Parkinson's Pose Estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4a0e3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "try:\n",
    "    import ujson as json\n",
    "except ImportError:\n",
    "    try:\n",
    "        import simplejson as json\n",
    "    except ImportError:\n",
    "        import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "from ipykernel import kernelapp as app\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import signal\n",
    "from scipy import stats\n",
    "from scipy.integrate import simps\n",
    "from scipy.spatial import ConvexHull\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "sns.set(font_scale=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb5a5951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of records :387\n"
     ]
    }
   ],
   "source": [
    "trajectory_file = 'UDysRS_UPDRS_Export/Communication_all_export.txt'\n",
    "\n",
    "with open(trajectory_file, 'r') as infile:\n",
    "    comm_dict = json.load(infile)\n",
    "\n",
    "print(\"total number of records :{}\".format(len(comm_dict.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57112c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key in Dictionary: dict_keys(['position', 'resp'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Lank',\n",
       " 'Lelb',\n",
       " 'Lhip',\n",
       " 'Lkne',\n",
       " 'Lsho',\n",
       " 'Lwri',\n",
       " 'Rank',\n",
       " 'Relb',\n",
       " 'Rhip',\n",
       " 'Rkne',\n",
       " 'Rsho',\n",
       " 'Rwri']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Key in Dictionary:\",comm_dict['26-1'].keys())\n",
    "comm_dict['26-1']['position'].pop('face', None)\n",
    "comm_dict['26-1']['position'].pop('head', None)\n",
    "comm_dict['26-1']['position'].pop('neck', None)\n",
    "sorted(comm_dict['26-1']['position'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e4aea65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "part = \"Lank\"\n",
    "horizontal_displacemt_array = np.array(comm_dict['26-1']['position'][part])[:,0]\n",
    "vertical_displacement_array = np.array(comm_dict['26-1']['position'][part])[:,1]\n",
    "fig = plt.figure(figsize=(15, 4))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(horizontal_displacemt_array,label='Horizental motion')\n",
    "ax.legend(loc='best')\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(vertical_displacement_array,label='vertical motion')\n",
    "ax2.legend(loc='best')\n",
    "\n",
    "plt.suptitle('Joint motion')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae7314c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combinied_horizental_and_vertical(horizental_array, vertical_array):\n",
    "    combined_array = np.sqrt(np.square(horizental_array) + np.square(vertical_array))\n",
    "    return combined_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e84d01f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_into_velocity(dispacement_array, plot=False):\n",
    "    velocity_array = np.diff(dispacement_array)\n",
    "    return velocity_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e660b143",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_into_acceleration(velocity_array, plot=False):\n",
    "    accelation_array = np.diff(velocity_array)\n",
    "    return accelation_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74328daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_into_jerk(accelation_array, plot=False):\n",
    "    jerk_array = np.diff(accelation_array)\n",
    "    return jerk_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06f979e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_kinetic_feature(motion):\n",
    "    max_motion = np.amax(motion, axis=0)\n",
    "    median_motion = np.median(motion)\n",
    "    mean_motion = np.mean(motion, axis=0)\n",
    "    standard_division_motion = np.std(motion)\n",
    "    IQR_range = stats.iqr(motion, interpolation = 'midpoint')\n",
    "    return [max_motion, median_motion, mean_motion, standard_division_motion, IQR_range]\n",
    "# print(get_kinetic_feature(displacemt_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a47bd00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'peak_magnitude': 4.616687312071252,\n",
       " 'entropy': 3.014613385810386,\n",
       " 'half_point': 2.5,\n",
       " 'total_power': 12.677554704215051,\n",
       " 'power_bands_0.5_to_1': 1.534431046257786,\n",
       " 'power_bands_0_to_2': 10.163044600116995,\n",
       " 'power_bands_0_to_4': 12.299129300143314,\n",
       " 'power_bands_0_to_6': 12.677554704215051}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_spectral_feature(signals, sample_frequancy=10, is_plot=False):\n",
    "    sf = sample_frequancy\n",
    "    win = 4 * sf\n",
    "    \n",
    "    # calcutate the Spectral entropy.\n",
    "    def spectral_entropy(psd, normalize=False):\n",
    "        psd_norm = np.divide(psd, psd.sum())\n",
    "        se = -np.multiply(psd_norm, np.log2(psd_norm)).sum()\n",
    "        if normalize:\n",
    "            se /= np.log2(psd_norm.size)\n",
    "        return se\n",
    "    \n",
    "    # calculate the power band for given frequancy.\n",
    "    def bandpower(psd, freqs, min_freqs, max_freqs, is_plot=False):\n",
    "        # Define delta lower and upper limits\n",
    "        low, high = min_freqs, max_freqs\n",
    "\n",
    "        # Find intersecting values in frequency vector\n",
    "        idx_delta = np.logical_and(freqs >= low, freqs <= high)\n",
    "\n",
    "        if is_plot:\n",
    "            # Plot the power spectral density and fill the delta area\n",
    "            plt.figure(figsize=(7, 4))\n",
    "            plt.plot(freqs, psd, lw=2, color='k')\n",
    "            plt.fill_between(freqs, psd, where=idx_delta, color='skyblue')\n",
    "            plt.xlabel('Frequency (Hz)')\n",
    "            plt.ylabel('Power spectral density (uV^2 / Hz)')\n",
    "            plt.xlim([0, 10])\n",
    "            plt.ylim([0, psd.max() * 1.1])\n",
    "            plt.title(\"Welch's periodogram\")\n",
    "            sns.despine()\n",
    "\n",
    "        # Frequency resolution\n",
    "        freq_res = freqs[1] - freqs[0]  # = 1 / 4 = 0.25\n",
    "\n",
    "        # Compute the absolute power by approximating the area under the curve\n",
    "        delta_power = simps(psd[idx_delta], dx=freq_res)\n",
    "        #print('Absolute delta power: %.3f uV^2' % delta_power)\n",
    "        return delta_power\n",
    "\n",
    "    freqs, psd = signal.welch(signals, sf, nperseg=win)\n",
    "    if is_plot:\n",
    "        sns.set(font_scale=1.2, style='white')\n",
    "        plt.figure(figsize=(8, 4))\n",
    "        plt.plot(freqs, psd, color='k', lw=2)\n",
    "        plt.xlabel('Frequency (Hz)')\n",
    "        plt.ylabel('Power spectral density (V^2 / Hz)')\n",
    "        plt.ylim([0, psd.max() * 1.1])\n",
    "        plt.title(\"Welch's periodogram\")\n",
    "        plt.xlim([0, freqs.max()])\n",
    "        sns.despine()\n",
    "    #print(dir(psd))\n",
    "    features = {}\n",
    "    features[\"peak_magnitude\"] = np.sqrt(psd.max())\n",
    "    features[\"entropy\"] = spectral_entropy(psd)\n",
    "    features[\"half_point\"] = freqs.mean()\n",
    "    \n",
    "    features[\"total_power\"] = bandpower(psd, freqs, freqs.min(), freqs.max(), is_plot)\n",
    "    features[\"power_bands_0.5_to_1\"] = bandpower(psd, freqs, 0.5, 1, is_plot)\n",
    "    features[\"power_bands_0_to_2\"] = bandpower(psd, freqs, 0, 2, is_plot)\n",
    "    features[\"power_bands_0_to_4\"] = bandpower(psd, freqs, 0, 4, is_plot)\n",
    "    features[\"power_bands_0_to_6\"] = bandpower(psd, freqs, 0, 6, is_plot)\n",
    "    return features\n",
    "get_spectral_feature(vertical_displacement_array, is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c74a8c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.84978188041217"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the Area of Convex Hull of joint movement\n",
    "def get_convexhull(darray):\n",
    "    hull = ConvexHull(darray)\n",
    "    return hull.area\n",
    "get_convexhull(np.array(comm_dict['26-1']['position'][\"Lank\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d1a01390",
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_convertion(position_array, position_name, record_id=\"1-1\"):\n",
    "    position_array = np.array(position_array)\n",
    "    horizantal_position = position_array[:, 0]\n",
    "    vertical_position = position_array[:, 1]\n",
    "    displacement_array = combinied_horizental_and_vertical(horizantal_position, vertical_position) \n",
    "    velocity_array = convert_into_velocity(displacement_array)\n",
    "    accelation_array = convert_into_acceleration(velocity_array)\n",
    "    jerk_array = convert_into_jerk(accelation_array)\n",
    "    record = record_id.split(\"-\")\n",
    "\n",
    "    row = [record_id, int(record[0]), record[1], position_name]\n",
    "    row.extend(get_kinetic_feature(velocity_array))\n",
    "    row.extend(get_kinetic_feature(accelation_array))\n",
    "    row.extend(get_kinetic_feature(jerk_array))\n",
    "    spectral_feature_displacemt = get_spectral_feature(displacement_array)\n",
    "    row.extend([value for key, value in spectral_feature_displacemt.items()])\n",
    "    spectral_feature_velocity = get_spectral_feature(velocity_array)\n",
    "    row.extend([value for key, value in spectral_feature_velocity.items()])\n",
    "    convex_hull = get_convexhull(position_array)\n",
    "    row.extend([convex_hull])\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0298ecff",
   "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>combine_record_id</th>\n",
       "      <th>record_id</th>\n",
       "      <th>term</th>\n",
       "      <th>position_name</th>\n",
       "      <th>speed_max</th>\n",
       "      <th>speed_median</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std_div</th>\n",
       "      <th>speed_iqr_range</th>\n",
       "      <th>acceleration_max</th>\n",
       "      <th>...</th>\n",
       "      <th>displacement_power_bands_0_to_6</th>\n",
       "      <th>velocity_peak_magnitude</th>\n",
       "      <th>velocity_entropy</th>\n",
       "      <th>velocity_half_point</th>\n",
       "      <th>velocity_total_power</th>\n",
       "      <th>velocity_power_bands_0.5_to_1</th>\n",
       "      <th>velocity_power_bands_0_to_2</th>\n",
       "      <th>velocity_power_bands_0_to_4</th>\n",
       "      <th>velocity_power_bands_0_to_6</th>\n",
       "      <th>convexhull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lkne</td>\n",
       "      <td>3.136361</td>\n",
       "      <td>0.076740</td>\n",
       "      <td>0.002021</td>\n",
       "      <td>0.676698</td>\n",
       "      <td>0.806606</td>\n",
       "      <td>4.786833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.684968</td>\n",
       "      <td>0.405524</td>\n",
       "      <td>4.188976</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.486046</td>\n",
       "      <td>0.019232</td>\n",
       "      <td>0.099006</td>\n",
       "      <td>0.373697</td>\n",
       "      <td>0.486046</td>\n",
       "      <td>36.917852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lwri</td>\n",
       "      <td>5.198535</td>\n",
       "      <td>0.075536</td>\n",
       "      <td>0.016402</td>\n",
       "      <td>1.054253</td>\n",
       "      <td>1.094453</td>\n",
       "      <td>5.487450</td>\n",
       "      <td>...</td>\n",
       "      <td>1.901095</td>\n",
       "      <td>0.581605</td>\n",
       "      <td>4.242219</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.192271</td>\n",
       "      <td>0.091408</td>\n",
       "      <td>0.389776</td>\n",
       "      <td>0.946561</td>\n",
       "      <td>1.192271</td>\n",
       "      <td>37.675818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Rkne</td>\n",
       "      <td>3.659338</td>\n",
       "      <td>0.093118</td>\n",
       "      <td>0.029247</td>\n",
       "      <td>0.903657</td>\n",
       "      <td>1.181396</td>\n",
       "      <td>4.980085</td>\n",
       "      <td>...</td>\n",
       "      <td>1.487423</td>\n",
       "      <td>0.563078</td>\n",
       "      <td>4.205455</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.890508</td>\n",
       "      <td>0.037900</td>\n",
       "      <td>0.210545</td>\n",
       "      <td>0.671195</td>\n",
       "      <td>0.890508</td>\n",
       "      <td>145.438051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lelb</td>\n",
       "      <td>2.388729</td>\n",
       "      <td>0.087028</td>\n",
       "      <td>0.006549</td>\n",
       "      <td>0.630261</td>\n",
       "      <td>0.611841</td>\n",
       "      <td>3.936843</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910218</td>\n",
       "      <td>0.399440</td>\n",
       "      <td>4.266713</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.429022</td>\n",
       "      <td>0.027512</td>\n",
       "      <td>0.135335</td>\n",
       "      <td>0.330622</td>\n",
       "      <td>0.429022</td>\n",
       "      <td>32.153650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lsho</td>\n",
       "      <td>3.377126</td>\n",
       "      <td>0.098620</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.633851</td>\n",
       "      <td>0.472858</td>\n",
       "      <td>4.691207</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903263</td>\n",
       "      <td>0.362294</td>\n",
       "      <td>4.248869</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.430376</td>\n",
       "      <td>0.028220</td>\n",
       "      <td>0.135963</td>\n",
       "      <td>0.341566</td>\n",
       "      <td>0.430376</td>\n",
       "      <td>29.343181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhip</td>\n",
       "      <td>3.092405</td>\n",
       "      <td>0.082036</td>\n",
       "      <td>0.021397</td>\n",
       "      <td>0.713963</td>\n",
       "      <td>0.864571</td>\n",
       "      <td>4.181641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.074064</td>\n",
       "      <td>0.473776</td>\n",
       "      <td>4.171686</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.525334</td>\n",
       "      <td>0.026714</td>\n",
       "      <td>0.127043</td>\n",
       "      <td>0.389940</td>\n",
       "      <td>0.525334</td>\n",
       "      <td>37.914752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Rank</td>\n",
       "      <td>4.579702</td>\n",
       "      <td>0.077804</td>\n",
       "      <td>0.006936</td>\n",
       "      <td>0.901460</td>\n",
       "      <td>0.899729</td>\n",
       "      <td>4.681347</td>\n",
       "      <td>...</td>\n",
       "      <td>1.739043</td>\n",
       "      <td>0.540726</td>\n",
       "      <td>4.231232</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.838308</td>\n",
       "      <td>0.044310</td>\n",
       "      <td>0.229761</td>\n",
       "      <td>0.664828</td>\n",
       "      <td>0.838308</td>\n",
       "      <td>61.161962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lhip</td>\n",
       "      <td>2.536174</td>\n",
       "      <td>0.077433</td>\n",
       "      <td>0.017686</td>\n",
       "      <td>0.624526</td>\n",
       "      <td>0.751404</td>\n",
       "      <td>3.091574</td>\n",
       "      <td>...</td>\n",
       "      <td>1.202803</td>\n",
       "      <td>0.427129</td>\n",
       "      <td>4.235774</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.405390</td>\n",
       "      <td>0.027890</td>\n",
       "      <td>0.109671</td>\n",
       "      <td>0.326465</td>\n",
       "      <td>0.405390</td>\n",
       "      <td>37.673377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Rwri</td>\n",
       "      <td>3.321649</td>\n",
       "      <td>0.087401</td>\n",
       "      <td>-0.006569</td>\n",
       "      <td>0.671034</td>\n",
       "      <td>0.690387</td>\n",
       "      <td>5.181227</td>\n",
       "      <td>...</td>\n",
       "      <td>1.015975</td>\n",
       "      <td>0.440222</td>\n",
       "      <td>4.219854</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.462217</td>\n",
       "      <td>0.031037</td>\n",
       "      <td>0.120819</td>\n",
       "      <td>0.318026</td>\n",
       "      <td>0.462217</td>\n",
       "      <td>29.350673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26-1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Lank</td>\n",
       "      <td>5.766201</td>\n",
       "      <td>0.074706</td>\n",
       "      <td>-0.006732</td>\n",
       "      <td>1.026969</td>\n",
       "      <td>0.958384</td>\n",
       "      <td>7.775271</td>\n",
       "      <td>...</td>\n",
       "      <td>2.051206</td>\n",
       "      <td>0.697988</td>\n",
       "      <td>4.114266</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.098217</td>\n",
       "      <td>0.072292</td>\n",
       "      <td>0.234923</td>\n",
       "      <td>0.852103</td>\n",
       "      <td>1.098217</td>\n",
       "      <td>95.849782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  combine_record_id record_id term position_name  speed_max  speed_median  \\\n",
       "0              26-1        26    1          Lkne   3.136361      0.076740   \n",
       "1              26-1        26    1          Lwri   5.198535      0.075536   \n",
       "2              26-1        26    1          Rkne   3.659338      0.093118   \n",
       "3              26-1        26    1          Lelb   2.388729      0.087028   \n",
       "4              26-1        26    1          Lsho   3.377126      0.098620   \n",
       "5              26-1        26    1          Rhip   3.092405      0.082036   \n",
       "6              26-1        26    1          Rank   4.579702      0.077804   \n",
       "7              26-1        26    1          Lhip   2.536174      0.077433   \n",
       "8              26-1        26    1          Rwri   3.321649      0.087401   \n",
       "9              26-1        26    1          Lank   5.766201      0.074706   \n",
       "\n",
       "   speed_mean  speed_std_div  speed_iqr_range  acceleration_max  ...  \\\n",
       "0    0.002021       0.676698         0.806606          4.786833  ...   \n",
       "1    0.016402       1.054253         1.094453          5.487450  ...   \n",
       "2    0.029247       0.903657         1.181396          4.980085  ...   \n",
       "3    0.006549       0.630261         0.611841          3.936843  ...   \n",
       "4    0.001108       0.633851         0.472858          4.691207  ...   \n",
       "5    0.021397       0.713963         0.864571          4.181641  ...   \n",
       "6    0.006936       0.901460         0.899729          4.681347  ...   \n",
       "7    0.017686       0.624526         0.751404          3.091574  ...   \n",
       "8   -0.006569       0.671034         0.690387          5.181227  ...   \n",
       "9   -0.006732       1.026969         0.958384          7.775271  ...   \n",
       "\n",
       "   displacement_power_bands_0_to_6  velocity_peak_magnitude  velocity_entropy  \\\n",
       "0                         0.684968                 0.405524          4.188976   \n",
       "1                         1.901095                 0.581605          4.242219   \n",
       "2                         1.487423                 0.563078          4.205455   \n",
       "3                         0.910218                 0.399440          4.266713   \n",
       "4                         0.903263                 0.362294          4.248869   \n",
       "5                         1.074064                 0.473776          4.171686   \n",
       "6                         1.739043                 0.540726          4.231232   \n",
       "7                         1.202803                 0.427129          4.235774   \n",
       "8                         1.015975                 0.440222          4.219854   \n",
       "9                         2.051206                 0.697988          4.114266   \n",
       "\n",
       "   velocity_half_point  velocity_total_power  velocity_power_bands_0.5_to_1  \\\n",
       "0                  2.5              0.486046                       0.019232   \n",
       "1                  2.5              1.192271                       0.091408   \n",
       "2                  2.5              0.890508                       0.037900   \n",
       "3                  2.5              0.429022                       0.027512   \n",
       "4                  2.5              0.430376                       0.028220   \n",
       "5                  2.5              0.525334                       0.026714   \n",
       "6                  2.5              0.838308                       0.044310   \n",
       "7                  2.5              0.405390                       0.027890   \n",
       "8                  2.5              0.462217                       0.031037   \n",
       "9                  2.5              1.098217                       0.072292   \n",
       "\n",
       "   velocity_power_bands_0_to_2  velocity_power_bands_0_to_4  \\\n",
       "0                     0.099006                     0.373697   \n",
       "1                     0.389776                     0.946561   \n",
       "2                     0.210545                     0.671195   \n",
       "3                     0.135335                     0.330622   \n",
       "4                     0.135963                     0.341566   \n",
       "5                     0.127043                     0.389940   \n",
       "6                     0.229761                     0.664828   \n",
       "7                     0.109671                     0.326465   \n",
       "8                     0.120819                     0.318026   \n",
       "9                     0.234923                     0.852103   \n",
       "\n",
       "   velocity_power_bands_0_to_6  convexhull  \n",
       "0                     0.486046   36.917852  \n",
       "1                     1.192271   37.675818  \n",
       "2                     0.890508  145.438051  \n",
       "3                     0.429022   32.153650  \n",
       "4                     0.430376   29.343181  \n",
       "5                     0.525334   37.914752  \n",
       "6                     0.838308   61.161962  \n",
       "7                     0.405390   37.673377  \n",
       "8                     0.462217   29.350673  \n",
       "9                     1.098217   95.849782  \n",
       "\n",
       "[10 rows x 36 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_df = pd.DataFrame(columns=[\"combine_record_id\",\"record_id\", \"term\",\"position_name\",\n",
    "                                  \"speed_max\", \"speed_median\", \"speed_mean\", \"speed_std_div\", \"speed_iqr_range\",\n",
    "                                 \"acceleration_max\", \"acceleration_median\", \"acceleration_mean\", \"acceleration_std_div\", \"accelerati_iqr_range\",\n",
    "                                 \"jerk_max\", \"jerk_median\", \"jerk_mean\", \"jerk_std_div\", \"jerk_iqr_range\",\n",
    "                                 \"displacement_peak_magnitude\",\"displacement_entropy\", \"displacement_half_point\", \"displacement_total_power\",\n",
    "                                 \"displacement_power_bands_0.5_to_1\",\"displacement_power_bands_0_to_2\", \"displacement_power_bands_0_to_4\", \"displacement_power_bands_0_to_6\",\n",
    "                                 \"velocity_peak_magnitude\",\"velocity_entropy\", \"velocity_half_point\", \"velocity_total_power\",\n",
    "                                 \"velocity_power_bands_0.5_to_1\",\"velocity_power_bands_0_to_2\", \"velocity_power_bands_0_to_4\", \"velocity_power_bands_0_to_6\",\n",
    "                                 \"convexhull\"])\n",
    "index = 0\n",
    "\n",
    "for record_id, values in comm_dict.items():\n",
    "    positions = values[\"position\"]\n",
    "    resp = values[\"resp\"]\n",
    "    for position_name, position_array in positions.items():\n",
    "        row = record_convertion(position_array, position_name, record_id)\n",
    "        record_df.loc[index] = row\n",
    "        index += 1\n",
    "\n",
    "record_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20deedc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Communication', 'Drinking', 'Higher'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_file = 'UDysRS_UPDRS_Export/UDysRS.txt'\n",
    "\n",
    "with open(rating_file, 'r') as infile:\n",
    "    ratings = json.load(infile)\n",
    "\n",
    "ratings.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74b5b000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 1.5, 1.5, 2.5, 1.5, 1.5]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['Communication']['2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "366eac73",
   "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>combine_record_id</th>\n",
       "      <th>record_id</th>\n",
       "      <th>term</th>\n",
       "      <th>position_name</th>\n",
       "      <th>sub_score</th>\n",
       "      <th>speed_max</th>\n",
       "      <th>speed_median</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std_div</th>\n",
       "      <th>speed_iqr_range</th>\n",
       "      <th>...</th>\n",
       "      <th>velocity_peak_magnitude</th>\n",
       "      <th>velocity_entropy</th>\n",
       "      <th>velocity_half_point</th>\n",
       "      <th>velocity_total_power</th>\n",
       "      <th>velocity_power_bands_0.5_to_1</th>\n",
       "      <th>velocity_power_bands_0_to_2</th>\n",
       "      <th>velocity_power_bands_0_to_4</th>\n",
       "      <th>velocity_power_bands_0_to_6</th>\n",
       "      <th>convexhull</th>\n",
       "      <th>UDysRS_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lkne</td>\n",
       "      <td>Lleg</td>\n",
       "      <td>7.621226</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>-0.009828</td>\n",
       "      <td>1.698062</td>\n",
       "      <td>1.190246</td>\n",
       "      <td>...</td>\n",
       "      <td>0.963329</td>\n",
       "      <td>4.253132</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.739219</td>\n",
       "      <td>0.199188</td>\n",
       "      <td>0.984690</td>\n",
       "      <td>2.129187</td>\n",
       "      <td>2.739219</td>\n",
       "      <td>88.221826</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lwri</td>\n",
       "      <td>Larm</td>\n",
       "      <td>9.038624</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.131159</td>\n",
       "      <td>1.042972</td>\n",
       "      <td>0.602927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.828982</td>\n",
       "      <td>3.990733</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.826367</td>\n",
       "      <td>0.133442</td>\n",
       "      <td>0.527075</td>\n",
       "      <td>0.732966</td>\n",
       "      <td>0.826367</td>\n",
       "      <td>257.148438</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rkne</td>\n",
       "      <td>Rleg</td>\n",
       "      <td>3.524555</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.007824</td>\n",
       "      <td>0.466179</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>...</td>\n",
       "      <td>0.230852</td>\n",
       "      <td>4.182930</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.157589</td>\n",
       "      <td>0.006297</td>\n",
       "      <td>0.030704</td>\n",
       "      <td>0.112235</td>\n",
       "      <td>0.157589</td>\n",
       "      <td>21.371593</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lelb</td>\n",
       "      <td>Larm</td>\n",
       "      <td>3.696377</td>\n",
       "      <td>-0.000203</td>\n",
       "      <td>-0.065752</td>\n",
       "      <td>0.972254</td>\n",
       "      <td>0.676540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.660396</td>\n",
       "      <td>4.244662</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.914604</td>\n",
       "      <td>0.122146</td>\n",
       "      <td>0.459827</td>\n",
       "      <td>0.700054</td>\n",
       "      <td>0.914604</td>\n",
       "      <td>116.737246</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lsho</td>\n",
       "      <td>Larm</td>\n",
       "      <td>2.462362</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>0.557721</td>\n",
       "      <td>0.008510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322026</td>\n",
       "      <td>4.285978</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.276645</td>\n",
       "      <td>0.018851</td>\n",
       "      <td>0.094961</td>\n",
       "      <td>0.194003</td>\n",
       "      <td>0.276645</td>\n",
       "      <td>39.668787</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  combine_record_id record_id term position_name sub_score  speed_max  \\\n",
       "0               1-1         1    1          Lkne      Lleg   7.621226   \n",
       "1               1-1         1    1          Lwri      Larm   9.038624   \n",
       "2               1-1         1    1          Rkne      Rleg   3.524555   \n",
       "3               1-1         1    1          Lelb      Larm   3.696377   \n",
       "4               1-1         1    1          Lsho      Larm   2.462362   \n",
       "\n",
       "   speed_median  speed_mean  speed_std_div  speed_iqr_range  ...  \\\n",
       "0      0.000055   -0.009828       1.698062         1.190246  ...   \n",
       "1      0.000233    0.131159       1.042972         0.602927  ...   \n",
       "2      0.000038    0.007824       0.466179         0.007461  ...   \n",
       "3     -0.000203   -0.065752       0.972254         0.676540  ...   \n",
       "4     -0.000009   -0.009549       0.557721         0.008510  ...   \n",
       "\n",
       "   velocity_peak_magnitude  velocity_entropy  velocity_half_point  \\\n",
       "0                 0.963329          4.253132                  2.5   \n",
       "1                 0.828982          3.990733                  2.5   \n",
       "2                 0.230852          4.182930                  2.5   \n",
       "3                 0.660396          4.244662                  2.5   \n",
       "4                 0.322026          4.285978                  2.5   \n",
       "\n",
       "   velocity_total_power  velocity_power_bands_0.5_to_1  \\\n",
       "0              2.739219                       0.199188   \n",
       "1              0.826367                       0.133442   \n",
       "2              0.157589                       0.006297   \n",
       "3              0.914604                       0.122146   \n",
       "4              0.276645                       0.018851   \n",
       "\n",
       "   velocity_power_bands_0_to_2  velocity_power_bands_0_to_4  \\\n",
       "0                     0.984690                     2.129187   \n",
       "1                     0.527075                     0.732966   \n",
       "2                     0.030704                     0.112235   \n",
       "3                     0.459827                     0.700054   \n",
       "4                     0.094961                     0.194003   \n",
       "\n",
       "   velocity_power_bands_0_to_6  convexhull  UDysRS_rating  \n",
       "0                     2.739219   88.221826            0.5  \n",
       "1                     0.826367  257.148438            1.0  \n",
       "2                     0.157589   21.371593            0.0  \n",
       "3                     0.914604  116.737246            1.0  \n",
       "4                     0.276645   39.668787            1.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_score_dict = {\"Neck\":[\"face\"],\n",
    "        \"Larm\":[\"Lsho\", \"Lelb\", \"Lwri\"],\n",
    "        \"Rarm\":[\"Rsho\", \"Relb\", \"Rwri\"],\n",
    "        \"Trunk\":[\"Rsho\", \"Lsho\"],\n",
    "        \"Rleg\":[\"Rhip\", \"Rkne\", \"Rank\"],\n",
    "        \"Lleg\":[\"Lhip\", \"Lkne\", \"Lank\"]}\n",
    "\n",
    "#sub_score_dict\n",
    "groups = record_df.groupby(\"combine_record_id\")\n",
    "processed_df = pd.DataFrame(columns=[\"combine_record_id\",\"record_id\", \"term\",\"position_name\",\"sub_score\",\n",
    "                                  \"speed_max\", \"speed_median\", \"speed_mean\", \"speed_std_div\", \"speed_iqr_range\",\n",
    "                                 \"acceleration_max\", \"acceleration_median\", \"acceleration_mean\", \"acceleration_std_div\", \"accelerati_iqr_range\",\n",
    "                                 \"jerk_max\", \"jerk_median\", \"jerk_mean\", \"jerk_std_div\", \"jerk_iqr_range\",\n",
    "                                     \"displacement_peak_magnitude\",\"displacement_entropy\", \"displacement_half_point\", \"displacement_total_power\",\n",
    "                                 \"displacement_power_bands_0.5_to_1\",\"displacement_power_bands_0_to_2\", \"displacement_power_bands_0_to_4\", \"displacement_power_bands_0_to_6\",\n",
    "                                 \"velocity_peak_magnitude\",\"velocity_entropy\", \"velocity_half_point\", \"velocity_total_power\",\n",
    "                                 \"velocity_power_bands_0.5_to_1\",\"velocity_power_bands_0_to_2\", \"velocity_power_bands_0_to_4\", \"velocity_power_bands_0_to_6\",\n",
    "                                 \"convexhull\", \"UDysRS_rating\"])\n",
    "\n",
    "def find_rating(record_id, sub_group):\n",
    "    order = {\"Neck\":0,\n",
    "             \"Rarm\":1,\n",
    "             \"Larm\":2,\n",
    "             \"Trunk\":3,\n",
    "             \"Rleg\":4,\n",
    "             \"Lleg\":5}\n",
    "    try:\n",
    "        rating = ratings['Communication'][str(record_id)][order[sub_group]]\n",
    "    except:\n",
    "        rating = 0\n",
    "    return rating\n",
    "    \n",
    "for record_id, group in groups:\n",
    "    #print(record_id)\n",
    "    for index, dict_ in group.iterrows():\n",
    "        position_name = dict_[\"position_name\"]\n",
    "        for sub_score, values in sub_score_dict.items():\n",
    "            if position_name in values:\n",
    "                #print(key, position_name)\n",
    "                dict_[\"sub_score\"] = sub_score\n",
    "                dict_[\"UDysRS_rating\"] = find_rating(dict_[\"record_id\"], sub_score)\n",
    "                #print(dict_)\n",
    "                processed_df = processed_df.append(dict_, ignore_index=True)\n",
    "                \n",
    "    #print(group.head(17))\n",
    "processed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd973c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>sub_score</th>\n",
       "      <th>speed_max</th>\n",
       "      <th>speed_median</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std_div</th>\n",
       "      <th>speed_iqr_range</th>\n",
       "      <th>acceleration_max</th>\n",
       "      <th>acceleration_median</th>\n",
       "      <th>acceleration_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>velocity_peak_magnitude</th>\n",
       "      <th>velocity_entropy</th>\n",
       "      <th>velocity_half_point</th>\n",
       "      <th>velocity_total_power</th>\n",
       "      <th>velocity_power_bands_0.5_to_1</th>\n",
       "      <th>velocity_power_bands_0_to_2</th>\n",
       "      <th>velocity_power_bands_0_to_4</th>\n",
       "      <th>velocity_power_bands_0_to_6</th>\n",
       "      <th>convexhull</th>\n",
       "      <th>UDysRS_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Larm</td>\n",
       "      <td>2.378699</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.006151</td>\n",
       "      <td>0.434431</td>\n",
       "      <td>0.145590</td>\n",
       "      <td>2.563187</td>\n",
       "      <td>-0.000183</td>\n",
       "      <td>-0.002905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290431</td>\n",
       "      <td>4.180208</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.257181</td>\n",
       "      <td>0.032530</td>\n",
       "      <td>0.128346</td>\n",
       "      <td>0.204443</td>\n",
       "      <td>0.257181</td>\n",
       "      <td>51.447111</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lleg</td>\n",
       "      <td>2.801376</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-0.002543</td>\n",
       "      <td>0.607632</td>\n",
       "      <td>0.229252</td>\n",
       "      <td>4.019147</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>-0.002594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.331893</td>\n",
       "      <td>4.239766</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.481217</td>\n",
       "      <td>0.034714</td>\n",
       "      <td>0.156380</td>\n",
       "      <td>0.365744</td>\n",
       "      <td>0.481217</td>\n",
       "      <td>26.924760</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Neck</td>\n",
       "      <td>3.563685</td>\n",
       "      <td>-0.206350</td>\n",
       "      <td>0.016804</td>\n",
       "      <td>1.104360</td>\n",
       "      <td>0.673869</td>\n",
       "      <td>6.374849</td>\n",
       "      <td>0.025236</td>\n",
       "      <td>-0.001832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.744844</td>\n",
       "      <td>4.114691</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.166132</td>\n",
       "      <td>0.048349</td>\n",
       "      <td>0.234551</td>\n",
       "      <td>0.750230</td>\n",
       "      <td>1.166132</td>\n",
       "      <td>80.342687</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Rarm</td>\n",
       "      <td>1.805619</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.349983</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>2.890424</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.001641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200216</td>\n",
       "      <td>4.175002</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.101611</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.024399</td>\n",
       "      <td>0.073403</td>\n",
       "      <td>0.101611</td>\n",
       "      <td>12.233846</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Rleg</td>\n",
       "      <td>2.280942</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.001827</td>\n",
       "      <td>0.471195</td>\n",
       "      <td>0.038344</td>\n",
       "      <td>2.987845</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.266186</td>\n",
       "      <td>4.204739</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.212301</td>\n",
       "      <td>0.011795</td>\n",
       "      <td>0.052896</td>\n",
       "      <td>0.152180</td>\n",
       "      <td>0.212301</td>\n",
       "      <td>17.774876</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Trunk</td>\n",
       "      <td>1.723720</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.316521</td>\n",
       "      <td>0.004802</td>\n",
       "      <td>2.431101</td>\n",
       "      <td>-0.000032</td>\n",
       "      <td>-0.002697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181466</td>\n",
       "      <td>4.227550</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.096851</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>0.028385</td>\n",
       "      <td>0.069674</td>\n",
       "      <td>0.096851</td>\n",
       "      <td>15.987596</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>Larm</td>\n",
       "      <td>3.313730</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>-0.006432</td>\n",
       "      <td>1.024204</td>\n",
       "      <td>1.352590</td>\n",
       "      <td>2.674307</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>-0.004236</td>\n",
       "      <td>...</td>\n",
       "      <td>1.240900</td>\n",
       "      <td>3.201629</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.293431</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>1.080049</td>\n",
       "      <td>1.207227</td>\n",
       "      <td>1.293431</td>\n",
       "      <td>46.703959</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   record_id sub_score  speed_max  speed_median  speed_mean  speed_std_div  \\\n",
       "0          1      Larm   2.378699      0.000008    0.006151       0.434431   \n",
       "1          1      Lleg   2.801376      0.000005   -0.002543       0.607632   \n",
       "2          1      Neck   3.563685     -0.206350    0.016804       1.104360   \n",
       "3          1      Rarm   1.805619      0.000063    0.001008       0.349983   \n",
       "4          1      Rleg   2.280942      0.000060   -0.001827       0.471195   \n",
       "5          1     Trunk   1.723720      0.000010    0.000035       0.316521   \n",
       "6          2      Larm   3.313730      0.000220   -0.006432       1.024204   \n",
       "\n",
       "   speed_iqr_range  acceleration_max  acceleration_median  acceleration_mean  \\\n",
       "0         0.145590          2.563187            -0.000183          -0.002905   \n",
       "1         0.229252          4.019147             0.000188          -0.002594   \n",
       "2         0.673869          6.374849             0.025236          -0.001832   \n",
       "3         0.031725          2.890424            -0.000061          -0.001641   \n",
       "4         0.038344          2.987845             0.000008          -0.001380   \n",
       "5         0.004802          2.431101            -0.000032          -0.002697   \n",
       "6         1.352590          2.674307             0.000632          -0.004236   \n",
       "\n",
       "   ...  velocity_peak_magnitude  velocity_entropy  velocity_half_point  \\\n",
       "0  ...                 0.290431          4.180208                  2.5   \n",
       "1  ...                 0.331893          4.239766                  2.5   \n",
       "2  ...                 0.744844          4.114691                  2.5   \n",
       "3  ...                 0.200216          4.175002                  2.5   \n",
       "4  ...                 0.266186          4.204739                  2.5   \n",
       "5  ...                 0.181466          4.227550                  2.5   \n",
       "6  ...                 1.240900          3.201629                  2.5   \n",
       "\n",
       "   velocity_total_power  velocity_power_bands_0.5_to_1  \\\n",
       "0              0.257181                       0.032530   \n",
       "1              0.481217                       0.034714   \n",
       "2              1.166132                       0.048349   \n",
       "3              0.101611                       0.005229   \n",
       "4              0.212301                       0.011795   \n",
       "5              0.096851                       0.005998   \n",
       "6              1.293431                       0.236842   \n",
       "\n",
       "   velocity_power_bands_0_to_2  velocity_power_bands_0_to_4  \\\n",
       "0                     0.128346                     0.204443   \n",
       "1                     0.156380                     0.365744   \n",
       "2                     0.234551                     0.750230   \n",
       "3                     0.024399                     0.073403   \n",
       "4                     0.052896                     0.152180   \n",
       "5                     0.028385                     0.069674   \n",
       "6                     1.080049                     1.207227   \n",
       "\n",
       "   velocity_power_bands_0_to_6  convexhull  UDysRS_rating  \n",
       "0                     0.257181   51.447111            1.0  \n",
       "1                     0.481217   26.924760            0.5  \n",
       "2                     1.166132   80.342687            1.5  \n",
       "3                     0.101611   12.233846            0.0  \n",
       "4                     0.212301   17.774876            0.0  \n",
       "5                     0.096851   15.987596            0.5  \n",
       "6                     1.293431   46.703959            1.5  \n",
       "\n",
       "[7 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df = processed_df.groupby(['record_id', 'sub_score']).mean().reset_index()\n",
    "grouped_df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2342d22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Larm\n",
      "0.4074074074074074\n",
      "Lleg\n",
      "0.25925925925925924\n",
      "Neck\n",
      "0.4074074074074074\n",
      "Rarm\n",
      "0.5555555555555556\n",
      "Rleg\n",
      "0.4444444444444444\n",
      "Trunk\n",
      "0.4444444444444444\n"
     ]
    }
   ],
   "source": [
    "sub_score_gr = grouped_df.groupby([\"sub_score\"])\n",
    "for sub_score, sub_score_group in sub_score_gr:\n",
    "    print(sub_score)\n",
    "    y = sub_score_group[\"UDysRS_rating\"].astype(str)\n",
    "    X = sub_score_group.drop(['record_id', 'sub_score', 'UDysRS_rating'], axis=1)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    #print(X.head())\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf.fit(X_train, y_train)\n",
    "    filename = '{}_decision_tree_model.sav'.format(sub_score)\n",
    "    pickle.dump(clf, open(filename, 'wb'))\n",
    "    print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f62c4395",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "try:\n",
    "    import ujson as json\n",
    "except ImportError:\n",
    "    try:\n",
    "        import simplejson as json\n",
    "    except ImportError:\n",
    "        import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "from ipykernel import kernelapp as app\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import signal\n",
    "from scipy import stats\n",
    "from scipy.integrate import simps\n",
    "from scipy.spatial import ConvexHull\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "sns.set(font_scale=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ab6e236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of records :12\n"
     ]
    }
   ],
   "source": [
    "# add these graphs and default input as one \n",
    "\n",
    "comm_dict = {'Lank': [[941, 847], [955, 849], [973, 858], [981, 876], [1011, 876], [1035, 878], [1072, 888], [1097, 897], [1124, 908], [1149, 919], [1174, 927], [1196, 933], [1210, 941], [1228, 946], [1240, 952], [1252, 962], [1257, 972], [1261, 978], [1258, 980], [1256, 980], [1254, 983], [1248, 987], [1249, 985], [1248, 986], [1248, 986], [1252, 987], [1254, 986], [1256, 986], [1257, 985], [1257, 985], [1256, 980], [1268, 969], [1295, 931], [1313, 940], [1334, 949], [1331, 965], [1336, 964], [1356, 939], [1370, 928], [1406, 921], [1415, 908], [1435, 916], [1450, 919], [1477, 927], [1501, 929], [1538, 995], [1582, 1024], [1596, 1036], [1602, 1050], [1608, 1087], [1616, 1125], [1612, 1147], [1628, 1106], [1635, 1102], [1640, 1116], [1623, 1106], [1642, 1100], [1645, 1091], [1646, 1087], [1662, 1140], [1659, 1137], [1654, 1172], [1650, 1192], [1656, 1224], [1651, 1230], [1647, 1235], [1644, 1281], [1643, 1310], [1642, 1338], [1637, 1357], [1606, 1342], [1582, 1316], [1588, 1312], [1599, 1258], [1567, 1240], [1465, 1314], [1487, 1255], [1462, 1286], [1425, 1332], [1433, 1329], [1367, 1347], [1371, 1335], [1354, 1377], [1456, 1362], [1463, 1359], [1539, 1354], [1382, 1352], [1297, 1419], [1282, 1397], [1336, 1410], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [406, 137], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [548, 211], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [182, 978], [178, 978], [168, 978], [163, 987], [158, 1005], [159, 1006], [159, 1006], [160, 1014], [159, 1028], [159, 1037], [160, 1032], [162, 1028], [167, 1015], [174, 999], [180, 977], [187, 959], [195, 953], [211, 923], [224, 918], [240, 904], [256, 895], [275, 868], [293, 854], [309, 842], [326, 819], [341, 798], [360, 785], [386, 766], [410, 746], [430, 751], [497, 774], [698, 747], [611, 765], [586, 757], [622, 749], [659, 621], [642, 627], [613, 671], [636, 658], [642, 617], [665, 596], [664, 608], [656, 629], [717, 625], [681, 700], [693, 705], [679, 696], [684, 695], [690, 670], [714, 670], [721, 670], [753, 658], [737, 659], [754, 659], [751, 650], [772, 606], [766, 624]], 'Lelb': [[1157, 199], [1161, 200], [1168, 201], [1177, 201], [1188, 201], [1196, 201], [1204, 201], [1219, 199], [1232, 198], [1242, 197], [1252, 196], [1261, 196], [1273, 199], [1284, 204], [1291, 215], [1306, 230], [1314, 240], [1324, 247], [1336, 257], [1340, 269], [1352, 276], [1361, 285], [1382, 292], [1397, 293], [1415, 294], [1431, 292], [1444, 289], [1460, 288], [1476, 283], [1485, 274], [1494, 264], [1502, 255], [1514, 256], [1518, 254], [1520, 251], [1524, 252], [1528, 256], [1529, 267], [1531, 279], [1536, 288], [1541, 298], [1544, 319], [1549, 339], [1548, 348], [1550, 351], [1548, 358], [1546, 362], [1546, 356], [1544, 356], [1541, 355], [1539, 353], [1536, 337], [1533, 315], [1537, 297], [1547, 278], [1541, 256], [1580, 229], [1591, 249], [1625, 246], [1656, 243], [1530, 239], [1264, 239], [1701, 226], [1770, 217], [1807, 222], [1828, 223], [1839, 223], [1867, 224], [1876, 224], [1869, 223], [1847, 219], [1830, 216], [1824, 213], [1822, 206], [1787, 190], [1172, 248], [1555, 211], [1114, 223], [1075, 223], [1044, 219], [1011, 214], [996, 206], [970, 191], [951, 169], [915, 156], [877, 154], [852, 146], [827, 146], [808, 149], [807, 152], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [1114, 222], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [388, 187], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [24, 286], [24, 296], [36, 306], [48, 314], [66, 321], [93, 322], [111, 326], [143, 326], [164, 324], [184, 323], [205, 286], [231, 284], [279, 276], [327, 222], [344, 195], [348, 225], [359, 221], [365, 223], [376, 222], [373, 225], [369, 226], [367, 247], [367, 267], [371, 281], [376, 298], [376, 313], [374, 319], [376, 317], [382, 321], [383, 319], [382, 319], [381, 315], [380, 312], [379, 298], [383, 274], [391, 258], [399, 247], [410, 237], [420, 232], [436, 223], [448, 221], [460, 223], [474, 225], [494, 222], [508, 225], [525, 226], [544, 230], [561, 238], [576, 240], [597, 240], [613, 239], [638, 234], [650, 216], [648, 217], [665, 214], [676, 211], [672, 210]], 'Lhip': [[1014, 513], [1019, 517], [1027, 517], [1037, 517], [1048, 517], [1056, 516], [1070, 512], [1083, 507], [1093, 504], [1105, 499], [1115, 496], [1127, 490], [1138, 486], [1151, 484], [1162, 484], [1175, 481], [1189, 482], [1202, 483], [1215, 488], [1223, 491], [1237, 499], [1251, 502], [1263, 516], [1276, 524], [1283, 527], [1293, 524], [1299, 522], [1307, 522], [1315, 521], [1324, 520], [1335, 519], [1342, 508], [1354, 500], [1359, 494], [1370, 487], [1377, 489], [1391, 490], [1408, 491], [1417, 492], [1433, 493], [1445, 499], [1454, 523], [1470, 547], [1478, 559], [1490, 570], [1500, 571], [1508, 570], [1513, 568], [1525, 572], [1523, 568], [1533, 566], [1527, 565], [1531, 550], [1532, 530], [1528, 521], [1526, 519], [1525, 508], [1541, 508], [1553, 510], [1561, 512], [1556, 513], [1533, 530], [1541, 535], [1562, 549], [1572, 560], [1577, 569], [1576, 588], [1579, 604], [1578, 613], [1578, 613], [1568, 616], [1540, 612], [1541, 593], [1543, 581], [1514, 573], [1377, 574], [1437, 567], [1369, 563], [1326, 561], [1319, 552], [1295, 554], [1304, 554], [1285, 579], [1309, 585], [1314, 605], [1303, 624], [1247, 630], [1217, 655], [1203, 661], [1227, 678], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 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252], [898, 263], [898, 274], [906, 286], [915, 300], [916, 304], [924, 311], [930, 313], [940, 315], [948, 313]], 'Rwri': [[917, 131], [917, 130], [921, 130], [927, 130], [935, 130], [940, 130], [951, 130], [967, 122], [979, 106], [990, 92], [997, 84], [1005, 65], [1009, 62], [1009, 60], [1012, 62], [1017, 66], [1028, 78], [1046, 111], [1054, 144], [1064, 169], [1069, 198], [1068, 238], [1065, 251], [1063, 260], [1062, 276], [1060, 274], [1058, 269], [1041, 265], [1037, 258], [1054, 256], [1060, 254], [1049, 250], [1030, 236], [1022, 226], [998, 218], [994, 208], [985, 199], [964, 195], [931, 191], [913, 185], [904, 180], [900, 175], [895, 170], [890, 167], [891, 163], [894, 160], [894, 159], [898, 157], [901, 156], [901, 155], [902, 154], [910, 153], [934, 154], [953, 155], [986, 161], [1360, 281], [1142, 245], [1090, 226], [1093, 222], [1108, 231], [1469, 310], [1872, 286], [1289, 278], [1180, 285], [1161, 290], [1163, 295], [1159, 293], [1153, 291], [1152, 289], [1140, 281], [1134, 269], [1120, 256], [1096, 237], [1073, 226], [1034, 211], [1949, 281], [1036, 206], [1901, 320], [1885, 335], [1856, 352], [1828, 363], [1809, 383], [1778, 390], [1751, 400], [1726, 414], [1705, 420], [1697, 423], [1677, 428], [1609, 420], [1550, 418], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [1331, 369], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [264, 280], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [436, 444], [436, 455], [436, 461], [437, 474], [439, 477], [443, 476], [445, 474], [449, 468], [450, 452], [457, 434], [464, 416], [472, 397], [481, 381], [485, 373], [492, 356], [491, 342], [505, 341], [513, 338], [524, 338], [535, 344], [554, 348], [581, 359], [590, 370], [606, 390], [622, 407], [642, 425], [671, 439], [683, 435], [708, 431], [726, 428], [752, 409], [759, 375], [770, 344], [785, 306], [791, 281], [797, 261], [807, 241], [819, 218], [826, 201], [832, 193], [844, 189], [855, 190], [858, 189], [857, 196], [869, 206], [878, 215], [887, 232], [894, 252], [898, 263], [898, 274], [906, 286], [915, 300], [916, 304], [924, 311], [930, 313], [940, 315], [948, 313]]}\n",
    "\n",
    "print(\"total number of records :{}\".format(len(comm_dict.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "298538a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key in Dictionary: dict_keys(['Lank', 'Lelb', 'Lhip', 'Lkne', 'Lsho', 'Lwri', 'Rank', 'Relb', 'Rhip', 'Rkne', 'Rsho', 'Rwri'])\n"
     ]
    }
   ],
   "source": [
    "print(\"Key in Dictionary:\",comm_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3054820a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    %matplotlib inline\n",
    "    part = \"Lank\"\n",
    "    horizontal_displacemt_array = np.array(comm_dict[part])[:,0]\n",
    "    vertical_displacement_array = np.array(comm_dict[part])[:,1]\n",
    "    fig = plt.figure(figsize=(15, 4))\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(horizontal_displacemt_array,label='Horizental motion')\n",
    "    ax.legend(loc='best')\n",
    "\n",
    "    ax2 = fig.add_subplot(122)\n",
    "    ax2.plot(vertical_displacement_array,label='vertical motion')\n",
    "    ax2.legend(loc='best')\n",
    "\n",
    "    plt.suptitle('Joint motion')\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "417f09d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'peak_magnitude': 298.65453369034236,\n",
       " 'entropy': 2.338088145992246,\n",
       " 'half_point': 2.5,\n",
       " 'total_power': 44065.442764816486,\n",
       " 'power_bands_0.5_to_1': 5161.005037572766,\n",
       " 'power_bands_0_to_2': 42023.144328976035,\n",
       " 'power_bands_0_to_4': 43543.04962467708,\n",
       " 'power_bands_0_to_6': 44065.442764816486}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QAQ8PDywsLCos4KqkcMLNloQrhBCiBDoTbmE1a9akX79+9OvXD4Do6Gh+//131q1bx+TJk/nzzz/LNciqSjvhypCyEEII3fRKuPdq1qwZzZo1w9/fn5ycnIcd0yOjcMLNu5PNnTt3MDMzq8SIhBBCVFUPPEvZ1NT0YcTxSCqccAGSZLUpIYQQOshjQQ+g8CxlgJspstqUEEKI4knCfQCa53ANjU0ASEqVhCuEEKJ4D5xws7KyHkYcjyRND7dG3QaAJFwhhBC66Uy4OTk5hISE4OnpiaurKwEBAUUqCN28eZO2bduWe5BVUUFBASkpKQDY1HkGgBRJuEIIIXTQmXA//fRT9u7dy4wZM/jwww+5ffs2L7/8MpGRkVrHKYqi98VOnDjBwIEDcXNzo2fPnnz33XfA3eQeFBSEh4cHXl5erFy5Uqvdzp076dmzJy4uLrz55ptak5USEhLw9/fH1dWVHj16sG/fPq3YQkND8fb2xt3dnZCQEPLy8vSOtySadZTNrKphYWMLQIpUDBJCCKGDzoT7448/EhISQp8+fejTpw8bN27khRdeYPz48fz666/qcQYGBnpdqKCggDFjxvD6669z9OhRFi5cyIcffsi5c+cICwsjLi6OPXv2sGnTJiIiIti6dSsAMTExTJ8+nTlz5hAVFUXDhg2ZOHGiet5Jkybh5OREVFQUs2fPZuLEicTHxwMQHh7Onj17iIiIYPfu3Zw+fZoVK1bcx9dUlCbpW9aww8yqGiA1cYUQQuimM+Gmp6djb2//74GGhsyaNYsBAwYwfvx4Dh06VKYLpaWlkZSUhKIoKIqCgYEBxsbGmJiYEBERwahRo7CxsaF+/foEBASwceNGALZt24aPjw/u7u6YmZkRGBjIsWPHuHTpEnFxcZw5c4Zx48ZhamqKt7c3Pj4+bNq0CYCtW7cybNgwateujZ2dHWPHjiU8PPx+vqciNAnXwsZWTbhSE1cIIYQuOhNumzZt+OKLL4oMwc6aNYsuXbowevRodu3apfeFbG1tee2115g6dSotWrRg4MCBTJw4EXt7e27cuIGjo6N6bOPGjYmOjgbu9nAdHBzUfRYWFtSpU4fo6GguXrxInTp1sLS0VPc3adKE8+fPF9u2SZMmJCYmkpqaqnfcuqg9XBs7TP+XcKUmrhBCCF10Jtxp06Zx8OBB2rdvz+HDh/9tYGjIokWL6NmzJ7Nnz9b7QgUFBZiamrJw4UJOnjzJunXrWLZsGXv37gXA3NxcPdbCwoLs7GwAMjMzi6zVbG5uTlZWFhkZGVrtSmurOVaz/0FoZihb1LDFzPp/PVxJuEIIIXTQubSjg4MDO3bs4MCBAzzzzDPajYyN+eSTT/D19eWnn37S60K7d+/m+PHjTJkyBQAPDw9efPFFIiIiALhz5456bFZWltprLZxANbKzs7GyskJRFK12pbXVvC/cI75fmmdwzav/ew83XUr0CSGE0KHE53CtrKzw9fVVy/Ht379fa3/37t2ZP3++Xhe6du1akXWXjY2NsbOzw97entjYWHV7XFycOsTs6OhIXFycui8rK4urV6/i4OCAg4MDCQkJWkk1NjZWZ9vY2Fjs7e2pXr26XjGXRO3hFrqHmykJVwghhA6lLnxx69YtvvrqK3r27MnkyZPv+0IdOnTgwoULhIeHoygKZ86c4dtvv6V379707duXZcuWkZyczOXLl1m1ahV9+/YFwM/Pj8jISKKiosjJyWHhwoU0b96cxo0b06RJE5ydnQkNDSUnJ4dDhw4RGRmJn58fAH379mX16tVcuXKF5ORkwsLC1IpHD+rfSVP/9nClJq4QQghddA4pnzlzhq+//pqdO3fSunVrxo0bh6+v731fqGnTpixdupTFixfzySefULNmTQIDA+nRowedOnVi7ty5+Pn5UVBQwMsvv8yQIUMAcHJyIiQkhODgYK5fv06bNm1YvHixet6wsDCCgoLw9vbG1taWjz/+mGbNmgEwZMgQkpKSGDx4MNnZ2Tz33HOMHz/+vn+GwjQJ19zGFjPruz1mqYkrhBBCFwNFx8oVzs7OPPXUUyxYsABvb++KjqtKuHz5Mt27dycyMpL69etr7fPx8eH8+fMMCfuO6rXrs/Ilb4zNzPk79mIlRSuEEKIq0zmk/PXXX+Pt7c3o0aMZNmwY33///UOZ3fu4KPwcrom5BQaGRmpNXCGEEOJeOhNu27ZtWbBgAb/88gsdOnRgyZIltG/fviJjq7IKCgoKzVKugYGBgfpokNTEFUIIUZxSJ03Z2try1ltv8fPPPxMaGloRMVV5qampFBQUYGZdHaP/lebTTJySmrhCCCGKozPhDhw4kPXr16s9OQMDA7p06VJhgVVl/64yZatu0yRcKdEnhBCiODoTbqtWrfjss8/o3Lkzo0ePZvfu3eTm5lZkbFWW5hlcyxp26jZTSbhCCCFKoDPhBgcH89tvv/HZZ59Ro0YN3n//fTp27MjMmTM5fvx4RcZY5RR+BldD08OVmrhCCCGKU+I9XENDQzp16sScOXP4448/mD17Nmlpafj7+9OrVy+WLl2qlsJ7kvybcGuo2zSTpqQmrhBCiOLoXPjiXqampvTq1YtevXqRmZnJr7/+ypdffsmyZcv466+/yjPGKkdNuNWL9nClJq4QQoji6J1wNU6dOsWPP/7Inj17SElJoX///uUQVtVWeJUpDamJK4QQoiR6JdzTp0/z448/smvXLq5du0aHDh2YOHEiPXr0wMzMrLxjrHL+LVxQdNKU1MQVQghRHJ0J9/Tp0/z000/89NNPJCQk0Lx5c4YOHYqfnx9PPfVURcZY5RQ7aUpq4gohhCiBzoT70ksvUadOHfr06UO/fv1wcHCoyLiqNM2zyRbFDClLTVwhhBDF0Zlw16xZg5eXV0XG8shQh5RrFJ00JTVxhRBCFEdnwi2cbDds2FDiSV599dWHF1EVV1BQQEpKCgDm1WzU7VITVwghREn0mjS1atUqrc/5+fkkJSVhbGxM27Ztn6iEW9w6yoDUxBVCCFEivRLu3r17i2zLyMggKCiI5s2bP/SgqrLilnWEf2cpZ6fLkLIQQoiiSq0WpIuVlRVjx45lzZo1DzGcqq+4wgWA1MQVQghRovtOuAAXL1584pKL2sO10e7hSk1cIYQQJdFrSHn8+PFFtqWnpxMVFcULL7zw0IOqyv59Bte2yD4zq2pk30rlZkoadZ+2r+jQhBBCVGF6JVxLS8si22xsbAgODqZfv34PPaiqTPMMrnn14hMuSIk+IYQQRemVcOfMmVPecTwyNEPK5vdMmgKpiSuEEEI3nfdwhw8fzqlTp/Q+0dGjRxk6dOhDCaoq+7dSkO4ertTEFUIIcS+dPdzAwECCg4MxNDSkV69edOrUCQcHB0xNTQHIycnh3Llz/Pnnn2zbtg2Ajz76qGKirkTFraOsITVxhRBC6KIz4bZq1YpNmzaxZ88e1q9fz+LFi1EUBQsLCxRFISsrCyMjI9zc3BgzZgy+vr4YGBhUZOyVQk24xQwpS01cIYQQupR4D9fQ0BBfX198fX1JT0/nr7/+4ubNmxgaGlKzZk2cnJywtrauqFirhNJmKYPUxBVCCFGU3gXora2tadeuXXnGUuXl5+f/Wymoeo0i+03VHq4kXCGEENoeaOGLJ01qaiqKomBezQZDo6J/q2ju4d6WmrhCCCHuIQm3DEoaTgapiSuEEEI3SbhloGtZRw1Nws2QhCuEEOIeeiXciIgI0tPTyzuWKk9X4QINTcLNkhJ9Qggh7qFXwv3ss8/o2LEjEyZMYO/eveTl5ZV3XFVSqUPKUhNXCCGEDnrNUt69ezcnTpxgx44dzJw5k9zcXHx9fenbty/u7u7lHWOVoUm45jqGlNWauBmScIUQQmjT+x6ui4sLM2bMYP/+/YSGhmJsbMyIESPo1q0boaGhXL58uTzjrBL+TbjF93AL18TNzs6uyNCEEEJUcWWaNJWfn89vv/3G999/zw8//IC1tTXdu3fn0qVL9OnTh6+//rq84qwSNJOmilvWEaQmrhBCCN30SriHDh0iKCiI9u3bM2HCBPLz85k/fz779+9nxowZLF68mPHjx7No0aISz5OYmMiYMWNwc3Ojffv2fPrpp8DddZmDgoLw8PDAy8uLlStXarXbuXMnPXv2xMXFhTfffFPtaQIkJCTg7++Pq6srPXr0YN++feo+RVEIDQ3F29sbd3d3QkJCHuj+s7rohY4eLvw7cepmiix+IYQQ4l96JdyAgAASEhKYNm0av//+OwsWLKBz584YGv7bvEWLFvj5+ZV4njFjxmBvb88ff/xBeHg4W7duZfv27YSFhREXF8eePXvYtGkTERERbN26FYCYmBimT5/OnDlziIqKomHDhkycOFE956RJk3ByciIqKorZs2czceJE4uPjAQgPD2fPnj1ERESwe/duTp8+zYoVK8r6HalKKlygITVxhRBCFEevhPvJJ5+wYsUK+vfvr1WMPicnhz179gDQrl07Zs2apfMcJ0+eJD4+nhkzZmBmZkaDBg1Yt24dnp6eREREMGrUKGxsbKhfvz4BAQFs3LgRgG3btuHj44O7uztmZmYEBgZy7NgxLl26RFxcHGfOnGHcuHGYmpri7e2Nj48PmzZtAmDr1q0MGzaM2rVrY2dnx9ixYwkPD7/f76rU53Dh34lTyZJwhRBCFKJXwn333Xe5fbvozNv4+HgCAwP1utCZM2do1qwZS5cupVOnTvTo0YM9e/Zgbm7OjRs3cHR0VI9t3Lgx0dHRwN0eroODg7rPwsKCOnXqEB0dzcWLF6lTp47WHwFNmjTh/PnzxbZt0qQJiYmJpKam6hVzYfn5+aSkpABgXt1G53FmknCFEEIUQ+djQV9//TVhYWHA3Xuhzz//fJHye5mZmTz77LN6XSgtLY2jR4/i4eFBZGQksbGxjBgxAju7u71Fc3Nz9VgLCwt1lm9mZiYWFhZa5zI3NycrK6tIu9Laao69nxnEKSkpJa6jrKGZNCUFDIQQQhSmM3MMGjQIS0tLCgoKeP/99xkzZgzVqlVT9xsYGGBpaYm3t7deFzI1NcXa2pqxY8cC4OzszMCBA4mIiADgzp076rFZWVlqr7VwAtXIzs7GysoKRVG02pXWVvO+cI9YX+oqU8XUwS1MauIKIYQojs6Ea2xsTP/+/QGoX78+bdu2xdhY72p+RTRp0oSsrCxycnIwNTUF7g7T2tjYYG9vT2xsLLVq1QIgLi5OHWJ2dHQkLi5OPU9WVhZXr17FwcEBRVFISEggOztb7b3GxsYWaevm5qbus7e3p3r16mWOX0241XXPUIZ/E27aLenhCiGE+JfODPrJJ5/wzjvvYGlpya+//sqvv/6q8yTvvfdeqRfq0KEDdnZ2zJs3jylTphAXF8emTZuYNWsW9evXZ9myZTg5OZGZmcmqVasYOnQoAH5+frzyyitERUXh6urKwoULad68OY0bNwbu9pRDQ0PVyVSRkZHqxKi+ffuyevVqvL29sbCwICwsjH79+pXl+1Gpz+CW0sPVTJpKkx6uEEKIQnQm3NOnT6vPrJ4+fVrnCe69r6uLmZkZ69evZ/bs2XTq1AlTU1NGjBiBr68vXbt2Ze7cufj5+VFQUMDLL7/MkCFDAHByciIkJITg4GCuX79OmzZtWLx4sXresLAwgoKC8Pb2xtbWlo8//phmzZoBMGTIEJKSkhg8eDDZ2dk899xzjB8/Xq9476XPM7ggNXGFEEIUz0BRFKWyg6iqLl++TPfu3dVe86JFi/AY/Baer72ts03cn/vY8eE4nD07EbllYwVGK4QQoirTe2nHQ4cOqb28bdu2MXLkSJYuXfrEVA7SDCnrKlygITVxhRBCFEevhLtq1SrefPNNdaGJ999/H2tra7Zu3crChQvLO8YqobTSfBpSE1cIIURx9Eq433zzDQsXLsTNzY2tW7fSokULFi5cyLx589i+fXt5x1gl6P1YkNTEFUIIUQy9Em5iYiKtWrUCYN++fXTu3BmAWrVqkZGRUX7RVSFqD7eUx4KkJq4QQoji6PVgbcOGDTl48CC1a9cmPj6e7t27A3fv5Woez3nc6VO4AIrWxL13JSwhhBBPJr0S7rhx45g4cSL5+fn07dsXZ2dn5s6dyzfffMPSpUvLO8ZKV1BQcHcdZQODEtdRhn9r4mbfSiUp7Rb1JOEKIYRAz4Tbs2dP9u3bR2JiIs2bNwegf//+DB8+nNq1a5drgFVBWlra3XWUq9cocR1lDTOruwn3Zkoa9Wo9XQERCiGEqOr0fizIxsYGS0tLLl68SExMDMbGxqSnpxMTE1Oe8VUJmipBlqXMUNaQmrhCCCHupVcP95dffuH9998nNTUVzToZBgYGKIqCgYEBf/31V7kGWdn+Tbgl37/VkJq4Qggh7qVXwg0NDaVdu3a8/fbbWFtbl3dMVY4m4Zb2DK6G1MQVQghxL70S7j///ENoaKhWMfcnidrDLeUZXA2piSuEEOJeet3DbdWqFdHR0eUdS5WVmpoKgFkpz+BqSE1cIYQQ99J7lvLMmTP5888/adiwISYmJlr7X3311XIJrqrQ9HDNy5hwpSauEEIIDb0S7po1a6hWrRr79u0rss/AwOCJSbj6DilLTVwhhBD30ivh7t27t7zjqNI0Q8p6T5qSmrhCCCHuofdzuAUFBezbt481a9Zw69YtTp06RXp6ennGVmWoxefLOKScLiX6hBBC/I9ePdzExEQCAgK4cuUK2dnZdO/enc8//5yTJ0/y1Vdf4ejoWN5xViq1h6vvLGWpiSuEEOIeevVwP/74YxwdHTl06BBmZmYAzJ8/HxcXF0JCQso1wKrg1q1bd9dRrlZDr+OlJq4QQoh76ZVwDx06xNtvv42pqam6zcLCggkTJnDy5MlyC66qUBQFi2o1MDQy0ut4qYkrhBDiXnrfw71z506RbSkpKUUeEXpc6TthCqQmrhBCiKL0Sri9evVi7ty5JCYmYmBgAMC5c+f48MMP1dq4jzt9CxdA0Zq4QgghhF4Jd+rUqdjb29O5c2cyMzN5/vnneeGFF6hfvz7Tpk0r7xirBH2fwYV/a+ICJMmzuEIIIdBzlrKVlRWLFi1i4sSJXLx4kby8PBwcHGjcuHF5x1dl6PtIkIbUxBVCCFGYzoSbkJBQZJuRkRHNmjUrckzdunXLIbSqxawMQ8ogNXGFEEJo05lwfXx81Pu1pXnc6+ECWOhZC1dDauIKIYQoTGfC3b59u/r+8OHDfPnll7z77ru0atUKU1NTzpw5w4IFCxg6dGiFBFrZyjJLGaQmrhBCCG06E27Tpk3V9++88w4ff/wx3t7e6rZatWphY2PD5MmTGTJkSPlGWQWUtYcrNXGFEEIUptcs5cTEROzsiiYcMzOzu6swPQHut4crNXGFEEKAngm3Q4cOzJo1i4sXL6rbzp07R3Bw8BPzHO79JlypiSuEEAL0fCzoww8/ZPz48fTu3VtdSzknJ4fOnTsTHBxcrgFWCWVYR1lDauIKIYQoTK+Ea2dnx7p167h48SIXLlwAwMnJ6Yl5Dtfcqrre6yhrSE1cIYQQhemVcDUcHBxwcHAor1iqLIvqNmVuIzVxhRBCFKZ38YInmVm1+0+4UhNXCCEESMLVi8UDJFypiSuEEAIqIeHeunWLrl27smXLFuDu5KugoCA8PDzw8vJi5cqVWsfv3LmTnj174uLiwptvvklSUpK6LyEhAX9/f1xdXenRowf79u1T9ymKQmhoKN7e3ri7uxMSEkJeXt59xWxevUaZ20hNXCGEEIXpTLhZWVl6v8oiODiY69evq5/DwsKIi4tjz549bNq0iYiICLZu3QpATEwM06dPZ86cOURFRdGwYUMmTpyotp00aRJOTk5ERUUxe/ZsJk6cSHx8PADh4eHs2bOHiIgIdu/ezenTp1mxYkWZYtUwtS57D1dq4gohhChM56QpV1fXh76WckREBOnp6VoFECIiIpg7dy42NjbY2NgQEBDAxo0b6d+/P9u2bcPHxwd3d3cAAgMDadeuHZcuXUJRFM6cOcPq1asxNTXF29sbHx8fNm3axMSJE9m6dSvDhg2jdu3aAIwdO5YpU6bwzjvv6BVrYeb3MaR8b01cc3PzMp9DCCHE40Nnwl27du1DvVB8fDxLly5l48aNjBgxArg7vHzjxg0cHR3V4xo3bkx0dDRwt4fbsmVLdZ+FhQV16tQhOjoaQ0ND6tSpg6Wlpbq/SZMmnDp1Sm1beEZ1kyZNSExMJDU1lRo1apQp9vsZUtbUxM2+lUpS2i3qScIVQognms6E6+HhodcJCg8P65Kfn8/kyZOZMmUK9vb26vbMzEwArd6fhYUF2dnZ6n4LCwutc5mbm6vD2Pf2GktqqzlWs78szP93P7aspCauEEIIDb2ew42Li+OTTz7h4sWL5OfnA3cnJeXk5JCcnMzZs2dLbL98+XIaN25Mr169tLZrEuKdO3fUbVlZWWqvtXAC1cjOzsbKygpFUbTaldZW875wj1hfFvfRwwWpiSuEEOJfes1SnjVrFlevXmXIkCEkJibyyiuv0LlzZ1JTU5k9e3ap7X/44Qd27dqFu7s77u7uREdH88EHHxAaGoq9vT2xsbHqsXFxceoQs6OjI3Fxceq+rKwsrl69qi7AkZCQoJVUY2NjdbaNjY3F3t6e6tXL3lu9n+dwQWriCiGE+JdePdyTJ0+ydu1aWrduzc6dO2nTpg0BAQE0atSIHTt28OKLL5bY/qefftL63K9fP4YNG8aAAQOwtLRk2bJlODk5kZmZyapVq9Qau35+frzyyitERUXh6urKwoULad68ubqkpLOzM6GhoQQGBnLs2DEiIyMJDw8HoG/fvqxevRpvb28sLCwICwujX79+Zf6C4N+e6v22k4QrhBBCrx6uoijUrFkTuDv5SDMruXv37qUOJ5dm/PjxNG3aFD8/PwYOHIivr69aX9fJyYmQkBCCg4Px9PQkJiaGxYsXq23DwsK4ePEi3t7ezJgxg48//lidAT1kyBCee+45Bg8ejK+vL46OjowfP/6+YjQwLNs6yhpSE1cIIYSGXj1cZ2dndu/ezfDhw3F0dOTw4cO8/vrrXL9+HUVRynzR77//Xn1vZmZGcHCwzqpDvr6++Pr6FruvTp06fPnll8XuMzQ0ZNy4cYwbN67M8T0sUhNXCCGEhl4J95133mHMmDGYmJjQt29fPvvsM15//XViY2Pp3Llzecf4yJKauEIIITT0SrgdO3Zk165d5OfnU6tWLTZu3MjWrVvp1KmTer9VFCU1cYUQQmjodQ/3hRde4NatWzRo0ACAZs2a8d577/HWW2/JCkolkJq4QgghNPRKuDdu3MDExKS8Y3nsSE1cIYQQGnoNKQ8aNIhRo0bx8ssv06BBgyK92i5dupRLcI86qYkrhBBCQ6+E+9lnnwGwYMGCIvsMDAz0Ll7wpJGauEIIITT0Srjnzp3Tue9+Hgt6UkhNXCGEEBp63cPt3r07qampRbZfv36d9u3bP+yYHhtSE1cIIYSGzh5uZGQkR48eBeDKlSssWbKkyL3bf/75p3yje8RJTVwhhBAaOhOus7Mz//nPf9Qh47Nnz2rNVDYwMMDS0pK5c+eWf5SPKKmJK4QQQkNnwq1Xr55ahH7atGlMnz4da2vrCgvscSE1cYUQQoCe93Bnz57NqlWr2Lhxo7pt0KBBLFu2jIKCgnIL7nEgNXGFEEKAngn3k08+4fvvv1dXmgJ45ZVXiIiI0KreI4qSmrhCCCFAz4S7c+dOFi5cSIcOHdRt/fv3Z+7cuWzZsqXcgnscSE1cIYQQoGfCzcrKwtLSssh2Gxsb0tPTH3pQjxOpiSuEEAL0TLienp7Mnz9f61ncW7duERoaioeHR3nF9liQmrhCCCFAz5WmZsyYgb+/P507d6Zu3boYGBiQkJDAM888w/Lly8s7xkea1MQVQggBeibcunXrsn37dv744w8uXryIiYkJjRo1omPHjhga6tVJfmJJTVwhhBCgZ8IFMDU1pX79+uTm5tKhQweSkpIwMDAoz9geC1ITVwghBOiZcNPT05k4cSK//fYbhoaG7Nq1izlz5hAfH88XX3xB7dq1yzvOR5bUxBVCCAF6TpqaO3cuOTk57Nu3DzMzM+Dufd3q1asTEhJSrgE+6qQmrhBCCNAz4f7666+899571KpVS91Wt25dgoKCOHToULkF9ziQmrhCCCFAz4SbmZlZbKWb/Px8WdqxFFITVwghBOiZcDt16kRYWBi5ubnqtuTkZObNmyf1cEshNXGFEEKAngk3KCiIa9eu4enpSXZ2Nv7+/nTt2pX09HSmT59e3jE+0u6tiSuEEOLJpNcs5Zo1a7Jx40YOHTrExYsXycvLw8HBgQ4dOsijQaWQmrhCCCFAzx6uxtNPP03NmjWpV68eDRs2lGSrJ83EqZspstqUEEI8qfTq4aampjJ+/Hj+/PNPTExMUBSFvLw8unXrxoIFC4otbCD+JTVxhRBC6NXDnTlzJrdv3yYiIoJTp05x+vRpNm3axLVr1/j444/LO8ZHnqkkXCGEeOLplXAPHDjARx99hLOzs7qtRYsWzJo1i927d5dbcI8LTQ83RRKuEEI8sfRKuHZ2dlql+TRyc3OxsrJ62DE9djTrKadITVwhhHhi6XUPd+LEiQQFBTFhwgTc3NwwMjLi7NmzzJ07l1deeYWYmBj1WEdHx3IL9lFlJhWDhBDiiadXwg0MDARg8uTJ6sxkRVEAWLRoEaGhoSiKgoGBAX/99Vc5hfrokpq4Qggh9Eq4kZGR5R3HY01q4gohhNAr4darVw9A7cUmJSVx+PBhmjdvTsOGDcs1wMeB1MQVQgih16SpU6dO4ePjw+HDh0lNTWXgwIFMnjyZ3r1788svv+h9sd9//50BAwbQtm1bevbsycaNGwHIyckhKCgIDw8PvLy8WLlypVa7nTt30rNnT1xcXHjzzTdJSkpS9yUkJODv74+rqys9evRg37596j5FUQgNDcXb2xt3d3dCQkLIy8vTO96HRWriCiGE0LserpeXF82bNyciIoL8/HyioqKYNm0an376qV4Xunr1KmPHjmX06NEcOXKEhQsXsmjRIn777TfCwsKIi4tjz549bNq0iYiICLZu3QpATEwM06dPZ86cOURFRdGwYUMmTpyonnfSpEk4OTkRFRXF7NmzmThxIvHx8QCEh4ezZ88eIiIi2L17N6dPn2bFihVl+4YeAqmJK4QQQq+E+9///pcxY8ZQrVo19u7dS9euXbG0tKRr167ExcXpdaErV67g5+dHz549MTQ0pHXr1nh4eHDs2DEiIiIYNWoUNjY21K9fn4CAALX3u23bNnx8fHB3d8fMzIzAwECOHTvGpUuXiIuL48yZM4wbNw5TU1O8vb3x8fFh06ZNAGzdupVhw4ZRu3Zt7OzsGDt2LOHh4ff5Vd0/qYkrhBBCr3u41apVIy0tDUtLS44fP87w4cMB+Pvvv7Gzs9PrQu7u7ri7u6ufU1NTOXLkCP369ePGjRtajxM1btyY6Oho4G4Pt2XLluo+CwsL6tSpQ3R0NIaGhtSpU0drackmTZpw6tQpta2Dg4PWvsTERFJTU6lRo4ZecT8MUhNXCCGEXgnX19eXSZMmYWFhQa1atejcuTM7d+4kJCSE/v37l/mit2/fZvTo0bRp04YWLVoAaBW4t7CwUEvZZWZmYmFhodXe3NycrKysIu1Ka6s5tqLL5ElNXCGEEHol3Pfff59169Zx5coVXnvtNUxMTMjIyMDf3x9/f/8yXTAuLo4xY8bg6OjIggUL1OR3584d9ZisrCy111o4gWpkZ2djZWWFoiha7Uprq3lf0cUW7q2Je+8fCUIIIR5/eiVcIyMjdRhZ46WXXirzxQ4fPsyYMWMYPHgwkyZNulsr1swMe3t7YmNjqVWrFnA3KWuGmB0dHbXuE2dlZXH16lUcHBxQFIWEhAStJBYbG1ukrZubm7rP3t6e6tWrlzn2B1G4Ju7lq9dwbNyoQq8vhBCi8pWpHu6D+Oeffxg5ciTjxo0jMDBQq5Zu3759WbZsGcnJyVy+fJlVq1bRt29fAPz8/IiMjCQqKoqcnBwWLlxI8+bNady4MU2aNMHZ2ZnQ0FBycnI4dOgQkZGR+Pn5qeddvXo1V65cITk5mbCwMPr161dRP7KWei3v3r9esXpNpVxfCCFE5TJQNGs0lrM5c+awZs2aIsO5r7zyCuPGjWPu3Lns2rWLgoICXn75ZSZMmKAm5V27dhEaGsr169dp06YNISEh1K1bF7j7uFFQUBDHjx/H1taWSZMm8fzzzwNQUFDA0qVL+e6778jOzua5554jKCgIU1NTvWK+fPky3bt3Z+CSTVR7us4D/fyJMWcJnzAEU3MLjh05jK2t7QOdTwghxKOlwhLuo+hhJlyA72eO5p9jf/DmO+OZNe29hxChEEKIR4VeQ8ojR47k4sWL5R3LY8990AgA1v/nK9LT0ys5GiGEEBVJr4R74sQJTExMyjuWx169lm7UedaVrNu3+Ezu5QohxBNFr4Q7fPhw3nvvPX788UfOnDlDTEyM1kvoT9PLXfXFFxX+PLAQQojKo9djQYsXLwbu9nTvJTVwy6ahWwfsHZpz4+JffLX+G0aPKNtzzEIIIR5NUg+3ghkYGOA+aAQ/zglk+WfLGTHsNRmuF0KIJ4BeQ8r16tWjXr16ZGVlcfbsWWxtbSkoKKBu3bpqrVyhPwdvH2zrNyb5WgLfbNpc2eEIIYSoAHol3PT0dN588038/PwYP348SUlJzJkzh759+3Lt2rXyjvGxY2BoiNtLAQAsXrKU/Pz8So5ICCFEedO7Hm5OTg779u3DzMwMgBkzZlC9enVCQkLKNcDHVbMuz1G9Vl2u/RPH1h07KzscIYQQ5UyvhPvrr7/y3nvvqWsdA9StW5egoCAOHTpUbsE9zoyMTWj74t0JUwsXL0HWHxFCiMebXgk3MzOz2Ao3+fn5FBQUPPSgnhTNe/TD0rYmf58/y67IvZUdjhBCiHKkV8Lt1KkTYWFh5ObmqtuSk5OZN28e7du3L7fgHnfGpma4vjAUgPmhSyo5GiGEEOVJr4QbFBTEtWvX8PT0JDs7G39/f7p27Up6ejrTp08v7xgfay2fG4iZdXXOnTjCgT8OVnY4Qgghyolez+HWrFmTjRs3cujQIS5evEheXh4ODg506NBBq8yeKDtTSyva9H2VP7/+jLmhi9nR3ruyQxJCCFEO9Eq4r7/+Ol27dqVz5854eXmVd0xPnDZ9hnA84j8c/+M3jh0/QVtXl8oOSQghxEOm9z3c/fv3M2DAAHx8fJg1axa//vqrrAX8kJhXs6HV/w0CYI7cyxVCiMdSmerhZmVlERUVxYEDB/j9999JSEjAw8ODL774ojxjrDQPux5uSTKSb/CfgOfJz80hcu9enJ2cyvV6QgghKpZePVyNO3fukJ+fj7GxMRYWFuTm5vLPP/+UV2xPFCs7e57t9QIAwXM+kedyhRDiMaNXwg0ODsbPz4/27duzYMECMjMz8ff355dffmHXrl3lHeMTo+2LwzEyNePAnp+YNn2GJF0hhHiM6DVpaseOHWRmZtK1a1d8fX3x8PCgbt265R3bE6f603XpPT2UHz6awLr/rCE/P595c0IwNCzTQIQQQogqSK/f5IcPHyY8PJy2bduyY8cOevfujY+PD1OnTmXLli3lHeMTpaFbB3rP+BQjE1O+Xr+OyVOnyWpeQgjxGCjTpCmNv//+m6+++orNmzeTl5f32Bagr8hJU/f659gf7PhoAvk5d3h5yCss+GSe9HSFEOIRpteQclJSEocOHVJfV69excXFhXfeeYfOnTuXd4xPpGfatsdv5hJ+mD2e8G++Jr+ggNAF8yXpCiHEI0qvhNuhQweefvppOnbsSGBgIB06dKBatWrlHdsT7xkXL/xmLmHHh+PYFL6Rgvx8Pl20ECMjo8oOTQghRBnplXC3bt2Ks7MzcLcYvdxTrDgN2njSJ3gp2z94hy2bvkNRFBaHLpKkK4QQjxi9xiednZ35+uuv6dKlC+3atcPT05OOHTvy+eefl3d8Aqjfuh19Zy3DxNyCiM2bGDthIvn5+ZUdlhBCiDLQq4e7atUqli9fzqhRo3Bzc0NRFI4dO8YXX3yBmZkZw4YNK+84n3j1WrnTZ9Zyts8aw/dbNpOfn8+yJYsxNtbrP6EQQohKptcsZR8fHyZNmoSfn5/W9p07d/Lpp5+ye/fucguwMlXmLGVdEv57jG2z3iY3K5NmTs6MGT2Kfv36YWpqWtmhCSGEKIFeQ8opKSm0atWqyPYWLVpw7dq1hx6U0K1ui7b0+/AzrOzsiT5/jgkTJuDp5cWyZctIS0ur7PCEEELooFfCbdq0KT/99FOR7Tt37qRJkyYPPShRsjrNXRi2aifdx3/AU884kHj9OiEhIbi3a8fMmTOJj4+v7BCFEELcQ68h5QMHDjBy5Eg6deqEq6srAMePH+fAgQOEhYXRrVu3cg+0MlTFIeV7KYrCP8f+4PiW/xB/MgoAQ0ND/Pz8GDlyJC4uLpUboBBCCKAMK00dPnyYdevWERMTg7m5OU2aNGHEiBHq40KPo0ch4RZ2I/YcxyPWcmH/Lgry8wBwb9eO7j4+eHh40KZNGywsLCo5SiGEeDLd19KOT4pHLeFqpN+8zsltX3Pmp03kZKar201MTGjZsiXt2rXDw8ODdu3aUbNmzUqMVAghnhw6E25WVhYhISHs2rULU1NTevTowbvvvou1tXVFx1hpHtWEq5GTmc6lw7+RcPY41/46wY24aLjnP3ejRo3w8PCgVatW1KxZk6eeekr919bWVhbYEEKIh0Rnwp03bx7bt29n6NChGBoasmHDBtq0acOnn35awSFWnkc94d7rTsZtrp07xdW/TnDtrxNcPXeKvDvZOo83MDCgRo0aagK2s7Ojdu3a1K1bl7p161KvXj3q1avH008/LYlZCCFKoXPVhF27drFgwQK8vLwA8PDw4NVXXyU3NxcTE5MKC/BBnTt3juDgYM6fP0+DBg34+OOPad26dWWHVSnMrKrR0K0DDd06AJCfl8vNuGiunj1BSnws2bdSyE5LIfNWCpmpyWTfTiMlJYWUlBQuXLig87zGxsbUrl1bTcB169bFzMxM65ji/q4zNDSkVq1a1KlTR31Vr14dAwODh/uDCyFEFaAz4V6/fl3rkZ9WrVqhKApJSUnUrl27QoJ7UDk5OYwZM4ahQ4eyfv16du/eTUBAAL/88ssTNTSui5GxCbWatqBW0xbF7i/IzyP7VhpZaclkpiWTlZZCRlIi6Tevk37jKrdvXOP2jatkpiZz+fJlLl++/MAxWVlZaSXgunXrYmtrS0FBAQUFBeTl5ZGfn1/sy8DAAGNjY0xMTHT+a2JigrW1NTY2NuqrRo0aWFpalproFUUhOzub27dvc/v2bdLT08nJycHKyorq1atjbW1NtWrVpLcvhCiWzoSbn5+v9YvDwMAAExMTcnNzKySwh+HPP/8kNzeX4cOHA9C7d2/Wr1/Pzp07GTRoUOUG9wgwNDLG0vYpLG2f4qkSjsvLuUP6zevcvnGV9BvXSL95HWvDAlo9Za513L0JLTc3l8TERBISErh69SoJCQlkZGQQExNDTExMOfxEuhkbG2slYWtra7Kzs0lPT1eT6+3bt/Vaw9rS0lIrAVerVg0TExPy8vLIzc0lNzdXfX/vvwUFBSiKUuyrcNEQQ0NDjI2NMTIyUv8t/N7Y2BhDQ0P1eoWvce/L2NhY6969nZ2d+l7zqlmzJs8++6ysaCbEA3isF+KNiYnBwcFBa1uTJk2Ijo7Wq73ml2tGUuJDj+1xY2RsQo06z1CjzjMA1LU0ol/j6mU6h6IoZGRkcP36dRITE7lx4waJiYmkpaVhaGioJpXi3mvqBOfm5pKfn1/k38KJLSMjQ+2l3r59m1u3bnHnzh2Sk5NJTk4uMUZzc3MsLS2xsrLCysoKExMTsrKySE9PJyMjg4yMDLKyssjKyuL69ev392VWsPz8fBISEkhISCjxuPbt27No0aIKikqIqq127dplXsu+xKO3bNmCpaWl+jk/P5/vv/8eW1tbreNeffXVMl20omRmZmJurt3LsrCwICsrS6/2N27cAODHD8Y89NieBEsqOwA9GRsbl+n/ODk5OeTk5JCSklJkX+H/vzxuTp48Sffu3Ss7DCGqhMjISOrXr1+mNjp/y9StW5dvvvlGa1vNmjXZsmWL1jYDA4Mqm3AtLS25c+eO1rasrCy9fym2bNmSDRs2YG9vL/flhBBCqO5nLpPOhLt3794HCqYqcHBwYM2aNVrbYmNj6d+/v17tzc3NcXd3f/iBCSGEeOLoVbzgUeXp6YmiKKxZs4bc3Fx++OEHzp8/T8+ePSs7NCGEEE+Yx35px+joaIKDgzl37hz169fn/fffx9vbu7LDEkII8YR57BOuEEIIURU81kPKQgghRFUhCVcIIYSoAJJwhRBCiAogCVcIIYSoAJJwi3Hu3DlefvllXFxc6NOnD6dOnarskB4Jv//+OwMGDKBt27b07NmTjRs3VnZIj5Rbt27RtWvXIovLCN0SExMZM2YMbm5utG/f/okqH/ogTpw4wcCBA3Fzc6Nnz5589913lR1SlXbq1Cmtp1tycnIICgrCw8MDLy8vVq5cqd+JFKHlzp07Srdu3ZSvvvpKycnJUXbs2KG4u7srt2/fruzQqrSEhATF1dVV2b17t5Kfn6+cPHlSadeunbJ///7KDu2RMWHCBMXZ2VnZvHlzZYfyyHjxxReVmTNnKtnZ2co///yjdOnSRdm2bVtlh1Wl5efnK97e3srWrVsVRVGUkydPKi1btlT++uuvSo6s6ikoKFC+/fZbxc3NTXFzc1O3L1iwQHn11VeV1NRUJT4+XvH19VUiIiJKPZ/0cO9RuMKQiYkJvXv3xtHRkZ07d1Z2aFXalStX8PPzo2fPnhgaGtK6dWs8PDw4duxYZYf2SIiIiCA9PZ1mzZpVdiiPjJMnTxIfH8+MGTMwMzOjQYMGrFu3Dk9Pz8oOrUpLS0sjKSlJrUJVuKyl0LZkyRK++eYbRo8erbU9IiKCUaNGYWNjQ/369QkICNBrRE8S7j0etMLQk8rd3Z0PP/xQ/ZyamsqRI0d49tlnKzGqR0N8fDxLly4lJCSkskN5pJw5c4ZmzZqxdOlSOnXqRI8ePdizZw9PP/10ZYdWpdna2vLaa68xdepUWrRowcCBA5k4cWKR33sCBg8ezJYtW2jZsqW67datW9y4cQNHR0d1W+PGjfXKEZJw7/GgFYYE3L59m9GjR9OmTRupLlOK/Px8Jk+ezJQpU7C3t6/scB4paWlpHD16FGNjYyIjI1m6dCmrV69m+/btlR1alVZQUICpqSkLFy7k5MmTrFu3jmXLlnHgwIHKDq3KqVWrVpFtmZmZAFp5wsLCguzs7FLPJwn3Hg9aYehJFxcXx6BBg6hZsyZLlixR69SK4i1fvpzGjRvTq1evyg7lkWNqaoq1tTVjx47F1NQUZ2dnBg4cyJ49eyo7tCpt9+7dHD9+nN69e2NiYoKHhwcvvvgi4eHhlR3aI8HCwgJAK0/omyPkt+E9HBwciIuL09oWGxurNXwginf48GEGDRpEjx49WLJkCWZmZpUdUpX3ww8/sGvXLtzd3XF3dyc6OpoPPviAWbNmVXZoVV6TJk3IysoiJydH3Zafn1+JET0arl27pvWdQdlrQj/JbGxssLe3JzY2Vt0WFxenV46QhHsPqTB0f/755x9GjhzJuHHjCAwMxMDAoLJDeiT89NNPHDt2jCNHjnDkyBGaNWtGcHCwJFw9dOjQATs7O+bNm0dOTg7nz59n06ZN9O7du7JDq9I6dOjAhQsXCA8PR1EUzpw5w7fffivfWxn07duXZcuWkZyczOXLl1m1ahV9+/YttZ0k3HuYmpryxRdfsGvXLjw8PFixYgXLli3Dzs6uskOr0jZs2EBGRgaLFi3C1dVVfc2fP7+yQxOPKTMzM9avX098fDydOnVixIgRjBgxAl9f38oOrUpr2rQpS5cuJTw8HHd3dwIDAwkMDKRHjx6VHdojY/z48TRt2hQ/Pz8GDhyIr68vQ4YMKbWdVAsSQgghKoD0cIUQQogKIAlXCCGEqACScIUQQogKIAlXCCGEqACScIUQQogKIAlXCCGEqACScIWoAD4+Pjg5ORX7ysvLq+zwqoTTp08zdOhQALZs2aKz6s/UqVMZN25cqecrKChg4MCBWisCCVGZZC0vISrIpEmTGDBgQJHtsqTe3SUZZ86cycyZMx/aOQ0NDXn77bcJDg5m3bp1D+28Qtwv6eEKUUGsrKywt7cv8hLw888/Y2BggKur60M9b7du3bh69SpRUVEP9bxC3A9JuEJUAWFhYYwYMYKAgADc3NyIiIgAYNWqVXTr1g1XV1eGDBnCiRMn1DZ5eXnMmzcPLy8vPD09+fzzzxkwYABbtmwB4PXXX2fevHla13FycuKXX35RP5d0/qlTpzJr1iymTZuGq6srPj4+LF++XN2fn5/PsmXL6Nq1K66urrz++utcuHCBxMREnn32WQ4ePKh17Z49e7J58+Zif/7169ffV8WkqVOnFjtMv3TpUq3rrl27tsznFuJhk4QrRBXx22+/4e7uzrfffkuXLl3YuHEja9euJTg4mIiICLp06cKwYcOIj48H7ibp77//nnnz5rFmzRr27t3LhQsX9L5eaecH2LRpE7Vq1WLz5s0MHDiQxYsXc+bMGQCWLVvGunXreP/994mIiKBOnTqMHDmSp556Ci8vL3bs2KGe58SJE1y/fr3YpJqens7Ro0fp1KlTmb+z6dOnc+DAAfXl7++PnZ0dL774onpMp06d+OOPP+Reuah0knCFqCBz587VKuzg6uqqNdRpbm7OyJEjcXBwwM7OjpUrVxIYGEjXrl1p1KgRo0aNws3Nja+//hpFUfj2228ZPXo0Xbp0oXnz5ixYsKBM5elKOr9GgwYNmDBhAk2aNGHMmDHUqFGD//73vyiKwjfffMPo0aPp1asXjRo1YubMmfTq1Yu0tDT69u3Lnj17yM3NBWDHjh107dqVatWqFYnj7NmzFBQUFClvlpqaWuT7cnV11SowX61aNXVo/q+//mL9+vV8+umn1KlTRz3GwcGBzMxMmTwlKp3M1hCigowcObJICa9atWqp7+vXr4+h4d2/gTMyMkhISCAoKIjg4GD1mJycHExNTUlOTiY5OZkWLVpota9du7ZesZR2fo2GDRtqtbOysiIvL4+UlBSSk5Np1aqVus/a2pqpU6cCd4dxZ82axYEDB+jcuTM//vgjH3zwQbGx3Lx5EwsLiyL1k21sbPjuu++KHL9gwYIi2y5dukRgYCDvvvtukdnNtra2ACQlJRV7fSEqiiRcISqIra1tkQRWWOGEU1BQANztFT/77LNax5mbm2Nubl7sOQony3sVHlIt7fwlnU9RFExMTAB01j22srKie/fu7Ny5E1NTU3JycujcuXOxx2r+yLiXgYFBsd+XlZUVmZmZ6uf09HTefvttOnfuzPDhw4scr/lZjYyMir2OEBVFhpSFqII0Q6XXr1+nYcOG6mv9+vX89ttvWFlZUa9ePa1JTikpKVy5ckX9bGpqyu3bt9XPhe/NlnZ+feJ76qmnOHv2rLotOzub9u3bc/LkSeBuke79+/fz888/89xzz+n8Y6BmzZpkZmaSnZ2t9/ejoSgKU6ZMwcjIiI8++qjYY5KTk9XrCFGZpIcrRBU1YsQIli9fztNPP02rVq3Yvn07GzZsUJ8pfeutt1i0aBENGjSgcePGLFiwgJycHLV9y5YtWbt2Lf3798fa2pq5c+dqJb3Szl+a4cOHs3z5curVq0ejRo1YsWIF1apVo3nz5gB07NgRY2NjNm/ezJdffqnzPM7OzpiYmHDu3DlcXFzK9B0tW7aMw4cPs2bNGjIyMkhPTwfAxMSEGjVqAHDu3DlsbGxKHF0QoiJIwhWiiho6dCjZ2dnMnz+fmzdv0qhRI5YsWYKbmxsAgwcPJjU1lVmzZnHnzh0CAgI4fvy42v6NN94gNjaWgIAAbG1tGTdunFYPuLTzlyYgIICMjAxmzJhBRkYGbdu2ZeXKlWpSNzIy4v/+7//Ys2cP7dq103kea2tr2rZty59//lnmhLtlyxbS0tJ44YUXtLZ7eHiofzgcPnyYTp06yZCyqHQGiqIolR2EEOLh8PT0ZMqUKcWuaFUZxo0bR8OGDQkMDCzxuB9//JHly5drzUB+GAoKCujWrRsLFy7E3d39oZ5biLKSe7hCiIfuzz//ZM2aNezbt49BgwaVeryvry8AR44ceahxREZG0qBBA0m2okqQhCuEeOh++OEHlixZwuTJk2nQoEGpxxsaGvLRRx+xePHihxZDQUEBn332mc7HkYSoaDKkLIQQQlQA6eEKIYQQFUASrhBCCFEBJOEKIYQQFUASrhBCCFEBJOEKIYQQFUASrhBCCFEB/h9h0nEeDlkgqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def combinied_horizental_and_vertical(horizental_array, vertical_array):\n",
    "    combined_array = np.sqrt(np.square(horizental_array) + np.square(vertical_array))\n",
    "    return combined_array\n",
    "\n",
    "def convert_into_velocity(dispacement_array, plot=False):\n",
    "    velocity_array = np.diff(dispacement_array)\n",
    "    return velocity_array\n",
    "\n",
    "def convert_into_acceleration(velocity_array, plot=False):\n",
    "    accelation_array = np.diff(velocity_array)\n",
    "    return accelation_array\n",
    "\n",
    "def convert_into_jerk(accelation_array, plot=False):\n",
    "    jerk_array = np.diff(accelation_array)\n",
    "    return jerk_array\n",
    "\n",
    "def get_kinetic_feature(motion):\n",
    "    max_motion = np.amax(motion, axis=0)\n",
    "    median_motion = np.median(motion)\n",
    "    mean_motion = np.mean(motion, axis=0)\n",
    "    standard_division_motion = np.std(motion)\n",
    "    IQR_range = stats.iqr(motion, interpolation = 'midpoint')\n",
    "    return [max_motion, median_motion, mean_motion, standard_division_motion, IQR_range]\n",
    "# print(get_kinetic_feature(displacemt_array))\n",
    "\n",
    "def get_spectral_feature(signals, sample_frequancy=10, is_plot=False):\n",
    "    sf = sample_frequancy\n",
    "    win = 4 * sf\n",
    "    \n",
    "    # calcutate the Spectral entropy.\n",
    "    def spectral_entropy(psd, normalize=False):\n",
    "        psd_norm = np.divide(psd, psd.sum())\n",
    "        se = -np.multiply(psd_norm, np.log2(psd_norm)).sum()\n",
    "        if normalize:\n",
    "            se /= np.log2(psd_norm.size)\n",
    "        return se\n",
    "    \n",
    "    # calculate the power band for given frequancy.\n",
    "    def bandpower(psd, freqs, min_freqs, max_freqs, is_plot=False):\n",
    "        # Define delta lower and upper limits\n",
    "        low, high = min_freqs, max_freqs\n",
    "\n",
    "        # Find intersecting values in frequency vector\n",
    "        idx_delta = np.logical_and(freqs >= low, freqs <= high)\n",
    "\n",
    "        if is_plot:\n",
    "            # Plot the power spectral density and fill the delta area\n",
    "            plt.figure(figsize=(7, 4))\n",
    "            plt.plot(freqs, psd, lw=2, color='k')\n",
    "            plt.fill_between(freqs, psd, where=idx_delta, color='skyblue')\n",
    "            plt.xlabel('Frequency (Hz)')\n",
    "            plt.ylabel('Power spectral density (uV^2 / Hz)')\n",
    "            plt.xlim([0, 10])\n",
    "            plt.ylim([0, psd.max() * 1.1])\n",
    "            plt.title(\"Welch's periodogram\")\n",
    "            sns.despine()\n",
    "\n",
    "        # Frequency resolution\n",
    "        freq_res = freqs[1] - freqs[0]  # = 1 / 4 = 0.25\n",
    "\n",
    "        # Compute the absolute power by approximating the area under the curve\n",
    "        delta_power = simps(psd[idx_delta], dx=freq_res)\n",
    "        #print('Absolute delta power: %.3f uV^2' % delta_power)\n",
    "        return delta_power\n",
    "\n",
    "    freqs, psd = signal.welch(signals, sf, nperseg=win)\n",
    "    if is_plot:\n",
    "        sns.set(font_scale=1.2, style='white')\n",
    "        plt.figure(figsize=(8, 4))\n",
    "        plt.plot(freqs, psd, color='k', lw=2)\n",
    "        plt.xlabel('Frequency (Hz)')\n",
    "        plt.ylabel('Power spectral density (V^2 / Hz)')\n",
    "        plt.ylim([0, psd.max() * 1.1])\n",
    "        plt.title(\"Welch's periodogram\")\n",
    "        plt.xlim([0, freqs.max()])\n",
    "        sns.despine()\n",
    "    #print(dir(psd))\n",
    "    features = {}\n",
    "    features[\"peak_magnitude\"] = np.sqrt(psd.max())\n",
    "    features[\"entropy\"] = spectral_entropy(psd)\n",
    "    features[\"half_point\"] = freqs.mean()\n",
    "    \n",
    "    features[\"total_power\"] = bandpower(psd, freqs, freqs.min(), freqs.max(), is_plot)\n",
    "    features[\"power_bands_0.5_to_1\"] = bandpower(psd, freqs, 0.5, 1, is_plot)\n",
    "    features[\"power_bands_0_to_2\"] = bandpower(psd, freqs, 0, 2, is_plot)\n",
    "    features[\"power_bands_0_to_4\"] = bandpower(psd, freqs, 0, 4, is_plot)\n",
    "    features[\"power_bands_0_to_6\"] = bandpower(psd, freqs, 0, 6, is_plot)\n",
    "    return features\n",
    "get_spectral_feature(vertical_displacement_array, is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de804050",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc4f1015",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53212e9",
   "metadata": {},
   "outputs": [],
   "source": []
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