Climate_Coding_Challenge--Part_4!.ipynb

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        "# Part 4: Plot your results\n",
        "\n",
        "First things first – make sure to load the climate `DataFrame` you\n",
        "stored in the previous notebooks using Jupyter cell magic:"
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        "# Load all stored variables\n",
        "%store -r"
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        "You’ll also need some libraries later on. This is an extension to\n",
        "`pandas` that will allow you to easily make beautiful, interactive\n",
        "plots, and a related library that will let you save your plots:"
      ]
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      "source": [
        "## Plot the precpitation column (PRCP) vs time to explore the data\n",
        "\n",
        "Plotting in Python is easy, but not quite this easy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "b4227691",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='DATE'>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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t2xeDBw/GiBHWJ7LNmzcjOTkZ58+fx5gxYzB+/HjMnz8fADfJZKdOnZCeno7Vq1fj7NmzWLFiBWJjY5GWJmPo58yChpXChmiOxjIQ8ygdSD6mTelFvhE4OB/IvCO+nMVJwJvwfpO8R9xIsGL+W8fdVfLlFnR5vnWOKwVwxNTw11F36ExBI9+0G1yPDWfI3edyRfLB1KjR1Ej34d3CPBE21s7OKGwMKXdkZEelOaoRuRi6IqDn/3YPhccvb/v8xtLOtF/hn+OcPf5N6bG1vdT/gNN/OF5HTibX6NjRPmrkDXrH7+YuLAkSnrOBgn3ztOO0qIy6LutMeHi4+XFoaCgMBoPFawBw7do1DB8+3DxZpMnw4cPh7++P999/H71790aLFi3M75UtW9a8nqpVq2L+/Pk4fPgw3nzzTZw+fRqJiYnYunUrqlatal6mTZs2an5V58xqxdUV9/8NiHFlwy8Dd+eTdARY96ET3Tnd546lkBMXZf4dZRqvS/PBeeJDrCfuAFb05x4L8/L+Na5Rs633lDK1YKj7T64AgaWkfSZNpMu2VMJGyKfWcCUn5WsD7+4DJtfgqhgG7YTV7/BDO65dUN9l8qtxpAZcNw4XPk7cAVRrL+1zl/9xPk2qEBx3B+cXPhbmgb1g6Y6DRvX2ZIj1IhIQ69EEcA2QAaD/KiBGpPRwXmdusMGePwENekvb9n3eKL/p14FQXvfknZOtlzfN7fbObiCsnrRtqMD7SlYY46JRV/8pWCKwatUq5Obm2ixBGTRoEEqUKIFly5bZ/fzBgwdx+PBhczBTvnx5+Pj4YNWqVTAaFZ6/R6nvbWrUdnqtMuuTymCwbntjf2FVk6Ir/GJl/gUOgGg+8C8eQslHi5Ii56TfkL6s5N9fgL/r8xshA8DxX7n/twq6kpraQiTusD5mTJ89tZqrynG4sSLgNzQ968S0ABe32nnDxe0jhAHIzm8lpkVu/sn8Do7avpg46mEkZ1Rkq+OVZ88M++8VZRoKBXhfyUruQ2BChOu3+1kS4B+syKrOnTuH0NBQVKxoPfmWv78/qlWrZtXWpHXr1vDx8UFOTg5yc3Px9ttv47XXXgMAVKpUCd9//z1GjhyJMWPGoGnTpujYsSP69++PatUcTAzmjNyHXO+aBc8ALYcAXSZwjSvLVnfNQGL3r3FzfLR6F6ghqCfONwKr3wYiGgGt35O3fv6J0mDgLui/DwWavwVUacWtP7Yn0KAPcGot11ajx4/chGhuh/ddhUXHcor3r+4FKtQpWpJMbhwCto4DOo+zX6RvGl/C3v53/xqwbhi3nyrBmGv5PFOkqotfXZpfhGqGQwu5btYv2ggQj6/ketf0nAsUF5/t1q6Hd7nuxI36yfu8UvjB3eV/gZKVgG3jgc7jLZfLfWg/IDaND2RizAW+KgeUCAdGnJWfLv6x8Cid604c+6ITA/g5EUTlZgGr3gRqPQM0ftX+cs5Wg+qE95WseAHGGAyCC8aKFStw9OhRHDt2DCtWrMDvv/+OTz8tHGHx3XffRUpKChYvXoxWrVph5cqVqFevHjZt2lS0xPDTsWcWF6gAXN/+q3uAY0uBrV85scIi3EH+PgS4uAVY3NP6vfMJ3Mk74XPBG0W4+/trBNcteGkfrkfIuQ3cyQoAVr7ObXPLWPnr15JYQCLnvX/juZ5BSpj7BHenv+h5rn2MTUx8//vjPa5L6uKeypQOHlls+fyGnTFGDAZu9Fjz534pfHzG3neB7TT++QH3mcM/W7+3eiBwbmPhGBy2iA6OBm7fPf83ty/b5YLSRn7J1/4fuWrbi1utj/P/BPMUBZUtfCzsDr2x4Nz4QMGB0XZN4fLcNNqzPXd4XeIlz0Nk4HqInV0P/DFUfFFhqQ5/39Fxj0bvK1nxC+JKObTYrkJq1qyJtLQ0JCUlISLCspQoJycHiYmJeOKJJyxej4yMRI0a3CBdderUQWJiIr744gvExcWhePHiAICQkBA8//zzeP755zFu3Dh06dIF48aNQ6dODnpcANydyb0rXMkEH3/nN01yZmJqVAlwd5A+EmLnohxLYiMyWrTq57F1cc19xDX2rdxUuLDlU35D0SxBmwWTh8KRb92QcF4aud1YhXlx9xJXh/7cFMCveNHXx2eqDrZHkQap/H1fYhscYUmQ8I5fDrFtix0T/EaZJg9SuUbLYfWk77v5+cCBuUDdF4CQsMLXDQbuvRsHgfD6gF+gtPUJjzN7JU4ZyVwpiz2+/oWPhd9V6uitzpQiWjXwtYP/ffjnSJOUk0CJMKBE+cLXHqRYn1/5JAch+g1WvK9kxWDgqmNc/adgy/devXqhWLFi+O4767uiH374AZmZmXj55ZdF1+Hr64u8vDzk5NieGt1gMKB27drIzHTQs8MkvgE3IqJovaZIHhwSactgQaWDiV83nM4bclxYfJ+fz3WFnN/ZdmM0dyY8oRlFekXw92fhYHL8YMWZIbuF2/++EVdVNj7M5uJFcnWvgxO4AserRc8difutsJuzVGLnl30/AFfsHJdibR3O2WjM+20MMLu145GdTTKSgcU9gA0juRGPLRiAvTO57udLnehSLdbLSkgs2LNb6qax5GP230s9A/zQxnp0aP5ovIDjUjE+fqAmnC7g6l7p61GZ95WseIAqVapg0qRJGDFiBIoXL45XX30Vfn5++P333/HZZ59h+PDhFj2BAODOnTtISUlBXl4eTpw4gWnTpqFjx44oWbIkjh49ii+//BKvvvoq6tatC39/f+zYsQPz58/HJ598Ii1RphPzOeEYFRJP0od/Bpo5KB5V042DhY/5JwurOxtW2MjRNGGZiduPrSL4rUS7mvO/q7CXBS9YMTUglbN9NV3YJL6/KTHIGf+CoXbxutj6hd23lSB1tNzUM8DNE7bfM6BwcDu545wAEN1vbJVMmKSesv+elhK323/vqsRqIWe6Glsc54K8dDTKsAt5X8mKXqUnAfevSj6pffjhh1izZg3++ecfNG3aFLGxsVi6dClmz56Nb7/91mr5p556ChUrVkRUVBTefvttPPvss1ixYgUAoHLlyoiKisKYMWPQokULNG7cGNOmTcOYMWMwerSCU4Xn2KlqARyPRGrCGNdQbUlv4GhBj6e4UO5POFKoI1f3cu0axCZVE7uDFE5kZ2uMAne0ewY3T4iwVIlPNDDjvSfssssn3Nf5+efsxf3PYdxcMs7gtw0Q7daqcqCRZ7t00yFH49koYVc8d2ydWCVzBSrlXdp14OcXgHP6uZhaY1wbuLhQrg2N3N85IwX4pYd1SZCwFMSiuk+Q72JBm9RjTeP2LFSyohemOvLgCub6+QEDBliMWCtkal8iJioqymKkWlvKlStnHmyu6OTu0E7Uqf4bz0X85xOARrzqrs1xQNsPpW9yfhfu/5I+lq/zL1QH54kkRdBWg393aDDAbbsyJxQEqFHtRBaS2Ih223jhm4UPr+2zvw5+IOHI3UTg0ALu8ZNfSvyQAfh7lMjbvPs4Jc7RYsfg8RUi6RDJ579Gyk+PVJsL8vO3N4H6L1q+p8jFS+Y6/vwASNzG/b3sRLddV9s9nfv/Sw/BG050o974KRfsXNwKdI0vfP3oUsvlDvzEW4XgxkmsC3SeWC8z/ZzDqGRFd9zk7tzUQNFRaUJRTmjJx4AMGw0dZTc6tHPgWQ3mJPMAlTWCrX5OBlay0xwvYwv/Qi9WsiIcfZa/rxgFd41i+Hesku8SHYxz4UyV3uk/gE22giSJpTPOzCfDZ1G94+K7XsYUGCzPID/ZabzJBJ0ZcVZxKhy/wn1YOKKxiVg1rXAdcvcxHaGSFSLPwztctdWDNADV5a1DbBTH1P+AHx/nHvNHMzWNjyHHbZnjJUjFT5e93kV6JsxW2YGmYLwZu9sTqQZyhqlUhVuJtM+YZso1sRokzs44MsLfNeMm8GvBmBYhgnGPDHbWIZQgUtUq7G4rldRqVWcIA097DUEfOTMlgMTfS1gl6VRbKBWdFKkeuyVyvhFtYyRhugnAQT5LHK3XEf7nzv4FtBwsbz0KoJIVIo+pdEPY5c/qIBQ5KPNF2kTYrSJwwR2kEg1lnekFo1fCLslSWVShyPy9nPncUfujNUsmbLfE/w78fVw4b5FpBFFAfLwguYGYWENWsf1U8vgcTpBaksLf98V+R4NB+u8s1uZCy9LJ3SIjvkpthCwkWgXKyy+xMWCs9jcF8qhIjaCLjoIVvdFvN3flCasBLv0DLOzKTWbHx69eEavfl02pk51gcCV36x2UdtXyudQhwYX4F3rh+DJiDVcdXbi2jgd+e0u8RE6p4IjfO4yfZrEqB7GARO3GicIxPOT+dlLZGzfIioPvnX5d/H0T0VFXNexpJf5BkbdEfh9hKZK9eZfEhs0X61HkDB11GqBqIKKdw79YPl/Ulfu/oj83JL7JoYWCD6p8cpI7R801/p2UMI12Ahc9BzRyT1TCoMfu+kWqgWxdnHZO4v6Hx8pLl2z8onDB3DxiVT38Uhi1T/pbBQ2Zd0xSfhv8thNWIz2rbE4H126Pv2+KzQ0lNumh2G8u1iVYrBqIH6DwS/UAoGob4Mq/3OP9cyzfE85yLpVSQY8CvKJkJd/RDJdEGmOuRXfJfAY4DBzE7krstesQjh4q7CLsSMpJrj2NaftX9wKZIqNHCqujhF0CpeI3CrVqbOsmRWYWd3UyAymp1UfCUgr+yV2sijDrvmA9/GXFqh3lljbwG/4Ku5/y8kh4R8y/e1YlWOEHSoLvJjaaqVySG7Ly8itVZLwPHd21i7I1km9R5T5yvIwtYsMJhDcQ+ZzM7yD3XKgCjy5Z8ff3h4+PD5KSklC+fHn4+/tbzZmjG3kFB3h2NpCv0xiy4MTDSkUjJysPt+49gk/WXfj7+zv4oAx3LxU+tvrJRH7DtBvcCI8A1zD3/CZgaW8gwInJ2sQCLP706mKf2/09N3mhu3F2rBJbpB5jSwVdxvkjk94TyWfhDL+i7Rl4xEobxIZC519UEwX19keX8JYTBkO8fLgtqNp0R8L5jYpqp8i8RE5R+Zwu1lBWzAORwfikDu7mjIxk++/Jve4JS2805NHBio+PD6Kjo5GcnIykJA3mA3LG/YIdO93Xcs4KPTGl8W4ukJ2JoOTdqHJmAXyeUGHWVeHw0RZEgolbgoaSptFms53poSAiReTg9fF1fn16mzhMOCqvLApcPMROkmLVdGL5KZysjk9q41HhsPUnVtpfln+BsOpt5Ib++0PZ9YmO76Ex/m8n1lC2Ql37pUdi7WzUqF6RG1S5CY8OVgCudKVKlSrIy8uD0ahyo7OimNGb+99nCXApgWsR/vTX0ib3cxVTGtt8CN9tY1AsJx0GYeCgRJHpozTA14/3gp5Kw0QuhsUEk+2JFYHrlcGnsCRB7G7srhODtvFJnVNGGHRctzNLsfUHRd4SqXY4tdb+e7L3aT3tty4kNwDPuGk50aFUSs6MbML/DmLVX6KTE4pNAyCzGkiMM/MBuSGPD1YAblI+Pz8/+Pn5OV5YKw8K7uz8DMC2gqL4Rj2Bah00S5IVUxrxCMixM8jQ8V+lr0/sYuhTzP5yYidDq7fkXDBE1n9tv/33hF37pAzEpLdqSf4FXexEbDUiJ4/Yd0qROv6H4DeYJ2Hmb0B83xAbaE5snpj7EhsMCykxv5Do+nW275jJDFYSPgd6zZXxuS/kbU+qs3/Zf09sdm4lxlJxhuggjnrdV6TT0W27F7MXZasRfTuSngRc53XbPLvRuXlBrBr2yZzZ1kduHC3YnpzBo0QveCo0tiuKa/stZ4lWkhLjrChFcldcHVWrqd2A1F0aqEolt1Gw1DZLzuA32s5RYf3EaV5RsqJ7f7xn+/WzfwG1nnZtWqbU4f4P3gWUrQEsK5i6PbKF/c+ogV8N5EyxsnBZU1c+p4hsT+5AT2q4caiwxCFOQimOs6xm0JZKw7s4qdVMrvBvvLrrP7dR3ueE4xhJZkCRg0Fh42ir9esEv2ux7KBQhcBZbmmaKya9VBmVrOjBCTtVJ1pOz339oOVwzmq0v5A6c69TVL6zFhtzwdXUGKWUT25bAC2rJ+SOkeNNErfJ+5wSv6tYsKLXai3ZwYoa30fmOkXb1rgHClaICIkXfrFupq6eQEuJ3jWyJiQkuqC33lV6JLcqU+pFW4k5pXRF7qjIKlTTye1B5QFVhhSskKL783377y3sJvJBkZMT/y7L6o7LienV5dj3Q9HXYcXedy3KCVqnJ3enJrKzQ/aw+Tru8acXl3aqvAGxBvAO5gryKCoEzqfWyPyg+wfxFKzojmB+GUeu7rWcLt0kPx+4uM16plSpnJlkTKyIUayFutRqIFdMhueOhPl356L4fCGuomXA4Om/uRK0HJVU8ii4RFFUskI0lXQEmN8FmFrP+r3DC4FfXgDmtC/CBvgnfrHuqGIDuIkQC3L4F2Lh3ED86ROEjQX5FyvhPC6ebnpjYG5HbhRfLQnHmyH6YpoxXS1iwZBYqaUHXFAt6Clw1lNaZKJgxZ2JjflhGuRK7hgRgPo7uNSLqnB0UX6du1XvHF6aL26RlSx1qJGXdgJIuQO2KaV4Ke22LXv+Hy8iNjaIEuR2Jfa4YEVP38f9gxXquqw7/AuQox1MpLRDOEDZ+hHcHVWvnyTWDQu6KaoxdobcOuobIqOZ8ie5ExsfQWxCMDXI7i5KnHJTZimfNxGWfPz6OnB6LeAXpElyzHR1cVeAp30fjVHJiu6oEAHnG4EDc4GTq7h2DUWmcUM4sZKD34cUPj621P5yZ9Yplx4pxAIsohylJ9vzRMLB106v5f4rNbia3GomKhUjIlQNVvLy8vD5558jOjoagYGBqFatGsaOHYt8XpsDxhji4uIQERGBwMBAdOjQAadOiQx97en4VS+P0rjnidttj1Iqp2SCGbkJr/4eDeQ56MJotxrIFUWKKgdErm5kKDbUu1wGO42Qhb/b3UTg6r6ib+/2BcvRjQmxRW7Q4XElEe5f9aInqlYDffPNN/jhhx+waNEi1KtXDwcPHsT//vc/hIaG4oMPPgAATJo0CVOmTMHChQtRs2ZNjBs3Dp06dcLZs2cREhKiZvL06cKmwsd5j7iB4Zb24Z7LHaVUeIGZ2Zz7f+Vf4O3tIp/T0WitSks6qnUKFMALVsRKir5/jPs/9CBQLkb+5mY04f4Pc0VVC53o3ZeOxiXRkgc0atUTVUtW9uzZg+7du+O5555DVFQUXnzxRXTu3BkHD3IXT8YY4uPjMXr0aPTs2ROxsbFYtGgRHj58iKVLRYrwPVmioK2J6PDUEl3dY/t1sdFYDQauW7T5edGTYWMjaqyUt3qR3fvKLnW37WpS9hO5vbaE7lxQZj1i6ETvvmioAaICVYOVtm3bYsuWLTh3jmtceOzYMezatQvPPvssAODSpUtISUlB586dzZ8JCAhA+/btsXu37aHEs7OzkZ6ebvHnUYRFqM4UqZ7dAMzpwFXzWOCdBKQ2LP3jPcu2IfxqhL8/k54mMWJ3UlJHvhWrZ49obP+9ByrMeqoluaPuqjEJ4g0FqoruXSr6Ooh78bQB/TytpEhjqlYDffLJJ0hLS0Pt2rXh6+sLo9GI8ePH4+WXXwYApKRwc4+EhYVZfC4sLAxXrtgewn3ixIkYM2aMmsnW1lVBkHZNpK3BXd4JPT8fWNaXe7zqDcvl+HcspsZ0UlzYzFu/Gr1nRO6kzq6XtooD8+RtWu3um1IVZdRO/mePLeO94czEjzIuEI7ugE+sdH6dQkqVAhENUDUQAGowrDBVS1ZWrFiBxYsXY+nSpTh8+DAWLVqEb7/9FosWLbJYziA4YTPGrF4zGTVqFNLS0sx/165ds7mcx+B3xQW4eXhMVUU5D2x/RlgqkXm78HHeI3npUKOIVoneB2JzZUQ0Kvr61ZZ6pggf9rThyYlHkF0N5GHBihqN6r2YqiUrH3/8MT799FP07cvd8devXx9XrlzBxIkT8frrryM8PBwAV8JSsWJF8+dSU1OtSltMAgICEBAQoGay9SVNMKjbtAbc/zcSgMP8oE/kBLF3ZuHjwz/LS8ex5fI+p6VjK7ROgWO3/lNhpRTEEDfkacFK4natU+BRVC1ZefjwIXx8LDfh6+tr7rocHR2N8PBwbNpU2AMmJycHO3bsQOvWrdVMmvu7Lhi9VqzrKp/cWZAfpMj7nJZyMrROgbo8buI34tWo2oSIULVkpVu3bhg/fjyqVKmCevXq4ciRI5gyZQreeINrU2EwGDBs2DBMmDABMTExiImJwYQJExAUFIR+/fqpmTT3JwxI+BOEyb1DccfW+EaaGM2ag9/x0CJupuyWQ4BKTVyTJEIc8bSSFaIoVYOV6dOn44svvsCQIUOQmpqKiIgIDBo0CP/3f/9nXmbkyJHIysrCkCFDcO/ePbRo0QIJCQneOcaKM5KPWj7nFzlmJMlbpyKj27pYuo0Zp4kdBUHMn+9z//fO0i4phAi5480ScRlVg5WQkBDEx8cjPj7e7jIGgwFxcXGIi4tTMyme5+Fdy+dye+s8SgM2fAJ0/Ex+41viXaj6iYiRW52Tm6lsOohHobmB3FXiNsvnYgO8ifm6CtftNb6+5WzGRP8oaCB6dGihvM/dTVQ0GcSzULDiKWiiPGJCxelES8KxoghRAAUr7kTsIqREN7lbRRnzg7gelawQQrwDBSvuRO0hyNe+o+76iWuoXj1EQRIhxLUoWHEnVLzv3RgDzm4E7l3WOiWEEOJSqvYGIgq7fV7rFBAtXdgMLHuJexyXZr8EhYJaQoiHoZIVd3JbOJsy8SpX9whecLI6hoIYQoibomDFneTTaK1EB6jLNCHExShYcScXtmqdAkIIIcTlKFhxJ5mpWqeA6IndEg6q7iGEeBYKVtzJ7XNap4AQQghxOQpWCHEbemkropd0EEK8BXVd1lo+TYtOZBJOZmlmJ5j47U3A11+BDVM1EyHEtahkRWs0pw+R6994O2+IBBO/vlr07d6iLvSEENeiYEVr1B2ZyJF1H8jTaJZsGq+FqC33kdYpIDpDwQoh7oLf++fX17Qb74TGWSFq2/yl1ikgOkPBiuboLpXIcGkHtGvoSsEKUdnp37VOAdEZClYIcVe5mbZfv7wL2P41MLMlV12kNCpZIYS4GPUG0hrV/xOl/fNd4eP9c7VLByGEKIRKVghxGzJKNKgBN3FHdBNHBChY0RwdlMTNGOi0QVT2IEXrFBCdobMOIW6DAltCiHeiYIUQT7bja+XXSQMZEkJcjIIVrVHdLHE71BuIEOJaFKwQ4jZ0EiRQ12VCiItRsKK1K7u1TgEhhBCiaxSsaO3mSa1TQIhzqDcQIcTF6KyjOWqzQtxMynGtU0AI8TIUrBDiLvTSViTpiNYpIIR4GQpWCCGEEKJrFKxozZirdQoIIYQQXaNgRWsUrBBCCCGiKFjRHDWwJYQQQsRQsEKI29BJA1tCCHExCla0RsPtE0II58xfWqeA6BQFK5qjYIUQQgAAy18G0q5rnQqiQxSsaI1KVgghpNCDm1qngOgQBSuEEEII0TUKVjRHJSuEEEKIGNWDlRs3buCVV15B2bJlERQUhEaNGuHQoUPm9xljiIuLQ0REBAIDA9GhQwecOnVK7WTpB1UDEUIIIaJUDVbu3buHNm3awM/PDxs2bMDp06fx3XffoVSpUuZlJk2ahClTpmDGjBk4cOAAwsPD0alTJ2RkZKiZNEIIIYS4iWJqrvybb75BZGQkFixYYH4tKirK/Jgxhvj4eIwePRo9e/YEACxatAhhYWFYunQpBg0apGbyCCGEEOIGVC1Z+eOPP9C0aVP07t0bFSpUwGOPPYa5c+ea37906RJSUlLQuXNn82sBAQFo3749du/ebXOd2dnZSE9Pt/hza5f/0ToFxG1QlSEhxDupGqwkJiZi9uzZiImJwd9//43Bgwfj/fffx88//wwASElJAQCEhYVZfC4sLMz8ntDEiRMRGhpq/ouMjFTzKxCiH3cvaZ0CQlyARmom1lQNVvLz89G4cWNMmDABjz32GAYNGoS33noLs2fPtljOYLDcORljVq+ZjBo1Cmlpaea/a9euqZZ+QgghhGhP1WClYsWKqFu3rsVrderUwdWrVwEA4eHhAGBVipKammpV2mISEBCAkiVLWvwR4hXsBPCEEOLpVA1W2rRpg7Nnz1q8du7cOVStWhUAEB0djfDwcGzatMn8fk5ODnbs2IHWrVurmTRCCCGEuAlVewN9+OGHaN26NSZMmIA+ffpg//79mDNnDubMmQOAq/4ZNmwYJkyYgJiYGMTExGDChAkICgpCv3791EwaIYQQQtyEqsFKs2bNsGbNGowaNQpjx45FdHQ04uPj0b9/f/MyI0eORFZWFoYMGYJ79+6hRYsWSEhIQEhIiJpJI8QNUTUQIUQjfkGabl7VYAUAunbtiq5du9p932AwIC4uDnFxcWonhRD3Rm1WiDeg/VyfgspqunmaG4gQQgghukbBCiFug+44iReg+dKIDRSsEOIu0m9onQJC1Jefp3UKiC0aB5EUrBDiLi7t0DoFhKjvxiGtU0B0iIIVQrRUOkrrFBBCiGMaN3ymYIUQQoiOUNssYo2CFUI05cSJuUx19ZJBCCE6RsEKIVpypmjVQIcr8QI0zoo+UTUQIV7MmRb25Wuplw5CCBHTe6Gmm6dghRB3QSUrhBCtVGqi6ebp7EeIlio21DoFhBCiexSsEKKlOt2cWJhG9iSEeCcKVrSUm6V1CojWnGm0RsOQE69ADWyJNQpWtHTlX61TQDRHJ2ZCiM5FP651CihYIcRtnE/QOgWEqI+6LutPyUpap4CCFULchjFH6xQQoj6q7tQfHXQEoGCFEE3RiZkQCwEhWqeACMV01joFFKxoykhToRNCiIXiJbVOAdEhCla0dPus1ikghBCdoTYrxBoFK1rKp5IVQidmQizcPKl1CogOUbCipZKVtU4BIYToS9o1rVNAdIiCFS2VjtI6BYQQojNU2kisUbBCCCGEEF2jYEVLNPgRIYQQvdPBtYqCFUIIIfqhgwsjEdL+N6FghRBCiDJCIhRYifYXRqI/FKxoiYaVJoR4kmZvaJ0C4qEoWNEUBSuEEA/iU6zo66BqIGIDBSuEEEII0TUKVgghhCiESkWIOihY0RQd2ESi2+e1TgEhrmGgyxKxRnuFpqjNiteTWj+feVvddBCiG3QTR6xRsEKIW6DAlhDivShY0RTdQRCJqJs7cQfUk4eohIIVQrREQQjxKAoEKxTw6I8OfhMKVghxCxTUEEK8FwUrhBBCFEJBNVEHBSuEaElq8SpVFxFvQV2XiQ20VxDiDli+1ikgRAIl2jZo3z6C6I/LgpWJEyfCYDBg2LBh5tcYY4iLi0NERAQCAwPRoUMHnDp1ylVJIsR95D3SOgWEOKaDhpjEM7kkWDlw4ADmzJmDBg0aWLw+adIkTJkyBTNmzMCBAwcQHh6OTp06ISMjwxXJIsR9/DNF6xQQ4hoU8BAbVA9WHjx4gP79+2Pu3LkoXbq0+XXGGOLj4zF69Gj07NkTsbGxWLRoER4+fIilS5eqnSxC3MutM1qngBBCNKN6sPLuu+/iueeew1NPPWXx+qVLl5CSkoLOnTubXwsICED79u2xe/duu+vLzs5Genq6xZ/bojsI4uMncUFqYEsI8V7F1Fz58uXLcfjwYRw4cMDqvZSUFABAWFiYxethYWG4cuWK3XVOnDgRY8aMUTahhLhamw+AG4eBml20TgkhCqIGtkQdqpWsXLt2DR988AEWL16M4sWL213OIChdYIxZvcY3atQopKWlmf+uXbumWJoJcZlOY4EB6wBfiSUrVLBCvAWVOOuQ9r+JaiUrhw4dQmpqKpo0aWJ+zWg0YufOnZgxYwbOnj0LgCthqVixonmZ1NRUq9IWvoCAAAQEBKiVbELUFd1e5gcpWiGEeC/VSlaefPJJnDhxAkePHjX/NW3aFP3798fRo0dRrVo1hIeHY9OmTebP5OTkYMeOHWjdurVaySJEW+1Hap0CQtRDpSJEJaqVrISEhCA2NtbiteDgYJQtW9b8+rBhwzBhwgTExMQgJiYGEyZMQFBQEPr166dWsvSFRiX1PpIb1ArQvkII8WKqNrB1ZOTIkcjKysKQIUNw7949tGjRAgkJCQgJCdEyWYQQQgjREZcGK9u3b7d4bjAYEBcXh7i4OFcmQz+oyNT7yP7NqWSFeAk6LxIbaG4gQtwBVQMRr0HBCrFGwQohhBCFKBBoUMkKsYGCFUIIIcpQJNCgYIVYo2CFELdA1UCEEI3ooLSLghVC3AG1WSGEeDEKVghxKZl3KHlZyiaDEELcCAUrmtK+aI0QQpSjwDnt3Mair4N4HApWtKSDekBCCFGMn/1JayW7dabo6yDKMvhqnQIKVjTl6691CgghRDmxL2qdAqKG0Epap4CCFUIIIQopFqB1CoiHomCFEFeiqj/iyajXGlEJBSuEuBQFK8SDUTBOVELBiqboLoQQQghxhIIVQgghhOgaBSuEEEII0TUKVrREjdEIIYQQhyhYIYQQQoiuUbBCiCtRZwlCCHEaBSuEEOINOo3VOgWEyEbBiqaozQohxEVKaj9kOiFyUbBCiCtRfEoIIU6jYIUQQoh+NHtL6xQQHaJghRBXoga2RCsuGSpBgR08sHTR10E8DgUrWqJxVgghxFIxf61TQHSIghVCiOfxD9E6BYQQBVGwQggh3sBdZkT21gLngJJap0DXKFghxKXc5ILh9rz1iieCqp31reNorVOgaxSsaIpOHoSog4JCt0U/HbGBghVCXMnHV+sUEKKu134HqnXUOhXuh+VrnQJdo2CFEEKIcqp1AF5bK//z3lrgTMGKKApWtOStByUhaitZUesUEL3wdZeu0HRBEEPBCiHE81DPCu9Wr0fh41fXaJcOZ1DJiigKVgghHojuUq2J5ImPnzKbUKJ7tBLtuooVL3xscJN2YnKDleAKyqZDpyhYIcSe4qXkfa7mM4omg8hBXUqc0nGU1ikoFFSm6OvwVSj4ciW5wUrbYYomQ68oWNEU3f3pWmQLeZ+LbqdsOojz3GUANFcSG2fF4+bjccPfX+44OP4llE2HTlGwQog9dMEjRANKHHe8C7/bHMd08yqGghXiGcIbaJ0Coic0Wqsb89LfjvZZURSsEM/Ab1CnGJl3ZEo1ViTEVQwedikoHsp74iYlKxZpdoZ3BDketoe6GYqk9U1u8TGN8eF6DftpnYJCQeW0ToHr+AWpsFIFgov6fXirc5NgpfHrWqdA11QNViZOnIhmzZohJCQEFSpUwAsvvICzZ89aLMMYQ1xcHCIiIhAYGIgOHTrg1KlTaiaLeCQK/DxGRGPnP1MyQvl0yFWpidYpcB1VghUFuGNvID81Soc9h6rByo4dO/Duu+9i79692LRpE/Ly8tC5c2dkZmaal5k0aRKmTJmCGTNm4MCBAwgPD0enTp2QkZGhZtI8h1jxbfUnXZcOralSSiX3jsxN7uT0yj9Y6xR4JtEuwTL3WXcptVCCXqvKvKSEXtXc37hxIwYMGIB69eqhYcOGWLBgAa5evYpDhw4B4EpV4uPjMXr0aPTs2ROxsbFYtGgRHj58iKVLl6qZNPfVbKDl85eX21+2zQfqpkVP5J40xbr9qXIi9qKTu1yy7oq944TtkFi7h2IB9t+Tu6+7xYVSoWPOp5j997Qc0t9LAkaXhoppaWkAgDJluAj/0qVLSElJQefOnc3LBAQEoH379ti9e7fNdWRnZyM9Pd3iz33JONAr1LF8LjY6o5fsxADknzTdsbjY08mp0lGlgbUbkhs7yC7N0mmwIhaYySZ2PtXwXCsWRHkQlwUrjDF89NFHaNu2LWJjYwEAKSkpAICwsDCLZcPCwszvCU2cOBGhoaHmv8jISHUTrjePvWb5XPQY8aJgReyk+dir9t+jOWT056mx0pYrW6Pwcelo5dMRItJQml+CIRw8UKy6oP0nhY+f+FxeukSJBQ8i54PKzZXZvBJD5SuhTDX779XuKnOlOg3MvOQc5rJgZejQoTh+/DiWLVtm9Z5BUALAGLN6zWTUqFFIS0sz/127dk2V9OpWMXeZQdTFxEpWSnjH3BlF8vhIrVNQKListOXUbkMgFqxU61D4uH5vy/fESjT5AVZsL1nJEiW3hNGTS2GF302NKhtPzj+dcEmw8t577+GPP/7Atm3bULlyZfPr4eHhAGBVipKammpV2mISEBCAkiVLWvwRgoZ9ZX5Q5OTeqL/MdZIiKxYoYSH+BUKNu16563TDC5fsticqfFe1L/yy16/T35X/fWJf1C4dKlM1WGGMYejQoVi9ejW2bt2K6GjLotro6GiEh4dj06ZN5tdycnKwY8cOtG7dWs2k6YMijdN0egC5WkwnkTdF8kjsJ6j9LODnJT1TlLhAdJlY9HWYjDgHvHdYx0XcIvklNS/VapxaspK9DaqzPbejxDlTuA6dnIef/ELe59yg3YuqKXz33XexdOlS/P777wgJCTGXoISGhiIwMBAGgwHDhg3DhAkTEBMTg5iYGEyYMAFBQUHo109HgzwR7xVWF7h+wLnPeGuRsJLVMsVLcn+yeemFObgsF2Cn39A6JToiOB49+fiUewy6Qa8uVUtWZs+ejbS0NHTo0AEVK1Y0/61YscK8zMiRIzFs2DAMGTIETZs2xY0bN5CQkICQkBA1k0ZCdDSIlj2SqgKKSuWDtF4Pddcv1ws/uHZ7DeRW08kswdDy5KtluloOUXf9zoh4zPbrpao4+KDawYTK62/4srrrd0aw57TXU7VkhUk4MA0GA+Li4hAXF6dmUnRKgROX3LsEvQ5wxFehDpB0WNs0FPXiotegsER5126vXA3HyyhJ07tnDbct2oVbLF0qBFERjwFJR2y/fv+qstuKagekXQPuXXa8rOxzpsTAWWzsJlWIpKtkBJCZ6rqkqMgNrlheQtj9kcCpEyg/qCgbo3xSnOEGRaqq15LUeErlDQjoaQRj0YshL52eUB1Rs4vzn1Hqt4psKW05q2z2gHyXqlp7rVOgGApW9MLlk1i5wwVVZhrL17J8rsZFQbcNP3VCbGRlxUjsDSR7wDiRdYruUy6uBrKo0mOWaVN7P61QV8aHnMgDsWotq0b1Eo9z4W8XXl96eoqqfB3HyxSJ4LuJjTdj8TH9B3AUrOiFq7vTucPdvysokQ8+IqPg6vUkoPb8Oy4ZGVjib+fqfV1qFatS6RLbnjBwt8dX7oivIt/B3vdz9L0tjhkFel1ZTbYo+FyJcGnrEcVbp9SSNaXo9RyjMApWtOTKk2jFhq7blmJUyJ+63Z3choQ0dPzM8rk7nDzC6gle0DDNpaPsv6dpVmo4uZ9S1cIvr3C8DACE2B7XShNKnRc7fQW0Gmo9RYlX0bgbvYIoWPFk/JOm8O7fHS6oLF+hFfG+a0xn+4vJVbwk0Pp95dfrLUqIXSjd8WSrwLEVFlv0dQDSRwOWqsVgwQsyShEUO64dbK/N+0CX8QquX2rpicR1KEaB0ic3QMGKbgh2qiYDJH5Mp903tdRIMEYPP4+EeeKteSRU+9mir0PuibFUVZE3eb+PsP5d6m8nrCYJddR11sa2hRq9IrI9JaoBBMsFlZP4MQa7Fy+9XLicqQYSS3PNZ+Rtv/Zz8j4nhp/O8rWVX7/UbQsFlnZdOlRGwYpePTdV3fVLPdGPtj2hpE1ic6nIITeO4M/bUuQ02EuEzIGm5F4wpHaHrNpW3vorCKuFZJAb+AVIHFMp+nF5aRHmebDEC7+YGLHeTioEBSWlHlsSf4N2I+Slw6nf2F4+KHSDEMZr3OvMcRVW10Fpnh1iNz18gaWcWKfKl2DZbZH0h4IVTYns8D5Sfxq5J0aJJww/ZwZmU/ok7YpSDzcpWSnr4nFKXEriTMHCE7vDwcVMq3fxbywWfPFna1aqpMOZi6NJOYW698vZNj8PlCK5tKwAf9/hT3Sq9A2XkFj7LCKKghV3J3WgIldQ+i5BlcnVeOss0rw//LS5IJ/7/Kzs+lyxbzR+Tdpycn9ni/YIYutQIFhxpouuWFdYfumQ2Pd2Jk/4JYmOPtdzLjdMgtwJ74T7TbSMcTyUbDf22u9A/T5A56+c+JDgOzzGq9J7aozMhEjswdRtmuVzteeH00vVnwL0P3uRt1Ct0Zk7U/mOOCDEfdqslBZr18Gno+9TprrEBWWmObCMvM/Z418CqNQYuLSTe87fN6q0AlJPF30bqhT7C6skRRZt0If7U2zTanTTlVjdAnCBmjlYk3lh9vUvfKx2Gw899briMxh0deqwhUpW9OLsX8qv010uxGqwaONh7+THoM4RquHdjNQRRYX7hhJ3YFbrUCBvW78n73Nyvo+j40WsnYPa1XSSs1Kkga1eVGmldQosf2t+lRh/v3Gm1KiSnXmQHNFLyYcbXCsoWNESfwfJfShzJW4+KFyroZbPnSnSFmM1uqV5pfLXKUYv+al2T4TwBuquX0i0FEBqnvOOkR4/Sl9WuK0QkcHDFB8p1on9SWrDT7lkjwBsR2glZdfnLGGAYK8NTdsPxdfDz5dgF8+15YUoWNENFSJsvUTtYoqJtFaX3ANCwGAAQitLW9bhPEJFPPkL78atRtNUGL9IWw1q7FP8C6yj0UaLymGDcRm/t8Eg+JwCAYPsnlWungZC5rQDoqtUYjwTBVhtW5AupYM4ucTyyIOmBaFgxd2p3WVWS8IqgHbDpX+2/SdAo/5A/9/El+s5x/l02WIvf5u/bfm8rNR2HDKJ1bmL9kRQYP9Q4q6+7gvytic6fo6wMbSddGo9348SE3BWbKjyse7EusXSwW/gW7yU7NTII/F3VqOqVA1i+19kc9elQ2UUrGhKiRlY5VYDqdCgV5GDmbeOYoK74OAKsEt4wAaEAC/Msh4PQ3iBU6JIWux7++nk7gsA/HldanU1AieP5C77DkidX0YVMrenRONOvV5QhYLK2n7sMnICTDfJW37ppLvsDxJQsKIXYr0EShZcUJ0duMtq/he1yTkwZA6u5vJ1uguVu/CWjCj6OqzIaHui6LKmZMjMO7H9q2Ijy+f5xsLH96/K2551AngfU6HNyjPfyPuc5LQolWYF1iO220S1ESzLW1jqiONqsNj/JOaBXqqwnEDBim6IHCURjwEfngLe3u7cKkXv1GQe2C4vsrVHiQBEQh7ImTlWL8FRVDtl1tPincLHYm2M1P7eTq1fTlWP6MZhd5+z2hd4z+sLxzPhvZefJzMtLqb2hdjVwzbIrTq3GjCO33PIidGVVSfh+41MFLygkw4CIihYcRehlYFiNhpPyj35yr4DU2CnViOql9reQJMiZ404s2+oPbigWLdfH7nDPfH2xSCRMVesGmqLfJ/S0bzHUXISJZ/YyLf87rUO5wmy9/3U6J4ul8TziFq9m+yNARSgwOi6TuWRCiXJUvgXZUBMbVCwoiWx+UvECIuW5W286B9r1N/+cmIDgilypyZzQsLaXRXYNrQtPRFO4BbR2M6CgjRq2b26YT+gzTDb70mdG8iWHnO4kp8YkfFlmr0lbV0GA9BpDDdhYplqQKexggWULq0RKF+LtynBtp77rvDxW1tEtq//O2T1SPwdSlQAnv6am4zyfxu517pN4/bPyk1FVq+TEtOqItVRjnQYBTwziXs8cCt/JZbLRcgcN0ZFNIKtbjjR20DOJFxqCI20/15IOHD3ou33RO+kFRgRU+zg5TfglHTxllENpLYSgjEd1B5nQwm+xYBSIvuLXA1f4v6E+Plgq0TSnsDSwPtHuMeP0qR/Tqxnkhi/IDtjLAnWEVQGiHMiPWpx5rsp0YhbrQAhuCzwwszC57ZuoPR6LFXrCFz5186bDvKrw6eFjys3USxJrkAlK3rh6i5mqh+ILmpTIoUr7ojq9+b+OzOHjCtU5u1X/KG+heOZSM4jkeVMc76ULBjjpnY37r+paqVGwUB9cieLc2ZEUYu2IiJpbv+J/ff4oyDbGvQrpKCxcQ2xGZiFRNLSwEbQ5c6knmP0GhSI0riUpXIz++91GsP9l1qi6CaoZEUvXD0CouwLuMTBr1w+/oucE55SJ0kDN3ndR2e4NjEXtzj+SFEYxRpmCqoW+y4Fvi0YCp4/YJyPr/LpKhMNDD9XOCJouRrA8LOFDb1LVy18fzwvcJLaWNmZbuZSe8J1GAXs+MZ2Ovh5ZKt05v0jXOmLUvO9RDQqfFyUXVPN4Fy4blXmBhIoVcVBzyktaBxgDdwMXNkDRLawPt80fwuo+bT0gTHdBJWsaErqnYdYa3kXR/hiDVSlJkX1wEzBPHFmoK6SFZ2rcpBL6ngcpaPVCUrEhIRZji0TEm7Zg0j4vlNktsERG3OlKBd2v+K8QMVeWlxxfLpBNaCQ3UH7ID5yrNKTV7qzqq0KqrVt7GOlIvXTxkYhFKy4BRW6yco9qdXhNVC1WodIXfPjIwsf8yfbEx0l0tWt6m14ZhLQ+DXgqTjBG2L5p1JaXlrCjYjb6GXx5f63AWj8uo0065ReTqqalvK5gJb5LDbkgcPs4i3w9EQFEiOXJ4/fJHIu1wmqBvJaMk+oFoPXOTEcNb8IX2wAPFWGNS/CgRdcFnh+OnDZXoM2F6rTlfu7edrydWG+VG3N/QHAw7uuSZsm3LFNhIZp0TIfRBvVO5EuJUb5lUxP+40I/cUVqqCSFU8gZ/I6uQMxuboRoORiXxknFsVO3m5yUlPi+zZ3caM9RX4j4RABGgS9tjAGdB7HPW75Lvff1C21yetOJEvYdVnNq5fBcgA0h2O+2FsNv+qKdy4qVcWyIXWDl+A2V2NTFXmV1upuxyo7VMgfHZYaUcmKlhQZL8XAFff//ZkC65JArOGixYVFiTE+GNBiELDdTtGvrz9gzLF+3SUHmv4OZpuUnr6gqsonYtFkOPFd5OxvDj+jROAk+A7N3gRqPVPYQ+q1P4CHt7m2PlJUaWX53BWlJ6//CVw/xI3FwR8KQGwUV7F2KPxg5fU/ubZfw89yx3apKpYf01UpmcCHp4GcTK401u3o/3xGwYqWivOm71ZiKHFnKHHQC9dRMgK4f4V7LFrVw6PDCN4mp2bk1bKoX/Dcx6/wsdhQ+R7NxQ00nT22+PMt+RaTHqgA0Owi4/QYHSLtPSwGxyxoEO5UHriIo/Z0fsW1mbhUN1OgqIuqgdyB8OQnNvLtS0u4/92+d26dSuBXETl1cee/JbMrpJ7vuBTnxHcNKAE8/Q1X3SA2JL07cuY37/Ej0G4EUKWl/WWKEjjL6pmkBBvzEml1A+DUMWinZMWtqJfPD3xKOl7IhD/ysWL0dxNJJStuT7BT1elqf6TLgFAgW+FRMPlVMWKlKXaGBL+Q+gA1lE0RrO/iFN+Ac9tXmrOBWcvB3P8/P7D9frWORUuPO2jYV+sUWFMqqHDlmEZqpNlRsMJf1qWBmEjVFaBqWowGkUuzKvdl+r/Zo5IVd1D9Ca1TYAcrnD7e3rwvDuy/VITeKpLnVlK43Qbg+vFLLCh9YtHgRMUfa6eBSCChxKBjrh4dWikdR3P/u0ywv4y7lCj2+IH7X7KgVyB/BOVgJxrpuvT7apC3zQcBAH4r87brt/3EF4WPXdrrShoqWXEHqtTfKtRmpekbQK3nuMnBjvzi9Cqsr0WuvHOSOc/JZ0nAVIkjpKrB6oQt9XvoqGj3w1NAdgb32JmLlVT830vTmbaLUHXZfiQ3Xo7oCLluEqzUeBIYealwCgN+Kayvn+3PeIIW7wD7Ztt/32Cw/Amf+QZoNxx711zDG7e+VjYtjmY3f3wEUK8Hdy5frUGw5AAFK3oRXEHe55SYLKyobJ1M+Y0GxVJhVdLKe8E0bLt5OyJBmyq9P+zQenr1gBKOl1GSGsXdxQKK3uC3mEKNGX38gPxcbsoE2VQKGB0N5V+upiAZynddNuYzmMsRizKEu5NtpnKN+cjKNcKJ1hv64+yxYzAU/ObXlEtDseJA3iNpvU/LVlduuwqjaiCt9V4EtP0IiOkkspBSE/rx1sjvcuirUC8R/sW/bg+JSbJxMHefhSsNP8L880Fg/PpsieuUfIIoU03ackptTymhVUTelLOviKf/dHK6jHXKc+WOrRmIbej0lXiJjDOB6OB/gKZvAi/Ol/yRn/5JlL5+CwodywO3cBPVdf7K+j2F98ev1p0GXvkNaDkEN2L6IT/fNaU5T8fvRIO4BBjtbO+q1H3Fi9zPyrV+8e0d3P7d52cn1qSjUtgCFKxord4LwFNfWp1gMh7Z2OkUlPlIbDI8qUROWj6CXcvexcPWifWx/mi/rynGrjuNa3ez7K9TKnsnb/7UAY5XIm/bGlm+/yp2nb8tcWnxi8+/F6Sup+j2Jkpsw9RyiOR1Ory0VqgDdJ0iuTQQAMat/0/ysnYVJaio3BR47ltZbQtyjfk4dEV6W7GFuy8DNZ7CynJD0GbyLoz87bjT2xRl5we6eCsTAJCVy7th4eXZ45O3WX5A7ZsGF96UOLepwgx8dto/WLLviuXbFWpz+7czE4HqEAUrEqTZilZVVj8uofAJYyJpcLBXN+zH/efPzQMgn9k+ASjl3kNpeeZoy+kiQRsTeSbH6fyqRV6HXI9yjdh98TZyjRK6cTr4vU7eSMOnq0/glXn7nPqcHGNyX+UePPl/iq3z/kMbA/0BhSVKBl8JDZwL94cuU3fi4GUXTjtQ6xnXbcuK4zmrvlp3Gr1m73F6zfGbzwMAVh267vRnr90VlII06FP42OG5iPed5FbddhjF/X/uO5tvn03JwOztF/Eo11j0bfH8eSypyOsAgCPBba1ey8zOw+bTNy3TDGD0mpMytiDI96g2hY+LBcpYn/IoWHFg25lUNByTgL5z9oA52Hm3nUnFjK3ncUBwYmSM4WxKBrLzuJ1qyb4rePK77bh+T1ox5tFr99FwTAImbTxjWmPhm4J97PO1Jyy2ixdmAR9fBGp2tlhOicsWY9yFMSvHdLAUpuv7LeckrcOnCA1s7V/Y7a/jU/5doeD3vM0sa8dXHbou8ptLGytG6m/8/rIj6Dd3H779+6yk5fmuCy4EKWmPnFyD4LvUtl/idOTqPYvnC4zPYFzdP7A+tJ/1BUmmmdsu2H6jmD8w6gYw6jrSHuU5PB5N7mXl4c1FBxVJm1BaVi7i/jiFhzm8C4ZIu445O3nVR7z0p2XlIifP9v7814lkvDxnL1LTH2HY8iOYtPEM0rJynd43swuOl5/3XLH5viPCGwdjPrObhpM30rCP19Ov3aRtWHHgKhhjyDXmY+vtwmMtX3gcPsi2mwbhsgCwL/EODl25Jx5cdPiUOw82G2jz7S7xO/HNxjP29z2bwzI4PlelZtj4LhUb2lx2z8U7yMy2XeK97X5he71bBfkzdOlhDPz5ICZucP6cYU2Qd/xG6c3eVGD9RUfBigMT/uKKe/cm3sX2s7eQn89w9c5Dmwdpwumb+DbhHPZevGPx+u9Hk9AlficGzD8AgIt8L97KRJ8fuLsbe3WyJnMOcWOjzNp+EZtP37Q4YLNyjBbF178dugGAO4Cbjd+Cv06mFNbtFzSwMjLLg8zWCQAAGAxW9cL8730hNQNdp+/CS3P24Mb9LMzYU/i9z6Zafu7avSzY5OzdfgmuweF/+ZHCFUn6+PIDhQ3XVhy8ZvcCAQAjVh7D36e4O5ez6fK6Krf9xrKoeu7ORDDG8CjXaK7qm7b5PBJO3wRQUOTupLuCkohbNk72Kw9ew6bkIKvXOQy7+VU9wobNPD1m7bZ67afDD/Du0sNoN6nwu9o76VptmTGrZTNzjHaWBph/MLYlPkDDMQnmY1MKi3YWFepKSter/JKpktZF6Ceup+HrDf9h4e7LuHQ70/w6/3Nrjtyw+Ez8lsKL4aztF3DuZgbuPMhGwzEJeOK77TbTMmTJYexJvIMnp+zA2qNJmLX9IhqOScAnTlbH9J+7125bm7k7HbfByeBVHT/KNeLxSdswZMnhwgV454au03fhYmqGxecn/30Obyw8gJjRG/DGwsLg8dzNwuV+2XsFTcdtxvxdlwpXyzu2f9xhmc6Ltx7gpTl70Wv2bov9vuWELfhlz2UAwKjVx/H6/P3IDyyLS7czzTeNl29n4ou1Jy2C7OlbC3+fA5ctA/McCaWe2XlGHLt23+K1/fmFg7Zx53reuSqiMfc53xJ4ee5evD5/PwA7bfkKLNx9BTvO3cK2s7cAAKsO2W6May+QPHczA4N/OWQRfIpdgfIZw4XUB5JvDtRCwYoT1h69gfeWHcHjk7dhoI07tfXHuSK/pDTLC/PivdydzJ5EyyAmKe0Rrt97iEZjEjDRxol3SM77QPtPcdSnsJfCwJ8P4p9zt8zP+8zZB9TpjvmGHhiYMxxZuUbk5OXjpTl7cftBNoYsOYy7mTl4c+EB9L87EAvyuuDpnG9g4O2e9i7YjFnXC0eP+sv8eMt/3AX2+PU0pKY/wqobhRc5/glmrbE1Zm0vPAnwG2zeuG+ZVxduPcDKg4UHn1Xa/rcBK32ewcCcERYv/3rQ+oDNNebj3wu3kWvnHHPlzkOsOy5eTDtj23nU/mIjuiy+iUM1hwEvWHdDZIwLGv+9cBtDlx7Gwxz7F+rxf/2HbWdT0WriFtSPS0DGo1xM3VxYCuVrXdQEALiVkY1HuUZM3PAfqn32l8V7/N9y/Pr/MGr1CYv3GWP4eNVxvHuxGa7VfpObf4bHmM/Q7yfehblMtN30O3Lt7kN8vvYE6n35N/r/tNfh8tGj/kK9L/+2ev1hTh7i/jiFw7ySnO4zdqHel3/jfwu5oH/uP9wFbdrm84j6dL3F53ONlidWfkBu7DQeaDEYeW9uxaztF3A6yboB8dTN5/HP+dsYkPMxHjZ7D79mt8CF1AcWy3SbsQtnUrgLLf9X+4fXVuiUjXWb7L98F52n7jRXFVy3F9AXyBC0M/v14HWMX3+aV7IJZIgEiQbYb2sz3k7gdyGfuxESBjNrjtzAjftZ2HAyhfeq+MXs9oNs8wXWnlnbLgIAxq4rnFmcf408IGhr8+R3O8yPM7ML8yEl/RG++P0U8vMZlu2/hh3nbuGHnRfR8dvtqPX5RszZeRH9f9qHX/ZewesL9ttMy/dbz1ukYem+q4XPAYvStLSCau/3lh5B95mWM7SbzlUL8zqj+md/mUu4AKDFhdewIK8LumePAQAcvHIP+y/dxcZTKRBjCmqETOfdWxnZaPvNNkzc8B9yjfn4esMZ/HvhNjaeTEbnqTux8VQKGvCaGuTnM4tSef5+NG/XJTw1ZQeW8L6/FnTRdXnWrFmYPHkykpOTUa9ePcTHx6Ndu3aapmnjyRTM25WI87wTVF4+w/oTyQCALWdSkZVjRKB/4R13esHJZNn+a3ixSWW8ueggJvSojwu3Ctfxw46LFtuZvuUCMrLz8OPORIwS9MT8K78lnj4WgqR0yzsUoTd+PoStWb3Nz2t+vsHi/cZfbSp45It/8br4F5epx6zdiLJzM5DKLBsBvrfsCLaYOyBZfujS7Ux8vKrwjnHn+VtoXLCX9v5hd8HdzqtW2/hx50X0KVjnuhNJGLrsmPm94YEX8Z6ddH/06zH0FOkBe/JG4cWm1/HmWNS0OdKOJaEbL+WfrjmJFas2mpfLPHkEC0QmwubfVVq0TQJ3Auw7Zw9iKoTgqxdi8V9yOp6Z9o/FMmJ3GAcv3wFg2UX0ySncCT0Hfvg6/xU88yAco+L+hunUtNtGSWB3kW2IuXE/C4v3cie1fy/cwb7EO2geXQaGghK0jSeTsWj3FUx6sYFFUMq36/xtTNtyHgt3X8bG3Xewt+D3uXjrAR7CsnTohZn/4qjgThYA/rfwABbzfoPMHCMu387Ec9//g8wcIxI+/Bydp+4EkIJJG8/i30+fwM5zt/DXiWS81ioK32/hLlTb8x/D+QZtMHLmv/jT32iV+UeuWm/bWXF/Fl6Ybz/IxrxdlzB7+0V88GQM/nZw0Zr7zyXM//cyLhZ8174/7sG66oX75tydiSjKPNmprBQA62BGGBAD3E1I1ew8ZOXaLxmT425mDkrKvK1+f/kR8+NJGwurSyb8dcb8OPFWJhzJZ8ziBsyYz5CZk4eggpcajk1AcT8fPLJxZ5SOYEQ9Wmp+fjYlAw0Kvs9NlMGYPMtzcp8frdsTGZxsk9ds/GYAXEmUqTRKeO0RWrz3KiqEFMeUTefwgs8pxAvOYbO2XcArLbVr16d5sLJixQoMGzYMs2bNQps2bfDjjz/imWeewenTp1GlilgXTXXde5hjVQy4/niyxfPGX23CE7Ur4Jn64ehYy3KcFFMDNotiUgBfbzhj8XyFnRO2ienOje8KKxx7wQCGrWdSRddhC3/n/yLvf5jsN8fpdajhxzzL9hL8UEb4e9grKB267KjFuw9zjIBC406Z7mjaBuSgjEqdA/Ym3sXexLtYc+QGHkisTrFHWOKw/nhy4X5sJ0g7d/OB7PzqO8eyNOWlObZLV/hVRgCwxfgYnvQ9gp+NnXD17kOr4n57bAUqgO2Te4dvt5sfc4FKoTZfbzU//kfQi0p4p6yUa8x6bKWm4zabH0/bct7qfVuE1ciHrt5H04KL4fi//sNbMoajWWV8HC/67sQM4wuSP5PxKBf1vvwbEaHSNrg8rwP6FtuOaXk9Mdt/mvOJlGCd4JwtF5NQzWwrUJG7LiGDwTVVMFM2Wbc1XGHsAEBaNZiaNK8GmjJlCt58800MHDgQderUQXx8PCIjIzF7tsiofy4QHOA4jsvKNWL9iWQMXXoETcZtcri8UrIVuPLyD5eVBTuj8ywPoKJeu6+zcjjIahdxLZ5DTqDi7B2YXgzMHY4Gj+bgFJNfBaUlKfm+wdjMYikljmNnOLNnjMgdhIaP5mB3fqzT20mS2MD707y30PDRHOzMb+D0NtyZux2jFxjXYPz2Azu99FxE02AlJycHhw4dQufOlj1VOnfujN27rRvyAUB2djbS09Mt/tRQKtDxieSPoW0wuH11VCkTJDmq1guxA+Yu40ZIPcrkj2Z4nxWOsnrVxh2kLfvy68jeXiazfzd3g9kePCyZWQ7DngVlBsdzr1MRcIFZNh5NYmVxMb8iAOBSvoMRVBXC4IN0uHhkXiedyS9aSS+DAcbCsWDxiCk0GCNPBuw1onaWAWmq/x7KbsPVx93xfIUGldRQ4bnecjpZPZ7DNK0Gun37NoxGI8LCLE+IYWFhSEmxXVc7ceJEjBkzRvW0tawmPp9I8+gyaFC5FBpULoVPnq6F08npeO77XaqnSyliwUrPnDF4zXeTVZWMM86xyuiX8xna+ZzAcmNH9PHdYXfZ57InoIfvP/g+T+IItTakoCzicl9DJopDWMbzZ35LxORdx6F8bmjy13M+QTOfM/g9v43FcvwAS8+UOpF0zx6Lrr57EZ/XC4BlvuzPqY03i23AfOMzeN3XugGsN/oq71WkIRhrjNZjXkiVAz98kvsW/JGL27Df68pZH+UMRmlDBq4y1wSXYiqVCsSN+1mqDKOol1IJBgNG5r6NIewPLDeqP2u5weKxcnlg71xvq6rq4y61rF5zJQPTsD9SUlISKlWqhN27d6NVq1bm18ePH49ffvkFZ86csfpMdnY2srMLu6ilp6cjMjISaWlpKFnSrWeRAOJ4J6+4NLuLsY2fwbB3JvfkrW1ApcaSVp+fz+Bj6m0yLoybL8K0LbFtC98zPW/9vuVw37cvADOacI+/uAP48mLhg/OBdR9yj9/dD8wsmAm3/SdAx8/sJ3rbBGDHN7bTNbYcN6cL7z3GmLkxp2Sm7/PYK8CRxbzX7f8G+CYayCromdDte6AJr5HcuQRgae/CdUj8XSXLzwfG8hotV2wIJBc0KH4jAajSQsYqefuG0N+jgT0zuMdqfB8xaTeAqQVdjT+9BhQXOcb56XplNbC4J/d4+FmVJgPlmd0GuFkwGBc/jzqPAxI+5x7X7e7kkOcyzesCXNtrnZb/bQSqFp5n7R7XpudSmT5XpTXwBq9x/x/vAYd/dry+7AxgYsHYNB+esh6n5vvHgLsFbZheXg4s62t7nfzlirJfmr4Pfx8y+ALN3wL2Fcwe7VMM+L87tj/PX0fLIcDTEy3f+7E9kHzUcTr5v8fjI4Gdk7jHHT8H2n9c+J6j/JPj+Epg9UDHaSyi9PR0hIaGSrp+a1oNVK5cOfj6+lqVoqSmplqVtpgEBASgZMmSFn/exumLcQGLi1HzIvQRqP4k97/xa5avl4oEYAACQh2PMBpV0NurYV/56bBBbt64NQXuN+wGKprTx520Q0/Fcf+bD9I0GfoicZ/yC+b+fIqZx1Eq8jqVZjAot22p56hnvy18XLmpMtt2Y5pWA/n7+6NJkybYtGkTevQorALYtGkTuneX23mSSBLmfMM5s/6rgJwH1ne5xQKA0cncaI+ODsjX/rC9DnfkjQGSFvSczzGdgE+vig6o53Wk/l4+PsAnl7hh932L0OhY40HLFNf8LeCvgvGk/N2jilpNmndd/uijj/Dqq6+iadOmaNWqFebMmYOrV69i8ODBWifNs4VUlP9ZHx/7QYafhHkk/EuIr4OvlAv79Zet4XgZE/6J2NNOknrlTD5rEdjoPVBRPU+KcBwUE2lsXLFRYfWOS0vadHpc6zhmV5PmwcpLL72EO3fuYOzYsUhOTkZsbCz++usvVK2q3eAzXiH6ceCJL7gZZ13Nmdk/G/YF7l0CqrZWLz3/2wic/5urX94cp952iAwyz8y6DCBddJXRcwmUHM99x50zGvYD7sub14g4SYf7kObBCgAMGTIEQ4ZIn/KdKMBgAB4f4Xg5rfn4Ak98ru42qraybHhYVDo80AlxOaUCxqAyXENlgIIVL6b5oHCEkCJo+6HgBT2WKHihqgXd4uv11DYdHkfabOeKeapgmIzusyxnbO4+qwjrjOP+S2mMXTqK+29npmZvoouSFVIEXn0XTxdm1O8DXNiidSqI0OvrgNyHQIAXNYx0SdWbi4/5tsOAZm8CASHc81HXue9ZlI4B1Tpw6zGtU8zQQ4AxB/BXarA/90XBijsq44YjJ5ZUoO+/Xrk6YLTaHgVtuuPjo2GgYmd/DCxj+3XVkuHmN1LB5bn//KBCSoAhhdT1+BazHK/Ki1EuuKPGrwPrP9I6Fc6J6cQ16KXiTEJcq/dCIOMmUL6m1ilxD31+AdKu0blKZyhYcUfuGGm7S4NevyCu+N5dGAxUsOIutCppqCd/GgvneMiOWPd5rVNAbKAGtoQorUpL7n95DbqFu8Jz33H/n5+hbTrEFC9V+DjI9kSWHuvJ/+P+az2aboW62m5fS3UKAp6mb6q/Lb9grsoqIBQoofK0Ehpyw1t0QlSkRCPBgBBgdArg61/0dTnLFY0cmw0EGr4M+Aervy0LMgeFc8eSyKKo2gr4LMn1v49w3xMb6M3T9fmZK6F1xW/g4wN89B+X/x68r1PJCiGyOCjS9wt0PD+SO3PVhdDdG2lacOF3kfP7mEa1rqLiAIxqqqSj+XMMBvWOkdLR1q/5+gHFFLw5KunEwJ0u4rlhmNfwpJO5u9L6N/CQtgJEW29sBA4tAlooNdWJi4+LZycBpaoADfq4druu8vqfwLV9rhm7p2oroPN4oFyM+tuSiIIVQggh3ABkT32pdSrkCywNPPmF1qlQT/Tj3J+rtB7qum1JQNVAbs8Fd9Wmqcp7zVN/W1rrOYf732WCtulwpHQUN3Femerqb6v5W9z/ujQTOtFYVFvAxw+IeEzrlBAXo5IV4ljzt4DHXpE2o7K7q/s81zjW4XfVsOrFYOBGtmT5ytZT21M6isuTYsXV35Yn86j2N7a44JgICAE+u8EFLMSrUMmKnpQtqB+MaOz6bZcrGDCqUhPb7+sxUIlqy/0vEabsevX4XYV8i9kOVILKqrM9v0AvuNh6GS16qymhWADXA4Z4FSpZ0ZPX1nIN3Jq5oG++0KtrgEMLLSfr0ruePwH75wCP9ddg4zq9cNMopdDtb6M3H50B4mOBLuO1TgkhDlGwoiehlYEnRmu47c+12bZcJcprl198VOKgL7r9PXSWruCywOhkrVNBiCRUlkaIu3PJbLcaKRHGjflQqgrg72DyN9PIur0Xqp4sIoFug0bijqhkxe3RCYF4MB9f4IPjBY8d3Fs1Gwg89irXpuHGYfXTRix5ctBMNEfBCiFE35wZQlzPQ7xTSQMhslE1ECFy0IWHEEJchoIVQgghCqBqIKIeClYIKaraz4m/3+cX7n+379VPCymgowtn80Hc/8c/1jYdaqM2K0RF1GaFkKIKLC3+ft3ngc9vuWa0WaI/z04COo/zwt+fqkqJcqhkhRA5TKPnFg+VtryaF6rI5uqtmyjD6wIVQpRFJSvujhp6aqPrVCCsHhD7otYpATp/xQ3qRxMNEkI8FAUrhMgRWFo/bRACQoDHR2idCkIIUQ1VAxFCCFEANbAl6qFghRBCCCG6RsEKIcTzBJXTOgWkTLTWKSAehNqsEEI8T+mqQI8fHXcrJ+qJags8NwUoX0vrlBAPQMEKIcQzNeyrdQpIsze1TgHxEBSsuKsqrYAHqUCFulqnhBBCaARboioKVtzV/zYALB/w8dU6JYQQQoiqKFhxVwYDYKBAhRBCiOej3kCEEEII0TUKVgghhBCiaxSsEEIIka/LRMC/BNBtmtYpIR6M2qwQQgiRr9UQoMVgwIfufYl6aO8ihBBSNBSoEJXRHkYIIYQQXVMtWLl8+TLefPNNREdHIzAwENWrV8eXX36JnJwci+WuXr2Kbt26ITg4GOXKlcP7779vtQwhhBBCvJdqbVbOnDmD/Px8/Pjjj6hRowZOnjyJt956C5mZmfj2228BAEajEc899xzKly+PXbt24c6dO3j99dfBGMP06dPVShohhBBC3IiBMdeNkTx58mTMnj0biYmJAIANGzaga9euuHbtGiIiIgAAy5cvx4ABA5CamoqSJUtarSM7OxvZ2dnm5+np6YiMjERaWprN5QkhhBCiP+np6QgNDZV0/XZpm5W0tDSUKVPG/HzPnj2IjY01ByoA0KVLF2RnZ+PQoUM21zFx4kSEhoaa/yIjI1VPNyGEEEK047Jg5eLFi5g+fToGDx5sfi0lJQVhYWEWy5UuXRr+/v5ISUmxuZ5Ro0YhLS3N/Hft2jVV000IIYQQbTkdrMTFxcFgMIj+HTx40OIzSUlJePrpp9G7d28MHDjQ4j2DwWC1DcaYzdcBICAgACVLlrT4I4QQQojncrqB7dChQ9G3b1/RZaKiosyPk5KS0LFjR7Rq1Qpz5syxWC48PBz79u2zeO3evXvIzc21KnEhhBBCiHdyOlgpV64cypUrJ2nZGzduoGPHjmjSpAkWLFgAH8HAQa1atcL48eORnJyMihUrAgASEhIQEBCAJk2aOJs0QgghhHgg1XoDJSUloX379qhSpQp+/vln+Pr6mt8LDw8HwHVdbtSoEcLCwjB58mTcvXsXAwYMwAsvvCC567IzrYkJIYQQog/OXL9VG2clISEBFy5cwIULF1C5cmWL90zxka+vL9avX48hQ4agTZs2CAwMRL9+/czjsBBCCCGEuHScFTWkpaWhVKlSuHbtGpWsEEIIIW7CNE7a/fv3ERoaKrqs28+6nJGRAQA03gohhBDihjIyMhwGK25fspKfn4+kpCSEhITY7e6sJ6ZIkkqCLFG+WKM8sUZ5YhvlizXKE9v0lC+MMWRkZCAiIsKqA46Q25es+Pj4WLWJcQc0RoxtlC/WKE+sUZ7YRvlijfLENr3ki6MSFROXDrdPCCGEEOIsClYIIYQQomsUrLhYQEAAvvzySwQEBGidFF2hfLFGeWKN8sQ2yhdrlCe2uWu+uH0DW0IIIYR4NipZIYQQQoiuUbBCCCGEEF2jYIUQQgghukbBCiGEEEJ0jYIVQgghbof6hljz5DyhYEVFnrzjyEV5Yo3yxDbKF2uUJ5zU1FTzvHAA5Qvg+XlCXZcVkpOTg6lTpyI4OBj169dH+/bttU6S5ihPrFGe2Eb5Yo3yxFpeXh4GDRqELVu2oEKFCoiJicG0adNQrlw5rZOmGa/JE0aK7K+//mJly5ZlLVu2ZI0bN2alS5dmo0ePZllZWVonTTOUJ9YoT2yjfLFGeWItNzeX9e/fn7Vs2ZJt376dTZkyhcXGxrJ27dqx06dPa508TXhTnlCwooDevXuzQYMGMcYYu3v3Llu5ciULCAhgU6dOZQ8fPtQ4ddqgPLFGeWIb5Ys1yhNrV69eZTExMeyXX34xv5acnMwqVarE3nvvPZaSkqJh6rThTXlCwUoRJSYmskqVKrHFixdbvP7ee++xJk2asISEBI1Sph3KE2uUJ4Xy8vLMjylfrF28eJHyxIYjR46wwMBAdv78ecYYY48ePWKMMTZjxgxWq1Yt9uuvv2qZPE14U55QA1snnT17Fnl5eebnVatWRW5uLtLT0wEAWVlZAIAvv/wSmZmZ2LBhAx48eKBJWl0lJSXF4nnVqlWRl5fn1Xny888/48KFC+bnlCecL774Al988YX5eVRUlNfnS0JCAo4dOwaj0QgAiI6O9vpzyoQJE/Dll19i+fLl5tfq1KmDChUqYPHixQAAHx/u8vXuu+8iJCQEGzZsQHZ2tibpdYU5c+Zg7ty52Llzp/m1mJgYhIeHe0eeaB0tuYt58+axSpUqsdq1a7OGDRuyhQsXspycHMYYY2+//TZr0KCBeVnT619//TWLjIxk9+7d0yLJqps7dy577LHHWMuWLVm3bt3Y9u3bGWOMZWVlscGDB3tlnhiNRtajRw9mMBjY5MmTzW0McnJyvDZPGGNs7dq1LCwsjLVo0YLNmDGD3b17lzHGlbJ4a74sWLCAhYeHs/r167OQkBA2ZMgQduPGDcYYY4MGDfLKPNm3bx+rUqUKa9y4MXvmmWdYSEgI69WrF7t48SJjjLERI0awmjVrsps3bzLGmPn4WrRoEStVqpRHtulZunQpq1ChAmvVqhVr1KgRK1++PBs/fjxjjLG0tDQ2cuRIr8gTClYkWL16NYuKimILFy5kW7ZsYR9//DELDg5m06dPZ0ajka1bt47VrFmTxcfHM8YKi+Ju3brFAgMD2T///KNl8hWXnJzM+vTpw6pWrcrmzZvHfvjhB9a1a1cWHh5uXmbVqlWsdu3aXpMnjHGBCmOMDR06lDVr1oxVrFiRHT582Pz+b7/95nV5whhjDx48YN26dWNjx461+f5vv/3GatWq5VX58tNPP7EaNWqwZcuWsVu3brElS5aw4OBgdvToUcaY9+4rH330EXvuuecYY9zxdOLECVa1alU2ePBgdv/+fbZ3717WuHFjNmTIEMYYY/n5+YwxxrZt28YqVKjAjh07plna1bBkyRLWsGFD9sMPPzDGGLtx4wabMWMGCw4OZmlpaYwxxjZt2sSaNWvm8XlCwYoI08Xnww8/ZG3btrV4b+jQoaxFixbszz//ZNnZ2ey9995jkZGR5jsjxhhLSEhgVapUMZ+APMWqVatY69at2alTp8yvnT9/nlWtWpWtXbuWMcbYzZs32QcffOA1eWJy69Yt1rRpU5aWlsaqVavG3njjDZaamsoYYywpKYm9//77Xpcn69atY+XLl2dGo5HdvXuXffLJJ+zrr782t8m4f/++1xw/+fn5LC8vj/Xr14+9+uqrFu/VrFnTHNympKR41b6Sn5/P7t+/z9q2bctGjBjBGCs8/86aNYs99thj5gv21KlTWVBQEFu9ejXLzs5mjDE2btw41qFDB/OF2t2ZvsfChQvZ22+/bdGoeteuXaxmzZpsz549jDGuJGXq1KksODjYo/OEghUJOnfuzN5++23GWGFx7I0bN9hTTz3F+vXrx9LT09n58+dZ69atWaNGjdjixYvZ+fPnWd++fdlTTz3FMjMztUy+4k6fPs2WLl1q8VpiYiKrWLGi+QBijLHjx4+zdu3aeUWeMMZVaWRlZbHHH3+cGY1GtmLFClasWDF28OBBxhh3d3z58mWv2U9MJ8l58+axF154gW3evJlFR0ezLl26sOeff575+vqyoUOHsrt377JLly6Zi7k9PV8YY6xRo0Zs4MCB5t4a7733HqtVqxaLi4tju3fvZoxxDW09OU8OHTrE7t+/b/Fa06ZNzb2gTKVJOTk5rGfPnuz5559nN27cYDk5Oezjjz9mISEhrH379qx3794sMDCQzZw5kzHG3PrifOjQIYsqvvv371s0SGeMsaNHj7Lw8HBzVSpjjKWnp7ORI0d6ZJ6YULDC8+uvv7KBAwey+Ph4dvz4cfPr48aNs6jiMO08s2bNYg0aNGCbN29mjHF3Q08//TSrU6cOq1SpEmvTpg27dOmSS7+D0vh5Yqs4MT8/31xcGxkZyc6dO2fxvqfnCX8/YYyxCxcusKioKPMJp2PHjqxu3bosIiKCTZ48mTHmmXnCmP18WbZsGQsNDWVDhgxh//d//2cO+BcuXMhatGjBvv32W8aYZ+aLvTxZvnw5q1q1KuvcuTMrW7Ysq127Nhs7dizr2LEja9CgAfv6668ZY56ZJ6tWrWKVK1dm1atXZ1WqVGH/93//x65fv84YY2zatGmsRIkS5mDMVErw22+/scqVK7N///3XvJ6VK1eyL7/8kg0ePJj9999/rv8iChLmyRdffGHR7dhUysQYY1OmTGFt2rRhjBXmj4kn5YkQBSuMsdu3b7MXX3yRhYeHs8GDB7O2bduyiIgItnDhQsYYY6dOnWKlSpViU6dOZYwVRvx5eXksLCzMHL2a3ktOTmYnTpxw+fdQkr08WbRokXmZvLw8c8Q+d+5c1rx5c8ZYYRRvOsCysrK8Jk+2bNnC+vfvzxjjqsbq16/PDAYD69mzp8Udk6fsJ4w5Pn7y8/NZbGwsMxgMbMGCBebP5efns169erE333zTfNL1lHyxlyf875+amsomT57M2rdvz9LT082vv/XWW6xHjx4WDSY9IU8YY+zAgQPmtjjHjh1js2bNYuXLl2fvvPMOu3//Prty5QqrXr26uXTFFNgyxljZsmXZvHnztEq6asTy5M6dO4wx7lyam5vLGGOsR48e7N1339UyyZqgYIVx0Wjz5s3N0T1jjHXv3p1FRUWxdevWMca4diuhoaHmYkvThbhly5Zs6NChrk+0yuzlSXR0NFuzZg1jzDLaf/7559mwYcPMz69evWquZ/WEIkjGxPNk1apVjDHG/vzzT1arVi326quvMj8/PzZ06FD20ksvsbp161qUOnlKnjAmbV+ZNWsWMxgMbObMmeaTLmOMDRgwgLVq1cr83FPyRSxPVq9ezRjjRh/t27cvGzduHGOs8C75o48+YtWrV2cPHjxgjHlGnpi+w+zZs1nlypXNjUMZ48YEad68OZs4cSJjjLGZM2cyX19ftmPHDvMyFy9eZNWrV2e//fabaxOuIkd50rJlS/bVV1+ZXzMajSw/P59Vr17dfF06e/Ys69u3L7t69aprE68BGmcFwNKlS1G5cmVUqlTJPH5Bjx49cOXKFUydOhX379/Hhx9+iMjISPTp0wdXr16Fj48Pzpw5g/v376NHjx4afwPl2cuTy5cvY8aMGbh9+zZ8fHyQm5uLe/fu4fjx4+jWrRuysrIwbNgwVK1aFdu3bwcAGAwGDb+JcsTyZPbs2bh37x6Cg4ORkZGB5ORkbN26FdOnT8fPP/+M//77D/PmzUNubi4Az8kTwPG+cufOHQwaNAidOnXC9OnTsXXrVgDc+DxJSUl44403zOvylHwRy5OZM2ciNTUVxYoVw507d3Dw4EEAgL+/P27evIlz586hb9++CA4OBuAZeWL6DpcuXULNmjVRrFgx83sDBgxAs2bN8Pvvv+PcuXN455130LdvX7z00ksYO3Ysjh49ikmTJiEoKAgtW7bU6isozlGeNGnSBBs2bMCpU6cAcGOoHDhwAEFBQWjcuDGGDRuGBg0a4M6dO6hQoYIm38GVvC5Y2blzJ/7++2+Lgd1iYmLMO0SJEiUAAGfOnMETTzyBhw8fYvXq1YiMjMTKlStx5swZtGvXDn369EG7du1QvXp1NGrUSIuvohhn8+TRo0dYs2YNAMDPzw+XL1+Gn58fdu7ciejoaOzcuRN79uzBM8884/ovoxA5+8n69evRunVr/P777/jjjz/Qtm1b5OXlwd/fHytXrsSAAQPg5+enyfdRirP5kpWVhdWrV8PHxwdLlixBhQoV0K9fPzz77LNo1KgRcnNz8dxzz2nyXZQi5/hZu3YtAGDUqFFYv3492rRpgyFDhqBp06ZIT0/H22+/7fLvoaRNmzbh/fffx7Rp07B//37z623atMHu3bvNA0kajUYEBweje/fu8PHxwfr162EwGLB48WL07t0ba9asQe/evXHgwAEsWbIEERERWn2lIpOTJwaDAQkJCeZl//rrL5w8eRK1atXCpk2b8O+//yIhIQEBAQEu/z4up3XRjqvcunWLvfbaa8xgMLCGDRtaNFK7ePEiK1++PGvfvj375ptvWKtWrVh0dDTbsmULa9iwIfv888/NVR6nT59mixcvZh9++KHbD2VclDz54osvzMvGx8czg8HAqlatyubPn6/BN1FOUfcTIU8owmes6PliapR+8+ZNlpCQwCZPnmyuInJXSu0ra9asYZ988gnr16+f259TkpKSWNeuXVmFChVY//79Wf369VloaCjbt28fY4xrf1O7dm1z70p+VXK7du3YO++8Y35uNBpZZmYmO3PmjGu/hMKKmiem8VMY4zp7lC9f3qOqw6TyimAlNzeXzZo1i3Xp0oUtX76cBQUFsYkTJ5obyjLG9V1/6623WOPGjdnQoUPZrVu3GGOMvfrqq6xXr15aJV01SubJvn372IwZM1z+HZRG+4ltlC/WKE+sZWZmstdff5299NJLLDEx0fx6s2bN2IABAxhjXKP8n3/+mfn4+Fj07GGMsf79+7OOHTuan3tCoK9EnnTo0MH83DRmkzfyimCFMcb27t3L/vzzT8YYY2PGjGHly5dnR44csVqO3xXs5s2bLDY21twAjh/xegIl8sTT0H5iG+WLNcoTa2+//TbbsGEDY4yZG1KPGTOGtWjRwrzMo0ePWI8ePVidOnXY9u3bWX5+PktOTmbNmzdnP/30kybpVhPliTK8JlgRRukRERHs7bffNncZ5L+flZXFcnJyzCMnCsfS8BSUJ9YoT2yjfLFGeWKN39XY9P1feeUV9tZbb1m8lpWVxTp06MAqVKjAOnfuzCIiIljLli09slcL5YkyvCZYMTHd5fz666+sWLFiVtOtX79+nc2aNYs1bdqUlSlTxmqkVk9EeWKN8sQ2yhdrlCfi2rVrZx5fxjTdAGPcgHcJCQls/PjxbMmSJRqm0PUoT5xnYIwxrRv5aqV169YIDg4291K4desWypcvj2XLliEpKQnDhw/XOokuR3lijfLENsoXa5QnlhITE9G6dWusX78eTZo0AQDk5OTA399f45Rph/JEJq2jJS2Y6g1PnjzJfH192bRp09j777/PGjdu7BGjRMpBeWKN8sQ2yhdrlCeWTFUbixYtYtWrVze/HhcXxwYPHmwendebUJ4UjVcGK3zNmjUzd7vduHGj1snRBcoTa5QntlG+WKM8KfTuu++ykSNHsoSEBBYVFcUqVKjA/v77b62TpSnKE3m8Nli5cOECi42NZUFBQdTaugDliTXKE9soX6xRnljKyspiNWrUYAaDgQUEBJgnZ/RmlCfyFXNcUeSZfH190atXL3zyyScIDAzUOjm6QHlijfLENsoXa5QnlooXL46oqCh06tQJU6ZMQfHixbVOkuYoT+Tz6ga2hBBC1GM0GuHr66t1MnSF8kQeClYIIYQQomteN5EhIYQQQtwLBSuEEEII0TUKVgghhBCiaxSsEEIIIUTXKFghhBBCiK5RsEIIIYQQXaNghRBCCCG6RsEKIYQQQnSNghVCiGoGDBgAg8EAg8EAPz8/hIWFoVOnTpg/fz7y8/Otlu/cuTN8fX2xd+9eAMDly5fNn7f3FxcXJ7qcaV2EEPdFI9gSQlQzYMAA3Lx5EwsWLIDRaMTNmzexceNGTJw4Ee3atcMff/yBYsW4KcquXr2KevXq4Y033sDDhw8xd+5cGI1G3Lp1y7y+b7/9Fhs3bsTmzZvNr5UoUQK3b99GdHQ0Nm/ejHr16lmkoWzZsvDz83PNFyaEqMJrJzIkhLhGQEAAwsPDAQCVKlVC48aN0bJlSzz55JNYuHAhBg4cCABYsGABunbtinfeeQfNmzdHfHw8goODzZ8FuMCkWLFiFq8BwO3btwFwgYnwPUKI+6NqIEKIyz3xxBNo2LAhVq9eDQBgjGHBggV45ZVXULt2bdSsWRO//vqrxqkkhOgFBSuEEE3Url0bly9fBgBs3rwZDx8+RJcuXQAAr7zyCubNm+f0Olu3bo0SJUpY/BmNRiWTTQjRAFUDEUI0wRiDwWAAAMybNw8vvfSSuf3Kyy+/jI8//hhnz55FrVq1JK9zxYoVqFOnjsVrvr6+yiWaEKIJClYIIZr477//EB0djbt372Lt2rXIzc3F7Nmzze8bjUbMnz8f33zzjeR1RkZGokaNGmoklxCiIaoGIoS43NatW3HixAn06tULS5YsQeXKlXHs2DEcPXrU/BcfH49FixYhLy9P6+QSQjRGJSuEEFVlZ2cjJSXFquty165d8dprr6FJkyZ48cUXERsba/G5qlWr4pNPPsH69evRvXt3Sdu6c+cOUlJSLF4rVaoUihcvrtj3IYS4HpWsEEJUtXHjRlSsWBFRUVF4+umnsW3bNnz//ff4/fffcfToURw7dgy9evWy+lxISAg6d+7sVEPbp556ChUrVrT4W7t2rYLfhhCiBRoUjhBCCCG6RiUrhBBCCNE1ClYIIYQQomsUrBBCCCFE1yhYIYQQQoiuUbBCCCGEEF2jYIUQQgghukbBCiGEEEJ0jYIVQgghhOgaBSuEEEII0TUKVgghhBCiaxSsEEIIIUTX/h9IjB/GWeDxsAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "climate_df.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1cc5ab6-60d6-4289-ba80-44eeca351a76",
      "metadata": {},
      "source": [
        "Looks like we have *both* precipitation and temperature on the same\n",
        "plot, and it’s hard to see what it is because it’s missing labels!\n",
        "\n",
        "> ****Label your plot****\n",
        ">\n",
        "> <figure>\n",
        "> <img src=\"https://imgs.xkcd.com/comics/convincing.png\"\n",
        "> alt=\"Source: https://xkcd.com/833\" />\n",
        "> <figcaption aria-hidden=\"true\">Source: https://xkcd.com/833</figcaption>\n",
        "> </figure>\n",
        ">\n",
        "> Make sure each plot has:\n",
        ">\n",
        "> -   A title that explains where and when the data are from\n",
        "> -   x- and y- axis labels with **units** where appropriate\n",
        "> -   A legend where appropriate\n",
        "\n",
        "When plotting in Python, you’ll always need to add some instructions on\n",
        "labels and how you want your plot to look.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Plot your data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Change <code>dataframe</code> to <strong>your</strong>\n",
        "<code>DataFrame</code> name.</li>\n",
        "<li>Change <code>y=</code> to the name of your <strong>observed\n",
        "temperature</strong> column name.</li>\n",
        "<li>Use the <code>title</code>, <code>ylabel</code>, and\n",
        "<code>xlabel</code> parameters to add key text to your plot.</li>\n",
        "<li>Adjust the size of your figure using <code>figsize=(x,y)</code>\n",
        "where <code>x</code> is figure width and <code>y</code> is figure\n",
        "height</li>\n",
        "</ol>\n",
        "<blockquote>\n",
        "<p><strong>HINT:</strong> labels have to be a <em>type</em> in Python\n",
        "called a <strong>string</strong>. You can make a string by putting\n",
        "quotes around your label, just like the column names in the sample code\n",
        "(eg <code>y='TOBS'</code>).</p>\n",
        "</blockquote></div></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06764365-a1f5-40a7-8034-f04bc4ec77de",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-extra\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Looking for an Extra Challenge?</div></div><div class=\"callout-body-container callout-body\"><p>There are many other things you can do to customize your plot. Take a\n",
        "look at the <a\n",
        "href=\"https://pandas.pydata.org/docs/user_guide/visualization.html\">pandas\n",
        "plotting galleries</a> and the <a\n",
        "href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html\">documentation\n",
        "of plot</a> to see if there’s other changes you want to make to your\n",
        "plot. Some possibilities include:</p>\n",
        "<ul>\n",
        "<li>Remove the legend since there’s only one data series</li>\n",
        "<li>Increase the figure size</li>\n",
        "<li>Increase the font size</li>\n",
        "<li>Change the colors</li>\n",
        "<li>Use a bar graph instead (usually we use lines for time series, but\n",
        "since this is annual it could go either way)</li>\n",
        "<li>Add a trend line</li>\n",
        "</ul>\n",
        "<p>Not sure how to do any of these? Try searching the internet, or\n",
        "asking an AI!</p></div></div>\n",
        "\n",
        "## Clean up time series plots by resampling\n",
        "\n",
        "You may notice that your plot looks a little “fuzzy”. This happens when\n",
        "Python is trying to plot a value for every date, but the resolution of\n",
        "the image is too low to actually do that. You can address this issue by\n",
        "**resampling** the data, or summarizing it over a time period of your\n",
        "choice. In this case, we will resample annually, giving us one data\n",
        "point per year.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Resample</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Set the frequency of your final data by replacing\n",
        "<code>DT_OFFSET</code>with a <strong>Datetime Offset Code</strong>.\n",
        "Check out the table in the <a\n",
        "href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects\">pandas\n",
        "datetime documentation</a> to find the one you want (we recommend the\n",
        "start of the year).</li>\n",
        "<li>Choose how to summarize each year of data by replacing\n",
        "<code>agg_method_here</code> with a method that will calculate the\n",
        "<strong>average annual value</strong>. Check out the <a\n",
        "href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#basics\">pandas\n",
        "resampling documentation</a> for a list of common built-in options.</li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "8f52fbc4",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRCP_IN</th>\n",
              "      <th>TOBS_F</th>\n",
              "      <th>TOBS_C</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1893-01-01</th>\n",
              "      <td>0.025543</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894-01-01</th>\n",
              "      <td>0.058841</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1895-01-01</th>\n",
              "      <td>0.117090</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896-01-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1897-01-01</th>\n",
              "      <td>0.068922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>0.057644</td>\n",
              "      <td>54.426997</td>\n",
              "      <td>12.459443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>0.046721</td>\n",
              "      <td>57.691460</td>\n",
              "      <td>14.273033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-01</th>\n",
              "      <td>0.056658</td>\n",
              "      <td>57.538462</td>\n",
              "      <td>14.188034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-01</th>\n",
              "      <td>0.051479</td>\n",
              "      <td>56.139726</td>\n",
              "      <td>13.410959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-01</th>\n",
              "      <td>0.076740</td>\n",
              "      <td>58.996337</td>\n",
              "      <td>14.997965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             PRCP_IN     TOBS_F     TOBS_C\n",
              "DATE                                      \n",
              "1893-01-01  0.025543        NaN        NaN\n",
              "1894-01-01  0.058841        NaN        NaN\n",
              "1895-01-01  0.117090        NaN        NaN\n",
              "1896-01-01       NaN        NaN        NaN\n",
              "1897-01-01  0.068922        NaN        NaN\n",
              "...              ...        ...        ...\n",
              "2019-01-01  0.057644  54.426997  12.459443\n",
              "2020-01-01  0.046721  57.691460  14.273033\n",
              "2021-01-01  0.056658  57.538462  14.188034\n",
              "2022-01-01  0.051479  56.139726  13.410959\n",
              "2023-01-01  0.076740  58.996337  14.997965\n",
              "\n",
              "[131 rows x 3 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ann_climate_df_w_units = climate_df_w_units.resample('YS').mean()\n",
        "ann_climate_df = climate_df.resample('YS').mean()\n",
        "ann_climate_df_w_units"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12bf0d69-8bfd-48f5-b76f-7827504ed784",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Plot Annual Data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Try plotting your new DataFrame in the cell below. Can you see what\n",
        "is going on more clearly now? Don’t forget to adjust your labels!</li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "22a823d6",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Annual Temperature in Boulder, CO'}, xlabel='Date', ylabel='Obs. Temperature (C)'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the ann# Plot the data using .plot\n",
        "ann_climate_df_w_units.plot(\n",
        "    y='TOBS_C',\n",
        "    title='Annual Temperature in Boulder, CO',\n",
        "    xlabel='Date',\n",
        "    ylabel='Obs. Temperature (C)',)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9da60a12-6ece-4043-8870-5a3d0388c494",
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        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-respond\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Reflect and Respond: Interpret your plot</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li><p>Create a new Markdown cell below this one.</p></li>\n",
        "<li><p>In the new cell, answer the following questions using a\n",
        "<strong>bulleted list</strong> in Markdown – what are 2 things you\n",
        "notice about this data? What physical phenomena or data anomaly could be\n",
        "causing each one?</p></li>\n",
        "</ol>\n",
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        "data-__quarto_custom_context=\"Block\" data-__quarto_custom_id=\"6\">\n",
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        "\n",
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        "## Interpretation of the above annual temperature in Boulder CO\n",
        "* There is a data gap near 1940. The datapoint preceeding the gap is anomalously large and the datapoint proceeding the gap is anomalously low. It is likely that the data gap begins mid-year 1937 and excludes late-year wintertime temperatures such that the average temperature is skewed warm. The opposite is likely true for the 1941 year.\n",
        "* There is a anomolously low value in 1994. This may also be due to missing values mid-year. Displaying the number of NaNs each year would determine if this is true. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de00a236-e176-4362-88c0-a2e8de9f8517",
      "metadata": {},
      "source": [
        "## Check specific values with an interactive plot\n",
        "\n",
        "You can use the `.hvplot()` method with similar arguments to create an\n",
        "interactive plot.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Interactive Plot</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Copy your plotting code into the cell below.</li>\n",
        "<li>Replace <code>.plot</code> in your code with\n",
        "<code>.hvplot</code></li>\n",
        "</ol>\n",
        "<p>Now, you should be able to hover over data points and see their\n",
        "values!</p></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "4bff67b5",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
            "  return dataset.data.dtypes[idx].type\n",
            "/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
            "  return dataset.data.dtypes[idx].type\n"
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            ],
            "text/plain": [
              ":Curve   [DATE]   (TOBS_C)"
            ]
          },
          "execution_count": 17,
          "metadata": {
            "application/vnd.holoviews_exec.v0+json": {
              "id": "p1073"
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Plot the annual data interactively\n",
        "an_obsT_BOCO = ann_climate_df_w_units.hvplot(\n",
        "    y='TOBS_C',\n",
        "    title='Annual Temperature in Boulder, CO',\n",
        "    xlabel='Date',\n",
        "    ylabel='Obs. Temperature (C)',)\n",
        "\n",
        "an_obsT_BOCO"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdc24928-65f1-4176-b8f9-eacca630730f",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Explore the data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li><p>Create a new Markdown cell below this one.</p></li>\n",
        "<li><p>Hover over the lowest point on your plot. What is the overall\n",
        "minimum annual average temperature?</p></li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2e84f6f",
      "metadata": {},
      "source": [
        "## Minimum average annual temeprature in Boulder, CO\n",
        "* The minimum occurs in 1994 at 3.7 C. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42513d90-2d8a-4c42-a1bc-a66194d8951e",
      "metadata": {},
      "source": [
        "## BONUS: Save your work\n",
        "\n",
        "You will need to save your analyses and plots to tell others about what\n",
        "you find.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Save Your Plot</div></div><div class=\"callout-body-container callout-body\"><p>Just like with any other type of object in Python, if you want to\n",
        "reuse your work, you need to give it a name.</p>\n",
        "<ol type=\"1\">\n",
        "<li>Go back to your <code>hvplot</code> code, and give your plot a name\n",
        "by assigning it to a variable. HINT: if you still want your plot to\n",
        "display in your notebook, make sure to <strong>call</strong> its name at\n",
        "the end of the cell.</li>\n",
        "<li>Replace <code>my_plot</code> with the name you gave to your\n",
        "plot.</li>\n",
        "<li>Replace <code>'my_plot.html'</code> with the name you want for your\n",
        "plot. If you change the file extension, <code>.html</code>, to\n",
        "<code>.png</code>, you will get an image instead of an interactive\n",
        "webpage, provided you have the necessary libraries installed.</li>\n",
        "</ol>\n",
        "<p>Once you run the code, you should see your saved plot in your files –\n",
        "go ahead and open it up.</p>\n",
        "<div data-__quarto_custom=\"true\" data-__quarto_custom_type=\"Callout\"\n",
        "data-__quarto_custom_context=\"Block\" data-__quarto_custom_id=\"8\">\n",
        "<div data-__quarto_custom_scaffold=\"true\">\n",
        "\n",
        "</div>\n",
        "<div data-__quarto_custom_scaffold=\"true\">\n",
        "<p>You may need to right-click on your file and download it to be able\n",
        "to view it.</p>\n",
        "</div>\n",
        "</div></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "3ca1b32d",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
            "  return dataset.data.dtypes[idx].type\n",
            "/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
            "  return dataset.data.dtypes[idx].type\n"
          ]
        }
      ],
      "source": [
        "hv.save(an_obsT_BOCO, 'an_obsT_BOCO.html')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "12175da4",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stored 'ann_climate_df_w_units' (DataFrame)\n"
          ]
        }
      ],
      "source": [
        "%store ann_climate_df_w_units"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "mimetype": "text/x-python",
      "name": "python",
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      "pygments_lexer": "ipython3",
      "version": "3.10.11"
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