Geothermal Feasibility Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b2910c",
   "metadata": {},
   "source": [
    "# Geothermal Feasibility Study\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72996758",
   "metadata": {},
   "source": [
    "### Sketch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7915fe",
   "metadata": {},
   "source": [
    "<img src=\"roofdiagram.jpg\" alt=\"my awesome sketch\"  width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b0d51c",
   "metadata": {},
   "source": [
    "### Objective \n",
    "The objective of the final heat transfer project was to create computational models to study the feasibility and energy cost of using geothermal to supplement the cooling thermal management of a building. The building is assumed to be a currently standing building, with steel decking insulation and neglecting wall loads. The model was created with the following assumptions:\n",
    "\n",
    "### Assumptions\n",
    "-Ground was assumed to be at 10C at 2m deep\n",
    "-Soil was assumed to be clay\n",
    "For the heat exchange between soil and piping, two possibilities were examined:\n",
    "\n",
    "-Option 1: The outside surface is at soil temperature in the heat exchanger\n",
    "\n",
    "-Option 2: Introduced a parametric distance at which the soil was at 10C from the surface of the pipe. Studied the effect of that distance on the pipe length\n",
    "\n",
    "-Recommended material for piping was assumed to be HDPE\n",
    "\n",
    "-Thermal conductivity of HDPE =\t0.35 - 0.49 \tW / mK\n",
    "#Cost of HDPE Pipe: \n",
    "#Pipe Roughness: : 0.00006\n",
    "\n",
    "The heat exchanger is assumed to require cool temperature of 12C and return temperature of 22C. Heat exchanger efficacy is estimated at 0.75\n",
    "\n",
    "Indoor temperature of 20C is assumed\n",
    "\n",
    "Properties of the pipe were acquired from \n",
    "\n",
    "https://hdpesupply.com/1-ips-sdr11-pe4710-black-hdpe-pipe-500-coil/ : \\\\$0.82 per foot OR \\\\$2.69 per meter\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8db24f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  \n",
    "sys.path.insert(0, 'Libraries/') \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79d4d219",
   "metadata": {},
   "outputs": [],
   "source": [
    "xl = pd.ExcelFile('Climate/Burlington, VT 2021-01-01 to 2021-12-31.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd912f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "      <th>temp</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>dew</th>\n",
       "      <th>humidity</th>\n",
       "      <th>precip</th>\n",
       "      <th>precipprob</th>\n",
       "      <th>preciptype</th>\n",
       "      <th>snow</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>...</th>\n",
       "      <th>sealevelpressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>visibility</th>\n",
       "      <th>solarradiation</th>\n",
       "      <th>solarenergy</th>\n",
       "      <th>uvindex</th>\n",
       "      <th>severerisk</th>\n",
       "      <th>conditions</th>\n",
       "      <th>icon</th>\n",
       "      <th>stations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>Burlington, VT</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1030.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>72622564776,KPBG,KBTV,KMVL,72611454771,7261701...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  temp  feelslike  dew  humidity  precip  \\\n",
       "datetime                                                             \n",
       "2021-01-01  Burlington, VT  -3.9       -7.7 -8.8      68.6     0.0   \n",
       "\n",
       "            precipprob  preciptype  snow  snowdepth  ...  sealevelpressure  \\\n",
       "datetime                                             ...                     \n",
       "2021-01-01         NaN         NaN   0.0        6.3  ...            1030.1   \n",
       "\n",
       "            cloudcover  visibility  solarradiation  solarenergy  uvindex  \\\n",
       "datetime                                                                   \n",
       "2021-01-01        48.8        16.0             NaN          NaN      NaN   \n",
       "\n",
       "            severerisk        conditions                 icon  \\\n",
       "datetime                                                        \n",
       "2021-01-01         NaN  Partially cloudy  partly-cloudy-night   \n",
       "\n",
       "                                                     stations  \n",
       "datetime                                                       \n",
       "2021-01-01  72622564776,KPBG,KBTV,KMVL,72611454771,7261701...  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.read_excel(\"Climate/Burlington, VT 2021-01-01 to 2021-12-31.xlsx\",\n",
    "                        sheet_name=xl.sheet_names[0],header=0,\n",
    "                        converters= {'datetime': pd.to_datetime},)\n",
    "weather = weather.set_index('datetime')\n",
    "weather.head(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37a2b77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'temp', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob',\n",
       "       'preciptype', 'snow', 'snowdepth', 'windgust', 'windspeed', 'winddir',\n",
       "       'sealevelpressure', 'cloudcover', 'visibility', 'solarradiation',\n",
       "       'solarenergy', 'uvindex', 'severerisk', 'conditions', 'icon',\n",
       "       'stations'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ebbc8c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "interp_method = 'linear' \n",
    "weather['solarradiation'] = weather['solarradiation'].fillna(0.0)\n",
    "weather['cloudcover'] = weather['cloudcover'].fillna(0.0)\n",
    "weather['precip'] = weather['precip'].fillna(0.0)\n",
    "weather['windspeed'] = weather['windspeed'].interpolate(interp_method)\n",
    "weather['temp'] = weather['temp'].interpolate(interp_method)\n",
    "weather['solarradiation'] = weather['solarradiation'].interpolate(interp_method)\n",
    "weather['cloudcover'] = weather['cloudcover'].interpolate(interp_method)\n",
    "weather['dew'] = weather['dew'].interpolate(interp_method)\n",
    "weather['precip'] = weather['precip'].interpolate(interp_method)\n",
    "weather['humidity'] = weather['humidity'].interpolate(interp_method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8445752d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4aBjkDNvkHefSQj7jaCGvaRUM7d7WdZ/XEJ8v1KmXhJxKLsUFIe9msw/Cwp+fg69MMBPgMIbGkILrw1X2lbByCkOuuBs2/oSnC6Noi++qCLLGKRFW7d5wal/XckqbfAY9Yhq1d03G0UI+z6mtb7KtyGytiIGxZDe+O15Z6n6gWVRO2FFR7AxFwBjQaPq5F6XozdOzgzHiOH2QkQhtRHUlGmNqLdqNvXX2+8XPyeHmmQRjiMWZt5AX9nVpm/S4Ed0kAeBoU7KOT8xc73o+pU3e7DGfn7vJZpoKgnxf0gmLrFGjhXyeM+be6Rh1z3T/gjmOmEZP1sK577sKXrRjhT1o17lDuzrKxhPMEjJu5hov3v/xafjoFsOv/uwhVVh+z7kY2bMSE/p0dJQ9a7AyG6aSf954su17xNLkjToXeHRI4ojklP6drM+XPzYHADC8hxGGQRWvRwX/7UdWt7OOjSUYYvEE7nx1KS5/zD2QmQxjzHFdba7JPFrI5zmpLyLKLcRhvN+CIxFeVD5EZccPYpP3olNFsc0+XlZkRA357pn9HWX5AqcglEgB0Lh3TV1jHPM27cf+OvfJYFGTV6UflH/LyrJCXDyqu+v5rCQijFlzD/EEs7yGeJIUvv0TYX7h7teXYfa65Hc5Ng+gF0M1B1rIa1oFooYXSsibuvz5klBV9X0JwVzjpR27oXJlNM7lfM2iEUI7IUZ9Ryk8MEdlR+c2+TcWGhEjl28/5ChjXVuo0xHF5KssVEsLo2jw0Or5zxaLM2uyNhZnSjPNEzPX46tPzcWMVbtw8GgTnp69Edc8Odfa/9dZGx3H6MVQmUcLeU2rQLTVepltJx9vN4MwBsxZt9dhg1Zq8gkmuFCGfzX8AoXJ3CXEhldlngKAZ64b79jGRwtBEoOLI5JeHcoc++WRnq+QNzvYLfuOoCASAZFxb/hoQ0xK8tC7qwAAby7ajjMe+tDavvXAUazfnZxj+c3lI/HCt0406qMXQ2UcHYVS0ypoCqjJ9+5YjpdvPAlz1u3Fw9NXg0HttqgKL5Bg6dnkSxRumW68umArLhLMIm5eJap+g8evD9KniKMLVccl28QLooQGj4li/rNVlBRgz+EGKxolH2mJ1+Ore+U5k8v/NBtd2yU7tYIooajAOK5Ja/IZR2vymlaBTZP3EPLdKktxQu8OmGhOMrrFb1eG/GXA794zAoalImrcslOpONoYty1icrNFq6rPRwyyP70K0ewkr2rtfds0y0+eU9cQx6y17gm6E4xhy74j2HO4EQOqKlAQiSCeYFYnFWQ0s+1gvS0gW0Ekogwj8e6yHXh1gfukuiYYWshrcpJNe+02XptN3mVEP7hrG3zj5N4A7B4oKtwmXheZK2GPCvbr6T88NWCtgxONkK1TCOrdAsA3SYiIOCJ5b8VOj5IGstCXYcwINxFPMFM4E2LxpJkraM5acWS2ametNQIQbfI3/G0+fviPRY5jNeHQQl7T7GzZdwTn/PYj7KpVJ91QcdqDHwJImiaaAnjXnDm4ixCOl68KddHkXcw13KRRJfiUD6hqgx6V6hW3YUw0IqN7VdqiR7pq8or6u03wqhDLnjOkKngFFVS1LXa4r0ajhL/M2oBfvLbM+C5krQpKbX2TkO3K2CaGH9akhxbymmbnmdkbsXrnYby2IHz+UC6kRM3PLVyvLYKu+T+IJv/9swYY500wfOOU3gCAvp0rbOVn/uQMXDjS6Vr43TMHeFXflavH97KZa7wmO2XkWDU/mOxeh0JB7b9z6vEhaqg4VzQC0ZD1t083WWaWVTsNUxDvZMOsXC0ritpW8QLAopoDadVVk0QLeU1Ow+O+xAJo8h3Kky6J5GOuETsK7hHCN6m8VqIRwu+vHu1av7AUFURsmnx9k3qys6qN0+tG1uTH9+7geh3RJh9mzkBFzf6jeG+FPeSCvNqWfw9jfiorKhBW8RrbDtero3VqwqOFvCan4a55otfFsm12v/BzhxpmiOsm9rG2WYk1XM4rKppcaCYYQ9wnTIDMM7M3+RcCcP0pfWzfo2S3ycuujK9/ZyLW3D8F7RX+8/Lk5rxN+x1lOEHa0r9LhW8ZN+QOh2vyfGQS5PrFQofHzVOlRdrxL1NoIa9pdmauMZJQhFnExOEanqjJq8L1DqpqY1t0JA//ZcS68GsYYQ3U8dndqGsMpnFePKqHY5tXbtae7ctc3Tjlw7xWiQYJtMaDqYUJtcCRJ2r5wIFr8qpOSiYidHi8L5+/aZ/HEQZLtx5EzX730M0aAy3kNc3O6p3GwpeDR71jsavgJmXRu6ZXR/uiHsbcfcbdNfnkHpsmn/DW5Pt0KkefTuXK83ghdxwMgJf8jXqYgWSzi1fnyZN/cx6+YqSjzKQBnXHdxD64/5LhtralQlQy17SX4vG7IXbK9U1xfL7RfXTCOf+RT3DKr2f4ljvW0UJek3Gen7splCeNjOi+yE0TYjhhOQRAgjkFn5tNftxx7TH2uPY24VwguO/FGXNkYhKZccvpmHHL6db3oAG15E5INtfIFIbwk2xT4i5ISyUhP6K60lGmKBrBzy8Ygq7tSnDO0OAeOCrPIt4mbq7ZVxcsAqroXePWcb40b4s1cli9M1gmKzf+vWCrw003VZZtO4jpy/3dU7OFFvKajFKz/wjufHUpbhSSM3MGVrUJdI7fvrfa+sxtvKIwdQoB5khmzW3yNz5nr0c0QkgwZtN+o8I1jjbGPTVsmZP7GREmr+Hx410QJ2jPHVqF9uVFHqm7w7lJfmmYe7CzctO2fekYw1yk6lfEbV4mJJnvnzXQsY13ylyTFwOWuXHusK72bFeKkcmRxhh+8s/FuMZcvSymS0yFH/xjIc5/5JO0zsGZ+vtP8K1n52XkXM1BRoQ8Ef2FiHYR0VJhWwcimk5Ea8z/7TNxLU3uwhjDL98ycnbuqm2wtnPvDzmaohs7DyVHAVxoiItkZPNEQmGucVOEC6MRJCRtkXugNMQSeHXBVuw81KA+WKCi2BCendsY/vQ87K4bHcqTfvdfHtvT9/xhJn9VWa44kQhh4wNT8fAVowBA2bGIC5hCXBZnD3Ha8KPWxKsxGvOa1L1tymCsvX8KelSW2vLWqhaq8TSFm8z5mKA5ab2oPUY8eDKlyT8N4Dxp220A3meMDQDwvvld04KEWZCSCfbVNeKtJTvMaye3c6EcdOJ1t9BBqDRaeZ6RMWeeU3LRk6MRAiRNnvt6/3b6auUxKj645TRM+94pgct3KC9CmxKjY+BN8jLX+K0cHd/H3W3SC/F3unp8T/z1GyfYFnqpNPl/3zwRb3zH2db+XZwjs4hkk7/7gqGudfn2qX2tiWF7tqvkvWlr/mb/Wbrd9TwabzIi5BljMwHI0+EXAXjG/PwMgIszcS1NcPiEZxj4xFcqiEJL9Lrgr2zQSUqxnEro8P0zVu7Czc9/YWjyjrqoz22Ya6C0yc9a5x8LhtOlTQmGdvfW3mVOG9gZQLDokX6I3jphvJbETrO4IIozBtm1cVXH07VtCYZX29vaqULtNcNPz8MflykycAFA745ltmvxzoEx+0KqtmY45qMuOWqD8PbS7bY5opZWfrJNc9rkqxhj2wHA/K/0zyKiG4hoHhHN2717dzNW59jjhr+FtxM+P3czBt/1dkquaW6p21hITV5Mui0KJW7X5uf5xtOfY9qS7fho9W6HF42bHhwh7kWT3BYVBExzct/Fw3DruYMwsV8n1zIdyovw7dPcc6xyxN8yTL17CuGGX5q3xbH/g5XOCUTVgq/fXjlKeX7eed43zTDbuSUIr9lvd73kV0gwhneWJkMaWFEvi1Pzm69viuPG577AV/+cjGN/jMn47E+8MsaeYIyNY4yN69y5c7ark1dsUviT+/HWku0pH+vmacKV5qCavIiYe5T7javOI9to3TR5IkOTt5lrUly1GpbKsiLcfEb/ZHwdRZl5d07G7VP8ww+I2mjYhay8U1MlEVm61ZmAhJtURgjaPI9/f8W4altZuSN3E/LyHbS8awAM6ppM2s7Px107B5mT90Hj/fPjN+9LPs/HmIxvViG/k4i6AYD5f5dPeU0LsHTrQQy/+x3sOqR2cawzX/yv/Hlu6CGySsjH4gks3HIAQDAhX9dgnwwTV3dyM4fK+8K5jF4t+QyTPJP85FN/DU7sa3jXDO4azHPIj6BRHMX6u80/uMEzUgUVlFFFp8RNXOKEMgDMXb8Po+551/peXKA218jPguhdwxSjFL4imD8DQUeF/DJi8XSThV/1xBz84YM1aZ2jJWlOIf86gGvNz9cCeK0Zr6UJyF9mbUBtfQwfrlabxtYI/sf/+87KUOcWXx4uSA4JHgxBXsxVkv9zYYForjFf8ACdhZt2GyFyuOmF8WSRuXh0D8z72WSM7hXeeUyuY5h6pJNb444vGSOF0wcFGzlzU4nddGbcCzk4WizBcOBIctFbUUEEi35+Ds70W01rCXl7J86fGb5OgneCbkHqZHhnIv7W3Ksm1bmRT9fvw0PvBp+kzzaZcqF8AcAcAIOIqIaIrgfwAICziWgNgLPN75oswwWkm0ARh/BhV6iKbo78BdoiDJNlO6zMlN99jEv/ONu27c4vJVPkca+beILZzqvCLcDV0m0HsXrnYUtbfOyrY9I213SqKPYvpECWU0G1eMDeYYphkYNQZi6QCuKL/+vLhlufxTAL/NiSwihGSpOy8rXalRW6mm04ycVQzNaB8c88LSC/VRUBY9vw510c7Rwyn+s2Kdr5WxuZ8q65mjHWjTFWyBirZow9xRjbyxg7izE2wPzvH4xC0+zwlybICx7WDCDGUOFmFjGYmJi4WsUKRULqDorYJwnGsNFntaLbqGHLvqPW/0FVbXDesG5pafLpIGujYXLE8kOvP6VP6OiS/FjVYVOlhOdXnpBc5CVqvmLH+M1J7hPFfBTAO7BuZtq/0b0qbeW4kN9d22Ddu4riAqsz5goEL1dtTiAPrHL3w99X12jFFhLbKpt+8p1jo5Uah4eLm2Do2UGdHCMIok2edyLii5TKxKtK/sYT/p2Un2loydaDlmlItsk/de24cJVMEdmV72gI11XevlT6J36PVSGKzxzkblbh8w9A8LAL/DmrNDv460/pg6q2xfg/yTuHt+ORD9Zav0u/zuXWJKmceSpumm+8BPWYe6fjwj/McmznZsUwq3tbM1rIHyNw+eonHET/66DvgBFQap/NJs/95MXrBLWjioid0ZeGdwVgtMFtQs+6Voi5Nfm3OOv49DIoBSVMHWX4/Qxj4uGMqK7EBz8+DdeaqRJFLhtbbYvNI/ItQWP3CqCm4ifnDsb3zuyPa0/ujbl3TMZxHe2B0MRRI3+MeAgKIKl98+ZyhULlDQQYYRCAZOwcsbZc2Uhjvt2TjXvqfM2JLYkW8nlMpRABkD/Y/L+baSAVH+I7XlmCyx+bg4177A92zf4jNnt1PGAwLxFRhrUvM0w3cuwZFWMkc4AXciz3liKdRTnJzjo1bbRv5wrX0ZxbJErRrBUmgBoAtCsrxI/OGeQePlnYzJWFgkjEsqlzTV6VC1bFkzM32M8vtDXm8w6ky+kPfYhJ/zujWc6dClrI5zGDhIBgydACxnc3DVCM9shL1NY3eXq0LN12EIBzovZwQwwVJcnJrVQ0+YjKhTLBfKM/BomjzmmNo/ZEInVzTaqIz4wc3TLtcytSIUYjJLhQ2k0sfqa/JVsP2L4rNfnWeONTQAv5PEZ8ESwh7/OAFwvCsTGeQG19E4bf/S4efHeV73Vk7TqesPs8p2aTVwv5VBKQuNHWI1RvrmJ11nkiqMRWiEJeNtckc/5627oc8xvCBbiCkIqpKwzrd4cPK9IcaCGfx4hD2m0H6rHzUL310qgU3dlr92CvEP+7Y3kx9tcZ2vnrC92TcHN564gOmbD7c6ci5EUZxhfvJBjDtgPe7phh6NymOPDCoObgJGFCMyhxZhd6rR1XTd7ctq+uwVbO71kaZsYValPidJP0M1lmilRiRzUHWsjnMaLQnfzwR5jwy/cR99AAr/nzXDw/d7P1vawoilcW1ABwpnlTXUdWriIR+8vIP3+xeT963zbNFlLYDbGa3Ic/ngD+Omuj77F+Md5FBpkrVu+9yD1qYqaZNKAzThvYGb+8dLh/YQnuXdKcgkrlvnrNhF4O76NMVEE8x13/NiKWF0bJemb2mnHpCySbvJz5isODnPE5IbGK3Obf3IOgbLnmyhwbqwHyiAWb96NtaSH6dfZOvswYw+Kag47t1uIQ6QlXacbFBRGs2KH2XhCJM7W5RhxuA0kh/+TM9QCAzzbsw9Th3TyHzWJnVN2+1Lrecsmn/ifnDVIc61t1B0ETm2SC0qIonrlufErH8g61uUwOK+89TykEf3mJs0PKhOVMda2CSMTV5TdpIlSfj5tzuEBnMN4JomTH0dyjoLAeSM2F1uRbGZf8cTbO+s1HvuVUAh5ICmLZs+On/1rsKFtSGEXnAKs5+Vztxj115nHJx0q8DO8M/mNGGfzuCwvQ9463PM+tssmLk8Dc5/uswU63R7/FXCf2TfqJ88s0t502U/D72FzaYklh1NdNlTO6V6XvQjc/VCPLooIImuLGvA53iUza6BO27zLc7s49umrrY3jhMyPqprywqrnIlRW1WsjnKW5Zb7gWI78bYnhfzraDR1FsZnM6Z4i777i1QtG0f/JEEYxBqcmr4C+xjCjDVFEo3/jOKdj4wFTL3CKieofvu3iY9fm6iX08r5fLcHdSlUmlpaluX4aFPz/bGmn99LzBoc+h6qzEifZVZpRROaKp2yii0dLkkwX+vWCr7dgwHfqmvXVgUvA0P9zcRVua3OhqNBnHzV3R0uSlgKslBRFHJEfR7l3skbrPzXPHEPJCnTyE/DVPzlVuF4fp4sQrJ6zmLQp+8dz8U2vxVvnmpD7o3KYYl47u4V+4BSBKujtOHd4Ns9buwZUn+Kc5FI+X4UJyb12jFUqae2zFLHON+pni3jiikOeXSPrJB6vbnHV7cfWTn+LBL4/AZWOq/Q8wyaQHWDrkRlejyThufu3WyldpktQv/omXWYDbh3nHYmX5AVNq8l3NWOQiPByxjM27psCpyXtNPKreMT8TTtg4MNmiMBrBl8dW55R5yfLcihKe++YEXDCye6jj775giO07D2pWK0UytQcwczPXJGz/gWQHHpN87v1YZc5LLd16MGcEdxi0kM9T3FYEuvm0+8FfiNMfnIErHp9j2+dYaGW+OzsO1uN/30761/Nri+GDAWD5NvXk7i8vGW4T1LyjEUcpXi/q+j2GC5sYj8emyYuFhSiImtTgSbtLUgz8JeeM5Z06TwoOGM8Qt8cXRkk58bq45oA172NLJWlWKxbSXCOWD+MFnCtPkjbX5CluphFmmWvU293gAnbj3iPYKGWNcjPXfOfvC2yLUrhwli/1pd9/rLzmNRN62ZKbFJovvXi81+r6WWv3Gucf3g2Pf7TerFtyvyjw+fZceTFbI3+4ZgwWbN6PjimGXpaDjfFnTsw5HE8kE74URiNoisctrxnO+yuS+Yls5hrYfeyDup9aIxQirclrcoeDRxuV25OC1v6w+j27TZJ9RxS+XLjLE1ryqsN4gmHv4QbfuPIiYl9VrJh4DTLkjtps7+ry/DxBEpJo1LQrLcTpHlEs/ZBzyXIBLeY4YCy5ndvsHXH5hfsd97DJcwXh7aU7PBfXWTb8KPm+J+L1cqU/0EI+T3GzLXMTpfwA+sWVkWPFnP/IJ9bnZIRL47/bKDieYPjKn9UTrG6IQdYKUhTytvkE4aPoJ51MPxeqehnj8a+NxRNfG5udi+cIsjcKN/+IQj7OmGVT5+X73vGWzeQnPhLic71w8wFjm2CTb4jFceNz8/Gjlxa61ou7YRZECCt91o3Ywy3khpTXQj5POeSS1YlJ9nOO3zJxOS/mLjNLk3Euu53fTfDGE8yRcNuPEsGrpyBKIAJmrjFSF3ZvVxJoQQtvWp9O5VKe0uTjzzvFVEIvZIJzh3bFOUO7ZuXauUKD4N01dUQ3S7MXcw3P37Qft7+yBABQJGj+8zYlXYDFR0LUXWrN/MGWZh4hy6Ps0/XG8bsO1ePPH6+3x1wSzDUNjlzCdvyiY2YDbZNvRYQxJdw3bYVyu9vEq5+t0SvqoxzG2EuTT4VeHcqwed8RRIkQJcICUyOb7OG7L/LSvC1Yfd8URAj4txCDR2XPbyXONXnJACHLU5TISuZyREoo/+7ynQDcE4b4eUiJNnn5uf7v57/A/E37ccbgLtaq8oTVKUTQvZ13Uh3Rm0ebazShyYSWkHCZeBUFsGqlXizBXBcs8Yc57qPJpzpplQyqRjaPiDDyuKgggoJoxFWT5wkxBrVgWAONnbYlhbjlnIEAjGep0LzXbs9dmHDSIqK3jPxO7T9izGWJmjwvUxAlX7NmtnITeKGFfCtCNpmkAn+m5YlXvr2kMIIZt57uOK4pnsArX2xVnlOezFUJ+RHV7VLW5HlVoxGyT6L6aGyDzVWw3z6tn3BMcr9o6jl7SBU2PjAV7XNgBemxDDfPNcUSlhCvd0mL6OYd4xdRVBxxJlMBGvuS+W+dk7eRAN414juaK+JeC/lWhJuWMGfdXoy6513U1qvt8CJu5hr+TJ81uMqWzYnz8Zo9+JkZHdDtnG5p1cqLoogQYXOaKdGiEZImS72FPE9vJ4byFQOQ5UuY3nziwBHjGV6+/ZA1YS6bazhuax7Kit1XZ/e+bZqlmROS5hou1FXzSsmAZv6uxqL5J8zAdfrynVi7q3lCE2sh34qIuSRK+M27q3DgSBNWbHdOao7qWWn7ngxQZi/Xq0MZAODOqcenXD8rMqIkfCOmcF63uy6l84ojBFEu+8noq07oic/uOAtDure1tg3r0c5aSZkroWA1ST5cbfi41+w/ioKoXcjL90vUtsXH2S9rWNyKTMmEjFPGPlUAv5hgk/fLy+uXzMSNbz07D5Mf9g88mApayOcAjDE89M4qPDpjLV5bqDaJAO42edFbQKZHe/tEUcLS5KVzxBm+PLYa3Su9J5a8cDPXRIjSinvO61ogafJ+pyQidFGEUOhommS0Jp97lIreVOaQ8OnZGwEkwxxwxLs33ZyMBfwFLX9fRJ973mFY80uKrGpRco6AZc1efEdzZfW0FvI5wK7aBvxhxlo8+M4qfP/Fha7l/EIVqDTTLw3rZi/rshiqIZaw+Sk/fMXIQHVXnVsWvsaEaejTWfBgamHNNW7wn1EL+dxDXExVIC2O+sM1Y2zfRfv3x2v2JOPV+Mz9cL93Bjg1efNY8Rxi/Hl5XikuCXWxgwkq4t1G6JlCC/kcQI7+6MbSreoY8V5JEORVhMmFS8lHsL4pjj2HGzB3/V5r26Uhou2J9SByriqNUHoClVc1EiGbYE81mJjoraPJLfp1Lrc+y0rL0B5tbd9lgfv+SsPUw4XmmF6VymskNflkQvioZZN3ntuy00ecK17tmru/qUhFoxby+U9Q18hfvaX2fecajVLIS0PcZD7W5LZdh4yFTev32G3mvwqZlo4xYwi9bJu9MyLB5zkVeFULHEI+tfM1d8INTepM7N/J+iyvgO1cUYzzhAVjcif/7b/NB5B0UCh1SQ0YV5hrItLEqyptJWPOleGissQgedcElPdNseY162ghnwOkO1wTbYwybrZwsWNpjKu9F64e758j1bYyMMEQIXKMTCIUPHa3+hr8PLK5JrXzxaUXW5M7cMEum+YAQ6h/f/KA5HeXc8QSCRREyDVOkRiL3go7zCOcKs01sMrLNnmnuSa8wN64NzWHhKBoIZ9lDjfEAj8YbqUSwkPLKTO1mHLHwiZTUxE6Fr5U+54QSay5Nw53eQMMLYcIKC60P1aycA5P0tYvDghSFdL895brqck+/DlJMKZcVyGaH91ufyzOrBAYKuJC3lfZaYF/l4U3YLxn8lyWrRwkF8oAVnnGGC56dJZvuXRo9qeciM4jolVEtJaIbmvu6wVl894juPFv810XWrQEby7ehmG/eMc1YYYMF6wyqgfztIGdMbCqwmGT57y1ZIf1mQv5ni7nV7GvrhGMMcsOChgvAYFQ3yRr8pSW1mwtUIEcUTI1+EgjaA5TTcvB729hJKIcmdriDSmegD2HG9AUZyj0MA9yQcyYOPEqa/LO0XWCweFCKUedtEVrDaC7tUSsm2YV8kQUBfAogCkAhgC4moiGeB/VMtzz5jK8vWwHZq3dk7U6zFhpBNpaXHMgUPlJAwx75WApn6lqgVOCMU/h+tnGZECnpNAL/jgcbojhlS+24paXFyXrYWryby3Z7igfRpO/ZkIvfPLTM6zv4gIVW1iDFDsOPtFVojX5nCMSIfzo7IH4980TlR5ZosfNaMXE6sWPzjLMNVFyfT64UpMQJ17Na1nvksKCqjTXMLvmHjZUtWjabK45ouZ+yscDWMsYW88YawTwIoCLmvmagdh6wIiHvj7FBTqZIKwfLTczyK5lqnyX8YQhBINo0A0pCHkAjrjwCWaaVKRrGmaW4A9w17YlqG6fHFVUmb7u0ai9PamODviKXr/l75rs8L2zBmBI97bK+ytOxvYVPHE4NfuPYt3uw2Bwn7MRM01ZTguSJr/nsOGMwBjDbvNzgjmFuKzJi690kLdbdLmU3+tM0dxPeQ8AW4TvNeY2CyK6gYjmEdG83bt3N3N1kqzYbsSFvt/FY6UlcPMrd4P3+rKnSjJpR3IbYwzRiEqDdl6Mn7co6jRfDO/RDreeO0hZn9++t9pRDwLhl5JXDhGw3SMpg4xc5WeuG4+HrxiJtiWF2F+nToYShldvOhmPXD261eRzPVbpqIgjFETb7VBejCMNcVdzHjcnJhhzLIbijwQf7f39s834eM0es3zSK42Xk+cNwhpfRE2+XWmhR8nUaW4hr/qdbb8DY+wJxtg4xti4zp07N3N1coemeALvWav0ggkbtwBlKk2em2tkZVUVYInHnldNRL7x3VNw8xn98dS14wAApw9yv0eGd40zlAKBsONgvfogBbLwrWpbYvntj+vd3toeZGJLRc8OZaGTTGtaHtVqZdEm7zaSi8UT6N2pzMNcY2jyol87V4Yst0vz0Zohzjkx5oi0ajPBM/voPMhAXYxP/5vLR/kfkALNLeRrAPQUvlcD2OZSNm163zYNT85c31ynd/Cdv3+B3rdNw4Y97iafOev2ovdt0xzpxaYt3o46MyZHUIXS8sJxmeEXh5KG6cRpl1S5ax4yA5vJfskiA8wky/IKWpGElGuTQ+T013fj/BHd8LWTjnPdv1X4HVONE6JpvRTYvGvUL05T3Fi97fZaLd1qjOLFideo5ELJJ1BFIZxIJG3y/Nyi4nX547Px7efmW99FJWT68p2Ysy652BAw5NWk/52hbFsmaW4h/zmAAUTUh4iKAFwF4PXmuBC/OUHNL988pQ8AYKCQqCAsby42JhjfX7HTtczzczcBAOZt2m/bzm1+YeAPpKwg8FyqcYcm7/STV7ulGY9BucviEQDo1bEMK+89D1eckOyzTxEWrgCGAFaNFAjqobeKP1wzBm1L3Iet/AUFUltdqGnd2IS8S5n3VuzC5r1HXD1X2pQYbsUJxlAfs4c14PD3RBTysQSzslTx10p83pduPeSqvX/r2Xm4+slPXWps0ConXhljMQDfAfAOgBUAXmKMLWuOa4XV6rhADBpSwAv+0Kjg99xhGU/BFdAS8tKDJIf65WVU/umqB98KLObzkImp+ACgbam93eVFBcqwsBEi2zA7U/xzfk3Gz6nJbQpt5hr3crUNMdd3m6euZADuMsNnO96TuFM+/N97a3DT818AENNFutdBJfDjCYZbX16EtbucEWPFlJqZpNndCxhjbzHGBjLG+jHG7m+u64jC64vN+z1dmRhj+OusjQDSE/LVZoTHnu09/MuliRrOmp3JmxzYXBNz2t5tlxI2c01ePjf/nUQ3zFRXgKqq0VVhR03ZoV3Bt0/ta33em4FJWE1uI8/vhHGhFb1o+nZyeuKsEvINy88+N8PsrVMLXl76cIM6a5Ubq3bU4uX5NfjO3xc49okLCzNJ3viQibbmS/84G28v2+Fadseh5CRgOsGBkkM29zJJG17yIapviuPFz7e4HeIKtxO6DQnleBtE5Fjww8tUCCthudUjnXDAgNGBuPnDi6d+7eaJ+NLw1JJWqxKaaPKXbu2cSsPU4d3w5NfH+Ya1+MLMBQwA7cqcJsCDQrJ7p5A3XoraerUQ5+Uv+9Ns1+urXlN+GdU7PLhb86SezBshL4cG4MMqFeIPvOdw6togH9J5pQRjCk0+1VW2TUKIVOW1pOtGiVxNSU3mQzx/0358sNKYUwhrUZGbnTAXQ8lEyL7EfGTPShzX0alZ9QgQy15Hjjy24Kuw774guYby0a+MwdlDqjw1+Wopj4Kfp4v1XJnleDhit7UjQR7DRII5HB14lVfttJtr/vP9SRjTqz2aA3djcisjTP7TTLlHW9HpPMrwGXbxkrKJKOgiuZhlk3cz19gnXgsj5LCjy+cSNZGw5ho5Lk4snkCEnI/UdRP74PVF9mQochO+c0Z/fEswxbghhmk4Z0hViNpqWiPfPbM/4gmGqxTB8rwe1/Ii+3Po94pxMxAvx5Ugt7hSQd6V776wAEO6tcVLN57kW1Y1YskUeaPJh/G0eGep3ZQTdikyhx8VVpOXTUTxgHUPM7nM/eQBYOMDUx37VV42YbXk+ph9RBJXmGvm3nEWrpnQS7lMXOSmM/oFWgxSILh5XjhK+7rnO21KCnHX+UOUygp/vq8c19OxT3ZH9FtdLrtE8iBmTW5zdgFelcMNMVv4EOO86np4uS+nS95o8kEEYFM8gauf+NThzhhLMBSFEHB1DTFc9qfZ2M1nwz2eH8u7RpDysiYfNEgR1ypW7qjF7LV7cLLkwiiehfvJu7Fyh3N2P+wIZ9pie4yauMJcw7/LD7u8kCloB7NmZzLZcY5kV9NkCTkvq4j8PPk9K3y3HGq4wUWupGoMcBsZNJePPJBPmnwAQbmrtsEh4AFhAUQ84ViwoOKLzfttQtJbk3eaa+QbHcTUtG73YXy0Ohn24Y5XlyiuZb+un9yU7YXpTrzyePKBzin9ZEHjyGzel71YQ5rcQswzMNIlYb1V1sdgwxizxYNXuVCKcPPOpWN6KPe74ZY7witqZrrkjZBXafJb9h2xfXczy3y4ahd63zYNP35pEa5+8lMs2OzsCGznkU7jpSUkzTXpafJn/eYj3zKi5Ewwu91QFaZYvmq6k5qxBHMIdTf/ePnaqcSROWeotskfy4wyo1CePaQKV51gN9nIz6GfHmVEmEx+dzOrcPjZwyYJcbXxN6NDQd4IeZVNvkESpkddvFq4z/zri4yIC34eN3Jn4anJm/9tE69SJqagNnmRZHZ5+7F1DTEcaYxhiZQP9rtn9nfWTbpsqgG7uDskY06Tj5uCkuo8iNgR6XjwxzaDu7bF2vunYPKQKof5pHMb+0Sm39OWSNh93v1G11yBaoyF85TLRiiO/BHyipsib1OtxgSc9mK/5cXyIhxP7xpFpEmeUzVZz1SEvPFf1DhiCYahv3gHQ37+DgDgAyG4kkpLTzXAl4zoc79dCkTmNjr48rjqlEL9NsfKWU3rhU/Eq8Jbc35/9WiMO87bPTHBGJYJipGvJm+eP6wmH8YLMFPkzRuj+rFls8gP/7Ew0Lm4b3lDLI5/zq9xaMtyJ7CrtsERgIyTnHhNbpOjPfIbf8low763u7YB2w96h+Zdv7sOq3bU2joILw8j8SXg7lqZmrgsEnyJxQUm4nXPltwdB3dti9X3Twl9LZ2yT6NE0iWOE8yTF4zohp+dfzxuOr2f6+FiUm8AONoYt8KRq4gnGD7bsM+KZeOFGB47lRyw6ZI3b4xKwIlCfuOeOs9okSJcHp750Ee45eVF+O10e9x0WQO+699LcfIDHyjPlUxdl3wKeWc+rEdbAEmtgWu9J9z/Hk76lfp8Ij9/bantwfTSzMVOhq8azZSQ93L/4m0qCpmQxI2vn9QbAFDq4v+vOTYRlZg/f30cfnLeYOs7X/ntFV5azin774XbMOV3H7uW33+kCVc8Pgdz1vs7aoiRJrW5Jg3E3Ipc0RaFvLg8+fwR7uFygWRvy8PazpY8bngMmSBYqesE7Z8ffeNphmZhxbQOaRPfVdtgm633GgmKLwF31/KaSwiDp5B3mTtIlQ5lRjTLTm2CRbXUHBvw1+uS0T0weUiVUqk4vltb1+MTjDWbALbZ+iVldEKfDlauhuYib4S8+OMNr64EYPdxFRcgyavhZGR7nLxI5yf/Why4Xsn8pEZI4qdnbbC2cbOPlYIspK9sQnD5Eq+lQrSNL64xbI9//nhDqOtx5NyoXuEIeOfWt5M6pPO5Q6vQs4N/OAOOV+wPzbGLKvRvGBjzt8NnArkjOev4Ljjr+Ob1EsvLxVC3nTcYVz/5qU2T5/svHd3DEqZnD6nC9OXOWPDyjfDKhuQH164JhOufmQcAeOyrYwEk0/hxk0tYTd6wIyZs390QpxHalxVhz+EGPPLBmlDX48z8yRm4+olPsc7Mj8sTjIt8dsdZ2HckaYv8weQBOLlfR8cCrse/lpoWo4W8RiRijRhTO15MBVgUjaQVuNAL2SYvp/JsDvJGkxcFc6cKYyj/tOkaKe6/ekIvS+C5BR+Se3Qxml1YeNx6UcPgpgtu5Ujmbg0p5MFsIxg5zICI6B7J48CnuvapS5sSDO3eDoDhf6+K49GlbQkGd00OjwuiEYeA12gyRb/OxkjxhD4dPMvJo1BOQtDkwya0D4PsXdNciUJE8kbIc2E3plclOrcxJhbFSREu5AujEUsoublVyb3tqwvswbUqioMPgLhAF8/IP/NefNm2Q+b3VDT55Jl///5a237xARIF8ZRhhl97OqNTfu7CKGUs4FsQdO5tjYphPdph1m1n4qsTkoHMXv/ORDx6zRhbuQe/PBIA8ICUbF7U5IubcVJfdp9uiaiq+SPkzR7ykWvGoLLMOSnXaE6WFkYpObQDcFLfjq7ncqNL2+AxzbkglZNsA85eXL7hfguGjETEybrKKQUrhDDD4qn5IqJ0Jl5XmGEd4glm8xxqbjqYaQS98sBqjk16VJbaRqwjqisxVXKyuGBkd2x8YKojqqX4LjWnJi+vsWkJTT6PbPKmEDd/tPG9O+CzjfvAzOTSXHAXRSM2Tw9VYCA+bDu+W1ulr+wOacGPF5bpR5CnfJOslcpCPs4Y3NMRwxZrQ4V4pKjJFwoLSOIpCnr+u2zceyT0hHE6lBUVKKNqajTpwARN3s2kkwle+Gyz7bvW5EPAe2G+Au70wcZkaZ/b3wJgN9dcPaEXigoiOGdoV6U9mQvOtoqEGzsP1buunFWRNNc4bfKyfC2UhbyfJg/vUYctj6xwah6TPVPeBIUtKOQ1mkzTo7IUCQa8vtAIa+KWg6E50EI+BKt3GSFo3UJ2ct/2woKIsdryvinoUVmqHC7xWNLcnMFt/IDTpuaHZa4RZDEX7rKQl2faE1ZnoBbGsk2eL67iiE1TafIi9148THmNIKQSnkCjyRUmDeiEBGNWeJNMLdwLQpmPO3cmyJu38+9zjWEQD9kproj87fTVlm+7rHWqelIr3Khi+Ca6BQYhoZx4Nb5VSnkn5Q5Kjm0NAL07Jpdry6v0nEq9euJV1WZlAu6ANGfCA42muehUUYSSwggiEcKu2qTytiANb7qwlBc3/6gh795OLijFG/W795P+4LLWqRJ4XHBaQlawe/NOIqiHDT+HbeLVFMZtSgpQXpS8yfKogstvfv3bpgzGsB7tbPtF11HZ/EI2TV787GxzOqNGLeQ1rZE5t5+Fxb84N61nP13KirSQDw0XlG5mG1kgqTV5ngLMqUlzAa26OT96aaEjUFlCYX/nHyNEKBWGawVS3bh3DQ/ZUBAhm519z+EGKQqlXZXfLWgnbvZ5r21B0TZ5TWukMBpBUUEEG/cc8S/cTLREuOy8EPLiylYuzK6b2EdZVhby+xXmF6cmL8SHMaX1984a4DjulS+24hevL7NtS8pgdfgBUbjKspJ7vnBNXqUx26JQekykRly0eqseabhBEhGuViRa1mhaA5+s3ZPxcwZdJR/GHTtV8kLIH2mMObb176KOlSJrnXylnAgXlmvNyVxReHLB27FcHSBLNJ+c+dCH1jlE+cs19EiELIEbIWd2mITUyRREnaJYTDiyaa+7RiKem5+lg9CGdBcZ/ejsgemdQKPJEaYO9w5gGIQOLvJBpkub1OfCgpIXQl6OYQ6424nl7EcqzxAuqPmiKtEmzz1d3M7PyyYSDOuF0MaiuaaJC3lK2sejEYUAZ8xWvjAScWjh3i6Uyc/icXz7PmFhRqpZoTiZimip0WSbaUvsCeovGNkdE4RwCRd6hCzm/PS8wbZoty1he3cjL4Q815ZFRFu716qyQoW7FDfTNJixYI42xS2tmivqquOA5NCvSRK+ohDkmjkhufo2QuQQtFaC8VhSk5cnTb1NNGrvGpVAH9y1jet5NJpjme+d2d+KtNqvczmGdncPWcwpLy7AzWckU24O697Otp/H12oJ8kLIj6iuxLdP64u/fuME5X4vQajW5I3yYgz6HYeMVa4JS5P31nxlTxfxW32TIbQjlNSqoxFnDBjeT3BtvSAacWTA8VrQJNbRLvDt5TY+MBVVIV0orx5vJE7+niJ3rEbTmnjia2Nt36+VQmYURiOWYkTkfE+/eqJzPipC9vAIbaSFlW99fxJeuenkdKodmLSEPBFdTkTLiChBROOkfbcT0VoiWkVE56ZXTW86tynG7VOOxxmDuti2v/Ttk3yPVS18iCcSlhbPV71yYepnruHIHYu4oOnXb68EYMS4d9O2gWSHIoZskI06Xin/zh7SVXnuTAT5Gt3LCO42wYz9U5rF4ahGkw5dJAXnpH72aKmFBRHrnSE4nRTaltjXuwDG+yYGOmsnrYnp0qYEY3qpAyRmmnSXWy0FcCmAx8WNRDQEwFUAhgLoDuA9IhrIGAuX2jxNBlapJ19F3DR5Llg7VhTjUH3Msqlb5hofIR+XhK/KZh2LM0urPtIYd7XJc0Euu1gC3jb5hy4fYX0WBXsmAopdPrYa445rj77mxHXbkkL8679PQh+X5CAaTa6yVwrsJwcoM4IaGp8jRA63a5XDQ4TIJlvEc/7Xyb3TrHE40hLyjLEVgNLGexGAFxljDQA2ENFaAOMBzEnnemEJ4oOq0uT3H2nEETNlF5/U5UI6sLlGEuqqlIEJxrw1edlPPkqoklyuvExRYvszrckTkSXgOWOP847lrdHkOiN7VjpkQlE0YilGRM75uKNNTt01Qvak86LAVy1GbE6ayybfA8AW4XuNuc0BEd1ARPOIaN7u3bszWokgIUNVQv61hdvwwcpdAJIeKFyUciHvF69FtpU3KDLNVLcvswtcPz/5SAQ/mGx3VQwaZKwFEtBoNK2SUT0rrc+PXDXasXamMBqxvT9yIEExhysnQmSTP6KcaekF4r6XI6L3iGip4u8ir8MU25TSiDH2BGNsHGNsXOfOqafZUxGJEPp0Kvcs42Z2ESddgaRN3YoFH1LIiwu2ACO2TlFBxFOT54MBPpwsiJKjU/KyyYu0tPag0bQWyoUQJb06lqEgIptrIuAiLULkkBmiMOeefESQzDXCqLqF4yj4mmsYY5NTOG8NgJ7C92oA21I4T9r4BeV328snSkoLozjaFLd6KK6Q++Vj9RPyvF42M4p0jqSJyPiuCoHql+CEI/4Mn5vR9jQajdN82VFybxRt8kTOHNCcQVVt8NsrR+G9FTud63FETb6FFa7minP5OoC/E9HDMCZeBwD4rJmu5Ymfxt2+3Dkz3rdTORZuOQAAuOn0fvjN9NWWVs0Fr5/5Q55olYV8ROjxOfK956c4VG/MC8j2eMDbJi/CH7oIAW8s2u5TWqM5dpDn7sS0oGvun2JzmyRymiTiCYa/f2sCBndtiw7lRRii8KMXtf+WiCEvkq4L5SVEVAPgJADTiOgdAGCMLQPwEoDlAN4GcHNLe9Zw/CZIVe5P3SpLrNDFZeZQLpnog69W9T6vLHwb4/bmRy0hb/w/Y1BnVxfKOtPmV1bo7JNlL56xLnlrxUVXcoej0WiSiFq4mEUNMDzTZCE9pld7nNyvk2coA9Gk06omXhljrzLGqhljxYyxKsbYucK++xlj/Rhjgxhj/0m/qqnht9xepemLphYeS567VPKRWoQIPz1vsPt1fcw10UhSswb4ggu57sZ/3gRVmj25Mxnvkq1edAG7Yly1a701mmOReT+bjM/vdLdM8zdvX12jTUi/96NT8cMAcZvsE6+tSMi3BpZudeZoFVFp+qKZ++PVRpiCf86vMfYJ5pr/Pr2f8pyMMYcLpZwyMCpo1kY9Ig47Hr9W3Bo9OK8l2+TlmX+OpYlQ+nFqNJp8o1NFsS0DHABMPj65uJK/M1sPHLXNnfXv0iaQ0M6mkM+bRN6povKuEQX09oNGfPg1u2oBCELeQ1Aamd/tQv75ueoEvtz9qlARYVL26FFdU9bk5RSCHNGmWNyMiYo1mnxg9X1TbMLYFuwvhden1Zpr8gG3GO08zZ5DuzaFqtcMeYIxXzMRP3yDGalyw94jCpu8/Zqqh0O2ybslSxFtil8eo801Go0XRQURu5CH+nNQinPZTz7fkV0sI2QI1V4dyzGqZ6U1k86FPZepXr1xXMq9KnLmYGMIqLLZOwOU2V0o+UMn2t1jCWZ7GFWx9cX6NsUTLT5c1GhaO+Ir0z5grHiRIq3JZw95YUJBNIJ4goExI67MxH5GAK5BZhycZMIP93My5j7he7J5PtnMQgBeXbDVto0XiSfsNnlxFBFLJGwd1aMz1imvy+ceYgmG9mUtF+ZUo8kHavYn03qeOqCTR0k1rdaFMh8pikYsc0uECJeNNUwbPIE2F95eNyrBmHIlauc2xVZkut1SUKRIBFi5wz5JzBjD/rpGvLl4m23CVLx2PMF8QywAQIUQ6jQVTUSjOZZ5e9kO63MYx4X7Lh6G3h3LbIqZFvIZ5sS+SdPG41LcaMBY0SpSGCXEE4a5JULJJB1OTxcvIe8MUAYAXduWWNY8eXc0EsElo+zhfRIM+OVbK7Bud52tvDj6iMWZLWCSmI1GhHcEqty0Go2mefjqicfhw1vPsMkDba7JMHedPwQA0LtjGc4d2tWxv0N5EV741onW90LTXJNgxuQo74G5f7w8CTqkm3N124bddVBFG1AlBrH2EXDKAHvsnvqmOF42XTflspx4gtnMNW4JtYkIGx+YqnOxajQpkK5crixNjp61Jp9heLAhL1+Xk0w7OWAKeWbY5KMRsmzvchwZfqNUsaEv+MMnSk2+MEr4YMUuZR1UHcA3n53nWpYTSzCbvU8HItNoMk+6b1VZcdJioIV8hgn7g3JzTYLBbq6xcrzaJ0H7dlZHuYwrVPmCSMRmGxchCu6YZRfyCduCLr6vrct1NBpNeAKGiHKlOIsByo4ZIe/jtm6RNNcww1wT4a6TyYVJ4iSo2ySMKlCdKhG3SNB7f+9FwwAYq/RicWYLzRCNEN794an44JbTPc8h57XUaDTNhxhBVmvyWaaoIKLW5IUolGJP7CaYucb/8o3JPLOF0YiPkA9287u0LcGV43oiGgEOHGlyuGcNrGqDThXOiJUip6TgBqbRaFJDFPI5F0++tcM84r6oKDRdKLmfPD8uaa6x273dhDYX8kWSAP7mpD741xfOyVSwcHa/SISw81ADdh6yu2IGHQpmIs+rRnOswbXwK8f1xOZ9ztyubojmGhbUrJAh8l7IJwOKBRNqRdEIYoILpWWuSSTNNeJCqBE92qGsKOoIQBZX+NMXRgnHK7xxAAAhA4e5JUMJOhTU87MaTXj46/XrL48IdZw42t64J3jnkAny3lzTodwwW1w4snug8oUFwsRrhCzByzuLJ2auR31T0uAeiRDunHq84zxc8xdvrpxWTOScIVWhdGs3Yd4QCxa2X3vhaDThyUQE18ICba7JKB3Ki7D47nNQURSsqYVSWAMuTL0CjikDhyV4LtjkPv75tIGdHck9rj+lD5ZsPRiojm7XBIAdB+sDHh/4UhrNMU+vDmXYvO8Ibp/inkMiKPmS/i+nUGV/ckP0rokQORZDqVDdNC7kCwXtnX9+5rrxjvKGC2Xwm+8WyYCHX/BDx5TXaIJTVmRMnJ7Yt6NPSX/SdccMyzEh5MNQFI2gIZbA9gP1GCAkBFD5vXNU9n4egEwcmqnCAH96+1lWDtcwctdtjqFnh7Jgx2sZr9EEhg/kM6EbtXT6TS3kJfjCotqGGNbuOmx9b1IEHOMs2+Y0szSatvFohFDVthg7DzUoJ0u7titB13YloeuZ7pCPiHDm4C64aFSwuQqN5liGmWvmM+GVdny3NmmfIwxayEuIZoxVO2tBpoeNnGZPhCf+EGk07TtRImvyVZVPVsTNzv5/V47CoK72ByMTCyr+8l8npH0OjeZYIJNej+coYmg1J1rIS6iGUgURwqMz1rnGalfZ2LjmH40kF1T5iWU35fzi0T0c2+QOoTBKnqMNjUaTOsnkQVmtRkrkvQtlWE7s55xYkVMEykHJxMUN3GuGdxaRCFnZmsqKvftU8QHy09RF00+EgFdvmoh/CqtrNRpN5uDveCuU8VrIy/Tp6Aw4Jk+YOtL0CUJ+6nAjnnuTaa4piBD2HG4E4G9HF+19w7q7LJoyESdeCyIRDOvRDuN6d/A4QqPRpEr3ylIALR+SIBNoIS9RWhR1bJM1edlU8t0zk4k4uAZuafJC2THHVXpeWzwtnxu4bmIfZVkx8mQq2eM1Gk1w7r5wKL55Sh+lEpjraJu8RHmxQsgrkn2LiMHAjjYZXjX1gncNp2tbby8a8bRc4I/uVaksawtK1hoNhRpNK6Jf5wr8zExA1NrQQt7kN5ePxPNzN2FAF6d7k+wVIy8k6t+lwvr867dXAgDeXLwdAKSIlT7mGlGTN/+72eZtiUJa4RBSoznWeOcHp2JfXWOLX1cLeZPLxlZbSbtl5JAGbrK6R2Upth4wsrofOGIscBIFsF9cGbET4GYeNyEvR7fUaDS5jewG3VJoa24AavYftX1XLYh494en4s3vnuJ5nqONPkJe/Gx+cYs2WSSELuUdikaj0cikJeSJ6EEiWklEi4noVSKqFPbdTkRriWgVEZ2bdk1zCKbIGDuwqg3alxehXDFxy/Ezq5DCtON2jDwZrNFoNCrSlRTTAQxjjI0AsBrA7QBAREMAXAVgKIDzAPyRiNylX47x+6tHe+73Wv12/yXDHdu4J4zvYijhM7flu62CLVTEwdFoNBqZtIQ8Y+xdxljM/PopAG7UvgjAi4yxBsbYBgBrAThDL+YofrHnEx5h5NqWGtMcpYVRjO9j+K1fPMpYsVoRYjFUnbmAyi2YUaFgrqluX+p5Xo1Gc+ySyYnX6wD8w/zcA4bQ59SY2xwQ0Q0AbgCAXr16ZbA66fG1E4/DmYO7KPfFPVR5bmY52hS3Un7de/EwXDCyOwZUeU+8iLb+xTVG0LNXF9Tg7CFVjrJ84rW0MIpPfnqm53k1Gs2xi6+QJ6L3AKgi6tzJGHvNLHMngBiA5/lhivJKycgYewLAEwAwbty4nAm+cu/Fw1z3eZlrxIlSLohLCqM4dWBn32uqLDNRl5VO2iav0WiC4CvkGWOTvfYT0bUAzgdwFksGcakB0FMoVg1gW6qVzDW8skSJfvFhBbFKyJ8/opuyLPeu0eugNBqNF+l615wH4KcALmSMidlpXwdwFREVE1EfAAMAfJbOtXIJz1SAgib/9rIdoc4reteMN+PQjHeJR8NHDHICcY1GoxFJ1yb/BwDFAKabAupTxtiNjLFlRPQSgOUwzDg3M8byRhp5pe9KZ2GSeOSfvjoG8zftR/vyImXZmv0tm/Fdo9G0TtIS8oyx/h777gdwfzrnz1VYyKTeQREP7VhR7JNcQNtpNBqNP3r2LgCycr5ie61rWVGTb18WPIE4EC612ICqCv9CGo3mmEcL+QDM/MkZ+N1Vo6zvC7cccC0rTryeMsDfo0YkzCCgyoxoqcPWaDQaL3SAsgBUty+zfe9UobaTA/bY7mFXpYYpHfUJYKbRaDSA1uQDUyBI71E9KwOVC53ZPURxrvX7hS/WaDTHNlrIB0TUmGMe7jWia3xxYbifN8ykLXeh/MqE3FklrNFocg8t5AMihvaNxYN517wb1k8+RNmCaASr7jsPd01tndlqNBpNy6Bt8gEpFoR8Y1wdNAywa/wdXHzc3eCpA4PXqdUE9tRoNFlCa/IBEYW8VxRKUZMf1qNdqGt4xcTRaDSaVNBCPiDiBGelh/97uRhOOKTQblsazq9eo9Fo/NDmmhD07VSOBGN48MsjXcuIC6D+a2LvUOdvV1qI6T88FT07lPkX1mg0mgBoIR+CD2453bcMEYEIuOqEnhhRXRn6Gn4x5zUajSYMWsg3Axt+NTXbVdBoNBoA2iav0Wg0eY0W8hqNRpPHaCGv0Wg0eYwW8hqNRpPHaCGv0Wg0eYwW8hqNRpPHaCGv0Wg0eQx55SttaYhoN4BNaZyiE4A9GapOtsiHNnDypS350g5OPrVHt8XgOMaYMhVdTgn5dCGieYyxcdmuRzrkQxs4+dKWfGkHJ5/ao9vijzbXaDQaTR6jhbxGo9HkMfkm5J/IdgUyQD60gZMvbcmXdnDyqT26LT7klU1eo9FoNHbyTZPXaDQajYAW8hqNRpPHaCGv0eQhJOar1BzTtCohnw8PLhF1ED636vYQ0elEpFyA0dogoh8T0Tnm51Z9X0ysFGOtvT2tvf6cbLWjVQh5IrqIiJ4B4J5cNcchovOIaCaA/yOi3wAAa6Wz3kJbvgKgIdv1SQciOoeI3gHwUwBfB1rvfQEAIjqbiD4B8BAR/QRove3Jh/ceyH47cjb9HxERY4wR0RkA7gXQBOAkItrEGNuf5eoFwuy5IwCuB3AdgF8BWADgWSKawhj7TzbrFwazLQTgSgCPA7ieMfZydmuVGmZbCgH8HMBpMO5LEYATiKgQQKw1CkYiqgZwN4AHAHwI4EUi6sgY+yl/n7JZvzC05vceyC35lZOavPRAbgBwLoBbAUwAMCJrFQsBbwNjLA7gEwCnMMZeA1APYBeAZUQU4WWzWFVfhLYkAGwD8CyAtea+y4mo2hSOraktjQBeY4xNYoy9BWA/gKsYY02tTBiKv/dgAEsYY28wxmoBPArgh0Q0wBQ4OX1vJDYAOAet7L0Hck9+5ZyQJ6LvAHiFiH5IRF0ZYxsZY9sZYx8A2AngNCLqkeVqeiK1oRtjbDljLEZEYwD8G0BvGOaBh/kh2ampP0JbfkREnWB0WIsB/ImIVgK4AsAjAP7ID8lOTf1R3JfPze2FjLGPAKwnoinZrWVwpPa0BbAawClEdJJZpAuAZQB+lq06BoWIbiKiy8zPBGALY2xHa3rvgRyVX4yxnPkDcAmAzwGcAeCvAP4AYJSwfwSA5wBcKh1H2a57kDYA6AOgl/m5HMABAOOyXecQbXkUwCAA3WGYOEab5ToA2A1gbLbrHPK+jOTPj9mGPwM4J9t1TbE9fwJQBcM0+DSAWQD+bj5ziwD0znadXdrRBsBjAHYAOAygwNwe4e91a3jvPZ6xUcL+rLQj1zT5CQD+xBibAcO2uAHA9/hOxthiGD/iMCI6k4h+am7PpeG1qg3fBwDG2AbG2Gbzcx2AlwC0zVI9gyC3ZSOAWxlj2wD8D2NsAQAwxvbBGKFUZKeagfC6L8xsQymMFxTclJbDqNrzP4yxpwB8C8APGWPXANgM4DMAh7JVUS+YYVb6iDHWFcCbMBQJwBB8zCzTGt57IEflV048yIKtcD2AawCAMbYJwDQA5UR0oVD8BQDfBPAPGPGXc8IO7NOGMqkNIKKfARgKYHlL1jMIHm15A0AbIrqQMVYvlL8LRltWtnRd/Qj5bD0HYDwRlTBj/iHn8GjP6wDaE9ElzJhX+Mwsdy+MUWNti1fWB6Etr5v/fwDganMOIU5EBUKZnHnv5evmuvzKmpAnoij/LPRk/wRwhIguMr9vh+ElMIQMKgD8DsASACMYY7dKx7coYdtgHjOFDBe3gQC+zBjb0XI1difFtkwiohkw2nIZY2xny9XYnVSeLXNbKYAXAcRbqKqBCNmeQeYxA4joNQDDYGj1TS1XY3dUbWGM1RFRxHwX/gjDbAbGWIwxxoioHMDvkSPvPSS5mevyq0WFPBGdRET3AAAzvE74dv6S7QfwKoD/NmeoD8IwAZSYP0Q9gO8zxqYyxra3ZN2FuqbahlJz/woANzLGvp6tNnAy0JaNAG5mjH2tFbelWHjJXmOMPZkLAjGdd8XcvwPGvbkw252vV1tksxhj7DYAfcxjqojoBNO0+b1svvcAQETjieg5AL8iouGU9I7jHVdOyq8WE/JEdC2AZwD8jIiuMLcVALaerBTAOzB6wCeIqDuA0TB8THnPvqul6iyTZhsazXIbGWNLW7ruMhlqyxbGWNbNTWm2JcbPIwqgbJKhd6WWMVbT0nWX8WsLYyxharjthMN+DWPi+GMAZWbZbL73ESL6BYwRxn9grC+6GebiJuG5yU35xVpu5nkygGoYvq+bhe1R8//d5g80Goanw30whjp/5GWy/ZcPbdBtye225Ft7ArTlFwDeBjDJ/D4FxtzOQwAKs11/ob7XAxhjfu4IQ9iPE/bn7D1pzh/lYgB3ADif31R+02D4Wt8rlO0Cw92rn3SOsizf2FbfBt2W3G5LvrUn3bbAmO/pmUPtmMp/X7Mtxeb3lwBc0CruSTP8OJ1huNPNBHAjjNWdl5j7isz/QwEcBFClOD6S9R8lD9qg25Lbbcm39mSgLTkxAvFqh1CmEMBsAANz+Z5YdWqGH+lEGL7U/PvXAMyWbyYM+9Zfzc9Tsv1D5FsbdFtyuy351p58aYtfO8xtwwG8an5uA2C8+TmnFmdZ9c3QD/N1AKfDGNIU8p4axvBmLIDH+Y8g9nQAEjBmpG/Ldg+YD23QbcnttuRbe/KlLSHawVfjng7geQD/BcMd8nrkqIBnjKUehdJ05eoKwxaVALAOxkq77zPGdhJRlBkLGo4H0B6wPAMYER0H4LcwZs9vZlnyNsmHNnB0W3KzLUB+tSdf2pJiO7gn1rkAroYRZvsrzFjJmruk2PPxoddAAM/xXg5GoKpXpDLPArjC/NzZ/F8Jc4iTrb98aINuS263Jd/aky9tSaMdXMOfCODKbLcj6F8oTd70b70HQJSI3oIRdyUOGL0cEX0PwDYiOo0ZUf0AI+jQBnMxxKVE9CVmxG/5THGJZicf2sDRbcnNtgD51Z58aUuG2jGFMTYrG/VPlcCLoYjoNADzYQxd1iIZCP8MIhoPWMOye2D4jPKVYNfBWO7bFsAZ5o3OCvnQBo5uS262Bciv9uRLWzLYji0tXvl0CTHEmQTga8L3PwL4bxiTD/PNbREYdq6XABwHoB+A/4O5iCDbf/nQBt2W3G5LvrUnX9qSL+1Iqe0hfqQyAMVI2qq+AuBX5ueFAL5rfh4H4MVsNyxf26Dbktttybf25Etb8qUdqfwFNtcwxo4wxhpYMk7D2TASRQDANwAcT0RvwgilOR/IjRDAIvnQBo5uS262Bciv9uRLW/KlHakQ2oXStFMxGFloeBzoWhhLgIcB2MAY2wrkZFB/APnRBo5uS+6ST+3Jl7bkSzvCkEoUygSMBQN7AIwwe7+7ACQYY5/wHyjHyYc2cHRbcpd8ak++tCVf2hGcVGw8MJb+JmAEHLo+2zanY7UNui25/5dP7cmXtuRLO4L+8US5oSCiahgxHR5mjDWEPkEOkA9t4Oi25C751J58aUu+tCMoKQl5jUaj0bQOciKRt0aj0WiaBy3kNRqNJo/RQl6j0WjyGC3kNRqNJo/RQl6j0WjyGC3kNXkPEd1NRLd47L+YiIYEOI+tHBHdQ0STM1VPjaY50EJeowEuBuAr5OVyjLGfM8bea6Y6aTQZQQt5TV5CRHcS0Soieg/AIHPbt4jocyJaRET/IqIyIjoZwIUAHiSihUTUz/x7m4jmE9HHRDTYpdzTRPRl89wbieiXRDSHiOYR0RgieoeI1hHRjUK9bjXrsJiI/icLP43mGCPlHK8aTa5CRGMBXAVgNIxn/AsYkQVfYYw9aZa5D8aS9keI6HUAbzLG/mnuex/AjYyxNUQ0AcAfGWNnKsrJl97CGDuJiH4L4GkYaeJKACwD8BgRnQNgAIDxMJJbv05EpzLGZjbbj6E55tFCXpOPTALwKmPsCACYwhkAhpnCvRJABYB35AOJqALAyQBeFoR4ccDr8ussAVDBGKsFUEtE9URUCeAc82+BWa4ChtDXQl7TbGghr8lXVPE6ngZwMWNsERH9F4DTFWUiAA4wxkalcE0eByUhfObfC2Bo779ijD2ewrk1mpTQNnlNPjITwCVEVEpEbQBcYG5vA2A7ERXCyAzEqTX3gTF2CEbi5ssBI3EEEY2Uy6XIOwCuM0cLIKIeRNQljfNpNL5oIa/JOxhjXwD4B4y0bv8C8LG56y4AcwFMB7BSOORFALcS0QIi6gejA7ieiBbBsKdf5FIubL3eBfB3AHOIaAmMBNHpdBoajS86CqVGo9HkMVqT12g0mjxGC3mNRqPJY7SQ12g0mjxGC3mNRqPJY7SQ12g0mjxGC3mNRqPJY7SQ12g0mjzm/wFYCxa2CWJ1bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mljgPvPl+u7uBzuBdRMMP18WvYMFdVTe5/28FxgGDCrUuU0ARPYsKla3ymBN9mlrSX8Owhyr57O+mpj192K1AXp5bw0OTV/KHtzp/ZM1sLsrFUS0TkQeqItJTRHp7n4FLgCWFWJcpjEMlpIhG9wKJuRtu8+79eV92+CHKcdC9K2lsSv/uJJVMj7J0gmXYF0G/MJJSqOEHjgPGuS0uyoFnVfX1Aq3LmLyr3raPfQeb+cTxH+hwusST1rso7mpoKkzCikCYQbO10FFEgTsdYaS3IMFdVdcAZxRi2cbkQ6pS8ND7pwJQfe+wjJYbC2gbWWqBKF1htJb1qlgiuknzyppCmkCtJ1FEz6JCZatUugfksl8LUX9ciGUWVdVixJpCGlNUOuMkj5VIcM+Hzsjqii31/NcLC1o7hWVSLVMMD1I9UeyhakyotrvtzjtLqfbsDbt0m2y73fzsPF6Zv7F1OIx0HWyO84tXFuUjaSWr5IP76rq99B/+Gitr68NOSqTk48HVW8tq6T/8tYxPzHxK1mqlUCG4EMt9aPJKpq7YWoAlF498V8u8uayWPY3N7rLzs8yf/3UR4xdszGreqPVQ7RSvuV3Nxy/YFHJKosULUrmU6F5b7OybhRt25Z6gLHVWQdpbTSFK7g+8+T7fStXDtBMVIk5l3BQyxRzJqsfeXbWNbXsPZLg2xwtVG7jl+QVZzRu1HqqdItMgdKC5hfELNto45QXQ2NTCOyuLayiJZPWu+dr7icdRJsfVrycsY9z8mrSnn/yeU3rf35T+uCqFlM8LWarN5v3urTPV9LEk0f0bT87iq0/MyDR5JanLvWbv/jdWMPqdtRzRo4ILP35swdYzxT0RLzy1cOsoFlv3NDLot5Nb/570089wynG9W/8uhnbRYacjyJOV2b2LNtuSZ7505nZMvH6kezkp883opde78K6pK+xQD0Ei00O1M2VaN7xlj3Ni7Nlf2E4m3x4zh2+PKZ5b6Uxlsl3XbGt7snjbNsxHi97JlCwN+UpbGA9QOyNObNjRwCNvr+z0oFS5chtXjXyX5gzG5glS5iu5e3f1YV7crVomC5meXK3VOEVWiis+ebzlztuS8mtlbX3rKI7Zalctk9PSDrno91P53jPhvePmxrFzuH/S+9TszP8wCkG+9afZPD2jmltfWsC89bvYtrftfvEC9KFChzJv/U4mLAp+1hYUFvJ9HH71iRmcdkfxdrwv+eDuSXfHlWhLtZLSuo2LYFt3dPG/+MFpXPqHaQVfTzbW1O3jjaW1SdaV11U5y0zYWV69fqaFoHhc+dhtE3lm5rqM5qtvbOaX45e2ru/ZJG/Q8lI5dsY6rho5nZufnR84XVlAnXu+70Jmr92R9vMPay3TicJu19tZ7p6wjImLN6eeMIlJy2pTlm7bn0ZFENXTtLU+P/XXjU0tvLtqW+BJrChNLXFuG7eYLbsbmb56W0avietIw8H8DLubeD5s2LE/8PtUmuJxmuPKXX9fmlN6Hpq8Esi+p3TgMBA5pSg31okpC5nWuXeVapk9jU3s3t/EU5Vr+eFfMn/tnP/c+N0b72U9L+ReYnps6momLw8uxSZzqHVFTqvmvHsms7gmeOx2v+37DvKNJ2cl7W/xzxV1PDtrPdc9NYuvj57FbeMW55YwYOLizZz+yzf48XPBpddC6Whv5jpshX9/7WlsYoW7PTNdXtCD9HgXq3Qv/eDeOpBQeluvVHsQZmrAiEmc8atJeVlWru8DzXWb3/f6e9w4Nrv6Z/+as8nFpt2NPPz2yrSn353kQb237nq3Y82KLbl3unupagMAf19YPH088vkyjUm+ainv/F6yaU9a8/pL7l5aOrrg7z3QzLCH3mFZmssvBSUf3FtleDRFveSeq2bfyyZSxfZkwdtbxuZdnfNQrhgk21be3Ussj8EvqF45Xeke/5meJ4nNDnPhL2l7H/86N71+AUHVMh2ZvXY7Szftyfgu1e/NZbXsagiuwrTWMlnItFB4qNNT13PVyHd5dlbwg6pEm3wBOdPbWW8bV67aBsDv3+z8N/bkU0fHWMattVo74eR+BHavKMt5GbkIbpGSvzMrnsMdY9B1r7XkHjD9nOqdAFm3Dtq29wDffbqK7z0zl5fn1lCzsyFw3Z2p5IO7J+1tl0MNwYhXl7Z2Tio1W+sbmbd+V9p1vf6Anu1JFmYvYG/Nha6GSwwiqYJbPl82ccHJx+S+kCyks19zrMlLuYxB/Y/qcF7/fn9zWS0LNuxqPaaDjonHpq4GYOXW7MZBOtDstMtfu20ft760kPPvm5LVcvKp5IP7oQek6R1NXlVBNoFnzPTqku2YdP8bKzKavu8He7R+9rbUgebgFh6J54p3Uu4sgrcRdfYTlmQXQu9bryollxKud+gmtgUvhHQenvrNXbczb+tuCaiW8aS6m0y86P56wrJ2edl/sCVpZ6llm/Zw3VOzkh7ziS9ATxVPrLVMFjItmL3qPnx6ZuY6Vm3tOiNJ9jwss5Emju19WOtnL149PnVNWvP+duLyjNZVSG1bTaQ+wd7bsod/JDQdzWRc8GQPn71Ve3XBuZRsvVl7dgu3WsavvrGJJ99Zw9dHz8rbMv37S1G2+4ZdSDUEQ2Kde1y1Xa/l0375Ojc9Mzdw/l+MW8w7K7clfcCa6RAGVi2Tg9cWbWbJxtRN1jyLanbzuQfS78BS6gONHVaeWSDwnxpeKWne+vRKZfksvSWat34n/Ye/xqKaXR1Od+hEloTvO17+pX94hx8kaTp65/glTFq6pcPltaRYvr+HZa76HXl4zsvwJEtOYgk1mbsnLOPXr2V+Uf/o0cnz4L8LUoVx8w8Nt1u9vSFollaJwX3e+l2BF9S3k1Szpno2l+xZdjFFiZIP7t5BuWl3I5c/XFmw9TTnoxKxhPhH1fMCUbLWGZlWfcTjmnVw89q7T3s/zdEn21UZtW21ko7Xl26hrv4AY2esS1rSa11+0uPEW29m7cA72k65PE5It5rg5RStU1SVlrgmbQKaat51HQRp/6bsKLX+bd7UEudbf5rNwoCL/7vuA/50tluqi3BivX3KUS1TrzLvSj64PzplVUbTp3oQk0ydrydj/+Gv0X/4a2zY0UDDwWZenLOhqEr2jXkYErZNyT1F4S3VyfKxY3u1fj7xF6/xL7dN5Bev5N6JJxvebmpsymxgql+OXxK8vIS/WwKOA38A8y4q6bZACppMcAb2etFt515IBzsouYvAqGlrOOm2iSzfnHkVZ1DLFH9+2zaFTL69/Nt83fZ9TF1Rx+1/a7+/MjkvvEP62VkbWL+9od3daKatUF8NeN+EqjJx8eacB0lLpuSDu/e2lXRlOxb2TQGDOC2s2cXdE5bzPy8vYvrq7YHzFWrHdaSjEzJd/oDtnTzZ9vDzt+rwFvH8nI4D057GJl5f0n7YhEx7QCaeg15e6g9kVtIMqksPKqUHTXf735a0q3NPVa3gSZbNr42amXTsmXwKHE7B9523H9fvSC8/mWgT3DuYzr/NO2od5U3WHFe27G4MnOZzpx3XZjkvz6vhM7+bwpcfm95mumTrSXZcBrWye2NpLT/8yzwenbI6aZpzUfLBPVOJ46RcdP9U/uvFBSmv6kG3j3GFunrnIGnwjRXir6f0vyEqHlcenrySR95eSTyu7Go4yC3Pz2fCok0Zd68Hp5djUADULGL7+u0NSV8c4XW/TwxcW3Y38nySAZ7apEdh3Pwa1qcZ0AD++8WFfP/P81ibMJywV13kBenmljijp61p16rBS2l5WeLtc3BdPMCoaclPsqBOMU0BtzTJqmW8dGc8EFeSGTZ2UsewDquFKGxrJH8ZpaN0+KtMO0qPty1V4daXFgROc1hFLOVy/Mvy1LkPeJM96PXfXcxeu4MZq7ez0+3wlOxVkLkqyMs6RORS4P+AMuBJVb23EOsJsrW+kYPN8aQPm3Ym9CBbs20fa7bto9dh5dx84cc49ojubNjRwIot9Xzu9ONapzvY3P5E3rRrf2sJzP8k399RqKklzqqte2lqibOmbl9rh54pK+r4xPFHMH7BptYLwLgfnkffD/RAUfp+oAeJlmzczcc/1JuKMucA/Lw7omH1vcNap2mJa2B9Y+2e4JKK54pHK9nZ0MSVZ/UDYMe+QyXbLe68/gfWqsoXH6lka/0Bfn/1Ge2W599ecVV++sLCDtefyCsJJg6Mtd/9+69za/juBf/CuPkb+c3E5fxm4nJW/PrSdsvxn4PqS9eGhE4mm3fv57cTk/dO3BcwQFdTS/tnB8mezXQrd/ZZR3dV79fWt3nJCQQH91wqAFdtree4I7q3e35StW4n3xlbxS3/djJ9P9i99ftU/QQ6+nlxzW5O69ub8rLgMmTQvP66e3/eNycpaUPbQkdHd5f+jnlB5zNARcy7s0reGubuCcvo42tNBnDVyOlJpm6fRu9NUMMvOxVwnunc++UBHc6fjbyX3EWkDHgUuAw4HbhWRE7P93o8J/Xp2ebvQb+Z3GEHgn5Htg+aAE/PWNf6NqEL/ncK33m6qs2AUQcCDoZ7//Eeq9xOD8N9dch7DxwKBLGYcMOYOVz2f++wp/HQgTt33U6entF2WNQrR05n8D2TOfeet9uta932fVz+cCW/CWiR4A8wj05ZxX/8cXa7aVI1HfPapHsH4e1/a18n7r9gvli1oXVExVtfah+473v9UKDM5mF0WZKS7uh31gJOfe33npnbZnTFj9/efmztxPl/57b3T7wTa07RzOWdldvafdcUcEwke81gNzfAJQsqQOBIkemW9PsPfy2tMWY+98A0vvlkcHPFt5bX8oVHKhn20DvprZSOg/8XHqnssHdy0Lz+88x/F3Tzs/MpT1LR7Q+cB5uTb7A/vHVojKBk6T6qpxO0O+pD8FTlWu79R2bDFCwIeI+wt4xdBeoPUoiS+yBglaquARCR54ErgGX5XtGGHQ1JuwuPnhbcJjtVm2X/fA+/vZJzMngA++iUVbTEtc0b0l+eW9NaCs3kIWJi+scvdJY5Zno1H/5g2wvUyKmrW4PH4/8MrlrwB6dk2wbgiWmrqYjFqN3T9mIwetoaFvtK7v9vXPADRs/Y6dWtn6uqdwROM3raGhRl48799O5eQXmZUCZCLCYsddsXP+er9klMd+Wqba1DHCR65O1V9DqsnBlrDj0LUQ1+RjN62pqsXl03Zno1zQlVM/PW7wqc9h9u9VlH1Sl/W7CR2WvbbquRAQ0GkrVg+fFz85PWJcOhaqeFNbsZPW1t0un8+378go1s2d1IvyN7cNwRTon+j+86805fvb21cJPMY1NXc9Th3dp9P21lHQdSPNROfI71ZpKqy7HTq+nl9uPoqITv59/O/uq492vrOzw/gjySZGC5TJeTb5LvVh4i8hXgUlX9jvv3dcCnVfXmhOluAm4C+MhHPvKpdesyG9wfYMqKrXy7iN4Kb4wx2fBXrWZCROaq6sCg3wpRcg8qGre7gqjqKGAUwMCBA7O6wlzwsWNY8qvPUx4TmlriiMihB2Yd3C4eXlHGwZY4Ik5J/kBzCzGRNoksj0m76oTymNASd1oIl4m0ltrKYzEUpSwmlMdixMSpj42Jk46DzXGnhxzOxvGqHJrjSreyGHFVtwcdSce/SJYvof3GLROhokw40BynzLdtupXFOqzzVdU2y+9WFqOiTNjnqy5I3C7lMaE8JsSV1vx6LZIE54Gmv25aRDisPNbm9rtMhB7dyojHlRa33bS3HcpjwWnwtp/gbLPyWIwWtxdi4jbqVhajJa6IOJ+b4xq4HcpjQkyc7aXu9uhWHkPVWZd/2WUirQ/JvDR4Klr3advlx8TJj7cN46o0x5UyEUTaV1+VidCt3Dme9h5oRkSIyaFWH+UxZ1vub2px89f+mE88Pvx58D57tR1e9Yb3u7euoG3qT4e3Dm+b9OxWRsPBFuIB8/nT5e03EWfdMXd+/zHmvei6pfV4cI5j7xmIt9+D0uavxWmJO/syHj+0LP8+jImz37zj0l/oFXHu9yvKnLQ2tcRpiTvnuyCUxZzzrTmuHGiOu+dD+/1/WHmsdV7/dk5W3ZSrQgT3GuAE39/9gIIMOF1eFqOXWx2R6Qh53WOHpvcedmUu+Tq7lR/aYT1C6iZeHrBtsslrrwyHLkicPmj2ioCHbLGYEEMI2pWZpqEj3WLS4XbI/njILA2Z6N29Iulvh3crSLuInGQ63EWmOtoeuQg6LtOdpqJMUs6fzvLzpRBrmgOcLCInikg34GvAqwVYjzHGmCTyfnlV1WYRuRl4A6do+0dVze2FisYYYzJSkHsnVZ0ITCzEso0xxqTW5XqoGmNMV2DB3RhjIsiCuzHGRFDeOzFllQiROiDzXkyOY4DgboqlJSr5gOjkJSr58EQpP5YXx0dVtU/QD0UR3HMhIlXJemiVkqjkA6KTl6jkwxOl/FheUrNqGWOMiSAL7sYYE0FRCO6jwk5AnkQlHxCdvEQlH54o5cfykkLJ17kbY4xpLwold2OMMQksuBtjTARZcDcmQiTVS09Nl1ESwT0KB6yIHOX7XNL5EZGhIhLYcaLUiMitInKJ+7mk94ur9Q3bpZyfUk57orDyUtTBXUSuEJGxwBlhpyVbInKpiEwD/iAivwfQEn2K7cvLN4DMXzpaRETkEhF5A/g58B9QuvsFQEQuFpFK4H4R+R8ozfxE4Zz3hJ2XonuFi4iIqqqIXAjcDTQB54rIOlXdGXLy0uJeqWPAjcANwD3AfOBpEblMVf8RZvoy4eZFgGuAJ4AbVfWlcFOVHTcvFcAvgc/i7JduwDkiUgE0l2hA7AeMAO4FpgLPi8jRqvpz73wKM33pKuVz3lNM8auoSu4JB+Ja4PPAz4BPAwNCS1gGvDyoagtQCZyvquOBRmArsFREYt60ISY1JV9e4jivSnwaWOX+drWI9HODYinl5SAwXlUvcN87sBP4mqo2lUoQhHbb+1Rgsar+XVXrgUeBn4rIyW6gKep947MWuIQSO+c9xRa/iia4u29vekVEfioiH1LValXdrKpvA7XAZ0XkwyEns0MJeeirqsvcN1OdDfwN6I9TDfCAN0s4KU3Nl5f/EpFjcC5Ui4DHROQ94KvAw8BIb5ZwUppawH6Z435foar/BNaIyGXhpjJ9Cfk5AngfOF9EznUnORZYCtweVhrTISI/FJEvu58F2KCqW0rpnPcUZfxS943xYf4DrsR59+qFwJ+AR4Azfb8PAP4MXJUwn4Sd9nTyAJwIfMT93BPYBQwMO80Z5OVR4OPA8ThVGWe50x0F1AGfCjvNGe6XM7zjx83Dk8AlYac1y/w8BhyHUwU4BngXeNY95hYC/cNOc0AeegOPA1uAvUC5+33MO6dL4ZxPcYyd6fs9lLwUS8n908BjqjoFp+5wLfAT70dVXYSz8T4pIheJyM/d74vpNjooD7cAqOpaVV3vft4HvAgcEVI605GYl2rgZ6q6CfiVqs4HUNUdOHckvcJJZlo62i/q5qEHzomJV2VWxILy8ytVfQr4LvBTVf06sB6YDewJK6HJqFN19E9V/RAwAafwAE6wU3eaUjjnPUUZv0I9kH11gWuArwOo6jrgNaCniHzRN/lzwHeAF3DGPy6Ket4UeTg8IQ+IyO3AJ4BlnZnOdHSQl78DvUXki6ra6Jv+Dpy8vNfZaU0lw2Prz8AgEemuzvOFotNBfl4FjhSRK9V5bjDbne5unLvE+k5PbAd8+XjV/f8/gWvd5wMtIlLum6aozvnEdRd7/Or04C4iZd5n35Xrr0CDiFzh/r0Z56n/6eLoBfwfsBgYoKo/S5i/U2WaB3eey8RpqnYK8BVV3dJ5KU4uy7xcICJTcPLyZVWt7bwUJ5fNseV+1wN4HmjppKSmJcP8fNyd52QRGQ98EqcU39R5KQ4WlA9V3SciMfc8GIlTNYaqNquqikhP4CGK5Jx3tYmXxR6/OiW4i8i5InIXgDqtSLzvvZNrJzAO+IH7xHk3zq1+d3cDNAK3qOowVd3cGWlOlEMeeri/Lwe+r6r/EVYePHnISzXwI1W9roTzcpjv5BqvqqOLJBBmfa64v2/B2TdfDPOi21E+Equ+VHU4cKI7z3Eico5bffmTMM95j4gMEpE/A/eIyL/KodZu3kWrKONXwYO7iFwPjAVuF5Gvut+VQ5srVw/gDZwr3igROR44C6eNqHc131rotCaTYx4OutNVq+qSzk57ojzlZYOqhl6tlGNemr3l+INPmPJ0rtSrak1np90vVT5UNe6WZj/gm+0+nIfB7wCHu9OGds6D8/xFRO7Euav4B06/oB/hdkryHTfFGb+08E+SPwf0w2m/ut73fZn7/wh3w5yF03Lh1zi3NCO9acL+F4U8WF6KOy9Ryk8a+bgTeB24wP37MpznNvcDFWGnPyEvNwJnu5+PxgnyA32/F+0+KcTG+BJwG3C5t0O9HYbTVvpu37TH4jTbOilhGYeHvENLPg+Wl+LOS5Tyk2s+cJ7lnBB2PhLyMszbvm5+DnP/fhH4QrHvE9U8BnegD06zuGnA93F6Y17p/tbN/f8TwG7guID5Y6FvjAjkwfJS3HmJUn7ykI9iuttImhffNBXAdOCUYt0nbdKUx40zGKcttPf3dcD0xB2JU3/1J/fzZWFvgKjlwfJS3HmJUn6iko908uJ+96/AOPdzb2CQ+7noOlap5hjccUbTG4pz61LhXZ1xbmM+BTzhZd5/ZQPiOE+Yh4d9xYtCHiwvxZ2XKOUnKvnIMC9eD9qhwF+Ab+E0a7yRIg3sqpr5qJBuk6wP4dQ1xYHVOD3jblHVWhEpU6czwmnAkdD6pF9F5KPAgzhPxH+kIbUeiUIePJaX4swLRCc/UckHZJ0Xr2XV54FrcYa7/oY6PU+LV4ZXOu826xTgz95VDWcAqVcSpnka+Kr7uY/7/wdxb2XC+heFPFheijsvUcpPVPKRY168Ev0Q4Jqw85Huv7RK7m4b1buAMhGZiDMuSgs4VzUR+QmwSUQ+q84oe+AMCLTW7chwlYj8uzrjq8wOWEXBRSEPHstLceYFopOfqOQD8paXy1T13TDSn62UnZhE5LPAXJxblFUcGoD+QhEZBK23YHfhtPn0em7dgNMt9wjgQncnhyIKefBYXoozLxCd/EQlH5DXvGzo9MTnKo1bmQuA63x/jwR+gPNQYa77XQynHutF4KPAScAfcBv/h/0vCnmwvBR3XqKUn6jkI2p5yTjvaWycw4HDOFQX9Q3gHvfzAuDH7ueBwPNhZyiqebC8FHdeopSfqOQjannJ9F/KahlVbVDVA3poHIWLcV7QAPBt4DQRmYAzpOVcKI6heP2ikAeP5aU48wLRyU9U8gHRykum0m4K6dZDKc5bX7yxmOtxuup+Elirqhsh9GE5k4pCHjyWl+IVlfxEJR8QrbykK5NRIeM4Df23AQPcq90dQFxVK70NU+SikAeP5aV4RSU/UckHRCsv6cmw/mowzkaqBG4Mu04pm39RyIPlpfj/RSU/UclH1PKSzj/vZbRpEZF+OGMuPKCqB7K7nIQrCnnwWF6KV1TyE5V8QLTyko6MgrsxxpjSUOxvejfGGJMFC+7GGBNBFtyNMSaCLLgbY0wEWXA3xpgIsuBuIktERojIf3fw+5dE5PQ0ltNmOhG5S0Q+l690GlMIFtxNV/YlIGVwT5xOVX+pqm8VKE3G5IUFdxMpIvL/RGSFiLwFfNz97rsiMkdEForIyyJyuIicB3wR+J2ILBCRk9x/r4vIXBF5R0ROTTLdGBH5irvsahH5rYjMEJEqETlbRN4QkdUi8n1fun7mpmGRiPwqhE1jupiM36FqTLESkU8BXwPOwjm25+GM9PeKqo52p/k1Ttfzh0XkVWCCqv7V/W0y8H1VXSkinwZGqupFAdMlrnqDqp4rIg8CY3Bex9YdWAo8LiKXACcDg3BeHP2qiHxGVacVbGOYLs+Cu4mSC4BxqtoA4AZlgE+6Qf2DQC/gjcQZRaQXcB7wki94H5bmer31LAZ6qWo9UC8ijSLyQeAS9998d7peOMHegrspGAvuJmqCxtMYA3xJVReKyLeAoQHTxIBdqnpmFuv0ximJ+z57f5fjlNbvUdUnsli2MVmxOncTJdOAK0Wkh4j0Br7gft8b2CwiFThv4vHUu7+hqntwXoh8NTgvbBCRMxKny9IbwA3u3QEi8mEROTaH5RmTkgV3ExmqOg94Aef1aS8D77g/3QHMAt4E3vPN8jzwMxGZLyIn4QT+G0VkIU59+RVJpss0XZOAZ4EZIrIY58XLuVwsjEnJRoU0xpgIspK7McZEkAV3Y4yJIAvuxhgTQRbcjTEmgiy4G2NMBFlwN8aYCLLgbowxEfT/AT2KmGXc6/DzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax0 = weather.plot(y='solarradiation')\n",
    "ax0 = weather.plot(y='temp')\n",
    "ax0 = weather.plot(y='cloudcover')\n",
    "ax0 = weather.plot(y='windspeed')\n",
    "ax0 = weather.plot(y='dew')\n",
    "ax0 = weather.plot(y='precip')\n",
    "ax0 = weather.plot(y='humidity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7965c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "interp_method = 'cubic'\n",
    "\n",
    "t_data = np.arange(0,weather.shape[0]*15*60,15*60)\n",
    "U_atm = np.abs(weather['windspeed'][:].to_numpy()/3.6) #converted from km/h to m/s\n",
    "T_atm = weather['temp'][:].to_numpy()\n",
    "q_sun = weather['solarradiation'][:].to_numpy()\n",
    "# CC = weather['Cloud Cover'][:].to_numpy()\n",
    "cc = weather['cloudcover'][:].to_numpy()/100. # converted from % to fraction\n",
    "rh = weather['humidity'][:].to_numpy() # left as %\n",
    "p_r = weather['precip'][:].to_numpy()*1e-3/(15*60) #converted to mm to m/s \n",
    "T_dp = weather['dew'][:].to_numpy()\n",
    "f_U_atm = interp1d(t_data,U_atm,kind=interp_method)\n",
    "def U_atmospheric(t):\n",
    "    global f_U_atm\n",
    "    return np.maximum(f_U_atm(t),0.)\n",
    "f_T_atm = interp1d(t_data,T_atm,kind=interp_method)\n",
    "def T_atmospheric(t):\n",
    "    global f_T_atm\n",
    "    return f_T_atm(t)\n",
    "f_q_sun = interp1d(t_data,q_sun,kind=interp_method)\n",
    "def sun_irradiation(t):\n",
    "    global f_q_sun\n",
    "    return np.maximum(f_q_sun(t),0.)\n",
    "f_CC = interp1d(t_data,cc,kind=interp_method)\n",
    "def cloud_cover(t):\n",
    "    global f_CC\n",
    "    return np.clip(f_CC(t),0.,1.)\n",
    "f_T_dp = interp1d(t_data,T_dp,kind=interp_method)\n",
    "def dew_point(t):\n",
    "    global f_T_dp\n",
    "    return f_T_dp(t)\n",
    "f_RH = interp1d(t_data,rh,kind=interp_method)\n",
    "def relative_humidity(t):\n",
    "    global f_RH\n",
    "    return np.clip(f_RH(t),0.,100.)\n",
    "f_rain = interp1d(t_data,p_r,kind=interp_method)\n",
    "def rain_rate(t):\n",
    "    global f_rain\n",
    "    return np.maximum(f_rain(t),0.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fbdef5",
   "metadata": {},
   "source": [
    "### Importing Heat Transfer Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d70d3cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.constants as csts\n",
    "from scipy.integrate import solve_ivp\n",
    "from Libraries import thermodynamics as thermo\n",
    "from Libraries import HT_external_convection as extconv\n",
    "from Libraries import HT_natural_convection as natconv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc0b09a",
   "metadata": {},
   "source": [
    "### Convection heat transfer on upper surface\n",
    "\n",
    "Convection heat transfer is determined by the relative strength of forced and natural convection. Here the forced convection is the wind over the roof. Regardless of the direction of the wind, the length of the slab is always assumed to be the dimension of the square making the roof $L$. Thermodynamics properties with a subscript $f$ denotes properties evaluated at the film temperature\n",
    "$$\n",
    "T_f = \\frac{T_s+T_\\infty}{2}\\,,\n",
    "$$ where $T_s$ is the temperature at the surface of the slab. For every time iteration, the following non-dimensional numbers are computed:\n",
    "* Reynolds number quantifies the wind strength\n",
    "$$\n",
    "Re=\\frac{U_\\infty L}{\\nu_f}\n",
    "$$\n",
    "* Grashoff number quantifies the natural convection strength\n",
    "$$\n",
    "Gr=\\frac{g\\beta\\left\\vert T_s-T_\\infty\\right\\vert L^3}{\\nu_f^2}\n",
    "$$\n",
    "* Richardson number quantifies the relative strength of natural convection vs forced wind convection\n",
    "$$\n",
    "Ri=\\frac{Gr}{Re^2}\n",
    "$$\n",
    "* Rayleigh number is required for Nusselt number convection in case of natural convection.\n",
    "$$\n",
    "Ra=\\frac{g\\beta\\left\\vert T_s-T_\\infty\\right\\vert L^3}{\\nu_f\\alpha_f}\n",
    "$$\n",
    "\n",
    "The following algorithm determines the local average convection coefficient\n",
    "$$\n",
    "\\bar{h}=\\bar{h}_{forced}+\\bar{h}_natural=\\frac{\\overline{Nu}_{forced}k_f}{L}+\\frac{\\overline{Nu}_{natural}k_f}{L}\n",
    "$$\n",
    "where $\\overline{Nu}_{forced}$ is the correlation for the flow over a plate at constant temperature and $\\overline{Nu}_{natural}$ is the correlation for natural convection over a horizontal surface at constant temperature. \n",
    "\n",
    "* If the wind velocity drops below $U_\\infty=0.15\\mathrm{m/s}$, there is no forced convection: $h_{forced}=0$\n",
    "* If $Ri<0.1$, natural convection is negligible, $h_{natural} = 0$, compute $h_{forced}$\n",
    "* If $Ri>10$, forced convection is negligible, $h_{forced}=0$, compute $h_{natural}$\n",
    "* If $0.1\\leq Ri\\leq 10$ compute both $h_{forced}$ and $h_{natural}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72907188",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outsideconvection(Uinf,T_f,Tinf,air_f,Lroof,Ts):\n",
    "    #yourcodehear\n",
    "    A = 100\n",
    "    g = 9.81\n",
    "    Ra = natconv.Ra(beta=air_f.beta,DT=np.abs(Ts-Tinf),D=Lroof,nu=air_f.nu,alpha=air_f.alpha)\n",
    "\n",
    "    if Uinf < 0.15:\n",
    "        Ri=11\n",
    "    else:\n",
    "        Gr = g*air_f.beta*(Ts - Tinf)*Lroof**3/(air_f.nu**2)\n",
    "        Re = Uinf*Lroof/air_f.nu\n",
    "        Ri = Gr/Re**2\n",
    "    \n",
    "    if Ri < 0.1:\n",
    "        #natural convection is negligible: Forced Convection\n",
    "        air_bl = extconv.FlatPlate('mixed','isothermal',Uinf,air_f.nu,air_f.alpha,Lroof,xi=0,Re_xc=5e5)\n",
    "        air_bl.average(Lroof)\n",
    "        Nu_ave = air_bl.Nu_ave\n",
    "\n",
    "        h_forced = (Nu_ave*air_f.k)/Lroof\n",
    "        h_nat = 0\n",
    "               \n",
    "    elif 0.1 <= Ri and Ri <= 10:\n",
    "        #Mixed Regime \n",
    "        air_bl = extconv.FlatPlate('mixed','isothermal',Uinf,air_f.nu,air_f.alpha,Lroof,xi=0,Re_xc=5e5)\n",
    "        air_bl.average(Lroof)\n",
    "        Nu_ave = air_bl.Nu_ave\n",
    "        kf = air_f.k \n",
    "        h_forced = (Nu_ave*air_f.k)/Lroof\n",
    "        if Ts >= Tinf:\n",
    "            airFlow = natconv.FlatPlate(Ra,air_f.Pr,'upper','hot')\n",
    "        else:\n",
    "            airFlow = natconv.FlatPlate(Ra,air_f.Pr,'upper','cold')\n",
    "        h_nat = airFlow.Nu*air_f.k/Lroof\n",
    "            \n",
    "    elif Ri > 10:\n",
    "        #forced convection is negligible: Natural Convection\n",
    "        if Ts >= Tinf:\n",
    "            airFlow = natconv.FlatPlate(Ra,air_f.Pr,'upper','hot')\n",
    "        else:\n",
    "            airFlow = natconv.FlatPlate(Ra,air_f.Pr,'upper','cold')\n",
    "        h_nat = airFlow.Nu*air_f.k/Lroof\n",
    "        h_forced = 0\n",
    "    \n",
    "    h = h_nat + h_forced\n",
    "    qpp = h*(Tinf - Ts)\n",
    "    return qpp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a70fac",
   "metadata": {},
   "source": [
    "### Radiation between outer surface and sky\n",
    "\n",
    "The radiation between the surface and sky depends on cloud coverage (variable $cc$ between 0 and 1), air temperature and the dew point temperature $T_{dp}$ for the correlation chosen below. \n",
    "\n",
    "#### Definition of $T_{sky}$\n",
    "* $T_{\\infty}$ Air temperature $\\mathrm{^\\circ C}$\n",
    "* $T_{dp}$ Dew point temperature $\\mathrm{^\\circ C}$\n",
    "* $cc$ Cloud coverage\n",
    "* $\\varepsilon_{clear}$: emissivity of clear blue sky\n",
    "* $\\varepsilon_{sky}=1$\n",
    "\n",
    "$$\n",
    "\\varepsilon_{clear}=0.711 + 0.56\\frac{Tdp}{100} + 0.73\\left(\\frac{Tdp}{100}\\right)^2\n",
    "$$\n",
    "\n",
    "$$\n",
    "Ca = 1. + 0.02224cc + 0.0035cc^2 + 0.00028cc^3\n",
    "$$\n",
    "\n",
    "$$\n",
    "T_{sky}  = (Ca\\varepsilon_{clear})^{0.25}(Tinf+273.15)\n",
    "$$\n",
    "\n",
    "#### Radiation coefficient between surface and sky\n",
    "$$\n",
    "h_{rad,sky}= \\varepsilon_{sky}\\sigma((T_{sky}+273.15)+(T_s+273.15))\n",
    "        ((T_{sky}+273.15)^2+(T_s+273.15)^2)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4d67ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_sky(Tdp,cc,Tinf,Ts):\n",
    "    eps_clear = 0.711 + 0.56*(Tdp/100.) + 0.73*(Tdp/100.)**2\n",
    "    Ca = 1. + 0.02224*cc + 0.0035*cc**2 + 0.00028*cc**3\n",
    "    Tsky  = (Ca*eps_clear)**0.25*thermo.C2K(Tinf)\n",
    "    hr = eps_sky*csts.sigma*(Tsky+thermo.C2K(Ts))* \\\n",
    "        (Tsky**2+thermo.C2K(Ts)**2)\n",
    "    return Tsky,hr\n",
    "def skyradiation(Tdp,cc,Tinf,Ts):\n",
    "    # Ts must be in Celsius\n",
    "    Tsky,hr = T_sky(Tdp,cc,Tinf,Ts)\n",
    "    qsky = eps_sky*csts.sigma*(Tsky**4 - thermo.C2K(Ts)**4)\n",
    "    return qsky\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330a2224",
   "metadata": {},
   "source": [
    "### Heat transfer with rain\n",
    "\n",
    "Rain has a cooling or heating effect on the surface depending on the difference between the wet bulb temperature and the surface temperature. The wet bulb temperature is a function of air temperature and humidity. The heat flux of the rain onto the surface is then calculated as the difference of the rate of enthalpy of the rain at wet bulb temperature hitting the surface and the rate of enthalpy at surface temperature for the same flow rate (variable $Pr$ for precipitation).\n",
    "#### Wet Bulb Temperature Correlation\n",
    "\n",
    "$$\n",
    "T_{WB}=T_\\infty\\arctan\\left[0.1515977\\sqrt{RH + 8.313659}\\right] + \\arctan(T_\\infty + RH) - \\arctan(RH - 1.676331) + 0.00391838RH^{1.5}\\arctan(0.023101RH) \\\n",
    "            - 4.686035\n",
    "$$\n",
    "\n",
    "#### Heat Flux of Cooling by Rain\n",
    "$$\n",
    "q''=\\rho C_p P_r(T_{WB}-Ts(t))\n",
    "$$\n",
    "\n",
    "where $\\rho=1000\\mathrm{kg/m^3}$, $C_p=4.19\\times10^3\\mathrm{J/Kg.K}$ and $P_r$ is the precipitation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9964153c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_wet_bulb(T,RH):\n",
    "    return T * np.arctan(0.1515977*(RH + 8.313659)**0.5) + np.arctan(T + RH) \\\n",
    "            - np.arctan(RH - 1.676331) + 0.00391838*RH**1.5*np.arctan(0.023101*RH) \\\n",
    "            - 4.686035\n",
    "def rain(pr,Tinf,RH,Ts):\n",
    "    Twb = T_wet_bulb(Tinf,RH)\n",
    "#     print(\"rain\",pr,Twb,Tinf,RH,Ts)\n",
    "    rho = 1000.\n",
    "    Cp = 4.19e3\n",
    "    return rho*Cp*pr*(Twb - Ts)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d6f986",
   "metadata": {},
   "source": [
    "### Conduction Through Roof -Flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a305799",
   "metadata": {},
   "outputs": [],
   "source": [
    "def roof_cond(k_ins,k_steel,H_ins,H_steel,Ts_lower,Ts):\n",
    "    Rcondroof=(H_ins/k_ins+H_steel/k_steel) #Assumes flat top\n",
    "    qpp=(1/Rcondroof)*(Ts-Ts_lower)\n",
    "    return qpp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012c3411-658d-4ac5-9809-de22c1d62881",
   "metadata": {},
   "source": [
    "## Governing equation of the system\n",
    "\n",
    "Conservation of energy equation is applied to the outer surface of the slab, which is assumed to be at uniform temperature $T_s(t)$:\n",
    "\n",
    "$$\n",
    "\\iint_A\\rho_{ins}C_{p,ins}\\frac{dT_s}{dt}\\rho_{ins}C_{p,ins}A\\frac{dT_s}{dt}=q_{sun}+q_{rain}+q_{conv}+q_{rad,sky}-q_{cond}=(q''_{sun}+q''_{rain}+q''_{conv}+q''_{rad,sky}-q''_{cond})A\n",
    "$$\n",
    "\n",
    "Therefore the equation govering the surface temperature is:\n",
    "$$\n",
    "\\frac{dT_s}{dt}=\\frac{1}{\\rho_{ins}C_{p,ins}}(q''_{sun}+q''_{rain}+q''_{conv}+q''_{rad,sky}-q''_{cond})\\;\\;\\;\\;\\;\\mathrm{Eq.\\;(1)}\n",
    "$$\n",
    "\n",
    "The cooling demand fluctuates over time:\n",
    "\n",
    "$$\n",
    "q''_{cool}(t)=\\begin{cases}\n",
    "\\frac{1}{R''_{tot}}(T_s(t)-T_{s,lower}) &\\text{if }T_s(t)\\geq T_{s,lower}\\\\\n",
    "0 & \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$\n",
    "where $R''_{tot}=R''_{cond,ins}+R''_{cond,concrete}$.\n",
    "\n",
    "Note that the irradiation from the sun is only partly absorbed by the surface:\n",
    "$$\n",
    "q''_{sun}=\\alpha_{abs}I_{sun}(t)\\;,\n",
    "$$\n",
    "where $\\alpha_{abs}$ is the surface absorptivity ($0\\leq \\alpha_{abs}\\leq 1$, always) and $I_{sun}(t)$ is the measure irradiation from the sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76a7279e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(t,T):\n",
    "    global Lroof,k,H,eps,f_U_atm,f_T_atm,f_q_sun,rho,C_p,alpha_s,f_CC,f_T_dp\n",
    "    # film temperature between ambient and upper surface\n",
    "    Tinf = T_atmospheric(t)\n",
    "    Uinf = U_atmospheric(t)\n",
    "    \n",
    "    T_f = (T[0]+Tinf)/2\n",
    "    air_f = thermo.Fluid('air',T_f,\"C\")\n",
    "    Tdp = dew_point(t)\n",
    "    cc = cloud_cover(t)\n",
    "    pr = rain_rate(t)\n",
    "    rh = relative_humidity(t)\n",
    "    \n",
    "    \n",
    "    qpp_sun = alpha_s*sun_irradiation(t)\n",
    "    \n",
    "    qpp_sky = skyradiation(Tdp,cc,Tinf,T[0])\n",
    "    \n",
    "    qpp_conv = outsideconvection(Uinf,T_f,Tinf,air_f,Lroof,T[0])\n",
    "    \n",
    "    qpp_rain = rain(pr,Tinf,rh,T[0])\n",
    "    \n",
    "    qpp_cond = roof_cond(k_ins,k_steel,H_ins,H_steel,Ts_lower,T[0])\n",
    "    \n",
    "    qpp_total = qpp_sun + qpp_sky + qpp_conv + qpp_rain - qpp_cond\n",
    "    \n",
    "    return qpp_total/(rho*C_p)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12aea8a0",
   "metadata": {},
   "source": [
    "## Main code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e43077cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho = 20.\n",
    "C_p = 2500.\n",
    "alpha_s = 0.5\n",
    "eps_sky = 1.\n",
    "\n",
    "#Inputs\n",
    "k_ins = 0.04 #W/(mK)\n",
    "H_ins = 0.0508 #m\n",
    "k_steel = 0.046 #W/(mK)\n",
    "H_steel = 0.0009525 #m\n",
    "Lroof = 100\n",
    "A = Lroof**2\n",
    "Ts_lower = 20 #C\n",
    "\n",
    "tmax = t_data[-1]\n",
    "mask = np.where(t_data <= tmax)\n",
    "sol = solve_ivp(f,[0,tmax],[-5],method='LSODA',t_eval=t_data[mask],max_step=10*60.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ffece0",
   "metadata": {},
   "source": [
    "### Maximum Cooling Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a986a0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum cooling demand: 214275 W\n",
      "on 06/07/2021, 16:00:00 \n"
     ]
    }
   ],
   "source": [
    "weather['Tsu'] = sol.y[0]\n",
    "Rcondroof=(H_ins/k_ins+H_steel/k_steel)\n",
    "weather['q_cool'] = np.maximum((1/Rcondroof)*(sol.y[0] - Ts_lower)*A,0.)\n",
    "\n",
    "#Input\n",
    "#q_HUMANITY=100*4035 \n",
    "\n",
    "print(\"Maximum cooling demand: %.0f W\" %(weather['q_cool'].max()))\n",
    "print(\"on %s \" %weather.index[weather['q_cool'].argmax()].strftime(\"%m/%d/%Y, %H:%M:%S\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff54c1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With a 75 percent efficient heat exchanged, the geothermal cooling system must supply 285700 W\n"
     ]
    }
   ],
   "source": [
    "q_Geo=((weather['q_cool'].max())/.75)\n",
    "print(\"With a 75 percent efficient heat exchanged, the geothermal cooling system must supply %.0f W\" %q_Geo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea65e876",
   "metadata": {},
   "source": [
    "## Piping length inside the geothermal system\n",
    "\n",
    "We consider a close loop system containing water which should arrive at the heat exchanger with a temperature of C and exits the heat exchanger at 12C. Assuming that these temperatures to be the temperatures at the entrance of the underground pipe (20C) and the exit (12C), the heat transfer between the geothermal system and the water circulating in a pipe is:\n",
    "\n",
    "$$\n",
    "q=\\dot{m}C_p(T_{m,i}-T_{m,o})\\;\\;\\;\\;\\;\\mathrm{Eq\\;(3)}\n",
    "$$\n",
    "where $\\dot{m}$ is the mass flow rate.\n",
    "\n",
    "Eq. (3) is used to determine the flow rate, which in turn defines the convection coefficient of the flow within the pipe using the Dittus Boettler correlation. \n",
    "\n",
    "Assuming that the outside surface of the pipe is at $\\mathrm{12^\\circ C}$, the total resistance between the flow and the ground per unit length is\n",
    "\n",
    "$$\n",
    "R'_{tot}=R'_{conv,i}+R'_{cond}\n",
    "$$\n",
    "\n",
    "$L$ is then the solution of the equation for the variation of temperature between inlet and outlet of the pipe\n",
    "\n",
    "$$\n",
    "\\frac{T_\\infty-T_{m,o}}{T_\\infty-T_{m,i}}=\\exp\\left(-\\frac{1}{\\dot{m}C_pR_\\text{tot}}\\right)\n",
    "$$\n",
    "for the target outlet temperature $T_{m,o}$. First, assuming 1D, steady convection on the outside of the pipe, we must solve for $R'_{tot}$. Since\n",
    "$$\n",
    "R_{tot}=\\frac{R'_{tot}}{L}\n",
    "$$\n",
    "the pipe length is\n",
    "$$\n",
    "L=-\\dot{m}C_pR'_\\text{tot}\\ln\\frac{T_\\infty-T_{m,o}}{T_\\infty-T_{m,i}}\n",
    "$$\n",
    "\n",
    "Here we assume that the analysis for a straight pipe applies to a system where the pipe will most likely spiral within the ground."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9c81aa90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mass flow rate: 6.8244 kg/s\n",
      "Average velocity: 0.843 m/s\n",
      "Re_D_pipe= 79194\n",
      "h_int=2048.9 W/m^2.K\n",
      "The necessary length of the pipe is 340 m\n"
     ]
    }
   ],
   "source": [
    "Tmi = 22. #C\n",
    "Tmo = 12. #C\n",
    "#qcool_max = 285700. #W\n",
    "Delta_T = Tmo - Tmi\n",
    "Tm = (Tmo + Tmi)/2\n",
    "water_Tm = thermo.Fluid('water',Tm,\"C\")\n",
    "mdot = q_Geo/(water_Tm.Cp*abs(Delta_T))\n",
    "print(\"mass flow rate: %.4f kg/s\" %mdot)\n",
    "Di = 4*0.0254\n",
    "Do = Di + 0.3*0.0254\n",
    "kpipe = 2.25\n",
    "A = np.pi*Di**2/4\n",
    "Um = mdot/(water_Tm.rho*A)\n",
    "print(\"Average velocity: %.3f m/s\" %Um)\n",
    "\n",
    "import HT_thermal_resistance as res\n",
    "import HT_internal_convection as intconv\n",
    "Rp = []\n",
    "Rp.append(res.Resistance(\"$R'_{conv,i}$\", \"W/m\"))\n",
    "Rp.append(res.Resistance(\"$R'_{cond}$\", \"W/m\"))\n",
    "pipeflow = intconv.PipeFlow(D=Di,L=1,Um=Um,nu=water_Tm.nu)\n",
    "print(\"Re_D_pipe= %.0f\" %pipeflow.Re)\n",
    "pipeflow.Dittus_Boelter(mode='cooling',Pr=water_Tm.Pr)\n",
    "hint = pipeflow.Nu*water_Tm.k/Di\n",
    "print(\"h_int=%.1f W/m^2.K\" %hint)\n",
    "Rp[0].convection(h=hint,A=np.pi*Di)\n",
    "\n",
    "# conduction\n",
    "Rp[1].cond_cylinder(k=kpipe,ra=Di,rb=Do,L=1.)\n",
    "Rptot = Rp[0].R + Rp[1].R\n",
    "T_infty=10.\n",
    "L = intconv.L_given_other_params(T_infty,Tmo,Tmi,mdot,water_Tm.Cp,Rptot)\n",
    "print(\"The necessary length of the pipe is %.0f m\" %L)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d5c5cc",
   "metadata": {},
   "source": [
    "### Pipe Headloss, Required Power, and Cost\n",
    "\n",
    "A uniform flow entering a pipe first experiences streamwise variation of velocity to accommodate the wall boundary conditions. A boundary layer, of thickness $\\delta$, forms on the wall and grows until its edge reaches the pipe centerline. This region is the hydrodynamic entrance region. Beyond that point, the flow becomes fully developed, which means that\n",
    "<ul>\n",
    "<li> In the laminar regime, the velocity profile is only a function of $r$,</li>\n",
    "<li> In the turbulent regime, the <b>mean</b> velocity profile is only a function of $r$.</li>\n",
    "</ul>\n",
    "Friction drag or the force exerted by the flow onto the pipe wall governs the pressure gradient necessary to generate a desired flowrate. Calculation of the friction drag leads to the design of the mechanical force creating the pressure gradient. In fully developed (laminar or turbulent) regimes, the pressure gradient may be determined by\n",
    "<p class='alert alert-danger'>\n",
    "$$\n",
    "-\\frac{\\Delta\\overline{P}}{L}=f\\,\\frac{1}{D}\\,\\frac{\\rho U_m^2}{2}\\;\\;\\;\\;\n",
    "$$\n",
    "</p>\n",
    "where $D=2R$ and $L$ are the diameter and length of the pipe, respectively, and $f$ is the <b>friction factor</b>. The bulk velocity or average velocity is\n",
    "<p class='alert alert-info'>\n",
    "$$\n",
    "U_m=\\frac{\\dot{m}}{\\rho A_c}\\;\\;\\;\\;\n",
    "$$\n",
    "Where $A_c$ is the cross sectional area of the pipe.\n",
    "</p>\n",
    "where $\\dot{m}$ is the mass flux\n",
    "$$\n",
    "\\dot{m}=\\int_0^{2\\pi}\\int_0^R\\rho \\overline{u}(r)\\,r\\,dr d\\theta=2\\pi\\int_0^R\\rho \\overline{u}(r)\\,r\\,dr\\;\\;\\;\\;\n",
    "$$\n",
    "and $A_c=\\pi R^2$\n",
    "The Reynolds number of the flow is based on the bulk velocity and pipe diameter:\n",
    "<p class='alert alert-danger'>\n",
    "$$\n",
    "Re_D=\\frac{\\rho U_mD}{\\mu}=\\frac{4\\dot{m}}{\\pi D\\mu}\\;\\;\\;\\;\n",
    "$$\n",
    "</p>\n",
    "\n",
    "Beyond the critical Reynolds number, $f$ is a function of the roughness to diameter ratio $\\varepsilon=e/D$ (e is typically the standard deviation of the roughness height) and the Reynolds number.\n",
    "<p class='alert alert-danger'>\n",
    "$$\n",
    "\\frac{1}{\\sqrt{f}}=-2\\log_{10}\\left[\\frac{\\varepsilon}{3.7}+\\frac{2.51}{Re_D\\sqrt{f}}\\right]\\;\\;\\;\\;\n",
    "$$\n",
    "</p>\n",
    "\n",
    "Often there is a need to determine the pump or blower power $P$ necessary to move the flow at a prescribed pressure drop:\n",
    "<p class='alert alert-danger'>\n",
    "$$\n",
    "P=\\frac{\\dot{m}}{\\rho}\\Delta p= \\underbrace{(\\Delta p)A_c}_\\text{force}\\cdot U_m\\;\\;\\;\\;\n",
    "$$\n",
    "</p>\n",
    "\n",
    "\n",
    "Here we assume that the analysis for a straight pipe applies to a system where the pipe will most likely spiral within the ground.\n",
    "\n",
    "## Pipe Cost\n",
    "\n",
    "Taking the pipe cost to be $11.88 per meter of pipe based on an online source (https://hdpesupply.com/4-ips-sdr17-pe4710-black-hdpe-pipe-straight-length-per-foot/), the cost of the pipe is simply calculated by multiplying that cost by the length of the pipe found in meters.\n",
    "\n",
    "$$\n",
    "Cost_{Pipe}=Length*$11.88\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "61f036db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cost of pipe = $4041\n",
      "Kinematic viscosity : 1.08e-06 m^2/s\n",
      "Re= 7.92e+04\n",
      "Mass flowrate= 6.8 kg/s\n",
      "Volume flowrate= 0.007 m^3/s\n",
      "Friction Factor = 0.05750\n",
      "Head Loss Due to Friction = 6.98 m\n",
      "Pressure Gradient -dP/dx= 68279 Pa/m\n",
      "Pumping Power Required = 467 Watts\n",
      "Pump Power Spec With a Pump Efficiency of 0.85 is 549 Watts\n"
     ]
    }
   ],
   "source": [
    "from Libraries import thermodynamics as thermo\n",
    "waterflow = thermo.Fluid('water',Tm,\"C\")\n",
    "#Importing thermo libraries\n",
    "\n",
    "#Setting fluid temperature for properties (Set as average temp T_in+T_out/2)\n",
    "#Setting up opbject for water flow\n",
    "\n",
    "g = 9.8\n",
    "#Gravity Constant\n",
    "#Length of pipe\n",
    "#Diameter of pipe\n",
    "#Velocity of flow in pipe\n",
    "Cost_Per_Meter = 11.88 #$/m\n",
    "#Cost Per Meter of Pipe - https://hdpesupply.com/4-ips-sdr17-pe4710-black-hdpe-pipe-straight-length-per-foot/\n",
    "Pipe_Cost_Tot = L*Cost_Per_Meter\n",
    "#Total cost for pipe\n",
    "vdot = mdot/1000 #(m^3/s)\n",
    "#Volume Flow Rate \n",
    "P_eff = 0.85\n",
    "#Efficiency of Pump\n",
    "\n",
    "\n",
    "pipe = intconv.PipeFlow(D=Di, L=L, rho=waterflow.rho, nu=waterflow.nu, Um=Um)\n",
    "\n",
    "print(\"Total cost of pipe = $%1.0f\" %Pipe_Cost_Tot)\n",
    "print(\"Kinematic viscosity : %0.2e m^2/s\" %waterflow.nu)\n",
    "print(\"Re= %1.2e\" %pipe.Re)\n",
    "print(\"Mass flowrate= %1.1f kg/s\" %mdot)\n",
    "print(\"Volume flowrate= %1.3f m^3/s\" %vdot)\n",
    "pipe.f_turbulent(eps=0.03)\n",
    "print(\"Friction Factor = %1.5f\" %pipe.f)\n",
    "h_lf = pipe.f*L/Di*(Um*Um)/(2*g)\n",
    "print('Head Loss Due to Friction = %.2f m' %h_lf)\n",
    "\n",
    "print(\"Pressure Gradient -dP/dx= %1.0f Pa/m\" %pipe.dPdx)\n",
    "\n",
    "\n",
    "P_power = (pipe.mdot/waterflow.rho)*pipe.dPdx\n",
    "print(\"Pumping Power Required = %1.0f Watts\" %P_power)\n",
    "P_spec = (1/0.85)*P_power\n",
    "#Power Spec of Pump\n",
    "print(\"Pump Power Spec With a Pump Efficiency of %1.2f is %1.0f Watts\" %(P_eff,P_spec))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bdbc1bc",
   "metadata": {},
   "source": [
    "#### Why does the room model need three temperatures?\n",
    "\n",
    "\n",
    "\n",
    "*`T[0]` : Temperature of the roof exposed to the outside. This discribes the boundary conditions for the energy entering the control surface\n",
    "\n",
    "\n",
    "\n",
    "*`T[1]` : Temperature of the ceiling. this defines the interior control surface exposed to the volume of the room.\n",
    "\n",
    "\n",
    "\n",
    "*`T[2]` : Average temperature of the room\n",
    "\n",
    "\n",
    "\n",
    "*`T[3]` : is NOT a temperature, it is a coefficient equal to 1 when heating is needed, -1 when cooling is needed, 0 when `T[2]` is between $T_{sp}-\\Delta_{sp}$ and $T_{sp}+\\Delta_{sp}$\n",
    "\n",
    "\n",
    "\n",
    "The code solves four equations\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{dT_i}{dt}=\\frac{1}{\\rho C_p}(\\sum E_{in}-\\sum E_{out}+ \\sum E_g)\n",
    "$$\n",
    "for i=0,1,2,3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19475c96",
   "metadata": {},
   "source": [
    "### Inside Convection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4832207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def insideconvection(T_f_in, T_vol, air_f_in, Lplate, T_surf):\n",
    "    Ra = natconv.Ra(beta=air_f_in.beta,DT=np.abs(T_surf-T_vol),D=Lplate,nu=air_f_in.nu,alpha=air_f_in.alpha)\n",
    "    \n",
    "    #No forced convection inside\n",
    "    NaturalConvection = True\n",
    "    \n",
    "    #Natural convection flux\n",
    "    if NaturalConvection and Ra > 1e4:\n",
    "        if T_surf >= T_vol:\n",
    "            airflow = natconv.FlatPlate(Ra,air_f_in.Pr,'upper','hot')\n",
    "        else:\n",
    "            airflow = natconv.FlatPlate(Ra,air_f_in.Pr,'upper','cold')\n",
    "        hconv_n = airflow.Nu*air_f_in.k/Lplate\n",
    "    else:\n",
    "        hconv_n = 0\n",
    "    h = hconv_n\n",
    "    qpp = h*(T_surf - T_vol)\n",
    "    \n",
    "    return qpp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b13ef4",
   "metadata": {},
   "source": [
    "### Variable Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6492e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "T_hvac = 4*60 #4mins in seconds \n",
    "q_hvac = 10000 #W, power delivered by the hvac\n",
    "V_room = 100*100*5\n",
    "\n",
    "T_sp = 20\n",
    "DT_sp = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef1bc1d",
   "metadata": {},
   "source": [
    "### Updated Building Model Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bfcca890",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code for building:\n",
    "\n",
    "def f_room(t,T):\n",
    "    global Lroof,eps,f_U_atm,f_T_atm,f_q_sun,rho,C_p,alpha_s,f_CC,f_T_dp,T_sp,DT_sp\n",
    "    global q_hvac,T_hvac,V_room\n",
    "    dTdt = np.zeros(len(T))\n",
    "    # film temperature between ambient and upper surface\n",
    "    Tinf = T_atmospheric(t)\n",
    "    Uinf = U_atmospheric(t)\n",
    "    \n",
    "    T_f = (T[0]+Tinf)/2\n",
    "    air_f = thermo.Fluid('air',T_f,\"C\")\n",
    "    Tdp = dew_point(t)\n",
    "    cc = cloud_cover(t)\n",
    "    pr = rain_rate(t)\n",
    "    rh = relative_humidity(t)\n",
    "    \n",
    "    #input number of people\n",
    "    #q_HUMANITY=(100*4035)\n",
    "    \n",
    "    qpp_sun = alpha_s*sun_irradiation(t)\n",
    "    \n",
    "    qpp_sky = skyradiation(Tdp,cc,Tinf,T[0]) #h_rad(T_s - T_sky)\n",
    "    \n",
    "    qpp_conv = outsideconvection(Uinf, T_f,Tinf, air_f,Lroof, T[0]) #h_conv_tot(T_s -T_inf)\n",
    "    \n",
    "    qpp_rain = rain(pr,Tinf,rh,T[0]) #h_rain(Twb - T_s)\n",
    "    \n",
    "    qpp_cond = roof_cond(k_ins,k_steel,H_ins,H_steel,T[1],T[0])\n",
    "    \n",
    "    T_f_in = (T[1] + T[2])/2.\n",
    "    air_f_in = thermo.Fluid('air',T_f_in,\"C\")\n",
    "    qpp_conv_in = insideconvection(T_f_in, T[2], air_f_in, Lroof, T[1])\n",
    "    qpp_total = qpp_sun + qpp_sky + qpp_conv + qpp_rain - qpp_cond\n",
    "    dTdt[0] = qpp_total/(rho*C_p)\n",
    "    dTdt[1] = (qpp_cond - qpp_conv_in)/(air_f_in.rho*air_f_in.Cp)\n",
    "    air_room = thermo.Fluid('air',T[2],\"C\")\n",
    "\n",
    " \n",
    "\n",
    "    dTdt[2] = (qpp_conv_in*A + T[3]*q_hvac)/(air_room.rho*air_room.Cp*V_room)\n",
    "    if T[2] > T_sp + DT_sp:\n",
    "        if T[3] >= -1:\n",
    "            dTdt[3] = -1./T_hvac\n",
    "    elif T[2] < T_sp - DT_sp:\n",
    "        if T[3] <= 1.:\n",
    "            dTdt[3] = 1./T_hvac\n",
    "    return dTdt\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681fbefa",
   "metadata": {},
   "source": [
    "### Updated Model Main Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0234a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho = 20.\n",
    "C_p = 2500.\n",
    "alpha_s = 0.5\n",
    "eps_sky = 1.\n",
    "\n",
    "#Inputs\n",
    "k_ins = 0.04 #W/(mK)\n",
    "H_ins = 0.0508 #m\n",
    "k_steel = 0.046 #W/(mK)\n",
    "H_steel = 0.0009525 #m\n",
    "Lroof = 100\n",
    "A = Lroof**2\n",
    "Ts_lower = 20 #C\n",
    "\n",
    "tmax = t_data[-1]\n",
    "mask = np.where(t_data <= tmax)\n",
    "\n",
    "sol = solve_ivp(f_room,[0,tmax],[f_T_atm(t_data[0]),T_sp,T_sp,0],method='LSODA',t_eval=t_data[mask],max_step=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2d47af",
   "metadata": {},
   "source": [
    "### Cooling Load Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60edfdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather['q_hvac_cooling']=-np.clip(sol.y[3],-1.1,0.)*q_hvac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8135aedf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wombat=weather['q_cool'].plot()\n",
    "weather['q_hvac_cooling'].plot(ax=wombat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "75bceec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 29182251.6044614$"
      ],
      "text/plain": [
       "29182251.604461364"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['q_cool'].sum()*15*60/3600 #data taken every 15 mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85047e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 8143342.28810111$"
      ],
      "text/plain": [
       "8143342.28810111"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['q_hvac_cooling'].sum()*15*60/3600"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7507a1f1",
   "metadata": {},
   "source": [
    "### Temperature Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "882cc042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather['tempcheck']=sol.y[2]\n",
    "weather['tempcheck'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23b1cb3",
   "metadata": {},
   "source": [
    "### Variable q_hvac Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e93e6a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Input_q_hvac=[1000,5000,8000,10000,15000,25000]\n",
    "q_hvac_cooling_tot=[870819.202910989,4279252.62903607,6667905.73483349,8143342.28810111,11639636.1737659,16750322.7737699 \n",
    "]\n",
    "\n",
    "plt.plot(Input_q_hvac,q_hvac_cooling_tot)\n",
    "plt.xlabel('q HVAC')\n",
    "plt.ylabel('Total Cooling Load')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b8a7a1",
   "metadata": {},
   "source": [
    "## Model Analysis and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78d90e1",
   "metadata": {},
   "source": [
    "Updating the model to include interior convection reveals several things about the system. First, it shows how unfeasible it is to use a geothermal cooling system to keep the temperature on the ceiling constant. By setting the fixed temperature to be in the middle of the room, the model not only becomes more accurate to realistic building design desires, it also drastically reduces the required cooling load. This is because interior convection adds a hysteresis affect which increases the time required to heat up the set point temperature, which means the cooling system requires less power to maintain temperature.\n",
    "\n",
    "The second conclusion reached about this system is that simple steel and insulation roofs are extremely poor choises for climate controlled rooms. Even with 50 kWs of HVAC cooling, the temperature in the room still exceeded 21$^{\\circ}$C consistently throughout the summer months, peaking at around 28$^{\\circ}$C. The graph of Total Cooling Load versus q HVAC only reaches a stable point where increasing the HVAC power has negligible impact on the total cooling load when q HVAC is well over 100 kW, at which point the temperature still ocassionally exceeds 2$1^{\\circ}$C. Reducing the net thermal conductivity of the roof should be able to reduce this point down to around 10-15 kW. Such a change would also increase the degree of the first conclusion, further demonstraighting how ineffectual balancing a cooling system off of ceiling temperature is. These results can be taken as a case study emphasizing the importance of proper building insulation. \n",
    "\n",
    "Despite the poor building insulation, this rough estimate still shows geothermal cooling to be a viable option with pump power, pipe length, and pipe cost not being prohibitively large. With a better insulated model building these costs would reduce even further. This supports the conclusion that low depth geothermal cooling would indeed be a viable means cooling buildings in Vermont over the summer.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdb81d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e15e744-880c-47ea-8316-e13dabb84e5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edd68cc5-200f-4e58-9c0b-ef35ede590a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae09cd0f-dbc9-4b83-96df-ba094f944869",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf12564-4ef3-472f-9f9f-fb965cb4e157",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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