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    "### Lab 3-2:  Wind profiles\n",
    "We typically have a measurement of wind at one height above the ground but need to estimate (a) the wind at the height of the smokestack plume or (b) the average wind speed within the mixing depth (to determine the dilution rate or ventilation coefficient)."
   ]
  },
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   "id": "2670b035-032a-4385-9667-a93f63f7850c",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97385ee2-3df2-4327-9a6c-26c2af4c9d05",
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   "source": [
    "From lecture notes, we define\n",
    "$$ u(z) = u_{ref}(\\frac{z}{z_{ref}})^p $$\n",
    "where $u(z)$ is the wind at any elevation, $u_{ref}$ is the reference wind (this is what is measured), $z$ is the elevation, $z_{ref}$ is the elevation at which the reference wind was measured, and $p$ is an exponent, which varies with stability and roughness. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df607e99-bcb4-4af7-b5b4-b179c5df5cd5",
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   "source": [
    "When using a reference height of 10 m, the EPA recommends using values of of p from the following table (from the User’s Guide for the ISC3 Dispersion Models, Vol II, EPA_454/B-95-003b, U.S. EPA, Sept. 1995):\n",
    "\n",
    "| Stability Category | Urban | Rural |\n",
    "| --- | --- | --- |\n",
    "| A | 0.15 | 0.07 |\n",
    "| B | 0.15 | 0.07 |\n",
    "| C | 0.20 | 0.10 |\n",
    "| D | 0.25 | 0.15 |\n",
    "| E | 0.30 | 0.35 |\n",
    "| F | 0.30 | 0.55 |\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "f3cd9531-4c80-4a46-9283-8c70333a5a34",
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>time</th>\n",
       "      <th>pres</th>\n",
       "      <th>qc_pres</th>\n",
       "      <th>tdry</th>\n",
       "      <th>qc_tdry</th>\n",
       "      <th>dp</th>\n",
       "      <th>qc_dp</th>\n",
       "      <th>wspd</th>\n",
       "      <th>qc_wspd</th>\n",
       "      <th>deg</th>\n",
       "      <th>...</th>\n",
       "      <th>qc_u_wind</th>\n",
       "      <th>v_wind</th>\n",
       "      <th>qc_v_wind</th>\n",
       "      <th>wstat</th>\n",
       "      <th>asc</th>\n",
       "      <th>qc_asc</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>alt</th>\n",
       "      <th>potential_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-10 11:34:00</td>\n",
       "      <td>713.79047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.979830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.522203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.867797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>328.13560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.741468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.147457</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>38.959520</td>\n",
       "      <td>-106.98986</td>\n",
       "      <td>3055.1375</td>\n",
       "      <td>16.678686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-10 11:35:00</td>\n",
       "      <td>687.39090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.463167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-21.827170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.898333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.98334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.746618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.701667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.958880</td>\n",
       "      <td>-106.98948</td>\n",
       "      <td>3348.7185</td>\n",
       "      <td>24.837664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-10 11:36:00</td>\n",
       "      <td>662.85834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.424000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-26.478666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.211667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.70000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.953473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.918333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.958366</td>\n",
       "      <td>-106.98947</td>\n",
       "      <td>3634.4617</td>\n",
       "      <td>29.118020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-10 11:37:00</td>\n",
       "      <td>640.70935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.980833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-30.277832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.588334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>335.06668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.305604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.791666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.957546</td>\n",
       "      <td>-106.98897</td>\n",
       "      <td>3901.4053</td>\n",
       "      <td>31.435535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-10 11:38:00</td>\n",
       "      <td>623.39840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.733000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-32.042500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.016666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302.40000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.379487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.956264</td>\n",
       "      <td>-106.98776</td>\n",
       "      <td>4116.1970</td>\n",
       "      <td>32.968940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                  time       pres  qc_pres      tdry  qc_tdry         dp  \\\n",
       "0  2022-01-10 11:34:00  713.79047      0.0 -9.979830      0.0 -18.522203   \n",
       "1  2022-01-10 11:35:00  687.39090      0.0 -5.463167      0.0 -21.827170   \n",
       "2  2022-01-10 11:36:00  662.85834      0.0 -4.424000      0.0 -26.478666   \n",
       "3  2022-01-10 11:37:00  640.70935      0.0 -4.980833      0.0 -30.277832   \n",
       "4  2022-01-10 11:38:00  623.39840      0.0 -5.733000      0.0 -32.042500   \n",
       "\n",
       "   qc_dp      wspd  qc_wspd        deg  ...  qc_u_wind    v_wind  qc_v_wind  \\\n",
       "0    0.0  1.867797      0.0  328.13560  ...        0.0 -1.741468        0.0   \n",
       "1    0.0  0.898333      0.0  154.98334  ...        0.0 -0.746618        0.0   \n",
       "2    0.0  1.211667      0.0  252.70000  ...        0.0 -0.953473        0.0   \n",
       "3    0.0  2.588334      0.0  335.06668  ...        0.0 -2.305604        0.0   \n",
       "4    0.0  3.016666      0.0  302.40000  ...        0.0 -2.379487        0.0   \n",
       "\n",
       "   wstat       asc    qc_asc        lat        lon        alt  potential_T  \n",
       "0    NaN  5.147457  0.135593  38.959520 -106.98986  3055.1375    16.678686  \n",
       "1    NaN  4.701667  0.000000  38.958880 -106.98948  3348.7185    24.837664  \n",
       "2    NaN  4.918333  0.000000  38.958366 -106.98947  3634.4617    29.118020  \n",
       "3    NaN  3.791666  0.000000  38.957546 -106.98897  3901.4053    31.435535  \n",
       "4    NaN  3.650000  0.000000  38.956264 -106.98776  4116.1970    32.968940  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can read in our sounding data from lab 2 and see how the wind profile observed by the balloon matches this\n",
    "# copy one stable day '2022-01-10_radiosonde.csv' and one stormy and well-mixed day '2021-12-26_radiosone.csv' \n",
    "# into this folder where you're working.\n",
    "\n",
    "sonde_temp_profile = pd.read_csv('2022-01-10_radiosonde.csv')\n",
    "# take a look at what is included in the file\n",
    "sonde_temp_profile.head()\n",
    "# Here, we are interested in the wind speed (wspd) in m/s.\n",
    "# Note that the sounding also provides info on wind direction, where\n",
    "# u_wind is the magnitude of winds moving from west to east, and \n",
    "# v_wind is the magnitude of winds moving from south to north."
   ]
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   "id": "4fa598f8-4cc5-4a4c-af2a-2974870313dd",
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    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wind speed with height')"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a plot of height vs. windspeed\n",
    "plt.figure()\n",
    "plt.plot(sonde_temp_profile['wspd'],sonde_temp_profile['alt'])\n",
    "plt.ylabel('altitude (m)')\n",
    "plt.xlabel('wind speed (m/s)')\n",
    "#plt.xlim(0,10000) \n",
    "plt.title('Wind speed with height')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d727c917-1c44-4b93-9e68-bd978eb8eadd",
   "metadata": {},
   "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>time</th>\n",
       "      <th>pres</th>\n",
       "      <th>qc_pres</th>\n",
       "      <th>tdry</th>\n",
       "      <th>qc_tdry</th>\n",
       "      <th>dp</th>\n",
       "      <th>qc_dp</th>\n",
       "      <th>wspd</th>\n",
       "      <th>qc_wspd</th>\n",
       "      <th>deg</th>\n",
       "      <th>...</th>\n",
       "      <th>qc_u_wind</th>\n",
       "      <th>v_wind</th>\n",
       "      <th>qc_v_wind</th>\n",
       "      <th>wstat</th>\n",
       "      <th>asc</th>\n",
       "      <th>qc_asc</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>alt</th>\n",
       "      <th>potential_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-26 11:21:00</td>\n",
       "      <td>693.21545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.425254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.821864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.249152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.91525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.935613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.352543</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>38.960567</td>\n",
       "      <td>-106.990940</td>\n",
       "      <td>3016.0867</td>\n",
       "      <td>25.267000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-12-26 11:22:00</td>\n",
       "      <td>670.93726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.650167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.674833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.146667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.85000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.685246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.863333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.963493</td>\n",
       "      <td>-106.992420</td>\n",
       "      <td>3272.3267</td>\n",
       "      <td>25.573175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12-26 11:23:00</td>\n",
       "      <td>640.13590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.671000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-11.190167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.874998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215.58333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.083486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.098333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.967400</td>\n",
       "      <td>-106.989940</td>\n",
       "      <td>3638.2449</td>\n",
       "      <td>26.193165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-12-26 11:24:00</td>\n",
       "      <td>606.30780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.815167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-13.453666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.878333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215.41667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.834729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.453333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.970660</td>\n",
       "      <td>-106.987015</td>\n",
       "      <td>4054.3499</td>\n",
       "      <td>27.242916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-26 11:25:00</td>\n",
       "      <td>581.16330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-14.862834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.385002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.359997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.30000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.322145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.468333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.976658</td>\n",
       "      <td>-106.977410</td>\n",
       "      <td>4376.5483</td>\n",
       "      <td>28.515516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time       pres  qc_pres       tdry  qc_tdry         dp  \\\n",
       "0  2021-12-26 11:21:00  693.21545      0.0  -4.425254      0.0  -8.821864   \n",
       "1  2021-12-26 11:22:00  670.93726      0.0  -6.650167      0.0  -9.674833   \n",
       "2  2021-12-26 11:23:00  640.13590      0.0  -9.671000      0.0 -11.190167   \n",
       "3  2021-12-26 11:24:00  606.30780      0.0 -12.815167      0.0 -13.453666   \n",
       "4  2021-12-26 11:25:00  581.16330      0.0 -14.862834      0.0 -18.385002   \n",
       "\n",
       "   qc_dp       wspd  qc_wspd        deg  ...  qc_u_wind     v_wind  qc_v_wind  \\\n",
       "0    0.0   4.249152      0.0  131.91525  ...        0.0   2.935613        0.0   \n",
       "1    0.0   7.146667      0.0  174.85000  ...        0.0   6.685246        0.0   \n",
       "2    0.0   8.874998      0.0  215.58333  ...        0.0   7.083486        0.0   \n",
       "3    0.0   9.878333      0.0  215.41667  ...        0.0   7.834729        0.0   \n",
       "4    0.0  25.359997      0.0  238.30000  ...        0.0  13.322145        0.0   \n",
       "\n",
       "   wstat       asc    qc_asc        lat         lon        alt  potential_T  \n",
       "0    NaN  4.352543  0.135593  38.960567 -106.990940  3016.0867    25.267000  \n",
       "1    NaN  3.863333  0.000000  38.963493 -106.992420  3272.3267    25.573175  \n",
       "2    NaN  8.098333  0.000000  38.967400 -106.989940  3638.2449    26.193165  \n",
       "3    NaN  5.453333  0.000000  38.970660 -106.987015  4054.3499    27.242916  \n",
       "4    NaN  6.468333  0.000000  38.976658 -106.977410  4376.5483    28.515516  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add in the other day for comparison\n",
    "sonde_temp_profile2 = pd.read_csv('2021-12-26_radiosonde.csv')\n",
    "sonde_temp_profile2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36d9dce0-a290-4c00-9ca0-39b5686aa0b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x796490bc07f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YsGABtra2ap0lS5YwevRodbXboEGDmDVrVtU3rpo52dtyJbeAvIJCrUMRQgjLVFgIe380Xrd5SttYhKbMah8kc5aRkYFer8dgMJjt/J7u/9rEiUtX+enlcO6t76l1OEIIYXmObYbvBoGjHiYcAnsnrSMSd+lOf3+bxRwkUTncnewByLqWr3EkQghhofZeH14LeUSSoxpOEiQr4uZoHDHNuJancSRCCGGBcq/Awd+N122GaBuL0JwkSFbE3cmYIGVKD5IQQlRc4krIuwK1G0BgR62jERqTBMmKFA2xSYIkhBB3YO8Pxj/bPA1ypFSNJwmSFSnuQZIhNiGEqJCMc3AsxnjderCmoQjzIAmSFfG4niBl5UgPkhBCVMi+/wEK1OsMng20jkaYAUmQrIibzEESQoiKU5Qbhtdk7yNhJAmSFSmegyRDbEIIUW7J8XDhb7BzgpYPax2NMBOSIFmRojlIGdKDJIQQ5Ve091GzgeCk1zYWYTYkQbIisopNCCEqqCAPEn4yXrd5WttYhFmRBMmKuKuTtGWITQghyuXIerh6CVx9oGEPraMRZkQSJCviIZO0hRCiYoomZ7ceDLaan98uzIgkSFbEzbF4iE3OIBZCiNu4ehmS1hivZfWaKEESJCtSNMRWUKiQnVegcTRCCGHmDiyDglzwDQG/VlpHI8yMJEhWxMXBFlsb4/b4WTLMJoQQt1a0ek16j0QZJEGyIjqdDjdHWeovhBC3dekonNkJOhtoJUeLiNIkQbIych6bEEKUQ1HvUaNe4O6rbSzCLEmCZGUc7Ix/pdfyCjWORAghzFRhIeyT4TVxa5IgWRFFUThvuAaAr4ejxtEIIYSZOrUN0k+Bo4dx92whyiAJkhXJuJbPlVzj6jV/vbPG0QghhJk6Em38M7g/2MvPSlE2SZCsyLn0bAA8XR1wdrDVOBohhDBTHvcY/7x6Uds4hFmTBMmKJBuMCVJALSeNIxFCCDNWL8z45+lYKJQ940TZJEGyImfTjfOPZHhNCCFuwacFOLhDbiakHtQ6GmGmJEGyIkVDbPfUkgRJCCFuysYW6nYwXp/arm0swmxJgmRFihIkGWITQojbUIfZdmobhzBbkiBZkWQZYhNCiPIJ7GT887T0IImySYJkRc6qPUiSIAkhxC3V7WA8ZiT9FGQkax2NMEOSIFmJgkKFlAxjD5LMQRJCiNtwdAfflsZr6UUSZZAEyUpcyMyhoFDBzkZHHXfZRVsIIW4rUOYhiZuTBMlKFA2v+Xo4YWuj0zgaIYSwAEUTtWUlmyiDJEhWQpb4CyFEBRVN1E7ZB7lXtY1FmB1JkKxE0S7a/rLEXwghykdfF9wDoDAfzsZpHY0wM5IgWYlz15f4ywo2IYQoJ50O6hUt99+hbSzC7EiCZCVkib8QQtwBdaK2JEjClOYJ0tmzZ3n22Wfx8vLCxcWFtm3bEhdX3NU5bNgwdDqdySssLMzkGTk5OYwaNQpvb29cXV0ZNGgQZ86cMamTlpZGZGQker0evV5PZGQk6enp1dHEaqEeVKuXITYhhCi3G3uQCgu1jUWYFU0TpLS0NLp06YK9vT1r1qzh4MGDfPrpp9SqVcukXv/+/UlOTlZfq1evNikfO3Ysy5YtY+nSpWzZsoWsrCwiIiIoKCg+pXnIkCHEx8cTFRVFVFQU8fHxREZGVkczq4UMsQkhxB3wDQF7F7hmgItJWkcjzIidlh/+8ccfExgYyPz589V79evXL1XP0dERPz+/Mp9hMBiYN28eixYtonfv3gAsXryYwMBA1q9fT79+/UhMTCQqKort27fTqZPx/xbmzp1LeHg4SUlJNG3atPIbV42ycwu4fCUXkARJCCEqxNYe7gmFE38al/v7NNc6ImEmNO1BWr58OR06dOCJJ57Ax8eHdu3aMXfu3FL1YmJi8PHxITg4mBEjRpCamqqWxcXFkZeXR9++fdV7AQEBhISEsHXrVgC2bduGXq9XkyOAsLAw9Hq9WqeknJwcMjIyTF7m6kJmDgD2tjrcHTXNeYUQwvLIwbWiDJomSMeOHeOrr76iSZMmrF27lpdffpnRo0fz3XffqXUGDBjAkiVL2LhxI59++imxsbH07NmTnBxjUpCSkoKDgwO1a9c2ebavry8pKSlqHR8fn1Kf7+Pjo9Ypafr06ep8Jb1eT2BgYGU1u9L5eDji7mRHXoHC9mOXtA5HCCEsizpRWzaMFMU0TZAKCwtp374906ZNo127drz00kuMGDGCr776Sq3z5JNPMnDgQEJCQnjwwQdZs2YNhw4dYtWqVbd8tqIo6HTFO0rfeH2zOjd6++23MRgM6uv06dN32Mqq52Rvy6A2AQD8HHfmNrWFEEKYqNsB0MHlY7BzLiiK1hEJM6BpguTv70+LFi1M7jVv3pxTp07d8j1BQUEcPnwYAD8/P3Jzc0lLSzOpl5qaiq+vr1rn/PnzpZ514cIFtU5Jjo6OeHh4mLzM2eOhdQFYvT+ZzGt5GkcjhBAWxLkWtB1ivF49ARY/BhnJmoYktKdpgtSlSxeSkkxXDRw6dIigoKCbvufSpUucPn0af39/AEJDQ7G3tyc6Olqtk5yczP79++ncuTMA4eHhGAwGdu4sHl/esWMHBoNBrWPp2gbWolEdV67lFbI6Qf7DFkKIChk0CwbMBDsnOLoBvgqHA8u0jkpoSNME6fXXX2f79u1MmzaNI0eO8P333zNnzhxeffVVALKyspgwYQLbtm3jxIkTxMTE8OCDD+Lt7c0jjzwCgF6vZ/jw4YwfP54NGzawZ88enn32WVq1aqWuamvevDn9+/dnxIgRbN++ne3btzNixAgiIiIsfgVbEZ1Ox+OhxnlSMswmhBAVZGMDnV6Cl/4A/7aQnQY/DYNfR0J2usbBCS1omiDde++9LFu2jB9++IGQkBA+/PBDvvjiC5555hkAbG1tSUhI4KGHHiI4OJihQ4cSHBzMtm3bcHd3V5/z+eef8/DDDzN48GC6dOmCi4sLK1aswNbWVq2zZMkSWrVqRd++fenbty+tW7dm0aJF1d7mqvRIu3uw0UHsiTROXLyidThCCGF56jSFF9fD/RNBZwP7foSvusDxP7SOTFQznaLIbLTyyMjIQK/XYzAYzHo+0tBvd7L50AVG9WzM+L7W0TsmhBCaOL0Tlr1knLwNEP4a9Pwn2MuJBZbkTn9/a37UiKhcRZO1f4k7Q2Gh5L5CCHHHAjvCS39C6PPGr7fNgjndIXmfpmGJ6iEJkpXp08IXdyc7zhmusU32RBJCiLvj6AYPfgFD/geuPnAhEeb2hD8/g8KC275dWC5JkKyM7IkkhBBVILgfvLINmkVAYR5smALzH4DLx7WOTFQRSZCs0BMdjKvZ1sieSEIIUXlcveHJxfDwV+Dgbtx5++uusPs72VzSCkmCZIXa1NXT2MdN9kQSQojKptMZN5X8x19QLxxys2D5KFj+mtaRiUomCZIVMu6JZJysLcNsQghRyc4fgG3/hdTE4nun5Bw3ayNHv1upR9rdw8yov4k9kcbxi1do4O2qdUhCCGG5crJg/y/G4bSzu4rv6wOh3bPFK92E1ZAEyUr5ejhxf3AdYpIu8EvcGSb0kz2RhBCiQhQFzu6G3QuNyVFulvG+jR00fQDaD4VGPcDG9tbPERZJEiQr9nhoXWOCtPsMr/cJxtZGp3VIQghh/rLTYN9PxsTo/P7i+56NoP1zxjlIbj7axSeqhSRIVqx3c188nOxINlxj29FLdG3irXVIQghhnhQFTv5lHEI7+DvkXzPet3WElg8bE6OgLsZJ2qJGkATJijnZ2zKobQCLt5/i57jTkiAJIURJWRdg7/fGxOjSkeL7Pi0hdCi0HgzOtbWLT2hGEiQr93hoIIu3nyLqQAoZ1/LwcLLXOiQhhNBWYQEc2wRxCyFpNRTmG+87uEHIY8a5Rfe0l96iGk4SJCvXpq6eJj5uHE7NIiohhcH3BmodkhBCaMNwFvYsNr4Mp4rv39PBOIQW8ig4umsXnzArkiBZOZ1OR6t79BxOzeJCVo7W4QghRPUqyINDa40Tro+sB6XQeN+pFrR5CtpFgl+IpiEK8yQJUg1w6vJVAAI9XTSORAghqkFBPpyNg6RVsHcpZJ0vLqt/n7G3qPmDYO+sXYzC7EmCVAOcvJ4g1feSBEkIYaUMZ+HoBmMv0bEYuGYoLnP1MS7Nb/8ceDXSLERhWSRBsnJXc/O5kGkcWgvylN20hRBWIj8HTm0zJkRHNkDqQdNyp1rGTRxbPgpNB4CtLFARFSMJkpU7ecnYe1TLxR69i/yAEEJYsEtHjcnQ0Q1w/A/Iu3pDoQ7qdoBGvaBxb+MqNNnhWtwFSZCsXFGCFCTzj4QQliYnC05sud5LtB7SjpuWu/kak6HGvaBhD3Dx1CZOYZUkQbJyJy9dAaCelwyvCSHMnKJAamJxQnRqGxTkFpfb2EG9cGNC1Lg3+IbIXkWiykiCZOVkgrYQwqxlpxknVR9ZD0c2QuY50/Ja9aBxH2NC1OA+2adIVBtJkKyc2oMkQ2xCCHNQWAjJe4xziY6shzOxxXsTAdg5GZfiN+5tfHk1kl4ioQlJkKxc0Ryk+t4yxCaE0EhWKhzdaEyIjm6Eq5dMy+s0uz65uhcEdZb9iYRZkATJiuXmF3IuPRuQSdpCiGpUkAendxbPJUrZZ1ru6AENuxl7iBr1glpyBJIwP5IgWbEzaVcpVMDZ3pY67o5ahyOEsGbpp4qHzY5thtxM03L/NsXDZnXvlX2JhNmTBMmKFU3QDvJyQSdj+EKIypSXDSf/Mk6sPrIeLiaZlrt4FQ+bNeoJbj7axCnEHZIEyYqdvGicoB0kK9iEEHdLUeDSkeJhsxNbIP9acbnOBup2LN6XyL8t2NhoFq4Qd0sSJCtW3IMkE7SFEHcgJ9O4Y3VRUpR+yrTcPaB4T6KG3cC5tjZxClEFJEGyYqeur2CTJf5CiHJRFEhJKD7f7PR2KMwvLrd1MK4yKzrOw6e5LMEXVksSJCt24voeSPWlB0kIcTNXL19fgn/9jLOs86blng2LJ1fX7woO8vNE1AySIFmpgkKF05evL/GXOUhCiCKFBXB2d/Gw2dk4QCkut3eBBvdfX4Lf07hRoxA1kCRIViol4xq5BYXY2+rw1ztpHY4QQksZycbeoSPr4egmuJZuWu7TsnguUb0wsJNtQYSQBMlKFR0xUre2C3a2spJEiBolP9c4f6hoLtH5/ablTnpo2KN4xZlHgDZxCmHGJEGyUmfSjMNrdWvLlv1C1AiXjxcf5XFsM+RduaFQB/e0L55cfU8o2MqPfyFuRfOuhbNnz/Lss8/i5eWFi4sLbdu2JS4uTi1XFIXJkycTEBCAs7Mz3bt358CBAybPyMnJYdSoUXh7e+Pq6sqgQYM4c+aMSZ20tDQiIyPR6/Xo9XoiIyNJT0+vjiZqouiIEUmQhLBSuVfh0DpYPRH+3R7+3RZWT4Ck1cbkyLUOtHkaHpsHbxyFERuh57tQr5MkR0KUg6b/laSlpdGlSxd69OjBmjVr8PHx4ejRo9SqVUutM3PmTD777DMWLFhAcHAwH330EX369CEpKQl3d3cAxo4dy4oVK1i6dCleXl6MHz+eiIgI4uLisLW1BWDIkCGcOXOGqKgoAEaOHElkZCQrVqyo9nZXh+R04wZu/npJkISwCooCF5KKJ1ef3AoFOcXlNnYQ2Kl4LpFvK9moUYi7oFMURbl9tarx1ltv8ddff/Hnn3+WWa4oCgEBAYwdO5Y333wTMPYW+fr68vHHH/PSSy9hMBioU6cOixYt4sknnwTg3LlzBAYGsnr1avr160diYiItWrRg+/btdOrUCYDt27cTHh7O33//TdOmTW8ba0ZGBnq9HoPBgIeHRyV9B6pO5Lwd/Hn4Ip880YbHQ+tqHY4Q4k5kp8PxzcVziTLOmpbrA4uX4De4H5zM/2eTENXtTn9/a9qDtHz5cvr168cTTzzB5s2bueeee3jllVcYMWIEAMePHyclJYW+ffuq73F0dKRbt25s3bqVl156ibi4OPLy8kzqBAQEEBISwtatW+nXrx/btm1Dr9eryRFAWFgYer2erVu3lpkg5eTkkJNT/H9nGRkZVfEtqDJnrw+xBcgKNiEsR2EhpOy9YaPGnaAUFJfbOhr3IipKirybyEaNQlQRTROkY8eO8dVXXzFu3Djeeecddu7cyejRo3F0dOS5554jJSUFAF9fX5P3+fr6cvLkSQBSUlJwcHCgdu3apeoUvT8lJQUfn9IHJfr4+Kh1Spo+fTpTpky56zZqQVEUdYgtoJYMsQlh1rIuGCdWH91gTIquXjQt92pSnBAFdQYH2ddMiOqgaYJUWFhIhw4dmDZtGgDt2rXjwIEDfPXVVzz33HNqvZIn0SuKctvT6UvWKav+rZ7z9ttvM27cOPXrjIwMAgMDb98oM5B+NY/sPOP/dfpJD5IQ5qUgH87EFs8lSo43LXdwg4bdjXOJGvWC2kFaRClEjadpguTv70+LFi1M7jVv3pxffvkFAD8/P8DYA+Tv76/WSU1NVXuV/Pz8yM3NJS0tzaQXKTU1lc6dO6t1zp8vsX0+cOHChVK9U0UcHR1xdLTMzdKKhte83RxwsrfVOBohBIYzxt6hI+uNS/BzDKblfq2Ke4nqdgQ7B23iFEKoNE2QunTpQlJSksm9Q4cOERRk/D+mBg0a4OfnR3R0NO3atQMgNzeXzZs38/HHHwMQGhqKvb090dHRDB48GIDk5GT279/PzJkzAQgPD8dgMLBz5046duwIwI4dOzAYDGoSZU2SDTK8JoSm8q7Bqa3Xk6INcCHRtNy5tvEYj6LjPNz9tIlTCHFTmiZIr7/+Op07d2batGkMHjyYnTt3MmfOHObMmQMYh8XGjh3LtGnTaNKkCU2aNGHatGm4uLgwZMgQAPR6PcOHD2f8+PF4eXnh6enJhAkTaNWqFb179waMvVL9+/dnxIgRzJ49GzAu84+IiCjXCjZLc06doC0JkhDVQlHg8rHiYbPjf0J+dnG5zgbu6VDcSxTQFmykd1cIc6ZpgnTvvfeybNky3n77bT744AMaNGjAF198wTPPPKPWmThxItnZ2bzyyiukpaXRqVMn1q1bp+6BBPD5559jZ2fH4MGDyc7OplevXixYsEDdAwlgyZIljB49Wl3tNmjQIGbNmlV9ja1G5wzGH8z+tWT+kRBVJicLTvxZnBSlnTAtd/MrPsqjYXdw8dQiSiHEHdJ0HyRLYkn7II36YQ8r9p7jvYHNefG+hlqHI4R1UBQ4f6A4ITq1HQrzistt7CEo/PqwWS/wbSlL8IUwAxa5D5KoGkVDbLKLthB36eplOBZjnEd0dANkJpuW1wqCJn2MSVH9+8DRTZMwhRCVTxIkK5RcNAdJhtiEqJjCAjgXX9xLdHYXKIXF5XbO0OC+4rlEng2ll0gIKyUJkpXJLygkJcO4iu0eWcUmxO1lnr++SeN644aN2Wmm5XWaF59vVi8c7OV/PISoCSRBsjLnM3MoVMDeVoe3m2Xu4yRElcrPhTM7i3uJUhJMyx09rm/UeH2CtV7OMhSiJpIEycoUDa/56Z2wsZGufyFMZJyDb/tD+knT+/5tb9iosQPY2msSnhDCfEiCZGXOyh5IQpStIA9+GmZMjpxrQ5O+xoSoYQ9wq6N1dEIIMyMJkpWRXbSFuInoSXB6BzjqYcRG4wRrIYS4CRutAxCV6+SlK4CsYBPCxIHfYPt/jdePfCXJkRDitiRBsjKxJ4wrcNrUraVtIEKYi4tH4PfXjNddxkCzgdrGI4SwCJIgWZGLWTkcSc0CoGMDOdZACHKvwv+eg9xMCOoCPd/XOiIhhIWQBMmKxB6/DEAzP3dquThoHI0QGlMUWDUOUg+Aqw88/i3YyrRLIUT5SIJkRXZcT5A6Se+RELB7Iez9AXQ2xuTI3U/riIQQFkQSJCuy/dglADo28NI4EiE0di4eVk80Xvd633g8iBBCVIAkSFYi/WouSeczAZl/JGq47DTjvKOCHAgeAJ3HaB2REMICSYJkJWJPpKEo0LCOK3Xc5YgRUUMVFsKyfxg3g6wVZFzSbyM/5oQQFSc/OazEzuPG4bVOMrwmarKtX8KhNWDrCIO/M+6YLYQQd0ASJCshE7RFjXf8T9jwgfH6gZkQ0FbTcIQQlk0SJCuQlZPP/rMGQOYfiRoqMwV+fgGUQmgzBNoP1ToiIYSFkwTJCuw6cZlCBQI9neUMNlHzFOQbk6MrqeDTEgZ+Cjqd1lEJISycJEhWYKc6vCbzj0QNtPEDOPkXOLgb5x05uGgdkRDCCkiCZAWK5h/J8Jqocf5eBX99abx+aBZ4N9Y2HiGE1ZAEycJl5xaw70w6AGHSgyRqksvHjEv6AcJehZYPaxqOEMK6SIJk4facSiOvQMFf70Sgp8w/EjVEXrZxM8gcAwR2gj5TtI5ICGFlJEGycDcOr+lkYqqoKVa/ASkJ4OINTywAW3utIxJCWBlJkCzcDtkgUtQ0exbDnkWADh77BjwCtI5ICGGFJEGyYDn5Bew5lQ7IBG1RQ6QkwKrxxuse70KjHtrGI4SwWpIgWbC/kzPJyS/E09WBRnVctQ5HiKp1zWCcd5R/DRr3gfvGax2REMKKSYJkwextjX99OpD5R8K6KQr8/qpx5Zo+EB6dI4fQCiGqlPyEsWABtZwAuHQll2t5BRpHI0QV2jYLEleAjT0MXgguMqQshKhakiBZML2zPS4OtgAkG65pHI0QVeTkNoieZLzuPx3uCdU2HiFEjSAJkgXT6XT46429SMnp2RpHI0QVyEqFn4aBUgCtnoB7X9Q6IiFEDSEJkoUrOpz2rCRIwtoUFhgPoc1KAe+mEPGFHEIrhKg2kiBZuAC9MUGSITZhdTZNhRN/gr0rPLkIHN20jkgIUYNIgmTh/K9P1E42SA+SsCKH1sKfnxqvB/0b6jTVNh4hRI2jaYI0efJkdDqdycvPz08tHzZsWKnysLAwk2fk5OQwatQovL29cXV1ZdCgQZw5c8akTlpaGpGRkej1evR6PZGRkaSnp1dHE6tc8RCb9CAJK5F2En4dabzuOBJaPa5tPEKIGknzHqSWLVuSnJysvhISEkzK+/fvb1K+evVqk/KxY8eybNkyli5dypYtW8jKyiIiIoKCguJl70OGDCE+Pp6oqCiioqKIj48nMjKyWtpX1dQhNpmDJKxBfo5xM8hr6XBPB+g7VeuIhBA1lJ3mAdjZmfQaleTo6HjTcoPBwLx581i0aBG9e/cGYPHixQQGBrJ+/Xr69etHYmIiUVFRbN++nU6dOgEwd+5cwsPDSUpKomlTy+66L9oL6Vx6NoqiyIaRwrJFvQXJ8eDsaTyE1s5B64iEEDWU5j1Ihw8fJiAggAYNGvDUU09x7Ngxk/KYmBh8fHwIDg5mxIgRpKamqmVxcXHk5eXRt29f9V5AQAAhISFs3boVgG3btqHX69XkCCAsLAy9Xq/WKUtOTg4ZGRkmL3Pkf70H6UpuARnX8jWORoi7sPdH2PUtoINH50KtQK0jEkLUYJomSJ06deK7775j7dq1zJ07l5SUFDp37sylS8YT6gcMGMCSJUvYuHEjn376KbGxsfTs2ZOcnBwAUlJScHBwoHbt2ibP9fX1JSUlRa3j4+NT6rN9fHzUOmWZPn26OmdJr9cTGGieP6ydHWyp7WIPyERtYcHOH4SVY43X3SZCk96ahiOEEJomSAMGDOCxxx6jVatW9O7dm1WrVgGwcOFCAJ588kkGDhxISEgIDz74IGvWrOHQoUNqvZspOdRU1rDT7Yaj3n77bQwGg/o6ffr0nTSxWhRN1D4n85CEJcrJNM47yrsKDXtAtze1jkgIIbQfYruRq6srrVq14vDhw2WW+/v7ExQUpJb7+fmRm5tLWlqaSb3U1FR8fX3VOufPny/1rAsXLqh1yuLo6IiHh4fJy1wVDbOdk5VswtIoCiwfBZcOg8c98Ng3YGOrdVRCCGFeCVJOTg6JiYn4+/uXWX7p0iVOnz6tloeGhmJvb090dLRaJzk5mf3799O5c2cAwsPDMRgM7Ny5U62zY8cODAaDWsfS3SN7IQlLtWM2HFgGNnbGSdmu3lpHJIQQgMar2CZMmMCDDz5IvXr1SE1N5aOPPiIjI4OhQ4eSlZXF5MmTeeyxx/D39+fEiRO88847eHt788gjjwCg1+sZPnw448ePx8vLC09PTyZMmKAO2QE0b96c/v37M2LECGbPng3AyJEjiYiIsPgVbEX8a0kPkrBAp3fCuneN130/gsCO2sYjhBA30DRBOnPmDE8//TQXL16kTp06hIWFsX37doKCgsjOziYhIYHvvvuO9PR0/P396dGjBz/++CPu7u7qMz7//HPs7OwYPHgw2dnZ9OrViwULFmBrW9xNv2TJEkaPHq2udhs0aBCzZs2q9vZWlaIDa2UOkrAYVy4aD6EtzIeWj0Cnl7WOSAghTOgURVG0DsISZGRkoNfrMRgMZjcfadeJyzz+9TYCPZ35c2JPrcMR4tYKC2DJ43B0I3g1gZGbwNH99u8TQog7cKe/v81qDpK4M/fULtpN+xqXr+RqHI0Qt7F5pjE5sneBwd9JciSEMEuSIFkBPw8nQu7xIL9Q4ec4892OQAgOr4fNHxuvIz4H3xbaxiOEEDchCZIV0Ol0PNspCIAlO05RWCijpsIMpZ+GX0cACoQ+D22e0joiIYS4qbtKkIp2tBbaG9Q2AHcnO05eusqfRy5qHY4QpvJzjZOysy+Df1voP0PriIQQ4pYqlCCtXbuWYcOG0ahRI+zt7XFxccHd3Z1u3boxdepUzp07V1VxittwcbDjsfZ1AVi8/aTG0QhRwrr34OwucKoFgxeCvZPWEQkhxC2VK0H67bffaNq0KUOHDsXGxoY33niDX3/9lbVr1zJv3jy6devG+vXradiwIS+//DIXLlyo6rhFGZ4NqwfAhsTznJUl/8JcJPwMO417kPHIbKhdX9NwhBCiPMq1zL9jx47885//ZODAgdjY3DynOnv2LF9++SW+vr6MHz++UgPVmjkv87/R03O2s+3YJUb1bMz4vtaxEaawYBeSYE4PyLsCXcdB70laRySEqGHu9Pe37INUTpaSIK3al8yr3++mjrsjW9/qib2tzMMXGsnJgm96wYW/of59EPkb2Gq6N60QogaSfZAEAH1b+lLH3ZELmTmsO1D6kF4hqoWiwMrXjcmRmx88/q0kR0IIi1Lhn1iKovDzzz+zadMmUlNTKSwsNCn/9ddfKy04UXH2tjY8dW8g/9l4hEXbTzCwddkH/wpRpXbNg4T/gc4WnpgPbj5aRySEEBVS4R6kMWPGEBkZyfHjx3Fzc0Ov15u8hPae7lgPGx1sP3aZI6mZWocjapqzcRD1tvG6zxQI6qxtPEIIcQcq3IO0ePFifv31Vx544IGqiEdUgoBazvRq7kv0wfMs3n6KyYNaah2SqElWT4SCXGgWAeGvaR2NEELckQr3IOn1eho2bFgVsYhK9GyYcWftX+LOcDU3X+NoRI2RnwPJ8cbrftNAp9M0HCGEuFMVTpAmT57MlClTyM6WfXbM2X2NvQnyciEzJ5/l8bKBp6gmF/6Gwnxw0kOtelpHI4QQd6zCCdITTzxBWloaPj4+tGrVivbt25u8hHmwsdHxTCfjL6jFO04iuzmIapGy3/inX2vpPRJCWLQKz0EaNmwYcXFxPPvss/j6+qKTH4Jm64nQQD5Zd4j9ZzPYe8ZA28BaWockrN356wmSb4i2cQghxF2qcIK0atUq1q5dS9euXasiHlGJars6ENHan193n2XRtpOSIImql5Jg/NNPEiQhhGWr8BBbYGCgWe8kLUwVTdZeue8c6VdzNY5GWDVFkR4kIYTVqHCC9OmnnzJx4kROnDhRBeGIytYusBYt/D3IyS/k57gzWocjrFnGOchOM24OWaeZ1tEIIcRdqXCC9Oyzz7Jp0yYaNWqEu7s7np6eJi9hXnQ6HU93DASQo0dE1SoaXvMOBnsnbWMRQoi7VOE5SF988UUVhCGqUrdgH+AAu0+lcSUnH1dHORNLVIHzMv9ICGE9KvybcujQoVURh6hC9bxcqFvbmTNp2ew8cZkeTeVcLFEF1CX+rbSNQwghKkG5htiuXLlSoYdWtL6oel0bewPw1+GLGkcirJZM0BZCWJFyJUiNGzdm2rRpnDt38x2ZFUUhOjqaAQMG8O9//7vSAhSVo0tRgnT0ksaRCKuUewUuHTVeSw+SEMIKlGuILSYmhvfee48pU6bQtm1bOnToQEBAAE5OTqSlpXHw4EG2bduGvb09b7/9NiNHjqzquEUFdW7kBUBicgYXs3LwdnPUOCJhVVITAQVcfcBNhnCFEJavXAlS06ZN+emnnzhz5gw//fQTf/zxB1u3biU7Oxtvb2/atWvH3LlzeeCBB7CxqfDCOFENvNwcae7vQWJyBluPXmJQmwCtQxLWRDaIFEJYmQpN0q5bty6vv/46r7/+elXFI6pQl0ZexgTpyEVJkETlKkqQZP6REMJKSHdPDdKliXEe0p+HL8rhtaJynZcVbEII6yIJUg3Ssb4n9rY6zqZnc+ryVa3DEdaisBDOHzBeS4IkhLASkiDVIK6OdrQLrA3AX0dkNZuoJOknIDcLbB3Bq4nW0QghRKWQBKmGUZf7H5H9kEQlKdog0qcZ2Mou7UII6yAJUg3TpbFxuf/WoxcpLJR5SKISqBtEyvCaEMJ63FGC9Oeff/Lss88SHh7O2bNnAVi0aBFbtmyp1OBE5WsTWAtXB1vSruZxMDlD63CENVCPGJEVbEII61HhBOmXX36hX79+ODs7s2fPHnJycgDIzMxk2rRplR6gqFz2tjaENTT2Iskwm6gUssRfCGGFKpwgffTRR3z99dfMnTsXe3t79X7nzp3ZvXt3hZ41efJkdDqdycvPz08tVxSFyZMnExAQgLOzM927d+fAgQMmz8jJyWHUqFF4e3vj6urKoEGDOHPmjEmdtLQ0IiMj0ev16PV6IiMjSU9Pr2jTrUbn6/OQtkiCJO5WdjoYThmvpQdJCGFFKpwgJSUlcf/995e67+HhcUdJR8uWLUlOTlZfCQkJatnMmTP57LPPmDVrFrGxsfj5+dGnTx8yMzPVOmPHjmXZsmUsXbqULVu2kJWVRUREBAUFBWqdIUOGEB8fT1RUFFFRUcTHxxMZGVnhWK1F0cG1sScuk5NfcJvaQtxC0fJ+fSA419Y2FiGEqEQVXnLi7+/PkSNHqF+/vsn9LVu20LBhw4oHYGdn0mtURFEUvvjiC959910effRRABYuXIivry/ff/89L730EgaDgXnz5rFo0SJ69+4NwOLFiwkMDGT9+vX069ePxMREoqKi2L59O506dQJg7ty5hIeHk5SURNOmTSscs6UL9nXD282Ri1k57D6ZTvj1c9qEqDB1grb0HgkhrEuFe5BeeuklxowZw44dO9DpdJw7d44lS5YwYcIEXnnllQoHcPjwYQICAmjQoAFPPfUUx44dA+D48eOkpKTQt29fta6joyPdunVj69atAMTFxZGXl2dSJyAggJCQELXOtm3b0Ov1anIEEBYWhl6vV+uUJScnh4yMDJOXtdDpdCar2YS4Y3IGmxDCSlW4B2nixIkYDAZ69OjBtWvXuP/++3F0dGTChAm89tprFXpWp06d+O677wgODub8+fN89NFHdO7cmQMHDpCSkgKAr6+vyXt8fX05efIkACkpKTg4OFC7du1SdYren5KSgo9P6dPFfXx81DplmT59OlOmTKlQeyxJc38Pfo8/x9m0bK1DEZZMepCEEFbqjnZ1mzp1Ku+++y4HDx6ksLCQFi1a4ObmVuHnDBgwQL1u1aoV4eHhNGrUiIULFxIWFgYYeztupChKqXsllaxTVv3bPeftt99m3Lhx6tcZGRkEBgbeukEWxNHO2HmYU1CocSTCYhXkQ2qi8VqOGBFCWJk73ijSxcWFDh060LFjxztKjsri6upKq1atOHz4sDovqWQvT2pqqtqr5OfnR25uLmlpabesc/78+VKfdeHChVK9UzdydHTEw8PD5GVNHK4nSLn5kiCJO3TpCORfA3tXqN1A62iEEKJSlasHqWiSdHn8+uuvdxxMTk4OiYmJ3HfffTRo0AA/Pz+io6Np164dALm5uWzevJmPP/4YgNDQUOzt7YmOjmbw4MEAJCcns3//fmbOnAlAeHg4BoOBnTt30rFjRwB27NiBwWCgc+fOdxyrpXOwNSZIedKDJO6UOrzWAmxkU34hhHUpV4Kk1+vVa0VRWLZsGXq9ng4dOgDGydLp6ekVSqQAJkyYwIMPPki9evVITU3lo48+IiMjg6FDh6LT6Rg7dizTpk2jSZMmNGnShGnTpuHi4sKQIUPUuIYPH8748ePx8vLC09OTCRMm0KpVK3VVW/Pmzenfvz8jRoxg9uzZAIwcOZKIiIgauYKtiPQgibumTtCW4TUhhPUpV4I0f/589frNN99k8ODBfP3119ja2gJQUFDAK6+8UuFhqDNnzvD0009z8eJF6tSpQ1hYGNu3bycoKAgwTgjPzs7mlVdeIS0tjU6dOrFu3Trc3d3VZ3z++efY2dkxePBgsrOz6dWrFwsWLFBjA1iyZAmjR49WV7sNGjSIWbNmVShWa1PUgyQJkrhjMkFbCGHFdIqiVOjE0jp16rBly5ZSvS9JSUl07tyZS5cuVWqA5iIjIwO9Xo/BYLCK+UgbEs8zfOEuWtfVs/y1rlqHIyzRJ00hKwWGR0NgR62jEUKIMt3p7+8KTxzIz88nMTGx1P3ExEQKC6U3wlLIEJu4K1cuGpMjdODTQutohBCi0lV4mf/zzz/PCy+8wJEjR9Sl+Nu3b2fGjBk8//zzlR6gqBrqEJtM0hZ3omj+kWcDcKycVaxCCGFOKpwgffLJJ/j5+fH555+TnJwMGI8fmThxIuPHj6/0AEXVsJceJHE3ihIkmX8khLBSFU6QbGxsmDhxIhMnTlSP37CGOTk1jUzSFnelaIK2rGATQlipO9pJu4gkRparaCdt2QdJ3JEUSZCEENatwglSgwYNbnlER9Fhs8K8ySRtccfyc+BikvFahtiEEFaqwgnS2LFjTb7Oy8tjz549REVF8cYbb1RWXKKKqQmS9CCJirqQBIX54KQHfV2toxFCiCpR4QRpzJgxZd7/73//y65du+46IFE97NWjRhQKCxVsbG59ALAQKnWDyFZwm4OjhRDCUlXaAUoDBgzgl19+qazHiSpW1IMEkCf7V4nyKiyEfT8ar2X+kRDCilVagvTzzz/j6elZWY8TVaxoFRvIPCRRATvnwLEYsHOCDi9oHY0QQlSZCg+xtWvXzmSStqIopKSkcOHCBf7v//6vUoMTVUcSJFFhqYkQ/b7xuu9HUCdY23iEEKIKVThBeuihh0wSJBsbG+rUqUP37t1p1qxZpQYnqo6NjQ57Wx15BYpM1Ba3l58Dv4yAghxo3AfufVHriIQQokpVOEGaPHlyFYQhtOBga0NeQYH0IInb2/ghnE8AFy946L8yOVsIYfUqPAfJ1taW1NTUUvcvXbqEra1tpQQlqoejvfHvK0cSJHErxzbD1lnG60H/AXdfbeMRQohqUOEESVGUMu/n5OTg4OBw1wGJ6lO0m3ZOniRI4iay0+C3fwAKtB8KzQZqHZEQQlSLcg+x/fvf/wZAp9PxzTff4OZWfIJ3QUEBf/zxh8xBsjDFm0UWaByJMEuKAivHQcZZ8GwE/adrHZEQQlSbcidIn3/+OWDsQfr6669NhtMcHByoX78+X3/9deVHKKqM9CCJW0r4CQ78CjpbeHQuOLhqHZEQQlSbcidIx48fB6BHjx78+uuv1K5du8qCEtXD0U7mIImbSD8Fq8Ybr7u/BXVDtY1HCCGqWYVXsW3atKkq4hAaKBpikwRJmCgsgF9fgpwMCOwEXcdpHZEQQlS7ciVI48aN48MPP8TV1ZVx4279w/Kzzz6rlMBE1VOH2PJlDpK4wV9fwqmt4OAGj8wG2wr/f5QQQli8cv3k27NnD3l5eQDs3r3bZKNIYbkcpQdJlHQuHjZNNV4PmAmeDTQNRwghtFKuBOnGYbWYmJiqikVUMxliEyZyr8KvI6AwH5oPgrZDtI5ICCE0U+F9kF544QUyMzNL3b9y5QovvCCHV1qSoknaspO2AIznrF08BG5+8OCXslu2EKJGq3CCtHDhQrKzs0vdz87O5rvvvquUoET1kDlIQnVoHcTONV4/8hW4eGobjxBCaKzcsy8zMjJQFAVFUcjMzMTJyUktKygoYPXq1fj4+FRJkKJqOMg+SAIg6wL8/orxOuwVaNRT23iEEMIMlDtBqlWrFjqdDp1OR3BwcKlynU7HlClTKjU4UbVkHySBosCK0XDlAtRpDr0maR2REEKYhXInSJs2bUJRFHr27Mkvv/yCp2dxF7yDgwNBQUEEBARUSZCiajjaXz9qRBKkmmv3QkhaDbYO8NhcsHe6/XuEEKIGKHeC1K1bN8C4o3a9evVkqb8VcLCVOUg12qWjEPW28brX++DXStt4hBDCjJQrQdq3b5/J1wkJCTet27p167uLSFSboh4kGWKrgQry4JcXIe8qNLgfwl7VOiIhhDAr5UqQ2rZti06nQ1GUW9bT6XQUyMnwFkPmINVgm2fCud3gpIeHvwabCi9oFUIIq1auBKnooFphXWyvj5Lm5ElSW6Oc2gF/fmK8jvgC9PdoGo4QQpijciVIQUFBVR2HqGaKovDrnrMANPJx0zgaUW1yMo27ZSuF0PopCHlU64iEEMIs3fEplAcPHuTUqVPk5uaa3B80aNBdByWq3obEVPadMeBsb8vwrnLeVo2x5i1IPwn6evDATK2jEUIIs1XhBOnYsWM88sgjJCQkmMxLKlrVJnOQzF9hocJn0YcAGNq5Pt5ujhpHJKrFwd8hfjHobODR2cb5R0IIIcpU4ZmZY8aMoUGDBpw/fx4XFxcOHDjAH3/8QYcOHe7qINvp06ej0+kYO3asem/YsGHq5pRFr7CwMJP35eTkMGrUKLy9vXF1dWXQoEGcOXPGpE5aWhqRkZHo9Xr0ej2RkZGkp6ffcayWbu2BFA4mZ+DqYMtL9zfUOhxRHTLOwYoxxuuur0NQZ23jEUIIM1fhBGnbtm188MEH1KlTBxsbG2xsbOjatSvTp09n9OjRdxREbGwsc+bMKXOLgP79+5OcnKy+Vq9ebVI+duxYli1bxtKlS9myZQtZWVlERESY9GQNGTKE+Ph4oqKiiIqKIj4+nsjIyDuK1dIVFip8vt7Ye/RC1wbUdnXQOCJR5QoL4bdXIDsN/NtCt7e0jkgIIcxehYfYCgoKcHMzTur19vbm3LlzNG3alKCgIJKSkiocQFZWFs888wxz587lo48+KlXu6OiIn59fme81GAzMmzePRYsW0bt3bwAWL15MYGAg69evp1+/fiQmJhIVFcX27dvp1KkTAHPnziU8PJykpCSaNm1a4Zgt2cqEZA6dz8LdyY4Xu0rvUY2wczYc2wR2zvDoXLCTpFgIIW6nwj1IISEh6saRnTp1YubMmfz111988MEHNGxY8V+4r776KgMHDlQTnJJiYmLw8fEhODiYESNGkJqaqpbFxcWRl5dH37591XsBAQGEhISwdetWwNjjpdfr1eQIICwsDL1er9YpS05ODhkZGSYvS1dQqPDF9d6jEfc1RO9ir3FEosqdPwjR189X6/cR1Cl9jqIQQojSKtyD9N5773HlyhUAPvroIyIiIrjvvvvw8vLixx9/rNCzli5dyu7du4mNjS2zfMCAATzxxBMEBQVx/Phx/vnPf9KzZ0/i4uJwdHQkJSUFBwcHateubfI+X19fUlJSAEhJScHHx6fUs318fNQ6ZZk+fbrVHb77e/xZjl24Qi0Xe57vUl/rcERVy88xLukvyIEm/aDDcK0jEkIIi1HhBKlfv37qdcOGDTl48CCXL1+mdu3aFTqf7fTp04wZM4Z169bh5FT2AZlPPvmkeh0SEkKHDh0ICgpi1apVPProzfdvURTFJJay4ipZp6S3336bcePGqV9nZGQQGBh4yzaZs7yCQr7ccBiAkfc3xN1Jeo+s3oYP4Px+cPGGh2aBnJ8ohBDldsf7IN3I09Ozwu+Ji4sjNTWV0NBQ9V5BQQF//PEHs2bNIicnB1tbW5P3+Pv7ExQUxOHDxl/0fn5+5ObmkpaWZtKLlJqaSufOndU658+fL/X5Fy5cwNfX96bxOTo64uhoPcvfl+0+y8lLV/FydWBoeH2twxFV7VgMbJtlvH5oFriV7kUVQghxc5odwNSrVy8SEhKIj49XXx06dOCZZ54hPj6+VHIEcOnSJU6fPo2/vz8AoaGh2NvbEx0drdZJTk5m//79aoIUHh6OwWBg586dap0dO3ZgMBjUOtYuN7+49+jlbo1wdayUvFiYq+w0WPYP43Xo89B0gLbxCCGEBdLsN6W7uzshISEm91xdXfHy8iIkJISsrCwmT57MY489hr+/PydOnOCdd97B29ubRx55BAC9Xs/w4cMZP348Xl5eeHp6MmHCBFq1aqVO+m7evDn9+/dnxIgRzJ49G4CRI0cSERFRY1aw/RR3mrPp2dRxd+TZMDk2xqopCqx8HTLPgVdj6DdV64iEEMIimW1Xgq2tLQkJCXz33Xekp6fj7+9Pjx49+PHHH3F3d1frff7559jZ2TF48GCys7Pp1asXCxYsMOmBWrJkCaNHj1ZXuw0aNIhZs2ZVe5u0cC2vgFkbjwDwSvdGODuU7pkTVmTfj3BgGdjYGZf0O7hqHZEQQlgknVJ0Voi4pYyMDPR6PQaDAQ8PD63DKbcFfx1n8oqD+Oud2DShO072kiBZrbST8FUXyM2Enu/B/W9oHZEQQmjuTn9/azYHSVS9a3kF/DfmKACv9mgsyZE1KyyAZS8Zk6PAMOg67vbvEUIIcVOSIFmxxdtPciEzh3tqOTO4g+VuUSDK4a8v4NQ2cHA3HkRrI8mwEELcDUmQrNTV3Hy+ut57NLpXYxzs5K/aap3bA5umGa8f+BfUrq9pOEIIYQ3MdpK2uDsLt57k0pVc6nm68Gj7ulqHIypbQT6c/Av+Xgn7f4HCfGjxMLR5SuvIhBDCKkiCZIUyr+Ux+w9j79GYXk2wt5XeI6uQlw1HN0LiSji0xrjfURHPRhDxueyWLYQQlUQSJCu04K8TpF/No2EdVx5qG6B1OOJuZKfDobXw9wo4sgHyrhaXuXgZN4Fs9iA07A72ZR/ZI4QQouIkQbIy1/IKmPfXccDYe2QnvUeWJyMZklYZe4pO/GkcPiuiD4RmEdA8wrhazVb+ExZCiKogP12tzNoDKaRfzeOeWs5EtJbeI4tx6SgkrjDOKToTa1pWp7kxIWoWAf5tZBhNCCGqgSRIVub7HacAGNwhEFsb+UVqthQFkvcaE6LElXAh0bS87r3Xe4oeBK9G2sQohBA1mCRIVuTohSx2HL+MjQ4G3ysr18xOYYFxr6LElfD3KjCcKi6zsYP69xl7ipoOBA9/7eIUQgghCZI1+TH2NAA9mvrgr3fWOBoBQN41OBZjnGSdtAauXious3eBxr2Mk6yD+4Jzbc3CFEIIYUoSJCuRk1/Az3FnAHi6Yz2No6nhrhngcLRxTtGR9ZCbVVzmXBuCBxh7ihr1BHtJZIUQwhxJgmQlog+e5/KVXHw9HOnetI7W4dQ8WanGYbO/V8KxzVCYV1zmcQ80G2icUxTURVaeCSGEBZCf1Fbih53G+SxPdgiUpf3V5fLx4knWp3cASnGZd3DxcvyA9rLyTAghLIwkSFbg5KUr/HXkEjodDL5XDqWtMooC5/dfn2S90nh9o4D215fjPwh1grWJUQghRKWQBMkKLL0+Ofv+JnWoW9tF42isTGEBnN55vadoBaSfLC7T2UL9LsaEqNlA0N+jXZxCCCEqlSRIFi6voJCfdsnk7EqVnwPH/zAmREmr4cqF4jI7J2jUy9hTFNwfXDy1i1MIIUSVkQTJwm1IPM/FrBy83Rzp1dxH63AsV06mceXZ3yvh0DrIzSwuc9Ibk6FmEcZl+Q6u2sUphBCiWkiCZOG+32kcXhvcoS72Mjm7Yq5cNPYQJa407lVUkFNc5uZnHDZrHmHcwNHWXrMwhRBCVD9JkCzY6ctX+fOwcfjnSZmcXT5pJ4uX45/aBkphcZlno+JJ1veEgo0knEIIUVNJgmTB/rfrNIoCXRt7E+Qlwz5lUhRITSyeZJ2yz7Tcv40xIWoeAXWayXJ8IYQQgCRIFiu/oJD/7TIOr8nk7BIKC+HsLmNC9PdKuHysuExnA/U6X+8pGgi15HsnhBCiNEmQLNSmpAucz8jBy9WBPi18tQ5He/m5cOJPY0L092rISikus3WERj2Mk6ybDgBXb+3iFEIIYREkQbJQRTtnPx5aFwe7GjpXJicLjm4wTrI+tBZyDMVljh7QpK+xp6hxb3B01y5OIYQQFkcSJAt0Lj2bmKRUoAZOzr5yCQ6tub7ybBPkXysuc/WBZg8Y5xQ1uB/sHLSLUwghhEWTBMkC/W/XaQoVCGvoScM6blqHU/UMZ4qP9zi5FZSC4rLa9a+fefYg1L0XbGw1C1MIIYT1kATJwhQUKvwvtgZMzr6QBInLjYlRcrxpmW+r65OsI8C3paw8E0IIUekkQbIwB89lcM5wDXcnO/q19NM6nMpTWAjn9sDfK4xJ0aXDNxTqoF6YMSFqNhA8G2gWphBCiJpBEiQLc/LyFQCa+bnjZG/hw0kFeXDyr+vDZ6sg81xxma0DNOhm7Clq+gC4yTEqQgghqo8kSBbm1OWrAATWdtE4kjuUexWObjTOJ0paA9fSi8sc3KBJH2NPUZO+4OShWZhCCCFqNkmQLMzpy9kABHpaUIKUnWZchp+4Ao5sgPzs4jIXb+PeRM0fNPYY2TtpF6cQQghxnSRIFuZ0UQ+SuSdIGeeKzzw7sQUK84vL9PWKJ1nXC5OVZ0IIIcyOJEgWpmiIrZ45J0h7lsDvrwJK8T2fFteX40eAX2tZeSaEEMKsSYJkQfILCjmXXjTE5qxxNLeQuAJQwKcltHnSmBh5NdI6KiGEEKLczOaMiunTp6PT6Rg7dqx6T1EUJk+eTEBAAM7OznTv3p0DBw6YvC8nJ4dRo0bh7e2Nq6srgwYN4syZMyZ10tLSiIyMRK/Xo9friYyMJD09vRpaVbmSDdfIL1RwsLXB192M5+pcTDL+2X86dBkjyZEQQgiLYxYJUmxsLHPmzKF169Ym92fOnMlnn33GrFmziI2Nxc/Pjz59+pCZmanWGTt2LMuWLWPp0qVs2bKFrKwsIiIiKCgo3m15yJAhxMfHExUVRVRUFPHx8URGRlZb+yrL6TTj8Frd2s7Y2JjpEFV+DqSdMF7XaappKEIIIcSd0jxBysrK4plnnmHu3LnUrl1bva8oCl988QXvvvsujz76KCEhISxcuJCrV6/y/fffA2AwGJg3bx6ffvopvXv3pl27dixevJiEhATWr18PQGJiIlFRUXzzzTeEh4cTHh7O3LlzWblyJUlJSZq0+U5ZxATtS0dBKTQeFuvmq3U0QgghxB3RPEF69dVXGThwIL179za5f/z4cVJSUujbt696z9HRkW7durF161YA4uLiyMvLM6kTEBBASEiIWmfbtm3o9Xo6deqk1gkLC0Ov16t1ypKTk0NGRobJS2tFS/zNeoL2xUPGP72DZSK2EEIIi6XpJO2lS5eye/duYmNjS5WlpKQA4Otr2gvh6+vLyZMn1ToODg4mPU9FdYren5KSgo9P6V2YfXx81DplmT59OlOmTKlYg6qYukmkOU/QvjFBEkIIISyUZj1Ip0+fZsyYMSxevBgnp5tPONaV6IVQFKXUvZJK1imr/u2e8/bbb2MwGNTX6dOnb/mZ1cEilvhfuD5sWUcSJCGEEJZLswQpLi6O1NRUQkNDsbOzw87Ojs2bN/Pvf/8bOzs7teeoZC9PamqqWubn50dubi5paWm3rHP+/PlSn3/hwoVSvVM3cnR0xMPDw+SltTPqJG0zTpDUHiSZoC2EEMJyaZYg9erVi4SEBOLj49VXhw4deOaZZ4iPj6dhw4b4+fkRHR2tvic3N5fNmzfTuXNnAEJDQ7G3tzepk5yczP79+9U64eHhGAwGdu7cqdbZsWMHBoNBrWMJruTkczErF4B6XmaaIBUWwsXDxmsZYhNCCGHBNJuD5O7uTkhIiMk9V1dXvLy81Ptjx45l2rRpNGnShCZNmjBt2jRcXFwYMmQIAHq9nuHDhzN+/Hi8vLzw9PRkwoQJtGrVSp303bx5c/r378+IESOYPXs2ACNHjiQiIoKmTS2nl+NMmnGCtt7ZHg8ne42juYmMM8Zz1mwdoHZ9raMRQggh7phZ76Q9ceJEsrOzeeWVV0hLS6NTp06sW7cOd3d3tc7nn3+OnZ0dgwcPJjs7m169erFgwQJsbYvP91qyZAmjR49WV7sNGjSIWbNmVXt77oZlzD+6Przm2QhszfqflhBCCHFLOkVRlNtXExkZGej1egwGgybzkeZtOc6HKw8ysJU//32mfbV/frls+y+sfQeaD4InF2kdjRBCCHHHv7813wdJlE/RJpF1LWGJv+ygLYQQwsJJgmQhjl7IAiDQnFewXZA9kIQQQlgHSZAsgCE7jx3HLgNwb31PjaO5BdkkUgghhJWQBMkCRB88T25BIU183Gjq5377N2jh6mW4etF47d1E21iEEEKIuyQJkgVYue8cABGtAzSO5BaKdtDWB4KDq7axCCGEEHdJEiQzl341ly2HjT0zEW38NY7mFmR4TQghhBWRBMnMrT2QQn6hQnN/DxrVcdM6nJuTBEkIIYQVkQTJzK3clwxARGsz7j2CG5b4S4IkhBDC8kmCZMYuZeWw9eglwAISpKI5SHJIrRBCCCsgCZIZW7M/hYJChVb36AnyMuOJz3nZkH7KeC1DbEIIIayAJEhmrHj1mpn3Hl06AijgXBtcvbWORgghhLhrkiCZqdTMa+w4btwccqC5J0g3Dq/pdNrGIoQQQlQCSZDM1JqEFBQF2gbWoq45Hy8CcPGw8U/ZIFIIIYSVkATJTFnM8BrAxes9SHJIrRBCCCshCZIZSjZkE3siDbCA4TW4oQdJJmgLIYSwDpIgmaFV1/c+urd+bfz1zhpHcxuFBZIgCSGEsDqSIJmh4s0hzfjstSLpJ6EgB+ycoFY9raMRQgghKoUkSGbm9OWrxJ9OR6eDAa38tA7n9op6j7wag42ttrEIIYQQlUQSJDOzOsHYe9SpgSc+7k4aR1MO6hJ/GV4TQghhPSRBMjMWNbwGN5zBJivYhBBCWA9JkMzIiYtXSDhrwNZGx4AQCxheg+IESfZAEkIIYUUkQTIjq64Pr3Vu5IWXm6PG0ZSDosghtUIIIaySJEhmZMVeC9ocEuDKRbiWDujAq5HW0QghhBCVRhIkM3EkNYu/UzKxs9HRr6WlDK9d7z2qHQT2Zr5fkxBCCFEBkiCZiaLNIbs28aaWi4PG0ZSTOv9IhteEEEJYF0mQzMS6gymABa1eAzi72/inrGATQghhZSRBMhPn0rMBaF1Xr3Ek5VRYAIeijNeNe2kbixBCCFHJJEEyA4WFCobsPABqOdtrHE05nYmFKxfASQ9BXbSORgghhKhUkiCZgcxr+RQqxmu9i4UkSH+vMv7ZpB/YWkjMQgghRDlJgmQG0q7mAuDiYIujnQWcZ6YoxQlSswe0jUUIIYSoApIgmYH068NrtS1p9drlo2DrAI17ax2NEEIIUekkQTIDRT1IekuZf/T3SuOfDbqBo7u2sQghhBBVQBIkM2C4er0HydVSEqTVxj+bDdQ2DiGEEKKKSIJkBop6kGo5W8AQW0YynN1lvG46QNtYhBBCiCqiaYL01Vdf0bp1azw8PPDw8CA8PJw1a9ao5cOGDUOn05m8wsLCTJ6Rk5PDqFGj8Pb2xtXVlUGDBnHmzBmTOmlpaURGRqLX69Hr9URGRpKenl4dTSyX9Os9SLUsYQXboet/P3XvBXcLORJFCCGEqCBNE6S6desyY8YMdu3axa5du+jZsycPPfQQBw4cUOv079+f5ORk9bV69WqTZ4wdO5Zly5axdOlStmzZQlZWFhERERQUFKh1hgwZQnx8PFFRUURFRREfH09kZGS1tfN20ot6kCwhQSpavdZUVq8JIYSwXnZafviDDz5o8vXUqVP56quv2L59Oy1btgTA0dERP7+yeyoMBgPz5s1j0aJF9O5tXE21ePFiAgMDWb9+Pf369SMxMZGoqCi2b99Op06dAJg7dy7h4eEkJSXRtKn2x2RYzCq2axlwbLPxulmEtrEIIYQQVchs5iAVFBSwdOlSrly5Qnh4uHo/JiYGHx8fgoODGTFiBKmpqWpZXFwceXl59O3bV70XEBBASEgIW7duBWDbtm3o9Xo1OQIICwtDr9erdcqSk5NDRkaGyauqFA2xmf0qtiProTAPvBpDnWCtoxFCCCGqjOYJUkJCAm5ubjg6OvLyyy+zbNkyWrRoAcCAAQNYsmQJGzdu5NNPPyU2NpaePXuSk5MDQEpKCg4ODtSuXdvkmb6+vqSkpKh1fHx8Sn2uj4+PWqcs06dPV+cs6fV6AgMDK6vJpRQNsZl9D5K6OaSsXhNCCGHdNB1iA2jatCnx8fGkp6fzyy+/MHToUDZv3kyLFi148skn1XohISF06NCBoKAgVq1axaOPPnrTZyqKgk6nU7++8fpmdUp6++23GTdunPp1RkZGlSVJRUNsZj0HKT8XDkcbr5tKgiRETVZQUEBeXp7WYQgBgL29Pba2lX8KheYJkoODA40bNwagQ4cOxMbG8uWXXzJ79uxSdf39/QkKCuLw4cMA+Pn5kZubS1pamkkvUmpqKp07d1brnD9/vtSzLly4gK+v703jcnR0xNHR8a7aVl5pV4omaZtxD9LJLZBjAFcfqNtB62iEEBpQFIWUlBSzWgUsBECtWrXw8/O7ZcdHRWmeIJWkKIo6hFbSpUuXOH36NP7+/gCEhoZib29PdHQ0gwcPBiA5OZn9+/czc+ZMAMLDwzEYDOzcuZOOHTsCsGPHDgwGg5pEaamgUCHjWj5g5j1IRZtDNu0PNhZwXpwQotIVJUc+Pj64uLhU6i8jIe6EoihcvXpVnZ9clB9UBk0TpHfeeYcBAwYQGBhIZmYmS5cuJSYmhqioKLKyspg8eTKPPfYY/v7+nDhxgnfeeQdvb28eeeQRAPR6PcOHD2f8+PF4eXnh6enJhAkTaNWqlbqqrXnz5vTv358RI0aovVIjR44kIiLCLFawGbKLu6lrmeskbUWBpKLds2X1mhA1UUFBgZoceXl5aR2OECpnZ2fAOHrk4+NTacNtmiZI58+fJzIykuTkZPR6Pa1btyYqKoo+ffqQnZ1NQkIC3333Henp6fj7+9OjRw9+/PFH3N2Lz//6/PPPsbOzY/DgwWRnZ9OrVy8WLFhg8g1asmQJo0ePVle7DRo0iFmzZlV7e8tSNEHb3dEOO1vN58yXLTkeMs6Cvavx/DUhRI1TNOfIxcVF40iEKK3o32VeXp51JEjz5s27aZmzszNr16697TOcnJz4z3/+w3/+85+b1vH09GTx4sV3FGNVSyvaRducz2ErWr3WuBfYO2kbixBCUzKsJsxRVfy7NNMui5rDkG0B57D9LcNrQghhrWJiYtDpdLecfL9gwQJq1apVbTGZA0mQNGb257BdPg6pB0BnC036aB2NEEJU2LBhw3j44Yer9DPmzJlD9+7d8fDwuGmyUdXngtbEJKYqSYKkMXWIzVyX+BdNzq7fBVw8tY1FCCHM1NWrV+nfvz/vvPPOTeuY+7mgwpQkSBozqLtom2kPkno4rWwOKYSwfFFRUXTt2pVatWrh5eVFREQER48eVctPnDiBTqfj119/pUePHri4uNCmTRu2bdt2y+eOHTuWt956i7CwsDLLi84F/eabbwgPDyc8PJy5c+eycuVKkpKSyh3/3r176dGjB+7u7nh4eBAaGsquXbuIiYnh+eefx2AwoNPp0Ol0TJ48GTCeUdqhQwfc3d3x8/NjyJAhJsd2Ffnrr79o06YNTk5OdOrUiYSEhFvGsmLFCkJDQ3FycqJhw4ZMmTKF/Pz8crfF3EmCpDG1B8kcl/hfuQSnrv9QaPaAtrEIIcyOoihczc2v9peiKHcc85UrVxg3bhyxsbFs2LABGxsbHnnkEQoLC03qvfvuu0yYMIH4+HiCg4N5+umn7+qX/52eC1rSM888Q926dYmNjSUuLo633noLe3t7OnfuzBdffIGHhwfJyckkJyczYcIEAHJzc/nwww/Zu3cvv/32G8ePH2fYsGGlnv3GG2/wySefEBsbi4+PD4MGDbrpjulr167l2WefZfTo0Rw8eJDZs2ezYMECpk6dWrFvjBkzu40ia5riY0bMcIjtUBQoheDXCmrV0zoaIYSZyc4roMX7t19tXNkOftAPF4c7+/X12GOPmXw9b948fHx8OHjwICEhIer9CRMmMHCgsed8ypQptGzZkiNHjtCsWbM7+tw7PRe0pFOnTvHGG2+ocTRp0kQt0+v16HQ6/Pz8TN7zwgsvqNcNGzbk3//+Nx07diQrKws3Nze1bNKkSfTpY5xrunDhQurWrcuyZcvUjZhvNHXqVN566y2GDh2qPvfDDz9k4sSJTJo0qdztMWfSg6Sx7NwCAOxszXDprHo4raxeE0JYh6NHjzJkyBAaNmyIh4cHDRo0AIyJx41at26tXhftzlzWsFRF3Mm5oCWNGzeOF198kd69ezNjxgyT4cGb2bNnDw899BBBQUG4u7vTvXt3oHSbw8PD1WtPT0+aNm1KYmJimc+Mi4vjgw8+wM3NTX2NGDGC5ORkrl69Wu72mDPpQdJYsK8b6xPPc+BshtahmMq9Ckc3Gq+byvCaEKI0Z3tbDn7QT5PPvVMPPvgggYGBzJ07l4CAAAoLCwkJCSE3N9eknr198bSHogSm5DBcRdzpuaAlTZ48mSFDhrBq1SrWrFnDpEmTWLp0qXrCRElXrlyhb9++9O3bl8WLF1OnTh1OnTpFv379SrW5LDdL3goLC5kyZUqZB8c7OVnHfnmSIGmsXT3jIbt7TqdpHEkJxzZBfjbo6xmH2IQQogSdTnfHQ11auHTpEomJicyePZv77rsPgC1btlTLZ1fmuaDBwcEEBwfz+uuv8/TTTzN//nweeeQRHBwcKCgoMKn7999/c/HiRWbMmEFgYCAAu3btKvO527dvp14943SKtLQ0Dh06dNMhxfbt25OUlKQeNm+NLOdftpVqG1gLgMOpWWRey8PdyUwma6ubQz4AsnOuEMIK1K5dGy8vL+bMmYO/vz+nTp3irbfeqpRnp6SkkJKSwpEjRwBISEjA3d2devXq4enpWSnngmZnZ/PGG2/w+OOP06BBA86cOUNsbKw6r6p+/fpkZWWxYcMG2rRpg4uLC/Xq1cPBwYH//Oc/vPzyy+zfv58PP/ywzOd/8MEHeHl54evry7vvvou3t/dN9496//33iYiIIDAwkCeeeAIbGxv27dtHQkICH330UQW/e+ZJ5iBprI67I3VrO6MosO+MQetwjAoL4NAa43UzWd4vhLBshYWF2NnZYWNjw9KlS4mLiyMkJITXX3+df/3rX5XyGV9//TXt2rVjxIgRANx///20a9eO5cuXq3WWLFlCq1at1CGv1q1bs2jRIpPn1K9fX12eX5KtrS2XLl3iueeeIzg4mMGDBzNgwACmTJkCQOfOnXn55Zd58sknqVOnDjNnzqROnTosWLCAn376iRYtWjBjxgw++eSTMp8/Y8YMxowZQ2hoKMnJySxfvhwHh7IXEPXr14+VK1cSHR3NvffeS1hYGJ999hlBQUEV/daZLZ1yN+sla5CMjAz0ej0GgwEPD49KffaoH/awYu85JvQN5rWeTW7/hqp2civMHwBOteCNo2ArHY1C1HTXrl3j+PHjNGjQwOLmmPTv35/GjRubzSHlN5OdnY2npyerV6+mR48eWodjUW717/NOf39LD5IZKBpmiz+drmkcqsSVxj+D+0tyJISwWGlpaaxatYqYmBh69+6tdTi3tXnzZnr27CnJkZmQ335moF29WgDsOZVe4SWfle78Adg1z3jd/EHt4hBCiLv0wgsvEBsby/jx43nooYe0Due2+vfvT//+/bUOQ1wnCZIZaBnggYOtDZeu5HL6cjb1vFy0CST3Cvw0DPKvQeM+srxfCGHRli1bpnUIwoLJEJsZcLSzpXmAcVxU0+X+qybAxUPg7g+PfA028s9DCCFEzSS/Ac1Eu+vzkPacStcmgPjvYe/3oLOBx+aBq7c2cQghhBBmQBIkM1E0D0mTidoXkmDVeON193egfpfqj0EIIYQwI5IgmYl2gcYdtQ+eyyAnv+A2tStRXrZx3lHeVWjYHe4bV32fLYQQQpgpSZDMRKCnM16uDuQWFHLgXDWey7bmTUg9CK4+8MgcsLnzM46EEEIIayEJkpnQ6XTF+yFV1zykhJ9h90JAB4/NBffyH5gohBBCWDNJkMyIuh9SdcxDunQUVowxXt//hnF4TQghhKhkMTEx6HQ60tPTb1pnwYIF1KpVq9piKg9JkMxIu3rGeUh7TlXxUv+8a/DTUMjNgqAu0O3Nqv08IYTQ0LBhw9DpdOh0Ouzt7fH19aVPnz58++23FBYWVuln//rrr/Tp04c6derg4eFBeHg4a9euLVUvPT2dV199FX9/f5ycnGjevDmrV6+u0tgqyhyTmKokCZIZaV1Xj04HZ9KyuZCZU3UfFP1PSEkAFy947Bs5TkQIYfX69+9PcnIyJ06cYM2aNfTo0YMxY8YQERFBfn5+lX3uH3/8QZ8+fVi9ejVxcXH06NGDBx98kD179qh1cnNz6dOnDydOnODnn38mKSmJuXPncs8991RZXOL2JEEyI+5O9jTxcQOqcLn/wd9h5xzj9SNzwCOgaj5HCCHMiKOjI35+ftxzzz20b9+ed955h99//501a9awYMECtZ7BYGDkyJH4+Pjg4eFBz5492bt3r8mzli9fTocOHXBycsLb25tHH330pp/7xRdfMHHiRO69916aNGnCtGnTaNKkCStWrFDrfPvtt1y+fJnffvuNLl26EBQURNeuXWnTpk2523fixAl0Oh3x8fHqvfT0dHQ6HTExMUDxUNeGDRvo0KEDLi4udO7cmaSkJPU9e/fupUePHri7u+Ph4UFoaCi7du0iJiaG559/HoPBoPbGTZ48GYDFixfToUMH3N3d8fPzY8iQIaSmppaK8a+//qJNmzY4OTnRqVMnEhISbtmmFStWEBoaipOTEw0bNmTKlClVmsyWJAmSmSk+uLYKhtnSTsDvo4zXXcZCE/M/vFEIYcYUxXhEUXW/FKVSwu/Zsydt2rTh119/vd4chYEDB5KSkqL2+LRv355evXpx+fJlAFatWsWjjz7KwIED2bNnj5pslFdhYSGZmZl4enqq95YvX054eDivvvoqvr6+hISEMG3aNAoKqmbLl3fffZdPP/2UXbt2YWdnxwsvvKCWPfPMM9StW5fY2Fji4uJ46623sLe3p3PnznzxxRd4eHiQnJxMcnIyEyZMAIw9YB9++CF79+7lt99+4/jx4wwbNqzU577xxht88sknxMbG4uPjw6BBg8jLyyszxrVr1/Lss88yevRoDh48yOzZs1mwYAFTp06tku9JWWRsxcy0q1eb/+06U/k7aufnwk/PQ44B6naEnu9V7vOFEDVP3lWYpkEv9DvnwMG1Uh7VrFkz9u3bB8CmTZtISEggNTUVR0dHAD755BN+++03fv75Z0aOHMnUqVN56qmnmDJlivqMivT0fPrpp1y5coXBgwer944dO8bGjRt55plnWL16NYcPH+bVV18lPz+f999/v1LaeaOpU6fSrVs3AN566y0GDhzItWvXcHJy4tSpU7zxxhs0a9YMgCZNmqjv0+v16HQ6/Pz8TJ53Y4LVsGFD/v3vf9OxY0eysrJwc3NTyyZNmkSfPn0AWLhwIXXr1mXZsmUm34sbY3zrrbcYOnSo+twPP/yQiRMnMmnSpEr6TtyaJEhmpmgl297T6RQUKtja6CrnwRumwLnd4FQLHv8WbO0r57lCCGHBFEVBpzP+nI2LiyMrKwsvLy+TOtnZ2Rw9ehSA+Ph4RowYcUef9cMPPzB58mR+//13fHx81PuFhYX4+PgwZ84cbG1tCQ0N5dy5c/zrX/+qkgSpdevW6rW/vz8Aqamp1KtXj3HjxvHiiy+yaNEievfuzRNPPEGjRo1u+bw9e/YwefJk4uPjuXz5sjrx/dSpU7Ro0UKtFx4erl57enrStGlTEhMTy3xmXFwcsbGxJj1GBQUFXLt2jatXr+LiUvWHukuCZGaa+Ljj6mDLldwCjqRm0dTP/e4fmrQGts0yXj/8FdQKvPtnCiGEvYuxN0eLz60kiYmJNGjQADAmKv7+/uqcnRsVrd5ydna+o8/58ccfGT58OD/99BO9e5tOb/D398fe3h5b2+KNeps3b05KSgq5ubk4ODjc9vk21w8XV24YfrzZ8JW9ffH/IBclh0VJzeTJkxkyZAirVq1izZo1TJo0iaVLl/LII4+U+awrV67Qt29f+vbty+LFi6lTpw6nTp2iX79+5Obm3jbuos8vqbCwkClTppQ5v8vJyem2z60MkiCZGVsbHa3r1mLbsUvsOZV29wlS+mlY9rLxOuwVaPbA3QcphBAAOl2lDXVpYePGjSQkJPD6668D0L59e1JSUrCzs6N+/fplvqd169Zs2LCB559/vtyf88MPP/DCCy/www8/MHDgwFLlXbp04fvvv6ewsFBNdA4dOoS/v3+5kiOAOnXqAJCcnEy7du0ATCZsV0RwcDDBwcG8/vrrPP3008yfP59HHnkEBweHUvOi/v77by5evMiMGTMIDDT+z/euXbvKfO727dupV68eAGlpaRw6dEgdyiupffv2JCUl0bhx4ztqQ2WQSdpmqNIOri3Ig1+Gw7V0CGgHvafc9i1CCGGNcnJySElJ4ezZs+zevZtp06bx0EMPERERwXPPPQdA7969CQ8P5+GHH2bt2rWcOHGCrVu38t5776m/9CdNmsQPP/zApEmTSExMJCEhgZkzZ970c3/44Qeee+45Pv30U8LCwkhJSSElJQWDwaDW+cc//sGlS5cYM2YMhw4dYtWqVUybNo1XX3213O1zdnYmLCyMGTNmcPDgQf744w/ee69ic02zs7N57bXXiImJ4eTJk/z111/ExsbSvHlzAOrXr09WVhYbNmzg4sWLXL16lXr16uHg4MB//vMfjh07xvLly/nwww/LfP4HH3zAhg0b2L9/P8OGDcPb25uHH364zLrvv/8+3333HZMnT+bAgQMkJiby448/VrhNd0UR5WIwGBRAMRgMVf5Za/cnK0FvrlT6frb57h4UPUlRJnkoyrS6inLpWKXEJoSombKzs5WDBw8q2dnZWodSYUOHDlUABVDs7OyUOnXqKL1791a+/fZbpaCgwKRuRkaGMmrUKCUgIECxt7dXAgMDlWeeeUY5deqUWueXX35R2rZtqzg4OCje3t7Ko48+etPP7tatm/rZN76GDh1qUm/r1q1Kp06dFEdHR6Vhw4bK1KlTlfz8fLV8/vz5yu1+ZR88eFAJCwtTnJ2dlbZt2yrr1q1TAGXTpk2KoijKpk2bFEBJS0tT37Nnzx4FUI4fP67k5OQoTz31lBIYGKg4ODgoAQEBymuvvWbyd/7yyy8rXl5eCqBMmjRJURRF+f7775X69esrjo6OSnh4uLJ8+XIFUPbs2WPyuStWrFBatmypODg4KPfee68SHx9v0j69Xm/SnqioKKVz586Ks7Oz4uHhoXTs2FGZM2dOmW2/1b/PO/39rVOUSlovaeUyMjLQ6/UYDAY8PDyq9LNSM6/RceoGdDrYN6kv7k53MKH6yHpY/Jjx+omF0PLhSo1RCFGzXLt2jePHj9OgQYNqmwMiik2ePJmYmJgy50eJW//7vNPf3zIHyQz5uDsR6OmMg60N5zOuVTxBykiGX18yXt/7oiRHQghh4dauXcuXX36pdRg1iqZzkL766itat26Nh4eHekbNmjVr1HJFUZg8eTIBAQE4OzvTvXt3Dhw4YPKMnJwcRo0ahbe3N66urgwaNIgzZ86Y1ElLSyMyMhK9Xo9erycyMvKWh+aZg+jXu7FhfHca+1RwknZhAfzyIly9CH6toG/1baolhBCiamzbto2OHTtqHUaNommCVLduXWbMmMGuXbvYtWsXPXv25KGHHlKToJkzZ/LZZ58xa9YsYmNj8fPzo0+fPmRmZqrPGDt2LMuWLWPp0qVs2bKFrKwsIiIiTGbaDxkyhPj4eKKiooiKiiI+Pp7IyMhqb29FONnb3r5SWTZ/DCe3gIMbPL4A7KUrXAghhKiwCs1Yqga1a9dWvvnmG6WwsFDx8/NTZsyYoZZdu3ZN0ev1ytdff60oiqKkp6cr9vb2ytKlS9U6Z8+eVWxsbJSoqChFUYyT1gBl+/btap1t27YpgPL333+XO67qnKR9xwxnFeUDb+PE7L3/0zoaIYQVseRJ2sL6VcUkbbNZ5l9QUMDSpUu5cuUK4eHhHD9+nJSUFPr27avWcXR0pFu3bmzduhUw7rSZl5dnUicgIICQkBC1zrZt29Dr9XTq1EmtExYWhl6vV+uUJScnh4yMDJOX2fMIgKEr4b4J0PoJraMRQgghLJbmCVJCQgJubm44Ojry8ssvs2zZMlq0aEFKSgoAvr6+JvV9fX3VspSUFBwcHKhdu/Yt69y4pXsRHx8ftU5Zpk+frs5Z0uv16gZYZq9eJ+j1T62jEEJYKUUWPgszVBX/LjVPkJo2bUp8fDzbt2/nH//4B0OHDuXgwYNqecltyJUbzs25mZJ1yqp/u+e8/fbbGAwG9XX69OnyNkkIIaxO0fEUV69e1TgSIUor+nd54zEqd0vzZf4ODg7qVuIdOnQgNjaWL7/8kjfffBMw9gAVHaYHxgP1inqV/Pz8yM3NJS0tzaQXKTU1lc6dO6t1zp8/X+pzL1y4UKp36kaOjo7qac5CCFHT2draUqtWLVJTUwFwcXG57f+sClHVFEXh6tWrpKamUqtWLZPz7O6W5glSSYqikJOTQ4MGDfDz8yM6Olo9VyY3N5fNmzfz8ccfAxAaGoq9vT3R0dEMHjwYMJ5Ds3//fnXr9/DwcAwGAzt37lSXSO7YsQODwaAmUUIIIW7Pz88PQE2ShDAXtWrVUv99VhZNE6R33nmHAQMGEBgYSGZmJkuXLiUmJoaoqCh0Oh1jx45l2rRpNGnShCZNmjBt2jRcXFwYMmQIAHq9nuHDhzN+/Hi8vLzw9PRkwoQJtGrVSj0tuXnz5vTv358RI0Ywe/ZsAEaOHElERARNmzbVrO1CCGFpdDod/v7++Pj43PSkeCGqm729faX2HBXRNEE6f/48kZGRJCcno9frad26NVFRUfTp0weAiRMnkp2dzSuvvEJaWhqdOnVi3bp1uLsXb574+eefY2dnx+DBg8nOzqZXr14sWLDA5Ju1ZMkSRo8era52GzRoELNmzarexgohhJWwtbWtkl9IQpgTOYutnKrzLDYhhBBCVI47/f2t+So2IYQQQghzIwmSEEIIIUQJZreKzVwVjURaxI7aQgghhACKf29XdEaRJEjlVHRArsXsqC2EEEIIVWZmJnq9vtz1ZZJ2ORUWFnLu3Dnc3d3veHO0jIwMAgMDOX36tNVP9Ja2Wqea1FaoWe2Vtlonaaux5ygzM5OAgABsbMo/s0h6kMrJxsaGunXrVsqzPDw8rP4fahFpq3WqSW2FmtVeaat1qultrUjPURGZpC2EEEIIUYIkSEIIIYQQJUiCVI0cHR2ZNGlSjTgEV9pqnWpSW6FmtVfaap2krXdOJmkLIYQQQpQgPUhCCCGEECVIgiSEEEIIUYIkSEIIIYQQJUiCJIQQQghRgiRI1eT//u//aNCgAU5OToSGhvLnn39qHVKl+OOPP3jwwQcJCAhAp9Px22+/mZQrisLkyZMJCAjA2dmZ7t27c+DAAW2CvQvTp0/n3nvvxd3dHR8fHx5++GGSkpJM6lhLWwG++uorWrdurW64Fh4ezpo1a9Rya2rrjaZPn45Op2Ps2LHqPWtq6+TJk9HpdCYvPz8/tdya2gpw9uxZnn32Wby8vHBxcaFt27bExcWp5dbS3vr165f6e9XpdLz66quA9bQTID8/n/fee48GDRrg7OxMw4YN+eCDDygsLFTrVFp7FVHlli5dqtjb2ytz585VDh48qIwZM0ZxdXVVTp48qXVod2316tXKu+++q/zyyy8KoCxbtsykfMaMGYq7u7vyyy+/KAkJCcqTTz6p+Pv7KxkZGdoEfIf69eunzJ8/X9m/f78SHx+vDBw4UKlXr56SlZWl1rGWtiqKoixfvlxZtWqVkpSUpCQlJSnvvPOOYm9vr+zfv19RFOtqa5GdO3cq9evXV1q3bq2MGTNGvW9NbZ00aZLSsmVLJTk5WX2lpqaq5dbU1suXLytBQUHKsGHDlB07dijHjx9X1q9frxw5ckStYy3tTU1NNfk7jY6OVgBl06ZNiqJYTzsVRVE++ugjxcvLS1m5cqVy/Phx5aefflLc3NyUL774Qq1TWe2VBKkadOzYUXn55ZdN7jVr1kx56623NIqoapRMkAoLCxU/Pz9lxowZ6r1r164per1e+frrrzWIsPKkpqYqgLJ582ZFUay7rUVq166tfPPNN1bZ1szMTKVJkyZKdHS00q1bNzVBsra2Tpo0SWnTpk2ZZdbW1jfffFPp2rXrTcutrb03GjNmjNKoUSOlsLDQ6to5cOBA5YUXXjC59+ijjyrPPvusoiiV+/cqQ2xVLDc3l7i4OPr27Wtyv2/fvmzdulWjqKrH8ePHSUlJMWm7o6Mj3bp1s/i2GwwGADw9PQHrbmtBQQFLly7lypUrhIeHW2VbX331VQYOHEjv3r1N7ltjWw8fPkxAQAANGjTgqaee4tixY4D1tXX58uV06NCBJ554Ah8fH9q1a8fcuXPVcmtrb5Hc3FwWL17MCy+8gE6ns7p2du3alQ0bNnDo0CEA9u7dy5YtW3jggQeAyv17lcNqq9jFixcpKCjA19fX5L6vry8pKSkaRVU9itpXVttPnjypRUiVQlEUxo0bR9euXQkJCQGss60JCQmEh4dz7do13NzcWLZsGS1atFB/yFhLW5cuXcru3buJjY0tVWZtf6+dOnXiu+++Izg4mPPnz/PRRx/RuXNnDhw4YHVtPXbsGF999RXjxo3jnXfeYefOnYwePRpHR0eee+45q2tvkd9++4309HSGDRsGWN+/4TfffBODwUCzZs2wtbWloKCAqVOn8vTTTwOV215JkKqJTqcz+VpRlFL3rJW1tf21115j3759bNmypVSZNbW1adOmxMfHk56ezi+//MLQoUPZvHmzWm4NbT19+jRjxoxh3bp1ODk53bSeNbQVYMCAAep1q1atCA8Pp1GjRixcuJCwsDDAetpaWFhIhw4dmDZtGgDt2rXjwIEDfPXVVzz33HNqPWtpb5F58+YxYMAAAgICTO5bSzt//PFHFi9ezPfff0/Lli2Jj49n7NixBAQEMHToULVeZbRXhtiqmLe3N7a2tqV6i1JTU0tluNamaHWMNbV91KhRLF++nE2bNlG3bl31vjW21cHBgcaNG9OhQwemT59OmzZt+PLLL62qrXFxcaSmphIaGoqdnR12dnZs3ryZf//739jZ2antsYa2lsXV1ZVWrVpx+PBhq/p7BfD396dFixYm95o3b86pU6cA6/xv9uTJk6xfv54XX3xRvWdt7XzjjTd46623eOqpp2jVqhWRkZG8/vrrTJ8+Hajc9kqCVMUcHBwIDQ0lOjra5H50dDSdO3fWKKrq0aBBA/z8/Ezanpuby+bNmy2u7Yqi8Nprr/Hrr7+yceNGGjRoYFJuTW29GUVRyMnJsaq29urVi4SEBOLj49VXhw4deOaZZ4iPj6dhw4ZW09ay5OTkkJiYiL+/v1X9vQJ06dKl1FYchw4dIigoCLDO/2bnz5+Pj48PAwcOVO9ZWzuvXr2KjY1p6mJra6su86/U9t7ZPHJREUXL/OfNm6ccPHhQGTt2rOLq6qqcOHFC69DuWmZmprJnzx5lz549CqB89tlnyp49e9QtDGbMmKHo9Xrl119/VRISEpSnn37aIpeX/uMf/1D0er0SExNjspz26tWrah1raauiKMrbb7+t/PHHH8rx48eVffv2Ke+8845iY2OjrFu3TlEU62prSTeuYlMU62rr+PHjlZiYGOXYsWPK9u3blYiICMXd3V39WWRNbd25c6diZ2enTJ06VTl8+LCyZMkSxcXFRVm8eLFax5raW1BQoNSrV0958803S5VZUzuHDh2q3HPPPeoy/19//VXx9vZWJk6cqNaprPZKglRN/vvf/ypBQUGKg4OD0r59e3V5uKXbtGmTApR6DR06VFEU45LLSZMmKX5+foqjo6Ny//33KwkJCdoGfQfKaiOgzJ8/X61jLW1VFEV54YUX1H+vderUUXr16qUmR4piXW0tqWSCZE1tLdoPxt7eXgkICFAeffRR5cCBA2q5NbVVURRlxYoVSkhIiOLo6Kg0a9ZMmTNnjkm5NbV37dq1CqAkJSWVKrOmdmZkZChjxoxR6tWrpzg5OSkNGzZU3n33XSUnJ0etU1nt1SmKotxJN5cQQgghhLWSOUhCCCGEECVIgiSEEEIIUYIkSEIIIYQQJUiCJIQQQghRgiRIQgghhBAlSIIkhBBCCFGCJEhCCCGEECVIgiSEqBQLFiygVq1ad/2c7t27M3bs2Lt+TnWbPHkybdu2vW29f/7zn4wcObLqA7rBrFmzGDRoULV+phCWThIkIUSlePLJJzl06JDWYZi18+fP8+WXX/LOO+9UyvNOnjyJo6MjGRkZt6w3YsQIYmNj2bJlS6V8rhA1gSRIQohK4ezsjI+Pj9ZhmLV58+YRHh5O/fr1K+V5v//+O927d8fDw+OW9RwdHRkyZAj/+c9/KuVzhagJJEESQpRpxYoV1KpVSz0lOz4+Hp1OxxtvvKHWeemll3j66aeB0kNsRUNOixYton79+uj1ep566ikyMzPVOleuXOG5557Dzc0Nf39/Pv3009vGtXfvXnr06IG7uzseHh6Ehoaya9cukxh+++03goODcXJyok+fPpw+fbpU20JDQ3FycqJhw4ZMmTKF/Px8tdxgMDBy5Eh8fHzw8PCgZ8+e7N271+QZM2bMwNfXF3d3d4YPH861a9duG/vSpUtLDXV1796dUaNGMXbsWGrXro2vry9z5szhypUrPP/887i7u9OoUSPWrFlT6nm///67+ryYmBg6duyIq6srtWrVokuXLpw8eVKtO2jQIH777Teys7NvG6cQQhIkIcRN3H///WRmZrJnzx4ANm/ejLe3N5s3b1brxMTE0K1bt5s+4+jRo/z222+sXLmSlStXsnnzZmbMmKGWv/HGG2zatIlly5axbt06YmJiiIuLu2VczzzzDHXr1iU2Npa4uDjeeust7O3t1fKrV68ydepUFi5cyF9//UVGRgZPPfWUWr527VqeffZZRo8ezcGDB5k9ezYLFixg6tSpACiKwsCBA0lJSWH16tXExcXRvn17evXqxeXLlwH43//+x6RJk5g6dSq7du3C39+f//u//7tl3Glpaezfv58OHTqUKlu4cCHe3t7s3LmTUaNG8Y9//IMnnniCzp07s3v3bvr160dkZCRXr15V35Oens6ff/7JoEGDyM/P5+GHH6Zbt27s27ePbdu2MXLkSHQ6nVq/Q4cO5OXlsXPnzlvGKYS4rtKO2BVCWJ327dsrn3zyiaIoivLwww8rU6dOVRwcHJSMjAwlOTlZAZTExERFURRl/vz5il6vV987adIkxcXFRcnIyFDvvfHGG0qnTp0URVGUzMxMxcHBQVm6dKlafunSJcXZ2VkZM2bMTWNyd3dXFixYUGbZ/PnzFUDZvn27ei8xMVEBlB07diiKoij33XefMm3aNJP3LVq0SPH391cURVE2bNigeHh4KNeuXTOp06hRI2X27NmKoihKeHi48vLLL5uUd+rUSWnTps1N496zZ48CKKdOnTK5361bN6Vr167q1/n5+Yqrq6sSGRmp3iv6Xm/btk29t2TJEqV9+/aKohi/b4ASExNz089XFEWpXbv2Tb93QghT0oMkhLip7t27ExMTg6Io/Pnnnzz00EOEhISwZcsWNm3ahK+vL82aNbvp++vXr4+7u7v6tb+/P6mpqYCxdyk3N5fw8HC13NPTk6ZNm94ypnHjxvHiiy/Su3dvZsyYwdGjR03K7ezsTHppmjVrRq1atUhMTAQgLi6ODz74ADc3N/U1YsQIkpOTuXr1KnFxcWRlZeHl5WVS5/jx4+pnJSYmmsQNlPq6pKKhLScnp1JlrVu3Vq9tbW3x8vKiVatW6j1fX18A9XsHpsNrnp6eDBs2jH79+vHggw/y5ZdfkpycXOpznJ2dTXqhhBA3JwmSEOKmunfvzp9//snevXuxsbGhRYsWdOvWjc2bN992eA0wGfoC0Ol06pwmRVHuKKbJkydz4MABBg4cyMaNG2nRogXLli0r9TklFd0rLCxkypQpxMfHq6+EhAQOHz6Mk5MThYWF+Pv7m5THx8eTlJRkMv+qory9vQHjUFtJZX2fbrx3Y+wAeXl5REVF8dBDD6l15s+fz7Zt2+jcuTM//vgjwcHBbN++3eS5ly9fpk6dOnfcBiFqEkmQhBA3VTQP6YsvvqBbt27odDq6detGTExMuRKkW2ncuDH29vYmv8TT0tLKtVVAcHAwr7/+OuvWrePRRx9l/vz5all+fr46aRsgKSmJ9PR0taerffv2JCUl0bhx41IvGxsb2rdvT0pKCnZ2dqXKi5Kc5s2bl0o+Sn5dUqNGjfDw8ODgwYO3/+bcxqZNm6hVq1apfZfatWvH22+/zdatWwkJCeH7779Xy44ePcq1a9do167dXX++EDWBJEhCiJvS6/W0bduWxYsX0717d8CYNO3evZtDhw6p9+6Em5sbw4cP54033mDDhg3s37+fYcOGYWNz8x9L2dnZvPbaa8TExHDy5En++usvYmNjad68uVrH3t6eUaNGsWPHDnbv3s3zzz9PWFgYHTt2BOD999/nu+++U3uiEhMT+fHHH3nvvfcA6N27N+Hh4Tz88MOsXbuWEydOsHXrVt577z018RozZgzffvst3377LYcOHWLSpEkcOHDglu21sbGhd+/elbIX0fLly01Wwx0/fpy3336bbdu2cfLkSdatW8ehQ4dMvi9//vknDRs2pFGjRnf9+ULUBJIgCSFuqUePHhQUFKjJUO3atWnRogV16tQx+QV8J/71r39x//33M2jQIHr37k3Xrl0JDQ29aX1bW1suXbrEc889R3BwMIMHD2bAgAFMmTJFrePi4sKbb77JkCFDCA8Px9nZmaVLl6rl/fr1Y+XKlURHR3PvvfcSFhbGZ599RlBQEGAczlq9ejX3338/L7zwAsHBwTz11FOcOHFCnQv05JNP8v777/Pmm28SGhrKyZMn+cc//nHb9o4cOZKlS5eqQ2V3avny5SbDay4uLvz999889thjBAcHM3LkSF577TVeeukltc4PP/zAiBEj7upzhahJdMqdTgQQQggzs2DBAsaOHUt6errWoZRJURTCwsIYO3asun9URe3evZuePXty4cKFUnOXbmb//v306tWLQ4cOodfr7+hzhahppAdJCCGqiU6nY86cOSabUlZUfn4+//nPf8qdHAGcO3eO7777TpIjISpAepCEEFbD3HuQhBCWQxIkIYQQQogSZIhNCCGEEKIESZCEEEIIIUqQBEkIIYQQogRJkIQQQgghSpAESQghhBCiBEmQhBBCCCFKkARJCCGEEKIESZCEEEIIIUqQBEkIIYQQooT/B2FHXdrv7upMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a plot of height vs. windspeed for both days\n",
    "# Zoom in near the surface\n",
    "plt.figure()\n",
    "plt.plot(sonde_temp_profile['wspd'],sonde_temp_profile['alt'],label='Jan 10, stable')\n",
    "plt.plot(sonde_temp_profile2['wspd'],sonde_temp_profile2['alt'],label='Dec 26, unstable')\n",
    "plt.ylabel('altitude (m)')\n",
    "plt.xlabel('wind speed (m/s)')\n",
    "plt.ylim(2890,7000) \n",
    "plt.title('Winds with height')\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2184dcfc-cb4d-4540-9b46-d90ee6c090d1",
   "metadata": {},
   "source": [
    "### How do we get the 10 m wind speed needed for the Gaussian model?\n",
    "We presume that 2890 m is the true surface height where the balloon was launched.\n",
    "The first measurement height is at about 3000 m, which is 110 m above the surface.\n",
    "We can use the power law relationship to scale down the wind to 10 m.\n",
    "\n",
    "We will call this location rural, and say that Jan 10 is type F. and Dec 26 is type A.\n",
    "To learn more about how type A and type F are defined, see page 10 and 11 of the typed lecture notes on dispersion and smokestack plumes.\n",
    "\n",
    "Basically, these are the Pasquill-Gifford-Turner Stability Classifications. Type A is very unstable.  Type F is stable.  These inform the choice of p value in the scaling \n",
    "calculations below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ae79c5f-c81d-4376-bd27-b76f9c31f414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5001216204828121\n",
      "3.593283160069813\n"
     ]
    }
   ],
   "source": [
    "# Presuming the first sonde-recorded wind speeds at ~3000 m as 110 m above the ground\n",
    "# Note that this is not the 10 m that the EPA wants.  \n",
    "# We use the scaling equations to estimate the 10 m value.\n",
    "uref1 = 1.87\n",
    "zref1 = 110\n",
    "p1 = 0.55\n",
    "z1 = 10\n",
    "u10_1 = uref1*np.power(z1/zref1,p1)\n",
    "print(u10_1)\n",
    "\n",
    "uref2 = 4.25 \n",
    "# note that I read this off the lowest measurement from the sounding\n",
    "zref2 = 110\n",
    "p2 = 0.07\n",
    "z2 = 10\n",
    "u10_2 = uref2*np.power(z2/zref2,p2)\n",
    "print(u10_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c715be4-0879-48cd-b08f-2844ebb5c2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x796490a323b0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "# and we can create plots of these vertical profiles and compare them\n",
    "z =  np.arange(0,500,1)\n",
    "u1 = uref1*np.power(z/zref1,p1)\n",
    "u2 = uref2*np.power(z/zref2,p2)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(u1,z,label='Jan 10, stable')\n",
    "plt.plot(u2,z,label='Dec 26, unstable')\n",
    "plt.ylabel('height above the surface (m)')\n",
    "plt.xlabel('wind speed (m/s)')\n",
    "#plt.ylim(2890,7000) \n",
    "plt.title('Winds with height')\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cac751-7aac-4834-9390-8b887ce3baa9",
   "metadata": {},
   "source": [
    "We can see that the p parameter determines the shape and how winds scale with height.  The stability is very important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7fc3945-aa0c-4837-a576-1e66ba0e0fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}