17 2.0
1919 9 1 10 162.0 90.0 -1.1647083184890923e-16 3.784366730434151e-17 2.0
1920 0 2 10 0.0 90.0 1.8369701987210297e-16 0.0 3.0
1921 1 2 10 18.0 90.0 1.7470624777336384e-16 5.676550095651224e-17 3.0
1922 2 2 10 36.0 90.0 1.4861401089256375e-16 1.079743991708995e-16 3.0
1923 3 2 10 54.0 90.0 1.079743991708995e-16 1.4861401089256375e-16 3.0
1924 4 2 10 72.0 90.0 5.676550095651226e-17 1.7470624777336384e-16 3.0
1925 5 2 10 90.0 90.0 1.1248198369963932e-32 1.8369701987210297e-16 3.0
1926 6 2 10 108.0 90.0 -5.676550095651223e-17 1.7470624777336386e-16 3.0
1927 7 2 10 126.0 90.0 -1.0797439917089949e-16 1.4861401089256375e-16 3.0
1928 8 2 10 144.0 90.0 -1.4861401089256373e-16 1.0797439917089951e-16 3.0
1929 9 2 10 162.0 90.0 -1.7470624777336384e-16 5.676550095651227e-17 3.0
1930 0 3 10 0.0 90.0 2.4492935982947064e-16 0.0 4.0
1931 1 3 10 18.0 90.0 2.3294166369781847e-16 7.5687334608683e-17 4.0
1932 2 3 10 36.0 90.0 1.9815201452341832e-16 1.4396586556119933e-16 4.0
1933 3 3 10 54.0 90.0 1.4396586556119933e-16 1.9815201452341832e-16 4.0
1934 4 3 10 72.0 90.0 7.568733460868301e-17 2.3294166369781847e-16 4.0
1935 5 3 10 90.0 90.0 1.4997597826618576e-32 2.4492935982947064e-16 4.0
1936 6 3 10 108.0 90.0 -7.568733460868299e-17 2.329416636978185e-16 4.0
1937 7 3 10 126.0 90.0 -1.439658655611993e-16 1.9815201452341832e-16 4.0
1938 8 3 10 144.0 90.0 -1.981520145234183e-16 1.4396586556119935e-16 4.0
1939 9 3 10 162.0 90.0 -2.3294166369781847e-16 7.568733460868302e-17 4.0
1940 0 4 10 0.0 90.0 3.061616997868383e-16 0.0 5.0
1941 1 4 10 18.0 90.0 2.9117707962227305e-16 9.460916826085374e-17 5.0
1942 2 4 10 36.0 90.0 2.4769001815427294e-16 1.7995733195149918e-16 5.0
1943 3 4 10 54.0 90.0 1.7995733195149918e-16 2.4769001815427294e-16 5.0
1944 4 4 10 72.0 90.0 9.460916826085377e-17 2.9117707962227305e-16 5.0
1945 5 4 10 90.0 90.0 1.8746997283273223e-32 3.061616997868383e-16 5.0
1946 6 4 10 108.0 90.0 -9.460916826085373e-17 2.911770796222731e-16 5.0
1947 7 4 10 126.0 90.0 -1.7995733195149913e-16 2.4769001815427294e-16 5.0
1948 8 4 10 144.0 90.0 -2.4769001815427284e-16 1.799573319514992e-16 5.0
1949 9 4 10 162.0 90.0 -2.9117707962227305e-16 9.460916826085378e-17 5.0
1950 0 5 10 0.0 90.0 3.6739403974420594e-16 0.0 6.0
1951 1 5 10 18.0 90.0 3.494124955467277e-16 1.1353100191302449e-16 6.0
1952 2 5 10 36.0 90.0 2.972280217851275e-16 2.15948798341799e-16 6.0
1953 3 5 10 54.0 90.0 2.15948798341799e-16 2.972280217851275e-16 6.0
1954 4 5 10 72.0 90.0 1.135310019130245e-16 3.494124955467277e-16 6.0
1955 5 5 10 90.0 90.0 2.2496396739927864e-32 3.6739403974420594e-16 6.0
1956 6 5 10 108.0 90.0 -1.1353100191302446e-16 3.4941249554672773e-16 6.0
1957 7 5 10 126.0 90.0 -2.1594879834179898e-16 2.972280217851275e-16 6.0
1958 8 5 10 144.0 90.0 -2.9722802178512745e-16 2.1594879834179903e-16 6.0
1959 9 5 10 162.0 90.0 -3.494124955467277e-16 1.1353100191302454e-16 6.0
1960 0 6 10 0.0 90.0 4.286263797015736e-16 0.0 7.0
1961 1 6 10 18.0 90.0 4.076479114711823e-16 1.3245283556519524e-16 7.0
1962 2 6 10 36.0 90.0 3.4676602541598207e-16 2.519402647320988e-16 7.0
1963 3 6 10 54.0 90.0 2.519402647320988e-16 3.4676602541598207e-16 7.0
1964 4 6 10 72.0 90.0 1.3245283556519527e-16 4.076479114711823e-16 7.0
1965 5 6 10 90.0 90.0 2.624579619658251e-32 4.286263797015736e-16 7.0
1966 6 6 10 108.0 90.0 -1.3245283556519522e-16 4.0764791147118236e-16 7.0
1967 7 6 10 126.0 90.0 -2.5194026473209875e-16 3.4676602541598207e-16 7.0
1968 8 6 10 144.0 90.0 -3.46766025415982e-16 2.519402647320989e-16 7.0
1969 9 6 10 162.0 90.0 -4.076479114711823e-16 1.324528355651953e-16 7.0
1970 0 7 10 0.0 90.0 4.898587196589413e-16 0.0 8.0
1971 1 7 10 18.0 90.0 4.658833273956369e-16 1.51374669217366e-16 8.0
1972 2 7 10 36.0 90.0 3.9630402904683664e-16 2.8793173112239865e-16 8.0
1973 3 7 10 54.0 90.0 2.8793173112239865e-16 3.9630402904683664e-16 8.0
1974 4 7 10 72.0 90.0 1.5137466921736602e-16 4.658833273956369e-16 8.0
1975 5 7 10 90.0 90.0 2.999519565323715e-32 4.898587196589413e-16 8.0
1976 6 7 10 108.0 90.0 -1.5137466921736597e-16 4.65883327395637e-16 8.0
1977 7 7 10 126.0 90.0 -2.879317311223986e-16 3.9630402904683664e-16 8.0
1978 8 7 10 144.0 90.0 -3.963040290468366e-16 2.879317311223987e-16 8.0
1979 9 7 10 162.0 90.0 -4.658833273956369e-16 1.5137466921736605e-16 8.0
1980 0 8 10 0.0 90.0 5.51091059616309e-16 0.0 9.0
1981 1 8 10 18.0 90.0 5.241187433200915e-16 1.7029650286953675e-16 9.0
1982 2 8 10 36.0 90.0 4.458420326776912e-16 3.239231975126985e-16 9.0
1983 3 8 10 54.0 90.0 3.239231975126985e-16 4.458420326776912e-16 9.0
1984 4 8 10 72.0 90.0 1.7029650286953678e-16 5.241187433200915e-16 9.0
1985 5 8 10 90.0 90.0 3.3744595109891796e-32 5.51091059616309e-16 9.0
1986 6 8 10 108.0 90.0 -1.7029650286953673e-16 5.241187433200916e-16 9.0
1987 7 8 10 126.0 90.0 -3.2392319751269845e-16 4.458420326776912e-16 9.0
1988 8 8 10 144.0 90.0 -4.458420326776912e-16 3.2392319751269855e-16 9.0
1989 9 8 10 162.0 90.0 -5.241187433200915e-16 1.702965028695368e-16 9.0
1990 0 9 10 0.0 90.0 6.123233995736766e-16 0.0 10.0
1991 1 9 10 18.0 90.0 5.823541592445461e-16 1.8921833652170748e-16 10.0
1992 2 9 10 36.0 90.0 4.953800363085459e-16 3.5991466390299835e-16 10.0
1993 3 9 10 54.0 90.0 3.5991466390299835e-16 4.953800363085459e-16 10.0
1994 4 9 10 72.0 90.0 1.8921833652170753e-16 5.823541592445461e-16 10.0
1995 5 9 10 90.0 90.0 3.7493994566546446e-32 6.123233995736766e-16 10.0
1996 6 9 10 108.0 90.0 -1.8921833652170746e-16 5.823541592445462e-16 10.0
1997 7 9 10 126.0 90.0 -3.5991466390299825e-16 4.953800363085459e-16 10.0
1998 8 9 10 144.0 90.0 -4.953800363085457e-16 3.599146639029984e-16 10.0
1999 9 9 10 162.0 90.0 -5.823541592445461e-16 1.8921833652170756e-16 10.0
c:\users\adrian\appdata\local\programs\python\python36\lib\site-packages\vtk\util\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.
assert not numpy.issubdtype(z.dtype, complex), \
Out[9]:
1
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Content source: opengeostat/pygslib
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