Rogachev_3block_alignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добрый день,\n",
    " \n",
    "В Папку Лекции выложил лекции по начальной выставке БИНС.\n",
    " \n",
    "Каждому из вас даю одинаковое задание:\n",
    "\n",
    "    Даю каждому свой файл данных с колонками: время, показаний 3-х акселерометров и трех датчиков угловой скорости (ДУС). Размерности указаны.\n",
    "    Ориентация осей либо продольная ось (X), вертикальная ось (Y), правое крыло (Z), либо правое крыло (s1), продольная ось (s2), вертикальная (s3. Это легко уточняется по колонке, где показание акселерометра равно примерно 9.81….\n",
    "    Требуется определить три угла: тангаж, крен, курс (в градусах), географическую широту места (градус).\n",
    "     Требуется построить графики накапливающихся средних значений показаний акселерометров, ДУС, а также соответствующих среднеквадратичных отклонений.\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dt следует брать из разности показаний\n",
    "следует внимательно посмотреть на оси\n",
    "и внимательно на интервалы вибрационного и прочего воздействия на объект выставки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "from math import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_earth = 6371000\n",
    "a = 6378137.0\n",
    "aa = 6378137.0\n",
    "b = 6356752.0\n",
    "e2 = 6.6943799901413 * (10 ** -3)\n",
    "delta_t = d_t = dt = 0.0025\n",
    "phi = phi_init = phi_0 = 31.2*pi/180.0          #initial latitude in radians - FALSE\n",
    "lambda_init = lambda_0 = 121.3*pi/180.0      #initial longitude in radians - FALSE\n",
    "h = h_init = h_0 = 100                        #initial altitude in meters - TRUE\n",
    "u = 2 * pi / 86164.090530833\n",
    "global R_E \n",
    "R_E = a / ((1 - e2 * (sin(phi) ** 2)) ** 0.5)\n",
    "global R_N\n",
    "R_N = a * (1 - e2) / ((1 - e2 * (sin(phi) ** 2)) ** 1.5)\n",
    "con = (u**2)*((R_E+h) * math.sin(phi) * math.cos(phi))\n",
    "#chi = 1.68502813 #азимутальный угол, радианы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNFparser(filename):\n",
    "    '''\n",
    "    Тут мы расписываем парсер\n",
    "    CNFnav - кусок для работы в режиме навигации\n",
    "    CNFalign - кусок для выставки (причём секунд 10-20 из него для алгебраической, остальное на кинематику)\n",
    "    '''\n",
    "    CNFnames = ['Time','AccX','AccY','AccZ','OmX','OmY','OmZ','H']\n",
    "    CNF = pd.read_csv(filename, sep = '\\s+' ,header = None, names = CNFnames, skiprows = 1)\n",
    "    #CNFalign = CNF[CNF.FlagAlignment == 1]\n",
    "    #CNFalign = CNFalign.loc[:,['Time','OmX','OmY','OmZ','AccX','AccY','AccZ']]\n",
    "    #CNFnav = CNF[CNF.FlagAlignment == 0]\n",
    "    #CNFnav = CNFnav.loc[:,['Time','OmX','OmY','OmZ','AccX','AccY','AccZ']]\n",
    "    #CNF = []\n",
    "    CNF['Invariant'] = CNF.AccX*CNF.OmX +  CNF.AccY*CNF.OmY +  CNF.AccZ*CNF.OmZ\n",
    "    #CNF['g\\''] = (CNF.AccX**2 + CNF.AccY**2 + CNF.AccZ**2)**(0.5)\n",
    "    CNF['W_Std500'] = (CNF.Invariant.rolling(window = 500, center = False).std()).dropna()\n",
    "    return CNF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "CNF = CNFparser('./INS_3_2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0025000000000545697"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF.Time[0]-CNF.Time[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>OmX</th>\n",
       "      <th>OmY</th>\n",
       "      <th>OmZ</th>\n",
       "      <th>H</th>\n",
       "      <th>Invariant</th>\n",
       "      <th>W_Std500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.0</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>675752.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2754.674061</td>\n",
       "      <td>0.180927</td>\n",
       "      <td>-0.108482</td>\n",
       "      <td>9.812357</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.004880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>488.050287</td>\n",
       "      <td>0.004446</td>\n",
       "      <td>0.071474</td>\n",
       "      <td>0.055071</td>\n",
       "      <td>0.012124</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004907</td>\n",
       "      <td>0.000557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1909.348041</td>\n",
       "      <td>0.156578</td>\n",
       "      <td>-0.279866</td>\n",
       "      <td>9.676982</td>\n",
       "      <td>-0.025576</td>\n",
       "      <td>-0.001963</td>\n",
       "      <td>-0.002049</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.023161</td>\n",
       "      <td>0.003813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2332.011051</td>\n",
       "      <td>0.177940</td>\n",
       "      <td>-0.175552</td>\n",
       "      <td>9.760486</td>\n",
       "      <td>-0.011611</td>\n",
       "      <td>-0.000279</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002650</td>\n",
       "      <td>0.004476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2754.674060</td>\n",
       "      <td>0.180932</td>\n",
       "      <td>-0.108247</td>\n",
       "      <td>9.812340</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.004652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3177.337070</td>\n",
       "      <td>0.183913</td>\n",
       "      <td>-0.041506</td>\n",
       "      <td>9.864075</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003792</td>\n",
       "      <td>0.005427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3600.000080</td>\n",
       "      <td>0.208240</td>\n",
       "      <td>0.067274</td>\n",
       "      <td>9.949390</td>\n",
       "      <td>0.025093</td>\n",
       "      <td>0.001930</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.023477</td>\n",
       "      <td>0.006515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time           AccX           AccY           AccZ  \\\n",
       "count  676251.000000  676251.000000  676251.000000  676251.000000   \n",
       "mean     2754.674061       0.180927      -0.108482       9.812357   \n",
       "std       488.050287       0.004446       0.071474       0.055071   \n",
       "min      1909.348041       0.156578      -0.279866       9.676982   \n",
       "25%      2332.011051       0.177940      -0.175552       9.760486   \n",
       "50%      2754.674060       0.180932      -0.108247       9.812340   \n",
       "75%      3177.337070       0.183913      -0.041506       9.864075   \n",
       "max      3600.000080       0.208240       0.067274       9.949390   \n",
       "\n",
       "                 OmX            OmY            OmZ         H      Invariant  \\\n",
       "count  676251.000000  676251.000000  676251.000000  676251.0  676251.000000   \n",
       "mean       -0.000038      -0.000013       0.000061     100.0       0.000579   \n",
       "std         0.012124       0.000415       0.000440       0.0       0.004907   \n",
       "min        -0.025576      -0.001963      -0.002049     100.0      -0.023161   \n",
       "25%        -0.011611      -0.000279      -0.000213     100.0      -0.002650   \n",
       "50%        -0.000046      -0.000010       0.000056     100.0       0.000570   \n",
       "75%         0.011541       0.000252       0.000338     100.0       0.003792   \n",
       "max         0.025093       0.001930       0.002131     100.0       0.023477   \n",
       "\n",
       "            W_Std500  \n",
       "count  675752.000000  \n",
       "mean        0.004880  \n",
       "std         0.000557  \n",
       "min         0.003813  \n",
       "25%         0.004476  \n",
       "50%         0.004652  \n",
       "75%         0.005427  \n",
       "max         0.006515  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>OmX</th>\n",
       "      <th>OmY</th>\n",
       "      <th>OmZ</th>\n",
       "      <th>H</th>\n",
       "      <th>Invariant</th>\n",
       "      <th>W_Std500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1909.348041</td>\n",
       "      <td>0.179717</td>\n",
       "      <td>-0.173474</td>\n",
       "      <td>9.867469</td>\n",
       "      <td>0.015186</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>-0.000367</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1909.350541</td>\n",
       "      <td>0.184395</td>\n",
       "      <td>-0.116951</td>\n",
       "      <td>9.804570</td>\n",
       "      <td>-0.011850</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1909.353041</td>\n",
       "      <td>0.177705</td>\n",
       "      <td>-0.036077</td>\n",
       "      <td>9.760693</td>\n",
       "      <td>-0.002734</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>0.000496</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1909.355541</td>\n",
       "      <td>0.175688</td>\n",
       "      <td>-0.181746</td>\n",
       "      <td>9.877270</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>-0.000457</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002237</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1909.358042</td>\n",
       "      <td>0.185866</td>\n",
       "      <td>-0.095217</td>\n",
       "      <td>9.799156</td>\n",
       "      <td>-0.012572</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1909.360542</td>\n",
       "      <td>0.188293</td>\n",
       "      <td>-0.044645</td>\n",
       "      <td>9.767680</td>\n",
       "      <td>-0.000916</td>\n",
       "      <td>-0.000097</td>\n",
       "      <td>-0.000578</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.005813</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1909.363042</td>\n",
       "      <td>0.172833</td>\n",
       "      <td>-0.204624</td>\n",
       "      <td>9.876915</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.006681</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1909.365542</td>\n",
       "      <td>0.175460</td>\n",
       "      <td>-0.081272</td>\n",
       "      <td>9.787695</td>\n",
       "      <td>-0.014638</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.000566</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1909.368042</td>\n",
       "      <td>0.185467</td>\n",
       "      <td>-0.043622</td>\n",
       "      <td>9.774943</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001326</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1909.370542</td>\n",
       "      <td>0.179064</td>\n",
       "      <td>-0.208546</td>\n",
       "      <td>9.884666</td>\n",
       "      <td>0.011459</td>\n",
       "      <td>-0.000321</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.002023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1909.373042</td>\n",
       "      <td>0.173480</td>\n",
       "      <td>-0.069311</td>\n",
       "      <td>9.785008</td>\n",
       "      <td>-0.016843</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002618</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1909.375542</td>\n",
       "      <td>0.180159</td>\n",
       "      <td>-0.058934</td>\n",
       "      <td>9.778024</td>\n",
       "      <td>0.006604</td>\n",
       "      <td>-0.000177</td>\n",
       "      <td>-0.000220</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.000956</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1909.378042</td>\n",
       "      <td>0.183865</td>\n",
       "      <td>-0.211797</td>\n",
       "      <td>9.892679</td>\n",
       "      <td>0.010794</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000445</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.006335</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1909.380542</td>\n",
       "      <td>0.183607</td>\n",
       "      <td>-0.040389</td>\n",
       "      <td>9.760002</td>\n",
       "      <td>-0.017850</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>-0.000177</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.005011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1909.383042</td>\n",
       "      <td>0.177192</td>\n",
       "      <td>-0.075853</td>\n",
       "      <td>9.785118</td>\n",
       "      <td>0.009081</td>\n",
       "      <td>-0.000405</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1909.385542</td>\n",
       "      <td>0.171593</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>9.903666</td>\n",
       "      <td>0.008761</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004894</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1909.388042</td>\n",
       "      <td>0.186531</td>\n",
       "      <td>-0.021806</td>\n",
       "      <td>9.751023</td>\n",
       "      <td>-0.019287</td>\n",
       "      <td>-0.000254</td>\n",
       "      <td>-0.000227</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.005808</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1909.390542</td>\n",
       "      <td>0.176763</td>\n",
       "      <td>-0.099460</td>\n",
       "      <td>9.800285</td>\n",
       "      <td>0.012360</td>\n",
       "      <td>-0.000495</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1909.393042</td>\n",
       "      <td>0.175409</td>\n",
       "      <td>-0.209310</td>\n",
       "      <td>9.886482</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1909.395542</td>\n",
       "      <td>0.184929</td>\n",
       "      <td>-0.016613</td>\n",
       "      <td>9.731046</td>\n",
       "      <td>-0.016744</td>\n",
       "      <td>-0.000494</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002683</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1909.398042</td>\n",
       "      <td>0.184514</td>\n",
       "      <td>-0.108170</td>\n",
       "      <td>9.818773</td>\n",
       "      <td>0.013617</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1909.400542</td>\n",
       "      <td>0.180960</td>\n",
       "      <td>-0.188411</td>\n",
       "      <td>9.876599</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>-0.000125</td>\n",
       "      <td>-0.000359</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.003171</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1909.403042</td>\n",
       "      <td>0.178148</td>\n",
       "      <td>-0.014823</td>\n",
       "      <td>9.729397</td>\n",
       "      <td>-0.015997</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1909.405542</td>\n",
       "      <td>0.173736</td>\n",
       "      <td>-0.124116</td>\n",
       "      <td>9.838128</td>\n",
       "      <td>0.015752</td>\n",
       "      <td>-0.000713</td>\n",
       "      <td>-0.000235</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000514</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1909.408042</td>\n",
       "      <td>0.179088</td>\n",
       "      <td>-0.185085</td>\n",
       "      <td>9.859499</td>\n",
       "      <td>-0.000975</td>\n",
       "      <td>0.000610</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1909.410542</td>\n",
       "      <td>0.187688</td>\n",
       "      <td>-0.014771</td>\n",
       "      <td>9.730080</td>\n",
       "      <td>-0.013522</td>\n",
       "      <td>-0.001202</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001257</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1909.413042</td>\n",
       "      <td>0.177566</td>\n",
       "      <td>-0.139955</td>\n",
       "      <td>9.838489</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003057</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1909.415542</td>\n",
       "      <td>0.181310</td>\n",
       "      <td>-0.171530</td>\n",
       "      <td>9.854868</td>\n",
       "      <td>-0.003718</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1909.418042</td>\n",
       "      <td>0.188026</td>\n",
       "      <td>0.006141</td>\n",
       "      <td>9.734034</td>\n",
       "      <td>-0.013282</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003330</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1909.420543</td>\n",
       "      <td>0.183145</td>\n",
       "      <td>-0.156904</td>\n",
       "      <td>9.850579</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-0.000293</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676221</th>\n",
       "      <td>3599.927579</td>\n",
       "      <td>0.175995</td>\n",
       "      <td>-0.164095</td>\n",
       "      <td>9.898097</td>\n",
       "      <td>0.017749</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>-0.000073</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.002400</td>\n",
       "      <td>0.005404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676222</th>\n",
       "      <td>3599.930079</td>\n",
       "      <td>0.178831</td>\n",
       "      <td>-0.153956</td>\n",
       "      <td>9.808633</td>\n",
       "      <td>-0.012596</td>\n",
       "      <td>-0.000446</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.005395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676223</th>\n",
       "      <td>3599.932579</td>\n",
       "      <td>0.183366</td>\n",
       "      <td>0.006010</td>\n",
       "      <td>9.741156</td>\n",
       "      <td>-0.004369</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>-0.000111</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001877</td>\n",
       "      <td>0.005386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676224</th>\n",
       "      <td>3599.935079</td>\n",
       "      <td>0.181991</td>\n",
       "      <td>-0.186428</td>\n",
       "      <td>9.905919</td>\n",
       "      <td>0.015999</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.007627</td>\n",
       "      <td>0.005395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676225</th>\n",
       "      <td>3599.937579</td>\n",
       "      <td>0.184933</td>\n",
       "      <td>-0.131127</td>\n",
       "      <td>9.787286</td>\n",
       "      <td>-0.013580</td>\n",
       "      <td>-0.000741</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003368</td>\n",
       "      <td>0.005392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676226</th>\n",
       "      <td>3599.940079</td>\n",
       "      <td>0.185123</td>\n",
       "      <td>0.004007</td>\n",
       "      <td>9.764983</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.015118</td>\n",
       "      <td>0.005436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676227</th>\n",
       "      <td>3599.942579</td>\n",
       "      <td>0.181187</td>\n",
       "      <td>-0.215548</td>\n",
       "      <td>9.898887</td>\n",
       "      <td>0.013924</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>0.005476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676228</th>\n",
       "      <td>3599.945079</td>\n",
       "      <td>0.181486</td>\n",
       "      <td>-0.100787</td>\n",
       "      <td>9.774061</td>\n",
       "      <td>-0.015324</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000297</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.005652</td>\n",
       "      <td>0.005476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676229</th>\n",
       "      <td>3599.947579</td>\n",
       "      <td>0.176953</td>\n",
       "      <td>-0.012024</td>\n",
       "      <td>9.778200</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.000475</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.004258</td>\n",
       "      <td>0.005474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676230</th>\n",
       "      <td>3599.950079</td>\n",
       "      <td>0.180064</td>\n",
       "      <td>-0.236031</td>\n",
       "      <td>9.886716</td>\n",
       "      <td>0.012750</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.015030</td>\n",
       "      <td>0.005507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676231</th>\n",
       "      <td>3599.952579</td>\n",
       "      <td>0.187160</td>\n",
       "      <td>-0.057037</td>\n",
       "      <td>9.762747</td>\n",
       "      <td>-0.016584</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000831</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.011212</td>\n",
       "      <td>0.005531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676232</th>\n",
       "      <td>3599.955079</td>\n",
       "      <td>0.181830</td>\n",
       "      <td>-0.038105</td>\n",
       "      <td>9.790835</td>\n",
       "      <td>0.004923</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004812</td>\n",
       "      <td>0.005533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676233</th>\n",
       "      <td>3599.957579</td>\n",
       "      <td>0.180147</td>\n",
       "      <td>-0.238400</td>\n",
       "      <td>9.877928</td>\n",
       "      <td>0.011091</td>\n",
       "      <td>-0.000283</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.005535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676234</th>\n",
       "      <td>3599.960079</td>\n",
       "      <td>0.182241</td>\n",
       "      <td>-0.030087</td>\n",
       "      <td>9.758900</td>\n",
       "      <td>-0.016538</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>-0.000767</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.010500</td>\n",
       "      <td>0.005554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676235</th>\n",
       "      <td>3599.962579</td>\n",
       "      <td>0.181302</td>\n",
       "      <td>-0.069600</td>\n",
       "      <td>9.798723</td>\n",
       "      <td>0.007601</td>\n",
       "      <td>-0.000506</td>\n",
       "      <td>0.000651</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.007792</td>\n",
       "      <td>0.005559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676236</th>\n",
       "      <td>3599.965079</td>\n",
       "      <td>0.182382</td>\n",
       "      <td>-0.228578</td>\n",
       "      <td>9.864134</td>\n",
       "      <td>0.008168</td>\n",
       "      <td>-0.000091</td>\n",
       "      <td>-0.000507</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.003487</td>\n",
       "      <td>0.005562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676237</th>\n",
       "      <td>3599.967579</td>\n",
       "      <td>0.180786</td>\n",
       "      <td>-0.012406</td>\n",
       "      <td>9.758993</td>\n",
       "      <td>-0.016736</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.005562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676238</th>\n",
       "      <td>3599.970079</td>\n",
       "      <td>0.179676</td>\n",
       "      <td>-0.105127</td>\n",
       "      <td>9.814013</td>\n",
       "      <td>0.009947</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001855</td>\n",
       "      <td>0.005562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676239</th>\n",
       "      <td>3599.972579</td>\n",
       "      <td>0.181578</td>\n",
       "      <td>-0.195622</td>\n",
       "      <td>9.856699</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>-0.000135</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.005561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676240</th>\n",
       "      <td>3599.975079</td>\n",
       "      <td>0.184245</td>\n",
       "      <td>-0.016796</td>\n",
       "      <td>9.763838</td>\n",
       "      <td>-0.014608</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001324</td>\n",
       "      <td>0.005560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676241</th>\n",
       "      <td>3599.977579</td>\n",
       "      <td>0.182892</td>\n",
       "      <td>-0.131770</td>\n",
       "      <td>9.811121</td>\n",
       "      <td>0.010641</td>\n",
       "      <td>-0.000714</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004710</td>\n",
       "      <td>0.005560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676242</th>\n",
       "      <td>3599.980079</td>\n",
       "      <td>0.176144</td>\n",
       "      <td>-0.165815</td>\n",
       "      <td>9.847615</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.006236</td>\n",
       "      <td>0.005567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676243</th>\n",
       "      <td>3599.982579</td>\n",
       "      <td>0.180519</td>\n",
       "      <td>-0.023160</td>\n",
       "      <td>9.774400</td>\n",
       "      <td>-0.012229</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.000868</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.006261</td>\n",
       "      <td>0.005572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676244</th>\n",
       "      <td>3599.985080</td>\n",
       "      <td>0.186611</td>\n",
       "      <td>-0.145888</td>\n",
       "      <td>9.810749</td>\n",
       "      <td>0.011847</td>\n",
       "      <td>-0.000531</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.005575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676245</th>\n",
       "      <td>3599.987580</td>\n",
       "      <td>0.173388</td>\n",
       "      <td>-0.144582</td>\n",
       "      <td>9.851936</td>\n",
       "      <td>-0.000535</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>-0.000776</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.007787</td>\n",
       "      <td>0.005583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676246</th>\n",
       "      <td>3599.990080</td>\n",
       "      <td>0.177886</td>\n",
       "      <td>-0.048791</td>\n",
       "      <td>9.780684</td>\n",
       "      <td>-0.010690</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.005582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676247</th>\n",
       "      <td>3599.992580</td>\n",
       "      <td>0.181166</td>\n",
       "      <td>-0.145863</td>\n",
       "      <td>9.815701</td>\n",
       "      <td>0.012286</td>\n",
       "      <td>-0.000163</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.007332</td>\n",
       "      <td>0.005587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676248</th>\n",
       "      <td>3599.995080</td>\n",
       "      <td>0.179195</td>\n",
       "      <td>-0.128630</td>\n",
       "      <td>9.849484</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.008896</td>\n",
       "      <td>0.005602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676249</th>\n",
       "      <td>3599.997580</td>\n",
       "      <td>0.187667</td>\n",
       "      <td>-0.057140</td>\n",
       "      <td>9.759937</td>\n",
       "      <td>-0.008005</td>\n",
       "      <td>-0.000450</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.010283</td>\n",
       "      <td>0.005616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676250</th>\n",
       "      <td>3600.000080</td>\n",
       "      <td>0.177385</td>\n",
       "      <td>-0.139698</td>\n",
       "      <td>9.837146</td>\n",
       "      <td>0.013255</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>-0.001468</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.012130</td>\n",
       "      <td>0.005642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>676251 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time      AccX      AccY      AccZ       OmX       OmY  \\\n",
       "0       1909.348041  0.179717 -0.173474  9.867469  0.015186  0.000801   \n",
       "1       1909.350541  0.184395 -0.116951  9.804570 -0.011850  0.000058   \n",
       "2       1909.353041  0.177705 -0.036077  9.760693 -0.002734 -0.000388   \n",
       "3       1909.355541  0.175688 -0.181746  9.877270  0.013422  0.000420   \n",
       "4       1909.358042  0.185866 -0.095217  9.799156 -0.012572  0.000293   \n",
       "5       1909.360542  0.188293 -0.044645  9.767680 -0.000916 -0.000097   \n",
       "6       1909.363042  0.172833 -0.204624  9.876915  0.014042  0.000335   \n",
       "7       1909.365542  0.175460 -0.081272  9.787695 -0.014638  0.000152   \n",
       "8       1909.368042  0.185467 -0.043622  9.774943  0.002733  0.000394   \n",
       "9       1909.370542  0.179064 -0.208546  9.884666  0.011459 -0.000321   \n",
       "10      1909.373042  0.173480 -0.069311  9.785008 -0.016843  0.000416   \n",
       "11      1909.375542  0.180159 -0.058934  9.778024  0.006604 -0.000177   \n",
       "12      1909.378042  0.183865 -0.211797  9.892679  0.010794  0.000257   \n",
       "13      1909.380542  0.183607 -0.040389  9.760002 -0.017850  0.000100   \n",
       "14      1909.383042  0.177192 -0.075853  9.785118  0.009081 -0.000405   \n",
       "15      1909.385542  0.171593 -0.212664  9.903666  0.008761  0.000152   \n",
       "16      1909.388042  0.186531 -0.021806  9.751023 -0.019287 -0.000254   \n",
       "17      1909.390542  0.176763 -0.099460  9.800285  0.012360 -0.000495   \n",
       "18      1909.393042  0.175409 -0.209310  9.886482  0.005645  0.000580   \n",
       "19      1909.395542  0.184929 -0.016613  9.731046 -0.016744 -0.000494   \n",
       "20      1909.398042  0.184514 -0.108170  9.818773  0.013617  0.000108   \n",
       "21      1909.400542  0.180960 -0.188411  9.876599  0.001934 -0.000125   \n",
       "22      1909.403042  0.178148 -0.014823  9.729397 -0.015997  0.000169   \n",
       "23      1909.405542  0.173736 -0.124116  9.838128  0.015752 -0.000713   \n",
       "24      1909.408042  0.179088 -0.185085  9.859499 -0.000975  0.000610   \n",
       "25      1909.410542  0.187688 -0.014771  9.730080 -0.013522 -0.001202   \n",
       "26      1909.413042  0.177566 -0.139955  9.838489  0.016806  0.000613   \n",
       "27      1909.415542  0.181310 -0.171530  9.854868 -0.003718  0.000703   \n",
       "28      1909.418042  0.188026  0.006141  9.734034 -0.013282 -0.000635   \n",
       "29      1909.420543  0.183145 -0.156904  9.850579  0.018025 -0.000572   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "676221  3599.927579  0.175995 -0.164095  9.898097  0.017749  0.000032   \n",
       "676222  3599.930079  0.178831 -0.153956  9.808633 -0.012596 -0.000446   \n",
       "676223  3599.932579  0.183366  0.006010  9.741156 -0.004369  0.000197   \n",
       "676224  3599.935079  0.181991 -0.186428  9.905919  0.015999  0.000673   \n",
       "676225  3599.937579  0.184933 -0.131127  9.787286 -0.013580 -0.000741   \n",
       "676226  3599.940079  0.185123  0.004007  9.764983  0.000276  0.000541   \n",
       "676227  3599.942579  0.181187 -0.215548  9.898887  0.013924 -0.000335   \n",
       "676228  3599.945079  0.181486 -0.100787  9.774061 -0.015324 -0.000361   \n",
       "676229  3599.947579  0.176953 -0.012024  9.778200  0.002186  0.000059   \n",
       "676230  3599.950079  0.180064 -0.236031  9.886716  0.012750  0.000210   \n",
       "676231  3599.952579  0.187160 -0.057037  9.762747 -0.016584 -0.000048   \n",
       "676232  3599.955079  0.181830 -0.038105  9.790835  0.004923  0.000940   \n",
       "676233  3599.957579  0.180147 -0.238400  9.877928  0.011091 -0.000283   \n",
       "676234  3599.960079  0.182241 -0.030087  9.758900 -0.016538  0.000124   \n",
       "676235  3599.962579  0.181302 -0.069600  9.798723  0.007601 -0.000506   \n",
       "676236  3599.965079  0.182382 -0.228578  9.864134  0.008168 -0.000091   \n",
       "676237  3599.967579  0.180786 -0.012406  9.758993 -0.016736  0.000871   \n",
       "676238  3599.970079  0.179676 -0.105127  9.814013  0.009947 -0.000043   \n",
       "676239  3599.972579  0.181578 -0.195622  9.856699  0.005020 -0.000135   \n",
       "676240  3599.975079  0.184245 -0.016796  9.763838 -0.014608  0.000484   \n",
       "676241  3599.977579  0.182892 -0.131770  9.811121  0.010641 -0.000714   \n",
       "676242  3599.980079  0.176144 -0.165815  9.847615  0.001620  0.000354   \n",
       "676243  3599.982579  0.180519 -0.023160  9.774400 -0.012229  0.000685   \n",
       "676244  3599.985080  0.186611 -0.145888  9.810749  0.011847 -0.000531   \n",
       "676245  3599.987580  0.173388 -0.144582  9.851936 -0.000535  0.000358   \n",
       "676246  3599.990080  0.177886 -0.048791  9.780684 -0.010690  0.000469   \n",
       "676247  3599.992580  0.181166 -0.145863  9.815701  0.012286 -0.000163   \n",
       "676248  3599.995080  0.179195 -0.128630  9.849484 -0.004350  0.000498   \n",
       "676249  3599.997580  0.187667 -0.057140  9.759937 -0.008005 -0.000450   \n",
       "676250  3600.000080  0.177385 -0.139698  9.837146  0.013255  0.000254   \n",
       "\n",
       "             OmZ      H  Invariant  W_Std500  \n",
       "0      -0.000367  100.0  -0.001030       NaN  \n",
       "1       0.000412  100.0   0.001843       NaN  \n",
       "2       0.000496  100.0   0.004373       NaN  \n",
       "3      -0.000457  100.0  -0.002237       NaN  \n",
       "4       0.000404  100.0   0.001595       NaN  \n",
       "5      -0.000578  100.0  -0.005813       NaN  \n",
       "6       0.000438  100.0   0.006681       NaN  \n",
       "7       0.000206  100.0  -0.000566       NaN  \n",
       "8      -0.000186  100.0  -0.001326       NaN  \n",
       "9      -0.000010  100.0   0.002023       NaN  \n",
       "10      0.000034  100.0  -0.002618       NaN  \n",
       "11     -0.000220  100.0  -0.000956       NaN  \n",
       "12      0.000445  100.0   0.006335       NaN  \n",
       "13     -0.000177  100.0  -0.005011       NaN  \n",
       "14     -0.000020  100.0   0.001443       NaN  \n",
       "15      0.000346  100.0   0.004894       NaN  \n",
       "16     -0.000227  100.0  -0.005808       NaN  \n",
       "17      0.000158  100.0   0.003785       NaN  \n",
       "18      0.000289  100.0   0.003729       NaN  \n",
       "19      0.000042  100.0  -0.002683       NaN  \n",
       "20      0.000165  100.0   0.004125       NaN  \n",
       "21     -0.000359  100.0  -0.003171       NaN  \n",
       "22      0.000471  100.0   0.001733       NaN  \n",
       "23     -0.000235  100.0   0.000514       NaN  \n",
       "24      0.000169  100.0   0.001382       NaN  \n",
       "25      0.000130  100.0  -0.001257       NaN  \n",
       "26      0.000016  100.0   0.003057       NaN  \n",
       "27     -0.000136  100.0  -0.002131       NaN  \n",
       "28      0.000599  100.0   0.003330       NaN  \n",
       "29     -0.000293  100.0   0.000502       NaN  \n",
       "...          ...    ...        ...       ...  \n",
       "676221 -0.000073  100.0   0.002400  0.005404  \n",
       "676222  0.000266  100.0   0.000424  0.005395  \n",
       "676223 -0.000111  100.0  -0.001877  0.005386  \n",
       "676224  0.000489  100.0   0.007627  0.005395  \n",
       "676225  0.000591  100.0   0.003368  0.005392  \n",
       "676226 -0.001554  100.0  -0.015118  0.005436  \n",
       "676227  0.001431  100.0   0.016761  0.005476  \n",
       "676228 -0.000297  100.0  -0.005652  0.005476  \n",
       "676229 -0.000475  100.0  -0.004258  0.005474  \n",
       "676230  0.001293  100.0   0.015030  0.005507  \n",
       "676231 -0.000831  100.0  -0.011212  0.005531  \n",
       "676232  0.000404  100.0   0.004812  0.005533  \n",
       "676233  0.000189  100.0   0.003937  0.005535  \n",
       "676234 -0.000767  100.0  -0.010500  0.005554  \n",
       "676235  0.000651  100.0   0.007792  0.005559  \n",
       "676236 -0.000507  100.0  -0.003487  0.005562  \n",
       "676237  0.000499  100.0   0.001832  0.005562  \n",
       "676238  0.000006  100.0   0.001855  0.005562  \n",
       "676239 -0.000031  100.0   0.000631  0.005561  \n",
       "676240  0.000141  100.0  -0.001324  0.005560  \n",
       "676241  0.000272  100.0   0.004710  0.005560  \n",
       "676242 -0.000656  100.0  -0.006236  0.005567  \n",
       "676243  0.000868  100.0   0.006261  0.005572  \n",
       "676244  0.000222  100.0   0.004462  0.005575  \n",
       "676245 -0.000776  100.0  -0.007787  0.005583  \n",
       "676246  0.000619  100.0   0.004128  0.005582  \n",
       "676247  0.000518  100.0   0.007332  0.005587  \n",
       "676248 -0.000818  100.0  -0.008896  0.005602  \n",
       "676249  0.001205  100.0   0.010283  0.005616  \n",
       "676250 -0.001468  100.0  -0.012130  0.005642  \n",
       "\n",
       "[676251 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g_phi(phi, h, a, e2):\n",
    "    #g1 = 9.78030 * (1 - 2 * h / a + 3 * e2 * sin(phi) ** 2 / 4)\n",
    "    g = 9.78030*(1+0.005302*sin(phi)*sin(phi)-0.000007*sin(2*phi)*sin(2*phi))\n",
    "    return g#(f_x**2 + f_y**2 + f_z**2)**(0.5) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.79416166528857\n",
      "g' =  9.814624541745097\n",
      "\\delta g =  0.020462876456527113\n"
     ]
    }
   ],
   "source": [
    "g_phi(phi_0, h_0, a, e2)\n",
    "global g\n",
    "g = g_phi(phi_0, h_0, a, e2)\n",
    "print(g)\n",
    "temp = np.mean(CNF.loc[:,['AccX','AccY','AccZ','OmX','OmY','OmZ']].values, axis = 0).reshape(6,1)\n",
    "print ('g\\' = ', np.linalg.norm(temp[0:3]))\n",
    "print ('\\delta g = ', np.linalg.norm(temp[0:3]) - g_phi(phi_0, h_0, a, e2))\n",
    "#del temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u*cos(phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u*sin(phi)"
   ]
  },
  {
   "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>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>OmX</th>\n",
       "      <th>OmY</th>\n",
       "      <th>OmZ</th>\n",
       "      <th>Invariant</th>\n",
       "      <th>W_Std500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.0</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>675752.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.180927</td>\n",
       "      <td>-0.108482</td>\n",
       "      <td>9.812357</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.003198</td>\n",
       "      <td>1.213445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.004446</td>\n",
       "      <td>0.071474</td>\n",
       "      <td>0.055071</td>\n",
       "      <td>0.012124</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.212565</td>\n",
       "      <td>0.028050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.156578</td>\n",
       "      <td>-0.279866</td>\n",
       "      <td>9.676982</td>\n",
       "      <td>-0.025576</td>\n",
       "      <td>-0.001963</td>\n",
       "      <td>-0.002049</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-2.554327</td>\n",
       "      <td>1.132493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.177940</td>\n",
       "      <td>-0.175552</td>\n",
       "      <td>9.760486</td>\n",
       "      <td>-0.011611</td>\n",
       "      <td>-0.000279</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-1.160515</td>\n",
       "      <td>1.194156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.180932</td>\n",
       "      <td>-0.108247</td>\n",
       "      <td>9.812340</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.003811</td>\n",
       "      <td>1.210279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.183913</td>\n",
       "      <td>-0.041506</td>\n",
       "      <td>9.864075</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.154657</td>\n",
       "      <td>1.229127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.208240</td>\n",
       "      <td>0.067274</td>\n",
       "      <td>9.949390</td>\n",
       "      <td>0.025093</td>\n",
       "      <td>0.001930</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.514225</td>\n",
       "      <td>1.420204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time           AccX           AccY           AccZ  \\\n",
       "count  676251.000000  676251.000000  676251.000000  676251.000000   \n",
       "mean        0.180927      -0.108482       9.812357      -0.000038   \n",
       "std         0.004446       0.071474       0.055071       0.012124   \n",
       "min         0.156578      -0.279866       9.676982      -0.025576   \n",
       "25%         0.177940      -0.175552       9.760486      -0.011611   \n",
       "50%         0.180932      -0.108247       9.812340      -0.000046   \n",
       "75%         0.183913      -0.041506       9.864075       0.011541   \n",
       "max         0.208240       0.067274       9.949390       0.025093   \n",
       "\n",
       "                 OmX            OmY       OmZ      Invariant       W_Std500  \n",
       "count  676251.000000  676251.000000  676251.0  676251.000000  675752.000000  \n",
       "mean       -0.000013       0.000061     100.0      -0.003198       1.213445  \n",
       "std         0.000415       0.000440       0.0       1.212565       0.028050  \n",
       "min        -0.001963      -0.002049     100.0      -2.554327       1.132493  \n",
       "25%        -0.000279      -0.000213     100.0      -1.160515       1.194156  \n",
       "50%        -0.000010       0.000056     100.0      -0.003811       1.210279  \n",
       "75%         0.000252       0.000338     100.0       1.154657       1.229127  \n",
       "max         0.001930       0.002131     100.0       2.514225       1.420204  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CNFinit = CNF.loc[:1999,['Time','AccX','AccY','AccZ','OmX','OmY','OmZ']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=676251, minmax=(array([ 1.90934804e+03,  1.56577800e-01, -2.79865900e-01,  9.67698190e+00,\n",
       "       -2.55762800e-02, -1.96283000e-03, -2.04910000e-03,  1.00000000e+02,\n",
       "       -2.31607302e-02,             nan]), array([3.60000008e+03, 2.08240400e-01, 6.72744000e-02, 9.94939040e+00,\n",
       "       2.50931400e-02, 1.93040000e-03, 2.13127000e-03, 1.00000000e+02,\n",
       "       2.34770326e-02,            nan])), mean=array([ 2.75467406e+03,  1.80927402e-01, -1.08481945e-01,  9.81235710e+00,\n",
       "       -3.80032056e-05, -1.26768643e-05,  6.09481060e-05,  1.00000000e+02,\n",
       "        5.79143907e-04,             nan]), variance=array([2.38193083e+05, 1.97685116e-05, 5.10847592e-03, 3.03281218e-03,\n",
       "       1.46997014e-04, 1.72503043e-07, 1.93733543e-07, 0.00000000e+00,\n",
       "       2.40746584e-05,            nan]), skewness=array([ 2.30041566e-10, -3.84338076e-03,  2.93182308e-03,  3.35367735e-03,\n",
       "       -1.23134774e-04,  2.56991260e-03,  5.62403147e-04,  0.00000000e+00,\n",
       "        9.47613299e-03,             nan]), kurtosis=array([-1.2       ,  0.06017408, -1.32746283, -1.34066942, -1.380565  ,\n",
       "        0.29792126,  0.38774325, -3.        ,  0.16834421,         nan]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe(CNF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_calibration_naive(A, phi, h, a, e2, u):\n",
    "    L = np.zeros((3, 3))\n",
    "    means = np.mean(A[:, 1:], axis = 0).reshape(6,1)\n",
    "    theta = math.atan2(means[0], math.sqrt(means[1]**2+means[2]**2))\n",
    "    gamm = -math.atan2(means[2], means[1])\n",
    "    L[:, 2:3] = means[0:3] / 9.812357#(g_phi(phi, h, a, e2))\n",
    "    L[:, 1:2] = (means[3:6] - (L[:, 2:3] * u * sin(phi))) / (u * cos(phi))\n",
    "    L[:, 0:1] = np.cross(L[:, 1:2].T, L[:, 2:3].T).T\n",
    "    L[:, 2:3] /= np.linalg.norm(L[:, 2:3], 2)\n",
    "    L[:, 1:2] /= np.linalg.norm(L[:, 1:2], 2)\n",
    "    L[:, 0:1] /= np.linalg.norm(L[:, 0:1], 2)\n",
    "    psi = math.atan2(L[0][0], L[0][1])\n",
    "    return L, theta, gamm, psi, means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_algebraic_alignment():\n",
    "    '''\n",
    "    Это функция начальной алгебраической выставки - в глобальные переменные записываются значения углов, которые \n",
    "    позднее буду использовать как начальные приближения для алгоритмов точной выставки\n",
    "    '''\n",
    "    global g\n",
    "    global L, L_al\n",
    "    global theta, gamm, phi, psi, chi, chi_al, psi_al, theta_al, gamm_al, L_al, om\n",
    "    DELIMITER0 = 0 \n",
    "    DELIMITER = (15*int((1/dt)))  # 15 секунд\n",
    "    om_x = CNF['OmX'][:].mean()\n",
    "    om_y = CNF['OmY'][:].mean()  \n",
    "    om_z = CNF['OmZ'][:].mean()\n",
    "    f_x = CNF['AccX'][:].mean()\n",
    "    f_y = CNF['AccY'][:].mean()\n",
    "    f_z = CNF['AccZ'][:].mean()\n",
    "    theta = math.atan2(f_x, math.sqrt(f_y*f_y+f_z*f_z))\n",
    "    gamm = -math.atan2(f_z, f_y)\n",
    "    om = math.sqrt(om_x**2+om_y**2+om_z**2)\n",
    "    \n",
    "    phi = math.atan2(om_z, math.sqrt(om_x**2+om_y**2))\n",
    "    g_mean = math.sqrt(f_x**2+f_y**2+f_z**2)\n",
    "    print ('phi_rude = ',phi*180/pi)\n",
    "    print ('g_phi = ', g_phi(phi, h, a, e2))\n",
    "    print('g_mean - g_phi = ',-g_phi(phi, h, a, e2) + f_z)\n",
    "    \n",
    "    l13 = sin(theta)\n",
    "    l23 = cos(theta)*cos(gamm)\n",
    "    l33 = -cos(theta)*sin(gamm)\n",
    "    l12 = (om_x - u*l13*sin(phi))/(u*cos(phi))\n",
    "    l22 = (om_y - u*l23*sin(phi))/(u*cos(phi))\n",
    "    l32 = (om_z - u*l33*sin(phi))/(u*cos(phi))\n",
    "    l12 = l12/(sqrt(l12*l12+l22*l22+l32*l32)) #нормировка\n",
    "    l22 = l22/(sqrt(l12*l12+l22*l22+l32*l32))\n",
    "    l32 = l32/(sqrt(l12*l12+l22*l22+l32*l32))\n",
    "    #теперь нужно векторно умножить второй на третий\n",
    "    l11 = l22*l33 - l32*l23\n",
    "    l21 =-l12*l33 + l13*l32\n",
    "    l31 = l12*l23 - l22*l13\n",
    "    L = np.matrix([[l11, l12, l13], [l21, l22, l23], [l31, l32, l33]])   #записали в матрицу\n",
    "    psi = math.atan2(L.item(0,0),L.item(0,1))\n",
    "    theta = math.asin(L.item(0,2))\n",
    "    gamm = -math.atan2(L.item(2,2),L.item(1,2))\n",
    "    chi = (psi*180./math.pi - 90.)*math.pi/180 #угол азимутальной ориентации\n",
    "    chi_al = chi\n",
    "    psi_al = psi                                      \n",
    "    theta_al = theta\n",
    "    gamm_al = gamm\n",
    "    L_al = L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phi_rude =  56.68269169910939\n",
      "g_phi =  9.816452584905193\n",
      "g_mean - g_phi =  -0.0040954859146413725\n",
      "[[-2.99037932e-01 -9.56022967e-01  1.84344701e-02]\n",
      " [ 9.55814156e-01 -2.99114264e-01 -1.10530917e-02]\n",
      " [ 1.60810225e-02  6.53984440e-04  9.99768973e-01]] 1.003262984716304 7.293571246532863e-05 1.4553912328633045e-08\n",
      "1.0562771630061651 0.018435514308082424\n",
      "-90.63341604243499 -1.5818515222703393\n",
      "-162.6306475922038 -2.8384402651345444\n"
     ]
    }
   ],
   "source": [
    "initial_algebraic_alignment()\n",
    "print(L, np.linalg.det(L), om, om-0.000072921158553)\n",
    "#thhh = atan2(L[0][2], (L[0][0]**2+L[0][1]**2)**(0.5))\n",
    "#gmmm = atan2(-L_0[2][2],L_0[1][2])\n",
    "#pssss = atan2(L_0[0][0], L_0[0][1])\n",
    "print(theta*180/pi, theta)\n",
    "print(gamm*180/pi, gamm)\n",
    "print(psi*180/pi, psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_create(theta, gamm, psi):\n",
    "    L = np.array([[sin(psi)*cos(theta)                      , cos(psi)*cos(theta)                                , sin(theta)],\n",
    "         [cos(psi)*sin(gamm)-sin(psi)*sin(theta)*cos(gamm), -sin(psi)*sin(gamm)-cos(psi)*sin(theta)*cos(gamm), cos(theta)*cos(gamm)],\n",
    "         [cos(psi)*cos(gamm)+sin(psi)*sin(theta)*sin(gamm), -sin(psi)*cos(gamm)+cos(psi)*sin(theta)*sin(gamm), -cos(theta)*sin(gamm)]])\n",
    "    return L.reshape((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.018435514308082424 -1.5818515222703393 -2.8384402651345444\n"
     ]
    }
   ],
   "source": [
    "print(theta, gamm, psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.99037932e-01 -9.56022967e-01  1.84344701e-02]\n",
      " [ 9.55814156e-01 -2.99114264e-01 -1.10530917e-02]\n",
      " [ 1.60810225e-02  6.53984440e-04  9.99768973e-01]] 1.003262984716304\n",
      "[[-2.99037932e-01 -9.56022967e-01  1.84344701e-02]\n",
      " [ 9.55814156e-01 -2.99114264e-01 -1.10530917e-02]\n",
      " [ 1.60810225e-02  6.53984440e-04  9.99768973e-01]] 1.003262984716304\n"
     ]
    }
   ],
   "source": [
    "print(L, np.linalg.det(L))\n",
    "print(L_al, np.linalg.det(L_al))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Графики накапливающихся средних значений акселерометров, ДУС, а также соответствующие среднеквадратические отклонения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "CNF_det = CNF.loc[:,['Time','AccX','AccY','AccZ','OmX','OmY','OmZ']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.79716500e-01 -1.73473800e-01  9.86746880e+00  1.51859800e-02\n",
      "  8.01270000e-04 -3.66870000e-04 -1.02990646e-03]\n",
      "[ 0. -0.  0.  0.  0. -0. -0.]\n",
      "[[ 1.79716500e-01 -1.73473800e-01  9.86746880e+00  1.51859800e-02\n",
      "   8.01270000e-04 -3.66870000e-04 -1.02990646e-03  0.00000000e+00\n",
      "  -0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "det = np.zeros((0, 14))\n",
    "mu = CNF.loc[0,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values\n",
    "#time = CNF.loc[0,['Time']].values\n",
    "sigma = mu*0\n",
    "print(mu)\n",
    "print(sigma)\n",
    "det = np.append(det, np.append(mu, sigma).reshape((1,14)), axis = 0)\n",
    "\n",
    "print(det)\n",
    "x_prev = CNF.loc[0,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values\n",
    "i = 0\n",
    "#coordinates = np.append([phi_0, lambda_0, h_0], V_0.T)\n",
    "for x in CNF.loc[1:23900,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values:\n",
    "    mu_prev = mu\n",
    "    sigma_prev = sigma\n",
    "    i = i+1\n",
    "    mu = (i*mu_prev + x)/(i+1)\n",
    "    if i==1:\n",
    "        sigma = ((x_prev - x)**2) / 2\n",
    "    else:\n",
    "        sigma = (1-1/i)*sigma_prev + (((x-mu)/i)**2)*(i+1)\n",
    "\n",
    "    det = np.append(det, np.append(mu, sigma).reshape((1,14)), axis = 0)\n",
    "\n",
    "##for i in range(1,23900):\n",
    "#    CNF_det = CNF.loc[:i,['Time','AccX','AccY','AccZ','OmX','OmY','OmZ']].values\n",
    "#    L_0, theta, gamm, psi  = initial_calibration_naive(CNF_det, phi_0, h_0, a, e2, u)\n",
    "#    #print L_0, np.linalg.det(L_0)\n",
    "#    det = np.append(det, np.array([CNF.Time[i], np.linalg.det(L_0), theta, gamm, psi]).reshape((1,5)), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.figure(figsize=(18,5));\n",
    "\n",
    "time = CNF.loc[:239000,['Time']].values\n",
    "del2 = 10000\n",
    "fig.suptitle(u\"Зависимость параметров выставки от времени\")\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.3)\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax1.plot(time[50:del2,0], det[50:del2,1])\n",
    "ax1.set_ylabel(u'|L\\'|')\n",
    "ax1.set_xlabel(u'время, c')\n",
    "\n",
    "ax2.plot(time[50:del2,0], det[50:del2,4])\n",
    "ax2.set_ylabel(u'курс, rad')\n",
    "ax2.set_xlabel(u'время, c')\n",
    "\n",
    "ax3.plot(time[50:del2,0], det[50:del2,3])\n",
    "ax3.set_ylabel(u'крен, rad')\n",
    "ax3.set_xlabel(u'время, c')\n",
    "\n",
    "ax4.plot(time[50:del2,0], det[50:del2,13])\n",
    "ax4.set_ylabel(u'тангаж, rad')\n",
    "ax4.set_xlabel(u'время, c')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1296 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,18))\n",
    "time = CNF.loc[:239000,['Time']].values\n",
    "del1 = 100\n",
    "#plt.figure();\n",
    "#fig.suptitle(u\"Оценки математического ожидания на интервале выставки\")\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.3)\n",
    "ax1 = fig.add_subplot(721)\n",
    "ax4 = fig.add_subplot(722)\n",
    "ax2 = fig.add_subplot(723)\n",
    "ax5 = fig.add_subplot(724)\n",
    "ax3 = fig.add_subplot(725)\n",
    "ax6 = fig.add_subplot(726)\n",
    "ax7 = fig.add_subplot(714)\n",
    "ax1.plot(time[del1:del2,0], det[del1:del2,0])\n",
    "#ax1.axis([200, 1000, 97, 98.2])\n",
    "ax1.grid(b=True, which='major', axis='both')\n",
    "ax1.set_ylabel(u'E(f\\'_x), м/с²')\n",
    "ax1.set_xlabel(u'время, c')\n",
    "\n",
    "ax2.plot(time[del1:del2,0], det[del1:del2,1])\n",
    "#ax2.axis([1000, 1025, 98.005, 98.02])\n",
    "ax2.grid(b=True, which='major', axis='both')\n",
    "ax2.set_ylabel(u'E(f\\'_y), м/с²')\n",
    "ax2.set_xlabel(u'время, c')\n",
    "\n",
    "ax3.plot(time[del1:del2,0], det[del1:del2,2])\n",
    "#ax3.axis([200, 1000,  -90.002,-89.997])\n",
    "ax3.grid(b=True, which='major', axis='both')\n",
    "ax3.set_ylabel(u'E(f\\'_z), м/с²')\n",
    "ax3.set_xlabel(u'время, c')\n",
    "\n",
    "ax4.plot(time[del1:del2,0], det[del1:del2,3])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax4.grid(b=True, which='major', axis='both')\n",
    "ax4.set_ylabel(u'E(ω\\'_x), рад/с')\n",
    "ax4.set_xlabel(u'время, c')\n",
    "\n",
    "ax5.plot(time[del1:del2,0], det[del1:del2,4])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax5.grid(b=True, which='major', axis='both')\n",
    "ax5.set_ylabel(u'E(ω\\'_y), рад/с')\n",
    "ax5.set_xlabel(u'время, c')\n",
    "\n",
    "ax6.plot(time[del1:del2,0], det[del1:del2,5])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax6.grid(b=True, which='major', axis='both')\n",
    "ax6.set_ylabel(u'E(ω\\'_z), рад/с')\n",
    "ax6.set_xlabel(u'время, c')\n",
    "\n",
    "ax7.plot(time[del1:del2,0], det[del1:del2,6])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax7.grid(b=True, which='major', axis='both')\n",
    "ax7.set_ylabel(u'E(f\\'_x*ω_z+f\\'_y*ω_y+f\\'_z*ω_z),\\n м*рад/с³')\n",
    "ax7.set_xlabel(u'время, c')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1296 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,18))\n",
    "time = CNF.loc[:239000,['Time']].values\n",
    "del1 = 200\n",
    "#plt.figure();\n",
    "#fig.suptitle(u\"Оценки математического ожидания на интервале выставки\")\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.3)\n",
    "ax1 = fig.add_subplot(721)\n",
    "ax4 = fig.add_subplot(722)\n",
    "ax2 = fig.add_subplot(723)\n",
    "ax5 = fig.add_subplot(724)\n",
    "ax3 = fig.add_subplot(725)\n",
    "ax6 = fig.add_subplot(726)\n",
    "ax7 = fig.add_subplot(714)\n",
    "ax1.plot(time[del1:del2,0], det[del1:del2,7])\n",
    "#ax1.axis([200, 1000, 97, 98.2])\n",
    "ax1.grid(b=True, which='major', axis='both')\n",
    "ax1.set_ylabel(u'σ(f\\'_x), м/с²')\n",
    "ax1.set_xlabel(u'время, c')\n",
    "\n",
    "ax2.plot(time[del1:del2,0], det[del1:del2,8])\n",
    "#ax2.axis([1000, 1025, 98.005, 98.02])\n",
    "ax2.grid(b=True, which='major', axis='both')\n",
    "ax2.set_ylabel(u'σ(f\\'_y), м/с²')\n",
    "ax2.set_xlabel(u'время, c')\n",
    "\n",
    "ax3.plot(time[del1:del2,0], det[del1:del2,9])\n",
    "#ax3.axis([200, 1000,  -90.002,-89.997])\n",
    "ax3.grid(b=True, which='major', axis='both')\n",
    "ax3.set_ylabel(u'σ(f\\'_z), м/с²')\n",
    "ax3.set_xlabel(u'время, c')\n",
    "\n",
    "ax4.plot(time[del1:del2,0], det[del1:del2,10])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax4.grid(b=True, which='major', axis='both')\n",
    "ax4.set_ylabel(u'σ(ω\\'_x), рад/с')\n",
    "ax4.set_xlabel(u'время, c')\n",
    "\n",
    "ax5.plot(time[del1:del2,0], det[del1:del2,11])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax5.grid(b=True, which='major', axis='both')\n",
    "ax5.set_ylabel(u'σ(ω\\'_y), рад/с')\n",
    "ax5.set_xlabel(u'время, c')\n",
    "\n",
    "ax6.plot(time[del1:del2,0], det[del1:del2,12])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax6.grid(b=True, which='major', axis='both')\n",
    "ax6.set_ylabel(u'σ(ω\\'_z), рад/с')\n",
    "ax6.set_xlabel(u'время, c')\n",
    "\n",
    "ax7.plot(time[del1:del2,0], det[del1:del2,13])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax7.grid(b=True, which='major', axis='both')\n",
    "ax7.set_ylabel(u'σ(f\\'_x*ω_z+f\\'_y*ω_y+f\\'_z*ω_z),\\n м*рад/с³')\n",
    "ax7.set_xlabel(u'время, c')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}