iGEM2020_logistics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "logistics.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UddeshyaPandey/Uddeshya_Rosalind/blob/main/logistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXuvUopY3XV7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "93581bbb-9ba6-4f6d-f64e-26eb9310b756"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "plt.style.use(\"fivethirtyeight\")\n",
        "x = np.linspace(10, 1000)\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(16, 9))\n",
        "\n",
        "t = 0.0005 * x + 0.124\n",
        "plt.plot(x,t)\n",
        "\n",
        "plt.legend()\n",
        "#plt.grid(True, linestyle=':')\n",
        "\n",
        "plt.xlabel('Amount of excreta (kg)')\n",
        "plt.ylabel('Time of degradation(hr)')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5Y-kpoloZcJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRjvMgxVoZhV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "30de20ee-a508-4d3d-c457-a352357889a1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "a1=np.array(['sulfonamide','tetracycline','ampicillin','macrolides', 'floroquinalones','penicilin']).reshape(6,1)\n",
        "r1 = np.array([40, 50,10,23,45,60]).reshape(6,1)\n",
        "\n",
        "final=np.concatenate((a1, r1), axis=1)\n",
        "print(final)\n",
        "ax=sns.heatmap(final)\n",
        "print(ax)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['sulfonamide' '40']\n",
            " ['tetracycline' '50']\n",
            " ['ampicillin' '10']\n",
            " ['macrolides' '23']\n",
            " ['floroquinalones' '45']\n",
            " ['penicilin' '60']]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-57e0ede6c8b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m     plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[1;32m    546\u001b[0m                           \u001b[0mannot_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticklabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m                           yticklabels, mask)\n\u001b[0m\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;31m# Add the pcolormesh kwargs here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;31m# Determine good default values for the colormapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         self._determine_cmap_params(plot_data, vmin, vmax,\n\u001b[0;32m--> 166\u001b[0;31m                                     cmap, center, robust)\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;31m# Sort out the annotations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m_determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;31m# plot_data is a np.ma.array instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mcalc_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'sulfonamide'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UleyQz_bZSWo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "c77c2e71-aee9-416a-aed9-1d23147d6052"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "plt.style.use(\"fivethirtyeight\")\n",
        "x = np.linspace(1, 10)\n",
        "plt.figure(dpi=1200)\n",
        "\n",
        "fig = plt.figure(figsize=(16, 9))\n",
        "\n",
        "y = 0.16783* x\n",
        "plt.plot(x,y)\n",
        "\n",
        "plt.legend()\n",
        "#plt.grid(True, linestyle=':')\n",
        "\n",
        "plt.xlabel('Volume of growth medium (L)')\n",
        "plt.ylabel('Amount of L-arabinose (gm)')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 7200x4800 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MtHDadHu/DP3q1Ay5HBQTaBgoKAAAAACgATBNUx9urdKsQp/WlEcsZzwphib1ztDE3hnyptqSnBD4aSgoAAAAAKCe+2JnlWYW+FSwL2y5nmaXbsrO0G19M9QyzZ7kdEDtoKAAAAAAgHrqf/ZUK6/QpyW7QpbrDkO6vodbd/b3qIObYgINGwUFAAAAANQzqw+ENavIpw+3VlmuG5Ku7ubSfQO86urlZR0aB57JAAAAAFBPbPJF9EiRT29tqpRZw8zILmm6P8er3i1SkpoNSDQKCgAAAACoYzuDUT2+wqfX1lcoUkMzcW4Hpx7I9eq01qnJDQckCQUFAAAAANSR/VVRzS0O6Pm1AYWsdwzVaa1TNC23mc7p4ExuOCDJKCgAAAAAIMl81TH9fnVAv18VUKCGUyZ6ZTr0wECvftY5TYZhJDkhkHwUFAAAAACQJJURUy98H9DclQEdCMUsZ7p67Lo/x6srT3bJRjGBJoSCAgAAAAASLBwz9dr6Cj2+wqddFdbFRId0m+4e4NV13dOVYqOYQNNDQQEAAAAACRKNmXq7tFL5RT5t9lvfZKKF06Y7+mXoxlMz5HJQTKDpoqAAAAAAgFpmmqb+trVKswp9+r48YjnjSTF0S58M/bpXhryptiQnBOofCgoAAAAAqEVf7KzSzAKfCvaFLdfT7NL47Azd1jdDLdLsSU4H1F8UFAAAAABQC/5nT7XyCn1asitkue4wpP/T0607+3vUPp1iAvjfKCgAAAAA4CdYfSCshwt9+vu2Kst1Q9I13Vy6L8erLA8vwYCacHQAAAAAwAnY5Isov8intzdVyqxh5uIuabo/16tezVOSmg1oiCgoAAAAAOA47AhG9fhyn14rqVC0hmZieAenHsj1amDr1OSGAxowCgoAAAAAiMP+qqh+WxzQC2sDClnvGKpBrVM0bWAzDWvvTG44oBGo071sli5dqtGjRys7O1uZmZlauHBh3I/duHGjOnXqpI4dOyYwIQAAAICmzlcdU36RT/3f2q3fr7YuJ3o1d+hP57fQxyNbU04AJ6hOC4pgMKhevXrpkUcekcvlivtx1dXVGjdunAYPHpzAdAAAAACassqIqadX+tX/7TI9utyvQOTI6zlO9tj1wjnN9dVlbfSzLi4ZhlEHSYHGoU4v8RgxYoRGjBghSZo4cWLcj3vwwQfVu3dvDRkyREuXLk1UPAAAAABNUHXU1B9LKvT4Cp92VcQsZzqk23TPAK+u7Z6uFBulBFAbGtw9KBYvXqzFixdryZIl+stf/lLXcQAAAAA0EtGYqbdLK5Vf5NNmv/VNJlo6bbqjv0c39nQrzUExAdSmBlVQ7Nq1S5MnT9Yf//hHZWRkxP24kpKSBKZCU8HzCDg2jhMgPhwrwLEl8zgxTemLA3bN35KiTRXWV8G77aZ+0TGsMR0icjsC2laatHjAUTWknyndu3c/6nqDKihuvvlmjRs3TqeddtpxPe5YXwTgWEpKSngeAcfAcQLEh2MFOLZkHif/3FmlvAKfCvaFLdfT7NL47Azd1jdDLdLsSckExKux/UxpUAXFkiVLtHTpUj366KOSJNM0FYvF1LJlS82ZM0djx46t24AAAAAAGoT/2VOtvEKfluwKWa47DOn/9HTrzv4etU+nmACSoUEVFF9//fVhb3/44YeaM2eOPv30U3Xo0KGOUgEAAABoKFYdCGtWoU9/31ZluW5IuqabS/fleJXlaVAvl4AGr06PuEAgoE2bNkmSYrGYtm/fruLiYjVv3lydO3fWjBkzVFBQoEWLFkmSevXqddjji4qKZLPZjng/AAAAAPynTb6I8ot8entTpY7cLPRHF3dJ09Rcr7KbpyQ1G4Af1WlBUVRUpEsuueTQ2/n5+crPz9eYMWM0f/58lZWVqbSUu88AAAAAODE7glE9vtyn10oqFK2hmRjewalpuV7ltk5NbjgAh6nTgmLo0KEqLy+vcX3+/PlHffx1112n6667rrZjAQAAAGjg9lVFNbc4oBfWBhSy3jFUg1qnaNrAZhrW3pnccAAscVEVAAAAgEbDVx3TM6sDmrcqoEDE+pSJ3s0dmjbQqws6pckwjCQnBFATCgoAAAAADV5FJKYXvg9q7kq/fghZFxMne+y6P9erK7q6ZKOYAOodCgoAAAAADVZ11NRrJUE9vtyvssqY5UyHdJvuGeDVtd3TlWKjmADqKwoKAAAAAA1ONGbq7dJK5Rf5tNlvfZOJlk6b7ujv0Y093UpzUEwA9R0FBQAAAIAGwzRN/W1rlWYV+vR9ecRyxptiaFKfDE3snSFPii3JCQGcKAoKAAAAAPWeaZr6YldIMwt8KtwXtpxJs0s3Z2doct8MtUizJzkhgJ+KggIAAABAvfbtnpDyCnz6sqzact1hSGN7ujWlv0ft0ykmgIaKggIAAABAvbTqQFgPF/r00bYqy3VD0qhuLt2b41WWh5c2QEPHUQwAAACgXtlWaeixLw7o7U2Vst4wVLq4S5qm5nqV3TwlqdkAJA4FBQAAAIB6YUcwqseX+/Ta+jRFVWk5M7yDU9NyvcptnZrkdAASjYICAAAAQJ3aVxXV3OKAXlgbUCgq/XjxxuFOb52qBwZ6Nay9M+n5ACQHBQUAAACAOnGwOqbfrw5o3qqAAhHrizl6N3do2kCvLuiUJsM4srgA0HhQUAAAAABIqopITC98H9TclX79ELIuJk722DU116vLu7pko5gAmgQKCgAAAABJUR019VpJUI8v96usMmY50zHdrrEdKnTbkK5KsVFMAE0JBQUAAACAhIrGTL21qVL5RT5tCUQtZ1o6bbqjv0c39nRrW+kGygmgCaKgAAAAAJAQpmnqr1urNKvQp7XlEcsZb4qhW/pk6Ne9M+RJsSU5IYD6hIICAAAAQK0yTVP/3BlSXqFPhfvCljMuu6Hx2W5N7puhFmn2JCcEUB9RUAAAAACoNd/uCWlmgU9flVVbrjsMaWxPt6b096h9OsUEgP+PggIAAADAT7bqQFgPF/r00bYqy3VD0qhuLt2b41WWh5chAI7EdwYAAAAAJ2zjwYjyl/v0zqZKWW8YKl1yUpruz/Equ3lKUrMBaFgoKAAAAAAct+2BiB5f4dcfSyoUraGZOK+DUw/kepXbOjW54QA0SBQUAAAAAOK2ryqq3xb79eLaoELWO4bq9NapmjbQq6HtnckNB6BBo6AAAAAAcEwHq2N6ZlVA81cHFIhYnzLRp0WKpuV6NaKTU4ZhJDkhgIaOggIAAABAjSoiMT3/fVBzi/0qr7YuJk722DU116vLu7pko5gAcIIoKAAAAAAcoTpq6rWSoB5f7ldZZcxypmO6XffkeDTmlHSl2CgmAPw0FBQAAAAADonGTL21qVL5RT5tCVjfZKKl06Yp/T0a19OtNAfFBIDaQUEBAAAAQKZp6q9bqzSr0Ke15RHLGW+Kod/0ydCE3hnypNiSnBBAY0dBAQAAADRhpmnqnztDmlnoU9G+sOWMy27o5l5uTe7rUXMnxQSAxKCgAAAAAJqob3aHlFfo01dl1ZbrKTZpbA+3pvT3qF26PcnpADQ1cRcUy5cv17Jly7Ru3Trt379fhmGoZcuW6tGjh8444wzl5OQkMicAAACAWrLyQFgPF/q0eFuV5brNkEZ1S9c9AzzK8vA3TQDJcdTvNnv37tXzzz+vP/3pT9qxY4dM01RqaqoyMzNlmqYOHjyo6upqGYahDh06aMyYMbrpppvUpk2bZOUHAAAAEKcNB8PKL/LrndLKGmcuPSlN9+d6dWpmShKTAcBRCoqHHnpIzz//vDwejy699FINHz5cAwYMUPv27Q+b27Vrl5YvX67PPvtMr732mubNm6fx48frwQcfTHh4AAAAAMe2PRDRYyv8WlhSoahpPXNeB6emDfQqp1VqcsMBwP9TY0GxZMkSLViwQBdffLEMo+atg9q3b6/27dvrZz/7mR577DF98MEHevLJJxMSFgAAAED89lVF9dtiv15cG1TIesdQndEmVQ/kejW0vTO54QDgf6mxoPjss8+O+4MZhqFLL71Ul1566U8KBQAAAODEHayO6ZlVAc1fHVAgYn3KRJ8WKZqW69WITs6j/kESAJKFO94AAAAAjURFJKbnvw9qbrFf5dXWxUQ3r11Tc7z6eVeXbBQTAOqR4y4oQqGQduzYofLycpnmkd/0Bg4cWCvBAAAAAMSnOmrq1fVBPb7Cr92VMcuZTm677h7g0bWnpMtho5gAUP/EXVD88MMPuv/++/Xuu+8qHA4fsW6apgzD0IEDB2o1IAAAAABr0ZipNzdVKr/Ip60B65tMtEqzaUo/j27o6Vaag2ICQP0Vd0ExceJELV68WFdeeaUGDhwor9ebyFwAAAAAamCapj7YUqXZRT6tLY9YznhTDN3a16MJvdzKSLElOSEAHL+4C4p//vOfmjBhgmbPnp3IPAAAAABqYJqmPt8ZUl6hT0X7jjyrWZJcdkM393Jrcl+PmjspJgA0HHEXFC1btlTXrl0TmQUAAABADb7ZHdLMQp+WllVbrqfYpLE93JrS36N26fYkpwOAny7uguKGG27Q22+/rXHjxslu5xseAAAAkAzF+6s1q9CnxdtDlus2QxrVLV33DPAoy8MmfQAarri/g02ZMkWhUEhDhw7V6NGj1aFDB8ui4vLLL6/VgAAAAEBTtOFgWPlFfr1TWlnjzKUnpen+XK9OzUxJYjIASIy4C4rt27fr008/1ffff68HH3zQcsYwDAoKAAAA4CfYHojosRV+LSypUNS0njm/o1MP5HqV0yo1ueEAIIHiLihuueUWrVq1SnfccQe7eAAAAAC1bG9lVL8t9uvFtUFVx6xnzmiTqmkDvTq7nTO54QAgCeIuKL799ltNnjxZ999/fyLzAAAAAE1KeSimZ1YHNH91QMGI9SkTfVqkaHquV//dySnDMJKcEACSI+6Cok2bNsrMzExkFgAAAKDJqIjE9NyaoJ5c6Vd5tXUxcYrXoam5Hl2W5ZKNYgJAIxd3QfGb3/xGzz//vH75y1/K4/EkMhMAAADQaFVHTb2yPqgnVvi1u9L6Wo5ObrvuGeDRmFPS5bBRTABoGuIuKAKBgFJSUpSTk6PLLrtMHTt2PGIXD8MwdOutt9Z6SAAAAKChi8ZMvbmpUvlFPm0NRC1nWqXZNKWfRzf0dCvNQTEBoGmJu6B46KGHDv33Sy+9ZDlDQQEAAAAczjRNfbClSrMKfVp3MGI54001dGsfjyb0cisjxZbkhABQP8RdUKxYsSKROQAAAIBGxTRNfb4zpJkFPi3fH7accdkNTejl1q19PWrupJgA0LTFXVB06dIlkTkAAACARmPZ7pDyCn1aWlZtuZ5ik8b2dGtKP4/apdstZwCgqYm7oAAAAABwdMX7qzWr0KfF20OW6zZDGt0tXfcM8OgkD7+KA8B/ivu7Yr9+/Y6657JhGEpLS1OHDh00dOhQ3XDDDWxLCgAAgCZhw8GwZhf59W5pZY0zl56Upqm5XvXMTEliMgBoOOK+0G3IkCFyu93aunWrMjIy1K9fP/Xr108ZGRnaunWr3G63evbsqb1792rmzJkaPHiwNm/efNSPuXTpUo0ePVrZ2dnKzMzUwoULjzr/5ZdfasyYMerZs6fat2+vwYMH67XXXov3nwAAAADUqu2BiG5d+oPOeG9PjeXE+R2d+vyS1nr1vJaUEwBwFHGfQXHRRRfpo48+0t/+9jcNHjz4sLWvvvpK119/vaZNm6YLL7xQX375pUaNGqWZM2fWuOOHJAWDQfXq1UtjxozRhAkTjpnh22+/Ve/evTV58mS1a9dOn376qW677TalpaXp6quvjvefAgAAAPwkeyuj+m2xXy+uDao6Zj1zZptUPTDQq7PbOZMbDgAaqLgLivz8fI0fP/6IckKSzj77bP3qV79SXl6eLrzwQg0dOlRjx47Vn//856N+zBEjRmjEiBGSpIkTJx4zw5QpUw57+8Ybb9SXX36pRYsWUVAAAAAg4cpDMT2zOqD5qwMKRkzLmb4tUjQt16v/7uQ86iXSAIDDxV1QbNq0Sc2aNatxPTMzU5s2bTr0ds+ePVVRUfHT0sXB7/erQ4cOR50pKSlJeA40fjyPgGPjOAHiw7HS8FRFpTd2OfTq9hT5ItalQxdXTM5TF7UAACAASURBVBO6hHV+qwrZqg5qw4Ykh2xkOE6A+DSkY6V79+5HXY+7oMjKytKf/vQnjR07Vunp6YetBYNBLVy4UCeddNKh9+3atUutWrU6zrjH56OPPtIXX3yhxYsXH3XuWF8E4FhKSkp4HgHHwHECxIdjpWGpjpp6ZX1QT6zwa3el9bUcndx23TPAozGnpMth44yJ2sBxAsSnsR0rcRcU9957r8aNG6dBgwZp1KhRysrKkiSVlpbqzTffVFlZmV588UVJUjQa1ZtvvqkzzjgjIaEladmyZbrpppv06KOPauDAgQn7PAAAAGh6ojFTb2ys0CPL/doaiFrOtEqz6c7+Ho3t4Vaag2ICAH6quAuKn//853K5XJoxY4bmzp172Fp2drbmzJmjCy+8UJJkmqbef//9hG0z+q9//UvXXHON7rvvPt14440J+RwAAABoekzT1KItVZpd6NO6gxHLGW+qoVv7eDShl1sZKXFvigcAOIa4CwpJuuCCC3TBBReorKxM27ZtkyR17txZ7dq1O/yDOhzq0qVL7aX8D0uXLtWoUaN07733xnVjTQAAAOBYTNPU5ztDmlng0/L9YcsZl93QhF5u3drXo+ZOigkAqG3HVVD8W7t27Y4oJU5EIBA4dGPNWCym7du3q7i4WM2bN1fnzp01Y8YMFRQUaNGiRZJ0aPvSG2+8UVdffbV2794tSbLb7Qm/3wUAAAAap2W7Q8or9GlpWbXleopNGtvTrTv7edQ23Z7kdADQdNRY/S5ZsuSEP2i8jy0qKtKwYcM0bNgwVVZWKj8/X8OGDdPs2bMlSWVlZSotLT00//rrr6uiokJPP/20evbseeh/w4cPP+GsAAAAaJqK91dr1D/26cIP91mWEzZDGnNKur67oq0ePzOTcgIAEswoLy+33MC5ffv26tOnj8aNG6eRI0fK6/Ue9QP5fD799a9/1csvv6zVq1dr586dCQkM1IXGdndcIBE4ToD4cKzUvQ0Hw5pd5Ne7pZU1zlyWlab7c7zqmZmSxGT4N44TID6N7Vip8RKPgoICPfroo5o8ebJuu+025eTkaMCAAcrKylJmZqZM01R5ebm2bNmi5cuXq6ioSJJ07bXX6tVXX03aPwAAAACIx7ZARI8t9+v1DRWKWv6JTvqvjk49kOvVgFapyQ0HAKi5oOjQoYOeeuopTZ8+XX/+85/14Ycf6tVXX1Vl5eFNc3p6ugYOHKgZM2bommuuUYsWLRIeGgAAAIjX3sqo5hT79dLaoKpj1jNntknVtIFeDWnnTG44AMAhx7xJZsuWLTVp0iRNmjRJkUhE27dv14EDByRJLVq0UOfOnWW3cz0eAAAA6pfyUEzPrA5o/uqAghHrUyb6tkjRtFyv/ruTU4ZhJDkhAOA/HdcuHg6HQ1lZWcrKykpQHAAAAOCnCYZjev77oJ5c6Vd5tXUxcYrXoam5Hl2W5ZKNYgIA6oUT2mYUAAAAqG+qo6ZeWR/UEyv82l1pfS1HJ7dd9wzwaMwp6XLYKCYAoD6hoAAAAECDFo2ZemNjhR5Z7tfWQNRypnWaTVP6e3RDT7ecdooJAKiPKCgAAADQIJmmqUVbqjS70Kd1ByOWM95UQ5P7eHRzL7cyUmxJTggAOB4UFAAAAGhQTNPUZztDyivwafn+sOVMusPQhF5u3drHo0wnxQQANAQUFAAAAGgwlu0OaWaBT1/vrrZcT7FJY3u4dWd/j9qms9McADQkFBQAAACo91bsr9asQp8+3h6yXLcZ0uhu6bpngEcnefgVFwAaouM6323Dhg0aP368srOz1bp1a33xxReSpP3792vSpEn67rvvEhISAAAATVPJwbBu+PyAzlm0t8Zy4rKsNP3r5200b2hzygkAaMDi/g6+cuVKjRw5Uk6nU2eddZb++te/Hlpr2bKlvv/+e7344os67bTTEhIUAAAATce2QESPLffr9Q0ViprWM//V0akHcr0a0Co1ueEAAAkRd0ExY8YMtW3bVp988okikYg++OCDw9bPP/98vfPOO7UeEAAAAE3H3sqo5hT79dLaoKpj1jNntknVtIFeDWnnTG44AEBCxV1QLFu2TPfee6+aNWumAwcOHLHeuXNnlZWV1Wo4AAAANA3loZieWRXQ/DUBBSPWp0z0bZGi6QO9+q+OThmGkeSEAIBEO66L9JzOmlvqPXv2HHUdAAAA+N+C4Zie+z6oJ1f6dbDaupjo3syhqTleXZqVJhvFBAA0WnEXFP3799fixYt10003HbEWDof1zjvvaNCgQbUaDgAAAI1TKGrqlXVBPVHs155K62s5OrntujfHo9Hd0uWwUUwAQGMXd0ExZcoUXXXVVbr11lt15ZVXSpLKysr0ySefaM6cOdqwYYPmzp2bsKAAAABo+CIxU29srNAjy/3aFohazrROs+nO/h6N7emW004xAQBNRdwFxXnnnadnn31Wd999t/74xz9Kkn7961/LNE01a9ZMzz33nM4888yEBQUAAEDDFTNNfbClSrMKfVp/MGI54001NLmPRzf3cisjxZbkhACAunZc96C4+uqrNXLkSH3++efauHGjYrGYunbtqvPOO08ejydRGQEAANBAmaapT3eElFfo04r9YcuZdIehm7PdmtzXo0wnxQQANFXHVVBIUnp6ukaOHJmILAAAAGhE/rU7pJkFPv1rd7XleopNuqGnW1P6edQ23Z7kdACA+ibuinrdunX629/+dtj7li5dqiuuuELnn3++5s2bV+vhAAAA0PCs2F+tqz/ep599uM+ynLAZ0nXd0/XdFW312JmZlBMAAEnHcQbFAw88IMMwDp09sWPHDo0aNUpOp1OtW7fWAw88oMzMTF177bUJCwsAAID6a315WLOL/Hp/c2WNMz/Pcun+HI96ZKYkMRkAoCGI+wyKFStWaMiQIYfefuONNxSLxfTVV19p2bJluuCCC/TCCy8kJCQAAADqr62BiCZ99YPOfH9PjeXEf3d06p+XtNYfhregnAAAWIr7DIqDBw+qZcuWh97+xz/+oaFDh6p9+/aSpAsuuEDTp0+v/YQAAACol3ZXRDWn2K8/rAuqOmY9c1bbVE3L9WpwO2dywwEAGpy4C4rWrVtr69atkqTy8nJ99913ysvLO7QeCoVqPx0AAADqnfJQTL9b5deCNUFVREzLmX4tUjR9oFfnd3TKMIwkJwQANERxFxTDhw/Xc889J6/Xq6+++kqSdNFFFx1aX7t2rTp27Fj7CQEAAFAvBMIxPbsmqKdW+eWrti4mujdzaGqOV5dmpclGMQEAOA5xFxTTp0/Xhg0bNG3aNKWmpmrmzJnq0qWLJKmqqkrvv/++rrnmmoQFBQAAQN0IRU39YV1QT6zwa2+V9bUcndx23Zvj0ehu6XLYKCYAAMfvuC7x+Pvf/66DBw/K5XIpNTX10Jppmlq0aJE6deqUkJAAAABIvkjM1J83VuiRIr+2B6OWM63TbLqzv0dje7rltFNMAABOXNwFxb81a9bsiPe5XC717du3VgIBAACgbsVMU4s2V2lWkU8lByOWM81SDU3u69H4bLcyUuLeGA4AgBodV0Hxww8/aO7cufr4448P3TCzS5cuuvDCCzV58mQ1b948ISEBAACQeKZp6pMdIeUV+FR8IGw5k+4w9Otebv2mj0eZTooJAEDtibug2L59uy688ELt2LFDAwYM0CWXXCJJKikp0VNPPaW3335bH330EZd5AAAANEBfl4WUV+jTv3ZXW66n2qQbero1pb9HbVz2JKcDADQFcRcUDz30kA4ePKgPPvhAZ5999mFrX3/9tUaPHq0ZM2bo+eefr/WQAAAASIzl+6r1cKFPn+yw3jLeZkhjTknXPQM86pJx3FcHAwAQt7h/ynz22We6+eabjygnJGnw4MEaP368XnrppVoNBwAAgMRYVx7W7CKf/rK5qsaZn2e5dH+ORz0yU5KYDADQVMVdUFRWVqpVq1Y1rrdq1UqVlZW1EgoAAACJscUf0aPL/frzxgrFTOuZ/+7o1NRcrwa0SrUeAAAgAeIuKE499VS99dZbuuGGG+R0Og9bq66u1ptvvqns7OxaDwgAAICfbndFVHOK/Xp5XVDhmPXMWW1TNS3Xq8HtnNYDAAAkUNwFxW233aaxY8dq+PDhGjdunE455RRJP94k8+WXX9batWv1yiuvJCwoAAAAjl95KKbfrfJrwZqgKiLWp0z0a5Gi6QO9Or+jU4ZhJDkhAAA/iruguOyyy7RgwQJNnz5dd91116EfXqZpqk2bNpo/f/6hnT0AAABQtwLhmJ5dE9RTq/zyVVsXE92bOfRArleXnJQmG8UEAKCOHdetmEeNGqUrr7xSRUVF2rZtmySpc+fOysnJkcPBXZ0BAADqWihq6uV1Qc1Z4dfeKutrOTq57bovx6NR3dLlsFFMAADqh+NuFRwOhwYNGqRBgwYlIg8AAABOQCRm6k8bKvTocr+2B6OWM21cNt3Zz6P/09Mtp51iAgBQvxx3QbF27Vpt3rxZ5eXlMs0jTxccM2ZMrQQDAADAscVMU3/ZXKlZhX5t8EUsZ5qlGrqtr0fjs91yp9iSnBAAgPjEXVCUlpZq/PjxKigosCwmJMkwDAoKAACAJDBNU5/sCCmvwKfiA2HLmXSHoYm9MnRLnwxlOikmAAD123Ht4rFmzRrl5+frrLPOUmZmZiJzAQAAoAZfl4WUV+jTv3ZXW66n2qQbero1pb9HbVz2JKcDAODExF1QfPPNN7rjjjt08803JzIPAAAAarB8X7UeLvTpkx0hy3WbIV17SrruHuBRlwxuYA4AaFji/snVsmVLeb3eRGYBAACAhXXlYc0u8ukvm6tqnLk8y6X7cz3q3iwlickAAKg9cV+MOG7cOL355puKRq3vCg0AAIDatcUf0cQvf9BZ7++psZwY0cmpLy5trZeHt6CcAAA0aHGfQZGVlaVIJKIhQ4ZozJgx6tixo+z2I69pvPzyy2s1IAAAQFOzuyKqJ4r9+sO6oMIx65mz2qZq+kCvzmrrTG44AAASJO6C4le/+tWh/37ooYcsZwzDoKAAAAA4QeWhmJ5a6dez3wdVEbHeNa1/yxRNH+jVeR2cMgwjyQkBAEicuAuKDz74IJE5AAAAmqxAOKZn1wT11Cq/fNXWxUSPZg5NzfXq0pPSKCYAAI1S3AXF2WefncgcAAAATU4oaurldUHNWeHX3irrazk6Z9h13wCPRnVLl91GMQEAaLzYfwoAACDJIjFTf9pQoUeX+7U9aH0D8jYum+7q79H1Pdxy2ikmAACNX40FxaRJk2QYhp566inZ7XZNmjTpmB/MMAw988wztRoQAACgsYiZpv6x166Xivdogy9iOZOZaui2vh7dlO2WOyXuDdcAAGjwaiwolixZIpvNplgsJrvdriVLlhzzekeuhwQAADiSaZr6x/aQ8gp9WnnAKenIcsLtMPTr3hm6pXeGMp0UEwCApqfGgmLlypVHfbs2LF26VE8//bRWrFihXbt26fe//72uu+66oz5m9erVuuuuu1RYWKjmzZtr7NixuvvuuylHAABAvbS0LKS8Ap+W7am2XE+1STee6tYd/Txq7TpyC3cAAJqKOr0HRTAYVK9evTRmzBhNmDDhmPM+n0+XX365Bg8erM8++0wlJSWaNGmS0tPT9Zvf/CYJiQEAAOKzfF+18gp9+nRHyHLdbkjXnpKuuwd41DmD24IBAHDcPw03bdqkjz/+WFu3bpUkdenSRSNGjNDJJ5983J98xIgRGjFihCRp4sSJx5x/6623VFlZqfnz58vlcqlXr15av3695s2bp1tuuYWzKAAAQJ1bVx7WrEKfFm2pqnHmiq4u3ZfjUfdmKUlMBgBA/RZ3QRGNRnXPPffo5ZdfVix2+DZYU6dO1dixY/XYY4/Jbk/cqYnffvutzjrrLLlcrkPvO//88zVr1ixt2bJFWVlZCfvcAAAAR7PFH9Gjy/3688YKxUzrmQs6OfXLVuW6OKdjcsMBANAAxF1QPPzww3rxxRc1atQoTZgwQd26dZMkbdy4UQsWLNDLL78sr9erBx98MGFh9+zZow4dOhz2vtatWx9aq6mgKCkpSVgmNB08j4Bj4zhBU7SvWnp5W4reLXMoYlqfzZnjjWpSVlj9vRWSOFaAeHCcAPFpSMdK9+7dj7oed0GxcOFC/fznP9eCBQsOe/+AAQO0YMECVVZWauHChQktKE7Usb4IwLGUlJTwPAKOgeMETc0PoZh+t9KvBWuCqoxanzIxoGWKpg306rwOzkOXonKsAMfGcQLEp7EdK3EXFBUVFTr77LNrXB82bJg+/fTTWglVkzZt2mjv3r2Hve/fb7dp0yahnxsAAECSAuGYFqwJ6ner/PJVWxcTPZs5NDXXq0tOSuMeWQAAxCnuTbYHDx6sZcuW1bi+bNkyDR48uFZC1eT000/Xv/71L1VV/f+bTn3++edq3769TjrppIR+bgAA0LSFoqYWrAko5+3derjQZ1lOdM6wa97Zmfr65210aZaLcgIAgOMQd0ExZ84cFRcXa8qUKVq3bp3C4bDC4bDWrVunO+64QytXrtRvf/vb4/rkgUBAxcXFKi4uViwW0/bt21VcXKxt27ZJkmbMmKFLL7300PxVV10ll8uliRMnas2aNVq0aJGefPJJTZw4kV8AAABAQkRipl5bH9TAd3br3m8Oam9V7IiZNi6bHj+zmb67oq2u7e6W3cbvJQAAHK8aL/Fo167dES/6o9Go1q9fr5dffvnQmmn++NcDh8Oh008/XTt37oz7kxcVFemSSy459HZ+fr7y8/M1ZswYzZ8/X2VlZSotLT203qxZM7333nu68847NXz4cGVmZmrSpEm65ZZb4v6cAAAA8YiZpv6yuVKzCv3a4ItYzmSmGrqtr0c3ZbvlTon77z4AAMBCjQXF5ZdfnvCzEoYOHary8vIa1+fPn3/E+3r37q2///3viYwFAACaMNM09Y/tIeUV+rTyQNhyxu0w9OveGbqld4YynRQTAADUhhoLCqtyAAAAoDFbWhZSXoFPy/ZUW66n2qQbT3Xrjn4etXbZk5wOAIDGLe5dPAAAABqr5fuqlVfo06c7QpbrdkO6rnu67urvUecMfn0CACARjvsn7M6dO7VixQr5fD7FYkfeJGrMmDG1EgwAACDR1pWHNavQp0VbqmqcuaKrS/fleNS9WUoSkwEA0PTEXVCEQiFNmjRJ7733nmKxmAzDOHSDzP+8VwUFBQAAqO+2+CN6ZLlfb2ysUOzI3UIlSRd0cmpqrlf9WqYmNxwAAE1U3AXFrFmz9P7772vq1Kk644wzdPHFF2v+/Plq166dnnnmGe3du1cLFixIZFYAAICfpKwiqjkr/PrD+qDCR54IKkka3DZV0wd6dWZbZ3LDAQDQxMV92+n33ntPo0eP1h133KHs7GxJUvv27XXuuefqrbfeUnp6ul566aWEBQUAADhRP4Rieui7g8p5e7eeX2tdTgxomaJ3R7TU337WinICAIA6EHdBsWfPHg0aNEiS5HD8eOJFVdWP12sahqHLLrtMixYtSkBEAACAExMIx/T4cp/6v1WmJ1cGVBk98nqOns0cenV4C31+SWud1zEt4dusAwAAa3Ff4tGqVSv5fD5JksfjkcvlUmlp6aH1cDisYDBY+wkBAACOU1XE1MvrgppT7Ne+KutrObpk2HVfjlfXnOyS3UYpAQBAXYu7oOjbt68KCgok/XjGxJAhQzR//nz1799fsVhMzz33nPr27ZuwoAAAAMcSiZl6fUOFHlvu1/Zg1HKmrcumu/p7dH0Pt1LtFBMAANQXcRcU119/vV5//XVVVVUpLS1NM2fO1MUXX6yRI0fKNE21aNFCs2bNSmRWAAAASzHT1PullZpV5NNGn3UxkZlq6PZ+Ht2U7Va6I+6rXAEAQJLEXVBcdNFFuuiiiw69nZ2draKiIn355Zey2+0688wzlZmZmZCQAAAAVkzT1MfbQ8or9GnVgbDljNthaGLvDE3qnaFMJ8UEAAD1VVwFRWVlpX73u99p0KBBOu+88w693+v1auTIkQkLBwAAUJOvykLKK/Dpmz3VluupNunGU926o59HrV32JKcDAADHK66CwuVyae7cuXrssccSnQcAAOCoivZVK6/Ap892hizX7YZ0Xfd03dXfo84ZcZ8sCgAA6ljcP7X79OmjTZs2JTILAABAjdaWhzWr0KcPtlTVOHNFV5fuz/HolGYpSUwGAABqQ9wFxbRp0zR27FidddZZuuCCCxKZCQAA4JDN/ogeKfLpzU2VipnWMxd0TtPUHI/6tUxNbjgAAFBr4i4onnnmGTVv3lxjxoxRhw4dlJWVJZfLddiMYRh68803az0kAABoesoqonpihV+vrA8qHLOeGdIuVdNzvTqjrTO54QAAQK2Lu6BYu3atDMNQp06dJElbt249YsYw2EscAAD8ND+EYnpqpV/PrgmqMmp9ykROqxRNz/Xq3A5Ofv8AAKCRiLugWLlyZSJzAACAJs4fjmnB6oCeXhWQL2xdTJya6dDUXK8u7pJGMQEAQCPDra0BAECdqoqYemldUL8t9mtflfW1HF0y7Lovx6trTnbJbqOYAACgMTqhgsLv98vn8ykWO/KXiM6dO//kUAAAoPGLxEy9vqFCjxb5taMiajnT1mXTXf09ur6HW6l2igkAABqz4yoo/vCHP+jpp59WaWlpjTMHDhz4yaEAAEDjFTNNvVdaqdlFPm30WRcTmamGbu/n0U3ZbqU7bElOCAAA6kLcBcWrr76q22+/XcOHD9d1112nvLw8TZw4UWlpaVq4cKHatm2r8ePHJzIrAABowEzT1MfbQ8or9GnVgbDljNthaGLvDN3SJ0PNUikmAABoSuIuKBYsWKBzzz1X7777rg4cOKC8vDyNGDFC55xzjn7zm9/onHPOkc/nS2RWAADQQH1VFlJegU/f7Km2XHfapRtPdev2vh61dtmTnA4AANQHcf9pYtOmTbrooot+fJDtx4eFwz/+9SMzM1PXX3+9XnjhhQREBAAADVXRvmpdsXifLv77Pstywm5I1/dIV8EVbTX79EzKCQAAmrC4z6Bwu90yzR+3/MrIyJDdbteuXbsOrbdo0UI7d+6s/YQAAKDBWVse1qxCnz7YUlXjzFUnu3TfAK+6NWNTMQAAcBwFRY8ePbRu3bofH+RwqG/fvnrjjTc0evRoRaNRvfHGGzrppJMSFhQAANR/m/0RPVLk05ubKhUzrWcu6JymB3K96tsiJbnhAABAvRb3JR4XXXSRPvroI1VV/fiXkDvvvFNff/21srKydMopp+ibb77R7bffnrCgAACg/iqriOrO/8vefcZHWefrH79mJskkmcwQSgiGKr2nqYB6UHBZ5fBXBGVFdy1YAEURRSlSdIkICqK4i1ggu7rqEVRcCyprwY7sShKalNAUREIoycykz8z9f+DrcJblDgyQzEySz/uR3L/vJJeaX5hcucuaIp2/okCv7zQvJy5qEaNV/91My37TlHICAACcIOgzKO655x7dc889x/48ZMgQrVy5Uu+++65sNpuuuOIKXXzxxbUSEgAARKYj5X4t3OjVC1tKVOY3P2UivVm0Zma4dGmKXRaLJcQJAQBAXXFWF33269dP/fr1q6ksAACgjvBUBbR4s1d/3uSVu8q8mOiaGKVpGS79vzaxFBMAAOCUzrigKCkp0Z///GeNHDmSe08AANBAlPsMZW8r0YINHh0qD5jOtE2waWq6SyPax8lmpZgAAADBOauC4vHHH1ffvn0pKAAAqOd8AUOv7SjV47ke/VzqN51pEWfVg2lO3djJoRgbxQQAADg9Z3WJx/8+dhQAANRPAcPQ27vL9FiuWzvd5sVEY7tF9/Vy6vZuDsVHBX3/bQAAgOPw4HEAAHACwzC0al+5sta5tfmoz3QmIcqiu3omaFyPBDWKoZgAAABn54wLCpvNptatWysuLq4m8wAAgDD76pcKZa1z65+Flabrdpt0W1eH7u/tVLNYW4jTAQCA+uqMC4qmTZtqw4YNx/68fv165ebm6pZbbqmJXAAAIMRyCiuVlePW6v0Vpus2i3Rjp3g9mOZSSwfFBAAAqFk1dj7mxx9/rPvvv7+mPhwAAAiRrUVVuvGzwxr4fmG15cS17eP0z2HJevqixpQTAACgVnAPCgAAGqg9Hp/m5rq1bGeZqrvt9RWtYzUtw6VeTaJDmg0AADQ8FBQAADQwB0r9mr/eo5e2l6gqYD5zcYsYzcx06YLm9tCGAwAADRYFBQAADcSRcr8WbvTqhS0lKvObnzOR0SxaMzNduuQcuywWS4gTAgCAhoyCAgCAes5TFdDizV79eZNX7irzYqJbYpSmZbg0pE0sxQQAAAiLkxYUCxcuDPoDrVmz5qzDAACAmlPuM7R0W4kWrPfocIX5tRxtE2yamu7SiPZxslkpJgAAQPictKB45JFHTuuD8RsXAADCrypg6LX8Uj2R59HPpX7TmRZxVj2Y5tSNnRyKsfH3NwAACL+TFhTr168PVQ4AAHCWAoaht3eX6bFct3a6zYuJxnaL7uvl1O3dHIqPqrGnjQMAAJy1kxYUbdq0CVUOAABwhgzD0Kp95cpa59bmoz7TmYQoi+7qmaBxPRLUKIZiAgAARB5ukgkAQB321S8Vylrn1j8LK03X7Tbp9q4Juq93gprF2kKcDgAAIHgUFAAA1EE5hZXKynFr9f4K03WbRbqxU7weTHOppYNiAgAARD4KCgAA6pAtR6s0O8et938qN123SLq2fZympLnUoRF/zQMAgLqDdy4AANQBezw+zcl1a/nOMhnVzFzROlbTM1zq2SQ6pNkAAABqQrUFxaZNm9S6dWs1atQolHkAAMC/OVDq17z1Hr20rUS+apqJi1vEaGamSxc0t4c2HAAAQA2q9jbe/fv31z/+8Y9jf77yyiv1xRdfhCQUAAAN3ZFyvx7+V7HS3yzQ0q3m5URGs2j9/fKmHDWKogAAIABJREFUeu+KZpQTAACgzqv2DIr4+HiVlpYe+/PXX3+tm266KSShAABoqDxVAS3e7NWfN3nlrjI/ZaJbYpSmZbg0pE2sLBZLiBMCAADUjmoLil69eumZZ55RRUWFnE6nJGnNmjXy+cyfr/6/rr/++ppNCABAA1DuM7R0W4kWrPfocEXAdKZtgk0PZbh07blxslkpJgAAQP1iKSoqMv31zIYNGzRq1Cjt2rXr10GLRYZR3W25dGzmyJEjpxVgyZIleuaZZ1RQUKCuXbtqzpw5uvDCC6udf+ONN7Rw4ULt3LlTTqdTl156qbKyspScnHxanxc4Hfn5+erUqVO4YwARjX1yZqoChl7LL9UTeR79XOo3nWkRZ9WkNJf+0CleMTaKibqOvQKcGvsECE592yvVnkHRu3dvff/99/rll1908OBBDRgwQNOmTdNvfvObGvvkK1as0JQpU/Tkk0+qb9++WrJkiUaMGKHvvvtOrVu3PmH+u+++05gxY5SVlaUhQ4aosLBQEydO1B133KF33323xnIBAFDbAoahFbvL9FiOW7s85sVEY7tF9/dy6rZuDsVHVXvbKAAAgHrhpI8ZtVgsSklJUUpKiq6//npdeumlSktLq7FPvmjRIt1www26+eabJUnz5s3Tp59+quzsbD388MMnzP/rX/9SSkqKxo0bJ0lq166dRo8ercmTJ9dYJgAAapNhGFq1r1xZ69zafNT8ssmEKIvu6pmgcT0S1CiGYgIAADQMQb/refbZZ3XeeedJ+vXN1aFDh3To0KFTXvZRncrKSuXl5WngwIHHHR84cKDWrl1r+po+ffqooKBAH374oQzD0OHDh7VixQoNGjTojDIAABBKX/1SoctXHtLIT46YlhN2mzSuR4LyRiTroXQX5QQAAGhQTnoGxX/atWuXZs2apU8//VQlJSWSJIfDoUGDBmn69Olq37590B/r8OHD8vv9SkpKOu54UlKSDh48aPqaCy64QEuXLtXo0aNVVlYmn8+nAQMGaPHixSf9XPn5+UHnAqrD1xFwauwTc5s9Vi3+MVpri2ym6zYZuqqFT7e19inZXqqjew/qaIgzIrTYK8CpsU+A4NSlvXKq+2UEXVBs2bJFl19+ucrLyzV48GB17txZkrR9+3atXLlSn332mT788EN169bt7BKfxNatWzV58mQ9+OCDGjhwoAoKCjRjxgxNmDBBzz//fLWvq083DUF41LebzwC1gX1yoi1HqzQ7x633fyo3XbdIurZ9nKamu9TedVq/M0Adxl4BTo19AgSnvu2VoN8NPfLII4qPj9fnn39+wpkSu3fv1uDBg/XHP/5Rr7/+elAfr2nTprLZbCosLDzueGFhoZo3b276mgULFigjI0Pjx4+XJPXs2VPx8fEaPHiwZs6cqZYtWwb7rwMAQK3Z4/FpTq5by3eWqboLIQe3jtW0DJd6NokOaTYAAIBIFfTFrWvWrNHtt99uehnHueeeq9tuu03ffvtt0J84JiZGaWlpWr169XHHV69erT59+pi+pqysTDbb8afH/u+fAwHzZ8YDABAqv5T6NXFNkc57q0DLqikn/qtFjD4ekqT/+U1TygkAAIB/E/QZFH6/X3a7vdr12NhY+f3mj0mrzrhx4zRmzBhlZmaqT58+ys7O1oEDBzRq1ChJ0pgxYyTp2OUbV1xxhe69914tXbpUl112mQ4cOKCpU6cqNTXV9LGkAACEwpFyv57e6NULW7wqr+avwoxm0ZqZ6dIl59hlsVhCGxAAAKAOCLqgSE1N1csvv6wbb7xRiYmJx60VFRXp5ZdfPu1HkA4fPlxHjhzRvHnzVFBQoG7dumn58uVq06aNJGnfvn3Hzf/+97+X1+vViy++qOnTp8vlcql///565JFHTuvzAgBQEzxVAT272atFm7xyV5lfzNEtMUrTMlwa0iaWYgIAAOAkLEVFRUE9J/Trr7/WsGHD1KhRI91www3q2LGjpF9vyvH666+ruLhYb7/9ti666KJaDQyEQ327+QxQGxrSPinzGVq61aunNnh1uML8EsN2TpumpLk0on2cbFaKCfyfhrRXgDPFPgGCU9/2StBnUFx88cV66623NH36dP3pT386bi01NVXZ2dmUEwCAeq0qYOjV/FI9kefW/lLzYqJFnFWT0lz6Q6d4xdgoJgAAAIJ1Ws8069+/v7788ksVFBRo7969kqTWrVsrOTm5VsIBABAJAoaht3aV6bFct3Z7zG8y0dhu0f29nLq9W4LioigmAAAATtcZPXQ9OTmZUgIAUO8ZhqGP9pYrK8etH476TGcSoiwa1zNB43okyBUT9MOxAAAA8B/OqKAAAKC++/KXCmWtK9a/CqtM1+026Y6uCbqvd4KaxtpMZwAAABA8CgoAAP7NusJKZeW49fn+CtN1m0W6qXO8Hkh1qaWDYgIAAKCmUFAAACBpy9EqPZrj1sqfyk3XLZJGtI/TlHSX2rv46xMAAKCm8Q4LANCg7Xb7NCfPrTd2lqm6527/d5tYTUt3qUeT6JBmAwAAaEiCLij27t2rZs2aKS4uznS9rKxMhw4dUuvWrWssHAAAtWV/iV/z13v08vYS+appJvqfY9eMDJfObx4T2nAAAAANUNC3G09NTdX7779f7fqHH36o1NTUGgkFAEBtOVzu14x/FSvjrQPK3mZeTmQ2i9Y7lzfVu1c0o5wAAAAIkaDPoDCM6k58/ZXP55PFwnPfAQCRyV0Z0LObvVq02StPlfnfad0To/RQhktD2sTydxoAAECIndY9KKp7s1ZcXKxPPvlESUlJNRIKAICaUuYztHSrVws2eHWkImA6085p00PpLl1zbpxsVooJAACAcDhpQTF37lw98cQTkn4tJ0aPHq3Ro0ebzhqGobvuuqvmEwIAcAaqAoZezS/VE3lu7S81LybOibdqUqpLf+gcr2iKCQAAgLA6aUGRmZmp2267TZK0ZMkSDRgwQB06dDhuxmKxyOFwKC0tTVdddVXtJQUAIAgBw9Bbu8r0WK5buz1+05kmdqvu652g27smKC6KYgIAACASnLSgGDRokAYNGiRJKikp0a233qrzzjsvJMEAADgdhmHow73lejTHrR+O+kxnnNEWjeuRoLt6JMgVE/R9ogEAABACQd+D4tlnn63NHAAAnLEv9lcoK6dY3xdWma7H2qQ7uiVoQq8ENY21hTgdAAAAgnFaN8n0+/369NNPtWfPHhUVFZ3wZA+LxaJJkybVaEAAAKqzrrBSWTlufb6/wnQ9yiLd2DleD6a6lOKgmAAAAIhkQRcUubm5uvHGG7V///5qHzlKQQEACIUfjlZpdo5bK38qN123SBrRPk5T0l1q7zqtLh4AAABhEvS7tokTJ6q8vFyvvvqq+vXrp8TExNrMBQDACXa7fZqT59YbO8tkXpVL/90mVtPSXerRJDqk2QAAAHB2gi4oNm/erOnTp2vw4MG1mQcAgBPsL/Fr/nqPXt5eIl81zUT/c+yamenSeUkxoQ0HAACAGhF0QZGSklKbOQAAOMHhcr+e3ujVi1u8Kjd/YqjOS4rWjAyXLkmJDW04AAAA1Kign7F233336aWXXpLb7a7NPAAAyF0Z0Nxct9LeLNCfNpmXE90To/TaZU308ZAkygkAAIB6IOgzKI4ePar4+HhlZGRo6NChatmypWy24++IbrFYNH78+BoPCQBoGMp8hpZs9eqpDV4dqQiYzrRz2vRQukvXnBsnm9US4oQAAACoLUEXFI888sixf87OzjadoaAAAJyJqoChV7aX6on1bv1Sal5MnBNv1aRUl/7QOV7RFBMAAAD1TtAFxfr162szBwCgAfIHDL21u0xzct3a7TG/yUQTu1X39U7Q7V0TFBdFMQEAAFBfBV1QtGnTpjZzAAAaEMMw9OHecj26zq0finymM85oi8b1SNBdPRLkign6lkkAAACoo4IuKAAAqAlf7K9QVk6xvi+sMl2PtUl3dEvQhF4JahprM50BAABA/RN0QdG7d29ZLCc/tdZisSgvL++sQwEA6p/vCyuVtc6tL36pMF2Pskg3dXbogVSnUhwUEwAAAA1N0AXFRRdddEJB4ff7tXfvXq1du1bdunVT7969azwgAKBu23ykSrNz3frgp3LTdYukER3iNDXNpXNdnNgHAADQUAX9TnDx4sXVrm3cuFHXXHONfve739VIKABA3bfb7dOcXLfe2FUmo5qZ/24Tq2npLvVoEh3SbAAAAIg8NfKrql69eumWW27Rww8/rC+++KImPiQAoI7aX+LXvPVu/W17qXzVNBOXptg1PcOl85JiQhsOAAAAEavGzqVt3ry5tm3bVlMfDgBQxxRVSTP+VawXt3hVbv7EUJ2XFK0ZGY10SYo9tOEAAAAQ8WqkoDhy5Ij+9re/KSUlpSY+HACgDnFXBvTsZq/+tDFOJX6v6Uz3xChNz3RpcOvYU95wGQAAAA1T0AXFlVdeaXq8uLhY+fn5qqys1PPPP19jwQAAka3MZ2jJVq+e2uDVkYqAfr3d5fHOddr0ULpLw8+Nk81KMQEAAIDqBV1QBAKBE37rZbFY1LZtW1166aX6wx/+oM6dO9d4QABAZKkKGHple6meWO/WL6UB05mUeKsmpbn0+07xiqaYAAAAQBCCLihWrlxZmzkAABEuYBh6c1eZHst1a4/H/CYTTexW3d87Qbd1TVBcFMUEAAAAgscD5wEAJ2UYhj74qVyzc9z6ochnOuOMtuj6cyo0/b/ayhVjDXFCAAAA1AenVVAcPXpUTz31lP7xj3/op59+kiS1adNGV1xxhe699141bty4VkICAMLji/0Vysop1veFVabrsTbpjm4JmtArQUf27qKcAAAAwBkLuqDYt2+frrjiCv38889KS0s7dtPM/Px8LVy4UG+++aY++ugjtWrVqtbCAgBC4/vCSmWtc+uLXypM16Ms0k2dHXog1akUh02SdCSUAQEAAFDvBF1QPPLIIyouLtZ7772niy+++Li1b7/9ViNHjtQf//hHvfjiizUeEgAQGpuPVGl2rlsf/FRuum6RNKJ9nKamu3Sui6sEAQAAUHOCfnf52WefacyYMSeUE5J04YUXavTo0crOzq7RcACA0Njl9mlurltv7CqTUc3MkDaxmpbhUvfG0SHNBgAAgIYh6IKirKxMzZo1q3a9WbNmKisrq5FQAIDQ2F/i17z1bv1te6l81TQTl6bYNSPDpcykmNCGAwAAQIMSdEHRtWtXvfHGGxo1apTsdvtxa5WVlVq+fLm6detW4wEBADXvcLlfT23w6sWtXlWYPzFU5yVFa0ZGI12SYjcfAAAAAGpQ0AXFhAkTdMstt2jAgAG69dZb1bFjR0m/3iTzL3/5i7Zu3aqXXnqp1oICAM6euzKgRZu9WrTJK281p0x0T4zS9EyXBreOlcViCXFCAAAANFRBFxRDhw7Vc889p5kzZ+rBBx889qbVMAw1b95cixcvPvZkDwBAZCnzGVqyxasFGz06WmFeTJzrtOmhdJeuaR8nK8UEAAAAQuy0bsF+3XXX6ZprrlFubq727t0rSWrdurXS09MVFcXd3AEg0lT6Db2SX6p56936pTRgOpMSb9WkNJd+3yle0VaKCQAAAITHabcKUVFROv/883X++efXRh4AQA3wBwy9ubtMc3Ld2uMxv8lEE7tV9/dO0G1dExQXRTEBAACA8DrtgmLPnj3as2ePioqKZBgnniY8bNiwGgkGADh9hmFo5U/lmp3j1pYin+mMM9qiu3sm6M7uCXLFWEOcEAAAADAXdEGxZ88ejRs3TmvWrJEk03LCYrFQUABAGBiGoS9+qdCsdW7lHKoynYm1SaO7JWhCrwQ1ibWFOCEAAABwckEXFOPGjdP333+v++67T5mZmXK5XLWZCwAQpH8drNSsdcX66kCl6XqURbqps0MPpDqV4qCYAAAAQGQKuqBYt26dJkyYoKlTp9ZmHgBAkDYdqdLsHLc+3Ftuum6RNKJDnKamuXSuixsZAwAAILIF/Y61ZcuWatSoUW1mAQAEYZfbpzm5br25q0zmDwyVhrSJ1bQMl7o3jg5pNgAAAOBMBX13tPHjx+tvf/ubvF5vbeYBAFTj5xK/JnxzVOevKNAb1ZQTl6bY9en/S9KrlzWlnAAAAECdEvQZFDfffLMCgYAyMjJ01VVXKSUlRTbb8dcyWywWjR8//rQCLFmyRM8884wKCgrUtWtXzZkzRxdeeGG185WVlZo3b56WLVumAwcOqHnz5rr77rs1duzY0/q8AFBXHCr366kNXi3Z6lWF+RNDdX5StKZnNNIlKfbQhgMAAABqSNAFxcaNG/Xkk0+qsLBQS5cuNZ053YJixYoVmjJlip588kn17dtXS5Ys0YgRI/Tdd9+pdevWpq+59dZbtX//fi1cuFDt27dXYWGhysrKgv6cAFBXFFcGtGizV89u8srrM7+Yo3vjKM3IcOmK1rGyWCwhTggAAADUnKALivvuu08ej0dPP/10jT3FY9GiRbrhhht08803S5LmzZunTz/9VNnZ2Xr44YdPmP/ss8/05ZdfKjc3V02bNpUktW3b9qxzAEAkKfUFtGRLiZ7a6NHRCvNi4lynTdMyXBp+bpysFBMAAACoB4IuKDZv3qypU6ceKxPOVmVlpfLy8nTPPfccd3zgwIFau3at6WtWrlyp9PR0LVq0SK+//rpiY2P1m9/8RjNnzlRCQkKN5AKAcKn0G/pbfonm5Xl0oCxgOpMSb9WkNJd+3yle0VaKCQAAANQfQRcU7dq1k2FUd7/403f48GH5/X4lJSUddzwpKUkHDx40fc2ePXv03XffyW636+WXX1ZxcbEmTZqkAwcO6OWXX672c+Xn59dYbjRcfB2htvgNaVWhTc//GK39Feb3Lm4UZWhU6ypd08KnWKtXe3aGOGSQ2CdAcNgrwKmxT4Dg1KW90qlTp5OuB11QTJkyRdOmTdPVV18dtssqAoGALBaLXnzxxWOPPJ03b56GDx+ugwcPqnnz5qavO9V/BOBU8vPz+TpCjTMMQ+//VK7ZOW5tLfKZzjijLbq7Z4Lu7J4gV0zQD14KC/YJEBz2CnBq7BMgOPVtrwRdUHz55Zdq1KiRLrjgAvXv318tW7Y0fYrH/Pnzg/p4TZs2lc1mU2Fh4XHHCwsLqy0akpOTdc455xwrJySpc+fOkqR9+/ZV+zoAiCSGYejz/RXKynEr51CV6UysTRrdLUETeiWoSazNdAYAAACoT4IuKLKzs4/98yeffGI6czoFRUxMjNLS0rR69WpdffXVx46vXr1aV111lelr+vbtq3feeUder/fYPSd27vz1POfqnvoBAJHknwcrNGudW18fqDRdj7JIN3dx6IFUp86Jp5gAAABAwxF0QXH06NEa/+Tjxo3TmDFjlJmZqT59+ig7O1sHDhzQqFGjJEljxoyRJD3//POSpGuvvVbz5s3TuHHjNGXKFBUXF2vKlCkaOnToCfeyAIBIsulIlbJy3Fq1t9x03SLpdx3iNDXdpXbOoL81AwAAAPVGjb4L3rVrl9q3bx/0/PDhw3XkyBHNmzdPBQUF6tatm5YvX642bdpI+vWyjX+XkJCgv//975o0aZIGDhyoxMREDRkyxPSRpAAQCXYW+zQnz603d5VVO/P/2sRqWoZL3RpHhzAZAAAAEFksRUVFZ/VojsOHD+utt97S8uXLlZOToyNHjtRUNiBi1Lebz6D27fP6NG+9R6/kl8pfzXfZASl2zchwKSMpJrThagn7BAgOewU4NfYJEJz6tlfO6AyKsrIyrVy5UsuXL9fnn3+uqqoqdejQQXfffXdN5wOAOuVQuV8LNni0dGuJKvzmMxckxWh6pkv9z7GHNhwAAAAQwYIuKAzD0OrVq7Vs2TJ98MEH8nq9slgsuvHGG3X33XfXq9YGAE5XcWVAf97k1eLNXnl95qdM9GgcpRmZLl3eKlYWiyXECQEAAIDIdsqCIi8vT8uWLdPbb7+tgoICdejQQXfddZcyMjI0cuRIXXbZZZQTABqsUl9AL24p0VMbPCqqNC8mOrhseijdpWHnxslKMQEAAACYOmlBccEFF2jHjh1KSUnRiBEjdM011ygtLU2StHv37pAEBIBIVOk39PL2Es1b71FBWcB0pmW8TZPTnbqhY7yirBQTAAAAwMmctKDIz89X27Zt9cgjj2jw4MGy27leGkDD5g8YemNXmebkuvWj1/wmE81irbqvt1O3dXEoNopiAgAAAAjGSQuKZ555Rm+88YZuu+02ORwODR48WNdee60GDhwYqnwAEBEMw9D7P5Vrdo5bW4t8pjOuaIvu6ZmgsT0S5Iy2hjghAAAAULedtKC48cYbdeONN2r//v164403tHz5ci1fvlxNmjTRRRddJIvFwo3eANRrhmHo8/0VmpXjVu6hKtOZOJtFY7o7dG8vpxrbKSYAAACAMxHUUzxSUlJ077336t5779WmTZu0fPlyrVixQoZh6P7779dHH32kwYMHa8CAAXI4HLWdGQBCYm1BhbJy3Pr6QKXperRVuqWzQxNTnWoRbwtxOgAAAKB+Cfoxo/+rZ8+e6tmzp/74xz/qq6++0rJly/Tee+/ptddeU2xsrH755ZfayAkAIbPxSJUezXFr1d5y03WrRbquQ7wmpznVznna30YBAAAAmDjjd9YWi0X9+/dX//79tWDBAn3wwQdavnx5TWYDgJDaWezTY7luvbW7rNqZq9rG6qEMl7omRocwGQAAAFD/1civ/ux2u4YNG6Zhw4bVxIcDgJDa5/Vp3nqPXskvld8wnxmYYteMTJfSm8WENhwAAADQQHBuMoAG61C5Xws2eLR0a4kqzJ8Yqj7NYzQ9w6X/OofHLAMAAAC1iYICQINTXBnQnzd5tXizV16f+SkTPZtEa0aGS79tZedpRQAAAEAIUFAAaDBKfQG9uKVET23wqKjSvJjo4LJpWrpLV58bJyvFBAAAABAyFBQA6r1Kv6GXt5do3nqPCsoCpjOtHDZNSnPqho7xirJSTAAAAAChRkEBoN7yBwwt31WmOblu/eQ1v8lEs1irJvZ2alQXh2KjKCYAAACAcKGgAFDvGIah934s12O5bm0t8pnOuKItuqdngsb2SJAz2hrihAAAAAD+EwUFgHrDMAyt3l+hrBy3cg9Vmc7E2Swa092he3s51dhOMQEAAABECgoKAPXC2oIKzcpx65sDlabr0Vbpls4OTUx1qkW8LcTpAAAAAJwKBQWAOm3jkSo9muPWqr3lputWi3Rdh3hNTnOqnZNveQAAAECk4t06gDppR3GV5uR69Nbusmpnrmobq4cyXOqaGB3CZAAAAADOBAUFgDpln9enJ9Z79Gp+qfyG+cxlLe2anuFSerOY0IYDAAAAcMYoKADUCYVlfi3Y4NHSrSWqDJjP9GkeoxmZLl3cwh7acAAAAADOGgUFgIhWXBnQnzZ5tXizVyU+81MmejaJ1swMlwa1sstisYQ4IQAAAICaQEEBICKV+gJ6cUuJntrgUVGleTHRwWXTQ+kuDTs3TlaKCQAAAKBOo6AAEFEq/YZe3l6iees9Kigzv5ajlcOmSWlO3dAxXlFWigkAAACgPqCgABAR/AFDy3eVaU6uWz95/aYzzWKtmtjbqVFdHIqNopgAAAAA6hMKCgBhZRiG3vuxXLNz3NpW7DOdccVYNL6nU2O7O5QQbQ1xQgAAAAChQEEBICwMw9Dq/RWatc6tvMNVpjNxNovGdndofC+nGtspJgAAAID6jIICQMitLajQrBy3vjlQaboebZVu6ezQxFSnWsTbQpwOAAAAQDhQUAAImY1HqvToumKt2ldhum61SNd1iNfkNKfaOfn2BAAAADQk/AQAoNbtKK7SY7kerdhdVu3MVW1j9VCGS10To0OYDAAAAECkoKAAUGv2eX16Yr1Hr+aXym+Yz1zW0q7pGS6lN4sJbTgAAAAAEYWCAkCNKyzza8EGj5ZuLVFlwHymT/MYzch06eIW9tCGAwAAABCRKCgA1JiiioD+vNmrxZu9KvGZnzLRs0m0Zma4NKiVXRaLJcQJAQAAAEQqCgoAZ63UF9ALP5To6Y0eFVWaFxMdXVGaluHU0HZxslJMAAAAAPgPFBQAzlil39BL20s0f71HBWXm13K0ctg0Oc2p6zvGK8pKMQEAAADAHAUFgNPmDxhatrNUc/M8+snrN51pFmvVA6lO3dLZodgoigkAAAAAJ0dBASBohmHovR/LNTvHrW3FPtMZV4xF43s6Nba7QwnR1hAnBAAAAFBXUVAAOCXDMLR6f4VmrXMr73CV6UyczaKx3R0a38upxnaKCQAAAACnh4ICwEl9V1ChrBy3vjlQaboebZVu6eLQxN5OtYi3hTgdAAAAgPqCggKAqQ2HKzU7x61V+ypM160WaWSHeE1Oc6qtk28lAAAAAM4OP1UAOM6O4io9luvRit1l1c4MbRerh9Jd6pIYHcJkAAAAAOozCgoAkqS9Xp+eyPPotR2l8hvmM5e1tGtGhktpzWJCGw4AAABAvUdBATRwhWV+PbnBo+ytJaoMmM/0bR6jGZkuXdTCHtpwAAAAABoMCgqggSqqCOjPm7xa/INXJT7zUyZ6NYnWjAyXBrWyy2KxhDghAAAAgIaEggJoYEqqAnphS4me3uhRcaV5MdHRFaVpGU4NbRcnK8UEAAAAgBCgoAAaiEq/oZe2l2j+eo8Kysyv5WjlsGlymlPXd4xXlJViAgAAAEDoUFAA9Zw/YGjZzlLNzfPoJ6/fdKZZrFUPpDo1qotDdhvFBAAAAIDQo6AA6inDMPTuj+V6LMetbcU+0xlXjEXjezo1trtDCdHWECcEAAAAgP9DQQHUM4Zh6LP9Fcpa51be4SrTmTibRWO7OzS+l1ON7RQTAAAAAMKPggKoR9YU/FpMfFtQaboebZVu6eLQA72dSo63hTgdAAAAAFQv7L86XbJkiXr37q3k5GRdcskl+vbbb4N63Zo1a9TL3/7kAAAgAElEQVS0aVP169evlhMCkW/94Ur97uNDGvzBIdNywmqRbugYr++HJ2te30TKCQAAAAARJ6wFxYoVKzRlyhRNnDhRX375pS644AKNGDFCe/fuPenrioqKNHbsWF1yySUhSgpEpvziKo1afUSXvFuof+yrMJ0Z2i5Wa65urmf/q7HaOjlpCgAAAEBkCmtBsWjRIt1www26+eab1aVLF82bN0/JycnKzs4+6evuvvtuXX/99Tr//PNDlBSILHu9Pt399VH1efug3t5TZjrzm5Z2fX5lkl4a0FRdEqNDnBAAAAAATk/YCorKykrl5eVp4MCBxx0fOHCg1q5dW+3rlixZosLCQj344IO1HRGIOAfL/JqytkiZbxXolfxSBYwTZ/o2j9HKwc305m+bKa1ZTOhDAgAAAMAZCNv53ocPH5bf71dSUtJxx5OSknTw4EHT12zevFmPP/64Pv74Y9lswV9Dn5+ff1ZZASm8X0cen/TKz9H6n5+jVBawmM50dgR0V9tKXdi4VBZPkfI9IQ4JiO+3QLDYK8CpsU+A4NSlvdKpU6eTrteZC9IrKip06623KisrS+3atTut157qPwJwKvn5+WH5OiqpCuiFLSV6eqNHxZUmp0tI6tQoStPSXbqqXaysFvPyAgiFcO0ToK5hrwCnxj4BglPf9krYCoqmTZvKZrOpsLDwuOOFhYVq3rz5CfMHDhzQtm3bNG7cOI0bN06SFAgEZBiGmjZtqjfeeOOEy0WAuqrCb+ilbSWav8Gjg2UB05lWDpumpDs1skO8oqwUEwAAAADqtrAVFDExMUpLS9Pq1at19dVXHzu+evVqXXXVVSfMp6SknPAI0qVLl2r16tV65ZVX1KZNm1rPDNQ2X8DQsp2lmpvn0V6v33QmKdaqB1KduqWLQ3YbxQQAAACA+iGsl3iMGzdOY8aMUWZmpvr06aPs7GwdOHBAo0aNkiSNGTNGkvT8888rOjpa3bt3P+71zZo1k91uP+E4UNcEDEPv/Viu2TlubS/2mc40irHo3l5Oje7mUEJ0WB/AAwAAAAA1LqwFxfDhw3XkyBHNmzdPBQUF6tatm5YvX37sbIh9+/aFMx5Q6wzD0Kc/Vygrx631h6tMZ+KjLBrb3aHxPZ1KtFNMAAAAAKifLEVFReZ33gNwTG3cfGZNQYVmrXNrTUGl6Xq0VRrVxaGJvZ1Kjg/+qTVAuNS3mzQBtYW9Apwa+wQITn3bK3XmKR5AfbH+cKVm57j1j30VputWi3R9x3hNSnWqrZMtCgAAAKBh4KcfIETyi6v0WI5Hb+8pq3bm6nZxeijdqc6J0SFMBgAAAADhR0EB1LKfvD49kefRaztKFajmgqpBLe2aluFSWrOY0IYDAAAAgAhBQQHUkoNlfj253qO/bCtRZcB8pl9yjGZkuHRhC3towwEAAABAhKGgAGpYUUVAf9rk0eIfSlTqMz9loneTaM3MdOmylnZZLJYQJwQAAACAyENBAdSQkqqAXthSoqc3elRcaV5MdGoUpWnpLl3VLlZWigkAAAAAOIaCAjhLFX5DL20r0fwNHh0sM7+Wo5XDpinpTo3sEK8oK8UEAAAAAPwnCgrgDPkChpbtLNXcPI/2ev2mM0mxVj2Q6tQtXRyy2ygmAAAAAKA6FBTAaQoYht77sVyzc9zaXuwznWkUY9G9vZwa080hR7Q1xAkBAAAAoO6hoACCZBiGPv25Qlk5bq0/XGU6Ex9l0djuDo3v6VSinWICAAAAAIJFQQEEIa/YqvEfHtKagkrT9RirNKqLQxNTnWoeZwtxOgAAAACo+ygogJNYf7hSj65z6+OfYyWdWE5YLdL1HeM1Oc2pNglsJwAAAAA4U/xEBZjYXlSlx3I9+vuesmpnrm4Xp4fSneqcGB3CZAAAAABQP1FQAP/mJ69PT+R59NqOUgUM85lBLe2aluFSWrOY0IYDAAAAgHqMggKQdLDMryfXe/SXbSWqDJjP9EuO0YwMly5sYQ9tOAAAAABoACgo0KAVVQT0p00eLf6hRKU+81MmejeJ1m3neHTT+SmyWCwhTggAAAAADQMFBRqkkqqAnt9SooUbPSquNC8mOjWK0vQMl65sG6udO4opJwAAAACgFlFQoEGp8Bv667YSPbnBo4Nl5tdytHLYNDXdqes6xCvKSikBAAAAAKFAQYEGwRcw9PrOUs3N9Whfid90pnmcVQ/0durmLg7ZbRQTAAAAABBKFBSo1wKGofd+LNfsHLe2F/tMZxrFWDS+p1NjuzvkiLaGOCEAAAAAQKKgQD1lGIY+/blCWTlurT9cZToTH2XRnd0duqenU4l2igkAAAAACCcKCtQ73x74tZhYU1Bpuh5jlUZ1cWhiqlPN42whTgcAAAAAMENBgXoj71ClZue49fHPFabrVot0Q8d4TUpzqk0CX/oAAAAAEEn4KQ113vaiKj2W69Hf95RVOzOsXZympjvVOTE6hMkAAAAAAMGioECd9ZPXp8fzPPqfHaUKGOYzg1raNT3TpdSmMaENBwAAAAA4LRQUqHMOlvn15HqP/rKtRJUB85l+yTGakeHShS3soQ0HAAAAADgjFBSoM4oqAvrTJo8W/1CiUp/5KRO9m0RrZqZLl7W0y2KxhDghAAAAAOBMUVAg4pVUBfT8lhIt3OhRcaV5MdG5UZSmZbh0ZdtYWSkmAAAAAKDOoaBAxKrwG/rrthLNX+9RYbn5tRytE2yakubUdR3iFWWlmAAAAACAuoqCAhHHFzD0+s5Szc31aF+J33SmeZxVE3s7dUsXh+w2igkAAAAAqOsoKBAxAoahd/eUa3auW/nFPtOZRjEWTejl1OhuDjmirSFOCAAAAACoLRQUCDvDMPTJzxXKWufWhiNVpjPxURbd1T1Bd/dMUKKdYgIAAAAA6hsKCoTVtwcqlJXj1pqCStP1GKs0qotDE1Odah5nC3E6AAAAAECoUFAgLPIOVerRHLc++bnCdN1qkW7oGK9JaU61SeDLFAAAAADqO37yQ0htL6rSY7ke/X1PWbUzw9rF6aEMpzo1ig5hMgAAAABAOFFQICR+8vr0eJ5H/7OjVAHDfOa3reyaluFSatOY0IYDAAAAAIQdBQVqVUGpX09u8Ogv20pUFTCf6Zcco5mZLvVLtoc2HAAAAAAgYlBQoFYUVQT0zCaPnvuhRKU+81MmUptGa2amSwNT7LJYLCFOCAAAAACIJBQUqFHeqoCe/6FECzd55K40LyY6N4rStAyXrmobSzEBAAAAAJBEQYEaUuE39JdtJXpyvUeF5ebXcrROsGlKmlPXdYhXlJViAgAAAADwfygocFZ8AUP/s6NUj+d5tK/EbzrTPM6qB3o7dXMXh+w2igkAAAAAwIkoKHBGAoahd/eUa3auW/nFPtOZRjEWTejl1OhuDjmirSFOCAAAAACoSygocFoMw9AnP1coa51bG45Umc44oiy6s3uC7u6ZoEQ7xQQAAAAA4NQoKBC0bw9UKCvHrTUFlabrMVbp1q4O3d/bqeZxthCnAwAAAADUZRQUOKW8Q5V6NMetT36uMF23WqQbOsZrUppTbRL4kgIAAAAAnD5+mkS1thVV6bFct97ZU17tzLB2cXoow6lOjaJDmAwAAAAAUN9QUOAEP3p8ejzPo9d3lipgmM/8tpVd0zJcSm0aE9pwAAAAAIB6iYICxxSU+jV/g0d/3VaiqoD5TL/kGM3MdKlfsj204QAAAAAA9RoFBVRUEdDCjR49v6VEpT7zUyZSm0ZrZqZLA1PsslgsIU4IAAAAAKjvKCgaMG9VQM//UKKFmzxyV5oXE50bRWlahktXtY2lmAAAAAAA1BoKigaowm/oL9tK9OR6jwrLza/laJ1g09Q0p37XIV5RVooJAAAAAEDtoqBoQHwBQ/+zo1SP53m0r8RvOtM8zqoHU526qbNDdhvFBAAAAAAgNCgoGoCAYeidPWWanePRDrfPdCYxxqIJvZy6o5tDjmhriBMCAAAAABq6sP8kumTJEvXu3VvJycm65JJL9O2331Y7++6772rYsGHq0KGDWrVqpcsuu0wffPBBCNPWLYZh6B97y3XJu4Ua9flR03LCEWXRA6lO5V3bQhN6OyknAAAAAABhEdafRlesWKEpU6Zo4sSJ+vLLL3XBBRdoxIgR2rt3r+n8N998o/79+2v58uX68ssvNWjQIP3hD384aanRUH1zoEKDPzik331yWBuPVJ2wHmOV7uzuUN61yZqe4VKinWICAAAAABA+lqKiIvPHN4TAZZddph49euiZZ545diwjI0NDhw7Vww8/HNTHGDhwoPr166fZs2fXVsw6Je9QpbJy3Pr05wrTdZtFuqFjvCalOdU6gSt8gpWfn69OnTqFOwYQ0dgnQHDYK8CpsU+A4NS3vRK2n1ArKyuVl5ene+6557jjAwcO1Nq1a4P+OF6vV4mJiSedyc/PP6OMdcnuUoue+zFanx2u/n/poGY+jW5TpXbxpSr/5ZDq/3+VmtUQvo6As8U+AYLDXgFOjX0CBKcu7ZVTlSlhKygOHz4sv9+vpKSk444nJSXp4MGDQX2MF198Ufv379d111130rn61Cj9px89Ps3N82jZzlIFqjkX5vJWdk3LcKl305jQhqtH6lszCdQG9gkQHPYKcGrsEyA49W2v1Nlz/N955x3NnDlT2dnZatOmTbjjhFxBqV/zN3j0120lqgqYz1yYHKOZmS71TbaHNhwAAAAAAKcpbAVF06ZNZbPZVFhYeNzxwsJCNW/e/KSvfeeddzR27Fg999xzGjx4cG3GjDhHKwJ6ZqNHz/1QojK/+SkTaU2jNTPTpQEpdlkslhAnBAAAAADg9IXt0Q0xMTFKS0vT6tWrjzu+evVq9enTp9rXvf322xozZoyeffZZDR06tLZjRgxvVUDz13uU+uYBPbXRa1pOdGkUpZcHNNHqK5M0sGUs5QQAAAAAoM4I6yUe48aN05gxY5SZmak+ffooOztbBw4c0KhRoyRJY8aMkSQ9//zzkqS33npLY8aMUVZWli688EIVFBRI+rXsaNy4cXj+JWpZuc/QX7aV6MkNHh0qN7+Wo02CTVPTXfpd+zjZrJQSAAAAAIC6J6wFxfDhw3XkyBHNmzdPBQUF6tatm5YvX37snhL79u07bj47O1s+n09Tp07V1KlTjx2/6KKLtHLlypBmr22+gKHXdpTqiTyP9pX4TWeS46x6MNWpmzo7FGOjmAAAAAAA1F1hv0nm7bffrttvv9107T9Lh/pWQpgJGIbe2VOm2Tke7XD7TGcSYyya0Mup0d0dio8K21U6AAAAAADUmLAXFPg/24qqdPsXR7XxSJXpuiPKojt7JOjuHglKtFNMAAAAAADqDwqKCJIcZ9Ne74lnTcRYpdu6OnR/b6eS4mxhSAYAAAAAQO3i1/ARJNFu1YRezmN/tlmkmzrHa901yZrTJ5FyAgAAAABQb3EGRYS5o5tDz/3g1YUt7Hoo3amOjaLDHQkAAAAAgFpHQRFhHNFWrR2erEYxnNwCAAAAAGg4+Ck4AlFOAAAAAAAaGn4SBgAAAAAAYUdBAQAAAAAAwo6CAgAAAAAAhB0FBQAAAAAACDsKCgAAAAAAEHYUFAAAAAAAIOwoKAAAAAAAQNhRUAAAAAAAgLCjoAAAAAAAAGFHQQEAAAAAAMKOggIAAAAAAIQdBQUAAAAAAAg7CgoAAAAAABB2FBQAAAAAACDsKCgAAAAAAEDYUVAAAAAAAICwo6AAAAAAAABhR0EBAAAAAADCjoICAAAAAACEnaWoqMgIdwgAAAAAANCwcQYFAAAAAAAIOwoKAAAAAAAQdhQUAAAAAAAg7CgoAAAAAABA2FFQAAAAAACAsKOgAAAAAAAAYUdBAZhYsGCBBgwYoNatW6tDhw667rrr9MMPP4Q7FhDRFixYoMTERD344IPhjgJEnAMHDmjs2LHq0KGDkpOT1adPH3399dfhjgVEFL/fr0cffVS9e/dWcnKyevfurUcffVQ+ny/c0YCw+eabbzRy5Eh169ZNiYmJevXVV49bNwxDc+bMUdeuXdWiRQsNGTJEW7ZsCVPas0dBAZj4+uuvddttt2nVqlV69913FRUVpauvvlpHjx4NdzQgIv3rX//SX//6V/Xo0SPcUYCIU1RUpMsvv1yGYWj58uVau3atnnjiCSUlJYU7GhBRnn76aS1ZskSPP/64/vnPf2ru3Ll68cUXtWDBgnBHA8KmpKRE3bt319y5cxUXF3fC+sKFC7Vo0SI9/vjj+uyzz5SUlKRhw4bJ4/GEIe3ZsxQVFRnhDgFEOq/XqzZt2ujVV1/V4MGDwx0HiCjFxcW65JJL9Mwzz+jxxx9X9+7dNW/evHDHAiLGrFmz9M0332jVqlXhjgJEtOuuu06NGzfWc889d+zY2LFjdfToUS1btiyMyYDI0LJlSz3xxBP6/e9/L+nXsye6du2qO+64Qw888IAkqaysTJ06dVJWVpZGjRoVzrhnhDMogCB4vV4FAgElJiaGOwoQcSZMmKChQ4eqf//+4Y4CRKSVK1cqMzNTo0aNUseOHXXxxRfrhRdekGHwOyLg3/Xt21dff/21tm/fLknaunWrvvrqKw0aNCjMyYDI9OOPP6qgoEADBw48diwuLk4XXnih1q5dG8ZkZy4q3AGAumDKlCnq1auXLrjggnBHASLKSy+9pF27dumFF14IdxQgYu3Zs0dLly7VXXfdpQkTJmjjxo2aPHmyJGn06NFhTgdEjgkTJsjr9apPnz6y2Wzy+Xx64IEHdPvtt4c7GhCRCgoKJOmESwaTkpL0yy+/hCPSWaOgAE7hoYce0nfffaePPvpINpst3HGAiJGfn69Zs2bpo48+UnR0dLjjABErEAgoPT1dDz/8sCQpNTVVu3bt0pIlSygogH+zYsUKvf7661qyZIm6du2qjRs3asqUKWrTpo1uuummcMcDEAIUFMBJTJ06VStWrNB7772ndu3ahTsOEFH++c9/6vDhw+rbt++xY36/X99++62ys7O1f/9+2e32MCYEIkNycrK6dOly3LHOnTtr3759YUoERKaZM2fq7rvv1jXXXCNJ6tGjh/bu3aunnnqKggIwkZycLEkqLCxU69atjx0vLCxU8+bNwxXrrFBQANWYPHmy3n77bb333nvq3LlzuOMAEWfIkCFKT08/7ti4cePUoUMH3X///YqJiQlTMiCy9O3bVzt27Dju2I4dO457MwlAKi0tPeFsVZvNpkAgEKZEQGRr27atkpOTtXr1amVkZEiSysvLtWbNGs2aNSvM6c4MBQVg4oEHHtCyZcv0yiuvKDEx8dj1XQ6HQwkJCWFOB0SGxMTEE24cGx8fr8aNG6t79+5hSgVEnrvuuku//e1vNX/+fA0fPlwbNmzQCy+8oBkzZoQ7GhBRrrjiCj399NNq27atunbtqg0bNmjRokUaOXJkuKMBYeP1erVr1y5Jv14yuG/fPm3YsEGNGzdW69atdeedd2rBggXq1KmTOnbsqPnz58vhcOjaa68Nc/Izw2NGARPVPa1j8uTJmjp1aojTAHXHkCFDeMwoYGLVqlWaNWuWduzYoVatWumOO+7QmDFjZLFYwh0NiBgej0ezZ8/W+++/r0OHDik5OVnXXHONJk2apNjY2HDHA8Liq6++0pVXXnnC8euvv16LFy+WYRiaO3eu/vrXv6qoqEiZmZmaP39+nf1lEQUFAAAAAAAIO2u4AwAAAAAAAFBQAAAAAACAsKOgAAAAAAAAYUdBAQAAAAAAwo6CAgAAAAAAhB0FBQAAAAAACDsKCgAA/k1iYqLmzJkT7hi1bvHixUpLS1OTJk108cUXhztOrUlMTNR9990X7hhn7M4771SvXr2OOxapX6P79+9XixYt9MUXX5zW61atWqWWLVvq0KFDtZQMAFBXUFAAAOqkkSNHKjk5WUVFRdXOTJo0SYmJidqxY0cIk0W+NWvWaOr/b+/Og6oq3wCOf/VqoU7I6AUuIiWImoClmNplkVxGEDQV8tJcF6TRZkxRp7QxN9LMoAU1d7RJxl0gzQXFVDQI1HFFotwSl5QSkkuCoF74/cHc8+vKpkAi+Xxm7gy857zvec97zj/vc97lo4/o3r07S5cuZc6cOfVdpVrZt2/fU9lhf9ZERkbi6uqKj4+PkvbZZ59hZWXFH3/8UWk+X19f2rVrR1RU1JOophBCiKeYBCiEEEI0SMHBwRQXF7Njx44KjxuNRrZt24a7uzvOzs5PuHZPt5SUFACioqLQ6/UMGDCgnmtUOz/88AORkZH1XY0nJjs7m2nTptV3Nczk5OSwceNGQkNDa5Q/NDSUtWvXkp+fX8c1E0II0ZBIgEIIIUSDNHDgQCwtLYmLi6vweFJSErdu3UKn0z3hmj39bt26BUDLli3/1esYjUaKi4v/1Ws8iywsLGjSpEl9V8PMli1bABg0aFCN8g8ZMoTi4mK2b99el9USQgjRwEiAQgghRINkYWHB4MGDSUlJ4ebNm+WOb926FZVKRVBQEAC5ublMnjyZjh07Ymtri1arJSYmptrrmIaoP2zDhg1YWVlx5coVJa1Lly4EBQWRnJzMG2+8gUajQavVKnPyd+3ahYeHB7a2tvTu3ZvTp0+XK/fixYuMGTMGR0dHbG1t8fb25vvvv3+kNjEajXz55Zd069YNGxsb3NzcmDNnDnfv3lXOsbKyIjo6WvnbysqKDRs2VFnu9u3b6dWrl9JuO3bsKLc2wpUrV7CysmLhwoVER0fj7u6OjY0Nx44dA+Ds2bMMHz4cBwcH2rRpQ0BAAKmpqUp+g8FAq1atWLJkiZJWUFCAWq2mTZs23L9/X0mfP38+tra2FBUVMX78eFavXm12Pw8/Fyhre61Wi42NDa+//jr79++vtj3/eU9r1qzh1Vdfxc7OjiFDhnD16lVKS0v56quvcHV1RaPR8Pbbb5Obm1uunIMHD+Lv74+9vT329vYEBQWRnp5e7jxTHU3tvHPnzgrr9fAaFE/6Ha3I7t276datW4X1eBTW1ta4urqya9euGuUXQgjx3yABCiGEEA2WTqejpKSE+Ph4s/TCwkISEhLo06cP1tbWFBUVMXjwYDZs2EBgYCDz5s3D2tqayZMns3DhwjqtU1ZWFmPHjmXAgAGEh4djMBjQ6/XExsYyffp0hg8fzowZM8jKyiIkJASj0ajkPXfuHP369SMzM5PJkyczf/58WrVqRUhIiPKFuipTpkxh/vz5uLm5sWDBAnx8fPj6668ZPXo0paWlAKxatYo+ffoof69atQpPT89Ky0xMTCQ0NBSVSsXs2bMZNGgQkyZN4syZMxWev2XLFpYsWcKIESNYsGABGo2Gc+fO4e/vT3p6OmFhYUyfPp3s7GyGDh3KTz/9BJSN5nBxcTELWhw7dgyj0UhhYSGnTp1S0tPS0nB3d8fCwoLQ0NBy97Nq1SrUarVZOdOmTSMwMJC5c+dSVFTE6NGj+euvv6ptU4D4+Hiio6MZN24cEyZMIC0tjTFjxrBgwQL27t3LpEmTGDNmDImJicyYMcMsb2xsLEFBQVhYWDBnzhymT59OVlYW/v7+nD9/Xjnv4MGDjB49GoDZs2cTEBDAxIkTHzlA8Dhq845W5P79+5w6dYquXbvWql5du3bl2LFjyrsqhBDi2fN0jQ8UQgghHoO3tzf29vbExcUxceJEJT0hIYE7d+4o0zvWrl1LZmYmy5cvR6/XAzB27FgCAwOJiIggJCSEVq1a1UmdLl26REJCAh4eHgB06tSJwMBAJk6cyNGjR2nXrh1Q1iGfMmWK8iUbYPr06djZ2ZGUlESzZs0AGDduHMOGDWPu3LnodDoaNWpU4XUzMjJYt24der2e5cuXK+lt27YlMjKSxMRE/Pz8CA4O5vjx4yQlJREcHFzt/cydOxeNRkNiYiIvvPACAD4+PgwaNAgHB4dy51+7do0TJ06g0WiUtJEjR1JUVMShQ4do3749ACNGjKBHjx7MnDmTQ4cOAaDVaomLi6O0tJRGjRqRmpqKq6srRUVFpKam0rNnT+7du8eJEyeYMGECAD179sTZ2bnK+zl//jxHjx7FyckJKHtvvLy8iIuL49133622DW7cuMHJkyeV0QElJSVERUVx9+5dfvzxR5o2bQqUrcPw3XffsWjRIpo1a0ZBQQHTpk1Dr9ezbNkypbxRo0bx2muv8fnnn7NmzRoAwsPDsba2Zu/evcrUG29vb4YMGVJhO9dGbd7Rily/fp27d+8q+WqqXbt25OXlkZ2djZ2dXa3KEkII0TDJCAohhBANVuPGjQkKCuL06dNmO3XExsbSokULAgICgLJdHtRqtVkHVqVSMX78eIqLi5UOcl1wdnZWOn4A3bt3B8DLy8usA2dKz8rKAuD27dscOnSIoUOHUlhYSG5urvLr168fN27cqHI3kn379gEoHXeT9957D5VKpRx/HDdv3iQzMxOdTqcEJ0z34uLiUmGegIAAs+CE0Wjk4MGD+Pn5KcEJgNatW6PX6zl9+jR//vknAB4eHty+fZvMzEwAUlNT8fDwQKvVKiMrTp06RVFREVqt9pHvw9vbWwlOALi5uWFpaam0fXXefPNNs6kLpmen0+mU4IQp/f79+/z+++9A2TooeXl5DB8+3Ox5Go1GtFotycnJQNmil2fPniU4ONhsXRAfHx86d+78yPf5qGr6jlbGNBKlptM7TEz5K5omI4QQ4tkgAQohhBANmmmUxNatW4Gyzs2BAwcICAigRYsWQNlXfScnJ1QqlVneTp06AXD16tU6q0/btm3N/jd1OO3t7c3SLS0tAZRtUn/77TdKS0uJiIigffv2Zr9Zs2YB/1/csiLXrl2jUaNG5XYsadmyJRqNpkb3eO3aNQCzzr1JRWlAua/oOTk5FBYW0qFDh3LnduzYEfh/+5uCDqmpqcpICU9PTzw8PDhy5AglJSWkpaWhUqno1avXI9/Hw88Eytqlqi1qq8pvenbVPdNLly4BMMixnBYAAAaCSURBVHTo0HLPdOfOncrzNLXzPwM4JhWl1VZN39Hq1HZqhil/ZaOEhBBC/PfJFA8hhBANmpubGy4uLsTHxzNjxgy2bdvGgwcP6mz3jso6S5XNy384CFJduqlTVlJSApSNeKhs28/KRi08TUxTU2pCo9Hg5OREamoqXbp0oaioCA8PDwoKCsjPz+fs2bOkpaXh5uZmNqKjOtW1fU3zP+ozXb58OW3atHmka9XEk3pHK9O6dWvg0QMZlTHlN5UnhBDi2SMBCiGEEA1ecHAw4eHhnDx5ktjYWGxsbJSFEwEcHBxIT0/HaDSadcJMixS++OKLlZZtGnael5dnNoTd9NW7rphGHjRp0qTK+f6VcXBwoLS0lIsXL+Lq6qqk5+fnk52dja+vb43KhLLRHQ+rKK0iarWa5s2bc+HChXLHTGn/bH+tVsuBAwd45ZVX6NChA9bW1lhbW2Nvb09KSgpHjhxR1hF52jk6OgJlbVDVMzW1s2nExT9VlPawJ/WOVqZt27Y0b9683M4pj8u0a4qtrW0d1UwIIURDI1M8hBBCNHhvvfUWjRs35osvvuDo0aMMGzbMLBDh6+tLTk4OsbGxSlpJSQkrV67k+eefr7LzaOpk/nN3iYKCAjZt2lSn92BtbY23tzcxMTHcuHGj3PGcnJwq85tGXaxYscIsfeXKlRiNxhoFKOzs7HBxcWHr1q38/fffSnpKSoqyTkR1VCoV/fr1Y+/evVy+fFlJv337Nps2bVK2RDXRarVkZ2ezceNGs3UStFota9euxWAwmKUDylSe2n7Br2t9+/alZcuWREVFce/evXLHTc9Uo9HQpUsXtmzZgsFgUI4fPnyYX375pdrrPKl3tDJNmjShe/futd5x5PTp0/To0UOmeAghxDNMRlAIIYRo8Ozt7fH09GTPnj0A5XZzCAkJISYmhrCwMNLT02nXrh27d+/m8OHDhIeHV7mDR9++fWnbti1hYWFcuHCBxo0bs2HDBtRqNdevX6/T+4iKisLX1xdPT09CQkJwdHTk1q1bHD9+nHPnzplttfkwNzc3Ro0axbp168jPz6d3796cOXOG9evX079//0qnjVRn9uzZ6PV6fH19GTFiBAaDgdWrV+Pi4sKdO3ceqYxZs2aRlJSEn58fY8eOxcLCgpiYGAwGAzExMWbnmoIPFy5cYOrUqWbpcXFxAOUWyOzWrRsA06ZNo3///jRp0gQ/Pz8lcFFfLC0tWbhwIePGjaN3794EBQVhY2PDtWvXOHDgAC+//LISUAoPD0en0zFw4ED0ej0Gg4Ho6Gg6d+5cbTs/yXe0Mv7+/syZM6fcKA6TFStWVPg83n//fVQqFbdu3eLnn3/mnXfeeRLVFUII8ZSSAIUQQoj/BJ1OR3JyMs7Ozri7u5sds7CwYOfOncydO5fY2FgMBgNOTk4sXryYkJCQKstt2rQp69evZ+rUqXz66afY2Ngwfvx4rKysyu2YUVsdOnQgKSmJyMhINm/eTG5uLmq1Gjc3N2bOnFlt/kWLFvHSSy+xfv169uzZg42NDWFhYXz00Uc1/io9cOBAvvnmGyIiIpg3bx7t27dn2bJlbN68mV9//fWRyujUqRN79uxh3rx5LF68mJKSErp27crixYvx9PQ0O9fJyQmNRkN2dna5ERSmstRqtVmewYMHM378eOLj45VtSs+cOVPvAQqAwMBANBoNUVFRLF26lOLiYjQaDb169SI0NFQ5r3///qxdu5ZPP/2UTz75BEdHR5YuXUpCQgIpKSlVXuNJvqOV0el0hIeHs2vXLkaOHFnu+KJFiyrMFxYWhkqlYseOHTz33HMMGzbs366qEEKIp1ijvLy82i25LIQQQohnjpeXF2q1mu3bt9d3VcRTYsqUKWRkZLB///7Hzuvl5YWXlxcRERH/Qs2EEEI0FLIGhRBCCCEqdf/+fR48eGCWlpycTEZGBl5eXvVUK/E0+vDDD8nIyODw4cOPlS8xMZHLly/zwQcf/Es1E0II0VDICAohhBBCVOrKlSsMHToUnU6HnZ0d58+f59tvv8XS0pK0tLQq1+8QQgghhHgcsgaFEEIIISplZWVF165dWbduHTk5OTRv3pwBAwbw8ccfS3BCCCGEEHVKRlAIIYQQQgghhBCi3skaFEIIIYQQQgghhKh3EqAQQgghhBBCCCFEvZMAhRBCCCGEEEIIIeqdBCiEEEIIIYQQQghR7yRAIYQQQgghhBBCiHr3P6CqtLkgwysnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAnieQ6VhHuZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "cde05b99-3086-4158-f853-880f2c1bef25"
      },
      "source": [
        "  print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  10.           30.20408163   50.40816327   70.6122449    90.81632653\n",
            "  111.02040816  131.2244898   151.42857143  171.63265306  191.83673469\n",
            "  212.04081633  232.24489796  252.44897959  272.65306122  292.85714286\n",
            "  313.06122449  333.26530612  353.46938776  373.67346939  393.87755102\n",
            "  414.08163265  434.28571429  454.48979592  474.69387755  494.89795918\n",
            "  515.10204082  535.30612245  555.51020408  575.71428571  595.91836735\n",
            "  616.12244898  636.32653061  656.53061224  676.73469388  696.93877551\n",
            "  717.14285714  737.34693878  757.55102041  777.75510204  797.95918367\n",
            "  818.16326531  838.36734694  858.57142857  878.7755102   898.97959184\n",
            "  919.18367347  939.3877551   959.59183673  979.79591837 1000.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4-Nq70kQGtJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "442fc001-0f7d-4bc1-d625-2c7c9990a8c2"
      },
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        " \n",
        "\n",
        "data = pd.read_csv('/Heatmap logistics - Sheet1.csv')\n",
        "\n",
        "\n",
        "sns.heatmap(df, cmap=\"YlGnBu\")\n",
        "sns.heatmap(df, cmap=\"Blues\")\n",
        "sns.heatmap(df, cmap=\"BuPu\")\n",
        "sns.heatmap(df, cmap=\"Greens\")\n",
        " \n",
        "#add this after your favorite color to show the plot\n",
        "plt.show()\n",
        "#left\n",
        "sns.heatmap(df, vmin=0, vmax=0.5)\n",
        "plt.show()\n",
        "#right\n",
        "sns.heatmap(df, vmin=0.5, vmax=0.7)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-5bcbe9eb71da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Heatmap logistics - Sheet1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1169\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Heatmap logistics - Sheet1.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhd0oxb_psjm"
      },
      "source": [
        "\n",
        "z = 4 * np.tan(np.random.randint(10, size =(500))) + np.random.randint(100, size =(500))\n",
        "x = 4 * np.cos(z) + np.random.normal(size = 500)\n",
        "y = 4 * np.sin(z) + 4 * np.random.normal(size = 500)\n",
        "\n",
        "\n",
        "sctt = ax.scatter3D(x, y, z,\n",
        "                    alpha = 0.8,\n",
        "                    c = (x + y + z), \n",
        "                    cmap = my_cmap, \n",
        "                    marker ='^')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GA8-1KYGrVgN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "a9238e0d-023c-45d4-9896-214bd14bd3e6"
      },
      "source": [
        "data = pd.read_csv('/Heatmap logistics - Sheet1.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-d63f4138c89c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Heatmap logistics - Sheet1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1169\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Heatmap logistics - Sheet1.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os6O4Uy8toE1"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(\"fivethirtyeight\")\n",
        "fig = plt.figure(figsize=(100, 100))\n",
        "excreta = np.linspace(1,100,100)\n",
        "enzyme = np.linspace(0.01,1,100)\n",
        "\n",
        "def f(a,b):\n",
        "    return (0.02036*a + 5.085)/b\n",
        "t = []\n",
        "for i in range(100):\n",
        "    t.append(f(excreta[i],enzyme[i]))\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = plt.axes(projection='3d')\n",
        "\n",
        "ax.plot3D(excreta,enzyme,t, c='r', marker='o')\n",
        "ax.set_xlabel(\"excreta in kg\",fontsize = 10)\n",
        "ax.set_ylabel(\"enzyme in uM\",fontsize = 10)\n",
        "\n",
        "ax.set_zlabel(\"time in minutes\",fontsize = 10)\n",
        "plt.colorbar();\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOWJ2KRTLmSa"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "data=pd.dataframe(2)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}