etl_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.interpolate import CubicSpline\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"C:\\\\Users\\\\ninja\\\\Desktop\\\\6_proje_makalesi\\\\1_fall_detection\\\\proje dosyaları ve kodları\\\\veri_etiketleme_etl_pipeline\\\\veriler\"\n",
    "detections_path=path + \"\\\\alp_sag_detection_dataDogru___20240821_175540_detections.csv\"\n",
    "imu_path=path +\"\\\\alp_sag_imu_data_20240821_175456_imu_data.csv\" # imu \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class extract_fall_data():\n",
    "    def __init__(self):\n",
    "        try:\n",
    "            pass\n",
    "        except Exception as e :\n",
    "            raise extract_fall_data(e,sys)\n",
    "        \n",
    "    def ekf_filter(self,z, x_est, P, F, Q, H, R):\n",
    "    \n",
    "        x_pred = np.dot(F, x_est)  # x_pred = F * x_est\n",
    "        P_pred = np.dot(F, np.dot(P, F.T)) + Q  # P_pred = F * P * F.T + Q\n",
    "    \n",
    "    # Measurement update step\n",
    "        y = z - np.dot(H, x_pred)  # Measurement residual\n",
    "        S = np.dot(H, np.dot(P_pred, H.T)) + R  # Residual covariance\n",
    "        K = np.dot(P_pred, np.dot(H.T, np.linalg.inv(S)))  # Kalman gain\n",
    "    \n",
    "        x_est_new = x_pred + np.dot(K, y)  # State update\n",
    "        P_new = P_pred - np.dot(K, np.dot(H, P_pred))  # Covariance update\n",
    "    \n",
    "        return x_est_new, P_new\n",
    "    \n",
    "    def process_and_interpolate(self, filtered_df, desired_freq=50/1000):\n",
    "        \n",
    "        filtered_df['timestamp'] = pd.to_datetime(filtered_df['timestamp'], errors='coerce')\n",
    "        filtered_df = filtered_df.dropna(subset=['timestamp'])\n",
    "        filtered_df['timestamp_unix'] = (filtered_df['timestamp'].astype(np.int64) // 10**6)\n",
    "        filtered_df = filtered_df.sort_values(by='timestamp')\n",
    "        timestamps_unix = filtered_df['timestamp_unix'].values\n",
    "        values = filtered_df[['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered']].values\n",
    "\n",
    "        # Yeni zaman damgalarını oluşturma\n",
    "        start_time = timestamps_unix.min()\n",
    "        end_time = timestamps_unix.max()\n",
    "        new_timestamps_unix = np.arange(start_time, end_time, 1 / desired_freq)\n",
    "\n",
    "        # Cubic Spline interpolatör oluşturma\n",
    "        cs = CubicSpline(timestamps_unix, values, axis=0, bc_type='natural')\n",
    "        # Yeni zaman damgalarındaki verileri interpolasyon ile hesaplama\n",
    "        new_values = cs(new_timestamps_unix)\n",
    "        interpolated_df = pd.DataFrame(new_values, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        interpolated_df['timestamp_unix'] = new_timestamps_unix\n",
    "\n",
    "        return interpolated_df\n",
    "    \n",
    "    def apply_threshold(self,interpolated_df):\n",
    "        columns_to_zero = ['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered']\n",
    "\n",
    "        for column in columns_to_zero:\n",
    "            if column in interpolated_df.columns:  \n",
    "                interpolated_df.loc[interpolated_df[column] > 20, column] = 0\n",
    "                interpolated_df.loc[interpolated_df[column] < -20, column] = 0\n",
    "            \n",
    "        return interpolated_df\n",
    "    \n",
    "    def two_esp_extractor(self,detections_path,imu_path):\n",
    "        detections_df=pd.read_csv(detections_path)\n",
    "        imu_df=pd.read_csv(imu_path)\n",
    "        imu_df_1 = imu_df[imu_df['topic'] == 'esp32_1/data']\n",
    "        imu_df_2 = imu_df[imu_df['topic'] == 'esp32_2/data']\n",
    "        imu_df_2 = imu_df_2.sort_values(by='timestamp')\n",
    "        duplicate_timestamps = imu_df_2[imu_df_2.duplicated(subset='timestamp', keep=False)]\n",
    "        duplicate_counts = duplicate_timestamps['timestamp'].value_counts()\n",
    "        imu_df_2 = imu_df_2.drop_duplicates(subset='timestamp')\n",
    "        imu_df_1 = imu_df_1.sort_values(by='timestamp')\n",
    "        duplicate_timestamps = imu_df_1[imu_df_1.duplicated(subset='timestamp', keep=False)]\n",
    "        duplicate_counts = duplicate_timestamps['timestamp'].value_counts()\n",
    "        imu_df_1 = imu_df_1.drop_duplicates(subset='timestamp')\n",
    "        F = np.eye(6)  \n",
    "        H = np.eye(6)  \n",
    "        Q = np.eye(6) * 0.01  \n",
    "        R = np.eye(6) * 0.1  \n",
    "        P = np.eye(6)  \n",
    "        x_est = np.zeros(6)  \n",
    "\n",
    "        \n",
    "        filtered_values_1 = []\n",
    "        filtered_values_2 = []\n",
    "\n",
    "        \n",
    "        for i in range(len(imu_df_1)):\n",
    "            z = np.array([imu_df_1.iloc[i]['ax'], imu_df_1.iloc[i]['ay'], imu_df_1.iloc[i]['az'], \n",
    "                          imu_df_1.iloc[i]['gx'], imu_df_1.iloc[i]['gy'], imu_df_1.iloc[i]['gz']])\n",
    "            x_est, P = self.ekf_filter(z, x_est, P, F, Q, H, R)\n",
    "            filtered_values_1.append(x_est)\n",
    "\n",
    "        \n",
    "        for i in range(len(imu_df_2)):\n",
    "            z = np.array([imu_df_2.iloc[i]['ax'], imu_df_2.iloc[i]['ay'], imu_df_2.iloc[i]['az'], \n",
    "                          imu_df_2.iloc[i]['gx'], imu_df_2.iloc[i]['gy'], imu_df_2.iloc[i]['gz']])\n",
    "            x_est, P = self.ekf_filter(z, x_est, P, F, Q, H, R)\n",
    "            filtered_values_2.append(x_est)\n",
    "\n",
    "       \n",
    "        filtered_df_1 = pd.DataFrame(filtered_values_1, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        filtered_df_1['timestamp'] = imu_df_1['timestamp'].reset_index(drop=True)\n",
    "\n",
    "        filtered_df_2 = pd.DataFrame(filtered_values_2, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        filtered_df_2['timestamp'] = imu_df_2['timestamp'].reset_index(drop=True)\n",
    "\n",
    "        filtered_df_1 = pd.DataFrame(filtered_values_1, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        filtered_df_1['timestamp'] = imu_df_1['timestamp'].reset_index(drop=True)\n",
    "\n",
    "        filtered_df_2 = pd.DataFrame(filtered_values_2, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        filtered_df_2['timestamp'] = imu_df_2['timestamp'].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        interpolated_df_1 = self.process_and_interpolate(filtered_df_1)\n",
    "        interpolated_df_2 = self.process_and_interpolate(filtered_df_2)\n",
    "\n",
    "        interpolated_imu_1 = self.apply_threshold(interpolated_df_1)\n",
    "        interpolated_imu_2 = self.apply_threshold(interpolated_df_2)\n",
    "\n",
    "\n",
    "        return interpolated_imu_1,interpolated_imu_2,detections_df\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Tekra\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract=extract_fall_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1,df2,detections_df=extract.two_esp_extractor(detections_path,imu_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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       "      <td>-0.448961</td>\n",
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       "       ax_filtered  ay_filtered  az_filtered  gx_filtered  gy_filtered  \\\n",
       "0        -1.028198     7.406667    -2.838919    -0.418559     0.063694   \n",
       "1        -1.020539     7.627713    -2.904742    -0.430347     0.050697   \n",
       "2        -1.013342     7.828984    -2.935427    -0.440230     0.041012   \n",
       "3        -1.003664     7.984988    -2.912928    -0.446552     0.034748   \n",
       "4        -0.980148     8.063047    -2.868723    -0.448961     0.024543   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "10787    -5.037125     9.130744     2.043917    -0.420748    -0.557826   \n",
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       "10790    -4.359680     9.429144    -0.768182    -1.042094     0.091744   \n",
       "10791    -1.188117     8.780438    -2.056490    -0.630043     0.234186   \n",
       "\n",
       "       gz_filtered  timestamp_unix  \n",
       "0         0.027297    1.724263e+12  \n",
       "1         0.045961    1.724263e+12  \n",
       "2         0.059841    1.724263e+12  \n",
       "3         0.066260    1.724263e+12  \n",
       "4         0.068392    1.724263e+12  \n",
       "...            ...             ...  \n",
       "10787    -0.418317    1.724263e+12  \n",
       "10788    -0.710905    1.724263e+12  \n",
       "10789    -0.879079    1.724263e+12  \n",
       "10790    -0.843613    1.724263e+12  \n",
       "10791    -0.525279    1.724263e+12  \n",
       "\n",
       "[10792 rows x 7 columns]"
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     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.106760</td>\n",
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       "      <td>-10.261009</td>\n",
       "      <td>-1.458750</td>\n",
       "      <td>-1.378814</td>\n",
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       "      <td>-11.091636</td>\n",
       "      <td>-1.340353</td>\n",
       "      <td>-1.018545</td>\n",
       "      <td>1.914958</td>\n",
       "      <td>1.724263e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8847</th>\n",
       "      <td>-6.300267</td>\n",
       "      <td>-2.165874</td>\n",
       "      <td>-11.474019</td>\n",
       "      <td>-1.237328</td>\n",
       "      <td>-0.747149</td>\n",
       "      <td>1.728567</td>\n",
       "      <td>1.724263e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8848</th>\n",
       "      <td>-6.094230</td>\n",
       "      <td>-1.978118</td>\n",
       "      <td>-11.087933</td>\n",
       "      <td>-1.156209</td>\n",
       "      <td>-0.605860</td>\n",
       "      <td>1.553912</td>\n",
       "      <td>1.724263e+12</td>\n",
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       "<p>8849 rows × 7 columns</p>\n",
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       "      ax_filtered  ay_filtered  az_filtered  gx_filtered  gy_filtered  \\\n",
       "0        2.138565     8.406895    -2.373958    -0.012593     0.503110   \n",
       "1        2.661174     8.628310    -2.221580     0.042824     0.582171   \n",
       "2        2.965917     8.797170    -2.114343     0.075497     0.639309   \n",
       "3        3.073905     8.921844    -2.017026     0.090435     0.679164   \n",
       "4        3.135588     9.040206    -1.858517     0.106760     0.719177   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8844    -5.026528    -5.062301   -10.102765    -1.571690    -1.652236   \n",
       "8845    -5.152721    -4.135279   -10.261009    -1.458750    -1.378814   \n",
       "8846    -5.882477    -2.932780   -11.091636    -1.340353    -1.018545   \n",
       "8847    -6.300267    -2.165874   -11.474019    -1.237328    -0.747149   \n",
       "8848    -6.094230    -1.978118   -11.087933    -1.156209    -0.605860   \n",
       "\n",
       "      gz_filtered  timestamp_unix  \n",
       "0       -0.328804    1.724263e+12  \n",
       "1       -0.263299    1.724263e+12  \n",
       "2       -0.207668    1.724263e+12  \n",
       "3       -0.154188    1.724263e+12  \n",
       "4       -0.087611    1.724263e+12  \n",
       "...           ...             ...  \n",
       "8844     2.247382    1.724263e+12  \n",
       "8845     2.098301    1.724263e+12  \n",
       "8846     1.914958    1.724263e+12  \n",
       "8847     1.728567    1.724263e+12  \n",
       "8848     1.553912    1.724263e+12  \n",
       "\n",
       "[8849 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df2"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
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   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <th>3783</th>\n",
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       "                    Timestamp   Class Name  Confidence  Track ID\n",
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       "2     2024-08-21 17:55:44.976      falling    0.290418       NaN\n",
       "3     2024-08-21 17:55:45.026      falling    0.515806       NaN\n",
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       "...                       ...          ...         ...       ...\n",
       "3782  2024-08-21 17:59:06.176  not_falling    0.590795       3.0\n",
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     "execution_count": 38,
     "metadata": {},
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   "source": [
    "detections_df"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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