geocene_data_api.ipynb

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   "cell_type": "code",
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   "source": [
    "### Import Libraries\n",
    "# Built-in system libraries\n",
    "import json\n",
    "import time\n",
    "from tempfile import NamedTemporaryFile\n",
    "import tempfile \n",
    "import shutil\n",
    "import zipfile\n",
    "import os\n",
    "import argparse\n",
    "import datetime as datetime\n",
    "from datetime import timedelta\n",
    "\n",
    "# 3rd party libraries that need to be installed\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "from sqlalchemy import create_engine\n",
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions\n",
    "\n",
    "#Get Data Update\n",
    "def get_update(url_request,id_request,token_headers):\n",
    "\n",
    "    get_request_id = url_request + str(id_request)\n",
    "    result = requests.get(get_request_id,headers = token_headers)\n",
    "    update = result.json()\n",
    "\n",
    "    if update['progress'] is None:\n",
    "         update['progress'] = 0\n",
    "            \n",
    "    return (update)\n",
    "\n",
    "#Eait for export\n",
    "def wait_for_export(url_request,id_request,token_headers):\n",
    "    progress = 0\n",
    "    while progress <100:\n",
    "        update_var = get_update(url_request,id_request,token_headers)\n",
    "        if update_var['status'] == \"error\":\n",
    "            print(\"error processing export\")\n",
    "            return ()\n",
    "\n",
    "        progress = update_var['progress']\n",
    "        print(str(progress) + \"% of data export completed\")\n",
    "\n",
    "        if(progress<100):\n",
    "            time.sleep(3)\n",
    "\n",
    "#Get URL\n",
    "def get_url(url_request,id_request,token_headers):\n",
    "        \n",
    "    while (requests.get(url_request + str(id_request) + \"/\", headers = token_headers)).json()['download_url'] is None:\n",
    "        time.sleep(1)\n",
    "\n",
    "    download_url_var = (requests.get(url_request + str(id_request) + \"/\", headers = token_headers)).json()['download_url']\n",
    "\n",
    "    return(download_url_var)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# living_energy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def sensor_summary(this_sensor_df):\n",
    "def sensor_summary(this_sensor_df,a_house_id):\n",
    "    ### Gathering data for the last month\n",
    "    this_house_sensor = this_sensor_df.reset_index()\n",
    "    this_house_sensor['timestamp'] = this_house_sensor['timestamp'].astype('datetime64[ns]') \n",
    "\n",
    "    ### Adding dates so that the function works\n",
    "    this_house_sensor['date'] = this_house_sensor['timestamp'].dt.date\n",
    "    last_date = pd.datetime.now() - timedelta(days=40)\n",
    "    test_var =  this_house_sensor[this_house_sensor['timestamp'] >= last_date.date()]\n",
    "\n",
    "    if len(test_var['index']) > 0:\n",
    "        data_last_month  = this_house_sensor[this_house_sensor['timestamp'] >= last_date.date()]\n",
    "\n",
    "        #Last Day Collected\n",
    "        last_day = data_last_month['date'].reset_index()['date'][len(data_last_month['date'])-1]\n",
    "\n",
    "        # Number of Days in Sample\n",
    "        ndays = len(data_last_month['date'].unique())\n",
    "\n",
    "        # Number of Samples\n",
    "        nsamples = len(data_last_month['value'])\n",
    "\n",
    "        # Count number of NA samples\n",
    "        count = 0\n",
    "        for sample in data_last_month['value']:\n",
    "            if pd.isnull(sample) == True: \n",
    "                count += 1\n",
    "\n",
    "        # Interquartile range\n",
    "        sum_stats = data_last_month['value'].describe()\n",
    "\n",
    "        print(\"Sensor ID is \" + str(data_last_month['sensor_id'].unique()[0]))\n",
    "        print(\"Sensor Type is \" + str(characteris[characteris['sensor_id'] == str(this_house_sensor['sensor_id'].unique()[0])]['type'].reset_index()['type'][0]))\n",
    "        print(\"Sensor Type is \" + str(data_last_month['sensor_id'].unique()[0]))\n",
    "        print(\"Last Date: \" + str(last_day))\n",
    "        print(\"Number of days in sample: \" + str(ndays))\n",
    "        print(\"Number of samples: \" + str(nsamples))\n",
    "        print(\"Number of NAs: \" + str(count))\n",
    "        print(sum_stats)\n",
    "\n",
    "    else: \n",
    "        print(\"Warning! \" + str(a_house_id) + \" doesn't have any data!\")\n",
    "        data_last_month  = this_house_sensor[this_house_sensor['timestamp'] >= last_date.date()]\n",
    "\n",
    "\n",
    "    return(data_last_month)\n",
    "\n",
    "\n",
    "def energy_variable(this_power_df):\n",
    "    \n",
    "    this_power_df['timestamp'] = this_power_df['timestamp'].astype('datetime64[ns]') \n",
    "    this_power_df.loc[:,'power'] = this_power_df['value'] * 220\n",
    "    # Borrow recipe from http://www.datasciencemadesimple.com/difference-two-timestamps-seconds-minutes-hours-pandas-python-2/\n",
    "    this_power_df.sort_values(by='timestamp', inplace=True)\n",
    "    this_power_df.loc[:,'duration_hr'] = (this_power_df['timestamp'] - this_power_df['timestamp'].shift(1)) / np.timedelta64(1,'h')\n",
    "\n",
    "    # Energy\n",
    "    this_power_df.loc[:,'energy_wh'] = this_power_df['power'] * this_power_df['duration_hr']\n",
    "    \n",
    "    return(this_power_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define API URLs\n",
    "\n",
    "base_url = 'https://collect.geocene.com/'\n",
    "data_export_request_url = base_url +  'data_export_request/'\n",
    "api_token_auth_url = base_url + 'api-token-auth/'\n",
    "PARAMS = '{\"username\":\"diegoleonbarido\",\"password\":\"stistutorapa\"}'\n",
    "post_headers = {'content-type': 'application/json'}\n",
    "filter_date = \"2019-010-01T00:00:00Z\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://collect.geocene.com/data_export_request/'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make Request\n",
    "r = (requests.post(api_token_auth_url, data= PARAMS, headers = post_headers)).json()\n",
    "data_export_request_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://collect.geocene.com/data_export_request/'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_export_request_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "663\n"
     ]
    }
   ],
   "source": [
    "#Make Request\n",
    "r = (requests.post(api_token_auth_url, data= PARAMS, headers = post_headers)).json()\n",
    "\n",
    "#Get Tokens\n",
    "token = r['token']\n",
    "token_header = 'Token ' + token\n",
    "token_headers = {'Authorization':token_header,'content-type': 'application/json'}\n",
    "\n",
    "#Get ID\n",
    "export_request_body = '{\"after\":\"' + filter_date + '\"}'\n",
    "id_request = ((requests.post(data_export_request_url, data= PARAMS, headers = token_headers)).json())['id']\n",
    "print(id_request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function wait_for_export at 0x1120d2510>\n",
      "https://geocene-collect-prod.s3.amazonaws.com/exports/a5490ea7-ff46-437c-80b4-0672c7af93ed.zip?AWSAccessKeyId=AKIAILDV44LTVHS7RX5A&Signature=Lzs4%2FhFOvW94RwOCqS3hmx7UHUw%3D&Expires=1604868586\n",
      "/var/folders/cd/d1kdqrt52tbfbjlm6vz9hd4w0000gn/T/tmpllo149dj\n"
     ]
    }
   ],
   "source": [
    "#Path\n",
    "url_path = get_url(data_export_request_url,id_request,token_headers)\n",
    "print(wait_for_export)\n",
    "print(url_path)\n",
    "\n",
    "#Temp Directory\n",
    "temp_dir = tempfile.TemporaryDirectory()\n",
    "print(temp_dir.name)\n",
    "\n",
    "empty_zip_data = b'PK\\x05\\x06\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n",
    "zip_path = temp_dir.name + '/this_zip.zip'\n",
    "with open(zip_path, 'wb') as zip:\n",
    "    zip.write(empty_zip_data)\n",
    "    \n",
    "#Get Data\n",
    "urllib.request.urlretrieve(url_path, zip_path)\n",
    "\n",
    "#Extract Data\n",
    "zipzip = zipfile.ZipFile(zip_path,'r')\n",
    "zipzip.extractall(temp_dir.name)\n",
    "zipzip.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function wait_for_export at 0x1120d2510>\n",
      "https://geocene-collect-prod.s3.amazonaws.com/exports/a5490ea7-ff46-437c-80b4-0672c7af93ed.zip?AWSAccessKeyId=AKIAILDV44LTVHS7RX5A&Signature=Lzs4%2FhFOvW94RwOCqS3hmx7UHUw%3D&Expires=1604868586\n"
     ]
    }
   ],
   "source": [
    "print(wait_for_export)\n",
    "print(url_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
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'5cf69077-0000-0000-0000-A0E6F850C43B.csv', '5cf6937c-0000-0000-0000-CC78AB77F5BF.csv', '5cf69550-0000-0000-0000-CC78AB77FFA6.csv', '5cf6965d-0000-0000-0000-A0E6F850C612.csv', '5cf69ac4-0000-0000-0000-CC78AB77F995.csv', '5cf69c5e-0000-0000-0000-CC78AB77FE01.csv', '5cf6a136-0000-0000-0000-A0E6F850C649.csv', '5cf6a3d5-0000-0000-0000-CC78AB77FCC9.csv', '5cf6a591-0000-0000-0000-CC78AB77FFC3.csv', '5cf6a71c-0000-0000-0000-CC78AB77FE29.csv', '5d1d286a-0000-0000-0000-CC78AB77FCE4.csv', '5d1d2a8c-0000-0000-0000-CC78AB77FE44.csv', '5d1d2ce9-0000-0000-0000-A0E6F850C2D5.csv', '5d1d2d09-0000-0000-0000-A0E6F850C2D5.csv', '5d1e332f-0000-0000-0000-CC78AB6E0556.csv', '5d1e350c-0000-0000-0000-CC78AB77FF89.csv', '5d1e3717-0000-0000-0000-CC78AB6E0574.csv', '5d1e38e6-0000-0000-0000-A0E6F850C2DF.csv', '5d1e3b4c-0000-0000-0000-A0E6F850C61E.csv', '5d1e4874-0000-0000-0000-CC78AB77EF2C.csv', '5d1e4a90-0000-0000-0000-CC78AB77F33E.csv', '5d1e4c7b-0000-0000-0000-A0E6F850C417.csv', '5d1e56a2-0000-0000-0000-CC78AB77F9D1.csv', '5d1e589c-0000-0000-0000-CC78AB6E0581.csv', '5d1e5aab-0000-0000-0000-A0E6F850C40B.csv', '5d1e83b4-0000-0000-0000-CC78AB77FADD.csv', '5d1e860e-0000-0000-0000-CC78AB6E054D.csv', '5d1e8a1e-0000-0000-0000-CC78AB77F32C.csv', '5d1e8db6-0000-0000-0000-A0E6F850C641.csv', '5d1f99a3-0000-0000-0000-CC78AB77FCFE.csv', '5d1f9c27-0000-0000-0000-CC78AB77F991.csv', '5d1f9e94-0000-0000-0000-A0E6F850C602.csv', '5d1fa1aa-0000-0000-0000-A0E6F850C612.csv', '5d1fa33a-0000-0000-0000-CC78AB77F995.csv', '5d1fa4d0-0000-0000-0000-CC78AB77FE01.csv', '5d1faee3-0000-0000-0000-CC78AB77FAF2.csv', '5d1fb1b9-0000-0000-0000-CC78AB77F9E2.csv', '5d1fb493-0000-0000-0000-A0E6F850C46A.csv', '5d1fb8cf-0000-0000-0000-CC78AB77FFB9.csv', '5d1fbb6d-0000-0000-0000-CC78AB77FE40.csv', '5d1fbe47-0000-0000-0000-A0E6F850C462.csv', '5d1fbe5a-0000-0000-0000-A0E6F850C462.csv', '5d1fd387-0000-0000-0000-CC78AB77FCD0.csv', '5d1fd6ac-0000-0000-0000-CC78AB77FE2C.csv', '5d1fd761-0000-0000-0000-CC78AB77F5DF.csv', '5d1fe157-0000-0000-0000-CC78AB77F9E0.csv', '5d1fe4bd-0000-0000-0000-CC78AB77F99E.csv', '5d1febd5-0000-0000-0000-A0E6F850C44A.csv', '5d1fefb9-0000-0000-0000-A0E6F850C41B.csv', '5d1ff396-0000-0000-0000-CC78AB77FF88.csv', '5d1ff726-0000-0000-0000-CC78AB77FAF4.csv', '5d200476-0000-0000-0000-CC78AB77FE0B.csv', '5d200843-0000-0000-0000-CC78AB77FAFD.csv', '5d2010f5-0000-0000-0000-CC78AB77F5BF.csv', '5d201305-0000-0000-0000-CC78AB77FFA6.csv', '5d20175d-0000-0000-0000-A0E6F850C43B.csv', '5d202b1f-0000-0000-0000-CC78AB6E05C5.csv', '5d202f49-0000-0000-0000-CC78AB77F731.csv', '5d2033cd-0000-0000-0000-A0E6F850C450.csv', '5d20f642-0000-0000-0000-CC78AB77F9B3.csv', '5d20f66f-0000-0000-0000-CC78AB77F9B3.csv', '5d20f72f-0000-0000-0000-A0E6F850C411.csv', '5d20f75b-0000-0000-0000-CC78AB77FAE9.csv', '5d210367-0000-0000-0000-CC78AB77FCC9.csv', '5d2105b4-0000-0000-0000-CC78AB77FFC3.csv', '5d211146-0000-0000-0000-CC78AB77FA8B.csv', '5d21158c-0000-0000-0000-CC78AB77FCA1.csv', '5d2163dd-0000-0000-0000-A0E6F850C649.csv', '5d21671c-0000-0000-0000-CC78AB77FE29.csv', '5d2390c6-0000-0000-0000-A0E6F850C622.csv', '5d2392e8-0000-0000-0000-A0E6F850C2FE.csv', '5d239b5f-0000-0000-0000-CC78AB77FE5A.csv', '5d239c2b-0000-0000-0000-A0E6F850C445.csv', '5d239d84-0000-0000-0000-A0E6F850C60D.csv', '5d239dc8-0000-0000-0000-A0E6F850C60D.csv', '5d449d54-0000-0000-0000-CC78AB77FF89.csv', '5d449d80-0000-0000-0000-CC78AB6E0574.csv', '5d449ff4-0000-0000-0000-CC78AB6E0556.csv', '5d44a1b3-0000-0000-0000-A0E6F850C2DF.csv', '5d44a1c5-0000-0000-0000-A0E6F850C2DF.csv', '5d44a1df-0000-0000-0000-A0E6F850C2DF.csv', '5d44fcd3-0000-0000-0000-A0E6F850C417.csv', '5d44fcf2-0000-0000-0000-A0E6F850C417.csv', '5d44fd32-0000-0000-0000-CC78AB77F33E.csv', '5d44fe1f-0000-0000-0000-CC78AB77EF2C.csv', '5d44fe33-0000-0000-0000-CC78AB77EF2C.csv', '5d450221-0000-0000-0000-CC78AB77FE44.csv', '5d450252-0000-0000-0000-CC78AB77FCE4.csv', '5d45028d-0000-0000-0000-A0E6F850C2D5.csv', '5d4502a0-0000-0000-0000-A0E6F850C2D5.csv', '5d4502c2-0000-0000-0000-A0E6F850C2D5.csv', '5d4502d5-0000-0000-0000-A0E6F850C2D5.csv', '5d4502f0-0000-0000-0000-A0E6F850C2D5.csv', '5d45a057-0000-0000-0000-CC78AB6E05C5.csv', '5d45a093-0000-0000-0000-CC78AB77F731.csv', '5d45b5a9-0000-0000-0000-A0E6F850C612.csv', '5d45b660-0000-0000-0000-CC78AB77FE01.csv', '5d45d2cf-0000-0000-0000-A0E6F850C411.csv', '5d45d356-0000-0000-0000-CC78AB77FAE9.csv', '5d45d367-0000-0000-0000-CC78AB77F9B3.csv', '5d485279-0000-0000-0000-CC78AB77F991.csv', '5d48530b-0000-0000-0000-CC78AB77FCFE.csv', '5d4853f4-0000-0000-0000-A0E6F850C602.csv', '5d485732-0000-0000-0000-CC78AB77F9D1.csv', '5d4857ad-0000-0000-0000-CC78AB6E0581.csv', '5d485834-0000-0000-0000-A0E6F850C40B.csv', '5d485f21-0000-0000-0000-CC78AB77F99E.csv', '5d485f42-0000-0000-0000-CC78AB77F9E0.csv', '5d4860be-0000-0000-0000-A0E6F850C44A.csv', '5d4860d7-0000-0000-0000-A0E6F850C44A.csv', '5d486306-0000-0000-0000-A0E6F850C41B.csv', '5d486381-0000-0000-0000-CC78AB77FF88.csv', '5d486702-0000-0000-0000-CC78AB77FAF4.csv', '5d487953-0000-0000-0000-CC78AB77FAF2.csv', '5d487980-0000-0000-0000-CC78AB77F9E2.csv', '5d487d35-0000-0000-0000-CC78AB77FE40.csv', '5d487daa-0000-0000-0000-CC78AB77FFB9.csv', '5d487e29-0000-0000-0000-A0E6F850C462.csv', '5d487e39-0000-0000-0000-A0E6F850C462.csv', '5d487e4b-0000-0000-0000-A0E6F850C462.csv', '5d487e83-0000-0000-0000-A0E6F850C462.csv', '5d49b818-0000-0000-0000-CC78AB77FCD0.csv', '5d49b835-0000-0000-0000-CC78AB77FE2C.csv', '5d49b99a-0000-0000-0000-CC78AB77F5DF.csv', '5d49ba89-0000-0000-0000-A0E6F850C622.csv', '5d49bba2-0000-0000-0000-A0E6F850C2FE.csv', '5d49c479-0000-0000-0000-CC78AB77FFA6.csv', '5d49c4b8-0000-0000-0000-CC78AB77F5BF.csv', '5d49cbcf-0000-0000-0000-CC78AB77FAFD.csv', '5d49cbe0-0000-0000-0000-A0E6F850C441.csv', '5d49cc08-0000-0000-0000-A0E6F850C441.csv', '5d49cc1a-0000-0000-0000-CC78AB77FE0B.csv', '5d49d19d-0000-0000-0000-A0E6F850C43B.csv', '5d49e1ed-0000-0000-0000-A0E6F850C43B.csv', '5d49e3ab-0000-0000-0000-CC78AB77FFC3.csv', '5d49e3d3-0000-0000-0000-CC78AB77FE29.csv', '5d49e471-0000-0000-0000-CC78AB77FCC9.csv', '5d49f010-0000-0000-0000-CC78AB77FA8B.csv', '5d49f013-0000-0000-0000-CC78AB77FCA1.csv', '5d49f0de-0000-0000-0000-A0E6F850C445.csv', '5d49f14c-0000-0000-0000-CC78AB77FE5A.csv', '5d49f21e-0000-0000-0000-A0E6F850C60D.csv', '5d49f74d-0000-0000-0000-CC78AB77FADD.csv', '5d49f774-0000-0000-0000-CC78AB6E054D.csv', '5d49f7b3-0000-0000-0000-A0E6F850C641.csv', '5d49f86b-0000-0000-0000-CC78AB77F32C.csv', '5d4a206a-0000-0000-0000-A0E6F850C43B.csv', '5d4b5650-0000-0000-0000-A0E6F850C630.csv', '5d4b5710-0000-0000-0000-A0E6F850C2FB.csv', '5d4b79e7-0000-0000-0000-A0E6F850C46D.csv', '5d4b7e81-0000-0000-0000-A0E6F850C649.csv', '5d4b7e96-0000-0000-0000-A0E6F850C649.csv', '5d52f148-0000-0000-0000-A0E6F850C649.csv', '5d52f89a-0000-0000-0000-A0E6F850C46D.csv', '5d52f8b4-0000-0000-0000-A0E6F850C46D.csv', '5d52fd0d-0000-0000-0000-CC78AB77F995.csv', '5d532305-0000-0000-0000-A0E6F850C61E.csv', '5d6e925a-0000-0000-0000-CC78AB77EF2C.csv', '5d6ee751-0000-0000-0000-A0E6F850C41E.csv', '5d6eeeaf-0000-0000-0000-A0E6F850C605.csv', '5d6ff115-0000-0000-0000-A0E6F850C465.csv', '5d6ff444-0000-0000-0000-A0E6F850C42F.csv', '5d718a86-0000-0000-0000-CC78AB77F9B8.csv', '5d977f2e-0000-0000-0000-A0E6F850C43B.csv', '5d977f3a-0000-0000-0000-A0E6F850C43B.csv', '5d97a5eb-0000-0000-0000-A0E6F850C61E.csv', 'events.csv', 'mission_logs.csv', 'missions.csv', 'sensors.csv', 'this_zip.zip']\n"
     ]
    }
   ],
   "source": [
    "#Get Files\n",
    "list_files = os.listdir(temp_dir.name)\n",
    "print(list_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# living_energy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sensor Type List\n",
    "sensor_type = {}\n",
    "sensor_type['id_1']= (\"living_temp\",\"living_energy\",\"ambient_temp\")\n",
    "sensor_type['id_10'] = (\"ambient_temp\",\"living_energy\",\"living_temp\")\n",
    "sensor_type['id_9'] = (\"ambient_temp\",\"room_energy\",\"room_temp\")\n",
    "sensor_type['id_20'] = (\"ambient_temp\",\"living_energy\",\"living_temp\")\n",
    "sensor_type['id_3'] = (\"ambient_temp\",\"living_energy\",\"living_temp\")\n",
    "sensor_type['id_4'] = (\"living_temp\",\"living_energy\",\"ambient_temp\")\n",
    "sensor_type['id_11'] = (\"ambient_temp\",\"room_energy\",\"room_temp\")\n",
    "sensor_type['id_12'] = (\"room_temp\",\"room_energy\",\"ambient_temp\")\n",
    "sensor_type['id_5'] = (\"living_temp\",\"room_energy\",\"living_energy\",\"room_temp\",\"ambient_temp\")\n",
    "sensor_type['id_8'] = (\"living_temp\",\"living_energy\",\"ambient_temp\")\n",
    "sensor_type['id_16'] = (\"ambient_temp\",\"room_temp\",\"living_energy\",\"room_energy\",\"living_temp\")\n",
    "sensor_type['id_17'] = (\"living_temp\",\"ambient_temp\",\"living_energy\",\"room_energy\",\"room_temp\")\n",
    "sensor_type['id_13'] = (\"room_temp\",\"room_energy\",\"ambient_temp\")\n",
    "sensor_type['id_19'] = (\"living_temp\",\"room_energy\",\"living_energy\",\"ambient_temp\",\"room_temp\")\n",
    "sensor_type['id_18'] = (\"room_temp\",\"room_energy\",\"ambient_temp\")\n",
    "sensor_type['id_7'] = (\"living_energy\",\"living_temp\",\"room_energy\",\"room_temp\",\"ambient_temp\")\n",
    "sensor_type['id_14'] = (\"room_energy\",\"room_temp\",\"ambient_temp\")\n",
    "sensor_type['id_15'] = (\"living_energy\",\"living_temp\",\"ambient_temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Senor Data Analysis\n",
    "\n",
    "#Sensor list\n",
    "house_sensors = {}\n",
    "house_sensors['id_1']= (\"FCFE\",\"C602\",\"F991\")\n",
    "house_sensors['id_10'] = (\"FE44\",\"C2D5\",\"FCE4\")\n",
    "house_sensors['id_9'] = (\"F9B8\",\"C417\",\"F33E\") #Sept (\"F9B8\",\"C417\",\"F33E\") #Aug (\"EF2C\",\"C417\",\"F33E\")\n",
    "house_sensors['id_20'] = (\"0581\",\"C40B\",\"F9D1\")\n",
    "house_sensors['id_3'] = (\"FAF4\",\"C41B\",\"FF88\")\n",
    "house_sensors['id_4'] = (\"F99E\",\"C44A\",\"F9E0\")\n",
    "house_sensors['id_11'] = (\"FFB9\",\"C462\",\"FE40\")\n",
    "house_sensors['id_12'] = (\"FAF2\",\"C42F\",\"F9E2\") # Sept (\"FAF2\",\"C42F\",\"F9E2\") # Ag (\"FAF2\",\"C63C\",\"F9E2\") # Jun Jul(\"FAF2\",\"C46A\",\"F9E2\")\n",
    "house_sensors['id_5'] = (\"FF89\",\"C2DF\",\"C41E\",\"0574\",\"0556\") # Sept (\"FF89\",\"C2DF\",\"C41E\",\"0574\",\"0556\") # Jun, Jul, Aug(\"FF89\",\"C2DF\",\"C61E\",\"0574\",\"0556\")\n",
    "house_sensors['id_8'] = (\"05C5\",\"C605\",\"F731\") # Sept (\"05C5\",\"C605\",\"F731\")# Ag (\"05C5\",\"C649\",\"F731\") # Jun, Jul (\"05C5\",\"C450\",\"F731\") \n",
    "house_sensors['id_16'] = (\"FADD\",\"F32C\",\"C641\",\"C61E\",\"054D\")#Sept (\"FADD\",\"F32C\",\"C61E\",\"C46D\",\"054D\") #Aug (\"FADD\",\"F32C\",\"C641\",\"C46D\",\"054D\")\n",
    "house_sensors['id_17'] = (\"FCA1\",\"FA8B\",\"C465\",\"C445\",\"FE5A\") # Sept (\"FCA1\",\"FA8B\",\"C465\",\"C445\",\"FE5A\") # Jun Jul Aug (\"FCA1\",\"FA8B\",\"C60D\",\"C445\",\"FE5A\")\n",
    "house_sensors['id_13'] = (\"F9B3\",\"C411\",\"FAE9\")\n",
    "house_sensors['id_19'] = (\"FCD0\",\"C2FE\",\"C622\",\"F5DF\",\"FE2C\")\n",
    "house_sensors['id_18'] = (\"FAFD\",\"C441\",\"FE0B\")\n",
    "house_sensors['id_7'] = (\"C630\",\"FCC9\",\"C2FB\",\"FE29\",\"FFC3\") # Jun Jul (\"C401\",\"FCC9\",\"C649\",\"FE29\",\"FFC3\")\n",
    "house_sensors['id_14'] = (\"C43B\",\"F5BF\",\"FFA6\")\n",
    "house_sensors['id_15'] = (\"C612\",\"F995\",\"FE01\")\n",
    "\n",
    "house_list = list(house_sensors.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_total = pd.read_csv(\"/Users/diego/Desktop/this_data/5d239b5f-0000-0000-0000-CC78AB77FE5A.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id_1',\n",
       " 'id_10',\n",
       " 'id_9',\n",
       " 'id_20',\n",
       " 'id_3',\n",
       " 'id_4',\n",
       " 'id_11',\n",
       " 'id_12',\n",
       " 'id_5',\n",
       " 'id_8',\n",
       " 'id_16',\n",
       " 'id_17',\n",
       " 'id_13',\n",
       " 'id_19',\n",
       " 'id_18',\n",
       " 'id_7',\n",
       " 'id_14',\n",
       " 'id_15']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_list = list(house_sensors.keys())\n",
    "house_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Creating data frames for each house\n",
    "\n",
    "house_list = list(house_sensors.keys())\n",
    "house_df_list = []\n",
    "            \n",
    "for house in house_list:\n",
    "    \n",
    "    list_dfs = []\n",
    "    sensor_list = house_sensors[house]\n",
    "\n",
    "    for file in list_files:\n",
    "     \n",
    "        for sensor_id in range(0,len(sensor_list)):\n",
    "            \n",
    "            if file[-8:] == (house_sensors[house][sensor_id] + \".csv\"):\n",
    "                \n",
    "                path = temp_dir.name + \"/\" + file\n",
    "                this_df = pd.read_csv(path)\n",
    "                this_df['sensor_id'] = str((file[-8:]).replace(\".csv\",\"\"))\n",
    "                list_dfs.append(this_df)\n",
    "     \n",
    "    #Appending the house together\n",
    "    appended_data = pd.concat(list_dfs, axis=0)\n",
    "    appended_data['house_name'] = house\n",
    "\n",
    "    #Adding \n",
    "    house_df_list.append(appended_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# room_temp_df = room_temp_df[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name']]\n",
    "# room_temp_df['energy_wh'] = \"NA\"\n",
    "# room_temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# room_energy_df = room_energy_df[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "# room_energy_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_path = \"/Users/diego/Desktop/this_data/csv/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # this_house_df['house_name'].unique()[0]\n",
    "\n",
    "# #  this_export_df = pd.concat([living_energy_df, living_temp_df,ambient_temp_df\n",
    "\n",
    "# room_energy_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_1\n",
      "    \n",
      "Sensor ID is C602\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C602\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58568\n",
      "Number of NAs: 0\n",
      "count    58568.000000\n",
      "mean         0.155208\n",
      "std          1.264450\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         38.562500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C602\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C602\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58568\n",
      "Number of NAs: 0\n",
      "count    58568.000000\n",
      "mean         0.155208\n",
      "std          1.264450\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         38.562500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FCFE\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FCFE\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58567\n",
      "Number of NAs: 0\n",
      "count    58567.000000\n",
      "mean        28.257319\n",
      "std          2.175627\n",
      "min         15.812500\n",
      "25%         26.937500\n",
      "50%         28.687500\n",
      "75%         29.687500\n",
      "max         35.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F991\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F991\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58569\n",
      "Number of NAs: 0\n",
      "count    58569.000000\n",
      "mean        28.449506\n",
      "std          1.935796\n",
      "min         20.937500\n",
      "25%         26.937500\n",
      "50%         28.750000\n",
      "75%         29.812500\n",
      "max         35.312500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f510358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d25e63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_10\n",
      "    \n",
      "Sensor ID is C2D5\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C2D5\n",
      "Last Date: 2019-09-30\n",
      "Number of days in sample: 1\n",
      "Number of samples: 908\n",
      "Number of NAs: 0\n",
      "count    908.000000\n",
      "mean       0.394067\n",
      "std        1.377029\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        6.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2D5\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C2D5\n",
      "Last Date: 2019-09-30\n",
      "Number of days in sample: 1\n",
      "Number of samples: 908\n",
      "Number of NAs: 0\n",
      "count    908.000000\n",
      "mean       0.394067\n",
      "std        1.377029\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        6.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FCE4\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FCE4\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58697\n",
      "Number of NAs: 0\n",
      "count    58697.000000\n",
      "mean        27.241421\n",
      "std          4.028101\n",
      "min         11.625000\n",
      "25%         24.812500\n",
      "50%         26.750000\n",
      "75%         30.687500\n",
      "max         36.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE44\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FE44\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58695\n",
      "Number of NAs: 0\n",
      "count    58695.000000\n",
      "mean        27.931466\n",
      "std          2.599107\n",
      "min         20.750000\n",
      "25%         26.375000\n",
      "50%         27.812500\n",
      "75%         29.375000\n",
      "max         35.500000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120cd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2db91e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_9\n",
      "    \n",
      "Sensor ID is C417\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C417\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58694\n",
      "Number of NAs: 0\n",
      "count    58694.000000\n",
      "mean         0.191293\n",
      "std          0.768257\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         20.062500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C417\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C417\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58694\n",
      "Number of NAs: 0\n",
      "count    58694.000000\n",
      "mean         0.191293\n",
      "std          0.768257\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         20.062500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F33E\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is F33E\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58693\n",
      "Number of NAs: 0\n",
      "count    58693.000000\n",
      "mean        25.464997\n",
      "std          4.655905\n",
      "min          5.250000\n",
      "25%         23.937500\n",
      "50%         26.000000\n",
      "75%         28.125000\n",
      "max         34.500000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F9B8\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F9B8\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58692\n",
      "Number of NAs: 0\n",
      "count    58692.000000\n",
      "mean        26.506830\n",
      "std          1.873484\n",
      "min         20.500000\n",
      "25%         25.312500\n",
      "50%         26.375000\n",
      "75%         27.437500\n",
      "max         34.000000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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aDIt4NzxOPZlqj0XQ9gj5cOwa3Tq5XuhD36zBUY/+pDl2p/sTAIC7zkj+9dminfhZGMK0yfzj2nZAG9JXkO2Fj0gDNESNE5+yylGhFegDiWTWGrTrv6bPXL7jkFHjjoLNunjsLUzWZjBs/n7l1UH0s9Deu7ZWnbg8G7qL0e7rfEbuea/bfOgv01VOUnCgOhA1NHT1bskJ97HnEazwXa05tuewNg/Cao30rs5MEgiL2EMlSufZwiDT63jmIqE6uokwYnKJeqYRgbBg2mfJSu7WJw2xYIn12EnUn6utPCa2RA2dSlBGsVv+RwGcCECpofUegLOS0kIAXfb8CADo15o/3Cil3IGs2Lone57Duc7f4apSbfDBsIhaMBmGjEAPhEUsF6Uf3+HNlJ+RWIdszh5jubhzjuyAsU5zSl8WNZPHAT/8O/J3z7bZ6CkvyXm9NGWuNBmamSfEKEPh57V7ceYrf6Dv/TNjap+Cm/+7TPM3+31bmSycDoLForkGv3LV8sh2bSAYYVX8ee1e/LGpHEeQberJPCEnUIxyrORSC5g5TMc8PRsnPWvOHwMA/1si/QZsPoECNaGLL+VY5+Usnc8hEBIi0TyixVTA69L1v39h1WRNi+qT/frg16tx1it/YPuBplfTls1l0K9eWqqGDkKIkxCyDMA+AD8C2AzgEKVUUYN2Auhgdn1DoQihz5cZPzRKKbr+6zt0/ZfRudarKEdzPTtYD9eF8A8XEwPNHLv9k+VYI3bBXpoHEKmLSBwCXa/FKrQCbBHnQJaxu0K6JbmVbXiEYx0w96XI3+U1QUxwLpCvAz74c5vmfAICJwS86H5Z3qEKhUBYQB+HdVWm2z5ZbnncCic4luI0h8R6yL5RmYXTMBgW8ZD7PdPjNVSN5vCGq3D161IuwJXvLcLFb81HLbyR4zwNXqAUH3iewC/efxqOSQ5zI2L14XjA1/x8UUyGLOeMXmRv3l8Np/wegsVn+wNnMqoQszhnahHJJq2Hiq7Uxz1U1zQEZOc2mXjb/RRKfRfh1dlqRJp+shPrKtHcEJNAp5QKlNLBADoCGA6gT6wPIIRcQwhZRAhZtH9//aJDFGHK00x4glzBCz9txACyBR2IFLFCGSGmtxUSqgrhnm2z0c1RJtnRidJFsQt0fYo2b5le18bYhcYoF+3fVkkfjzPkWgQU//5yleb4X/q3w4XO2dzMusq6MEJy+nxFRhfDcUCaAOuDm8f2xBTPU+jqkELGCNPv+jayKK+2rkg03qk6+Y50bMIy37Wa4yVEDVHjTcZWYXTfr25YbPqFzlnc/cbUfm0bOmSoE8aBGu373/fV6ohAt8qM/W6lMfM2q8tQq+YCANqHtuETz0O47cO5Uc/VgzQxrpjtB2sjK912TI6JqBsHbKBDc0FcUS6U0kMAZgEYCSCPEKIYMjsC4JaeoZS+QSkdSikdWlhYWK9GkshS07q5ela9BaUH8Y1XNUuwA65GnwzE/NgDOraSNGCA0dC1o/UUx3yUmKSt600JhPMBhjKMdt2Q3omrEzqshq9HDeMk4mn2h2qDeNytZruyGrqDALe4/idtU/4gv+svMc/hGhh5Yyhe/3Uzet07A7eP62163edLrCsZ/VvDeWPEJPebzCONAj0Ysp6gznDMRUdipoBQfOx5GB0CxnyEDnkZyDThM5+/5SAcELHQx4+9PyWoZhxv36UdW13yM+GC1JfjnObp+zzH6+Jt0el2zy2fjOGO9TjaxBH9/eo9pv6Dplyq82bX55FtA5VLyJzbKV0RS5RLISEkT97OAHAygLWQBLtSKuYyAF/x79BwKBpWNM6OCL3u7ElA2XJ0LTBfak7Rx+Yygiw/S12uRwS6TkN/zfMCZntv5967VLdk/1MmC2IHvuAyFkAWQnpBoP04rdgdLxyqhmTxeunBr/VxwNqz/hQl5+++nL7c+2dYOBGtMJlJ7ACkN5o0Yx2CgojcDBfqqAc1jhzDdSO752OecITpfRe4zZOOAKCAqMtpnsnlw98YwcVRLV/0vIwvPfcZ9gNAH7IDIxzrcO/2qw3ZoLsO1eFfJgle7fMycJvrU+4xAFhBVafdSzlac1PH1hkRga6HKFK4EUZXUoYSzpgXY3DoR5PJ105dHNV/kGgFfc6G/Tj/deOKIZqdn2V66NxJjfzST3Y01PSKujQUsXylxQBmEUJWAFgI4EdK6bcA7gJwGyFkE4B8AG9b3KNhiFGg+zzy68x+AnhjjCFFmv05u7TJxDKxu5qGzQwSTTgaR0N3mnxYket1ppOpsj37zy1qWrhe4wfUAXYoV9ZcdZrlTIvsRsVfAAAOYrx3pza6CYSZXQSRYj+VwgBrvMaVA6BddcTjOHPpYsnZ9xYEERkkiIDDOLnlZbpRjlxUZfFNQL85JQ70/Y7oqz5eXw9zMJOjiX+EnRRYnOxQ+V8GP/xj1Ocr8LgcGOlYo7ZLJwJPEv6IbB8fUil2/7tgO/7YdACjnHwTVUgUMdn9LGZ5b8eGrdvwyzqtaad9npFeOJHwUT9OdCyp17V7DvtNywT+3zsLsLC0AjNXacd9tPj/HgzR3rFl6sRoMLPWJr4SWaoRS5TLCkrpEErpQEppf0rpw/L+LZTS4ZTSHpTS8ymlySvD7pc+rGgJJJrfi4oaMwSgFejdCrMx2LGZuac6SH5mPwii8G6ox800JQU1slM0A35c7/wSNXWSoF6/hxUQxnc5uF362FtVbjA9xwxv6+z2F+jsuLsPm2sjYZHiOpcUFx92eLnnBMMiMuGHByGDLX/mqj1YsJX/Ub6cPUW3h2G/k1ckQadRoIcFCh9CEJ18YXR2UHJo681w5zjmoDvRmmt4NvSznKrwjLcafA2M6fWx4PVfN6OGqu/DdqMoUhwQ+O969+cSTe95Tj7vzYqdh3GiU4omyiW1+Nu7WsKxzq3ja6/VhD1DZ6P/cP42TNz3LN7xPA3vwXjKwUk4+blfo5YJvO4DrYmJl6HLYsNe1TS0WuwSyQXR+01ylhoTztIdaZEpqrCisY4w7mmUaqT6j3MXaI5Tqmql+oQfMA6SbK9E/FRVNFx1isoffTyl2u51fYg73J/gHTcnWYeKklFvlzpYPTskIaNMMjzN0gx67vD/sDZkADsO6gW62hdLtx/CDGGY1CyTISEIItb4/oYNvssMS9frPliMCybzP8oBxLzsGQlLPgHKMcKKlMKLIESnOsGs9Q2ObPcSpfuG4NZc96zndfzsvQPTheE45MxX7ma4/xlOpr1xCnRWschCfMv2bZRfmPydP7ZqFIWVWSNjvme5PiFNh4Wl1pro4doQ9lWp9mSrYff+PG301L1frIoQvjlCxhVNtCEcb/Y3IAl0XrlCBazv4wthFDbKtn+9hl7VI2mR1ilDegh0GSXZ1pqxfvD0J6Wav8PMCQa6WOaj7laYhWrqA9oPAXFIXXT01lcBAGe/OhdZTJWUx11v4mn367jtE228NQB0l52mxzklDauzpswdBeY8Bbx5YkSo+7pJqfu7i06Qz4hdoP99THfUUfNk3b8d21Xz98Fa1SlYHQjhFHmy5K2C7vxsOXrOUcvDRVvysvgt40TtDuY3mPKTtEyvdbUxXBcWKXwkCJHxNbDC+5XwGQCAvU6+iWiCcwHyBNl3YeLoVdsUn0Bni5C849FO1v3a55ped8WxJajThFOqffHo9LWRVRIACEQ7UQHAKrGEe982WervHi1wgIdBD/8QCYV0QIzbFh55ZgLDXPYzk1T7VtqVy65DdYZyhSxY2mUnRJTKwRJ6Db0inLTk9pQhrQR6bY1WA9AvDb9evlszqPSmEfZlOxNdCBlrQw8LyEAAxJMR0dCdVBWAPZgl/UWuWTjPOQd/LDFWr9lKteXcWOJ9Z7AKmP249EeFpPUc4ZcEnFOUB3McgoaKFBnEPA54+U5tJt/KXWpfdshjTR7Gj/KTRTvRm6hx6uEoESIsRANFqXr/q2W63+7VRk7ysCAiAwGILtZcoF5LAASpM2q1IoC/0nktfDpz2/onjY1waB3VvhC/qMUv6/Ziyh+lmhh1h25l8kF4rOWz/OALoLxMdT9PpurphQNB4zhRVmZOiKYml7ao4JqvImcnUKB/u0JN+Pr7CT00x/Q2dT1Odanjycm8u751XywqrXf7mirSSqAfploP/god+ZE+CqQcrTR/h0LqQD6lv+Qwrc2UEnwIQ3YfDgXgJBROTyYTh87cB0bucEVjmL1eTTteTUu01zG8Ej1mq6Friq1/WMV0AED+QSVbVDsE2+ZI2t1xvYyOQE/Ausq9vpIN62CuZkI4zRKo2AiMeAR6+eEqiJTvzF4qc5NsaDXKcOyjBTswwFGK/DImsoIRGKc75sFDBAwLWZvhAP47/d31DXPfxGUB5xzgJ2B9LTOF9nVsj+xz6rqFdZjyAgAGET5t865Dajirk4go1mm0+jst3mgkylI1dOO6cOPeKnQme7HAdz1Or/7YcK3S1rW7td+jIFJc+Ia1fZw9l0WWh6V20ELPC6OfgH4VB0a2HRBNwyrdxOZDTw0mPANA0obCzHJftWdTOCDihhN6gBWC7XR2tkBAXcYpIYJhWQNsveTlyDEalD4QJw1rBTql6Ej24QvvA6ZNZScZ/QpBT+a0QJSiWZ75XRLG3+VfBgBY3/OayPNYKORdczj0vULIOkuvMFOryfqYMERtSCNfy5rg+DOyHTLhwHjJ/SLe1vkLMhHQZG1K95d+L79sIupQY6RWdcrNY7XLgYHFwKafIYoUnRyxJ6lFc6YnUqDf55qq+Vuh0FVawGr0WVQb193doToc9X4FF8JwE77p6IWfNjLnCSjTccboNfR21Wugh5K097rneYOiffJzc9BeTs4bFDDGwCsCfeNe7Qp6ze5Kg9LFghXi+m+j2qJojN6spY/AclD1WidEEBBQSg029GjBDemI9BDoPU4GACwTu6OGIZmqkO3AL3onY4vvEvTJ1TqHirO1r/frOnUZN2+9pKV4gtIg9FSoH0V2pcTD4V4xTSvQF76Fk6KEZ7EkRfrUdT09boBKdtIMWRmhsnki4JVsyr+s5S8tJw7vZNjXodI6Nb9foVags5Vt2OLZZhr6i+FzItthA82vhNOdf2q4aCil6EbKDKaCSa43scV3CYa2lmybi4rOB3MRAGBsH8k2fqjLeO1DPjgHYZHiG+FoHPbFyDahe6fJv27GS+GzTI9HwxKxh+mxV8Jnav62iuBw6gTt1wLrCNUK9C56E+GMu5hT1XP19wSALI/2O8gs6Gg4h83CZnmLIpS/lF9lqSDbExHoWW7p/x0HaxEMi3jhZynqZYLjT4zn0Pf+yRRz1jvaX5nFL5UHAAU52kgsls5aECn+z6kmaDmIpKFPePF3g6nGFuipgleKsb7T/YlmNh4q042eQaRwrsnT52q02ruCL4PF8M7qzN6VSD+uYnLxFx0ZOVYD2bQz9j5oPqzv/okMWGvCLNObBntX46WftYOUEknInjFAsrVnhqQVR1gO1ftiyU6NCUdBcStjGNr/ohRR2r9dF1LG9NOlR/NjvVmwH7LAkBpZpdALIkUNfMh1qBNt951f4a+u2QCA7YelCbk0dxjTUKl8mLda8lPkbTOy6gkihRchhFzROUrkVmr+emLGOtzo+lLdEaft90gO8ZaCCmiTpJTyh7E8gi1Mrj89F7os4flqyN0FQ1UB7UYYmbpC371Cut+em2jECnT16Yp5w8xOXl4djJjUjijOxeG6EEb/Zxbu+3JVpKj1K54XMZlD38tqzHpeH5aiWt8XV0zRTg4jn/glsn2wNogZwojI3ze7vgARQ1hTZozA8cTIt59OSA+BnqlGQWzcp4bnPfODdqBWC/y6mFXtpKzC9cvVrLMRrSThWZknZSNW9VQ1ti8XSPclGW0Mec1hiy7TUsXqhuFrx+D0Qdrq8z6PpKH7nNIHFqRS+wW3JKi6Ovbgrd900TgmONlpvXLQmj20KGYST8w0dAfzPqyGblX5JyRQnO78Ex4mRaGQMYPlyELK72IqE/38kPQMuR3lg41p8mFRRAnZA8Hpw/deRoOEYGIQAAAgAElEQVTXtaW2o2SbZ/nSuROQ7p15iVlmWND2fM3fimnC8IgY7jXGwa6ytOPOLAYdABYzZe++896DGeRmzfFb3f/TXiAaBdnJDtWUwlJFKxw+igbPM18pGro/GIpQaszZuB+BsGhILGPxGVP0ItPb8Jq2CvSad2YNn3jO1tCbAC56U6WdPbpbvuaYAyKX5rbGK0eb7FZDC/e4JbNFeVt5mcuEtl3ikrP/qnYbeFhcFiRd/37u1cj2kWSj4XhBhvZefQUp/bzNask0U1crTQiCQxKwT7nfwNnbHgHAL6bA4jrWycfByEL9ykL9MJ+ZaZ69GNnPCLn5W9RVgxVhWLQEkIFyjPqBDCakcrdksvlxuRT5Ey7sZ7hOEClq4YUnXI2uAjPhPZSnOe/Q0Jsl7ZEZEyGedqozMcRT/pINL6SU4mLnz9zzFK23hppPrF7C+ibURmSjFrtovvGCSsmE+PlSbSJVFxKlXioz1hWBzT6bMsenr5Ds+te6JIc9O4Ir/Vphz2aoKnOrGa8NoC0pGeaM7/GOhVjrvRyEMwFpofZVICTiNvdnmqNmgttqsqkXHmwFvHJ0Yu8ZJ9JOoLNFcHMztLP6yb05gx5A6+MkJr4eOeoP2CosLQfDsnZImA+9d44s/LzGmOIz2QxDHT7yPBbZftHzsuH4pT9pWe9yqGRD9lRLmkphoBQAQBm7/bnO3wBAU429/WHVTj13k7R/rVf17PNwbu0n2h2MNquhezXRuFlBv3y7qmVbVbipCRjNUztpQWRbyW4EgI/DY6SNKkmA7NonabpBGOOxwyLFYMcWhIkHvcLm2YkZ2XmKSyyyj1sMm3mHaMJc72Bko1G2HajFt8xyXw8CEVlEFXD7PVpfyAGaw5yr4gvPA5HCKBrIfaVvUzQQJomOR/hGRRGz1u3D4doQHvtOUjpOjhCCqR10sFr7++47XG2IKAlHCSstwkGMdKzm/i6TPc8hgwRx0qJrOVeqYFePPBNlzyIjVxAAeEyczPVCtfzc/eZVthoDaSfQj+yilun6z8z1yINqgmmb7eDKI29QWpKOrlO1p8tr3pG3pAscAdUbvybnWGmjy7GGe0XjDVfQkViHEWrad1jSVE9zyqsPXuak/M1moQ7nrVAr4TiXTwMAzPca64myWs9/64bpjqod1b21i7ufBfvRTOinhk0uKjXPnF29XRrkIaY6YZW7wHAeAfCGMEH+QxqSNxRLH0bh3t8N5++Qyc8Kq83JygCAON2SQDerRqWAOV5RG7SMitFznR9ZLnHSHagOYG1ZJQY4+CYyChj8L5UurQLChuWybejpMGGeXCBlA7M5ArGAMgJdSUqa5VLHekW1H1e8uxDXTF2EUT20vxfbLmWYjpY5Zq50zlCfAYpnLxgU1dQ033cDPvI8ZmA/ZUMv2x3U2sz1mnUmk+iX43NjnaidKIlJ3GJbxJ71HRXvjI9+TiMg7QT6N8u1FWZYHmxyeJcminax2BO72hwNFPU33OdLQbKv1mZLbGxt5z6o3icsDxB3/KRGhfUdJJ9eEdnkpd+HZYk+03O3Zv9R658GALhEqc21DM9IgKGuLYQ+fEztpzFdGUFiYkNnP+QlTCq53gHHwilIjq6FPW6J7HOZxARvpnLEivz8Q/lSKT5/vwsN5+a7JS23tN04wzFNm2vL4SECOu9VJ3Ku6Wqeupr6IQoXut456ROqsWHuVzjq0Z/w7cqySMatAdQoiKhD23fdHKqpJEswD/eLYLnE6tiW8Mu/mYIxYYQEiaPnhLC68lSisbaW1+DCYbrCyoI6pvRF2P3waLjR3c7YxctLv2gdzeOOUCkSdhVo8xTydLVap3vuiWwX5foMSpcZncVxzpWWTv24cNCc4qIxkXYC3cruVbNDG7rnQxBB4gHy1CgOca1UEOMCl5SwQh2MdirzI++rkD8ml7VAn8uhd13oux5dTXjSLbH6c/zqHIEyb1dk+o1CJUNOtNDHX7vksEu34IcIghBR34cNk9SHDrLZk46wGmGg106VAc9q6Keuvi2yXZRr7KNdFdIH98dayZSU10o1XUWNCde1iXqMy+Un3pbqngbyrIsmC+2PAgC0qi2N7ONq6PNeBv3hfgDAo9ONMdosTuZwkTvnSZWjFHuzFur7evXMk06jOUlBr9rYShECQBWNkyyMsZGHBBET9XZ/5nhY53MIMmNq2gItrwsF0Swuf1yzV2M60k+mrGZd7deuXthott35Wl6bE3RlGrs42BqiFpmsHOzXczrNvAd0mlGJSBeknUBvC3NtpDrk0BSJ6OfYhuLqNYBDfU3HxxMBAIsgCWN3iCG1qigFAFwvSmYMOCUhWEO9Ee4QFheHVM0gTNVnnC3bveOFRwwiTDwo2mPknWbjxg2Y9yqcoh8h4oWPiSgJhlQBojcVsIK1pEoVUgPLtHzdyofFCvS+jh3ANrmkHAVOc8zDaMeKyPHdM6REsD9XSY7hTJ86mcQq0DtukjnFRWMS07EBqX8ph1NeAzcT1rh+BhAOIiiIEcoBFmTuC9I9ozRvOzVyx2zPNvdfPOB6X76viOOdulwBh3Wha8XpGQ0l7fi+I8AkqoehYzhcF8K5Tq1ZS60/ICXKfe65n2mniqO6tEEHqArGOOdiIFSLUt9FuEz4XKLiYKDP8PQyJqiikNas9OMapuKUrm/Wip1hhn2VRiesSAk+8zyIe93TDMf0K378+QrIhplATewmUwCoO/Lq6Cc1AtJGoIfGPQEAcFk4Mi5x/YSL39YyLPrklPgr3U9q9u+iBajwaPnS8ark0NoBOSqGSBpHv8AUPBX+q+F57FJuivCXyPZNTIxz8Oa1mNzlGdM2sxhJlyInVM6lG3jiOwtny/f/QidhB7zUj0NEjfSorlM1oEyHvkKT+qHnBbSDevS/3olEryhalUHgTJHed/H2g3jZ8xKmeiZFDrm3zQZ2Loqw3rUtUvu5uy4mutzJ4TOv3o+hDikenTg9KBW1DIWXuGSNUmcSezZ0nuZvjbr40V+BRwsxd30Z98NWMGFgMbJhXslGG1ooYXemWs1prdgJAnFjvijtu8IlxdEfc3g6nnZP1l2pto9bHelZbbGROi+f+/3YLubx+GGRGiJkWLPaua/NwxEOraYdqT9AgSl/lGri7lkHbNscL45xarN8HX5pdXu+KNnT2Qlcn6LPRoxd1kFLR1BRzawadeRqVlURHp2+BpvE9thYcJJm/1AH33k+faGJU/2p7hZPMaKctop+UiMgbQS60ylpM9c4v40MjC452p+2GAewaqfWhl1WIDkLA0VDNPt91A9KnBAdumXvg63gd2Yx1KsqvhBUx9FPgvZ+fbp31Z8OAPC0bo+JEy/DY6GLzF5NgzzhAESis0vPnoTVO60pUEeEJS3bT9SwOHZ5fJPzM90V6se1y12iOfKb91b8vFj6UANhEaMdKyJhayxEkeLWj40CbnBgMfDW2EgyiSfEL10GAC4aNn6hn1waSXMX2vTAd6I00d4TulJ7nkcryCKOVQt4qbmwppTis8U78bj7Le7xe1wf4krXDMN+Vk4dojnY5OhqIO3q4TcWp+heoa7kTBPWGBOCYMJV7+bQ1kauESlqqXbic1RrJ/APdaRguWvMJzx2pRYMixqiOhYRCmjmfD2F8xCHGtr783otTQcbaqgvi2gwEQGRH+GGE3sii/jRro1q5uvxyRhuGwGgc8Vc85DgOBLONBW9UlhcNW0EuiMomUYucf2MNZtLAQC3ZGurxTgJxRFEq20Ul0vJRCO7awX0eMcCtAnsRCBDp6UD6ClsRojz8cwU1LJnV4W01eKz27TTnx5Brs+tIWWywi4UoiZLt6Sc/QQ2+f7PULghGgJ1fNpQQFu/9NK9TxqOt5ouOZt/WbsPf3MahRhgHbLIgnYbY3psbeZRxp3bmXR5hxMvhM/B3aGrME3QUvFSQWuO8VskTykY5Dcn81Je53Tnn9zj13AmNQBou/G/ke2RzjXoLRizcnkFzlmc4TQpzlyjau6Cn+MopRQDN7xoet+QKMKts90XzrpD87c+7HFvKAv/dH2MLtRI4tWP0eZ3HarDTqpdNZBIQRiKswa3x4Pu9yPHftuo9Q2xmZpbKrX905qZg/Rx6Be5Zhnaha1S4lWmx4lichA5Gz7Hhn63GM/T4QXnC/jHfY9E/l4hqopZxVrrknssPvyzVP0jatx88pA2Ah0FakHhmo2SzS8rbIwo0RNnVWVKwrGE4SIXSlWBITr5lKSFIaP9soyyvN3SAPw0U3KgOHuehDMDD5s2/xzGTnla4FHT8w4WHo2y4pO4x3723sHdb4YPfpfMFjsqavG1MBKbRXXyYqOBlrgGG67dFJbMHD2Ksk05tq2yRDXI5nOWA4A/U23TzUF+8eQAPPivcCK6FmRr9vddxK/5qYAnQkWLOpIxv48O45yL8Y3nHox0qOaHfUdcrp6w5itzmt85EpnZDS4p/PE1onPILVO511tRbRETAECgEu/WGtkqFVT7w/AQa3bMiToBucGfhxtcX+EVwXw81wUFPPD16ghHS8CRKY8vtde/XLZbm+FarbVLl7nVCJp9VJsU1rtQdfSaFS7X3kxaKbKKSm12dEoLAHjT8yzW7yjD7kN1mhVF60/OtLhKC4HR8sPB1BWfTh+B3ku1UQ+fL1HV7quQl5q+PN4VAABPJ0lYsRq6813pXpW+9txrzLCeGkmxpmVeiqH+1xDKKkaHGGPP19HOmH+JutzcPeRWtb00oHHixoNaXRZi1WYprt3ldOAvjgUaNr9hjE1xfeaR0ONi2U7tIMRUu6SCgM3ei6M3jOMTUOBxqyavmaK28PNqj9bZqK+LunX8u4b79fFPAQBMKnqa+7zWu821LpHSuCsQKRjgKMXjLtVUU9uLIf/69DJzV/Av2sn9SKqLsmll7gAEAISDBlppFku2V6A1OCavsEWVI9kR7YKAdroopvfDElHev7+UTEijZBu6V6zVjC8eVqzTmqFYwfc31wxsY4urC6oJKmqBEgD4UZrcwzKLaqjHX0Bo7Jpy77f74Ngnf4ELorY4edCYeMVDSb7aT/6ARd8mGekj0B1GDSfiHBtnrvGK/SRHGVvVRUGuP7YoAgV3nz4YJf5pKPGrNkaROFCOViAEePxmnaf7tOcjmwGqRjQIcGje59O96sQiRgmV1IO979q84zWZi2/INmwCk6y4yccBAPr5zUPkDlb7TXli6K7FcMbBe8K9f6a6xA3oQiszxGrNVDKyW77G8el0GseEH16U+Kch1MmYFAYA7XYayb4UUAqs9uns9AeisJ4x6OrYi/20FQ72uRiegH71aG1yUTCbaCc1fH6V9QU1++Cx4PXO8rj4hU8WS3QTAzu2wlKxB/yZ6hjsnqkKsf4dtM6+0W5p1Vd6QBsLrqxAglziLwnHdVLH6v6qgMYUNMG5QCOMKBOJE5NAByTTVlCekEtGodOWT63PN14OJwR4cplkqseNJlkeehSqk2owYGvo0aHP9qpjwhe7GLMkFTg86tLtH8GbLG/7dOh8w3EWJ/Xl1IOkivMHyGur0+CLB0U2Twg8q16icS0BH2xRB0Or6s2a1P9o6Bt4N7J91OEf8EjWPYZzTJ0+8jJ1YNBcoG/YoIuu6XZCZNP72aWm110QsDaHAMAToYnYUDhey5dztbr87xbWJmt0bpOJz8XRkb/DRYPwRO6/I39/e6NqemD53mOFEOYIxpeOjISzxgKvnPuQ1UM7oUSzoSvYTIyrQEvMfQkFPB3gN2m8BU2ojrFbmqRH9yyAD0EQl7q6G7j4XgCS2zys65OuVPIF6d9msihVgNq4c598nGJ0T22W6XFrH4psBwXRwO/u/VGlBA5XqvZ2lt/cijsIe1bAXSetkp17VyDnsHUmMW5fD9ymHd9OiNrcFAA0wDF16cBm3/r9tkCPH9NvV7fD5h3o3jQzsv2dqCXOWdVXcpr09b+D7v6peFk4G3eEpOISB9ySxsIO3MIcc6cbIcQ46TCCeTeMKe88PFhxSlzFofX27V3ODngypA2xDAkU28S22NHxdBhQexDfecYjQHz4U9SGyUEIoWdQ91H835eYEJBK5/mPNxfaC2hfbBHb4YrgHaaibLLAaU8Ho/lHASHQOOFcWfkoc6qaJatNvjIrds06cv9SE3PMC4OA9TM1u3r430df/zuGU3NJHdqtnYJW+UV4I/fGyP5A2OI3ZbIMedw1llj+EUryOPZ5mbVy6h8m/SBnmb4yazP6OrbDW8mjLCBYvFHOuhzxd2wWi7FV5Cg1AMZmSu/QbpfKRR7WJXGxdBgOYqSvbbtOLQ7S1sUPW+QlDkXw/b0gdVI0mCPst4z+AQDktANy2+N5mevfCQHdHWXo7F8P8d9qW8kTRv54PURGoH+9dJvFmclF+gr0VUwYXhvzmFHHYPNwQSVTrw4+CHBi+k2j8KkwBpcH78SZ4lOG831uJz7/xzH442412sJS9DLebpZUzNBGJspgzLgz4RCtOdejYaao5W0JCaLEpufiTEiL3kFtXR0qSS7eDp+iPRaowodLjX6BKpleIHOGccUDAFcHpUzSE4PPYpY4xHB8fGASxgSk2Hz93CWKFN/J0URTulrH71fUhSEQc9pVs9JjZsj82GKF9pHWWRmGC3WwNo91HqcWMharTXjyAeDTy5n7mlMphEaqtLgbRbW4R/EBflQOKkqxeKs1lQE3oYnBcIeswc5/Dd0dZejqkO6n9O0asQvEXqdia1vJke/crIYU9gysgBlECnQn5iZPTe1VVqDzyNUUlP6GA35ZpA0y5o2YoUImRcuHNAG0De2Cw+XGtLC6Gq192dzxDAAiM9GMFGPP8k000kqgH+hxDv+A2wfk9wDVx5QDQL55dZluG9+NbHtdDvRrL2l4s8XBuPYkI/8LABzZubXBUQQwmvy9zAdUpsZo/37XCdBjtUuihmWrzLTJbxtds2DQs60a+bG5RBrEW6nW7hcWqPSBOD34XNANzF8egQ8BVIadhhA07F7CLQKwQ3+eDj+KKqukwvXS2/9uZN962hmllG+bDIki/hG6BSX+aVjkOtJArHTHeDXaqbiVDxWONvpbGM6zwovhszAtfGL0ExlUZFg7KkM9JKKmY3uojvg+PjXOemLwXqwXGa2PYfU8BCPDpwJXVpuIKeucoGq+GIVl/AvmPI0bRlubcM7rba5oUADeTJl6oZiJhGLCRbuTXSBuH8o7Se+cI0jvSUAREGXxUtBLvVbWZEvLa3C/W1uuj8WVoY8i2+0OzJeoaSmNSsk8vEY22ZX+zq3LysNxvaRVxyiHttD7xEe+iGxnlhuLwLNgOeQLDxiLnjcW0kqg51/8DgaKH/IPXr8A5F4OD3SGGgHTOtMNP1WF/uIcVcjq5/2Vu2IgRwInh8DtAwZfIm0zkTk8xrd7Wk1Cd/9UOIg0GHbSAmR5nRAttE6MfQCri8+O/PnBVSpda1lP/mrkl3X70IZU40CAP8BPc85Hd0cZtlHdcvqTy/G653nD+WZkRwrG9FYF/isXSSYUvcPTDAu2qoJv+kpj1MRrs1UTAiEEdQ5t5ItSuu76E8wnchbPhi/AQ+H/i+lcBW7R2kZK5IIsOT41Y7QkpLZ7ntgP44P/US8oVROM9hC1707XhbeS5R9hAe2LEv80VEF97+mCzpGqYOlU9G9nzfOS4ZQm7PL+f+Mef1CQicsqmRyIFWqh6Fr4QMIBDOsn8eoUBSRzAwGwu0YebycypjnZ98X7bVlsZHwJrWplE8ZDeZi2wDqfozRD5s/vd07Mvqis9tKE86xHqgKl0HgQQrB0rBo2igdbgT7REThkbENRpSrwsyus+YCSiahvTAjpRAiZRQhZQwhZTYhUDoUQ8iAhZBchZJn879Skt5YQjBtgonE4nIDThfMC9/OPA7h4RBcso+qHXlLcFmbRB2N5DlAOlHhujbw+6xXgwcNAK13Ny1G34kBnVchT4oAAZ0RDD1MnMtxO/kpDQYejsLSjKoBYjpfMdqomFKl7KYSx57AUteAKVePZsC49nkGt3oQQ5DuDBnTgpzm/HT4FJf5p6Ng6A5sfPxVTrxyOE/qYx6ArYPuO5fvo1EYrjAiAcf3U34VnUnn78mEoncTPGH2kw2v4WRgSMekoiHWyUXAoioZO+qjPfz58rul5P5fcbtxJSCSSagPV2W4ZBYHFBOcC7n6MuQcFO1Wb9pth3SdaVwG3TO4WaN0LA/1vGm6xPCy/a5/T1J1fXQ83DWKUYyVak2qgsBcyfNJvVe5QfEUUVdVyJIw7AztEeaLaKvkpCIBvBE4xiMrdoJTiNMqv0LSzQo3A+Z9+tQmACHLIoK8VYo0sysrS5jfsHvtSZHvIaK2fhwSqgOcHGIR6pyp1lZRbuw3wx77KTiRimcLCAG6nlB4B4GgA1xNClEDN5yilg+V/3yWtlQzYslUAUJ6lZdxbRPvADH8d3gnPMJEsB49SbZL6n35gx/i4GfSVjbg46UGsP/4VAFrN/gCVltlThZNRFQibahbH+F8Euh2PGobTw+NyoKf/fQzzv4JNFap5JMK/MecpjOkmLZvblhyBnbStppCCgk2tRhr2mYHlqmbB2uCdDoLRPbWmmdViF5RTrUlBL5R9btWGrE8VB4Cbx6q/d5wmcmz39sKVoTvwj9DNeDt8Ck4K/Cf6RRy4K1Ruk2pq7AunW52IBGr+iS1td4HlcwwTzVGXxdhCGbnF6LdUTQ56LHyJ9viTJRGK41BmESqRhaH+1yKHKYDpitAdeQMuZCKXJux6ER94JH4llP4RiT4pEFWfy1EyHw8ObcPHHjkuf6OU3X3yEUU4RLWCFACwbS72VPoxNcxPritdr5ox75IDGFhctFN+X3cG9nQ6xXCch4wcbR5L5+O0/XRHR45V4PkBEOtUof1n9smaw5uWG3n8GwNRBTqltIxSukTergKwFkCM5dYTj5vG9tRURy+oMZZ6e5XDjAgAHVtnYiEj8J0eo5OwdNIElE6awC3EzEO8yYU8wV+NTJT4p+Ft4VRU+cOmtr8pt0g+hLAzAx+Hx+Dlnu/A43QgBBf2ozUGdzImWNHt84CQrNW4pWX6faErsJfmadKcBaf1+7Lx94+e3R//DBmryKj8Hdr2K+amCcEnMDTwuuE6Fp1aq+2YMMBoZ+/CZPwSYj2NEhC8GJYEiTYkleCR8KXYpNeAY0QRwz/+JIe0jXU+L6cWJE+cJYbleGrTTfOnUpxZwUXBe3B84Fk8HpIYRfH1jSgrlnhaNoj8T/aIGin5jDqlNldBOw4ipGGeTATaq872HmzRjWA1uuQbmS/dCh9LZgGCdXJyk1yQhVKggHDMmkunYun2Q7jU9RO3va9R1QwVhoVpMiMPm/reYH6cgaeVdYLhU1edhvUnv4cTAs9olADHk50iXDsup0NTjStYfdBwn8ZAXDZ0QkgJgCEAlMKeNxBCVhBC3iGEtDa55hpCyCJCyKL9+zmMcnFiwoBi3Bb6u+U5L4bPxpfCMbi6gO90UYRTlV/VaM2+I7NqJ5HrqHKe5WkxY2S3fFCGjuCzDEmLu7foNfRulxN51l3ha1CW2QsupohAj0KjxkO2/oq1WyQbpBCWnFnfiUdjROBVBJgQObaQM4tleSdjv06rbpvjw2fC8YZzdyM/0r6GoijXi+f/OpgrsCcG78Ub4QmRYy+Hz8RL7fna9rPh83FO4EG8LJzFPQ6Ym5AUvBA2ccYD+Frg5EAwyWHxmnPiwZ1hrYY6V+yPbbQdZjAZt9s6SsrNraHrAQBTwuNxeVClkDirUtI+s8rmc9pLUOyQE6Qy81Feq5rD+hLG5DDuEe53EqHKKOqP2qO0CsBLv2xEBoKoduTg7MBDGBeQ+YS2zLac1PKC/Hqpz+iZNl0ZCHnMHcws8ttGzxjvfexZmPXEVfjpiWu1gQUPt8bvq0vhEIORWsAAv/xiYyBmgU4IyQbwPwC3UEorAbwGoDuAwQDKAHBjzCilb1BKh1JKhxYWWkdHxILe7XIQhisSC31DibY4ckl+Jvzw4pbQDajxWttv+7TLSZggrg94j87NcMGfodqJp2ZfjjMLv8NOr9HJp2+7w6HuUJxxAPDTcilGeJuo9v8tJ/XElLBqkw14+dEigw/9aBobzcYk3xO6MuIsbQjZnHLpPaf2Na14M0/sh8fDKuXA0+ELsS5bX2JPAcES2gsAwaJSvtbkdBAM979i2qbnLPwONbzQxVrV7FCi01xrR/3L9F6A8Te9LHgX/0QAX3BsyIDWae32S20JytrsQ+HLMJsJJa0lUvsO9NdlyEo3wsVOWVN2eXEyU0UolzAp8fLKj2UUJaDoqlRg8mQhO0Or+e+sqMMY53Jki1VYSntiA0Or4XU58D9hFNecpce5gQdwbfBWvCToJl2nC7sPqSa7d8Pm1a2yfPHF/p/zyHT8LqjFy0d9OghHVs1CBlWfl+9LjWCJSaATQtyQhPmHlNLPAYBSupdSKlBKRQBvAjBxtScHq2kJSvzTUFSgTdiZectxke25m60pZz2MQ7G+3Z9ookyvS6X0nSlIQopSCgengYrBQTETsahmqtjc2U7KCuztVON++7TLxQqqLuH77NFOjCyClL+0PSH4XGS1M00Yyz0nFsTa94a8rTh/tIpaPkkVIcA+tEaJ3ySCSoctfVUSMe6yP0dl3hzZXTs+g0xJvW0HayOJbGaYIw4wPSaYxKzvoepi+aiVj8jn8j/1TCoJZtFtNJkUkYOYI6jPv2BoJ7wePs1wnpJJ+5FTdSASUHRSzDVZBVi9W2tesYpCKsjxoi0OQchqa2pLnxiUslkX0974XuRP5hsrpd/mX6Er8UQ4Nvrq1W34z9Nj1CNGdsy2UOWNK87C3YlCLFEuBMDbANZSSp9l9rMGzrMBGAmfk4QhnVVb8YiuWs2Sdarx8M9xaiRIRpRz40GizAwAIDp9OCHwDG6Wl8kipXBwHvCRRQjXtJzLI9vHVXwOAHB102p0huvHWMEAACAASURBVLhzBiz7XcjKVplgxKPdx+SIjgvEQFlwFodBU/BFcZi3UrXNo7vpVj7M7/jN8t34WTDPjAWih4gq+JG5D2+SsUpYAmAaWZVLalGRWQIA6FqQhY8FYz4FSqRxVR1ki1kA5yqVu5xu/LaRSVCj1FDsgoUQDsNDwvCIftwX/htWiiW4KKiltKjgOPb1EIkDJf5p+EgYG7Ppq+e1sU3qAIAHD+PfvbWUyocLpRyMgDs2c0+iEctoORbApQBO1IUo/ocQspIQsgLACQButbxLAsEmjehJggDgBDkO+prjuhmOsXbzaPbxWGA1MC2vi6Lbb6XFCMCDLfursWpXpWbCUB4ZtuC1GDZitGEfyVITXaT7qTdd0F3K+lwq9sAPwlGa5XO86egN61YlDJREvxeJ8hvWox0LaF/cE7oSfxUewXdd7oyEud4WvC5yzsodUYoyM9quy+HQJFXpMzOrEd35fn3wJowNGDOXWehrxuppCfTRNqzmLTWM31lVNANuUfKv+NxObKUc3n+X8R3yiPa7HNO7UPVFrPhYUzNUj6y9C1CIQxHT4+nBxzFX1Cb6haJMUIBWObh6dFf08r+HwZRfvGPD+b/gz/4PwuONjxzv0YmjsPem7bgk+C9c3uFr7BwkRc4FneYMmMlELFEuv1NKCaV0IBuiSCm9lFI6QN5/BqW0HpWR6wdWs+YJteqAJLRbZxpn5Zpgw8nneUM/Vm1R893EIPmUCejndWrq+J7D0cl/+rc3apFuwXhdd/9UXBG8A+WDJEfz2cGHcU3odpQyH25/R2nU5yUasfRmfSeOHK9Wg9XfZpowFitILyxtqyZwscv6zgOs08DBhC3mZ3s0zmcxSysQrSZLJSN5ung0CrvyTS93hyQ2xvtCV2j262kJ9Br6FaE7TZ/LIov4UZlVwuzhdLo7+qT09PmDVHv8F9fih1V7IFCC9b2kibK4lQ9bRKlvXJt/QjfHHrSu0GZnTgmPj2zzVo03Bc2jWjI9LgThNo2M6dXvKBx9Xv100qI2rfDB43fj3auPhy8oOZE922IvjpFIpFWmqAJ/SJ3dTxtg9FAvLJU69cmZRra1h8+QZvqLR0gJEw3V0RMd5QLw28RqG1P/jE7+k5fpluLWGTgLVXMTpcBJfdtCgBOzxCHI8GgH+lpqXRzAY1W0Wod4usaKTM/qvsl2QdXAh6ViD3wrHI1WfbQRPgY6BWYw5PhckdbNFIZpCkPrcX7gfrBv0q+9umzvWsDX+P4rnIgS/zTcfiYnSYdtkm5FqLe/s+P3D8bhd6RjE9ofmAdLMAJ9tcgfN60zPbiXKSG4fHs5nISCyiGeQzrn4dmwFFraY+Pb3Ht8yZSADFOjhv61KEUcfd36CsMxZbz6QzFS8dYTnRdKJrqe663Dc5OFtBToHZlYZb8ZPagJHA6C0kkT8NjZ5s4mFo3pq060TVjP8OhkqIS/W1mG+05Tifz15eSimVl+vs0Ytqgg3rdghcm456QMwVnreGRW/Pj2hoICeP0StRTep9fxkqwIzg4+jBtCNxkm71VMPL8e3eQqS139H+C60C3IyzT26/jAJFwRvEOTIwEARUwC10cLdli+g5fjD9pTrHLUmDlFeZgnHhH9JBYstz8TzrpC7IptraW+dDoIlolqTP6pfaQVpBLqR0A0WdwAsLu3Ss/co202ljPH9aaqlyZKkTsl/mn4NHuioYlK4pOVmTIR2DdeEuSlXcwjo5KJtBTobOWabhzN5eNrJG3l/tPiHJjNDIU5Xk0BDMJUQqqoDWq4pfdXGePQh/lfBQDcGbracKxTm0xMuZwfXZAIQfvjGmuWQKABky3nwrF91RDXIXKCFutvYZGnM+VNEcZzzwOADJmcTHJuEm7frKeduayU87dYR2mxcHLuu3yoGpu/H9w0ES70An13Z36iHg8fC2Mi2z4EkZurmv72QnUQ9/NJTlJBZjwlBNhNtXV/269X80iUt3siNBG/CgNRCe13fwxTkYwtN6lgw97onOYJgVwqc/EhY9RQYyAtBToAvHHpUfi/kV00iTUKRnTLx/x7xuKKY0uS3g4ul0tsFyYdHfIy4DWpZrO1vAYdmJXOMbrwOo/Tgf3IQ4l/Gj7hRTYAMfG0RINZt1XJfhCrVQsh1kI91t+EAJGYd7eTRMbU5v2c0m0ACrK1GcaxRqLECzayaWS3fIsz+VUL9wWtV1kD/W8AAF4bplZx6twmE4tpL8157bd/Hdl+aeIQ9PS/Dx6uOLZEY7vv5dgFoqvZe49sdmlTJ5N4yeyLhBADt39dTklkW+mLycLpuCx0t+HZHpcDLjm218sxB+qLbWBg7PS68YDKDvFzD/P7KNlIW4E+rl87PHwmn+IWAIpyfTFpig3VJiM29Fidog16WuwgBFi245CG01l/3MNMhnU62+LTFwzSX9LkkGgTVemkCdj4mEpgpfhiUgUnk3wQzWfBC2u976vVmN3uCnwj8svxVSIbJf5pqHapIarSc7T3KmeSwKv8YdMwVjaRJ9IuJqpnWElrDGstRb+M2C+Vh6vJlOgXeL9kYKTqpIzlM1WKfL/1u7FYx+b9TNTNg4eBcyZHv2E90LFImjj8RFWWKmqCmLGyDPuqkl/JKG0FelOBwg5Yn3nBjNs5UQ7WHK8LT4elRJaHQ8Zycexkpufi6NMuepxvqsH2U6Ls6fXFvSGJfnZKsZHt86IRUQo9m4B9p183WNNmmL3/C+L5uDF4veW1H/yp5jPw7lLuUPMVeluMi2ElxmxjMVNdWeytDOC5AxLdc6daqXCGoAt5XCuqfeVsozpYY/l99RHE7J//GGPBqZME+Jis0S3lNfj7h0uwtiz5Zh9boDcQu2StRJ8JFwvCFrG4icCjZ/fHQeRiiP91zC3gV+OZ9c8x+PeEvoaErF5FiRPoVt8iRT1DPhvSHsN9G37jD4WTMND/BjfTkGcCiAXxtIqXSQwAS7dHiZkHcNlIVnAaj4vDVB9KryLJyXuazNX+YoE6gY2U7djsqjBv1XuR7e0Ha7GHaoW+4NSGV94QUsv2sc7WePqCJ7z1fo/mClugy2joN11t4kBLJc4cLDHsVSAXNSbhWl0LsnDVaGMCVkOQYmU5KvSFhuubHKZHJbJxiEMvcLiOTzkQDQ4HsPBeaYI4vpc1D9KGvXx7fyxYwRRz4XVFl2q1lFyOzHuyinZDiX8avmeIwNrLDKWtiXlb9NFTCocQhRSLvpmqrJBCB/XeDgdwz6nm1NgsXmWKoLDgUWQ0N9gCXYbZNx2zcKqHFGsMwffXYVIaet92qUlFjoaoXWCVCEqs+9DsUE0wsbHILGlV90JjhMXnS3YZ9plBY0YCQWGOF6WTJuC9v1lTJfHDPCXwuP3/c97AyPauijrLVZJ/xI2mx1bvVjnBW2VIwvogw85Ze4Zqq27P4dGnRNXCy+SEud7+dzE68BwIYTV0gvOPsi6n1+RwULHlN0IEhIwWL9BTpUzGY2qoD5R7r9sj2e2cujV5XYKFWirA9l+ie7JW1z+jekjOLp5z8oTearTPPk74p5LxGY2mVw8zMwoPPYs4xSJkrNhpNAd2YUJ/7z/dGN7L8t278s1XcO9eoYauKmyfkwWVwMvN5D7s5mQ412WoiYEPyu0IwIMdunKIDgK0zvJgxYPjsOFRfuEKhRJk6pXS5JeghVfcUOLtd373pGZ/Y8iaFi/QGxusvVbP56IcSqSgXybzjsxYpeWRrq8ZIBYkc6LSPCdRNvUYbqRkavIc2ROHq5rj3M3lhuOFOVKYY6x1apmGWR5e8aBKCXt1nGazMka45mWo9mXlkYtENXTRKsKGV1RlOyOMXX2jmDiYdzyyi0WsvHxers9t2p7rT+iB0kkTmEpZqZHoCnV1x00fRTkz8bAFuoymbvdNBdY8PB5fXs8PeUsk4okXt/o7mRjfT+IZOY5jy2YnhH+MMdLCspnN8SDa+2UyjmyJYiB29C1WzSJ7K42a8z6Zgvfx0ES4ndqWZHnU55pFag30v4mB/jdBnGq7TpLr9CrUtywItL4Ntn1A9NVKqqOcWJz5V2M1r8aCLdBTiGRqsor2/8JfBwOoX9hWpsfF1cDiRUM+NksbeSN+xCO75+Pp8wfh9Uus6W4f+Hq1Yd9pAyWzwolxJmJFE2IupwOjexbA43JY9oWeYhrQhqnqaR/uGN8btfChxD8NbwinG5L3XrpIzWo145ivRBbat9MSkf1NTvSbJ/bDl94z8GLRo5rj2w6oRTOO0yUC8eLsmyra9WdoMcJBPP+TsUxmstB4RNdNHA22t6XKYBcFZw7uEIl2YaGP9EgkGuvbI2hE8w6A846qXw3SCQOLUZQ7Ekd25psUnrtwEG79eLlhfywT1tQrpbjuXZykHgXztxorNbFhqsf3LsTmfWriTbQ6Aev3qFEsndqYrz707W+bqzpFX/FehZ652WCpvBSnKiAR0LGrnfQR51pUbpiDrZt2YDCpBCHJrwHU4jX0aN9MU1rKxYNogi6ZHEVx9ViCtHfebVLx2ykrIj2GlrTRlAhkkePlp+jH4xRtCLwurQA/N8rE1T5PFcwuHefAf85Vo2fWllVqjrE/x8Z91RGHvQKWXbI2KGgGUjpp6ACwzSURtuV+ci5+996CL733wxmqf2hprGjxAl1BPOMlEWOLp9Art02zsdswNGBl05Qm2+cuHIRje+RzV0P1RaLejyWuYsHGZbOPYjVlHsKC+pvp7etnDDYvuNxdV8R8y35tEQxWgzeg6fzUMaHivE8N+3LKjauwRMMW6IlCjB9fNK2yJaI5dMPZQzriw6usOcnNYDYOEtUv9TUVmeEkJu5eP+n43M56O4FZ9O+gdYou4JiNmjL69TT6rGjI3CyWKNgCXUYqTODJfGa0EnfpAlZcJEsjT/WEYirQ41k1muy/99S+OKV/scnR+iGaBu+Kw1ak/waO7SGtJh46ox/n7HiRul/WzWGBdZfOSvpzW7xT1NaSE49G7dNm9vtpJrAEvNzVx3WLq0pPrM98aeIQU8FeykSr6PH2ZUNx5XuLjM+VH8uuchqeK5FapWZf/jC0PbAw8neHsp+S/kxbQ5fR3AR7Y0V/NBgJ6njeXdKkB0zB4zhvKjh9UHtuTH40sJrrjScaY/ZZpPs3mX/tN5q/H6s7J+nPbMJDpvnjQE0w1U1IChJhGmlM+l6rCvSpRNpMynGAjXw5fZC5AzURSHUksdOTgfv7/xj5+7LrjdTKiYYt0FMECoryaiPvB5AYzTKdbOi8EEolvbsxIll4XCdNAemuofLQmeGQyc9KHKVtU+2qh89TY8+PaJ98gjxboDcykj3wmkIoX7wtmDzHSHdqFdbZUhDPb9kEfvaYUJynRsDkM6X8Uq1NNxe0eIHe3Ja1ieL2bkyEBGOb68vvku5g3ztRiUWx9KX+nN/vOiExD9eht65wSjInojT8FBqMFh/lkigkYlwmUrtO94kq3tbz+i5dtFYzpLL5HVtnYvpNo9CjrTktrxmGdmmNRdsqcM1xRgbIDI82K7UlCt1kIqpAJ4R0AvA+gCJIcUBvUEpfIIS0AfAxgBIApQAuoJSmtqpuCmGPSxv1BTv5sgKuMdPdeYK1X/v4+NsVfPb3Y1BaXoOSAmOxDzNwqRvq9fSmh78F/4kqmglj7mjiEYuGHgZwO6V0CSEkB8BiQsiPAC4H8DOldBIh5G4AdwO4K3lNbTlICLVAKqeYBH6JzeWjrg9S4Q9J1COthPn3txyX9qunePCLaM3QmUhEFeiU0jIAZfJ2FSFkLYAOAM4EMEY+7T0As5GOAj1FAytZS82m4RRNUkZnCl8tqc82oYOIL1M09b97rOjdiCGpLQ1x2dAJISUAhgCYD6BIFvYAsAeSSYZ3zTUArgGAzp0717edTR6xfk7JFkrp6BTlgtNRvLj9JjB/JRSsYG4stsXmiqYSujv1yuHI9VnTJSQKMUe5EEKyAfwPwC2UUg0vJpWkCLf3KKVvUEqHUkqHFhbGn1lmo35IJ40tVuysMCc34meKpncfNGb7m4se0BQxumchBiWgUEwsiEmgE0LckIT5h5TSz+XdewkhxfLxYgDmZcfTAOn+8TclJIJUqj73ag5gtcqW9u4NAd+p2vI6MKpAJ5JR9m0AaymlzzKHvgZwmbx9GYCvEt+8xkM8y7Pk26kbfv+msty0UX80Jkd/qiYPq3Ha0O+sJX4DsdjQjwVwKYCVhJBl8r57AEwC8Akh5EoA2wBckJwmJhfNbQ5vCk7RRKB5vEXDkCgNMx001WS0sCWakWKJcvkd5v09NrHNSV/EO3aSNdaaglM0no+zoa1NB2EVD1L1Pk1g2NhIAFp86r+CxvuQGuc5qRR0iYmj5+20kDo8G2qayPo0aaaNNIAt0GW0RHtbOqC5aeA2bCQTLV6gKzZnf6hhnNhNTeykywRl1m9NrT+bOuz+MiI9voDEosUL9EQhkYMnLNZ/cmk2TtHm8RoxwfQ3a0F9wENcvpiWKL05sAV6E0JzEWKJMJPE+oEqfdZcTDOa1P84rgvxqoTYaHGwBXqKkKxolKYQ5dKhdUb0k2IEK+BS/2ZNF6It0G3AFugJQ1Phcok8J4Ua69lDOjT4Hs1ltWKjcWCPFwktXqA313GQSqdoIjIc+dwsqUNT901YNS+WpqeLEz0eNIHFaqOjxQv0poRE2IObguCJp/0t8aPTw6y3UtE1TWH82Kg/bIFuI21gJeBsOdT8YP+m8cMW6M0MTcEpmgjwNEXeqzXU1GAjtUjUcOXdpzmakaKhxQv01LHMmSMRbWouYXw2YkO6m0rSvf1NBS1eoDc2GmvYNkftpDm+E1D/2HPNPRLSEhvpDlugNyEkQqtuCppOsqJcbNiwYQ1boCcIiWEYbJ4aaGPAngCaHxqs4LTAz8kW6AlCU/FFNh+nqPZ/wMQpavHRp7sfoTFbn+7DpgksTJsEWrxAT/eP3gzN8b3SXOakDPGMhHQdNdwJKV1fpgFo8QI9ZYivVkM9bp/CTNEk3TfdtUgzJMZ3koCGpABJ/U2b6Xixgi3QE4RYPyjFaXnFuwuNx+QPuyEfZ1NwitpofDTHFZmN+GELdBtNGqygspOIzJGuDvVk/m7p2SMNQ4sX6M1NEDQXpygPLS1TtKWvtlr469cLLV6g20g8kiWI0lULjQctXYgnEs1ZuTGDLdCbEBKS8m8LhLSDTfWQHLQ8cW4LdBtNFLaAsmEjfkQV6ISQdwgh+wghq5h9DxJCdhFClsn/Tk1uM5sPbDGlRTTBrdFeLUM9W3bPJmph1tgLvBZoFUkqYtHQ3wXwF87+5yilg+V/3yW2WY2H5iYGmoLdMGlx6HGe39x+2/oiHcxwyWhhE/gUGh1RBTqldA6Ag43QlrRGS9cQGwNNYbJKBsxGTipGVDPt4haDhtjQbyCErJBNMq0T1qI0RX62p8H3SMQHnA7aGAuzyJWWENGSSKTXr944aIkjqL4C/TUA3QEMBlAG4BmzEwkh1xBCFhFCFu3fv7+ej2uayPa60CEvAwDQuygnxa1pnohVUKXZPNZkYfdjeqNeAp1SupdSKlBKRQBvAhhuce4blNKhlNKhhYWF9W1n8lDPAfzY2f3x9Q3HwumI7wYHa4Kmx1SNIr2/qrj40O2C0jYSAHtoSHDV5yJCSDGltEz+82wAq6zOb464eEQXzd+xCrGt5TVJaI2K5mJnjlXQW5mY0s38ZCOxaC7fQjyIKtAJIR8BGAOggBCyE8ADAMYQQgZDmhhLAVybxDY2K7S8IWajUdDQWhApGpj295BYRBXolNKJnN1vJ6EtLQKrdh02PaZ+k/Uf5umglfZrn4vVuyvjvq7ZfvxsTdEU/3wpez7nuQ1tS7MdLxawM0UbiON6FQAAcnzumM5fWxa/IEs3RJtUpt80Gjed2CPue1lpkakWhA0C814t0EogIRnv3QL7ssUL9IbGjz9wej/8ducJaJMVW9jiKf2LY2pVc0fUb635d0FikaCCKY09oSTqZ7aHi4QWL9AbCrfTgU5tMmM+//RBsQj0+iPdHEFmmrXHGdvQtPqQ0+YjN2loWq86Ghm8Ud8Scxlsgd7I6NA6w/RYOti/E4145p+W+IHGjDTlcrGRWNgCvZHhSPIXky6TQqytZMP802zxETuY92J/vvT4JZOHhppDm+14sUCLF+j+kNCoz6vvEL3+hO4JbUe6IPYJqnmIv5YmhJJaI7qF9SVgC3R8tGB7oz4vXTToZKMFfmtNGik3ZyXhsxBboERv8QK9sX/yOJkCIijK9cV0XqxO0cIcb/0akmCYzW+xkp01t5qiqW5zc2INFVuePLcFemODEIIPrxrBP2ZyzWsXH4lLdFQDDcXE4Z1jPndgx1ZRz/nmhlENaY4BEwYYo4HSLYKnPqivQG9OgjhRaAnjRY8WL9CP7pbf6M88tkdBXOefMqAYjhhV+1hNOgVx0P12ah09LHNADEI/HrQ009SS+07GwntPSnUzUm96qSd4Ya4t0eRSL3Ku5oQRXdukugmW6Fuca3osXqZHFvW58raTe9X7efEiXu0q3eV/rIlp9UG69k087fa4eAI9gY1JE7R4Db2pD/YZN482PdbYTe9akNXIT2whaEKhmc3JdNMSNfQWL9CTHRdeH8TaJF7b08VueFwviRs/HvMT780sM0Wb4G9rQ4tkjtfCbMnx39Eima+5ocWbXNL5m2+Ixux0pHYuH1bSBqWTJsR1TZrMVQ1CvZ2iaTyOgeSsDP6/vXMNkqO6DvB3tLtaSYtWz12tHqweq0UPhABJKHo/kEAPbIMhrgo2GLCIZGwZKAIB4irbcZUt41Scyo+kHIIxxEVIsAEbgzBGsmKRwsYmWFLAAgFGcUSEVhZgvYyMxM2PviONZrt7umd6pl/nq5qann7ce8/07dPnnnvvuS3Njnpbu2hC5Gknldwr9CRa6EG57PzRvfYFtUqbGrzP+9jMMSWJeqfzi88v48ix6CdnNQWM5ZIJPF5UnSFiBFVdhAy+LAsulzy11HL01LiThFu94uwRp/1+ee+hQNd1t59RcZ7NTQ2ex+66Yrrrfrdnvn1gv8h967dcdBaLz+q9XKHfCIwk3MeoGRdDn0Vcuq8Wo2sKnaJVjB1IHarQE3Czb1sx+bTff7dpl+/5184bB8CkjsoXpp42ynv0jNcQyXr8VRsuP4cbl3UHHqapOERlYdc9fK7fEoJVpl3wz6e5FR4WVegx3exz3cZt26IsnzKi97EiPn/JFJ66eVGosL2lTGgLbt1fOKkdqO4FEpQrZozxPOambLLcnL5kem1DLReTxb/xDOtDH9DXuzWaNXLvQ4+rHj/ymfl0/dVGj6P+ZlJTQ5+6KNcCV8wcw4ppHScfkFqSRcVSDq9VmZobvRXRhLbT3TFVL9eWQR/6X1w8ifbWZj40fVTcRakbubfQ42qO+U0K8ptMFBf1UObg/4LNoM4B4PzOwSe3jxw7Huiai6d2BE4/TCum3o9DpcMWF3aXH+7av28Daxd1VTUBL23kXqEn0SJccXbwhzVKZo0dEku+QcnqmqL9ijqo3zn6fowliY+wwxY/uzTYmrR5I/cKPRkdJqdrqlqXyWs43EPr5vL6V1fXNO9yZNknrkTHnAnDeO0rq+IuRuLIvQ89SRRUWRRzfrys2cc/t4DRg91nziV9ZImbrt/zztHQ6Wy6ZTEtzQ3M3fCTCEqVDZLozir3cm/M01yFgOReoSdRiTXU0EqdNjraqIj1xO0ltWvf4dDpTKxi/H6tmdDWwm/2Hwl9XVQ1JnlPgxKG3Cv0JFbgKPR5Wj0XfsX2Wy4wK0Gl4nYBJtFS9+OSc0YGXgwlD5Rts4jIvSLSIyIvFu0bKiJPi8ir9jvZvWk+xP0Awam4Kq39mwD1I3vx6K/ejLsINaeWrTM/0lrj/uETM/jypdPiLkZiCOKEug9YWbLvDmCzMaYb2Gx/p5Ik6M7xw1v4woem8s2rZgLxPdRxUTxsL2ei9yJv8qetRZB0yip0Y8xW4O2S3ZcC99vt+4HLIi5X3UjKA/SpBeNPrhsaRashTRNFrpk7LtB5bvFdPuoSoCzNVNo6q7ZVF3d1cSt+Qh7NVFFpN/EIY8xeu/0W4D9XPcEk0fcac2TbujOvK9gygOOG9R5uGfblN3JQsMW268U/XT3ztN9B++hrZYgk72lQwlC16jDOVC/PF7yIrBWR50Xk+f3791ebXeQkcJBLJBZ6UloeQWhvPaVk/SzNK/+k98LWhZFrQeXd+pdLQ5Wt1pROIktCn46SXipV6PtEZCSA/e7xOtEYc7cxZpYxZlZbW+8mc9zE2QHpZnFCPh/qZpc1IUtpdHn7Hv1juFjsbnHWV1YxMzfsIh3lqNTAKLfc2vIp7ZUlXCfS5CJMMpUOW3wMuAb4mv3+QWQlqjPvHP1jbHk/ceNCV4VUTeyJA4ePVVOkqnj8cwt47o3S7pZgPLZ+AVte8bQLvPPc4Xj+/hBSsRczymOiVRwENTBKFWA5+a+dN55NO73/37iWLsyf6VJbggxbfBD4GTBJRPaIyBocRX6RiLwKLLe/U0lTjA7rluZG2gY299o/eEBTxWn2HHIU+ttH6v+imjZ6EGsWjPc9Z+kk91bapI6BfHpxV8V5H69iifdaLK5QKesCLpdWWuZyoZSTJKNSO8pa6MaYKz0OLYu4LLHgplDjpprl17rbz+DVnsMM6l/5S6GWfPu62RVfO2SA9wSSchZmR2s/blzWXXHe9WLWuKHBToxYP8flevQTI4eex6rRmaIJrTQv/fWKiq7rHuEo9CwF9f/RzQvZ9Ot9DLOruD9500K2/++7p51TTr9dv3A8H3fpVIV0rl8aVp8n3Ued1OcwbeReoQf1V0+u44IScGrF8rDcdcV0PnLuaCa217e8tWRyRyuTO07FiJ8ysrVXzPgDh8O7mO5cNZkNT77MJ+eO9T3vSx+eGjrtShkS0N3m1iIpdNCe+MCw/Bs/5bYVkwLnG5cPXYmW3Cv08uvIrgAACRNJREFUICNKtty6hOEpiRcxsF8TK6fFE089Tv7ztd+Fvmbd4i7WlfHbf2ZJF9fO9+8XCMvP71zGnA2bXY8FjSDop38b+ghbbl1y+vlBC6emcqpJX1szYoIY6OOHtzCwXzJ90kowduz5fajzb1/pLNxdi+iUHRFMbqqiD1jJMLm30DUQVn3YdMviWJv1YYdErl00ga62Fi6amsxJ0GFHrahLJR/kXqEXKDfcTqmOuGOQH3ov2FqdBRr6CBfHtBRgLUijOldjKzyq0Il+tp9SPwb2awytrKPghiVdPLFjb/kTKyDuZQDrShrfNAkm9z50Jd388ydnxZLv7Ssn1ywuTEMfKTv6KrQHJeGKU23xaFCFrqSaC4JOxMkYfQPEvilGZ4rmA1XoSqqpJu5NGrncxn8f0RptGODCqJl8/ZvZQxW6oqSIk8sUhrxu/sThvscL0Rrz9oLMGqrQFSVFFCzz4SFjEDU3NnD1HO8ZsSesiR7X8ofqEIoGHeWipJ75E4fx4emjXI9dO28c9z27m7NGxDtsspSHb5hbkdvkzxeOp3PoAFafE35IpZ+uLljoddfn2iCIFFXoSup54Po5nseunjuW+57dzfsnkmUDzhxbWWduY0MfLpk+sqJr/XTn2GEtABWnXTHJui2pRxW6kmlGDXIWr1i/dGLMJUk2owf3Z+eXV9KvKR4vrBrq0aAKXck0/fs26MQxyyCfePLg/FdKutFOUUXJCUOrWAlLSQeq0BUlJ2hslOyjCl1RcoLq8+yjCl1RlNjQkATRogpdUXJCkg10bT1Egyp0RckJzU06iiXrqEJXlJyQx7Vm84YqdEXJCc025O6qFCl2XUksHDqxSFFyQnNjA1tvW0p7a7jAXnHS1KA2ZxiqUugishs4BJwAjhtj4lk+RlGUQHQOGxB3EUIxc+wQANYtmhBzSdJBFBb6UmPM7yJIR1GUnNHYx7HAvSzx5VPa+c6a2czv8o/nrjioy0VRlNj4yHmj2NVziM96BE8TERZ2t9W5VOmlWgeVAX4sIv8lImujKJCiKPmhqaEPd66aQms/jTMTBdVa6AuMMW+KSDvwtIi8bIzZWnyCVfRrATo7O6vMTlEURfGiKgvdGPOm/e4BHgVmu5xztzFmljFmVlubNp0URVFqRcUKXURaRGRgYRu4GHgxqoIpiqIo4ajG5TICeNSG5GwE/tUY86NISqUoiqKEpmKFboz5DXBuhGVRFEVRqkCnYSmKomQEVeiKoigZQRW6oihKRhBj6rdiiIjsB/6nRskPB7IUgkDlSTZZkweyJ1OW5BlrjCk77ruuCr2WiMjzWQoOpvIkm6zJA9mTKWvyBEFdLoqiKBlBFbqiKEpGyJJCvzvuAkSMypNssiYPZE+mrMlTlsz40BVFUfJOlix0RVGUfGOMifwDnAlsAX4NvATcZPcPBZ4GXrXfQ+z+ycDPgGPArSVp3YQT9Osl4GafPO8FeoAXS/a75uly/XjgOeA14N+Bvnb/IuAF4LgtRxZk6gSexVk+8D2coaRpkGe9lcUAw4v2LwEOFsnTkxJ5HgBesXndCzTZ/Z8AdgKHgaNW5jTLMwQnGutOe59eJ/5n6GP22g+AWT7Xe5VtCfB7YJv9fMErjXp+apMojARm2O2BwC5gKvB14A67/w7gLrvdDlwAfKX4xgHT7E0bgBN3ZhMw0SPPRcAMlxvnmqfL9Q8Bf2a3vwncYLfHAdOB7wK3ZUSmu4HbbdpTcRR6GuQ5396P3fRW6E+nsM6tBsR+Hiy6P/NwlNkMYBXwy5TL8zfAF3H0wuXA5gTcoynAJOA/8FfoXmVbAjzupwfj+NTE5WKM2WuMecFuH8J5M48GLgXut6fdD1xmz+kxxvwSeL8kqSnAc8aYo8aY48BPcSqEW55bgbddDrnmWYw4ISMvBL7nUrbdxpgdwBHgjSzIhGPhYu/RIODNpMtjr/+VMWa32zHgWJrqnL1+o7EAvwDG2P3PGmNetvL8HBiVZnlwlPZPrF54BOelPCBOmYwxO40xr7hdU0Ig2ZNCzX3oIjIOx7J6DhhhjNlrD72FE4LXjxeBhSIyTEQG4FgAZ4YsQpA8hwHv2soBsAenormSAZm+BFwlInuAjcBXUyBPOeaKyHYReVJELiJF8ohIE3A14BZ+eg3wDOmWZztW4YrIbGAsjuUdp0xB8StbcZ07u0b5h6Kmi0SLyBnAwzg+roM2djoAxhgjIr5DbIwxO0XkLuDHOBbyNuBEpeUJkmc5MiLTlcB9xpi/FZELgSeAq1Iszws4U6MPi8jlwA+Bj6dInn8EthpjnineKSJLgetx+gXSVN9K5fka8Pcisg34bxwF/3USIlNQSspWXOdWA98HumtdhnLUzEK3b+mHgQdsMwtgn4iMtMdH4nRW+GKM+ZYxZqYxZhHwDrBLRM4UkW328+kySbjmKSJP2evvAQ4Ag0Wk8IIbg+OGKKUhIzKtAR6y9+h2nI63Z/zSToA8fvkftA9WE7DOylNY2zbR8ojIF4E24JbiBERkOnAPsB/4lxTUN0957P25zhhzHvApHBfMd2KWyRUR+ba9fqNf2Qp1zm5vBJpEZHgleUZJTSx0cV653wJ2GmO+UXToMeAanDf2NcAPAqTVbozpEZFOnGbbHGPMu8B5AYvjmqcxZkVJPluAPwX+zadsNwDbMiDTb4FlOD72t3BGEexPujw++XcA+3Dq3Ns4o0MOJF0eEbkeWAEsM8Z8ULS/E3gEp9PwlRTVNy95BuO8ZN/HedG+YYzZELNMrhhjrivZ5Vq2Qp2zVvtsHOP4AHFjatDTCizA6XjbwalhPatx/LqbcYYAbQKG2vM7cHy8B4F37XarPfYMzlDB7TgVxSvPB4G9OJVmD7DG7nfN0+X6CTgdOa/hjGhptvsvsOn9wcr0XgZkmmrvjbFyvZ4SeW601x0H/g+4x+5fj9NhbXCa4LtSIs/xov/+5NA3HMv8YNH9OZpyeebae/JbK9NLxP8MfdT+PoZjDDzlcb1X2dZbObbjdFzPq4UuDfvRmaKKoigZQWeKKoqiZARV6IqiKBlBFbqiKEpGUIWuKIqSEVShK4qiZARV6IqiKBlBFbqiKEpGUIWuKIqSEf4fWzqbZEVvtQwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9ea1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dbb33198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_20\n",
      "    \n",
      "Sensor ID is C40B\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C40B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58561\n",
      "Number of NAs: 0\n",
      "count    58561.000000\n",
      "mean         1.124429\n",
      "std          3.074579\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         41.562500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C40B\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C40B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58561\n",
      "Number of NAs: 0\n",
      "count    58561.000000\n",
      "mean         1.124429\n",
      "std          3.074579\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         41.562500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F9D1\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is F9D1\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58561\n",
      "Number of NAs: 0\n",
      "count    58561.000000\n",
      "mean        23.858065\n",
      "std          5.474437\n",
      "min          2.062500\n",
      "25%         23.812500\n",
      "50%         25.750000\n",
      "75%         27.125000\n",
      "max         35.437500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is 0581\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is 0581\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58562\n",
      "Number of NAs: 0\n",
      "count    58562.000000\n",
      "mean        26.617074\n",
      "std          1.313749\n",
      "min         20.937500\n",
      "25%         25.937500\n",
      "50%         26.812500\n",
      "75%         27.500000\n",
      "max         34.750000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2db931ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d280f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_3\n",
      "    \n",
      "Sensor ID is C41B\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C41B\n",
      "Last Date: 2019-10-03\n",
      "Number of days in sample: 4\n",
      "Number of samples: 5702\n",
      "Number of NAs: 0\n",
      "count    5702.000000\n",
      "mean        0.070414\n",
      "std         0.808864\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max        10.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C41B\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C41B\n",
      "Last Date: 2019-10-03\n",
      "Number of days in sample: 4\n",
      "Number of samples: 5702\n",
      "Number of NAs: 0\n",
      "count    5702.000000\n",
      "mean        0.070414\n",
      "std         0.808864\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max        10.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FF88\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FF88\n",
      "Last Date: 2019-10-03\n",
      "Number of days in sample: 4\n",
      "Number of samples: 5703\n",
      "Number of NAs: 0\n",
      "count    5703.000000\n",
      "mean       30.001764\n",
      "std         0.441390\n",
      "min        27.562500\n",
      "25%        29.812500\n",
      "50%        30.000000\n",
      "75%        30.250000\n",
      "max        30.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FAF4\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FAF4\n",
      "Last Date: 2019-10-03\n",
      "Number of days in sample: 4\n",
      "Number of samples: 5697\n",
      "Number of NAs: 0\n",
      "count    5697.000000\n",
      "mean       27.749561\n",
      "std         0.663212\n",
      "min        25.125000\n",
      "25%        27.250000\n",
      "50%        27.812500\n",
      "75%        28.125000\n",
      "max        29.500000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a911dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d280f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_4\n",
      "    \n",
      "Sensor ID is C44A\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C44A\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58587\n",
      "Number of NAs: 0\n",
      "count    58587.000000\n",
      "mean         0.551738\n",
      "std          1.930073\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          9.937500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C44A\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C44A\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58587\n",
      "Number of NAs: 0\n",
      "count    58587.000000\n",
      "mean         0.551738\n",
      "std          1.930073\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          9.937500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F99E\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is F99E\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58590\n",
      "Number of NAs: 0\n",
      "count    58590.000000\n",
      "mean        26.883374\n",
      "std          4.081040\n",
      "min          9.437500\n",
      "25%         26.187500\n",
      "50%         27.750000\n",
      "75%         29.125000\n",
      "max         43.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F9E0\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F9E0\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58592\n",
      "Number of NAs: 0\n",
      "count    58592.000000\n",
      "mean        27.684882\n",
      "std          1.936114\n",
      "min         21.250000\n",
      "25%         26.250000\n",
      "50%         27.687500\n",
      "75%         28.937500\n",
      "max         38.875000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a28ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12edfbd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_11\n",
      "    \n",
      "Sensor ID is C462\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C462\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58584\n",
      "Number of NAs: 0\n",
      "count    58584.000000\n",
      "mean         0.783399\n",
      "std          1.198212\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.812500\n",
      "max          7.187500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C462\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C462\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58584\n",
      "Number of NAs: 0\n",
      "count    58584.000000\n",
      "mean         0.783399\n",
      "std          1.198212\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.812500\n",
      "max          7.187500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE40\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FE40\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58584\n",
      "Number of NAs: 0\n",
      "count    58584.000000\n",
      "mean        23.782392\n",
      "std          6.013433\n",
      "min         11.812500\n",
      "25%         16.750000\n",
      "50%         26.437500\n",
      "75%         28.687500\n",
      "max         39.187500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FFB9\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FFB9\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58585\n",
      "Number of NAs: 0\n",
      "count    58585.000000\n",
      "mean        26.915995\n",
      "std          2.282641\n",
      "min         19.562500\n",
      "25%         25.375000\n",
      "50%         26.937500\n",
      "75%         28.625000\n",
      "max         39.437500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122cbe5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dc024b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_12\n",
      "    \n",
      "Sensor ID is C42F\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C42F\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58722\n",
      "Number of NAs: 0\n",
      "count    58722.000000\n",
      "mean         0.079274\n",
      "std          0.674288\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C42F\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C42F\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58722\n",
      "Number of NAs: 0\n",
      "count    58722.000000\n",
      "mean         0.079274\n",
      "std          0.674288\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FAF2\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FAF2\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58723\n",
      "Number of NAs: 0\n",
      "count    58723.000000\n",
      "mean        27.961894\n",
      "std          2.227754\n",
      "min         13.375000\n",
      "25%         26.812500\n",
      "50%         28.062500\n",
      "75%         29.312500\n",
      "max         51.375000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F9E2\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F9E2\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58723\n",
      "Number of NAs: 0\n",
      "count    58723.000000\n",
      "mean        27.802355\n",
      "std          1.939154\n",
      "min         19.750000\n",
      "25%         26.812500\n",
      "50%         28.062500\n",
      "75%         29.062500\n",
      "max         43.375000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c631b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a6186470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_5\n",
      "    \n",
      "Sensor ID is C41E\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C41E\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58538\n",
      "Number of NAs: 0\n",
      "count    58538.000000\n",
      "mean         0.072158\n",
      "std          0.827277\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         12.687500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C41E\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C41E\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58538\n",
      "Number of NAs: 0\n",
      "count    58538.000000\n",
      "mean         0.072158\n",
      "std          0.827277\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         12.687500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2DF\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2DF\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58541\n",
      "Number of NAs: 0\n",
      "count    58541.000000\n",
      "mean         0.333786\n",
      "std          1.288607\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         28.000000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2DF\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2DF\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58541\n",
      "Number of NAs: 0\n",
      "count    58541.000000\n",
      "mean         0.333786\n",
      "std          1.288607\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         28.000000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FF89\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FF89\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58543\n",
      "Number of NAs: 0\n",
      "count    58543.000000\n",
      "mean        28.242011\n",
      "std          2.323439\n",
      "min          9.250000\n",
      "25%         27.312500\n",
      "50%         28.750000\n",
      "75%         29.500000\n",
      "max         38.500000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is 0556\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is 0556\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58543\n",
      "Number of NAs: 0\n",
      "count    58543.000000\n",
      "mean        28.263785\n",
      "std          1.855644\n",
      "min         21.250000\n",
      "25%         27.062500\n",
      "50%         28.625000\n",
      "75%         29.500000\n",
      "max         39.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is 0574\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is 0574\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58538\n",
      "Number of NAs: 0\n",
      "count    58538.000000\n",
      "mean        26.482010\n",
      "std          4.815586\n",
      "min          5.500000\n",
      "25%         25.437500\n",
      "50%         27.125000\n",
      "75%         29.250000\n",
      "max         41.750000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a18c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12818ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1281a3da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_8\n",
      "    \n",
      "Sensor ID is C605\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C605\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58667\n",
      "Number of NAs: 0\n",
      "count    58667.000000\n",
      "mean         0.057051\n",
      "std          0.548618\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.375000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C605\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C605\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58667\n",
      "Number of NAs: 0\n",
      "count    58667.000000\n",
      "mean         0.057051\n",
      "std          0.548618\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.375000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is 05C5\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is 05C5\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58666\n",
      "Number of NAs: 0\n",
      "count    58666.000000\n",
      "mean        29.610681\n",
      "std          3.197653\n",
      "min         20.562500\n",
      "25%         27.125000\n",
      "50%         29.812500\n",
      "75%         31.875000\n",
      "max         37.812500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F731\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F731\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58668\n",
      "Number of NAs: 0\n",
      "count    58668.000000\n",
      "mean        29.121763\n",
      "std          3.288467\n",
      "min         20.000000\n",
      "25%         26.625000\n",
      "50%         29.062500\n",
      "75%         31.500000\n",
      "max         37.437500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15417a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bde979e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_16\n",
      "    \n",
      "Sensor ID is C641\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C641\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57724\n",
      "Number of NAs: 0\n",
      "count    57724.000000\n",
      "mean         0.506702\n",
      "std          1.536232\n",
      "min          0.000000\n",
      "25%          0.187500\n",
      "50%          0.187500\n",
      "75%          0.187500\n",
      "max          9.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C641\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C641\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57724\n",
      "Number of NAs: 0\n",
      "count    57724.000000\n",
      "mean         0.506702\n",
      "std          1.536232\n",
      "min          0.000000\n",
      "25%          0.187500\n",
      "50%          0.187500\n",
      "75%          0.187500\n",
      "max          9.750000\n",
      "Name: value, dtype: float64\n",
      "Warning! C61E doesn't have any data!\n",
      "Warning! C61E doesn't have any data!\n",
      "Sensor ID is 054D\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is 054D\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57726\n",
      "Number of NAs: 0\n",
      "count    57726.000000\n",
      "mean        28.188284\n",
      "std          2.373808\n",
      "min         19.250000\n",
      "25%         26.687500\n",
      "50%         28.687500\n",
      "75%         29.812500\n",
      "max         36.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FADD\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FADD\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57727\n",
      "Number of NAs: 0\n",
      "count    57727.000000\n",
      "mean        28.267336\n",
      "std          2.062576\n",
      "min         18.812500\n",
      "25%         26.875000\n",
      "50%         28.625000\n",
      "75%         29.687500\n",
      "max         34.187500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F32C\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is F32C\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57725\n",
      "Number of NAs: 0\n",
      "count    57725.000000\n",
      "mean        25.742699\n",
      "std          3.686937\n",
      "min         12.000000\n",
      "25%         24.812500\n",
      "50%         26.812500\n",
      "75%         28.062500\n",
      "max         38.187500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1552ae1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dbb22828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vJrln4K+B/x75uT4FnD/t/azmz3jka8x8GPyXz5Kkhn+VJElqGAZJUsMwSJIahkGS1DAMkqSGYZAkNQyDJKlhGCRJjf8FFDYZsBaypoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a61b2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_17\n",
      "    \n",
      "Sensor ID is C465\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C465\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58551\n",
      "Number of NAs: 0\n",
      "count    58551.000000\n",
      "mean         0.725860\n",
      "std          2.789843\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         65.937500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C465\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C465\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58551\n",
      "Number of NAs: 0\n",
      "count    58551.000000\n",
      "mean         0.725860\n",
      "std          2.789843\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         65.937500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C445\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C445\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58548\n",
      "Number of NAs: 0\n",
      "count    58548.000000\n",
      "mean         0.142780\n",
      "std          0.751694\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         16.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C445\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C445\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58548\n",
      "Number of NAs: 0\n",
      "count    58548.000000\n",
      "mean         0.142780\n",
      "std          0.751694\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         16.750000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FCA1\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FCA1\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58554\n",
      "Number of NAs: 0\n",
      "count    58554.000000\n",
      "mean        26.383768\n",
      "std          3.037732\n",
      "min          9.500000\n",
      "25%         25.312500\n",
      "50%         27.062500\n",
      "75%         28.375000\n",
      "max         41.625000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FA8B\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FA8B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58553\n",
      "Number of NAs: 0\n",
      "count    58553.000000\n",
      "mean        27.259014\n",
      "std          1.437667\n",
      "min         21.187500\n",
      "25%         26.437500\n",
      "50%         27.562500\n",
      "75%         28.062500\n",
      "max         45.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE5A\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FE5A\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58551\n",
      "Number of NAs: 0\n",
      "count    58551.000000\n",
      "mean        28.145172\n",
      "std          4.723798\n",
      "min          8.750000\n",
      "25%         25.312500\n",
      "50%         27.312500\n",
      "75%         30.875000\n",
      "max         42.125000\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a172860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d280f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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44NtsF32h4eAEnfg3DvjGoMHJ8tXLzSrC9fT1TpJJn1+o9OyztbetXJkunma14uqfpG9ldY9ex4suSi5zxx2NPwswefLgYznjjOg2GJWv5YTqb91qYP786AOI7Rg0lT+zIs0JOvHXO0CbOXjzvoyuqOo9V41G7Vlfgyyy/keRJOm6+vJ7EbXeO/rUp5I/h1F50l+7NntszWjmOf/Sl5IHJH/8xzBnztAMiPK2eHEx404r6MSf9U3JbpwOSiPL81CvbKuvjW/F65dUZ9p22nF8NdPmrFnZ6+gkjz8OH/pQ/anfbhN04m/naLNZnRJHPa2KsdV9L8JzWyRFmJZM8uKL0f3y5e2NYygFnfjrjSibSQpKHJFWjfiLmPjzHPG3Qx6xFqm/oQo68esAzUdRR/z6MfuBsjzn7fpm3KyKGncWQSf+epcaasTfvCwJtJ0j/oUL868zzxF/0aZOihZvWVHjziLoxJ81sQxF4i/CQVnUqZ5WXNVTdCFP9RQ17jSCTvxZ5/glUtSpnlYIeY6/CIOUJJrqCUwRRvxFUNTLOVuh6MdEiCP+op6wsgg68WvEnw+N+PsVMeZKIb65W1bUuNMIOvFrxJ+Pos7xt0IRY66kqZ4wBJ34i3AdfxEORiX+fknHVJH6EeJUT4hSJ34zO8LMFprZU2a20swuTdhnopltM7Nl8e0b2cLNVxHnkDuRpnr6FTHmvGjEXxx7Zyi7C/gbd19qZgcAS8xsgbs/VbXfQ+5+XoZ22qJTRvxF+GMq6nX8rVDEmPNWtOegCH9jeUs94nf3l9x9abz8OrAKOCyvwIZCtyWddtGIv74i9UNv7oYhlzl+MxsLnAQsTth8mpktN7N5ZnZCnTpmmFmvmfX29fXlEVZDenM3H5rj71fEmCvpzd0wZE78ZrY/MBv4mrtX/wzFUuBIdx8H/BNwd6163H2mu5fcvdTT05M1rKYU4c3dItCIv1/IH+DKs46hVNQTVhaZEr+ZDSNK+je7+4Af4HP37e7+Rrw8FxhmZiOztJmnoh2gnUof4OpX9GNKUz1hyHJVjwHXAavc/R9q7HNIvB9mNiFub0vaNvOmEX8+NNXTr4gxVyp6/GkU/YSVRparej4MfA540syWxeuuBMYAuPu1wIXAV8xsF7ADmBr/GnxHKOKIshNpqqdfnjG3Ywoi5BF/SFInfnd/GKh7aLr7NcA1adtoNY3486ERf7+iz/FnUdS58hBPWPrkbmBacXC36nns1D/EbjtZ5a1oz4ESf2Cy/gGHdKDU06oRf6ca7BvSRerjYGKt3jfEBFpUXZ/4W3nVSBEP8FbErKmefkWMuVLR408jxBNW1yf+erot6bRLq56rTp2KG+xxU6RjKcQRf1Hfm8ii6xN/2oOwW6d6NOLProgxNyuPxF9URX/tBkOJv4A6bU5dl3P2K2LMlUL+5G7R4s6i6xN/Wp084u+0A1Qj/ua2FUGIUz0h6vrE38qDsIgHeKeN+IuYRDXH311CPGF1feJPq5MPgk6LLbTr+OspYsxpdcuIv6hxZ9H1iV8j/j1pxJ/dYOPq1H4kCfnN3ZB0feJPq5P/WDstNl3O2S/PmNvxOuvN3TB0feLXiH9PGvG3VtHn+AejW6Z6QqTEn6FcEa/q6bTE3456s2rVyaq6bFFH/EUT4gmr6xO/tJ5G/P2KPuIP8XLOosadRdcnfo348yvbijqLmPg14k/eN8QEWlRdn/jT6uSDt9Nia1XC6rR+lhXxZNXtslxRFOIJq+sTv97c3ZNG/K2lEX/nx11Nib8LtXKqp106baqn+hLGvNrolss5i3QMdvJxL/np+sTfSvojiYQ24h+qOf52KOqIP4uixp1F1yd+vbmbT9nBJLssySNtPZ0iz6t6Or3/3ZL4y4oadxqZEr+ZnWtmz5jZGjO7PGH7O83stnj7YjMbm6U96Uy6jr+5bUVQ9PjTCPGrJlInfjPbC/hnYDJwPDDNzI6v2m068Kq7vw/4R+CqtO2lVfQRf97XhWvE35y0cWV5vfTmbnsUNe4ssoz4JwBr3P05d/8tcCtwQdU+FwA3xst3AGebhXh+7W6hjfjrKWLMlYoevzRn7wxlDwPWVzzeAJxaax9332Vm24CDgFcytFtTqZS8bq+9apc54YTk9X/4hzBsWP32zj8fRo1qPr40pk4duO6UU+AddU7ZtfoE8MEP1n4+6pU78cTa/xI//fSeZauTR716r74a7r03eduDD9Yv26juLNtPOKF2f089tfZz+Pd/D3feuee63/2uuXjGjduzzR07mos1y7ZqTz3VfNnqY2l9nA3OPhve+c7m28zDm2/2Lw+mvwBbtkT3vb2DL5u3gw6CRYta306WxJ8rM5sBzAAYM2ZMqjre//7ofsmS/nW///v9y6NGwcKF/Y8nToSRI/sfr1rVn7TGjduz7iOPhHnz9lw3cWL/H2r1H0wz3vteWLsWxo6FdeuS9ymVBm478cT+5ep2DzgAjo8n3PbfHx57bM/tlc/H1q2waVP/43K5pL5U/0GYwcqV0fLZZ8OIEXtuX7Uquj/66P56k2KeMgX23Td526RJ8J731C5bGXOtuOttP+UUOOqo5O3V/a3cVn4Ok9o7/3zYZ5+B61evHhhPdd1JSef556P7k06KjpdKu3dHJ91G9SZtq+W442D27Gh5333r11t5LJX37+2NYm2H8t/JYPoL0d/87Nlw+ulw6KG5hzUow4cPTTtZEv9G4IiKx4fH65L22WBmewO/B2xJqszdZwIzAUqlUqp/OP/t39KUEhEJS5Y5/seBY8zsKDPbB5gKzKnaZw5wUbx8IfAf7ppFFBFpp9Qj/njO/hLgfmAv4Hp3X2lm3wF63X0OcB1wk5mtAbYSnRxERKSNMs3xu/tcYG7Vum9ULL8FfCpLGyIikq+u/+SuiIjsSYlfRCQwSvwiIoFR4hcRCYwSv4hIYKwTL6s3sz7ghRZUPZIWfV1EG3Vbn9SfztdtfeqW/hzp7j3N7NiRib9VzKzX3RO+0ae4uq1P6k/n67Y+dVt/mqGpHhGRwCjxi4gEJrTEP7PdAbRAt/VJ/el83danbutPQ0HN8YuISHgjfhERcfe23Yi+q38h8BSwErg0Xn8gsABYHd+PiNe/H3gE2An8j6q6LgVWxPV8rU6b1wObgRVV6xPbTCh/FLAYWAPcBuwTrz8DWArsiuPohj6NAX4NvA68RXSJbRH6c0ncFwdGVqyfCGyv6M/mgvTnZuCZuK3rgWHx+j8DngCeBrYBawt0zNXq0wjgl8AbwI74dWxnfz4Vl90NlOqUrxXbxPi1WRbfvlGrjqG8tbdxGA2cHC8fADxL9MPtVwOXx+svB66Klw8GPgh8t/IFBk6MX9x9ib5x9AHgfTXaPAM4OeEFTmwzofwvgKnx8rXAV+LlscAHgNuB/9klfZoJfD2u+3iixF+E/pwUvx7rGJj4FxTwmJsCWHy7peL1OZ0oUY4mOtkt7oI+/TC+nUyU0H/V5v4cBxwbx1Ev8deKbSJwb7082I5bW6d63P0ld18aL78OrCL6nd7KH2m/Efh4vM9md38c+F1VVccBi939TXffBfxf4BM12lxE9NsA1RLbrBT/UPxZRD8cXx3bOnd/AvhP4Plu6BPRiJn4Nfo9ol9U6+j+xOX/n7uvS9oG7CzSMReXn+sx4DGiX7vD3X/t7q+6+0tEI+jDi94nogQ/x92XuvvTRLMCz7WrP+6+yt2fSSpTpal+d4qOmeM3s7FEI7XFwKj4YAbYBDT6SfMVwB+Y2UFmti/RaOKIBmWqNdPmQcBr8UEE0Q/MH1arwi7o07eAz5rZBqLfXfheAfrTyGlmttzM5pnZRylQf8xsGPA5oqmQatOBeQU55v5LQp+WEydnM5sAHAmMo339aVa92CqPuTb/nHukI35s3cz2B2YTzcFtt/IvmAPu7mZW99Ijd19lZlcB84lG3MuAt9PG00ybjXRJn6YBN7j7j8zsLOA+4LMF7s9Soo+1v2FmnwDuAT5ToP78BFjk7g9VrjSzM4kS/znAXRTrmKvu0w+A/21my4j+c9kJ/LAT+tOsqtgqj7kpwN3AMa2OoZG2j/jjM/5s4GZ3vzNe/bKZjY63jyZ606Uud7/O3U9x9zOAV4FnzewIM1sW377coIrENs3s/rj8LKIfih8e/3A8JP/APEQ/RdkNfZoO/CJ+jb4OvAk8VK/uDuhPvfa3x3+Aw4CL4/4sKkJ/zOybQA/w15UVmNkHgFnAJ4GfUYxjrmaf4tfovxHN2R9IlPhn1au7xf1JZGb/Gpcv/wJhYmzlYy5engsMM7ORadrMU1tH/Badwq8DVrn7P1RsKv9I+w/i+39voq6D3X2zmY0h+lfxQ+7+GjC+yXAS23T3c6raWUj0w/G31ontK8CyLujTb4Czid4D2ET0R9jX6f2p0/4hwMtEx9xWoitHtnR6f8zsi0Sj+bPdfXfF+jHAnURTJZdTrL+jWn0aTnRCnkU0gLrX3be3sT+J4pNTpcTYysdc/F/ABKLB9hbazdv4zjLwEaI3EJ+g/3KnKUTzzg8SXRr1AHBgvP8hRHPQ24HX4uX3xNseIrqEcjnRwVSrzVuAl4jeCNoATI/XJ7aZUP5oojej1hBdwfPOeP0H4/p2xH16qwv6dHz82njcr7UF6c9fxuV2AS8Cs+L1lxC98e5E//o/W5D+7Kp47v/rkkCi5PhqXN6JEmZRjrlafTqNaMDhRJdBPtnm/vxJ/Hgn0aDh/hrla8V2CdHloMuBR4HT25lzyzd9cldEJDBtn+MXEZGhpcQvIhIYJX4RkcAo8YuIBEaJX0QkMEr8IiKBUeIXEQmMEr+ISGD+Py0Rem/89XOHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a425e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_13\n",
      "    \n",
      "Sensor ID is C411\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C411\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58599\n",
      "Number of NAs: 0\n",
      "count    58599.000000\n",
      "mean         0.558633\n",
      "std          1.722347\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         30.312500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C411\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C411\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58599\n",
      "Number of NAs: 0\n",
      "count    58599.000000\n",
      "mean         0.558633\n",
      "std          1.722347\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         30.312500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F9B3\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is F9B3\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58596\n",
      "Number of NAs: 0\n",
      "count    58596.000000\n",
      "mean        25.473706\n",
      "std          9.512745\n",
      "min      -2048.000000\n",
      "25%         23.812500\n",
      "50%         26.250000\n",
      "75%         27.875000\n",
      "max         34.812500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FAE9\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FAE9\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58600\n",
      "Number of NAs: 0\n",
      "count    58600.000000\n",
      "mean        27.037370\n",
      "std          1.811432\n",
      "min         20.875000\n",
      "25%         26.000000\n",
      "50%         26.812500\n",
      "75%         27.750000\n",
      "max         33.562500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dbb20f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2db9310f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_19\n",
      "    \n",
      "Sensor ID is C622\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C622\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58577\n",
      "Number of NAs: 0\n",
      "count    58577.000000\n",
      "mean         0.356434\n",
      "std          1.912870\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         12.250000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C622\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C622\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58577\n",
      "Number of NAs: 0\n",
      "count    58577.000000\n",
      "mean         0.356434\n",
      "std          1.912870\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         12.250000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2FE\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2FE\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58575\n",
      "Number of NAs: 0\n",
      "count    58575.000000\n",
      "mean         0.048334\n",
      "std          0.441186\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          5.125000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2FE\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2FE\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58575\n",
      "Number of NAs: 0\n",
      "count    58575.000000\n",
      "mean         0.048334\n",
      "std          0.441186\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          5.125000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FCD0\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FCD0\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58580\n",
      "Number of NAs: 0\n",
      "count    58580.000000\n",
      "mean        27.804022\n",
      "std          4.075338\n",
      "min          6.625000\n",
      "25%         26.812500\n",
      "50%         29.062500\n",
      "75%         29.937500\n",
      "max         40.312500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F5DF\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is F5DF\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58574\n",
      "Number of NAs: 0\n",
      "count    58574.000000\n",
      "mean        28.679721\n",
      "std          1.957024\n",
      "min         21.187500\n",
      "25%         27.375000\n",
      "50%         29.125000\n",
      "75%         30.062500\n",
      "max         39.312500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE2C\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FE2C\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58577\n",
      "Number of NAs: 0\n",
      "count    58577.000000\n",
      "mean        28.049742\n",
      "std          2.633472\n",
      "min          7.750000\n",
      "25%         26.750000\n",
      "50%         28.625000\n",
      "75%         29.625000\n",
      "max         40.687500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ecad7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2db932208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cd219588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_18\n",
      "    \n",
      "Sensor ID is C441\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C441\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51904\n",
      "Number of NAs: 0\n",
      "count    51904.000000\n",
      "mean         0.017792\n",
      "std          0.265293\n",
      "min         -0.125000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.250000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C441\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C441\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51904\n",
      "Number of NAs: 0\n",
      "count    51904.000000\n",
      "mean         0.017792\n",
      "std          0.265293\n",
      "min         -0.125000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          6.250000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FAFD\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FAFD\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58690\n",
      "Number of NAs: 0\n",
      "count    58690.000000\n",
      "mean        27.636769\n",
      "std          3.548364\n",
      "min          8.562500\n",
      "25%         26.250000\n",
      "50%         28.187500\n",
      "75%         29.937500\n",
      "max         36.437500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE0B\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FE0B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58688\n",
      "Number of NAs: 0\n",
      "count    58688.000000\n",
      "mean        27.456158\n",
      "std          2.322619\n",
      "min         19.562500\n",
      "25%         26.187500\n",
      "50%         27.750000\n",
      "75%         29.187500\n",
      "max         33.187500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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/hJDI9U2ThN5BaXy4mnTOL2MERFBmGtxhEguVW1pVp2jxTFxOkdagyjsbhXIDXvfcg2dL54aPHTegOJzsoqLKPE6DHTSVu+YNzv+Y7Ov6aos9opSIbQ/IKC/0WG770dD2DoCpdscby5mw1cd+q0RqTm08z8rt9s7HAwwx9nob+YuLqsLLj7mfxEmObzHLsYjb/0XXgwCA0jw3fu94R9vgitCOr89Y7uPbK3ZihhLGeJnzA9PuO/bzD/UIwguF9d9o133W/TC3zeh7MQoIvanojvc6Fk+uj0yaaOEgblT8RbFEUmU76gzHOCuPxPSHv8SRD1rnUgDWipKe3m3MPOhE1y34FouG7gMwjVI6FsA4ADMIIZMB/AXAo5TSIQD2A/h18oYZGx3JsPMFZHzmuQGvue/Dw66n8LDrSTyu05DVuOYhoc04hKzDcS2aYNDb7fUs+LkGVft0U+mSSm775c55qCD22Z7TDfVfjLQHQ7aam77BQ4FSpXB9B6IwFimZrMVogtHbn+NyoA/2oco7G6dIiyKet6yAf+Gs2Rn7AwcAhzmYv6O2yYfrXW/EdpCXL4Ggtv4DAKrc4nrzx38Ns4iwiUzhCud74eXJ0jpO6w7JNPwydyJomult0nWx6kyGqt/iXh5zJ3PgjrjN3DA7EgVoRZV3Nq5xvBl95wwhnCwVR/lp+4xn/lyr5Qru88gWNrN1ouu+UKMKdMpQ71KX8o8CmAZAfRJeAnBqUkbYAQKG6bCVlqPSptNyfun4Cr90fI0Bzd8DhP9KQiTyVzTkyf6o8s6GBBkXv/BduBk0xxHXhRfHS4YsM92Qdxocm4t7nc193rK3BV6nAw5fPa5wvMdt662z9V973DAAwJTBPbl9ehVYa/cA8MGqapRhP1Z6L8fvHO9y2zxOBwZLzCT1uPsJU1EwlTLsx1XP8g5hY8PsWF66W2oaTWGltpzwIPexzRfkzEdjpE04RVqEBz7mo2KeCc4ML/sNvdLHSXzo6cbd2oxEf4954YfLwd8f+brff7jiX9HTU+d8tvIphMcUJWvYir9/tiH8YtajzlKuc72J+lbr3y5TiceEbpfxbLShN5XyM78NXladMaMFOgAQQhyEkJUAagDMB7AJQD2lVL0jdwDol5whxo5RWIQoxXXON1ACpinpnYRWmmPe2v+Y1pXK5pooBw8sNq3b7D0fAPDFzxYhf9NuCy/e63weALCv71Fshe6OzPM4caL0bfizX8rhTlOU40JDWwBjXh2HOa5/o9dWbdawXydkh5Uzu32xLlzvrRU7UKOLvLCatvcmzH9wkmMJ913d9+E61FPNb6C3hev5znslVniv4DTa/kozbAIZVd7ZuPX2P1keiwKtnvnKtx+JvY75yJO5j/e+8DpXmKyc1ONx9xN4dyFfI14vxD8KTYp4ibdf0MJH9Rq5BGoyweg1ayvTlN701+a3Fwx681SsPDx/PWY/a/aPeKDN3sbdbY7AykTUgmOJjKyXwP+Wh+/nFZs6B1OQXCTUsTdJw07giUns/yQTk0CnlIYopeMA9AcwCcCIWC9ACLmMELKMELKstrZj2YsdRW2gHL72jqW4xvkWVnhZUacnF2iRAy9ZPDBnOBbCaEm7se0R034/brOLGqCwvMUkzUziJOym2VNxmmm39kAIT7oftzk3c+YN7qUJ1oCrKLw85y2WrHT+5IHcaFSMmbDLDc0xAOB3TnYDj5K2crOFNn8QDt3NbhVRo48L37RbO/ezXzFtt4KwSol/cT1rPhgArlsTXjyt+lHMXaqza5/OjtlccbbxKKCQb2xx3+4rLAXlnaWfcp+vcmoP66FS5Pj5S33/Ci/rlQYnQuHyvSqPRYhkWhKlFk8O2nGx4yPMdnyGVTtiS5LqtkQxeP/9sw22obKXTa20XB8NSdaUjJ82dyCjesVLwN6fge//FX3fTtKhKBdKaT2ABQAOA1BMCFHVnP4ALF8/lNJnKKUTKaUTy8rKrHaJG/3DVYb9KAQfb9sDvK1XP8U971Cb9PkY8qSLYF34fqa0BIPJLsttRmSnqn1rYneJoUKiMS62vtWPXpL2N4VcmnAfp9iNr57aD0e+OgRV3tkRlYjvt/ECvT+p5eqhvKmrbVO1r5UT6Fbn/Wev18PL7zyhaeGba1vwpvsOLPD8IbyuTjebaKI5aBh7GSA50HStZurYp3die5iNvPdR9m6a1gOmh5dfXGhOOjqh5R3TOpWLnPMtzR87Rl0GAOhBtPtKHyFTSXbhuyr+N5u3uhoOhHC38wUUGu6TP3/Ej8sYAnmr81Xc4foX7nc9h/ud/+ReTNHC8/TbjftG0yXbAyE0Z2hnJ7uv5WHFH2ZVgM8YqaRifN7WecZwn/c3a7b3Pe/cyh/82T3AnUVpbywdS5RLGSGkWFnOAXAcgHVggv0MZbeLALxrfYbkoU6tnQjiO++VWOW9jIs6COniRQlkjO6rabSrbZx1Nr81xyhpq+X6/3M/jr5E0cJm/V/Ec1CLEngmrdlwt9Y0+TCr7oXwZ6LbfqiS4NTr8UHa4SH7WiRvruDfv197ruE+f/6TNps6Y0J/nOPQ7I5GL//Fjo9Q3KBpuX90acJ97IBiTJD4h+qGf2jOOSdCyMthduWCYq2kwc5anaBUwsQ85cNs/x7PlN+Fl/uQyJqwVSjjsirzjKX8pFsAAA8Fzgyv+4vOFv+G526USmYH28uuB3Chcz5WeS/l1htNMD5D8tRMh2Zum+1cgA8XaVm5VsXM9Nz6jjbDaTQkUl3ujFyEbMRtH4fDKTMFAhadRC1CCPWRVY9YhAI/b9FhjJ2Tf94Kg/x9pLed1xBD+Y1Fysw6wjOXCmLR0PsAWEAIWQXgOwDzKaUfALgRwPWEkI0ASgE8l7xhWqPa0cYQTbP7YasmiBp0dt+HXP/Afp1DSE2HNrK31rp92w+5k8PLPZ2Gh+vGKu287gfYgosP3zvC9zfT6BnaTaQmJ9nhcTpwdLPu4dTdzE8vNNeQkUL2QsDYB9XIMRWak3VPYzvOcmpx/Es8V3L73uGyn0oaHb0A8FyTVtc8h/jhtBjnK7n674t9R44cs+9CxTFU09B/Z3AYq6h+hsfc5petW7Ez62tgu7zMqal/QV08pYI7buoy/rs4WVqEKQ7rcMXrj+NfSH8xOGqLCf+b/GLh6eHl6ob2cKTR065HTDOKV5dswzWON9EXe/HJmuxvNddr31J87JmDI+reNm1bvDlyKef+xNr0O0jin323zIc657mA3ZQFWgweNNhwtPl5TgexRLmsopSOp5SOoZQeSCm9W1m/mVI6iVI6hFJ6JqU05W1h1OzPBmiC+6NvtCzHFmhC6ZeOr7nyrL89WveD3KI9AKeQryJe0x+UURYy2NBzLKJpevAmnT9fchL3WTPsaDfAVxv22uzDMDsy+ZvHqHlKwdjTx/VOTwDYu1RzEBvHVUqih0M272ffqd4JuGbMzdY7f2e2q0+UdTZ/9cVFCNbJOh/BGS9q++hMZfqXj54/3sdetn2I2dFds5/ZwjftYZr6B6HJnO8jPFSDWcw4+7jR9W/LawMIR8SoYY/zlFIQdhTI2ve8pbYFz7v/CgA43rEMFz7H13jvh1pc53oTi7xXY8U282wj28hvZSbBfu1mk4pTshZrr7juw9OuR0yzUStaqQc+g/I/Tl4bDhwwJSF1kaLzGZ0pqtrHPvPcEF53T+1V4eVc8O+YUyWt+FOjPrXclYOqwefbXqeRFISnY43tARQQbZpNJcWNMOYc/qC+B3Mfjxyq8x+UjQSN6Y2ubfMFzXZO/RRxwgE98F/PPdz2Hm9aOBFtMKYz3+N8Mbysr12joobVuWySLOY9fDkAoA+UaStx4MDTb7S+uCNKTpruIXFf8N/wMjnQ7FiOxHOGJCEAeDeffUcvfsFe9rv3MrPI97JRA2O0BULYQe1nUsUGP45ek5Yp5ZLRnnfxIZc+ahHyqtCn2IuRkuYoXmYwz3mJNvv893fbbc+zymPvh7jm39/bbutqaM+P2eRy1/vWvVOPcPyI4x3LLLcZ2Y98UxKg/rPZp6EK9PRmkWasQH91yVbLeOgSxYFFKcVc933ctqudb3HHAwAKWMnSivOfsL3WPp29rD0QwiyH9mLYf8j1bOHUp/iDrJyrkxU778iTtO26339oL/uiPy2+EKZKhprtupsq3yL+PRS0tuedIC3BJY6PwiF47H9+vB6iHWssbgVomalPuR7VVv5Ri7E/2/kF/EEZo6UqZawRnEUh7XfcNupy83bdQzJ46EhslXvhEbfFfnEwhTI79VttlwAAhgSZzfVXed9y+1ElPLFvcQ781Im24Vrahd55mU9489ErH2jRNcbY8mmOldzn52VlFne5eZZozLEoAy/Q9fZdY/idnkLSZqrxo/Luytgc+l0C5fnxW4TfJqKHbD+yD2WkkRPcTchDC2GmVJPtXs1XiXSfp4CMFei3vL3GMvROZWd9G3IJ/8N+K2udhspylD99oqKxRIhuCRAniPK7bqxpxhpZczzmb1B8wTbTPA6vagMmlpfbYKyKp7uZgrKMscRYsEnbbhW3PVQyBx4NLsvDU+7HcLvrX/h0LTOLnPPMYpQRs5M4UkneHxWn8rEOnVaXXwbfhR+GP26ra0EJUcL6cpjT9vlK3QtAZY6meZZM+KV5u+HhcV6/Cpdef59pt9Y/WvtFMN2+w0yPAG8+213DPktFfFpFSwOzu971/lpUSrvhaNHsrW3tmmlrqTycO+6yVdos6cuf7G3bwZCMWllJRCoeaNpuLFb2nVez3e9t9uEih+bUvMX5KiJhbERyMFmPT91/RA7aE1rsKqkoArSmyWxWPOFA1re3siwPjkilQyPQJLHIKr1PpVoqx06n8tsYBTcRGnpS2CCzB5ErVnXwRQD4hggONSfK37EmBe+t3KUJKQC+XuO0jbfWAhd/DNxu16DZ6mGhttv1JpU2f8gcequ7eZZttb7mz7ubOM3QRTVtuPy/LFvyO4sIDwDYuYQlAo8iVaZtH82zTiP3VE4JLw95sj8eVOPOJ7EQwAvP+5X5IK8WfZRfaZHkY3hI+hXnWBZNy83XnKbzD9KZV478g2lfFelPusxdWYZ/NXtB1wT42jEbqpkp5rTx7P5y71gc3tbUoH1/34QOtL3W2p32jbtbfCGtZLNknm1d/Zq9OSQYopjt1KKQfu2M3MR45zI+6uVp9yMYIu3CSLItql2/y0CYidCY3QkAToeE0UV+fN40C+8555i26xk0533L9ds9QwEAbW2ao1qiIYSIct/ZvfgivRBT8LLMOoGuaqVccsnhV5v2O8CnhDN981h43a1O+wdfFbYzDuyNQyQtFKpl6CxtF6cbOOAwS2ea+Wy8DZ1SihLYv1xafCEUEmNkinaDFOqqAu4ZfEZ4+eZn3sDGmmaMJlW40PEJPm7WQvDGSRYlWntUhBerG5lWGNbeJSf29TwEAHCDNNd4ZGT6TwTAHrYx7c+EVx/r4+3IIASf58/E16HReC90GFsXx4OQn8umxn7wgr+mkQ8zJE5dOQR/E8b7mQmmfOxxAIB1PaYBAHZ+8xoAIMei+TZ541fhZa8jhBBxgFq81C8/wlx6WNWIm/1BnKqa8nQCffWX0euvWE0OmyK02Ttvw7Xc5zJFQaEg2BtHHfe0oGjEXotmMu//sAvzfL8CAIyWtppmN5tkrTPUFq+17yzgYEECfp1Ad9AgQhK7n4wmF6qaXCzj0FPXAzDjBfowYnYAVe1twfKt+/FY8DTIIECp5uBauYRpMld7zKFtt/zBnJYeggQKEtaWf9rNC10azaGnp3y09r/B5uILyhY3p05DDwTxG4PmpY9Dn+ytCi/X6AT6m/K12FXfhnmem3G36yXbob0WPIYtXL0S22TmwB2+mH0fIfU2OfNFFE2+EAAwzliTRkfDtRamj4ojw4vf3Hk6TvHdg7Htz+Dp681lcY/5w6v4+vDnMXOMmgXacYHucDMNO2AQ6O99Yu5Q9M/gCQCAli8eQ06QvbzqnKxQWk4eM4OctJOZiuYu2YoglYAj/4CWApZxOLCBOdqiJq7RAAAgAElEQVRCMoVDDiAIF4hVhMwmc4bxh99XAWAhlaPV/AZd/aCKxSyBxcpHogoqq0SZnbvM9vAt/U4xrTPy7FcdyIBMI2urmXnyBMd3CEapD3Tcw19wn42OfNnieNnJ7h9OoCMEWRHoMJR8UC0CdS2Ja0wSDxkv0J9yGeO7gUfmrUBlWR6ucb7NpmQ64Zm3gNVVec2vlLY9TosMycnRptkr+jPBFYALVGf8eP8H/kHx7OGbQURk5MnAlUuB0aeGY+hV7XNXfRtnylE2hpfmLtmOFmosrqVp94cHdV3vDS+LdhsnGAC0tjONTAJFi6cXQAg2HvMkAKAITQiEZJSrDrjcnnCOPNH6RIpZCwCKii3ixXXlbwu8Ltxxxfn49JZTMbjM7AgmhGDOCSPgUIViB+ySE9ufwtj2Z+ByMQEYIrxg/c2PF5mOUTXjvG8fxltg8ez5w9j9UT7lAm7fnl7Cyje4ciD/ijdd7GvxwYUg2mXrx2pTtdm01foWmz1yJkKnG9W5LGa9oJ3dby0+syb6+TpmHjFWfASAPGjO2a9CB6Ke5qHifHNZCX1npYOlDZZdv7oiy6u0UNoht3wYYU/gunY+78BF+Odh4eqNJn/R+Hr24pd2as8VE+jsGTRq6KrTurEtvcXPMlagE8ggkOGRzMLqT5t/hf/9aJ0gtLKZxYzf41IyLvOsQ9AC/VkiUUCxmaki8tTxvLOsvd9hHRt4meo0400uMqUoJ/ZO3sMHl2qNM1SUm8oXlNEIXU9UQ4XISNELDbtY6KeDyGhVFJdjDtOSqGqafHjY/Q/2wZ1r+r4+Dyk+hFN4YbF1duR+mxMOKDGV2jWh1mnxFEbeT8deFKEB+doLU2E/sa+8+fvA78PLAwZWIEQJ+pQyu37OyOO4fSf1VTTloB8FpXwdGUoBN4Jwe3IM6xVz3Sjluzv2TqwrmwEAONPJvidjjkHrGa+Fl0MyxbheZp9B+RdsBqUvIRBQBM66HdpsIMfrxV5XXxBdvoQ6Jn35jNtcr9iGoUaiI2WrE8Vj7ifDy792RPYZnKvzLwDmTOcSR5upDMPKcpbU1Zw3ILzOTQNamLLR5KLcb5JwisbHD55L8ZXnWvSHduMGhjLtsT/Za+pK03YyE0pnOheCUorv5SFswwFTuP1WnrkIrw3RHGrMBqsJh637DHZsyfygxYJ6A6hWk2ZfCF7wtj69SNq+35xirs4b1u9pwtHGkEYdpjLCeVpMfJ+X2d8vQUZbgJ2P6OqLc9EzpUNN57Yrg+DJjV0I23LMzcBpTwPDT4j5kHeunIIPrz4S1GC2XFpqYW5QOh0tlkeHV5FQAEE44bKJWirdp8Qx15n9D8u37kcxaQIJ8L+VqrV9uU6JOirog6EX8mGyry/bjn1UK7c7uHJIePmm227AYfsUW3rFkdjZgzmOD29kgiykm/5vrGQzy5LtWrjkxMAyDAnyCTiLf2Cx2m2GF8kG74Wmv+uj1dW4cq71TPTSl5dh6C0fcc05Us3FTr4+fLQGFGWGmXBpzWLTLKfVzV6+jbr6LWWkHuMbWWCFMRpIrSO1/dN/dGDkiSdjBXohaUN/ostgLB0C6WwtBV1fXAoAcsafFV7eVNusxQAbQsTGjR6Nc8//TbjWiip4VeFZ7DZIihiKeVmhHabGgsuYZKz6p7tp/qar4rdv+Lncscuq9mMT1WuL/JhO/pzXMlF5DDZOfoBb5YCM4nxeswTYzCGMmy9nEArJ4cy5pOD0AGPP6dB3PG5AMUb1LUT41lbGT/tO4PZ7wzkTGMTs+v84X9vWq/pzeEgAkk242+8Cih9i+1LTth37WzHTsRReypst6rez3zWs/UpOOAv4QnUDS/NsM3AfdD2LP7mUhKrKo9F4wpPcdn2MetGwIwAANEKZBAA4/J3DAbCorUi0B0L47asrMG9VNZZVmR2989eymfCFz5u/j0TQ7Avi592RI9H0ciAYknGn0+wr8vnsTUmhfVtMZZBzFUXc28qH/rY5FBOhjSY+ZWfKK6BwZKxAN1E8EA6n5jjqB4PGoHNScfZKW2GhCXRFJAAAyluNtcA758FWHZuUgiuAZeQ0naknLNCV5/jNFTswmXsZ8GPqayxWNXwGynWVC2dISzHLsQiFueY2bz9X7TCtU6m/375YFsdE++zEpGH4XSdOPJT7XDFLK0MwQ4lbBoCBsv3fCwAtqmnruLu59c31tRjRm5+VbBr5W3bMXhZnf4ykKBHrzR2HXLHGSzdsx/AhmvaOUBAfLtdmC66eLEeitMq6INeSIddxn62qTO7Zp72k9QJ//jprM2ay2F7XigPv+ATH/20h5/iM5AT1BWWUmnxRgOfPvS32ZjT0HG9yLBcF2cy/YDdLMKOUooV6sKaUzRbzN7yHxRvTNyuxI3sEuqHRwdk55kL/KqvXrI7hhCT8P9UJyKCxdWosCUWWp+cL9FPwiU/gthrb3vEO1eNGlVvWJ7FlwKEoyNH+jn+4FcdywKzFDP7iKtM6ldJQhBtaL1APu9J+vyRBDS+1ngNHhKNZAGDo4OHGQ2zZSVmmcMO+PRgYUpKgFFPderAZ3vqFr5vMF7XK1/n5VyzzcwtVhMp43tFKKcXD862bhpgIBbnZg6+lDru3awK9WHFIVzZ+ZzoUgKn8Qs98DxppDppGnRtel/+49t3o+8k+/aW5AJweq6SkijnzbOuSR+Ovn2jfSYvOhGpV6lc1mayrbtTKQE+71bSfFVLjLpMNvaGc+cZ2lyq+tBBl2bgOZmIdJW3FildjO38qyR6BXjSA+3g1VeKkR5xk2jV32ZOmdSYMAlfV0/MDxtCzTsaY6jT0oxyrDGe2qdscfqDZ9n98abDnKmPfJtk0kSq0Wd+ghYCuyWMa7YHty027bTm9gw+oIz4/Q+cw/y4zrtOKgBXlxj6mfsoM58tvFmI7Vfq+FpQDAHZMYALSTyVUGyJEeh/EYtivUuLuH3D9U9nC/65bNvyIoT3Mj+J22aJ/wNQ/AgD2Uzb137dvH/Zs1/IiXCU2df4V8nJ5s5pLIigkbfC27EIbZS95vfN9fY29ucNodzYWBdPHf9daxLdTSjHt4S9s+86+p4so02e3GiPNAGD+gs8AGPIEDjoTgVuslY4g1b7vA37+p8nkIrnY99TUzHxmIZkJdH2Y8lEuc939yIjEotgZONl6/ZizTKtO8ccikPT1ELXlaTufNuwWr0A3JxZFYtaYctttVx0zxHJ9SDKbUNilo4/ZebA5Plxl0Jgjoh7PCVS7F0gSIRZ/Y//SAhzpexSHt5vD97b0Nb/4Vb6krNDaKSt+g8ESn0lZeQDT0EesfxoOQ3/RAwZWcJ/DCVr1fO5E7Yr3UdmkvDgHHh5e3zztz+bBlDCTitp0o8/Lh2PsKObUrT32MW5X2sDMR3tQguUlFn+fLOO779l1XVu/xNbTzVmT2+u0l9QlUwZx24zRLUah/dzXWky7XtNX+fynGmyubcFJf//aPDYApWhAlXc2znfMx8uLq8LriUW9lGMXnmlahx4VcLms80T0PqdVvX9pMrm4vMy0luNg1wqGgnAQyhWS25kTo8kxbhnRcbJHoCsCpP7aKn71CM0Us//MtxA77Hy9KF86dq2SOWjcr+Pwx3HKziGXsmQenZD/bJVVwgfbrm+KzJ07ytAel+wrTA6aygt02ZVnsyew2HFI5AvFkDmbaKjNQ/Turefjoztmm9YXz9acWcZ4/8G97R2MhRVM2O+Xc7F3B0u2og52PCnTHnguznkcu/5/S1hd+MdXEYyCMssaowmm/pN4M6KefwTZNkJlfPYjE9yevCJuH/IoE/QSlS1/g8Dn9+OkWi1rd8RYG6UIrErkZcv4HASjQL/iFT4S5gOdJq1vwKGiF/hGZJliuZf5IO51vYBFOg29d7VWwqPx1JcBaK0df6qOrZTHcEnzlRT5q02zDTWPof9uFi0UCjIzj6Sbbcp1XS8JK+MEensghIc+sbA3Kg9wcbEhRE9n43YNOYrb5Dv7v7CDD3sjYeflUdUv8DuSzn6FqsuV/R90FQAzH0IrvNBP0fQ9MKnBhm6XGk8pwWV+5gS7NXCxafuLfuPLScPj4jMTyQxeW2ygWsSLe6JZm0+hUmKD9QBK8twoyjGbW3rka7OZFzGL2/bFUJs67gAK8tj3MKhtDTas+IKtHGL+Xjn7uiJcRw9kJpVzHZ/jGqfSqEF3PxVYOKlVSqdr5SyKHUwzznGzv6sdvFZaRuoxYa+5oZhv0zeY6VCiU7xFpu0AMLpvIV5zsUJoxogmNbrmRe9DqPLORn/CmyM3R2miohfSxkqUxnT9sUTLTF60QgvRzTuIf+mZzI8Amqn99wgAo3e+zgdKAHB6mAKT085MNkGlcilxaM+FFnpseP6W22dkJ5uME+hH3vYa/rg4cpd2lXnD7uU+ew2arLvycNijM7noZMNX5Uyr1RpCxBm2SNkN4vAp03AKyJSgoe9U5SN/3opinYAlfCilw9DxRz/eO/50I+6asAgzjzB/Z1/cegpeD06NbbwGX8RbpVr5WmffMcbdkcr6FdZ0/PpT2h/D68Gp6H/andz6Ew8ba3uMS2dmGeJgUSBk0NGm/VrbzRmEldN/AwA4yaFz4I861bSfFb+YPD68PNd5FxvLR6wW0VdH6ZpsWNQW+aIf++3yqxdpK6/knai0nUWKHDuiDIc5tPrieuEVDMnohf04Gkwzf8fNV7Ws7Gk/qzNiFMQvLa7iPr/r0c6txuu/OfIxk5lLrVjanF8RXrd0wl8jXntV/hQs2sTPxNWw0g0lTAmU1VLUOoE+wM2utb5qG3cs3r8aaO1AkEICyTiBri8bymN+gA/d/Hfus9Pw4xNPAewxno/dyH7FLh3uVxqnKlq2hTUtHrjy4fDZ/XAi5NScVvoz1zoUG/r5b5nGVtZoXdAfhFUmvOPk0fC6zD91odeFgZK5vogleXwPxfOvvB0+6sJOWooxYw62OSiNxPG7fHHfhai89GXMGsfb/EvyeI13dd8zYMX1DqXLU34v07ZvfzbXHPLmW2jFBk25ov1VLAwdhM9C47Hzap2ZwGKWgZ4s8WvaUdoMgSqRS9sKtKqgBcfeABMFvI+m9SsWOFCz5jNu/e49mi3cH5KxVPc89jSECw4o4fMWrKGYRNbhqS/42kB/+3QDlsgjLI84z8nGdFhf3ozUsn9PuGJl+3BtlnX4DLOJTU/AXYQD+/KywOF0I0RJuFZ/UOkVKukSCUvUvAGfoex1Gsk4gW6LxQP8Sbt9KdO5ajEqO7hwRO3catldVaBbOd9igaiak8zORwKt8JIA8vazeHKmoWva0OmNr7CF/Xq7nWKmcVin0HNavs04d1GdoD70Cm7bWT5W9+atkNkJ6nJIeGvmCiyc+UXc30FSUUwXdpFCVrgcEiYYs2otcE7nw9WeCxoyWYf+Iry4OYfdg/4f3jAP0ep7M6w7rLInLgzchF8HbkC/kkgKCICz2T2irwG+Zz+bAT5fp82ixgw0NDjWsYaygmPVG1nM/LA6vp3f5ys05cHYdMOIVY0ZI2c4FuK/nntwXMjsGD1U4qNI1ixgL8xJEjO5Fo/gZ5ctz87ECRIzIfXcrZ3P6+bNhz9N5it8OkI+0JAhFFJywEEo9m1jz6McULbrNPQmZdLlcVn4iDptio2P7BHoOuGlOoz6VtjHGh919T9ttxnPxz4pafGKhz2ofnVx/nDEsCQF2Nte7QNKwVdT/KhAabc2+nTtaGWzx8+HfRHL58ha6PbKd9nuc9+1l2NY+0u4PvBby2PPnTQQ504yN2OIcLmsoEfPPtznRmrQRD1awbHKNuYM/OVOQ5ngGPn9dBbBdMuJI6PvbGEHD7Yx7bFfoSaIXIaZ6i2BS8LLb+cxp+yQPayswAm5vFDN3a7V6LFK8NF3C/p64144EEKVdzauc75u2hcAhhI26xgiRU7oAoA9W3nfWW4Be/l+S9jsY5tjIA6UFIVntH17wqr+vI/EFWyBbBDokqLQnexgiUUhRVPX29BHSGzWRSJ0iEo1mSXQm2NLZBk7gN3YxjczAHwx+Ea8H5qMfr3s+0ICxrYTWqkniYYgg4AqX53R1h0r7QUV7P/CQcp52Fu+ZpTqvOTPWxRSHFKeQl27K3WUxhtKdbTqXhs2w9Q3vNC3ggOAA0rzTLVsMoV4f5dYMBYVGzbCfiZYn8uH+i0dZZ+M8rZnlmnd4YN7ouqBmbh0aqVp2wchQ1SKxY+c8+0jAIBTCtabtqm8GpoeXj7keN7B3dvP1+o5ukXLcjU6EgFgw0a+ZswmL0uiCjt9dUiQcbmThRBf7XzHdnwqQwKGv0GpOdT3Yhbpsr2+HRernZucvCN0XDsLN77Mfx2OHcmbxIoDNSYNXS1//HM+8z3JSpQLsWg+YtLuYdXQHaLBhRH/okgJQTrhFeGLO/qCm3HyPZ/Yblcp2ad1iNGfTaJBzX4OxG1D3zeQVdvbM0QJU5O1Oh8auip6auSCwwljDLuproTF368KuAbCT9vzZJ39bzefQet2Svjw6iPx413HR/xbrEnzSyDBZqAbAqzjUg0tNrU1I1vsK0vmXvEp97n3UXy00UchLeTzuobYm3oDwP96RS+pUPozc5DuquTt/nzUk/b3HHeQ9YyrecpNAIAPJM1U2dhmbqKxe8uP4eVLJ/GJUcZen4dLPyIq7gLsn8IcomW1zInbKPFhpH36sBnTaY5vtF64B/F/7/d/PhsV7XPxP/kQky+tTs6FLBs0dCJhJykPt6JraGFFuva3W9ROt3A8+wLad6NlgwuBzuFe9Ij9RqI3YlDTuo7Ss2aRYY1mcgnqBXq8US7qN69misrsBqCKZqBvqgEAvYM7UCeVWF5SUsxAgV88AJyk79lpHlsN4R8yH9Fpm33M0Ryj+hYiz6K5QtcnsQL9+mtZK7PXQuaQxAMO1ULnNst8zRB3IT8TbA7xDtbii7Qyuf2KzcXRIvHbU+wjlG7s9zL3eX0Nn8X6Skgr2Lbu7hnhZePLqpGyMUkjmJ/g9P3Ph7eFLBSHcWvuDy/vXMYnKp1873/Cy+2BEH7j4OuYhwwmnEaaA4w/H57+zP6f62dhjutzxmIr0bo/WSYP5fC+EEIIPrrmSNx8otnRmiMFIbUbzJaSA80hF1pbWOhldR1TfKqbeOFN5ZDJXAOw/ACVtdXMWRxrjHxnyCiBHhn9jUgt1nWMnnu16nH66btD0dA7+9IwVnEkIV5DNz4qAeJGvmy8Idhe737P7I/ywCnAxEtssk5jGGeaazknlARr6D1LemBI+8v4eYQ5yioga9d628KBrKe8kDfXHDakDC8Ej8eNgUtxxgRzi7pIFBdrNvPPvcdy234980ju85gpvON20/0n4t/Bo7FJ7mPZVk+lkLAXQU45s+XrywKopZWb+mv5HYNCVeFlY/u/v7i0JKaGtoCpocveD/kwYw+CgNON3OHTufWS7EdI6kCnMIWRfQpx2dTBpvUOOQAS5F94kkNCO9wgSs/hgT3Y7zbmAN6h7G/aCzlk1tD1Qr6hlSlrDRHaAiaK7BHoxGxyoZ3485rz+ZoY6jklGtIcop3CMG1Xm1arPQsNUS4AsMepOuMIN6YzHCwSQa3xQixs6LGRwClhmrz8ugEk9Gwuh4SND8zCk+dPNG1z99JKL/w9ZO+MA4DSfHNE0lHXPI+yqZfiqmnWJRzs6FOUg9eDU7GTluKS+ku4bZXlvFnCncPHhDskgjnByzDd/zDsoHu1UELi1h2vmBhqd7OCcfm7jLNZxvB8vi78VIdm0pMpxRjVgalQvlwby4jyfGY+qV0fjjj7WWb1mg5uW4zKUORCYR2hQK5HgPC/iyRJcEkU/R3MdyWHwxb52aqvvd1SQw9ZCPkUWFwyV6Bf6/9d2NHBsAoBi//8DcV6R5cmXCUaQgjOxP02ilAu3KuUVtWbXHSaNoEMOfxzaTb0UMCPYx3f88dSGPaDlowU6TvpSH/Urk4KQymHjz0MW+Ry3BC4DJdbaIB+yn6XS/x/tDy+siwffzx+uCn6JBZuCF6BKb6/49BBJdx6o524so+50NfSW6bjqz+Zw3erKTvX9hXmMr8A8MN3TIEo8SlRHnIAuF4XDaPcgFP81oIe4DNDT6GPmraPrlXMMeu1bkTDJXMsv8r/DdBeBhPbn7LdzwovbQdkPiCASE543S74nMznpAp0Ymho4/O3g1rY0EOh5GvjVmSsQH9HPgIHDR1ks7XzJpctQ38FAHjBe4EmvOu34dC6d1GK+vB+8coNLQaZApRi1FJmo7VzirLoGnMyk+M+3YNKVHONag7irhhxPK1FQ1iHoISReZEx8eJwuvCXoa/h9dDRllUcR/lewEzfffhcTl4C1mnjIxdAs4p571XgtUz++W+ImVAGLrrF8lxffs3KAf93LTNTBKbeBBTqQjl3sKzTobSKfZ7BN1NZu6sRr3yrRc88dZ1WuldtHBJue2igrdlc6xwAZp+jdVoy1rSJhg/ucBRLGImg0dkTLlkxMSlauOTkNfS25oawyYWV62AEI/TxTSZRBTohZAAhZAEhZC0h5EdCyDXK+jsJITsJISuVfzbdgxOEwWlx/2kH4b5TD9IPVFtUwvg6E7rW0OMgVLa/gv94z1LOSYG/adfrvLhiZ/A2beMbzupMLvprSJAhRzNjqA2R1eCXDoxy68FzbOt5ZCTh8gipYWIFc8JZOTaDcOJHOghvXNHB/rMd4ByLfIBz/dYCORoHTudDF2spi/TYls/ivWfksOiUiiY2M3T66rn95XZmd14hKy0LdW0eQ74WnPj4V3j2K83cUqY3Qz13HB/yN/l3ALRywc89zsocfBniy030yHPjCN9juNb/O1x0lHWGqR1OGgRkXqOWiIQ6HwGCrKxGWEM3lIL2bvw4HOWit+uHdGaYVObdxaKhBwH8gVI6CsBkAFcSQtRODI9SSscp/yK33u4Mq14HHuBv2NmHDsTAUp12YWFD78w3SQgsTBwanTW5uNpYTP0BKx8CWnTx9WGnKOHCDwmoNh717zI4P4lDdW5ZhS12L1T/SUcyRTvDxVMG4R/nT8ApY/uatv36CDaTNJYQSDaL5dE423cbJnTQBIFyPq7+p36/BACsOVgRprvYfXZqAcuiJPVM274un9VMqd7KzC/hPrcl2kw68CbLRj6YaDHlbqeED0NKraGyEdjfqjN/TGPZyksLmGP0Kj9LCFxFzXH5V58+He/IR2BQz3zTNjv81AHJQqATENT5HeEwSNVOLjl4Db2+58HhOHSXW9PQQ0aNP0VEFeiU0mpK6QpluQnAOgCpLXC98VP7bRYZYV/3+zU+CE3Gz70tGgPHyVCfdcxs3Kn/ekHzri5ywqHZ0HmTi6zTuA1x6OGTGvpoWiQWJTPhhh9Lek0uJMVOWYdEMOPA3pb3w5wTRmDe1Uegsix2QRMr3940HUtunm65bcN9J2AJHYlrZkUqQmdm2kg+9LLHISw+/shhrN7LpU6mu41vYaYXHMa6Wjn6sHK9u9Z/j5BMtZhwZw5+UqKDvOvfAwDMdmolcAHgdiUuPtSwky8ZoPSx9U3mW+edKJk7kp05sT/evXIKjhtl3zvASBBOuBEEDRoEugT0LikK9yuVbUwuvpZGLYZdp72HLBylXa7BBSGkAsB4AOq3eRUhZBUh5HlCiGURDELIZYSQZYSQZbW18fbgi/BFnP4scAPv8W519cBVgasRcMZe7S3y1RMvnCjRQsWI/oXFmVyUv3vNmxjrX4E8qiQBEWuBrma3qZt57bT72LSBFL64YsDlkDC6b3LMWb2LvCgvtC4P63JIqHpgJi48rKJD5ySEhLsXAYCjnE3I8/tqpTT++ZXumevPkqNmH8n2O6TG0I7P4cTivloi02hShVMlvnbLXjCzztb2PGyuNZfdPflwPkfildCxpn0IIRg7IHJzbCNBOOBCECur+GqLhDhQIrWGa+M7FLOSw6AoDF5+D6DY0AM5mj9L5mzoqVMuYr4SISQfwJsArqWUNgJ4CsBgAOMAVAOwjH+ilD5DKZ1IKZ1YVmbRUisWVv3HfpvDZaoEmAo6PZW30yB1cejha7zBQtL6BdV6F3zYot050yvUuo5AFXSc10NabHmOUkKD6ArW3TtP15TcyYT/0F7aDMSY+j5piJZuP89zc7ghhf9QtWctu18qpd2gu809f4mhd2+kDl4dIUQckAiFHOBDLAkh2EnKWR9RIBzWKDn4mP19JePDNvSaMVpxu5BsjnJJhfEvJoFOCHGBCfNXKaVvAQCldA+lNESZR+9ZALEVKc864kwsshXo1pmiVuQ2VfEjCUfIWByXYhNI2gswpn0Amc17IebA3UlL0avQHDs/kZj7aRZ4NZPD5g18SedRfQotr+PKYyGShV7NlHHU55Fj+QFgoSuWNojRCYJdd0Ifg3+DEPxY0w4PCWJ/czuoGofu4p3ee4rGauYYt/ZC47Jeu5JTlDCj4HMA1lFKH9Gt15ecOw2AucdUmojWn7OjjA+utN0W929l15ZNiQU3OkWtLlq2y+BbiKHVm1Frz16xl71/WSrwKbWD6mgBci2K3L3huTvi8QOX3gMA+CCPCWc7XxMZzRp6PDHbHNL51VGvmdapfLcvMQ5mtYyH5DeaeQgG92EzfwcC4dBEycleWmqt9t47PgKU0huSW3vxWdrQu0hi0RQAFwCYZghRfJAQspoQsgrAMQCui3iWNNAZJS3aoZ2PnrC5gk4o211DFcoFDXz1uUiOwNSbX7KrOFd3YxtlJhK96cWKdTIffbY4xOzovatZE4px0WzapSwR68B+Zh9D8RDemfu9rGXSPnFeYgwCeVBS/o0mF4lgYCF7FgPtbeHieZLTiTHtz+Ji/58AAP0bv9eEvS4xT589mso7MZYol68ppYRSOkYfokgpvYBSepCy/hRKaXW0c2Ujds2I40Yftmj70oj8MqCULwEQ8ZhsRQj0TtGndx9UtM/FyyG+0uY2N1+eYF6vS/khDbIAAB1USURBVLnP/wkdzX3eeYhWLvgi/4221yvJc+OhwJncumF9+MqgO3/5Xni5MKdzBeM2FBwKANhLmMlnzPZXuO0EEoZvZz2H5ZqfQRWB7nA40Yi8cBLRT6XTwxo6XJpz2qq+i6i22IVZQlmIlhpW1VHs5A3R2dDdchtwZ+zREeqxHUmq0aoEZJsAzLa/J7U8c4G5Zg0AlJ7Jp+m/Xc8L+PohfE/UnroInO0F47lty9XEI4UndHVwVsmD4HHyJsSTxvZDRftcVLTP7XSXrJocFsceUrKri3y7uO2EAN9VslDLBuSDqlq4oZZLTX1rOPXf4dR8CFYCPRVkhkAfe270fXSkoI48bqOXYbrvr6De6C3LOoIazkgBjG40t+UCYC98w+Ya8/aUt4lL8wtCczp3t5SqxOCx6EELAHmVfLbrESP4CpGTB/Plgr269mwXHzOK2zZB4pth/OG4YeFlY+EulaoHZqLqgZk2o+4I7O8LKE5RY+YpIQQbmpiA3lS9V9PQnXym6NTQ4rB5hejMpdZx6MknMwT6IN6O960cQzsuwJA8n1gCcGETTXx+lSZ4I43dxsFEjE7R6MJM6LECK9Qa+GdNNJT0NaS+u5z8HXTeoQPxlNICclj7S+hbpGnop4zhs2jP89/Eff79dE1j3yInJizRFuU5aw4yEbgP5iicycPYePvlS2EbusOQWNSEvHD1SX0Wqb5hxt589qKqzh+dqNHbkhkCfeRJ3Mdz/Lcl/ZKRFMwXgsd3fhZgc37K2dDtjo2ioVskFtmfL1kabLpfFem+fmaT73Fiy59PxINnmJue6Jk1jldqCrwu/CV4Lira58IPFzczLPDywvAb+SAYuTnAujCd5b893qHHxN79LFFIfUZmSt9y2x1OF1wOZexNOy3NKt9LB2KPsy+oYkN36AW6TkMPOphZ1u9ITKJjJDJDoOuiNyra56ZxIMAyeRjuCl4U/pzoWQAJhy1G3MtmNeH/j7fRRRaQchNTFhLtO/y9/yqMtIkvt0KSCM72MWVsjVxhuc/c0HRUtM9FLRJryjQyCSx5STX7eIgmgJf2PgdEciC3mZXrzd26QNPQdULbFwyhKFCj09C1GfLL32iZtGoP2o52pIqHzBDoXUgIqba2zssL6xPIuSybtnNhhlZO0RQnFqX0ama6Uup/tnGO/1aslivwgTwZ+R1sT7iEjsSRvkdxpv92DLQo3fvHXzDzxIzRvU3bEknelV9ga84ofD/FukwvANDBrN1gXa/JmoauE+iNNBc+uLSwRcmJbaezSJyLHP8L76eWVLZqbpJoMqNZZNq732gskMcl9wLKm6II5v6D/woeiwsQXVipYYtW57W/bIIFYNo15HRfP3spGHEMTl47KvqONmynzD5+SEWJadvvjmZRM+cdeoBpWyIpLCpB4Y2L0bj8G9M2h9I9zOVlL5yQv4018QBLLPrLLw/C+IE9cMCTF8FDAqjSxaj39zHN/HDYJyMmk64jKSORduGgsUYp29n5SBobx6byfwnMhfyboGg0sX4fXedrSz1d6J7JNp690Dqk0YhaNtiOkYY4c4CZZa6aNhQ9UlRq2Okxm0Em7HkDAODxMpt3qK4KU7c+Ed5+9iEDMay8AE0ulkkqKU0wHA4npB4Dkj3kiGSGQE+LZIrtmqlUbP8ZZD1EJEO7LNtz0eiJRa8U/Bor5Uo0lB0S6xAzBCHQk8lB/YowyULDBoCv/nQMxvYvwlXHRO6RevYh6RV+AOB0WVeqBIC8QmbHp84c7MhXHLhObf9G5TH01P0MQIlRH2xdyjhVZIjJhT2cQRrb+yfRcRvn9vg3RtbMw16qOYCSZUOP5GStU0KrSBSBrmWv6r4JYloDANjhGoRT/ffiNVeCPfCpSAaIhJDnSeX939sXxxpQkot3r7Le/tbvDsfpT7Jeo/piXulCX3/FiMvtRYA60NLUgC35E9C76Uc4dbHmK51jUCnvRm4La5btcDo5weALBOBxpfZvzCgNfSvtWGxqorTnZqkAz4dOwHuy1kqrs/KKuuLLMGWY/7AzfLebtscStpg8sZtegZ7wkgyChDC6b+xRManAmxPhOSQEfjhBan8CkQMIGfI88nozB+7gtlUAAIehtK7nPj7JKhVkhkB3OIFzXktJ/HlHiVdstPcYark+NjnE7/QL31+wjGp9FONxcCZc/qVdQ1db0Am6Ek6pa4kct8cs0DcXadmwEiha4QFoMFyZUWXEQayUgZvwYYt7XX2QLrrWtxuJESeiFh3rRtLV2UH5N/jpvjtjPFITlqf77sR6am2L5IRZyjXWdKfcs783V62mJ+gSOKSu9Yp1e3IgG6LC9uRpytYG2g/DyXYQOYiQQaDnDZ7CfVb7EZTcmL5K4plhQ+8giVAOky3/CAAPeFv4CjrM8rqPBU/DR6FDw5/djdu4Y4xo96f5i+haj1MSoekpjiSIzvXHDcMxw3tF3zEFeFwO+OBCju5Z1D8jufChCbmWAr1HsUHBVEwy+r6j7f4A1u1qAt92O3lkpUDPFBzQupo8GDjLdr9Hg3xZUX9Bf5s9Gdblc1NNejV0h6G+taDrcPV0a3NjOvA4JTTBAX3wYotLi97Z5R6Efv7N8NGgyYbucBtCHi0azCy45wR8GDoaZ6YmCjODTC5dlU6ovCWkObz8ZIiVHY3F/h3MjdKb1aJ8LqHs5VFEG/h9kyV3HSm6g+2IoXuTQEAIQSHhzXLLe2vKVb1PxmCpGkQOhtvVWSFTwk3rW7ws0/UEx3d4wf3XBI/anowR6EF9j74sJqYa5p5odS7YzxrS/bw9drOMuHK5Js6RdQzqZmGQu2lya3LYXl9K8wtFkLFQnSbu8njRQj0gNGQyueiRCK8Z5V3/vWmfPY3J9+dkjED/26cbou+UJJJhT7ftsZiAa7UUD8c/gifh7z21qCBJtqnPnESj+iHtT2Ka7+HkXUAgSAA/kUrus/6R6FveG3nEB4T8kE3lqSNg0fhmwc+1cY4wdjJGoP+4qyH6TmkgEdUW1T6MCYMQPBCcjVqnFj61v/ywCAckh1oUh1t1pZqgm8U7NxBzerlAoKcpxJtS9Lr2sJqPAQA9/TvC3Y307C4/OubrXHJ4RRyj6xgZI9BT8XbTk8pokFuCl+ium7gr66N9WoqYI6qexN7SLpOhDg8q2ufirML0llsWdH2K0BJefid0OCpKNe161UClVHYoaKmhtxz2B9vzts58kvs8iG7v5EijkzECPdvQi239DRVKQMylldlGbZzhhMH0ku5w8SQhEkUFsVKXy4qIvRKcjmsDV+GsiVpeh6uMmWMKaCNkCw198LiptufNPeQ8fkUHW2nGQ0YI9PZAx2KKU9FdO5FX2EztM8tm+2/u8Pms3gmqQHfYxGcL+SforjQEWb0VWRGHev/W5n3tAIAy1Ee0obd4bOLqJ1+pLXvyOznS6GSEQN+6L76Y4lR0rUnEJRqg/dDG09l1dtkk98H80ISI59WPLeRgMbOv5c6OZ4gCQdaiyGzIFmrN+FFaSY2QXdjinQ3Iu8kmaGPG/Z0dXofIiMSiVGjckQiEUpdxKRneELLNO3e6v4PRI5IDFe1zMaZnES6JvnenSWaDboEgkfQtdAGt1s9aj9Yt4eWR/tWpHFZcZISGHrQQqJFITOq/JpDWVZubTXT+/Kw/6juz1nLrjaUuQhY/UTImHllqShe2dEFU5DxWxdUqs9o5LL31zTtKRgj0R+evT/cQTCRNABoEkOiN2TnSXfRR0PU5ooZFQh1a0oIf7vgFty2/h+bf+jl/UkrHFQ9RBTohZAAhZAEhZC0h5EdCyDXK+hJCyHxCyAbl/6SlBP68x9xfMxZSIQo7ew2jOSlWk0tHieZPyLbXRrb9PYLksbDfpQAAWXKjKIdvSKEvtOVzxhnym9cLmPCreIfXIWKxoQcB/IFSuoIQUgBgOSFkPoBfAfiMUvoAIWQOgDkAbkzeULMLOxuzuvY89+M4vPVT+JH+ri4CQTbjLR8C7AJIMHJq/pj6+fFd4IbUZblHVf8opdWU0hXKchOAdQD6AZgF4CVlt5cAnJqsQXYnVE26ShqAvwbP6dS5hLlB2NAF0em5bxkAYHTz4oj71XoGpmI4naJD83lCSAWA8QCWACinlFYrm3YD6Fh/uC5OrHKgs6GRRqGrOkWTJYiEfBMIeFoHHQ8A2OGqsNy+SWZ29E3lM1I1pLiJWaATQvIBvAngWkopF/ZBKaWw8RMSQi4jhCwjhCyrrY0vfT8kZ5+qqQpso0DP8yQukjSWl0K6Q0KTjZilCKKhRrnIIeukO7X1XP9dn6RsTPESk0AnhLjAhPmrlNK3lNV7CCF9lO19AFjWZaWUPkMpnUgpnVhWFqWOtw3VDe1xHZcJ1LXwXYtcjowIPOrypCKpTJAd5OezxL5eNqWlh0s7AAAbpYpUDSluYolyIQCeA7COUvqIbtN7AJTKNbgIwLuJH176SbZguO/DdR0+plNDMhycrASgriJPu8o4BF0XbxFL23+DTou4X+++1r17uxKxqINTAFwAYBohZKXy70QADwA4jhCyAcCxyucuRSoe5mRdImljFzYIgYCjrGcZJrY/hcaj7rTcvue367GqeDqGze76tf2jGmwppV/DXm5lVhpVHNAsEYBCURUIrMnzOPHdn+0rIZaXl6P82rdst3clMqKWSzqIqiF3Us5vq0tTE2PDH5btTlGBIBayxeeSlR64VGrV8d4HchfT/LPlhlbJsj9HIIiJrBToKol4pm0FXSdPLqoRCgSCRJPVAr0rE02DTFr0SVLOKhAIugJCoEfB1nzTSYtJY1ugcydIMNni/BUIujNCoNsQqw02Xk26MzbeTDDXdP0RCgTZhxDoUUiWDd0pmb/6a48dGl7e2+wzbR/Ru6BzF41AtjlFBYLuiBDo8dJJC0Vxrrks7uTK0vByq99cV+KUcX3jvl53k9fd7M8VCABkqEC/Z9bodA+h01gJWL1AtzxGiCmBQBCBjEss+umeGfC6HBH3SaR7z8pZWFmWhx11SjH8OGWsXjgXeJ149TeHRj3G2G/Ujkh/f3fT1AWC7kTGaejRhLmeztiFI2nDn1w7Ne7z6i4Q5vaTRmFM/+Koh7idif+5khXcImJmBILUk3ECPdVYvRQSUeJW3zvU2EfUjplj+kTfCfFNGoTmLhBkPkKgd5DnLprIfY5XEOoPi/UcvQq8nbqm8bp6si0MXbygBN2RrBToyRROxwzvlZDz6AVOOoWPEHwCQfaQlQJdJRnCSorVMxkFvY1+y940VV4UCARZRVYL9K6M/mXz+Gcbkn89G2NLtplaBILujBDodljIv4+uOTKW3Tp8+gElOXGexZp4ZLQwvQgEmU9GCPTjRpV3aP8jhvYEABzUryih4xjZpzCh51MpV5ydkRhYkpuUaycLofkLBKknIxKLhvbKx/y1e2Le//jRvbHmruOR70nen9fpTj86jfjmmSMj7nrvqQfi6OFlnbueetluo4p3l79TINDICIEej+hMpjDXkwgBWZRjruui5/zJB3TofEKUCQTdk4wwuchy15u/J7KuSqyJRdq1uz6iV6lAkHoyQkMPpUGgJ1to6l8Ijk5q+XMvPRRDeuXHeF2BQJCtZISGPm1kYpJ5EkkiNVCL0ugxc9tJo3BYZWk4izQaQm8WCLKXjNDQEx2tkkgSofEGQvGL2V8fMSim/bqNL1Shu/29AgGQIRp6OiMz7K7cWRu6/k9q8QU7dS4jEcvnJvRKAoGgK5ERAj2d2AnHRJpcElG9MV6ECUYgyB6EQLch1llBIqotepJQ57yjJFxzF28KgSDlRJUkhJDnCSE1hJA1unV3EkJ2EkJWKv9OTO4ws5tUyj67F1C2yt/uk0glEMSmob8IYIbF+kcppeOUfx8mdlg82f5IDujRsVou0WRUR76vbP9urVoICgTZSlSBTildCKAuBWPpkqRC4DnTaENPFukWo9n+ohIIrOiMJLmKELJKMcn0sNuJEHIZIWQZIWRZbW1tJy7XNYk32iVdpgDjeNMteAUCQeKIV6A/BWAwgHEAqgE8bLcjpfQZSulESunEsrLEFJhKJZko8CKN2S46J1s1WmFDF3Qn4hLolNI9lNIQpVQG8CyASYkdFk86nslsEwPZ9vcIBAIzcQl0Qoi+/fxpANbY7ZvpJEsQpktxTGRRsUxAOEUF3Ymoqf+EkNcAHA2gJyFkB4A7ABxNCBkHNruvAnB5EsfYpREz+q6JMLUIuiNRBTql9FyL1c8lYSxR6QoJOImiq4ibsf2LsXzrfpTkuRN63nQrxtvrWOPtn3Y3pXcgAkEKyYjiXOk0E9jJpc4U1AKAsQOKO3F05O8j4lbDxptOHIHTD+6HyrLYyu9mClv3taR7CAJByskolTeVs+hkX6sz9VsSWR/e5ZBwYBeuZikQCGInowR6OugqphE9zQmuzpgMRMcigSD1ZIRA787CoV+xuSxAtNlD9/22BILuTUbY0NPJ2urGpJ17yc3To84ArIR3XLOGrjjVSCIiykXQHckIDT1bKS/0olehdeu4/103FUDmhkV2t3h3gaArkFECPZVCIt0Cya04TSULid4Z7TNVf1W6zWRzl2xL6/UFgnSQEQI93THNVriTHBMvK3+0pUCP43y+oAwA2Nvs68ywMoad9W3pHoJAkHIyQqB3R9TIxERp1B+uqgYAbKoV8dkCQbYiBHqXhUn0eKwrVofsakitxtoVZ1UCQbaTUQI9lQ7CXWmesue4WQDSiN6Fpm3RZKXV9hZfqPODEggEXRoRtmhHtJdHkjXQfsU5mHvpoRhnUSIgngqCK7fXJ2JYAoGgCyMEug0uR2zTgWTOGg4f3DN5JxcIBFlHRplcuiLpsBUL87RAILAiIwR6OgSYLEfZIY1h6sLhKBAIrMgIga6SShkaVWYKoRoR8fUIBKknIwS6pEjywhxXyq4Zq+MxU1PzBQJB9pERTtFctxN3zxqNo4f1Stk1u7aG2bVHJxAI0kNGCHQAuPCwitReUMjMTiGaMwsEqScjTC7pIFpxqWNGlAGwrrWSbISsFAgEVmSMhp5qonV5e+yc8aht8nWqlVy8CHkenVnj+uLdlbvSPQyBIKUIDT1OvC4HBpTkpuXam2qa03LdjpDsapTROCBNv41AkE6EQLehK5s16tsC6R5CVHoVsMYdl0wZlJbrF+W603JdgSCdCJOLDXIXlui9bbocRWJs/yL8sKMhCaOxp+qBmSm9nkDQ3REaug1dV5wDo/qyCoyTK0tiPmb8wB4Auk/cvCfNJh+BIB2Iu96OLqyhD+2VjztOHoW/n3uw5fYCL5t49S3KCa/rVegBAFw2tTL5A+wCnDtpIPr3yMGn1x+V7qEIBCkjqkAnhDxPCKkhhKzRrSshhMwnhGxQ/u+R3GGmHjXK5YLJB6R3IBYQQnDxlEEoK/BYbj94YA88MXs87jxldHidrPxBjm6iojskgq9vnIYhvfLTPRSBIGXEoqG/CGCGYd0cAJ9RSocC+Ez5nJVkqvw7aUxf5Lgd4c9qY2lnGsIsBQJBaojqFKWULiSEVBhWzwJwtLL8EoAvANyYwHGlHTXTMUPluYmLp1SgprEdl3cTk4tA0B2JN8qlnFJarSzvBlCeoPF0GcJNmjNVRTeQ63birlkHpnsYAoEgiXR6/k2ZKmvrQSSEXEYIWUYIWVZbW9vZy6WMrusSFQgEAmviFeh7CCF9AED5v8ZuR0rpM5TSiZTSiWVlZXFeLvWETS7ZoaALBIJuQLwC/T0AFynLFwF4NzHD6XqQrLGiCwSCbCeWsMXXACwGMJwQsoMQ8msADwA4jhCyAcCxyuesgoZt6Okdh0AgEMRKLFEu59psmp7gsXQp1PK5Qp4LBIJMQQQl26Bq6JIkRLpAIMgMRHEuG6r2tQAANtc2Y9WdvxCaukAg6PIIgW7Dki11AIBvNu5DoTd1zakFAoEgXoTJxYazJg4AAJx36MA0j0QgEAhiQwh0G9Tyqy5RhlUgEGQIwuRiw7mTBmJ3QzuuOmZIuociEAgEMSEEug1elwM3nTgy3cMQCASCmBH2BIFAIMgShEAXCASCLEEIdIFAIMgShEAXCASCLEEIdIFAIMgShEAXCASCLEEIdIFAIMgShEAXCASCLIGordZScjFC/r+9cw2xqori+G8RUyA91MxH+SqK0kLMVFJKTAnJL5UU9DDEFEqQjCj0U/alh0ZBXyJELQuxLO09ZWqWA5YZNmPapCmaWOqYZqOZ4uTqw17W4XbuQ5njvWe7fnDwzD5377X+7nXX3nfvfbn7gJ8zar4L8FtGbVcD11PbxKYH4tMUk54+qlr2NzzPaELPEhH5VlUHV9uP9sL11Dax6YH4NMWmpxJ8ycVxHCcSPKE7juNEQkwJfU61HWhnXE9tE5seiE9TbHrKEs0auuM4ztlOTDN0x3GcsxtVbfcL6AWsAn4ANgHTrLwzsBz4yf7tZOXXAF8Bx4DHC9qaBmy0dh4tYXM+0AJsLChPtZlS/3JgLbAVeAs418pHAOuBNvMjBk29gTXAIeAo4ShpHvRMNS0KdEmUjwRaE3pacqJnIbDZbM0H6qz8fqAZOAwcMc151tMJeNc0tQLbqP576G6rewIYXKJ+Md9GAn8AjXY9WayNM3ll0yj0AAbZ/QXAFqA/MBuYYeUzgFl23xUYAjyd7DjgOuu0DoQf41gBXFnE5ghgUErHpdpMqb8YuMfuXwGm2H1fYADwNvBEJJrmANOt7f6EhJ4HPddbf+zg/wl9eQ5jbiwgdi1K9M9wQjIbBNwGrMu5nueBmYS8MA5YWQN91A+4GviC0gm9mG8jgY9K5cFqXJksuajqblVdb/eHCCPzZcDtwAJ72QLgDntNi6quA44XNNUPWKuqR1S1DfiSEBBpNlcDB1IepdpMIiICjALeSfFth6puAP4EtsegiTDDxfroIuCXWtdj9b9T1R1pz4BjeYo5q1+vBvAN0NPK16jqj6bna+DSPOshJO3PLS8sJQzKHaqpSVWbVXVzWp0CKtJeK2S+hi4ifQkzq7VAN1XdbY/2AN3KVN8I3CwiF4tIB8IMoNcpulCJzYuBgxYcALsIgZZKBJqeAsaLyC6gHngmB3rKMUxEmkTkExG5lRzpEZE64AHg05THk4AG8q2nCUu4IjIU6EOYeVdTU6WU8i0Zc9dmZP+UyPQ3RUXkfGAJYY2rNUwaA6qqIlLyiI2qNovILOAzwgy5Efj7dP2pxGY5ItF0L/Caqr4gIqOAj4HxOdaznvDV6MMiMg74ELgvR3peBlarakOyUERuASYT9gXyFG+Fep4DXhKRRuB7QoKfTY1oqpQC35IxNxZ4D7gqax/KkdkM3UbpJcBC+5gFsFdEetjzHoTNipKo6jxVvUFVRwC/A1tEpJeINNr1cJkmUm2KyDKrPxfYD3QUkZMDXE/CMkQh50SiaRKw2PpoOmHjraFU2zWgp5T9Vntj1QEPmZ7VedAjIjOBS4DHkg2IyABgLrAPeD0H8VZUj/XPRFUdCDxIWIJ5o8qaUhGRV61+fSnfTsac3dcDdSLS5XRstieZzNAlDLnzgGZVfTHx6ANgAmHEngC8X0FbXVW1RUR6Ez623aiqB4GBFbqTalNVxxTYWQXcBbxZwrcpQGMEmnYCowlr7HsIpwj21bqeEva7A3sJMXeAcDpkf63rEZHJwBhgtKqeSJT3BpYSNg035yjeiunpSBhkjxMG2u2q+myVNaWiqhMLilJ9OxlzNmsfSpgc76faaAY7rcBNhI23Dfx3rGcsYV13JeEI0Aqgs72+O2GNtxU4aPcX2rMGwlHBJkKgFLO5CNhNCJpdwCQrT7WZUv8KwkbOVsKJlvOsfIi195dpOhqBpv7WN2q6tuVEzyNWrw34FZhr5VMJG9ZK+Ai+JSd62hL/9/8efSPMzFsT/XMk53qGWZ/sNE2bqP576E77+xhhMrCsSP1ivk01HU2EjevhWeTSU738m6KO4ziR4N8UdRzHiQRP6I7jOJHgCd1xHCcSPKE7juNEgid0x3GcSPCE7jiOEwme0B3HcSLBE7rjOE4k/ANJMjs/9VmP1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128992358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077c2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_7\n",
      "    \n",
      "Sensor ID is C630\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C630\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51597\n",
      "Number of NAs: 0\n",
      "count    51597.000000\n",
      "mean         1.078967\n",
      "std          2.980896\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         10.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C630\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C630\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51597\n",
      "Number of NAs: 0\n",
      "count    51597.000000\n",
      "mean         1.078967\n",
      "std          2.980896\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         10.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2FB\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2FB\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58609\n",
      "Number of NAs: 0\n",
      "count    58609.000000\n",
      "mean         0.811520\n",
      "std          1.378636\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.875000\n",
      "max          6.500000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C2FB\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C2FB\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58609\n",
      "Number of NAs: 0\n",
      "count    58609.000000\n",
      "mean         0.811520\n",
      "std          1.378636\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.875000\n",
      "max          6.500000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FCC9\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is FCC9\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58611\n",
      "Number of NAs: 0\n",
      "count    58611.000000\n",
      "mean        24.684250\n",
      "std          5.035577\n",
      "min          9.000000\n",
      "25%         23.437500\n",
      "50%         25.875000\n",
      "75%         27.875000\n",
      "max         36.312500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FFC3\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FFC3\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58607\n",
      "Number of NAs: 0\n",
      "count    58607.000000\n",
      "mean        26.210653\n",
      "std          2.055123\n",
      "min         18.687500\n",
      "25%         24.937500\n",
      "50%         26.312500\n",
      "75%         27.437500\n",
      "max         36.125000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE29\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is FE29\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58611\n",
      "Number of NAs: 0\n",
      "count    58611.000000\n",
      "mean        21.349735\n",
      "std          5.640483\n",
      "min          6.375000\n",
      "25%         15.812500\n",
      "50%         23.875000\n",
      "75%         25.812500\n",
      "max         36.312500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a8d8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f83feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dc035710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_14\n",
      "    \n",
      "Sensor ID is C43B\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C43B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51876\n",
      "Number of NAs: 0\n",
      "count    51876.000000\n",
      "mean         0.525471\n",
      "std          1.308390\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         15.125000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C43B\n",
      "Sensor Type is room_energy\n",
      "Sensor Type is C43B\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 37\n",
      "Number of samples: 51876\n",
      "Number of NAs: 0\n",
      "count    51876.000000\n",
      "mean         0.525471\n",
      "std          1.308390\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         15.125000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F5BF\n",
      "Sensor Type is room_temp\n",
      "Sensor Type is F5BF\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58673\n",
      "Number of NAs: 0\n",
      "count    58673.000000\n",
      "mean        23.905847\n",
      "std          6.613979\n",
      "min          5.187500\n",
      "25%         23.062500\n",
      "50%         26.375000\n",
      "75%         28.000000\n",
      "max         41.437500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FFA6\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FFA6\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 58674\n",
      "Number of NAs: 0\n",
      "count    58674.000000\n",
      "mean        26.678812\n",
      "std          1.916770\n",
      "min         21.062500\n",
      "25%         25.250000\n",
      "50%         26.562500\n",
      "75%         28.125000\n",
      "max         38.187500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16cebf2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12819c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_15\n",
      "    \n",
      "Sensor ID is C612\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C612\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57710\n",
      "Number of NAs: 0\n",
      "count    57710.000000\n",
      "mean         0.996246\n",
      "std          2.992138\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         62.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is C612\n",
      "Sensor Type is living_energy\n",
      "Sensor Type is C612\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57710\n",
      "Number of NAs: 0\n",
      "count    57710.000000\n",
      "mean         0.996246\n",
      "std          2.992138\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         62.875000\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is F995\n",
      "Sensor Type is living_temp\n",
      "Sensor Type is F995\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57713\n",
      "Number of NAs: 0\n",
      "count    57713.000000\n",
      "mean        27.526887\n",
      "std          5.636082\n",
      "min          7.812500\n",
      "25%         26.875000\n",
      "50%         29.062500\n",
      "75%         30.625000\n",
      "max         37.062500\n",
      "Name: value, dtype: float64\n",
      "Sensor ID is FE01\n",
      "Sensor Type is ambient_temp\n",
      "Sensor Type is FE01\n",
      "Last Date: 2019-11-09\n",
      "Number of days in sample: 41\n",
      "Number of samples: 57714\n",
      "Number of NAs: 0\n",
      "count    57714.000000\n",
      "mean        29.701888\n",
      "std          2.985322\n",
      "min         19.312500\n",
      "25%         27.812500\n",
      "50%         29.812500\n",
      "75%         31.500000\n",
      "max         39.437500\n",
      "Name: value, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d2609b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a172710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for house_id in house_list:\n",
    "for idx,house_df in enumerate(house_df_list):\n",
    "    \n",
    "    # for house_id in house_list:\n",
    "    #df_total = pd.read_sql(\"SELECT DISTINCT ON (index,timestamp, value, sensor_type_id) * FROM \" + house_id + \" order by timestamp;\",engine)\n",
    "    df_total = house_df_list[idx]\n",
    "    \n",
    "    #Get house \n",
    "    this_house_df = df_total\n",
    "    house_sensors_list = house_sensors[this_house_df['house_name'].unique()[0]]\n",
    "    sensor_type_list = sensor_type[this_house_df['house_name'].unique()[0]]\n",
    "    this_current_house_id = str(this_house_df['house_name'].unique()[0])\n",
    "\n",
    "    characteris = pd.DataFrame([house_sensors_list,sensor_type_list]).T\n",
    "    characteris.columns = ['sensor_id','type']\n",
    "    \n",
    "\n",
    "    print(\"    \")\n",
    "    print(\"    \")\n",
    "    print(\"HOUSE ID \" + str(this_current_house_id))\n",
    "    print(\"    \")\n",
    "\n",
    "    if len(sensor_type_list) == 3:\n",
    "        if 'room_energy' in sensor_type_list:\n",
    "            room_energy_str = characteris[characteris['type'] == 'room_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            room_temp_str = characteris[characteris['type'] == 'room_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "            room_energy_df = this_house_df[(this_house_df['sensor_id'] == room_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            room_energy_df = energy_variable(room_energy_df)\n",
    "            room_temp_df = this_house_df[(this_house_df['sensor_id'] == room_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "            #Implement Summary Function\n",
    "            room_energy_df_plot = sensor_summary(room_energy_df,room_energy_str)\n",
    "            if len(room_energy_df['timestamp']) >0:\n",
    "                room_energy_df_plot = sensor_summary(room_energy_df,room_energy_str)\n",
    "            else:\n",
    "                room_energy_df_plot = room_energy_df\n",
    "            room_temp_df_plot = sensor_summary(room_temp_df,room_temp_str)\n",
    "            ambient_temp_df_plot = sensor_summary(ambient_temp_df,ambient_temp_str)\n",
    "\n",
    "            plt.clf()\n",
    "            plt.plot(room_temp_df_plot['timestamp'],room_temp_df_plot['value'])\n",
    "            plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'])\n",
    "            plt.show()\n",
    "            plt.clf()\n",
    "            plt.plot(room_energy_df_plot['timestamp'],room_energy_df_plot['value'])\n",
    "            plt.show()\n",
    "            \n",
    "            room_energy_df_plot = room_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "            room_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            \n",
    "            this_export_df = pd.concat([room_energy_df_plot, room_temp_df_plot,ambient_temp_df_plot])\n",
    "            this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "            this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )\n",
    "\n",
    "        else:\n",
    "            living_energy_str = characteris[characteris['type'] == 'living_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            living_temp_str = characteris[characteris['type'] == 'living_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "            living_energy_df = this_house_df[(this_house_df['sensor_id'] == living_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            living_energy_df = energy_variable(living_energy_df)\n",
    "            living_temp_df = this_house_df[(this_house_df['sensor_id'] == living_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "            #Implement Summary Function\n",
    "            living_energy_df_plot = sensor_summary(living_energy_df,living_energy_str)\n",
    "            if len(living_energy_df['timestamp']) >0:\n",
    "                living_energy_df_plot = sensor_summary(living_energy_df,living_energy_str)\n",
    "            else:\n",
    "                living_energy_df_plot = living_energy_df\n",
    "            living_temp_df_plot = sensor_summary(living_temp_df,living_temp_str)\n",
    "            ambient_temp_df_plot = sensor_summary(ambient_temp_df,ambient_temp_df)\n",
    "\n",
    "            plt.clf()\n",
    "            plt.plot(living_temp_df_plot['timestamp'],living_temp_df_plot['value'])\n",
    "            plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'])\n",
    "            plt.show()\n",
    "            plt.clf()\n",
    "            plt.plot(living_energy_df_plot['timestamp'],living_energy_df_plot['value'])\n",
    "            plt.show()\n",
    "            \n",
    "            living_energy_df_plot = living_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "            living_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            \n",
    "            this_export_df = pd.concat([living_energy_df_plot, living_temp_df_plot,ambient_temp_df_plot])\n",
    "            this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "            this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )\n",
    "\n",
    "\n",
    "    else:\n",
    "        living_energy_str = characteris[characteris['type'] == 'living_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        living_temp_str = characteris[characteris['type'] == 'living_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        room_energy_str = characteris[characteris['type'] == 'room_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        room_temp_str = characteris[characteris['type'] == 'room_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "        room_energy_df = this_house_df[(this_house_df['sensor_id'] == room_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        room_energy_df = energy_variable(room_energy_df)\n",
    "        room_temp_df = this_house_df[(this_house_df['sensor_id'] == room_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        living_energy_df = this_house_df[(this_house_df['sensor_id'] == living_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        living_energy_df = energy_variable(living_energy_df)\n",
    "        living_temp_df = this_house_df[(this_house_df['sensor_id'] == living_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "\n",
    "        #Implement Summary Function\n",
    "        living_energy_df_plot = sensor_summary(living_energy_df,living_energy_str)\n",
    "        if len(living_energy_df['timestamp']) >0:\n",
    "            living_energy_df_plot = sensor_summary(living_energy_df,living_energy_str)\n",
    "        else:\n",
    "            living_energy_df_plot = living_energy_df\n",
    "        room_energy_df_plot = sensor_summary(room_energy_df,room_energy_str)\n",
    "        if len(room_energy_df['timestamp']) >0:\n",
    "            room_energy_df_plot = sensor_summary(room_energy_df,room_energy_str)\n",
    "        else:\n",
    "            room_energy_df_plot = room_energy_df\n",
    "        living_temp_df_plot = sensor_summary(living_temp_df,living_temp_str)\n",
    "        ambient_temp_df_plot = sensor_summary(ambient_temp_df,ambient_temp_str)\n",
    "        room_temp_df_plot = sensor_summary(room_temp_df,room_temp_str)\n",
    "\n",
    "        plt.clf()\n",
    "        plt.plot(room_temp_df_plot['timestamp'],room_temp_df_plot['value'],color='blue')\n",
    "        plt.plot(living_temp_df_plot['timestamp'],living_temp_df_plot['value'],color='red')\n",
    "        plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'],color='yellow')\n",
    "        plt.show()\n",
    "        if len(living_energy_df['timestamp']) >0:\n",
    "            plt.clf()\n",
    "            plt.plot(living_energy_df_plot['timestamp'],living_energy_df_plot['value'],color='blue')\n",
    "            plt.show()\n",
    "        if len(room_energy_df['timestamp']) >0:\n",
    "            plt.clf()\n",
    "            plt.plot(room_energy_df_plot['timestamp'],room_energy_df_plot['value'],color='blue')\n",
    "            plt.show()\n",
    "            \n",
    "        room_energy_df_plot = room_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "        living_energy_df_plot = living_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "        room_temp_df_plot['energy_wh'] = \"NA\"\n",
    "        living_temp_df_plot['energy_wh'] = \"NA\"\n",
    "        ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "\n",
    "        this_export_df = pd.concat([room_energy_df_plot,living_energy_df_plot, room_temp_df_plot,living_temp_df_plot,ambient_temp_df_plot])\n",
    "        this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "        this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \n",
      "    \n",
      "HOUSE ID id_12\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "df_total = house_df_list[7]\n",
    "\n",
    "#Get house \n",
    "this_house_df = df_total\n",
    "house_sensors_list = house_sensors[this_house_df['house_name'].unique()[0]]\n",
    "sensor_type_list = sensor_type[this_house_df['house_name'].unique()[0]]\n",
    "\n",
    "characteris = pd.DataFrame([house_sensors_list,sensor_type_list]).T\n",
    "characteris.columns = ['sensor_id','type']\n",
    "\n",
    "print(\"    \")\n",
    "print(\"    \")\n",
    "print(\"HOUSE ID \" + str(this_house_df['house_name'].unique()[0]))\n",
    "print(\"    \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>power</th>\n",
       "      <th>duration_hr</th>\n",
       "      <th>energy_wh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [created_at, timestamp, value, sensor_type_id, channel, sensor_id, house_name, power, duration_hr, energy_wh]\n",
       "Index: []"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_energy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>energy_wh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-04T16:47:06Z</td>\n",
       "      <td>2019-08-07 22:56:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>C2FB</td>\n",
       "      <td>id_7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-09-04T16:47:06Z</td>\n",
       "      <td>2019-08-07 22:57:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>C2FB</td>\n",
       "      <td>id_7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-09-04T16:47:06Z</td>\n",
       "      <td>2019-08-07 22:58:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>C2FB</td>\n",
       "      <td>id_7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-04T16:47:06Z</td>\n",
       "      <td>2019-08-07 22:59:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>C2FB</td>\n",
       "      <td>id_7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-04T16:47:06Z</td>\n",
       "      <td>2019-08-07 23:00:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>C2FB</td>\n",
       "      <td>id_7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             created_at           timestamp  value  sensor_type_id  channel  \\\n",
       "0  2019-09-04T16:47:06Z 2019-08-07 22:56:16    0.0               7        0   \n",
       "1  2019-09-04T16:47:06Z 2019-08-07 22:57:16    0.0               7        0   \n",
       "2  2019-09-04T16:47:06Z 2019-08-07 22:58:16    0.0               7        0   \n",
       "3  2019-09-04T16:47:06Z 2019-08-07 22:59:16    0.0               7        0   \n",
       "4  2019-09-04T16:47:06Z 2019-08-07 23:00:16    0.0               7        0   \n",
       "\n",
       "  sensor_id house_name  energy_wh  \n",
       "0      C2FB       id_7        NaN  \n",
       "1      C2FB       id_7        0.0  \n",
       "2      C2FB       id_7        0.0  \n",
       "3      C2FB       id_7        0.0  \n",
       "4      C2FB       id_7        0.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_energy_df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-d4b4d746ec34>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m#Implement Summary Function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m#         if len(room_energy_df['timestamp']) >0:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mroom_energy_df_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msensor_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroom_energy_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mroom_temp_df_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msensor_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroom_temp_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mambient_temp_df_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msensor_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mambient_temp_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-55-d0dd5b072d8b>\u001b[0m in \u001b[0;36msensor_summary\u001b[0;34m(this_sensor_df)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Warning! \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis_house_sensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sensor_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" doesn't have any data!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0mdata_last_month\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mthis_house_sensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mthis_house_sensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlast_date\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "if len(sensor_type_list) == 3:\n",
    "    if 'room_energy' in sensor_type_list:\n",
    "        room_energy_str = characteris[characteris['type'] == 'room_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        room_temp_str = characteris[characteris['type'] == 'room_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "        room_energy_df = this_house_df[(this_house_df['sensor_id'] == room_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        room_energy_df = energy_variable(room_energy_df)\n",
    "        room_temp_df = this_house_df[(this_house_df['sensor_id'] == room_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "        #Implement Summary Function\n",
    "#         if len(room_energy_df['timestamp']) >0:\n",
    "        room_energy_df_plot = sensor_summary(room_energy_df)\n",
    "        room_temp_df_plot = sensor_summary(room_temp_df)\n",
    "        ambient_temp_df_plot = sensor_summary(ambient_temp_df)\n",
    "\n",
    "        plt.clf()\n",
    "        plt.plot(room_temp_df_plot['timestamp'],room_temp_df_plot['value'])\n",
    "        plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'])\n",
    "        plt.show()\n",
    "        plt.clf()\n",
    "        plt.plot(room_energy_df_plot['timestamp'],room_energy_df_plot['value'])\n",
    "        plt.show()\n",
    "\n",
    "        room_energy_df_plot = room_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "        room_temp_df_plot['energy_wh'] = \"NA\"\n",
    "        ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "\n",
    "        this_export_df = pd.concat([room_energy_df_plot, room_temp_df_plot,ambient_temp_df_plot])\n",
    "        this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "        this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Exploring one\n",
    "\n",
    "# #for house_id in house_list:\n",
    "# for idx,house_df in enumerate(house_df_list):\n",
    "\n",
    "# # for house_id in house_list:\n",
    "    #df_total = pd.read_sql(\"SELECT DISTINCT ON (index,timestamp, value, sensor_type_id) * FROM \" + house_id + \" order by timestamp;\",engine)\n",
    "    df_total = house_df_list[7]\n",
    "    \n",
    "    #Get house \n",
    "    this_house_df = df_total\n",
    "    house_sensors_list = house_sensors[this_house_df['house_name'].unique()[0]]\n",
    "    sensor_type_list = sensor_type[this_house_df['house_name'].unique()[0]]\n",
    "\n",
    "    characteris = pd.DataFrame([house_sensors_list,sensor_type_list]).T\n",
    "    characteris.columns = ['sensor_id','type']\n",
    "\n",
    "    print(\"    \")\n",
    "    print(\"    \")\n",
    "    print(\"HOUSE ID \" + str(this_house_df['house_name'].unique()[0]))\n",
    "    print(\"    \")\n",
    "\n",
    "    if len(sensor_type_list) == 3:\n",
    "        if 'room_energy' in sensor_type_list:\n",
    "            room_energy_str = characteris[characteris['type'] == 'room_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            room_temp_str = characteris[characteris['type'] == 'room_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "            room_energy_df = this_house_df[(this_house_df['sensor_id'] == room_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            room_energy_df = energy_variable(room_energy_df)\n",
    "            room_temp_df = this_house_df[(this_house_df['sensor_id'] == room_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "            #Implement Summary Function\n",
    "            if len(room_energy_df['timestamp']) >0:\n",
    "                room_energy_df_plot = sensor_summary(room_energy_df)\n",
    "            room_temp_df_plot = sensor_summary(room_temp_df)\n",
    "            ambient_temp_df_plot = sensor_summary(ambient_temp_df)\n",
    "\n",
    "            plt.clf()\n",
    "            plt.plot(room_temp_df_plot['timestamp'],room_temp_df_plot['value'])\n",
    "            plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'])\n",
    "            plt.show()\n",
    "            plt.clf()\n",
    "            plt.plot(room_energy_df_plot['timestamp'],room_energy_df_plot['value'])\n",
    "            plt.show()\n",
    "            \n",
    "            room_energy_df_plot = room_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "            room_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            \n",
    "            this_export_df = pd.concat([room_energy_df_plot, room_temp_df_plot,ambient_temp_df_plot])\n",
    "            this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "            this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )\n",
    "\n",
    "        else:\n",
    "            living_energy_str = characteris[characteris['type'] == 'living_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            living_temp_str = characteris[characteris['type'] == 'living_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "            ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "            living_energy_df = this_house_df[(this_house_df['sensor_id'] == living_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            living_energy_df = energy_variable(living_energy_df)\n",
    "            living_temp_df = this_house_df[(this_house_df['sensor_id'] == living_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "            ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "            #Implement Summary Function\n",
    "            if len(living_energy_df['timestamp']) >0:\n",
    "                living_energy_df_plot = sensor_summary(living_energy_df)\n",
    "            living_temp_df_plot = sensor_summary(living_temp_df)\n",
    "            ambient_temp_df_plot = sensor_summary(ambient_temp_df)\n",
    "\n",
    "            plt.clf()\n",
    "            plt.plot(living_temp_df_plot['timestamp'],living_temp_df_plot['value'])\n",
    "            plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'])\n",
    "            plt.show()\n",
    "            plt.clf()\n",
    "            plt.plot(living_energy_df_plot['timestamp'],living_energy_df_plot['value'])\n",
    "            plt.show()\n",
    "            \n",
    "            living_energy_df_plot = living_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "            living_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "            \n",
    "            this_export_df = pd.concat([living_energy_df_plot, living_temp_df_plot,ambient_temp_df_plot])\n",
    "            this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "            this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )\n",
    "\n",
    "\n",
    "    else:\n",
    "        living_energy_str = characteris[characteris['type'] == 'living_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        living_temp_str = characteris[characteris['type'] == 'living_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        room_energy_str = characteris[characteris['type'] == 'room_energy']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        room_temp_str = characteris[characteris['type'] == 'room_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "        ambient_temp_str = characteris[characteris['type'] == 'ambient_temp']['sensor_id'].reset_index()['sensor_id'][0]\n",
    "\n",
    "        room_energy_df = this_house_df[(this_house_df['sensor_id'] == room_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        room_energy_df = energy_variable(room_energy_df)\n",
    "        room_temp_df = this_house_df[(this_house_df['sensor_id'] == room_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        living_energy_df = this_house_df[(this_house_df['sensor_id'] == living_energy_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        living_energy_df = energy_variable(living_energy_df)\n",
    "        living_temp_df = this_house_df[(this_house_df['sensor_id'] == living_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "        ambient_temp_df = this_house_df[(this_house_df['sensor_id'] == ambient_temp_str)].drop_duplicates(subset=['timestamp','value'])\n",
    "\n",
    "\n",
    "        #Implement Summary Function\n",
    "        if len(living_energy_df['timestamp']) >0:\n",
    "            living_energy_df_plot = sensor_summary(living_energy_df)\n",
    "        if len(room_energy_df['timestamp']) >0:\n",
    "            room_energy_df_plot = sensor_summary(room_energy_df)\n",
    "        living_temp_df_plot = sensor_summary(living_temp_df)\n",
    "        ambient_temp_df_plot = sensor_summary(ambient_temp_df)\n",
    "        room_temp_df_plot = sensor_summary(room_temp_df)\n",
    "\n",
    "        plt.clf()\n",
    "        plt.plot(room_temp_df_plot['timestamp'],room_temp_df_plot['value'],color='blue')\n",
    "        plt.plot(living_temp_df_plot['timestamp'],living_temp_df_plot['value'],color='red')\n",
    "        plt.plot(ambient_temp_df_plot['timestamp'],ambient_temp_df_plot['value'],color='yellow')\n",
    "        plt.show()\n",
    "        if len(living_energy_df['timestamp']) >0:\n",
    "            plt.clf()\n",
    "            plt.plot(living_energy_df_plot['timestamp'],living_energy_df_plot['value'],color='blue')\n",
    "            plt.show()\n",
    "        if len(room_energy_df['timestamp']) >0:\n",
    "            plt.clf()\n",
    "            plt.plot(room_energy_df_plot['timestamp'],room_energy_df_plot['value'],color='blue')\n",
    "            plt.show()\n",
    "            \n",
    "        room_energy_df_plot = room_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "        living_energy_df_plot = living_energy_df_plot[['created_at','timestamp','value','sensor_type_id','channel','sensor_id','house_name','energy_wh']]\n",
    "        room_temp_df_plot['energy_wh'] = \"NA\"\n",
    "        living_temp_df_plot['energy_wh'] = \"NA\"\n",
    "        ambient_temp_df_plot['energy_wh'] = \"NA\"\n",
    "\n",
    "        this_export_df = pd.concat([room_energy_df_plot,living_energy_df_plot, room_temp_df_plot,living_temp_df_plot,ambient_temp_df_plot])\n",
    "        this_export_df = this_export_df[['created_at', 'timestamp','value','sensor_type_id','sensor_id','house_name','energy_wh','channel']]\n",
    "        this_export_df.to_csv(export_path + str(this_house_df['house_name'].unique()[0]) + \".csv\" )           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Post Gres Details\n",
    "\n",
    "# In POSTGRES DATABASE\n",
    "# CREATE DATABASE sonora_sensors;\n",
    "# GRANT ALL PRIVILEGES ON DATABASE sonora_sensors TO diego;\n",
    "\n",
    "# CREATE TABLE taqueria (\n",
    "#  index bigint,\n",
    "#  created_at timestamp,\n",
    "#  timestamp timestamp,\n",
    "#  value double precision,\n",
    "#  sensor_type_id text,\n",
    "#  channel bigint,\n",
    "#  sensor_id text,\n",
    "#  house_name text);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87231</th>\n",
       "      <td>2019-06-18T20:23:51Z</td>\n",
       "      <td>2019-06-18T20:20:08Z</td>\n",
       "      <td>5.1875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87232</th>\n",
       "      <td>2019-06-18T20:23:51Z</td>\n",
       "      <td>2019-06-18T20:20:23Z</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87233</th>\n",
       "      <td>2019-06-18T20:23:51Z</td>\n",
       "      <td>2019-06-18T20:20:38Z</td>\n",
       "      <td>5.8750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87234</th>\n",
       "      <td>2019-06-18T20:23:51Z</td>\n",
       "      <td>2019-06-18T20:20:53Z</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87235</th>\n",
       "      <td>2019-06-18T20:23:51Z</td>\n",
       "      <td>2019-06-18T20:21:08Z</td>\n",
       "      <td>5.6250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at             timestamp   value  sensor_type_id  \\\n",
       "87231  2019-06-18T20:23:51Z  2019-06-18T20:20:08Z  5.1875               1   \n",
       "87232  2019-06-18T20:23:51Z  2019-06-18T20:20:23Z  5.5000               1   \n",
       "87233  2019-06-18T20:23:51Z  2019-06-18T20:20:38Z  5.8750               1   \n",
       "87234  2019-06-18T20:23:51Z  2019-06-18T20:20:53Z  6.2500               1   \n",
       "87235  2019-06-18T20:23:51Z  2019-06-18T20:21:08Z  5.6250               1   \n",
       "\n",
       "       channel  \n",
       "87231        0  \n",
       "87232        0  \n",
       "87233        0  \n",
       "87234        0  \n",
       "87235        0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ao1JmHDFYiRzdsDTpjQA2UkqdAOaHYBlyD5TSOyil0yml0xsbG/0/Gyoc2ax3skQ94LqWTU8a2U/ibK9Ov7+s5JESZmDv4FA3Ef740rrC54Z6uZQBHzx6qOdvlt11ouruu65OhZew/lNHWgQdxBpuSulWABsIIU4pnQXgLa1aGQwq0eQN+/uXT1Em64LJw5XJGj2gvvC5Q3LS2L0pQ1c3xQdumM0cEeLH37nubJFLF3DCWPlJ56zC+hJ5FYAHCCFvAjgWwC/0qaQX/+KTvE5OTohYOVZpVAm84rNMxNX1rAYA9OlVwy1f59SK2zWkcp/HzXvasH5nq7Bbwj96rmGI4NBVTtmYggyHyXBTShfbbpAplNIPU0rVJjZOiKH9euUpq6uHy04ck7QKZUOkii++ZR7TccP61eJChaNlFTTUFV0l7ZIjbnf0jDS+inhokdwmCDoe3aysKk7RtE356ehy7/KejQqTwX2LRw+X8c/q5c9fOFGpPJERt25UabRhV+mGCT1rio91i+QGuxdPKe2UROcM/APs2+esFZJT0IMAf/niSVIyskruDLfbeHV2dSee7zep/iLNHdX4oX2VytNtt5McpYVFkPzHBycAsBaUfOvBN3D2jXOE5Ad1eqL3KxKVEnUGAcHJh+fTz83vnKsgKiVplAjpNdtA717VSuXptNsqO4WRDXXKFpIcOczq/CgFHl28WViOyvsTEhUVLSShW9i5KR7PeMjdiDuMjNSXMtLcQFW/BSW9yIqVCyYPQ31PNZ2WKveQSjeTX9QFk4cpkXvyuEHC+WiySq4NNyEQGgak2c0QhXertnTcw4m/eBbf8+UkIQQYPbAOF09lz24XVY2i4Ws6EbWHrIbUOUo2u17Q5URF+jvkcyaqMdyE8OsUdnw6nop4Mmu4/zz/PaUrxHgqPqN220tK7mHbvkP4a8Bu6TWcyx2jbmfOKr0recvZHvx2dNW24GXtadztyN8JsAweot6+CnlfCH9zduuyYsu+8ANTSmYN9w8eWYpzbprLfZ5n1EnF/G7+RpLFEXjaNSZQV66ir/ssZyXthPnlU95dbn7xkckAXIZbUn4aI3IKEOc/wt1W3Hd1/q9fVKdTmcis4U4SfyORsS9JGVBZm6jzea6uIgDnKCpKnWMEl6anvXML4pxjrGXljl9ftvNTOjnpH3EzqBZ1/d49awrHqKqrrAzCjOEWoGTEnYgW/FDPZ8kHWk6VSKqI/YKsqGBPPSJ9uXNCfawx98xqSIs+bmaVQuQULyidS1yg1USd0du1YjUj9lYZxnDb8BgyfyPJSi/tRn7Erc90VxFLPk+ddEZYqDS/7bthKVPWeivKkqto96IZ1W9pqh4bq63wkb0n1kvuDHdJrhKB9u2XkYFNoUuQVVlvbDRBFWekwO1zg/OLi+QR4aWc1c96LVU5eHTGcasqN+vtTNzH7SYrj3LuDLcfkde3khF3Zqq7SJp8n4HyQbhC2XaFbOTwi0snq1IpkLTFiLsjLQAVhkjN/Q3rVyu2cpLhHLGoknTVGy+5N9wqyIqnxG2s5Ufcehs+b2zu7pAUoQTpdJXobjJVhclJWTkKlIE14Vwy4lb04FgRSHznZNG96SZ3hltFfZX6uOVl5pF570TvmsNTrFsjkvInnY+Gh7aOLmzcXZo4ihd1C3BKy453Y17ASjUr0oGynMM7HxJFVp7l3BnuMHjqy99IZBpNYj1/gk5uSimeW7ENn7lrfuG7vW1eVwchROHklRo5unHUnPHL2aHHMJeJogU47qLrsidzugQmdaoIKekEVPq4ee9zioYNpMtJ7g23yAqzShhxJx0O6B8h+/fAtF/0Ja8C9K/roT87oICawsvGmcMBnRKUjCpxWYhq229SJ5BPpVpjJYgseR/XmN39zgFjuIUe6oqI404w2oB14YWKDvHU8YNT6SoR3eCA1edcOE5hHLeUHFuMR39lDw5/OGD45GQ2nubcGW4V1eJ3b8j6EZNA/hVa/IFmubSq1XCEkFS6SuZqzp/iGCaZUNUR/Ws9r1Yy9XH5SWM8eqnE6uSz9wzKkDvDrYLSXCWJqCGFrMptAhNUhWszFJhI/olwWZVDXJE4boxiOKB4GQ5vqAvMVSJSnsMb6krOldHt22dPkNInDG8uo/Q+2LneSMGNzMrJjLxdeUhzowTU5p+opB1w4q5U7+TvcI6XUK2hrkfg1mgi9LK3U3OvcGXRLeiY1354Fgb37lX4W8StxtIk9rR2YEDvnvEHJkDuRtz+Ci5M4vCFlXjIpKskhdeudjlARSIFwlFruaeOLkYkKO0UGGSxL3m3jxfXBjd8fCoedm3oK1Mf08ao2+hgSN9aVHnairpwQDeLN+xRLlMVuTPcfsQmJ/3hgOKUdbk0Df4sQl0P8Z1agq7d2d2NCUP74oMTrex2EMg/EYbqEffwfrVK5Y0brD7CQUV2wIG9e+LDx410fSMuK8i3rbJ+VXXybjHVqlYfaSD3htuBa8BdEg6YvRG37GMjsy8kWwJ9tSvrWBjE+FrcW3H+k6+fcYRSeYC6XCVHKtq4WW9uGz0DoFqJwYlujOEWoAJc3ApGKBJRJYzhgKpgjWS49PiR8QcBGNxXrd+T515ZXQKFEbdsvL7GCQJlC6xENlJguC3HL59G0qtZmThkL/zgqfiKCAeUPN/f8C+aMlxSok8+VD7YmpHU85Jj2ToMgMPHzXl8qJyAtK4yxryvjmyNSjdSKH4WjbUvBzk03N4qFlmnUHJsZuw234x+FP5Ht1eN2tdKlfkndEaVqJCtw5eqKsmUKs2ccvJ0BAz1yxY6Cu5nkGUdwu1z1vIJLSM5NNxeePJxhy2Pz4zddiH/Ci1x7YBL+193efNxR5HGlZNBsOjJvOTdPk70bXDyyP5c12PFPVpPciMFFto6OjVIVUPuDbdQnomSjRTEm01iOaYUr5zkioOPOLaQT5ozH3cUaVw5KQqv31W+BF2GVkqKJUdHoIbIRHZYG3TLcVZ7phFjuO16Yo10sA8OlJElVPu4ZQWWlL/CETezDozXu+KUJu956lUJ5ZyJw5iOE0meFnS+d+sy+Tt1r8RUGg4ooUcWyZ3hFt2kFXDP1HtZvf2AnFJlQuVyXhm7zVTWnDIjZamO4+5fp1YgB/WMmfmI0OyN+3z7f0WFV/Rxa8hVAj0rJ1M8N5k/w+3HGemxGpOgY3e2HFKrVAaQeQBZnjGi0HLrDGlLq//cade7W4O3dIvDWeqt+u48VcHw0LE0AQpwbz4RJtf9fWd3ei137g23A7MxQelr/eSR3qTsc1c1Y/nmvYo004NqN4RMOCVQagBZljH3q2ULLWM1Phn0eIXiRKqIbJZ8weRhuOkTxwLwR4GI4yxmkfVx3/xvx5Z8t/NAO4YpXs0KpDvMN3eGO7Sn5TI80ed+9u7XcOFv5nFqVl5UR5WobuKElG6uUHpMOke7osjezjlOugAANVXBbr04RjbU4feXTXONuOWjQCYO71f4LOvjHta/1EAHfSeK+x47uozhTi3Fycl4iruKeOlKcc/sxq2l6o0UuHYQYjimpb0Lu1qDNwDmhdUg9vb5j1nvKS0pD7542jjXX2K5Shr79vL8rbpv1DEpWKVpyft//2u5BqlqMIbb+Z8rjtsXDijhCnOPfJtmPo7vPfSmuDCe66p2lai+NqUY3KdX/HERnDB2IAB2P3RDPf9S9rQO+kX1mjC0T+hvTrXxynYfH7QSk0tWwHdVRF3oqJuWdvGc87phNtyEkGpCyBuEkMd0KlRuikaYIRwwJMRKZaP564INymRFkegYkcbHgfev7xn7YMeNJut9mwpUOiTgM3+0hW+ugbPsnIU7YTK8hltVnD7h3uknNLrM1Q4/NHWEhFZ64RlxXw1ghS5FkoYtqiR4GXGaJzHCUP16L7sAojRPuryOhdwaUlKyQ5MrPaxokim/oeaNmmmo7xH5O6+rpKRdBO3IQ/S4q0YNSC7sMw4mw00IGQXgQgB36lVHP/4KZh9vh0eVdMls7FdGPHHcyakRaEz8uSxUpOosPOOCljvIxsQZJt1ElYnbtSQ64q6pDh9xM+UNCdzqrPidjgU4VURub80wOjmEuidgywHriPtmANcASG9goyBCS94rYMSt2nLL+riDR9wyGrll6R1zK1sopEgOIL5ysm+tt2PidZXEGXdZt1XQ+VVE3f6k7sp8/u3tamRqINZwE0IuArCdUrow5rgvEUIWEEIWNDfr3cFaBn/1ck1OhsjoTHHYUBiy4YAy+26yHFqV0uyAflFpdcOERUDxoiIKZMeB4gI1zvU3TFSJ+LgZSoZnRXS551FYRtzvB/AhQsg6AA8COJMQcr//IErpHZTS6ZTS6Y2NjYrV1IjdephylYRsB9XSnt4sYmGojypRK5AQuWgdjyzG41ju4KPHj5JRpWyERUDF0XLI25aDyi7qDSbIVbJl78HCZ9mOIFAfktG3XgliDTel9PuU0lGU0iYAnwTwPKX0M9o1KzMyI+4XVoq/YZSzvbmNq+rLcsVxMx0cn6qT9ZI1VeqiXq86a7wyWToR3Sy4r281qmz4nh9eHzdb8jeiZc/JM45M7wA0d3HcYbm0meo9xG/4p1fWS2pVftQveec4luGYKoWRAkRhKw8aMEapyZoUSjWimwWXjqbV+gD0+Lj1RJX0qE6veeTSjFL6AqX0Il3KJIFYfWf/tUy1a4MrHzfjYifZZ/GCydZ2aqKv5zsPqFm5yYoOXzxvGfpzifDuWBMv372EXk0brKqSj+PeY6/SdX/PI7Pcnpr0dillYukmKxkU8xZJKH8l6SDt98CSZCqO0QPqAYgnNrrgNy/GHpPWfCm8rhJng4YJw7y7uvPeXXtn9GpD+eIKjuOW9XEfe+0z2H/Qm0kxLakMgsiV4W6a+TjmrBL3R4fl484KOuO4Zy3fxq5HwNX936gYcTtkPWm+CGGLxcI4f5K1QcNFU7yrBd0d00urd8bKORSTGExPrhI1Pu59B70Ts2l+znNluIMY2WCtjuLLVWL9P7SfteCBdzupVJDgaOLmZ98p8aXW96j2qFSlcB9BlbaiX638ApwNu/hyR4sQtlgsijGD6kvlRMgOvC6jXgBrzpr4axOoiSqhlHrKK82RKhm0OGpxVorxbF3mHOsYn7hRRhpJskn+ef57oeVdMOgsr78xP4+3EybpHnHzluWp18/WoocbVW49FUU344jBhc/b9h2MOFIMVZ186cI6BUI1kXvD7RgHvnA2TcpkCEqBwX2K2fROlwydKnGVBH3JibMEnNVwi/g0U+uEEQwHLBXDd4dBxu7+L5xY+DxqQHFULzKHEZwdkH90zDTY58nRz3V1eYzhtgfLXHtOZtRwu9VWcw/uCAEV8oqodJXo2FmcFZ5yUbk0vyBLdm9RTpXiDGi1jj0nbR+3iqRk3n1ZJRXTSO4Nt1PZbEtC7HPAvtoyrSjPDqhUmnikwFG+qAhLVmrHxdoQXYATJoeVpkG9PX9/+sTDQuUJ5eMOyQ4oKi/8OsbHnWqcqmGpJB0NJCnUr5yUXejh/12snKeNGcB/UoZgLRN1Pm4+y13te70Z6zPk7t/VvVGpj/ZSFamii9wb7oLB5lk66cOJLskSSTfKgx3eeF+/4ScMSaaCfk1icJ3GeF/RlZMlchi/C8Nfh+75ht8+9w7D+fHXdvoCnhFyULm4Y0pE/OblJPeGu8vxcUvsgLNt3yEsXL9bsWbqcTdWFU1S5rX37pfejZWd4uemiGBH8bMPTwIAPPefH1CoTJGqhFwlfko3Qih+VrU1mNNJyRpa9+mEc8Rd7s4794a74OPmqaSAx+Gjt74seH2h06RR7+Pmk7dxd1vk76yJg6aO8m6V9fn3jwUAfO+8o7j0KTc97DDUuh56cpk4rijZkDbZCVP/9aWzA4bk4wbknyX36dYGxOkdOdTEH1LZFMIBGY6tpCXvqpFNwRq8cjK+oI8e3g9LNu4t/H14Yx+su+5COWXKwMEOzbH/gmldS8Rw2tnSHab8rhIpdQI5cMhaqi67ExWltKC/SI5vADhtQnkyCuZ+xL271arLqE71AAAgAElEQVR0rpWT9t9ZNuBpzMftlmGNeOJJQ8BIlJ4UFJf5IiuAUh+/alSVi6wYfztzT05eevxIAX1KNarvaY0/u2RdJa7PIrvqTBzeD3dcPk1KB1Yq3nAP71/r+XuCvZrOD2veXyCdk1G8qDC07keIpUhGD4zffLWw5yRIqieHeOjTq/TFNmg/wySzA4YeJqlTt+8+3VEqPRnSprLo77ib/NfihdJiORw41IlF7+3hOn/UgDrUanJ9+al4w+1vd587pSnwOJERd5ZRYRO9KT/jcW9myyI7dsU7pYnHaMteXZf6Iru8B0eQhMdNs/Dg6xs8f7tdJSKujaBrO6N4+U27s/NkV7zh9lfFtr3BuRJ4qsxtUD51wuhM+FT9KG+ishNDAdEHPPMOWUN2dBhHWnKV+BNXuScnZV0bBZmO4U74Da2cY4iKN9x+nPzbJXDk41Zp9pKauU6jj9sNazgW68MyrrF3/EEZ5sIpwz1/K1s5yXm8+3pfmDEWD7jylABew62q82q3k7zxpBYOglIUbiANcydR5M5wh83ms02EhYcdpT38DPDlYVBsaFUPIAmAXS2HGI5je8Km61xRKXDvqrvrYf28czm8+bjDkAnf+9FFE0tcWe4/RdpMkDr/XLwJAPDjR5cxywkqF/dXU0c1oF9teoPucme4zzlmaOFzQ30xt7LInonuUy45dkTQoalFiY/bk2QqXmDUIf6OZOeB9pLl04E6MNoVXaldk/axO4QtdJHtoFXf3o4Dxc5YlWtjSF/1K5dZXXVJkQvD/bFpowpG4AOuOMuaKj7DE7xyknh+yyuqG/nwhlomw81KUgY2rFmVSxtpH7eApqMG1GHVz84P/G1I3+KbAYurxN/xBOnT0aWm9VlRJcU4bp5GXW73enrfBRRBqTWT/eI1Z2CZz7/Nm/CmaLjT3BezoXzlpOIi2bbvYOwiFQr2kXSSqV2DbJ/qFuQvBmX9VICcuE6wpoqgZ8iuUM7GJQDw/NvbpVRzqO2hZvzpX0fAG46qMi1vHLkYcRMQjGiowznHDPM0OmfxDcBneJoPHMLslduxu6V8u4BvV7BziLth8hpaJwb7wsnDA3+XNkQ+AU8s3QoAWLh+l6xkAHp3wRFxRwSVP4uGrNdStTuT6mJz14OIbkH6XHXmeBmVCnhylUBdPngd5MJwh9HuajhsoWdWq/nGn9/A5+953bOIQndve8IvnsPe1o74AxnhbZQD6q3dbiaO6GedT6n3IVKy51/pdx+99ZXIc9h93GLXzwphPu7fMGTgi4I7qiSmDHW8+Uwa2T/+IB9hHaCjf9qTnOXacLvh8XGrvS77sfsOKjTcgo3yhlkrC5/dxaEqqsQp4x9ecLQagTZVmnwlolJ1h4Eq85QINPqoc2QHOGGpAo4Z0Q+HS4Z8erMDpjvJVO4Md1izYYoZDviurb1TRp1A/rZgQ/xBkqhulCzyeK747s4WpuNqqkhgLhA/zfvjQwsriRp7Ofn5k4ZJyVHd3VVxWhz/c/nWln2Bx721ZR/WNLdIrZ50t2HW7JRB55aD3BluNxe5Fi2w5eMubcaPLt5s/6ZOr2seelOdMBcy++nF3R6vvCm+dKz+091urLBRlnPNHzCMzh97cwuzbleEpEWQwT/gF34N5zivRzXB2MFyo1DVb5nuZ+jEsQO5z5/3zo7A753yfGD+eiG9HNmOnKoqfneiWTmpkJJwIuL+7A4HlLxOet+qAuFVN+541oHOByY0Yt11F+InFx8TedzE4f0Kn9u7oiexeveqkTZQbr5z7pFcaQxY6v7XnzxOQiNxWOs5PGwxIFeJxPXcHZjIIxM3obl9H9ubVdD9/n3hxsJn3nDAclPxhhsI7wk9c2ssclQokxY4e5q40Cje8MJpYwbg9sun4d+mjw78/dMnHoZRA6xIlraInVKczvfhr56Cx66awaVDOXBK5eKpIwK/F4UlJSrv637URrw8RJ1S5Rks8ZfCwN49I3+/ZfZqHOoUS5m7evsBXPMP622XkHRnp8yk4ZaJQQ6bHGESGXCqs4hH9WvS72avxi5fuKHKa/CWYFAKTtlFLeceMwxTRzdY+vgUqu1RXdgl57kV8fG+A3r3FIouiILFZcJSBE6be+2HZxW+q+9Z7fmNWZjNx44fxXLh1K2clA1EOuuoIYHfuzuyJ+1QUhGcZ44g1QPubBpuVXjTksZXU1As8ICYEYAoN8xaieP/5xnPd7KGUsbH7RC2vJhpdBJwTNQtvf+IQQCAkQPi83jH8bXTD+c+52PTGIwjB0P61uLHF02UkuGUoH9V6WkTBpccS9wnCBI40JFohu42zNJm/Ec44ah+Pndyk7hSAbDuwORgVk4mhGhUifR1Nchku67YlcPOYvVx8/Q9H5s2Ci+t3omX1+zwpCoQ4ZrzjsLvX1gTeUxYmYwb3BsPfvkkNHLkEw8j8vY5nn5CCFb+7DzsbulAQ32PwAT+Kty0sluX+fG4SgT0GTMoeC5DZXoEwN4Bh/McMzmpEW2J6zXIPCZkdKEC0RGCc57/dOkdtgMek232RNPtc9Yyn6MS55bqelZjSN/a0DcekVuPy07HQq+aagzrXxu664rl45Z0lSg+yTM5yala1EYc3g6BYSQfcwhhOCZJKt5wRxW+198mtgBHZ+WOawzeZk0FMtGuDkTiIYyWbFHDkh2QQ/7ZRw9h6gz9MqM6e94FJVGyVLclFav/VCfn8rYZPuV+9Ympob+5R9yvr9vNrZefYgrndFrvijfcQERUCWc44KptBxRpxEbvnt6RlPSeeu7PkiPu0u/FBJYYSdc372uy4nwHKZxHSMtzGDQqVDFSdKNigi3o0ZFZ/SjqKvnHV0+JdJe5DffQvrWhx0Ux1bW2wBGXlvbiJxeGOwzecEA3xd2c1dfs3VdMB1C6X59KkvJx8+BEnLz/iNKJNzESCOj05xCJOpQrdI/lGL5wQNHruIm7HO/kJCvuBVuiux25dXPWDpxz81y8+E5z7Lnltu+xhpsQMpoQMpsQ8hYhZDkh5OpyKBaFTH2HNUT+bHnevfRU2oTTJwSHPCnt/TllOYeHjaxll7xH3dsA14YXaSTu3svtYitcFyrCAQVylUT8xuvjZn2TW7mtuBReJFoF8NbTqq37AVix3aw766QtrWsngP+klE4EcBKArxNC5GKa0gJnOOCRQ/sC8IaWya+4ZBegcoQiKilsZN3NmKGTx38MWLsUpfRtVYiybuiQwgx3nj0nFb5hHD1cfiLfXVb96/WE+aoi1nBTSrdQShfZn/cDWAEgftlWxmBp4JNH9cfIhjpcc95RhYZUzudQ6YBb8Il2nycz0cRK1Ox+GoySyvpftW1/7DE8I+g0rvTV1WbcESeiA5zFG/YUPrvnl9KyPZ0bLh83IaQJwHEA5utQRgf+KnRXgmfPRFH5GoxHVRUp2fwVAL779yXojMnbwcqWvWIbM+jycYedbsXTMoYGSVynXETZgKW+HZoi5TBdiz0cUFm5xIXZaTKC3m0I5eV5chrJi1MOs+EmhPQB8A8A36KUluRWJIR8iRCygBCyoLk53plfXhiKXsM2RRt2tXLJdPPBiUNLvluwfjfecI0KZBgiOPMe9mB2U4ofP7oMv+VM3B9XjoSoz/Udhr8JHDOiHz538hj87tPHq1HAc62AVaSKTQTvAhzWq982Zw32tIbv/hRlnN0+7s5uij+9sg6b97QxXjmcatWGO5XmugiT4SaE9IBltB+glD4cdAyl9A5K6XRK6fTGRrlVbjpxV4d3ybsYUeedev1sbnl3ftaKKAnbs0/VArFOVqe0j+Ikpff77fsP4b5X1+NXz6xS4oYpIh8ZwYvTLqqqCP77kkmhq/UceEP0ws5J4Rt5KMde+0z8QQG4fdyrtx/Af/1zOU657vnQ41mLtsaV6JtpcpJlBQ4H5Y73ZokqIQDuArCCUnqjfpWSgTfVRsnmrCE13TTzcWYdrj5rPM62R9phBjpuKy9WOrspmmY+zqUf4PNxh9zz2O8/gWfe2hZwLoNcf7mqSLiRIKFbZAV8x+K+4o7jTlnR3fPSu0LnxdlRFSPut649l/l6gaRsyfv7AVwO4ExCyGL73wWa9WJm425xd4S7nEUnNFh62qjXSjdXnXlE4fMgBXkxovjz/Pe4jg9a6h41Qvzinxbg7nmlDymvjzNycpJLUnJ47th3/6KjbJbzdrd24L5X14tdQJC4ydPdIfum8g4g/Hh83IItQ+eG0qphiSqZRykllNIplNJj7X9PlEO5UJ1cn2f8MtodEbaJqv8zb1XzVPG5N88N/c193RpX6tTh/QV90BGoeJ3jEXHtY29hWdyEW0xBVilYROImLUuYU6KGFqKq9OcfmaTlmu49RWV3tge8tqErhZWVi5WTLB2pzhVm2xh25fjRhd7tt9IYggTwv5lc9Nt5UtezJifDr8kziZSGEi34uDPzvqCWy04co/0a//XP5bHHxJX+q2t3FT6ncUOFXBhuN+4H3f35tjlr8NQy9n0JeexqY0gO60j53GeUB5EmvLsl3lUUvnVW+l0i5dQvibJ4aeaZCVw1PTDNf+lXw0PuDHcUX7l/EVZujV8E4YalwkSMcJS/TfZ1f2g/Cf+5e4KW8ZRnV5ROVLLKUJFvQyfcPntlyYvUdu1RbWpkQx1zPnSW+/rVx8Oz/JUKZD9UF/vagv3yfso52Mqd4Q7zcTtE+6M9U3PWd8HBEL7z+NHpKXnoK6cInyvyil9IkSl0bvncCimwEckS0eZ4Ovu4tvvRaaNw0ji+Hd55OsiJCpa/u9l3sFOpPBXkznDLUq6l7qqyyKlG5Nok5HOJ7KBzo/JtZMDSlkyOOx1+wLFO+toe1Wob11lHDUFDyhJ1fevsCdpkzxivKptkesnB1mURE1sKng9Zt0WobzfKVSJ1RTGo7/9yEbeLC08dsh7Lu2oudi0HoziWsuVpb3dcPg0HDnViyYY9WPie/OYCKknrHE5WyKTh5jWWrCM+UZlK5HHE91r3n0zTd8qeK9lRhKol9x1wbgYG1swUbld2SzGG6j/nmGEAgCUK0iSwdmast5XWqClhytxIc+4qSW/jUe0zV4V3AQ5b+cmEY6qO41aNjhaUxvvly0oYXyrsbyHJF8b0MQOYjitnZ5Q/wx0zOcksRnMl6VjFpeIR8Cz7VyAvDgLFecgTtgPFOO7ykvR9++FtO0kOsdL4cpA/w+1CRX04z0OkT1pkQi/SVcIvTyUqVx/G1kGEqyQNozGVsJSrWIRSfEuPk6sjc6FqVCVg85PGTIG5Ntwy6K7KSMMtvR2V1OncHYfUVnOAsuGpvgeQ39+fdOcbhJqBDOuNlV5t2z6xHPEFiSnfmV0lFW+4w8KxAEW9vqY2onoErxLey7OtPAs+iMRspJD0WCg2x3fo96W/8JRr0vcdBdtGxqXfybbrJHdmNysnNaDjtaw0rati+Yrl+blyxli+E1xPQ1gKVhHi6qYqKo5bMbquE5RmIaoIdaiRtlFo8H0HdGYcak8Z1WDLSQazclIjqo0405J3gWvqnvzs3Us8EpR7xC1xIAFRvElyugwYNxlXXxSWx8FZSl+jeAFTGl9vMmm4VbVdGZ8nz7kididqokWFHbtoynDhc3mvz7YjifV/UDx7Ujvg6BFu/Rd1S2kbHfOQZBx30+DeqO1Rpby9pNBuZ9Nwy6CrEqLboUCOjghNVYwaD2/sI3wu9/Ul1Q31E3PK1WWQk7Cz5V7Aovpyga4SBeUYt9K2Usid4XYjF8ft/Vu4rYQueY84RUkDF4fXxR05uRjr4075AhzOgoxaOKnzPmNFxxygfBSrqd+pIuo2l3ZIY/PLteFWgarePWipd1qhtPRBPlpxRjYHy1WiN99M2oh0o2g0I+UcxQe9Uaq4My1pgFNouXNnuN2NU2UzVf8qqSHJlHvVo4S+jvFwl2VNhFNeJhG96lwlSY/eo1Lc6nrFV9E2VSbK4pHHi440wGYHnJShYoShq0rjk0zJyhe/96DLC296G9N9ZsFnmYR25X7RGNFQx3xskgmkCBg7Z45KM4Y7BQQ1qboe1WwnC45YxaJKNIy4bWQfrKDrsyTFEl36ryyKKAVulYIKQT7ucirCyddOPxxfP+NwZfKCF+CoGZCo7ujTWC+5M9yqKU7U6cvl8JHjRgZeMymcB8NzxxFWkS0cMPgFN8pnqd6VqTb6RweqJ8FZqamuwnfPPUpOiIvxQ/oqk+WmSlFHf815RxY+q57sVEEmDbcqw+W2NcP61XKdo/t10C29f126di/hHnFzvKmUTNJC3S7vaaCQqyTiGPVxyOUrI1bde9aUmh4l0VJEfMGW+zR3uGzvnoxv5GUkk4ZbhjDDMfeaM9C/rgfOPGpI2a4ZfU5klqlE4U8yJR4VksRK1yygvlzUlUySXSmzj5uBJT85B419e6G+Z/r2m6l4wx1Vh+6RSM+aKhw2sJ7ZP1bSOBXHXevMDihLUBlJZxwM+V7lg6iLqDbj/62YHTBjTu4yoCb6hSgrxv51PdDYpxfSWDEVb7j9BL02ymwAzGJERardrYqyxT4SuC8ZGFWi6bpVMdkBedDhMhA1NqJ1KHwewzFJO53UuErUhlWmYUI7iFwY7rAHNijDX9JxqA46o0pkca7vVjHKtSMzuUMI0N0dokfah+I2nnLy7/Lu+i2J0FL1JFsnqt7Qirlz0vnGlwvD7SGmEZdzkwAgfMSe1p4eCI8AiTpeFAJ1I+5KIs2Tskm2XR0pEtLY+vJnuF2U+qnZ/WM8Dw7LkaWj/6iViIKz5oqaYJAU2dFv6OkxIx5eI6FjlM4jMc074FQChKhdMJPWBWD5NtxBrhLOSlK8SKuoS3qDSgJj1yN37ImQ5T4vqOxVxeWykMLnUxlpND464HF3MskrY/vjIZOGW2bkGGUQeUZvJUZf8RJ1ndkBZd9ki7lK2GTK7TmpcumkIjkKRIq2YV1GRJVdl5GjKo5bZR+V1qimTBpuVZQk7U9IjyB05+P2yON27Os4vLQzcP4ud66IpHy0PPXANcgQ0EWGJH3c6qJKXLOTKSR3hjuoGpzveHpr3dVZ5aoZ3RNR/DvalH6nKylW1KtqCgdCsRR3Ik9YkQCStlEqBiQyro2g66t2vagid4bbTVA7Fd3dJarNiyyPjzTWCc+aBzdwsac+7izVk0M6HkIe9ViScekgjcZHB1USS97DSOP8QK4Nt0MhLhkcDyGHnVLu4+aWppbA25H0yYcdEzeCSueLrD7SaETcyGinamcn1XHcaYTJcBNCziOErCSErCaEzNStlE5IxIyaSLC9rpWTujcL9sqTj6SJHknKuEqIsuxsWp5Bzidb95Z0vNfUcj3BklZx+1UKl7wDGZ6cJIRUA/gdgPMBTATwKULIRN2KlRN3eBurkSmsgCv0zKqfjjJOTvIez9mSowwvU7bAND45kqR95JwESspEdRy3wpQLKmEZcZ8AYDWldC2ltB3AgwAu0auWPjw5QEqG3BxyOI6dIJB7OK2vaECwHT1p3KDQ4wf36SV8rbTG0YoSNRo9bcJg65gU171OotI8sGJ19GLnVrtec0cOqCvKSyEs+QpHAtjg+nsjgBP1qGPxwRvnRP7u7lGrSPTxe1rbQ39z2kmnnQyDAFi8YU+ovK17D6Khtzc3NksFr91xIFRmV8hw1N+In/72aTjnprkAgE//YX7kHo9h7G3r8Px9/qRheHLZVpz/6xdjG+j6Xa2Fz5ffNR+7Wto9Bvnqs8ajvasbYwf1xuiB9RgzqB6nXPc8AOCmZ1bh9jlrsH5XK4b3D857fuW9C7Blbxumjm7wfF9FCN7euj+w/EQGVht2tYbWxa6W8LYSxevrdoXKDHvbeHTx5pLvfv3J4/Aff1uMJ5ZuDZV34FCnkI6t7V2Rz8mWvQcxoiE+J/1fvngSPvWHVwEEP3eb9rR5cllHsey/z8WFv3kR63dabetz97yGntXesWQL5/1WEYI5q5pjbYjzLNx62fH46gOL8MuPTkavmmq8eM0ZmP/uLkwZZbVDQoBF68NtgsPG3a2YPLI/l64yKEs0Swj5EoAvAcBhhx0mJGPMoHps3XsQ44fGV/zEEf1x8rhBeGn1jshXmQlD++KSY0cU/q7vWY0vf2AcCAguOXYENuxqLexQfvnJYzBoac9QWeOH9sHJ9shyUO+e+MKMsTh/8nAAQO9eNTj76CHYtOcgxjX2xpUzxuK40Q247qm3scFl9IKYNLI/Tj/Smwd89IA6VBFg9MB6XDR1OCYM7YsXrzkDv3p6Jdq7QrIuMdDYp1dh04hvnT0BNdVV6ArL4uRi/NA+OHHsILzx3m60d3XjmBH98fHpo/Dk0q0Y0VCHqiqC753n3SFlyU/Owc8eewst7Z0FGZceN8pzzAlNA3HpcSNxsLMLk0b2w8emeX//1AmHobZH8IvhkcP64txJw5jv/RPTR8e+RjcN6o1eNeyJ8684ZQyeeWtb6O9HDuuLc48p6vi+sQMK9zu0Xy0G9y52frU9qvGVDxweu/3W6XU9cMQQNuMIABdOGYHNew9Gyhw/tA9OG98YK+vkwwfhsatm4LY5awLLcvzQPjjzqKFMevXpVYM53z0DOw8cws+fWIGDHV2Bx51W2wMThrK9tV45YyzmvtPMdGxjn14455hhWHfdhYXvRg+sx+iB9YW/P3PSGMxavjVW1vihfUrark5InF+JEHIygJ9SSs+1//4+AFBK/zfsnOnTp9MFCxao1NNgMBgqGkLIQkrpdJZjWXzcrwMYTwgZSwjpCeCTAP4lo6DBYDAYxIl1lVBKOwkh3wAwC0A1gLsppcu1a2YwGAyGQJh83JTSJwA8oVkXg8FgMDBgVk4aDAZDxjCG22AwGDKGMdwGg8GQMYzhNhgMhoxhDLfBYDBkjNgFOEJCCWkGsF6x2MEAdiiWqRKjnxxp1w9Iv45GPzmS1m8MpTR++So0GW4dEEIWsK4qSgKjnxxp1w9Iv45GPznSrp8b4yoxGAyGjGEMt8FgMGSMLBnuO5JWIAajnxxp1w9Iv45GPznSrl+BzPi4DQaDwWCRpRG3wWAwGABrnzeRfwBGA5gN4C0AywFcbX8/EMAzAN6x/x9gf38UgFcAHALwHZ+sqwEss+V8K+Ka5wFYCWA1gJmu7wmAnwNYBWAFgG8G6QdgLIAFAFoB7AfwrE+/lbA2Pro5hfp9y/6+G8CiNOkH4DAA8wG02PrdmZB+3wCw1q7DNU77s8+/D0Cbrd9NuvULeUYess+lAF5A8Rn5IoA37fP3A2hHcs9IYBm6npE37d/uTaF+n0WxDa4pRx0HnP8NVx0Pdn3fH8D/A7DE1uHzoraXUipluIcDON7+3Ne+oYkArnduGMBMAL+0Pw8B8D775r/jkjPJLtB6WNkKnwVwRMD1qu2KGgegp10AE+3fPg/gTwCqXNcK0u8pWLnEZwK4DcAjLv3GAlgEYBNchjsN+gEYD2ApgDPt8vtpyvS7A8B37fq9HcCuhPS71NZrN6yY3Jm2fhcAeM7W7x4A63XrF/KMrAdwNoC9AK51PSP3wepghsAyWJuQ3DMSWIb2McNg5edfDZfhToN+ABpgGdyLUKZnJMQuHgegCcA6eA33D1zl2AhgF4CeovZX2FVCKd1CKV1kf94PqxcaCWsj4Xvtw+4F8GH7mO2U0tcBdPhEHQ1gPqW0lVLaCWAOrMrxE7Vp8VdhPQjdrmsF6XcygCNtve6FVdkftmVcDeDHKdXviwB+TSl93tbvQMr0o7Aa9OuwGv++hPTbD+AMV/k47e8SWG8Br8MynnWEkOE69bP/9+u4GNbIrR7AX106nkAp3W2fdx8sI+UmDWUIWJuo/BHe9pcW/T4N4O+U0sdQpmck4HxQSt+glK4L+glAX0IIAdAHluEW20AUinzchJAmWD3NfABDKaVb7J+2AojbgG4ZgFMJIYMIIfWwRkejA44L2rR4pP35cAD/RghZQAh5khAyPkC/abBWRTn6bYTV8w0lhBwPYDSl9PE06gdgAoAJhJCXAHwBlvFMk34/BfAZQshGAJfBGomXW79C+wPgbF7otD+/7L0u2dr1C9CxGoCzUaX/GbkS1ojWTeJlSAgZCeAjAG4NuG7i+sF6RgYQQl6AtffttHLqx8AtsDqQzbDenq92OgERpA03IaQPgH/A8hu5R1qg1ntBZNgKpXQFrFedp2G9ii9GsVJY6QXgILVWPf0BwN0B+v0wRBcK4EYA/5li/WpguUtOh+Un/TghpCFF+n0KwB8ppaMAPADg04SQqjLrl8r2x6MjIeQMWIb7mXLqyKjfzQC+F2RsUqJfDSxjfSGA+wGcTQiZUA79GDnXvu4IAMcCuIUQ0o9TRgEpw00I6QGrQB+glD5sf73NeQ21/w98pXBDKb2LUjqNUnoaLN/VKkLIaELIYvvfV2D5/dy95Cj7O8DqGZ3rPwJgin39p2H1cB2w/FMNLv1GAWiGNYqcBOAFQsg6+/vPE0IKS18T1m+7ff6/KKUdAPbY3xd6/BTodyWAv7lk1cDyP5ZNP3f7gzWidbc/v+z+Ltk69ZtFCFkM4G14n5Eu2KNsR0dCyBQAd8J6NW+DjxSU4XQAD9rPyEQAlxJCHBdKGvTbCGAWpbQF1uT5WgBTy6DfLPv8OxHN5wE8TC1WA3gX1mSvGFR8cpLAepj9ERg3wDs5eb3v95+idMbcmcw5DFYjbwi4Xg2syhiL4sTBMfZv1wH4d/vz6bAmUEr0A/B3WDO7zuTaowH6rQu4p0T1gzXTfa99zPWwjPegFOn3JIAr7GNugeWKIOXWz9X+3BNX18MahT1pn3MngPd012/MM+KfnLwVlnvklKSfkbAy9P2+GKVRJUnX8dGwJqBrYE1ObgEwSXcdR9jHdfBOTt4Ke8IUVqe9yf077z8Zwz0D1ivKm3ZFLoblOxpkF+A7sGZvB9rHD4PVa+2DZXg2Auhn//YirJCfJQDOirjmBbBmltcA+KHr+wYAj8PyHarix3gAAADUSURBVL0Cq6cN0u/fYUWOOOFszwXo12n/lhr97AZ8G6yRbxeskKc06TcRwGuwwti6YE0MJaHfbbBGZ912PW5yld899ndd5ajfkGdkI6w3vE5YYbF7YT0j98EyRMtcZZjUMxJYhr5npB3JtcEo/a5F8RlpK0cdB5z/TRTtyGbYobGwXCRP2+cvA/AZUdtLKTUrJw0GgyFrmJWTBoPBkDGM4TYYDIaMYQy3wWAwZAxjuA0GgyFjGMNtMBgMGcMYboPBYMgYxnAbDAZDxjCG22AwGDLG/weVsbjFVnCS4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c192e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Analyze one file\n",
    "file = \"5cf55047-0000-0000-0000-A0E6F850C2D5.csv\"\n",
    "path = temp_dir.name + \"/\" + file\n",
    "this_df = pd.read_csv(path)\n",
    "this_df.tail()\n",
    "\n",
    "this_df.loc[:,'datetime'] = this_df['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n",
    "\n",
    "plt.plot(this_df['datetime'],this_df['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['25A4', '07A3', 'F335'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Analyze Data\n",
    "taqueria_data = house_df_list[0]\n",
    "house_data = house_df_list[1]\n",
    "\n",
    "appended_data = taqueria_data\n",
    "\n",
    "appended_data['sensor_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# Taqueria\n",
    "ener_25A4 = appended_data[(appended_data[\"sensor_id\"] == \"25A4\") & (appended_data[\"sensor_type_id\"] == '1')]\n",
    "temp_F335 = appended_data[appended_data[\"sensor_id\"] == \"F335\"]\n",
    "temp_07A3 = appended_data[appended_data[\"sensor_id\"] == \"07A3\"]\n",
    "\n",
    "ener_25A4.loc[:,'datetime'] = ener_25A4['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n",
    "temp_F335.loc[:,'datetime'] = temp_F335['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n",
    "temp_07A3.loc[:,'datetime'] = temp_07A3['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sonora Hotel\n",
    "temp_FCE4 = appended_data[appended_data[\"sensor_id\"] == \"FCE4\"]\n",
    "temp_FE44 = appended_data[appended_data[\"sensor_id\"] == \"FE44\"]\n",
    "\n",
    "temp_FCE4.loc[:,'datetime'] = temp_FCE4['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n",
    "temp_FE44.loc[:,'datetime'] = temp_FE44['timestamp'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86500</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:08:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:08:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86502</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:09:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:09:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86504</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:10:17Z</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:10:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86505</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:11:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:11:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86506</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:12:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:12:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at             timestamp   value sensor_type_id  \\\n",
       "86500  2019-05-06T00:53:12Z  2019-05-02T22:08:17Z  0.0000              1   \n",
       "86502  2019-05-06T00:53:12Z  2019-05-02T22:09:17Z  0.0000              1   \n",
       "86504  2019-05-06T00:53:12Z  2019-05-02T22:10:17Z  0.0625              1   \n",
       "86505  2019-05-06T00:53:12Z  2019-05-02T22:11:17Z  0.0000              1   \n",
       "86506  2019-05-06T00:53:12Z  2019-05-02T22:12:17Z  0.0000              1   \n",
       "\n",
       "       channel sensor_id house_name            datetime  \n",
       "86500        0      25A4   taqueria 2019-05-02 22:08:17  \n",
       "86502        0      25A4   taqueria 2019-05-02 22:09:17  \n",
       "86504        0      25A4   taqueria 2019-05-02 22:10:17  \n",
       "86505        0      25A4   taqueria 2019-05-02 22:11:17  \n",
       "86506        0      25A4   taqueria 2019-05-02 22:12:17  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ener_25A4.tail()\n",
    "# temp_FE44.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/usr/local/lib/python3.6/site-packages/ipykernel/__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Josiah\n",
    "#Please take a look at these energyh calculations. We are measuring current and assuming 220V. \n",
    "#In the DF 'value' is current value. I'm turning into power by multiplying by 220.\n",
    "\n",
    "\n",
    "##### Energy Setup\n",
    "# Calculating Power\n",
    "ener_25A4.loc[:,'power'] = ener_25A4['value'] * 220\n",
    "\n",
    "#Josiah: I sort by timestamp, lag the values and calculate the seconds between samples\n",
    "# Borrow recipe from http://www.datasciencemadesimple.com/difference-two-timestamps-seconds-minutes-hours-pandas-python-2/\n",
    "ener_25A4.sort_values(by='datetime', inplace=True)\n",
    "ener_25A4.loc[:,'duration_hr'] = (ener_25A4['datetime'] - ener_25A4['datetime'].shift(1)) / np.timedelta64(1,'h')\n",
    "\n",
    "# Energy\n",
    "ener_25A4.loc[:,'energy_wh'] = ener_25A4['power'] * ener_25A4['duration_hr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2019, 4, 10), datetime.date(2019, 4, 11),\n",
       "       datetime.date(2019, 4, 12), datetime.date(2019, 4, 13),\n",
       "       datetime.date(2019, 4, 14), datetime.date(2019, 4, 15),\n",
       "       datetime.date(2019, 4, 16), datetime.date(2019, 4, 17),\n",
       "       datetime.date(2019, 4, 18), datetime.date(2019, 4, 19),\n",
       "       datetime.date(2019, 4, 20), datetime.date(2019, 4, 21),\n",
       "       datetime.date(2019, 4, 22), datetime.date(2019, 4, 23),\n",
       "       datetime.date(2019, 4, 24), datetime.date(2019, 4, 25),\n",
       "       datetime.date(2019, 4, 26), datetime.date(2019, 4, 27),\n",
       "       datetime.date(2019, 4, 28), datetime.date(2019, 4, 29),\n",
       "       datetime.date(2019, 4, 30), datetime.date(2019, 5, 1),\n",
       "       datetime.date(2019, 5, 2)], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "that_day_ener['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Josiah: \n",
    "# I do a smaller sample to see the relationship between vars\n",
    "\n",
    "that_day_ener = ener_25A4[ener_25A4['datetime'] >= datetime.datetime(2019, 4, 10,20,0)]\n",
    "that_day_temp1 = temp_F335[temp_F335['datetime'] >= datetime.datetime(2019, 4, 10,20,0)]\n",
    "that_day_temp2 = temp_07A3[temp_07A3['datetime'] >= datetime.datetime(2019, 4, 10,20,0)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>power</th>\n",
       "      <th>duration_hr</th>\n",
       "      <th>energy_wh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86500</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:08:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:08:17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86502</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:09:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:09:17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86504</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:10:17Z</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:10:17</td>\n",
       "      <td>13.75</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.229167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86505</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:11:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:11:17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86506</th>\n",
       "      <td>2019-05-06T00:53:12Z</td>\n",
       "      <td>2019-05-02T22:12:17Z</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25A4</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-05-02 22:12:17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at             timestamp   value sensor_type_id  \\\n",
       "86500  2019-05-06T00:53:12Z  2019-05-02T22:08:17Z  0.0000              1   \n",
       "86502  2019-05-06T00:53:12Z  2019-05-02T22:09:17Z  0.0000              1   \n",
       "86504  2019-05-06T00:53:12Z  2019-05-02T22:10:17Z  0.0625              1   \n",
       "86505  2019-05-06T00:53:12Z  2019-05-02T22:11:17Z  0.0000              1   \n",
       "86506  2019-05-06T00:53:12Z  2019-05-02T22:12:17Z  0.0000              1   \n",
       "\n",
       "       channel sensor_id house_name            datetime  power  duration_hr  \\\n",
       "86500        0      25A4   taqueria 2019-05-02 22:08:17   0.00     0.016667   \n",
       "86502        0      25A4   taqueria 2019-05-02 22:09:17   0.00     0.016667   \n",
       "86504        0      25A4   taqueria 2019-05-02 22:10:17  13.75     0.016667   \n",
       "86505        0      25A4   taqueria 2019-05-02 22:11:17   0.00     0.016667   \n",
       "86506        0      25A4   taqueria 2019-05-02 22:12:17   0.00     0.016667   \n",
       "\n",
       "       energy_wh  \n",
       "86500   0.000000  \n",
       "86502   0.000000  \n",
       "86504   0.229167  \n",
       "86505   0.000000  \n",
       "86506   0.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "that_day_ener.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113829a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(that_day_ener['datetime'],that_day_ener['energy_wh'])\n",
    "plt.show()\n",
    "\n",
    "#Watt-hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133d9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(that_day_temp1['datetime'],that_day_temp1['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123d61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(that_day_temp2['datetime'],that_day_temp2['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11485</th>\n",
       "      <td>2019-04-10T21:04:41Z</td>\n",
       "      <td>2019-04-10T20:00:27Z</td>\n",
       "      <td>24.9375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F335</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-04-10 20:00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11486</th>\n",
       "      <td>2019-04-10T21:04:41Z</td>\n",
       "      <td>2019-04-10T20:01:27Z</td>\n",
       "      <td>24.8125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F335</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-04-10 20:01:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11487</th>\n",
       "      <td>2019-04-10T21:04:41Z</td>\n",
       "      <td>2019-04-10T20:02:27Z</td>\n",
       "      <td>24.8125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F335</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-04-10 20:02:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11488</th>\n",
       "      <td>2019-04-10T21:04:41Z</td>\n",
       "      <td>2019-04-10T20:03:27Z</td>\n",
       "      <td>24.8125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F335</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-04-10 20:03:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11489</th>\n",
       "      <td>2019-04-10T21:04:41Z</td>\n",
       "      <td>2019-04-10T20:04:27Z</td>\n",
       "      <td>24.8125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F335</td>\n",
       "      <td>taqueria</td>\n",
       "      <td>2019-04-10 20:04:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at             timestamp    value sensor_type_id  \\\n",
       "11485  2019-04-10T21:04:41Z  2019-04-10T20:00:27Z  24.9375              1   \n",
       "11486  2019-04-10T21:04:41Z  2019-04-10T20:01:27Z  24.8125              1   \n",
       "11487  2019-04-10T21:04:41Z  2019-04-10T20:02:27Z  24.8125              1   \n",
       "11488  2019-04-10T21:04:41Z  2019-04-10T20:03:27Z  24.8125              1   \n",
       "11489  2019-04-10T21:04:41Z  2019-04-10T20:04:27Z  24.8125              1   \n",
       "\n",
       "       channel sensor_id house_name            datetime  \n",
       "11485        0      F335   taqueria 2019-04-10 20:00:27  \n",
       "11486        0      F335   taqueria 2019-04-10 20:01:27  \n",
       "11487        0      F335   taqueria 2019-04-10 20:02:27  \n",
       "11488        0      F335   taqueria 2019-04-10 20:03:27  \n",
       "11489        0      F335   taqueria 2019-04-10 20:04:27  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "that_day_temp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-06T01:37:56Z</td>\n",
       "      <td>2016-01-02T07:47:24Z</td>\n",
       "      <td>8.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FE44</td>\n",
       "      <td>hotel_sonora</td>\n",
       "      <td>2019-04-02 23:49:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-06T01:37:56Z</td>\n",
       "      <td>2016-01-02T07:47:39Z</td>\n",
       "      <td>8.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FE44</td>\n",
       "      <td>hotel_sonora</td>\n",
       "      <td>2019-04-02 23:49:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-06T01:37:56Z</td>\n",
       "      <td>2016-01-02T07:47:54Z</td>\n",
       "      <td>8.4375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FE44</td>\n",
       "      <td>hotel_sonora</td>\n",
       "      <td>2019-04-02 23:50:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-06T01:37:56Z</td>\n",
       "      <td>2016-01-02T07:48:09Z</td>\n",
       "      <td>8.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FE44</td>\n",
       "      <td>hotel_sonora</td>\n",
       "      <td>2019-04-02 23:50:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-06T01:37:56Z</td>\n",
       "      <td>2016-01-02T07:48:24Z</td>\n",
       "      <td>8.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FE44</td>\n",
       "      <td>hotel_sonora</td>\n",
       "      <td>2019-04-02 23:51:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             created_at             timestamp   value  sensor_type_id  \\\n",
       "0  2019-05-06T01:37:56Z  2016-01-02T07:47:24Z  8.2500               1   \n",
       "1  2019-05-06T01:37:56Z  2016-01-02T07:47:39Z  8.2500               1   \n",
       "2  2019-05-06T01:37:56Z  2016-01-02T07:47:54Z  8.4375               1   \n",
       "3  2019-05-06T01:37:56Z  2016-01-02T07:48:09Z  8.5000               1   \n",
       "4  2019-05-06T01:37:56Z  2016-01-02T07:48:24Z  8.5000               1   \n",
       "\n",
       "   channel sensor_id    house_name            datetime  \n",
       "0        0      FE44  hotel_sonora 2019-04-02 23:49:12  \n",
       "1        0      FE44  hotel_sonora 2019-04-02 23:49:12  \n",
       "2        0      FE44  hotel_sonora 2019-04-02 23:50:12  \n",
       "3        0      FE44  hotel_sonora 2019-04-02 23:50:12  \n",
       "4        0      FE44  hotel_sonora 2019-04-02 23:51:12  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Sonora Hotel \n",
    "that_day_temp1 = temp_FE44[temp_FE44['datetime'] >= datetime.datetime(2019, 4, 10,20,0)]\n",
    "that_day_temp2 = temp_FCE4[temp_FCE4['datetime'] >= datetime.datetime(2019, 4, 10,20,0)]\n",
    "\n",
    "\n",
    "temp_FE44.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJrln4K+B/x75uT4FnD/t/azmz3jka8x8GPyXz5Kkhn+VJElqGAZJUsMwSJIahkGS1DAMkqSGYZAkNQyDJKlhGCRJjf8FFDYZsBaypoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3a1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(that_day_temp1['datetime'],that_day_temp1['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJrln4K+B/x75uT4FnD/t/azmz3jka8x8GPyXz5Kkhn+VJElqGAZJUsMwSJIahkGS1DAMkqSGYZAkNQyDJKlhGCRJjf8FFDYZsBaypoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e73de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(that_day_temp2['datetime'],that_day_temp2['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>house_name</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [created_at, timestamp, value, sensor_type_id, channel, sensor_id, house_name, datetime]\n",
       "Index: []"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}