{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GEONE - DEESSE - Simulations with connectivity data\n", "\n", "## Main points addressed\n", "\n", "- how to set hard data with connectivity data\n", "- how to set connectivity constraints" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import what is required" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "\n", "# import package 'geone'\n", "import geone as gn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sys.version_info(major=3, minor=13, micro=7, releaselevel='final', serial=0)\n", "geone version: 1.3.0\n" ] } ], "source": [ "# Show version of python and version of geone\n", "import sys \n", "print(sys.version_info)\n", "print('geone version: ' + gn.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training image (TI)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 1., 2.])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read file \n", "data_dir = 'data'\n", "filename = os.path.join(data_dir, 'ti.txt')\n", "ti = gn.img.readImageTxt(filename)\n", "\n", "# Values in the TI\n", "ti.get_unique()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setting for categories / colors\n", "categ_val = [0, 1, 2]\n", "categ_col = ['lightblue', 'darkgreen', 'orange']\n", "\n", "plt.figure(figsize=(5,5))\n", "gn.imgplot.drawImage2D(ti, categ=True, categVal=categ_val, categCol=categ_col, title='TI')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation grid\n", "Define the simulation grid (number of cells in each direction, cell unit, origin)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "nx, ny, nz = 100, 100, 1 # number of cells\n", "sx, sy, sz = ti.sx, ti.sy, ti.sz # cell unit\n", "ox, oy, oz = 0.0, 0.0, 0.0 # origin (corner of the \"first\" grid cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hard data and connectivity data\n", "A hard data point is defined by:\n", "\n", "- its spatial coordinates (`x`, `y`, `z`) in the simulation grid,\n", "- the value of the simulated variable, whose the name (`code` in the example below) identical to the name given by `varname` in the class `geone.deesseinterface.DeesseInput` (input for deesse),\n", "- a connected component (geobody) label, whose the name (`geobody_label` in the example below) identical to the name given by `varname` in the class `geone.deesseinterface.Connectivity` describing the connectivity constraints.\n", "\n", "Points with the same *positive* connected component label means that they should be connected in the output realizations. A negative or zero label means no connectivity constraints on that point." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "npt = 5 # number of points\n", "nv = 5 # number of variables including x, y, z coordinates\n", "varname = ['x', 'y', 'z', 'code', 'geobody_label'] # list of variable names\n", "v = np.array([\n", " [ 10.5, 40.5, 0.5, 1, 1], # x, y, z, code, geobody_label: 1st point\n", " [ 50.5, 35.5, 0.5, 1, 1], # 2nd point: should be connected to the 1st point\n", " [ 90.5, 48.5, 0.5, 1, 1], # 3rd point: should be connected to the 1st and 2nd points\n", " [ 75.5, 50.5, 0.5, 0, 0], # no connectivity constraint on that point\n", " [ 20.5, 80.5, 0.5, 2, 0], # no connectivity constraint on that point\n", " ]).T # variable values: (nv, npt)-array\n", "hd = gn.img.PointSet(npt=npt, nv=nv, varname=varname, val=v)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the colors for values of the variable of index 3 in the point set, \n", "# according to the color settings used for the TI\n", "hd_col = gn.imgplot.get_colors_from_values(hd.val[3], categ=True, categVal=categ_val, categCol=categ_col)\n", "\n", "# Get the colors for geobody label (variable of index 4)\n", "gb_col = gn.imgplot.get_colors_from_values(hd.val[4], categ=True, categVal=[0, 1], categCol=['gray', 'red'])\n", "\n", "# Set an image with simulation grid geometry defined above, and no variable\n", "im_empty = gn.img.Img(nx, ny, nz, sx, sy, sz, ox, oy, oz, nv=0)\n", "\n", "# Plot\n", "plt.figure(figsize=(8,5))\n", "\n", "# Plot empty simulation grid and specify colors\n", "gn.imgplot.drawImage2D(im_empty, categ=True, categVal=categ_val, categCol=categ_col)\n", "\n", "# Add hard data points\n", "plt.scatter(hd.x(), hd.y(), marker='o', s=80, color=hd_col, edgecolors=gb_col, linewidths=2)\n", "\n", "plt.title('Hard data (and geobody label - edges)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Specifiy the connectivity constraints (class `geone.deesseinterface.Connectivity`)\n", "The way how to deal with the connectivity data is described by the class `geone.deesseinterface.Connectivity`, whose the parameters (keyword arguments) are:\n", "\n", "- `connectivityConstraintUsage`: integer with the following meaning:\n", " - 0: no connectivity constraint\n", " - 1: paste connecting paths before simulation by successively binding the cells to be connected in a random order\n", " - 2: paste connecting paths before simulation by successively binding the cells to be connected beginning with the pair of cells with the smallest distance and then the remaining cells in increasing order according to their distance to the set of cells already connected (the distance between two cells is defined as the length (in number of cells) of the minimal path binding the two nodes in an homogeneous connected medium according to the type of connectivity `connectivityType`)\n", " - 3: check connectivity pattern during simulation (*not working properly...*)\n", "- `connectivityType`: a character string indicating which type of connection is used:\n", " - `connect_face`: connection through the *faces* of the cells (\"6-neighbors connection\")\n", " - `connect_face_edge`: connection through the *faces* or *edges* of the cells (\"18-neighbors connection\")\n", " - `connect_face_edge_corner`: connection through the *faces* or *edges* or *corner* (*vertices*) of the cells (\"26-neighbors connection\")\n", "- `nclass` and `classInterval`: define the number of classes, and for each class the ensemble of values that can be connected together as a (union of) interval(s) (these parameters are defined as for probability constraints, see the corresponding example)\n", "- `varname`: the name of the connected component label, should be present in a conditioning data set (`geobody_label` in this example)\n", "- `tiAsRefFlag`: boolean (used if `connectivityConstraintUsage=1 or 2`) indicating that the paths pasted in the simulation grid are borrowed from the TI (`True`) or from the image given by `refConnectivityImage` (`False`)\n", "- `refConnectivityImage` and `refConnectivityVarIndex` : image (class `geone.img.Img`) and index of the variable used (integer) from which the paths pasted in the simulation grid are borrowed (if `connectivityConstraintUsage=1 or 2` and `tiAsRefFlag=False`)\n", "- `deactivationDistance` and `threshold`: parameters for `connectivityConstraintUsage=3`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Define connectivity constraints\n", "co = gn.deesseinterface.Connectivity(\n", " connectivityConstraintUsage=2,\n", " connectivityType='connect_face_edge_corner',\n", " nclass=3,\n", " classInterval=[[-0.5, 0.5], [0.5, 1.5], [1.5, 2.5]],\n", " varname='geobody_label',\n", " tiAsRefFlag=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fill the input structure for deesse and launch deesse" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deesseRun: DeeSse running... [VERSION 3.2 / BUILD NUMBER 20230914 / OpenMP 19 thread(s)]\n", "deesseRun: DeeSse run complete\n", "deesseRun: warnings encountered (1 times in all):\n", "# 1: WARNING 00143: conditioning variable(s) is (are) ignored from data point set (not a simulated variable!)\n", "Elapsed time: 4.8 sec\n" ] } ], "source": [ "nreal = 20\n", "deesse_input = gn.deesseinterface.DeesseInput(\n", " nx=nx, ny=ny, nz=nz,\n", " sx=sx, sy=sy, sz=sz,\n", " ox=ox, oy=oy, oz=oz,\n", " nv=1, varname='code',\n", " TI=ti,\n", " dataPointSet=hd, # set hard data and connectivity data\n", " connectivity=co, # set connectivity constraint\n", " outputPathIndexFlag=True, # get simulation path in output (to plot pasted paths)\n", " distanceType='categorical',\n", " nneighboringNode=24,\n", " distanceThreshold=0.02,\n", " maxScanFraction=0.25,\n", " npostProcessingPathMax=1,\n", " seed=444,\n", " nrealization=nreal)\n", "\n", "# Run deesse\n", "t1 = time.time() # start time\n", "deesse_output = gn.deesseinterface.deesseRun(deesse_input)\n", "t2 = time.time() # end time\n", "print(f'Elapsed time: {t2-t1:.2g} sec')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrieve the results, do some statistics, and display" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Retrieve the results\n", "sim = deesse_output['sim']\n", "path = deesse_output['path']\n", "\n", "# Gather the nreal realizations into one image\n", "all_sim = gn.img.gatherImages(sim) # all_sim is one image with nreal variables\n", "\n", "# Do statistics over all the realizations: compute the pixel-wise proportion for the given categories\n", "all_sim_stats = gn.img.imageCategProp(all_sim, categ_val) # do statistics (pixel-wise proportion) \n", "\n", "# Add 10 to cells coming from hard data set or from a pasted path (simulation path index is nan):\n", "# this trick will allow to highlight these cells...\n", "for i in range(nreal):\n", " sim[i].val = sim[i].val + 10 * np.isnan(path[i].val)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display\n", "new_col=['blue', 'lightgreen', 'yellow'] # colors for the \"new\" facies: 10, 11, 12\n", "\n", "prop_col=['blue', 'darkgreen', 'orange'] # colors for the proportion maps\n", "cmap = [gn.customcolors.custom_cmap(['white', c]) for c in prop_col]\n", "\n", "plt.subplots(2, 3, figsize=(17,10), sharex=True, sharey=True) # 2 x 3 sub-plots\n", "# ... the first three realizations\n", "for i in range(3):\n", " plt.subplot(2, 3, i+1) # select next sub-plot\n", " gn.imgplot.drawImage2D(sim[i], categ=True, \n", " categVal=categ_val + [10, 11, 12], # category to be displayed (+ for concatenation)\n", " categCol=categ_col + new_col, # colors used\n", " title=f'Real #{i}')\n", " plt.scatter(hd.x(), hd.y(), marker='o', s=80, \n", " color='none', edgecolors='black', linewidths=1) # add hard data points\n", "\n", "for i in range(3):\n", " plt.subplot(2, 3, i+4) # select next sub-plot\n", " gn.imgplot.drawImage2D(all_sim_stats, iv=i, cmap=cmap[i],\n", " title=f'Prop. of categ. {i} (over {nreal} real.)')\n", " plt.plot(hd.x(), hd.y(), '+', markersize=20, c='black') # add hard data points\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Do statistics on pasted paths\n", "for i in range(nreal):\n", " path[i].val = 1 * np.isnan(path[i].val)\n", " \n", "all_path = gn.img.gatherImages(path) # all_path is one image with nreal variables\n", "all_path_stats = gn.img.imageCategProp(all_path, [1]) # do statistics (pixel-wise proportion)\n", "\n", "# ...multiply by nreal to get the number of times a cell is in a pasted path (or coming from the hard data set)\n", "all_path_stats.val = nreal*all_path_stats.val " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the number of times a cell is in a pasted path (or coming from the hard data set)\n", "plt.figure(figsize=(8,5))\n", "gn.imgplot.drawImage2D(all_path_stats,\n", " cmap=gn.customcolors.cmap1, # color map used (this is the default one)\n", " vmin=1, # ... value zero will be displayed with color 'cunder' \n", " # from gn.customcolors.cmap1\n", " title=f'Coming from HD or pasted paths (over {nreal} real.)')\n", "plt.scatter(hd.x(), hd.y(), marker='o', s=80, \n", " color='none', edgecolors='white', linewidths=2) # add hard data points\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "py313", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 4 }