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‎make_nonlinear_MV_spatial_data.ipynb

+69-55
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@@ -39,6 +39,48 @@
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"from copy import copy # deep copies"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Some Truth Model Parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Truth Model Parameters \n",
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"seed = 73073 # random number seed for repeatable random samples and rejection sampler \n",
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"name = 'Por' # name of the feature of interest\n",
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"mean = 0.13 # univariate distribution mean - assuming Gaussian distribution\n",
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"stdev = 0.03 # univariate standard deviation \n",
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"nug = 0.0 # nugget effect - variogram model\n",
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"it = 1 # type of structure (1 -spherical, 2 - exponential, 3 - Gaussian)\n",
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"azi = 45.0 # primary direction/azimuth of continuity (0 = y positive, 90 = x positive)\n",
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"hmaj = 800 # variogram range in the major direction\n",
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"hmin = 300 # variogram range in the minor direction (major direction + 90)\n",
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"corr1 = 0.90\n",
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"corr2 = -0.8\n",
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"\n",
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"# Make the variogram object\n",
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"vario = GSLIB.make_variogram(nug,nst=1,it1=it,cc1=1.0,azi1=azi,hmaj1=hmaj,hmin1=hmin) # make model object\n",
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"\n",
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"# Make the dummy dataset (should be outside the range of correlation of the model)\n",
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"df = pd.DataFrame({'X':np.full(100,-9999),'Y':np.full(100,-9999),name:np.random.normal(0.0,1.0,100)})\n",
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"\n",
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"# Truth Model Grid Parameters\n",
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"nx = 100; ny = 100 # number of cells in the x and y directions\n",
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"xsiz = 10.0; ysiz = 10.0 # size of the cells in the x and y directions\n",
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"xmn = xsiz * 0.5; ymn = ysiz * 0.5 # assume origin at 0,0, calculate the 1 cell centroid\n",
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"xmin = xmn - 0.5*xsiz; ymin = ymn - 0.5*ysiz # assume origin at 0,0, calculate the min and max x/y coordinates\n",
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"xmax = xmin + nx * xsiz; ymax = ymin + ny * ysiz;\n",
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"vmin = -3; vmax = 3 # feature min and max for color bars\n",
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"sim_ns = np.zeros([6,ny,nx])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -108,11 +150,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def regular_sample_MV(array,array2,array3,array4,xmin,xmax,ymin,ymax,spacing,minsx=xmin,maxsx=xmax,minsy=ymin,maxsy=ymax,name='Value',name2='Value2',name3='Value3',name4=\"Value4\"):\n",
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"def regular_sample_MV(array,array2,array3,array4,xmin,xmax,ymin,ymax,spacing,\n",
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" minsx=xmin,maxsx=xmax,minsy=ymin,maxsy=ymax,name='Value',name2='Value2',name3='Value3',name4=\"Value4\"):\n",
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" x = []; y = []; v = []; v2 = []; v3 = []; v4 = []\n",
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" nx = array.shape[1]; ny = array.shape[0]\n",
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" xsiz = (xmax-xmin)/nx; ysiz = (ymax-ymin)/ny\n",
@@ -240,10 +283,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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"Setting up rotation matrices for variogram and search\n",
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"Working on realization number 0\n",
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" currently on node 0\n",
@@ -264,10 +307,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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" Secondary Data: Number of data = 10000\n",
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" Equal Weighted Average = 0.0\n",
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" Equal Weighted Variance = 1.0\n",
@@ -293,10 +336,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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" Secondary Data: Number of data = 10000\n",
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" Equal Weighted Average = 0.0\n",
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" Equal Weighted Variance = 1.0\n",
@@ -322,10 +365,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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"Setting up rotation matrices for variogram and search\n",
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"Working on realization number 0\n",
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" currently on node 0\n",
@@ -346,10 +389,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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" Secondary Data: Number of data = 10000\n",
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" Equal Weighted Average = -0.0\n",
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" Equal Weighted Variance = 1.0\n",
@@ -375,10 +418,10 @@
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"\n",
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" Data for SGSIM: Number of acceptable data = 100\n",
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" Number trimmed = 0\n",
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" Weighted Average = -0.0678\n",
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" Weighted Variance = 0.9855\n",
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" Weighted Transformed Average = -0.0678\n",
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" Weighted Transformed Variance = 0.9855\n",
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" Weighted Average = 0.016\n",
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" Weighted Variance = 1.1133\n",
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" Weighted Transformed Average = 0.016\n",
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" Weighted Transformed Variance = 1.1133\n",
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" Secondary Data: Number of data = 10000\n",
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" Equal Weighted Average = -0.0\n",
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" Equal Weighted Variance = 1.0\n",
@@ -405,35 +448,6 @@
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}
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],
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"source": [
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"# Truth Model Parameters \n",
409-
"seed = 73073 # random number seed for repeatable random samples and rejection sampler \n",
410-
"name = 'Por' # name of the feature of interest\n",
411-
"mean = 0.13 # univariate distribution mean - assuming Gaussian distribution\n",
412-
"stdev = 0.03 # univariate standard deviation \n",
413-
"nug = 0.0 # nugget effect - variogram model\n",
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"it = 1 # type of structure (1 -spherical, 2 - exponential, 3 - Gaussian)\n",
415-
"azi = 45.0 # primary direction/azimuth of continuity (0 = y positive, 90 = x positive)\n",
416-
"hmaj = 800 # variogram range in the major direction\n",
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"hmin = 300 # variogram range in the minor direction (major direction + 90)\n",
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"corr1 = 0.90\n",
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"corr2 = -0.8\n",
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"\n",
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"# Make the variogram object\n",
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"vario = GSLIB.make_variogram(nug,nst=1,it1=it,cc1=1.0,azi1=azi,hmaj1=hmaj,hmin1=hmin) # make model object\n",
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"\n",
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"# Make the dummy dataset (should be outside the range of correlation of the model)\n",
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"df = pd.DataFrame({'X':np.full(100,-9999),'Y':np.full(100,-9999),name:np.random.normal(0.0,1.0,100)})\n",
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"\n",
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"# Truth Model Grid Parameters\n",
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"nx = 100; ny = 100 # number of cells in the x and y directions\n",
429-
"xsiz = 10.0; ysiz = 10.0 # size of the cells in the x and y directions\n",
430-
"xmn = xsiz * 0.5; ymn = ysiz * 0.5 # assume origin at 0,0, calculate the 1 cell centroid\n",
431-
"xmin = xmn - 0.5*xsiz; ymin = ymn - 0.5*ysiz # assume origin at 0,0, calculate the min and max x/y coordinates\n",
432-
"xmax = xmin + nx * xsiz; ymax = ymin + ny * ysiz;\n",
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"vmin = -3; vmax = 3 # feature min and max for color bars\n",
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"sim_ns = np.zeros([6,ny,nx])\n",
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"\n",
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"\n",
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"# Calculate the Simulated Truth Model over the Specified Grid and Variogram \n",
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"sim_ns[0,:,:] = geostats.sgsim(df,'X','Y',name,wcol=-1,scol=-1,tmin=-9999,tmax=9999,itrans=0,ismooth=0,dftrans=0,tcol=0,\n",
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" twtcol=0,zmin=-3.0,zmax=3.0,ltail=1,ltpar=-3.0,utail=1,utpar=3.0,nsim=1,\n",

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