{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GEONE - GEOSCLASSIC - categorical variable - non-stationary covariance model\n",
    "\n",
    "## Estimation (kriging) and simulation (Sequential Indicator Simulation, SIS)\n",
    "\n",
    "See notebook `ex_geosclassic_indicator_1d_1.ipynb` for detail explanations about estimation (kriging) and simulation (Sequential Indicator Simulation, SIS) in a grid - using stationary covariance model.\n",
    "\n",
    "## Non-stationary covariance model over a grid\n",
    "See notebook `ex_geosclassic_1d_2_non_stat_cov.ipynb` for detail explanations on how to set non-stationarities in a grid.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples in 1D\n",
    "In this notebook, examples in 1D with a non-stationary covariance model are given."
   ]
  },
  {
   "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",
    "\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: 1.3.1\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": [
    "### Remark\n",
    "The matplotlib figures can be visualized in *interactive* mode:\n",
    "\n",
    "- `%matplotlib notebook`: enable interactive mode\n",
    "- `%matplotlib inline`: disable interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Category values\n",
    "A list of category values (facies) must be defined. Let `ncategory` be the length of this list, *i.e.* the number of categories:\n",
    "\n",
    "- if `ncategory == 1`: the unique category value given must not be equal to 0; this is used for a binary case with values (\"unique category value\", 0), where 0 indicates the absence of the considered medium; conditioning data values should be \"unique category value\" or 0\n",
    "- if `ncategory >= 2`: this is used for a multi-category case with given values (distinct); conditioning data values should be in the list of given values\n",
    "\n",
    "Then, set color for each category, and color maps for proportions (for further plots).\n",
    "\n",
    "**Below: select the case with `ncategory` greater than one or equal to one below, comment the undesired cell.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Case with ncategory > 1\n",
    "# -----------------------\n",
    "category_values = [1., 2., 3.]\n",
    "ncategory = len(category_values)\n",
    "\n",
    "# Set colors ...\n",
    "categVal = category_values\n",
    "categCol = ['lightblue', 'orange', 'darkgreen'] # must be of length len(categVal)\n",
    "cmap_categ = [gn.customcolors.custom_cmap(['white', c]) for c in categCol]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Case with ncategory = 1\n",
    "# # -----------------------\n",
    "# category_values = [2.] # all categories are 2. and 0.\n",
    "# ncategory = len(category_values)\n",
    "\n",
    "# # Set colors ...\n",
    "# categVal = [category_values[0], 0]\n",
    "# categCol = ['tab:red', 'lightblue'] # must be of length len(categVal)\n",
    "# cmap_categ = [gn.customcolors.custom_cmap(['white', c]) for c in categCol]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid (1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 1000  # number of cells\n",
    "sx = 1.0  # cell unit\n",
    "ox = 0.0  # origin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariance model\n",
    "\n",
    "In 1D, a covariance model is given by an instance of the class `geone.covModel.covModel1D`.\n",
    "\n",
    "### Base covariance model (sationary)\n",
    "The weight `'w'` to every elementary contribution is set to `1.0`; the method `multiply_w` will be used to set non-stationarities about this parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the base covariance model (stationary)\n",
    "cov_model = gn.covModel.CovModel1D(elem=[\n",
    "    ('exponential', {'w':1.0, 'r':100}),  # elementary contribution\n",
    "    ('nugget', {'w':1.0}),                # elementary contribution\n",
    "    ], name='model-1D example')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure()\n",
    "# cov_model.plot_model()\n",
    "# plt.title('Covariance function - base')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining non-stationarities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set an image with grid geometry defined above, and no variable\n",
    "im = gn.img.Img(nx, 1, 1, sx, 1.0, 1.0, ox, 0.0, 0.0, nv=0)\n",
    "\n",
    "# Get the x coordinates of the centers of grid cell\n",
    "x_grid = im.x()\n",
    "\n",
    "# Coordinate of center of the grid\n",
    "x_center = 0.5 * (im.xmin() + im.xmax())\n",
    "\n",
    "# Set weight over the grid for the elementary contributions of the covariance model (array)\n",
    "nug_w = 9. * 1. / (1. + np.exp(-(np.abs(x_grid-x_center)-300)/30))\n",
    "exp_w = 9 - nug_w\n",
    "\n",
    "# Set list to handle non-stationarities for further estimation/simulation in the grid\n",
    "cov_model_non_stationarity_list = [\n",
    "    ('multiply_w', exp_w, {'elem_ind':0}), # multiply weight by `gau_w` for elem. contrib. of index 0\n",
    "    ('multiply_w', nug_w, {'elem_ind':1}), # multiply weight by `nug_w` for elem. contrib. of index 1\n",
    "]\n",
    "# Note: `gau_w`, `nug_w` could also be a function of one parameter (x location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "# ----\n",
    "# Set weight in images ...\n",
    "im_exp_w = gn.img.Img(nx, 1, 1, sx, 1., 1., ox, 0., 0., nv=1, val=exp_w)\n",
    "im_nug_w = gn.img.Img(nx, 1, 1, sx, 1., 1., ox, 0., 0., nv=1, val=nug_w)\n",
    "\n",
    "# Set spacing in y direction (1 cell) to visualize images using gn.imgplot.drawImage2D\n",
    "im_exp_w.sy = .2 * im_exp_w.sx * im_exp_w.nx     \n",
    "im_nug_w.sy = .2 * im_nug_w.sx * im_nug_w.nx \n",
    "\n",
    "# Plot\n",
    "plt.subplots(2,1, figsize=(12,5), sharey=True)\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "gn.imgplot.drawImage2D(im_exp_w, cmap='terrain', title=\"exponential weight\")\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "gn.imgplot.drawImage2D(im_nug_w, cmap='terrain', title=\"nugget weight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings - using data (optional) and probability (constant, optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ncategory > 1:\n",
    "    # Case with ncategory > 1\n",
    "    # -----------------------\n",
    "    # Data\n",
    "    x = [50.5, 300.1, 550.2, 849.4]  # data locations (real coordinates)\n",
    "    v = [ 1., 2., 1., 3.]            # data values\n",
    "    # x = None\n",
    "    # v = None\n",
    "\n",
    "    # Probability, proportion of each category\n",
    "    probability = [.1, .2, .7] # should sum to 1\n",
    "    # probability = None\n",
    "\n",
    "    # Type of kriging\n",
    "    method = 'simple_kriging'\n",
    "\n",
    "else:\n",
    "    # Case with ncategory = 1\n",
    "    # -----------------------\n",
    "    # Data\n",
    "    x = [50.5, 300.1, 550.2, 849.4]  # data locations (real coordinates)\n",
    "    v = [ 0., 2., 2., 0.]            # data values\n",
    "    # x = None\n",
    "    # v = None\n",
    "\n",
    "    # Probability, proportion (of non-zero category)\n",
    "    probability = [.7] # list of one number in the interval [0, 1]\n",
    "    # probability = None\n",
    "\n",
    "    # Type of kriging\n",
    "    method = 'simple_kriging'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of probabilities (by kriging)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 8 thread(s) (OpenMP)] ...\n",
      "estimateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 0.0031 sec\n"
     ]
    }
   ],
   "source": [
    "# Computational resources\n",
    "nthreads = 8\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.estimateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            use_unique_neighborhood=False, # search neighborhood (unique cannot be used with non-stationarities)...\n",
    "                            searchRadius=None,             \n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nthreads=nthreads,              # computational resources\n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "krig_img = geosclassic_output['image'] # output image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of warning(s), and warning messages\n",
    "geosclassic_output['nwarning'], geosclassic_output['warnings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 1 thread(s) (OpenMP)] ...\n",
      "estimateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 0.0049 sec\n",
      "Same results ? True\n"
     ]
    }
   ],
   "source": [
    "# %%script false --no-raise-error # skip this cell! (comment this line to run the cell)\n",
    "\n",
    "# Equivalent, using other computational resources\n",
    "# -----------------------------------------------\n",
    "# Computational resources\n",
    "nthreads = 1\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.estimateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            use_unique_neighborhood=False, # search neighborhood (unique cannot be used with non-stationarities)...\n",
    "                            searchRadius=None,             \n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nthreads=nthreads,              # computational resources\n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "krig_img_2 = geosclassic_output['image'] # output image\n",
    "\n",
    "print(f\"Same results ? {np.allclose(krig_img.val, krig_img_2.val)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [2 process(es) of 4 thread(s) (OpenMP)] ...\n",
      "simulateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [2 process(es)] complete\n",
      "Elapsed time: 2.9 sec\n"
     ]
    }
   ],
   "source": [
    "# Number of realizations\n",
    "nreal = 1000\n",
    "\n",
    "# Seed\n",
    "seed = 321\n",
    "\n",
    "# Computational resources\n",
    "nproc = 2\n",
    "nthreads_per_proc = 4\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.simulateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            searchRadius=None,             # search neighborhood ...\n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nreal=nreal,                   # number of realizations\n",
    "                            seed=seed,                     # seed\n",
    "                            nproc=nproc,                   # computational resources ...\n",
    "                            nthreads_per_proc=nthreads_per_proc, \n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "simul_img = geosclassic_output['image'] # output image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of warning(s), and warning messages\n",
    "geosclassic_output['nwarning'], geosclassic_output['warnings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 1 thread(s) (OpenMP)] ...\n",
      "simulateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 14 sec\n",
      "Same results ? True\n"
     ]
    }
   ],
   "source": [
    "# %%script false --no-raise-error # skip this cell! (comment this line to run the cell)\n",
    "\n",
    "# Equivalent, using other computational resources\n",
    "# -----------------------------------------------\n",
    "# Computational resources\n",
    "nproc = 1\n",
    "nthreads_per_proc = 1\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.simulateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            searchRadius=None,             # search neighborhood ...\n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nreal=nreal,                   # number of realizations\n",
    "                            seed=seed,                     # seed\n",
    "                            nproc=nproc,                   # computational resources ...\n",
    "                            nthreads_per_proc=nthreads_per_proc, \n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "simul_img_2 = geosclassic_output['image'] # output image\n",
    "\n",
    "print(f\"Same results ? {np.allclose(simul_img.val, simul_img_2.val)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set sapcing (cell size) in y direction (1 cell in 1D) to visualize images using gn.imgplot.drawImage2D\n",
    "sy = 0.2 * (simul_img.xmax() - simul_img.xmin())\n",
    "simul_img.sy = sy\n",
    "krig_img.sy = sy\n",
    "\n",
    "if x is not None:\n",
    "    # Set y-coordinates for conditioning data points for visualization\n",
    "    y = len(x) * [simul_img.oy + 0.5*simul_img.sy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute proportion of each category (pixel-wise)\n",
    "simul_img_prop = gn.img.imageCategProp(simul_img, category_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.subplots(1+ncategory, 2, figsize=(12, 2*(1+ncategory)), sharex=True, sharey=True)\n",
    "\n",
    "for i in range(2):\n",
    "    plt.subplot(1+ncategory, 2, i+1)\n",
    "    gn.imgplot.drawImage2D(simul_img, iv=i, categ=True, categVal=categVal, categCol=categCol)\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'real #{i}')\n",
    "\n",
    "for i in range(ncategory):\n",
    "    plt.subplot(1+ncategory, 2, 2*i+3)\n",
    "    gn.imgplot.drawImage2D(simul_img_prop, iv=i, vmin=0, vmax=1, cmap=cmap_categ[i])\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'Proportion of categ {categVal[i]} over {nreal} real')\n",
    "\n",
    "    plt.subplot(1+ncategory, 2, 2*i+4)\n",
    "    gn.imgplot.drawImage2D(krig_img, iv=i, vmin=0, vmax=1, cmap=cmap_categ[i])\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'Estimate probability of categ {categVal[i]} (kriging)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation: all data respected ? True\n",
      "Simulation: all data respected ? True\n"
     ]
    }
   ],
   "source": [
    "# Check data\n",
    "# ----------\n",
    "if x is not None:\n",
    "    # Get index of conditioning location in the grid\n",
    "    data_grid_index = [gn.img.pointToGridIndex(xk, 0, 0, sx, 1., 1., ox, 0., 0.) for xk in x] # (ix, iy, iz) for each data point\n",
    "    # Check estimation\n",
    "    krig_v = [krig_img.val[:, iz, iy, ix] for ix, iy, iz in data_grid_index]\n",
    "    if ncategory == 1:\n",
    "        print(f'Estimation: all data respected ? {np.all(np.asarray(krig_v).reshape(-1) == np.asarray([1 if vi == category_values[0] else 0 for vi in v]))}')\n",
    "    else:\n",
    "        print(f'Estimation: all data respected ? {np.all([np.all(krig_v[i] == np.eye(ncategory)[np.where(np.asarray(category_values) == v[i])[0][0]]) for i in range(len(x))])}')\n",
    "    # Check simulation\n",
    "    sim_v = [simul_img.val[:, iz, iy, ix] for ix, iy, iz in data_grid_index]\n",
    "    print(f'Simulation: all data respected ? {np.all([np.all(sim_v[i] == v[i]) for i in range(len(x))])}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prescribed probabilities                                    = [0.1, 0.2, 0.7]\n",
      "Estimation: probabilities (mean over the grid)              = [0.15260573 0.23212455 0.61526972]\n",
      "Simulation: probabilities (mean over the grid and all real.)= [np.float64(0.144588), np.float64(0.23767), np.float64(0.617742)]\n"
     ]
    }
   ],
   "source": [
    "# Compare probabilities\n",
    "# ---------------------\n",
    "if probability is not None:\n",
    "    print(f'Prescribed probabilities                                    = {probability}')\n",
    "    print(f'Estimation: probabilities (mean over the grid)              = {krig_img.val.mean(axis=(1,2,3))}')\n",
    "    print(f'Simulation: probabilities (mean over the grid and all real.)= {[np.mean(simul_img.val == cv) for cv in category_values]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Example - using non-stationary probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting probability (proportion) maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coordinates of the center of grid cells\n",
    "xg = ox + sx*(0.5+np.arange(nx))\n",
    "\n",
    "if ncategory > 1:\n",
    "    # Case with ncategory > 1\n",
    "    # -----------------------\n",
    "    # Define probability maps for each category\n",
    "    c = 0.9\n",
    "    p1 = np.linspace(c, 0., nx)     \n",
    "    p2 = c - p1\n",
    "    p0 = (1. - c) * np.ones_like(p1) # 1.0 - p1 - p2 # constant map (0.1)\n",
    "\n",
    "    probability = np.array((p0, p1, p2))\n",
    "\n",
    "else:\n",
    "    # Case with ncategory = 1\n",
    "    # -----------------------\n",
    "    c = 1.0\n",
    "    # Define probability map for non-zero category\n",
    "    probability = np.linspace(0., c, nx)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "# ----\n",
    "# Fill image for display\n",
    "probability_img = gn.img.Img(nx, 1, 1, sx, 1., 1., 0., 0., 0., nv=ncategory, val=probability)\n",
    "probability_img.sy = .2 * probability_img.sx * probability_img.nx # set spacing in y direction (1 cell) to visualize images using gn.imgplot.drawImage2D\n",
    "\n",
    "# Display probability maps\n",
    "plt.subplots(ncategory, 1, figsize=(10,8), sharey=True)\n",
    "\n",
    "for i in range(ncategory):\n",
    "    plt.subplot(ncategory, 1, 1+i)\n",
    "    gn.imgplot.drawImage2D(probability_img, iv=i, title = f'Probability for categ. {categVal[i]}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings - using data (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ncategory > 1:\n",
    "    # Case with ncategory > 1\n",
    "    # -----------------------\n",
    "    # Data\n",
    "    x = [50.5, 300.1, 550.2, 849.4]  # data locations (real coordinates)\n",
    "    v = [ 1., 2., 1., 3.]            # data values\n",
    "    # x = None\n",
    "    # v = None\n",
    "\n",
    "    # Probability : `probability` defined above\n",
    "\n",
    "    # Type of kriging\n",
    "    method = 'simple_kriging'\n",
    "\n",
    "else:\n",
    "    # Case with ncategory = 1\n",
    "    # -----------------------\n",
    "    # Data\n",
    "    x = [50.5, 300.1, 550.2, 849.4]  # data locations (real coordinates)\n",
    "    v = [ 0., 2., 2., 0.]            # data values\n",
    "    # x = None\n",
    "    # v = None\n",
    "\n",
    "    # Probability : `probability` defined above\n",
    "\n",
    "    # Type of kriging\n",
    "    method = 'simple_kriging'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of probabilities (by kriging)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 8 thread(s) (OpenMP)] ...\n",
      "estimateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 0.0027 sec\n"
     ]
    }
   ],
   "source": [
    "# Computational resources\n",
    "nthreads = 8\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.estimateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            use_unique_neighborhood=False, # search neighborhood (unique cannot be used with non-stationarities)...\n",
    "                            searchRadius=None,             \n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nthreads=nthreads,              # computational resources\n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "krig_img = geosclassic_output['image'] # output image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of warning(s), and warning messages\n",
    "geosclassic_output['nwarning'], geosclassic_output['warnings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 1 thread(s) (OpenMP)] ...\n",
      "estimateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 0.0089 sec\n",
      "Same results ? True\n"
     ]
    }
   ],
   "source": [
    "# %%script false --no-raise-error # skip this cell! (comment this line to run the cell)\n",
    "\n",
    "# Equivalent, using other computational resources\n",
    "# -----------------------------------------------\n",
    "# Computational resources\n",
    "nthreads = 1\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.estimateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            use_unique_neighborhood=False, # search neighborhood (unique cannot be used with non-stationarities)...\n",
    "                            searchRadius=None,             \n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nthreads=nthreads,              # computational resources\n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "krig_img_2 = geosclassic_output['image'] # output image\n",
    "\n",
    "print(f\"Same results ? {np.allclose(krig_img.val, krig_img_2.val)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [2 process(es) of 4 thread(s) (OpenMP)] ...\n",
      "simulateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [2 process(es)] complete\n",
      "Elapsed time: 3 sec\n"
     ]
    }
   ],
   "source": [
    "# Number of realizations\n",
    "nreal = 1000\n",
    "\n",
    "# Seed\n",
    "seed = 321\n",
    "\n",
    "# Computational resources\n",
    "nproc = 2\n",
    "nthreads_per_proc = 4\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.simulateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            searchRadius=None,             # search neighborhood ...\n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nreal=nreal,                   # number of realizations\n",
    "                            seed=seed,                     # seed\n",
    "                            nproc=nproc,                   # computational resources ...\n",
    "                            nthreads_per_proc=nthreads_per_proc, \n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "simul_img = geosclassic_output['image'] # output image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of warning(s), and warning messages\n",
    "geosclassic_output['nwarning'], geosclassic_output['warnings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulateIndicator: call `run_MPDSOMPGeosClassicIndicatorSim` [1 process of 1 thread(s) (OpenMP)] ...\n",
      "simulateIndicator: `run_MPDSOMPGeosClassicIndicatorSim` [1 process] complete\n",
      "Elapsed time: 14 sec\n",
      "Same results ? True\n"
     ]
    }
   ],
   "source": [
    "# %%script false --no-raise-error # skip this cell! (comment this line to run the cell)\n",
    "\n",
    "# Equivalent, using other computational resources\n",
    "# -----------------------------------------------\n",
    "# Computational resources\n",
    "nproc = 1\n",
    "nthreads_per_proc = 1\n",
    "\n",
    "t1 = time.time() # start time\n",
    "geosclassic_output = gn.geosclassicinterface.simulateIndicator(\n",
    "                            category_values,               # list of categories (required)\n",
    "                            cov_model,                     # covariance model(s) (required)\n",
    "                            nx, sx, ox,                    # grid geometry (nx is required)\n",
    "                            x=x, v=v,                      # data\n",
    "                            probability=probability,       # probability\n",
    "                            cov_model_non_stationarity_list=cov_model_non_stationarity_list, # non-stationrities\n",
    "                            method=method,                 # type of kriging\n",
    "                            searchRadius=None,             # search neighborhood ...\n",
    "                            searchRadiusRelative=1.2, \n",
    "                            nneighborMax=12,\n",
    "                            nreal=nreal,                   # number of realizations\n",
    "                            seed=seed,                     # seed\n",
    "                            nproc=nproc,                   # computational resources ...\n",
    "                            nthreads_per_proc=nthreads_per_proc, \n",
    "                            verbose=2                      # verbose mode\n",
    "                        )\n",
    "t2 = time.time() # end time\n",
    "print('Elapsed time: {:.2g} sec'.format(t2-t1))\n",
    "\n",
    "simul_img_2 = geosclassic_output['image'] # output image\n",
    "\n",
    "print(f\"Same results ? {np.allclose(simul_img.val, simul_img_2.val)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set sapcing (cell size) in y direction (1 cell in 1D) to visualize images using gn.imgplot.drawImage2D\n",
    "sy = 0.2 * (simul_img.xmax() - simul_img.xmin())\n",
    "simul_img.sy = sy\n",
    "krig_img.sy = sy\n",
    "\n",
    "if x is not None:\n",
    "    # Set y-coordinates for conditioning data points for visualization\n",
    "    y = len(x) * [simul_img.oy + 0.5*simul_img.sy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute proportion of each category (pixel-wise)\n",
    "simul_img_prop = gn.img.imageCategProp(simul_img, category_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.subplots(1+ncategory, 3, figsize=(15, (1+ncategory)*2), sharex=True, sharey=True)\n",
    "\n",
    "for i in range(3):\n",
    "    plt.subplot(1+ncategory, 3, i+1)\n",
    "    gn.imgplot.drawImage2D(simul_img, iv=i, categ=True, categVal=categVal, categCol=categCol)\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'real #{i}')\n",
    "\n",
    "for i in range(ncategory):\n",
    "    plt.subplot(1+ncategory, 3, 3*i+4)\n",
    "    gn.imgplot.drawImage2D(simul_img_prop, iv=i, vmin=0, vmax=1, cmap=cmap_categ[i])\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'Proportion of categ {categVal[i]} over {nreal} real')\n",
    "\n",
    "    plt.subplot(1+ncategory, 3, 3*i+5)\n",
    "    gn.imgplot.drawImage2D(krig_img, iv=i, vmin=0, vmax=1, cmap=cmap_categ[i])\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'Estimate probability of categ {categVal[i]} (kriging)')\n",
    "\n",
    "    plt.subplot(1+ncategory, 3, 3*i+6)\n",
    "    gn.imgplot.drawImage2D(probability_img, iv=i, vmin=0, vmax=1, cmap=cmap_categ[i])\n",
    "    if x is not None:\n",
    "        plt.plot(x, y, '+', c='black', markersize=5) # add conditioning point locations\n",
    "    plt.title(f'Prescribed probability for categ {categVal[i]}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation: all data respected ? True\n",
      "Simulation: all data respected ? True\n"
     ]
    }
   ],
   "source": [
    "# Check data\n",
    "# ----------\n",
    "if x is not None:\n",
    "    # Get index of conditioning location in the grid\n",
    "    data_grid_index = [gn.img.pointToGridIndex(xk, 0, 0, sx, 1., 1., ox, 0., 0.) for xk in x] # (ix, iy, iz) for each data point\n",
    "    # Check estimation\n",
    "    krig_v = [krig_img.val[:, iz, iy, ix] for ix, iy, iz in data_grid_index]\n",
    "    if ncategory == 1:\n",
    "        print(f'Estimation: all data respected ? {np.all(np.asarray(krig_v).reshape(-1) == np.asarray([1 if vi == category_values[0] else 0 for vi in v]))}')\n",
    "    else:\n",
    "        print(f'Estimation: all data respected ? {np.all([np.all(krig_v[i] == np.eye(ncategory)[np.where(np.asarray(category_values) == v[i])[0][0]]) for i in range(len(x))])}')\n",
    "    # Check simulation\n",
    "    sim_v = [simul_img.val[:, iz, iy, ix] for ix, iy, iz in data_grid_index]\n",
    "    print(f'Simulation: all data respected ? {np.all([np.all(sim_v[i] == v[i]) for i in range(len(x))])}')\n"
   ]
  }
 ],
 "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
}