{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7da34b83-dc10-4208-b7de-3d51e78bec05",
   "metadata": {},
   "source": [
    "# MPSlib: Hard and soft data\n",
    "\n",
    "MPSlib can account for hard and soft data (both co-located and non-co-located).\n",
    "Details about the use of the preferential path and co- and non-co-located soft data can be found in:\n",
    "\n",
    "[Hansen, Thomas Mejer, Klaus Mosegaard, and Knud Skou Cordua. \"Multiple point statistical simulation using uncertain (soft) conditional data.\" Computers & Geosciences 114 (2018): 1-10](https://doi.org/10.1016/j.cageo.2018.01.017)\n",
    "\n",
    "`mps_snesim_tree` and `mps_snesim_list` can account for co-located soft data only.\n",
    "\n",
    "`mps_genesim` can account for both co-located and non-co-located soft data.\n",
    "\n",
    "## Define hard data\n",
    "\n",
    "Hard data (model parameters with no uncertainty) are given by the `d_hard` variable,\n",
    "with X, Y, Z, and VALUE for each conditional data point. Three conditional hard data can be given by:\n",
    "\n",
    "    O.d_hard = np.array([[ ix1, iy1, iz1, val1],\n",
    "                         [ ix2, iy2, iz2, val2],\n",
    "                         [ ix3, iy3, iz3, val3]])\n",
    "\n",
    "## Define soft/uncertain data\n",
    "\n",
    "Soft data (model parameters with uncertainty) are given by the `d_soft` variable,\n",
    "with X, Y, Z for the position and a probability for each possible outcome.\n",
    "When considering a training image with two categories [0, 1], setting P(m=0)=0.2\n",
    "at position [5, 3, 2] is done as:\n",
    "\n",
    "    O.d_soft = np.array([[ 5, 3, 2, 0.2, 0.8]])\n",
    "\n",
    "If a training image has 3 categories and P(m=0)=0.2, P(m=1)=0.3, then:\n",
    "\n",
    "    O.d_soft = np.array([[ 5, 3, 2, 0.2, 0.3, 0.5]])\n",
    "\n",
    "## Preferential path\n",
    "\n",
    "MPSlib supports a preferential simulation path such that model parameters with more\n",
    "informative conditional data (i.e. lower entropy) are simulated before less informed nodes.\n",
    "This is especially useful when using sparse soft data.\n",
    "\n",
    "    O.par['shuffle_simulation_grid'] = 0  # Unilateral path\n",
    "    O.par['shuffle_simulation_grid'] = 1  # Random path\n",
    "    O.par['shuffle_simulation_grid'] = 2  # Preferential path\n",
    "\n",
    "### Co-located soft data\n",
    "\n",
    "By default, only co-located soft data are considered during simulation:\n",
    "\n",
    "    O.par['n_cond_soft'] = 1              # Only 1 soft data point is used\n",
    "    O.par['max_search_radius_soft'] = 0   # Only co-located soft data is used\n",
    "    O.par['shuffle_simulation_grid'] = 2  # Preferential path\n",
    "\n",
    "It is advised to use the preferential path whenever using co-located soft data.\n",
    "\n",
    "### Non-co-located soft data\n",
    "\n",
    "`mps_genesim` can handle non-co-located soft data using a rejection sampler.\n",
    "In practice, it becomes computationally expensive to condition on many soft data points.\n",
    "To use up to 3 soft data points within a search radius:\n",
    "\n",
    "    O.par['n_cond_soft'] = 3\n",
    "    O.par['max_search_radius_soft'] = 10000000\n",
    "    O.par['shuffle_simulation_grid'] = 2  # Preferential path\n",
    "\n",
    "Using the preferential path is always recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a3ff962-4a75-461f-a96f-bb2334808197",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mpslib as mps\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09d678f",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c39b0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using mps_genesim installed in /mnt/space/space_au11687/PROGRAMMING/mpslib (scikit-mps in /mnt/space/space_au11687/PROGRAMMING/mpslib/scikit-mps/mpslib/mpslib.py)\n"
     ]
    }
   ],
   "source": [
    "#O = mps.mpslib(method='mps_snesim_tree', parameter_filename='mps_snesim.txt')\n",
    "O = mps.mpslib(method='mps_genesim', parameter_filename='mps_genesim.txt')\n",
    "\n",
    "TI1, TI_filename1 = mps.trainingimages.strebelle(3, coarse3d=1)\n",
    "O.par['soft_data_categories'] = np.array([0, 1])\n",
    "O.ti = TI1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c316fc4d-ec6b-44bd-b27e-f2e609175354",
   "metadata": {},
   "outputs": [],
   "source": [
    "O.par['rseed'] = 1\n",
    "O.par['n_multiple_grids'] = 0\n",
    "O.par['n_cond'] = 16\n",
    "O.par['n_cond_soft'] = 1\n",
    "O.par['n_real'] = 500\n",
    "O.par['debug_level'] = -1\n",
    "O.par['simulation_grid_size'][0] = 18\n",
    "O.par['simulation_grid_size'][1] = 13\n",
    "O.par['simulation_grid_size'][2] = 1\n",
    "O.par['hard_data_fnam'] = 'hard.dat'\n",
    "O.par['soft_data_fnam'] = 'soft.dat'\n",
    "O.delete_local_files()\n",
    "O.par['n_max_cpdf_count'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333082ff-bdd1-405e-84b2-44ad641aea99",
   "metadata": {},
   "source": [
    "## Hard data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ab6dbe8-c405-4512-befa-4e22ed65a7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set hard data\n",
    "d_hard = np.array([[ 15, 4, 0, 1],\n",
    "                   [ 15, 5, 0, 1]])\n",
    "# Optionally use hard data\n",
    "# O.d_hard = d_hard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc1df53-bef1-4bb3-b840-33b8951ec880",
   "metadata": {},
   "source": [
    "## Soft/uncertain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f0147a5-9486-42f7-9dba-e9891cd4d0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set soft data\n",
    "d_soft = np.array([[ 2,  2, 0, 0.7,   0.3  ],\n",
    "                   [ 5,  5, 0, 0.001, 0.999],\n",
    "                   [10,  8, 0, 0.999, 0.001]])\n",
    "\n",
    "O.d_soft = d_soft"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e197c6a-2a45-4f9b-a702-eb040f00020b",
   "metadata": {},
   "source": [
    "### Example 1: Co-located soft data only\n",
    "\n",
    "In this example only one soft data point is used, and only if it is located at the same\n",
    "position as the node being simulated in the sequential simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "665499e5-ac48-4c46-9c53-0513169c2be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Only co-located\n",
    "O.par['n_cond_soft'] = 1\n",
    "O.par['max_search_radius_soft'] = 0\n",
    "\n",
    "gtxt = ['unilateral', 'random', 'preferential']\n",
    "shuffle_simulation_grid_arr = [0, 1, 2]\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "for i in range(len(shuffle_simulation_grid_arr)):\n",
    "    O.par['shuffle_simulation_grid'] = shuffle_simulation_grid_arr[i]\n",
    "\n",
    "    O.delete_local_files()\n",
    "    O.run_parallel()\n",
    "    m_mean, m_std, m_mode = O.etype()\n",
    "\n",
    "    plt.subplot(2, 3, i + 1)\n",
    "    plt.imshow(m_mean.T, zorder=-1, vmin=0, vmax=1, cmap='hot')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)\n",
    "    plt.title('%s path' % gtxt[i])\n",
    "    plt.subplot(2, 3, 3 + i + 1)\n",
    "    plt.imshow(m_std.T, zorder=-1, vmin=0, vmax=0.4, cmap='gray')\n",
    "    plt.title('std')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5022deb4-8368-44f2-b956-4005e0e65299",
   "metadata": {},
   "source": [
    "### Example 2: One non-co-located soft data point\n",
    "\n",
    "In this example still only one soft data point is used, but it can be located anywhere\n",
    "in the simulation grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4005444-9ae6-4303-96e6-e014e97059c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One non-co-located soft data point\n",
    "O.par['n_cond_soft'] = 1\n",
    "O.par['max_search_radius_soft'] = 1000000\n",
    "\n",
    "shuffle_simulation_grid_arr = [0, 1, 2]\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "for i in range(len(shuffle_simulation_grid_arr)):\n",
    "    O.par['shuffle_simulation_grid'] = shuffle_simulation_grid_arr[i]\n",
    "\n",
    "    O.delete_local_files()\n",
    "    O.run_parallel()\n",
    "    m_mean, m_std, m_mode = O.etype()\n",
    "\n",
    "    plt.subplot(2, 3, i + 1)\n",
    "    plt.imshow(m_mean.T, zorder=-1, vmin=0, vmax=1, cmap='hot')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)\n",
    "    plt.title('%s path' % gtxt[i])\n",
    "    plt.subplot(2, 3, 3 + i + 1)\n",
    "    plt.imshow(m_std.T, zorder=-1, vmin=0, vmax=0.4, cmap='gray')\n",
    "    plt.title('std')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a706ba0-39e4-45bf-9c7b-d24a460b3d5f",
   "metadata": {},
   "source": [
    "### Example 3: Three (all) non-co-located soft data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7da138e6-70d3-408c-9e3b-77d26f0f800c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n",
      "parallel: Using 25 of max 26 threads\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ev9+vr776Svv371deXp5Wr159yOW/+uor/f3vf9f555+v3bt3a8eOHdqxY4d27typwsJCrVu3Tlu2bAlZZsaMGS2uTde1a9fgv/ft26edO3dq6NCh6tmzZ7vG0ZbmM9iazZ49W5L0wgsvtLru2tpa7dixQyeffLI2bNig2tradq/r0ksvDTkT5MQTT1RTU5O++OKLSIePqBDOLbvDvb4CEF0uv/zyFtO+v43cu3evduzYoX/7t3+TpFa3zwfWOPHEE7Vz5075fD5J321/r7rqqpD55syZE/JzU1OT/va3v2ny5MkaPHhwcHr//v3105/+VK+//nqwZrNLLrkkZDucn5+vQCCgSy65JDgtISFBeXl57c7LrKyskLMhUlNTNW3aNL377ruqrq6WJCUlJSk+Pj447p07d6p79+466qijwsqw7t2762c/+1nw58TERI0bNy7sbIfbkDNwl/Z8vpakk08+WcOHDw/+HAgE9Kc//UmTJk1SIBAI7jfs2LFDhYWFqq2tPeQ2849//KNOPPFE9erVK2T5goICNTU16bXXXguZ/5xzzgl+pVKKbN/F9Gf87+dqXV2dduzYoeOOO06BQCDiu0Iedthhuuyyy4I/JyYm6rLLLtO2bdu0atUqSc73+76vtawnq6KZd3OGr6DCiCOOOKLFtF69eunrr78Ou9ajjz6qu+66S59++qn27dsXnD5o0KBDLrt+/XoFAgHNnz9f8+fPb3Webdu2acCAAQet+80336isrEwPP/ywtmzZEnJtnnCaYAcaNmxYyM9DhgxRfHx8yLXl/u///k+lpaWqrKxscZ2i2tpapaWltWtdB/5NevXqJUkR/U0QTbx70VJ4T2vb56+++ko33XSTnnzySW3bti3kd61tnw+2LUxNTdUXX3yh+Pj4kINIknTUUUeF/Lx9+3bV19e3mC5JP/jBD+T3+1VVVaURI0a0ue7m7Xd2dnaL6e3dNg8dOrTF12yPPPJISd9eKyczM1N+v1+/+c1vdN9992njxo0h18EJ57IQhx9+eIt19erVS++//367a8CNyBm4S3s+X0stM2X79u3atWuXHnjgAT3wwAOt1j4wZw60bt06vf/++yFNtYMtf+AYItl3Mf0Zf/PmzSopKdFzzz3Xokak+z1ZWVktbpz0/axqPnDmZL+vWXJycovXP9L9UHQU7+YMDTi06sAP3M3auphlW3c3DbTjotLf9/vf/14XXXSRJk+erF/84hdKT09XQkKCysrK9Pnnnx9y+eaLnV577bUqLCxsdZ6hQ4eG/Pz9oz7NZs+erYcfflhz5szR+PHjlZaWpri4OE2dOjXkgqpOHfg6f/755zr11FN19NFHa+HChcrOzlZiYqJeeOEF/fd//3dY6zb1N0G08W5gwXta2z6ff/75euONN/SLX/xCubm56t69u/x+v84444xWt5GduS1sa92tTTc5nttuu03z58/XxRdfrFtuuUW9e/dWfHy85syZQ46gHcgZuFtb+zEHZkrz9vBnP/uZpk+f3uoy37/WWGv8fr9OO+00XXfdda3+vrnpdKgxhLPvYnLb3NTUpNNOO01fffWVrr/+eh199NFKSUnRli1bdNFFFxnd7zmQ0/2+Zm29Hohm3s0ZGnBoVa9evbRr164W021/ffHpp5/W4MGD9cwzz4SEZ2lpach8bQVr89eCunTpooKCAkfjmD59uu66667gtL1797b6moRj3bp1IUd01q9fL7/fH7zj7P/+7/+qoaFBzz33XMjRre/fDahZW68BYp2HL1oKz/v6669VUVGhm266SSUlJcHp69ati7jmwIED5ff79fnnn4ec3bZ27dqQ+fr166du3bq1mC59e/e6+Pj4Fme22dB8tsT3M+Czzz6TpGCWPP300zrllFP0u9/9LmTZXbt2qW/fvsGfyRG0jpyBuxzq83Vbmu983dTUFPF+w5AhQ7Rnz56Ilze173Kg9m7fP/jgA3322Wd69NFHQ24u8dJLLzla/9atW1VXVxdyFlxrWeVkvw9u5t2c4RpwaNWQIUNUW1sb8jWTf/7zn/rzn/9sdb3NRzC+fwTnrbfeUmVlZch8zXchPbAhlp6ergkTJuj+++/XP//5zxb1v3+L7kON48CjSPfcc4/j21kvWrSoRU1JwbvJtfb8a2tr9fDDD7eolZKS4rghCDfy7jUTgNa2kZJa3GUuHM3b37vvvvugNRMSEnT66afrf/7nf0K+1lRTU6OlS5fqhBNOUGpqasTjaK+tW7eGZLHP59Njjz2m3NxcZWZmBsd64Gv0xz/+scV1hJp3jMgShCJn4C6H+nzdloSEBJ1zzjn605/+pA8//LDF79uz33D++eersrJSL774Yovf7dq1S/v3H/z/h6l9lwO1dz+htVwNBAL6zW9+E9F6m+3fv1/3339/8OfGxkbdf//96tevn8aOHdvmusPZ74ObeTdnOAMOrZo6daquv/56nX322brqqqtUX1+vxYsX68gjj3R0E4JD+fGPf6xnnnlGZ599ts4880xt3LhRS5Ys0fDhw7Vnz57gfF27dtXw4cO1bNkyHXnkkerdu7dGjhypkSNHatGiRTrhhBM0atQozZgxQ4MHD1ZNTY0qKyv15Zdf6r333mvXOB5//HGlpaVp+PDhqqys1MsvvxzWtXNas3HjRv3kJz/RGWecocrKSv3+97/XT3/6U40ZM0aSdPrppysxMVGTJk3SZZddpj179ujBBx9Uenp6i1AeO3asFi9erFtvvVVDhw5Venq6fvSjHzkaH9zAu6dsA6mpqTrppJO0YMEC7du3TwMGDNDf/vY3bdy4MeKaubm5uuCCC3TfffeptrZWxx13nCoqKrR+/foW895666166aWXdMIJJ2jmzJk67LDDdP/996uhoUELFixw8tTa7cgjj9Qll1yit99+WxkZGXrooYdUU1MTcqDmxz/+sW6++WYVFRXpuOOO0wcffKAnnngi5OYR0rcH23r27KklS5aoR48eSklJUX5+fljX3kEsImfgLof6fH0wt99+u1555RXl5+drxowZGj58uL766iutXr1aL7/8sr766quDLv+LX/xCzz33nH784x/roosu0tixY1VXV6cPPvhATz/9tDZt2hRy5nFrTOy7HKi9+wlHH320hgwZomuvvVZbtmxRamqq/vSnPzm+flpWVpbuuOMObdq0SUceeaSWLVumNWvW6IEHHlCXLl0kmdnvg1t5N2dowKFVffr00Z///GcVFxfruuuu06BBg1RWVqZ169ZZbcBddNFFqq6u1v33368XX3xRw4cP1+9//3v98Y9/1IoVK0Lm/e1vf6vZs2frmmuuUWNjo0pLSzVy5EgNHz5c77zzjm666SY98sgj2rlzp9LT03XMMceEfGXpYH7zm98oISFBTzzxhPbu3avjjz9eL7/8cpvXZmivZcuWqaSkRHPnztVhhx2mWbNm6de//nXw90cddZSefvpp3XDDDbr22muVmZmpK664Qv369dPFF18cUqukpERffPGFFixYoN27d+vkk0+mAecJ3g0sQJKWLl2q2bNna9GiRQoEAjr99NP117/+VVlZWRHXfOihh9SvXz898cQTevbZZ/WjH/1Izz//fIuvlI4YMUL/+Mc/NG/ePJWVlcnv9ys/P1+///3vlZ+f7/SptcuwYcN0zz336Be/+IXWrl2rQYMGadmyZSH59F//9V+qq6vT0qVLtWzZMv3whz/U888/r7lz54bU6tKlix599FHNmzdPl19+ufbv36+HH36YBpznkTNwl0N9vj6YjIwMrVy5UjfffLOeeeYZ3XffferTp49GjBihO+6445DLd+vWTa+++qpuu+02/fGPf9Rjjz2m1NRUHXnkkbrpppvadfM0E/suB2rvfkKXLl30v//7v7rqqqtUVlam5ORknX322Zo1a1a7Gpht6dWrlx599FHNnj1bDz74oDIyMnTvvfdqxowZwXlM7PfBrbybM3EBrqQLWHfjjTfqpptu0vbt2w95FAxojc/nU1pammpr/6TU1JRDLxBcrk5paeeotra2Q74eB8CenJwcjRw5Un/5y186eyiIQeQM3IbP19FpwoQJ2rFjR6tf64W3kTOcAQcALuPdi5YCADoCOQMAsMm7OUMDDgBcxbunbAMAOgI5AwCwybs5QwMOAFzFu4EFAOgI5AwAwCbv5gzXgAMAF/jumgmPKTW1WxjL1SstbVpMXDMBAGAPOQMAsImc4Qw4AHAZ7x4xAgB0BHIGAGCTd3OGBhwAuIp3AwsA0BHIGQCATd7NmahrwPn9fm3dulU9evRQXFxcZw8HAIwKBALavXu3srKyFB8fH0EF7941yCSyBkCsImeiAzkDIFaRM5GLugbc1q1blZ2d3dnDAACrqqqqdPjhh0ewZJPCC6HYCSyTyBoAsY6c6VzkDIBYR86EL+oacD169JAkXS4pqXOHIkmqNVDD1NvlGwM19hmo0WighiQlGqjR10CNXgZqSNIAAzUaDNSQpDQDNUxc3rK3gRqSmedjanvS/suFtm6PpGP13bYufN49Zduk5tf/Gjl7b5jYLktmcsLE9l2S/AZqRNOHCxNZYyInEgzUkMzkXlcDNSQpxUANE9vmLgZqSFJ3AzVM/Z0jOZ+gWZ2kU0XOdLbm1/9SOdsOmdgXkcxs2019To0WTv6fmWZiO2bq0vQmPr87/bzcLN1ADROvrYl8kKSeBmqYem2d7NPXSTpT5EwkoukzsiQFT9FOUnQ04Ex8cDfVgIuWt52p2+aa2Bia+PuYep+Z2KEx9QUFE2MxsXE3sXMmmQk9U39nU88p8q+jeDewTPp+1iQ7qGNih0YykxOmdiRMjMVUQ8QEEznh5D3SzFRjJlq276bqmHhtacC1jpzpXM2vf6KcfQYxsQ2TzOSVqcyLFtHUgIumfRoT22UTNSQzOWMiI0x9/jdRx9RYTJxUQ86EL+oacACAg/HuNRMAAB2BnAEA2OTdnKEBBwCu4t0jRgCAjkDOAABs8m7OWDvzdtGiRcrJyVFycrLy8/O1cuVKW6sCAA/ZH8EjNpEzAGADOdOMnAEAG7ybM1YacMuWLVNxcbFKS0u1evVqjRkzRoWFhdq2bZuN1QGAh3g3sL6PnAEAW8gZiZwBAHu8mzNWGnALFy7UjBkzVFRUpOHDh2vJkiXq1q2bHnrooRbzNjQ0yOfzhTwAADiYcHJGImsAAOEhZwAAphlvwDU2NmrVqlUqKCj4biXx8SooKFBlZWWL+cvKypSWlhZ8ZGdnmx4SAMQQ7x4xahZuzkhkDQC0HzlDzgCATd7NGeMNuB07dqipqUkZGRkh0zMyMlRdXd1i/nnz5qm2tjb4qKqqMj0kAIghzXcNau8jdu4a1CzcnJHIGgBoP3KGnAEAm7ybM51+F9SkpCQlJSV19jAAwCX2S0oIc36QNQDQXuRMJMgZAGgv7+aM8QZc3759lZCQoJqampDpNTU1yszMNL06APAY7wZWM3IGAGwiZ8gZALDJuzlj/CuoiYmJGjt2rCoqKoLT/H6/KioqNH78eNOrAwCP8e41E5qRMwBgEzlDzgCATd7NGStfQS0uLtb06dOVl5encePGqby8XHV1dSoqKrKxOgDwkOZrJoQzf+whZwDAFnJGImcAwB7v5oyVBtyUKVO0fft2lZSUqLq6Wrm5uVq+fHmLC5kCAMK1X+GdvBw7R4y+j5wBAFvIGYmcAQB7vJsz1m7CMGvWLM2aNctWeQDwKO8G1oHIGQCwgZxpRs4AgA3ezZlOvwtqW2olJTpYvs7QOBoM1NhuoIYkNRqoEc6lDm3WkKTBBmr0NlAjy0ANKbrG0sdAjW4Gaph675s46fgrAzUk59uEPY5H4N3AsqFW0l4Hy39haBymMitamMiJZAM1JGmggRomno+pc2ZMbN9N5JUkdTFUx6kUQ3VMvOdMfFaTnD0n5+9XciaaOMmo76s1UCOaPteZ+P9qKmd6GqiRaqCGqW3hPgM1TN2qxEQdE/cmNrX/a+JvZOpeyz0cLOukT/Mt7+ZM1DbgAACtaVJ4H11j55oJAICOQM4AAGzybs7QgAMAV/HuRUsBAB2BnAEA2OTdnAnnvD8AAAAAAAAAYeIMOABwlf2S4sKcHwCA9iJnAAA2eTdnaMABgKt4N7AAAB2BnAEA2OTdnKEBBwCu4t3AAgB0BHIGAGCTd3OGBhwAuIp3AwsA0BHIGQCATd7NGRpwAOAqTQovsGLnrkEAgI5AzgAAbPJuznAXVABwlf0RPMKzaNEi5eTkKDk5Wfn5+Vq5cuVB5y8vL9dRRx2lrl27Kjs7W9dcc4327t0b9noBANGAnAEA2GQ/Z6TozBoacADgKnYDa9myZSouLlZpaalWr16tMWPGqLCwUNu2bWt1/qVLl2ru3LkqLS3VJ598ot/97ndatmyZ/uu//ivSJwgA6FTkDADAJvsNuGjNGhpwAOAqdgNr4cKFmjFjhoqKijR8+HAtWbJE3bp100MPPdTq/G+88YaOP/54/fSnP1VOTo5OP/10XXDBBYc8wgQAiFbkDADAJvsNuGjNGhpwAOAqTQovrL69ZoLP5wt5NDQ0tKjc2NioVatWqaCgIDgtPj5eBQUFqqysbHU0xx13nFatWhUMpw0bNuiFF17Qv//7vxt6vgCAjkXOAABsspczUnRnDTdhAABX2S8pEMb83wZWdnZ2yNTS0lLdeOONIdN27NihpqYmZWRkhEzPyMjQp59+2mr1n/70p9qxY4dOOOEEBQIB7d+/X5dffjlfDQIA1yJnAAA22csZKbqzhgYcALhKZIFVVVWl1NTU4NSkpCQjo1mxYoVuu+023XfffcrPz9f69et19dVX65ZbbtH8+fONrAMA0JHIGQCATdGVM1LHZQ0NOADwgNTU1JDAak3fvn2VkJCgmpqakOk1NTXKzMxsdZn58+fr5z//uf7zP/9TkjRq1CjV1dXp0ksv1S9/+UvFx3OlAwDwAnIGAGBTe3JGiu6sIbEAwFXsXbQ0MTFRY8eOVUVFRXCa3+9XRUWFxo8f3+oy9fX1LQIpISFBkhQIhHNkCwAQHcgZAIBNdm/CEM1ZwxlwAOAqkZ2y3V7FxcWaPn268vLyNG7cOJWXl6uurk5FRUWSpGnTpmnAgAEqKyuTJE2aNEkLFy7UMcccEzxde/78+Zo0aVIwtAAAbkLOAABsspszUvRmTdQ24PpKcvKN3vD/RK37wkANU2PZa6BGsoEaRxmoIUm5BmoMNFDD1PNp/WTW8PQwUEOSdhmokWighonXRJK2Gqix00ANE3XqHI+gSeEFlj+s6lOmTNH27dtVUlKi6upq5ebmavny5cGLmG7evDnk6NANN9yguLg43XDDDdqyZYv69eunSZMm6Ve/+lVY6+0sA+Vsu2ji/4kktX452PDUGqghmcksEx9TcgzUMFVnsIEa2YeepV16Gqhh6usPJrbxiV0MFDFRQ5Kv3kwdE3Y5WNb5/2FyxqRkOdunMfHZXTKTEaY+p5rYp+lmoMYAAzUkM/sSwwzUMJUzJv7OPQ3UkMzklYnPJKb+H+4yUGOfgRqSs6yI9pyRojdr4gJRdu62z+dTWlqarpWzsKo2NJ61BmrEWgNuuIEaEg241sRaA87QPlFUNeB2O1y+TtKPJdXW1rbrGgbNmreNtbXZSk1t/8cRn8+vtLSqsNcX65pfz9vlbLto4r0p0YBrjamsMbGNpwHXOhpw9uxysOxuSSNFznS2aNunqTn0LIdkYl/EVB0acC3RgGsdDbjWOfm8t1vSMSJnIhG1Z8ABAFqzX+F9HAn/iBEAwMvIGQCATd7NGRpwAOAq3g0sAEBHIGcAADZ5N2dowAGAq3g3sAAAHYGcAQDY5N2coQEHAK7SpPBCKKou8wkAiHrkDADAJu/mjKnr8AaVlZXp2GOPVY8ePZSenq7Jkydr7VoTtzIAAHx7xCjcR2whZwDAJnKGnAEAm7ybM8YbcK+++qquvPJKvfnmm3rppZe0b98+nX766aqrqzO9KgDwIO8GVjNyBgBsImfIGQCwybs5Y/wrqMuXLw/5+ZFHHlF6erpWrVqlk046qcX8DQ0NamhoCP7s8/lMDwkAEEPCzRmJrAEAtB85AwCwwfgZcAeqra2VJPXu3bvV35eVlSktLS34yM7Otj0kAHAx7x4xasuhckYiawCg/ciZA5EzAGCSd3PGagPO7/drzpw5Ov744zVy5MhW55k3b55qa2uDj6qqKptDAgB3C/ilQFMYj9i5a1Br2pMzElkDAO1GzoQgZwDAMA/njNW7oF555ZX68MMP9frrr7c5T1JSkpKSkmwOAwBih1/h3TQodvKqVe3JGYmsAYB2I2dCkDMAYJiHc8ZaA27WrFn6y1/+otdee02HH364rdUAgLc0/esRzvwxipwBAAvImSByBgAs8HDOGG/ABQIBzZ49W3/+85+1YsUKDRo0yPQqAMC7PBxYzcgZALCInCFnAMAmD+eM8QbclVdeqaVLl+p//ud/1KNHD1VXV0uS0tLS1LVrV9OrAwBv8fAp283IGQCwiJwhZwDAJg/njPGbMCxevFi1tbWaMGGC+vfvH3wsW7bM9KoAwHuaInjEGHIGACwiZ8gZALDJwzlj5SuoAABLPHzEqBk5AwAWkTPkDADY5OGcsXoXVCdOkpTiYPlGQ+NIMFQnWgw3UGOAqfMmBxuocZSBGskGakiSibvN1xqoIanXXgNFtjov8fU+5zUkMwc9TP2ZnT4lx8/FH2aRGAosG46Ss6wxsQmSpFMM1DCxCZKkngZqZBmocbSBGpLUp7eBIv0M1NhtoIZk5rsLJjIiigR2mKnTw0ANU/8Paxwsu8fpyskZoybIWc6Y+u9q4iOZqbH0NFDDxKY910ANSUowcX+ObAM1TDHxhzb1ZjGRnV85LxGod15DMhPhBnbRJDl7acmZyEVtAw4A0AoPX7QUANAByBkAgE0ezhkacADgJh4+ZRsA0AHIGQCATR7OGeM3YQAAAAAAAADwHc6AAwA38fAp2wCADkDOAABs8nDO0IADADfxcGABADoAOQMAsMnDOUMDDgDcxMPXTAAAdAByBgBgk4dzhgYcALiJh48YAQA6ADkDALDJwzlDAw4A3CSg8I4CBWwNBAAQk8gZAIBNHs4ZGnAA4CYePmIEAOgA5AwAwCYP5wwNOABwEw8HFgCgA5AzAACbPJwzNOAAwE08fNFSAEAHIGcAADZ5OGdowAGAm3j4iBEAoAOQMwAAmzycMzTgAMBNPBxYAIAOQM4AAGzycM7QgAMAN/HwKdsAgA5AzgAAbPJwzsR39gAAAAAAAACAWMYZcADgJn6Fdxp2DB0xAgB0AHIGAGCTh3OGBhwAuImHT9kGAHQAcgYAYJOHcyZqG3AnSUp1sHzcMYYGkmOgxg8N1JCkowzUGGOgRk8DNSRpt4EamwzUWGeghiS9Z6DGVgM1JGmDgRpdnJdI3ue8hiSZKLPXQI2o4OGLltrwo0QpNc5BgT6GBnK4gRqjDNSQzGTWcAM1sg3UkKQtBmp8aKBGlYEakvSxgRqmsma7gRrVzkvscl5CklRroEajgRqSs2vEOL6+DDlj1IldHeaMqW3hUAM1TjRQQzKzP2KiRv/eBopICnzlvMY/nJfQuwZqSGZyZqeBGpKZsZjYCag3UEPmPgq4nodzJmobcACAVng4sAAAHYCcAQDY5OGcoQEHAG7i4VO2AQAdgJwBANjk4ZyhAQcAbuLhI0YAgA5AzgAAbPJwztCAAwA38XBgAQA6ADkDALDJwznj+Dqth3L77bcrLi5Oc+bMsb0qAIh9AX132nZ7HoHOGWZHImcAwCBypgVyBgAM8nDOWD0D7u2339b999+v0aNH21wNAHiHh48YtYacAQDDyJkQ5AwAGObhnLF2BtyePXt04YUX6sEHH1SvXr1srQYAvCWco0XhXuDUZcgZALCAnAkiZwDAAg/njLUG3JVXXqkzzzxTBQUFB52voaFBPp8v5AEAwKG0N2cksgYAED5yBgBgkpWvoD755JNavXq13n777UPOW1ZWpptuusnGMAAg9nj4lO3vCydnJLIGANqNnJFEzgCANR7OGeNnwFVVVenqq6/WE088oeTk5EPOP2/ePNXW1gYfVVVVpocEALGjKYJHjAk3ZySyBgDajZwhZwDAJg/njPEz4FatWqVt27bphz/8YXBaU1OTXnvtNd17771qaGhQQkJC8HdJSUlKSkoyPQwAiE3hXgchhq6Z0CzcnJHIGgBoN3KGnAEAmzycM8bPgDv11FP1wQcfaM2aNcFHXl6eLrzwQq1Zs6ZFWAEAwtABR4wWLVqknJwcJScnKz8/XytXrjzo/Lt27dKVV16p/v37KykpSUceeaReeOGF8FfcTuQMAFhEzpAzAGBTB50BF41ZY/wMuB49emjkyJEh01JSUtSnT58W0wEAYfIrvBAK84jRsmXLVFxcrCVLlig/P1/l5eUqLCzU2rVrlZ6e3mL+xsZGnXbaaUpPT9fTTz+tAQMG6IsvvlDPnj3DW3EYyBkAsIicIWcAwCbLOSNFb9ZYuQkDAMCSCE/ZPvBubG19VWbhwoWaMWOGioqKJElLlizR888/r4ceekhz585tMf9DDz2kr776Sm+88Ya6dOkiScrJyQljgACAqELOAABsspwzUvRmjfGvoLZmxYoVKi8v74hVAUBsi/CU7ezsbKWlpQUfZWVlLUo3NjZq1apVKigoCE6Lj49XQUGBKisrWx3Oc889p/Hjx+vKK69URkaGRo4cqdtuu01NTR17tVRyBgAMIWdaRc4AgCEWc0aK7qzhDDgAcJMIjxhVVVUpNTU1OLm1o0U7duxQU1OTMjIyQqZnZGTo008/bbX8hg0b9Pe//10XXnihXnjhBa1fv14zZ87Uvn37VFpaGsZAAQBRgZwBANhkMWek6M6aqG3AxQ2V4pxc33SYoYGcbKDGjw3UkKQjfm6gSLGBGvcZqCEp+UHnNbY4L6F6AzUkaa+BGtsN1JCkfdFRw9RLa+JUXVOXS/7K4fJ1TgcQ7oVI/zVvampqSGCZ4vf7lZ6ergceeEAJCQkaO3astmzZol//+tfu2DEaI2dJOMbQOP7dQI1TDNSQpO7XGChylYEaNxuoISn7Yec1TGxTTWSEJCUbqOF4Q/Qvu52XaGpwXiPGolOSlOJg2YDTlZMzZp0mqYuD5QcbGseJBmqcYKCGJPUykTMLDNQwsV8kad89zmuY/68TOQPbdtUYqGGozjdOP7zL3D6NiQg3NRYn53U5PicsynJG6risidoGHACgFREGVnv07dtXCQkJqqkJ/bRTU1OjzMzMVpfp37+/unTpEnJHuB/84Aeqrq5WY2OjEhMTwxgsAKDTkTMAAJss5owU3VnTIdeAAwAY4o/g0U6JiYkaO3asKioqvlud36+KigqNHz++1WWOP/54rV+/Xn7/dyv67LPP1L9/f3aKAMCNyBkAgE0Wc0aK7qyhAQcACCouLtaDDz6oRx99VJ988omuuOIK1dXVBe8gNG3aNM2bNy84/xVXXKGvvvpKV199tT777DM9//zzuu2223TllVd21lMAAEQxcgYAYFu0Zg1fQQUAN/ErvNOwwzxiNGXKFG3fvl0lJSWqrq5Wbm6uli9fHryI6ebNmxUf/92xm+zsbL344ou65pprNHr0aA0YMEBXX321rr/++vBWDACIDuQMAMAmyzkjRW/W0IADADeJ8K5B4Zg1a5ZmzZrV6u9WrFjRYtr48eP15ptvhr8iAED0IWcAADZ1QM5I0Zk1NOAAwE0sX7QUAOBx5AwAwCYP5wwNOABwEw8HFgCgA5AzAACbPJwzNOAAwE066JRtAIBHkTMAAJs8nDM04ADATTx8xAgA0AHIGQCATR7OGRpwAOAmHg4sAEAHIGcAADZ5OGdowAGAmwQU3mnYAVsDAQDEJHIGAGCTh3OGBhwAuImHjxgBADoAOQMAsMnDOUMDDgDcxMMXLQUAdAByBgBgk4dzhgYcALiJh48YAQA6ADkDALDJwzkT39kDAAAAAAAAAGIZZ8ABgJt4+IgRAKADkDMAAJs8nDPR24CLl7Pz8xIMjWOrgRr/Y6CGJCU/7rzGbgM1PnBeQpL0pYEaVQZq7DVQQ5L2GahhaiwG6tQZeD5dnJeQJCUbqJFooIYJXZ0W8PA1E6zoIWdJ2NPQOExsy94yUEOS9v638xo1BmpscF5CkpnMMvFZwMTfWDKTNY0GakgK7HFew9TLYoKJzaWpfQQndRyPgZwxa4ec5cwAQ+Mw8Z9ttYEaktTFQEasNVDjQ+clJElvG6ixzkCNWgM1DAmYyCqZid86AzXqDdSQzLUoTOjhYNk4pyv3cM5EbwMOANCSh48YAQA6ADkDALDJwzlDAw4A3MSv8EIoho4YAQA6ADkDALDJwzlDAw4A3MTDp2wDADoAOQMAsMnDOUMDDgDcxMOnbAMAOgA5AwCwycM5QwMOANzEw0eMAAAdgJwBANjk4Zxxcp/RNm3ZskU/+9nP1KdPH3Xt2lWjRo3SO++8Y2NVAOAtTRE8YhA5AwCWkDOSyBkAsMbDOWP8DLivv/5axx9/vE455RT99a9/Vb9+/bRu3Tr16tXL9KoAwHs8fMp2M3IGACwiZ8gZALDJwzljvAF3xx13KDs7Ww8//HBw2qBBg0yvBgC8ycOnbDcjZwDAInImopxpaGhQQ0ND8Gefz2dtfADgah7OGeNfQX3uueeUl5en8847T+np6TrmmGP04IMPtjl/Q0ODfD5fyAMAgLaEmzMSWQMAaL9IcqasrExpaWnBR3Z2dgeNFgDgFsYbcBs2bNDixYs1bNgwvfjii7riiit01VVX6dFHH211fsIKAMLgV3jXS4ihI0bNws0ZiawBgHYjZyLKmXnz5qm2tjb4qKqq6sARA4CLeDhnjH8F1e/3Ky8vT7fddpsk6ZhjjtGHH36oJUuWaPr06S3mnzdvnoqLi4M/+3w+dowAoC1NCu/QSQxdM6FZuDkjkTUA0G7kTEQ5k5SUpKSkpI4cJgC4k4dzxvgZcP3799fw4cNDpv3gBz/Q5s2bW50/KSlJqampIQ8AQBv8ETxiTLg5I5E1ANBu5ExEOQMAaCcP54zxM+COP/54rV27NmTaZ599poEDB5peFQB4j4ePGDUjZwDAInKGnAEAmzycM8bPgLvmmmv05ptv6rbbbtP69eu1dOlSPfDAA7ryyitNrwoAvMfDR4yakTMAYBE5Q84AgE0ezhnjDbhjjz1Wf/7zn/WHP/xBI0eO1C233KLy8nJdeOGFplcFAN4TzgVLmx8xhpwBAIvIGXIGAGzycM4Y/wqqJP34xz/Wj3/8YxulAcDbPHzK9veRMwBgCTkjiZwBAGs8nDNWGnAAAEsCCu807ICtgQAAYhI5AwCwycM5E70NuIR/PSL1tqFxvOq8RONW5zUkaYOBGlUGahh6OtpuoEaygRpdDNQwpaehOmkGavQ0UCOa9DZUp4ehOhFrkhQX5vxo25dyljUmNsyStNdAjRoDNSTt3Oe8hokIXm+ghiRtMVDDxKVHsg3UkKQkAzX6GKghSf0M1DCR47Fol4Nl65yunJwx61M5u+iPqY2hiQ8w6wzUkPSZgY1qpfMSxiLcZ6CGiZwx9Rm1p4EaJvZFJCnFQA0Tr4uJcUhmctPUJvcrB8uSM5GL3gYcAKAlDwcWAKADkDMAAJs8nDM04ADATcK9E1AM3TUIANAByBkAgE0ezhnjd0EFAAAAAAAA8B3OgAMAN/HwKdsAgA5AzgAAbPJwztCAAwA38fAp2wCADkDOAABs8nDO0IADADfx8BEjAEAHIGcAADZ5OGdowAGAm/gVXgjF0BEjAEAHIGcAADZ5OGdowAGAm/gV3hGjGAosAEAHIGcAADZ5OGdowAGAm4R7CnYMnbINAOgA5AwAwCYP5wwNOABwEw8HFgCgA5AzAACbPJwzNOAAwE08fMo2AKADkDMAAJs8nDM04ADATTx8xAgA0AHIGQCATR7OGRpwAOAmHj5iBADoAOQMAMAmD+cMDTgAcJNwAyiGAgsA0AHIGQCATR7OmfjOHgAAAAAAAAAQy2jAAYCbNEXwCNOiRYuUk5Oj5ORk5efna+XKle1a7sknn1RcXJwmT54c/koBANGBnAEA2NQBOSNFZ9ZE7VdQN3wi9XCw/G5T4zBQo9pADUlaa6DGTgM1GgzUkKRGAzXqDdTYbqCGJO0yUCPHQA1JyjNQI9NAjQwDNSSpp6E6JuxzuHyd0wFYPmV72bJlKi4u1pIlS5Sfn6/y8nIVFhZq7dq1Sk9Pb3O5TZs26dprr9WJJ54Y5gA71/rPpO4Olt9qaBxfGKixyUANKXrGYurbBiayxsRnCqfbjmYmPlPkG6ghmcmsaMqafgZq9DFQwynHn9PIGaM++cpZzpj4rCuZ2aepMlBDktYYqPGVgRp7DdSQzOSVibGY2qeJppwZZaDGQAM1ehqoIZnZX+xtoIZTjt+vHfAV1GjNGs6AAwA3sXzEaOHChZoxY4aKioo0fPhwLVmyRN26ddNDDz3U9pCamnThhRfqpptu0uDBgyN4UgCAqEHOAABs6oAz4KI1a2jAAYCbRBhYPp8v5NHQ0PIcicbGRq1atUoFBQXBafHx8SooKFBlZWWbQ7r55puVnp6uSy65xMhTBAB0InIGAGCTxZyRojtraMABgJv4I3hIys7OVlpaWvBRVlbWovSOHTvU1NSkjIzQL3RlZGSourr1LyS8/vrr+t3vfqcHH3zQyNMDAHQycgYAYJPFnJGiO2ui9hpwAIBW+CUFwpj/X/NWVVUpNTU1ODkpKcnxUHbv3q2f//znevDBB9W3b1/H9QAAUYCcAQDYFEU5I3Vs1tCAAwA38UuKC2P+fwVWampqSGC1pm/fvkpISFBNTU3I9JqaGmVmtrxU+ueff65NmzZp0qRJ3w3P/+0hqsMOO0xr167VkCFDwhgsAKDTkTMAAJss5owU3Vlj/CuoTU1Nmj9/vgYNGqSuXbtqyJAhuuWWWxQIhNPiBAC0yuJFSxMTEzV27FhVVFQEp/n9flVUVGj8+PEt5j/66KP1wQcfaM2aNcHHT37yE51yyilas2aNsrOzI32WB0XOAIBF5Aw5AwA2Wb4JQzRnjfEz4O644w4tXrxYjz76qEaMGKF33nlHRUVFSktL01VXXWV6dQDgLU2K6IhRexUXF2v69OnKy8vTuHHjVF5errq6OhUVFUmSpk2bpgEDBqisrEzJyckaOXJkyPI9e/aUpBbTTSJnAMAicoacAQCbLOeMFL1ZY7wB98Ybb+iss87SmWeeKUnKycnRH/7wB61cubLV+RsaGkLuXuHz+UwPCQBiR4SnbLfXlClTtH37dpWUlKi6ulq5ublavnx58CKmmzdvVnx8596/J9yckcgaAGg3coacAQCbLOeMFL1ZY7wBd9xxx+mBBx7QZ599piOPPFLvvfeeXn/9dS1cuLDV+cvKynTTTTeZHgYAIEKzZs3SrFmzWv3dihUrDrrsI488Yn5ABwg3ZySyBgCiCTkDALAtGrPGeANu7ty58vl8Ovroo5WQkKCmpib96le/0oUXXtjq/PPmzVNxcXHwZ5/PZ+16DgDgeh1wyna0CzdnJLIGANqNnCFnAMAmD+eM8QbcU089pSeeeEJLly7ViBEjtGbNGs2ZM0dZWVmaPn16i/mTkpKM3T4WAGKehwOrWbg5I5E1ANBu5Aw5AwA2eThnjDfgfvGLX2ju3LmaOnWqJGnUqFH64osvVFZW1mZgAQDaKaCYCqFIkDMAYBE5Q84AgE0ezhnjDbj6+voWF7NLSEiQ3+83vSoA8Jxw78Qd5l27XYGcAQB7yBlyBgBs8nLOGG/ATZo0Sb/61a90xBFHaMSIEXr33Xe1cOFCXXzxxaZXBQCe4+XAakbOAIA95Aw5AwA2eTlnjDfg7rnnHs2fP18zZ87Utm3blJWVpcsuu0wlJSWmVwUAnuP/1yOc+WMNOQMA9pAz5AwA2OTlnDHegOvRo4fKy8tVXl5uujQAeJ6Xjxg1I2cAwB5yhpwBAJu8nDPGG3CmrJTU1cHyew2No9ZAjQ0GakhSnYEaDQZqmLq/UzdDdZzKiqI6yQZqSGbec/sM1DAlwUANUxtup69LvcPlvXzEyIZ35CxrthsaxxcGapgai4msMbUtM6GLgRo9DNRw+n+/WU8DNXYbqCFJVQZqmBiLqc98JuqYyCtJ6u1g2fhDz3JQ5IxZ78lZzpjIB0mqNlDD1LbDxGfMngZqmPqs22ighon9IhN5J0kZBmqY2i7sNFDDxGubYqCGJO0yUMPpNr6Zk89qTvPSyzkTtQ04AEBLXj5iBACwj5wBANjk5ZyhAQcALuJXeCEUS0eMAAD2kTMAAJu8nDM04ADARbx8yjYAwD5yBgBgk5dzxtRXiAEAAAAAAAC0gjPgAMBFvHzNBACAfeQMAMAmL+cMDTgAcBEvBxYAwD5yBgBgk5dzhgYcALiIl6+ZAACwj5wBANjk5ZyhAQcALuLlI0YAAPvIGQCATV7OGRpwAOAiXj5iBACwj5wBANjk5ZyhAQcALuJXeEeBYimwAAD2kTMAAJu8nDM04ADARbx8yjYAwD5yBgBgk5dzhgYcALiIl0/ZBgDYR84AAGzycs7QgAMAF/HyESMAgH3kDADAJi/nDA04AHARLwcWAMA+cgYAYJOXc4YGHAC4iJdP2QYA2EfOAABs8nLOxHf2AAAAAAAAAIBYxhlwAOAiXj5lGwBgHzkDALDJyzkTtQ24LyUld/YgJO0zUKOfgRqSlGWgxl4DNUz9XXYZqDHcQI0EAzUkM6+Lib+PFD0bqTpDdaoN1EgxUEOSejhc3uk2JaDwTsMOOFxfrKuV1OhweRNM/F8xkVeSlGighontak8DNSQzp/qbeD6mssbE9r2LgRqm6qQaqJFmoIYk9TFQw9RXS5x8HnD6WYKcMetrSd84WN7UZzoTGWHqs5SJOtG0LTTx+d1E9nYzUEMy87qY2qcxkREmtsum9mlM7C/WG6ghOft/6PQzjZdzJmobcACAlrx8xAgAYB85AwCwycs5QwMOAFzEy4EFALCPnAEA2OTlnKEBBwAu4uW7BgEA7CNnAAA2eTlnaMABgIt4+YgRAMA+cgYAYJOXcybsaxK+9tprmjRpkrKyshQXF6dnn3025PeBQEAlJSXq37+/unbtqoKCAq1bt87UeAHA05oieLgNOQMAnYecIWcAwCYv5Exbwm7A1dXVacyYMVq0aFGrv1+wYIHuvvtuLVmyRG+99ZZSUlJUWFiovXtN3QsFALzLH8HDbcgZAOg85Aw5AwA2eSFn2hL2V1AnTpyoiRMntvq7QCCg8vJy3XDDDTrrrLMkSY899pgyMjL07LPPaurUqc5GCwAe51d4R4HcGFjkDAB0HnKGnAEAm7yQM20J+wy4g9m4caOqq6tVUFAQnJaWlqb8/HxVVla2ukxDQ4N8Pl/IAwDQOi8fMZIiyxmJrAGA9iJnyBkAsMnLOWO0AVddXS1JysjICJmekZER/N2BysrKlJaWFnxkZ2ebHBIAIIZEkjMSWQMAaB9yBgBgi9EGXCTmzZun2tra4KOqqqqzhwQAUcvLFy11gqwBgPYhZyJDzgBA+3g5Z8K+BtzBZGZmSpJqamrUv3//4PSamhrl5ua2ukxSUpKSkpJMDgMAYpaXb9stRZYzElkDAO1FzpAzAGCTl3PG6BlwgwYNUmZmpioqKoLTfD6f3nrrLY0fP97kqgDAk7x8zQSJnAEA28gZcgYAbPJyzoR9BtyePXu0fv364M8bN27UmjVr1Lt3bx1xxBGaM2eObr31Vg0bNkyDBg3S/PnzlZWVpcmTJ5scNwB4kheOGJEzANB5yBlyBgBs8kLOtCXsBtw777yjU045JfhzcXGxJGn69Ol65JFHdN1116murk6XXnqpdu3apRNOOEHLly9XcnKyuVEDgEd5IbDIGQDoPOQMOQMANnkhZ9oSFwgEAp09iO/z+XxKS0vTfEnREHH7DNQwdcpkFwM19hqoYervsstAjX4GaiQYqCGZeV1M/H2k6NlImXo+PQzUSDFQQ3I+lnpJMyTV1tYqNTW13cs1bxsfkNQ1jPV9I+nSCNYX65pfzwUK7/U80HZD49lioEatgRqmmNgG9TRQQzJzrQ0TOWEqa0y8tiY+T5iqY2KrlGaghiT1MVDD1FicfKaok3SuyJnO1vx63i5nf89dhsaz1UANU9sxE6JpW2ji826igRrdDNSQomefUzKTESY+B5jYF5HM7C/2NFBDcrYfXSfpbJEzkTB6EwYAgF1ePmIEALCPnAEA2OTlnInaBlyGnJ2VYKrrbuJIj6mjKyY65iZqRNMRABN6G6qz20ANU+9bE6JpLCZeWxNns0rO/z87XT7cC5HG0kVLbUj61yNSprYfJu6b12CghiRVG6hhIveiKTtNnBllKvNMnGFl6iwWE58HTPydTZ3hHE1nfTgZi9OzPcgZs7rK2T6Nqf+vJrZjpt7fjYbqOGXqtTWx7YimM62jZdsumXlO0XRvYhOf1UzdRbPOwbL1Dtft5ZyJ2gYcAKAlLx8xAgDYR84AAGzycs7QgAMAF/FyYAEA7CNnAAA2eTlnTJ3BCADoAP4IHuFatGiRcnJylJycrPz8fK1cubLNeR988EGdeOKJ6tWrl3r16qWCgoKDzg8AiG7kDADApo7IGSk6s4YGHAAgaNmyZSouLlZpaalWr16tMWPGqLCwUNu2bWt1/hUrVuiCCy7QK6+8osrKSmVnZ+v000/Xli0m7usJAIg15AwAwLZozZq4QCAQMFrRoeZb094rbsJwIG7CYAc3YWhdNI0llm7CUC/pIkV+2+4FCv+23deFsb78/Hwde+yxuvfeeyVJfr9f2dnZmj17tubOnXvI5ZuamtSrVy/de++9mjZtWhgj7VjNr+dv5CxrnF6EtpmTC+E24yYMreMmDC3F2oW6uQlDqDpJhSJnOpupnDG1bTeBmzC0jpswtMRNGFoXTTdhcPI3qpd0oaI3Z6TozRrOgAMAF/Hru+smtOfRfMq2z+cLeTQ0tPwI0NjYqFWrVqmgoCA4LT4+XgUFBaqsrGzX+Orr67Vv3z717m2qtQ0A6EjkDADAJps5I0V31tCAAwAXifSaCdnZ2UpLSws+ysrKWtTesWOHmpqalJGRETI9IyND1dXtOy/q+uuvV1ZWVkjgAQDcg5wBANhkM2ek6M4a7oIKAC4S6V2DqqqqQk7ZTkoyf0L+7bffrieffFIrVqxQcnK0fMkcABAOcgYAYFM054xkN2towAGAi4R7J6DmeVNTUw95zYS+ffsqISFBNTU1IdNramqUmZl50GXvvPNO3X777Xr55Zc1evToMEYIAIgm5AwAwCabOSNFd9bwFVQAcJFwrpcQ7tGlxMREjR07VhUVFcFpfr9fFRUVGj9+fJvLLViwQLfccouWL1+uvLy8MJ8RACCakDMAAJts5owU3VnDGXAA4CKRnrLdXsXFxZo+fbry8vI0btw4lZeXq66uTkVFRZKkadOmacCAAcFrLtxxxx0qKSnR0qVLlZOTE7yuQvfu3dW9e/cw1w4A6GzkDADAJts5I0Vv1tCAAwAXifSU7faaMmWKtm/frpKSElVXVys3N1fLly8PXsR08+bNio//7uTpxYsXq7GxUeeee25IndLSUt14441hrh0A0NnIGQCATbZzRorerIkLBAIBY9UM8Pl8SktL072Sujqos9fQeBIM1OhioIYkmbj8n4kaPQzUkMyMxQRTNxbebaCGqfetCdE0FhOv7T4DNSTn/5/rJV0kqba2tl3XMGjWvG28XlI4lxttkHRHBOuLdc2v52/kLGvqDY2nzkCN1m/EHr723Rvq4EzkXjRlZx8DNUxlXpqBGiY+20hmPg+Y+DunGKghmRmLqex0MpY6SYUiZzqbqZwxtW03wdT7u9FQHadMbQtNbDtMjCXWtu2Smedk5zL9kTHx/9nUNcSc/I3qJV0ociYSnAEHAC7SEadsAwC8i5wBANjk5ZyhAQcALtIRp2wDALyLnAEA2OTlnOEuqAAAAAAAAIBFnAEHAC7i5VO2AQD2kTMAAJu8nDNR24AbImcX1TV1seNouhC9iZsFmLo4pwkmLs5p4u/cz0ANycxF1E39fUzctMDEhs7EOKToOu3Y6TZhj8PlvXzKtg29JXVzsLyJi/NL0fX/zUTWmLjpSTTdhMFETpj6XGLiqwsDDNSQzNwQItFADVMXdDexvdxloIZk5jNFpMgZs7rJWc5Ey03LJHOfmU1sx0x8Zjb12prI8GjaR3Pyfm0WTTfti5b9IsnMTRhMjcXJa+v07+LlnInaBhwAoCUvHzECANhHzgAAbPJyztCAAwAX8XJgAQDsI2cAADZ5OWdowAGAiwQU3mnYAVsDAQDEJHIGAGCTl3OGBhwAuIiXjxgBAOwjZwAANnk5Z8K+BuZrr72mSZMmKSsrS3FxcXr22WeDv9u3b5+uv/56jRo1SikpKcrKytK0adO0detWk2MGAM9qiuDhNuQMAHQecoacAQCbvJAzbQm7AVdXV6cxY8Zo0aJFLX5XX1+v1atXa/78+Vq9erWeeeYZrV27Vj/5yU+MDBYAvM4fwcNtyBkA6DzkDDkDADZ5IWfaEvZXUCdOnKiJEye2+ru0tDS99NJLIdPuvfdejRs3Tps3b9YRRxwR2SgBAJK8cco2OQMAnYecIWcAwCYv5ExbrF8Drra2VnFxcerZs2erv29oaFBDQ0PwZ5/PZ3tIAOBa4R4FiqUjRm05VM5IZA0AtBc50xI5AwDmeDlnwv4Kajj27t2r66+/XhdccIFSU1NbnaesrExpaWnBR3Z2ts0hAQBiSHtyRiJrAACRIWcAAKZYa8Dt27dP559/vgKBgBYvXtzmfPPmzVNtbW3wUVVVZWtIAOB6Xr5o6YHamzMSWQMA7UXOfIecAQDzvJwzVr6C2hxWX3zxhf7+978f9GhRUlKSkpKSbAwDAGKOX+GFUCydsv194eSMRNYAQHuRM98iZwDADi/njPEGXHNYrVu3Tq+88or69OljehUA4FlevmZCM3IGAOwhZ8gZALDJyzkTdgNuz549Wr9+ffDnjRs3as2aNerdu7f69++vc889V6tXr9Zf/vIXNTU1qbq6WpLUu3dvJSYmmhs5AHhQk8K7doAbT9kmZwCg85Az5AwA2OSFnGlL2A24d955R6ecckrw5+LiYknS9OnTdeONN+q5556TJOXm5oYs98orr2jChAmRjxQA4InAImcAoPOQM+QMANjkhZxpS9gNuAkTJigQCLT5+4P9DgDgjBdO2SZnAKDzkDPkDADY5IWcaYuVmzA40Rx4dQ7r7Hc+FElSg6E6Jpg44T3BQA1TTLz59hmoYepyuU7fs5K5v88eAzVMHGkwMQ4puja6ex0u3/w+ifTDvZePGJnU/Pp/08njaGbi72TquTh9j0tmts2m3rsmdqPrDdQwtR2LM1DD1LY5nG1RW0x8tmk0UEMy8zcy9do6ec+RM9HBVM5E02cgU/tXJrYdJj4zm3ptTfwfiKZ9NBO5aSKrJDPvORP7aKa2cyZ6C6bG4qQOORO5qGvA7d69W5J0biePAwBs2r17t9LS0sJezstHjExqzppLO3kcAGALOdO5mnPmqk4eBwDYQs6EL+oacFlZWaqqqlKPHj0UF9d679zn8yk7O1tVVVWHvCU4wsNraw+vrR1ue10DgYB2796trKysiJb38m27TTpU1rjtfeUmvLb28Nra46bXlpyJDuzTdB5eV3t4be1x02tLzkQu6hpw8fHxOvzww9s1b2pqatS/Od2K19YeXls73PS6RnKkqFmTwjutP5ZO2TapvVnjpveV2/Da2sNra49bXltypvOxT9P5eF3t4bW1xy2vLTkTmahrwAEA2ublU7YBAPaRMwAAm7ycMyaugQkAAAAAAACgDa48Ay4pKUmlpaVKSjJ1/0o047W1h9fWDq+9rl4+Zbsjee191ZF4be3htbXHS68tOdNxvPS+6ki8rvbw2trjpdfWyzkTF4j03rEAgA7j8/mUlpamf1N4R072S3pTUm1trSuuJwEA6BzkDADAJnLGpWfAAYBXefmaCQAA+8gZAIBNXs4ZGnAA4CJePmUbAGAfOQMAsMnLOUMDDgBcJKDwjgJxjQEAQDjIGQCATV7OGRpwAOAi4R4BiqUjRgAA+8gZAIBNXs4ZGnAA4CJeDiwAgH3kDADAJi/nTHxnDyASixYtUk5OjpKTk5Wfn6+VK1d29pBc78Ybb1RcXFzI4+ijj+7sYbnOa6+9pkmTJikrK0txcXF69tlnQ34fCARUUlKi/v37q2vXriooKNC6des6Z7Auc6jX9qKLLmrxHj7jjDM6Z7AW+SN4IHzkjHnkjDlkjR3kzLfImY5BzphHzphDzthD1ng7Z1zXgFu2bJmKi4tVWlqq1atXa8yYMSosLNS2bds6e2iuN2LECP3zn/8MPl5//fXOHpLr1NXVacyYMVq0aFGrv1+wYIHuvvtuLVmyRG+99ZZSUlJUWFiovXv3dvBI3edQr60knXHGGSHv4T/84Q8dOMKO0RTBA+EhZ+whZ8wga+wgZ75FzthHzthDzphBzthD1ng7Z1z3FdSFCxdqxowZKioqkiQtWbJEzz//vB566CHNnTu3k0fnbocddpgyMzM7exiuNnHiRE2cOLHV3wUCAZWXl+uGG27QWWedJUl67LHHlJGRoWeffVZTp07tyKG6zsFe22ZJSUkx/x728inbHYWcsYecMYOssYOc+RY5Yx85Yw85YwY5Yw9Z4+2ccdUZcI2NjVq1apUKCgqC0+Lj41VQUKDKyspOHFlsWLdunbKysjR48GBdeOGF2rx5c2cPKaZs3LhR1dXVIe/ftLQ05efn8/41ZMWKFUpPT9dRRx2lK664Qjt37uzsIRnn5VO2OwI5Yxc5Yx9ZYxc5Q844Rc7YRc7YR87YF+tZ4+WccVUDbseOHWpqalJGRkbI9IyMDFVXV3fSqGJDfn6+HnnkES1fvlyLFy/Wxo0bdeKJJ2r37t2dPbSY0fwe5f1rxxlnnKHHHntMFRUVuuOOO/Tqq69q4sSJamqKpWMmsI2csYec6RhkjT3kDEwgZ+whZzoGOWMXWRPbXPcVVNjx/dNgR48erfz8fA0cOFBPPfWULrnkkk4cGdA+3z/dfdSoURo9erSGDBmiFStW6NRTT+3EkZkV7hGgWDpiBHcjZ+B25IyZ+QFbyBnEAi9kjZdzxlVnwPXt21cJCQmqqakJmV5TUxPT35HuDD179tSRRx6p9evXd/ZQYkbze5T3b8cYPHiw+vbtG3PvYS+fst0RyJmOQ87YQdZ0HHKGnIkEOdNxyBk7yJmOFYtZ4+WccVUDLjExUWPHjlVFRUVwmt/vV0VFhcaPH9+JI4s9e/bs0eeff67+/ft39lBixqBBg5SZmRny/vX5fHrrrbd4/1rw5ZdfaufOnTH3HvbyXYM6AjnTccgZO8iajkPOkDORIGc6DjljBznTsWIxa7ycM677CmpxcbGmT5+uvLw8jRs3TuXl5aqrqwveRQiRufbaazVp0iQNHDhQW7duVWlpqRISEnTBBRd09tBcZc+ePSFHJzZu3Kg1a9aod+/eOuKIIzRnzhzdeuutGjZsmAYNGqT58+crKytLkydP7rxBu8TBXtvevXvrpptu0jnnnKPMzEx9/vnnuu666zR06FAVFhZ24qjNa5IUCGP+WDpi1FHIGTvIGXPIGjvImW+RM/aRM3aQM+aQM/aQNR7PmYAL3XPPPYEjjjgikJiYGBg3blzgzTff7Owhud6UKVMC/fv3DyQmJgYGDBgQmDJlSmD9+vWdPSzXeeWVVwL6dnsS8pg+fXogEAgE/H5/YP78+YGMjIxAUlJS4NRTTw2sXbu2cwftEgd7bevr6wOnn356oF+/foEuXboEBg4cGJgxY0agurq6s4dtTG1tbUBSIF0KZIbxSP/X61RbW9vZT8FVyBnzyBlzyBo7yBlypiORM+aRM+aQM/Z4OWvImUAgLhAIhNN8BAB0Ap/Pp7S0NPVReNcO8EvaKam2tlapqal2BgcAcD1yBgBgEznjwq+gAoCX+RXeKdscYQEAhIOcAQDY5OWccdVNGADA6zrirkGLFi1STk6OkpOTlZ+fr5UrVx50/j/+8Y86+uijlZycrFGjRumFF16IYK0AgGhAzgAAbOqou6BGY9bQgAMAF7F916Bly5apuLhYpaWlWr16tcaMGaPCwkJt27at1fnfeOMNXXDBBbrkkkv07rvvavLkyZo8ebI+/PDDyJ4gAKBTkTMAAJs64i6o0Zo1XAMOAFyg+ZoJ3STFhbFcQFK92n/NhPz8fB177LG69957JUl+v1/Z2dmaPXu25s6d22L+KVOmqK6uTn/5y1+C0/7t3/5Nubm5WrJkSRgjBQB0JnIGAGBTR+WMFL1ZwxlwAOAird426RAP6dvA+/6joaGhRe3GxkatWrVKBQUFwWnx8fEqKChQZWVlq+OprKwMmV+SCgsL25wfABDdyBkAgE02c0aK7qyhAQcALpCYmKjMzEx9o2+PALX38Y2k7t27Kzs7W2lpacFHWVlZi3Xs2LFDTU1NysjICJmekZGh6urqVsdVXV0d1vwAgOhEzgAAbOqInJGiO2u4CyoAuEBycrI2btyoxsbGsJcNBAKKiws90TspKcnU0AAAMYCcAQDYRM7QgAMA10hOTlZycrK1+n379lVCQoJqampCptfU1CgzM7PVZTIzM8OaHwAQvcgZAIBNtnNGiu6s4SuoAABJ354WPnbsWFVUVASn+f1+VVRUaPz48a0uM378+JD5Jemll15qc34AgHeRMwAA26I5azgDDgAQVFxcrOnTpysvL0/jxo1TeXm56urqVFRUJEmaNm2aBgwYELzmwtVXX62TTz5Zd911l84880w9+eSTeuedd/TAAw905tMAAEQpcgYAYFu0Zg0NOABA0JQpU7R9+3aVlJSourpaubm5Wr58efCipJs3b1Z8/HcnTx933HFaunSpbrjhBv3Xf/2Xhg0bpmeffVYjR47srKc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",
      "text/plain": [
       "<Figure size 1500x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Three non-co-located soft data points\n",
    "O.par['n_cond_soft'] = 3\n",
    "O.par['max_search_radius_soft'] = 1000000\n",
    "\n",
    "shuffle_simulation_grid_arr = [0, 1, 2]\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "for i in range(len(shuffle_simulation_grid_arr)):\n",
    "    O.par['shuffle_simulation_grid'] = shuffle_simulation_grid_arr[i]\n",
    "\n",
    "    O.delete_local_files()\n",
    "    O.run_parallel()\n",
    "    m_mean, m_std, m_mode = O.etype()\n",
    "\n",
    "    plt.subplot(2, 3, i + 1)\n",
    "    plt.imshow(m_mean.T, zorder=-1, vmin=0, vmax=1, cmap='hot')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)\n",
    "    plt.title('%s path' % gtxt[i])\n",
    "    plt.subplot(2, 3, 3 + i + 1)\n",
    "    plt.imshow(m_std.T, zorder=-1, vmin=0, vmax=0.4, cmap='gray')\n",
    "    plt.title('std')\n",
    "    plt.colorbar(fraction=0.046, pad=0.04)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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