{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Detailed Look at the SceneWalk Code in Python\n",
        "This is a basic expanation of the original (subtractive scenewalk model)\n",
        "## Load libraries and data\n",
        "The data we are using here is from the Memory3 experiment. We import it from a csv using pandas.\n",
        "\n",
        "It may be interesting to note that the data is such that the bottom left hand corner is the axis origin."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from scenewalk.scenewalk_model_object import scenewalk as scenewalk_model\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scenewalk.plotting.sw_plot import plot_3_maps"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:38.469Z",
          "iopub.execute_input": "2020-03-28T10:36:38.476Z",
          "iopub.status.idle": "2020-03-28T10:36:39.562Z",
          "shell.execute_reply": "2020-03-28T10:36:39.623Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Data\n",
        "SAC = pd.read_csv('../../SceneWalk_research/DATA/Mem3/SAC_mem3.csv')\n",
        "SAC"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "        Unnamed: 0   Start     End     PeakVel      Ampl    sacLen  VP  trial  \\\n",
              "0                1     NaN     NaN         NaN       NaN       NaN   1      1   \n",
              "1                2    99.0   109.0  204.060792  1.493026  1.492857   1      1   \n",
              "2                3   739.0   781.0  381.255743  7.085431  5.909781   1      1   \n",
              "3                4  1009.0  1065.0  406.522941  9.542654  8.586285   1      1   \n",
              "4                5  1330.0  1362.0  481.183495  5.460476  3.963403   1      1   \n",
              "...            ...     ...     ...         ...       ...       ...  ..    ...   \n",
              "224314      224315  7963.0  7974.0  288.413440  2.362354  2.362354  30     80   \n",
              "224315      224316  8714.0  8727.0  292.242848  2.884906  2.882165  30     80   \n",
              "224316      224317  9108.0  9139.0  455.156418  5.037857  4.807370  30     80   \n",
              "224317      224318  9394.0  9426.0  317.557028  5.585248  5.585248  30     80   \n",
              "224318      224319  9804.0  9839.0  423.712189  5.753254  5.666740  30     80   \n",
              "\n",
              "        Img  mode  ...  fixdur  blinkSac  blinkFix  viewnr        dtc  \\\n",
              "0        86     1  ...      98     False     False       1   0.254938   \n",
              "1        86     1  ...     630     False     False       1   1.744902   \n",
              "2        86     1  ...     228     False     False       1   8.408326   \n",
              "3        86     1  ...     265     False     False       1  12.847886   \n",
              "4        86     1  ...     183     False     False       1  16.963105   \n",
              "...     ...   ...  ...     ...       ...       ...     ...        ...   \n",
              "224314  303     3  ...     405     False     False       1   5.811551   \n",
              "224315  303     3  ...     381     False     False       1   5.831994   \n",
              "224316  303     3  ...     255     False     False       1   8.625651   \n",
              "224317  303     3  ...     378     False     False       1  13.697656   \n",
              "224318  303     3  ...     169     False     False       1  17.375090   \n",
              "\n",
              "        imtype2  conformity  gazepathLen  conformityNorm  conformityHans  \n",
              "0             1    0.001490           23      -13.914239       -9.390677  \n",
              "1             1    0.002793           23      -13.007765       -8.484203  \n",
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              "4             1    0.000257           23      -16.449903      -11.926341  \n",
              "...         ...         ...          ...             ...             ...  \n",
              "224314        4    0.000953           34      -15.122182      -10.034719  \n",
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              "224316        4    0.000037           34      -19.824252      -14.736790  \n",
              "224317        4    0.000476           34      -16.125454      -11.037991  \n",
              "224318        4    0.000003           34      -23.234150      -18.146688  \n",
              "\n",
              "[224319 rows x 28 columns]"
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              "      <th>Start</th>\n",
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              "      <td>-13.996878</td>\n",
              "      <td>-9.473316</td>\n",
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              "      <td>1009.0</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>406.522941</td>\n",
              "      <td>9.542654</td>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:39.571Z",
          "iopub.execute_input": "2020-03-28T10:36:39.578Z",
          "iopub.status.idle": "2020-03-28T10:36:40.466Z",
          "shell.execute_reply": "2020-03-28T10:36:40.589Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the data frame we extract the relevant data:\n",
        "\n",
        "- positions of all fixations in the image for the emprirical saliency map. \n",
        "- positions and fixation durations of the gaze path we are analyzing. \n",
        "\n",
        "Both of these should be numpy arrays (not pandas) to stay consistent with indexing etc. (pandas is 1 indexed, normal python is 0 indexed)."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "use_img = 35\n",
        "\n",
        "img_x = np.asarray(SAC.x[SAC['Img'] == use_img])\n",
        "img_y = np.asarray(SAC.y[SAC['Img'] == use_img])\n",
        "\n",
        "use_vp =  np.asarray(SAC.VP[SAC['Img'] == use_img])[0]\n",
        "use_trial = np.asarray(SAC.trial[SAC['Img'] == use_img])[0]\n",
        "\n",
        "path_x=np.asarray(SAC.x[(SAC['Img'] == use_img) & (SAC['VP'] == use_vp) & (SAC['trial'] == use_trial)])\n",
        "path_y=np.asarray(SAC.y[(SAC['Img'] == use_img) & (SAC['VP'] == use_vp) & (SAC['trial'] == use_trial)])\n",
        "path_dur=np.asarray(SAC.fixdur[(SAC['Img'] == use_img) & (SAC['VP'] == use_vp) & (SAC['trial'] == use_trial)])"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:40.477Z",
          "iopub.execute_input": "2020-03-28T10:36:40.484Z",
          "iopub.status.idle": "2020-03-28T10:36:40.496Z",
          "shell.execute_reply": "2020-03-28T10:36:40.594Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the data we imported. Each blue point is a fixation. Each red point is part of the scan path we're going to analyze."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "im=plt.scatter(img_x, img_y)\n",
        "im=plt.plot(path_x, path_y, '.r-')\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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wWgYjhk3ip6ZteS1/MgN//lr23gLIZiD0F5zBedLzC4qon+YIvhSA4vdyUIqofCx/E70nL4+JYzkYQnW7LATCEuAJALOKzuoV+P4MAiPiL8PpkC3Qq8Re0WqPRxRlQ66fyenEDb1a+YSzXRBknZ45XbOYMqQzDhm7jpSPRQki6GLd+AtxfwaHEpMH4HCd+tw8ZCLTeo9g0Oav+GT6A7Q9sNN3zaOpdbhxyNOsa3EGLy98ket//DzgngIwsjrS1F8LVqPrSWaGJwd2DKFvOuwCTw4MTVOgBCWT3szqyNB4QETZ5HYy46RyYiYyjDBF9CYLkkNWmAwCh00ISDoEXsGQnpwka1+VHE9Gk1HJRWlq9U1+QVHAffxLtGlFEertAz2RgXL933vbBl5emEeau4xH+t5Dfsc+vt9S3WX8e/4kLtxWwGOX/5MZ3fpjFwSmDOkMYJgR5B9dGYmzPhaOyXAgPd/JCIuFcgJAD1tFK6TZn/mhd6JqsUmCIWmQz+R0Ir+giNy5GwPSy+qBJIQhVJApsRKUUpWqna+XzaFHeCgtBo2PHeSVBS/Qc9dmPuzcl4mXjqLckQJASmUFr34ymct//56n+9zOO+cM4s/J/cOi55kVfh4NaqAZOJnD6y0WSi1EsCNJqRqM/9ZSS9MaO6em9qQek4s0aYoNUO9EahyYOV2zyLu+c4CpQY899nCp21f/Um+CLTm7LcDg7lkB5phurTKYuHAz2eMWVy923jzmAjUsm25FWwIiJwVB2warZPPfV7chI4Y/y+u9rmPExqXMn5FL68O7AShPSuafOY+wqN35PL7ibcavnweiGFaEsFnh50oZCKON+mkOxbEh5xOwYJVUS1jI5U5WQvBkV6psDl67c+7cjUxcuJnDpW7VYgv+dnijyaP82yTZjCXo1XrVSpX5Xz+/oIixczaG0A+lOpbllVUBVXFWbT0UdFwVlR6RaUO7MGHBZk79/Udm/vdRkj1uKuw1ler981TLaftKfVQ/zUGZ284LF93CDy06Mm3RFBa/dz8PX3U/i9udT6U9ifuvzqXi0xRGff4u0y8pQzxnhGyIqCAEJDz0QbL39568PMR0YtSk4p8rXC2eQP45q8J2cBY8cYXs2JB2dIlC+00kWCaUGEMu0GbFL/sjinCzCwJVohhgNggnM1yaw0Zykp0jLndY9nV/SLZsNXv9mNkbwra1SjsDM6NHs6oF8F2r5/DgyunYAA8CUy6siZyUnkvOLCNVppcz10CNQOwqHuWFuZM47c/NvNt9IM/2uQ233UGSIPL00tcZXvAp73S/mqcuvSNAiAvAaY3T+X1fSUC/OWwCyUk2SioC+8BIm/QIdqWFUmrbtGoeerjZFyXzXqIG3MUTSiYUSwOPIR7L38TMNTt8k6+o2BUQMu6/7TeyhQ6mkw3uHt5gF/EWVVDKyw0ERGweL6uUtW1LxQi0iuHqndoOuxDgPPXfGcgxJsKFtGAeSUn37UxsiPzeoKZ82u5ilyJLY8Uv+3nu2k6Kwsd/8bqqysnDX73H7Ws/oevuQu6+ZhxFGY0Zf/lduGwOblu3gNTKCh7texei4LV0isBv+0pC2l0FIcJbatOs73bK7kwmLtwcoC3rqYQjfa+lIQfvtvQu1FIVoOAdmz8s4R4ISwOPEYwMZIneprQdT0tO0qynGC6CtXktjSxY2/LXvM1gf4DXPJCekiQ7adUcsU6HnVSHzVDofHp5KZ++ey8Oj5tP2/Vm6Mal/NT0dIYPf5Yqm51Mp4MjLrdqjm0t+D97v8JVvLDkZapsNh7o/wDLTzsHRJHcldO5e81c5nXsw0NXjcZjMx60FA70sm2MCNHscYt13Vur/xI9n1E0YWngcUbe0kLdGufuYhfThnaRHawSPQ68wstsBGvzoK6RydH91Baq3cUu8guKDNnTj7jcink81GzzqQ4b/c9qpmm79cejK96hxZF9DB3xHD+0PJOfmp7G1MXT+Md383j93CEIgnpNUT3w31191q43Pzc+ldc/mcw7857i/3oO5sULbiTvopspdaSS+78PSKl0M3rgg1Taoz9d9ez81DRkOWTp9J9o9Z9W/c54Ix67A4uFEiMYjZLUk2g/3FS4emtKGslBEcwCUUJmmsNw8n+1Arol5ZWK5x0udTNvXREt6qfqus9Ff6xjxMbPeOucQfzQ8kwA5ne8hEXtL2DMNzPptOc3ikvdEQdeBT/PjvrNGHxDHjO79OMf383jw/8+QpNjB3jtvKE83ed2BhR+wxv5z/qSYUUKp8OuGORkdEzpKVOop7Scnv5LxJz+/qkNxgQVHFGqJ2smLA08RtDL4vAfyFqaTp/2jQynXZVMHHoDbPRODj22aKfDjihiyGbt3x/+Gk6G00FJRaVmkQmX2yNrNw5GvbLjPP/py/zasBVTL7ih5gdB4NEr7qL7rp95adGLjLr/TVmWhhFtS84JWp6UzKN97+H7Fh15dulrLH7vfkYPeJC3zxlEuSOFZ5a9zlvznmbUtY9S5tBekJTYRXZB8Dkw5XZ4akJUK9Ol0q5NidUi57xXQ6Q7H7MRbNIJ7u9Y7A4sG3iMYDRKUu78YIFhtD5lsL2w61PLNO3DeoMn1OycUn7n3L7tNP0AUnRnMBPG7FzlwXhpYR79f/mGQTdO4aemp4X8fu72jcz872Nsv+4G2sydHvBbOFtntcWo7cGdvJ7/HKcf2Mkr5w3j5d7DuHbzCp7/9F+sbXEGtw1+gpKUtIDrSQI7S0c/+9NMJVqiVp1SJXqf3D2iFXATDxu42rvV48cxK3rUsoHHGdJLl/jX/tAahHKccL3CzN+52Kd9I5+NunmmU5dzT69ZQIkyZhcEtj53le+z2qKjJkTMZJsE48pfviHn56+Z1nuErPAGWN26Mx+cN5ibP/qAh27+G+eNvlV2YSkqdjF69gZGz95AptOhyupRShdQ1vZv/PrJF/xxz93c/+0suhf9zOiBDzJ6wFimLZrCjNmPc/OQiRxNreM7X+r5Pu0bAag6uP13XqLoXTS1Fp1wMl2ajUh3PkahNO+ktuh5zmjvDiwNPA4wqrEZDW0W8Nqai0trtFiQp39pvf1tGqwA6TnUruN/DSNalN7rq0HrGU8pOczn79zN3vpNGTj8BaqSHIo8Z0elm48/GEuT4wfJGfUGD950oeYuKJJqOPnrd/HDk1N4/LPXOZJah3uvfohM1zFe/eR5fm3UmhuHPMXhtIyQ82wCyGUuUOsLLa3ZSH6UeIa8m+lIjDSPjpm7A0sDTyAY9eKrrfTBk9VhF0DEp11LWkNKkk1Wg1Kb1MGOruDKPUo8cH8EZ/zTq0WZYTIRgPPaNmD9jiPy1xFFnl/2GvUqy6m/7GN+O+OMEK6+dB0RqEhycP/AB1n0/mieWjCVxxs3puhIWeh1/eCuEsO3gwoCS86+knWN2vJ6/nPMmvUIr116C6OufYz/y3+W/84azw1DJ7G/Tv2A0+ReiVZQjZY2qWR/lhs/JeWV5BcUxZwZoqUxBx/7yPwfKXVXBXzvvwtU6pOiaiZVn/aNFMeKlknKLFgaeC2A2kovCNA8o6Z+YEl5peHqK3JlxoI1x3AEaiQaiFkJlYIjQjOcDgTBm9v89q0reeyjF+DFF2HsWN123pvWLeSpL97k8cv/wYxuAzQ103DsoMFtqVNeSt6yV7ny55UsP7UHc866nCmLp7G3TgNGDpvEnnqNNNug5kjXEzkr9/6dDhs2QZCNAtXz7mOpMUv303Lg69mZOmwCVRBS+ESt1msksLIR1mJoZRiUeLZGQ5ehplai1kQyIlD9nZZ6MgbKTVizUpoqCs8dO6BTJ+jcGVasALtd8RlD+lUUeW/uBHrt3MSAm1/i91NaqbYhHJOCbFtEkZs2LOHRL9/iQFp93jn7Gu7/5kOOOOsyYtgkdmY2VW2DEmvJJsCInq101U6V8+FomWaMFNGOZNFXcqRLYf5ybTcb9dMcFDxhfu1RKxthFKHFhdXDlVVDTtcs1VqUwRVa5KB0vpRqQ6teqJG8LEqQUsv6c2Vz526U7Q+znD+yVX+qquC228DjgffeA7uXo6y0ZfaIYiCPWRB4/JoHKHGk8vLCF3F4lIWCwyaElUVPti2CwPSu/Rl58xSqbDbGffUuX5zek7rlJcyZ+TCnHtxV/cyBp0n0QKUSd1UizNConQreMSJXU1RtoVXjQ5tVCxZQLU6S4fTGHkRbeAMxuYc/LBt4hFBiIUxcuFk2n7WSXU4pl7X0XarDhivIXqcXTodd0fRxuNQtm8EuGHq1e7VIzgkLNofYzN1VIhMWbA7pi9IK5QAdI5Btz6qP4csv4c034dRTfccqmRjkzAtj+3bhhYO5PP/B44z93wwmX3xryHlqLBQI9SmIIj76ZIZCRsmsTCc3DB3MHS1a88CsyVy7eQV/1m9G86MHmPPhOGY9+x9aXthLdpczJowEZ5K9V3qGcMxaSnxopWuFw2JRi3Q+WuaW9QucCLAEeIRQordJ+ay9glc5/FfOJldU7CL3o414PCJVvnO8f0lOS70CVSs3iUDNRFJz+qjdy5tHO7Q9wRNXye7o/71RW7see6V/e2bN/JKc1x+Cfv3gjjtC+NhKibNkHc9jbmX2T98y6rv5fHVqd9a0OkvVBKDG/fbX3IqKXdgVtkx92jeqacuEq2HqVNo8/DB4PKSUFnPvU6Pg88/JkTHZGE0JLMF/TIRjpoNQoSxpzHJXCicaVO25Yim81cr4RQOWCSVCqGkLLrdHcUsl5QQZP3+TrGBz+wlvf9RLdbBtcn+2PneVZk1HuyD4zCFK4cxK0WPBULpXVqaTPyf3p0phUhvVptT43sG1L50OOyN7tdI9aWxVHh6a9RykpMB//kP+ht0B4f/FLjeI+Io7yKUvgBqT2JjZG5h25T/Y0TCLqYum0j6lUlV4B99LLYo02DkmIcAMIggwdiysXAktqjMmHjoEl1wCq1eHnKsnpF0OLreHsXM20mbcYlXhrTYeg4WyksZstHCD1K+JAIfNm80zlrAEeIQI11bbPNMZVnCKv7DXmpDSZJM0P5fbo6uyipzQ1cr/odQP/t9rVUzX0qTqpCaF5IZ5JqcT6Sn6NpJ3fj+f7rt/gddeg6ws2f53V4mkJSf5fAEQWFj4sfxNAYL4L4+dB6/JpamrmM8KZ5HTpbnsvc0KRJJdEM87DwoKvLsKgCNHvN999VXAYVJ+nXAgFaBWguSofWloF115YpQWdhHtqlL+iGaAl1HUSU1i9OwNtB2/hOww/V1GYQnwCKElRDOdDsUBHWnEmjQhlRyHWZnOAM0P9Jkb5ISxVnItPQme5CqmgzcaUBKMaigudcs6WvX0Y7v92xjzv5n8dO7lMHy46nnS93Jl2uScfWsbteWtS26C2bNhxoyQ6xnNvqgGRYXhlFNg8WJ+vuuhmu/69OHb12aGHJrmMHfaB+fv0UrCBsrPobWrDEY8E1kFQ9ptB/tdoinELRu4TijR3/wddMGmEElYudyekHzZOV2zDOcyAa+2GtyW4T1bMvuHnSEV4yXbtxENRY01oRaApCdARymdQLHLrSspl5qWrxoN6XEzbdEUjqTWYVSv23how25yumYpOgql+xjpuxc6X8Odpb/i/uddDPvJznohIyDhk1FIAVn+Tl/NZFMb9zC+QR+6DMvkg9mPkSRWcd49N7CmopxeY27z8y+E5wwPhhJdVBon/umF85YWBhynVNXIKGMnXLt+rBDthFYWD1wH5Cqr24AMmXB1tUhFqXIJIFspRUL9NAf9z2omK5SHnt0yhK/rsHkXh4CIzOpAHKNly6LFY/VHl4nLDAcbaTkHcz/aqGhTHrvyA+5dPZu/X/s4X5ze01fxXu4c//dqpN+yMp0MalDBqHsHsyujMYvbX8Dq1mdRkNVB93WCE0tB6IKo9p2/IGt0/DDvzX2Sjvv+AGD9hKncm9JZl7ATBMhI9RauUMqposVt18PxjiSIx7+YSLAz1Omw061VBt9uPWRKLEGkMCOhlRXIEwH0CJzgwRlOJKEk4KVIrkgzEKpV9lFrQziDzchk1FuhRYKesOQzHv80JCwaoMvuQubNyGV+x0vI7T/a0H2NoHfbBny79RD3fzOT0atmIQKVNjv3XP0QS9v11n0drYUqWCg6bMx0gh0AACAASURBVAIIyC5etioPjy//D7euWwjAq+cO4cULbzJ0/3CDbfRERUrXDyeTo1LErP9YUavhGUuYkRvGyoUSAfRoi8FbpXBscyKBLAM5k4URLq9SZR+HTaBSFGWrm4fjlNXKQRE8SfXCSFSenPBOdZcxZfFU/qrTkKcuu0P3fcPBmj8Oe/Ol2B1UIWBDxFHl4c3859jSKJvPT+vJF6f3ZFPT03w1LuUgt+X21zaDoZaLpspmZ+Jld7KmZSfezH+We1bPoXvRFoYPfy7gOLUyenpMY3JCWE/xBSO5S/yhNzNiuNx3MxGOWcgILAGuASMOCP/BGa5tLtykQkrHKk1AMJ7QXwlaEXXBk1QPBCHwGuHYEB/6ejptDxUxYugzHEtJN3x+SJtQdgJLWt6aVp0oT3Lg8FRSabPzYee+nLF/G3evmct9q2ezt04Dvmx7Dp+f3pNvW51FuSMl5FpqQi4cLG13HheN+jdf/3sU5+7YxLbnB/C3sR9TkeTQtUiq+T6UhHCmTH4dAISaHZhc1kQ9NmO1ORK8CMTbRh7tep2WAFeBUY6pv3app3iB1jXkIOf8kdtG663sY0YiITVtK1yal7Q70KuVZQY5JM/d/iO3rVvAe90G8G12F1331CpQ8Fj+JkVnq+SkXp/VgZHDJtFrxybWtOrE+qwO3va5jnLxH+u47LfvuHrL14zY+BkuRyors7vwxWk9+fK0czhUnRrWfwyYRZPbXr85l0xczPInveaxX6cM4vrcDxh5bZ+IBIzS4p2SZJONAPbf9SltHrQErpZQdrk9jK52nAZXDYolsvwUqGjBsoGrwIgdW06TkUtNavQactAKu49VQVUJavZOtVzeRqIo9TjNJEdznfJSPnvnHirsSVx16790lSCzCwK9Tq0fknrW6bAzuHsWK37Zr5wREq/vQq+gyK5j56uzBf54exbOzxbT7NgBqhBYl9WBr9v1osvdN3HZoAuhOo+LagUjmcVbicHy3LWdyOnUBBo2hKNHvT98/DHk5Gi2WYKebH5Qk0BKGpNqBSaUzjVSHUgJ0vszWnowUphdKchyYoYBtcnTu20D1vxxOGBQyjnb1OyX/ohV/uBoQM3RpfTsWiH+wdDjXJWyNk7+9F9cv+kLrh/5vE8D1gN/Ya1U91EJ2yb316wZKd0jwEm4fhf57y2m8/qVXPnnD7Tf/Zv3wNNPh6uv5q5jLViacSoeW2isgVG2iuSPePHTLUz+dy7nb6+2D48ZA88/Dw71iNb8giIemLNBV2i60fcrd77Wgq332uGG/4cLuyAwZUh4RTyUELYAFwThHWAAsE8UxTOrv5sA3AFIHrdHRFFcotWI2ibAlTRLAUgKypkhQakCSzzq+cUSSmwCPc+td6cTvMjJ3fObl9/nxfcf5fVe1/HCRbf4ztWr7QcLDj1tUxM2hlkWO3fCwoWwYAEsXw5uN4ec9VjRtge/N8jC4fHwTZuubGp5huFKPwHvQhSZtOw1Rm74zPtjz54wdy60bBnSbimHut5Me5qFNHRe48/qRTFY4/dna0W7Vmo4eEll9xAuIhHgFwLHgelBAvy4KIovGmlEbRPg4Q4OKce23PX8B6PE9zZanTsRoSaotISY0S2xUlX1Zu4SlvznLv5Kqcs1N02jIsnhO6dbqwzfjklNI/PX9LXysIPXXJF3nbnalg9Hj8KyZSx8+v+4+JdvqVvhogpvBfuRwyaxt2M3Q/S0kMVIFHlo5fvcteYj7+fMTPjwQ/KbnhV3oeh02BCQZxdJuMFPiIer6ZuN3m0bMPOOc02/btg0QlEUVwqCkG16i+IMvQInw+mgvNJjKKOZmn2wvLJmQB4uDYxA1OuwMwtmVUPRooNplZALZsqoaXz+zJRgAfPIkldJP36EBwY/6RPeAtCtVQbrdxzxCW2PKGpmwtPrwE5PTjIcfKK7v+vVg+uuY9qyQ1xQ+B0i3kAjh6eSXjs28YYB8xDIOJsFgRcuvJmypBQe+GYmFBfDVVdR3GckFT2GgIzZJlbQEy0667udPJPTKSDyM54LT7Sq8ahBlw28WoAvCtLAbwGOAmuBsaIoHlY4dxQwCqBVq1bdt2/fbkKzI4Path5CNbtwIBdmbMRUEO2isGaadPQGbRiFkg9Cyqbi/9uALSt5dcEL5F1wI6+dNzTgeCWNO1iIS5+N2EyDnXVGanzq6u/vvuPwpX0RKitxVlZgr/LgticxctgkNrY4Q5G/LQfV95S0HnJzfd+tbtWJ+wbmsr9OA139EE/4512PpjbudNhJSbIp5mmP5pyNyIkpI8CbAAfwjvengWaiKN6mdZ1EMaGoDWQIL2m9EvSwGIJhRuitFswUumqCNpLn0PueGh0/xLK372Zb/eZcd8MLsg4/JUjC2ggjxh+ZTgfllVWagjms/v70U7juOkrqn8Lga54g7ejhEHqi2j2DobmIvPYa3HOP77f96ZncNzCX1a07a/ZDvBHsezIa7asFyf+ydvuhuBQyNrWkmiiKe0VR9IiiWAW8BZwTaQNjCTXestnZzVxuDzPX7DC0KJhVTkwNeiLl9CC/oAibQjbESJ9DLsOhVICipLyymi4nMvmzV0itrGBs/zGGhDeom1O04LAJvoAjf8jlVNfb31Ku8bH9H6BywACKW51K+trv+Mff+7G3YzfeOHcIG1ucEXIdPaXINDMF3n03vP02oiBwMC0Dty2JGbMf595VsxBEcxJgRQvuKpG8pYXkFxTR9allmscbyXoogG+RnbeuKGSsSJ9jkX0wGGEF8giC0EwUxT3VHwcBP5nXpOhDKRCguYoGnul0kJ6SFJZ2bkQ4SFkEzYSc7VWrD/Red/z8TbLmBjNCiP1t48FJi4pdbhw2gVt/Wc6lW39gwqWj+KNhi7DuEzbBTIWZESyY9fR3fkER4+f9yC3fzObhr9/nm9adGT3wcR7b4wnwI7RR0C71LL5a/ghuuw3B6aT+jTfyR6NWfNW4DWO/mUmPoi2MGTDWF2yUiJAEqJb5UxLIbccv0WUqM5KdMtrZB4OhqYELgjALWA20EwRhlyAItwMvCIKwSRCEH4E+wJgot9NUqOWuVvptwtUdNU0LRnMZy8GIU0wP5HJaj5+/iT7tG+lKvK8GpQFtFwTdAUnBxRKCiz/ndPUWW87KdIYI2iaH/yL3s//j21Zn8X73AbrbbRbcHlExF3vwQqgnX/qLn27hoc/e4OGv3+eTDhdx6/UTOGBLZcKCzarX1vreMIYPxzZ3Lqcd3MWlGR5eufRWeu3YxOJ376P7rp/NuUeUoMd3JfWTHuHt/4707k5jmaNcU4CLojhcFMVmoig6RFFsIYri26Io3iiKYidRFM8SRfFqP228VkBtK6m1zVQq35XpdChu+Y3giME0q1pQCnVe8ct+33OCV+hK2oPeLaDSQK0SRd0Z5YKLJQQvNFJbgu8liFXkLXkJD5B71WjVBFESsjKdious0fckwSOKIaXe5HKqa5ovyssZ98FT3LpuIf/pcQ2jB47FbfeOtWKXO+Cd6FkMwkHAglqYwbdT34aff+byjcsZde1jlCclM/vDcdzx3XxkM6HVAvj3k9JYsAuC7DvSu0DGwgQq4YSOxDSLJhd8zeDc4P4OFL3ReKkOm+z222xvtpaDMRI2SjiOOaMsAelawfe6de0nPPnlWzw7+EHmdLpcV8bIl4Z6c6LI5ZJx2AVVzrES6qc5OF5eGRLKbogbfuQIDBoEK1Yw6eLbeKvntSGHBGcMBGOpE8Lh4jtsAn12/8RLHz7BnrqncOegR3ngfzO46tdv+fy0noztP4ajqXX0PWMCID3ZzqRB4afK1UNTjJZD86QLpY9m5KPRhUEpd4me9kW6CGkJ2UjYKFp9rGcx04L/QiMVYGh7cCeL37ufb7O7cHT2PBCEkEVVDhLjJCXJFsDHDxem0Mr27IErr4TNm1n75BSuO95W132V+jhcCqMaxbX7rp95b+6THHbWY8SwSVz2+/c8suId9tZtyN3XPMyPzf6m/ZwJADPmV/Dx2Q2dISk11O4XLk46AR4tbrKZCEcrMjootK4RKQXQSAh9OGwPKapV2vl03v4T/1qYR53yUvrd+SYP3don6vxffwRrWEoZJ3X1X2GhtxDx/v0wbx707UvXp5bpClmX7q8Wz6AnkZR0Ha2I07P2/Mr0OU9Q6khl5LBJZJQd59VPJtP4+GGeueR2pncb4M0BnOBQy30OxgS6Ho3cLHlz0glws7jJ0TDD6IVZi5DaM+i5Rzh9EE5FIjlI5oi8pYU02bye2R+Ow1HlocKWxLARz8mGk0dLmGdWR4j6l9FTS9al+o6++w769webDZYsgR49fG3Xy6RQYrbIcdPV4LAJmrsXgA77/uCD2Y8jCgIjhz7D3joNmbp4Kpdu/YFF7S9gXL97OZ6SpuueiQA91YccNoHkJBslFd7v/IOG9I7xbSbEdJjKA68NMMNTr8TgiBXP0yyutsTkCK7mDtoOsXD7wGgbM50O0pNDOdxuj+hbPHrt2ERSlXci2cQqeu3YJHsf6XnN1AfTk+0Uu9wcLnVHzuZZsgQuuQQyMuDbb33CW2q7v7NTjeWi1MfFLrchM5Ue4Q2wpfGpDB0+GY9g47+zHqHF0X38ffDjPHfxLfQrXMWC90fToboGZ22Av9O+9+TljJ69IaTf3FWiT3iDt28fmLOB/IIiXWNcwFhRGKM4YQW4GZ56rUoz0UbU6WJosyPC7QOjbUxPSaK0Ql7oSJr/mlad8Ag2RMBtT2JNq04hPGp/GmKGAmMoHJTItC2YzSPLLgnGe+/B1VdDu3awahWcdlrIIf4L7pQhnWWZTX3aN4op20HC1lNaMmTE85Q6Upg16xG67P6VN3tex/Dhz5LmLuPjDx5k6MaltYalIi3ETTav567Vc+hWtEX2uG5FW3y/V4kwceFmXf0vQlTlRa03oUSSBU8L0QoRV0NwIq2SisqQSjuxTEEbbh8o2d6VNEM1k4C/vffRRf/ihg2fMnzoJDac3i0gTUGwjV2usIHZ0D0WRNGbc3v8eLjsMpg/H+rW1XUPucIgUoqGeFWbaX50Hx/OepRTSou57bon+b7lmXRMKuPxWZPo9UcB8zr24bEr7saVrF1MwwgEIEnB5GMTIMOpUMpNAXZBYMBPXzJl8UvYxCpfpkf/VAW9tm/k/bkTAvLQrM/qwEtDu2j6DqQ2R6sqfa3WwLW292qmA/9rBAePSFBaYW2CEJVtUfDzFLvcIHqparq0uygg3F2AkmavxL2VFli5XVOf9o18O4G1rc4EwNasqU+ASUI/eEq7PSJ1UpMCuO5mQ5cWXFUF99/vFd7Dh8PixbqFN3gLXQc/m7/2b+S56qc5QvpYolH6Q+uKu+s1ZsiIyeypewrvz3mSC/5cT9cef2PxC+8wrfcIBm3+ik+mP0DbAzt1t00PRCDv+s4h8Rj10xxMHdKFgieu0Gc6E0UuKvqJ/8x5gpcXTSVJrPJlerz2p+WMXTmdsSun8+/5z/DBnCdI8bhJEqt8mSDBO8aV4kL8Ec2dUq2uiam2vdcj5LTSoMp5+cEbvBGNtK9yz+OuEklLTqLgidD84rGAEtNBjylKKWxb6XrBaWXlqIc76jUG4J42STz4y35N7bO41B3Qd/kFRWHVKpWDANr9UF4ON90Ec+Z4K9+8+KLXcWkAar6QnK7GKq8Xl7plsydCYL/rcc7tq9uQYcOf44M5j/OfeU9xl8fNl6f1hPNHsLbFGby8MI+F00fzSN97yO/YR3cb1SDVmVSbd2rO3XpJAmd9/yV3r8unQ1Ehh+pkMrNzX67f9AXJVR6qBIERGz5DwJsjZ39aBl+2PZt+v63Bg+Az3UmCe8LVHVWdzlZVehVE6uTTWgCkQTJ2zsYQGpZ/4VSzmClmOS3NhJxQjeR5jV5v8Y97At7RzoymAHy2aA1F3bS3pZlpDnpPXh5gkjLLoCKisYAfOeKtN/nVV17BPXZsWPfRyqNipPJ6cxUBqCd7InijYFMqK0itrCDZ4+bRK+7m9fzneHP+JBa3v4D3uw9gVXYXrrrlX7yyMI+XFk3hnJ2bmXjZKMqTknW1Uwl6hKGc0tFAdPNuaQGd574Df/4Jf/sbvPkm/8rsxnvr9/JL4zY8/fn/8VOTtnTb8ysAlYKNd3tcwwfd+tPvpaF8fWp3Xj1vqLca0tUdgdDxnJnmQBS9EdWxYK3VGgEejYRMSoKxqNjly8OhpeGYWYTBjART0YBmAiSTrie3IwrG/vRMypKSySr+S/M+dpvA8bJKn01UT7SmEajmvvEL0OGDD+CGG8K+j9YuKLdvO8bP+xFPWTmpleWkusupW1VBqrsCR0WZ77sM0c1NnRvDm7+BywWlpTX/+//tcpG/9zBFuw+S7Paem1pZgVP6v7Jcsa1Xb/maK35b7bMTjxg2ibH/+4C71nxE5z2/clfOOLbXbx5WP+jdt/gL1fKiPdz982eMXLeI5CPFcO65MHWq15Fss/H55OUAfFudMvfXU1rTbc+vAdp2qtv7vF+edg57O3YjL0gomz0/jKBWCHAlU4ecE8fIlkVNc/EXyloajpLZxqgTNRJzxYkAPdneEAR21WtMiyN7Na/nqRKJlnsv+L34v+te7gO8Pesx0o4ehk8+gfPPh717VQWm4ncuFzmlpfTcc4idRQcRXC7qim6ykkXqTq/w/X6Ny4VQpSO6dF7QZ5sNnE5IS/P+q/67kdOJ2LIxhUcqOUwSrqQUyhzJuJJSKE9KweVIoSwpGZfD+3efrWu5+uevsSP67MTrszrgsdl54aJb+KFFR6YtmsKi9+7noSvv59P25xvu8yoImWdKcyynrouc7XO9rJ+yMrjmGm/Bit69A64pKXH7qgtXFKfVA7zC+v96Xcf6rA60PrwbgJSMeuwudvlYJfES2v6oFQJcKyFTuNv73L7tfOHZwfAXykq2cH/I5XVWs6/LwWxzRW2DXlPRrowmtDi6z9R79/tlFedvK+C3U1pRlNEEp7uM1MqKau3Vq3V6NVCvJnt2EyctNwMuFwf2HabtnkO84y6n3YEdgRfuHyb7QBKq1f83czpp1igNnBkhwhanE8H/c9BvqP2WnKwYQdm4+l9+QRETNMb/rowm9Pv1WxyeSp/m6o8Vbc+m/y3/4tVPnueNTybz7q6BPNvnNl/CLr3wHyNyc2zWK3M5Z/symq/4DBwOr/9h7Fho3172epJydiw5jbKkZFIqKwDY0Lydj4nirNbAd7uFALIExF+I1woBruXECaYNjpm9QZfwy+maFVLxWu6+wXmp5RBs5gjXwRrP7Vi8oeZ8OlZW6fND7MxsQudqO6UZ6Fa0hVcXPE+SRtGCcruDckcK9RrUgyM1QnBHcTnFzgx6HaipoflF27PZ3fI0brr0DGMCNS0NUlMTKixdGo9qlLn1WR0YOWySYsUggCbHD/LlaWezK6Mxt65bSNfdhdx9zTiKMhrrbotNEGgzbjHNM52UVlTicnsQxCou2foDo76bT89dm70JtsaPh3vvhaZNVa/nr5ztT69PvbLjlNsd1Csv8R2T5i4DwOWooUTGOu+3EmqFAFec2H4Oqsw0B8fLKn38UL2rpFr6Vn+hLAlWJX5zsJkjER2SiQ4lE9KEaoeR9NuujMbULztGnfJSU0K3e+3YhFC9OFQKAjO6XsUH3QZQ5mcqKEtKJiUlWZbGOXjcYgZv+oIL/1zPpiZtvXm80+sjADc9FqiBB275BXL7tiLnb7ETAuHGRuR0zVJUYKQkYeuzOsgKbvAukrNmPUKSp5KKJAfPX3gj/1wzj8Xv3ccD/R9g+Wn6inpJi3hRsYvkSjfX/7yCUd9/zOkHd7KrXiOeuuQO5px1OT9Nul7X9fyVs/3pmTQvO8KxlHTqldUIcMkG7nKkBJwb7bw7elArBLiSCeNwqdvnoFKqYD5x4eaQauf+uSyUFgclipheM0eiOiQTGXr6Nm9pIbvqNQGgxREvewC8XGYEQoKeurXK4Nuth1SZJ2tadaIiyeHb/i8442K2NmwZclyZ28Pa7YcC37Uo8vCGj/nH0rf5X+su/GPQI5RULyrB7zocs5qZiPT+Sgvs4O5ZzFizQ+VM7yKZ7HEjAMmV3v8H3PIyr38ymXfmPcXHZ1zM1oYt+LZ1Z8VFQEK9suOM3PApt6xbSJPjh9jc+FTuG/ggS9qdT6U9Sd25LAPfrve7DvD77xxr1IDGYpkvuKw+XtniSgoU4FKYfDy18FohwINNGEay2vkLeX9TiZojVABG9mqlaqvWemmJ4JCMZyKucKHWt77ffqgLC56nc2UxhdQEAYG88PfvB7nMfHq2/+Adc5KgeianE3g8MGYM/1j6Ngs7XswDV97vs+nKvetI4xYiRaT3V1tgF/+4R1aJqp/m4Kir0rtI2h2keNzYEbnst++Z1bkfg2/I4+UFLzDo56+oAu5ePSckElJCs6P7uW3tJwzfuJQ6FS5WZndl7FVj+Ca7i8/kFNEca9YMvvmGum3acFnDVF8a4+XffgZAqSMwqlQKk7cEuA5Ik9esLHdg3BFqRCDG2yEZb20vHOju3+xsAEY28fBNdVIniY8vlwHQf1FQqieptv0PxqzvdvLMlX+DG2+EuXPhgQfwjBhN489/U217vM1qZtxfaYF9cmBoQIvTYefJgV7z15jZIsOHP0ve4mm0PbybbnsKWfLufdxTnU/8it/W+CIhJQaLhA77/uCO7z9m4JaVCKLIwg4X8tY51/Jzk1NJc9jISk8xNMcUx1nTpnDgAJx5JhQXA975e141bbIsyIRitO+igVojwCWY3WHBjlAlhMsqUcslHE3hrpWEKpr3DufZ5Pp3zOwNrN1+iGdyOgVeMyOV5alOClZuoKhPD9/xo2dvYMKCzb50n3JQMm0l2wUqZNhIckgrO+7N4+0XoJMD5HQPNbvouXeszGrRvL+WwjJ69gbWZ3Xgn4Me4dN372NtizNoeuwAsz8cx4dd+uGx2RGqc42sadUJZ5LAFX9tZvCXs7hwWwEljlSmdxvAOz2uCXB6JifZDadWVpzHksOzvNwbhIVXPqRV28BLZQR4vE2itU6Aq3GyHXaB9OQkXxRUSXmlZvCG3hdg5vY3FtqxWpBSNO8t92xygjVYyJeUV4b0rwjMrDZZ+Ju5io6Usa1OI5oeDg3mKXa5GTN7A6Nnb5Ata6Vmx52/bpdmWbXGxw7y/twJcHgnzJgBI0fq7hslX05JeWVMbKnRNuupKSxZ1fP210bZzD7rcq7b9CWDbnyRu1fP4aaCJb7jvmx7Duf/WcDTn/8fHfduhSZN4NlnOW/vqRyRKd9mtIas0jweO2cjTZs46AVeDn61AG+e6fTRCIOdmODNCqmFaCprtS6ZlVzCI/Da2vKu68yGJ6/wJa+acHVH2WMlGBm8Zm5/Y5GmVmlhkooXR+veSsE4xS63L9GYXBIypYVWsjsHX3NnRhNaKgTzSHq0XO5yuSRbkh9ES3ifenAX82fkcuqxvd6EVAaEt/+966cFcp/9+yaaUEowFokwUUsG54/cvu18xZ+nnT8Stz2Ju9bM5a6c8Tx52Z2+4wYUfsMDqz4ko/Qok699ALZtg/HjqdNUXlDKjXO1NinNV48oMvXHo94PLpfPhJLbtx11Kr00wrKkUAE+c80OHsvfFPK9f1uiWVOg1mngRmzLwcfKsVD0Dl4zt5+xsIUqaVtKwRhm3VvtOv4LRaQpUHdlNOGcXT9rHie3SwrWFHtPXq7Zni67C3nno4kkO+ykfLMSuncPq90SHS/Y4RcrZ6aZcQZGdpLS5wkLNrOfBrzZczAPfDOTt4t+4f3uA+mw70+G/bgMAA8CH3btx5unX8K4VK/jUO/uQatNajv4XaneKExfFKzbTU7XLH47NQPX9ymy3Hxpl9ijdQPZfo2247rWCXAwNgjNGrBmbj9jYQtVWuiUuLxm3Vsr7YBZC8WujMbUKy+hXtlxzcroWvfU+r3P1h947ZPJVDVpSp0VX8gWYTCCeDszzYJR4RQwF0suhtOXM3/rPHp3fIY5Z13O0B+XIQIVSQ7WtDorJA5Duqea4qbVJrWo6gNp9atPqH4PR49Cw4acni5AZj1F9puIN+GdfzslRPtd10oBHg+YySqJFcVQbvFau/2QLGdXjy1PD7TSDkiTUk7IpzlsuNxVuiii+xp6EyK1OrKXnzQEuNbipLboXP/j5zz32Sscb9eRzK8+hyZNIrZpxtuZaRYiEk7p6fDMM3D77Uwb9Ds3Z5/JT01OpUHpMe695iG2ZJ/Jc0HzQY8yptUm6Xy5DKMVSQ6OOOuSIQnw4mJo2NCrjaelqY4TjygyevYGJi7czJMDa3w90X7Xtc4GHk/kdNUuEKH3OmbYIvXaH/2x4pf9hr43CiU7L9QsUkqFG5699iymDe2iWQ/SLggMGuRNStSiWD2plZ6FUdavIorctXoOeZ++zOrWncn8fpVPeAfbNMfM3kC2gXdgRrm/REDEJf9uvhk6deKcN1/g+QF/Y2vrM0hzl7G3Y7ewbfN62pTTNUu2VJ3TYUdo1qxGA692ZEoCPLdvO81iEYdLA/0Z0X7XCa+B18ZgFD2I1LQTLpMlFtt3/7QDWlGVcr/5M1XkdirPXduJPtneaMczKg7xmUI75FgoSu2FGq3MVuXhiS/f4pb1i8g/4yKmDR/P19UVdOS26MFOU/9rqt2vto/riHeSdjvk5UG/flz97Scw7BL4fhGr/n4WnHJKVNuk9A7qfdsC/qjOs1PtyKSkBNLTyemaxdrth0LK2wVDrqZAtN51QgtwOSGV+9FGJizYHLOE6YmKcJ0jsdy++w9guURjWvxd1cEvilCvHv3rlfNGkHM2nLqh0rFPzlnHpAV5DCj8hn+fPYiXrvg7z/Y/03ec1kKn10FlpjMxXjBFOPXtC1dcAU8/Da+84v2usDBsAW6U5BDyvX/yqyANHLwRuD1aN5A1wfjDf5xE810ntACXLTHmEX2Us3hEFybKjiBcQDltxgAAIABJREFUTToeIf6R8N4VB78gQJs2tD0eWUrhgHudWofeX0ymUeFqJvW5jSVXjOTZoGvpqX5TVOzyJVk7EZQMtTFvinDKy4MuXeCjj7yfCwtD8nYbQURtatas5m9JgJeUQKMaH5F0bT2+nmgjoQW4nm19LHNJJFJ4eriadDy271GjUmVnw9atYU3YYKH0eLcM+uXeRqMtW2DGDB4dOZJHZc7TkxteoMZJWxtSGKghJmP+rLPg1lvh/fe9nwvNi4cwDH8NXDKh+GngEvxpkcExDLH0ZyS0EzPS0mhmIxYBOHoRiXPELGesXqhFhRpxwIagTRtvfUOVrawcgh2RKVt/o9OQq3D/vlUzQMffAQ2h1dvlqGbxGiNmIGZj/qmnqHR462Uu+3hl+GMiUsiZUKpt4MHI6ZrFhiev4CU/x7sZwVFGkNAauB5tB2K3XUkk/m5tcoSpmR0iqnCSne2dXAcPGrKZ+gslKUDHI9i487YXeefyyzXPl7Pta1Vzr20cbwmxGvP5+6Do7EHc/b8P6b7rZ5psXs/4Em91nJiOaT8BPmPpjzzmWsza/cUs//kgyQrpDuLpz0hoAR4spIKLNoBX4zGLw6yFROPv1hZHWJ/2jTTzRYdlUqnOSsiffxoS4JLw6bP1B17Pn8zeOg24achT7ExvpnFmKOSiOhNpjESKWI35vKWFtGp+BncBDVxHmfnfRxk5bBJ5S5PjJsCTj3tD653uco4IDl6YKx+sE08ktAkFArf7BU9cwdBzWgZsW0W8iY5isd06Ufi7sYZejrlhra6Nt5gD27bpPiW/oAibIHD9j5/z1ryn+e2Ullx3wwvsqN/MFKF0oo2RWD3P7mIXXff8ShUCAjVpZc3S9HXHTPgJ8LrlpSCKON3llDpScVeJCWcKS2gNXA4rftmvaGOMRR4JqB1mi0SC3kloWID6a+A6kF9QxPh5P3Lnt7N5aOV0VmZ35Z854ylJSTNNKJ1oY0TpeQBTmTbNM50hlZHWtOpkyqJqxBGbv93FVbYkkqsqqVteQkplBTZEXy7wRDOF1SoBnl9QFHcbY20xWxhFsC23T/tGrPhlv+EJKkc500O9C0uA1qsHDRro1sCnfPozD3/6OresX8THZ1zMQ1d5K+jYBcGw48ms/qoNCB7z0WCm5PZtx/iSioDKSHLh9OHACAsq7/PfOCc9k+bHDlCvvMRX0FiqxmMThLiXUfOHpgAXBOEdYACwTxTFM6u/awDMBrKBbcAQURQPR6+ZNYNGCbXVxpgIkJuQ/jZrvRNUaWLLla1z2ATqpCaFlRkyANnZ+gR4WRnjp0/kqsJV/PvsQTzX51ZEwWtBrBJFw8LbjP6qrYgGLbRG00/mjawONM908pxJi6ARR+zuYhf70+vT/NgB6paX4KyuxiPVw/SIYkK9Wz028PeAfkHfjQO+FEXxdODL6s9RhVKeaajdNsZEgFrfStBDHVOa2FLZOn+q1dBzWpKWbMIGUKISquHIEbjySq4qXMUzfW7j2Utu9wlvML74m9VfehBOvptoI1rMlGjRW43kbMlwOtifnglAvbISnBWhxRz8363W+4n2+9OcQaIorhQEITvo62uAi6v/fh/4CnjYxHaFQG1wxJJ3eSJC78QLNy1r8Pcl5ZXM/mGnr4J8RBprdraXuy2Ksvma2b0brrwStmxh7aRXmOk6DSKMQjWrv7SQSIFj/kg0NpYEpYhRI9HHggD7071pZeuWl/pMKK6ggsa7i108lr8pIC9K8PuJxfsLl4XSRBTFPdV//wU0UTpQEIRRgiCsFQRh7f794We8UxocWZnOuAvvRNSSjEDvxNOTllUOGU5HQOBMscvtE94SjGg1AcjOhrIy2CuTlbCwEM47D/74AxYvpscj95iSBdKs/tJCIgWO+SMRmTZqlW+MZP8sLnWzr04DAJKrKsksOwaEllNLS7YzQyapVXDd2Wi/v4hphKIoisjnOZd+/7coij1EUezRqFH4fO1EHDRgfskkPcLL7AVDqUydP8JNy+p02BEEfRV4dhe7jPenEpVwzRpvPg2Xy1t8uDpAx4xtuln9pYVEChzzhxGBGCuYJSybZzp9GjhAk+OHvNcKEuAlFcrjuajYRdvxS2JCuAjXCLlXEIRmoijuEQShGbDPtBYpIFHpWbEudhyNbZlc34bDqlB6R2Nmb9DVjuaZTuP96UclzE9pSd7SQtqtW8nrn0ymsklT6nz1JbRtq+v+emFWf8nB3wxgEwTZjHd6NftoJl5LNDaWmrDUmjP+/ZThdHC4bo0Ab1wtwN3JofUw1aCWqdBMU5Mg6sgjUW0DX+THQskDDoqiOFkQhHFAA1EUH9K6To8ePcS1a9dG1uIEQ5txi2W3HwLw5+T+hq6lFMVnFwSqRJHmmU5KKypD6imCVwvSSs8aLyg9lz+kFLBjZm9Q3M7J5vcuKYE6ddh8zziuy7yIu794l3+umcufDZpz880vknvjBbKCJlGySga3SSt1hNRPoK7MqOVSj/dzmo38giLFcSPlrJEbf1LRkeD5dM7uLcz5IBeAeb2uYfCaT7ho1L/ZXr95xG112ATyru9s+B0IgrBOFMUewd9rmlAEQZgFrAbaCYKwSxCE24HJwOWCIPwGXFb9+aRExFVJ/KBWMVsyJ8gJb7VzEwFyJgeHTaB+miNkC67Wb7LmlPR0aNSI377fxPVr8rlnzRxsiGQd3U+T/btkt9DRrhQeLpTYLXZBCOgnQLP9iWo/jwbylhYqKlG5fdspzo3DpW7Z+bQnrUYDH9zMKyJdMhXpw4JWSR+D0MNCGa7w06XmNqV2IhbFjvWem6jQa/7KLyiipLxS9Vqy5pQ2bWi4bzd1UhvgQcCO6AvFfiOrQ8g1ol0pPFwoCZoqUQzYzfWevFyz/eHYz8PdlcR7N6P0TCLesadUyFsJEo0QgD1eroYrOVXhaGNwe0RTx1nC50JJdJjp0NHjHJNDIjhztaDlPJS04uDcynIImbDZ2bQ+to81rc6iIslBpWBTDcVOVOeg3t2cnvYb3RmGuytJhN2MGkMNjM+rMkcqx1Kq83/v3g2oa+D10xzc0KsVDrs+9drMcWYJcBNgVgBC8GKgUNMXp8OWUAwAM6AnOEZCyITNzibr6H5+aX0GI4dNYuoFNzBy2CS2ZJ8pu7CZafYyE3qZVnrab5S1Fa7JZeLCzXE31Wg9q5ySlekMLbrtj33VTBT3riLcNjuV9lBjhdNh46WhXSh44gp6tG6Ax6PtTwRzx1mtyoVyMsDfu9/1qWWyNrpUhz1hHZbhQq9WIiuE2rTB7q5g6gWNebpOqmYodjzKyumBXlOTnvYbZW2Fa3JJBJ+MnmfVyucSjAPp9Wl7qAhHlYejKenYBK9JRhS9PonhPVvyTE4n3/F5Swup0tFWs8eZJcAVoGXXi4Xdr1hhcih9H01E+3n12P8Vk05VUwn7pZfRT8fClqiUVNBHz9PbfiNUv3CiK9W07FjvZvQ8a/AYHtw9i0Ub94SY7QQI4IK7HClUiepMLz02dlkWVYSwBLgMlHija7cfYsUv+ykqdgWUzopWiHOihCzHIiRYq/qSKgXOP5jn/PND2i4n6BKNx2wUZrc/nF2JmpYd791MMOTG8Lx1RbKUzKJiV4Ajs1Qllaw0vrRwQ69WARq7WbAEuAyU7IH+eQ9ikZM8Ubb6sWBtBGuVGU4HgoC+bIWtW3v/D4rGTNRcIomIcHYlSgpGptORcP2rNoaD/Va9Jy/3hdMDlFU7MDPTauzm+QVFTFy4WdGEFIx564ro0bqB6f1iCXAZqNGSwjkvXERrq2/UHBIr1kbYWmVqKjRrFpKVMFHpgokKo/2vpGBMuLpjNJoXEYyM4dy+7Vi9qsaEIuUCP1zqputTy+h/VrOQ9MhaiNa4swS4DMLlY0fDtGH2VjkcrTRRTDmqkMkLnqh0wRMFiexLCIaeMeyv2FzoDLSBSzhc6g7YiRtBNMadRSOUQTh87ERgMehBOHSxRE0kJiG/oIhlpWnsWLc5ILlXotIFTyREK4e32dAaw8F89mAnpj/CEd4QnXFnCXAZyPFG1VCbuNjhaKWJmH1OgjTxfk1rSLNjB/jr0HFfIEmiLzwWYge1MZxfUMQDczYEKDaBAtycKMxojDvLhKKAYNOFUkKmRE4iJYdwzSGJytqQdhS76jXGUeWh6bGDFNka+5xT0jGJvsW3EH3IjeH8giJyP9pIVZBafTCtHh7Bhl2soiwpOeRa/iw0PaifFh3HriXAdSJRGCGR4kR5DgnSzmFnZlMAWhzZS1FGY9/3SpPWEuonJoy+27ylhSHFRQCqbHYOpmXQuOQwtqCMrU6HncHds3yUYi04HXaeHBgdx64lwHUilg6baOdxhhNHK5V2FLsyGgPQ4sg+vkM754dFLTzxEM67VRPA+9Pr07jkMElVNQnW7IJAt1YZzPpup2rOb/8U0NGcX5YAN4BYmBFiIWAS1RwSDqQdxe56jahCoOWRv8LO+XGi9MnJCqPvVivh1iFnPQCSPN5rOh12urXKYNXWQ6rnxTLvuiXAEwyJKmAS1ezgv6P4q25D/uY6qDp5LGrhiQuld1hU7KL35OUhY1crgvJ4dUbC5KpKMqsDy9SEtwAxnxuWAE8wJIKACRbWfdo3CghcSDSzg29HsaQDzYUyCCN60KIW1n4ovVuBGlOJ/9jVmlPHk70C3FHloaSiUtZW7o9pQ7vEfD5YNMIIEI1q9PHmLsvld565ZkfcU4bKwb//u0xcxqKjyRRt2KL6LnL7tgvJ2+ywC7XWiWuhBnK0UTm2iMvtYeLCzdiU8jVX43iKd87ZPdrCG9STe0ULlgYeJqJlq443S0TOhKM0dONpdgju/2KXm63pjbjy2EH2Hjym+i6C8zbrzeOcSEhUk5YZCPfZ5Bz0Sk5KPTlMjlVr4ElV+kLm4zEfLA08TESr5mC8g2aMDMJ4mh3k+n9XRhPsYhXNjh1QfBcTFmwOydtcVf19bUEiVMGJFiJ9tuDIUK0gPDWUVGvg/iwUNcRjPlgaeJjQY6uORJOIlzalZkf011PjzR2X6/9dGU0ALxd8Z2ZT2WOUSrbpKeWWKEhUR7cZMPvZtNIUq8FnA/foOzce88ES4GFCyxmm18QS762wlsMSAgMXEmXLLtf/OzO9Arxl8V5Wtz5xHZOJ4OiOFsx6Nv9xneF0kOqw+VITl5RX6lqwjyfr18AFAcbM3kDe0kKLhVIboGWr1qNJxDuoRCnJfaIJaznI9f+euqdQKdhocWSv4g6hfppD1v5ZP029RqIRxKt60YmwYCk9m4g3nYWevpTzjzgddh9LRKucmoQqwWth1mMD77pzC712bmJNq06ML6kAYjOHLQEeJrQiGvVoEvHeCivdf8Uv+xM+v4t8AQgHe+o1op3rgKLf4MmBHcn9aGMAq8BhF0wLdY5X9aJ4m7TMgprJQ29fKo3rCQs2+8aL06Ht/kut9Apih0ddA7/wj7VMnzsBDwIVSQ5GDptE3tJkS4AnOtRs1Xq0pHhvheN9/0gh2/8rOtDSrcwFj3YqgXhUL0rUXVI4OxH/Z5ObP3r6Umn8FrvcPtNJqVu7BLHTXQZAw9IjdCvawvqsDrLHTZ87AQA7Ig5PJb12bOJ1hWPNhiXAowQ9WlK8t8Lxvr/ZyC8ownEshe6FPzJYZbsdTSdxwlcvihEi2YlIz9Zm3GJZCqtWX4ZbkCUYpx3cCUBm2TFm/vdRRg6bFCLEL/xjne/vSsGG257EmladsGtwzM2CRSOMEvzpgOBNbiNpDxIlKt75qs24fzSCmcKBJDCOukWaHj9E1o/fx4VaF+9ArESBGTTbcPsynIIscmh7cBfgZWBJmrU/7FUeps99EoBxfe9m6gU3+IS8WqIrM2EJcJPhL9DylhbSp30jnA6774X681rjzfmO9P6JxEfOW1pIh20/Mfin5QDMmPM4Hf78KebRcfFelBMFZuxEwu3L4HEdrja8P70+IoGatQQBeHrZG4A36dV/u1zJ6+cOCdDQY6HQWCYUEyG3bZSrn+dvx4v3VjiS+5tt742EvbG72MU1OzZhF73tSfZU8va8p3hnxzUw4jRo1cpwe8KBEft0vCmk0YQZ5rlIbP3+47rNuMW67+mPY6npHE1O481e17GmVacA4ZxV/BcjNn4GQN/bXpM9PxasMkuAm4jaEoZuFsy090bK3mie6WRNq05U2B04PJVUCTZ2ZjRm7P9mQPZMuPRSuPVWGDQInKFCxExhqmdRjCWFNB4LhVlMGTMUHKXFxCaAKIJNEGRNHk53OcdS03n93CGBP4giS9+7z/v33//Oo7f3icjpGgksAW4iaksYulkw0wkaqTaf27cd40sqGDlsEr12ePm4W7LP5OWeGVyxdim89x6MHAkZGTBsmFeYn3MOCEJc+PixopDGK9YgnkwZvcFpkrlQSUNPdZdTlpQS8v31hStJLy/1fnjpJXLS0yNyukYCS4BjnoZSW8LQzYKZfORItfkagZHMG1kdaJ7p5Lm+7biiaxYMOBeeeAK+/hrefRemT4c334QOHeDWW3nncDauqrSA60Vbc4oVWyWesQbxMA+GE5ymNG/T3GWUJgcWNG5Ydoy8T/K8H958E9LTfb/Fg9V10gtwMzUUJYFWGyIbw4GZWpZZNlPFe9ts0KeP99+rr8KcOV5h/tBDzBdsfH1qd+Z2uowvTzsHt90blRlNzSlWk722c/2NwmhwWn5BEaUV8oE6zspyXH4auAC8XTCj5oDbbw84Ph4BVie9ADdTQ6ktARZmwiwtK6aDv149+Pvfvf8KC5l150QuX7eUS7f+wCFnPT454yLmdrqcI+2iU4gWYve8enL2nEjj1ciCpRVS73SXUZxaz/e51/aNdFlZbW5ZsgTsgQyZeMz/k16Am62hxJtVUlsRt8WvXTvqTsvj0o9Gcvava7lu0xeM2PApt65bSHG7jpA2CkaMgFNOMfW2sXpetYUi3rl4ogEjOxs55c0fTnc5e+p6NfAUdzmvfvI8ABtbn0nnfv1kz4n1/I9IgAuCsA04BniASlEUe5jRqFjiRItGNIpE0sDitfjVCFMn97btQbtkN89XbKbzFx/D/ffDgw/CwIFex2e/fpBkjt4Ti+dVWyh6T15+wqWlNbKzUVPSsjKdpLnLKXV4Bfi9q2fT0HUUgPF9RjFqw+6E6CMzRmIfURQPmHCduOBETgykhRNRAwsXocI0B3gUNm3y2spnzID586FpU7jxRq8w7xCbfBeRQmmhqI32cS2Fw8jORkl5y8p0smrcJRyaVE6ZI4V2+7dxz+o5AHx05qX83OTUhJknJ30kZryjIeOJaFUVOqHQqRNMnQq7dsHHH0PPnjBtGv/f3vkHSVVdefxzZpgB+WHIAI46wwT5IQEBgSDijliJhYoQIyBJUFSIulaszVbUsnYxJisxay0mrsluYnTdVUGFgIYVp1w3QjIkbq2g/Bjkt8hPYUSG3SwoPwZmhrt/3Nc9PUP30K/7db/7Zs6n6lW/fv3r1On3vu/cc+89l6FDYdw4OxLh6NGwrcyIqE37jwUcpZvXcd+qVyndsj7pzN8po1quypPqWk410/NrX+5D5bxqik+f4lRhMfP+85fx158cfwfgznWSbQRugOUiYoB/McY8F4BNeaej5q2jGIGFRnExTJlit0OHYOFCG5l/97tw//0wbRrMnm0nDBVEIy5yuvXZ2Ah798KOHfGtbPl7vHNwN71PHAGgoaATt3/7Jy1Kt/pJCSaL1uNjxk830vV0PdO2VNOz/hgAv7rqW3x6fnNfiAvXSbYCfrUxplZELgBWiMh2Y8w7iW8QkXuBewEq8jSdWUmPjp7/z5jSUnjwQXjgAVi3zgr5okV269sXZs2yYj5gQNiWtknoo6aMgYMHW4h0fNu1y4p4jC98gaKupRzqXkKvE0coAIrPNLLgtUdZvOMGmNSbZU29fKcEWwdvI3+8nJMNTXRuaqAAExfv/z3vfJ69cnqLz7pwnYgJqGqWiMwFjhljnkz1njFjxpi1a9cG8nuKf/zOUFN8UF8Pb7xhxXz5citO11xjc+XTp0P37mFbGB5HjsBHHyUX6mPHmt/XuTMMGgSXXnr21rs3lU+spHTLehYufoSipkaaCgpYU34ZYw9spbipge1ll/LKZROoGnINn3Vp9ncsp30ultXUcv+SDQD0PPkZG/75tvhrP7zuPl4ZPTn+XCC+wk8+EJF1yQaJZCzgItINKDDGfO7trwAeM8b8LtVnOrKAhz3aI9mY1/Y8yShUDhywsz3nz7fC1a0bfPObVszHj7cLKLY36utt1JxMpOvqmt9XUAD9+iUX6b5920w/xc7hIXs3tyiX8OS15UzeVM22v/85Qw7vpb5TMW8NruS14dexumIYSAF75k1O+b0xKudVx1uks9dWMfcPNiO8q6SMG+56msbC5oRF5YASFv7lVZn5KgNSCXg2KZRS4HWxJ2MnYFFb4t2RcWG0R5SXT4sc5eXwgx/Aww/Du+/aqHzJEivoAwbY9MqsWVawokRTE+zf31KcP/zQPu7bZ1sdMUpLrSjfdFOzQA8eDP3720g7A1KVS5g8qgy+Nox7jl9GyfZNfHvjcr6x9U9M27KSfT0v5HdjJ0HtSChr+1qL5bR7nDoeF2+A14ZNaCHeAOs/PhovCR0mgaVQ0qGjRuCJd/ZE0m3aBUGqQjsCaUUnSpYcPw5Ll1ox/+MfbRQ+YYKNyqdMSVohMRSMgcOHk0fSO3fCqVPN7+3RI3kkPWiQLRqWZxIDpS4N9UzcsYpbN63gyn0bbWQ/cSLcdZe9qRQXn/X52HX6k+W/5o6atwA7SqO+U3HS1Xjyef3mIgJX0sSF0R7aYRky3brBnXfabfduWLDAbrfdZsXu1lutmF9xRX5SLMeONeelY1F0bEscFllUBAMHWmGeNKmlUJeWOpUOatkpC2sqJ/HVxx6AHidt62f+fNsf0aePHct/9912OChW/I+fauQrB7ZyR81bbLngEobW7WmxGk9rAXdhFIpG4HnAhQg8VQ5cOyxD5MwZG42/+KKNzk+etIIye7YVmAsvzO77T5+GPXuSR9OffNLyvRUVZ0fSgwfb4wHNPA2dpiZ4+214/nmoqrKjXMaNo2bCVO45NZD+tTv49bJ5GITv3/QQLyx9jKKmRhoKOzkbgauA54Fci2e6HaRhd6S6hF9f5Nx3R482V0hctcoWSrrxRvjOd3ijfBQ/rd6T/LfPnIHa2uQivWePFa0YvXsnT3kMHOhOCidf1NXByy9bMd+2jfrCIro02RXrTxcU8uqI69lc2p+Sk5+ftRoP5D/4UQEPmbYEIBtx0MjaP359lncfb99um/svvQQHD/Lnrufz331HcKK4C5/26EVnEW7qeoyyuv02DXLiRPNnu3a1OejBg8/OS5eUBG9r1DGGqXc+xdzfP8vln+60h7ztlJf7/qB8KLde2TfU0Voq4I6SrTi4kJ6JGn59FpqPGxt56J6fMWXlYio/3kgs29yI8Emvi6kYN/LsaPriiyMzE9QVKudVU7plPUsWzaHoTPN12CgFPDX+dp656luhd/RrJ2YA5KIZnW09chc6SKOGX5/l08etz7HaC0dwQb/tjNu/mU7mDI1SwC+uvo2n/2JG6KLSXogtx3fPtB/xwm/nUoiNwGMr0bvc0a8CnoJzzVoMaix3tuKgo0v849dn+fJxsvkCAqyuGE5DYSfwOtTe/dLl+v8GSOz6nVtVwOo1I6jctxED3HXLo2zrN4x/cKE2TAq0rZWE2IVUe+QkBnshLVz9cU4q92VbES5VRTUnChI5il+f5cvHyVpjBqgpG8LMGY/z1PjbmTnjcbb1Gxb/7WU1tVTOq+aSOf9B5bzqsyrzKekxZVQZGx69nu4z7Ar0BUCPL/Zwvi9JI/AkpLqQkpFtMzrbinD5KkjUnkaw+PVZNj7247dU55IBDl02usXswymjypyY4dveuPyvZ8MTPwTgueGF4LgfVcCT4EeUs23KBiHAuS6H2x6Fwq/PMvGxX7+da4GB1vjpP2lPN+CcUlYGY8fC++9DTU3Y1pwTTaEkIZUot55zFlQzOt0C9LkgnSa4LvyQGX795jdVk27/SbKUYLKFEBSPW26xj1VV4dqRBirgSUh1Ic0cV9GuVu5J98LWkS6Z4ddvfleHSrf/RG/APpk61T7W1UFDQ7i2nANNoSQh9EL3eSLdJnjYI12i2vzPxG9+UjWp+k9iS4LFhyLqDdgfgwbFd6tf/xM/2l3g7LmnAp6CjrDMWroRYphLb0U5/55rv7W5JFiroYjJOuEzvQFH9Ybqh9rrvk7ZijdZ9cxvqL1ymj3m4LmnAt6BSTdCDLNFku1EpzDJh99aBxqV86qTjqBqLeKZ3kiyuaG6IPzp2LCsppaXK25gKW/y0H+9xLryIfFaKK6deyrgHRg/EWJYLZKo59/z7be2hiKW9Twva/HM9IbqQksqXRt+9vaHlNbXA9C5qZGFix9pUY3QpXNPBbwDE4Vcf9j596jhdyiiXzK9obrQkkrXhk+OnOTm/ZtoQijEnFUP3KVzTwW8g+N6rj/M/HsUybW/Mr2hutCSSteGi3uex+qK4ZzuVBSvB766Ynj89ROnG51YTg1UwBXHiUIrwSVy7a9MbxAutKTStSFW3GrmjMfjiycn1gP/vxMNznRmajlZxRlc6OTq6KTbyef3Pa1Hx0D+69b7Kd2caH+BCE1JdFJX5FEUj468MIUrN66g/oNU33PLV8pCXRQhZptfX7uwILjWA1ecxoVOrjBwYXRGjKD+g1Tfs3L74dAXGcmkz8eF9E8qdCq94gQudHKFgUvT3IP6D6L4X7ZVE8jlks0q4IoTZFsXPaq4JHZB/Qep3m8g0JrlQdVCP1dNIL81avKJCrjiBC5HObnEpRtXUP9Bsu+JEVQlxCArLKbTCgqzYmhbqIArTuBylJNLXLpxBfUfJH5PMoJIEQWZenKpFeQX7cRUnMH1SUUvaAUSAAAFaElEQVS5wLVx7kH9B7HvSTWCI1txDFJ0Xe6kPBcq4IoSMu35xpUrcQzye6M821dTKIqi5IxcpYiC/N4op+80AlcUJWfkKkUU9PdGtRWkMzEjgiuz9RRFyT86EzPCuDRbT1EUd9AceARwabaeoijuoAIeAaI8TlVRlNyhAh4BXJqtpyiKO2Ql4CIyUUQ+FJGdIjInKKOUlrg0W09RFHfIuBNTRAqBp4HrgAPAGhGpMsZsDco4xeLabD1FUdwgm1EoY4GdxpjdACKyGLgZUAHPAVEdp6ooSu7IJoVSBuxPeH7AO6YoiqLkgZx3YorIvSKyVkTWHj58ONc/pyiK0mHIRsBrgb4Jz8u9Yy0wxjxnjBljjBnTp0+fLH5OURRFSSQbAV8DDBKRS0SkGJgBVAVjlqIoinIuMu7ENMY0isj3gLeBQuAFY8yWwCxTFEVR2iSvxaxE5DCwz8dHegP/kyNzskVt84+rdoHalgmu2gXtz7YvGWPOykHnVcD9IiJrk1XgcgG1zT+u2gVqWya4ahd0HNt0Kr2iKEpEUQFXFEWJKK4L+HNhG9AGapt/XLUL1LZMcNUu6CC2OZ0DVxRFUVLjegSuKIqipEAFXFEUJaI4K+Au1xoXkb0isklENohIqKs0i8gLIlInIpsTjpWIyAoR+ch7/KIjds0VkVrPbxtEZFK+7fLs6CsiK0Vkq4hsEZHve8dD9VsbdoXuNxHpIiLvi8gHnm0/9o5fIiLvedfpEm9Wtgt2zReRPQk+G5lPu1rZWCgiNSLypvc8OJ8ZY5zbsDM7dwH9gWLgA2Bo2HYl2LcX6B22HZ4t1wCjgc0Jx34KzPH25wBPOGLXXOAhB3x2ETDa2+8B7ACGhu23NuwK3W+AAN29/SLgPWAc8Cowwzv+LHCfI3bNB6aHfa55dj0ILALe9J4H5jNXI/B4rXFjzGkgVmtcaYUx5h3gz60O3wws8PYXAFPyahQp7XICY8xBY8x6b/9zYBu2FHKofmvDrtAxlmPe0yJvM8C1wG+942H4LJVdTiAi5cBk4N+850KAPnNVwF2vNW6A5SKyTkTuDduYJJQaYw56+58CpWEa04rvichGL8WS99ROa0SkHzAKG7k547dWdoEDfvNSARuAOmAFtpV8xBjT6L0llOu0tV3GmJjPHvd89nMR6Zxvuzx+AfwNcMZ73osAfeaqgLvO1caY0cCNwF+JyDVhG5QKY9tprkQkzwADgJHAQeAfwzRGRLoDS4H7jTGfJb4Wpt+S2OWE34wxTcaYkdjS0WOBL4dhR2ta2yUiw4CHsfZdAZQAf5tvu0Tk60CdMWZdrn7DVQFPq9Z4WBhjar3HOuB17MnsEodE5CIA77EuZHsAMMYc8i62M8C/EqLfRKQIK5ILjTH/7h0O3W/J7HLJb549R4CVwFVATxGJVTUN9TpNsGuil44yxphTwIuE47NK4BsishebBr4W+CcC9JmrAu5srXER6SYiPWL7wPXA5rY/lXeqgFne/izgjRBtiRMTR4+phOQ3Lw/5PLDNGPNUwkuh+i2VXS74TUT6iEhPb/887GLm27CCOd17Wxg+S2bX9oQbsWBzzHn3mTHmYWNMuTGmH1bDqo0xMwnSZ2H30LbRczsJ2wu/C3gkbHsS7OqPHRXzAbAlbNuA32Cb1Q3YfNrd2DzbH4CPgN8DJY7Y9TKwCdiIFcuLQvLZ1dj0yEZgg7dNCttvbdgVut+AEUCNZ8Nm4O+84/2B94GdwGtAZ0fsqvZ8thl4BW+kSlgb8FWaR6EE5jOdSq8oihJRXE2hKIqiKOdABVxRFCWiqIAriqJEFBVwRVGUiKICriiKElFUwBVFUSKKCriiKEpE+X/rhXrny4tRXQAAAABJRU5ErkJggg==\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:40.507Z",
          "iopub.execute_input": "2020-03-28T10:36:40.515Z",
          "iopub.status.idle": "2020-03-28T10:36:40.681Z",
          "shell.execute_reply": "2020-03-28T10:36:40.738Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Empirical Fixation density Map\n",
        "Normally the fixation desity that is used comes with the data set (see spatstat or corpus data). For historical reasons, they are computed in R. \n",
        "\n",
        "`sw.empirical_fixation_density()` makes a desity map that is very similar to the R version. It also converts the data to px. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "d_range = {\n",
        "    \"x\" : (0.00000, 39.90938),\n",
        "    \"y\" : (0.00000,  26.60625)\n",
        "}\n",
        "\n",
        "sw = scenewalk_model(\"subtractive\", \"zero\", \"off\", \"off\", \"off\", d_range)\n",
        "img_x_t, img_y_t, fix_dens = sw.empirical_fixation_density(img_x, img_y)\n",
        "\n",
        "plt.scatter(img_x_t, img_y_t, s=1, color='white')\n",
        "plt.imshow(np.float64(fix_dens), origin='lower')\n",
        "plt.show()\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:40.690Z",
          "iopub.execute_input": "2020-03-28T10:36:40.697Z",
          "iopub.status.idle": "2020-03-28T10:36:42.296Z",
          "shell.execute_reply": "2020-03-28T10:36:42.441Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model\n",
        "For example we can use Heiko's estimated parameters (sigmas times 4 because this model uses pixels and not degrees). Heiko had 2 parameters that were unreasonable. I've swapped them out here for demonstration purposes."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "omegaAttention = 10 #2.4*(10^30)\n",
        "omegaInhib = 1.97 \n",
        "sigmaAttention = 5.9\n",
        "sigmaInhib = 4.5\n",
        "gamma = 1#44.780\n",
        "lamb = 0.8115\n",
        "inhibStrength = 0.3637\n",
        "zeta = 0.0722\n",
        "\n",
        "sw_params=[omegaAttention, omegaInhib, sigmaAttention, sigmaInhib, gamma, lamb, inhibStrength, zeta]\n",
        "\n",
        "path_durR = path_dur/1000"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.306Z",
          "iopub.execute_input": "2020-03-28T10:36:42.312Z",
          "iopub.status.idle": "2020-03-28T10:36:42.323Z",
          "shell.execute_reply": "2020-03-28T10:36:42.447Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Already previously we initiated a scenewalk model with the following command. To get into it more: the arguments are \n",
        "- subtractive: the way inhibition is added\n",
        "- zero: the initiation map is zero everywhere\n",
        "- off: no attention shifts\n",
        "- off: no occulomotor potential\n",
        "- off: no location dependent decay (i.e. facilitation of return is off)\n",
        "- data range\n",
        "\n",
        "This is the configuration of the original scenewalk model. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "sw = scenewalk_model(\"subtractive\", \"zero\", \"off\", \"off\", \"off\", d_range)\n",
        "sw.update_params(sw_params)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.335Z",
          "iopub.execute_input": "2020-03-28T10:36:42.342Z",
          "iopub.status.idle": "2020-03-28T10:36:42.352Z",
          "shell.execute_reply": "2020-03-28T10:36:42.453Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can get the likelihood of the scanpath."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "path_x"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "array([20.13561279, 30.63710112, 23.16872571, 19.94138714, 32.54210889,\n",
              "       32.8480808 , 31.62685376, 32.65119453, 36.71663009, 37.4722477 ,\n",
              "       37.00929888, 37.69041898, 37.27004017, 38.73072349, 36.81108229,\n",
              "       37.33921643, 37.04654764, 33.90967033, 25.34245664, 18.37161817,\n",
              "       17.27277989, 20.69035318, 22.55412125, 25.43557853, 18.25189003])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.363Z",
          "iopub.execute_input": "2020-03-28T10:36:42.369Z",
          "iopub.status.idle": "2020-03-28T10:36:42.383Z",
          "shell.execute_reply": "2020-03-28T10:36:42.457Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sw.get_scanpath_likelihood(path_x, path_y, path_durR, fix_dens)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "-297.28024961378914465"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.393Z",
          "iopub.execute_input": "2020-03-28T10:36:42.399Z",
          "iopub.status.idle": "2020-03-28T10:36:42.485Z",
          "shell.execute_reply": "2020-03-28T10:36:42.462Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Unpack scenewalk code\n",
        "\n",
        "Here we step through what happens in the model when we call `get_scanpath_likelihood`."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "sw = scenewalk_model(\"subtractive\", \"zero\", \"off\", \"off\", \"off\", d_range)\n",
        "sw.update_params(sw_params)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "shell.execute_reply": "2020-03-28T10:36:42.466Z",
          "iopub.status.busy": "2020-03-28T10:36:42.495Z",
          "iopub.execute_input": "2020-03-28T10:36:42.501Z",
          "iopub.status.idle": "2020-03-28T10:36:42.507Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will need to extract the individual fixations to step through the code"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fix_x = path_x[0]\n",
        "fix_x = sw.convert_deg_to_px(fix_x, \"x\", fix=True)\n",
        "fix_y = path_y[0]\n",
        "fix_y = sw.convert_deg_to_px(fix_y, \"y\", fix=True)\n",
        "duration = path_dur[0]"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.516Z",
          "iopub.execute_input": "2020-03-28T10:36:42.522Z",
          "iopub.status.idle": "2020-03-28T10:36:42.529Z",
          "shell.execute_reply": "2020-03-28T10:36:42.472Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the initial state of the model. The attention map is set to (almost) 0. The inhibition map is set to 1. We'll keep working with the configuration described above."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prev_mapAtt = sw.att_map_init()\n",
        "prev_mapInhib = sw.initialize_map_unif()\n",
        "\n",
        "plot_3_maps(inhib_map=prev_mapInhib, att_map=prev_mapAtt)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.538Z",
          "iopub.execute_input": "2020-03-28T10:36:42.544Z",
          "iopub.status.idle": "2020-03-28T10:36:42.746Z",
          "shell.execute_reply": "2020-03-28T10:36:42.812Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start with the first fixation: Gaussians around fixation position"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 5\n",
        "xx, yy = np.float128(np.mgrid[0:128, 0:128])\n",
        "rad = (-((xx - fix_x) ** 2 + (yy - fix_y) ** 2)).T\n",
        "gaussAttention = 1/(2 * np.pi * (sigmaAttention**2)) * np.exp(rad / (2* (sigmaAttention ** 2)))\n",
        "gaussInhib = 1/(2 * np.pi * (sigmaInhib**2)) * np.exp(rad / (2 * (sigmaInhib**2)))\n",
        "plot_3_maps(inhib_map=gaussInhib, att_map=gaussAttention)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:42.755Z",
          "iopub.execute_input": "2020-03-28T10:36:42.761Z",
          "iopub.status.idle": "2020-03-28T10:36:43.017Z",
          "shell.execute_reply": "2020-03-28T10:36:43.107Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Attention Gauss gets weighted by the empirical saliency. Then we add the exponent to develop the map over time.\n",
        "\n",
        "The Inhibition Gauss also gets developed over time using the exponent."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 8\n",
        "salFixation = np.multiply(fix_dens, gaussAttention)\n",
        "salFixation = salFixation / np.sum(salFixation)\n",
        "mapAtt = salFixation + np.exp(-duration * omegaAttention) * (prev_mapAtt - salFixation)"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.026Z",
          "iopub.execute_input": "2020-03-28T10:36:43.032Z",
          "iopub.status.idle": "2020-03-28T10:36:43.042Z",
          "shell.execute_reply": "2020-03-28T10:36:43.112Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 9\n",
        "gaussInhib = gaussInhib/np.sum(gaussInhib)\n",
        "mapInhib = gaussInhib + np.exp(-duration * omegaInhib) * (prev_mapInhib - gaussInhib)"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.051Z",
          "iopub.execute_input": "2020-03-28T10:36:43.057Z",
          "iopub.status.idle": "2020-03-28T10:36:43.068Z",
          "shell.execute_reply": "2020-03-28T10:36:43.116Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is what it looks like after the temporal evolution"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_3_maps(inhib_map=mapInhib, att_map=mapAtt)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.077Z",
          "iopub.execute_input": "2020-03-28T10:36:43.083Z",
          "iopub.status.idle": "2020-03-28T10:36:43.305Z",
          "shell.execute_reply": "2020-03-28T10:36:43.393Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, both maps get weighted with an exponent (lambda or gamma). In then step numerical problems often occur, because taking a large exponent of a number between 0 and 1 can easily make the number so small, that it gets flushed to 0 by the computer."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 10\n",
        "mapAttPower = mapAtt ** lamb\n",
        "mapAttPowerNorm = mapAttPower / np.sum(mapAttPower)\n",
        "# handle numerical problem 1\n",
        "if np.isnan(mapAttPowerNorm).any():\n",
        "    raise Exception(\n",
        "        \"Value of *lambda* is too large. All of mapAttPower is flushed to zero.\")\n",
        "    # set to uniform?\n",
        "\n",
        "mapInhibPower = mapInhib ** gamma\n",
        "mapInhibPowerNorm = mapInhibPower / np.sum(mapInhibPower)\n",
        "# handle numerical problem 1\n",
        "if np.isnan(mapInhibPowerNorm).any():\n",
        "    raise Exception(\n",
        "        \"Value of *gamma* is too large. All of mapInhibPower is flushed to zero.\")\n",
        "    \n"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.315Z",
          "iopub.execute_input": "2020-03-28T10:36:43.321Z",
          "iopub.status.idle": "2020-03-28T10:36:43.331Z",
          "shell.execute_reply": "2020-03-28T10:36:43.397Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "u = mapAttPowerNorm - inhibStrength * mapInhibPowerNorm     # subtractive inhibition"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.340Z",
          "iopub.execute_input": "2020-03-28T10:36:43.347Z",
          "iopub.status.idle": "2020-03-28T10:36:43.357Z",
          "shell.execute_reply": "2020-03-28T10:36:43.402Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_3_maps(ufinal_map=u, inhib_map=mapInhibPowerNorm, att_map=mapAttPowerNorm)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "iVBORw0KGgoAAAANSUhEUgAAAWAAAACECAYAAACuw/FsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAXeElEQVR4nO3dbYxk2X3X8e//nHtvPXRPT/c87Ky9zu7KjkLsPLAvHEKkBBBBipACsRCJIiA2jmDlF4EXyIpkSBBPG0NEXhiMZBkRDM6TTFASEBKJ8iRIFBxjhBQ7qzjx4tldb3Z3Zmd6+qnqPp0/L8651dU9PU+7PX2rav4fqbU9Vberz9bp87vnnHvuKVFVjDHGnD3XdwGMMeZRZQFsjDE9sQA2xpieWAAbY0xPLICNMaYnFsDGGNOTlQtgEdkTkXeewuv8IxH5qdMok7k7EflNEflbd3juyVSn/kGPvcMxp/L3YRaLiHxCRH6073I8qKUOYBH5iohMUqPaE5E94OtU9YW+y2Zm9fMX3sprqOqLqrququ2DHntSWKfn7e/jlKT3+KaIDOYeO1LvIvK0iKiIZKf0O/+miPzW/GOq+iFV/aen8fpnaakDOPlLqVF1X6/0XSBjHgUi8jTwHYACf7nXwiypVQjgI9KZ9mvT958SkX8jIv9NRHZF5LMi8q65Yz8mIi+JyI6IfF5EvqO/kq+ursciIv8y9Zb+n4j8xWOHPSUiv53q6VdE5FL62ZN6T+8Skd9N9fZLInLh+LEi8hwxHD6eRkcfT8fM/32cF5H/KCLXROSqiPyIiLgHKPOj7v3A/wI+BXwAQEQ+DTwJ/Nf0vv8w8D/S8dvpsW9Lx/6giDyf3t9fFpGnuhdO9fQhEflDEdlO7VhE5N3AJ4BvS6+1nY7/lIj8s7mf/9si8kcickNE/ouIvP1er/0w36g7WbkAPsH3A/8Y2AL+CHhu7rnPAc8AF4CfAf6TiAzPvISPhm8F/gC4BPw48O+O/dH/NeCDwGNAAXz4Lq/1fuAHgbcBDfCvjh+gqv8A+J/AD6WR0Q+d8Dr/GjgPvBP4s+l1P/gAZX7UvR/46fT1XSJyRVV/AHiRw5HpjwN/Jh2/mR77HRH5HuDvA38FuEysq5899vrfDXwL8M3A9wHfparPAx8Cfie91ubxQonInwc+mn7mbcBV4Ofu9dpv4X1401YhgH8xncW2ReQXT3j+F1T1d1W1If6hPNM9oao/papvqGqjqj8BDIA/cUblftRcVdV/m+Zn/wOxYVyZe/7fq+qXVHUCfIa5ejrBp1X1C6q6D/wo8H13u/B2knT89wMfUdVdVf0K8BPADzxAmR9ZIvLtwFPAZ1T188CXiSfR+/Uh4KOq+nxqmz8GPDPfCwb+uapuq+qLwG9w97+JeX8d+ElV/T+qWgIfIfaYnz6F1z5VqxDA71PVzfT1vhOef3Xu+wNgvfuHiHw4DYFupaHMeWJvx5y+WT2o6kH6dv2k5zlWTyd4ae77q0DOg9fbpfRzV4+91hMnlekOZX6UfQD4FVW9nv79M+mx+/UU8LGu8wTcAIQ7vP/c+29i3tuZq1dV3QPeOKXXPlWnclVyGaX53h8GvhP4oqoGEblJ/CMwi+1r5r5/EqiB68ceh3hx6E6up597Cvj9udf66imVcWWJyIg4bPci0gXZANgUkT/J7e/7SfXwEvCcqv70myjCvbZwfIVYr11514CLLGDdrkIP+M06R5w/vAZkIvIPgY1+i2Tu098QkfeIyBj4J8DP32GZ2mvE+d3bpOM/AzwnIufS0PfvAbb2+97eB7TAe4hD92eAdxPncd/P7e/7NSAce+wTwEdE5BtgdkH0e+/z978GvENEijs8/7PAB0XkGYnL434M+GyaZlooj3IA/zLw34EvEYcrU44Obc3i+jTxyvurwBD4u3c47mPAX01X2W+7UAf8HWAfeAH4LeIw+idPvbSr5wPEOfsXVfXV7gv4OHH+9aPAj6TphQ+n6ZvngN9Oj/1pVf0F4F8APyciO8AXgPtdZfLrwBeBV0Xk+vEnVfVXidcG/jPwx8C7iPP9C0dsQ3ZjjOnHo9wDNsaYXlkAG2NMTyyAjTGmJxbAxhjTEwtgY4zpyQPdiFHIQIesPayymPs0ZZ9Ky1O7YcTqdTFYva6uXW5eV9XLxx9/oAAessa3yneeXqnMm/JZ/bVTfT2r18Vg9bq6flV//upJj9sUhDHG9MQC2BhjemIBbIwxPbEANsaYnlgAG2NMTyyAjTGmJxbAxhjTEwtgY4zpiQWwMcb0xALYGGN6YgFsjDE9sQA2xpieWAAbY0xPLICNMaYnFsDGGNMTC2BjjOmJBbAxxvTEAtgYY3piAWyMMT2552fCicizwLMAQ8YPvUDmbFi9riar1+Vyzx6wqn5SVd+rqu/NGZxFmcwZsHpdTVavy8WmIIwxpicWwMYY0xMLYGOM6YkFsDHG9MQC2BhjemIBbIwxPbEANsaYnlgAG2NMTyyAjTGmJxbAxhjTk3vuBbHURO7/WNWHVw5jjDnBagbwfPCKS/+5PYw1zIdumHvCwtiYXtxvp2lF2uhqBXBXefOhm74nBbCIoKnyxANBQQPgYyBriK+zIhVszFI41nbvqmujsPTtdHUCeK4Cu+AV78C5+JxzSDpGYBbCtC2EgKoioqCSesapR7zkFWzMQrtbpwlmHSe60epJnSVY2na6GgEscnvweo94D95D92+Rw96taqzU0MYQbgO0Ldq2CC0anPWGjXmYRI4Gb9dGnTt8fp7qyZ2lJW6nyx/Ax8M3z2JFFjlkGZLnkHk082jXG04VKW2ApoW6hqaJX3UK6TagLUtducYsrDt0mnDpe3Gx9zvf9to2dpra2GlSFUQ0tlMAwtK10+UO4G5KYS58pcghL5BBAUWODgu0yNDcEzKX5h9A2oDUAVc1UNXItIpBLBXaNAg1gIXwIrvXBRurr8U0H75zI1XJsthpytLIVSSGcEgj1rZFQ0CaBupmFsQAaIij1iUL4eUOYIhnyvnwHQyQ0RAdDQhrA9q1nGaU0Q6EkAnqQAK4RvGl4qct/qDG7+fIpIx/DNMpChbCi+r40PX4hRsNNo+/qI6Hb5bFUWtexPZb5GiepVHrYb1Kq1A3SB3DV7MaqepZZ0lbELd8Iby8ATxXkeJdPHsOBsh4RFgb0W4MqDcKqg1PPRaaEbSFxFtPAvhKySaQH3jyPU9eeLLcI/uOrl81C+Hu7KrhLgUyD1034vH+6EVW7w+PCWHWWyIo2rYsU4NcafMj1tTzlTy12+EAHRToqCAMMkLh0dyhIogq0iiubnHTBilrZFLGuq9k1k7jVER7+LuWoM6XN4A5rEjyeOaU4QAdD2nPDym3CqYXPOWmUJ2DZl0JRTjsAZdCvhe/ilvCMBc0c2QiOFUkhNiAU4OeXZizxtyPO/WcsiyefFPj1hAvpkrToFUdh7Dp4qrV2wLoRqwih52m1G7D+pBmLacZe9qho82PjlizacBPcrI9j3cOcSd0lpgbsS6B5Qzg+SFo1xgHA3Q4oNmI4Xtw2TN5TCgvKs1WQ7FRsjao8S7QBMfkYMDBbk62ndGMhJB5NC15yds2zhFrusKqmoa07V0KZR6qbrSTZUhRwGg46zWFIgefeld1ezinP51CWaFVFaf+LYT7M9/7dXK007Q2ol0fUG0NqDY81bqjGQvtkFkA+xL8xFHsKcXAUWQO7wUHsZ3Od5ZUlqaztJwB3Okm6rMMipwwHlBv5Ey3PNNLwuTxgLsy5Wsu3uLpjTe4MtjFiTJpc16ZnOfl3U2urZ1jkg9iA1eHazJcNUCagHQrJKQBJ7FiW5ZmeLMyRA7DdzhARiN0fUy7PqLdKGhGGSGLQ1VXK9l+g98rcbs57B8gIoSyTGtJF79Rrqyu9+t9nDbM83gCXRtQbw4otzImFxzVeag3lHakqFcIgp/E0WpzSwg5IAUFcW5Y0jJS6VZHiGNZOktLG8DdrcWxB+zRIieMMup1T3VeKC8oXC5555XrPHPhZb5h9DJP5DfxKDthyNXRZb4weDvP+8d5OWxRVgN8KWRTR3aQ4SY5fpqGuL45rFjRpRnerIRutOM9UhTIcIieW6O9sEZ5YUC56anOCW0hiIKfKsVuxvBmRpF7PPGWc5nNCcPcuiVz1rqlZanTpMOcZi2n2vBMt4TpJSgvtbBZM14vyX07G7FObuW0g7icVFqQNsfVAVd1F+TS6omuF7wEnaWlDWCItxXjPWQZmme0w4x67KjOQX2+5fLWHt+4+Qp/au0Fvmnwx1x2ghPhVthh0x/gJNAEz15ZcPMgozrI4ll25MkGHs0zxLk4NeFcvFnDnLl4odVDkcNoSLs5Znp5yP4Vz+RyPNmGYQsK2b5jcMPRDHPWnDBsAq5t4xrvto0XVHWxG+Wqmu804WP70kGc863WHeWmUF5uyR+b8I6L2zy9foO1rKQMGV892OTqeIs9v4aEPHaWSh87S2n1BHV9eBNH/IUL31lavgDuekSSroA7Ae/iOt/C0Q6gHSkybrk03ueJwTZPZDe54GDDDQkoQUo23ZTL2Q5XBjtcXNvi1tqYduxpRhKXrOUe361H9D5OQ5iz1w1b0zx/WBtRbQ3Yf8yz/4QwfUfN1pUdLq4d0ATH6zvr7L22RsgyXOvJJgPyskbSXHA8iVoP+EzNX7NJa3vFOdR7QhEvuNVjod5QZKviyUs3+ZYLV/nm8Utc8Hsc6IA/HF/hc9nTfDE8zmR6jnzPUe8J+SitXnJH73ZdltPr8gXwcd3txS5O2KuPX5IFChcb2lRzdsOEWg9ogd3g2A5DpiHHiZK7Fp8FQqaELK0X9hKXwMwv9ndibfcsde/9bLVLho5y6nVPuSVMrzQ8/o4bfPuVF3jX8HVaHP9340k+659kvzxPsesY3szIbuUxwL1HqVPPyCqyN13nyTs0E9pcCAW0A2U4qnjb+BZfP3qFbxq8wmUfmKoylJqba2u8ur7BS+Mx7cDRFqmtZg71cWWFzv+OJWisyx/AJ1HQVthvCq7X63wlu8S+FuS0TDXnIAzYCSNuNmts12Pq4OOwVDj6dVxYlvPqaolTTQ7NYo+pGQrNOvjNiq/fep0/t/E878mvUyOsuZLXpuf4vTfWqNcKmqEjFB6X+RjkdhLt17F9HlRiu+s6T5kPrGcVG37KBddy0Y0pteGy32Ur32ecV0ge0Cx1tro55QfZ+3uBLH8AzzbWiVdEXQ2+FKpJxvW9Nb5UPMZeO2Ddl7Q4ypDRBE+tjkmbs12N2Z6MaEpPVgvSpCurQdMyND28FdKcuSP7OIugLo5OQqbkecuFfJ/H/Q5XfEFNy+PZLTbyKa5oUwPl8MLPkRe2eeBehDBb2olqWsWguAZcDVWdsV2NeKNZ50buyWVCrcpu2OCgHcTOUpB4k6Ny2EaP1+WCz/12li+AVdN+DukNDjpbguLqeHtxdiBkO55bozFf5hKvD88xyBqa4GiDI2hsjK0KZZ2xuzdCDjKyicTbk2tF6rRRT7pwQ1iOCl01GjQORmYNNqQTraMqM14rN3ipucBYXicgvFhf5EY5JpQ+Nuo23cZ6vP4sfM+OdreFz92x2C0dawK+UvxU8RNhulfw8t4mvz98O7k0PJ7dosbzQnmFLx9c4sb+GJl4fAW+Amk0LhltA9rGndJmW80uQQgvXwDDbE/Q2dk0BKRu8dOGfJKR72m8uSIv2G4cu8MRzsWr38ebXagdOsnIdxzZPmSTuJTJVe1s84+uYuOZe/ErddWophFJ0+Kqlnyi5HtQbhf8wY3H+PXi3XxldIlaPb+3+wRXb2zhtzOyPfDTgFRNvPgW1KaReqaqaevXgNQNrmrIJjn5gaPYcTQ3c14ZnOfz8iTb9ZgLxT5Bha9ON3lh+yK3bq6RdW11qvgytn2aNm4tO7uDtQvhxa7v5QzgZFaZTbw/3E0a8r2WIm28A46mzGkHGY0/VhHdrmiNkE3j7cjFjlLsBbJJg0zjxh/aNEtXqSujG+0EjZuulBVuUpPvNAzfcDQjz3W/ya+VX8fnxk+iKtzYGdO8NmZ8zTG8Gch3G2Rao3Wd7oSzk2hfNKR9fEOIYVk3uElNtp9T7DqaoRAyR6lDrpYXuba7xiCPq48OpgXlrSHZjYzBG8LglpLvxY20qOrDZYbtck3wL20AzyqzW+NZ1bhpRbabMcjSesNWaCZxkb5maT4QmL+B3NXgp1DsKoNbSrHb4vcqZFpCWR1ue2eNth8aQNN+DlWF7E8otnPGhQMyfJVR3tzg2uAcANm+cO6mMLoWGF2ryW5NkINp3BeibY99DqA5cxr36pjtaDat8HueQVp5hDp85aj2BpTjgkmhiIKrhOG+UOzA4IYyvNmS79S4g3QTRl0v5Uh1aQMYOJyb7SpzUuJzT+4l9m6Dp5lCO4CQNvbQ+Ws6gXjr6hSKvUC+25LvVLiDCikPe03a7QXRXTwwZ0q7TbirGiZT3K2MgROkHZBNYo8oFLHOfQmD3ZbBzYb8xhS3O0GnJVpVaHcjjdXh2ZubB9a0W51UNWQV4h3eOQYiSJuRTR35rtCMBE2dKVdDNlHyfaXYCRTbFdluiTuYotPysAe8ZCPV5QzgVJka3OEURBXvgpEDTyaCBMW1Oc00rRfsAri7qq7g2rR3wFTJDlqy/Rq/W8Ye07S8fdNnc/a6htumfTkmcW22V2VYNuR7A5rR4d6xrgr4SYPfLXF7B+j+BJ1O0aY5nIIwvYkXVdu4oqVpoCwRiZvq5Kq4uiCbZDR7sd1q6ky5Nu3fPWnjXh/7JW5vCpMpVDVaN0s5xbScATxPQ3zjqyreYQPpgk3AVS1+GPcWDXmqzK4HHOJxvgq4ssVNGty0mgvfKjXaYL3fvmm3r2/3T0WaWF9uNycb5IfrS5vUs6rqGLzTMjb0WeO0OuzNsY6TUM/uWpO2RZoWKeOHI+SFR/PDHQolxAvjUsU5fSkrmJZousOxq+Nl6v3CMgfwXGV2c8Faxf1AYwgHXN0g0xzNPWTu6EcSdWsQ61ip3RaGWlYxfCu7aLNoZiGcPtOPpombunS3oEI6YaY5xqY51jNajkb5SND0mYtNkzbQ71YzNcg0fSxR5g/Xb889T532eq6rWL/z4btk9XzPABaRZ4FnAYaMH3qBHlhXkaRd8SEtN4rbSUpVo2lPBze/IL+r0DbOIVPHjzchfcyJdlvbLdkZ9X4tfL0el95/7T6YsVsZkbYkPVI7ad5+WRvlW7Hw9TrfcXIBrRskhPhZbxpiG6zjJlh0H6Lb/Vzbxnn8Zi5059vpEtbzPQNYVT8JfBJgQy4s1v+darqj6VgIh3hjhjYNOH/4iavzd0N1H3Hd9aTaFq2bw0rW1Z56WOh6vZuuATch3u/f9YqP7Xw1q7vZzzwalqJej4dwyyxg4ycj1+C7W5bTfzUtBZ0FcToRz7fTJbS8UxCd4yHcBWZ38Sxt+jGryO5TViH9TFpJcVKvqXt9s1hmdRLm9tRp73CMWUhdCLeApP17xR3Wm8jRrSXTnYw62xogrEQ7Xf4AhqMhHFz8/LZUmSKCtkc3AJn9TAiHty2uUKU+Mqx+ltuxdgstktaJKhx2muD20c38Y0v8d7AaAQy394q6syocrcgjP5POqitUocYslbl2C6S227XX46Oa1dvPY3UCuHN8SuJe+4LOV+oKVKgxS+lI27vLfO6KtdHVC2C4/8q87VhjTO8eoTa5mgE87xGqTGPMcrnD5KgxxpiHzQLYGGN6YgFsjDE9sQA2xpieWAAbY0xPLICNMaYnFsDGGNMTC2BjjOmJBbAxxvTEAtgYY3piAWyMMT2xADbGmJ5YABtjTE8sgI0xpicWwMYY0xMLYGOM6YkFsDHG9MQC2BhjemIBbIwxPbEANsaYnlgAG2NMTyyAjTGmJxbAxhjTEwtgY4zpiQWwMcb0xALYGGN6YgFsjDE9sQA2xpieWAAbY0xPLICNMaYnFsDGGNMTUdW7HyDyLPBs+uc3Al942IV6ky4B1/suxAkeRrmeUtXLb+UFrF7fMqvXt+ZRqle4Q93eM4CPHCzyv1X1vadarFOyqGVb1HLNW+QyLmrZFrVc8xa5jItatrMul01BGGNMTyyAjTGmJw8awJ98KKU4HYtatkUt17xFLuOilm1RyzVvkcu4qGU703I90BywMcaY02NTEMYY0xMLYGOM6YkFsDHG9MQC2BhjemIBbIwxPfn/RegETNMBqfgAAAAASUVORK5CYII=\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.368Z",
          "iopub.execute_input": "2020-03-28T10:36:43.375Z",
          "iopub.status.idle": "2020-03-28T10:36:43.602Z",
          "shell.execute_reply": "2020-03-28T10:36:43.631Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this state u can can have negative elements. Those are now flushed to 0. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 12\n",
        "ustar = u\n",
        "ustar[ustar <= 0] = 0\n",
        "# Heiko handles case where sum(ustar)=0 by if else (path3)\n",
        "plot_3_maps(ufinal_map=ustar, inhib_map=mapInhibPowerNorm, att_map=mapAttPowerNorm)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.611Z",
          "iopub.execute_input": "2020-03-28T10:36:43.617Z",
          "iopub.status.idle": "2020-03-28T10:36:43.903Z",
          "shell.execute_reply": "2020-03-28T10:36:43.962Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lastly some noise is added through the zeta parameter"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "### equation 13\n",
        "uFinal = (1 - zeta) * ustar + zeta / np.prod(np.shape(xx))"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.915Z",
          "iopub.execute_input": "2020-03-28T10:36:43.922Z",
          "iopub.status.idle": "2020-03-28T10:36:43.934Z",
          "shell.execute_reply": "2020-03-28T10:36:43.966Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_3_maps(ufinal_map=uFinal, inhib_map=mapInhib, att_map=mapAtt)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:43.945Z",
          "iopub.execute_input": "2020-03-28T10:36:43.953Z",
          "iopub.status.idle": "2020-03-28T10:36:44.190Z",
          "shell.execute_reply": "2020-03-28T10:36:44.224Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the map after the first fixation. We can now continue evolving the map using the above procedure. Instead of passing in the initialization maps, we pass in the evolved maps (the version before the exponent and the noise are added).\n",
        "\n",
        "The `sw.evolve_maps` function takes the steps explicitely shown above to yield the three maps."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "uFinal2, mapInhib2, mapAtt2, _, _ = sw.evolve_maps(path_durR[0:3],                                                          path_x[0:3],\n",
        "                                                   path_y[0:3], \n",
        "                                                   mapAtt, mapInhib, \n",
        "                                                   fix_dens, 2)\n",
        "plot_3_maps(ufinal_map=uFinal2, inhib_map=mapInhib2, att_map=mapAtt2)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:44.201Z",
          "iopub.execute_input": "2020-03-28T10:36:44.208Z",
          "iopub.status.idle": "2020-03-28T10:36:44.472Z",
          "shell.execute_reply": "2020-03-28T10:36:44.507Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uFinal3, mapInhib3, mapAtt3, _, _ = sw.evolve_maps(path_durR[1:4],                                                          path_x[1:4],\n",
        "                                                   path_y[1:4], \n",
        "                                                   mapAtt2, mapInhib2, \n",
        "                                                   fix_dens, 2)\n",
        "plot_3_maps(ufinal_map=uFinal3, inhib_map=mapInhib3, att_map=mapAtt3)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-03-28T10:36:44.485Z",
          "iopub.execute_input": "2020-03-28T10:36:44.492Z",
          "iopub.status.idle": "2020-03-28T10:36:44.754Z",
          "shell.execute_reply": "2020-03-28T10:36:44.760Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "etc..."
      ],
      "metadata": {}
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "sw-temp"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.0",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "sw-temp",
      "language": "python",
      "display_name": "sw-temp"
    },
    "nteract": {
      "version": "0.22.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}