All_SensorData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2431548157.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import yaml\n",
    "import os\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/1830080973.py:36: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>luminance_APDS9002</th>\n",
       "      <th>voltage_APDS9002</th>\n",
       "      <th>raw_APDS9002</th>\n",
       "      <th>temperature_BME280</th>\n",
       "      <th>pressure_BME280</th>\n",
       "      <th>humidity_BME280</th>\n",
       "      <th>altitude_BME280</th>\n",
       "      <th>temperature_BME680</th>\n",
       "      <th>pressure_BME680</th>\n",
       "      <th>humidity_BME680</th>\n",
       "      <th>...</th>\n",
       "      <th>WTUnits_WIMDA</th>\n",
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       "      <th>absoluteHumidity_WIMDA</th>\n",
       "      <th>dewPoint_WIMDA</th>\n",
       "      <th>windDirectionTrue_WIMDA</th>\n",
       "      <th>windDirectionMagnetic_WIMDA</th>\n",
       "      <th>windSpeedKnots_WIMDA</th>\n",
       "      <th>windSpeedMetersPerSecond_WIMDA</th>\n",
       "      <th>pitch_YXXDR</th>\n",
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       "      <th>dateTime</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:10</th>\n",
       "      <td>59.65</td>\n",
       "      <td>0.21</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>100096.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>102.81</td>\n",
       "      <td>40.32</td>\n",
       "      <td>100.07</td>\n",
       "      <td>30.01</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.50</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:20</th>\n",
       "      <td>57.41</td>\n",
       "      <td>0.20</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>100096.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>102.81</td>\n",
       "      <td>40.32</td>\n",
       "      <td>100.07</td>\n",
       "      <td>30.04</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.10</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:30</th>\n",
       "      <td>56.29</td>\n",
       "      <td>0.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.91</td>\n",
       "      <td>100093.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>40.30</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:40</th>\n",
       "      <td>57.41</td>\n",
       "      <td>0.20</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>100092.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>103.15</td>\n",
       "      <td>40.33</td>\n",
       "      <td>100.06</td>\n",
       "      <td>30.08</td>\n",
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       "      <td>0.75</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:50</th>\n",
       "      <td>57.41</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 23:59:20</th>\n",
       "      <td>54.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.10</td>\n",
       "      <td>100020.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>109.20</td>\n",
       "      <td>42.23</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 23:59:30</th>\n",
       "      <td>55.17</td>\n",
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       "      <th>2023-07-30 23:59:40</th>\n",
       "      <td>55.17</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>100017.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>109.46</td>\n",
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       "      <td>0.65</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 23:59:50</th>\n",
       "      <td>54.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.01</td>\n",
       "      <td>100015.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>109.62</td>\n",
       "      <td>42.17</td>\n",
       "      <td>99.98</td>\n",
       "      <td>26.37</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.10</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30 00:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>39.99</td>\n",
       "      <td>100097.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>102.73</td>\n",
       "      <td>40.33</td>\n",
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       "      <td>30.24</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                     luminance_APDS9002  voltage_APDS9002  raw_APDS9002  \\\n",
       "dateTime                                                                  \n",
       "2023-07-30 00:00:10               59.65              0.21          43.0   \n",
       "2023-07-30 00:00:20               57.41              0.20          41.0   \n",
       "2023-07-30 00:00:30               56.29              0.20          40.0   \n",
       "2023-07-30 00:00:40               57.41              0.20          41.0   \n",
       "2023-07-30 00:00:50               57.41              0.20          41.0   \n",
       "...                                 ...               ...           ...   \n",
       "2023-07-30 23:59:20               54.05              0.19          38.0   \n",
       "2023-07-30 23:59:30               55.17              0.19          39.0   \n",
       "2023-07-30 23:59:40               55.17              0.19          39.0   \n",
       "2023-07-30 23:59:50               54.05              0.19          38.0   \n",
       "2023-07-30 00:00:00                 NaN               NaN           NaN   \n",
       "\n",
       "                     temperature_BME280  pressure_BME280  humidity_BME280  \\\n",
       "dateTime                                                                    \n",
       "2023-07-30 00:00:10               40.00         100096.0             28.0   \n",
       "2023-07-30 00:00:20               39.93         100096.0             28.0   \n",
       "2023-07-30 00:00:30               39.91         100093.0             29.0   \n",
       "2023-07-30 00:00:40               39.93         100092.0             29.0   \n",
       "2023-07-30 00:00:50               39.91         100097.0             28.0   \n",
       "...                                 ...              ...              ...   \n",
       "2023-07-30 23:59:20               42.10         100020.0             24.0   \n",
       "2023-07-30 23:59:30               42.06         100017.0             23.0   \n",
       "2023-07-30 23:59:40               42.05         100017.0             24.0   \n",
       "2023-07-30 23:59:50               42.01         100015.0             23.0   \n",
       "2023-07-30 00:00:00               39.99         100097.0             28.0   \n",
       "\n",
       "                     altitude_BME280  temperature_BME680  pressure_BME680  \\\n",
       "dateTime                                                                    \n",
       "2023-07-30 00:00:10           102.81               40.32           100.07   \n",
       "2023-07-30 00:00:20           102.81               40.32           100.07   \n",
       "2023-07-30 00:00:30           103.06               40.30           100.06   \n",
       "2023-07-30 00:00:40           103.15               40.33           100.06   \n",
       "2023-07-30 00:00:50           102.73               40.32           100.07   \n",
       "...                              ...                 ...              ...   \n",
       "2023-07-30 23:59:20           109.20               42.23            99.98   \n",
       "2023-07-30 23:59:30           109.46               42.21            99.98   \n",
       "2023-07-30 23:59:40           109.46               42.19            99.98   \n",
       "2023-07-30 23:59:50           109.62               42.17            99.98   \n",
       "2023-07-30 00:00:00           102.73               40.33           100.07   \n",
       "\n",
       "                     humidity_BME680  ...  WTUnits_WIMDA  \\\n",
       "dateTime                              ...                  \n",
       "2023-07-30 00:00:10            30.01  ...            NaN   \n",
       "2023-07-30 00:00:20            30.04  ...            NaN   \n",
       "2023-07-30 00:00:30            30.06  ...            NaN   \n",
       "2023-07-30 00:00:40            30.08  ...            NaN   \n",
       "2023-07-30 00:00:50            30.05  ...            NaN   \n",
       "...                              ...  ...            ...   \n",
       "2023-07-30 23:59:20            26.55  ...            NaN   \n",
       "2023-07-30 23:59:30            26.46  ...            NaN   \n",
       "2023-07-30 23:59:40            26.44  ...            NaN   \n",
       "2023-07-30 23:59:50            26.37  ...            NaN   \n",
       "2023-07-30 00:00:00            30.24  ...            NaN   \n",
       "\n",
       "                     relativeHumidity_WIMDA  absoluteHumidity_WIMDA  \\\n",
       "dateTime                                                              \n",
       "2023-07-30 00:00:10                     NaN                     NaN   \n",
       "2023-07-30 00:00:20                     NaN                     NaN   \n",
       "2023-07-30 00:00:30                     NaN                     NaN   \n",
       "2023-07-30 00:00:40                     NaN                     NaN   \n",
       "2023-07-30 00:00:50                     NaN                     NaN   \n",
       "...                                     ...                     ...   \n",
       "2023-07-30 23:59:20                     NaN                     NaN   \n",
       "2023-07-30 23:59:30                     NaN                     NaN   \n",
       "2023-07-30 23:59:40                     NaN                     NaN   \n",
       "2023-07-30 23:59:50                     NaN                     NaN   \n",
       "2023-07-30 00:00:00                     NaN                     NaN   \n",
       "\n",
       "                     dewPoint_WIMDA  windDirectionTrue_WIMDA  \\\n",
       "dateTime                                                       \n",
       "2023-07-30 00:00:10             NaN                      NaN   \n",
       "2023-07-30 00:00:20             NaN                      NaN   \n",
       "2023-07-30 00:00:30             NaN                      NaN   \n",
       "2023-07-30 00:00:40             NaN                      NaN   \n",
       "2023-07-30 00:00:50             NaN                      NaN   \n",
       "...                             ...                      ...   \n",
       "2023-07-30 23:59:20             NaN                      NaN   \n",
       "2023-07-30 23:59:30             NaN                      NaN   \n",
       "2023-07-30 23:59:40             NaN                      NaN   \n",
       "2023-07-30 23:59:50             NaN                      NaN   \n",
       "2023-07-30 00:00:00             NaN                      NaN   \n",
       "\n",
       "                     windDirectionMagnetic_WIMDA  windSpeedKnots_WIMDA  \\\n",
       "dateTime                                                                 \n",
       "2023-07-30 00:00:10                          NaN                   NaN   \n",
       "2023-07-30 00:00:20                          NaN                   NaN   \n",
       "2023-07-30 00:00:30                          NaN                   NaN   \n",
       "2023-07-30 00:00:40                          NaN                   NaN   \n",
       "2023-07-30 00:00:50                          NaN                   NaN   \n",
       "...                                          ...                   ...   \n",
       "2023-07-30 23:59:20                          NaN                   NaN   \n",
       "2023-07-30 23:59:30                          NaN                   NaN   \n",
       "2023-07-30 23:59:40                          NaN                   NaN   \n",
       "2023-07-30 23:59:50                          NaN                   NaN   \n",
       "2023-07-30 00:00:00                          NaN                   NaN   \n",
       "\n",
       "                     windSpeedMetersPerSecond_WIMDA  pitch_YXXDR  roll_YXXDR  \n",
       "dateTime                                                                      \n",
       "2023-07-30 00:00:10                             NaN         0.50        0.30  \n",
       "2023-07-30 00:00:20                             NaN         0.60        0.10  \n",
       "2023-07-30 00:00:30                             NaN         0.70        0.20  \n",
       "2023-07-30 00:00:40                             NaN         0.70        0.75  \n",
       "2023-07-30 00:00:50                             NaN         0.65        0.70  \n",
       "...                                             ...          ...         ...  \n",
       "2023-07-30 23:59:20                             NaN         0.85        0.40  \n",
       "2023-07-30 23:59:30                             NaN         0.70        0.40  \n",
       "2023-07-30 23:59:40                             NaN         0.65        0.40  \n",
       "2023-07-30 23:59:50                             NaN         0.90        0.10  \n",
       "2023-07-30 00:00:00                             NaN         0.70       -0.30  \n",
       "\n",
       "[8640 rows x 127 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def process_csv_files(yaml_file):\n",
    "    # Load the YAML file\n",
    "    with open(yaml_file, 'r') as file:\n",
    "        csv_file_paths = yaml.safe_load(file)['csv_files']\n",
    "\n",
    "    all_dataframes = []\n",
    "\n",
    "    for file_info in csv_file_paths:\n",
    "        file_path = file_info['name']\n",
    "\n",
    "        # Extract the name from the file path\n",
    "        filename = os.path.basename(file_path)\n",
    "        name_match = re.search(r\"_([A-Za-z0-9]+)_\\d{4}_\\d{2}_\\d{2}\\.csv\", filename)\n",
    "        if name_match:\n",
    "            name_suffix = name_match.group(1)\n",
    "        else:\n",
    "            name_suffix = 'Unknown'\n",
    "\n",
    "        # Load CSV file\n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        # Convert 'dateTime' to datetime and set as index\n",
    "        df['dateTime'] = pd.to_datetime(df['dateTime'])\n",
    "        df.set_index('dateTime', inplace=True)\n",
    "\n",
    "        # Keep only numerical columns\n",
    "        numerical_cols = df.select_dtypes(include=['number']).columns\n",
    "\n",
    "        # Rename numerical columns by appending the extracted name\n",
    "        df_numerical = df[numerical_cols].rename(columns=lambda x: f\"{x}_{name_suffix}\")\n",
    "\n",
    "        # Drop rows with NaN values in numerical columns\n",
    "        df_numerical.dropna(inplace=True)\n",
    "\n",
    "        # Resample and average each DataFrame at 10-second intervals\n",
    "        df_resampled = df_numerical.resample('10S').mean()\n",
    "\n",
    "        # Collect all resampled dataframes\n",
    "        all_dataframes.append(df_resampled)\n",
    "\n",
    "    # Concatenate all dataframes along columns\n",
    "    combined_dataframe = pd.concat(all_dataframes, axis=1)\n",
    "\n",
    "    return combined_dataframe\n",
    "\n",
    "\n",
    "yaml_file = '/Users/matthewlary/Desktop/MintsData/SensorData.yaml'\n",
    "process_csv_files(yaml_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_IPS7100_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_IPS7100_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_WIMDA_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_WIMDA_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPSGPGGA2_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPSGPGGA2_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SEN0232_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SEN0232_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_LIBRAD_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_LIBRAD_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPSGPRMC2_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPSGPRMC2_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_VEML6075_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_VEML6075_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TB108L_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TB108L_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_QLMRAD001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_QLMRAD001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SKYCAM003_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SKYCAM003_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SCD30_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SCD30_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SI114X_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_SI114X_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_APDS9002_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_APDS9002_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GUV001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GUV001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TMG3993_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TMG3993_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_BME280_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_BME280_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_AS3935_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_AS3935_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_HM3301_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_HM3301_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TSL2591_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_TSL2591_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GL001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GL001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPGGA_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_GPGGA_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_BME680_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_BME680_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_MBC001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_MBC001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_YXXDR_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e0636e547_YXXDR_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_IPS7100_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_IPS7100_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_WIMDA_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_WIMDA_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPSGPGGA2_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPSGPGGA2_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SEN0232_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SEN0232_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_LIBRAD_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_LIBRAD_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPSGPRMC2_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPSGPRMC2_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_VEML6075_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_VEML6075_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TB108L_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TB108L_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_QLMRAD001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_QLMRAD001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SKYCAM003_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SKYCAM003_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SCD30_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SCD30_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SI114X_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_SI114X_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_APDS9002_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_APDS9002_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GUV001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GUV001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TMG3993_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TMG3993_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_BME280_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_BME280_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_AS3935_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_AS3935_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_HM3301_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_HM3301_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TSL2591_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_TSL2591_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GL001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GL001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPGGA_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_GPGGA_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_BME680_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_BME680_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_MBC001_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_MBC001_2023_07_30.csv\n",
      "Checking file: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_YXXDR_2023_07_30.csv\n",
      "File not found: /Users/mlary/Desktop/sensorData/MINTS_001e06430225_YXXDR_2023_07_30.csv\n",
      "No dataframes to concatenate\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import yaml\n",
    "import os\n",
    "\n",
    "def process_csv_files(yaml_file):\n",
    "    with open(yaml_file, 'r') as file:\n",
    "        yaml_data = yaml.safe_load(file)\n",
    "\n",
    "    node_ids = yaml_data['node_ids']\n",
    "    sensor_ids = yaml_data['sensor_ids']\n",
    "\n",
    "    base_path = \"/Users/mlary/Desktop/sensorData/MINTS_\"\n",
    "    date_suffix = \"2023_07_30.csv\"  # Change this if the dates vary\n",
    "    all_dataframes = []\n",
    "\n",
    "    for node_id in node_ids:\n",
    "        for sensor_id in sensor_ids:\n",
    "            file_path = f\"{base_path}{node_id}_{sensor_id}_{date_suffix}\"\n",
    "            print(f\"Checking file: {file_path}\")  # Debugging line\n",
    "\n",
    "            if not os.path.exists(file_path):\n",
    "                print(f\"File not found: {file_path}\")  # Debugging line\n",
    "                continue\n",
    "\n",
    "            df = pd.read_csv(file_path)\n",
    "            df['dateTime'] = pd.to_datetime(df['dateTime'])\n",
    "            df.set_index('dateTime', inplace=True)\n",
    "            numerical_cols = df.select_dtypes(include=['number']).columns\n",
    "            df_numerical = df[numerical_cols].rename(columns=lambda x: f\"{x}_{sensor_id}\")\n",
    "            df_numerical.dropna(inplace=True)\n",
    "            df_resampled = df_numerical.resample('10S').mean()\n",
    "            all_dataframes.append(df_resampled)\n",
    "\n",
    "    if not all_dataframes:\n",
    "        print(\"No dataframes to concatenate\")  # Debugging line\n",
    "        return pd.DataFrame()\n",
    "\n",
    "    combined_dataframe = pd.concat(all_dataframes, axis=1)\n",
    "    return combined_dataframe\n",
    "\n",
    "# Usage\n",
    "yaml_file = '/Users/matthewlary/Desktop/Programming/Repos/sensor_calibration/SensorDataV2.yaml'  # Update with the correct path\n",
    "combined_data = process_csv_files(yaml_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n",
      "/var/folders/y7/_39t7j0n667f6p1cmsg_0b9c0000gn/T/ipykernel_53417/2068761951.py:42: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
      "  df_resampled = df_numerical.resample('10S').mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import yaml\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "def process_csv_files(yaml_file):\n",
    "    # Load the YAML file\n",
    "    with open(yaml_file, 'r') as file:\n",
    "        csv_file_paths = yaml.safe_load(file)['csv_files']\n",
    "\n",
    "    all_dataframes = []\n",
    "\n",
    "    for file_info in csv_file_paths:\n",
    "        file_path = file_info['name']\n",
    "\n",
    "        # Extract the name from the file path\n",
    "        filename = os.path.basename(file_path)\n",
    "        name_match = re.search(r\"_([A-Za-z0-9]+)_\\d{4}_\\d{2}_\\d{2}\\.csv\", filename)\n",
    "        if name_match:\n",
    "            name_suffix = name_match.group(1)\n",
    "        else:\n",
    "            name_suffix = 'Unknown'\n",
    "\n",
    "        # Load CSV file\n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        # Convert 'dateTime' to datetime and set as index\n",
    "        df['dateTime'] = pd.to_datetime(df['dateTime'])\n",
    "        df.set_index('dateTime', inplace=True)\n",
    "\n",
    "        # Keep only numerical columns\n",
    "        numerical_cols = df.select_dtypes(include=['number']).columns\n",
    "\n",
    "        # Rename numerical columns by appending the extracted name\n",
    "        df_numerical = df[numerical_cols].rename(columns=lambda x: f\"{x}_{name_suffix}\")\n",
    "\n",
    "        # Drop rows with NaN values in numerical columns\n",
    "        df_numerical.dropna(inplace=True)\n",
    "\n",
    "        # Resample and average each DataFrame at 10-second intervals\n",
    "        df_resampled = df_numerical.resample('10S').mean()\n",
    "\n",
    "        # Collect all resampled dataframes\n",
    "        all_dataframes.append(df_resampled)\n",
    "\n",
    "    # Concatenate all dataframes along columns\n",
    "    combined_dataframe = pd.concat(all_dataframes, axis=1)\n",
    "\n",
    "    return combined_dataframe\n",
    "\n",
    "# Path to the YAML file\n",
    "yaml_file = '/Users/matthewlary/Desktop/MintsData/SensorData.yaml'\n",
    "\n",
    "# Process the CSV files and get the combined dataframe\n",
    "combined_dataframe = process_csv_files(yaml_file)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(combined_dataframe.index, combined_dataframe['temperature_BME280'], label='Temperature BME280', color='blue')\n",
    "plt.plot(combined_dataframe.index, combined_dataframe['temperature_BME680'], label='Temperature BME680', color='red')\n",
    "plt.xlabel('DateTime')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Temperature Data Resampled')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}