diff --git a/Data_Analytics_Sitter.ipynb b/Data_Analytics_Sitter.ipynb
deleted file mode 100644
index 9e7a6b193721b92c0683cd4b5b6590fee16f72fc..0000000000000000000000000000000000000000
--- a/Data_Analytics_Sitter.ipynb
+++ /dev/null
@@ -1,4548 +0,0 @@
-{
- "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",
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- " .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": [
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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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- "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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\n",
- "text/plain": [
- "<Figure size 1440x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# plot der Luftfeuchtigkeit, Taupunkt und beider Tempsensoren, alles innen\n",
- "df1_1.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint'], 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, Taupunkt und beider Tempsensoren, alles innen\n",
- "df1_2.plot.line(x='Time', y = ['DS18B20.Temperature', 'AM2301.Temperature', 'AM2301.Humidity', 'AM2301.DewPoint'], 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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- "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",
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- " .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",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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",
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- " .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",
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- "\n",
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- "</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",
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- " .dataframe tbody tr th {\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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",
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- "</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": {
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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": {
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- " vertical-align: middle;\n",
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- "\n",
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- " 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",
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- " <th>43910</th>\n",
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- " <td>2022-12-01 20:00:35</td>\n",
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- " <tr>\n",
- " <th>43911</th>\n",
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- " <td>2022-12-01 20:01:05</td>\n",
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- " </tr>\n",
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- " <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",
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- " </tr>\n",
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- " <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",
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- " </tr>\n",
- " <tr>\n",
- " <th>66755</th>\n",
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- " <td>2022-12-09 18:58:50</td>\n",
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- " <th>66756</th>\n",
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- " <td>2022-12-09 18:59:20</td>\n",
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- " <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": {
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- " <th>Condition</th>\n",
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- " <th>Timediff</th>\n",
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- " </thead>\n",
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- " <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",
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- " <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>"
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- "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 "
- ]
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- "execution_count": 72,
- "metadata": {},
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- "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": [
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- " <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",
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- " <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",
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- " <td>0 days 10:56:37.200737376</td>\n",
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- " <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",
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- " <td>0 days 00:38:30</td>\n",
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- " <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"
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- ],
- "source": [
- "df1_4.describe()"
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- " <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",
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- " <th>57663</th>\n",
- " <td>0</td>\n",
- " <td>2022-12-06 14:52:44</td>\n",
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- " <td>1.0</td>\n",
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- " <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",
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- " <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",
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- "</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 = '2022-12-17 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": {
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- "<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",
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- " <tr>\n",
- " <th>71447</th>\n",
- " <td>0</td>\n",
- " <td>2022-12-11 10:28:31</td>\n",
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- " <td>0</td>\n",
- " <td>2022-12-11 10:29:01</td>\n",
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- " <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",
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- " <th>71458</th>\n",
- " <td>0</td>\n",
- " <td>2022-12-11 10:34:01</td>\n",
- " <td>1</td>\n",
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- " <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",
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- ],
- "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": {
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- " <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",
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- " <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"
- ]
- }
- ],
- "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
-}