mailsys_differential_feature_extraction/toysnippet/step-03-wavletdecomposition...

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append('..')\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import pywt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import modules.wavacc as wav\n",
    "import csv\n",
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#open csv file as dataframe\n",
    "filepath = os.path.join('../interpolation/', os.listdir('../interpolation')[1])\n",
    "filename = os.listdir('../interpolation')[1]\n",
    "with open(filepath, 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    #save row as a float type\n",
    "    for row in reader:\n",
    "        original_wave = row\n",
    "for x in range(0, len(original_wave)):\n",
    "    original_wave[x] = float(original_wave[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overthres_x = []\n",
    "overthres_y = []\n",
    "thres = 0.5\n",
    "for x in range(0, len(original_wave)):\n",
    "    if original_wave[x] < -thres or original_wave[x] > thres:\n",
    "        overthres_x.append(x)\n",
    "        overthres_y.append(original_wave[x])\n",
    "plt.plot(overthres_x, overthres_y, color = 'red', linewidth = 0.3, label = 'cubic')\n",
    "#plt.xlim(0, 1800)\n",
    "plt.title('Over threshold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[179832, 179834], [179835, 179836], [179849, 180033], [180632, 180633], [180810, 180817], [180818, 181158], [181720, 181721], [181722, 181723], [183128, 183272], [183549, 183550]]\n",
      "approx time of surgery 204minutes\n",
      "1331\n"
     ]
    }
   ],
   "source": [
    "stroke = []\n",
    "threshold = 0.15\n",
    "s_start = 0\n",
    "s_end = 0\n",
    "for i in range(1, len(original_wave)-1):\n",
    "    if original_wave[i] < -threshold and original_wave[i+1] > threshold:\n",
    "        s_start = i\n",
    "    elif original_wave[i] > threshold and original_wave[i+1] < -threshold:\n",
    "        if s_start != 0:\n",
    "            s_end = i\n",
    "    if s_start != 0 and s_end != 0:\n",
    "        stroke.append([s_start, s_end])\n",
    "        s_start = 0\n",
    "        s_end = 0\n",
    "print(stroke[-10:])\n",
    "print(\"approx time of surgery \" + str(len(original_wave)//1800) + \"minutes\")\n",
    "print(len(stroke))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'21004100020230224.csv'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x         y         z  frame  positionvectorvalue  velocity   \n",
      "0       0.000000  0.000000  0.000000    0.0             0.000000  0.000000  \\\n",
      "1      -0.124534 -0.158254  1.640717    1.0             2.732507  0.000000   \n",
      "2      -0.106343 -0.167455  1.646413    2.0             2.750027  0.132363   \n",
      "3      -0.085354 -0.187697  1.658508    3.0             2.793165  0.207698   \n",
      "4      -0.060168 -0.196898  1.662754    4.0             2.807139  0.118208   \n",
      "...          ...       ...       ...    ...                  ...       ...   \n",
      "367195  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367196  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367197  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367198  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367199  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "\n",
      "        acceleration  isValid status  \n",
      "0           0.000000      0.0   idle  \n",
      "1           0.000000      1.0   idle  \n",
      "2           0.000000      1.0   idle  \n",
      "3           0.075336      1.0   idle  \n",
      "4          -0.089490      1.0   idle  \n",
      "...              ...      ...    ...  \n",
      "367195      0.000000      0.0   idle  \n",
      "367196      0.000000      0.0   idle  \n",
      "367197      0.000000      0.0   idle  \n",
      "367198      0.000000      0.0   idle  \n",
      "367199      0.000000      0.0   idle  \n",
      "\n",
      "[367200 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# read csv file as dataframe with same name of filename\n",
    "df = pd.read_csv(os.path.join('../handletrace/', filename))\n",
    "#add new series to dataframe\n",
    "df['status'] = 'idle'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in stroke:\n",
    "    #set df['status'] == 'in' when sroke[i][0] is same as index of df.\n",
    "    df.loc[i[0]:i[0], 'status'] = 'in'\n",
    "    ##set df['status'] == 'out' when sroke[i][1] is same as index of df.\n",
    "    df.loc[i[1]:i[1], 'status'] = 'out'\n",
    "    for frame in range(i[0]+1, i[1]):\n",
    "        df.loc[frame:frame, 'status'] = 'suction'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x         y         z  frame  positionvectorvalue  velocity   \n",
      "0       0.000000  0.000000  0.000000    0.0             0.000000  0.000000  \\\n",
      "1      -0.124534 -0.158254  1.640717    1.0             2.732507  0.000000   \n",
      "2      -0.106343 -0.167455  1.646413    2.0             2.750027  0.132363   \n",
      "3      -0.085354 -0.187697  1.658508    3.0             2.793165  0.207698   \n",
      "4      -0.060168 -0.196898  1.662754    4.0             2.807139  0.118208   \n",
      "...          ...       ...       ...    ...                  ...       ...   \n",
      "367195  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367196  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367197  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367198  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "367199  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "\n",
      "        acceleration  isValid status  \n",
      "0           0.000000      0.0   idle  \n",
      "1           0.000000      1.0   idle  \n",
      "2           0.000000      1.0   idle  \n",
      "3           0.075336      1.0   idle  \n",
      "4          -0.089490      1.0   idle  \n",
      "...              ...      ...    ...  \n",
      "367195      0.000000      0.0   idle  \n",
      "367196      0.000000      0.0   idle  \n",
      "367197      0.000000      0.0   idle  \n",
      "367198      0.000000      0.0   idle  \n",
      "367199      0.000000      0.0   idle  \n",
      "\n",
      "[367200 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x         y         z     frame  positionvectorvalue  velocity   \n",
      "142    -0.125933 -0.239222  1.643649     142.0             2.774670  0.201903  \\\n",
      "143    -0.135728 -0.220820  1.625990     143.0             2.711026  0.252277   \n",
      "144    -0.142724 -0.213460  1.632714     144.0             2.731690  0.143750   \n",
      "145    -0.144123 -0.217140  1.633188     145.0             2.735223  0.059437   \n",
      "146    -0.134328 -0.231861  1.641874     146.0             2.767552  0.179804   \n",
      "...          ...       ...       ...       ...                  ...       ...   \n",
      "183267 -0.415578 -0.038644  1.564197  183267.0             2.620912  0.127917   \n",
      "183268 -0.423974 -0.016562  1.562180  183268.0             2.620434  0.021862   \n",
      "183269 -0.412780 -0.014721  1.565466  183269.0             2.621289  0.029239   \n",
      "183270 -0.395989 -0.031283  1.566644  183270.0             2.612158  0.095553   \n",
      "183271 -0.363806 -0.047844  1.561238  183271.0             2.572109  0.200123   \n",
      "\n",
      "        acceleration  isValid   status  \n",
      "142         0.176307      1.0  suction  \n",
      "143         0.050374      1.0  suction  \n",
      "144        -0.108527      1.0  suction  \n",
      "145        -0.084313      1.0  suction  \n",
      "146         0.120367      1.0  suction  \n",
      "...              ...      ...      ...  \n",
      "183267     -0.069454      1.0  suction  \n",
      "183268     -0.106055      1.0  suction  \n",
      "183269      0.007377      1.0  suction  \n",
      "183270      0.066314      1.0  suction  \n",
      "183271      0.104570      1.0  suction  \n",
      "\n",
      "[31689 rows x 9 columns]\n",
      "x                      -0.11334\n",
      "y                     -0.263144\n",
      "z                      1.653283\n",
      "frame                     141.0\n",
      "positionvectorvalue    2.815435\n",
      "velocity               0.025596\n",
      "acceleration          -0.151524\n",
      "isValid                     1.0\n",
      "status                       in\n",
      "Name: 141, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df[df['status']=='suction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[141, 165]\n"
     ]
    }
   ],
   "source": [
    "print(stroke[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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