Code/get_data.ipynb gelöscht
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-01-15T16:21:32.634426800Z",
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"start_time": "2024-01-15T15:30:44.375000700Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": " Date Time Temperature Barometric Pressure Humidity\n0 23-12-2023 10:31:44 9.14 962.54 61.900\n1 23-12-2023 10:33:26 9.19 962.56 62.583\n2 23-12-2023 10:34:26 9.14 962.59 61.835\n3 23-12-2023 10:35:26 8.95 962.61 62.270\n4 23-12-2023 10:36:26 8.91 962.67 61.414\n.. ... ... ... ... ...\n0 15-01-2024 17:16:34 1.18 951.71 79.291\n0 15-01-2024 17:17:34 1.19 951.74 78.544\n0 15-01-2024 17:18:34 1.22 951.72 78.812\n0 15-01-2024 17:19:33 1.23 951.74 79.127\n0 15-01-2024 17:20:33 1.23 951.65 78.431\n\n[530 rows x 5 columns]",
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"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>Date</th>\n <th>Time</th>\n <th>Temperature</th>\n <th>Barometric Pressure</th>\n <th>Humidity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>23-12-2023</td>\n <td>10:31:44</td>\n <td>9.14</td>\n <td>962.54</td>\n <td>61.900</td>\n </tr>\n <tr>\n <th>1</th>\n <td>23-12-2023</td>\n <td>10:33:26</td>\n <td>9.19</td>\n <td>962.56</td>\n <td>62.583</td>\n </tr>\n <tr>\n <th>2</th>\n <td>23-12-2023</td>\n <td>10:34:26</td>\n <td>9.14</td>\n <td>962.59</td>\n <td>61.835</td>\n </tr>\n <tr>\n <th>3</th>\n <td>23-12-2023</td>\n <td>10:35:26</td>\n <td>8.95</td>\n <td>962.61</td>\n <td>62.270</td>\n </tr>\n <tr>\n <th>4</th>\n <td>23-12-2023</td>\n <td>10:36:26</td>\n <td>8.91</td>\n <td>962.67</td>\n <td>61.414</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>0</th>\n <td>15-01-2024</td>\n <td>17:16:34</td>\n <td>1.18</td>\n <td>951.71</td>\n <td>79.291</td>\n </tr>\n <tr>\n <th>0</th>\n <td>15-01-2024</td>\n <td>17:17:34</td>\n <td>1.19</td>\n <td>951.74</td>\n <td>78.544</td>\n </tr>\n <tr>\n <th>0</th>\n <td>15-01-2024</td>\n <td>17:18:34</td>\n <td>1.22</td>\n <td>951.72</td>\n <td>78.812</td>\n </tr>\n <tr>\n <th>0</th>\n <td>15-01-2024</td>\n <td>17:19:33</td>\n <td>1.23</td>\n <td>951.74</td>\n <td>79.127</td>\n </tr>\n <tr>\n <th>0</th>\n <td>15-01-2024</td>\n <td>17:20:33</td>\n <td>1.23</td>\n <td>951.65</td>\n <td>78.431</td>\n </tr>\n </tbody>\n</table>\n<p>530 rows × 5 columns</p>\n</div>"
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import paramiko\n",
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"import pandas as pd\n",
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"from datetime import datetime\n",
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"import time\n",
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"\n",
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"\n",
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"def get_data(hostname, port, username, password, script_path):\n",
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" try:\n",
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" client = paramiko.SSHClient()\n",
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" client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n",
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" client.connect(hostname, port, username, password)\n",
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"\n",
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" stdin, stdout, stderr = client.exec_command(f'python3 {script_path}')\n",
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" output = stdout.read().decode('utf-8').strip()\n",
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"\n",
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" client.close()\n",
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"\n",
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" output_pairs = output.split(',')\n",
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"\n",
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" variable_values = [float(pair.split('=')[1]) for pair in output_pairs]\n",
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"\n",
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" variable1, variable2, variable3 = variable_values\n",
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"\n",
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" return variable1, variable2, variable3\n",
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"\n",
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" except Exception as e:\n",
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" print(f\"An error occurred: {e}\")\n",
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" return None, None, None\n",
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"\n",
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"\n",
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"df = pd.read_csv('Data.csv')\n",
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"while True:\n",
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" try:\n",
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" temp, pressure, humidity = get_data('192.168.1.123', 22, 'hoehenmichae', 'pi',\n",
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" '/home/hoehenmichae/Documents/project/code.py')\n",
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" time_1 = datetime.now().time().replace(microsecond=0)\n",
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" date_1 = datetime.today().date().strftime('%d-%m-%Y')\n",
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" df_temp = pd.DataFrame(\n",
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" {'Date': [date_1], 'Time': [time_1], 'Temperature': [temp], 'Barometric Pressure': [pressure],\n",
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" 'Humidity': [humidity]})\n",
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" df = pd.concat([df, df_temp])\n",
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" df.to_csv('Data.csv', index=False)\n",
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" time.sleep(59)\n",
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"\n",
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" except KeyboardInterrupt:\n",
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" break\n",
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"\n",
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"df.to_csv('Data.csv', index=False)\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "33ba79e654b7b277"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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