wavedirCSV.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import all necessary python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Howard Wang 05/14/19\n",
    "# CSE 145: Embedded systems and design\n",
    "\n",
    "# Main Database: https://surf.smartfin.org/\n",
    "\n",
    "# Analyzing data from Buoy Calibration experiment to get wave direction.\n",
    "# First, parse the data from the .CSV file containing ocean and wave motion data.\n",
    "# This data comes from a controlled experiment (CE3), so we are assuming that \n",
    "# all of the vertical accelerations are contained in IMUA2.\n",
    "\n",
    "# MATPLOTLIB\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "#from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "# DATAFRAMES\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# SCIPY\n",
    "from scipy import stats\n",
    "from scipy import constants\n",
    "from scipy import signal #added\n",
    "from scipy.interpolate import CubicSpline\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.integrate import simps\n",
    "from scipy.integrate import cumtrapz\n",
    "import pylab as pylab\n",
    "\n",
    "# SYSTEM and CONVERSION TOOLS\n",
    "import math\n",
    "import abc\n",
    "import sys\n",
    "import csv\n",
    "import io\n",
    "import os\n",
    "import datetime\n",
    "import pytz\n",
    "import re\n",
    "import statistics\n",
    "\n",
    "# MODELING AND GRAPHS\n",
    "import peakutils\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# URL REQUESTS\n",
    "import requests\n",
    "\n",
    "# VECTORS AND GRAPHICS\n",
    "import mpld3\n",
    "import folium\n",
    "# import cmocean\n",
    "import skinematics as skin\n",
    "from skinematics import quat, vector, misc, rotmat, imus, view\n",
    "import pygame\n",
    "\n",
    "# PLOTTING TOOLS\n",
    "from plotly import tools \n",
    "import plotly.offline\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read from CSV\n",
    "#Full csv:\n",
    "#scripps_final_combined = pd.read_csv('Scripps_Final_Combined.csv')\n",
    "#print(scripps_final_combined.head())\n",
    "\n",
    "#Gets following columns: UTC, Time, IMU A1, IMU A2, IMU A3, IMU G1, IMU G2, IMU G3, IMU M1, IMU M2, Date\n",
    "motion_df = pd.read_csv('Scripps_Final_Combined.csv', usecols = [i for i in range(2, 1)])\n",
    "#motion_df = motion_df.drop(['Time'], axis=1)\n",
    "print(motion_df.head())\n",
    "print(len(motion_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0                                                 ID  \\\n",
      "0           0  google_106807627751629413403_0006667E228C_1709...   \n",
      "1           1  google_106807627751629413403_0006667E228C_1709...   \n",
      "2           2  google_106807627751629413403_0006667E228C_1709...   \n",
      "3           3  google_106807627751629413403_0006667E228C_1709...   \n",
      "4           4  google_106807627751629413403_0006667E228C_1709...   \n",
      "\n",
      "                              UTC   Time    IMU A1     IMU A2    IMU A3  \\\n",
      "0  2017-09-09T18:02:11.2810+00:00  18:02  0.000000  11.070788  0.134075   \n",
      "1  2017-09-09T18:02:11.4800+00:00  18:02 -0.325611  11.185710 -0.459687   \n",
      "2  2017-09-09T18:02:11.7310+00:00  18:02  0.000000  10.419566  1.800440   \n",
      "3  2017-09-09T18:02:11.9810+00:00  18:02  0.823605  10.860099 -0.114922   \n",
      "4  2017-09-09T18:02:12.1800+00:00  18:02  0.670376  11.492168 -0.651223   \n",
      "\n",
      "      IMU G1     IMU G2     IMU G3  IMU M1  IMU M2  IMU M3        Date  \\\n",
      "0  46.829268  15.853659  15.487805   -37.0  -199.0   517.0  2017-09-09   \n",
      "1   8.902439  -2.439024  12.073171   -33.0  -179.0   533.0  2017-09-09   \n",
      "2 -16.341463   1.463415  14.268293   -46.0  -188.0   528.0  2017-09-09   \n",
      "3  -5.731707  17.439024  21.707317   -64.0  -186.0   522.0  2017-09-09   \n",
      "4 -28.658537   1.219512  21.341463   -91.0  -175.0   493.0  2017-09-09   \n",
      "\n",
      "      Hs(m)  Tp(s)  Dp(deg)  \n",
      "0  0.841248   5.88    284.0  \n",
      "1  0.841248   5.88    284.0  \n",
      "2  0.841248   5.88    284.0  \n",
      "3  0.841248   5.88    284.0  \n",
      "4  0.841248   5.88    284.0  \n",
      "1538168\n"
     ]
    }
   ],
   "source": [
    "## Read from CSV\n",
    "#Full csv:\n",
    "#scripps_final_combined = pd.read_csv('Scripps_Final_Combined.csv')\n",
    "#print(scripps_final_combined.head())\n",
    "\n",
    "#Gets following columns: UTC, Time, IMU A1, IMU A2, IMU A3, IMU G1, IMU G2, IMU G3, IMU M1, IMU M2, Date\n",
    "motion_df = pd.read_csv('Scripps_Final_Combined.csv')\n",
    "#motion_df = motion_df.drop(['Time'], axis=1)\n",
    "print(motion_df.head())\n",
    "print(len(motion_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target a specific ride (14888)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrape motion and ocean csv data from smartfin database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print out both dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make copy of motion data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Unnamed: 0                                                 ID  \\\n",
      "0                 0  google_106807627751629413403_0006667E228C_1709...   \n",
      "1                 1  google_106807627751629413403_0006667E228C_1709...   \n",
      "2                 2  google_106807627751629413403_0006667E228C_1709...   \n",
      "3                 3  google_106807627751629413403_0006667E228C_1709...   \n",
      "4                 4  google_106807627751629413403_0006667E228C_1709...   \n",
      "...             ...                                                ...   \n",
      "1538163     2131900  google_113399247482189200727_000666D32248_1901...   \n",
      "1538164     2131901  google_113399247482189200727_000666D32248_1901...   \n",
      "1538165     2131902  google_113399247482189200727_000666D32248_1901...   \n",
      "1538166     2131903  google_113399247482189200727_000666D32248_1901...   \n",
      "1538167     2131904  google_113399247482189200727_000666D32248_1901...   \n",
      "\n",
      "                                    UTC   Time    IMU A1     IMU A2    IMU A3  \\\n",
      "0        2017-09-09T18:02:11.2810+00:00  18:02  0.000000  11.070788  0.134075   \n",
      "1        2017-09-09T18:02:11.4800+00:00  18:02 -0.325611  11.185710 -0.459687   \n",
      "2        2017-09-09T18:02:11.7310+00:00  18:02  0.000000  10.419566  1.800440   \n",
      "3        2017-09-09T18:02:11.9810+00:00  18:02  0.823605  10.860099 -0.114922   \n",
      "4        2017-09-09T18:02:12.1800+00:00  18:02  0.670376  11.492168 -0.651223   \n",
      "...                                 ...    ...       ...        ...       ...   \n",
      "1538163  2019-01-04T20:16:02.7080+00:00  20:16  8.216900   5.152322  0.536301   \n",
      "1538164  2019-01-04T20:16:02.9080+00:00  20:16  8.178593   5.133168  0.517148   \n",
      "1538165  2019-01-04T20:16:03.1010+00:00  20:16  8.159439   5.171476  0.517148   \n",
      "1538166  2019-01-04T20:16:03.3020+00:00  20:16  8.159439   5.152322  0.536301   \n",
      "1538167  2019-01-04T20:16:03.5040+00:00  20:16  8.140286   5.171476  0.517148   \n",
      "\n",
      "            IMU G1     IMU G2     IMU G3  IMU M1  IMU M2  IMU M3        Date  \\\n",
      "0        46.829268  15.853659  15.487805   -37.0  -199.0   517.0  2017-09-09   \n",
      "1         8.902439  -2.439024  12.073171   -33.0  -179.0   533.0  2017-09-09   \n",
      "2       -16.341463   1.463415  14.268293   -46.0  -188.0   528.0  2017-09-09   \n",
      "3        -5.731707  17.439024  21.707317   -64.0  -186.0   522.0  2017-09-09   \n",
      "4       -28.658537   1.219512  21.341463   -91.0  -175.0   493.0  2017-09-09   \n",
      "...            ...        ...        ...     ...     ...     ...         ...   \n",
      "1538163   0.731707   0.609756  -0.487805   -46.0  -128.0   -66.0  2019-01-04   \n",
      "1538164   0.609756   0.853659  -0.365854   -42.0  -122.0   -78.0  2019-01-04   \n",
      "1538165   0.731707   0.487805  -0.487805   -44.0  -130.0   -72.0  2019-01-04   \n",
      "1538166   0.365854   0.731707  -0.487805   -44.0  -122.0   -70.0  2019-01-04   \n",
      "1538167   0.731707   0.731707  -0.609756   -52.0  -124.0   -70.0  2019-01-04   \n",
      "\n",
      "            Hs(m)  Tp(s)  Dp(deg)  \n",
      "0        0.841248   5.88    284.0  \n",
      "1        0.841248   5.88    284.0  \n",
      "2        0.841248   5.88    284.0  \n",
      "3        0.841248   5.88    284.0  \n",
      "4        0.841248   5.88    284.0  \n",
      "...           ...    ...      ...  \n",
      "1538163  0.420624  12.50    292.0  \n",
      "1538164  0.420624  12.50    292.0  \n",
      "1538165  0.420624  12.50    292.0  \n",
      "1538166  0.420624  12.50    292.0  \n",
      "1538167  0.420624  12.50    292.0  \n",
      "\n",
      "[1538168 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "#print(motion_df)\n",
    "\n",
    "saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data\n",
    "\n",
    "print(saved_copy_motion_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop NAs from dataset (excluding Lat and Long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the \"nan\" values from the columns that we care about. \n",
    "#dropped_motion_df = motion_df\n",
    "#print(dropped_motion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25094102885821834\n"
     ]
    }
   ],
   "source": [
    "#Reading data from Scripps_Final_Combined.csv\n",
    "#The name of the motion dataframe is: motion_df\n",
    "\n",
    "#Get times from the \"Time\" column to create time_o_list and time_e_list.\n",
    "#Get imus from the \"IMU A[*]\" column to create the imu acc arrays. \n",
    "\n",
    "#Analyze following time period:\n",
    "#September 9, 2017 -- 18:10 until 18:40\n",
    "\n",
    "#Analyze particular UTC with actual heights (m) -- 30 minute sessions\n",
    "# 2017-09-09T18:24:00.1810+00:00 to 2017-09-09T18:54:00.1810+00:00 -- 0.810768\n",
    "# 2017-09-09T18:54:00.1810+00:00 to 2018-06-14T15:13:06.7140+00:00 -- 0.819912\n",
    "# 2018-06-14T15:13:06.7140+00:00 to 2018-06-14T15:43:00.0050+00:00 -- 0.679704\n",
    "# 2018-06-14T15:43:00.0050+00:00 to 2018-06-14T16:13:00.2150+00:00 -- 0.70104\n",
    "# 2019-05-29T15:21:41.0170+00:00 to 2019-05-29T15:51:00.0140+00:00 -- 1.069848\n",
    "# 2019-05-29T15:51:00.0140+00:00 to 2019-05-29T16:21:00.0620+00:00 -- 1.179576\n",
    "\n",
    "START = '2018-06-14T15:13:06.7140+00:00'\n",
    "END = '2018-06-14T15:43:00.0050+00:00'\n",
    "\n",
    "time_e_array = []\n",
    "\n",
    "time_array = motion_df[['Time']].to_numpy()\n",
    "utc_time = motion_df[['UTC']].to_numpy()\n",
    "\n",
    "start_index = np.where(utc_time==START)[0][0]\n",
    "end_index = np.where(utc_time==END)[0][0]\n",
    "length = end_index-start_index\n",
    "\n",
    "mins_per_measure = 30/length\n",
    "sec_per_measure = 60*mins_per_measure\n",
    "\n",
    "print(sec_per_measure)\n",
    "\n",
    "#start_time = 18*3600 + 10*60\n",
    "\n",
    "for i in range(0, length):\n",
    "    time_e_array.append(sec_per_measure*i)\n",
    "deg = motion_df.loc[start_index:end_index-1,'Dp(deg)'].to_numpy()\n",
    "#imu_array1 = motion_df.loc[start_index:end_index-1,'IMU A1'].to_numpy()\n",
    "#imu_array2 = motion_df.loc[start_index:end_index-1,'IMU A2'].to_numpy()\n",
    "#imu_array3 = motion_df.loc[start_index:end_index-1,'IMU A3'].to_numpy()\n",
    "imu_array1 = motion_df.loc[start_index:end_index-1,'IMU M1'].to_numpy()\n",
    "imu_array2 = motion_df.loc[start_index:end_index-1,'IMU M2'].to_numpy()\n",
    "imu_array3 = motion_df.loc[start_index:end_index-1,'IMU M3'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to parse time string and calculate half hour\n",
    "def add_half_hour(time_str):\n",
    "    # Parse string\n",
    "    hrs = int(time_str[:2])\n",
    "    mins = int(time_str[3:5])\n",
    "    \n",
    "    # Set minutes and hours\n",
    "    if (mins < 30):\n",
    "        mins += 30\n",
    "    else:\n",
    "        mins -= 30\n",
    "        if (hrs == 23):\n",
    "            hrs = 0\n",
    "        else:\n",
    "            hrs += 1\n",
    "            \n",
    "    # Check if mins and hrs are single digits\n",
    "    if (mins < 10):\n",
    "        mins = '0'+str(mins)\n",
    "    if (hrs < 10):\n",
    "        hrs = '0'+str(hrs)\n",
    "        \n",
    "    return str(hrs)+':'+str(mins)\n",
    "\n",
    "# Parse int and compare functions in case we need them in the future\n",
    "\n",
    "def parse_int(time_str):\n",
    "    return 60*int(time_str[:2])+int(time_str[3:5])\n",
    "\n",
    "def is_less_than_eq(str1, str2):\n",
    "    if (str1[:2]=='23' and str2[:2]=='00'):\n",
    "        return True\n",
    "    \n",
    "    str1 = parse_int(str1)\n",
    "    str2 = parse_int(str2)\n",
    "    \n",
    "    return (str1 <= str2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session:  1 , Date:  2017-09-09\n",
      "At  18:10  and  18:40\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  2 , Date:  2017-09-09\n",
      "At  18:40  and  19:10\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  3 , Date:  2018-06-14\n",
      "At  15:20  and  15:50\n",
      "Start:  [286.]  and End:  [286.] \n",
      "\n",
      "Session:  4 , Date:  2018-06-14\n",
      "At  15:50  and  16:20\n",
      "Start:  [283.]  and End:  [283.] \n",
      "\n",
      "Session:  5 , Date:  2019-05-29\n",
      "At  15:50  and  16:20\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  6 , Date:  2019-05-29\n",
      "At  16:20  and  16:50\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  7 , Date:  2018-08-12\n",
      "At  14:46  and  15:16\n",
      "Start:  [257.]  and End:  [257.] \n",
      "\n",
      "Session:  8 , Date:  2018-08-12\n",
      "At  15:20  and  15:50\n",
      "Start:  [255.]  and End:  [255.] \n",
      "\n",
      "Session:  9 , Date:  2017-08-08\n",
      "At  16:40  and  17:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  10 , Date:  2017-08-08\n",
      "At  17:10  and  17:40\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  11 , Date:  2017-08-08\n",
      "At  17:40  and  18:10\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  12 , Date:  2017-06-23\n",
      "At  18:29  and  18:59\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  13 , Date:  2017-06-23\n",
      "At  19:10  and  19:40\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  14 , Date:  2017-06-23\n",
      "At  19:40  and  20:10\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  15 , Date:  2018-05-14\n",
      "At  18:20  and  18:50\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n",
      "Session:  16 , Date:  2018-05-14\n",
      "At  18:50  and  19:20\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  17 , Date:  2017-09-19\n",
      "At  19:49  and  20:19\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  18 , Date:  2017-07-31\n",
      "At  19:10  and  19:40\n",
      "Start:  [295.]  and End:  [295.] \n",
      "\n",
      "Session:  19 , Date:  2018-08-28\n",
      "At  16:59  and  17:29\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  20 , Date:  2017-08-28\n",
      "At  21:10  and  21:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  21 , Date:  2018-09-02\n",
      "At  16:45  and  17:15\n",
      "Start:  [283.]  and End:  [283.] \n",
      "\n",
      "Session:  22 , Date:  2018-09-02\n",
      "At  17:20  and  17:50\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  23 , Date:  2018-09-02\n",
      "At  17:50  and  18:20\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  24 , Date:  2018-10-31\n",
      "At  15:50  and  16:20\n",
      "Start:  [288.]  and End:  [288.] \n",
      "\n",
      "Session:  25 , Date:  2017-09-21\n",
      "At  00:10  and  00:40\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  26 , Date:  2017-09-21\n",
      "At  00:40  and  01:10\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  27 , Date:  2017-05-31\n",
      "At  19:10  and  19:40\n",
      "Start:  [275.]  and End:  [275.] \n",
      "\n",
      "Session:  28 , Date:  2017-05-31\n",
      "At  19:40  and  20:10\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  29 , Date:  2018-08-13\n",
      "At  16:50  and  17:20\n",
      "Start:  [251.]  and End:  [251.] \n",
      "\n",
      "Session:  30 , Date:  2018-09-14\n",
      "At  14:50  and  15:20\n",
      "Start:  [281.]  and End:  [281.] \n",
      "\n",
      "Session:  31 , Date:  2018-09-14\n",
      "At  15:20  and  15:50\n",
      "Start:  [275.]  and End:  [275.] \n",
      "\n",
      "Session:  32 , Date:  2018-04-03\n",
      "At  16:20  and  16:50\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  33 , Date:  2018-04-03\n",
      "At  16:50  and  17:20\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n",
      "Session:  34 , Date:  2017-06-23\n",
      "At  18:29  and  18:59\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  35 , Date:  2017-06-23\n",
      "At  19:10  and  19:40\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  36 , Date:  2017-06-23\n",
      "At  19:40  and  20:10\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  37 , Date:  2019-01-04\n",
      "At  17:20  and  17:50\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n",
      "Session:  38 , Date:  2019-01-04\n",
      "At  17:50  and  18:20\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  39 , Date:  2017-05-20\n",
      "At  00:27  and  00:57\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  40 , Date:  2017-05-20\n",
      "At  01:10  and  01:40\n",
      "Start:  [288.]  and End:  [288.] \n",
      "\n",
      "Session:  41 , Date:  2017-12-20\n",
      "At  17:10  and  17:40\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n",
      "Session:  42 , Date:  2017-12-20\n",
      "At  17:40  and  18:10\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  43 , Date:  2017-10-20\n",
      "At  23:10  and  23:40\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  44 , Date:  2017-10-20\n",
      "At  23:40  and  00:10\n",
      "Start:  [296.]  and End:  [296.] \n",
      "\n",
      "Session:  45 , Date:  2018-10-22\n",
      "At  16:20  and  16:50\n",
      "Start:  [279.]  and End:  [279.] \n",
      "\n",
      "Session:  46 , Date:  2018-10-22\n",
      "At  16:50  and  17:20\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  47 , Date:  2017-09-09\n",
      "At  18:10  and  18:40\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  48 , Date:  2017-09-09\n",
      "At  18:40  and  19:10\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  49 , Date:  2019-06-15\n",
      "At  20:20  and  20:50\n",
      "Start:  [277.]  and End:  [277.] \n",
      "\n",
      "Session:  50 , Date:  2017-07-21\n",
      "At  01:10  and  01:40\n",
      "Start:  [266.]  and End:  [266.] \n",
      "\n",
      "Session:  51 , Date:  2017-07-21\n",
      "At  01:40  and  02:10\n",
      "Start:  [268.]  and End:  [268.] \n",
      "\n",
      "Session:  52 , Date:  2017-08-23\n",
      "At  16:10  and  16:40\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  53 , Date:  2017-08-23\n",
      "At  16:40  and  17:10\n",
      "Start:  [295.]  and End:  [295.] \n",
      "\n",
      "Session:  54 , Date:  2017-10-10\n",
      "At  16:38  and  17:08\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  55 , Date:  2017-10-10\n",
      "At  17:10  and  17:40\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  56 , Date:  2017-06-22\n",
      "At  19:40  and  20:10\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  57 , Date:  2017-06-18\n",
      "At  23:10  and  23:40\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  58 , Date:  2017-06-18\n",
      "At  23:40  and  00:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  59 , Date:  2018-07-11\n",
      "At  16:50  and  17:20\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  60 , Date:  2017-06-28\n",
      "At  21:51  and  22:21\n",
      "Start:  [281.]  and End:  [281.] \n",
      "\n",
      "Session:  61 , Date:  2017-09-21\n",
      "At  21:40  and  22:10\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  62 , Date:  2017-06-28\n",
      "At  21:51  and  22:21\n",
      "Start:  [281.]  and End:  [281.] \n",
      "\n",
      "Session:  63 , Date:  2017-06-30\n",
      "At  01:40  and  02:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  64 , Date:  2017-06-30\n",
      "At  02:10  and  02:40\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  65 , Date:  2017-12-04\n",
      "At  19:10  and  19:40\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  66 , Date:  2017-06-24\n",
      "At  21:40  and  22:10\n",
      "Start:  [275.]  and End:  [275.] \n",
      "\n",
      "Session:  67 , Date:  2017-06-24\n",
      "At  22:10  and  22:40\n",
      "Start:  [267.]  and End:  [267.] \n",
      "\n",
      "Session:  68 , Date:  2018-11-09\n",
      "At  19:50  and  20:20\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  69 , Date:  2017-06-16\n",
      "At  21:10  and  21:40\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  70 , Date:  2017-06-16\n",
      "At  21:40  and  22:10\n",
      "Start:  [288.]  and End:  [288.] \n",
      "\n",
      "Session:  71 , Date:  2017-08-29\n",
      "At  18:37  and  19:07\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  72 , Date:  2017-08-29\n",
      "At  19:10  and  19:40\n",
      "Start:  [277.]  and End:  [277.] \n",
      "\n",
      "Session:  73 , Date:  2017-10-04\n",
      "At  17:40  and  18:10\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  74 , Date:  2017-10-11\n",
      "At  23:10  and  23:40\n",
      "Start:  [263.]  and End:  [263.] \n",
      "\n",
      "Session:  75 , Date:  2019-06-15\n",
      "At  20:20  and  20:50\n",
      "Start:  [277.]  and End:  [277.] \n",
      "\n",
      "Session:  76 , Date:  2018-04-10\n",
      "At  00:00  and  00:30\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  77 , Date:  2017-06-28\n",
      "At  00:40  and  01:10\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  78 , Date:  2017-06-28\n",
      "At  01:10  and  01:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  79 , Date:  2017-06-28\n",
      "At  00:40  and  01:10\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  80 , Date:  2017-06-28\n",
      "At  01:10  and  01:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  81 , Date:  2018-09-10\n",
      "At  17:50  and  18:20\n",
      "Start:  [281.]  and End:  [281.] \n",
      "\n",
      "Session:  82 , Date:  2018-09-10\n",
      "At  18:20  and  18:50\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  83 , Date:  2017-07-02\n",
      "At  02:10  and  02:40\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  84 , Date:  2017-07-02\n",
      "At  02:40  and  03:10\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  85 , Date:  2017-12-04\n",
      "At  19:10  and  19:40\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  86 , Date:  2017-12-04\n",
      "At  19:40  and  20:10\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  87 , Date:  2018-11-04\n",
      "At  21:20  and  21:50\n",
      "Start:  [287.]  and End:  [287.] \n",
      "\n",
      "Session:  88 , Date:  2018-11-04\n",
      "At  21:50  and  22:20\n",
      "Start:  [290.]  and End:  [290.] \n",
      "\n",
      "Session:  89 , Date:  2019-02-08\n",
      "At  17:50  and  18:20\n",
      "Start:  [288.]  and End:  [288.] \n",
      "\n",
      "Session:  90 , Date:  2019-02-08\n",
      "At  18:20  and  18:50\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  91 , Date:  2018-06-05\n",
      "At  15:18  and  15:48\n",
      "Start:  [293.]  and End:  [293.] \n",
      "\n",
      "Session:  92 , Date:  2018-06-05\n",
      "At  15:50  and  16:20\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session:  93 , Date:  2018-06-05\n",
      "At  16:20  and  16:50\n",
      "Start:  [290.]  and End:  [290.] \n",
      "\n",
      "Session:  94 , Date:  2017-09-07\n",
      "At  18:40  and  19:10\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  95 , Date:  2017-09-07\n",
      "At  19:10  and  19:40\n",
      "Start:  [281.]  and End:  [281.] \n",
      "\n",
      "Session:  96 , Date:  2017-09-07\n",
      "At  19:40  and  20:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  97 , Date:  2017-06-30\n",
      "At  01:40  and  02:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  98 , Date:  2017-06-30\n",
      "At  02:10  and  02:40\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  99 , Date:  2017-09-08\n",
      "At  23:20  and  23:50\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  100 , Date:  2017-09-09\n",
      "At  00:10  and  00:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  101 , Date:  2017-08-02\n",
      "At  17:10  and  17:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  102 , Date:  2017-08-02\n",
      "At  17:40  and  18:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  103 , Date:  2017-06-24\n",
      "At  21:40  and  22:10\n",
      "Start:  [275.]  and End:  [275.] \n",
      "\n",
      "Session:  104 , Date:  2017-06-24\n",
      "At  22:10  and  22:40\n",
      "Start:  [267.]  and End:  [267.] \n",
      "\n",
      "Session:  105 , Date:  2017-06-23\n",
      "At  17:40  and  18:10\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  106 , Date:  2017-08-10\n",
      "At  16:40  and  17:10\n",
      "Start:  [292.]  and End:  [292.] \n",
      "\n",
      "Session:  107 , Date:  2017-08-10\n",
      "At  17:10  and  17:40\n",
      "Start:  [285.]  and End:  [285.] \n",
      "\n",
      "Session:  108 , Date:  2017-08-10\n",
      "At  17:40  and  18:10\n",
      "Start:  [291.]  and End:  [291.] \n",
      "\n",
      "Session:  109 , Date:  2017-06-26\n",
      "At  22:39  and  23:09\n",
      "Start:  [277.]  and End:  [277.] \n",
      "\n",
      "Session:  110 , Date:  2017-06-26\n",
      "At  23:10  and  23:40\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  111 , Date:  2017-06-26\n",
      "At  23:40  and  00:10\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  112 , Date:  2017-07-02\n",
      "At  02:10  and  02:40\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Session:  113 , Date:  2017-07-02\n",
      "At  02:40  and  03:10\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  114 , Date:  2017-06-26\n",
      "At  23:10  and  23:40\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  115 , Date:  2017-06-26\n",
      "At  23:40  and  00:10\n",
      "Start:  [282.]  and End:  [282.] \n",
      "\n",
      "Session:  116 , Date:  2018-04-15\n",
      "At  18:20  and  18:50\n",
      "Start:  [297.]  and End:  [297.] \n",
      "\n",
      "Session:  117 , Date:  2018-04-15\n",
      "At  18:50  and  19:20\n",
      "Start:  [295.]  and End:  [295.] \n",
      "\n",
      "Session:  118 , Date:  2018-09-12\n",
      "At  01:20  and  01:50\n",
      "Start:  [279.]  and End:  [279.] \n",
      "\n",
      "Session:  119 , Date:  2018-09-12\n",
      "At  01:50  and  02:20\n",
      "Start:  [286.]  and End:  [286.] \n",
      "\n",
      "Session:  120 , Date:  2018-08-12\n",
      "At  14:45  and  15:15\n",
      "Start:  [257.]  and End:  [257.] \n",
      "\n",
      "Session:  121 , Date:  2018-08-12\n",
      "At  15:20  and  15:50\n",
      "Start:  [255.]  and End:  [255.] \n",
      "\n",
      "Session:  122 , Date:  2017-10-20\n",
      "At  23:10  and  23:40\n",
      "Start:  [294.]  and End:  [294.] \n",
      "\n",
      "Session:  123 , Date:  2017-10-20\n",
      "At  23:40  and  00:10\n",
      "Start:  [296.]  and End:  [296.] \n",
      "\n",
      "Session:  124 , Date:  2017-06-28\n",
      "At  00:40  and  01:10\n",
      "Start:  [278.]  and End:  [278.] \n",
      "\n",
      "Session:  125 , Date:  2017-06-28\n",
      "At  01:10  and  01:40\n",
      "Start:  [280.]  and End:  [280.] \n",
      "\n",
      "Session:  126 , Date:  2017-10-26\n",
      "At  17:40  and  18:10\n",
      "Start:  [284.]  and End:  [284.] \n",
      "\n",
      "Session:  127 , Date:  2017-10-26\n",
      "At  18:10  and  18:40\n",
      "Start:  [288.]  and End:  [288.] \n",
      "\n",
      "Session:  128 , Date:  2019-01-04\n",
      "At  18:50  and  19:20\n",
      "Start:  [295.]  and End:  [295.] \n",
      "\n",
      "Session:  129 , Date:  2019-01-04\n",
      "At  19:20  and  19:50\n",
      "Start:  [289.]  and End:  [289.] \n",
      "\n",
      "Total sessions:  129\n",
      "Session 0 array length:  3\n",
      "Total dgs:  129\n",
      "CPU times: user 2min, sys: 544 ms, total: 2min 1s\n",
      "Wall time: 2min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "### Goal: create an array of arrays of IMU A2 data ###\n",
    "surf_sessions = []\n",
    "degree_labels = []\n",
    "utc_labels = []\n",
    "date_labels = []\n",
    "\n",
    "## Step 1: get 5 columns of the data frame to iterate over - ID, Time, IMU A2, Date, Height ##\n",
    "df_parser = motion_df[['ID', 'UTC', 'Time', 'IMU M1','IMU M2','IMU M3', 'Date', 'Dp(deg)']]\n",
    "\n",
    "# Convert to numpy array\n",
    "id_parser = df_parser[['ID']].to_numpy()\n",
    "time_parser = df_parser[['Time']].to_numpy()\n",
    "utc_parser = df_parser[['UTC']].to_numpy()\n",
    "imuM1_parser = df_parser[['IMU M1']].to_numpy()\n",
    "imuM2_parser = df_parser[['IMU M2']].to_numpy()\n",
    "imuM3_parser = df_parser[['IMU M3']].to_numpy()\n",
    "date_parser = df_parser[['Date']].to_numpy()\n",
    "degree_parser = df_parser[['Dp(deg)']].to_numpy()\n",
    "\n",
    "## Step 2: loop through every data point ##\n",
    "working_array = []\n",
    "working_m1 = []\n",
    "working_m2 = []\n",
    "working_m3 = []\n",
    "skip_check = '00:00'\n",
    "end_index_check = None\n",
    "\n",
    "for start_index in range(0, len(id_parser)):\n",
    "    ## Goal 1: Append IMU data if we are in the half-hour segment ##\n",
    "    if (end_index_check != None):\n",
    "        # Check if we've reached the end of the half hour before appending\n",
    "        if (start_index==end_index_check):\n",
    "            working_array.append(working_m1)\n",
    "            working_array.append(working_m2)\n",
    "            working_array.append(working_m3)\n",
    "            surf_sessions.append(working_array)\n",
    "            \n",
    "            working_array = []\n",
    "            working_m1 = []\n",
    "            working_m2 = []\n",
    "            working_m3 = []\n",
    "            \n",
    "            end_index_check = None\n",
    "        else:\n",
    "            working_m1.append(imuM1_parser[start_index][0])\n",
    "            working_m2.append(imuM2_parser[start_index][0])\n",
    "            working_m3.append(imuM3_parser[start_index][0])\n",
    "        continue\n",
    "        \n",
    "    ## Goal 2: Check if degree of current and half-hour times are equal ##\n",
    "    start = time_parser[start_index][0]\n",
    "    \n",
    "    # Skip through a minute if it has already been checked and invalidated\n",
    "    if (start==skip_check):\n",
    "        continue\n",
    "        \n",
    "    # Set end point\n",
    "    end = add_half_hour(start)\n",
    "    \n",
    "    # Check if ride is still valid for half hour time\n",
    "    try:\n",
    "        # End index is where the time is equal to end and we are in the same ride as we started\n",
    "        end_index = np.where((time_parser==end) & (id_parser==id_parser[start_index]))[0][0]\n",
    "    except IndexError:\n",
    "        # Skip through minutes\n",
    "        skip_check = start\n",
    "        continue\n",
    "    else:\n",
    "        # (occurs after try block) skip if the degrees don't match\n",
    "        if (degree_parser[start_index]!=degree_parser[end_index-1]):\n",
    "            skip_check = start\n",
    "        else:\n",
    "            # add to array if degrees do match\n",
    "            working_m1.append(imuM1_parser[start_index][0])\n",
    "            working_m2.append(imuM2_parser[start_index][0])\n",
    "            working_m3.append(imuM3_parser[start_index][0])\n",
    "            \n",
    "            degree_labels.append(degree_parser[start_index][0])\n",
    "            utc_labels.append(utc_parser[start_index][0])\n",
    "            date_labels.append(date_parser[start_index][0])\n",
    "            \n",
    "            end_index_check = end_index\n",
    "            skip_check = '00:00'\n",
    "            \n",
    "            print(\"Session: \", len(surf_sessions)+1, \", Date: \", date_parser[start_index][0])\n",
    "            print(\"At \", start, \" and \", end)\n",
    "            print(\"Start: \", degree_parser[start_index], \" and End: \", degree_parser[end_index-1], \"\\n\")\n",
    "\n",
    "print(\"Total sessions: \", len(surf_sessions))\n",
    "print(\"Total degrees: \", len(degree_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Unnamed: 0                                                 ID  \\\n",
      "0                 0  google_106807627751629413403_0006667E228C_1709...   \n",
      "1                 1  google_106807627751629413403_0006667E228C_1709...   \n",
      "2                 2  google_106807627751629413403_0006667E228C_1709...   \n",
      "3                 3  google_106807627751629413403_0006667E228C_1709...   \n",
      "4                 4  google_106807627751629413403_0006667E228C_1709...   \n",
      "...             ...                                                ...   \n",
      "1538163     2131900  google_113399247482189200727_000666D32248_1901...   \n",
      "1538164     2131901  google_113399247482189200727_000666D32248_1901...   \n",
      "1538165     2131902  google_113399247482189200727_000666D32248_1901...   \n",
      "1538166     2131903  google_113399247482189200727_000666D32248_1901...   \n",
      "1538167     2131904  google_113399247482189200727_000666D32248_1901...   \n",
      "\n",
      "                                    UTC   Time    IMU A1     IMU A2    IMU A3  \\\n",
      "0        2017-09-09T18:02:11.2810+00:00  18:02  0.000000  11.070788  0.134075   \n",
      "1        2017-09-09T18:02:11.4800+00:00  18:02 -0.325611  11.185710 -0.459687   \n",
      "2        2017-09-09T18:02:11.7310+00:00  18:02  0.000000  10.419566  1.800440   \n",
      "3        2017-09-09T18:02:11.9810+00:00  18:02  0.823605  10.860099 -0.114922   \n",
      "4        2017-09-09T18:02:12.1800+00:00  18:02  0.670376  11.492168 -0.651223   \n",
      "...                                 ...    ...       ...        ...       ...   \n",
      "1538163  2019-01-04T20:16:02.7080+00:00  20:16  8.216900   5.152322  0.536301   \n",
      "1538164  2019-01-04T20:16:02.9080+00:00  20:16  8.178593   5.133168  0.517148   \n",
      "1538165  2019-01-04T20:16:03.1010+00:00  20:16  8.159439   5.171476  0.517148   \n",
      "1538166  2019-01-04T20:16:03.3020+00:00  20:16  8.159439   5.152322  0.536301   \n",
      "1538167  2019-01-04T20:16:03.5040+00:00  20:16  8.140286   5.171476  0.517148   \n",
      "\n",
      "            IMU G1     IMU G2     IMU G3  IMU M1  IMU M2  IMU M3        Date  \\\n",
      "0        46.829268  15.853659  15.487805   -37.0  -199.0   517.0  2017-09-09   \n",
      "1         8.902439  -2.439024  12.073171   -33.0  -179.0   533.0  2017-09-09   \n",
      "2       -16.341463   1.463415  14.268293   -46.0  -188.0   528.0  2017-09-09   \n",
      "3        -5.731707  17.439024  21.707317   -64.0  -186.0   522.0  2017-09-09   \n",
      "4       -28.658537   1.219512  21.341463   -91.0  -175.0   493.0  2017-09-09   \n",
      "...            ...        ...        ...     ...     ...     ...         ...   \n",
      "1538163   0.731707   0.609756  -0.487805   -46.0  -128.0   -66.0  2019-01-04   \n",
      "1538164   0.609756   0.853659  -0.365854   -42.0  -122.0   -78.0  2019-01-04   \n",
      "1538165   0.731707   0.487805  -0.487805   -44.0  -130.0   -72.0  2019-01-04   \n",
      "1538166   0.365854   0.731707  -0.487805   -44.0  -122.0   -70.0  2019-01-04   \n",
      "1538167   0.731707   0.731707  -0.609756   -52.0  -124.0   -70.0  2019-01-04   \n",
      "\n",
      "            Hs(m)  Tp(s)  Dp(deg)  \n",
      "0        0.841248   5.88    284.0  \n",
      "1        0.841248   5.88    284.0  \n",
      "2        0.841248   5.88    284.0  \n",
      "3        0.841248   5.88    284.0  \n",
      "4        0.841248   5.88    284.0  \n",
      "...           ...    ...      ...  \n",
      "1538163  0.420624  12.50    292.0  \n",
      "1538164  0.420624  12.50    292.0  \n",
      "1538165  0.420624  12.50    292.0  \n",
      "1538166  0.420624  12.50    292.0  \n",
      "1538167  0.420624  12.50    292.0  \n",
      "\n",
      "[1538168 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "#Drop the \"nan\" values from the columns that we care about. \n",
    "dropped_motion_df = motion_df.dropna(subset=['Time', 'IMU A1', 'IMU A2', 'IMU A3', 'IMU G1', 'IMU G2', 'IMU G3', 'IMU M1', \n",
    "                                            'IMU M2', 'IMU M3','Dp(deg)'])\n",
    "print(dropped_motion_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate time offsets, and normalize columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert raw units to actual units (acc to [m/s^2]) and (time to [s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1799.4981179422837\n",
      "1799.749058971142\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "#Raw acceleration constant 512 = 1g (accelerometer's measured force due to gravity)\n",
    "g_const = 512\n",
    "\n",
    "#Approximate measurement for gravity:\n",
    "gravity = 9.80665\n",
    "\n",
    "# Correct the IMU Acceleration columns into units of meters\n",
    "def convert_acc_units(acc_array):\n",
    "    ret_array = []\n",
    "    for a in acc_array:\n",
    "        #Acceleration is now in m/s^2, need to subtract gravity from vertical axis. (??)\n",
    "        new_a = a / g_const * gravity + gravity\n",
    "        ret_array.append(new_a)\n",
    "    return ret_array\n",
    "\n",
    "imu1_array = convert_acc_units(imu_array1) #new units in m/s^2\n",
    "imu2_array = convert_acc_units(imu_array2) #new units in m/s^2\n",
    "imu3_array = convert_acc_units(imu_array3) #new units in m/s^2\n",
    "\n",
    "\n",
    "# To check:\n",
    "#for x,y in zip(imu2_array, imu_array_A2):\n",
    "#   print(x,y)\n",
    "print(len(time_e_array))\n",
    "\n",
    "# To check:\n",
    "for t in time_e_array:\n",
    "    print(t)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate and plot magnitude of acceleration on X-Z plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph of XZ Acceleration vs. Time\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Square x and z acceleration values\n",
    "accel_xz_array = [];\n",
    "for i in range(0, len(imu1_array)): \n",
    "    currMagnitude = math.sqrt((math.pow(imu1_array[i], 2) + math.pow(imu3_array[i], 2)))\n",
    "    accel_xz_array.append(currMagnitude)\n",
    "\n",
    "print(\"Graph of XZ Acceleration vs. Time\")\n",
    "\n",
    "plt.plot(time_e_array, accel_xz_array)\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Acceleration-XZ (m/s^2)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Center and Calibrate Magnetometer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Offset variables help in recentering the magnetic data in order to define direction and use trig functions\n",
    "M1_offset_var = 219.786\n",
    "M2_offset_var = 180\n",
    "M3_offset_var = 280\n",
    "\n",
    "def calibrate_magn_data(magn_array, offset_value):\n",
    "    ret_array = []\n",
    "    for m in magn_array:\n",
    "        new_m = m - offset_value\n",
    "        ret_array.append(new_m)\n",
    "    return ret_array\n",
    "\n",
    "#imuM1_array = calibrate_magn_data(imu_array1, M1_offset_var)\n",
    "#imuM2_array = calibrate_magn_data(imu_array2, M2_offset_var)\n",
    "#imuM3_array = calibrate_magn_data(imu_array3, M3_offset_var)\n",
    "\n",
    "\n",
    "#imuM1_array = calibrate_magn_data(imuM1_parser, M1_offset_var)\n",
    "#imuM2_array = calibrate_magn_data(imuM2_parser, M1_offset_var)\n",
    "#imuM3_array = calibrate_magn_data(imuM3_parser, M1_offset_var)\n",
    "\n",
    "# Check \n",
    "# print(imuM1_array)\n",
    "print(\"Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop through entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For Magnetometer: \n",
      "Session:  1\n",
      "error:  6.174124718090793\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  2\n",
      "error:  4.938634107923763\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  3\n",
      "error:  10.891707950225356\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  4\n",
      "error:  0.17517783881166993\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  5\n",
      "error:  6.305142781962362\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  6\n",
      "error:  5.3081198078311465\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  7\n",
      "error:  1.4224369249570183\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  8\n",
      "error:  0.642289212050666\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  9\n",
      "error:  3.888651255962771\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  10\n",
      "error:  92.52147770773666\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  11\n",
      "error:  93.12551235842642\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  12\n",
      "error:  2.9979808298240154\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  13\n",
      "error:  2.5061743255112683\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  14\n",
      "error:  1.8905783207794977\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  15\n",
      "error:  8.170030859598453\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  16\n",
      "error:  6.843389418323666\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  17\n",
      "error:  3.3952835835779966\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  18\n",
      "error:  5.903991870976775\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  19\n",
      "error:  1.777741577475933\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  20\n",
      "error:  2.8742227042727375\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  21\n",
      "error:  0.8655751957941081\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  22\n",
      "error:  0.6512455010780002\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  23\n",
      "error:  0.3343892809002343\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  24\n",
      "error:  3.63086151271165\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  25\n",
      "error:  6.151024774376567\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  26\n",
      "error:  3.537229594671615\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  27\n",
      "error:  3.710392796160203\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  28\n",
      "error:  3.054047314765912\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  29\n",
      "error:  3.5663055587462673\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  30\n",
      "error:  6.366376717772808\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  31\n",
      "error:  3.0903315196710857\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  32\n",
      "error:  5.251564281559864\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  33\n",
      "error:  5.465094396817329\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  34\n",
      "error:  2.9979808298240154\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  35\n",
      "error:  2.5061743255112683\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  36\n",
      "error:  1.8905783207794977\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  37\n",
      "error:  3.0400623468558234\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  38\n",
      "error:  2.1151557364189033\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  39\n",
      "error:  4.782659095482544\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  40\n",
      "error:  2.136683207563536\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  41\n",
      "error:  6.484005636121827\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  42\n",
      "error:  10.423140067223912\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  43\n",
      "error:  6.123003298397518\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  44\n",
      "error:  5.662020606597334\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  45\n",
      "error:  6.038088700893444\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  46\n",
      "error:  5.209530167033893\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  47\n",
      "error:  6.174124718090793\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  48\n",
      "error:  4.938634107923763\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  49\n",
      "error:  3.0064410032604343\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  50\n",
      "error:  8.53783862881703\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  51\n",
      "error:  2.3219627944266477\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  52\n",
      "error:  9.58579274281891\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  53\n",
      "error:  4.586409322734308\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  54\n",
      "error:  0.4097054556007336\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  55\n",
      "error:  0.9643437268930365\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  56\n",
      "error:  3.9596629962324683\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  57\n",
      "error:  2.0270811447093657\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  58\n",
      "error:  2.5626458172207878\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  59\n",
      "error:  3.787875552563588\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  60\n",
      "error:  19.18766177753272\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  61\n",
      "error:  9.571890675962036\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  62\n",
      "error:  0.8498518495951439\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  63\n",
      "error:  3.6550857922639324\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  64\n",
      "error:  4.79044602150268\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  65\n",
      "error:  2.1937293837540994\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  66\n",
      "error:  2.9335747090307995\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  67\n",
      "error:  22.93633326540852\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  68\n",
      "error:  1.90877543268332\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  69\n",
      "error:  6.030328271386539\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  70\n",
      "error:  2.642071158694817\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  71\n",
      "error:  2.875524532184469\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  72\n",
      "error:  2.13110586744712\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  73\n",
      "error:  7.623423597276971\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  74\n",
      "error:  2.2471824588521256\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  75\n",
      "error:  4.572501726412034\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  76\n",
      "error:  7.270936829818943\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  77\n",
      "error:  6.252738302986442\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  78\n",
      "error:  4.984510297559787\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  79\n",
      "error:  6.072323753616382\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  80\n",
      "error:  5.945845800584792\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  81\n",
      "error:  5.819820561005364\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  82\n",
      "error:  8.332764984762242\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  83\n",
      "error:  7.196464265648146\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  84\n",
      "error:  6.916751209314709\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  85\n",
      "error:  2.9781637641436522\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  86\n",
      "error:  2.9103747734216485\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  87\n",
      "error:  9.321543829630262\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  88\n",
      "error:  5.813547316126129\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  89\n",
      "error:  7.173579945762261\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  90\n",
      "error:  54.27858888806865\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  91\n",
      "error:  7.719353945553373\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  92\n",
      "error:  3.8252085215349627\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  93\n",
      "error:  2.9257566859826207\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  94\n",
      "error:  2.603215500815655\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  95\n",
      "error:  0.7107418387894032\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  96\n",
      "error:  2.2086941021319113\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  97\n",
      "error:  3.6550857922639324\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  98\n",
      "error:  4.79044602150268\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  99\n",
      "error:  2.104408241161805\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  100\n",
      "error:  4.533505893854794\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  101\n",
      "error:  6.410621804684495\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  102\n",
      "error:  5.418868229158824\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  103\n",
      "error:  2.9335747090307995\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  104\n",
      "error:  22.93633326540852\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  105\n",
      "error:  8.593350903377736\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  106\n",
      "error:  3.393480431558763\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  107\n",
      "error:  83.64048305547179\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  108\n",
      "error:  89.908778386668\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  109\n",
      "error:  4.504025890378246\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  110\n",
      "error:  6.538926707847555\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  111\n",
      "error:  6.765411251932011\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  112\n",
      "error:  7.196464265648146\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  113\n",
      "error:  6.916751209314709\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  114\n",
      "error:  0.8027706857480619\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  115\n",
      "error:  15.281695772308437\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  116\n",
      "error:  10.258129968222457\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  117\n",
      "error:  2.500330341843589\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  118\n",
      "error:  0.43765710421665405\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  119\n",
      "error:  0.34348751242132636\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  120\n",
      "error:  4.97338926479985\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  121\n",
      "error:  5.479980465372166\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  122\n",
      "error:  6.123003298397518\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  123\n",
      "error:  5.662020606597334\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  124\n",
      "error:  6.072323753616382\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  125\n",
      "error:  5.945845800584792\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  126\n",
      "error:  1.2086689670408037\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  127\n",
      "error:  1.1567831777623114\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  128\n",
      "error:  6.8000533728733945\n",
      "\n",
      "For Magnetometer: \n",
      "Session:  129\n",
      "error:  6.56947025347002\n",
      "129\n",
      "Average error:  7.960970891404051\n",
      "Median error:  4.79044602150268\n"
     ]
    }
   ],
   "source": [
    "def azimuth(x,y,z):\n",
    "    real_y = y * (-1) # This is to account for y \n",
    "    return (180/math.pi * math.atan2(real_y,x)) % 360\n",
    "\n",
    "def altitude(x,y,z):\n",
    "    h = math.hypot(y, x)\n",
    "    return 180/math.pi * math.atan2(z,h)\n",
    "\n",
    "def printAltAzi(alt, azi):\n",
    "    print (\"Alt:\", alt, \"\\n\", \"Azi:\",azi,\"\\n\")\n",
    "\n",
    "err_list = []\n",
    "exp_degree_list = []\n",
    "\n",
    "for index in range(0, len(surf_sessions)):\n",
    "    imu_surf_M1 = surf_sessions[index][0]\n",
    "    imu_surf_M2 = surf_sessions[index][1]\n",
    "    imu_surf_M3 = surf_sessions[index][2]\n",
    "    \n",
    "    imuM1_array = calibrate_magn_data(imu_surf_M1, M1_offset_var)\n",
    "    imuM2_array = calibrate_magn_data(imu_surf_M2, M2_offset_var)\n",
    "    imuM3_array = calibrate_magn_data(imu_surf_M3, M3_offset_var)\n",
    "    \n",
    "    magn_height = len(imuM1_array)\n",
    "    magn_array = np.zeros(shape=(magn_height,3))\n",
    "    \n",
    "    print(\"\\nFor Magnetometer: \")\n",
    "    for x in range(len(magn_array)):\n",
    "        magn_array[x,0] = imuM1_array[x]\n",
    "        magn_array[x,1] = imuM2_array[x]\n",
    "        magn_array[x,2] = imuM3_array[x]\n",
    "    \n",
    "    board_magn = magn_array.copy()\n",
    "    temp_x_magn = board_magn[:,0] * (-1)\n",
    "    temp_y_magn = board_magn[:,1] * (-1)\n",
    "    temp_z_magn = board_magn[:,2] * (-1)\n",
    "    board_magn[:,0] = temp_x_magn\n",
    "    board_magn[:,1] = temp_y_magn\n",
    "    board_magn[:,2] = temp_z_magn\n",
    "    \n",
    "    heading_azimuth = board_magn[:,0].copy()\n",
    "    #heading_altitude = board_magn[:,0].copy()\n",
    "\n",
    "    i = 0     #iterator\n",
    "    #for i in range(len(M1_no_out)):\n",
    "    while i < len(heading_azimuth):\n",
    "        heading_azimuth[i] = azimuth(board_magn[i,0], board_magn[i,1], board_magn[i,2])\n",
    "        \n",
    "        #heading_altitude[i] = altitude(board_magn[i,0], board_magn[i,1], board_magn[i,2])\n",
    "        #printAltAzi(heading_altitude[i],heading_azimuth[i])\n",
    "        \n",
    "        i += 1\n",
    "\n",
    "        '''\n",
    "    heading_azi_plot = plt.figure(figsize=(10,5))\n",
    "    azi_plot = heading_azi_plot.add_subplot(111)\n",
    "    azi_plot.plot(time_e_array, heading_azimuth)\n",
    "    azi_plot.set_title(\"Azimuth vs. Time\")\n",
    "    azi_plot.set_xlabel(\"Time Elapsed[sec]\")\n",
    "    azi_plot.set_ylabel(\"Azimuth [deg]\")\n",
    "    azi_plot.set_ylim([-50,400])\n",
    "    '''\n",
    "    \n",
    "    exp_degree = sum(heading_azimuth/len(heading_azimuth)) \n",
    "    actual = sum(deg/ len(deg))\n",
    "    err = 100*abs(actual-exp_degree)/actual\n",
    "    print(\"Session: \", index+1)\n",
    "    print(\"error: \", err)\n",
    "    err_list.append(err)\n",
    "    exp_degree_list.append(exp_degree)\n",
    "\n",
    "    '''heading_alt_plot = plt.figure(figsize=(10,5))\n",
    "    alt_plot = heading_alt_plot.add_subplot(111)\n",
    "    alt_plot.plot(time_e_array, heading_altitude)\n",
    "    alt_plot.set_title(\"Altitude vs. Time\")\n",
    "    alt_plot.set_xlabel(\"Time Elapsed [sec]\")\n",
    "    alt_plot.set_ylabel(\"Altitude [deg]\")\n",
    "    plt.show()\n",
    "    '''\n",
    "    \n",
    "print(len(err_list))\n",
    "print(\"Average error: \", statistics.mean(err_list))\n",
    "print(\"Median error: \", statistics.median(err_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x-axis will be dates of surf sessions, sorted\n",
    "# y-axis will be their corresponding actual degrees and expected degree values\n",
    "\n",
    "# Build dataframe and sort by UTC time\n",
    "data_to_plot = list(zip(utc_labels, date_labels, degree_labels, exp_degree_list))\n",
    "plot_df = pd.DataFrame(data_to_plot, columns=['UTC', 'Date', 'Actual Degrees', 'Expected Degrees'])\n",
    "plot_df.sort_values('UTC', inplace=True)\n",
    "\n",
    "ax = plt.gca()\n",
    "plot_df.plot(x='Date', y='Actual Degrees', color='blue', ax=ax, style='.', figsize=(15,7))\n",
    "plot_df.plot(x='Date', y='Expected Degrees', color='red', ax=ax, style='.')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Degrees (dg)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up 3xN arrays for Magnetometer and Accel values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Accelerometer: \n",
      "[[16.146496    8.44674346 14.61420693]\n",
      " [16.0124207   8.5042043  14.48013164]\n",
      " [16.12734238  8.61912598 14.48013164]\n",
      " ...\n",
      " [13.92467686  7.83382783 14.69082139]\n",
      " [14.34605635  7.98705674 15.80173096]\n",
      " [14.84405029  8.44674346 16.68279717]]\n",
      "\n",
      "For Magnetometer: \n",
      "[[ 111.214 -251.     -29.   ]\n",
      " [ 104.214 -248.     -36.   ]\n",
      " [ 110.214 -242.     -36.   ]\n",
      " ...\n",
      " [  -4.786 -283.     -25.   ]\n",
      " [  17.214 -275.      33.   ]\n",
      " [  43.214 -251.      79.   ]]\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Create N x 3 arrays for functions that need them later on, such as Scikit Kinematics\n",
    "magn_height = len(imuM1_array)\n",
    "acc_height = len(imu1_array)\n",
    "\n",
    "acc_array = np.zeros(shape=(acc_height,3))\n",
    "magn_array = np.zeros(shape=(magn_height,3))\n",
    "\n",
    "print(\"For Accelerometer: \")\n",
    "for x in range(len(acc_array)):\n",
    "    acc_array[x,0] = imu1_array[x]\n",
    "    acc_array[x,1] = imu2_array[x]\n",
    "    acc_array[x,2] = imu3_array[x]\n",
    "print(acc_array)\n",
    "\n",
    "print(\"\\nFor Magnetometer: \")\n",
    "for x in range(len(magn_array)):\n",
    "    magn_array[x,0] = imuM1_array[x]\n",
    "    magn_array[x,1] = imuM2_array[x]\n",
    "    magn_array[x,2] = imuM3_array[x]\n",
    "\n",
    "print(magn_array)\n",
    "print(\"Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conversion from fin-frame to board-frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Orientation from here onwards will be from the board/surfers reference frame (yaw left = turning left)\n",
    "\n",
    "x = -IMU1, y = -IMU3, z = -IMU2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-16.146496    -8.44674346 -14.61420693]\n",
      " [-16.0124207   -8.5042043  -14.48013164]\n",
      " [-16.12734238  -8.61912598 -14.48013164]\n",
      " ...\n",
      " [-13.92467686  -7.83382783 -14.69082139]\n",
      " [-14.34605635  -7.98705674 -15.80173096]\n",
      " [-14.84405029  -8.44674346 -16.68279717]]\n",
      "\n",
      "\n",
      "[[-111.214  251.      29.   ]\n",
      " [-104.214  248.      36.   ]\n",
      " [-110.214  242.      36.   ]\n",
      " ...\n",
      " [   4.786  283.      25.   ]\n",
      " [ -17.214  275.     -33.   ]\n",
      " [ -43.214  251.     -79.   ]]\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# The new array for board reference frame will have the IMUs in columns according to X,Y,Z directions\n",
    "board_acc = acc_array.copy()       # Reassign to the correct axes as stated above\n",
    "temp_x_acc = board_acc[:,0] * (-1)\n",
    "temp_y_acc = board_acc[:,1] * (-1)\n",
    "temp_z_acc = board_acc[:,2] * (-1)\n",
    "board_acc[:,0] = temp_x_acc     # X acceleration\n",
    "board_acc[:,1] = temp_y_acc     # Y acceleration\n",
    "board_acc[:,2] = temp_z_acc     # Z acceleration\n",
    "print(board_acc)\n",
    "print(\"\\n\")\n",
    "\n",
    "board_magn = magn_array.copy()\n",
    "temp_x_magn = board_magn[:,0] * (-1)\n",
    "temp_y_magn = board_magn[:,1] * (-1)\n",
    "temp_z_magn = board_magn[:,2] * (-1)\n",
    "board_magn[:,0] = temp_x_magn\n",
    "board_magn[:,1] = temp_y_magn\n",
    "board_magn[:,2] = temp_z_magn\n",
    "print(board_magn)\n",
    "print(\"Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate magnitude of XYZ acceleration vs time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7173\n",
      "Graph of XYZ Acceleration vs. Time\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Square x, y and z acceleration values\n",
    "accel_xyz_array = [];\n",
    "for i in range(0, len(board_acc)): \n",
    "    currMagnitude = math.sqrt(math.pow(board_acc[i][0], 2) + math.pow(board_acc[i][1], 2) + math.pow(board_acc[i][2], 2))\n",
    "    accel_xyz_array.append(currMagnitude)\n",
    "\n",
    "# Check length\n",
    "print(len(accel_xyz_array))\n",
    "print(\"Graph of XYZ Acceleration vs. Time\")\n",
    "\n",
    "plt.plot(time_e_array, accel_xyz_array)\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Acceleration-XYZ (m/s^2)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azimuth and altitude calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting full data set analysis:\n",
      "[[-111.214  251.      29.   ]\n",
      " [-104.214  248.      36.   ]\n",
      " [-110.214  242.      36.   ]\n",
      " ...\n",
      " [   4.786  283.      25.   ]\n",
      " [ -17.214  275.     -33.   ]\n",
      " [ -43.214  251.     -79.   ]]\n",
      "Done.\n",
      "286.9251359263996\n",
      "258.8375933140107\n",
      "0.10851415458153449\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VO+tQ22DhoU8WYOXmXfjzx/PcMjmNDVFjdse7s3HjmzOU27t5zPIsmV07fCpufXuW57vyZOPUr7BFW9xDgkowy+eakPedzeU0GQG/8T3Tu3L0OUtxi6HvtSkzgDsA3EoIWQLb5+z5IrfHUOKo3qNNQobyUnjh8slhfxqNI/7yWd6OX8ppshJcRNmO2tIJ8sgGmSlF7Msy8v181m23tRJH79fO/a4smZ8ppLq2Afd+MAe1DelI+9WnLCzZGG5eTcQYgcgLMpQWviwSfwnfV6tAQ471W79alPFTL4V5oiQEM0rpWErpOc7fyyilR1NK96eUXkwpLa0cB4aSY/12uRr70dELPZ9L4YVrzJSyxpHNjWmFxqIxEdVPqFCZ/9lt5bVk+fLHeerzxRg+aSXemCL3H1Xxp4/m4vTHxmFDSBoMEqOjO3/fi2HK5E+3dltN1uZmdph566oDt9sT6dy6qfv3993HzGCIhVe+WSn9XlzND3lyQiGas8dSCgOWCtY0SmlJC5A6pCSr/yABolCZ/1lqD74pP3o6P+lemAYk6rP8ZtlmAEB1TXCaEaYxk93rXLCovM35XCvwWrL7PpqHW9+ehZr6aJpGANjsBEs1xgCfXG9vs4pk5lglMH4Ywcywx5IsMV+Jc54aj6HvflfsZmRNSQtmTtvSlldj0RhNO7KozCBTTaE0ZqxZfD8oNbMxa1mYnxT7OY4oxjJOg1jXkC64xlZ2vmza8PTYpXE0p1GStkxUpsFQEEpMLsOcymq8+W1pFMltrIyeux7jFvnzFrLB1KJeYUwVsVvKyPyqljvlcmS4V1ugDh9HEtVpK7fgR09/jbpUdM1OELoRqks27gQgF4KjctGAbu7fpz76Fajk9uTVlCn5LpvzXXXifjm3pVjkugAzzv97EPd+MEe7Gn0qbeHu92djzdbdeW6VgfGVZAIvZbbXNGDUnPUFPedDnyzADa9NC9+wRLj2lWn4xQtTfN+nXMHMa8psjILZnz+aF2l715SZZ7mMnachTdGCM/1kw53vzcaMVdsCBU4guhYwk2w3eE/WRy58eiK2a1RXCKJJmXcaLbzGLJ7jNCvP7Zk2ZngtmRHMGjnDJ63E7e/omabmravGq9+swi1vzcxzqwyM3Vn4WRSCbbvlKS1O+tsXuP7VaVi9pXDC+7+/WoqRs9fj/RmVBTtn3NQ2pDHDya22ozblKakSt0amEExZEc3Hp3DmWvs8DWkLrZuV53YkV5iUC1DsmqJqflxTZoR9cnV2F+9+oSf2uoiRqypoHjKZbdpRh8UFKIqea4qPlEcwy7U1uWMEswLBJPJSqFzfWDnhoS/wu//OCt/QoetezfLYmuy57hW/hmp3fQrVjr/Orvrc/HayqfX2uZChXGT03MJq8qJw7wdz8HtugcRffq5h9I2BQskB7Dwpi6Jlk+wTmQL584vLJtluroKUuP+R94cXno+TuPKm8fukYqr5edLDX+CMf4yL5VhB5Lo44e+h8TH7HsEk+uI/8sZL5bYavDNtjdSUJaN5juaWOOFf9smSqCfeifrOHJMlpvMwU18rESZ1+NNHc3HJvyfF3Bovsyu9Gg9+kC6FgsT5hk0qhRLQGtKWW/C6Iss8Zhkn/ZDfIx53laNtDipPtW57jedzNhGMPMW2fMkEiWyETX6PB0cuyKFFGWobGociwviYfU9hw0QJPPNGj8z5W0Yp3eowh+kk5yA0Y9W2nM5VCis+xotfr4hsmouKOAWv2FzafpyWRfHZvA2xac8L9bTdSMY0RYIQnHFQZ/Tu1DKrY4Vlmc81C33Qbje8Nt3z+eFPcxNCiv22SdNzZHMc7kCfzS9dDTkAdG7dJNbj8WNmKczRuemjDdqwgWJ25fbiNmQPIW1RjzAjo5RSJQQJZpTSQM0ApRS76tPa5qNSEswKgTgJ3/W/jMaxFFa/IsMnrcB9H83DwH3bxnI8V2MWy9GCsG90Q9pCIgGUJYgnzUAUqKYpMx/RjHwJIwBYs7VGsaUmRe5jsnEumyZ508zk0qL8E3f7eB+zUhg/jcasQASp1g3R0QnZL4H3yyUokaUqKSVjxOx16P/HTzFvrZ6TchwpAFZu3lXQIIRcCBpIS6kPMFhpo7CIRG0KdI1MSGpIW0gQgmSCIGVRVG6LLtiE5RvLhyM6w19DN7dzFbuLSft4VoIZt3uxLyqEuBdc/AKjFBZzRjArEOtDSoQYoiETzPYXzCqlsPJhBAmSFqWBE9GExVUAgJmrbRPnnMrt6Dl0BCY7Wc5FdK47lbbw2GeL3M8zV2/1/P6Dv4/FSQ9/Kd332F7tpN/nSl0qjU81gwx4s8uC9eqor1LSmjKYIBOHAA1wE0merzUhmDLLEgTLNu3CCQ99ESmqd932GqwMMTe7psws2xp0K8Tfcn0MOrc9nwvzuHzMvOa80ntveOIe2r1RmcW/diOYFYhrhk8tdhP2KHbV+R127zq7XxFaokdDoFaHBg7urNQOc+qf6vhsffzdOun2OtnMHx+zGE9+vtj9vHqLvtYj14g8FQ+PWojrXpmGSUvlAiePboBDCcnmLuy5befKBl2dQ3LPgvmYOcJFfdpCgnhLnt381kztyXz8oir377BJcKJGX5ARtNARz3lA51bu36PmrMP1r0yLVDy92EKMNCozi+MU+zqiEL/GjA8YivXQWWEEM0Oj5IOZlaipT+Mfny3Cwfu0BgCUCxFiFWXF694L1le7mi4ASAeYMikNHmhYaakXJiy3PzvXqaoRqqMx++eXS3zfpdIWXp64AiMUAl+U44v89o0Zofux5Ms6SZh129BYJptcImnZrYgjme667TXYrUjXkiljlNGY8dRnkWJB9XzYNX2SZcLl6hp1ypllggl5cP8u7t/Xvzodo+aux19Hztc+V7F7mOwWZtPvm3AJZot9TWHEHW3Njyf5iGqPihHMGjGrt+zGko35T95XirRqWo7HP1+EJz5fjLlrq3FotzYoS3i7czFzxg1+fDx+/vxk93PQyy5mqwe8AwULclhetQsL1+8INYpkW5j5jSmr8McP5+I3r08P3C6bw380a22oOf+7NXZgzB8/nBt6PN0Vc/GHWD1ymQvYJHz/CH1hQsVxf/0CP/nPN4HbpC2KRIL4gm+ySY3w908XSifZXPNo3fC6Or2L+P7I3AxWRwgIYM2/8vie2vvESVwaszZc0uBS94gWLzlXWSpVYmZcI5gViHxEF5308Jc4/bH8J+8rRfp2bukppZIgxFNMGPBnfS9mwtkwB3Vxcnp+wjL37/emr3H/XrtdPmF4VnxZriarJQWpZZOWbroSkRMe+gJXBOSgq3ZMezoVG3SvsRT8RfJN3Jeoihzn364EgU9jlo0w9encDRi/pMr3fa7+d0GmeXEsznYhw2ATeaeYUzjo0rujP2VJNv2+MeX8y6cpM6bcujlhBLMCMfjgLuEbOWzf3YC5a01ajSDEgTuZ8JtWRNNOMVdCsnOz5soGmfXb69y/eYFJvEbGQ59ktCW8do5Sivs+nItpK7PLJRZ3AEVQ/dIgPzwRbcGsBAZZkbMP8Y8FstQvSx44y/378O57KY+XzwhGFXZUpnf6WLxxJ7btrsfVL0/Fll3ysmMypBqzPAoJYiSouKDLFtW7mW+kaYOyuH38LrmWOMo3YvfI9R0wzv/fU6I87J88OwlDnpyQx9Y0fsSJOUmIz8esTjCtFHNBKDNl3nhaHwAAtfz9QzUuJhPE91t9ysJz45e7n0979CvXeXnt9lq8NHEFLnomPPu+7JyFHKOO69UeADCgh1wIsbLQCga9d8urdmFnXW7lr6JQn7Iwp3I72rewNSsdWmY0LDJtLu9czyJyRXbXpzByduGTgSYTfg31uEWbMHzSSoyZv8H1h9RC0u9ikpXkCF2iRYUdzMK7hUQ5PetiFx/ZPceGZYfsXcjO+T/3thQK8b3OdQFm0mXsIUSJ2gHEHDHBD56F/5eCrTsfLFy/AxtyTB8irqgJ8a8cawVTZjFfONng2aTMdraV+ZixK9kqaB7YJMLYXZ/C+u3+e8ki/qL0IVnKg0I6wp64fwcAwIAe8sSrfJ3UONp1yiNj8fNhk8M3jImfPDsJ5zw1AR/MtO8zy17+h7MPxBVZ+ict3rAzruZFgkic/ym4CidBooGG1NOxlX1vzj1sn+waGIAYtcz60vWvTue20e9f7FrLixRsFFetzFIQSHTxpzwxGjMDgCUbow2Ilse5MPP9SQ9/gTMe+0q6z55agPnMx8fhmAc/j7QPH+EI2CucWWsy5t5kgqBcWGbHna8oF2QruibOQG4LZt7GjXeud1mVt581KU9gQ3XGzKnShLE7EcUk0UKSBkM1SE1c6vcLyhXimnblv7/HCY78/Tyrv9pNQNX+BevtZL0qTVQ+YKW2mGma+e+dc9jeoVUsVIjJUrOlPmXhwQiRiDIfsyTJaHOjzG2yK2ATZeum8admUQXaJLl7GSVwiM+59vcfH5pr8yIjC6pRRdYG0ZhmG3FhlnMheuNjtmcQdSD9fMFG9+8dnPlk9ZYaLFYIeaM0k20G8e2KLeg5dIQya3ypOnwOn7QCD4yY536+9e2Znt8/nbMBO+syzv/JhN/nRaSYGkiZhocJkhb1T2QLN8ijbVdU7fLkH5u/rlpu9sxivj7WMSXyqPrHz56LX9PEhAydQZa/n51aqZ2uVd372xVb5T8UEDb5ZyuU1aXSmJql76DIO9PW4NlxywK34W+lzMfs+N7t3YVA0LCi8x6yfvfa5FWh2+YKE8wS3HPQqSzCYFdDiL/ck4o4x93v1vj9ke/9IDyy2UcJaIp08ZkyY4zKNBqzRkzzimT4RgqueVkv2eyWnXXhG4Xw8ay1AICznxzv++3NKavQ666RPnNZKXDvB3M9flMbd3jvxVtTV3s+y/IqiZSaKZNNBJRSj/M+j5ghXZbXSXpZAZf65cKN0u9lt6+QcntQMISI7sSmEgIKKaRTSvGrl771fc+CU8Q0L7r8deQC/OmjeeEbaqCTlJhHFgUNktF6Bid4FXaTrCyCTImbd9bl7AoBAK0cDbErmHHNiJKTjfXXBCHaueTy7SKwYnP0cl/8LS9l338qScida5CSN8GsEcz2ODZU1/oSf4qTwBQnc/vzIQ6yiwLMpSs1X7yypPoRvzrZbudqjYSepcalR3X3vJy2KTO4OxezRJM8KjOjXRgzXy4s3fr2LM9n3Wt43NGqycbX71bLI35lZrG0RUOFmGHjl+FfkoS1UWFn0dKYcfehU+umyu1UxyqkjF7bYOGLBf7nm6vGjJlji0GC+NNMUMqVHgq4v0mhn8n6TlA/P/L+MZFdIWTMuPcMEJLpI/xzyMaUCeibA0upXByjGNG92RBXQl0er8Ysp0PFghHMskRVn+/a4VNxz/tzPIV9VQ/6Lx8Hr3ZfD1Dj/+DvY0PbCKhDuHfWpTCn0h7Y8xmang1fSiYxke7tmnteUFZUGcj4bokUQ2HGVl9SjZnzaEQ/ssDjaV7ERzPXKrdXpQeQfWtRiqWb5IuAtEVhWRT3j5iPv3+6UKtdQbB7pNMdeY3D+MXqFBwvfL1C+n1BNWaKCa/e1ZjZd17mTzX8V0crj5utQCcj6pEShPgEw8uGTXaDooLubmsukSkATFnuN8eq8vJlqyn7YGYleg4d4SmDVZZMoCxB3OPzmrtsqhgQEjDGCDc439r7bA7P71PKGjPZvcvZlJm23PfQZP5vxFz3ijyz9JbdtlmQL8ETdXXUSuHwWtuQxt9GLUCNRgJOhkqL9O2KzGCYz364vGoX1imSoqqYr6EJsNXZmYYnE7zPlvyCimHKHD5pBYDglz2Kv5bM4iSb+OuciUV2WpXpWmZSsihVag963zUytEpAFFhbdUwJ/DZBtTtnrJT7khVyLaJa+DBfUyZgTRh6Kjq0rPBsM6hvR+Vx43L8B6I7ficTRHrfqxz3i6BnqCMU/3daJqkyb2ZdplgkhPGfr2z/udVbvNaBBCGc83/me7HPz1tbjZ5DR2AFV85pTuV27KpLuddD4E9l4xKz6Y2noiyBA/dunfNxSmx9rkTWzlzH9rqUhWZOSapSyIZgBLOYYRmn+ckyLoHg1W9W4pmxS31OukGpO1Sran+ge3445ZGxOO6vX0TaRzR1yKAU6A08LNkAACAASURBVNImY8JKciViVJGIxRh4ljsDuUygCoq6PbRbG+n3sr40TSJ8sCzssu07SpzlWzctk/aVtOWPGOXJtpahDHaeqM7/3do2j3yufBVilxFUJxXILJ5aNy1H2+a2YCbrwqJjeZyCmQ6iRqWZxM+WCv/KCHsPxYkxTiFmuVAnM8lpzPj+376FV0B+xxEUx8zfAMCOejznqQn4zevTM1GZRF/zGGfi406tmqBHu9yrmvDvHSnhokyy5MW59pH6lOXWCi0FM7MRzLJAZ0XPjy2RH7RicyaA1ae9gthR94+JeihP+2rqSyA+mIOfcFT3mgI45YBO7mfCmTIJ5JnSi6ExY82XacyCVmYqoU12O0Q/NCDz3GV9j09syqiuTUl9HiktnAnYvVca3ZH5Ny2v2oWXJq6IfC4mnBaiTFeYaYSXh9mWI288ybedGCkYZ6J58T2jlOL9GZW+smaZcxOpYMguNeiSw95D0YGe1zhmK4uy/cRFbZIQ9/nwC7pT+nVCEA0pe59vl2/B9FX2wogEtU/4Pk5zWVzO6mFRuaWCzAUo17E9bVEufVFOh4qFoglmhJDuhJAvCSHzCSFzCSE3Od+3I4R8RghZ7PwrzzZZYNIWxdNjl2B3fQp3fzAndPtHRmd8bqK+hGHClLia4dNvpNKWkJNFPsvxHfn6V9UFf+NGXLHK8DjhKmZp8UVMEoKmZUl0atUEf73wEKn2pxgaavbsZYNnUHNUdQd11eyuWVCyuSoIkPe/YYRpzHg+dCKAs4WdRyudgrPNKY+MDdyO16rK9o/TT0tF2MJMpuGVCT2bhMjkONsuCkNjF27CzW/NxCOc7yBvBQg7dXBUZvAiRWwLr3HM9YrFdiWTxH03eU39v75cotUPd9Wn8eVC28cxmSDaY0ycWpk0pWjZpDx8wwiUciCAbLGQ6+20aOZ9+r5HZaYA3EYpPRDAsQB+Qwg5CMBQAJ9TSvsA+Nz5XHQ+nFWJh0ctxKOjFwU65TM+/m6d+/e2Xf4JLxtYfwlaNe7/h09wwt8ypkP+heVX3O04VX0hy9Jc9bI/bYAIP+Go8gmJA2AyQZBIEEz5w+m4cEA3rJFEmhZFY+Y6tMsmI/V+bQVTirtPhHOLfniMKCawNNUfom98Y4YyX54ODQHmV1+7QgZP1r8vPaqH9He2eyH6RJRJmD0vmeAzduEmYducmuWhRnCHqK61x6z11fKUPYkEUbybTLhWn0v2G3+PxEUJrzGbEZAQWCeSkgU8MZKEuMfnFywNaYrJXFCCmDhXttgmhGC/Di1C22AfJ0bBzKJoUp7wzAu5Hr8Ua8wyZJeWqzBlUeoGRX2v85hRStdRSqc7f+8AMB9AVwDnA3jZ2exlABcUp4Veap26i6PnyfJIUVTXNuCoB/wmxcptNRj09y9931cF5ChrWi7x3aAU/xizCED4qnGdU6Jn1Jz1+NuoBe73w7i8YMXqfDqTlFcwU5syeURhY4NkQimOKdM+56tCChVAPnie55SgkUWqqfZRUZeypBMIf6+6BKSaYOeLcs6ahoyQH3VyeHyMneJD1kUuemai57PsWd4++ADXNKkbCFKILhFNMLP/lWnRROFJN2eW/xz+Zyoem2nn1m6TB+58uWCj9Pw691P2THjhS3zn+fv30CcLoOLRz9SRwaq1SCJBOO2pdzoMSjKryvv2w4MzVSj279RSuX+cpsy0RZEkxM3Ltqcju3M5Z/6n1NWYFl8sKxEfM0JITwBHAJgMoDOldB1gC28Ago39BYY59/NYFJi1epvP1AAAa7bIc4QNDPAL27+Tf9U1adnmCK20+UbYhzmuVu2sU+bNyje8UKDKks0LZiofF1DqGWxDUpgBKI7vABvbv1zoT+kga06YeSrKJUxftRWDH/cnFubP8c1dpwUeI21Fu2/8+Bi1bFnmnP4TigEO4pxZniS44eT90aTc7ggsaaus7dNWbnWjqgsRgRVFMNvllNLhE1iz5yW+CzqmzPqU5QpX01ZuxfbdDdjvzpE+v8Sj9m3n+Xz/iPnuPiyCm79VW3c34PbBByjPG3RfZbcj7RHMRI2ZngBauVUd/V1d4x1rWLRrUhGVCYT4yWk0aejgfsrfYjVlWjR2k3wpaI1U5KNt/D0shUsvumBGCGkJ4F0AN1NKte0ghJBrCSFTCSFTN21S5zEqBKqOkrYoarNY1bKXns9BxqdUyNaXh01uPx82Gc+MXZrVMXKFHz9k0TWA19dDZTYWxzXdganQodBRJ6iwfaOM56o0HOKtCopQtPt2BI2P4u8o6GiCxMS3TMvCBH/WH+7632zfvp/N25A5TgH6Q6SC2M6mfN1S5pRc1+C9Lzp9fui73+H4h75AbUMaFz0zERc+8zUA4H9CwfqkIrcdAFz870nSBVKnVn5ta5BvIyNMYybeLzGRrYy0RT3uIyKrhAXy7WfaQqUdlWl/J2rdvVGKNsywryMstmmu9vuK01TIhIpbzugb2zFLWTCTNW3F5tySpFMKY8pkEELKYQtlr1FK33O+3kAI2dv5fW8AUtUOpfRZSulASunAjh3VuX4KgepBnvPUBFzxwpTIx2MvfU+Fv0KunTDbXEBxwJtoVKtGvmbduEVyoVv0fNL1myq01oxN/L85pbfvN5ngxV8FE1BO69cJT/30COU+jJ7tddNGkIBPXtIWjVTU15NSQX83D2Pmb8D67fJEokf3tDU7FqXSaEwm1AeV56rghJBC9IcoA/2rVx+DW8/o60k2ywQwUYiUXaHonzXaEUJZX1IlCw5bsOheAnsvgzwTZefixwKxALeOYLttd7SychWOsFu5rQbvTrdTYSSEPiOmB2GMnL1OK5dk0D2L1ZRJbcHslyfslzl3jscsAdlECWtanEpCW7gtup7KpZhRmQTA8wDmU0of4376EMAVzt9XAPig0G2TEebMKsv7Mn9ddo7QbJBiA1i2pVdUq7psslrHBf8yqQZc3iw5S1KgF5A7/wfBfI8KvRrq4/iZNK+I7v9x13u2tqdtiwo3r1JQ8+8ecpDWcaNoDb9YsNF9Ztf9oJf2foC6ELsOx/5VXnKHjZ1pi7q5pXjYBBrUH/jFQSG6g47Gh9G3cyvceFofTxvZooNv6/rttfhcUiHDF9EojCUqdDQ4kQp7B2rM/N/xY5VYV1S1gKtU+L8B6iTdDFnvEL+TCZdTlm/BDa9Nx59DqrYAwfc8TlOmZfn7ezb9mq+YUgKBiUrYfT2021644PB9YjmmRam7mPu+R2WeAOByAKcSQmY6/50N4CEAZxBCFgM4w/lc0mzVXK399Gh5hBjP4MfHucIIe7muHZ5dOosvFxTXxCuD12xFGegZpxxga0efFkyxQRqzj397In52jH3vCy2YtXdyhokD8YAee8lrvnF/v+eYmxZv2IFMCUJ1+/fV1JhFuQP//mqp2/ZBfcI10/xk9H+vz4hwJj2Y71iayisSsAmqlEzbuU7CsuLus9bIoxPFKhtMMxOUzBjQ6xM6UY86pkzZ/fCWXfL66qoWmDX16mjyg/cJzoQvC64YzZm4AbmwynzVVmpYLQ7o0ipzPuG3OMehlGVpJeUOg4/UL4Xs92EQAvz5gv6xHItSGOd/AKCUTqCUEkrpoZTSw53/RlJKN1NKT6OU9nH+lYenlRANKYrKbeEvajNJtKUIX4MzageZLDj79w6IChLJx4sYVJEAUAtmTcrU96lP58xgx5tlRMHs9AM7u3/379qmaI6dSx0HeNEv8PVrjtXOFTRrzXb3+lhiSxni2Ny7o9cUzp6xOCkEtYLSzOSeIAQ/CCgRFHasIN7+drXWdnyuIZnml92noIL2fBsLki4j4Bw6CW7ZNekIeL943nadsCyKox8Y4wpTT32xOHC/sPtgURoq3AGZa31jijqlkGysEf3nPMfUSegtfN4akqJIlNuXbvIHqvDHdAU55x9VnkGevZpzgo7wW1waM0opLOo3w2YD3wdKwc9KhRu5DLtaRhwwczB//GJSOkbVRkzKsnDHu34nY5GoSfvYACY6rspYtGEHfvLsN57vzjl0b8/no3qqc/WKg+7/ZqzBDa/llnj27vf9iXj5Tq96AWSTKtMG/fbU/d3vJi6tcv8Wd2GfH7n4MAByrUMhGOZk0xcjFJuWJ6XX306Rv2yBYxafskK9ThGPVytMdmwuiHILLEozwShJgouO7Ba4fba3999f6QWjMHPDmq01aJBocBIRNWaFsFqokjwDwPNXDgzdf6Dz3rZtLu8bPGsd37y6lIWNXJT48En+dC08Oj5m3TTK/nw2N6N1Uh1Tds9vf/c733dMy63jYya+16IZ/aIB3n4rLuRk2kB5DkBn+wjmaRlxCWbsMKJPZTYJYvkmlUJZIhXs2lSl97LB5DHbA9HtxEHPW6ZditI9dtT6V4jiYNO+hb8UD0PUXt3y1iyMnO3N2bZ6y27c+vZM9Bw6QisVwsL1fh8jfsBUXZ9sQCxPJjDkkL3Rilsh8SYPccV49iG2UDqgh12aKYrWoVDIWnLhgK7u30ybsn+nlsoI1rDj8bBnPGOV1wwmu99XnWg7El83qJdrSkqQ8Ap62WYMlyU5vuOd73z+Hmzw/OOHc+VVDZwGBmqnC6wZCFKu6CQkvevsAwEAPdrp1wTVTTHBCLsNvA9OEHwVkolL5Sl+ZPdclbcP0HtnZZfL92txDhcFM1WdWBG2X1AeShn5qi/BnnMcPmbU817k1Ky8wmvM4sKyaMaXM8bjZosRzDQJGsBTFsWxvdopf9fhNkm9w5Wbd+PBkfO19hedyycurYrkw6WzKj3vnxPw3nTb7+m+D+dqtCkzOcrLaPjPee8Hc3DtK35NnSXkLRMRfSzOP7wrFj9wFnp1tM25bmRbCY04si7FTxgduGLj2Zgq2rf0aljYtb/wta3F69ZWrQG5bpDt6L9g/Y5o5YuyvL2yZ/vW1NU+B++yBO+g7D8Z6wfJBMHpB3bGgXsH+xoVxPk/QEjSKRbNIgh5k2hYu6MEHAAaqVtCtvnz+Qf7vrtsmDxdi+4rWO70N51xTGYu5rWE4iJV7G+y+1nLjVkkYLtsiOs4rGvFUdCefy7Z+P8WCnbv4rhmBivJRAgKMyiEYAQzTYIEs3lrq3FED6+ZULYSDjrGiNnyHDy6hWVFgePGN2b4BiMKqow40YlE2bo7WmmpMi4tQa2kULrsdogml8OcYuSUBr+IMqGBN4mWOX/r+MnEjboQe9hsSJ1/1M+Np7OQV0q81g3V3hQUL//qaKcdfti9Gz1vg2sSLWMDV1CTQ1uZG/xzlr1PrI8QYiedlZkRC+1jFqS80plbkhra3mtO2s/zeYFEWx1EmD8otbz3Sswy379rG+1zqUyc7HsWHXj0fu0BqK/71W8yfmyy92PCkoyrAx9xCPjvu+wc27jxTkxwnCtx1aJkQr+ONjMM/vmW0gJWxMqDyixtUSSIfchSuHQjmGkStAK97b+zfAO8zJSSzzlAjAyt2lmPHbV+85BKM6ZyUM4lKKAsJC+MzrEH9NgLSzbuCC1+HrZ6Kiuixkx9b4P3W7TBNhcv3bQLbTT8i9o0L8eQQzJ+haJQcsl/Jnk+swlftjpmGfSBjJk8QcKLNOsUqZeh2834Cahqp9+8y7ocIQTJBJG+t/y5CiGYBWvMwmG+NEHvy77tMwvB6toG/OvLJdrtA+wxLAgK6rlvj15ymOf38gg5oJYp+gh7N4/cty0G7tsW++xlLzRUYxafx072XleU8Qsz750Wx4vJy/1mV378nBqzYBbXMORqzOIQzCyKk/p0AAAcvV9uFqB8wm5drKZMapsyE4SURAF3I5hpEhq1JLxpsiLaYQ9cJxxdxcez/Bq3fwqDM6XqSUJ1fSoNE78aVREUGQfoaVfSFsUVL9h5jVRJR4FwM1syglkkbsRJ48IjbD+yCsn94ecLvn5hRZl/206t/D6DB3FpAsQJTRRk2Llkz5h/dswnTZzcZNz9/pyshF9V/4uar46fcNMWxbKqXT5tEC8oF0JO/3Suv74uQ8eBmV3zo58twpOfs+hKb8P5w/zhf3N8tS9zxaLefty6me3ryfLr6fQNhqqaRya1h4XyZMK9Nzr5IGWLH15L9sYUb9SveNu7tfX778n6sc6kPfqWQRj3+1MCt4krCp5dt6+cVBbHohTo06kVDt6ntVYGgaLBFGYxSmZMMCPEaMwaFU1COqr4MKsl2qqT+waX/QwTGlgOLxlvTdVLN6AStFSLeh0nYkqpL1s3kCkkDcgHNB0zbZpLi8BrcUTCFoxujpoCvHTioMsLHSseGoLHfnI4AODy4/b17SsL0Dhw79bSgVxWgoXPGB8mIAX5FfI+e286qSzYwBWGGIiik5hW1Rax3+gKAOMWbcInc2yBSKwQwC+iCpGvSRQKeHSuhj2Lbbsb8NhniwD4xxveV23zzjqtzPQ8fNCJjHXbazz9mAnAn9x8Et799fGhizAd2FBTn7JQUZZwo9EfHqUuTp7ZVy2YyZ4x+4pPqwN4ozez1a737dwKPRQ5Bdn7E9fk7zr/C/c/m25tCyeOZjyOxuUJNyozRp2ZnXLEPmYJuJgZwUyX1iGZpHVMIqcf1Bm/P9Nb+Jd3kA9z2H3xl0d7Ph+5rzr9xRAhVQZgLzRUg43K3MYEuSAfp+fGL8NB936KjTu8Gi1euyE7/GdCQkdpuyzqvn5B2oWddcETEbO0FMJ09bqQw0kVcda0POmbGDpKtGBvXXcsfixJUzGgh//580mMw/pT0O8y04iuH4t4i8VrlKE7CYaZrGXPVzw2K8Fjb6912ryhI+gSySgtXqZ4nHKJhlXks3kb8OhoW+gpTyTQuXUTnHuYPJP6kCcnePoL6wqdWjXFkfu2jaWINhuD6hzBLIrQzI9fTIvH2iTLd8f6xBFO1DZbFF9xfGaxJBsTd4WMM2HwS9U4YMJsHAlmWT60BCmNlBEqXBezPGnMjCmzERE2cej2Y7FUyF7NMukfGiKGuAetcmURd5ZFlYkRVYIX2/6Jz9UJKlnyVNHUyK+is3nR2zQr99z3oPcwLA+Wmy6jAAPOH/7nzd925YvfKrb0ahVlNCtPonXTcmlJJz6zOKOMu+dBJWuAzGT01e9PDtyOkUwQrVWqeId1oqeUARLC12HCoexc4le8SbfYE5CWKVOyjTh58FtQCrQNKKDNuGb4VDz1he3ukKYUZYkETj9QrdXnNfpiu6MIBoOcJMX3X9Afv/thRuvL3vX6tC2YiX1emlLIeX5s3zsG93OfKesLYpkqfnvRxYHvP9NW+P3KZlf6S8TJXApUsPsW14LANWXGMJOzyHdCSNEXLEEcv7/tB3f5sX6LQ7awdBmElERQphHMdAnrqLwAISZ2BeyyQIDfr4g/Lq9BEqOeZLRqWo4P/+8EeXsk2pDaVBoNqqhMRW9kJqYgPxlVyHa5UCz6y4XSevQuW4VcXa2alkkFYp3cTyKZeoOFfeuaBphfAa/vWIeW/gE+zkK9IqxtvON4ELoOxuI91tG0qU2ZXsIKDcvkgyChId/dQSdDfBgybRR/u47osRcGcNpzCoqxC6OVY7OLOAc/J17zJG4apf5z8/IkDujcCj8/dl/836l93O+ZcN6QtlCRTLgpiG4+3d7mg5mVvmOt2VqDe96f42r29+/Ukity7QhmkqoCLZzxlY17bH9+DAtK5swz5rYfYPJdp2ltG3faDTbOi+9Fs4roUzuLfLeFkxKQThR0bm2PkwMCLEZRcdNlgJTEtRvBTJOwdAW8JkY2wLFwctEXg9/vzvcy1QOSGr40k5ZWKQdT2US3vaZBOVGoNIIsIEH0f2PRO0BmcOPnv2fHLcXbUzMmo407avHLAM0R4DUxAfZ9THP5y9i/H/7fCfjp0d09256nMMEwEjGvVHUJU4Lec06m8PiNp9lVDfrxNfbi1Nc7sAoQ+3fya9yC0DZlCp/5PqoqLi3TagD2BMFrSqI4mTP++skCLHISGxc6+EPsb//++YDIx5B1AX7yePu649CXK1UWdV75YGYlUo5gFqTdbPAIZoLGLMIKImVRqZDPxsL6lC2YsWM+PsbW1u+W+M3d9vYsvPLNSrzs+BHuqG3APy6xfTiZ1UCmaWMuA099bmsMP59vL4qjCJgA8MxlA9C6aTk6t24auB17j9lti2vyZ0Ee4t38+THRtUkeH7MYmpevYuCqbBm53NO0Zc8ziVLXmBFCLtT47+xCNraYhJnA+E4YtEIXo+v49AY84iQoK1A9fnGVMiXFys3+kPQ5ldXK8k5hGjNR0GP+bQ1py82ZxA/OD45c4NlelSeJDxrwmUcSxHNe1sRWkoHw9IOC/ZiKVZIpLHiC15KxPsQ7y8ctlzU4ZqKBWaw2dRM6ireY34/3qdRhd30a/e4Z5X7uqdDuhQUYPOcEmuQS+ZwNYn878+AukY8hG0/49ylXx/s/fzQPacvKJNhU0ODxMcteMFNVEWD9nzn/8+fYVZfylRgDMlqt/82wtWkTFle5vpdBpkwGE2zWOzn+ovpq8bV7g6Du4jXeBeIUJ80HG+8/uekkANGeByOTMiKecTJfY617VOESc7mn1PUxKw0zbtAb/RyAcwCcG/DfU/luYKnAOtkBzovYq2MLvPjLo9zf+UFLFDAO7ZZJvsgPoskEwS9P6Ck9n/hirdzsF6iuPL6nUoPwpcKU8d0av48E4C0bs5oT3piGTXQUb+mYAu7m/KkGPz7eZ45kqDp7UOLGZZt2YcR369zSS0FjTdg45A6IeZiX/+/16Tj1kbHS36K85KyN/ITE//3rk3u7f7eIKOAwdtenkUrLNRZh6GvMqKdkDSHA3UPsskJh92OQUCSd19IcuW9bnHe4XDN651kHBh6XnbfQgploRstGAyoTiINq81IAJ+zfXvv4yQRB2rKFpSAfQo+/p7BZFIEmpTCbpjmzop0uI/NbbUNaGvktQghxgyXYmC2rOiLC+oXs+SyTFDhnRE3s6poyY3Iw79LG1gqecZAt8HcNqOYRhkWd+0dILEJV3vx5mZAr9NVc2px2FguNwfn/E0rpryilv1T9B2BSwP57FGw1x97bZZt2eSI1eTPcaiGHGd99Ksr4SEWq9O/RGegO3qeNdGAIWi2JphxmWqncttsdnH74j3Hc9kxjJhbEtr8X03SsU+QaU6m1/zZqgfT7V6462vddUJRnmDYnTo1Z2qKe+/jxd+uUSTOjEFQ0GfAKY+/dIPctDGNXXUq77qGvLRKNiswvDhQYeP+YzH6E4OqTeuGSgd2waUed1GzOTNFn9fdqlPhNj+vVPrTdKsGCDbaFNmXqllQLIrIQTUNqhQqUOYJZmMaMxyeYRdGYCYLZXy88BADn/C/RmFHADVQIgoKreej0nZmrM7VhX7zyKDz10yN8+2Wc//3H/NlzmdJSYgUB3ev2mzK1dgslo4nL7ThsfI4z+33e5DLnX52yWrqkHfM6yfE4caEUzCilPw/bWWebPQXWUSu3ZiLdVKtfX3oEbrvqGrXpjkeWUFQkQbxReIzmwqDMO6Bv2uEtvssGsV+9NBW3v2Nn/+aTU6oT0oY2z8MSxapziyR7OwCc1Mefs40IAzVP2PgoDta5cObj49D37k+kv8mKyesiL8qduTD++nt1jB4AAdim4+mrtgVOKMselHsoyPbpLWmHL8eWsxvzOWTpRKprG9zJOEFsc/3Sjd5+wvtEJkJ8oPhz+WAaswILZnyptT+d568nmQ8sSiO9nwnHZSCZyIi1QdGZgH8hFEV4TDlmUwazIliWrRmrT1tYuL7aI2Lr+is1KUt4FmErN+/yREmf0q+TJyXIwz8+FADQr4udmFnWx3lNXVNhbI1qMmR3OK7Jn90W1w836+Nkgh8SttooZ/JVZUXlY5aTxsyiSDrawkbh/K/wLTuNEBL85u5hvPLNSgDAjrrMS9pPkq5AxmVcbinedHdYN3l9ueN7t5c6uookE0SqQWgmmLl4LcJrQtZtfoD9fIE/alKZkDZi531ijDrdhi4eLaLv/CEasxjzmC3ZuFM5sIal7QiCHZI/tkp4Z8/t1H7RXsPTHxuHtEUxfrG3cgMfhKCaZMW+VpYgvokK8JswxMNV1zTAsigOvW80fu+UAmpwBANxX16Qqk9ZoROh6nd2VFl/Vg3EDWkLr3yzMqcJZic3XpyvMMPGTX3agkWpdhoHmcYs7D7nYspkZlN3X+fdTFPqare+XLjJ0wZdgfrHR3bzpMZ5f8bawO3379QSAHyRnDz8OygGT0UWzJjGLDZzmT+aFNAX/Kp21mHB+mpXwEsQe6wsaR8zwV8v8332x2T9PxGPTJozOl6jVwEYBuAy57/nANwK4GtCyOV5bFvJsHFHLZZs9Gt8mleUub4zjEcu9taQu25QL1xyVCaCUOaoK5qDfnREV2wUNFsyWD1Af3sz+z57+ZGBgwC/v2xQkoWoA9FfAn5y43Ms8S+XeHZRa8hfR1SNWcbptnCvXdRBmw04/HWKvloMdmj2zO6/oH+WrbTRMX0lBN+TlEWlGipRuyEOoJZF3eO85zhtp9P2ZH24U7SewZseLRqe0oEPhvGavtWmTJXcNWz8ctzz/hy8ISQMjsKNp+4vbU8uhGmP1m+vhUX1gwJ4HzOWguCK43sGpuwJcv7nK5Ss3rIbj45e6BF+xdQcriBlUbRuao8NbZqVgxDiarRYWbAwDujS2qMdD3vfmUA5c7W9YCYEmHTnqRjUtyPatajAko07PH6JomAfXWNmE1utTFFj5vzxgKYJ/dRHxmLw4+MzGjNHKx2LYJYn5bRryhTPl6vGLBGff12u6Ly5FoADKaUXUUovAnAQgDoAxwC4I5+NKxWCsj2LWgQxQ7s4KfHO/uyl4idfwBbMdEgmSGjx4I6tmgQKUbx2RDbJvjddLphFDYX2TrD+31dU7cKfP57n+U4UNj6dm8nzdvlx+3oiC8N9zOIXzGSpR1pwk1kfZzUeFzKz5l6OkMsKPmeLzhxelvBHLMnuuo7/n3ic/so3TQAAIABJREFUlGUnOD2sm1cw4ydFdpQ3rjnW/e6gvVvjvRuOdz/zufNeu/oY3/lkzv8qc/22GtvMvkNSXk0X5pwNZITGJy49POvjAcAXEs02z8YddbAsquUOAdh9KeUk2OzUqilWPDQEx/fugMsCEniKT5QXUPgAjmtfmYanvljiKW4vCmbsb4tSrHWSIjMNLhOcwoI2mPWhWXkyY8q0aKhZimk0V2+pcduyd5tmaFKWwJZd9Tj9sXEe1w4xCXhk5/+Y8ymKyXSjwkoHsubEmVYofxoz+1+fj1kOx0zTjCm/BOQyLcGsJ6WUr52zEUBfSukWANk71DQigurOiTXKAOAvnEAh/tyG0xbJXs6KsoTUb0xGgvjznS34y2DP57JEAr85ZX+o4E0Qsg75QycNhT8CRquJLvxK01Nzz7nUlyetCGybSKdWTfHOr493tW9h41IyxgGHITuW6N/Hs3ebYOEpbCCTzQH3nnsQhp7VL7QOaxhsQL4gwNyWSBCfQC4XugSNmfA7hd+Uk7YslCUJugj3qJ7XUEjMbDef3sdTmop/d/jqF+woMpNYvlb2gLyvn3+43sJLhY5Zb0ddyi00HsaSjTttjZkwlqhyzgH+BadKQKmRRFKyCDhGktOYXT18KoBMOhD2rMPSznhMcc7xHv1skecdZakkeEStIttXVS7Ot+jI1pQZm/O/044cfcxYOb20I6DHITjmKyozozET56Tsz2dZcDVmJSCXaQlm4wkhHxNCriCEXAHgQwDjCCEtAOjplxs5gaZAycRUwQ1w+7bzOke34MqM5CIk7NehBfpLojLFqKFEArjxtD6Q8ZcL+nsme94fhnGEIt+V6iVQ3St+1SnL3C87nE6yRzfFRGipHvvfOJMeMo2K5zxcO1hSYcatkqLjPOweqMYXWcRh66bluP4HvbNKf+E5tiRVBw/TBIv+VlJTpnABormHUv81MufzpuVJj8aF15SMcSZLXismChP8++CJ6nNO+KrjK+o9d/4kM36CU+UczAcNKStwkSBia7G87bv6pP2U24vPXeULSSW/p9LU82xY3+X7FuszmXqXwe8t29dO95A5BkssDMgDqsQ1cFTFU7HTZWTeNfvI2UZn/ukj21oxet4GJ49ZnG2LF/edEjVmObzGKctynP9Lo+qBzkjxGwAvAjgcwBEAXgbwG0rpLkrpKflsXKlwoBOxAwDjfu+9ZNmLyfwk+nVp5atnmUwQZRklQD/P0p1n9UMi4fcxEwfIoLxEPz2quyeYQebkrKoUQCnFf4VUGboMu2Kg+zcTVGUvsSgk9GjnT7JLFNv6tsuDxuzXr073fce3454hB3l+C9OUhDUt15D4wHNzPiYyWDqL04RoPdmELN5jsW6rHTXo3SiVzmhRxi3K5ODj34elm2xzGB/cIgoTLNHoIV3bSNOryEzzSrnM2UeV0kUH/l7E5GKmeV65KfOY/dr5viPETinB33cAaFKWVOZZDHrf2jlFxFk7+H8B+LRzGW12ZhtmrmXPcI0iMTZj3rpq7noIOrZqgksGdvf0AZ3bH1V4ztaEGPdaINe+lRKE4lL2MWOwW/+L42yTe7Ztpk4Uc8LxtXxjymqMDihBWAhCeyG1R+ypAEZQSm8GMBJAvM4zJQ4/WbVt4TUPyJw/B/fvgn//fABG3HiStlkyKlt329qa0LxOip//9bMBKEsmXJ8OFWJiWYZFM8XLPefTGP46tcqYqy4aYGtihk/yazLEQe8MSXZ/tkl4ugz73zhXQ/xqPHOeTEN4AeL+C/qH+vzE1bTbQjRzQefm7yNfdouxV/MK9OSqULDNTzmgo2vC95kyJZ2Q36S6tgErNu+SOqvLnPWblnGCmaKDi+WFRsxeh7GKWq351Jh5k7IWTjKzKJXez6cvG4AXrhzo+S6o38nq/gLBQs65h2bM4cx3y+P8T73auaREY7bNGd+Yb9p/nMoNutiRppbn2uRClPc7HWd+fpOoGjM2l8Q1CmXK4TkasyyNmW6ko3OsOFJd5N3HzPncu6M3sjYq7FKThGCLkyD9jx/Ozb6BMaCTLuMaAO8A+I/zVVcA7+ezUaWMONjJXmRCCAb331s7Yueta4/1fXdIV3kqDQabr/jIJV2alicwxBlww9ItMH8WcUxTDfxnPzne4+gbRnlA7UOZCUxFqPO/62Cs3bRQxMErlbY8g7au8zWDDWT8dbICzoB++ZffSkzXfNWAoHPz9/GVq46Rbsv7ULLt2zavQAdHU8IPyB0lKRuosM15T03Auu21aCPxiZJpkL0aM/lzTxD/b58qVsFpi+LO975TVm8AgG+WbVb+FgS7zjcl73jc8IW0LepNZs1IEBJJK6R6X4LeN2kdTIv/m4J/7TPvZuZkrLh5tgspVs6NNxnKmix+FzQe8ceW/R2ExQk+QHwLRNmCKhcIia9eZN7ymEEQRp1rr67JzuWdLcyyqcObL3RNmScAqAYASuliAN+rHGY8udSIE2EdbGBPv3lh+K+OxjWcj8clA73Rnh4fhYD+LxuMeC1DWPuZxkzcikq+Y5wSMMFFQWya3C8j2DdKPFa8UZneY9WlLKUpMFtlCS/8/qCvP+luGMf2aodv7jwN++wVXKolE3Yf3lC+3qW7OdEve8VMB4wVTrkxmaAvc3Tnc6epBlNW80+HNLXNF77qDdz+O7OMzGTdTfQ3fOWqo0OF5ai0502IFpW+E4kEiTQBqXwyo/Zn0ZTp0Zi5zv+Z7VlQT7a+kyw3G39MmTapq/Be6AitQeX3lAjvV+zpMnL0MeO1UHGZMvPtqsUudfIyO6H73e/PUW8cABuvVKXwioGOYFZHKXW9nAkhZSiNHGxFgamumd8Mr8r+zSl6A604QMjGnrYtKvAHx0fprP5d8PCP7fxoJ+5vm5f4ATPoJWLn4rP/85qcsIFIZuZpUpaAReU5rKIiaznz4+OrJKjQNWXmI4+ZGDoPZB8VBWRyifEtzDX3Vaum5ejSpmnoCj2jMfN+z5Ioe9uU6TNsACMg0YRfySYysxCvMatwBDe+Lys1ZgmiPamrzPVxjHKq+3pSn464Y3C/rI5JKdC9nV/Q5icTVc63ZIK491EHlQCRu2CW+Y11J17D0n8fW5CNWlScsWLzbnwwcy3S3DsqO1Tn1t4o4GxKlQXB8lqyMTdz9Lg0Zkx7lONxOC0UIf5Ez9mQt6hM4bCsb22olpcDDIO1M09eR1mh05SvCCF3AWhGCDkDwH8BfJTfZpUuiQTBs5cfifd+fbz7mXHKAdkpEoNWXdPvOQNPXJqp7cZK8fADps7q6+VfZmpP1nKFlcMmfpnGoi5l4T9fLYu0IlJpe4ZPWolRc7wmph7t/U7+gHwFxlofHpVJlMfIFkqBLzm/JRpwfB3fj8sdJ1aeXOcJ1p6waFTX+V/oi0c52lzezOiZvJw/bROI/eFtLihE1nxV4s8GSRv5/scCD5pwPmaqibRJWcI3qauejY4gGTbx1aXSUnNnJo1DvBO+NBCGO0Wa2hqzVk3L8PszD3DvU4IA23bLTT53nuUXFPt0lrsTR/WXm7xsCz5yfFJ9ecy4RdOR+7bFift3cN9n2fg09ncna5835fHxC98+1+hmEfE5sTbEpTF7Z5pd5izXdns0ZoTEMk7mzcfM+ZfdS5aJoE4zcE4knWaCWelIZjotGQpgE4DZAK6D7fx/dz4bBQCEkMGEkIWEkCWEkKH5Pl8UfnhwF3RyVlr8xKDbDQ/cuxUuGtANj//EX0xXpF2LCo+Gi5VZ2YvLhxb0AjCzUyduZcinrhAnNub8yFBqEwBMWbFF+RtPqyZl+EqI+GJZxZdX7cL1r06T7qejLYrq/B+338MvX/zW/ZtKog0ZOpMCM+Xx15L7hE65/2doLpTt4vNA8fxhyIF4/oqBnoz87Lk0r0i6ghdBRvPBB3LImk8VRumtu/zpRxq4wfayY/yCq6qPlCXC62oyUhp9IuxQ9388H5c++w0WrK/2fG/FpNHgeXvqany9xC8E8sKSZdl9Z/Z9Z+I3p+zvTtwJQlCtqOfaQpLpv0PLJtL8X1HlgD9/PA+/fWMG5lRutyPgJO4UaYvaiW5DDt5Tkm5HRZ9OGb/MQgZfiGSGBf8Ccdbqbeg5dARWbtb3zWVMduoyu+9hrqZMYv/H+u3Nb87AQ59kF5XMLwa3KxYD2ZARIpnwbg88q7bsdlPqRMHVmPHrzSK7m+lEZVqU0ucopRdTSn/s/J1XUyYhJAngXwDOgl1p4KeEkIOC9yoO2ZiaypIJPHrJYW6dtihcfVIvPPzjQ930BUCwQNjdWbEpo9cEfxPx0TIhbp1ETaybEV126g8CUoa4bfM5/6uvtBiZ/0Uo4lHf89opcTKJ2t3Y2BiWsV+M7mI0LU/itAO90bCyXGGEyCc+mak8bcnzidWl/Imcee3biZIoUfH4bPX8i+N7+vLgTVwqd+CPQ1j/bo2d0lGcgFSayFwQFzkyLEq9fYW7RFmQBaB2fJdpJbON/hsxex0oVeQxoxSWEBiQ6/vau1NGiIvSYtH3LFv8+d7sf/nrYlqvsQvDn6vueaLimjJBnASz9vfvz1ybdf1f/rWqS4fXfrYs6qklHdpW55p5E+T7ihKCQbD335NapVQFM0LIbELId6r/8tyuowEsoZQuc/zb3gRwfp7PmRVx1b/TpWl5EpcM7O6dBDUGL5WWlh9027eo8E0gr09ehdqGdE6qbdkKeO824QOf+HLImsDaqy+YhZ42ayhVmwwXrven1lDBR8qKc+VLvzwa950bfY0iCrW++pURTG6szxNknhEBkQr/sn73/oxKPPbZIvcz862Taa6qQ4R/8f0b3L8LACePmdCeVYpcWHPXbg88BxAuiMxaYx9D7Ov5MmWGIfqYPfCj/mjfogIVyQROVrhcMCdqEdkYFzbsqcqRlTmO5fz+rinToj4z5+nCoiCoGgEPS/PBv49RnkGY7uGTm07CM5cN0D5epg3O8SXfxbFozFZgdh3gE/Z7bVHqpizJFn7Bo5Of86WJK3DRMxOVaW0Y4m3i+0sU/0kGu++NxZR5DoBzAYxy/mNFzEfCTp+RT7oC4LOXrnG+KzmipIbIFzrChsrJn+/UR/VsJ80bVdsQvtoJQubTonoHxt9+ivI4gT5moeky2DHyKZkBD49aKP1p/rpq6fcyCCG4/FjbbCdO9IP6dsSVJ+ynfSw26Bzbq73n+94dveagjGYn/JisLxFCPBoz2TNg1wFk+tp6Qfvqfr89uvOuKDT87aJD8fFvT0SHlk20F023vDXL/TvXyhAqTWSB129IW17h++KB3THtnjOcItXyfWolGktAUQw95Ho+uekkHLh3a9/3doFsb9t4U6YoUPbq2NIz2f73+uOCT+zQrW1zVJQlhDxyWrvabQkZJw7cuzXOOkSe4y0I4poy/ZGduXS9XIc1FuRVnrQlM0qBhz+Vj2W68IKmTvsWb7QXr2u36Y0DGY1Z5sGWJQl+999ZOOSPn2q3M+VqzLhj5xTGlTtKwYxSupJSuhLACZTS2ymls53/hgI4M8/tkt0Vz6MlhFxLCJlKCJm6aVP2KuBckZUxKjTiSov5n7HCvoBaEOKLoKcpdQuJ8xGmY+YHr2Ci8NgldpSSOIn379oap/Xr5JpeZfDReAwxl42KgmjMQNFZUQ+zaYTyOIDatBgVdt39u7bx3KMXuWAQIGMCbC7xMxJh5m8Cr2+LOOE/+KNDcO2gTD9SCQTsHVqXhWAmmtmalifd1BTZ3DtZ0uQo+PP9se8LO9BT0ZTJoWqLanEj15gFX48qsXbS1Zj5zeEWpW5pLh4+AERlhgW8CagTxNHAce97lCeg4/8bBdH8Jkt8m8uiUXSIjwpvzpMJj9kQVTDTPV0mGa7dTl4zXp+y8M60NdhRl8KqzcHVItx2WvG7G+SKju6uBSHkRPaBEHI8AH3vy+xYA6A797kbAM+ISSl9llI6kFI6sGPH6PmdsqFza3+yTI/zfwyTfljCVxmisPHTo3sAsIMUGCofM14jY1nU9XOoqc8Mhne+F7/lWnwJ6lOWLyErP4H06dQSN5+uzmivm8csXyHcDJbORIR3ntdB5YwfFY+bkXPMJy493FM2BwB+clQP3HJ6X9ygkVvL7fMEmF253T22OOGLLv4qgeDgfWzNyhE9ot0jIH5Xgpvfmin/QfM04jWm812XRkFakS4jiKBAChGdI4t1e9k5KBUCXNx0Ger8a+7+zm8/Gdjd99sjTkohdh7L8VmL1GiH43q3D99IA/GU7PPNb83E65NX2d9JhLWo8Jn7s4Hl0rz0qO5Orczg56ADf+t1aoPyAQhRtlONK4P+/mXoOYGMUMrn94urlmm26AhmVwH4FyFkBSFkOYCnAfwqv83CtwD6EEL2I4RUALgUdvH0ojH2dyfj05sH+b6XRRflwu/PPCDyPuLK5mQnNcXx3OAS1LZ3f30cOrSswJJNO13tBd/XG4TIzOMEs5hOtuxMW+1/xebUpSzfQM5vcssZfaVRY4ywe++uyvOoMpOlgZj7pzNx2TE9cM2gXhGPFc8qTjZoiX47gJ1n6abT+2hp9ni/vsUbdwKwzZPiIxBvteoRtWhSBkJs/7moxJ13KlfE1vzry+wcp3MlTMCR8bsfyseebDRmgLxkmasx481PnPN/mEDJ9pNpz5t5Eh/bJlN+IaZqM8ub2KFlhfT3OOHfx0dHL3TaZX+OIgxMXFKFnkNHxNYuFuxwbK/2TiHv3N8t3ozckLawdNNO9Bw6AjNWyR38MwEIwbjaQfYvt0M2mumURGNWpPWUS6jdglI6DcBhhJDWAAilNNxTNkcopSlCyP8B+BRAEsALlNKiFq9ShWjzDzMbx0OR9i2iDw7iQHZMr/ZY8dAQz3dBNvMj922Hqp31qNqZcfZ861t1gfI+nVtiEpezqWlZEg3paCZd8QWSacz0jmP/GzaGyOrxxQ2FXzBr0aQMD/zokMjHEku4ZMuY+f7w8VwXEExo5H2PymQJXYV7odLapi2KE3p3CDRTqShU8I0slQeD98ssFXMIpXpt+fTmQTjz8XEA/MlWGTL/VJ3L3F3v91kjgC85NetHlVtrsHLz7sCkskHXVC4pjJ7ino1qzx7tm/vGy7iRaYMybhh6bhZzKrejb+dWqChL4GfDJiuOn13/+3zBBnd/AgIKmrMzPK8wuOnNmbjQqYv84ay1OKJHW+V+YZfAAofYtfLjUDbvHwv+mVOZEW1kvtaFJCgq8xz+M6W0WhTKxG3ihFI6klLal1Lam1L6QL7Okytn9s+YC1XJGKOQzYvFTJdxEuQ7J3b+IIFKLISturx122s9iUMBPSEqTJ3NSHKr8nzBlxqS1T+Ndiz731zn+X0kPm+5Cg+tHQHq92dmTMti0XDAH0WrOq/Mr0hk+j1nSL+PUvcxF259e5byt7v/lykFU2i5TDRJM9IBPmY8B3TJ5PlSaUhkJZxk79u7vz4OI2/05zzjsRcv3r7A3nsWjecrjcXBBK6fHuMf8/g2sUvJqnySBN2gA3+b1J9djwANU2blthqc89QEZXHtDi39bjbPjF2KKcv1ck2yvHgJYpuWKY1mCZHBj99z11ZnNIOK64w6NBPhXwAY2FMt8KlgCxI+QEont2E+CdKY/Z0QUongRfuDAD6Ot0mNi657NYt1tZWNAkDH/MTnK8tVyyAONjJfEsZ1g3pj/OIq5b48oirfk7Vb1Ra3Vqb6uEDhNGaUUnRq1QTH9IrLRyW3ZyULRsj1+TctT7p9/o53ZwMAPp27Adf/wOufJg60qmc/a/U2HBbig6cSQMQ8fHEyY9U2re3e4nKtFVJj1qFlBb79w+nS38S0EzqoErvKjiO7zCP39df8FSlLEF9gAhP8dN5NJof36+KP+PRuZx+zQUNjpkPQGJctGW0/05ipr58V6JaZAY/r1d414/LX+LdRdmLY568YiLXbanD5cT1D22S3xTYD51rU2+/KEHydUQNlZJtl0+IXJiwHkEnbA/gTrReaIMFsA4DHQvZfHGNbDMhf9FbLJmW49Yy+eOyzRVqDzLBfDMQXCze6Dqo8oqmhSYBgKI7pQYLG21PX4P4LMma/oyXF3UW0NWYk/4LZsk27MLtye8mYswDgoQsP9X2XL+ufeN3iAByU0X3Waj0hSKRZxGjXKIiVLdZs3Y1ubdVRw4WmLJFQ9vuURUPfif5dg4WbzHmy8zGT0bZ5hU9j5kYlauyvK2xu2lEHAHh6bMbHL5f3Mq70CbLrzmiS1HeAbbtAkg8x7LKuenkqAGgLZvbxcjdl+t5/Iv+eIfqYfThrLU7t1wktFb7Fsv6dzfA+mlULKJ1hOzBdxsmU0lNC/ruokI3dk9H1lcqFiwfaNn6dRIanH9QZ3RWTUI2Q1yzQSTRAlS9yzH5eQaxH++YYzEWW5gITCmTXvqJqF/47Ve1TB9hZ6T+YWRk4eF7yn0mYU1kd2Zx15L5t0VpInJmr+Pjn8w/Gv342wGOquvhI+/nnS/j3mTLFRJB5OG822j9Z8IMOl/x7Uug2eQ769RCk0RALhYuMv/0UvHWtnnlOdo91BGIWbcuzdXc90oLQGCaYsGSx9rZ6z/u96Wv8XxZx4mVXxidazZjiwn3M+Ge5bNNOz29evzW99uyobcBrk1f6vifEbhelQHmOk5FoDgzzpWOPP5mwc5Hd+MYMnP/PCZHOmUuS3mLnLuPRS6NsyDusP6ny/+jQvV1wNn02Mer2XdVLvtFZjbrHDXiBxc4eJBR8vaRK+Zv6+DZhOXfYtc9e449dOfepCdhRl8LFkhB8xhNjFuPpsUsxXaNkSNSV+bu/Pt733aC+HfHOtDU4SDK56fALyer4oYsOxV8u6J/V8XQQL1t8ItJEpVky+pZBvglKl2yjzdZq5FnLZ8kvEdl1nH1IF4ycvR4AsL1GXZ8wKF+giOydjVLHluf+EfMBAAu4hMvs+PPXyatj8MKM7rtV2+B33s5lXdC3S7b+w96T7uJ8d3fUpUC5QIigrsP7Um4WzGz8GKu76Lrn/Tl4f6Y/Z1+COCWZkLubgJgqJiwynpmdkwnilqlauik8gTtV/N2YKZ0aBAYA2U8ai+4/C1/cdnLgNkxrpC2YqY4j/BBFaxG0pex9DQshZwNR2CWxQea/0/wr6R0aSYKZaWSmRLDztyl0k1DOO2wffHffD91kqXGQTJDIiW6jHp9HFJbLy7K7MbISVH07t8Lg/tEzrwP5FZ4KKZjJ3rtDumZ89XRr2d537kE4SJKlP1eCUg7UeyJZ7X9FTTxjNFeYWnesuWbQfr7vsn0tVzw0xBeYlC19O2c02DtqU3hu/DJuDFP3HT5oSYx23bgjWmLm3fUpqVAG2M+CELsfl+doyhTTLC1YH1z9JJX2R3sDwMsTV+DO977D9pqG0AX4hMXFqzcaJ0YwKxFYXp5stQoVZYnQfZMaAwAAXOZEPF0qifb8x08O8w2ObCUkc9AWB3y+8793g19TpEL10mQ0ZsH765jRgnKcsWtu0Kj5FpePWeum0dNHFJoLDt/H/TvsuuPUmOVCPkWnQgZzySJS+VdTty1XnrAfRt4UHE2ZDbpCapT3RXcN+OMj/drvQldfkCH6WY6as94dwyxql797eNQCXxk8fmy6TYgQXrRBT3PMUodsEiwenvYR4poyw5z/V1Tt8mitLxv2DR4cOd/9LPrzDp/kN5162udI8qJy4o8fzsUbU1bjjne+w7jFfqsKv/Wnc/3pgaJwiePuU2xCR0pCSHNCyD2EkOecz33ymSbj+8r7vzkBdwzul9e8TBk/K/9vvFDV0vF3EvNKHd2zHX50RDffQMqOS5ApBwUAv/thX7Rp7j0Gr3Yf0KMtbjx1/+gX4j2gFvx9Va26giYSdo1akWPFH/8LBqsX2HWvZr7rPlFIlcLXzYxCfcw5hSi1faxG3zLIVzMUyDUiq4CmTMnEWai8bjq0DshLx7dSlJduOq2Pej9N4SrXVA9xoxpzpq/a5inJ9OLXK/D02KUYNn6ZZzteY1a1Uy1YBfHk53asXpAvFSH2PaaUhrrVnPzIWJz66FcAbKHv6yWb8ey4TLtVKScotQXNX730LSYtzeTDZJpA1QJu1ZbduOqlbwPblAsEwOHdo6fbyAc6S9gXAdQBYJ6iawDcn7cWfU/p16U1fq1RDicXkq5g5n9hftA3U9ZKlSj3riEHSr/v1KoJfnXCfnj16mOEosH+AUCcN4Ii9YAoztTBG/LC5PNOeDQADHlyvPu3joZBRwtQSlGZ+ebMg7vgneuPw4Q7TvE878O6tfGlNLjy+J5ZnSN+LRRF93bN0bdzK5x3WFffr/UaWlEVMhlSJ7o4G+RpLDh/o7ycVZ9//vQInO9oVHnNKiDmHPO29NgYUs3IxrBczcwtKqKbM3WGgmVVttaJ0kzfqxP6YBzR5OOXVOGDmZWBbSJOVCYFsERSuUGFLL+aqhzZrvoUttc04IsFG3H9q9Pc7yc6QppqTpi3rjrv+cVYpHKPCD6Y+UBHMOtNKX0YQAMAUEprUPx33pAFzR3/ohtP9a9IefWxalXKwpbFV6PrXs1w77kH4cC9W0vNgUPP6scd2/sbPyjfIqmFybQu+3WQO9+y3Fld2oQEPnDX9xFXqHru2ozfQ9DAzfbWSVD7PZLLANh19gghHqF7lsQXT1fb8e+fH+n5HLffVtvmGe2wbA7QVTpt212Pb7gKGADw0sTlns8dWlZg/xgST8uQ3c8gTVSh6dS6KZ649Ai8cOVA3Hfewcrt/GNC7ueWaV1yzUWWL5FgPRdUokrCGscrMGPVNtz05kxUbqsJ3I7Armf6smN65J/Psk07cf/H83wawE/nrnf/rm1I46wnxmPiEu+7wfj4u3WBfTPb1Dkyeg4d4dHKBUEIwaHdbB/NIYdm578aFzo9tZ4Q0gxOvySE9IatQTM0MhIJguV/PRu3nOEXgHi1tcofiwlvo+as93zPC168XMZWrd6aeUKUJvd3lzb+7NWXH7tVCcF2AAAgAElEQVQvptx1miftA88lA7tjxUNDlLluGPxgTwhBbUMa1bXeqDU21jw4cj6+FXJYsVuSShuNmYq4rntw/y548cqjcNWJtgN33P70f+SEBNnqXFeAvOLFb3Hps994vpuy3Bu125CmsaxiH5SU9ZIJMJ7UCTGcV+T1q4/BpDtPjbTPqf06e+pYiqhcIxjZTJLlghAWhwN/vgI7JnPZ+dmt2F7TgKWc/1ac+RdZSSMVhHhNr5QCY5wAjKuHT8WwCcuxnKvOUNuQ9pTzW161C/PXVUsDrUT4yGGmpQprn0jYneGFxiDYva9IJgqa9kaGjmD2RwCjAHQnhLwG4HMAt+e1VYa8oZp0+NI9KtcCVfZ8PtKPH7xYeYyvFmYiZXoJPj38INyswi9cEULQSVHDLwridZ/z1AQcet9oz3cH3jsKtQ1pPDtuGS5W5KxKafg7fV8Fszj9m07p18nVcIRFYkXh+N7tPUK8blZ7GQslUWZVO+vQc+gInPDQF1i9ZTe21zTgvemVWbeXcVZ/fz4/WTPz3fPatazA3iHaaRlihB/fTlG7JT6SQ7KITM6Hj1ku3ZD6/pBtk8nv9so3K3Ga478FxFtKLqwOJIG/mVcPt5PUsmbwgtg7ggAWdfxb7CzcV23ZHWk/Rtj4IGtP2qK+AAsXEq2gfD4IFcwopZ8BuBDAlQDeADCQUjo2v80yFBq+9pzKxq8TVccEs2//cLpbqJbPu8OHi4sMOaQw6uOZq7dhyUZ5JJPKsZY5zOrksvqeymVa13374AO0j/f/7d15mBxluffx7z2TdbInk4Qkk8lGFsIWhhASSAIhgZCwhEURjIqABhRkOQcXxAU8cEAOHhV9BVE5CvKKuB1QFFlecI8IGBJ2AgmCYUchigECz/tHVc3U9FR3Vy/VVT39+1xXX9NTXVP1dE119V3Pcj/NBQarxHXxUbty1fvn9thmoFAsGRUQ/P21rnO50Ofhr3//V2dNcb40EKWI+nJ5LGI0XvgGJImvlnJvOnKvKbk3Sh3tXWk+8iUqLiXZdKWpHqKUExuVcrRee+OtvM2MhUaMl6rYVGNNTRb5Xl/d9mbnyMljvt514xpMAh548qXiuceu+1NXQu9tb77do4WiFMUOTdRnfPEldzDr0zd3W2bhn1mtMTOzjuABTAKeAbYA7f4y6UXCIzDzNWW29PdqxnYqkPMo+JCEt9c2Iv8ddvginIURZZfc/Ejn88ee28pxV67lX29Ef7EeNDs6g3yj1pgVa06G0r5cu9IIlH+VPHZeOwfMyp/pP+p/FexuyICe72fO527tfF7sRqUaAVnAInZVLP9eEv2kq/URLdTXtOdgAG/wxPElDB4pNqioHMF5+K4Ciagr8T+/29xjCrwgILtm7ZOJ7DOKEf2Z2+28W3jq5Z6BY24Lyppr7umxTq6Lf/Fwt9/ztVDEUez60NRkPPfqNp59ZRv/eH07n/vpg50BcG63HKBz8EOaCl1Jv+D/HADMBe7D+5/tBvwRWJhs0aSW+jY3MXlUC5tfei1vgBQMHgjfvU0a1X30yuxxQ9nw11e6DSaYOXYIT/8t+k4wazHMjaGBARfc9BB/eOIl1m6K7jx65B4TuiW/DCRws14XctOrRCkll1SxKVzKsVtb91qw6MDM+fstvONiyaCr2S+onNk6qtkEHKjWTUePOXQt/Fr3F/doH8Gmi1aWnYdsz0nVSYEQnA+fOyL/QIZqu+LXj/Ph/XfkhjwJYRNRYmBS6UjJTTFq2Aopdpqbwd7/eXvka7c/9Fy39SAY/JDRpsxgPkzgSaDDOTfXObcnsAewsVYFlNoJ7jLzXXyDAQLbQ8Ogc0dbXXPSPK4/eUG3O9ZCI7IyUEmW168e9frGnfX9dT0CyCED+uQNKhu1xszMaCmSUiDfkSk0UXY1L5K5I3+jbkKCvRULNIsFZpWk3cgVd0RhuERZasqE7ikIcnNphf/FUbuoJDnsRyrNlegLihiV2DfuH5fad+k3j5Y+Td3ubZXNFmIlRmaVNrOe/r0/V/T3RWvUC7zcvejeOeYNfqioSBWLc4bNcs5tCH5xzt0PzEmuSJKW4KIb9WV1/IKu5KDhdAOv58xJN7ylH/NyJiMfPaTnaMvcfWbZ3197s0dA4X1RRpc9CxnG01Ls/5nv5ahkqf38YKSaTdy5STOjNh1clItdnMP9DaMmRq9mzqUBfZvZeOGK0v4okabM8v8XR3V05YzL3czdoTloq3VNGOcPaCorkMKbj/Wb7+vqn/gfq3ZhSP8+Jd1MVnotKOfPj4uYsaUUTVZa94Gkc4sVU2yauW/kJOsN+1HEZPdZuHrHOWMfMrNvmtn+ZrafPwPAQ0X/SupO0CH+z3/pOVH3+au6Jr/+2uquLoZxsqQXnOS8ToKY3/XIhWNFp4lqROWMkMq3/P37TObEfafwwUVTq1K2yP1GnJt3PvI816x9MrKZOvDHnPxlY4f2vPk458cbeiyrRLFM7LmSSO9QSTN9uE9eoY99tboCBCl2yg3sZ4wdwrJQP9L3zJ/EhvOX1/Sa9fvHX+Irfsb+uIqNuiym1D5W5Y6mrJYzl+WfKQLid4Xo1g2ggvJUQ/HeunAC8CHgDP/3XwOXJ1YiSd2fNncFZt89ae9uTZdAt/QVca55heaprJcgJncUp1n8Sd4bSbELWlSHeojuBzSwXzOfiZjAvJqizs1PFAmofv/4izzxQvd+MflG+dbaC6E0Bkk0x1RSmxVu+m0uEH1Vq8bskqN346rfbe5Rg5+Gn61/hiWzxpT1P/lCiXm9Ku2PZlhJ/SPXRySTrqWWiDRLlfCmpKrqJksWJ13GNufcF51zR/qPLzrnSpvOXupK+Jq5cHor+88ck3fdONfQQiOk6qTCrAeDvMelHppnk1Lsgja8pR+//ugSlswc3W15brb/Winnf/Wbx17sURsVTpSZq9gdfTX9fmNXn6Qkaswq2WK4BrJQmrG4/5PPHFo4aB8zdACfWJHs/MPFBHuOajJLSqUjUhv1xjKcLiPzeczMbJOZPZH7qEXhJB1x8pUFSqnWj/qCqtcgxszrAxU1D2K9vqdquPSduxddp31US2f/scCgGKk2ElHGv8ro2bF/eoH8fEd3tJW+kzzCx23prJ43BuEO9kl8tVTS0fuemP3I4gYGJy6cwgPnLy+7PL1VvrmOY6vD69dHl8fPj1hUnXT+nwvs5T8WAZcB302yUJKu8TEyex++uz8pccwTePPFh3BmxFyYwTXgPfMr67Baa8GosjWLe/Z/qsPrWtUE0+cUG51Zb8HrdWvmdz5/4oV/csFN3bvZDuyb/1Ja6RyN3fiftyve08GXju05BmtYS9dI0nIy5hfdfQ2+sN6MMe1ZILWAPgPy1QRWOuvBC1vrb8bFU5dUPvI2qGTIwpUpTlPmS6HHX51zXwJKmyxN6kLQB+SclbOKrNn1ZVNplW/wYUj7DqVUQQb7ZRFJZh/c0nOqnkZy42n7csfZ+xdcJxyY/eCUBQmXqICY5124vDdHzL1XqLkst3awEsHnbcmsMQwZUDidRzW+rHJV0jy6bKf8XSLCcvu09ibVusxNHDmQBVNHRb5WSotHlNe3Vy8xcj3pbMq0DOcxC4RnADCzuWZ2CpC/3l7qVvDlM6R/nESh3s9Kz9/gw5DyiOuSHRVqntpnWvcLZLGM7L3dbm3DGVtkftPg/PnysXPYK6I5OGuKf9fVJjDbebxXCxanxjGJvlUTCsziUcy7945XK96b4rKkKoYX7thaUuqZUlTcFFrnsp75P/CF0PPtwCbgmGSKI6nyP8/NMT7YQVNepSdw1xdM2h+F8k0YXv6XVaMKgoZqZscvR7jG99yVO3Hhz6MzARXrS1koBqrmF913TpjHo89vzVsrkvSNfiW1MeGksoWKmcSghd7me3c9xRlLp/Obx17sUeNcbt62wIPPNGaNf1fm//RbcOL8B08KZgFwzh3onFsDFE9eJXUnuGz2jXGnHXz2K64x83dVz3fJ+hopXZCkuFYXwB3HDGZIkf5IrUP65X2t3MS5UHr+sUKGtfStixrGSDErcsZE5ISTnk5fOp1fnrm4x/lQbEaKYsLpL/ZOOdXI11Z3MKhIf9VqM7Psj8oEfhhzmdS54MslzhfJPtNaAdhpXGWt2sE1pBYfhM0XH5L4PiSes5fP5NQl0zgsGESSsFvPWsz68w7K+3pH+/AiubUKbz8rgxlO3m8qZnBewvnfkjJu2ADGxRh8VC96Tj1V2XVu1CDv5uHkxVNpbrLOJLphlTZlzgpt8+BddqhoW5Vaueu4mqU7Cf5Xu0wYxoThLUXWTlbeW0gzmwXsDAwzs6NCLw3Fm9i8bGb2X8BheDVvjwMnOOf+7r92DnAS8BZwunPul5XsS+ILTsw4H+zDdh/Pvju2MnJQ/lqGOILkgINj9GvLqrSrvevR4P59+Ojy4oNMqqVYU+S00YMLBl9Fa8zKKVQCxg0byKaLsn8D8o49o1OIlHM9+cyhszNbi1itWQwCwaVmVoEb4kpraNtGtPDws1u9bTVQUrPgI371ifPSLQiF+5jNBA4FhuMFUYGtwAcr3O+twDnOue1m9nngHODjZjYbOBYvIBwP3GZmM5xzjTlMpMaCEzPu3X+lQRnAIbuO47lXt7F670nFV86otKu9pXLeTA6lJ0I+cPZYbn3wuYLzwYov9DHZd8fWqm32xIVTqrataqu0v1euoP9dv+b8zXtxuqIUEpzrK3bZIRNT5jXi1TXvWeOcu8E5dwJwqHPuhNDjdOfc7yvZqXPuFudcMHRtLRDcPq0CrnPOve6c2wRsBNIPXxtEMLy+qnmXimhqMj6waCoDa9yPoKoa8crRS4RrO/N9n91w6r55b1YufcfufOGdu3PyftMSKJ3Uu9zKKwcsml5+UBqchYVaNcqtMbvyvd1n31g1ZwJPvvTPPGunqxqVAlmW9z9oZh/zn77bzC7LfVSxDCcCv/CfTwCeCr32tL9MauDUJTuy+eJDKs6DU4/mTBxe9t8qLqt/lmdS+gNnj2X3icPzBmZ9mo2j92xryM9MyWJUvmSggqaqCvVbrGR7heYfzpffrJiDdvb6k4W3/LP1z5S1raSlnWcsaYXOmmDc+N3APRGPgszsNjO7P+KxKrTOuXgpOK4NFkVsKvI/YGZrzOxuM7v7hRdeKFYckYKmjR5c9t+esO/k6hVEaqr7xaXn5efPf/lbj2VhCshKEOO7tLd93xYKoMrann+6FYr3opJeR1mdJ69cV5FdJga1xA0K/t+/71fZftJ/q50KNWX+1H/6mnPuO+EH8FqxDTvnljnndol43ABgZsfj9WFb7brC36eBiaHNtAFb8mz/SufcXOfc3NGjR0etIhJbcMHr36ep6HRCuXZrGx5rjkjJLrPoC/OL//AyA+XrR1jN5LGN7PuhKa96k2p/2QeBXjUCpguP3JVlO/UM4jpzVDp47tVtJW2z0r6WrYN7NlEO6NvzehwVwE+t4OYaCvcxrbU4V5VzYi6LzcwOBj4OHO6cCwd5NwLHmll/M5sCTAfuqmRfInH079NMk8FnDpvNpFGDCq47oqXnCNIdx1R2UZD0FaqtKfeivbCKndzTlPTovEaZ87LSGsGmpuoFZlA4ma8DtpeYALrS0+QP5yztlq4D4Kr378VpS3bk2g/szQo/fUfDNmWa2Qoz+wowIad/2bfxmh8r8VW8aZ1uNbN1ZnYFgHPuAeB64EHgZuBUjciUWmhuMp646BBW7z2J2eOGFlw3qpZkzsThnLhvdkeHSbTw9b1aF/uvHLdH5/OOSSOqss20rf3kUu4sMv+pxFONkY7VCsyiZt6oZLq9qBuYkxdPjf33fZub+LA/CG3GWO9md5cJwzh7+Uz23bGVrxy3B/36NPGpQ+Pl6SvlhjlLTZmFblO24PUlO5zufcq2AmdVslPnXN7ZdZ1zFwIXVrJ9kVKFq/SLdRu69gN7Ry6vZB5BSVex+fFKSYkS9Ffs22ycsXR6hSXLhtbB/WkdXJ2UILlzy0Lv61uWhL0mj+CZV7ymxVLGFAzs28znVu3MR3+4vsdrUTVmnYFZCef8pw7ZibFDB3BRzpRmZy2bwRtvddWtvHf+JK5Z+2TBbR2++3gOz5N4uk9zE49esCJ2uf7twBl8+Np7Y6+fFYX6mN3nnPs2MC2nj9mPnXOFe8SK1JE/nHMAC0ND2Ivdje44Jjq5YwPlYuw1wl8+UcFBVJ+XuNucNnpwzbKW15NCAyayVGuRhtnjhrImooZp9d7t/OCUfXhjuzd3XSk1Znd/alne8zDqnM/XbL9qTv5ZOvacNILDdh/fozZw8IA+fGj/rnqY9pG1zahfyumUpXOvUFPmBjNbD9xrZutDj2C5SK+QO3Kqqcwv0x2GVjQhhqRgxlgvyO5oH1FxouCbz1zE+Yfv3Dnva2/PtVSuLH0B1lqhM+xrqzu44bR9GTbQ68N6dEfX7AhTWr1+r89vfR0oLTCLWvfHH94H6MpZGZVbzTmYG2qK//cDZwJwwRG79Fg3CLZz+4c55xjcvw8npZQEuF7PtUJNmYfWrBQiKcoNxMqt5Ig7TF2yY6/JI/nNx5bQNmJgZ86mQ3Ydx00bSs/fNGuHoczaYSjOOc5cNr2uZ7OQ5OS7vOw3Y3S32sSoEY7jhw1gyyvbina36La/nB1OHT2IjvYR/mvei++ZHzpXO5syYde2Ydz9pNdA1j6qhc0XH8KDW17tsY+gRu7Lx+3BH594iZ/et4X/XbelRx+2NAKlozva+NG9Txddry5GZTrnnox64KWw+Fi+vxOpN7lNK1G5h3LvBKM00rxyvcnEkS2YWVdtRoX/RjPjzGUzNE1TjkI1kuOGe7XNR8xp3HziwWUnCGairidd51T8kzR8OVuxyw7cfMbi0KvevsK1asEz5xwfOcDrIzkolEIoKrgKat4G9+/D0p3GMnaY9/8M4rJCfQiTrlm+9J27cd9nD+L6kxcUXC9LtWuxxiib2Rzg3cAxwCbgx0kWSqQWhgzow9Zt2xmSM1S/3FFTwd9FdW6W7AtGZXb/73u/DW6QdA5Jestv4o268Wkd3J9HL1hB3wJTDfV2QXAUBDHhqfGCa0s51ybDOgekLJze2m1UeRAEhmPA8D6CmrDwsnARTth3Mv/zu82MyhkYEryXqMEFwwb25fgFk3jvgsnc+5e/sfP4oSz8/B0lv694DDNj2MC+zJsykuYmixyJmjV5rzZmNgNvQvHjgJeA7wPmnFtSo7KJJOq8w3bm6D3beiyvpMP2rz+6hNYh6ltUz6K+/NpGtPCDUxZwxZ2Pc/vDz3Py4qnsN0OJrUvxlt/5Lt/nqyGS9RaoOgpOu5MWTeGFf2zjxIVT+MKtj3qv+esEx66U1C5NBrtPHM5vP76ECcO7jxwPYpRifdbCL4eb/D51yGxOP2B6Z7+4QBB8B0FQUFtqZtz32YM611vuTwM1f+rIRGrOct/WT09byC8feJYv3/4YAPMmj+SuzS9761Z97+Ur9El4GFgKHOacW+ic+wqgnGLSa/TN80Uwb8rIsrfZPqqFln6qXalHrvNLKvr1vSaPpMWvOZs9fij79JLksbXSx8/xMDwiQXMjyRcDWah29oIjdu2WdDf4m9yAJ97+vL9pG9HS46YjqNEKLw7Of+eia5G3vdkVBjQ3GSMiAqqg326w/eCzlS/4uW7NAr62es88r5YuakYD8D634f50s8YNYciA7F2vCwVmRwPPAneY2TfMbCnZCipFyjaldRCLI0YigXcXd/enlvVY/tPTFnJZKHmo9C6dd/XAkXt4fZ3m5iSIDfraZGEOwXpzwKwxfOzgmXw6ZnLQ3iB8mmx68Z8F1y1UUR9MExdsr5TWuEJnqouoMevsY4brDBbDN5tvBm3SBQTv5W2/oEt3GgNUdtNbipP3m0qT9fz8Qv6+jln6SOcNFZ1zPwF+YmaDgCPwksqONbPLgZ84526pURlFqu6OIlnMWwf37zY6zzlvhNKubcNqUDpJQ+ddvRlffNccPrBoClNbu2cO/+QhO9E6uH/n1DASX1OT8eH98+YW7/WWXHonuxW4fkQ1oT9w/nKuWfsk79jTm0K66xyNv99C6wY1WlHNy87BsJa+fHT5zG7n+25tw4vu8+iONq7+w5OdXUUWTR/NpotWVmXWgzj2mjySJy46JPrFnLisZ0iavqKN+s65fzrnrnXOHYo3InMd8InESyaSsq++ew9uOn1h2sWQGsltbtl5/DAG5kxoP3RAX85ePpM+peQrEAnJ9/UfVWM2qH8fTtlvWmfg9O6924Gu/Hux9lcgGHo7oqkyd/1Tl+zYbYLwOH0BJ45s4d5PH9ht3uFaBGVHdUwoOqVebm1jrYLFUpTUuOqcexn4uv8QqTt3nr0/A/o2F18R7wOrJqvGUa10GSL5RI1SPGnhFL71202xAoQj9pjAEXvETylyT0SXjO7l8X5aVFNm9gcv9vDfx8wpuk5uU2bw1rN0qddtnzSUya2D2GFY/Az9QWBW7mwAUj/q+QtJ6sNTL/+rx7JPHzqbzRfnaXar0KAiaV7mT/VS+4wLXxNDCWZ7o/Dn27ls3odlbziCSIZMHzOYDyycwnsXKIt7b9c+ypvHb9Ko2s7nJ40jqsYsScVqgc5YOp13dLR1nvthpaTkqCf53lWWAjQFZiIFNDUZn2qgUWSNbK/JI7n+5AV0tBfv3CwSR27z5NZt27stO2DWmGT3XyTcaG6yHkFZ3KmJ6nUATG56jCz2MVNTpoiIb96UkerYLzVz1fv3SnT75cQcFqMp85ELDuar7+4oq0xpGzqgLzefuYiBfZt5z/xJnWFolgI01ZiJiIj0QuWEGp1/UyAy698n3gCqrJq1w1Ae+o+Duy3LTlimGjMREZFeqZJaoEKTzvcmGaoo66TATEREpEZq2am+rBqz0JRMjSGYqD3lYoQoMBMREamRN2JMaZSmoPN/o8RlWQrIAgrMREREEhD1nb/+qVcS3+8nV87y9l9B5/9GE3c0ai2o87+IiEiNbH19e+L7WLN4GmsWT6toG43SlNk1KjPVYnSjGjMREZEa6ZfxdCxd6TIaIzLLUkAWUI2ZiIhIjTgcX3rXnEwGBB6/j1ljxGWZasIMKDATERGpEecoaSLyWstuwJiMLL7fbNepioiI1KmoL/16qYiql3JWS5YCNAVmIiIiNZL1ycG7Mv9nu5y9WaqBmZmdbWbOzFr9383MLjOzjWa23szqczIuERER34C+TZy831QA3s54vBNnrszeqClDVWapBWZmNhE4EPhLaPEKYLr/WANcnkLRREREquqw3canXYRYOtpHADB9zJCUS1IbWawYTLPz/xeBjwE3hJatAq52Xl3vWjMbbmbjnHPPpFJCERGRCmVx5F8+R3W0sdfkkUwc2ZJ2UWoiSAuSoQqzdGrMzOxw4K/OuftyXpoAPBX6/Wl/mYiISN3KUlNZMY0SlGVVYjVmZnYbsEPES+cCnwQOivqziGWRFY1mtgavuZP29vYySykiIpKMcE1ZHcVlkrLEAjPn3LKo5Wa2KzAFuM+8M7UNuNfM5uHVkE0Mrd4GbMmz/SuBKwHmzp2bwVZiERERjwKzbAr6mGWpubnmTZnOuQ3OuTHOucnOucl4wViHc+5Z4Ebgff7ozPnAK+pfJiIi9cysvpoyG1GW/j1Zy/z/c2AlsBF4DTgh3eKIiIhULkPf+5JxqQdmfq1Z8NwBp6ZXGhERkeoyslUjI12y2A9Kmf9FREQSp8gsiz5/9K7MHDuEkYP6pV2UTqnXmImIiPRG4VqyJsVlmXTArLEcMGts2sXoRjVmIiIiCTO1ZUpMCsxEREQSprBM4lJgJiIikiAzU7oMiU2BmYiISMIUl0lcCsxEREQSoFhMyqHATEREJGGqMZO4FJiJiIgkyNCUTBKfAjMREZGEKS6TuBSYiYiIJMnA1ONMYlJgJiIikjBl/pe4FJiJiIgkwfI8FylAgZmIiEjC1JQpcSkwExERSZA3KjPtUki9UGAmIiKSME1iLnEpMBMREUmQmRoyJT4FZiIiIglThZnEpcBMREQkAeF6MjVlSlwKzERERBKmuEziUmAmIiKSMMVlEpcCMxERkQSZaRJziU+BmYiISMIUl0lcCsxEREQSEA7GlDBD4lJgJiIikjDVmElcCsxEREQSZCgwk/hSC8zM7CNm9oiZPWBml4SWn2NmG/3XlqdVPhERkWpRU6bE1SeNnZrZEmAVsJtz7nUzG+Mvnw0cC+wMjAduM7MZzrm30iiniIhIpcxMNWYSW1o1Zh8CLnbOvQ7gnHveX74KuM4597pzbhOwEZiXUhlFRESqQukyJK60ArMZwCIz+6OZ/crM9vKXTwCeCq33tL9MRESkrlie5yKFJNaUaWa3ATtEvHSuv98RwHxgL+B6M5tK9Lnr8mx/DbAGoL29vRpFFhERqZrwl5cqzCSuxAIz59yyfK+Z2YeAHzvnHHCXmb0NtOLVkE0MrdoGbMmz/SuBKwHmzp0bGbyJiIikzRuVqchM4kmrKfN/gQMAzGwG0A94EbgRONbM+pvZFGA6cFdKZRQRESmbU5WBlCGVUZnAVcBVZnY/8AZwvF979oCZXQ88CGwHTtWITBERqWeqLJNSpBKYOefeAN6T57ULgQtrWyIRERGR9Cnzv4iIiEhGKDATERERyQgFZiIiIglw0dmeRApSYCYiIpIo9f6X+BSYiYiIiGSEAjMRERGRjFBgJiIikgR1MZMyKDATERFJkBLMSikUmImIiCRAFWZSDgVmIiIiCVKFmZRCgZmIiIhIRigwExEREckIBWYiIiIJcOpkJmVQYCYiIpIgjcqUUigwExERSYDmypRyKDATERFJkGlcppRAgZmIiIhIRigwExEREckIBWYiIiIJ0KhMKYcCMxERkQRpVKaUQoGZiIhIjSya3pp2ESTj+qRdABERkUaw6aKVaRdB6oACMxERkRowtWlKDGrKFBERSYD6/hOxpeAAAArlSURBVEs5FJiJiIgkSPVkUgoFZiIiIglwypchZVBgJiIikiD1LZNSpBKYmdkcM1trZuvM7G4zm+cvNzO7zMw2mtl6M+tIo3wiIiKVGjqwLwArdtkh5ZJIPUlrVOYlwPnOuV+Y2Ur/9/2BFcB0/7E3cLn/U0REpK4MHdCXdZ85kCED+qZdFKkjaTVlOmCo/3wYsMV/vgq42nnWAsPNbFwaBRQREanU8JZ+NDepKVPiS6vG7Ezgl2Z2KV5wuI+/fALwVGi9p/1lz+RuwMzWAGsA2tvbEy2siIiISC0kFpiZ2W1AVMP6ucBS4Czn3I/M7BjgW8AyokcVRw5rcc5dCVwJMHfuXA19ERERkbqXWGDmnFuW7zUzuxo4w//1B8A3/edPAxNDq7bR1cwpIiIi0qul1cdsC7Cf//wA4DH/+Y3A+/zRmfOBV5xzPZoxRURERHqjtPqYfRD4spn1Abbh9xUDfg6sBDYCrwEnpFM8ERERkdpLJTBzzv0W2DNiuQNOrX2JRERERNKnzP8iIiIiGaHATERERCQjFJiJiIiIZIQCMxEREZGMMK+/fX0zsxeAJ2uwq1bgxRrsp97ouETTcclPxyaajkt+OjbRdFzyy/KxmeScGx31Qq8IzGrFzO52zs1NuxxZo+MSTcclPx2baDou+enYRNNxya9ej42aMkVEREQyQoGZiIiISEYoMCvNlWkXIKN0XKLpuOSnYxNNxyU/HZtoOi751eWxUR8zERERkYxQjZmIiIhIRigwi8HMDjazR8xso5l9Iu3y1JKZTTSzO8zsITN7wMzO8JefZ2Z/NbN1/mNl6G/O8Y/VI2a2PL3SJ8/MNpvZBv8Y3O0vG2lmt5rZY/7PEf5yM7PL/GOz3sw60i19MsxsZui8WGdmr5rZmY16zpjZVWb2vJndH1pW8jliZsf76z9mZsen8V6qKc9x+S8ze9h/7z8xs+H+8slm9q/QuXNF6G/29D+DG/1jZ2m8n2rKc2xK/vz0tu+uPMfl+6FjstnM1vnL6/eccc7pUeABNAOPA1OBfsB9wOy0y1XD9z8O6PCfDwEeBWYD5wFnR6w/2z9G/YEp/rFrTvt9JHh8NgOtOcsuAT7hP/8E8Hn/+UrgF4AB84E/pl3+GhyfZuBZYFKjnjPAYqADuL/ccwQYCTzh/xzhPx+R9ntL4LgcBPTxn38+dFwmh9fL2c5dwAL/mP0CWJH2e0vo2JT0+emN311RxyXn9S8An6n3c0Y1ZsXNAzY6555wzr0BXAesSrlMNeOce8Y5d6//fCvwEDChwJ+sAq5zzr3unNsEbMQ7ho1kFfAd//l3gCNCy692nrXAcDMbl0YBa2gp8LhzrlAC6F59zjjnfg28nLO41HNkOXCrc+5l59zfgFuBg5MvfXKijotz7hbn3Hb/17VAW6Ft+MdmqHPuD877xr2armNZt/KcM/nk+/z0uu+uQsfFr/U6BvheoW3UwzmjwKy4CcBTod+fpnBg0muZ2WRgD+CP/qLT/CaHq4KmGBrveDngFjO7x8zW+MvGOueeAS+wBcb4yxvt2AAcS/cLpc4ZT6nnSCMeoxPxajMCU8zsz2b2KzNb5C+bgHcsAr39uJTy+Wm0c2YR8Jxz7rHQsro8ZxSYFRfV9txwQ1nNbDDwI+BM59yrwOXANGAO8AxeFTI03vHa1znXAawATjWzxQXWbahjY2b9gMOBH/iLdM4Ul+9YNNQxMrNzge3Atf6iZ4B259wewL8B/9fMhtJYx6XUz08jHRuA4+h+E1i354wCs+KeBiaGfm8DtqRUllSYWV+8oOxa59yPAZxzzznn3nLOvQ18g66mp4Y6Xs65Lf7P54Gf4B2H54ImSv/n8/7qDXVs8ILVe51zz4HOmRylniMNc4z8gQ2HAqv9pib8ZrqX/Of34PWdmoF3XMLNnb32uJTx+Wmkc6YPcBTw/WBZPZ8zCsyK+xMw3cym+DUAxwI3plymmvHb7b8FPOSc++/Q8nDfqCOBYJTMjcCxZtbfzKYA0/E6WvY6ZjbIzIYEz/E6Lt+PdwyCUXPHAzf4z28E3uePvJsPvBI0Z/VS3e5gdc50U+o58kvgIDMb4TdhHeQv61XM7GDg48DhzrnXQstHm1mz/3wq3jnyhH9stprZfP9a9T66jmWvUsbnp5G+u5YBDzvnOpso6/qcSXv0QT088EZKPYoXcZ+bdnlq/N4X4lXzrgfW+Y+VwDXABn/5jcC40N+c6x+rR8jYaJcqH5upeCOd7gMeCM4NYBRwO/CY/3Okv9yA/+Mfmw3A3LTfQ4LHpgV4CRgWWtaQ5wxecPoM8Cbe3fpJ5ZwjeH2uNvqPE9J+Xwkdl414/aKCa80V/rpH+5+x+4B7gcNC25mLF6Q8DnwVP3F6PT/yHJuSPz+97bsr6rj4y78NnJKzbt2eM8r8LyIiIpIRasoUERERyQgFZiIiIiIZocBMREREJCMUmImIiIhkhAIzERERkYxQYCYiiTKzUWa2zn88a2Z/Df3++wT2t7+ZvRLaxzozW+a/9o9q7y9mmTabWWvE8rf88o2v0n4G+tt7I2p/IpJ9fdIugIj0bs7Lvj0HwMzOA/7hnLs04d3+xjl3aML7qIZ/OefmVGtjzrl/AXPMbHO1tikitaUaMxFJTVCD5ddy/crMrjezR83sYjNbbWZ3mdkGM5vmrzfazH5kZn/yH/uWud/BZna7md3rb3+Vv3yymT1sZt/xJ4v+oZm1+K9dbGYP+ssvLVQev5bwFn8C5a8TPT9fbpmazezbZna/X6az/OXTzOxmM7vHzH5jZrP85WPN7Cdmdp//2KecYyEi2aIaMxHJit2BnYCXgSeAbzrn5pnZGcBHgDOBLwNfdM791sza8aYl2iliW4vMbF3o96Odc4+Hft8GHOmce9Vv8ltrZsF0NTPxMor/zsyuAj7s/zwSmOWcc2Y23F83X3k+C/zWOfc5MzsEWBPj/c8BJjjndgEI7eNKvKzmj5nZ3sDXgAOAy4BfOeeO9KeeGRxjHyKScQrMRCQr/uT8uUPN7HHgFn/5BmCJ/3wZMNub4g6AoWY2xDm3NWdbxZoyDfhPM1sMvA1MAMb6rz3lnPud//y7wOnAl/CCuW+a2U3AzwqVB1iMN6kyzrmbzOxvMd7/E8BUM/sKcBNwi5kNBvYBfhDaR3//5wF48/zhnHsLeCXGPkQk4xSYiUhWvB56/nbo97fpulY1AQv8vlSVWA2MBvZ0zr3p98ka4L+WO0+dc85tN7N5wFK8yaBPwwuMIsvjB1ElzXfnnPubme0OLAdOBY7BqyX8ezX7oYlItqmPmYjUk1vwgiIAzKzcgGUY8LwflC0BJoVeazezBf7z44Df+jVXw5xzP8cLloL95ivPr/GCP8xsBTCiWIH8JtUm59yPgE8DHc65V4FNZvZOfx3zgzfwJj//kL+82cyGlnQERCSTFJiJSD05HZjrd8B/EDglz3qLctJlvCPn9Wv97dyNF0A9HHrtIeB4M1sPjAQuB4YAP/OX/Qo4q0h5zgcWm9m9wEHAX2K8twnAnX7fuG8D5/jLVwMnmdl9wAPAKn/5GcASM9sA3APsHGMfIpJx5lxJte0iIr2WmU0GfhZ0wK/B/v7hnKt6p32/aXauc+7Fam9bRJKlGjMRkfS8mkSCWaAvXt88EakzqjETERERyQjVmImIiIhkhAIzERERkYxQYCYiIiKSEQrMRERERDJCgZmIiIhIRigwExEREcmI/w/XGM+DE9H+VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Azimuth and Altitude LEGEND:\n",
    "# Altitude is the angle between the ground and the vector \n",
    "# Azimuth is the angle going clockwise from 0 deg North:\n",
    "# N - 0/360deg, E - 90deg, S - 180deg, W - 270deg\n",
    "\n",
    "# This will get complicated (ie make cases or lots of if statements) when rotations about the heading become more prevalent\n",
    "def azimuth(x,y,z):\n",
    "    real_y = y * (-1) # This is to account for y \n",
    "    return (180/math.pi * math.atan2(real_y,x)) % 360\n",
    "\n",
    "def altitude(x,y,z):\n",
    "    h = math.hypot(y, x)\n",
    "    return 180/math.pi * math.atan2(z,h)\n",
    "\n",
    "def printAltAzi(alt, azi):\n",
    "    print (\"Alt:\", alt, \"\\n\", \"Azi:\",azi,\"\\n\")\n",
    "# These values are uncorrected values: still need to add or subtract 'declination'\n",
    "#     (for AziMuth) and 'inclination' (for Altitude) correction values for geographical location\n",
    "\n",
    "print(\"Starting full data set analysis:\")\n",
    "avg_error_list = []\n",
    "expected_heights = []\n",
    "\n",
    "#for index in range(0, len(surf_sessions)):\n",
    "\n",
    "#acc_array = surf_sessions[index]\n",
    "    #dacc_test_array = signal.detrend(acc_array)\n",
    "    \n",
    "    # The new array for board reference frame will have the IMUs in columns according to X,Y,Z directions\n",
    "\n",
    "\n",
    "board_magn = magn_array.copy()\n",
    "temp_x_magn = board_magn[:,0] * (-1)\n",
    "temp_y_magn = board_magn[:,1] * (-1)\n",
    "temp_z_magn = board_magn[:,2] * (-1)\n",
    "board_magn[:,0] = temp_x_magn\n",
    "board_magn[:,1] = temp_y_magn\n",
    "board_magn[:,2] = temp_z_magn\n",
    "print(board_magn)\n",
    "print(\"Done.\")\n",
    "    \n",
    "heading_altitude = board_magn[:,0].copy()\n",
    "heading_azimuth = board_magn[:,0].copy()\n",
    "\n",
    "i = 0     #iterator\n",
    "#for i in range(len(M1_no_out)):\n",
    "while i < len(heading_altitude):\n",
    "    heading_altitude[i] = altitude(board_magn[i,0], board_magn[i,1], board_magn[i,2])\n",
    "    heading_azimuth[i] = azimuth(board_magn[i,0], board_magn[i,1], board_magn[i,2])\n",
    "    #print(heading_azimuth[i])\n",
    "    #printAltAzi(heading_altitude[i],heading_azimuth[i])\n",
    "    i += 1\n",
    "\n",
    "anss = sum(heading_azimuth/len(heading_azimuth)) \n",
    "actual = sum(deg/ len(deg))\n",
    "print(actual)\n",
    "print(anss)\n",
    "print((actual-anss)/anss)  \n",
    "\n",
    "\n",
    "heading_azi_plot = plt.figure(figsize=(10,5))\n",
    "azi_plot = heading_azi_plot.add_subplot(111)\n",
    "azi_plot.plot(time_e_array, heading_azimuth)\n",
    "azi_plot.set_title(\"Azimuth vs. Time\")\n",
    "azi_plot.set_xlabel(\"Time Elapsed[sec]\")\n",
    "azi_plot.set_ylabel(\"Azimuth [deg]\")\n",
    "azi_plot.set_ylim([-50,400])\n",
    "\n",
    "heading_alt_plot = plt.figure(figsize=(10,5))\n",
    "alt_plot = heading_alt_plot.add_subplot(111)\n",
    "alt_plot.plot(time_e_array, heading_altitude)\n",
    "alt_plot.set_title(\"Altitude vs. Time\")\n",
    "alt_plot.set_xlabel(\"Time Elapsed [sec]\")\n",
    "alt_plot.set_ylabel(\"Altitude [deg]\")\n",
    "plt.show()\n",
    "\n",
    "#for t in range(len(time_e_array)):\n",
    "    #printAltAzi(heading_altitude[t], heading_azimuth[t])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot polar azimuth and altitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: acc_array = acc_list = imu2_array = imu_array2\n",
    "# ALL CODE RELATED TO PLOTTING INDIVIDUAL GRAPHS HAS BEEN COMMENTED OUT\n",
    "\n",
    "print(\"Starting full data set analysis:\")\n",
    "avg_error_list = []\n",
    "expected_heights = []\n",
    "\n",
    "for index in range(0, len(surf_sessions)):\n",
    "\n",
    "    #acc_array = surf_sessions[index]\n",
    "    dacc_test_array = signal.detrend(acc_array)\n",
    "\n",
    "    butter_t_lfilter = butter_bandpass_lfilter(dacc_test_array, lowcut, highcut, fs, order=5)\n",
    "    butter_t_filtfilt = butter_bandpass_filtfilt(dacc_test_array, lowcut, highcut, fs, order=5)\n",
    "\n",
    "    ##For entire data:\n",
    "    #Get as close to just the signal (don't want noise)    \n",
    "    new_t_test, new_acc_test = calculate_new_range(time_array, acc_array, 0, 1800)\n",
    "    new_t_test, new_dacc_test = calculate_new_range(time_array, dacc_test_array, 0, 1800)\n",
    "    disp_butter_integral_t = double_integral_bandpass(new_t_test, new_acc_test, new_dacc_test, lowcut, highcut, fs, order)\n",
    "    print(\"Done.\")\n",
    "\n",
    "    new_dacc = np.array(new_dacc_test)\n",
    "    new_t = np.array(new_t_test)\n",
    "\n",
    "    '''\n",
    "    indices = peakutils.indexes(new_dacc, thres=0.02/max(new_dacc), min_dist=100)\n",
    "    \n",
    "    col_t = new_t\n",
    "    col_a = new_dacc\n",
    "\n",
    "    index_max = peakutils.indexes(col_a, thres=0.66, min_dist=25)\n",
    "    index_min = peakutils.indexes(-col_a, thres=0.66, min_dist=25)\n",
    "\n",
    "    f0 = plt.figure(figsize=(12,5))\n",
    "    ax0 = f0.add_subplot(121)\n",
    "\n",
    "    ax0.plot(col_t, col_a, lw=0.4, alpha=0.2, color=\"black\" )\n",
    "    ax0.plot(col_t[index_max], col_a[index_max], marker=\"o\", ls=\"\", ms=3, color=\"red\" )\n",
    "    ax0.plot(col_t[index_min], col_a[index_min], color =\"orange\", marker=\"o\", ls=\"\", ms=3 )\n",
    "    ax0.set_title(\"Displacement vs. Time\")\n",
    "    ax0.set_xlabel(\"Time [s]\")\n",
    "    ax0.set_ylabel('Displacement [m]')\n",
    "    '''\n",
    "\n",
    "    # Use largest third method with medians\n",
    "    n = int(len(new_dacc)/3)\n",
    "    xn = new_dacc\n",
    "    largest_third = (xn[np.argsort(xn)[-n:]])\n",
    "\n",
    "    exp_wave_height = np.median(largest_third)\n",
    "    expected_heights.append(exp_wave_height)\n",
    "    # Note: arbitrarily dividing exp_wave_height by 1.1 yields a lower % error \n",
    "    \n",
    "    actual = height_labels[index]\n",
    "    err = percent_error(actual, exp_wave_height)\n",
    "    avg_error_list.append(err)\n",
    "    \n",
    "    print(\"Significant wave height calculated as: \", exp_wave_height, \" for session \", index+1)\n",
    "    print(\"Actual wave height: \", actual)\n",
    "    print(\"Percent error: \", err)\n",
    "    \n",
    "print(\"\\nMean error: \", statistics.mean(avg_error_list))\n",
    "print(\"Median error: \", statistics.median(avg_error_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# Compute areas and colors\n",
    "r = [i for i in range(0, len(board_magn))]\n",
    "theta = heading_azimuth/360 * 2 * np.pi\n",
    "area = 1\n",
    "colors = theta\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111, projection='polar')\n",
    "c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=1)\n",
    "\n",
    "# Compute areas and colors\n",
    "alt_r = [i for i in range(0, len(board_magn))]\n",
    "alt_theta = heading_altitude/360 * 2 * np.pi\n",
    "alt_area = 1\n",
    "alt_colors = theta\n",
    "\n",
    "fig2 = plt.figure(figsize=(10,10))\n",
    "ax2 = fig2.add_subplot(111, projection='polar')\n",
    "c2 = ax2.scatter(alt_theta, alt_r, c=alt_colors, s=alt_area, cmap='hsv', alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot first 10 directional changes \n",
    "magnfig = plt.figure(figsize=(20,20))\n",
    "magnaxi = magnfig.add_subplot(111, projection='3d')\n",
    "magnaxi.scatter(board_magn[0,0], board_magn[0,2], board_magn[0,1], c='black', s=300, marker = \"<\")\n",
    "magnaxi.scatter(board_magn[1:,0], board_magn[1:,2], board_magn[1:,1], c='black', s=10, marker = \"o\")\n",
    "magnaxi.plot(board_magn[:,0], board_magn[:,2], board_magn[:,1], color='red')\n",
    "magnaxi.set_xlabel(\"X Axis ->\", fontsize=30)\n",
    "magnaxi.set_ylabel(\"Y Axis ->\", fontsize=30)\n",
    "magnaxi.set_zlabel(\"Z Axis ->\", fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read from CSV\n",
    "#Full csv:\n",
    "#scripps_final_combined = pd.read_csv('Scripps_Final_Combined.csv')\n",
    "#print(scripps_final_combined.head())\n",
    "\n",
    "#Gets following columns: UTC, Time, IMU A1, IMU A2, IMU A3, IMU G1, IMU G2, IMU G3, IMU M1, IMU M2, Date\n",
    "motion_df = pd.read_csv('Scripps_Final_Combined.csv', usecols = [i for i in range(2, 13)])\n",
    "#motion_df = motion_df.drop(['Time'], axis=1)\n",
    "print(motion_df.head())\n",
    "print(len(motion_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(motion_df)\n",
    "\n",
    "saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data included\n",
    "\n",
    "#print(saved_copy_motion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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