Dead Reckoning

#Samprith Kalakata
#May, 2019 

#Attempting to calculate significant wave height from data generated from SmartFin (https://smartfin.org/)
#Research consulted: https://journals.ametsoc.org/doi/pdf/10.1175/2010JTECHO724.1

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

from mpl_toolkits.basemap import Basemap

import pandas as pd
import numpy as np

from scipy import stats
from scipy import constants
from scipy import signal #added
from scipy.interpolate import CubicSpline
from scipy.interpolate import interp1d
from scipy.integrate import simps
from scipy.integrate import cumtrapz
from scipy.signal import butter, lfilter, freqz

import os
import datetime
import pytz
import re

import peakutils
import statsmodels.api as sm

import requests

import mpld3
import folium
#import cmocean
import skinematics as skin
from skinematics import quat, vector, misc, rotmat, imus, view
import pygame

from plotly import tools #added all the plotly's
import plotly.offline
import plotly.graph_objs as go

import math #added
import re   #added

# For the definition of the abstract base class IMU_Base
import abc

import sys

# %matplotlib notebook
%matplotlib inline

ride_ids = ['15218']
# 14743 - Motion Control July 10th
# 14750 - Magnetometer Control July 11th
# 14814 - Pool Displacement Control July 17th
# 14815 - Compass Orientation (Lying on Charger Side) July 19th
# 14816 - Orientation w Higher Sampling (Lying on Charger Side) July 20th
# 14827 - Pool Displacement Control w Higher Sampling (Jul 23)
# 14888 - First Buoy Calibration Experiment (July 30)
# 15218 - Jasmine's Second Ride Sesh filmed with GoPro (Aug 29)

#%% Fin ID scraper
# Input fin ID, get all ride IDs
# base URL to which we'll append given fin IDs
fin_url_base = 'http://surf.smartfin.org/fin/'

# Look for the following text in the HTML contents in fcn below
str_id_ride = 'rideId = \'' # backslash allows us to look for single quote
str_id_date = 'var date = \'' # backslash allows us to look for single quote

#%% Ride ID scraper
# Input ride ID, get ocean and motion CSVs
# Base URL to which we'll append given ride IDs
ride_url_base = 'https://surf.smartfin.org/ride/'

# Look for the following text in the HTML contents in fcn below
str_id_csv = 'img id="temperatureChart" class="chart" src="' 

def get_csv_from_ride_id(rid):
    # Build URL for each individual ride
    ride_url = ride_url_base+str(rid)
    print(ride_url)
    
    # Get contents of ride_url
    html_contents = requests.get(ride_url).text
    
    # Find CSV identifier 
    loc_csv_id = html_contents.find(str_id_csv)
    
    # Different based on whether user logged in with FB or Google
    offset_googleOAuth = [46, 114]
    offset_facebkOAuth = [46, 112]
    if html_contents[loc_csv_id+59] == 'f': # Facebook login
        off0 = offset_facebkOAuth[0]
        off1 = offset_facebkOAuth[1]
    else: # Google login
        off0 = offset_googleOAuth[0]
        off1 = offset_googleOAuth[1]
        
    csv_id_longstr = html_contents[loc_csv_id+off0:loc_csv_id+off1]
    
#    print(csv_id_longstr)
    
    # Stitch together full URL for CSV
    if ("media" in csv_id_longstr) & ("Calibration" not in html_contents): # other junk URLs can exist and break everything
        
        ocean_csv_url = 'https://surf.smartfin.org/'+csv_id_longstr+'Ocean.CSV'
        motion_csv_url = 'https://surf.smartfin.org/'+csv_id_longstr+'Motion.CSV'
        
        print(ocean_csv_url)
        # Go to ocean_csv_url and grab contents (theoretically, a CSV)
        ocean_df_small = pd.read_csv(ocean_csv_url, parse_dates = [0])
        elapsed_timedelta = (ocean_df_small['UTC']-ocean_df_small['UTC'][0])
        ocean_df_small['elapsed'] = elapsed_timedelta/np.timedelta64(1, 's')
        
        motion_df_small = pd.read_csv(motion_csv_url, parse_dates = [0])
        
        # Reindex on timestamp if there are at least a few rows
        if len(ocean_df_small) > 1:
            ocean_df_small.set_index('UTC', drop = True, append = False, inplace = True)
            motion_df_small.set_index('UTC', drop = True, append = False, inplace = True)
            
            #print(ocean_df_small)
            
            
            #May need to change this sampling interval:
            sample_interval = '33ms'
            
            
            ocean_df_small_resample = ocean_df_small.resample(sample_interval).mean()
            motion_df_small_resample = motion_df_small.resample(sample_interval).mean()
            
            # No need to save many extra rows with no fix
            motion_df_small = motion_df_small[~np.isnan(motion_df_small.Latitude)]
            
            return ocean_df_small_resample, motion_df_small_resample

    else:
        ocean_df_small_resample = pd.DataFrame() # empty DF just so something is returned
        motion_df_small_resample = pd.DataFrame() 
        return ocean_df_small_resample, motion_df_small_resample
    
    

appended_ocean_list = [] # list of DataFrames from original CSVs
appended_motion_list = []
appended_multiIndex = [] # fin_id & ride_id used to identify each DataFrame

## Nested loops (for each fin ID, find all ride IDs, then build a DataFrame from all ride CSVs)
## (Here, ride IDS are either ocean or motion dataframes)
count_good_fins = 0
    
# Loop over ride_ids and find CSVs
for rid in ride_ids:
    try:
        new_ocean_df, new_motion_df = get_csv_from_ride_id(rid) # get given ride's CSV from its ride ID using function above
        #print(len(new_ocean_df))
        #print(len(new_motion_df))
        if not new_ocean_df.empty: # Calibration rides, for example
            # Append only if DF isn't empty. There may be a better way to control empty DFs which are created above
            appended_multiIndex.append(str(rid)) # build list to be multiIndex of future DataFrame
            appended_ocean_list.append(new_ocean_df)
            appended_motion_list.append(new_motion_df)
            print("Ride data has been uploaded.")
            #print("Ride: ", rid, "data has been uploaded.")
            count_good_fins += 1
        
    except: 
        print("Ride threw an exception!")
        #print("Ride ", rid, "threw an exception!")    

#%% Build the "Master" DataFrame

# appended_ocean_df.summary()
df_keys = tuple(appended_multiIndex) # keys gotta be a tuple, a list which data in it cannot be changed
ocean_df = pd.concat(appended_ocean_list, keys = df_keys, names=['ride_id'])
motion_df = pd.concat(appended_motion_list, keys = df_keys, names = ['ride_id'])

##Here, maybe just use info from the motion_df and don't worry about ocean_df data for now.
##If you do want ocean_df data, look at how Phil was getting it from "July 10th and 11th Calibration" jupyter notebook file.
#print(motion_df)
print("finished fetching data frame")

https://surf.smartfin.org/ride/15218
https://surf.smartfin.org/media/201808/google_105349665704999793400_0006667E229D_180829164842_Ocean.CSV
Ride data has been uploaded.
finished fetching data frame

#print(motion_df)

saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data

#Reading data from ride_ids = ['xxxxx']
#The name of the motion dataframe is: motion_df

#Get times from the "Time" column to create time_o_list and time_e_list.
#Get imus from the "IMU A[*]" column to create the imu acc arrays. 

#Drop the "nan" values from the columns that we care about. 
dropped_motion_df = motion_df.dropna(subset=['Time', 'IMU A1', 'IMU A2', 'IMU A3'])

#Can test that this works by printing this one:
#dropped_motion_df = motion_df.dropna(subset=['Time', 'IMU A1', 'IMU A2', 'IMU A3', 'Latitude'])
#print(dropped_df)

time_e_list = []
time_o_list = []

#Remove all nan instances in time:
time_array_nans = np.array(dropped_motion_df.loc[:,"Time"], dtype=float)
time_array = []

imu1_array_nans = np.array(dropped_motion_df.loc[:,"IMU A1"], dtype=float)
imu_array1 = []

imu2_array_nans = np.array(dropped_motion_df.loc[:,"IMU A2"], dtype=float)
imu_array2 = []

imu3_array_nans = np.array(dropped_motion_df.loc[:,"IMU A3"], dtype=float)
imu_array3 = []


#Get all the times and imus where time, imu1, imu2, and imu3 are NOT nan values:
for t,x,y,z in zip(time_array_nans, imu1_array_nans, imu2_array_nans, imu3_array_nans):
    if (np.isnan(t)==0 and np.isnan(x)==0 and np.isnan(y)==0 and np.isnan(z)==0):
        time_array.append(t)
        imu_array1.append(x)
        imu_array2.append(y)
        imu_array3.append(z)

#for x in time_array:
#    print(x)
    
start_time = time_array[0]
time_len = len(time_array)
    
i = 0
while (i < time_len - 1):
    prev = time_array[i]
    after = time_array[i+1]
    
    #print(prev, " ", after)
    #print(after - prev)
    
    offset = after - prev
    #if (np.isnan(offset)==0):
    time_o_list.append(offset)
    
    elapsed = time_array[i] - start_time
    #if (np.isnan(elapsed)==0):
    time_e_list.append(elapsed)
    
    i = i + 1


##Check to make sure there are no "nan" values:
i = 0
while (i < len(time_o_list)):
    if (np.isnan(time_o_list[i])):
        print("Error! Value at index: ", i, " is nan")
    i = i + 1

#Drop the last value from each of the imu lists to make it match the time list.
del(imu_array1[-1])
del(imu_array2[-1])
del(imu_array3[-1])
    
print(len(time_e_list))
print(len(time_o_list))
print(len(imu_array1))
print(len(imu_array2))
print(len(imu_array3))

8348
8348
8348
8348
8348

## Convert raw units to actual units (acc to [m/s^2]) and (time to [s])


#Raw acceleration constant 512 = 1g (accelerometer's measured force due to gravity)
g_const = 512

#Approximate measurement for gravity:
gravity = -9.80665


# Correct the IMU Acceleration columns into units of meters
# Dividing by 512 is equivalent to muliplying by 4 to correct the bit shifting by 2 places and dividing by 2048 to convert bits to G's
# Multiplying by the 9.81 afterwards is simply to convert G's into m/s^2

def convert_acc_units(acc_array):
    ret_array = []
    for a in acc_array:
        #Acceleration is now in m/s^2, need to subtract gravity from vertical axis. (??)
        new_a = a / g_const * gravity - gravity
        ret_array.append(new_a)
    return ret_array

imu1_array = convert_acc_units(imu_array1) #new units in m/s^2
imu2_array = convert_acc_units(imu_array2) #new units in m/s^2
imu3_array = convert_acc_units(imu_array3) #new units in m/s^2

##To check:
#for x,y in zip(imu2_array, imu_array2):
#    print(x,y)
    
    
def convert_time_units(time_array):
    ret_array = []
    for t in time_array:
        new_t = t * (10**(-3)) #converting units in milliseconds to seconds
        ret_array.append(new_t)
    return ret_array

time_o_array = convert_time_units(time_o_list) #new units in seconds
time_e_array = convert_time_units(time_e_list) #new units in seconds

#Seperate each of the subexperiments into its own acc lists.
#i.e. subexperiment1 corresponds to acc1, (subexperiment2 => acc2), etc.

time_e_list1 = []
time_e_list2 = []
time_e_list3 = []

acc_list = []
acc_list1 = []
acc_list2 = []
acc_list3 = []

time_array = []
acc_array = []

gravity = -9.80665

#For our controlled experiments, we know that imu2 is the vertical axis
acc_list = imu2_array


########## new db ##########
acc_list_FB = imu1_array
acc_list_SD = imu3_array

i = 0
while (i < (len(acc_list)) - 1):
    if (time_e_array[i] > 300 and time_e_array[i] <= 450):
        acc_list1.append(acc_list[i])
        time_e_list1.append(time_e_array[i])
    if (time_e_array[i] > 450 and time_e_array[i] <= 670):
        acc_list2.append(acc_list[i])
        time_e_list2.append(time_e_array[i])
    if (time_e_array[i] > 670 and time_e_array[i] <= 850):
        acc_list3.append(acc_list[i])
        time_e_list3.append(time_e_array[i])
    i = i + 1
    
    
print 
#Plot the subexperiments to verify correctness:
for a,t in zip(acc_list,time_e_array): #acc_array becomes only acc values we care about
    if t > 300 and t <= 850:
        acc_array.append(a)
        time_array.append(t)

time_array = np.array(time_array)
acc_array = np.array(acc_array)
time_array1 = np.array(time_e_list1)
acc_array1 = np.array(acc_list1)
time_array2 = np.array(time_e_list2)
acc_array2 = np.array(acc_list2)
time_array3 = np.array(time_e_list3)
acc_array3 = np.array(acc_list3)

##PSD Step 2: Detrend the data 
dacc_array1 = signal.detrend(acc_array1)
dacc_array2 = signal.detrend(acc_array2)
dacc_array3 = signal.detrend(acc_array3)

########## new db ##########
dacc_array_FB = signal.detrend(acc_list_FB)
dacc_array_SD = signal.detrend(acc_list_SD)
std1_FB = np.std(dacc_array_FB)*3
std3_SD = np.std(dacc_array_SD)*3


##Remove outliers--points greater than 3x the standard deviation
std1 = np.std(dacc_array1)*3
std2 = np.std(dacc_array2)*3
std3 = np.std(dacc_array3)*3

#Returns a new array that is the same as the array passed in, with its outliers removed.
def removed_outliers(a_array, time_array, std):
    i = 0
    count = 0
    ret_accs = []
    ret_times = []
    while i < (len(a_array)):
        #if smaller than std, keep that value (larger ones get removed)
        if abs(a_array[i]) < std:
            ret_accs.append(a_array[i])
            ret_times.append(time_array[i])
        else:
            count = count + 1  #could help with debugging to know how many outliers removed
        i = i + 1  
    return count, ret_accs, ret_times;
        
count1, ro_array1, ro_time1 = removed_outliers(dacc_array1, time_array1, std1)
count2, ro_array2, ro_time2 = removed_outliers(dacc_array2, time_array2, std2)
count3, ro_array3, ro_time3 = removed_outliers(dacc_array3, time_array3, std3)

########## new db ##########
count1_FB, ro_array1_FB, ro_time1_FB = removed_outliers(dacc_array_FB, time_e_array, std1_FB)
count3_SD, ro_array3_SD, ro_time3_SD = removed_outliers(dacc_array_SD, time_e_array, std3_SD)



#print(len(dacc_array1))
#print(count1)
#print(len(ro_array1))

##Set up data interpolation (using Cubic Splines) for use in next step
cs1 = CubicSpline(ro_time1, ro_array1)
cs2 = CubicSpline(ro_time2, ro_array2)
cs3 = CubicSpline(ro_time3, ro_array3)


########## new db ##########
cs1_FB = CubicSpline(ro_time1_FB, ro_array1_FB)
cs3_SD = CubicSpline(ro_time3_SD, ro_array3_SD)

##interpld returns a function that relates y=ro_array (without outliers) to x=time:
#cs1 = interp1d(ro_time1, ro_array1)
#cs2 = interp1d(ro_time2, ro_array2)
#cs3 = interp1d(ro_time3, ro_array3)

#Now, use this interpolation to put points back into the original graph:
def add_interpolated_pts(a_array, time_array, std, cs):
    i = 0
    ret_acc = []
    while i < (len(a_array)):
        if abs(a_array[i]) > std:
            ret_acc.append(cs(time_array[i]))
        else:
            ret_acc.append(a_array[i])
        i = i + 1  
    return ret_acc;


#These are the new arrays with the interpolated points (which we will
#feed into a Kalman filter later).
interp_array1 = add_interpolated_pts(dacc_array1, time_array1, std1, cs1)
interp_array2 = add_interpolated_pts(dacc_array2, time_array2, std2, cs2)
interp_array3 = add_interpolated_pts(dacc_array3, time_array3, std3, cs3)

########## new db ##########
interp_array1_FB = add_interpolated_pts(dacc_array_FB, time_e_array, std1_FB, cs1_FB)
interp_array3_SD = add_interpolated_pts(dacc_array_SD, time_e_array, std3_SD, cs3_SD)


#print(len(interp_array1))
#print(len(dacc_array1))

##To verify that the two arrays are different(i.e. pts were actually interpolated):
j = 0
count = 0
while (j < (len(interp_array1) - 1)):
    if interp_array1[j] != dacc_array1[j]:
        count = count + 1
    j = j + 1
        
#print(count)
    

##Now, feed interpolated arrays through a Kalman filter:
##(Actually I'm going to use a LOWESS filter for simplicity)

#print("\n")
#print("Here, I'm implementing a lowess filter instead of a Kalman \n filter right now because it seems simpler to implement.")
#print("\n")

lowess = sm.nonparametric.lowess

#Parameters: takes in (y,x, ...)
filtered1 = lowess(interp_array1, time_array1, frac=0.005, is_sorted=True, return_sorted=False) 
filtered2 = lowess(interp_array2, time_array2, frac=0.002, is_sorted=True, return_sorted=False) 
filtered3 = lowess(interp_array3, time_array3, frac=0.002, is_sorted=True, return_sorted=False)


########## new db ##########
filtered1_FB = lowess(interp_array1_FB, time_e_array, frac=0.002, is_sorted=True, return_sorted=False) 
filtered3_SD = lowess(interp_array3_SD, time_e_array, frac=0.002, is_sorted=True, return_sorted=False)

#First, find peaks and valleys of the waveforms:
#(Found that this works better when the data has been detrended.)

indexes0 = peakutils.indexes(acc_array, thres=0.02/max(acc_array), min_dist=100)
indexes1 = peakutils.indexes(dacc_array1, thres=0.02/max(dacc_array1), min_dist=100)
indexes2 = peakutils.indexes(dacc_array2, thres=0.02/max(dacc_array2), min_dist=100)
indexes3 = peakutils.indexes(dacc_array3, thres=0.02/max(dacc_array3), min_dist=100)

########## new db ##########
indexes1_FB = peakutils.indexes(dacc_array_FB, thres=0.02/max(dacc_array_FB), min_dist=100)
indexes3_SD = peakutils.indexes(dacc_array_SD, thres=0.02/max(dacc_array_SD), min_dist=100)



col_0t = time_array # First column data
col_0a = acc_array # Second column data

col_1t = time_array1 # First column data
col_1a = dacc_array1 # Second column data

col_2t = time_array2 # First column data
col_2a = dacc_array2 # Second column data

col_3t = time_array3 # First column data
col_3a = dacc_array3 # Second column data


#Index1 gets the peaks, while index2 gets the valleys
index_max0 = peakutils.indexes(col_0a, thres=0.66, min_dist=25)
index_min0 = peakutils.indexes(-col_0a, thres=0.66, min_dist=25)

index_max1 = peakutils.indexes(col_1a, thres=0.66, min_dist=25)
index_min1 = peakutils.indexes(-col_1a, thres=0.66, min_dist=25)

index_max2 = peakutils.indexes(col_2a, thres=0.66, min_dist=25)
index_min2 = peakutils.indexes(-col_2a, thres=0.66, min_dist=25)

index_max3 = peakutils.indexes(col_3a, thres=0.66, min_dist=25)
index_min3 = peakutils.indexes(-col_3a, thres=0.66, min_dist=25)

#First integral of acc to get velocity:
from scipy import integrate

def calculate_new_range(time_array, array_of_values, low_time, high_time):
    new_time_array = []
    new_value_array = []
    for t,v in zip(time_array, array_of_values):
        if (t > low_time and t < high_time):
            new_time_array.append(t)
            new_value_array.append(v)
    return new_time_array, new_value_array

new_time_array2, new_dacc_array2 = calculate_new_range(time_array2, dacc_array2, 500, 640)


############################# new dr ##########################
new_time_array_FB, new_dacc_array_FB = calculate_new_range(time_e_array, dacc_array_FB, 0, 200)
new_time_array_SD, new_dacc_array_SD = calculate_new_range(time_e_array, dacc_array_SD, 0, 200)


############################# new dr ##########################
int1_FB = integrate.cumtrapz(x=new_time_array_FB, y=new_dacc_array_FB, initial=0) #First integral is the velocity:
dint1_FB = signal.detrend(int1_FB)
int2_FB = integrate.cumtrapz(x=new_time_array_FB, y=dint1_FB, initial=0) #Second integral is the displacment:
dint2_FB = signal.detrend(int2_FB)


int1_SD = integrate.cumtrapz(x=new_time_array_SD, y=new_dacc_array_SD, initial=0) #First integral is the velocity:
dint1_SD = signal.detrend(int1_SD)
int2_SD = integrate.cumtrapz(x=new_time_array_SD, y=dint1_SD, initial=0) #Second integral is the displacment:
dint2_SD = signal.detrend(int2_SD)



#First integral is the velocity:
int1 = integrate.cumtrapz(x=new_time_array2, y=new_dacc_array2, initial=0)
dint1 = signal.detrend(int1)

#Second integral is the displacment:
int2 = integrate.cumtrapz(x=new_time_array2, y=dint1, initial=0)
dint2 = signal.detrend(int2)


f1 = plt.figure(figsize=(12,5))
ax1 = f1.add_subplot(121)
ax2 = f1.add_subplot(122)

f2 = plt.figure(figsize=(12,5))
ax3 = f2.add_subplot(121)
ax4 = f2.add_subplot(122)

ax1.plot(new_time_array2, int1)
ax1.set_title('Velocity vs. Time')
ax1.set_xlabel('Time [s]')
ax1.set_ylabel('Velocity [m/s]')
ax1.axhline(0, color="orange", ls='--')


ax2.plot(new_time_array2, dint1)
ax2.set_title('Detrended Velocity vs. Time')
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Velocity [m/s]')
ax2.axhline(0, color="orange",ls='--')

ax3.plot(new_time_array2, int2)
ax3.set_title('Displacement vs. Time')
ax3.set_xlabel('Time [s]')
ax3.set_ylabel('Displacement [m]')               

ax4.plot(new_time_array2, dint2)
ax4.set_title('Detrended Displacement vs. Time')
ax4.set_xlabel('Time [s]')
ax4.set_ylabel('Displacement [m]')


plt.show()

print(dint2_FB)
print(dint2_SD)

[ 2.44213007e+01  2.41713897e+01  2.39480902e+01  2.37479998e+01
  2.35507814e+01  2.33350190e+01  2.30935765e+01  2.28422136e+01
  2.26004016e+01  2.23601409e+01  2.21048245e+01  2.18418421e+01
  2.15781352e+01  2.13019413e+01  2.10098608e+01  2.06830724e+01
  2.03422975e+01  2.00006795e+01  1.96621083e+01  1.93090088e+01
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  1.74059191e+01  1.70078464e+01  1.66251497e+01  1.62471358e+01
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  16.56328861  17.36204445  18.09436183  18.74679215  19.30938344
  19.77191958  20.17765158  20.49284695  20.72220356  20.85626248
  20.86178315  20.75844728  20.56311332  20.21704941  19.65917879
  18.71265328  17.6052084   16.41882026  15.20776825  14.01202532
  12.89784209  11.90405482  10.92651743   9.98467599   9.09947569
   8.2258109    7.3471708    6.46912399   5.6259342    4.80271783
   3.99897684   3.21714687   2.46539533   1.78165687   1.09793395
   0.43595862  -0.20159027  -0.82864707  -1.45435252  -2.03795725
  -2.57260097  -3.04577538  -3.47886173  -3.89495344  -4.35899113
  -4.8861053   -5.48196905  -6.0483259   -6.56071801  -7.04779848
  -7.50602737  -7.94896237  -8.46168349  -9.06800108  -9.74183569
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 -22.32714025 -22.33327259 -22.30184794 -22.243101   -22.18389995
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 -18.17621537 -18.09353565 -18.06709477 -18.1253855  -18.20442117
 -18.24603861 -18.26693782 -18.27775011 -18.26390733 -18.22116379
 -18.16087864 -18.0848514  -17.99575639 -17.90475712 -17.82080934
 -17.72912163 -17.60621678 -17.45801273 -17.2996084  -17.13080898
 -16.95820866 -16.77653898 -16.5716882  -16.33697521 -16.08841339
 -15.84246817 -15.60677672 -15.37506055 -15.13232577 -14.93315295
 -14.75825522 -14.53194542 -14.24575445 -13.9449112  -13.64313139
 -13.31970318 -12.97121089 -12.6244589  -12.28173387 -11.9544856
 -11.63725089 -11.29343831 -10.95269    -10.63133867 -10.33777907
 -10.06761304  -9.8092025   -9.57631402  -9.37727242  -9.20101897
  -9.02134587  -8.83541518  -8.65355254  -8.48504704  -8.31044232
  -8.11607816  -7.89547063  -7.65013643  -7.39619729  -7.13172698
  -6.85359609  -6.5656389   -6.28741414  -6.01655169  -5.7431534
  -5.49024142  -5.28023811  -5.07232374  -4.83533624  -4.59234855
  -4.36753482  -4.13899836  -3.87393153  -3.57680141  -3.28983185
  -2.99988348  -2.71847072  -2.46352163  -2.24718766  -2.04951555
  -1.79765125  -1.47527523  -1.23624041  -1.10955571  -0.99759757
  -0.87873913  -0.75183995  -0.61570056  -0.46891481  -0.29994018
  -0.11806709   0.06630626   0.25531677   0.41613186   0.4801292
   0.47139273   0.48678675   0.54133997   0.59715912   0.63922735
   0.66068336   0.67758472   0.71717227   0.77721446   0.84366744
   0.92334167   1.04885516   1.24677748   1.48696342   1.71030709
   1.89873025   2.08929498   2.31250698   2.57610681   2.84630726
   3.10328492   3.35986633   3.62512395   3.89998503   4.16091616
   4.42103368   4.69655318   4.99906269   5.30393091   5.57836791
   5.82998318   6.05698891   6.21747461   6.34158014   6.4893212
   6.6924853    6.95290473   7.21458473   7.44321223   7.67133309
   7.92063224   8.16833332   8.375379     8.53028829   8.65276144
   8.74414206   8.80055168   8.84240403   8.92517894   9.05384228
   9.19681514   9.33993923   9.49615143   9.69253491   9.92666338
  10.18527832  10.46370654  10.77089621  11.10115766  11.45532233
  11.86455522  12.33333772  12.84506256  13.31599552  13.71237131
  14.07554154  14.4622736   14.88618037  15.32204403  15.75729819
  16.20346481  16.65517356  17.07303369  17.44292511  17.83186737
  18.22716565  18.63556561  19.07649009  19.55086634  20.03971111
  20.54636506  21.0874425   21.66496648  22.25621954  22.89173581
  23.58803899  24.22003272  24.78137829  25.32227742  25.86584336
  26.42601713  27.01902667  27.66148627  28.35733994  29.09303458
  29.86829073  30.69730932  31.54277416  32.45574903  33.39691073
  34.38106352  35.40303253  36.44693081  37.491008    38.46419913
  39.28818515  39.95315474  40.62422426  41.30583305  41.86764367
  42.31777468  42.73415213  43.15563248  43.59030645  44.06908465
  44.60506443  45.21636847  45.88642694  46.48873428  46.95499852
  47.3087965   47.54075702  47.63561331  47.66276372  47.67843619
  47.70330489  47.73112832  47.7615154   47.80384028  47.86064426
  47.93320939  48.03913232  48.20057223  48.3812956   48.52087035
  48.56264127  48.62456393  48.88596414  49.30523309  49.68024563
  49.87244175  49.88354464  49.83416762  49.79005063  49.74077059
  49.71189448  49.72396058  49.76762739  49.83090916  49.9134074
  50.00427186  50.09759278  50.20257106  50.32736275  50.46814413
  50.60208489  50.5517257   50.17054807  49.61975565  49.0779435
  48.53386726  47.98210535  47.4098391   46.84432554  46.31842379
  45.81514676  45.34395948  44.93129119  44.57272094  44.25365117
  43.96728793  43.71654492  43.46797566  43.21798549  43.00061065
  42.81205204  42.57612653  42.32137936  42.05786986  41.73274022
  41.36203405  40.93953581  40.5030252   40.09355213  39.73104934
  39.40045534  39.09527289  38.80581395  38.50845783  38.19280777
  37.86422147  37.54492447  37.20662196  36.90540753  36.63745601
  36.40577759  36.15526958  35.78571604  35.358742    34.8295069
  34.12009435  33.27119674  32.38339075  31.53673972  30.72564002
  29.9198869   29.10453867  28.32009336  27.57021808  26.86441513
  26.14171911  25.40740513  24.62617337  23.7447545   22.89981792
  22.11192252  21.20986659  20.13541738  19.03068843  18.03628934
  17.04231522  16.09308301  15.20044911  14.33901569  13.4777773
  12.60710259  11.73428199  10.87485322  10.06544163   9.31158563
   8.59924134   7.91809259   7.27533575   6.67055983   6.05144094
   5.45352018   4.90176237   4.38621803   3.89380551   3.44268403
   3.06451919   2.73120462   2.33921953   1.82659933   1.27979878
   0.68161738  -0.07006056  -1.04051871  -2.1447807   -3.26847134
  -4.33841259  -5.35788315  -6.41496836  -7.45461051  -8.44473378
  -9.39125273 -10.2968004  -11.18277652 -12.02160646 -12.84062785
 -13.64413719 -14.43041176 -15.15742338 -15.82301318 -16.43159708
 -17.0772732  -17.72864975 -18.31551589 -18.83916363 -19.31703773
 -19.81953902 -20.40291437 -21.02645165 -21.62976458 -22.19113741
 -22.70880896 -23.1824579  -23.62505202 -24.04549163 -24.47467575
 -24.84436573 -25.13046191 -25.36551983 -25.58124059 -25.79363902
 -25.98889476 -26.16999798 -26.34353485 -26.5061773  -26.69272562
 -26.92739962 -27.17879121 -27.42690494 -27.68138822 -27.94739392
 -28.2252015  -28.51780545 -28.80497137 -29.05244669 -29.26563489
 -29.4635964  -29.64316517 -29.79811341 -29.90570504 -29.96754859
 -30.00138527 -30.02765147 -30.09206136 -30.29257554 -30.63128351
 -30.98749335 -31.27068058 -31.43587853 -31.58369921 -31.77529721
 -31.94838669 -32.07009629 -32.12514993 -32.14994726 -32.17454928
 -32.21039738 -32.24363484 -32.25442453 -32.23909624 -32.17962145
 -32.06376325 -31.90445806 -31.68783447 -31.43231893 -31.19556308
 -30.99756016 -30.8081638  -30.58431614 -30.30024154 -29.94525819
 -29.56679807 -29.19775942 -28.82159839 -28.4287477  -28.01352914
 -27.56394964 -27.06223513 -26.5328694  -26.00699823 -25.52653211
 -25.1324765  -24.91705962 -24.96710888 -25.21994094 -25.46297201
 -25.56996729 -25.56817939 -25.51387624 -25.39527032 -25.23074152
 -25.04862499 -24.82238518 -24.53967348 -24.18758407 -23.81703035
 -23.4468737  -23.06774447 -22.64764298 -22.1951531  -21.73330752
 -21.22325842 -20.65261468 -20.13063966 -19.75265273 -19.46834405
 -19.20523551 -18.90357523 -18.56705552 -18.21646968 -17.85662224
 -17.46939054 -17.03755691 -16.56201941 -16.05687781 -15.5290898
 -15.02782    -14.56297886 -14.21481994 -13.93846118 -13.74054311
 -13.5866495  -13.36028199 -13.01206912 -12.5536147  -12.0103756
 -11.44390272 -10.87251652 -10.31498317  -9.73307852  -9.14177692
  -8.59671342  -8.05991548  -7.4863042   -6.89041721  -6.3166903
  -5.78169333  -5.29134726  -4.85540995  -4.51123851  -4.24333681
  -4.05105649  -3.89314359  -3.73609573  -3.55908039  -3.36054752
  -3.12371566  -2.79110192  -2.33096107  -1.7506189   -1.0889516
  -0.3767371    0.37864504   1.19130591   2.04470237   2.8836246
   3.6522338    4.42502387   5.14250161   5.66609687   5.90080449
   5.91640943   5.75637234   5.41430698   5.02667179   4.69720752
   4.41274485   4.18824945   4.05304182   3.9515199    3.76497763
   3.45297204   3.01085458   2.4595578    1.89572893   1.37578101
   0.9222021    0.53035079   0.18618321  -0.16486978  -0.52779904
  -0.9132655   -1.25649558  -1.50919917  -1.68627713  -1.84113933
  -2.04508349  -2.36505907  -2.81243286  -3.28168306  -3.67225568
  -4.09506362  -4.64814741  -5.27474113  -5.99362324  -6.80570532
  -7.68295999  -8.51958415  -9.29118607  -9.96219883 -10.49511683
 -10.87925986 -11.21558565 -11.5531984  -11.86208204 -12.08779362
 -12.23235577 -12.28210381 -12.28421066 -12.23601452 -12.02096765
 -11.59268002 -11.03323625 -10.38351721  -9.64795233  -8.85806054
  -7.9410321   -6.86435535  -5.69733419  -4.53926475  -3.46167671
  -2.39882682  -1.30685299  -0.09857721   1.2492944    2.70663918
   4.18279881   5.64663953]

#print(motion_df)

saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data
dropped_motion_df = motion_df.dropna(subset=['Latitude', 'Longitude'])

#print(saved_copy_motion_df)


#Create arrays with just the longitude and the latitude
lat_array = np.array(dropped_motion_df.loc[:,"Latitude"], dtype=float)
lon_array = np.array(dropped_motion_df.loc[:,"Longitude"], dtype=float)

#confirm that these two arrays are the same length
print(len(lat_array))
print(len(lon_array))

#Account for how data is represented divide lat by 10^5 and lon by 10^4
lat_array[:] = [x / 10.0**5 for x in lat_array]
lon_array[:] = [x / 10.0**5 for x in lon_array]
print(lat_array[0])
print(lon_array[0])

357
357
32.85846
-117.25781

# Place map
gmap = gmplot.GoogleMapPlotter(lat_array[0], lon_array[0], 13)
gmap.scatter(lat_array, lon_array, '#3B0B39', size=5, marker=False)
gmap.plot(lat_array, lon_array, 'cornflowerblue', edge_width = 3.0)


# Draw
gmap.draw("dead-reckoning-IA.html")