freezing_index_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate and visualize Freezing Index from a multi-sensor gait txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import mne\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt \n",
    "from mne.time_frequency import psd_welch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import gait txt file and delete the unit row\n",
    "\n",
    "f_folder = \"C:\\\\Users\\\\Lenovo\\\\1代码 上传\\\\\"\n",
    "f_name = \"gait_test.txt\"\n",
    "f_name_clear = f_name[:-4]\n",
    "name = f_name_clear + '_clear.txt'\n",
    "\n",
    "with open(f'{f_folder}{f_name}','r',encoding='gbk')as f:\n",
    "    lines = f.readlines()\n",
    "ge = ['mm']\n",
    "new = ''\n",
    "for line in lines:\n",
    "    mode = True\n",
    "    for i in ge:\n",
    "        if i in line:\n",
    "            mode = False\n",
    "            break\n",
    "    if mode:\n",
    "        new += line\n",
    "with open(f'{f_folder}{f_name_clear}_clear.txt','w')as f:\n",
    "    f.write(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>R.5MT.X</th>\n",
       "      <th>R.5MT.Z</th>\n",
       "      <th>L.5MT.X</th>\n",
       "      <th>L.5MT.Z</th>\n",
       "      <th>P.RSacWand.X</th>\n",
       "      <th>P.RSacWand.Z</th>\n",
       "      <th>P.LSacWand.X</th>\n",
       "      <th>P.LSacWand.Z</th>\n",
       "      <th>R.Thigh.1.X</th>\n",
       "      <th>R.Thigh.1.Z</th>\n",
       "      <th>L.Thigh.1.X</th>\n",
       "      <th>L.Thigh.1.Z</th>\n",
       "      <th>R.Shank.1.X</th>\n",
       "      <th>R.Shank.1.Z</th>\n",
       "      <th>L.Shank.1.X</th>\n",
       "      <th>L.Shank.1.Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>3855.774</td>\n",
       "      <td>40.249</td>\n",
       "      <td>3834.117</td>\n",
       "      <td>46.652</td>\n",
       "      <td>4373.186</td>\n",
       "      <td>838.767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.016</td>\n",
       "      <td>448.962</td>\n",
       "      <td>3865.588</td>\n",
       "      <td>608.133</td>\n",
       "      <td>3840.583</td>\n",
       "      <td>248.774</td>\n",
       "      <td>3857.111</td>\n",
       "      <td>405.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>3855.774</td>\n",
       "      <td>40.249</td>\n",
       "      <td>3834.117</td>\n",
       "      <td>46.652</td>\n",
       "      <td>4373.186</td>\n",
       "      <td>838.767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.016</td>\n",
       "      <td>448.962</td>\n",
       "      <td>3865.588</td>\n",
       "      <td>608.133</td>\n",
       "      <td>3840.583</td>\n",
       "      <td>248.774</td>\n",
       "      <td>3857.111</td>\n",
       "      <td>405.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>3855.548</td>\n",
       "      <td>40.684</td>\n",
       "      <td>3833.379</td>\n",
       "      <td>47.750</td>\n",
       "      <td>4372.274</td>\n",
       "      <td>839.326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.991</td>\n",
       "      <td>449.358</td>\n",
       "      <td>3865.823</td>\n",
       "      <td>608.925</td>\n",
       "      <td>3841.486</td>\n",
       "      <td>249.277</td>\n",
       "      <td>3858.543</td>\n",
       "      <td>404.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015</td>\n",
       "      <td>3855.799</td>\n",
       "      <td>40.305</td>\n",
       "      <td>3833.633</td>\n",
       "      <td>47.431</td>\n",
       "      <td>4371.752</td>\n",
       "      <td>839.311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3809.056</td>\n",
       "      <td>449.570</td>\n",
       "      <td>3866.683</td>\n",
       "      <td>608.866</td>\n",
       "      <td>3842.413</td>\n",
       "      <td>249.484</td>\n",
       "      <td>3859.090</td>\n",
       "      <td>406.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020</td>\n",
       "      <td>3855.804</td>\n",
       "      <td>40.102</td>\n",
       "      <td>3833.379</td>\n",
       "      <td>47.854</td>\n",
       "      <td>4370.424</td>\n",
       "      <td>840.126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3810.894</td>\n",
       "      <td>448.645</td>\n",
       "      <td>3866.984</td>\n",
       "      <td>609.393</td>\n",
       "      <td>3843.129</td>\n",
       "      <td>250.223</td>\n",
       "      <td>3860.524</td>\n",
       "      <td>406.086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "    Time   R.5MT.X  R.5MT.Z   L.5MT.X  L.5MT.Z  P.RSacWand.X  P.RSacWand.Z  \\\n",
       "0  0.000  3855.774   40.249  3834.117   46.652      4373.186       838.767   \n",
       "1  0.005  3855.774   40.249  3834.117   46.652      4373.186       838.767   \n",
       "2  0.010  3855.548   40.684  3833.379   47.750      4372.274       839.326   \n",
       "3  0.015  3855.799   40.305  3833.633   47.431      4371.752       839.311   \n",
       "4  0.020  3855.804   40.102  3833.379   47.854      4370.424       840.126   \n",
       "\n",
       "   P.LSacWand.X  P.LSacWand.Z  R.Thigh.1.X  R.Thigh.1.Z  L.Thigh.1.X  \\\n",
       "0           0.0           0.0     3807.016      448.962     3865.588   \n",
       "1           0.0           0.0     3807.016      448.962     3865.588   \n",
       "2           0.0           0.0     3807.991      449.358     3865.823   \n",
       "3           0.0           0.0     3809.056      449.570     3866.683   \n",
       "4           0.0           0.0     3810.894      448.645     3866.984   \n",
       "\n",
       "   L.Thigh.1.Z  R.Shank.1.X  R.Shank.1.Z  L.Shank.1.X  L.Shank.1.Z  \n",
       "0      608.133     3840.583      248.774     3857.111      405.225  \n",
       "1      608.133     3840.583      248.774     3857.111      405.225  \n",
       "2      608.925     3841.486      249.277     3858.543      404.911  \n",
       "3      608.866     3842.413      249.484     3859.090      406.138  \n",
       "4      609.393     3843.129      250.223     3860.524      406.086  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the clear gait txt file into python as dataframe\n",
    "\n",
    "df = pd.read_csv(\"%s%s\" %(f_folder,name),header=[2],sep = '\t')\n",
    "\n",
    "m_list = ['Time',\n",
    "          'R.5MT.X','R.5MT.Z','L.5MT.X','L.5MT.Z',\n",
    "          'P.RSacWand.X','P.RSacWand.Z','P.LSacWand.X','P.LSacWand.Z',\n",
    "         'R.Thigh.1.X','R.Thigh.1.Z','L.Thigh.1.X','L.Thigh.1.Z',\n",
    "         'R.Shank.1.X','R.Shank.1.Z','L.Shank.1.X','L.Shank.1.Z']\n",
    "df = df [m_list]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of the before-preprocessd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize vertical movement track of the left foot\n",
    "ax1 = df.plot(x='Time' ,y='L.5MT.Z', figsize=(20,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess and visualization of the post-preprocessd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4527: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[1:].replace(0, np.nan, inplace=True) # Replace nan as 0 \n",
    "\n",
    "\n",
    "for i in m_list[1:]:\n",
    "    #超过5个标准差，赋值为nan\n",
    "    bar = df[i].mean()\n",
    "    std = df[i].std()\n",
    "    df.loc[df[i] > bar + 5 * std, i] = np.nan #Find outliers exceeded 5 std\n",
    "    df.loc[df[i] < bar - 5 * std, i] = np.nan\n",
    "\n",
    "    #找到跳跃值，赋值为nan\n",
    "    df.loc[df.diff()[i].nlargest(15).index] = np.nan # Find possible jump value\n",
    "    df.loc[df.diff()[i].nsmallest(15).index] = np.nan\n",
    "    \n",
    "## 对nan进行插值    \n",
    "df = df.interpolate(method='linear')\n",
    "ax1 = df.plot(x='Time' ,y='L.5MT.Z', figsize=(20,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert displacement data to acceleration data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>R.5MT.X</th>\n",
       "      <th>R.5MT.Z</th>\n",
       "      <th>L.5MT.X</th>\n",
       "      <th>L.5MT.Z</th>\n",
       "      <th>P.RSacWand.X</th>\n",
       "      <th>P.RSacWand.Z</th>\n",
       "      <th>P.LSacWand.X</th>\n",
       "      <th>P.LSacWand.Z</th>\n",
       "      <th>R.Thigh.1.X</th>\n",
       "      <th>...</th>\n",
       "      <th>L.Shank.1.X</th>\n",
       "      <th>L.Shank.1.Z</th>\n",
       "      <th>acc_Rft</th>\n",
       "      <th>acc_Lft</th>\n",
       "      <th>acc_Rsh</th>\n",
       "      <th>acc_Lsh</th>\n",
       "      <th>acc_Rth</th>\n",
       "      <th>acc_Lth</th>\n",
       "      <th>acc_Rlm</th>\n",
       "      <th>acc_Llm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>3855.774</td>\n",
       "      <td>40.249</td>\n",
       "      <td>3834.117</td>\n",
       "      <td>46.652</td>\n",
       "      <td>4373.186</td>\n",
       "      <td>838.767</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.016</td>\n",
       "      <td>...</td>\n",
       "      <td>3857.111</td>\n",
       "      <td>405.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>3855.774</td>\n",
       "      <td>40.249</td>\n",
       "      <td>3834.117</td>\n",
       "      <td>46.652</td>\n",
       "      <td>4373.186</td>\n",
       "      <td>838.767</td>\n",
       "      <td>207.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.016</td>\n",
       "      <td>...</td>\n",
       "      <td>3857.111</td>\n",
       "      <td>405.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>3855.548</td>\n",
       "      <td>40.684</td>\n",
       "      <td>3833.379</td>\n",
       "      <td>47.750</td>\n",
       "      <td>4372.274</td>\n",
       "      <td>839.326</td>\n",
       "      <td>414.508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3807.991</td>\n",
       "      <td>...</td>\n",
       "      <td>3858.543</td>\n",
       "      <td>404.911</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>-0.738</td>\n",
       "      <td>0.903</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.235</td>\n",
       "      <td>-0.912</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015</td>\n",
       "      <td>3855.799</td>\n",
       "      <td>40.305</td>\n",
       "      <td>3833.633</td>\n",
       "      <td>47.431</td>\n",
       "      <td>4371.752</td>\n",
       "      <td>839.311</td>\n",
       "      <td>621.762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3809.056</td>\n",
       "      <td>...</td>\n",
       "      <td>3859.090</td>\n",
       "      <td>406.138</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.885</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.390</td>\n",
       "      <td>-2.842171e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020</td>\n",
       "      <td>3855.804</td>\n",
       "      <td>40.102</td>\n",
       "      <td>3833.379</td>\n",
       "      <td>47.854</td>\n",
       "      <td>4370.424</td>\n",
       "      <td>840.126</td>\n",
       "      <td>829.016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3810.894</td>\n",
       "      <td>...</td>\n",
       "      <td>3860.524</td>\n",
       "      <td>406.086</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>-0.508</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.773</td>\n",
       "      <td>-0.559</td>\n",
       "      <td>-0.806</td>\n",
       "      <td>5.684342e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time   R.5MT.X  R.5MT.Z   L.5MT.X  L.5MT.Z  P.RSacWand.X  P.RSacWand.Z  \\\n",
       "0  0.000  3855.774   40.249  3834.117   46.652      4373.186       838.767   \n",
       "1  0.005  3855.774   40.249  3834.117   46.652      4373.186       838.767   \n",
       "2  0.010  3855.548   40.684  3833.379   47.750      4372.274       839.326   \n",
       "3  0.015  3855.799   40.305  3833.633   47.431      4371.752       839.311   \n",
       "4  0.020  3855.804   40.102  3833.379   47.854      4370.424       840.126   \n",
       "\n",
       "   P.LSacWand.X  P.LSacWand.Z  R.Thigh.1.X  ...  L.Shank.1.X  L.Shank.1.Z  \\\n",
       "0         0.000           0.0     3807.016  ...     3857.111      405.225   \n",
       "1       207.254           0.0     3807.016  ...     3857.111      405.225   \n",
       "2       414.508           0.0     3807.991  ...     3858.543      404.911   \n",
       "3       621.762           0.0     3809.056  ...     3859.090      406.138   \n",
       "4       829.016           0.0     3810.894  ...     3860.524      406.086   \n",
       "\n",
       "   acc_Rft  acc_Lft  acc_Rsh  acc_Lsh  acc_Rth  acc_Lth  acc_Rlm       acc_Llm  \n",
       "0    0.000    0.000    0.000    0.000    0.000    0.000    0.000  0.000000e+00  \n",
       "1    0.000    0.000    0.000    0.000    0.000    0.000    0.000  0.000000e+00  \n",
       "2   -0.226   -0.738    0.903    1.432    0.975    0.235   -0.912  0.000000e+00  \n",
       "3    0.477    0.992    0.024   -0.885    0.090    0.625    0.390 -2.842171e-14  \n",
       "4   -0.246   -0.508   -0.211    0.887    0.773   -0.559   -0.806  5.684342e-14  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_list = ['acc_Rft','acc_Lft','acc_Rsh','acc_Lsh','acc_Rth','acc_Lth','acc_Rlm','acc_Llm']\n",
    "location_list = ['R.5MT.X','L.5MT.X','R.Shank.1.X','L.Shank.1.X','R.Thigh.1.X','L.Thigh.1.X','P.RSacWand.X','P.LSacWand.X']\n",
    "for acc, loc in zip (acc_list, location_list):\n",
    "    df[acc] = df[loc].diff().diff()\n",
    "df.replace(np.nan, 0, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert the accelaration data to the MNE raw file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=8, n_times=8092\n",
      "    Range : 0 ... 8091 =      0.000 ...    40.455 secs\n",
      "Ready.\n",
      "No data channels found. The highpass and lowpass values in the measurement info will not be updated.\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 0.1 - 10 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 0.10\n",
      "- Lower transition bandwidth: 0.10 Hz (-6 dB cutoff frequency: 0.05 Hz)\n",
      "- Upper passband edge: 10.00 Hz\n",
      "- Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz)\n",
      "- Filter length: 6601 samples (33.005 sec)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Info | 7 non-empty values\n",
       " bads: []\n",
       " ch_names: acc_Rft, acc_Lft, acc_Rsh, acc_Lsh, acc_Rth, acc_Lth, acc_Rlm, ...\n",
       " chs: 8 MISC\n",
       " custom_ref_applied: False\n",
       " highpass: 0.0 Hz\n",
       " lowpass: 100.0 Hz\n",
       " meas_date: unspecified\n",
       " nchan: 8\n",
       " projs: []\n",
       " sfreq: 200.0 Hz\n",
       ">"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define info\n",
    "n_channels = 8\n",
    "ch_names = acc_list\n",
    "info = mne.create_info( ch_names,sfreq = 1/df['Time'][1])\n",
    "\n",
    "##Create mne raw\n",
    "times = df['Time'].tolist()\n",
    "data = np.array([df['acc_Rft'].tolist(),df['acc_Lft'].tolist(),df['acc_Rsh'].tolist(),df['acc_Lsh'].tolist(),\n",
    "                  df['acc_Rth'].tolist(),df['acc_Lth'].tolist(),df['acc_Rlm'].tolist(),df['acc_Llm'].tolist()])\n",
    "raw = mne.io.RawArray(data, info)\n",
    "raw.filter(0.1, 10, fir_design='firwin',picks = 'all')\n",
    "raw.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize acceleration data together with displacement data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f8f929cc8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = raw.get_data(picks = 'acc_Lft').squeeze()\n",
    "fig, axs = plt.subplots(2,figsize=(20,4))\n",
    "\n",
    "axs[0].plot(data, color = 'b')\n",
    "ax1.set_ylim(data.min()*1.5, data.max()*1.5)\n",
    "\n",
    "axs[1].plot(df['Time'],df['L.5MT.Z'], color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate freezing index with a 6s calculation window and 0.1 rolling window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 5.120 (s)\n",
      "Effective window size : 5.120 (s)\n",
      "Effective window size : 5.120 (s)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 5.120 (s)\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:27: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n",
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n"
     ]
    }
   ],
   "source": [
    "RS_list = ['acc_Rft','acc_Lft','acc_Rsh','acc_Lsh','acc_Rth','acc_Lth','acc_Rlm','acc_Llm']\n",
    "\n",
    "t_time = int(raw.n_times/raw.info['sfreq'])\n",
    "n_fft = int(raw.info['sfreq']*5.12)\n",
    "fmin =0\n",
    "fmax = 8.3\n",
    "n_overlap = 0\n",
    "\n",
    "co_fogi = []\n",
    "time = []\n",
    "co_RS = []\n",
    "t = 0\n",
    "while 6+0.1*t<= t_time :\n",
    "    for RS in RS_list:\n",
    "        psds, freqs = psd_welch(raw, picks=RS, n_fft = n_fft, window = 'hann', \n",
    "                              n_overlap =n_overlap, fmin=fmin, fmax = fmax,\n",
    "                               tmin = 0.1*t, tmax = 6+0.1*t)\n",
    "        psds = np.squeeze(psds)\n",
    "        fogi = psds[17:].sum()/psds[:17].sum()\n",
    "        co_fogi += [fogi]\n",
    "        co_RS += [RS]\n",
    "        time += [3+0.1*t]\n",
    "    t += 1\n",
    "final = pd.DataFrame({'time':time,'co_fogi':co_fogi,'co_RS':co_RS})\n",
    "mean = final.groupby('time',as_index=False).mean()\n",
    "ex_folder = 'C:\\\\Users\\\\Lenovo\\\\1代码 上传\\\\'\n",
    "final.to_excel(\"%s%s_fi.xls\" %(ex_folder,name[:-4]))\n",
    "mean.to_excel(\"%s%s_average_fi.xls\" %(ex_folder,name[:-4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Freezing Index together with forward and vertical displacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=1, n_times=8092\n",
      "    Range : 0 ... 8091 =      0.000 ...    40.455 secs\n",
      "Ready.\n",
      "No data channels found. The highpass and lowpass values in the measurement info will not be updated.\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 0.3 - 8 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 0.30\n",
      "- Lower transition bandwidth: 0.30 Hz (-6 dB cutoff frequency: 0.15 Hz)\n",
      "- Upper passband edge: 8.00 Hz\n",
      "- Upper transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 9.00 Hz)\n",
      "- Filter length: 2201 samples (11.005 sec)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Filter Verticle position to range (0.3, 8) Hz\n",
    "n_channels = 1\n",
    "ch_names = ['Z']\n",
    "info = mne.create_info( ch_names,sfreq = 1/df['Time'][1])\n",
    "\n",
    "times = df['Time'].tolist()\n",
    "gait_data = np.array([df['R.5MT.Z'].tolist()])\n",
    "gait= mne.io.RawArray (gait_data, info)\n",
    "gait.filter(0.3, 8, fir_design='firwin',picks = 'Z')\n",
    "Z_list = gait.get_data(picks = 'Z').squeeze()\n",
    "df['Forward position'] = -1*df['L.5MT.X']\n",
    "df['Vertical position'] = Z_list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "ax1 = mean.plot(x='time' ,y='co_fogi', figsize=(10,4),color='red',ylim = (0,4),label='Freezing index')\n",
    "plot = plt.axhline(y=3, color='black', linestyle='solid')\n",
    "plot = plt.axhline(y=2, color='gray', linestyle='dashed')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "ax3= ax1.twinx() \n",
    "\n",
    "plot = df.plot(x='Time', y='Forward position', figsize=(10,4), color='green',\n",
    "               ax=ax2, ylim = (df['Forward position'].min()*1,df['Forward position'].max()*3)).legend(loc=2)\n",
    "ax2.tick_params(axis='y', labelsize = 0)\n",
    "\n",
    "plot = df.plot(x='Time', y='Vertical position', figsize=(10,4), color='blue',\n",
    "               ax = ax3,ylim = (df['Vertical position'].min()*2,df['Vertical position'].max()*3)).legend(loc=4)\n",
    "ax3.tick_params(axis='y', labelsize = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}