diff --git a/Data_Analytics_Sitter.ipynb b/Data_Analytics_Sitter.ipynb
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@@ -0,0 +1,4576 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# IoT WS22/23 Data Analytics\n",
+ "## Chapter 1: Data Preparation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load all the log files (JSON) and add the content into a data frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import glob\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Path to the json files\n",
+ "json_files = glob.glob(\"log*\")\n",
+ "#json_files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create empty df\n",
+ "df = pd.DataFrame()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = 0\n",
+ "for i in json_files:\n",
+ " # read line by line json files\n",
+ " df_read = pd.read_json(json_files[x], lines=True)\n",
+ " # concatenate the actual df with new json file\n",
+ " df = pd.concat([df,df_read])\n",
+ " # x+1 for iterate through file list\n",
+ " x = x + 1\n",
+ "\n",
+ "#df\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# open dataframe head\n",
+ "#df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Filter the dataframe on relevant topics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remove not used topics. Tasmota ID D863F9\n",
+ "df = df[df[\"topic\"].str.contains(\"D863F9/SENSOR\")==True]\n",
+ "#df \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remove not used columns\n",
+ "df = df.drop([\"time\", \"topic\"], axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#check a random message\n",
+ "test = df.iloc[66861]['message']\n",
+ "#test\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'\\nfor i, r in df.iterrows():\\n # read line by line json files\\n msg_raw = r[\"message\"]\\n msg_df = pd.json_normalize(msg_raw, errors=\\'ignore\\')\\n # concatenate the actual df with new json file\\n msg_df2 = pd.concat([msg_df2,msg_df])\\n\\nmsg_df2\\n'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# This code is outdated, we´ve improved the perfomance with Chat GPT, check out the next cell\n",
+ "\"\"\"\n",
+ "for i, r in df.iterrows():\n",
+ " # read line by line json files\n",
+ " msg_raw = r[\"message\"]\n",
+ " msg_df = pd.json_normalize(msg_raw, errors='ignore')\n",
+ " # concatenate the actual df with new json file\n",
+ " msg_df2 = pd.concat([msg_df2,msg_df])\n",
+ "\n",
+ "msg_df2\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#create two empty pd dataframes\n",
+ "msg_df = pd.DataFrame()\n",
+ "msg_df2 = pd.DataFrame()\n",
+ "\n",
+ "# put information into a dataframe\n",
+ "def normalize_json(row):\n",
+ " msg_raw = row[\"message\"]\n",
+ " msg_df = pd.json_normalize(msg_raw, errors='ignore')\n",
+ " return msg_df\n",
+ "\n",
+ "msg_df2 = df.apply(normalize_json, axis=1)\n",
+ "msg_df2 = pd.concat(msg_df2.tolist())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#msg_df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remove not used columns\n",
+ "cleandf = msg_df2.drop([\"TempUnit\", \"DS18B20.Id\"], axis = 1)\n",
+ "#cleandf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sort dataframe by date\n",
+ "sortdf = cleandf.sort_values(by=['Time'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert values for switch to int values\n",
+ "adaptdf = sortdf.replace({'Switch1': 'OFF'}, {'Switch1': 0}, regex=True)\n",
+ "adaptdf = adaptdf.replace( {'Switch1': 'ON'}, {'Switch1': 1}, regex=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert string time values to time values\n",
+ "finaldf = adaptdf\n",
+ "finaldf['Time'] = pd.to_datetime(finaldf['Time'], format='%Y-%m-%dT%H:%M:%S')\n",
+ "\n",
+ "#finaldf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# reset index to be able to delete the row with the date from 1970\n",
+ "finaldf = finaldf.reset_index()\n",
+ "#finaldf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop the line with the date from 1970, presumably the default initialization date of the tasmota\n",
+ "rows_to_delete = finaldf.loc[finaldf['Time'] == \"1970-01-01 00:04:02\"]\n",
+ "\n",
+ "# delete the selected rows\n",
+ "finaldf = finaldf.drop(rows_to_delete.index)\n",
+ "\n",
+ "#finaldf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>DS18B20.Temperature</th>\n",
+ " <th>AM2301.Temperature</th>\n",
+ " <th>AM2301.Humidity</th>\n",
+ " <th>AM2301.DewPoint</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-15 20:12:27</td>\n",
+ " <td>0</td>\n",
+ " <td>12.6</td>\n",
+ " <td>13.5</td>\n",
+ " <td>78.2</td>\n",
+ " <td>9.2</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-15 20:12:57</td>\n",
+ " <td>0</td>\n",
+ " <td>12.6</td>\n",
+ " <td>13.5</td>\n",
+ " <td>78.2</td>\n",
+ " <td>9.2</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-15 20:13:27</td>\n",
+ " <td>0</td>\n",
+ " <td>12.6</td>\n",
+ " <td>13.5</td>\n",
+ " <td>78.2</td>\n",
+ " <td>9.2</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-15 20:13:57</td>\n",
+ " <td>0</td>\n",
+ " <td>12.6</td>\n",
+ " <td>13.5</td>\n",
+ " <td>78.2</td>\n",
+ " <td>9.2</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-15 20:14:27</td>\n",
+ " <td>0</td>\n",
+ " <td>12.6</td>\n",
+ " <td>13.5</td>\n",
+ " <td>78.2</td>\n",
+ " <td>9.2</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88575</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-17 09:57:45</td>\n",
+ " <td>1</td>\n",
+ " <td>12.8</td>\n",
+ " <td>16.7</td>\n",
+ " <td>52.5</td>\n",
+ " <td>6.4</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88576</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-17 09:58:15</td>\n",
+ " <td>1</td>\n",
+ " <td>12.8</td>\n",
+ " <td>16.7</td>\n",
+ " <td>52.5</td>\n",
+ " <td>6.4</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88577</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-17 09:58:45</td>\n",
+ " <td>1</td>\n",
+ " <td>12.8</td>\n",
+ " <td>16.6</td>\n",
+ " <td>52.5</td>\n",
+ " <td>6.3</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88578</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-17 09:59:15</td>\n",
+ " <td>1</td>\n",
+ " <td>12.8</td>\n",
+ " <td>16.7</td>\n",
+ " <td>52.5</td>\n",
+ " <td>6.4</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88579</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-17 09:59:45</td>\n",
+ " <td>1</td>\n",
+ " <td>12.8</td>\n",
+ " <td>16.7</td>\n",
+ " <td>52.5</td>\n",
+ " <td>6.4</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>88579 rows × 8 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 DS18B20.Temperature \\\n",
+ "1 0 2022-11-15 20:12:27 0 12.6 \n",
+ "2 0 2022-11-15 20:12:57 0 12.6 \n",
+ "3 0 2022-11-15 20:13:27 0 12.6 \n",
+ "4 0 2022-11-15 20:13:57 0 12.6 \n",
+ "5 0 2022-11-15 20:14:27 0 12.6 \n",
+ "... ... ... ... ... \n",
+ "88575 0 2022-12-17 09:57:45 1 12.8 \n",
+ "88576 0 2022-12-17 09:58:15 1 12.8 \n",
+ "88577 0 2022-12-17 09:58:45 1 12.8 \n",
+ "88578 0 2022-12-17 09:59:15 1 12.8 \n",
+ "88579 0 2022-12-17 09:59:45 1 12.8 \n",
+ "\n",
+ " AM2301.Temperature AM2301.Humidity AM2301.DewPoint \\\n",
+ "1 13.5 78.2 9.2 \n",
+ "2 13.5 78.2 9.2 \n",
+ "3 13.5 78.2 9.2 \n",
+ "4 13.5 78.2 9.2 \n",
+ "5 13.5 78.2 9.2 \n",
+ "... ... ... ... \n",
+ "88575 16.7 52.5 6.4 \n",
+ "88576 16.7 52.5 6.4 \n",
+ "88577 16.6 52.5 6.3 \n",
+ "88578 16.7 52.5 6.4 \n",
+ "88579 16.7 52.5 6.4 \n",
+ "\n",
+ " BH1750.Illuminance \n",
+ "1 0.0 \n",
+ "2 0.0 \n",
+ "3 0.0 \n",
+ "4 0.0 \n",
+ "5 0.0 \n",
+ "... ... \n",
+ "88575 NaN \n",
+ "88576 NaN \n",
+ "88577 NaN \n",
+ "88578 NaN \n",
+ "88579 NaN \n",
+ "\n",
+ "[88579 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# calibrating temperature sensors: AM2301 needs +0.6 °C\n",
+ "finaldf['AM2301.Temperature'] = finaldf['AM2301.Temperature'].add(0.6)\n",
+ "finaldf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>DS18B20.Temperature</th>\n",
+ " <th>AM2301.Temperature</th>\n",
+ " <th>AM2301.Humidity</th>\n",
+ " <th>AM2301.DewPoint</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>88579.0</td>\n",
+ " <td>88579.000000</td>\n",
+ " <td>88579.000000</td>\n",
+ " <td>88579.000000</td>\n",
+ " <td>88579.000000</td>\n",
+ " <td>88579.000000</td>\n",
+ " <td>43457.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.464557</td>\n",
+ " <td>14.257093</td>\n",
+ " <td>15.786260</td>\n",
+ " <td>68.740939</td>\n",
+ " <td>9.262844</td>\n",
+ " <td>9.069011</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.498745</td>\n",
+ " <td>2.917659</td>\n",
+ " <td>3.463454</td>\n",
+ " <td>10.515844</td>\n",
+ " <td>2.093712</td>\n",
+ " <td>123.456020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>8.300000</td>\n",
+ " <td>4.700000</td>\n",
+ " <td>33.500000</td>\n",
+ " <td>-4.700000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>12.800000</td>\n",
+ " <td>15.500000</td>\n",
+ " <td>59.000000</td>\n",
+ " <td>6.800000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>15.600000</td>\n",
+ " <td>16.600000</td>\n",
+ " <td>68.300000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>16.700000</td>\n",
+ " <td>18.400000</td>\n",
+ " <td>78.000000</td>\n",
+ " <td>11.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>20.400000</td>\n",
+ " <td>33.200000</td>\n",
+ " <td>98.000000</td>\n",
+ " <td>14.700000</td>\n",
+ " <td>2517.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 DS18B20.Temperature AM2301.Temperature \\\n",
+ "count 88579.0 88579.000000 88579.000000 88579.000000 \n",
+ "mean 0.0 0.464557 14.257093 15.786260 \n",
+ "std 0.0 0.498745 2.917659 3.463454 \n",
+ "min 0.0 0.000000 8.300000 4.700000 \n",
+ "25% 0.0 0.000000 12.800000 15.500000 \n",
+ "50% 0.0 0.000000 15.600000 16.600000 \n",
+ "75% 0.0 1.000000 16.700000 18.400000 \n",
+ "max 0.0 1.000000 20.400000 33.200000 \n",
+ "\n",
+ " AM2301.Humidity AM2301.DewPoint BH1750.Illuminance \n",
+ "count 88579.000000 88579.000000 43457.000000 \n",
+ "mean 68.740939 9.262844 9.069011 \n",
+ "std 10.515844 2.093712 123.456020 \n",
+ "min 33.500000 -4.700000 0.000000 \n",
+ "25% 59.000000 6.800000 0.000000 \n",
+ "50% 68.300000 10.100000 0.000000 \n",
+ "75% 78.000000 11.000000 3.000000 \n",
+ "max 98.000000 14.700000 2517.000000 "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# check the data\n",
+ "finaldf.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import Outdoor Temp/Hum/Dew"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import the csv file generated on https://www.meteostat.net\n",
+ "out1 = pd.read_csv('outdoor_temp.csv')\n",
+ "# generate out1 and out2 for two timeframes\n",
+ "out2 = out1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop not needed columns\n",
+ "out1 = out1.drop([\"prcp\", \"snow\", \"wdir\",\"wspd\",\"wpgt\",\"pres\",\"tsun\",\"coco\"], axis = 1)\n",
+ "out2 = out2.drop([\"prcp\", \"snow\", \"wdir\",\"wspd\",\"wpgt\",\"pres\",\"tsun\",\"coco\"], axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>time</th>\n",
+ " <th>temp</th>\n",
+ " <th>dwpt</th>\n",
+ " <th>rhum</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>2022-11-15 22:00:00</td>\n",
+ " <td>6.7</td>\n",
+ " <td>6.1</td>\n",
+ " <td>96</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>2022-11-15 23:00:00</td>\n",
+ " <td>6.4</td>\n",
+ " <td>5.7</td>\n",
+ " <td>95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>2022-11-16 00:00:00</td>\n",
+ " <td>6.5</td>\n",
+ " <td>5.6</td>\n",
+ " <td>94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>2022-11-16 01:00:00</td>\n",
+ " <td>6.8</td>\n",
+ " <td>5.9</td>\n",
+ " <td>94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>2022-11-16 02:00:00</td>\n",
+ " <td>6.8</td>\n",
+ " <td>5.9</td>\n",
+ " <td>94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>125</th>\n",
+ " <td>2022-11-20 05:00:00</td>\n",
+ " <td>2.7</td>\n",
+ " <td>1.2</td>\n",
+ " <td>90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>126</th>\n",
+ " <td>2022-11-20 06:00:00</td>\n",
+ " <td>2.5</td>\n",
+ " <td>1.5</td>\n",
+ " <td>93</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>127</th>\n",
+ " <td>2022-11-20 07:00:00</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.7</td>\n",
+ " <td>91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>128</th>\n",
+ " <td>2022-11-20 08:00:00</td>\n",
+ " <td>3.4</td>\n",
+ " <td>2.2</td>\n",
+ " <td>92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>129</th>\n",
+ " <td>2022-11-20 09:00:00</td>\n",
+ " <td>4.1</td>\n",
+ " <td>2.3</td>\n",
+ " <td>88</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>108 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " time temp dwpt rhum\n",
+ "22 2022-11-15 22:00:00 6.7 6.1 96\n",
+ "23 2022-11-15 23:00:00 6.4 5.7 95\n",
+ "24 2022-11-16 00:00:00 6.5 5.6 94\n",
+ "25 2022-11-16 01:00:00 6.8 5.9 94\n",
+ "26 2022-11-16 02:00:00 6.8 5.9 94\n",
+ ".. ... ... ... ...\n",
+ "125 2022-11-20 05:00:00 2.7 1.2 90\n",
+ "126 2022-11-20 06:00:00 2.5 1.5 93\n",
+ "127 2022-11-20 07:00:00 3.0 1.7 91\n",
+ "128 2022-11-20 08:00:00 3.4 2.2 92\n",
+ "129 2022-11-20 09:00:00 4.1 2.3 88\n",
+ "\n",
+ "[108 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# filter rows based on needed time period\n",
+ "deleterows1 = out1.loc[out1['time'] < '2022-11-15 22:00:00']\n",
+ "deleterows2 = out1.loc[out1['time'] > '2022-11-20 09:00:00']\n",
+ "\n",
+ "# delete the selected rows\n",
+ "out1 = out1.drop(deleterows1.index)\n",
+ "out1 = out1.drop(deleterows2.index)\n",
+ "\n",
+ "# check the data\n",
+ "out1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>time</th>\n",
+ " <th>temp</th>\n",
+ " <th>dwpt</th>\n",
+ " <th>rhum</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>130</th>\n",
+ " <td>2022-11-20 10:00:00</td>\n",
+ " <td>4.4</td>\n",
+ " <td>2.4</td>\n",
+ " <td>87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>131</th>\n",
+ " <td>2022-11-20 11:00:00</td>\n",
+ " <td>5.1</td>\n",
+ " <td>2.6</td>\n",
+ " <td>84</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>132</th>\n",
+ " <td>2022-11-20 12:00:00</td>\n",
+ " <td>6.1</td>\n",
+ " <td>2.9</td>\n",
+ " <td>80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>133</th>\n",
+ " <td>2022-11-20 13:00:00</td>\n",
+ " <td>6.3</td>\n",
+ " <td>3.6</td>\n",
+ " <td>83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>134</th>\n",
+ " <td>2022-11-20 14:00:00</td>\n",
+ " <td>6.6</td>\n",
+ " <td>3.6</td>\n",
+ " <td>81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>230</th>\n",
+ " <td>2022-11-24 14:00:00</td>\n",
+ " <td>7.9</td>\n",
+ " <td>6.2</td>\n",
+ " <td>89</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>231</th>\n",
+ " <td>2022-11-24 15:00:00</td>\n",
+ " <td>8.2</td>\n",
+ " <td>5.7</td>\n",
+ " <td>84</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>232</th>\n",
+ " <td>2022-11-24 16:00:00</td>\n",
+ " <td>7.9</td>\n",
+ " <td>4.8</td>\n",
+ " <td>81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>233</th>\n",
+ " <td>2022-11-24 17:00:00</td>\n",
+ " <td>6.6</td>\n",
+ " <td>3.6</td>\n",
+ " <td>81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>234</th>\n",
+ " <td>2022-11-24 18:00:00</td>\n",
+ " <td>6.1</td>\n",
+ " <td>2.4</td>\n",
+ " <td>77</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>105 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " time temp dwpt rhum\n",
+ "130 2022-11-20 10:00:00 4.4 2.4 87\n",
+ "131 2022-11-20 11:00:00 5.1 2.6 84\n",
+ "132 2022-11-20 12:00:00 6.1 2.9 80\n",
+ "133 2022-11-20 13:00:00 6.3 3.6 83\n",
+ "134 2022-11-20 14:00:00 6.6 3.6 81\n",
+ ".. ... ... ... ...\n",
+ "230 2022-11-24 14:00:00 7.9 6.2 89\n",
+ "231 2022-11-24 15:00:00 8.2 5.7 84\n",
+ "232 2022-11-24 16:00:00 7.9 4.8 81\n",
+ "233 2022-11-24 17:00:00 6.6 3.6 81\n",
+ "234 2022-11-24 18:00:00 6.1 2.4 77\n",
+ "\n",
+ "[105 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# filter rows based on needed time period\n",
+ "deleterows3 = out2.loc[out2['time'] < '2022-11-20 10:00:00']\n",
+ "deleterows4 = out2.loc[out2['time'] > '2022-11-24 18:00:00']\n",
+ "\n",
+ "# delete the selected rows\n",
+ "out2 = out2.drop(deleterows3.index)\n",
+ "out2 = out2.drop(deleterows4.index)\n",
+ "\n",
+ "# check the data\n",
+ "out2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# visualize outdoor temp of out1\n",
+ "#out1.plot.line(x='time', y = ['temp'], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Aufgabe 1: Vorgegebene Versuchsaufbauten"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.1 Aufgabe 1.1: Thermische Untersuchung eines Wohnraums"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Datensatz nach Zeitraum filtern\n",
+ "#15.11. - 20.11.\n",
+ "start1 = '2022-11-15 22:00:00'\n",
+ "end1 = '2022-11-20 09:00:00'\n",
+ "df1_1 = finaldf.drop([\"Switch1\", \"BH1750.Illuminance\"], axis = 1)\n",
+ "df1_1 = df1_1[(df1_1['Time'] >= start1) & (df1_1['Time'] <= end1)]\n",
+ "\n",
+ "#20.11. - 24.11.\n",
+ "start2 = '2022-11-20 10:00:00'\n",
+ "end2 = '2022-11-24 18:00:00'\n",
+ "df1_2 = finaldf.drop([\"Switch1\", \"BH1750.Illuminance\"], axis = 1)\n",
+ "df1_2 = df1_2[(df1_2['Time'] >= start2) & (df1_2['Time'] <= end2)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df1_2\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Zeitraum 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 22.0)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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TzJkzNW3atLJl3bt31+zZs7V27VqdeOKJOvXUU+XxVBzEZIypU9s/+eQTzZ8/v+zxzp07tWvXLknS5MmTlZqaqiFDhqi0tFSTJk2SJA0ZMkQrVqwoe82ZZ55ZdnvllVdWud3jjz9emZmZkqTi4mJddtllys3Nldfr1aJFi2r8O3i9Xp1yyinas2ePPv30U82aNUujR4+WJBUUFKhDhw413mZ1kRwCAAAAAKApqaKHT2UKdu1S8+bNa/y6LVu26LPPPtPcuXNljFFpaamMMbr00ksluSFiI0eO1D//+U/NmzdP//vf/8q9/pNPPtHf//53TZs2rWwoWaguXbpo0KBB+vLLL3XKKado+/btKikpUUpKivLy8tSlS5fa/cJ+Pp9P33zzTVmyJlSgPR6PR6mpqWWJKI/HU65eUGiCKnC/su1mZWWV3b///vvVsWNH/fTTT/L5fMrIyIjYzpSUlLLhdZK0d+/esvsZGRnyer2S3NC7c845R3feeWfVv3w9oOYQAAAAAABJ7tVXX9XZZ5+tlStXasWKFVq9erV69+5dVj9Ikq6++mrdfffdatu2bbnX/vjjj7rooov09ttvl+vdkpeXp4KCAknStm3b9NVXX6l///4yxmjChAl69dVXJUnPPPOMTjjhhDq1/6ijjtJDDz1U9jg3N7fG23jppZfKbseNG1ej7e7YsUOdO3eWx+PRs88+W1YfqHnz5mU9jSRX2yg3N1c+n0+rV6/WjBkzIm5v4sSJevXVV7Vx40ZJ0tatW8tmgWsIJIcAAAAAAEhyL774ok466aRyy0455RTdcccdZY8HDRqkc845p8Jrr732WuXn5+u0007T8OHDdfzxx0uSFixYoAMOOEDDhg3TYYcdpmuuuUZDhgyRJN19992677771LdvX23ZskXnn3++JGn9+vXq1q2b7rvvPt1+++3q1q2bdu7cKUk65phjItY5kqQHH3xQM2fO1NChQzVw4EA9+uijNf4MCgsLdcABB+iBBx7Q/fffX6PtXnrppXrmmWc0duxYLVq0qKxX0dChQ5WSkqJhw4bp/vvv10EHHaTevXtryJAhuuaaa7T//vtH3N7AgQN1++2366ijjtLQoUN15JFHat26dTX+narLWGsrX8GY7pL+T1InST5JU6y1Dxhj2kh6SVIvSSsknW6t3Rbh9ZMkPSDJK+lxa22V/eNGjRplA1XQ4eTk5Gj8+PGxbgbqiDgmBuKYGIhj/COGiYE4JgbimBiIY2wtWLBAAwYMqPN2dtVyWFmy69Wrl2bOnKl27drFuin1FsNIf1PGmFnW2lHh61an51CJpKuttQMkjZX0e2PMQEk3SPrUWttP0qf+x+Fv6pX0sKTJkgZKOtP/WgAAAAAAADQBVSaHrLXrrLU/+O/vkrRAUldJJ0h6xr/aM5JOjPDyMZKWWGuXWWuLJE31vw4AAAAAAKBJWLFiRZPoNRQrNao5ZIzpJWmEpO8kdbTWrpNcAklSpDnVukpaHfI4z78MAAAAAAAATUCVNYfKVjQmW9I0SX+31r5ujNlurW0V8vw2a23rsNecJuloa+0F/sdnSRpjrb08wvYvlHShJHXs2HHk1KlTa/krJab8/HxlZ2fHuhmoI+KYGIhjYmiMOLbb9K1KvWna1iZyoUHUDftiYiCOiYE4JgbiGFstW7bUPvvsU2469dooLS0tmw4d8ak+Ymit1dKlS7Vjx45yyydMmBCx5lBKdTZqjEmV9Jqk5621r/sXbzDGdLbWrjPGdJa0McJL8yR1D3ncTVLE0uLW2imSpkiuIDWF0MqjOFxiII6JgTgmhgaP44486X7/SOqb1klpzRruvZIU+2JiII6JgTgmBuIYW8uXL1dRUZHatm1bpwQRBanjX11jaK3Vli1b1KpVK40YMaJar6kyOWTcX+UTkhZYa+8LeeptSedIust/+1aEl38vqZ8xprekNZLOkPSrarUMAIB4tvzL4P1ty6WOg2LXFgAA0OR169ZNeXl52rRpU522s3fvXmVkZNRTqxAL9RHDjIwMdevWrdrrV6fn0EGSzpI0xxiT6192k1xS6GVjzPmSVkk6TZKMMV3kpqw/xlpbYoy5TNKHclPZP2mtnVft1gEAEK82hnzdbV9FcggAAFQqNTVVvXv3rvN2cnJyqt1bBE1TLGJYZXLIWjtdUrQ+bRMjrL9W0jEhj9+T9F5tGwgAQJP20S3Sz+9L+xwuHfOP4PIfnwvez5sp9Z/c+G0DAAAAqqFGs5UBAIAwPz4nbVks5b4QXGatVLBN6jzMPS7eE5u2AQAAANVAcggAgNr67jGpYKu7X7RLWviuu//OH93t4FOlFl2l5V/EpHkAAABAdZAcAgCgtua86m5/97m7nfFfqXBXsBfRiN9I6c0lb1ps2gcAAABUA8khAABqY/cWKW+G1Kqn1HV/t2zZ59LHf5FKi1xiqFkbqeNgae/2mDYVAAAAqAzJIQAAamPWU+72kKvKL1/8sbud7C9OndlK2rpMmvemlDersVoHAAAAVBvJIQAAauObh9xtz4Pdbe/D3O2OVdKwM6W0LPc4JcPdvnKO9H8nNG4bAQAAgGqocip7AAAQZusyNxvZoddK7fq6Zee8HXndvkcEE0lFu6TSEsnL1y8AAACaDnoOAQBQU8ty3G2X/atet8twqc+E4ONtKxqgQQAAAEDtkRwCAKCmVn7jbvseUfW6ma2ls9+UznrTPf7x/xqqVQAAAECtkBwCAKCmdm+SZKSUGkxR32Ocu13zg7RxobR9tXvsK5V2ra/3JgIAAADVRXIIAICaKNrtpqzvNKRmr0vNkDoOkVZ8Kf3nAOlfg6Wda6VP/iL9s7+rYwQAAADEAMkhAABCFRdIs1+Rti6P/PzuTe62/+Sabzu9efnH3z0mff1vd3/NDzXfHgAAAFAPSA4BABBqwTvS6xdIb18e+fmCbe6287Cab7vvRHfrSXW3X/0r+NzaH2u+PQAAAKAekBwCACQ3n0+afr9UsN093r3R3a740k07H271DHeb0arm73XI1dI1S6RbNklef72iAy6WUjJJDgEA4sOu9dJ710nLv4h1SwDUI5JDAIDktuIL6ZNbpfeudY8DSSJJ2jCn4vqbF7vb9v1r/l7GSNnt3e2wM6TmXaR9j5YyWkolhTXfHgAAje3n96UZj7nvTgAJIyXWDQAAIKbW+xNAc16WNsyTdq0LPvfq+dLWpVLL7tKVc6Uv/yl9/1+pbV8pq13d3vf4fwfv9z7Uvf+2FVLrXnXbLgAADSkwvHrNrNi2A0C9oucQACC57d7sbnseLLXpLfU8UBp3mVu2dam73eGfdv7T2/zrHlS/bejl396q7+p3uwAA1Le92/13jGRtLFsCoB6RHALi0d29pbcui3UrgMSw/Aspq7103rvSGc+7nyP/VnG9j/4UvH9glGLVtTXwBHf75iX1u10AAOrbyq/9d6y0aWFMmwKg/jCsDIg36+dIBVulH5+VTngo1q0B4t+eza7mTyhPyLWTXoe44tSBKeeHniG161e/bchsLXUeLq3LlZblSDJueUYLN7PZni1uv+95kJTdoX7fGwCAmsjfELy/ZpbUYUD1XldaIuV9L5UWuceeFKnbaCklrf7bCKDGSA4B8WT7aunRg4OPS4slb2rs2gPEu5JCafsqqd/RFZ9r0VXauUaacLP00m9cEkmSjrmnYdoy5nfSW7+X/u+Eyte7dUfDvD8AAFUp2Oa+NzsNldbPln54Vhrxm+q9dt4b0usXlF923P3SqN/WfzsB1BjDyoCaWv6FtG1lbN5713p3G+jlsJeTRKBOAkU1e0WoIXTBp9KFOVKPsdKEm9yy1r1cb56GMPQM6fyPpXPfcz/H3R987sjbgvd/fC7y65d+Jk1roMQVAACSlL/J3Y46T+owUNr8c/Vfu2utu/3N69K570rGI638pv7bCKBWSA4BNfXML6QHhsXmvQMFAIec7m5Dp9wGUHOBfahF14rPtegsdRnhpp3vf4xbNrkBky/eFKn7GJeo6nWQNOAEKbWZ63Y/MKQ30Vu/l3bkVXz9sydJn98u5W9suDYCAJJb4Fi0ZXf3U7C9+kWpC7a777R9Dpd6HexeV1bcGkCsMawMqAmfz38nRjMzrPbPZNSmt7td+qnUrm9s2gIkgvWz3W1mq8rXa9G58YdzZbWVbsxzB8/eFPf+C96RXvq19PpFUnrzyK+7118PaZ/DJW+6u2880sF/dMknyf0ve/9aaccaaftKqVXP4Otb93QH7988JJ05Veo/ufz2d2+R7ukjtR/gelJFs+h9d/vnbeVrOBXuku7s5mo5pWVXfF1poesFJUnXLZeatXF1Kv7eUfKVSPtMlLyuPsXgLZultY8GX+tNlXqMczWchp8ZXD7zSWnRR5W3tW0/6fKZkZ9f+pk047/lT4C8qdIRt0pt94n8Gl+p9O7VwR6foQq2Sau/lW5eL6VmVnx+1XfSk0dJ+06u+JwkLf5Qsj5p8KmSxyvt3Sl1Giwd/qfI6zeWRR9KM58KPjZGGnup1PuQ8uvlvuCKr3cdJWW1Lx/H7qOlQ66Wina7dea/5YZ9erwurlntXd2trA5Sdnv3OKuD+zvxeGve5tXfS08c4e7fsjkxhmq/cIa0Ya7UcXD55VuXSpsXVdwnJWnFdOnpY6V9J6ms5llAwVZ3/HH5D+X/3vdslf7hPx7Zd3LF/XHtj1L+euk3r0l9jyi/zWeOl5ZPi/w3vmamtHuTdNEXUueQi3GlJdLf2rr9f5+JkX/3wP+dW7a4/5uSVJgv3dm1rJ0V1j/hP9KIX0feXlO2d4f06d9cDZ91udH/n1TFWjfhQmpm9f6HLP/C3Wa0cgmexR9KM5+QRl9Q6cskubamt3D/GySpz2HS4o+ku3pI168MLgcQEySHgJoILcAXC4W73O2AX0gf3iRtnB/b9gDxLtADp13/2LYjmvCT3e5jpB4HSoU73U9lln7makJI7n9Fy67B5NDONdL3jwfXLdguZbVzJ3uBkytJevGMikmxp491t5sWVO9EesNcqfPQ4OPA+674Mti+UIGEneTqU4w+352Y+kr8v9enZa9LL8yXdha65dbn3mvB2+5xaHLo64dcUe9WPaK3c8vi6M/9+Ly05FOpff/y79Xr4OjJoe0rpVlPuffMaBX5d/zmYenQayq+9smj/OvNcUmPcNZ/oWLuq+42vYW06APpsBuCJ8Sx8ONz0rLPpXb7usebFkqZbSomhwKz8q2ZKXUaGozjrnUuSXHI1dK62S4xJLki7e36SWtzXdLAV1zxvY1HatbOnzhqH5JEipRMah/8230iJGmx5JOKydB4U1oS3IczW5d/bvMid7t1acWi+oH9euU3LkEcKvD3+u/9y/8/eP604P2deUov3B3cHyWXGJKk504p/zqfzyWGJDckKTxJvNs/bOmxQ8u/bvGH/t+xyP0Pq8yqr6Xeh7r70+8LLt+x2v2tSNJufx25ty6Nz+TQt49I3/83+Hj2S9LIc2u+naLd7mKAJI2/ser1A599+/5Siv8CxE9Tq5ccMh6XOA8YfKrbv/fucMe4DTVsG0C1kBwCaiL0ZMznq3jlraEV5UvNu7iTjbb9GFYG1NbXD2l8zs3uCnRqltSqe6xbVD3ZHaTfvl/5Orf6a5IdeLl01O3u/r9HSjOmuJ9Ixt8gjTzHJWNeObfi9o7/t7T/2a7X0KYFwecu/rLqdjzmTwzcukNa84P0ya3ucfPOkV///vXSd/7eB+9e5X5CHX2nNO5SSdKsnByNHz/eLS8tkW5vH0ycBN5fkmSkMRdKx/yj8rbe2rJiMizwXPcDpPP9vY9KCqXbO0RP0H3y1+AJ6bH3S/3Cek38e6S0ZYn02d9cIiRwtXznOum+/YLrHflXacip0dsbMORU1zvqb22Dy25YVXEWvoayc5305NEuIdZ9rHS+/yT+0YOl3OfcCeRx/s8j9Htr38nSr6YG4/jFPdJnt0sb5klPTQqud8rj0sDj3X1rXc+r3ZvcEMrdm0Lub3T1UHZvdAmQ/E1SSUH1f48Xz3C3N65x+8Ksp6XffVrLD6UBbF8lTZkg/faD6DMmzgrpuRW+fwX+bp4+TrpqQfAYJrBPStLRt7t9PdLrAvdv3eHisCakp91FX2rWtGnB/THS6yTpvPelp0IScL96pWIP6PDXHXOv9F5YEjXa/57Aa5/5hXTlPOn+QeWfv+iLYNJ96efSsye6+w+OkP7wY+RtNiUf/1la+K77/xHugxtrlxwK1N6TXEK6Ouu37esSOZ2HSkNOk+a8Ur1JUgq2uTp+AfufJb19mbu/LCe4nwOICZJDQE2EFoAu3FHxqlxDK9gupTVz9zNbRR6uEEngYDrSFWjUXfFed0JaXOBOxupy5b5wl7RlqUsAEq/6U7greDVakj662d32OkQadFJs2tRQJv5Z+vQ26fBbyi97OeyE78A/uF48xXuCw+r6THBJpTmvBQuHStLbl7teMj+HJKYu/6Hydoy/Ucq5M/h4y1I3fECSUjJdse9IjvhrMDkUbtBJ0a9Oe1PcTHJzX5dWflX+uVHnuZ9oeh8W7MmwZWnkoQ2HXRe8n5LuhuwFenMWbHfJCV+xG5IX2lOh57iK2/rtR25onuR6IHlS3M/c14LrdBjkH+ITwa9ell44Pfj40GtdcijU/LcrFlr3+VyC0Ztau+EnkfhKXS+17f6JGkKHaO5/jjupn/mEdKD/BDD05PPE/5TfVtdR7nZZTsg2zpb2Oy742Bj3v7FZm2BPrmisdRdVwpNIP78vLfnYrdNxsItBqJVfB09YN/qToanNXO+6gu3upLhoj1S8O/g+zdpWPTzVWteDzVfivitqEoMda6TvHnOzJubc5RKHLbuVX6dgWzCJ0nFIxW1c9KVL1uavl9b/FEwe/vyBu9+qpzTo5Iqvu3apdE9ID7nNi8sPsRx/Y+R95rcfuqThyPOCSaunjgk+n9U+8tD4c98N9mSSKiaGfhmlKL8kHXtfMKH804vln5t0d/nemL0PC97fusz9bWR3iL5tX2nwuyQt2x2PZbR0n8XONe4YoFxvT+OOE9NbuGOEPVvc/9vS4sjHCc27SKkZkd97707Xe/2rB6K3r3iPa0tlQ7MKd7nfs1WPYCIntObPpoWSOkV/fWmJ24dDe0Nmd3S3ed9LrXu7zyRwrGqta7e1br0tSypOex+Y9eyDG0gOATFGcgioib0hV4m3LJO6jWzc91/3k5SW5e6nZgbHfVfl6wfd1aYrZlfsLo66+3vH8o/rUpvmzpCD/ZvWBuON2rO2/OcaavLd0a/Ax6tDrnY/oUILWgcc9TdXp2L5F27IjeRObo+6PdjjKPQK/oMjgvdb9Yg+nCpg/A3uKvY//Sfw/97f3XrTpZvWRK8Pk5oR3IfCe8ic9nTl7zn6AvcT/rpj76v8hOmct4OvCbQzXHjNlIwW7kSrtFi6u5ci1qIbeELkfTgrpIfPowdHfr9Lv47e3n2Plq5d5hJMg06SWnSpuE4guRHN1T9LzSs5Cayu28KS2Fntg/cHnxI8sQ/9+5Gki7+qmADvOtINO/nwpuCyw2+pfS9dY1xtrvTm5f9euwwPJofG/V4a/isXx7+1c8teCBku9Z+QXg5VuXlD9JN7ySU8QhOXgR55VZnx3/IJkrmvup/TnpEGneiWrZsd7KUnSUfdpgo6D5VO/z+XKJ4yvvxzI8+TfvGvyO+f1c4NWZx2l3v80Kjyz4+/IfLreowN7svZHf2vD9lPro3Q+0VyiehfvyY9f0rF5/60SUpJi/w6yQ1D/frf0rblrhdaQKTvZY+n/Pvc2y9Y5yyS508N1kML3e7Hf3bHWdGM+E30WSbDRTt+uKuK3q0T/yJ9+leX5O4zPvp6jxzoeqCFvlfoseTX/5YG/D3669+7xiV4+ock+fqMd8PSQnuE/WW72/++ecjVM5Jc8qt4j5QSto+MvkD63x+qHioIoMGRHAKqa9PPwYNJSdowp/6SQ1uXuzoLC99xB9MdB0sdB1Zcb/fmYAHY9vu5L/SdayOfGIT61H+Q+NHN0klTgld04lH+Rnd1KfyKujfNLavswHzl19Ir57lCvftMqPq9ti6TDr7KHYjmb3RDDFr3UocN86XZlcwINfvl8o/b9pW67u+/Wv2JG55SHbOecQflNVGwzdUdOOmxYNLD53M9P0oLpX5HubozrXu5k6EF/5O6jQrWYln9vTT9fndSG+1kekee+7ubfHfVBWCXfia17NGwhdMXf+IKppZr42qp16HuYDN06M/oC6Ruo6WSQi1eMFv9Ei0xVJnfvOamul/9retBILm/kzU/BGsRhbvgU3eler+Qq/ibFkqHXhd5/XDNO7mizZ/f4eqESC4RU93CwRd9KU05TOp7pOsVVF1X+K9CW+tqiVSnyOkfct2+G55A37m2fA+DgPTmrgbOZ7erQmKo01D3f/z4Sk4Yr/jJDcUIPYEdeoY7MR3126rbm9VW+t1nbippSfrjHGn6v1zNnw4DXW26cG9cFLy/YV7Nk0O7N0vfPxGcFCHULx5wPcL6hHxWzdpIR98RTPac9Ji7zWgpdRxUcRsZLaSz33b77Y7VUklR5T05aqvrSNeLpFlbaYC/V5I3Vbpwmvuul3W9QHatDyZEopl8jyvuLkk//F/lvYfCe7R9cFPwRHnDPNdrb/Nil3Sc87L7fy2VTwyNvdT9bb11qavJFNivlnwSXOfIv7nvuUj2neySrCUhtYFkKk8oSC4BtGWJ+5vp5O+VtGWpGxZUHYdd7xJ/rXtKP7/nkk2V6XeEdMLD7m8uu6NLMJzy38oTQwEXTXOF0Zt3cm2srJ5QvyOksb+Xvn3YPZ7xX9c7q1lbqU2f8uuGJ4YkV48sNDF03P2ul1novhYpMTT0l8EYbVkqfeEf8pr7gutFGCpQby3gFw+6XkjLv3D7VKAG3acK1lEKZ63ryRlIDEnBY5XVM9xti67Shrlq1+5bSeMrbmPDPGnVt+7+pJD9os+EYNIx9HdOSQ8mhqRgb9TQ7xPJJUj/9wd3XCu53tiL3nfHKIF2hfeCrE/fPOySVq2iXDy11tVDO+iKit8lBdukeW+6WnjN2ko9q2jnhnnu99m23M2GGlBaLM2eKmV3csnR6vYqXD/Hfc7Lv3D7Sbt9y3pUlh2rpmb623Zg+dcW73W16gL/QwJ8JdKqb9wxdXisorVhba7rvTrmQneMsX6u+x9z3P1ukgHEDWOrO/VgIxo1apSdOTPKjCFJKie0rgJi49FDyhdKHXRS1Vexw0SN4/+dUL4bfbt9pcu+L79OcYH0907u5OHkx9xJ/Uu/kQ76o+teHo210l9bhbT7ZOm0p6Ku3uSF9wgIddIUadgva/faaPaZKJ31eu1eG5DZRrp+ed22URuBq4LfPip9cH1weeB3+vDmYBHKaD01KnPqU9LgCEMQAnbkuXoPLbpKVzVQ8fStyyr2SKhMyGw7/F9tREs+lZ47Weqyv3Th5/W22ZjG8LFDXW/OSM55p2IR5mieOzV44eGymQ3bky10/67NDE2V/X+I1uOhpMjVghp3mXR05B4JTXpf/Fv7iidPAQf+wc3udHsDJLAiadFNumqeu3//YJdACzfwBHeiHgNNOo5VuXff+pl0JDAT3PT7y9dyCnfJ18EEaf4m6d5qXkDpNES6eHrF5dtWSA8Mi75ff3Gvq3EWTete7rh2+v3ucaT9+aHRrqj5iLOkEx6q+Hx1jx+uXVrxwteDI9z3+U1r3ZDYNy8OPmc8bubOhuhJXbRbuqOKC6wBJz5afqIDSXrkYHexOB786mXX6zTgp6nlk5iRVNUTPvwcozbbQFQN+T/VGDPLWjsqfDk9h4DqWj/bzaow+W7pyUmuWOXRd7oprutqR1hX2s2LpLyZrkdHQGAa+0Dmf8Av3PCMr/7lriQ2DxvaNPsVd/Vr4Inll8973Q0Zadm17u1uLL5S6eEDpB4hPW6GnBacVaNkr+sqPfNJ10uqYKt0w2o3pn/PVpegKA4rSjr+RreNaGY+6RInSz+V3rs2uPyCT/Xd7EU64ICQtgRm3jAeV1cgtFdEYDvvhgzz+d3n0QvFlha77RhP7aZ0DR0SE2j3rKeDy1r3ckOJ3ru2fHHi0N8xIFpNmcB7fHGPu7q0brZra+BqckCgxk91u4p//4TrwZTVziWuCne6aclnTJEOuESafJf00S1u5q02fVxsl/qLxZ70mOsRFN5GyfU+Sc10NRKy2wsx0HeiG8aU1Ugn0Y0hPWRWnSvnu55EJYWuZ0NNCkH/6iXXU8RXUvOegjV18waXiHrpN9Wbaa5otzTtH+6qeqjz3g/WGfGVVt67JyXNTbHtTa99u2PpxjWuJkvRbvddU1rs/senZbv/V8a4oUi71lUcLhPOWlenKLO1q1kUiMH8N4M9fEOlZLgkguQ+59DE4SVfVewl4it1PVVRc1fOd99VD4TMoBj+HWit+45v3dMlkgI9WwLPtewaHAJ50B9dza09WyQZ97rSIknWDa8KHVqa3d4lTHZvjr5felPd31z4zIcBKf6eJtEKsE+729227i1d+o27eBOYtU1yw0HTsoLJobt6SFcvCvbG3rnOnxj6jesNEsmNecEZQL0hPbxKCl2P9dIS97cf+rsHjLnIXcRamyu9c6Vbdum3rkf9Z7e7mp/RkkNzXnXD4S7MqXjclDdTenyi6zVsPFKbfaSxF7v32bWufK+saMc8/xnnel6/ebG0Nmyd8MTQZbOiH7st+TTYy1CSjvp7cHbE8OHMVdX0k6RFH0ofRphdzv/a7777Tgf07yQ966+r+MLprj7dhJvdxZoN/ot2l3wTnHVOckXvC/0JnXeudMexHQZKxz8kLXir/PF06MxzkrsYGt6T+92rXX3DEx52fz8Bd3STinZJo39X8TNb+6PrZR/lgkK9mfmk5Emtfg/IJEByCKiOPf5/dJmt3cF73yPctMfz35TGXlL37YfOFDH5H9L717luyqFX/5b6r7Z3DRnK1n+S61a+4G1pzO/Kb/N1f9HWZv4v4dG/C055+u+R0p+qWcy6KZj7uvu8Q6eaPvxPwSF2ktTzYGnjvOBnmfucq3fy8/sVT2wk91xlQyrG/T7Yq2bOK8HlXUaoYEl+1fVWAvad5Lpuhxaa7Ty84Wa663WImyJcCrY79AB29AXSl/8s/zuFrhvwy+ei/47edHegtCPPvS7wmW9aGH39qpSWBIuIDj0jOPxiu//K+HePSJPuDHbfz2wdfN+WPVz3/EgnqN3HuqRVbRJtqF/1Ud+mKQkdllOXZLvHW3Uh4/qSmhFM4ob+X4hm5dfuAkR6i/JJ726jq5dcKnvfeip+HQspaVUPbQsUyK6t4b+JnBz69avR/w9ntGy82eiSgTfFJX1OeUJ67Xw3lLSy7/nAUOxoQgunV0dWu7olhwMn9+WGC4Zo288dI532tNsfo/VQ7HmwtHK6S8Z8dLN07D/d8vlvutveh0Xf99ObVyw2XV2B333GYy7B1esQt62N/gRGoPh/JK+d724j1Vt6fKK7/f5xl0ArKXAzc07xD38NDN2Uosf7N6+62e+kisdK4SobQp/RqnxyaNiZwUTZpLuDPbxPeaJ6x5hDT3fHc3vCksT+1xY0W+3qfrXoGrxI98U90vBfB4dI9jqkYhmLkx8LztwYmOhg43zpcf8wyPSW0Y9hz3zRFaEP9f3j7vat3weTQ9tXu8SQ5M5Nwif4KdjmCpw3ZHLIVxpMRI74DceJfiSHgOoInIQGevIcfYc7Wf3ghvpJDhXvKd/lfv5b7qekKDi+fuXXbixyp8HB153ypDS/resuHJ4cCnjdv/ygK1xi6c2L3Zdj6LabunW5wfvRuqee967/ef/B8v+ucD+hatK1tUWX+ukK2/sQ6drFVa9XX859p+p1Dry8bu9xS1i9pcBnfv2Kiuu+cbG04quKy8M9MCx4f/bU4P3rVwS7LAcO5gLLA8MALvnK1Sop1ya6MaOB7TNByptR/fpLTYXHf2IXXsckkjz/EP+LvyyfjEf9at6R/1lNxZBT3U+8CSRgw3tJS65X08Z5Ur+jXTH2ypz3rrY8OEFtt/7gTuoDJ/YBlfW4rov05u52/luuF1PgWCbNvzxvputR7Ct2iZQO/vpEob3n5rzikkNvX+7qf4XqNMTVcnvnyuAFX8nNoBkYah9N70PrZ//Maht9O2Mvdj812l476bqlla+TmhEc1h84VgutnRXpmLH/5GA7p4x3vXhC/f7bymudhv+OH/85OMtepKGHR97mzlHKbaNlxfWvXhQcJbF+jqsVdfxDtZ8heGHI7/7sSdLZb9ZuOwmmgS5dQz6fuxJeWuIyk9FqO/lKg+tV9mN90bcf/uPzuZ/qtM9XWvW6UnDdZBWY5jNwla6yXh/WBj+r0M/W55MJxNvnC/5N+HwuOZSWHVy3hf8q9M684LLSoopFCr0prgZB/nqX7An8DRTmV2xXi67l68Os/s7/t2WDbQ787UT9O4z2d+x/TW1U5+8/UCTw7Leq3t6Q02u2HHV3yhPuJxLjlWwl/zusdTEO/VuXXNHUk/9b/kpOtr/nyQEXB9933GXBg8omxlqrklKfSkqr8T8W8SfQ86YmPWiagsD3SFXJIZ/PTQMvSS2rmCkJQGx50ySZyD2HAr1uwi+iRLFwvysjPzHgFw3XuyL0ezy1WcXlb13qhsQuy3EXZwPWhAy/2rvTHU+EJ4Ykd7wQGJK3JeyCXaL1ao2m3b7uNrSXdlV+E5I0a9NHGnV+8Fisug6O8vcUMDrCxe2Dr6q47M2Lg+cFr5wn/fSi67FenfOISD+hBdSXfV7xHCfSOXoSnAvTc6ihfPtw+Qr9UsVM6ivnufov1TBekqbJzdhy4GWugHF1RMpQRysYd+i1bqhOqNCpXT0p7uS818HVe+9EsuB/7jYzQvfgT251s/EEvHaBm2I21CXfSI+M02GSFDr7/J82BRNPoeN9B/zCzVQSSPJ8/GfXe2ZYWCE8STroD24Y2u1V1FLxeCRPyHs8c1zl60cz7FfSSSFfzD6fGyu9bbl7HJi+tCpv/t4N/aou46l6FgjJzWZyyn/L/51zRbZhVXaV1eOp/Mv0nT+Wr4kU8KuQ3kPR4tf7kOoX/Y2B3je+V3b/luMG6vyDI8zwhPgVOGkJ7w7f1FUnOWStm65861KXnK/u7HIAYsMYV6MqvOZQaYmruyi5kgjVUJwWJYl0VAMO8Qm9QNrvyOD99OyK6yrkGPMlf/HtVj1ciYW/RahnFDiGCNRDCj+HGnB8jZsbl465p/zvXp0JCZq1qfsxdGZrt40tS8vXVrp5Q/QZho/4i6vx9u1/gsuWflYxvo/W4yx2oYW1L/hUempyxckIRp7rZuZMYCSHGkr3sdKEP7nuj4EicMtygrU3dm8KJobGXOS6UIYr3Fl+ekzJZc1DZ8yaEJLM+fx2VbDiK3dVM7uD65IZqbtpwBf3uEKGvhKXXS7YXr5Wi6/E9TbpMc7Vcdm6zP3TbtZW6n9sxW59xQWuzorxuu7ovmJXjK1ot+s+2KoaVyI3LXLdJusylr8+BJI0ofV+znpTevZEadFH5ZND4YkhyXV9jGTDnGAX/9ATjMCXYVG+O0j/zl84OFImfchpLk7hPXe8qS6eL/3aFQ8NOOMFN9xqzEWR/2YkaeR5wd5LAYs/ckMofnrBnfC36++6MW+cH0wMSe7vfegvI091HGBt+cTQgZe7McyVabtPza7QX5gjTf2NdEmEWT3QeCrrOeQrdYXT2/WX2u/r/pZbdourwrXWWn2zdItSU1xvwi35ruBo62bBIZttstKU8/NGDe3WUsUlPh3Qp628Hsa2x71R57kr3EMrmSGxKfJWIzm0Z6tLDPU7un6GTgNoeN60iseC+Rvc+US7/lL/Y6q/rUu/ld65yh3r953ojulaR5nqvT6E9hzqd3Tk5QGBYvglRe7kvf1+bpa2wHCpbSvcMebYS4MFn6XyU8eH6nNY5OWJpseBbiKdPZul7atcvaHG1HYfV4dz6WfSsDOiJ4YCDrvODf3as9WdD024WWWJwZK90vIvyteMqqniPdKWJe7vO7wMxbw33N/WQX8sn7jsPFSJrsrkkDHmSUnHSdporR3sX/aSpP7+VVpJ2m6tHR7htSsk7ZJUKqkk0nRpCav7aPfj8wWTQ9F6+4y/IXLyo2B7xeSQFJxq89h/uuKyAXnfS4s/LL/u0zX4IpCqntJw3Wx3lf/dsCRFxyEVT8Lv7h191oTv/1u9TPTD/tmHYt3zY+92l+AKTYDtM8HdBoogV3b12J8IWd9xvDrtO0r68l63/L+HS+f4eyWFFp8LjLEuzJe+fcR9jukt3Al0uGZtKu+yGf7Z7Xes+5GiJ4cOvcadpIfyprrkkCT9WEmPn5w73U9lMVsZVoPmsBuiXB2qgy4jglP+InYCM7hFMv8tN3NP9zGRp8WNA899u1K3vFX539mAzs315eLN+nKxq41w8v5ddd/pwxuhdWhQma1rXiOiKahOz6EF/iG8Q04LftcBaNo83or79Xz/vjzxz9UeVibJFYP+7ftVr1dfQpNA3ccE76eFLL9slvTYIcHhroHhYSPOkrqNdD8BJ0a4KJvZ2v3/85W4IVWzp7p6Q/FcML8mUtKkcZfGtg2/eqn662a2lv44p+r16oOvtPy57TcPuQLmE26On/qs9aQ6PYeelvSQpLIBnNbasstkxph/SqrszH2CtXZzJc8nNo/HdU3buSY47e3Ht7hCWqN/Jw08IXqvmMxW0hU/Sd50LX77PvXrlOWm9UvJcNnOwAl+wOnPuCkz57zi/slmtApW8A815kJXSGzMRS4zWrDVjdP94h+R23HsP11X1KeOcbMVBGYsCLVhjvRVWCIrNDEUmP0hVPj64UJPKKtaN5J9j5ba9696vVB5s6TP/uZ+3z7jpTUz3WezbnblyZ8v75NyX3BXkNNbuJjud5yr09Cym5t+s3iPfl6boU4TDnfdX//3B/fan/zDZ6L1HApMU9kQ09NetcC1OzXTDR3Ytc5NcxqeGJJcnZfNi6MPBbt4uvRoyJDDymK27id3u//Z7u+wvhNDaDo83sjDyjYvcbO4SRWHs8aJpZvyyxJDbbPSlJHq1Zrt7v/epEGddMzQzjq0XzsVlfq0aH2+3sxdo1dn5en1H9Zov06R6yTd8d5CHdS3rZ6/YGyj/R5IMoHkUKTZynZvcRelvvqXexx+nAGg6QokPgL27nRTvEvumLgpC00Ohc7eGHp82KaP68WxdXn5QtnV7c1hjDse37vDXZC95OuKveSRnEac5UbNrPrGnQ9ld3DnykmWGJKqkRyy1n5hjOkV6TljjJF0uqTD67ldiaXbKEkhnaZ8t0rPn+KG0lTVRdM/O8iabseq3/jxla+bmulO6kN7kcx/M1gvJ+Cw64NTRoaON03PdrVtwgV6J7XpE5wKMZKPb4n+3IBf+Lu7FlVv/ZpsO5q1P0qnPVWz1wSmaVz2ecXnhv+m4rJuY1xvmkAPr2/9VyrG3+CmQg9j1+e4L6eR5wSTQ7nPu4RSaEImLcvdBq6OSNLhN9fsd6mOFl1cL6HqSM1wbYiWHOo0RGrWLjilZlUxy2ovTb6n6m6liG/GG7nnUM6d0qL3pTb7xG0xyPs/XlR2/57ThmpvsU+XPu+KY14+sa8GdQkOlezQPEPtmqfp1Vmu5sEd7y2Mut2vlmzRjj3FatkszgodIz6UzVYWIWmb+1wwMdR+PymtWcV1ADRNnhRXwiFgwdvSrrXuol9TL5yfku4mWAkvSZCS7i60pmW5C+7p2dLP77qf5v4Zs9rvV/336TRUWvGle03HQfXXfsS3lLQmX8eysdS15tAhkjZYa6PN02wlfWSMsZIes9ZOqeP7JYZ+RzTeMKlfPudmLvCVui+Gyr4cDrrC/fh87sqD9ZUvkjz5H9Ij49z9axa7rKrkrj5Gmh1Bcl9UKekuIXLLpuDyot3RZ74KVdmQlMo8cZQbj1pdf+9cvr5SqP3PcVPXBxI2oS74WCre64o7533vlt20NvK64W7dEfwcvGnls9OB8a2bfna3h99S7UKCDSp0endfacUipYEpNYv2VB23lPSmf7CCuovUc2jmU9Lyaa4n5LnvRX5dE7d2e4Hemb1Oo3u11vMXjFWav+bQirui97TYr1MLLbp9soqjzF729k9rdePrrgv1H6b+qMxUrz6Yt16S64kUuH/nyUN05pge9fnrIJl4PJJM5GFlM0MuqFz8VcXnATRdnpTy37ervnG3l8TJvnzFT+64P9wfcoMTnYTWf9m11l2UDJyPVMfZb0ulhckzlAyoobomh86U9GIlzx9krV1rjOkg6WNjzEJr7ReRVjTGXCjpQknq2LGjcnJy6ti0xJKfnx/zz8T4ijWk9TCVejM07/u5kf+BNxH7FxSquGSD5lTnM7M+jY+WGJI0u7iHtn4zs9JNZHU6S6PzvteC/a7Qhq+/j7pedeNofCU6TNLOuR+qhaT56/ZoI/tEk9EU9sd40SdvjbqWFuvLkM/rgG/vUkrJHq1IH6o1X8auYHhd4piz2l2d7eLN19fTI36t1VirkmDCfOnazSoMOcafu3JD2f0bX5+jznuW1ct7xjv2xdo51Hi1esVSLQ/97KzVodtXyyNpVfeTtawR903imBiIY2yNKSrWrnVrtMAfgwFrVqmjpJxKjksjacpxHF5QqlYhj4uKi/R1E21rrDXlOKJ6YhHDWieHjDEpkk6WNDLaOtbatf7bjcaYNySNUfmJvEPXnSJpiiSNGjXKjq9qCFWSycnJUZP4TA5300uOj20rqrakjZSWVfVnVlLoikIHROjRVe269MedowGSBlSySo3i+FW6WuxynfIGHn2eBlZndjc0iiazP8aDkhxpjQ1+XpuXSDnrpbGXqt+kO9Uvhk2LFsfXZuXp6ld+0m0nDJIxRp8v3Kgnz3XF8XfsKdbpj32jNdtd759/nDdR6Sn1N833ipAOgis279b4e3MkSdP/dIx63fBu2XPXf11JMeEa2LAz2OuzY4v0ssc3HzNA89bu0L/OiDy7yz8/+ln//mxJ2euq8x5jerXRyxe73qcXPTtTH84LJrzCtxF4zQ+3HKk2WcFelZMf+FIL1u0sW7+w0KP09JIKr+vYIl3f3RT8MB//cpluf3dBxPcLvOaRX++vyUM6R/09nvt2pf705tyyx33aZ+n5Cw5Q55ZxeAX6qzT17NZFPUP//ldMl6aVSEfcqh4HX6nG7JvG/9TEQBxjbG5zNWvfVh0DMZh5odRtdI1j0qTjuKabtCM4CUTaPoc23bbGWJOOI6olFjGsS8+hIyQttNbmRXrSGJMlyWOt3eW/f5Sk2+rwfkD1eVMrn4klYMtSaYP/YH+fJlY6Kz1b2uM/cYtUIBqIB+FT2QdmvOt5YGzaUwmfzyq/qERXv+IKpv85ZBaytf5C07PzdujnDbs0oX97HdS3Xb0mhsL1bOvqvZx7YC9J0oT+7fX5z5vUvU2mDtqnXb28x9TvV5fdP6hvO73+g6sr9/f3XCLlukmRazkEEkOuXdG79FsrvTTTvceMFVvLPsfQxFD4NrbuLtJH893zr/+Qp2NCEjYL1u0st/7adevUpXPwtYHfZ8POwrL3klQuMRT+foHXXPL8D/r6hujfA6GJIUlatmm3pi/erIP61k8sGorPWqV4PEpL8Sgj1aPMVK9M+PATScrz95Cty9TAIUpKfUrxRu5h7PNZbd5dqJJS11tuS4GvLF4pHqMOLRqvFp21VjsKipVfWCJPYOiK3OfWqlmastPr2sk+Nqy1KvVZlfisUr0eeT1GpT4rI8njMRXW9/msrNz+V1TqU+gaVlLLzNQKn8Xe4lJtzi8s+9xC4xhJVlqKWmSmaGdBiXYXVS/B7fUYtctO18Zde6tVjSC0zS0yUlRU4tPekvK/TyQtM1OVFaexLhNekNqT0qR7+ddKeJ3K9BrMwAagStWZyv5FuY4i7YwxeZL+Yq19QtIZChtSZozpIulxa+0xkjpKesPVrFaKpBestR/Ub/OBKDxeqaSo6vVev9DdnvM/qfehDdummkrLcrPP9RkfHGsNxBuPvyC1te7v+Af/xJe9D4ttuyI4Y8q3mrFia8TnDrzrs3KPbzxmgPbtGHnGsfpijClXw+ip88ZUsnbthCaH7jp5aFlyKCD89w53xICOuuuUyvtXBpJDlW0vdBtrtxeUJYduf3dBhcRO6Po5OVs1fnzwtaG/T7T36tchu9z7Vec10Vz76uward8UXH54X13t8VacrWylvy5Jh4F1fo9vl23RGVO+1WuXHKiRPSvO9Nnnpgi1xqYFP/t7Th2q00Y1Tm/Zuz/4WY9OWxr1+QW3TVJmWsMlgRvK1a/8VG5/XnHXsRrw5w80oHMLvfX7gyqsf+7T3+uLRZsqLA/11LmjNWE/l1jNXb1dJz4coZbNtMr3oebpKdpVWD89H+tT26w0fX/zERETZ3HD45VKw2YrG7B/7NrTEDYtKv84g+QQUJ+qM1vZmVGWnxth2VpJx/jvL5M0rI7tA2rHkyr5dkd/fsca14Mhf72U1UHqWfFAKeYCB+7t+se2HUBdGP9JlfW5+5sXS9kdY35AV1Ti08qdrufEe3PWqbjUV5YYOn5YF3k9RmP7tNGuvSVav2Ov+nUMFsFsmZmqfh2yI2433rxz+cF67tuVOntcL6WleHT1kftq5sptOqhvW+3aW6JurSMPmSrxWU1fvFkP/arqE4+nzh2tq17O1W/G9izb3rY9xZq/dqeGdG2pU0eW7xnZpVWmHjxzhL5ctEmjepVPLGzYWagBnaP/7Xx85aF68LMl6tEmUz3aBGfa2lvsU4nPavueIv1hYvnBjB9deaimfLFMQ7q2VEZq9KvshSU+vTxztdplp+uBM0Zo1sqt2rQrymQMTcj1r80p9/jfny3RZa28SvOVlO9Nkb/B7Zt1uBixt7hU0xZt0rPfrJQk/euTRTpjdA/9uGqbvF6joV1blVv/1JHdNLpXay38+Wft1999193y5jx9smCDmqU1XC8OK6uP52/QhP4dKiSG7j5liKTg5/bqrNVqk5WupZvytTm/UDdOHlCWLHpi+nLtKSzRJeP3idpLqrEVl/r0+g95FRK9785ep6ISn35avV3vzl4nSfpu+RaleDzar1PzComhwOcgBT+LP705Vzcd4wbPv5kb3P7VR+6rDi3Sy8UxXGAboYmh0PeIpNQn3fRG8O/XGOmukyt/jSS99P1q/bBqe9TfJ9w3S7fozdy1uu/jRerRtplOryQxWVTi0/99s0JnjOnR9HqVeUJ6zftKpaJdMf+urXd7/eUfWvaQdqySMlpWvj6AGmli/9WAelLVsLL3r5MWvuPuT7i54qxbTcEud/Cmrgl21QfJxeM/YfKVuoO6PZulPhNi2yZJd3+wUE98vVf7DNhcNv18wC9Hd2/yQ4Xqy+CuLcv1orl8YvWrQP36gJ7VWm/Cfh30459rNlTp+GFddPywLjV6jST169hc/z4zcp2kaPbt2Fz3nla9a1lnj+tVdv/w/TrW6H1iJTw5JElbC3zy7tytcgMCd6yRWtWt0tCbP67RDa8H3+/LxZv15eLNUde//PC+6tk2Szm7l2n8aPfeT0xfrg/nbagw9LAhvJW7ttzj44Z21i/97Qh8breEDC+VpIXrdunli8dp7fYC/e2d+ZKk/MIS3XhMZRUHG8+9H/6sx76oWLD+9y/8EPF+JFlp3rLPQQp+Fmu2F0R87dkH9lLLzNRycQy3YssePZITTMSdOLxLufeIJjQ59OfjBlbrNR1bZOjcp8oXYa7sdR1bZOjN3LV66HM3XPbU/btF7UH0xPTluvuDhbr93QWVzk4ZE6HDygp3udtEG3a1/9nStLuCs90Gfk8A9YLkEBKTJ6V819pQ21a4xFCPcdIvHpDa9m3UptXYgF/EugVA7QXqHdhSabf/yvSwM+r9bWat3KY/vPijhnVvqS4tMzWgcwudMrKb3spdoyum5urIgR3VM6QnyRPTl0uS7v8k2EX9gz8eohSPUd8ODTtcDGhMc/96tJZv2q3CklIZY7Rx516VvuqRryhk6LW1LnE7+JRqb3d23nYd/9BXOmN097IeFLmrt8sY6f0rDlFxiVV6qkc5P2/UHe8tlOSGiw3r3kolpVYZqR71bJtVYbuvXHygNuzcW7dfugrH/Xu6ikpcUfmPrjxUHmPUuWWG0lOCvX/m/vVordi8W2n+ZUfd7+ZTmbFiq25/Z77Wh7TxsS+WqdRXg4I4EXw4f71Wby1QVppXu4tK1b9jcx3Sr+ZJ6sf9/9sk6X+XHazCklJlpnmV6vWouNQnI6MUr9GDny7WO/4eRJL7/1dSapWe4lHvduXjsvjvkzVv7U41Cxtet6eoVF1bZaplZmqV7bru6P46Zf9u8vkLB1W39+VPfz5Ka7YXqE1WWpXF7wPG9++g728+Qjv3Fqu41KeebSr+nYU6bN/2mnbteB12T44k6db/zVNalJ5gT329ouz+7f7kYFNxzo4idWyVpjRJ2uEvCdtAPYc+mLteM/29bXu2baazQhLnlZm7Zofe/HFNheVej9GvD+ipHm2bRXhViMOul8b8Tvrsb9LWpVKr6l2kSCTvz1mn/p2aq0/7xOjBjKaF5BASkydF8hVHfu7H59ztvkdL7ZvwkK3xN0qLP3K1h4B4FTqsrGC7u59V/71yTnnka0nuynaq18hjjE7ev6uumJorSfp4/gZlRagbMm+t66J++qhu2q9Tgl1hBSRlp6doSLfg0IsVm3erxHrlKQn5jty2wt3WYIjG8Q+5ejNTv19dbt8a16dtuX2pZWZqWXJo0uBOap5ReSKhZWZqtZINdfHIr/fX+c/M1D9OHRq1dlh2eooGd438ebw4Y5XCU0EvzlhVpzbtLiotd/vzhl1avW1PlYWUKxMa93B/PKJfWXLo+kn7Vfr/L9Xr0fDurerQEldDrW8thuO2bJaqls1q/vfQvnm62jevXjLJGFMuUfnarIhz7UhSuSRgXWNen6ykw22R0lJ2q6Mkbf7ZPdGsbYO839/ema8NO/fKY4yKSn06cUTXKvdtySVS35m9Vs1Sy38f7y4qVYrX6NqjI0+CUMbjcccQo38n/fSStN8xdfk14tIl/t7OTa7nGhICySEkJm9qxWKbkivO98U9UlZ76eArG79dNTH+BvcTotRn9YepP2rd9gIN695Kf/nFoBg1DpvzCzXq9k8kSfv3aFXpup1aZujBM0aU1aS498Ofy7qvS9KIHq2qdQIQqKFQ2fuF1ll449IDNaJHsGbLbf+brye/Wq5WzVL15LmjdfJ/vi57bukdx8jr70a/ZGO+jrhvWqVtOWJAB/3n1yPLrqpL7mDxiZCr1redMEhmRp7OktywssBMZRkVC9TW1o+rtulO/4lnwHVH76e/v7dAvW8sX/R23m2Tyj1mmlcko6z0FO2QVymh35FL/UWEu1de9PzHVdt0Usj/jYDwfStUxxYZTe4kZuKAjjVuU0P/Dr1ueLfCsrm3Ht1gBZL7dmje5OISa/H8eRSWlOq7v94uW+LvNV/kr7vZqfIJA2pjS36h1mwv0EWH9lH/Ts111cs/acitH5U9v/zOY2Qi1C6z1up/P63V6F6t9crF5WcsHf33T/Tw50t10WH7qIU/ybRw/U5N+teXkqRldxxTfl/oNFj60/p6/91CvTorT/d/vEg3HTNAxw7tXOX6Q/7yYVlNreHdW8ljpFVbC7Q5v1D792hVdnz28kXj9Oy3K5W7eptWby3QMUM6KSPVW65OWFXHlZJ08n9cgj68vlZaikeDu7TQzp0FemD+V/px1XYduE9bvfC7sVG3NeWLpbr7g5/LJT+vn7SfLhm/T5XtQGJpGtXzgPrmSa04Ta8kbfDXDug2unHbU0/W7SjQu7PXaf66nXr2m5WyNZnXFfUqtHZCs7QUZaVH/tlTVKr35qzX2u3BIQihiSFJ2pJfFPX1oT8B1VlHUoWTuCe/comb7XuKyyWGJJUbIvGLf0+v8vf/ZMFGrdpavuh7aGJIclPBL9/i364tdclZSepQxZXBGvhs4UZ9v3KrAseM950+TBP2a19hvVt/UfcZmIBEkJ2eohJ55QvtORQYgtJ1ZKWvfTlk5rmA1y4ZV5/NS1pHDypfw+rE4V3ie+YsNKr0FK98nhT5SsNrDtX/MOmf8rZLknq3y9K4fdrqiAHl/3a37I48W/COAvc/p01WWoXnxvRqI0mat2Zn2bIHPllcdn9zfuMX/7/mlZ+i1tmKJLTY+uqte5SVnlLW7tCC9ac/9o3+99Nard5aIEl6b876CgXkqzrOa5Hh7mekVuwRXVTiU1Z6itK9UmGxGz779dItlbb9jvcWVhgae/cHC6OsjURGzyEkJm+UYWV7t7vbQ69p1ObUl+tfc9Mmj+7VRl8u3qw+N72n34/vq2uOrjg8buTfPtZvD+6t00Z205H3f6Gde93nEcgnxesVstCrq9Em1YmUM6vDBDwR3kCyCiZCnrvggKirTlu0Sec8OUOH3vN51DY8eOaIanXZD/zuz54f/f3Crz73vrHi1ehIDrrrs7L2VTfneMR9X1T5uZYGrkH4fFLxHik1q8qhkic8/JV+8tcuqYq1bvjA9zcfEXWdeP1bBxpCRqpHpfIETyIl18sgo5XUrE25dZ/5eoX++r95ZUOoQv83nD6qm/5xKpPS1pfHzhoV6yYg3hmv9hb5kyiF+e62Gsmhg+/+THnbCsoe33xAhsZHWO/O9xdoyhfLyv4PHNyvnTq3zNTj54wqd+wx6vZPIn5/B1537NCKkw1ceWQ/vTtnnc7877cRj0XG3PGpjJFSPEYP/2p/HTWoU9lzkx/4UgvW7dRnVx+mv7+7QD5r9eS5o8v1Hg5sMz3Fo1+O6q5n/DMqhj5XmeoeSwX8+1cjdOA+7XTcv7/U3DU79fR5ozXwzx9W+/WVHeeF8vms+txUvpd055YZevb8A5STk6NuA0eU9QSv6e9Q29c0pMDfhDHRj1Vrcsx3+D9zdNi+7RmJEYLkEBKTJyXysLJ8/+wnGa1qvekVm3erQ4v0ep1md9vuIs1es0PZ6V55PR6t2VagAZ3LF5ubk7dDKzbvkSTddcpQvTYrTy99v1pfLt6kIwaWv2pT6vNpy+4i3fPhz2qRmaodBcU6dWQ3dWmZoQc/c71Wcldvr7f2h9q8q1Art+7RYfu2r3Z9gR0Fxfp5/S5Za9UmK01rd+yNWHMi/KrG5RMiFxMP/I7VWbc2VqxcqZ49e+qRnKVVdrk9oHcb3TB5P+0JuaL0w6rtmr7EzeDTp31WtWs5TL1wbMQrbqFeuOAA/erx7yRJvxnbQ22aBddfuH6XPpq/QcO6tdQBfdpqin9Gm3bZafrVmOBMLj7rejf17ZCtod1a6qJD99H9Hy/S6m17NG/tTv3qgB7q2aaZdheWL/o+e80O5fy8Sa2bpWrbHvc358sNKUidv1FKLT81+t7iUv24anvZ1NCS9JP/b/OiQ/dRmrfqo7ah3VpVWPbdTRN1wB2f6sM/Hlrl64FkYoyRz6TIlksO5Zc7iSwp9WnBul36/OeNapGZqrPHBou+Lly/Syu37NHfThzcmM0GUAWf8SpFrqeICndKqc3KzcY7J2+HSiOcUYcmhiTpmXmFGj1qe4X1pi/erG6tM3XS8K5q3yJDXVsFv89f8c/g9/y3qzS2T5sKrw1IT/Xq8P06VFi+T/ts/eUXA7UtpNdR4FjkgN5tdEBvt82Hc5bqkwUb1KFFRtl6C9a53kaPTVumTxdulCT9sGpbue1fPqGv9hSV6vHpy/XijNUVnotk5dY9+mHVNg3r1kp92lVd//ODeeu1aEO+mqV5Na6Pq/X05Dmj9ePq7WqWlqJ7TxumG1+frd+M7alpP29S84wU/ZS3QyN6tFJGilffLHO9e+48eUiV7xXg8Rj97YRBuuWtefrdIb01pndbDQmplda3Q7bG9GqjwlKfDqukwP3ctTs1Z80OFRSVqrCkVMWlVscM6aS+TajotZX0b//x/eUT+pYd04aryfnNsk27tWzTbp0wvGu55TsKiiOeh7TNSlP3NlUUTY9zJIeQmDypkXsOLctxt7UsiLtuR4HG35ujift10BPn1t/QtEPv+Vy79lacXS2Q/Z61cqtOeeQbSdJFh/VR11aZ+sPEflqyMV9v/7RWJz78VdRt3/LmXEnSHw7vpx5tm5UlTip7TX34m6TZtx5VNna8MsP++lGV64S74ODeuuqoyAXFIyWHoq1bGzk56zR+fH9dXY1tZqR6dfFh9TNme2yfqgtLHti3XbWvmtxUydTL4b3RHj2r8uEm0by+ra20Vm6Y5+ZFUlgZ1/1u+SDqa6+f1D9i3YLqaIp1ToCmwnpSZEMvoBTll+vR9/x3q/SXt90w7LF92tTr/08ADSMrM0OewpBhZSEJ3/fnrCsrZFyVvHwb9RjxlP27Rfx/MNo/LCz8JLu6jDE676DeFZaHH4u8/uMavTwzTy/PrFg0/KWQYa+BY+aAq47qr6ISn579dqUK/TMVhj5XHyJtp0OLDB3t7+V06shuOnVkN/dEPU5EfNa4XpXOFvfyxYkz9Pffny3RyJ6tddVR/TV3zY6IyaHanN9U9zVnjulRo+RdPCI5FGdm+8f5/vOjRVq5ZbfOObBX1HXX7dirJ6cvV4nPqkPzdI3bp63en7teN06ufb2P7XuKdcLwLuraOlPTF2/WxLBxxk2GNzXyVPYbF0qZrWs0I0uodTtc/ZRPF27Uk9OX67cHV/wiq46NO/fq1Ee/0dQLx6pLq8xyiaE7Tx6iG1+fI8mNWW7fPF1XvvRT2fOXhVzhuPX4QTppROQv4k35hWqVmapUr0ctm6WWTQ/65XUT9MOqbdVK2tTGeU9/X3Z/yrRlapvteq5s31OsvG0FGty1ejNC3XTMfuoXYUrxlVt2y2dV6d/+Vzccrq8Wb9bwHq2Ut22PxvWp/9mxUD1eb2C2slIpJUNqHuxOvm5H8GrlQ78aoSx/b7yC4lK1yUqrdWIIQBU8XskXNqzMnxz6fOFG/eXteUpP8ejR34zUgM7M4gfEBU+qjA1JDqW5Xh+lPls2c6ckPRV2cTO/sERdWmVqn/ZZeuqrFTLbVmnY0MiFrOs6a11dPXv+AVqxuXy9wz1Fpfp5wy6N6N5KO/cWy2etWmWmacnGfOXmbdcdJ7qT+bQUj9667CCt275XfTtk68mvltfbxTs0ji+vm1B2XjG4a0t9fOWhat88Xa/MzFOzdK9SPEYdmmdUsZWgzfmFykzzlh1/StLTX6/QtEWbdPSgjjpjdI9y63duVf1txyuSQ3EmMHVswF//N79ar9u4q1Bv5a6t0WuieeDTxbpk/D56JGepXvzdWI3bp2GmyawTT0r5A9+AHaulFrW7qiEFi+lJ0m3vzNeRAzvWqnvhCQ9/pXU79urAuz5T7p+PLPfcSSO6liWHnv9ulYZ3b6VVW91wsiuP2LfcVKFtstI0IUL33Mp0b9Os0bpEhhdelqTXqnfhSqeP6q5WzSofQhVN11aZOn10d0mKOk0xGoc3xf/36it1vRNaBYenjLvzs7L7xwzuTPFVoJFYT6pslORQIMHfrXVmjb9fAMSO8abIE5iMJWSo6MwVW1VU6nrL3HbCoEr36yuP3Fc5OWs1vonu+73bZal3hCFex6ribGKRfs/9OrXQfp1cwps6M/En/Pyln/8Y/3eH9qm392iekaJpizbp8sP7aXDX2nUmiGckh2LI57M65B+fq3ubTLWvIsu5ccNevbbuxwrLzxnXU1ceuW/E1wy/7eOIyx87a2TZ2N2a+GTBRl3ziuvBEpip6U9vztHALk1vxzl202Yd7SsuPz14wXb3ZTnopBpv76JnZ2rh+l1ql51ebvmf3pyrFv4xqf/7ySXffjGsYqG9gEAcAz2QJOnql91n+uCZIzRpUCelpXi04q5jNeyvH+mDuev0zVJXm2b69RPUrXXTH+caGMqzp6hERSFddwN/j+HJsIBUr0dej1FRqa/BejWh8QV6DllfqUzhLik9Wxt27tU9H/5cts60a8eTGAIakydF8oXM/lOUr/yMzvrT1OBxRvhU0wCaNo83RR75k0NrZmlNWm9NvvVD9e/kTqD/d9nBGtKt6R2zA03JqF5tkrosAcmhGJq9ZofWbC/Qmu0FVRY627PHp00lOyosP/eg3lF7V1w6fh/9J2S67YDD9m0fcerDqoT2EGqblaYtu4tUVOrTvDUV2xVr/Xbs1SSvz82Q5PEXxA1MY9+s5omxD+e5Ma1eYzSmVxuN6NlKj01bptX+Hj2hBf4q+zwCccxOT1G+f1z48s27NbBzCw3v1kppKcGpLk8d2U2fL9yoXXtLdNi+7dWlZWa0zTZJzdJSFPqnef8vh+mbpVuq7A1Um79NNF1er/uaKS4pUVrRbiktW58v3KhXZwXrBYQWtQTQ8IwnRaakfM+h1SVGby5cW7aodTOS9EA8Md4Uea0/OeRN1+atW7WzqERb8os0pncb9evYdIoLA2iaSA41ssBUj51aZKgkZOalz64ZX+nrcnJyNH585euEu27SfrpuUu3rC4Xr2iozbjKpL/7zDWmXXFFqj7+3T7FL5KjL/hFfY63VhHtztGLLHnVrHflkNTRON04OFvMtLClV/z99UGGdcDWJ4y3HDdQtxw2s1rrx4KQR3XTSiG6xbgYamfEEew6pKF/fry3UnbMWSpIW/m0SyUAgBow3RSZkWJkt2q0ftxerbVaaZt0SuXcngCbOpCjF33PIV1Ko2T53DFnVOQYABJAcakDWWm3bU1xh+m1JapmZqkFdW+iNH9do6u/GxqB1iS0lxd87xVciyZ8cKsp3t2nlh2YVl/q0fU+x8gtLtGKLSyAN7NxC2RnB3WPb7vW6oZKZndJTvDp2SOeyrrsAHI8/OVRaXCiV7NWczT41z0jRhYf2ITEExIjxproi8X62MF+7lcF3GBDHfJ4UeQPJoaI92qs07d+jVWwbBSCukBxqQL1vfC/qc3efOlTDu7fSfacPb7wGJZHUVH93+NCpete7Is+hU3tK0q8f/04zlm8tt+zOk4eobWh9odOrfs+Hfx25RxKQ1PzJoV3bt6iZpLzdXh0+poN+HzLrHoBG5vHK4yvRtt1FapXplad4t/YoQ2eN7Vn1awE0TSZFXvn09ZLNGlNSqEKlcmwKoEZIDjWQ5SHTLP7txMFl939cuU2rt+2J+VSQiS41zZ/YCZ2NxeNPGLXoqm27i/T+3PVqk5WqBWt3alyftjpmaGcVl/i0T4fs8okhALVm/AWp5yxbrY6ShvbporHjSQwBsZSVmSmPSrV0U77apZWol6Ts5i2ZnQyIY9bjlVelypm/Vgcanw4Z0F2d46xeJYDYIjnUQD6Yu77sfuiVOK7KNY5i4xJB+Xv2KDtQ67soX0ptJnm8uvnNXL03JxijiQM6EBugAQRqDr07Y6GOSJNG7dtDnVpWPjsjgIaVlZmuIuPTqY9+o5P6puh+SWP368lQTyCOWU+KUlWqz+et1k2ShvTsGOsmAYgzJIcayKkju2lEj1bq24GZAWKhS+vm0mppx67dym7vX1iUL6VlK2/bnnKJofevOET7dqTOAtAQAjWHWhhXz6tbx/aVrQ6gEXhTUpUq17N22Vo3G+fAXp1j2SQAdeVJkcdYbd+xXcqQPGn0GgJQMySHGkj75ulq35yhSbGSmem+EHcVFAQXFu2W0rL08szgFNr3nDpUAzq3aOzmAUnDeNzXTHP5ZwtMI2EOxFp2s0ztkk9pKR733ZgiGfZNIL75v2+zjP/YN4XzEAA1Q3IICSkz0w1byV2+UfsN8i8s2q2tJWl66ftVapedppl/YrpeoKF5vOV7DimdE1Ag1tJT01Tk8al9ZrpSd+xxR4NpWVW+DkAT5u+pm6W97nFqs0pWBoCKPLFuANAQurZ1vYG27woWBldRvtYXeFVU4tNZY3vFpmFAkgnUHGoh/75I7wQg9jwpSlGp1mwvUJYpdMvYN4G4ZgM9dQM9h1IZVgagZkgOISFlZrgvxIK9e8uW5e/cro2FqZrQv4OuOKJfrJoGJBXrTw41NwwrA5oMb4q8KpUkZcl/IknPISCubSvwSZKyA/t0CpM/AKgZkkNITF43W9m2XXu0fU+RrLXanb9Du5WuXx3QI8aNA5JHszS3L7YoqznECSgQc54Ueaw/OWT8F1HYN4G4tnaXKzJflvCl5xCAGiI5hMTkTZMkLV6/Tac9+o3eyl2rkr352msyNbJn6xg3Dkge2ZmuIGawmzs1EICY8w8rk6yyxLAyIBF0bu32YYaVAagtkkNITB7XW2Fcz+ZavDFfL8xYpSzt1WGDe8sYE+PGAckjJcXVQMhQkaw3XfLwtQPEnL82iVc+tU4tcsvoOQTEtaOGdJMkXTy2g1uQQnIIQM1wlI7E5B9WNrCDG289Y/kWZWmv2rVpE8tWAUknkBzKVGFZjz4AMeZPDqWoVK28RZLxMu01EOdSUtyxb7dmbsgoPYcA1BTJISQm/0nowX1aSpLSVKJUU8qVUaCReb3+nkOmSMaftAUQYyHJoRbeIik9W6JXLRDf/Pu1Cne5W5JDAGqI5BASk/8kNN24qyfNFCi4SU0FoDF5Q4aV0TMBaCLKhpWVqrmnkO9GIBGQHAJQRySHkJj8ySHjK9aYXm2UJWZjAWIhtSw5VFy2XwKIMf++mCKfSgp28d0IJIJAcqjInxyi5hCAGiI5hMTk9fdQKCnUtZP665TB/hnKOAAGGpU3JdCLr5iaQ0BT4fFKcsPK0n17+G4EEkFZcmi3qyPmH9YNANXFfw0kplRXiFolezW6VxuN9naRlkhKbx7TZgHJJiXFG3xAcghoGkJqDjUzhVJquxg3CECdhSaHGMYNoBboOYTElBJMDkmSivLdLVdHgUaVmhIylIxhZUDT4HH7oteUKk0lnEgCiSDQU6hoDxdjANQKySEkpsCwsuJAcmi3uyU5BDSqwFT2koL7JYDY8vcwSFWpUlXCiSSQCAI9h4rpOQSgdkgOITF5PO5EtCQ8OcSMLEBj8nhCvmY4AQWaBn/NIa98/uQQVQaAuFeWHCrg+xZArZAcQuJKyQhJDvlnbiA5BDQq4wntOcSwMqBJKJutrFTtMg0nkkAi8DCsDEDdkBxC4krNcFdPJIaVAbFiKEgNNDn+k8j3Lx+ntiSHgMTg7xGoonyGlQGolSqTQ8aYJ40xG40xc0OW3WqMWWOMyfX/HBPltZOMMT8bY5YYY26oz4YDVUrNlIr3uPuB5FBqs9i1B0hGnpDkUAonoECTEOjFV1IklRYHexwAiF/+QvOypSR8AdRKdXoOPS1pUoTl91trh/t/3gt/0hjjlfSwpMmSBko60xgzsC6NBWokvblU6J+lrGi3Swx56CwHNCpDzSGgyQkUhy8tlEqL2DeBRBCa5KXnEIBaqPJM2Vr7haSttdj2GElLrLXLrLVFkqZKOqEW2wFqJ72FVOivNVS8h15DQCx4GFYGNDkpGe62pEgqZbYyICGUq/HHPg2g5urSjeIyY8xs/7Cz1hGe7yppdcjjPP8yoHGkZQcLURfvDR4MA2g85WoOUZAaaBICQzxL9vp7DrFvAnGv3DBueg4BqLnaDjJ/RNLfJFn/7T8l/TZsHRPhdTbaBo0xF0q6UJI6duyonJycWjYtMeXn5/OZ1NCAHQVqvmujZuTkaODaVcou9mlGjD9D4pgYiGP1GV+xDvPfX7N+kxY3oc+NOMY/Ylg7zXav1hhJ8+b8qIElhVqVt1bLY/g5EsfEQBxjK33vJo3z39+8bafm1jIWxDExEMf4F4sY1io5ZK3dELhvjPmvpHcirJYnqXvI426S1layzSmSpkjSqFGj7Pjx42vTtISVk5MjPpMa2vWmtHCB+9zWPSZ52sb8MySOiYE41kBpifSFu9u1Rx91bUKfG3GMf8SwlratkL6XBvXtJc33qWeffuoZw8+ROCYG4hhju9ZL37q77Tp2qXUsiGNiII7xLxYxrNWwMmNM55CHJ0maG2G17yX1M8b0NsakSTpD0tu1eT+gVkILUpfspYstEAsehpUBTU6gIHXBNneblhW7tgCoHxSkBlB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+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot des ersten Zeitraumes beider temp sensoren\n",
+ "df1_1.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature'], figsize=(20,8), grid=True)\n",
+ "plt.ylim(0, 22)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 22.0)"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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kSZpiNg5JkiRJkiRNMRuHJEmSJEmSptj/AEpws+RIJ+QiAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot der zugehörigen Außentemp\n",
+ "out1.plot.line(x='time', y = ['temp'], figsize=(20,8), grid=True)\n",
+ "plt.ylim(0, 22)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 80.0)"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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vBmDnzp0sXLiQTZs20aNHD7777jteffVVLr/8cn7++WdGjBgBQHBwMCtXruSLL77gwQcfZNasWWUed/Xq1fzxxx/4+/uTkZHBvHnz8PPzY/v27VxzzTWsWrWKl19+mddff92RMfX555+X+hpOnjxJu3bt+Pe//01CQgKvvPIKS5cuxdvbm3vuuYfJkydz4403ntb7VBoFfkRERERERETc3JQpU3jwwQcBK9AzZcoU7r33XgAGDx7Mk08+SUREBFdffbXL/b/66itWrVrF4sWLHesaNmzIunXrOHjwICNGjGDUqFF4eJTsWGQYxmmVff78+WzatMmxnJqaSlpaGgBDhgzB29ubmJgY8vPzGTx4MAAxMTHs2bPHsc8111zj+P+hhx4q97jDhg3D398fgNzcXO677z7i4+Px9PRk27ZtlX4Nnp6ejBw5koyMDBYsWMDq1avp0qULAJmZmYSHh1f6mBWlwI+IiIiIiIjIuVCBzJzSZKalERQUdEr7Jicn89tvv7FhwwYMwyA/Px/DMLjnnnsAq9tWp06deOONN9i4cSM//fST0/7z58/nxRdfZPHixY7uXUXVq1eP6OholixZwsiRIzlx4gR5eXl4eXmRkJBAvXr1TqncdjabjeXLlzsCMUXZy+Ph4YG3t7cjyOTh4eE0Pk/R4JP977KOW6NGDcffEyZMICIigr///hubzYafn5/Lcnp5eTm6vAFkZWU5/vbz88PT0xOwusONGTOG//3vf+W/+DNAY/yIiIiIiIiIuLHp06dz4403snfvXvbs2cP+/ftp0qSJY7wegEceeYRXXnmFWrVqOe27du1a7rzzTmbOnOmUlZKQkEBmZiYAx48fZ+nSpbRq1QrDMOjfvz/Tp08HYNKkSQwfPvy0yj9w4EDeffddx3J8fHylj/HNN984/u/Ro0eljpuSkkJkZCQeHh58+eWXjvF4goKCHBlCYI0lFB8fj81mY//+/axcudLl8QYMGMD06dM5cuQIAMeOHXPMlnY2KPAjIiIiIiIi4samTJnC5Zdf7rRu5MiRvPTSS47l6OhoxowZU2Lff//736Snp3PllVcSFxfHsGHDANi8eTPdunWjffv2XHDBBYwdO5aYmBgAXnnlFcaPH0/z5s1JTk7m1ltvBeDw4cM0aNCA8ePH88ILL9CgQQNSU1MBuOSSS1yOKwTw9ttvs2rVKmJjY2nbtq3T1PEVlZ2dTbdu3XjrrbeYMGFCpY57zz33MGnSJLp37862bdsc2UCxsbF4eXnRvn17JkyYQK9evWjSpAkxMTGMHTuWjh07ujxe27ZteeGFFxg4cCCxsbFcfPHFHDp0qNKvqaIM0zTP2sGL69y5s2kfKVwKLVq0iH79+lV1MeQ0qA7dg+rRPage3YPq0T2oHqs/1aF7UD1Wrc2bN9OmTZvTPk7aaXT1+qeLiopi1apV1K5du6qLckbq0dVnyjCM1aZpdna1vTJ+RERERERERETclAZ3FhERERERERG3VXR2r38iZfyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIjIP8APP/yAYRhs2bIFsAY9NgyDJ5980rFNUlIS3t7e3HfffQCMHz+etm3bEhsby4ABA9i7dy8Ae/fupVOnTsTFxREdHc0HH3zgOMbu3bvp1q0bLVq04OqrryYnJweALVu20KNHD3x9fXn99ddLLWe/fv1YtWqVY3nPnj20a9fujLwHq1at4v7773f5WFRUFElJSQD07NnT8dxff/31GXnuqqLAj4iIiIiIiMg/wJQpU+jduzdTp051rGvatCmzZs1yLH/77bdER0c7ljt06MCqVatYt24do0aN4tFHHwUgMjKSZcuWER8fz4oVK3j55Zc5ePAgAI899hgPPfQQ27dvJzQ0lE8++QSAsLAw3n77bcaOHXsuXq5LnTt35u233y53u2XLlgEK/IiIiIiIiIhINZCens7SpUv55JNPnAI//v7+tGnTxpFh880333DVVVc5Hu/fvz8BAQEAdO/enYSEBAB8fHzw9fUFIDs7G5vNBoBpmvz222+MGjUKgDFjxjBjxgwAwsPD6dKlC97e3qf8Oj7//HNHNhLApZdeyqJFiwAIDAzkscceo1OnTlx00UWsXLmSfv360bRpU2bOnAnAokWLuPTSSwFITk5m4MCBdOjQgTvvvBPTNB3HDQwMBGDcuHEsWbKEuLg4JkyYQJ8+fYiPj3ds16tXL9atW3fKr+dc8KrqAoiIiIiIiIj8E7yy8hW2HNtySvvm5+fj6elZYn3rsNY81vWxcvefMWMGgwcPpmXLloSFhbFmzRrCwsIAGD16NFOnTqVu3bp4enpSr149R/ZOUZ988glDhgxxLO/fv5+hQ4eyY8cOXnvtNerVq0dSUhIhISF4eVnhhgYNGnDgwIFKv97rrrsOf39/AHJycvDwKD9v5eTJk/Tr149XXnmFyy+/nCeeeIJ58+axadMmxowZw7Bhw5y2f/bZZ+nduzdPPfUUP//8MxMnTixxzJdffpnXX3/dkRUVFhbG559/zptvvsm2bdvIzs4mNja20q/vXFLGj4iIiIiIiIibmzJlCqNHjwasQM+UKVMcjw0ePJh58+YxZcoUrr76apf7f/XVV6xatYp///vfjnUNGzZk3bp17Nixg0mTJpGYmOiUNWNnGEalyzt58mTi4+OJj49n9uzZFdrHx8eHwYMHAxATE8MFF1yAt7c3MTEx7Nmzp8T2v//+O9dffz0AQ4cOJTQ0tNznuPLKK5k1axa5ubl8+umn3HTTTRV+TVVFGT8iIiIiIiIi50BFMnNKk5aWRlBQ0Cntm5yczG+//caGDRswDIP8/HwMw+Cee+4BrIBJp06deOONN9i4cSM//fST0/7z58/nxRdfZPHixY7uXUXVq1eP6OholixZwsiRIzlx4gR5eXl4eXmRkJBAvXr1Tqncrnh5eTm6lQFkZWU5/vb29nYEmTw8PBxl9fDwIC8vz+XxKhuUCggI4OKLL+bHH39k2rRpToNQn6/KzfgxDKOVYRjxRf6lGobxoGEYYYZhzDMMY3vB/+WHxkTOsP+L/z8W719c1cUQERERERE5b02fPp0bb7yRvXv3smfPHvbv30+TJk0c4/UAPPLII7zyyivUqlXLad+1a9dy5513MnPmTMLDwx3rExISyMzMBOD48eMsXbqUVq1aYRgG/fv3Z/r06QBMmjSJ4cOHn7HXEhUVRXx8PDabjf3797Ny5cpTPlbfvn2ZPHkyAHPmzOH48eMltgkKCiItLc1p3W233cb9999Ply5dHN3lzmflBn5M09xqmmacaZpxQCcgA/gBGAcsME2zBbCgYFnknHr/7/e577f7yt9QRERERETkH2rKlClcfvnlTutGjhzJSy+95FiOjo5mzJgxJfb997//TXp6OldeeSVxcXGOcXI2b95Mt27daN++PRdccAFjx44lJiYGgFdeeYXx48fTvHlzkpOTufXWWwE4fPgwDRo0YPz48bzwwgs0aNCA1NRUAC655BKX4woV16tXL5o0aUJMTAxjx46lY8eOp/amAE8//TS///47HTt2ZO7cuTRq1KjENrGxsXh5edG+fXsmTJgAQKdOnQgODubmm28+5ec+lwxX/e9K3dgwBgJPm6bZyzCMrUA/0zQPGYYRCSwyTbNVWft37tzZrA5pUOfaokWL6NevX1UXo9oxTZPYL6xBtNaPWV+lZVEdugfVo3tQPboH1aN7UD1Wf6pD96B6rFqbN2+mTZs2p32c0+nqJWfWwYMH6devH1u2bKnQoNNFnYl6dPWZMgxjtWmanV1tX9nBnUcD9hGgIkzTPARQ8H94qXvJeSU3P5cvN33pctCt85lpmjy97GkW7V8EQK4tt0rLIyIiIiIiIv8sX3zxBd26dePFF1+sdNCnqlQ448cwDB/gIBBtmmaiYRgnTNMMKfL4cdM0S4zzYxjGHcAdABEREZ2mTp16RgruTtLT0wkMDDwnz5WYm8gLB19wWtcjsAejw0bjYZy/H9q/0v/ii+QvHMvvNH6H9Px0Hk943LFclc5lHcrZo3p0D6pH96B6dA+qx+pPdegeVI9Vq2bNmjRv3vy0j1PadO5SvZyJetyxYwcpKSlO6/r3719qxk9lZvUaAqwxTTOxYDnRMIzIIl29jrjayTTNicBEsLp6KcWwpMqmXi7ct5Aa3jXw9fLl+tnW1HP3xd1HqF8oXet2JapmFFO3TGXb8W18u+3bco+3PH05y9OX83SPpxnebDjent6n+lJOyR8H/uDu+XeXWD+w8UDm7p3rcp9+/fqRkJYABWORXXDBBac0ReCZovRZ96B6dA+qR/egenQPqsfqT3XoHlSPVWvz5s1npIuWunq5hzNRj35+fnTo0KHC21cm8HMNhd28AGYCY4CXC/7/sRLHkiIO5hzkom8vIjEjEU/Dk5vb3czfR/8mxDeEY1nHWJ24utxjvBv/bqWe870B73Eo/RAvrCjM/nl2+bMczTzK3e1LBmEqYuuxrUzfNp3j2ccJ8Q0h/kg8W49vdTxet0ZdetXrxcrDKzmZe5KY2jEsTih9Ri5XQZ+BjQey5sgaADYlb3KsP5l7kkAftWKInCs/7viRUL9Q+jboW9VFERERETnvmaZZpQ3V4j5OZciWCgV+DMMIAC4G7iyy+mVgmmEYtwL7gCsr/ezV1L7UfZzMPcnulN28s/YdEtITXG73n27/YXSr0U5f8MMnD3Pb3NuoW6MuKw6tKLFPvpnPx+s/PmNl7RHZg2YhzRjVchS1/GpRw7sGgCOr5+rWVxMzKcax/ayds5wCP4v3L+bVv17l0MlDJcbUubTppczaNYvBUYP5Zc8v5Zbl8MnDfLf9u8JjFwv6+Hr6MvmSyaxOXM3/Vv7Psb51WGs+HvgxNX1r8vzy57GZNgDm75vv2OZ49nEFfkTOMNM0WbR/EX0b9MXTozAd9bnlzzmyCat6YHURERGR852fnx/JycnUqlVLwR85LaZpkpycjJ+fX6X2q1DgxzTNDKBWsXXJwIBKPZsbSMlOYegPQyu07UsrXuKlFS8R5FOYxpWWkwbA3tS9Lvd5rudz9K7fm6tmXUVSZhJta7XFNE0GRg0kpnYMHcI74O3hjWEY5Obn4uXhhWEY5NnyMDDwMDywmTbyzXzHdpWxL22fUyCoLLN2zQIoEfRpFdqKQVGD2JWyi2CfYL7e8jW3tLuFDUkbqOVfizm75wDQr2E/HuvyGGF+Yfh6+jpuLFuFteLaNte6fE7DMDBNk41JGx3HgcL3VURO37fbvuW55c9VaNstx7bQOqz1WS6RiIiISPXVoEEDEhISOHr06GkdJysrq9I3/HL+Od169PPzo0GDBpXapzJdvQTw9vCme2R3/jz0Z4nHxrQdw6RNk+jfsD+RNSJJzEikbo26TttM3jzZaXlo06FcmHshF/e/GMARqFl41cLyy1JkLB4vj8Kq9DQ88eTMD/p1Y9sbeaDjA9hMG1l5WUzaNImP13/MyBYjeajTQwT5BGFglAg2Pd7tcaflV/u+espl8DA8yDfzef7P553WH0w/SNtabU/5uO5sY9JG/L38aRrStNRtxq8ez2cbPnNat/aGtU6fK3EtKy+L77Z/R6vQVnSu63IstVKlZKfQe2pvHur0ELe0u6XC++Xm53L3grtZcWgFC65cQHhA4aSKpmkyZ/ccLm588SmN1/X88ueZtm1ahbf3NDTAoMjZsPWY1VW6VVirKi6JiIicLm9vb5o0aXLax1m0aFGlxnWR81NV1KPu6iopwDuAjwZ+BLjupzm2y9gy9x/XdVyJ/RYtWnTepPytvn41X2z6gpEtRhLqF1pqX1Q/Lz8e6PgAD3R84JyWz8PwwDRNDp08BMD/XfR/3D3/bo5kuBxb/B9rxo4Z/HX4L45nHWfJgSVAYZecrce28s7ad2ga0pSkjCR+2vWTy2N0+NI6GV3X5jrGdR13bgp+jq1OXE2ob2iZQbHSzN41m8eWPFbq47X9a5OUmeS0bt6oeSRnJnPzrzeTmZfpWD9h9QQ6hnckLjyu1OPl5Ofw3PLn+HGn83BqA761Ei//uu4vnlr2lCMTrqyyhfmFcSzrGP5e/k7lKO7Wdrdyf8f7+d+K/zF161TC/cN5Z8A7tAlrw9y9cxm7eCwG58e5Syyzd83mgoYXOLr1SqHDJw8zetZokrOSAejboC/vXviuy9+4rce2kpmXWeZ38mw6kXWCUT+NAqzvtp+XWndFRETk1CnwcxpONVhzvgR5XPHx9OG2mNscy+dbWT0MDzLzMknLTePeuHvpFNEJoMyb13+a0rrqFV/vamDtf3X4F23C2nDPgnsc6yZvnsxjXR4j38xnwb4FjF1cdnAT4KqWV3Fj9I00CGyAYRguM8FOl820nfJxp26ZyosrXnRad3Wrq/E/6U9fsy8GBvlmviOo8Z8//sOve34l38yv8HMUD/oAXDz94lK3v2HODSXWXdDggjIHQC+qy+QuFS7bsaxjQOnfm2HNhvFi78L357/d/8t/u//XaRv7e2PDVuHnldOz5dgWrvzpSlqHteahjg/Ro14PDMPgvfj3+ODvD5y2/W7Yd7QMbVlFJT2/xB+Jd/n9+j3hd2K/iC2xvqZvTVKyrelRq2IMq2lbp/H6qtcdy/bvtr+XPzbTxjsXvkOXul3wNDzPu99oEREROT8p8CPVigce5Jl5AEQERODn6YeXh5fG+MHKQGv/RXvH8tWtrmZc13F8vflrXlv1Wpn7/n3j33gYHo7ldTeuw6TweK5ujsoybds0l92Fnuv5HAnpCfx58E+iakbxXM/nnAYNLkuuLZcxc8awPqnkjdi0S6fRMrSly2PZBwNPy0mj99TepR7/m63fAPD5F59XqDx2xVvj8235TFw3kbo16jKs2TDivoxzuZ+H4cHN0TcTUSOCl1a85HKb0oI+T3Z/kqtaXQXAi3++yNStUx2PRQVH8b8+/+Oan68BoHlIcwK8Avh88OfM2DmD3Sm7+XLTlyWOGVkjkuHNh3Nru1srlF1g/7yMnDmS/3b7L6Nbjy53Hynfx+s/5q01bwHW7It9G/TFZtpIzkzmyp+sORS2HNvCnfPvLOswjJw50m0H3s7Nz+WxJY8xb+88xnUdx8gWI/EwPPAwPPA0PDExMU2T2btn858//lPp49uDPmCdN4qO01cZRzKOODLyomtFc22ba7ms6WWlBmvybfk8vuRx5uyZ4/Jxe6D2jnl3OK3vWrcro1uPJrJGJG1rtXU6l4uIiIiAAj9SzRS9oK3pWxPDMAjyDqqywE++LZ+MvAxspo3PN3zOG6vfAOCrS76ifZ325exdMU8ve5rvt3/vtG76ZdNpFdaK7PxsfD19Aeg/rT8m1tR+c0fOJTIwEoDwGtb4L/UD6/PLyPJnXwMcWTofXvwhd84reYPZLbIbUcFRDG82nJg6hZlEebY8Rs0c5bhBOXjyoNN+Ty17yvH3uqR1zNw5E19PX1Zdv6rEc4xfNZ5ViasY2nQoL698uczyXjXrqgq9ruIGRw3mpd4vMWvXLN5Y/YbTDZ8rXoYXUTWjuCn6JgY3Gex474vy9PDk7rjCmfEqcvNdWuBnaNOhzN87n+z8bPy9/Pn96t9LBGX+2/2/XNHiCkxMp3GuXD3vlS2twMGjXR4tt0zlKdrF68UVLyrwUwE208a7a9/lo/UfMXPETJrUbOKYjnPoD0PZn7bfaft7F9xb4WO/3OdlhjYdyrSt00qMgXY25Nvy+Xbbt47MuWtaX8N/ulU+yFJRpmny7PJnnWaGBHh55cvlnh/s/tfnf3Sr2406AXUAWJKwhMSMRJ5d/myp+0zfNp2b291c6bIWD5ZvTN7If//4L//9ozBz7ubom7mz/Z38ceCPEpmUw5sN5+Z2NzPixxGOdVe1vMplQH3l4ZWsPLzSsfzBRR/Qq36vSpVZ5J8gNz8XE9Mx+Ymm1haRfxIFfqRaKfoDHewTDECQz9kL/OTacvEyvBj500i2H9/OpMGT8PXyZWPSRlJzUh0t8wDsK/zz+tnXO8r4x+g/TvnCYvB3gzmQfqDE+lE/jeKhTg8xYfUEvrrkK7LyshzjVnw/7HtH0AcKb9BPZfDrnvV6MvXSqXganhWaucnLw4sZI2Y4rXtpxUtM2TLFKnfLUexO2c3WY1tJz00HIDs/u8yZ5Fxl+IzrOo5rWl/DhNUT+Hzj5xV/QQVWXb/KKWhzeYvLubzF5SxatIh+/fpV+nina/YVs0nOTHY9nkif8vdvU6vNGS9TeXSxXDnFuxcOmzGs0seIrhXNlKFTHO9918ldyczLJCo4iqFNrdkmi54L317zNvd3vP80S+5s2YFlLrONpmyZwrbj2/hs0Gflfjay8rJYeXilU2Drm0u/cXmOyrPlccE3F5Cak3pK5b248cXc3+F+ompGlXisTwPryzWq5Sjn9VP7cCL7BAAfrfuI4c2HM3D6QLLzswnxDWFs57Fc1Pgi/L38HY0RNtOGzbRx66+3subImgqV7bONn/HZxs9KrP9fn/9xadNL2XF8h2Pdre1u5cFODzKu2zjm751PdK1opm2dxqRNk6hboy6HTx52bDtv7zwFfkSKKe06I8Q3hF9H/oq3h7djplwREXekwI9UK0UzfoJ9iwR+cs984OeCby5wjIViN+aXMZU6RmpOKrFfxLrMvsjMy8Tfy7/UfX/a+ZMj6LPi2hX4ePrwxNIn+HnXz4A1IDDAI4seITEjEbBmTGsR2sLpOPauTqea/h9dK/qU9rMb1XKUI/DzdI+nHeszcjPo+01fsvOzK3QcV+/hI50f4ZHOjwDWzc6RjCN8vvFz/tP1P6w4vILa/rW5vPnl1PKvdVqv4WxrGNSQhkENq7oYlVL086SBhMs2beu0EmNKudIxvCOThkxyLK9OXE39wPolZoe0+/PaP1mwbwEXNrzQsW5g1EDeXPMmAB+t/4i48Dj6Nujrcn/TNPlkwye0CWvDXfPvcqzv37A/t8XcRkztGAzDINeWS5+pfTiZe7LM8q9OXE3sF7Hc0/4eboy+scTnYu6euTyy+BGX+14962qn5bva38UHez+Akr0S+WP0H9T0rem0Ls+Wx2t/vcbtsbcTfySe7ce307dBX6JrV/78VcO7hiPwk5abxgXfXOB47ET2CZ5Y+gRPLH2i3OMsvnoxYX5hjuW9qXtZtH8R78e/T0ZeRontP7zoQ3rW7+lYts+qOKzZMB7s9CBgzSw6pMkQwJpMwj6hxKH0QyRmJHLP/Hs0ELRIEaZplpkVeCL7BN2+7uZYnnX5LBoGNVSXSRFxOwr8SLVStHvJ6WT8PL/8eRLSE1h2cBm96vXi/y76P6dWnpTslBJBH1d+vvxnGgU34tVZrzI9ZTrzRs1jzu45vBf/nuPGoagjGUf4dMOnhPqG8m78u7zU+yUua3ZZie1ybbmOsSneG/AeAd4BgNWdw9fT16nrlz3oAzhuCIqq7V8bgKY1Kz9z1ZnQIqQFrcNal5iuPMA7gFXXryIzL5NXVr7iuBHy8/Tjhx0/APDGBW8wMGpghZ7n4sbWwMnXtbkOgP6N+p+plyAuFP0u5uTnVGFJzn/2gZeHNxvO2M5j6fONcxpXm7A2TLusZBce++D1pfEwPByfe7uGQQ1ZP2a9o3X73gX38vvVvxPqFwpYAZLEjETWJK4pdfybhfsXsnD/wjKfe3DUYF674DW+2vQVr/z1itNj7//9Pu///X6Z+3cM71hmZkzxwaqbhzTnjQveKHUGPi8PLx7v9jgAFzW+iIsaX1Tm85fFfr4d1XIUMbVj+GzDZ+xJ3VPh/R/u9DA3Rd9UInOgcXBjxkSPYUx0xRoQompG8eFFH9IxomO520YGRhIZGIlhGI5gv8j5KiM3g9ScVGymjTm75/Dmmje5utXVPNzpYcf3z9U+v+3/jceXPO60fkTzEdzc7mYCvAKo5V+LPFseXoYXeWYex7OOM+i7QU7bvzfgPWr51cLPy483V7/JooRFTo9f+sOlQMUHds/Nz+VwxmHH+GIRARGOoK1dUmYSGbnWNc6xrGO8H/8+yw8t547YO/ho3Ue0CG3Bm/3frHYNQCJVKSc/Bw/DgxPZJwj0DsTLw4ukzCTH8Ai1/GpVeBzRfwoFfqRaKfoFLhr4qeh07rn5uUzePNlpnISlB5fy8fqPuT32dsc6+yDA97S/h2NZx4iqGUXPej0ZNmMYj3d9nGvbXOt03K6BXXn0UmvclNGtRzvGO7l3wb0kniwMzNgH+rT7zx//IS48rsSPvT2r5564e0q01hft+vTBRR84WupXXLvC5WvuUrcLH178IV3rdi3tbTmrDMPg28u+LfVxfy9/nun5jNO653o9d5ZLJaer6E1tri23CktyfjuUfoijmUfpWa8nL/R+AYDY2rGsS1p3RscCK0vfb6xzSJe6Xfjr8F+lbvevDv/inbXvlPp4TO0YvhzypdN5OM9mDbY/pu0YHun8CDf9clO5XZ1Ku6FauG8h9y8s2TVt+TXLCfQJLPOYZ5KPhw9gBaGvaHEFV7S4osQ2NtPGN1u/oV6Netz32310qduFTwd9esbLUjQDqCI8DU8FfsqRa8vFNE18PH2quihuIzkzmX/99i/WJ62ne2R3Hu/2OEHeQY7xtEzTJCkziZTsFC6febnLY3yz9RvHJAshviF0j+xOx4iOpY6BZzdjxwxm7JhRbhmvb3M9j3V9zGndOwMKz3eJJxO5aHphwDhmUgwXNbqIZ3s9S7BPMPm2fPLMPHw9fTmedZy0nDTeXPMm8/bOK/U5X+j1QpnZgRPXTQRg2/FtXPL9JQyOGkyIbwjjuo7TDav8o2TmZfLe2ve4PfZ2lxm9b699m0DvQD5e/zH5tnyCfIIcQ1yUpk/9PjzR/Qn8vfzx8fQhJTuFeoH1zubLOO8p8COE+Ia4zE45H3lgpd56GV6OblJBPkGk56SXu6/NtNHxK9ctp0WzZvJthVN239n+Tqd038rOklM3oC7rj64nObP0k9Ml31/CuK7jHJkqgKNr1I1tbyyx/YhmI3ht1WvcHnM73SO70z2yO90iu5XaSgbWWD0iZ1LRjJ9z4UTWCX7e/TMvr3yZNTeswcvw4mjmUWymjffj3+eKFlfQKLjRKc/AdCbl5ucSfzSe6FrR/LLHGlD9jtjCmZg+vPhD9qbuPaVuSKejaNAnKjjKkcUy7dJpjnGi7OU0TZP3/36f5iHNyczLJK5OnMtxcuyzLNrHxrB3VXtn7TtMXDeRcP9w6gTUYWPyRp7v9Twjmo8otXz9G/V3OsdW1Zhb9tb64q32RXkYHlzT2po573yaPU0ZP64dyzpG4slEsvOzuWHODQDE3xCvm+vTVHTmOrs/D/3J8BnDT+u4J7JP8MueXxznz+K8PbzpWa8nB08eZPvx7eUe77ULXmNw1OAyt4moEVFi3fx985m/b37FCu1CRbqEFmV/vUczj/Jm/zcd6zNyM/Dx9CnznCRyPilrRswVh1Zw29zbXD42aZN1DWFvICt6rVJUeUEfgCUHlpTI+Cs6K+4/kc4gwswRMx0twuc7e5ZBoE+g4+8g7/LH+MnIzXDqww3w+9W/E+IbQuwXsXyz9Rue6G79QN8+z8r8ubv93afdxzuiRgTHs4/Tb1o/l4+PbjWaqVun8vLKl4kIiOCixhdhM23sSdlDdK1ol2On3ND2BqJrR9MxvCOGYfDRwI9Oq4wip+JsjH9gmiZrj6zFxHR8vsGafemeBfc4tuv4ZckArr17IMDDdR+mR34Pl7OunU05+Tlk52fTc0phoLWOv9XiXTSzJ9An8JwFfV7u8zLjloxzLA9oNIAJ/SZgGAaX/3g5O07scHnzaxgG98aVP6uYPdur+DH+1eFf/KvDv06z9FXD0/B0+r86UcaPdR75fOPnjF89nnFdx5GZl+k8EUOB0T+P5pZ2t7jsIi2lM02Tb7d9e1qzB/44/EeWHVxG4+DG9KzXE08PT0zT5Pa5t7PisOvsZbs1N6zB28PbsbwkYQm1/Gthmiajf7ayrRsHN+aLIV9wNOMo9QPrVzhjcMbwGaw8vLLcLKPimtZsymsXvMbnGz7np10/lXi8V/1evHvhu2xI2kBsnVjij8Tj5+VHVHAUAd4BxB+JdwQkARbsW8CRjCPsTd3La3+9xuZjm7mw4YW8dWHJz7G4p/1p+/Hx8HEZkDzfvbLyFb7a/BWtw1ozod8EIgIi8Pa0vrNpOWmlBn2KWpe0DsBl0GdAowH0bdCXY1nH6FO/D/P2zuPDdR8ytvNYRrcezbZj27h29rUl9gN4/s/nef7P55l48UR61Otx6i+ymlLgRwj1C2Vo06H0rX/+B3/sN5tFAyLBvsFk5mWSlZdVYlDLE1knSEhP4Jqfr3GsK+3LnpWXxcnck45W8Wtbuz5pVEZEQNkn7P92/y8Ngxry2qrXeGjRQ6wfs56/Dv9FRl4GV7e62uU+hmGUO/aHyNlWPONnY9LG0wpmbEzeyOhZZ2ZK+PGHxzP+q/Hc3f5u7om7p/wdzoCc/Bw6fVXye3k08ygtQ1ue85bap3s8zb60fVzU+CL67u5L3/p9aVenndNg7fZuWl7GqZfNfoyiN2LVnf21VMdsEMMwMDGruhhV6s01b/LpBqvbXVmD+m45toVHf3+Un3f9zOKExQC83f9tWoe1xtvT2zE+3j/FgfQDDP7OdVZMsGcwqZNKn1nvuZ7PcXkLqwvXiawT7E3by/Wzr8fA+jwObzacbpHdeHb5s1zT+hqahjQtMV6XYRh8POhjx/LvCb/TOLgxjYMbl1lu++x8AFMvnUrL0JaO73DRwdUrollIM+oE1HEEfuzZfJM2TuKX3b+wIXmDY9tvL/uW1mGt2XZ8Gw0CGxDgHcBLfV7ipT6FQaONyRtpEdLC0a3QPnNn8XG74sLjuCP2DvJt+Xyy4ROg5NAAv+3/rVKvRaqPE1knOJJ5hGY1m5Fry6XL5C6OxyYNnkSH8A7n9Wxz6TnpLD24lL8O/+XorgnWOXbI96UH1nvV60XbWm1Zn7Sem6Jvom2ttny77VsOph/ku+3fObZrEdqCDy76gPCAcJfHaRXWivs63OdYjqlT+kzBdnfMuwN/L3/eH/A+H6z7gK51u3J7zO3n9ft8JijwI4DVKlwd2AM/RWfDahTUCLBmTGkV1sqxPiM3o8Qgqr+O/LVE/847Y+/kw3UfcvmPlzsGSq1Xox4hfiGnXd5+Dfs5LT/Z/UlHK9mLva1Zfm6MvpHXVr3m2GZNojU+RrdI5wwlkfNJ8R/HhPSESgd+DqQf4GD6QW759ZbyNwbGdR3ndCMXWyeWx7s+Tr3Aehw+ebjEzFD/9/f/0ad+nwpdBJwu+42j3U8jfuLTDZ/yw44fuKf9uQk+FVV0ivL3BrzncptX+77Kx+s/plFwo1N+HnvGjzt1QbD/zlTH1+RheJBvyyctJ40JqyfwSOdH/lGz7pmm6XTDYHd9m+t5tMujbDm2hRahLejwZQfACnoeOnnIsV3RMaae6vEUjYMa0zSkKV6GF1uObyG2diwp2SnUrVHXbW4QTmSd4OZfb2bHiR2lbpOa7zro8+WQLx3BDLsQvxBC/EJcdoF0NZlFaUqbjbAspzsLKbjO9CtrUPaWoS3PSHnsGZL2wI+4vw1JG5wapl2xzyb82aDPaBjU8LzLAPo94XfuXVB+dnBxb/Z/kwGNBpRYb+9ufnO7m6nlV+uUx/dbfPVisvOy2ZC8gYcXPUxM7RinMVLBGlfo5l9vBqzuZ7fH3O7qUG6l+l3VyD+aPcvAz7Mws6dJzSYA7ErZRauwVry95m22n9jOov2LnPZ1NQUw4GhNSkhP4LONnwG4nGHnVNT0rVni4sce+LF3AQF4oOMDvLXmLVJzUtl+YjuNghr94wcgk/Nb8a5eRYOxFZGRm+GydfmbS79xBHDahLVhWLNhZOVncW3rawnwDnAEfp7p8QwjW4507BfmF8Zngz5z/IjbrTi8otzAzy+7f+G3/b/xfK/nT7l72NIDSwH4T7f/MKrFKLw9vXmu13Pn9UDlbWq14Y1+b5zWMVqHtgagVWircrasPuw39KeTCVVV7DMLXTjtQrLys2gY1JCb291c/o7V3O6U3fyy+xdm755NSnYKw5oNczSuFGUfy6r47/KaxDVM3TKVOXvmONY9t7zs7+4LvV6gln8tutbtWupA0SnZKYCVETlhzQSahzTnjtg7iKwRWSJDuSz2LuBBPoUDJruy7fg2Rs4cWWK9l4cXt7S7hRvb3oi3hzcfrf+IpMwkl4Mid6vbjRWHV3Bx44tJSEtg87HNJbaZOWKm49rL3VT1NO6Lr17M3tS97Dixg5jaMSw/uJzxq8dXaZnkzMrMy6Tr5MpNuGK/tpl1+awSj21I2sCe1D0sSVjCUz2eqlSwf9eJXYz4cQQmJt0ju/N8r+fZeWInHcI7uBw7NCEtgfCAcN5Z+w6fb/y8xONv9X+LtrXacvf8ux3B5MgakQxrNoz9afs5lnWMsZ3HOjXUu1Jepl957Nl+OTZr1tle9Xs5Aj+tQlux9fjWEvu4SzC/LNXvqkb+0ew/yEUvsuwDjj76+6NsP76dj9aXHPPmo4EfuQz6APRv6Dztt5+nX6nbnklFW5PtLUY7ju9g+/HttAhtcdafX+R0FO/qlZGXUeF9b/31VlYeXum0bkjUEB7o9AD1A+szf9R8An0CXV682Af6axbSrMRjnet2Zv2Y9Tz0w0PUrV+X+fvml9mKDXAy9yT//v3fgDUo843RN9IhvEOFX4t9P3uWgX3A33+KIU2G0KZWG7e8CayWXb0wyDfzycrPciy7q0Pph5izZw6/7P6Fzcc2Y2DQuW5nbmh7A5c3dz1zVGk6RnSkY0RHXuz9Iq/+9So7U3aWOQMeOA/ce2fsnXSK6MTulN00CGqAv5c/t8+9nXwz32mfLce2MGuXdeP2w7AfaB7avNyyzd41m8eWOM9G5evpy4prV+Dp4VliDDRX8mx5TFw30TGLlCvP9XyOQVGDXN7sLVi4gBadWpxWdmB1UdVje4X5hRHmF+b4HfLz9FPgx424yvJ5qfdL+Hr6YhgGTy99mtcveJ2GQQ255IdLSux/6Q+XWn9MgpEtRpbIcJy9ezYrrl1R5oQvAEmZSfSf5nz/8+ehP7l4+sUltr095nY6R3Tm3fh3S2TN2D3S6RFGthzpGND5q0u+Ij0nvcozlBoHN2bmiJk0Dm7MB39/AMDkoZM5lH6Iy2ZYGYhLrl5Cdn52VRbznFHgR6oVe+CnaKt80b+LB33eH/C+U/9vVwJ9All34zpiv4gFcHT3OttcBX6WHVzGvrR9DG5S9uwTIlWteMtIVl5WhfaLmVQy+2b25bNpGNzQsVzWhcL7A95n0qZJtKvdrtRthocOp1/XfuxJ3cOO42UHfn7aWTgQp30Gl++HfV/h4Otzy5/j223fAlZQ6p/GMAy3C/rYgyVVfQN4KjwMD6fBne0DarqLpMwk5u6Zy5zdc4g/Gg9Ys7882uVRBjYeeNo3Gd6e3vy3+39LfTw9J50dJ3aw/cR2pm2dxpZjWwD4cN2HlX6uy2dezhPdnuCqVlc5nU8rMt5Zdn42cV/Glfr4jW1v5IoWV/DiihfZeWInx7KOlbrto10e5fo215fZ2u1peP4jgj5w/nX1jKoZRZBPUKUbJKqDLce28MaqN3ij3xsE+wRXdXHOKvuAx0XNunxWicwW+z3IrpRdjnVfDvmSW3+91ZG9YueqWytAt6+78UDHB7gp+qYSn2ObaWPkzJHlNooV9dH6j1w2qgP4ePiw6vpVJc4fNbxrnDfdjO3XKB9c9AFbj2/F19PXaZbSMzG0R3VxfpzVRCrIEfjxcu6OcWHDC0sMfLfsmmUVntrZMAzC/cM5knmE+kH1z0xhy1G0G0FEQAReHl6Oi8fmIeW3AopUpeKZBJl5meXu8+zyZ52Wiwd8KqJhcEPHDHzlaRHSghWHVpBnyyv1It5+ITZv1Dzei3+PGTtm8NXmr3i257Mltt2fup9g32BHRmBGboYj6APWxZm4gYKP9vly41cZHoYHplk4uLN98O3qLDUnlQV7FzB792xWHl6JzbTRIrQF93e4n8FNBtMwqHLnkNMR6BNIXHgcceFxXNnySgCSM5PZm7qX9Nx0x1gXnSI6Ua9GPWLrxNIgqAFNazalXmA9jmYcZe7euY4uqy+seIFPN3zK7CtmY8PGl5u+ZMLqCSWed2SLkRxMP8gFDS8gIS2hxA0kQL8G/Xii+xNOwa9PB33q+DsnP4f9afsZ8eMIAP689s/z5sbsfOLl4cWdsXe6HH+kqkQFR5Fvyy9/w/NUvi2f7Se20zqstdP6N9e8yZ+H/uSH7T8wJnoMb615Cx9PH+5uf3cVlfTMc5VZAzBl6JQyuzNFBUdxfZvruab1NTQKbsTqG1Zz+ORhlxk5rry15i3eWvMWjYIasS9tX5nbfjnkS+bvnc8d7e8gJSuFpKwk9qTs4allTzm2uaDBBfh4+nBfh/uo5VfrnPSMONN61e9Fr/q9nNb90wbxr35XNfKPZg/8FB3jB2B8v/HEfRlHm7A2jv7oFQ362B3JPAI4T7t8NhXtRmAYBu3rtGd14moAmtUs2Y1F5HxSfByEsgI/h08eZuK6iUzfNh2AjuEd+Xzw52e9P3WL0Bbk2nLZl7qPpiFNmblzJj/u+JEPLvqAdUnrME2Tval76V2/N3Vr1OX5Xs+z7MAyly1hE9dN5J217wBWYPaH4T+w/cR2AO5ufze3x9zudtkV/3TVNeOnaPeik7knz8nzrj+6ntvm3kazkGb830X/57gp2HJsCz+f+Jmvfv2KHSd2kJyVzPO9nmdE8xFlHi8jN4PFCYuZvXs2Sw8sJdeWS8OghtwWcxtDooZUqIvUuVLLvxa1/GsBJccOKq5OQB2ua3Md17a+1pFlfPDkwTKzd/7vov+jd/3eTuv2p+1nccJi6tWoR+e6nXmh1wvlnk99PH1oFtKMFdeuwGbaFPQpQ9EZgs4HXh5e1TqIO+i7QSRmJJbIpm0S3ISlB5by9eavGRM9ho/XW7O6VcfAT2pOKpM3T2b3id38sucXrmp1FT/u+NHR7Ragac2mvHvhuxVq8PIwPHisq3MXz7o16rLwqoUsWbqEi/pexJZjW+hStwvX/XydY/rzeaPmccn3lzgmXigt6PNCrxfoEN6B+oH18fTwdAzSHuwTTMPghnQI7+CYqe9IxpFSZ9SqzpZds6xaNvCcjn/Wq5Vqz55lUHwAVk8PT8cFV9+pfR0nvMq4pd0tfLrhUzqGdyx/4zOg+MlmQKMBjsBP8WlORc43xW8ySgv8bEre5DTb1j1x93BHzB3nZBA9e+bcm2veZOH+hY71Dy56kN8Tfncs3xdXeJE/qMkgvtz0JSdzTzpujFJzUh1BH4AdJ3bw9eavScxIBGBE8xEK+riRTUmbAEqMz1Id2Gf1skvLSTurz1d8MOH1SevpPbV3yQ1TCv98cumTPLn0SSJrRHJJk0vw8/JjRPMRhPmF8ceBP/hl9y8sSlhEZl4m4QHhXNP6Gi5pcglta7V1m8E3DcNg3qh5jtb7drXaOU0VDrDq+lWlDjb/7oB3T/m5yxv7Q84/Xh5ep3Rde76w/1ZeMfMKx7X61mNbHZlrB08erFDWcFU7kXWCY1nHHNfo64+uZ0PyBnae2Ok0jTlQYrm0CWYqq7Z/bUK9QgnyCaJLXWva9+d6PefI5Ktboy5rblhDRm4GG5M3MnnzZIY2HYppmiw5sIT6gfW5osUVlQrkuGPQByqfIOAOFPiRaqms2TDmXTnPKdW9ou7vcD9josecs4ui4jPG3ND2Bi5retk/qq+pVF9Fu3r5efq5HOMnMy+zxBTrd8Xedc5u3uz9uosGfQCnoI+fpx9ta7V1LMfWtlrhf9n9i2PWsPfWWtOh39D2BoY2HcroWaN55a9XuLDhhRgYRNaIPKuvQ86t49nHAfh84+dc1+a6Ki5N5XgYHmTbCgepPFsZP6ZpOjJWTtWhk4ccU1e/F/8eQT5BpOWkEeobyrBmwxgcNZiOER2rfJals6Vujbo0CmpEYkYiUy6dQkp2CsE+wY73tTrOKidnh5fhRY6ZQ2pOKglpCU6/WdVB54jOrEpcBbge5w9wmuVqScISWoa2PKcDAz+z7Bl2peziwoYX0jKsJYknE1mXtI4gnyAaBjXE28ObJ5c+CcDzvZ53/F0eA4OV162s1Cx+leXqHBngHUCXul0cwSGAgVEDz1oZpHrQr4pUK/YWj9KmToWS2UAV5enh6Zj+71xwlV6ooI9UF0UvNPy9/F221nWb3A2wbnAOnzxM/cD657TFvviF1pwr5jDk+yFO6wZGDXQqk31ch2eWP8OOEzt4tMujfL3la8CatcLTw5PbY27no/UfMX/ffC5ufLHbZCGIs6Y1q1/mpYfhQXZeYeAnPTf9rDzPHfPucFpedNUiavnX4tW/XuXLTV/yfK/naRPWhlE/jQKs37sfhv1AZGAkn234jPfirWBq0Vlp+jfszyVNLqFrZFe8Pf4ZGXQzhs/AxGqoKp4NUB1nlZOzw9PDkzxbHr2mWOOTrLtxXbX63TEMg/qB9TmQfqBC29tnqSuv62RR2fnZ3DXvLq5ve32lxmdanbiam365ybG89sjacvcpLeiz4MoFhPqFnvPzl/0cIlIeBX6kWrFPt1d8jJ/q6J/Wr1TcS9GMH1eBn5TsFMfFyIzhM/hh+w/0b1RygMOzbc0Na5iyeYpjkNXiis+U4u3pzUu9X+I/f/yHrzZ/5dgnwCvAcSN2S7tbHDNc2GfkE/dTHYMPHoaHU/bd6Xb1SspM4kD6Aaex70bPGs3G5I2O5cgakY4xbh7t8iiPdnnU8diaG9bw++LfGdC/8EbsrvZ3cVf7uxzL/+3+XzDdbwayivgnvmapPC/Dy2nsuZO5Jwn0CazCElXcpI2T+OvwX0QFR7Hm+jV8u+1bgnyCiK0TWzg1eSnybfkVDoDuT93PqsRVrEpcxcrrVrI2cS096/csc5+EtASnoE95vDy8uCv2LrpFduPXPb9yW8xtGIZBiG9IlWYm5uZbjeKaGEbKoztPqVY2JFl94FOyU8rZ8vxXHQcOFbErepHj6+VL/JF4p8ft43xc3PhianjX4Pq215/L4jl4e3hzY/SNjuXPBn3Gzb/e7Fju26BviX0ua3YZfRv0pffU3o7Zdyb0K5xpJ9AnkFEtRzF923Q6RXQ6i6WXqlQdZy3xwLmrV3rOqWf8zNs7j4cXPexYnnX5LN5Z+44j6NO/YX8W7l/IJU0uKfUY3h7e5f7WVccAm8i55OXh5Wj4BEjJSakWgZ/4I/G8vup1APak7sHb05tr21wLwKH0Q+Xuf+jkIZcNNnYL9y2kW2Q39qXtY/nB5Y719m5jYzuPZUz0GJf7vrHqDT7f+DkA9QPrM2XoFGr61rTGdjOt99wwDPJt+eTYcvD38nfa3z4Y8vnAnv0VEXDuusZJ9aTAj1Qr9rE69qbtreKSnD5l/Ei1ViTLfHfKbsCaiSfAO8Ap++eVPq+c65KVqXPdzk7LpfW7L37T3zWyq9PyU92fYmznsZoZxw0ZGJiY/Lvzv6u6KJXmYXg4NYycalevFYdWOAV9AKfW+XcufIfe9XszefNkrm51dfHdReQMKp71svPETuoH1q+i0hQ6fPIw245vY9bOWTzX67kSv6c3zLmh1H2LvqYvhnzBjXNuLLHNvL3zuLmd1VBz3ezr8PX05c3+b/Lp+k8d44OV5fVVr3NN62vw8fTBNE1un3c7Kw6tKLHdnCvmOIInxTN3PD088ffwL7HP+aRFSAv+0+0/DI4aXNVFkfOc7jylWvn96t+56ZebeG/Ae1VdlNOmwI9UZx6UTGvelLyJ6NrRjJppjevRrla787orw52xdxLsE1zudmM7jy3xfTUMQ0EfN9UgqAH70/ZTw6f61a+H4cGRjCOO5VMJ/Ow8sZPb5t5W6uMv93mZfg37AZTami4iZ07xrLmD6QerqCSQZ8ujw5cdSqyfs2cOK69b6ciM2Zfqehpxu6IBlg7hHYgIiHDM/mU3efNkbm53MxdPv5jDJw8DOMY5Kk2YXxjHso45ljt9VX5WbnUaL8kVwzC4pvU1VV0MqQZ05ynVSqhfKD+O+LGqi3FGKPAj1VnRC6VPB33KLb/ewpebvuS3/b8Vrh/8aVUUrcKuaHFFmY9XZmBJcR+fDfqMtUfXVssuSMVbqyvT1evtNW8zZ/ccEtITAIitE8uXQ76k/ReF4/vYZ7YTkXOn+PXi2Rq0vTyrDq9y6ipdXM8pPVl7gzU48hNLnyjzWMVnrXM1G29iRmKps4B9eNGHjjF8lh1Yxp3z7wSsLqj2AeNdaR3Wmm8u/QYPw4PF+xfTKLhRmeUUcSe68xQ5x+rVqMfBkwfLnJlM5HxXdHDnLnW7UD+wvlPQZ9LgSSX6xJ9vFHwVVyJqRDC4RvVMmS/ecp2Vn0WuLbfcINbrf73OpE2TnNZNvmSy0/JPI34iqmbUGSmnlG5Ys2ElxkyTf7btx7cDhdksRcf7ORdcBXyua3Mdt7S7hX8v/jdvX/g2vaf2xmbayMrLYsLqCaw9stYpi6foNQOU7L5mw+a0PLzZcH7cWdjQ+92w72gZ2hLTNEuc53y9Cmfz7RDege+2f4e3hzdrbljD1mNbScxIpE/9PiX2u6DhBZV8J0SqN131ipxjnw/+nB0ndlTL1mQRu+KZBf/p9h/uXXAvADNHzKRJzSZVUaxKUeBH3I2rgZS3JG8hpo7rVnOwuo0UD/osumqR4++6Nepy+ORhNVacIy/2frGqiyDnmc3HNgNwLOsY3h7e5OTnnPYxd6fsZtiMYTSt2ZQRzUdwXZvrXH7H96TscQR9avvX5pOBn9A0pKnj8UlDrHPH+H7jeXjRw3SZ3MXx2PsXvc+0rdP4Zus3JYIuxc9VGbkZTsutwlrxWfPPeGftOzzf63lHZo6rblk+HoXlbhHaAgCbaXMcp1VYq3LeDZF/hqqbe07kHyoyMJI+DfpUdTFETou99c4ewOzboC+3xVjjgtQLrFdl5aoMBX7E3RS9KQrxDQFgf9r+MvcZ9N0gx99j2o5h8iWTHdOzgzWlMmgmSpHzga+n72kFflYeWknMpBiGzRgGwK6UXYxfPZ5xS8aV2PaZZc9w2YzLAOu3fuFVC52CPkXF1o51Wn6176u0DG3JvXFWg1DxrlzFM34y8pwDP76evnSu25lJQyaV2x2raMAq0Nua7SzfzC9zH5F/IgV+RESk0uwZP0WDJw90fID1Y9bj6+lb2m7nleJjDIhUd0WDM8/2fBbA5ffRNE1iJsU4jZ/x941/M7bLWGLrON/APdr1UUJ8QwjzCztLpRaRivLx9DnlwI9pmtw691andfbv9by981i4b6HTY/axctrXac+aG9aUeeyIGhHE1oklKjiK9WPWM6TJEKCwcai8jJ/iTjXDsLSZOkVEXb1ERORUFFzDVecui9W57CKu2G+uLm58saO7ZVZ+ltM2pmlyyfeXOK17odcLJbpv2g2OGqxpgkXOE94e3uTYTi3w8+3xbx1/L7tmGUE+QQDM3TOXRxY/wv0L73eMjQPg5+lHVn4WEy+eWKHjFx8XDCh1Zk974KdNWBun9U1rNmVXyi6CvIMq9JyAUyCsujQ8iVQFZfyIiEil2adzr87BE3X1Endj74I5b+88xw3QydyTTts8/sfjjpm7AML9w7ms2WXnrpAicsoSMxJZsHdBpffbnLyZJWlLAOegD8DAqIF8OsiahTPXlsvG5I3sS91HVn4W/Rr0I8A74JTLa79GKN4VzDAMvhzyJR8N/MhpvT0AXbS7aXmahTRz/K3Aj0jpdNUrIiKVZs8sKK01rzpwNUikSHWWm58LWAOc2m+Anv/zea5qdRUAH637iJ93/QzAp4M+xcfTh/Z12rs+mIicVzwMD2ymjbTctAptn5WXZf3Lz+KqWdY5YNLgSU5BH7sudbvwSKdHeGP1G2w4uoFj2ccAuC32ttMu89eXfE3jmo1LPBYXHuf4+7W+r/Hzrp+5s/2dvPbXa5UakLloYMp+3msVqgGdRYpT4EdERCrNnlmgcXJEzh+Z+ZkAXN3y6hJjXWw5toW3174NQI/IHnSp26XE/iJy/vl353/z2qrX8PHwKdF1szSpOan0mtILKBzwGKBjRMdS9xkTPYaJ6yey9fhWTmSfACCmdukzAlZUWbMK2g1uMpjBTawupfaZwirj8a6Ps/nYZgzDYPpl06lbo26ljyHi7nTFLiIilZZn5gHqLiVyPsnKs24KA7wDSnR5eGrpUwDc2u5WHuz04LkumoicogGNB/DaqtcwDIPbY27no/UfYTNtpY7Ldcuvt/DX4b8cy+m56QC83vD1Mp/HMAxahbbi223WWECDogaV+hznm2vbXOv4W9O3i7hWPb7NIiJyXsmzWYGf6tjV681+bzKyxciqLobIGZeZZ2X8BHgHOAVl/zz0J5uPbQZQ0EekmimaWRvqFwrAzhM7XW67/uh6R9BnVMtRjvVNazbF16P88W/a1mrr+Lvo2DkiUv0p8CMiIpWWa7PGEqmOXb0GNB7AMz2fqepiiJxx9sBPDe8aANwbdy8At8+9vcrKJCKnx9OjcOrzrnW7AjB/33yX2147uzDz5ekeTzsaOe5uf3eFnuua1tc4/r6kySVlbCki1Y0CPyIiUmmhvlarY896Pau4JCJidyLrBAA1vKzAj31QZ7unejx1roskIqep6OyZLUJbAPB+/PsMnD7QabujGUcdf68fsx6AsZ3H8umgT7m48cUVeq76gfXx8fAhzC+MxsElB2QWkeqr+jXViohIlasXWI/ZV8ymXo16VV0UESlQfOytML8wx2Ozr5hNw6CGVVIuETl1nkZhxo+H4UEd/zoczTzKoZOH6Da5G42CG7Hj+A7H9/9/ff7n2D7QJ7BSA7kbhsGM4TPw9/Y/cy9ARM4LCvyIiMgp0U2kyPnJPg4IwFv93yIhLUHfV5FqyjCsWTTtmT9ta7VlccJiADLyMthybIvT9qfbRathsM4VIu6oQoEfwzBCgI+BdoAJ3AJsBb4BooA9wFWmaR4/G4UUERERkbJ9eNGHrEpc5TSV8YWNLqzCEonI6QrwCuDO2DsZFDUIgAn9J/Dbvt/oHtmdp5Y+xW/7f3NsO77f+GozE5eInFsVzfh5C/jFNM1RhmH4AAHAf4AFpmm+bBjGOGAc8NhZKqeIiIiIlKFn/Z70rK9xt0TciWEY3NfhPseyt4e3Iwj01oVvVVWxRKSaKTckbBhGMNAX+ATANM0c0zRPAMOBSQWbTQJGnJ0iioiIiIiIiIjIqahILmBT4CjwmWEYaw3D+NgwjBpAhGmahwAK/g8/i+UUEREREREREZFKMkzTLHsDw+gM/An0Mk1zhWEYbwGpwL9M0wwpst1x0zRDXex/B3AHQERERKepU6eeweK7h/T0dAIDA6u6GHIaVIfuQfXoHlSP7kH16B5Uj9Wf6tA9qB7dg+rRPZyteuzfv/9q0zQ7u3qsIoGfusCfpmlGFSz3wRrPpznQzzTNQ4ZhRAKLTNNsVdaxOnfubK5ateoUXoJ7W7RoEf369avqYshpUB26B9Wje1A9ugfVo3tQPVZ/qkP3oHp0D6pH93C26tEwjFIDP+V29TJN8zCw3zAMe1BnALAJmAmMKVg3BvjxDJRVRERERERERETOkIrO6vUvYHLBjF67gJuxgkbTDMO4FdgHXHl2iigiIiIiIiIiIqeiQoEf0zTjAVcpQwPOaGlEREREREREROSMqcisXiIiIiIiIiIiUg0p8CMiIiIiIiIi4qYU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIiIiJtS4EdERERERERExE0p8CMiIiIiIiIi4qYU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIib8qrIRoZh7AHSgHwgzzTNzoZhhAHfAFHAHuAq0zSPn51iioiIiIiIiIhIZVUm46e/aZpxpml2LlgeBywwTbMFsKBgWUREREREREREzhOn09VrODCp4O9JwIjTLo2IiIiIiIiIiJwxFQ38mMBcwzBWG4ZxR8G6CNM0DwEU/B9+NgooIiIiIiIiIiKnxjBNs/yNDKOeaZoHDcMIB+YB/wJmmqYZUmSb46ZphrrY9w7gDoCIiIhOU6dOPVNldxvp6ekEBgZWdTHkNKgO3YPq0T2oHt2D6tE9qB6rP9Whe1A9ugfVo3s4W/XYv3//1UWG5nFSocGdTdM8WPD/EcMwfgC6AomGYUSapnnIMIxI4Egp+04EJgJ07tzZ7Nev3ym8BPe2aNEi9L5Ub6pD96B6dA+qR/egenQPqsfqT3XoHlSP7kH16B6qoh7L7eplGEYNwzCC7H8DA4ENwExgTMFmY4Afz1YhRURERERERESk8iqS8RMB/GAYhn37r03T/MUwjL+AaYZh3ArsA648e8UUEREREREREZHKKjfwY5rmLqC9i/XJwICzUSgRERERERERETl9pzOdu4iIiIiIiIiInMcU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIiIiJtS4EdERERERERExE0p8CMiIiIiIiIi4qYU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNVTjwYxiGp2EYaw3DmFWwHGYYxjzDMLYX/B969oopIiIiIiIiIiKVVZmMnweAzUWWxwELTNNsASwoWBYRERERERERkfNEhQI/hmE0AIYCHxdZPRyYVPD3JGDEGS2ZiIiIiIiIiIiclopm/LwJPArYiqyLME3zEEDB/+FntmgiIiIiIiIiInI6DNM0y97AMC4FLjFN8x7DMPoBY03TvNQwjBOmaYYU2e64aZolxvkxDOMO4A6AiIiITlOnTj2DxXcP6enpBAYGVnUx5DSoDt2D6tE9qB7dg+rRPageqz/VoXtQPboH1aN7OFv12L9//9WmaXZ29ZhXBfbvBQwzDOMSwA8INgzjKyDRMIxI0zQPGYYRCRxxtbNpmhOBiQCdO3c2+/Xrdyqvwa0tWrQIvS/Vm+rQPage3YPq0T2oHt2D6rH6Ux26B9Wje1A9uoeqqMdyu3qZpvm4aZoNTNOMAkYDv5mmeT0wExhTsNkY4MezVkoREREREREREam0yszqVdzLwMWGYWwHLi5YFhERERERERGR80RFuno5mKa5CFhU8HcyMODMF0lERERERERERM6E08n4ERERERERERGR85gCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIiIiJtS4EdERERERERExE0p8CMiIiIiIiIi4qYU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLipcgM/hmH4GYax0jCMvw3D2GgYxrMF68MMw5hnGMb2gv9Dz35xRURERERERESkoiqS8ZMNXGiaZnsgDhhsGEZ3YBywwDTNFsCCgmURERERERERETlPlBv4MS3pBYveBf9MYDgwqWD9JGDE2SigiIiIiIiIiIicmgqN8WMYhqdhGPHAEWCeaZorgAjTNA8BFPwfftZKKSIiIiIiIiIilWaYplnxjQ0jBPgB+Bfwh2maIUUeO26aZolxfgzDuAO4AyAiIqLT1KlTT7PI7ic9PZ3AwMCqLoacBtWhe1A9ugfVo3tQPboH1WP1pzp0D6pH96B6dA9nqx779++/2jTNzq4e86rMgUzTPGEYxiJgMJBoGEakaZqHDMOIxMoGcrXPRGAiQOfOnc1+/fpV5in/ERYtWoTel+pNdegeVI/uQfXoHlSP7kH1WP2pDt2D6tE9qB7dQ1XUY0Vm9apTkOmDYRj+wEXAFmAmMKZgszHAj2epjCIiIiIiIiIicgoqkvETCUwyDMMTK1A0zTTNWYZhLAemGYZxK7APuPIsllNERERERERERCqp3MCPaZrrgA4u1icDA85GoURERERERERE5PRVaFYvERERERERERGpfhT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIiIiJtS4EdERERERERExE0p8CMiIiIiIiIi4qYU+BERERERERERcVMK/IiIiIiIiIiIuCkFfkRERERERERE3JQCPyIiIiIiIiIibkqBHxERERERERERN6XAj4iIiIiIiIiIm1LgR0RERERERETETSnwIyIiIiIiIiLiphT4ERERERERERFxUwr8iIiIiIiIiIi4KQV+RERERERERETclAI/IiIiIiIiIiJuSoEfERERERERERE3pcCPiIiIiIiIiIibUuBHRERERERERMRNKfAjIiIiIiIiIuKmFPgREREREREREXFTCvyIiIiIiIiIiLgpBX5ERERERERERNyUAj8iIiIiIiIiIm5KgR8RERERERERETelwI+IiIiIiIiIiJtS4EdERERERERExE2VG/gxDKOhYRgLDcPYbBjGRsMwHihYH2YYxjzDMLYX/B969osrIiIiIiIiIiIVVZGMnzzgEdM02wDdgXsNw2gLjAMWmKbZAlhQsCwiIiIiIiIiIueJcgM/pmkeMk1zTcHfacBmoD4wHJhUsNkkYMRZKqOIiIiIiIiIiJyCSo3xYxhGFNABWAFEmKZ5CKzgEBB+xksnIiJyPtr5G8x/BtIOV3VJRERERETKZJimWbENDSMQWAy8aJrm94ZhnDBNM6TI48dN0ywxzo9hGHcAdwBERER0mjp16hkpuDtJT08nMDCwqoshp0F16B5Uj+7hTNVjwMl9RB6ah2l4srfx1eR7+QMQemwN7dc9C0BqUAvWdHr9tJ9LStL30T2oHqs/1aF7UD26B9Wjezhb9di/f//Vpml2dvVYhQI/hmF4A7OAX03THF+wbivQzzTNQ4ZhRAKLTNNsVdZxOnfubK5atarSL8DdLVq0iH79+lV1MeQ0qA7dg+rRPZx2PZomrJwIcx51Xn/Tz/DHBNgx33n92B3gEwA+NU79OaUEfR/dg+qx+lMdugfVo3tQPbqHs1WPhmGUGvipyKxeBvAJsNke9CkwExhT8PcY4MfTLaiIiEiV+/LywqDPqM8gsr319+dDraCP4QE3zYbrv7PWv94cXqoHu3+vmvKKiIiIiJTBqwLb9AJuANYbhhFfsO4/wMvANMMwbgX2AVeelRKKiIicKVkpYMuHgDDXj//1CexaCJ4+cN9fEBoFbUfA/KchpJG1X/QVYBhWZlBUH9izxNp30mXWtptmwLXfWvsufxdiRkGTvufk5YmIiIiIFFdu4Mc0zT8Ao5SHB5zZ4oiIiJxBqydBXhY07Q9mPnw6yAr+XDcdajUDDAhrYm17eD38/LD194MbICjC+tvDAwY+X/LYhgE3zbL+XvIGLHjOCvoAfF2kLWTNJHj8APiqT76IiJznFr0Mmcdh0P+s3z8RcQsVyfgRkXMhOw22/GzdfA58wbqpFJFTd+hv+Ol+149NHlX4d1Qf8PCEXYus5UEvFQZ9Kqr3wxAeDbWaw++vwt5lULOBFXQ6uBb+Vx9u+AEC7cc1IDgSUg9aGUi+gRDWtLKvUERE5MxJWAWL/mf97e0PFz1T8X3TEiEjqXC5ZgPwq3lGiycip06BHxG7XYth+9yqC7p8dgkcXmf9XaMO9H7w3JdBxJ38+l/r/253QYMu1t/e/rB1Nqz9qnA7e1ctgD6PQPd7Kv9chgGtBlt/XzGxcL1pwve3w/pvrbGDytJsAERfDnVjnNcf3w3f3gSXfwg+gVbwyNO78PHgehAYXricnQ7JO8AvGLJSC9d7eFlZTikJ4BsEQXVLluHYbjixr+yLddMGJ49C84tLtganH4Hje6yucq5kJEN+rtX1zSfAWpeVYt1s+NW0ylggMG0nHAwp3DesCeRmOQflbPlwZDPY8kopa761T1Sv0l9P0nbIOem8LqxJ2e9Bdhok73T9WE461O9kfdZcSd5p7V+a1IPWDVPNBlZdePpAeJuqbww4ug1yMwqXg+q6/gztX2l9Rrz8gCL16O0PtVtaryPjGCRutD4DhqcVePX0sT7Xnr5F/vYBL1/r8VNxYI31XE37gacbXPLmZsHRzbhMxM9Jh0Y9XWdoJG6ygtCGi8dOJkG9DlCjlvP643vg0DoIaVTyuwiQegDqxkJIw5JlPLim4PPvopzH90Djns7nLID0o3D4bwioXXIfR1mPWtmbResy7bBVzsA6zs+Xl2VdS9VqVvrxzmeZx63P/8F4aNDZ+h6citxM6z2v07pi288s0ljyxwRo3AtaXFz+fjknYXxr67vvYFiTIjTuWfXnLxFR4EfE4Yth1v9RvaHVkHP73EnbraBP84uswWPnPw097qvYhWryTgiuD95+Z7+c/zT5uVa9hDS2Li7rtjv1Y+Vmwp/vWzft7a9V+vSZkpMBG3+A2i0AA2x59Fs0HNY2hJT90LA7DHnFeZ8mfaHlECsY5BNgXeh6+sClEwqDEWeKYcDIj6HrnZB2sHD9umlwfC/U7wCph2DHPNi5wPpXmh/uPLNlA6jZ0BrAGmD2WDgUX/ljjP7auilZOxk2fn/GitYZYHU5G3l4lR70Ke6Kj61xl+z2/gHzn3G9bVQfqxtfdjocWG0Fy2y51jhP5QXwHM/3kRWoSz0AEe1gzRfw99cV27cs/Z+wAhlOTOumODKuIDB4hi7v9i6D9dOtes08XvJxDy+49hvwLQiSzXsS9i132sSpHmOuhDaXwbQbK1cOw6MgGFT0n7f1uSsaJPL0KQzkNu4Fe5c6H2fIa1CnFfz1EbQbaa1LSYC2wyHhL6jZyApYZCSBdw0reJB22Bqnq7wb18SN1rHqxloz/PkFV+y1Hd0Gx3ZZ54DgehDV1wpWFB2H7GQSJG6AL4aXf7wR/we1Wlh/H9sFf75nZT9WxFVfQFA9WPWp02e1zO9iswHWeavVJXB0i/WcFTXsXZh5X8W3t+t2txXoWvtlxba/9lvrO+NVSkAarKB38o6C2RkNaNjN+p1O3AiHNxR0CS74DOSkgX+o9XkJCLPGhcvLgeTt1u+KUSRQmZthfT4b9XD9GcrNhN1LrAD/zoVWJuiqT0tud99qqN289PLnZlrl9PCE+h0L10+70WrU7HwLBJbx+TFN67t+ZKN1/rvgMZh0qZUdO+Bp69wZ0sgKattfx7Hd1vc9+nL47UUr6HPBOIhoa3Wz3rkAPr/E2vaZlNKfW0TOiQpN536maDp31zQtXxXbsxSWvA47fytcN3ZHQetRxZRah0k74NfHrR9dsC48LnwKGnRy3u6rkVbA57YFVjkWvgh12lg3HjWKtX7lZVs/7pHtYfVn1s0aQIOucOVnVitxdXJiv9Vib+bDlNHWuqb9nbep0wpaXwon9loD69pvzpN3Wu9X5glY+IK1rkY4RESX/Zy7Flrv3+CXrYuyr0ZCeFuO5foQFhrqvF1RjXs7Z1oAxF1nXfis+gRqt7Iu3Et7zqKKv8by5GbC/j+tv8f8VJgZ8VlBkDKsKYQ2gV73g4c3zH3Cugm54kOr5X3/isKb3NKe+2QSJK6HmKug883WuoxkCKhVcttNMyE9EUa8X3pmg112uhWECW9j3YAc22XdnOZlWa2EbS6zAjj7lhceKyvV+k4k73A+VvJOSNkHLQdb34Xi72tR9TrCpeOt1uzqIGm79a8EE3YssD57Kz6wbu4veMx66FA8LC4W2KrVwroZ2b/CygDo+S9r/Yr/q/jMY6OnlP7YzgXw18elP+5XE0Z84PqxqdeUvl/XO50CGus3rCemXUH2087frH/HXGTZNO1n7evKyollf0YAgiKtTE/vgvPKuqmwqQITlXa6GVoMLLbShKnXlr9v9OXW96y4tIPw8yOFyz3uswYIr4xmF1rdCsuTcsA6p4J1fvnqCisrICjSWrd7ccnW+6u+sM49RzbCby+Ufuw6ra2bRQrqsU0r+HZMye16/sv6jJr51vc5Pxfyc4r9K1hX0cf3LavY+1RZZZ2zc05CwkrX2+9aCL7BVmBk3zIri6v44+U9X/Fthr3rfF7Oy4Tpt5Rd/nYjod2oYisr8Hkd8irr958o/C6CFRibWXBeqdfB6s5aXIfrodVQ53Unj8BPD5T9fKO/xmWm0MG1Vjfa0gx6yfoNtFv8SumB7IbdCr/vAEnbrABtUf5h1rVE0YzQ0+HhZQVUiivxPStFy8FWkLU404RXoiDrROG6yPZW+fOyS34fBv0P4q6xAld266dbY9TZzwf3rIDw1rBtrvN4dXZN+7s+NsCTSdZvlc0GW34qDPLetdR6DxY8V5g56OFldSUrrVHNlm/99pUWtNv5W2FQ2MPb+ix6elkZTiGN4eOL4MCqgt+VUgK3uxZaAenOxb4/uRkw+crCuqnXAfxCXB/Dfhy7gFpWABgAs7AbOVT82s/VeSGqD3h4cez48cJr1cSN0P0u63qnWX84mWxdS8V/7ZyhWfy4Iz6wgoRevtb9gy3Puk5z2nYRLH/fCnS6cv33VgDWPwyaFPlsZxyDlR9Bw66uM9VyMqDFReW+Bacl5yQc3WrV23mabVYV07kr8FNZ+blWCxhYX6jUg9DpppJpyPaTUWknmtwM66YneSf7t62j4fAnoE5L64Y+Zb/zDxJYz5Odat2YRURD9AgXZcuDBc9ARAzkZ1s/kv6h0OGGwsFLi8o8brU612kN4W0rFehwK+/3tC5iG/Wwbrrs/ZNv/NG5VTU/12qN2f6rdXFRs4FVD0e2sHHRd0R37m212HjXsFoPvXxg1sNWQKC4m362MotsNlj5Ifwyztrvvweteny9BWQes7Yd/p7VKm+3YbrVclyaXg9aJ/+dC61W5mM7rRuxvOzSb4A9va2sodDGzuszT1g/mikJ0HFMxU+e9tarmg1dp5Y7mK5bMBt0Lfy7+MV0RRTd35VSjpkS3IqawUW6dxzb5dxfvehx87IKu+ZV5LmLPmdgXavlrDJO5X0oTUXKWFG3zIVG3Uquz8+zjrftV1j6ZtnHCGlUeDPkSmmfB59A66IDrABc9BUAbP3rN1pd+WT5AalTlJqVy5JtSYQGeFMvxJ+o2jXOyvNUWHYaLH3b+rw26g5db7duBvYus1pei17g2+VlWxlotVtaXWvAOufUjbUu+CvixH4rE2TeU9byjTOtLIuKZJvY8q3AQeoB6ze0cc8Sm7i8KMrPs57zyCbr/NL5loqdl9ZPL3nRbtqs73HbYc7r46fAjLtcH6fbXVYwvv21ULO+621M0yqjzQbf32ati7sOmlxgdW9pVoEL/4xjVjAjqK71Xm2fa9VnRrIVKPUoFoDevRiWvW397R8Kj+0p/dhZKbBvhesbOij8vuWkW+/z4JchrJn1mxfRtnC71EPWb5enN8QWBO2Ngv2LZLs46vFkcmGAYN8y6Pd4yUD6mXBsFySstn5jgwuCWJknrNeSU3AjlJpgrZv/tOtjBNeHy96G356zgtV+Na3AflmKnz/t72Np59Xij1/+oXWTdGCNFbQten1u32b4e1YjiH+I62Me3Wp9L4uq2aD873TCKivY2aQvYEDuSavLVUEXyXJvUA6utW62czOt8250OVlxKQnWDX1wfeu1BtSCrndULBN21yIruyYv09ovqnfp2yZugg3fWY17vsHWdTRYgYLIuMLt8rNLZkXVjbUaTezv/ZBXre9BXhYsftkajxGssd2ObCzcr8ttVkap3eyx1vVQaJTVMOVKwkqrTPag19afreB+xjHrO/d+DyuL6O4/nPfLPA4/3gdbCiYZ8PKzytegC477D8OwyvT97aW/T3aNesKgF6ysHrukHdY5fuvP1nLNRoVdPO3vTXB961w+5FXoViwI//dUK1vVvg1YdVGntZVNGTPKeh0n9lmfgS63Wttkp8MHBdfVYF2TH99TduCw/3+txo3iwbrarVx33TXzrTJUVFnXlqV9/7NSIGlrxY5R1vHA+ryHNSMlNcW6Vi2+TbtR1j2Cnf19tss56fxZPV80GwC9CurVtMGyd6zuheFty96vNGa+c2bu9d9b3dsxrG6Tx3ZZ56CiDOOcz76qwE91kHEMXi0WRPHys6LrYP2Anar6nSp+AgppXBiZP1NunQefuOjHe913zsvZqTC9IBvAL8T6MSvaqnLlJOuGrCx/TLDS7K+ZWvICtjxBdSvf5SY3y0qTPrAaOt9q9ZE/mWSlcM+4y2q1ve5b6wL7/e5W6w9YJ6KQxtaJKGU/LH3L+bhFf8iKu+oLmPukFfgY9g7Ui7OCMdNusMpw0TPwcpGAzj1/WhkRYN2ULXyx5PO5cvlE66Luw3N0whr2bmGLsCuLXzm1AMKNP1otFx1vLDhBF8hKtcYL2P27NXOSKyM/sd7buu2s/X3KuRFP3mk9l/3mJC0RYq5k0e+/V+4kfGSzlfViy7N+RDrcePa6cJkmbJ1jXSAVveE8sd+6AB30ojUWQG7BWCUnk6yLcPv4C6ZptfR0ub3kmAx29pvqGrWtz23yTuuCsuudJbsS7l9pfUav/87qoli0nHuXwrQxzkEzO99g68e894PW53vfcivoemidVfbRU6wuNSs/gjbDrJbJop+HvGyrTEGR1gWUiyDD6f6YmqbJyt3HuHqilWHVqXEoq/e66OZSoEOjENbuO0Hz8ECeGNrG5TYpmbks3nqUV0fF4uWpbn4VUWXZsDkn4aWCzL3q1j3h69FWUOOuP0o+duhv63trzxIF63zQ5lLr77RE6ybsVMfTKcU/Pqs55YB1jZSbaQXz0g5ZgcPgMn5HzzNuU4dJO6ybwjrlBPHON7Metro0P7a7cN3hDfBBkbHLHtlW7qQE66e/QsyGl1w/aG+QPBve7lDYBfCqL6H1UOs8Y294LeqORVZ3w8kjC4NrZQmub2UeT7vRCrIUN+rTwm6drhxca90LuGooSjlgNbYf22k1LJXVyJB6yLpO9fKxAn9Fr5n2rbDunerGVnziiPSjVhAvcaMVhK9R29EY7RRM37XQulZbVKxeb55T0F2x2Pk8ead1P+RX07nhNaqPdS8UGVu4Li/HyiRLPwJx11qvP36KFYzc96fV0FRW99M2wwoDeXZJ251/g84Xnj7w5NFz+pQK/FQH+bnWhRNYNy3bf7UihIaHdcOze3HhtheMKz16uG2OdZNWNCrdtJ/VktFyMPQsMrhaXpaV6l20K1JY09L7UXt4WzdOFdXsQudjV1aNcCt991zx8IZx+8ofiyP1EEy5umJ92+1prWDV4/xnys9SAKt+d/9OSnBral4xvrAvc1Hd74XBRU7Inw6xAgXHd1tjMgD8a43rAQiTdliBheLqtIJtv1hdOuzZFvl51lTSgeFWq5+rE2v/J0q2rOdlwZzHrL7pLhnAKZ4nLn3Tyigoi6eP1R+9ojcbpml1AQpvW3qL+ylym4vbc+HQOviwD1z9lZWBANYFwdwnCgM+Yc3gsjcBwwrupB2sfKbTKSirHhdsTqR2oC/tG4aw4UAK0fWCMQou5vYfy2DL4TSenLGBw6lZjn26Nw3jz13HHMt+3h5k5dp48fJ2/LLhMKYJf+xwEeQqRfsGNfk7wbpAbVc/mA0HCgdg/ujGzqXliVba1sQ0fDw9aFK7BmnZuSzbkcwrI2PZdCiVdvVdD1xsmiafL9tDDR8vwmqUMR4GkJtvY+6mRIbF1aN/q8KW7A8W7yQjJ58gXy+aFMuGysm3kXA8g2u6NiLIrzDob7OZfLNqP3UCC9PCi3b1MoEl24/SqXEow+Ocv/dz1h9iW2I6YYE+RAYXXmznmyYbD6QwLK4+zcNLb4wwTZOZfx9k/uYjxNavSZPaNegSfJyaYeHO46xUB9/fYV2QP1gsG3Hxa4XdYcH63R/wtNUgcZbpvFr9qQ6r2ILnrQbTp5Ktm+/knfBOwTg+rS6xMsEqcK5y1OOepVbPA/ug6o16nPGAr5MZ90L8V3DJ61ZGqt3M+2HNJGgxyCpP8Uyd0CZwz3IryJWfY607utm6Bwpp7Hzd/Ot/rXslv5pWQDtxg5VFV1p2XDXm8vuYcsDKiMo8bjXGnetxP08mweafrIb/ioyJdni9dT+dc9Jq9C7a0+XkESu7yTiNz2RGMjS9wLpfiJ9ijaEGVq+YGrWtTN+iWZyG4TLz+GxS4McdpB+xsmDKGkCumFOu+ANrrA82hvV/u5Glp9gf32NtY7NZmUI16lhfCLDWLXrJCpC0HGRl8Jhm4ZfW18UNQvJ2K/upx73Wdrt/tzIuwppYY9OUJz/bCswUHWizIrbNsbI+HtzgOmshP88af2L/Siu115W6sVYXnbjrrDEa/ENdD5i3/y/AhO9uszJDhk6wxm9xEWxwqkNbvhXA8w22AoJ1Y5w/Dz+PtQaWBOuH664l58d0l5nHYfl71ue3YTerFaH5ACsTzTStz9vRLeUHcjw8rfe4Gs6goovbSrBfeF4+EdpfbXXvm1SQOdDqEugz1upaWAWDWH/5029cOfgCjqRms3jbEfJtJs/8tIkaPp6czMkHrNOW/eevTaSV+bX5UGEApkGoP88Nj6ZT4zBq+ntzJDXLmoHdzxs/75IXI/uPZbAn+SQ1fF1/7m+ftIrkkzk0q1ODhmEBLNpqBX3bRgazqcjznis+nh40cxEMOZSSyYmMSjQcFGgY5k+gr7fTe1ge+/sOVGq/AB9PGteyAkpJ6dkcTcuu1HMVV9prLmuf80Hx9yzYz4ufmv1IwwM/4zGuSEZw0cyAeh3g4uetlv1Kjnuwdt9xElOz+PLPvUTVqkGfFrU5mpbN3wkp9GlRG18vD46mZfPkj1YLvv39S09PJzDQ+qxFBPsyukspGYdnyIYDqQT7e1G3pj/3Tykce8Zenm2JaeTbTK7p2pALWtYhz2YyY+0BHh3cmpYRVnbhjiNpvPvbDh4Z2IqGYWd4wPfTsGbfcWbGH2TBlkT2H8sE4I0r27P9SDordidzS68meHsaHErJYuvhNJrUrkGAjyefLdvDrqNWNmjRz3VmTh57kjNoGRHIwxcX/rbf9dUaALw8DFpEBDnVoSubD6XSKiKIrYlpJZ6jrH2Kquj3rfh+resGOYL3rhjAwOgIDp7I5H9XxOLpUfq2i7YeIcjPm06NXXSPrUpL37K6Wz1+AHwDYeFLVnb18Pesa9kKfperLosyw7r3CG9Tcv2xndawBFt+hm+uc378gXUlhyAoTdHJOIqPjelmdK3qHqoi8FP97szOd8Wnpzybio7aX57QqMIgS8Muzo95eMCFT1TuuYsfo0nfc9M30j4rTtYJoNjF48G18PHFztlOFz5hBS6a9q/84F7211i85bQ8Hp5WwKQ0Rfva37fKERQ6cCKT1XuP4+vlwYDW4ee+O4h/qPPnoGgdG4Y1IHXxQand1LbENL5bk0B0vbIDcp6GwQWt6hBYcLOfkpnL6r3HmL3+ML9tOcK4Ia1dBgmKy7fZ2HnkJC3rBpW6jWmafLfmAFG1Arihe2NaRBRum5Wbz6RlezicmsXTl0XzzV/7WLD5COHBvjw+pI1TMOLXjYdZtecYxzNyaRMZzJ6kk9QK9OHbVQk0Cw/k0phIBsfUJbhINsbJ7Dxm/n2QEH9vdiWdZECbcPKO59AOCgcP/LFgCvTiXb9Ow7KdSazcfYw351vZaC0KAhUBvl6EBXizsCB40qJIACM338ae5EyeXPpLiePZgz4AF7YKZ8GWI/RqXosAH+v9aRjqT6/mtenWNIyW4UF4FLlBCA8uu/WsYVhAmTeIq5907kabkpHLrqR0OjQKJTffxoLNR/DxMqgTeGZa6fYdy+Der9dgGPDFLV2ZvjqBH+MPOrql9W5R2+UNUINQf+ZtSqR/qzo8fHHZ3SGW70ripdlbAGhd17ppCw/yZfE2q166RoXx5KXOffR/Xn+IDxbvpFVEEA1CC1Pra/p78eeuY3xxS1dCA6xz4urVq+jUybp2OZiSyZ1fWl2hezSt5aib+iH+zN9sZUX2bVmHfw8sLPPmw6k8On2d43WVxv6aAWLq16R/6/BKBaKqSvEypmbl8ePWk9znlercePP9Hdb/t/xqpeaXYv6mRCYt30P3prV47VdrTAr7dysn38be5MKBQpfuSGbyisJxuaavLjZeAoXveVL+SWqH+vPnrmQ2H0p1BD3Ptfoh/hhG4fs2ZeV+pqwsHA9n/uYjjte7/Yg1dtiM+INO55dTYT9WUeFBvtT0r1w39zybye6kkyXWP/JtYVbzv/a5GGS5GPv7ALC+IPNwW2K6I9hT/DkbhPo76tCVfJvJ5kOpjqAPlP19syv++a3IPq72q+FbenaizWayYMsRR3B92qqEUusz32ayq+D9Pd06P9MuyTnMQ2B1F/INtAbXr9nIGkD7DPt6xT4+X7bbcQrp1yocb08DXy9P7rqgGT5erq9N/95/grFFPotFhdXw4f3rOlJ0moj5mxL5fftRxg1pQ4BhFN7TBEYUZrpXNOgD1nhh9St2jZpvM1m6I4mwGj6lZr8WdSIjh7Hf/s2+Yxnc1qep47pu2+E0moVbjRCbD6VxQ/fGZOfl4+nhwdSV+7g0th6+3h6s3XecgyeyyMzNL/P5ft92lH3JGVzfw3rdJ7PzmLPhMIkpWdzUK4qktGwa167BpkN5pP59kKNp2WTn5XNPv9Jne8vIyWNdQgr7jmWQlZvPnPWHublXFAOj61bovRL3ooyf84Ait5Wwa7E17fqYWc4jyJsmPFfL6rvdpK81u0NQZKWi/juOpHMkLYuezSrfUlBaHaZn5/H50t0cz8glul4wh1OzWP73ZiY1nIVH7wchvA0rdiXz5Z97mbXukNO+TWvX4M4LmuJdLAD0x/Ykdief5IbujZn6135Ha/eB45nk5Nv496BWRNY88ymeOXk2xn2/nkti6vLKyFinbhqlyczJ572FO1iz7zi39GrCLxsP06FRCP4ugiHf/LWfFbut7jTFu4fYJaVlk5btPHVzadueioyMDAICAlxeXJfFXobK7nc6ir7u8p7Xvm2ezeZoJT7d4wdzknV+t5PU62lqdxxuZf+0ugSusWaDMk2T9xftJCLYD3t84WhaNv+bswU/bw8ia5Z9kZ+enVcik+PC1uH8tqVkt9KL2oQ7XYzOXn+YtpHBHE3PJsDHkw+u78TJ7Dyi69XE3+csprPLGaXfxrLl5dvYcDCVYyezybdZwYRN01/gmpSP4PEEa2wse2ZerebwLytw9vu2o2w9nMbkFXsdmRKuvm8Al8Q43xxc1bkhwf7e+Hh64Olh8OovW1i49ShvjY5zZMscO5lDtyZhjsYLez3m5dvYefQktrN43WmacOdXq4itH8J9FzYnIyePlhFBmOAIZufbTOL3nyCg4FwQv/8Ej3+/3un1zl5/GLC6dV7Y+vQa9OzHKqpRWADt6lc+o8x+rI9v7EyAryeehkGwvzemad3g2YP8D06NZ2tiGsPa1+Pufs04mZ2Hl6cHDUL9qV2kOyXAhgMpmCZ4eRYGgrPzbOTm2+jcOBTDMMr9Lh48kcmxkzlk5ubTMiKoQkGtnDwbWw6n4uftSYNQf0cAvjypWbkcPJFJvs3Ez9uTZnXKDtIcTctm5e5j3Pv1Gno0rUVojdLLZn9/i3/uq1rdfbN5Kvt1cu5chk/NSGus0dir4YqJgNUNduvhtBIBsP3HMpkwfxueHgb5NpNafgb/HRZb4vi7k046rkHtv/eDo+vyy8aSn93Srrns+/VpUZsgv8K6XLn7OEnp2U775uTZOHCi8FrEvr6FfzqDu8fQetuHHA+N4S+vTrw5fzsXtKxDq7pB/LblCGN6NOaDxbsc+xctT51AX46kZbGnIEhdXlkB6tX0w7ecBrpzeW13qiryWouqE+TraLQ8H9jLWdb19MMXt6xwgHj5zmQ2HUrl5l5NKNrGtTc5g5r+3oQEOJ8HPAyDER3O7PAR5VHGjxvZcCCFuRsP0yC0/BThLQm5zJi6lj93HXMaV6J+iOsP99G0bHLyXU/9WNo+5bGfQJ8dFs2kZXuIaxRC9yYupnCuYiEp2QwE52krwerba+ZD93tg8P8qfLwjaVlMXLyLlhFBvDh7MymZVrbQFR3rO15/enYey3YmMbBt6RcCWxJyOfLXfk5k5jhawF8dGcuE+ds4lJJVbGsPmh4axisNa/DRV4vZUaQ18Lnh0Ww+lMaGAymsP5DCY9+VPrDd2n3We1A70IeezWo7TpL2ltqzZfb6w8xef9jxWcvOyycpPQd/b88SFx1Ff9iX7UwGXLcMFxdTSovIzL8PlljXrn7NMzYmypEjWYSH13S8l1Pv6F7iIrmo79ckkHC88DWGBnizpqBeAF4Y0Y7uTcv/Hu08ms7ynclc370Rpc0EeCglkxs+KRw0u+h71CDUnyXbrTFmLo2NdAoiXtQmwnGDAzgFfu6/sDmTV+wj+WSOY13/VnVKBPUign2dxrgBqBUaCpmQkppKbfvYZj3v589dyWw4kMILP28u9fVm5dpKreOiDANu7BGFt6dBg9AAwmr4kJSeTecX5hPo68XnN3ehYVgAEcWycawfUxfT5oq4ES9PD+IahjitO1I7HFIgK+0Yfr5B1jgXwKoOL/H1tHi+X+M8GUGfFrUdGVZr9h0n4Xgmt/dpwqDourSsG+SU+efKZzdXcIaagvK2KiOr8UxZ8uiFZT7u6WE4deVpExnMNV3P3thjefk29iRnkJmTT3p2HlG1A8oNfJ+uXx+qeAZ2RTIeylMvxJ96lbz+9PHyILZBSKWfK9jPm+C6Fc+WqhPky9DYSIbGDi1/4/PUnBmbIR72HzpMs+M7AJiS3RPPv/bzxZ97nMaJcyXfZgVbk7NMHp5W+riXg6PrEtugJtd3b0yXqDBOZOSweu9x5m5M5JtV+53OF8XF1K9J35Z1GNWpgdN6m83k4z92lSij/fpwaEwknh4Gy3YmMzfJYO7+DYB90Gor23fxtqOOTFJ7V1K7tpHBeHoYLNicWCJY0CoiyGWGUovwQOYWZHl2jip/bKQavp6O8v/3kjb0bx2OzTT5bOluhrSLJCMnj7u+WkOAjye5+TZM08qWc+Xn+3vj61VaoMkkO8/meHzF7mT++4N1Dn/y0rakZeVyaWw9Vq5cSdeuXXl/0Q6+X3OAobGReJbSqyGypp/j+ruoHhW4Nj2X7HVnv6Z3FfgZP29bpY9bWhZacT6eHuc88FMVlPFzChJTs7j+4xWM6RnlWLdgc6Kj20FEsC+JqeWPOVCekR0buFy/Yney081mRfYpi4lZ4mLwfFWfoyz1e4C0gW8S1PPmwgeeq2UNmPxkkmN62OMnc/hl42HybSbzNyc60ssjggtv5M9EPVVEq4ggnri0DbPXH2bKypLTVl/RoT7PDo92uuE+nJJFdl5+iW1N0xq01MvDwMMwqB/ij4eHgWmaHChoBTsb1uw7zkPfWCfQ4XH18CoYu2V3Ujpr9p2gTWQwbYv10U/JzHV0w7D7/OYuLlsmTNNqZYyqHVDqj6JpmhxNz8bDMAjw8cQ0KXVMlVOhDIOKO3gikzoT6rOt2c1E1oCQ9Z/Sw2sqienOGVlf397NKSBtYNCo1tkdM0P16B5Uj5W39pfP6PDng6wc8jNdu/Um9ZfnCfzzDdpmfUoWhb99s/7Vm7o1/coMbJ8pqsfqT3VYtXatXUjTH0fwV6+JRJ5YQ4ONH9A+ayIpFGY7XdetEXf0bVpiXw/DoGFYAAnHM/hj2Z/06N7N5XOE+PtQM6DiAbUzzWazrmGLZwRm5ubj5+WJYVhdub09PbCZkJ6VR8Mwf0IKAlF5+VYWkb+PJ94eHuTabIQHneMBjs8Rd/w+5ttMcvNtjm50OXlWgkNqVi45eTaycvPLHJ+rOCv4ZivRa2LWukM0DAugfYOSAW/72IHnijJ+qoETGTl0e2kBAE/M2OBym/6twvk7IYW6wb68cHlMucf8c/lyuvfoQX6+dbLz9DSICPItc4yXjJw80rPz8PLwIMjPi7SsPEIDvMsc4K4sL46IISk9Gw8PA5vNxDA45WOdTVv2HIAZsG3P3v9v787D9azrO4+/v+ec5GQ5OUkIZGMNsobFhbjgqIBIXehV1HFmWGZUpi306rSjo1YZ247W6eZyKa2iI1Uvrb1GXGZAxoKUYhFLW+vQ0UJnyiICIksSBCQkJCH5zh/3/SRPTs5JzpZzP/fvvF/XxXWe5U7yDZ98n/Oc7/O7fzendTZff/JB2Pks+aJL+ZNbH9i14mY0K4fncebxh+y6f/9jm3ngp5v58qUvYaCvjxXDg2zctG2PFVXfuWsDH73xLq665CVjLgftZAjVvi/bd+zctQfFyuF5u16sXn7sIfz+G07m0Z9t3TW8Wb5ocNT/1ysneLpWRIxrhdlkHblsIW94/uiDxWe27xjXXjZTFRHFfiNvm+H5c9jCIHf/ZD1PbP4hC+MoHt30LOe/8HD+1brDWb1kHiuH5/Xk64hUqiNWV5cI/9A13+Udy07g8VtvYW0sZ3D+ENf+yuksmjdwwFebSJpe8xdVK9RuveNejvjpHQz0LeVJhrjh7a9g8fw5rBge/X1kt8OWLmDlwr4Z/+F2vPr6YkobqQ/09+7fTfvX3xf0d11ZrrNSa7o/nPgPZ429H9Js4OBngub09/HS5yzjb374GN9591kMztk9nJnb38fi+RMfviyb3zfhU7QWzB3Y43zo/V12d3/mz+3vqStXjGXFyWvYevUAt999L7u2cHukGsC96a9XcdvO3UOfi158BMcsH+LcU6s3wovnz9nH8srdDlm054vM+S86gvP3swx8IhlGxISHOr1uJoY+6i0L5vSzgblsefopTuh/gGeOfjX/fOFr/LcgNWjZsuqDjeHYzEWf/S43znmQgVUn8Q+XnjOhT0sl9Y7Boep0pA0b1/PS/g0sWn0cP7rkdX6wImlCHPxM0MLBAf77L499VQwdWAMD/Wyas4QFzzzBib/9TS4942h23HIN7+yDu3Yexi+9bA2/etYxLJo3sNfyPknTp68v2JyDHBHrWRZPwfHrwKGP1KzBavn6IjYzN7expu8R+o+/CBz6SK21YLha8bOIzZwwdyMLV7xw4leqlTTrOfhR6wwtXcFztz7LlvU7uPwv7+byOffz2MByvvWbv7DXah1JB862mMeaqDeSXnLgNkeVNE7zqsHPcGzm6HiYfnbC8hMbLkrSVMybP8SO6Of05c8y/PhjsPSopkuS1EIuiVDrDAyv4PihzXzmzdW+VS8ZWs+yNc9z6CPNsM0Msjrqq30tPGTfB0s68OZVG+wPs5ljo76C4iEOfqRWi6B//hLOWLK+uu8HLZImwcGP2mfxYfDkg7xq7Qru/a/nsHLr/bB8bdNVSbPO09l1BRAHP1LzBgbJgXksis2siMerxxZP/GqfknrM4DBsrC5vzqKVzdYiqZU81Uvts3QNPL0BtjxO36b1sHO7S9mlBmzJrk3Kh5Y3V4ikXWL+QbzykD5iaAH8cA4MLmq6JElTNW8YHv5RddvBj6RJcPCj9ll1avX14X+EZ5+pbh90dHP1SLPVnPmwE3LOAmKul1GVesLwKo4bfAoWLoKFB7sJrFSCev8uAIZWNFeHpNbyVC+1z6rnVV+vuggev7+6vfjwxsqRZquXra32GQhP85J6x/BquP9WePJBGD606WokTYfBav8uBubtOQSSpHFy8KP2WXgwHLoOtj0F1/9G9U3QTz+kGTd/QX0Kiad5Sb1jzkLYsQ2eegQWLGu6GknToTPsWbTSVXySJsXBj9rp4utgqD7HuX8u9PlPWZpxcxdUX13xI/WOo8+svm68ExYc1GgpkqbJ/KXV16c3NluHpNbyp2W108AgvP6K6vbKU5qtRZqt5tT7+nTekEpq3vCq3bfnO/iRitB5r7ttU7N1SGotN3dWex3zKnjLN+CgNU1XIs1OA4PVVzd2lnpH9yB2gUNZqQjPPR+2b4HjX9d0JZJaysGP2m3Ny5uuQJq9OlfVmzvUbB2Sdute5eOKH6kc6y5uugJJLeapXpKkydn80+qr+4hIvaN7xc+SI5urQ5Ik9QwHP5KkyVl+YvX10NOarUPSbt2nXi49qrEyJElS7/BUL0nS5LzgzXDkS+HgY5uuRFJHBBz2Qnjwe7Dk8KarkSRJPcDBjyRpciIc+ki96K1/Dk9v2L0BuyRJmtU81UuSJKkkA4Ow+LCmq5AkST3CwY8kSZIkSVKhHPxIkiRJkiQVysGPJEmSJElSoRz8SJIkSZIkFcrBjyRJkiRJUqEc/EiSJEmSJBXKwY8kSZIkSVKhHPxIkiRJkiQVysGPJEmSJElSofY7+ImIz0XE+oi4o+uxgyLixoi4u/669MCWKUmSJEmSpIkaz4qfzwOvGfHYZcBNmXkscFN9X5IkSZIkST1kv4OfzLwF+OmIh88DvlDf/gLw+uktS5IkSZIkSVM12T1+VmTmwwD11+XTV5IkSZIkSZKmQ2Tm/g+KOAr4RmaeXN9/IjOXdD3/eGaOus9PRFwCXAKwYsWK06666qppKLssmzZtYmhoqOkyNAVmWAZzLIM5lsEcy2CO7WeGZTDHMphjGQ5UjmedddZtmblutOcGJvl7PhoRqzLz4YhYBawf68DMvBK4EmDdunV55plnTvKPLNfNN9+M/1/azQzLYI5lMMcymGMZzLH9zLAM5lgGcyxDEzlO9lSva4G31LffAnx9esqRJEmSJEnSdBnP5dy/BPwtcHxEPBgRvwj8IXBORNwNnFPflyRJkiRJUg/Z76lemXnBGE+dPc21SJIkSZIkaRpN9lQvSZIkSZIk9TgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRJkiRJhXLwI0mSJEmSVCgHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFmtLgJyJeExF3RsQ9EXHZdBUlSZIkSZKkqZv04Cci+oErgNcCa4ELImLtdBUmSZIkSZKkqZnKip8XAfdk5r2ZuQ24CjhvesqSJEmSJEnSVE1l8HMo8OOu+w/Wj0mSJEmSJKkHDEzh18Yoj+VeB0VcAlxS390UEXdO4c8s1cHAxqaL0JSYYRnMsQzmWAZzLIM5tp8ZlsEcy2COZThQOR451hNTGfw8CBzedf8w4KGRB2XmlcCVU/hzihcR/zsz1zVdhybPDMtgjmUwxzKYYxnMsf3MsAzmWAZzLEMTOU7lVK/vAcdGxJqImAucD1w7PWVJkiRJkiRpqia94iczn42IXwNuAPqBz2XmP01bZZIkSZIkSZqSqZzqRWZeB1w3TbXMZp4K135mWAZzLIM5lsEcy2CO7WeGZTDHMphjGWY8x8jcaz9mSZIkSZIkFWAqe/xIkiRJkiSphzn4mQERsSoiouk6NHkRsaLpGjR19mIZ7Mcy2I9lsB/bz14sg71YBvuxDL3Yjw5+DqCIGIyITwHfBq6MiDc2XZMmJiKGIuJjwPUR8WkzbCd7sQz2YxnsxzLYj+1nL5bBXiyD/ViGXu5HBz8H1i8AqzLzOOAbwAci4riGa9I4RcShwBep+uR1VC/EH2q0KE2Wvdhy9mNR7MeWsx+LYS+2nL1YFPux5Xq9Hx38TLOIGOq6m8AGgMz8OvBN4NKIWNJAaZq4Z4DPZObbMvMR4CvA9yPi1Ibr0jjYi8WxH1vMfiyO/dhS9mJx7MUWsx+L09P96OBnmkTEMRHxFeDzEXFuRCwEtgA/i4jV9WEfBl4AnFT/Gs/f7CERcXxE/LeImA+QmY8BN3cdcjhwNHBnA+VpnOzFMtiPZbAfy2A/tp+9WAZ7sQz2Yxna1o8OfqZBRPQBlwO3Uy3v+nngt4G/BE4AnhsRczPzUaolX/8JIDOzkYK1l4h4GVV2lwDvqB+LzHy667C5wH2ZubWBEjUO9mIZ7Mcy2I9lsB/bz14sg71YBvuxDG3sRwc/02MV8ATw+/XSvN8FXgk8D/g88K/r2wBXAY9HxJyZLlL79Bjw74HjgIsj4shRXmCfD/wQICJ+uVeW7WkP9mIZ7Mcy2I9lsB/bz14sg71YBvuxDK3rRwc/k1RPawHIzJ8A64Bzuu5/EvidzPwqcBfwnyPinVQNfG9mbp/5qtVtRIb/D7gnM+8BbgQ+MPIY4GxgWUT8D+BCqvM41aCRy17txXYaJUf7saW6s7Qf22tEjvZjy9mLZbAXy2A/lqGN/ejgZwIi4tCI+Ei9/G5n/Vh//fQfAr/Vdfg1VOdpvrB+7qPAEcBHMvMPZrBsdRktwy6dZXjvBv5FRJw94pjDgBcDX8rMszLzrpmoWXuLiNURcSWwtr4f9mL7jJZj19P2Y0tExPKIWFcvcc76sYH6afuxJUbLsYv92AIRsSwihkc8Zi+2zGg5drEXW6LOcemIx+zHlhktxy6t6kcHP+MUEb8C3ADMA+Z2pnmZuaM+5IvAtoh4e/34E1SbdG3JynfqHb7/bMaLFzB2hh2ZmRHRn5lPAZ+gPqc2Ii6ofyD9YGaelJlfm+natVtE/BbwNeBi4A2dx+3Fdhkrxw77sR0i4jeBvwF+jyqnzjnuz9aH2I8tMFaOneftx94XEe8F/h64PCLe3XncXmyXsXLssBfboX6PcwdwZUS8r/O4/dguY+XY0bZ+HNj/IbNb1ydfJwHnZ+YdYxyzIyJ+A/haRNwHDAEnAyNXlWiGjSfDLjsBMvPyiHhfRDxJ9cPp1Zl584GvVmOJiEOAPwAWAOdRXengNfXqrW2d4+zF3jbeHGv2Yw+LiFcDZ1Cd394H3BcRH8jMRztDA/ux9+0rxxGH2o89KiJ+jirDE4DFwJcj4ifAVzNzW0T02Yu9b385dh1qL/awiFgLvAxYA6wA/jQi7gKuzcyn7cd22F+OXYe2ph9d8bMf9SQvgDOBDRFxUkR8OCLeGhEHdR3Tn5m3AW+n2sjpEuBtmfl/m6pdlfFkOOLYxRHxYeDHwOsy8xcz03Okm/cz4L9k5oWZuYHqjdHSzpvazkH1N1R7sXeNK0ewH1ugD9hE9SHSycD3gBOhys7vja0xZo7d7MeetojqcsH9mbkR+D/A26g3iM3MnX5vbIV95thhL/aeiFjSdfdpYAkwlJn3A5+iGuidCvZjL5tIjh1t6kcHPyPU5/FdGRFndD3cB1wNXAZ8DLiHasf1d0XECfUxCZCZX8/M92XmKzLzr2eydlWmkGHHU8AXM/PUzLx1RorWXkbmmJlbM/Oh2L2XzzXA2RFxcO55Pq292EOmkGOH/dgDxnhdfQi4F/g61caGP6A6PeEdEbG8PsZ+7CFTyLHDfmzYGBnuoBrc/ZuImFvf3gCcUv+awF7sKVPIscNe7AF1jp8AvhIRvx4Rx1MNDP6e3QOCLwPbgHVd733sxx4yhRw7WtGPDn66RMSRwGeAfwlc0LUaZCfVBO8FVMstPw28B1gJLIdqejvzFWukqWTYkZk7M/MfZ65qjbSPHDunjgTVZRRvoto0ja7nR25KqoZMJceu4+zHho2S4zKAzPxBZr6L6hPqf5uZ7wd+neoqFgvqY/ze2COmkmOH/disUTI8uH7qf1Ht0XQOcAvwAPBZ4N/B7hV4M1+xRjOVHDu/h73YvIh4CXA9sJHqkuyHA5fWq7W2AM+PiNV1bjcAF2a9H6X92DumkmNHW/rRwc+eNlFtbLgGOJLqU+iBOuibgLupluORmbcDB+O5mL3GDMswWo67Xq+6vmEuppq+j7x8onqDOZZhZI6v7ORUf+q1nt2Dnu/Utw8a/bdSg8yx/UZmeFb9HmdHVpvAXgqcl5kfBb4N3BsRgyNWiqh55liGJ4CPZub7M/MW4FZgsH7ueuAY4FUAmfnnwKaIWN1EodqnJ5glObq5c5fMfCwiNmfmloj4EnAB8F3ggcy8NyKuAN4TER8DjqYanP2owZI1ghmWYYwc/45q1Rb1viGbIuIh4JeAG11Z0HvMsQz7yrFeuTUIvD4i1gCnA08C9zdYskZhju23v9dU4JmsNo99PtVg4bbM3DrW76dmmGMZMvOfI+LHEbsuIrOd6mcLMvPmqC4BfnGd4ynAI1QDdvWQ2ZRjuNJsbBHxNaqp38ezvvxevTT6TGBJZn62wfI0DmZYhq4c/yirTfEiM7M+N/6gzLy64RI1DuZYhq4cP5GZ26O6UtuLgPOBv83MTzZaoMbFHNtvtNdUYC3wVeDKzLy8yfo0PuZYhoj4VeCIzLys67GjgDdSXab9U03VpvErOcdZN/iJiPdQbZ72R5m5vevxvs4nzfWn0DvqH0beCbwDeAXwV5np6pCGmWEZppjjtzLzvgbK1gjmWIYp5HgGcFNm3td9rJphju03xdfUmzLz/ogYdIVIs8yxDOPMsa8e2H2Yamj+PyPijcDfZeZDzVSubuZYmTV7KUTEvIh4L/AfqT7NWtv1XHSFfmru3njr28AQ8A/AhVRLv9QQMyzDNOX47IwXrj2YYxmmIccLgGe7j9XMM8f2m4YML6L6wQaHBc0xxzJMMMfOa+ZJwLERcT3Vxt2+x2mYOe5p1gx+gK1UG6QdAfwF8JaIWATVBqMRsTIi/hS4IiJWR7WJ2luB5wBvzsxXZeaDTRUvwAxLYY5lMMcyTEuOOduWD/cec2y/qWZ4tq+pPcEcyzDRHI8AXga8Fvh0Zl6Uma3cB6Yw5til2MFPRAxExLsi4nDYdfWY2+rp+seprux0esSuHfJPpVrW9fLMfKiesn8rM4/MzGua+DvMdmZYBnMsgzmWwRzLYI7tZ4ZlMMcyTEOODwBvy8wzzbE55rhvRe7xExGnAF8ADqV6Mb2g67nOZqJvp9rE8F054ry9qC6pWMyyrjYywzKYYxnMsQzmWAZzbD8zLIM5lmEacpybmdtmsmbtzRz3r9QVPxuBPwZOAI6KiJ+DahO1rmOuAOYD6yLijIh4Q31M+CLcE8ywDOZYBnMsgzmWwRzbzwzLYI5lmGqORQ8LWsQc96PIwU9mPgx8OTMfBz4PvLd+fAfs2rV7O3Ad8BXgs0DWx5S3BKqFzLAM5lgGcyyDOZbBHNvPDMtgjmUwxzKY4/4VeapXt4iYD1wNXJeZf9z1+HOBLwHXZuZlTdWn/TPDMphjGcyxDOZYBnNsPzMsgzmWwRzLYI6jK37wAxARrwY+kJkvjoiTgR9TTfjmZOZjzVan8TDDMphjGcyxDOZYBnNsPzMsgzmWwRzLYI57K/JUr5Ey8wbg8YjYCnwQGMzMn83W0NvIDMtgjmUwxzKYYxnMsf3MsAzmWAZzLIM57q34wU9E9EXE7wInAr+Wmedm5vqm69L4mWEZzLEM5lgGcyyDObafGZbBHMtgjmUwx9HNllO9Xkt1WbetTdeiyTHDMphjGcyxDOZYBnNsPzMsgzmWwRzLYI57mxWDH0mSJEmSpNmo+FO9JEmSJEmSZisHP5IkSZIkSYVy8CNJkiRJklQoBz+SJEmSJEmFcvAjSZIkSZJUKAc/kiRpVoiIZRHx/fq/RyLiJ/XtTRHxyabrkyRJOhC8nLskSZp1IuL9wKbM/EjTtUiSJB1IrviRJEmzWkScGRHfqG+/PyK+EBF/ERH3RcQbI+JDEXF7RHwzIubUx50WEd+OiNsi4oaIWNXs30KSJGl0Dn4kSZL29BzgXOA84M+Av8rMU4AtwLn18OfjwJsy8zTgc8DvNVWsJEnSvgw0XYAkSVKPuT4zt0fE7UA/8M368duBo4DjgZOBGyOC+piHG6hTkiRpvxz8SJIk7WkrQGbujIjtuXtDxJ1U750C+KfMPL2pAiVJksbLU70kSZIm5k7gkIg4HSAi5kTESQ3XJEmSNCoHP5IkSROQmduANwEfjIgfAN8HXtpoUZIkSWPwcu6SJEmSJEmFcsWPJEmSJElSoRz8SJIkSZIkFcrBjyRJkiRJUqEc/EiSJEmSJBXKwY8kSZIkSVKhHPxIkiRJkiQVysGPJEmSJElSoRz8SJIkSZIkFer/A8RNgulILuUaAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot der Luftfeuchtigkeit und beider Tempsensoren, alles innen\n",
+ "df1_1.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity'], figsize=(20,8), grid=True) \n",
+ "plt.ylim(0, 80)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Zeitraum 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 22.0)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot des zweiten Zeitraumes beider temp sensoren\n",
+ "df1_2.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature'], figsize=(20,8), grid=True)\n",
+ "plt.ylim(0, 22)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 22.0)"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot der zugehörigen Außentemp\n",
+ "out2.plot.line(x='time', y = ['temp'], figsize=(20,8), grid=True)\n",
+ "plt.ylim(0, 22)\n",
+ "#ax.set_ylim(-5, 23)\n",
+ "# plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 80.0)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot der Luftfeuchtigkeit und beider Tempsensoren, alles innen\n",
+ "df1_2.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity'], figsize=(20,8), grid=True) \n",
+ "plt.ylim(0, 80)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Behaglichkeit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>DS18B20.Temperature</th>\n",
+ " <th>AM2301.Temperature</th>\n",
+ " <th>AM2301.Humidity</th>\n",
+ " <th>AM2301.DewPoint</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1199.0</td>\n",
+ " <td>1199.000000</td>\n",
+ " <td>1199.000000</td>\n",
+ " <td>1199.000000</td>\n",
+ " <td>1199.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>17.822602</td>\n",
+ " <td>19.443703</td>\n",
+ " <td>57.999416</td>\n",
+ " <td>10.381151</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.266301</td>\n",
+ " <td>0.506016</td>\n",
+ " <td>1.616507</td>\n",
+ " <td>0.556817</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>16.100000</td>\n",
+ " <td>15.300000</td>\n",
+ " <td>51.900000</td>\n",
+ " <td>6.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>17.600000</td>\n",
+ " <td>19.300000</td>\n",
+ " <td>57.700000</td>\n",
+ " <td>10.400000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>17.800000</td>\n",
+ " <td>19.500000</td>\n",
+ " <td>58.700000</td>\n",
+ " <td>10.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>18.000000</td>\n",
+ " <td>19.700000</td>\n",
+ " <td>59.100000</td>\n",
+ " <td>10.600000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>18.300000</td>\n",
+ " <td>20.100000</td>\n",
+ " <td>59.900000</td>\n",
+ " <td>11.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index DS18B20.Temperature AM2301.Temperature AM2301.Humidity \\\n",
+ "count 1199.0 1199.000000 1199.000000 1199.000000 \n",
+ "mean 0.0 17.822602 19.443703 57.999416 \n",
+ "std 0.0 0.266301 0.506016 1.616507 \n",
+ "min 0.0 16.100000 15.300000 51.900000 \n",
+ "25% 0.0 17.600000 19.300000 57.700000 \n",
+ "50% 0.0 17.800000 19.500000 58.700000 \n",
+ "75% 0.0 18.000000 19.700000 59.100000 \n",
+ "max 0.0 18.300000 20.100000 59.900000 \n",
+ "\n",
+ " AM2301.DewPoint \n",
+ "count 1199.000000 \n",
+ "mean 10.381151 \n",
+ "std 0.556817 \n",
+ "min 6.500000 \n",
+ "25% 10.400000 \n",
+ "50% 10.500000 \n",
+ "75% 10.600000 \n",
+ "max 11.000000 "
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "startb = '2022-11-24 08:00:00'\n",
+ "endb = '2022-11-24 18:00:00'\n",
+ "dfb = finaldf.drop([\"Switch1\", \"BH1750.Illuminance\"], axis = 1)\n",
+ "dfb = dfb[(dfb['Time'] > startb) & (dfb['Time'] < endb)]\n",
+ "dfb.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.2 Aufgabe 1.2: Nutzungsroutine von Raumbeleuchtungen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Messreihe 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Datensatz nach Zeitraum sortieren\n",
+ "#24.11. - 01.12.\n",
+ "start3 = '2022-11-24 20:00:00'\n",
+ "end3 = '2022-12-01 19:00:00'\n",
+ "df1_3 = finaldf.drop(['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint'], axis = 1)\n",
+ "df1_3 = df1_3[(df1_3['Time'] >= start3) & (df1_3['Time'] <= end3)]\n",
+ "\n",
+ "#02.12. - 09.12.\n",
+ "start4 = '2022-12-01 20:00:00'\n",
+ "end4 = '2022-12-09 19:00:00'\n",
+ "df1_4 = finaldf.drop(['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint'], axis = 1)\n",
+ "df1_4 = df1_4[(df1_4['Time'] >= start4) & (df1_4['Time'] <= end4)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>18162.0</td>\n",
+ " <td>18162.000000</td>\n",
+ " <td>18162.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.015637</td>\n",
+ " <td>17.184066</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.124070</td>\n",
+ " <td>186.018026</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2517.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance\n",
+ "count 18162.0 18162.000000 18162.000000\n",
+ "mean 0.0 0.015637 17.184066\n",
+ "std 0.0 0.124070 186.018026\n",
+ "min 0.0 0.000000 0.000000\n",
+ "25% 0.0 0.000000 0.000000\n",
+ "50% 0.0 0.000000 0.000000\n",
+ "75% 0.0 0.000000 4.000000\n",
+ "max 0.0 1.000000 2517.000000"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Helligkeit\n",
+ "df1_3.plot.line(x='Time', y = [\"BH1750.Illuminance\"], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Bewegungserkennung\n",
+ "df1_3.plot.line(x='Time', y = [\"Switch1\"], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a boolean mask indicating which values in column are above 200\n",
+ "mask = df1_3['BH1750.Illuminance'] > 200\n",
+ "\n",
+ "# set the values in column to 1 if they are above 200, and False otherwise\n",
+ "df1_3.loc[mask, 'BH1750.Illuminance'] = 1\n",
+ "df1_3.loc[~mask, 'BH1750.Illuminance'] = 0\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>25627</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:22</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25628</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:52</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25629</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:29</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25630</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:59</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25631</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:02:29</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43784</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:57:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43785</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43786</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43787</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43788</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>18162 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance\n",
+ "25627 0 2022-11-24 20:00:22 0 1.0\n",
+ "25628 0 2022-11-24 20:00:52 1 1.0\n",
+ "25629 0 2022-11-24 20:01:29 1 1.0\n",
+ "25630 0 2022-11-24 20:01:59 0 0.0\n",
+ "25631 0 2022-11-24 20:02:29 0 0.0\n",
+ "... ... ... ... ...\n",
+ "43784 0 2022-12-01 18:57:35 0 0.0\n",
+ "43785 0 2022-12-01 18:58:05 0 0.0\n",
+ "43786 0 2022-12-01 18:58:35 0 0.0\n",
+ "43787 0 2022-12-01 18:59:05 0 0.0\n",
+ "43788 0 2022-12-01 18:59:35 0 0.0\n",
+ "\n",
+ "[18162 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# S1/Illum/Cond\n",
+ "# Zustand 1: 0/0 --> Gut\n",
+ "# Zustand 2: 0/1 --> Schlecht\n",
+ "# Zustand 3: 1/0 --> Gut\n",
+ "# Zustand 4: 1/1 --> Gut"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Erstelle eine Spalte Condition mit den Werten 0 und 1, basierend auf welche relevant werden\n",
+ "def new_column(row):\n",
+ " if ((row['Switch1'] == 0) & (row['BH1750.Illuminance'] == 0)):\n",
+ " return 0\n",
+ " elif ((row['Switch1'] == 1) & (row['BH1750.Illuminance'] == 0)):\n",
+ " return 0\n",
+ " elif ((row['Switch1'] == 1) & (row['BH1750.Illuminance'] == 1)):\n",
+ " return 0\n",
+ " else:\n",
+ " return 1\n",
+ "\n",
+ "df1_3['Condition'] = df1_3.apply(new_column, axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>25627</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:22</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25628</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:52</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25629</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:29</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25630</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:59</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25631</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:02:29</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43784</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:57:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43785</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43786</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43787</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43788</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>18162 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition\n",
+ "25627 0 2022-11-24 20:00:22 0 1.0 1\n",
+ "25628 0 2022-11-24 20:00:52 1 1.0 0\n",
+ "25629 0 2022-11-24 20:01:29 1 1.0 0\n",
+ "25630 0 2022-11-24 20:01:59 0 0.0 0\n",
+ "25631 0 2022-11-24 20:02:29 0 0.0 0\n",
+ "... ... ... ... ... ...\n",
+ "43784 0 2022-12-01 18:57:35 0 0.0 0\n",
+ "43785 0 2022-12-01 18:58:05 0 0.0 0\n",
+ "43786 0 2022-12-01 18:58:35 0 0.0 0\n",
+ "43787 0 2022-12-01 18:59:05 0 0.0 0\n",
+ "43788 0 2022-12-01 18:59:35 0 0.0 0\n",
+ "\n",
+ "[18162 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Condition = Werte die 1 sind, sind die \"schlechten\" Zustände\n",
+ "df1_3.plot.line(x='Time', y = ['Condition'], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To Do:\n",
+ " 1. Wenn außerhalb Messbereich Switch1, aber Zeitdelta <3min, dann muss es ok sein. \n",
+ " D.h. Eine 1 bei Condition soll zur 0 werden. \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# erstelle eine neue Spalte mit den Werten von Condition um eine Reihe verschoben um Zustandsänderungen ermitteln zu können\n",
+ "df1_3['CellBefore'] = df1_3['Condition'].shift(periods=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>25627</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:22</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25628</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:52</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25629</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:29</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25630</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:01:59</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25631</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:02:29</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43784</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:57:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43785</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43786</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:58:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43787</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43788</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 18:59:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>18162 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition \\\n",
+ "25627 0 2022-11-24 20:00:22 0 1.0 1 \n",
+ "25628 0 2022-11-24 20:00:52 1 1.0 0 \n",
+ "25629 0 2022-11-24 20:01:29 1 1.0 0 \n",
+ "25630 0 2022-11-24 20:01:59 0 0.0 0 \n",
+ "25631 0 2022-11-24 20:02:29 0 0.0 0 \n",
+ "... ... ... ... ... ... \n",
+ "43784 0 2022-12-01 18:57:35 0 0.0 0 \n",
+ "43785 0 2022-12-01 18:58:05 0 0.0 0 \n",
+ "43786 0 2022-12-01 18:58:35 0 0.0 0 \n",
+ "43787 0 2022-12-01 18:59:05 0 0.0 0 \n",
+ "43788 0 2022-12-01 18:59:35 0 0.0 0 \n",
+ "\n",
+ " CellBefore \n",
+ "25627 NaN \n",
+ "25628 1.0 \n",
+ "25629 0.0 \n",
+ "25630 0.0 \n",
+ "25631 0.0 \n",
+ "... ... \n",
+ "43784 0.0 \n",
+ "43785 0.0 \n",
+ "43786 0.0 \n",
+ "43787 0.0 \n",
+ "43788 0.0 \n",
+ "\n",
+ "[18162 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select the rows where the value in column BH1750.Illuminance is less than 1 = no light on\n",
+ "\n",
+ "rows_to_delete = df1_3.loc[df1_3['BH1750.Illuminance'] < 1] \n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_3 = df1_3.drop(rows_to_delete.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select the rows which are irrelevant, as they are the 'good' conditions\n",
+ "rows_to_delete2 = df1_3.loc[((df1_3['CellBefore'] == 1) & (df1_3['Condition'] == 0)) \n",
+ "| ((df1_3['CellBefore'] == 0) & (df1_3['Condition'] == 1))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_3 = df1_3.drop(rows_to_delete2.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Introduce column Timediff for calculate the time between the state changes\n",
+ "df1_3['Timediff'] = df1_3['Time'].diff()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# select the rows which have less then 3 mins time difference\n",
+ "rows_to_delete3 = df1_3.loc[((df1_3['Timediff'] < '0 days 00:03:00'))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_3 = df1_3.drop(rows_to_delete3.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>10.0</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>10.0</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.111111</td>\n",
+ " <td>0 days 18:20:27.333333333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.421637</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.421637</td>\n",
+ " <td>0.333333</td>\n",
+ " <td>0 days 19:48:57.861088975</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0 days 00:22:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0 days 14:12:01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1 days 09:35:25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2 days 00:00:04</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance Condition CellBefore \\\n",
+ "count 10.0 10.000000 10.0 10.000000 9.000000 \n",
+ "mean 0.0 0.800000 1.0 0.200000 0.111111 \n",
+ "std 0.0 0.421637 0.0 0.421637 0.333333 \n",
+ "min 0.0 0.000000 1.0 0.000000 0.000000 \n",
+ "25% 0.0 1.000000 1.0 0.000000 0.000000 \n",
+ "50% 0.0 1.000000 1.0 0.000000 0.000000 \n",
+ "75% 0.0 1.000000 1.0 0.000000 0.000000 \n",
+ "max 0.0 1.000000 1.0 1.000000 1.000000 \n",
+ "\n",
+ " Timediff \n",
+ "count 9 \n",
+ "mean 0 days 18:20:27.333333333 \n",
+ "std 0 days 19:48:57.861088975 \n",
+ "min 0 days 00:04:00 \n",
+ "25% 0 days 00:22:00 \n",
+ "50% 0 days 14:12:01 \n",
+ "75% 1 days 09:35:25 \n",
+ "max 2 days 00:00:04 "
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rows_to_delete4 = df1_3.loc[((df1_3['Condition'] == 0))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_3 = df1_3.drop(rows_to_delete4.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaT</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance Condition CellBefore \\\n",
+ "count 2.0 2.0 2.0 2.0 1.0 \n",
+ "mean 0.0 0.0 1.0 1.0 1.0 \n",
+ "std 0.0 0.0 0.0 0.0 NaN \n",
+ "min 0.0 0.0 1.0 1.0 1.0 \n",
+ "25% 0.0 0.0 1.0 1.0 1.0 \n",
+ "50% 0.0 0.0 1.0 1.0 1.0 \n",
+ "75% 0.0 0.0 1.0 1.0 1.0 \n",
+ "max 0.0 0.0 1.0 1.0 1.0 \n",
+ "\n",
+ " Timediff \n",
+ "count 1 \n",
+ "mean 0 days 00:04:00 \n",
+ "std NaT \n",
+ "min 0 days 00:04:00 \n",
+ "25% 0 days 00:04:00 \n",
+ "50% 0 days 00:04:00 \n",
+ "75% 0 days 00:04:00 \n",
+ "max 0 days 00:04:00 "
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>25627</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:00:22</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaT</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25637</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-11-24 20:05:29</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition \\\n",
+ "25627 0 2022-11-24 20:00:22 0 1.0 1 \n",
+ "25637 0 2022-11-24 20:05:29 0 1.0 1 \n",
+ "\n",
+ " CellBefore Timediff \n",
+ "25627 NaN NaT \n",
+ "25637 1.0 0 days 00:04:00 "
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Timedelta('0 days 00:04:00')"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sumdf1_3 = df1_3['Timediff'].sum()\n",
+ "sumdf1_3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Messreihe 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Datensatz nach Zeitraum sortieren\n",
+ "\n",
+ "#02.12. - 09.12.\n",
+ "start4 = '2022-12-01 20:00:00'\n",
+ "end4 = '2022-12-09 19:00:00'\n",
+ "df1_4 = finaldf.drop(['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint'], axis = 1)\n",
+ "df1_4 = df1_4[(df1_4['Time'] >= start4) & (df1_4['Time'] <= end4)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>22849.0</td>\n",
+ " <td>22849.000000</td>\n",
+ " <td>22849.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.005427</td>\n",
+ " <td>2.724277</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.073469</td>\n",
+ " <td>7.024208</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance\n",
+ "count 22849.0 22849.000000 22849.000000\n",
+ "mean 0.0 0.005427 2.724277\n",
+ "std 0.0 0.073469 7.024208\n",
+ "min 0.0 0.000000 0.000000\n",
+ "25% 0.0 0.000000 0.000000\n",
+ "50% 0.0 0.000000 0.000000\n",
+ "75% 0.0 0.000000 3.000000\n",
+ "max 0.0 1.000000 67.000000"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Helligkeit\n",
+ "df1_4.plot.line(x='Time', y = [\"BH1750.Illuminance\"], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Bewegungserkennung\n",
+ "df1_4.plot.line(x='Time', y = [\"Switch1\"], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a boolean mask indicating which values in column are above 33\n",
+ "mask = df1_4['BH1750.Illuminance'] > 33\n",
+ "\n",
+ "# set the values in column to 1 if they are above 200, and False otherwise\n",
+ "df1_4.loc[mask, 'BH1750.Illuminance'] = 1\n",
+ "df1_4.loc[~mask, 'BH1750.Illuminance'] = 0\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>43909</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43910</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43911</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43912</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43913</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:02:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66753</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:57:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66754</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66755</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66756</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66757</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22849 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance\n",
+ "43909 0 2022-12-01 20:00:05 0 0.0\n",
+ "43910 0 2022-12-01 20:00:35 0 0.0\n",
+ "43911 0 2022-12-01 20:01:05 0 0.0\n",
+ "43912 0 2022-12-01 20:01:35 0 0.0\n",
+ "43913 0 2022-12-01 20:02:05 0 0.0\n",
+ "... ... ... ... ...\n",
+ "66753 0 2022-12-09 18:57:50 0 0.0\n",
+ "66754 0 2022-12-09 18:58:20 0 0.0\n",
+ "66755 0 2022-12-09 18:58:50 0 0.0\n",
+ "66756 0 2022-12-09 18:59:20 0 0.0\n",
+ "66757 0 2022-12-09 18:59:50 0 0.0\n",
+ "\n",
+ "[22849 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# S1/Illum/Cond\n",
+ "# Zustand 1: 0/0 --> Gut\n",
+ "# Zustand 2: 0/1 --> Schlecht\n",
+ "# Zustand 3: 1/0 --> Gut\n",
+ "# Zustand 4: 1/1 --> Gut"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Erstelle eine Spalte Condition mit den Werten 0 und 1, basierend auf welche relevant werden\n",
+ "def new_column(row):\n",
+ " if ((row['Switch1'] == 0) & (row['BH1750.Illuminance'] == 0)):\n",
+ " return 0\n",
+ " elif ((row['Switch1'] == 1) & (row['BH1750.Illuminance'] == 0)):\n",
+ " return 0\n",
+ " elif ((row['Switch1'] == 1) & (row['BH1750.Illuminance'] == 1)):\n",
+ " return 0\n",
+ " else:\n",
+ " return 1\n",
+ "\n",
+ "df1_4['Condition'] = df1_4.apply(new_column, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>43909</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43910</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43911</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43912</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43913</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:02:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66753</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:57:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66754</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66755</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66756</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66757</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22849 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition\n",
+ "43909 0 2022-12-01 20:00:05 0 0.0 0\n",
+ "43910 0 2022-12-01 20:00:35 0 0.0 0\n",
+ "43911 0 2022-12-01 20:01:05 0 0.0 0\n",
+ "43912 0 2022-12-01 20:01:35 0 0.0 0\n",
+ "43913 0 2022-12-01 20:02:05 0 0.0 0\n",
+ "... ... ... ... ... ...\n",
+ "66753 0 2022-12-09 18:57:50 0 0.0 0\n",
+ "66754 0 2022-12-09 18:58:20 0 0.0 0\n",
+ "66755 0 2022-12-09 18:58:50 0 0.0 0\n",
+ "66756 0 2022-12-09 18:59:20 0 0.0 0\n",
+ "66757 0 2022-12-09 18:59:50 0 0.0 0\n",
+ "\n",
+ "[22849 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotte die Condition = Werte die 1 sind, sind die \"schlechten\" Zustände\n",
+ "df1_4.plot.line(x='Time', y = ['Condition'], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# erstelle eine neue Spalte mit den Werten von Condition um eine Reihe verschoben um Zustandsänderungen ermitteln zu können\n",
+ "df1_4['CellBefore'] = df1_3['Condition'].shift(periods=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>43909</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43910</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:00:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43911</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43912</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:01:35</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43913</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-01 20:02:05</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66753</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:57:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66754</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66755</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:58:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66756</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:20</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66757</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 18:59:50</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22849 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition \\\n",
+ "43909 0 2022-12-01 20:00:05 0 0.0 0 \n",
+ "43910 0 2022-12-01 20:00:35 0 0.0 0 \n",
+ "43911 0 2022-12-01 20:01:05 0 0.0 0 \n",
+ "43912 0 2022-12-01 20:01:35 0 0.0 0 \n",
+ "43913 0 2022-12-01 20:02:05 0 0.0 0 \n",
+ "... ... ... ... ... ... \n",
+ "66753 0 2022-12-09 18:57:50 0 0.0 0 \n",
+ "66754 0 2022-12-09 18:58:20 0 0.0 0 \n",
+ "66755 0 2022-12-09 18:58:50 0 0.0 0 \n",
+ "66756 0 2022-12-09 18:59:20 0 0.0 0 \n",
+ "66757 0 2022-12-09 18:59:50 0 0.0 0 \n",
+ "\n",
+ " CellBefore \n",
+ "43909 NaN \n",
+ "43910 NaN \n",
+ "43911 NaN \n",
+ "43912 NaN \n",
+ "43913 NaN \n",
+ "... ... \n",
+ "66753 NaN \n",
+ "66754 NaN \n",
+ "66755 NaN \n",
+ "66756 NaN \n",
+ "66757 NaN \n",
+ "\n",
+ "[22849 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select the rows where the value in column BH1750.Illuminance is less than 1 = no light on\n",
+ "\n",
+ "rows_to_delete = df1_4.loc[df1_4['BH1750.Illuminance'] < 1] \n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_4 = df1_4.drop(rows_to_delete.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select the rows which are irrelevant, as they are the 'good' conditions\n",
+ "rows_to_delete2 = df1_4.loc[((df1_4['CellBefore'] == 1) & (df1_4['Condition'] == 0)) \n",
+ "| ((df1_4['CellBefore'] == 0) & (df1_4['Condition'] == 1))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_4 = df1_4.drop(rows_to_delete2.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Introduce column Timediff for calculate the time between the state changes\n",
+ "df1_4['Timediff'] = df1_4['Time'].diff()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# select the rows which have less then 3 mins time difference\n",
+ "rows_to_delete3 = df1_4.loc[((df1_4['Timediff'] < '0 days 00:03:00'))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_4 = df1_4.drop(rows_to_delete3.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>25.0</td>\n",
+ " <td>25.000000</td>\n",
+ " <td>25.0</td>\n",
+ " <td>25.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.320000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.680000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 07:41:00.750000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.476095</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.476095</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 10:24:27.768365391</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:43:22.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 02:10:44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 17:46:17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1 days 15:28:34</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance Condition CellBefore \\\n",
+ "count 25.0 25.000000 25.0 25.000000 0.0 \n",
+ "mean 0.0 0.320000 1.0 0.680000 NaN \n",
+ "std 0.0 0.476095 0.0 0.476095 NaN \n",
+ "min 0.0 0.000000 1.0 0.000000 NaN \n",
+ "25% 0.0 0.000000 1.0 0.000000 NaN \n",
+ "50% 0.0 0.000000 1.0 1.000000 NaN \n",
+ "75% 0.0 1.000000 1.0 1.000000 NaN \n",
+ "max 0.0 1.000000 1.0 1.000000 NaN \n",
+ "\n",
+ " Timediff \n",
+ "count 24 \n",
+ "mean 0 days 07:41:00.750000 \n",
+ "std 0 days 10:24:27.768365391 \n",
+ "min 0 days 00:04:00 \n",
+ "25% 0 days 00:43:22.500000 \n",
+ "50% 0 days 02:10:44 \n",
+ "75% 0 days 17:46:17 \n",
+ "max 1 days 15:28:34 "
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rows_to_delete4 = df1_4.loc[((df1_4['Condition'] == 0))]\n",
+ "\n",
+ "# Delete the selected rows\n",
+ "\n",
+ "df1_4 = df1_4.drop(rows_to_delete4.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>17.0</td>\n",
+ " <td>17.0</td>\n",
+ " <td>17.0</td>\n",
+ " <td>17.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 07:12:14.764705882</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 10:56:37.200737376</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:38:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 02:10:58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 04:03:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1 days 15:28:34</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 BH1750.Illuminance Condition CellBefore \\\n",
+ "count 17.0 17.0 17.0 17.0 0.0 \n",
+ "mean 0.0 0.0 1.0 1.0 NaN \n",
+ "std 0.0 0.0 0.0 0.0 NaN \n",
+ "min 0.0 0.0 1.0 1.0 NaN \n",
+ "25% 0.0 0.0 1.0 1.0 NaN \n",
+ "50% 0.0 0.0 1.0 1.0 NaN \n",
+ "75% 0.0 0.0 1.0 1.0 NaN \n",
+ "max 0.0 0.0 1.0 1.0 NaN \n",
+ "\n",
+ " Timediff \n",
+ "count 17 \n",
+ "mean 0 days 07:12:14.764705882 \n",
+ "std 0 days 10:56:37.200737376 \n",
+ "min 0 days 00:04:00 \n",
+ "25% 0 days 00:38:30 \n",
+ "50% 0 days 02:10:58 \n",
+ "75% 0 days 04:03:00 \n",
+ "max 1 days 15:28:34 "
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>BH1750.Illuminance</th>\n",
+ " <th>Condition</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>49181</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-03 16:07:38</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 22:44:32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49682</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-03 20:18:08</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 03:59:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54414</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-05 11:48:12</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1 days 15:28:34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54904</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-05 15:53:12</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 04:03:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57264</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-06 11:33:14</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 17:31:32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57461</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-06 13:11:44</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 01:37:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57663</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-06 14:52:44</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 01:41:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60030</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-07 10:36:16</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:38:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60492</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-07 14:27:16</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 02:10:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>63238</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-08 13:29:46</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 02:10:58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>63248</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-08 13:34:46</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:04:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>63687</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-08 17:15:19</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 03:17:03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64142</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-08 21:02:49</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 03:47:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66370</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 15:42:21</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 18:30:32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66474</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 16:34:21</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:33:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66486</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 16:40:21</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:05:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66617</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-09 17:49:50</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0 days 00:04:00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 BH1750.Illuminance Condition \\\n",
+ "49181 0 2022-12-03 16:07:38 0 1.0 1 \n",
+ "49682 0 2022-12-03 20:18:08 0 1.0 1 \n",
+ "54414 0 2022-12-05 11:48:12 0 1.0 1 \n",
+ "54904 0 2022-12-05 15:53:12 0 1.0 1 \n",
+ "57264 0 2022-12-06 11:33:14 0 1.0 1 \n",
+ "57461 0 2022-12-06 13:11:44 0 1.0 1 \n",
+ "57663 0 2022-12-06 14:52:44 0 1.0 1 \n",
+ "60030 0 2022-12-07 10:36:16 0 1.0 1 \n",
+ "60492 0 2022-12-07 14:27:16 0 1.0 1 \n",
+ "63238 0 2022-12-08 13:29:46 0 1.0 1 \n",
+ "63248 0 2022-12-08 13:34:46 0 1.0 1 \n",
+ "63687 0 2022-12-08 17:15:19 0 1.0 1 \n",
+ "64142 0 2022-12-08 21:02:49 0 1.0 1 \n",
+ "66370 0 2022-12-09 15:42:21 0 1.0 1 \n",
+ "66474 0 2022-12-09 16:34:21 0 1.0 1 \n",
+ "66486 0 2022-12-09 16:40:21 0 1.0 1 \n",
+ "66617 0 2022-12-09 17:49:50 0 1.0 1 \n",
+ "\n",
+ " CellBefore Timediff \n",
+ "49181 NaN 0 days 22:44:32 \n",
+ "49682 NaN 0 days 03:59:30 \n",
+ "54414 NaN 1 days 15:28:34 \n",
+ "54904 NaN 0 days 04:03:00 \n",
+ "57264 NaN 0 days 17:31:32 \n",
+ "57461 NaN 0 days 01:37:30 \n",
+ "57663 NaN 0 days 01:41:00 \n",
+ "60030 NaN 0 days 00:38:30 \n",
+ "60492 NaN 0 days 02:10:30 \n",
+ "63238 NaN 0 days 02:10:58 \n",
+ "63248 NaN 0 days 00:04:30 \n",
+ "63687 NaN 0 days 03:17:03 \n",
+ "64142 NaN 0 days 03:47:30 \n",
+ "66370 NaN 0 days 18:30:32 \n",
+ "66474 NaN 0 days 00:33:30 \n",
+ "66486 NaN 0 days 00:05:30 \n",
+ "66617 NaN 0 days 00:04:00 "
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Timedelta('5 days 02:28:11')"
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sumdf1_4 = df1_4['Timediff'].sum()\n",
+ "sumdf1_4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3 Aufgabe 2: Eigener Versuchsaufbau"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Datensatz nach Zeitraum sortieren\n",
+ "# 10.12. - 17.12.\n",
+ "start5 = '2022-12-10 21:00:00'\n",
+ "end5 = '2022-12-16 10:00:00'\n",
+ "df1_5 = finaldf.drop(['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint', 'BH1750.Illuminance'], axis = 1)\n",
+ "df1_5 = df1_5[(df1_5['Time'] >= start5) & (df1_5['Time'] <= end5)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Time'>"
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 1 == Door closed, 0 == Door open\n",
+ "df1_5.plot.line(x='Time', y = [\"Switch1\"], figsize=(20,8), grid=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add column to be able to figure out if a condition shift was done\n",
+ "df1_5['CellBefore'] = df1_5['Switch1'].shift(periods=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# delete the rows where there was no state change\n",
+ "rows_to_delete1_5_1 = df1_5.loc[((df1_5['CellBefore'] == 1) & (df1_5['Switch1'] == 1)) | ((df1_5['CellBefore'] == 0) & (df1_5['Switch1'] == 0))]\n",
+ "df1_5 = df1_5.drop(rows_to_delete1_5_1.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>CellBefore</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>69831</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-10 21:00:00</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71447</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:28:31</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71448</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:29:01</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71457</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:33:31</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71458</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:34:01</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78701</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:05:37</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78702</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:06:07</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78704</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:07:07</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78709</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:09:37</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78710</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:10:07</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>141 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 CellBefore\n",
+ "69831 0 2022-12-10 21:00:00 1 NaN\n",
+ "71447 0 2022-12-11 10:28:31 0 1.0\n",
+ "71448 0 2022-12-11 10:29:01 1 0.0\n",
+ "71457 0 2022-12-11 10:33:31 0 1.0\n",
+ "71458 0 2022-12-11 10:34:01 1 0.0\n",
+ "... ... ... ... ...\n",
+ "78701 0 2022-12-13 23:05:37 1 0.0\n",
+ "78702 0 2022-12-13 23:06:07 0 1.0\n",
+ "78704 0 2022-12-13 23:07:07 1 0.0\n",
+ "78709 0 2022-12-13 23:09:37 0 1.0\n",
+ "78710 0 2022-12-13 23:10:07 1 0.0\n",
+ "\n",
+ "[141 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add new column that contains the calculation of the timedifference between the rows\n",
+ "df1_5['Timediff'] = df1_5['Time'].diff()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Time</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>69831</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-10 21:00:00</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaT</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71447</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:28:31</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 13:28:31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71448</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:29:01</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:00:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71457</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:33:31</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:04:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71458</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-11 10:34:01</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:00:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78701</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:05:37</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:00:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78702</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:06:07</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:00:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78704</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:07:07</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:01:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78709</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:09:37</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0 days 00:02:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78710</th>\n",
+ " <td>0</td>\n",
+ " <td>2022-12-13 23:10:07</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:00:30</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>141 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Time Switch1 CellBefore Timediff\n",
+ "69831 0 2022-12-10 21:00:00 1 NaN NaT\n",
+ "71447 0 2022-12-11 10:28:31 0 1.0 0 days 13:28:31\n",
+ "71448 0 2022-12-11 10:29:01 1 0.0 0 days 00:00:30\n",
+ "71457 0 2022-12-11 10:33:31 0 1.0 0 days 00:04:30\n",
+ "71458 0 2022-12-11 10:34:01 1 0.0 0 days 00:00:30\n",
+ "... ... ... ... ... ...\n",
+ "78701 0 2022-12-13 23:05:37 1 0.0 0 days 00:00:30\n",
+ "78702 0 2022-12-13 23:06:07 0 1.0 0 days 00:00:30\n",
+ "78704 0 2022-12-13 23:07:07 1 0.0 0 days 00:01:00\n",
+ "78709 0 2022-12-13 23:09:37 0 1.0 0 days 00:02:30\n",
+ "78710 0 2022-12-13 23:10:07 1 0.0 0 days 00:00:30\n",
+ "\n",
+ "[141 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If timediff is smaller than 1 min --> delete the row\n",
+ "rows_to_delete1_5_2 = df1_5.loc[((df1_5['Timediff'] < '0 days 00:01:00'))]\n",
+ "df1_5 = df1_5.drop(rows_to_delete1_5_2.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Delete the rows with Switch1 = 1 because we only need to consider the cases when the door is open\n",
+ "rows_to_delete1_5_3 = df1_5.loc[df1_5['Switch1'] < 1] \n",
+ "df1_5 = df1_5.drop(rows_to_delete1_5_3.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>Switch1</th>\n",
+ " <th>CellBefore</th>\n",
+ " <th>Timediff</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>32.0</td>\n",
+ " <td>32.0</td>\n",
+ " <td>31.0</td>\n",
+ " <td>31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:02:05.806451612</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:01:22.128930897</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:01:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:01:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:01:30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:03:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0 days 00:06:30</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index Switch1 CellBefore Timediff\n",
+ "count 32.0 32.0 31.0 31\n",
+ "mean 0.0 1.0 0.0 0 days 00:02:05.806451612\n",
+ "std 0.0 0.0 0.0 0 days 00:01:22.128930897\n",
+ "min 0.0 1.0 0.0 0 days 00:01:00\n",
+ "25% 0.0 1.0 0.0 0 days 00:01:00\n",
+ "50% 0.0 1.0 0.0 0 days 00:01:30\n",
+ "75% 0.0 1.0 0.0 0 days 00:03:00\n",
+ "max 0.0 1.0 0.0 0 days 00:06:30"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1_5.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Timedelta('0 days 01:05:00')"
+ ]
+ },
+ "execution_count": 87,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# calculate the sum of all values in the above df\n",
+ "sumdf1_5 = df1_5['Timediff'].sum()\n",
+ "sumdf1_5"
+ ]
+ },
+ {
+ "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": []
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}