Solutions (So Far)
By Brendon and Jasmine
The ride identification number for this experiment was 14888. This can be used to look up the "ride session" on the Smartfin website where you can download the data for your own analysis. Oceanographers at SIO let us use the CDIP buoy calibrator, and we got a really nice-looking sine wave for this experiment.
Data was recorded at a sampling rate of 5 Hz, and the buoy calibrator was set to generate "waves" with a 6.25 second period, corresponding to a peak frequency at 0.16 Hz. The vertical displacement (or “wave height”) was set to be 1.8m.
Image 1. A portion of the acceleration in the vertical direction obtained from the buoy calibrator experiment.
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(All included in cell 10):
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Step 1: Detrend the data
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Step 2: Take the FFT of acceleration
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Step 3: Shift the FFT of acceleration
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Step 4: Normalize the FFT of acceleration
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Step 5: Multiply the FFT of acceleration signal by 1/w^2 where w = 2pif (f is the peak frequency that we got from the FFT of acceleration, in this case it was approx 0.16)
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Image 2. Graph of vertical displacement in the frequency domain, produced from the buoy calibrator experiment.
Our algorithm produced the following results: a reported peak frequency of 0.159651669086 and a reported wave height of 1.74061502787. Again, the actual expected value for peak frequency was 0.16 and for wave height was 1.8m. We calculate the percent error in the table below.
| Peak Frequency (Hz) | Wave Height (m) | |
| Actual | 0.16 | 1.8 |
| Calculated | 0.159651669086 | 1.74061502787 |
| Standard Error | 0.00217706818 | 0.03299165118 |
| % Standard Error | 0.22% | 3.30% |
We can use a method of doubly integrating our vertical acceleration data to obtain vertical displacement data. We can then use this data to determine significant wave height. Significant wave height is a statistical measurement for determining wave height from irregular waves; it is calculated as the mean of the largest one third, or 33%, of all waves.
Image 3. Using a peak picking algorithm to determine the largest 1/3 of wave heights.
| Significant Wave Height [m] | |
| Actual | 1.8 |
| Calculated | 1.8887834075897778 |
| Standard Error | 0.04932411532 |
| % Standard Error | 4.93% |
In this experiment, we see that our double integration wave analysis performs similarly to our spectral analysis when determining wave height.
When analyzing near-perfect waves, our methodology for determining wave height and period closely matches what we expected. These experiments demonstrate that our methodologies are basically correct (<5% error) for each of these statistics in this best-case scenario. However, real world ocean data does not look exactly like the near-perfect data that we tested in this experiment, as exemplified by the image below.
Image 4. Theoretical perfect ocean waves (pictured above) and actual irregular ocean waves (pictured below). Source: https://cdip.ucsd.edu/m/documents/wave_measurement.html
Since ocean waves will always have irregular waves due to fundamental variability on the sea surface [CDIP], we will have to calculate wave characteristics using statistical terms. For significant wave height, we will take the mean of the largest ⅓ of waves recorded during the sampling period. CDIP normally calculates these characteristics over a short-term period, for example, for one hour or less. The majority of CDIP’s parameters are calculated over a 30 minute time period.
The next test that we did was a controlled pool experiment in the UCSD Canyonview pool. This corresponds to ride ID number 14827 on the Smartfin website. Our experiment contained 3 sub-experiments, each with the Smartfin mounted onto a pole vertically. We simulated different wave heights by vertically displacing the Smartfin in the pool different distances and speeds. This was done to collect data in a controlled environment that more closely mirrored the type of data that we would expect to obtain from the ocean. We viewed the raw acceleration readings in a graph and then processed the data to get the resulting displacement as well as FFT. Notes for each of the sub-experiments are included below.
We collected data at a sampling rate of 30Hz for each experiment.
The first controlled experiment that we did was creating a sinusoidal wave with a total vertical displacement of 1.8m (amplitude of 0.9m) and** period of 6 seconds (corresponding to a frequency of 0.16Hz).**
The second controlled experiment that we did was creating a wave with a total vertical displacement of 1.5m (amplitude of 0.75m) and** period of 4 seconds (corresponding to a frequency of 0.25Hz).**
The third controlled experiment that we did was creating al wave with a total vertical displacement of 0.9m (amplitude of 0.45m) and period of 2 seconds (corresponding to a frequency of 0.5Hz).
Image 4. Vertical Acceleration vs. time for each of the controlled pool sub-experiments.
After doubly integrating, detrending, and filtering the results of each integration step, we obtain the following graphs of Vertical displacement vs. Time for each of the sub-experiments.
Again, we can view the wave heights that were produced in order to analyze our standard error in calculating the significant wave height using each of these methods.
| Actual Wave Height [m] | Calculated Wave Height [m] | Standard Error | |
| Experiment #1 | 1.8 | 2.0632722103091106 | 0.1462633906 |
| Experiment #2 | 1.5 | 2.0850691485538615 | 0.39004609903 |
| Experiment #3 | 0.9 | 0.8514080931935583 | 0.05399100756 |
Here, we see that we often overestimate the actual wave height when computing significant wave height. However, because significant wave height is a statistical measure that takes into account only the top 33% of waves, this is to be expected. In particular, it seems like we have a great standard error for our second experiment; this is because of the unexpected large wave that we see in the middle of the graph. Because we were only sampling waves for a short period of time, this wave dominates our series. When we take more waves into consideration, for example looking at the top 50% of waves rather than the top 33% of waves (to account for errors in the manual creation of our waves), these errors drop significantly. The following table demonstrates the standard error present in this new wave height metric, which can be compared to the previous significant wave height metric.
| Actual Wave Height [m] | Calculated Wave Height [m] | Standard Error | |
| Experiment #1 | 1.8 | 1.9848564307387153 | 0.10269801707 |
| Experiment #2 | 1.5 | 1.619954942662752 | 0.07996996177 |
| Experiment #3 | 0.9 | 0.8514080931935583 | 0.05399100756 |
In order to determine how our methodology would perform in an actual real world setting, we had a surfer float in the ocean on their surfboard. We then calculated the significant wave height from that data, and attempted to compare it against CDIP data taken from the same day.
#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/10.1175/2010JTECHO724.1import 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 inlineride_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)
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#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")By Howard Wang
"The method I came up with to calculate and determine the direction of waves in the general surf region can be described simply as a physical analysis of the overall movement of the Smartfin, currently using buoy calibrated data for controlled behaviour. Basically, the movement of the Smartfin emulates the movement of the waves." - Howard Wang
# Howard Wang 05/14/19
# CSE 145: Embedded systems and design
# Main Database: https://surf.smartfin.org/
# Analyzing data from Buoy Calibration experiment to get wave direction.
# First, parse the data from the .CSV file containing ocean and wave motion data.
# This data comes from a controlled experiment (CE3), so we are assuming that
# all of the vertical accelerations are contained in IMUA2.
# MATPLOTLIB
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
#from mpl_toolkits.basemap import Basemap
# DATAFRAMES
import pandas as pd
import numpy as np
# SCIPY
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
import pylab as pylab
# SYSTEM and CONVERSION TOOLS
import math
import abc
import sys
import csv
import io
import os
import datetime
import pytz
import re
# MODELING AND GRAPHS
import peakutils
import statsmodels.api as sm
# URL REQUESTS
import requests
# VECTORS AND GRAPHICS
import mpld3
import folium
# import cmocean
import skinematics as skin
from skinematics import quat, vector, misc, rotmat, imus, view
import pygame
# PLOTTING TOOLS
from plotly import tools
import plotly.offline
import plotly.graph_objs as go
%matplotlib notebook
%matplotlib inline
print("Done!")Done!
ride_ids = ['16083']
# This is the recent (May 15th buoy float session ID)
# A smartfin was placed next to a real buoy and data was collected
# Now data must be scrutinized against buoy float data
print("Done!")Done!
#%% 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]
# 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)
oceanCSV = requests.get(ocean_csv_url).content
ocean_df_small = pd.read_csv(io.StringIO(oceanCSV.decode('utf-8')))
# Grab CSV from motion url
motionCSV = requests.get(motion_csv_url).content
motion_df_small = pd.read_csv(io.StringIO(motionCSV.decode('utf-8')))
return ocean_df_small, motion_df_small
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
print("Done!")Done!
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'])
# Print motion and Ocean dataframes
print(motion_df)
print(ocean_df)https://surf.smartfin.org/ride/16083
https://surf.smartfin.org/media/201905/google_105349665704999793400_0006667E229D_190515161151_Ocean.CSV
Ride data has been uploaded.
UTC Time IMU A1 IMU A2 \
ride_id
16083 0 2019-05-15T16:11:51.2410+00:00 762168371 -59.0 505.0
1 2019-05-15T16:11:51.4940+00:00 762168623 -56.0 493.0
2 2019-05-15T16:11:51.7460+00:00 762168873 -60.0 524.0
3 2019-05-15T16:11:51.9990+00:00 762169125 -58.0 551.0
4 2019-05-15T16:11:52.2510+00:00 762169375 -57.0 519.0
5 2019-05-15T16:11:52.5030+00:00 762169625 -56.0 450.0
6 2019-05-15T16:11:52.7540+00:00 762169875 -57.0 439.0
7 2019-05-15T16:11:53.0070+00:00 762170126 -56.0 497.0
8 2019-05-15T16:11:53.2580+00:00 762170376 -59.0 506.0
9 2019-05-15T16:11:53.5110+00:00 762170627 -57.0 501.0
10 2019-05-15T16:11:53.7630+00:00 762170877 -59.0 499.0
11 2019-05-15T16:11:54.0140+00:00 762171127 -60.0 502.0
12 2019-05-15T16:11:54.2680+00:00 762171379 -59.0 446.0
13 2019-05-15T16:11:54.5190+00:00 762171629 -56.0 478.0
14 2019-05-15T16:11:54.7710+00:00 762171879 -59.0 535.0
15 2019-05-15T16:11:55.0240+00:00 762172130 -59.0 553.0
16 2019-05-15T16:11:55.2670+00:00 762172372 -60.0 520.0
17 2019-05-15T16:11:55.5200+00:00 762172623 -61.0 567.0
18 2019-05-15T16:11:55.7710+00:00 762172873 -63.0 589.0
19 2019-05-15T16:11:56.0250+00:00 762173125 -64.0 601.0
20 2019-05-15T16:11:56.2770+00:00 762173375 -62.0 510.0
21 2019-05-15T16:11:56.5290+00:00 762173626 -59.0 470.0
22 2019-05-15T16:11:56.7810+00:00 762173876 -60.0 537.0
23 2019-05-15T16:11:57.0330+00:00 762174127 -56.0 457.0
24 2019-05-15T16:11:57.2850+00:00 762174377 -56.0 421.0
25 2019-05-15T16:11:57.5370+00:00 762174627 -58.0 456.0
26 2019-05-15T16:11:57.7880+00:00 762174877 -63.0 527.0
27 2019-05-15T16:11:58.0410+00:00 762175128 -59.0 492.0
28 2019-05-15T16:11:58.2940+00:00 762175379 -57.0 480.0
29 2019-05-15T16:11:58.5460+00:00 762175630 -56.0 440.0
... ... ... ... ...
6440 2019-05-15T16:35:41.0010+00:00 763589006 465.0 42.0
6441 2019-05-15T16:35:41.2040+00:00 763589207 495.0 48.0
6442 2019-05-15T16:35:41.4050+00:00 763589407 482.0 72.0
6443 2019-05-15T16:35:41.6070+00:00 763589608 511.0 47.0
6444 2019-05-15T16:35:41.8090+00:00 763589808 540.0 22.0
6445 2019-05-15T16:35:42.0110+00:00 763590009 546.0 -31.0
6446 2019-05-15T16:35:42.2120+00:00 763590209 547.0 -69.0
6447 2019-05-15T16:35:42.4130+00:00 763590409 486.0 -21.0
6448 2019-05-15T16:35:42.6160+00:00 763590610 453.0 -31.0
6449 2019-05-15T16:35:42.8160+00:00 763590809 445.0 -22.0
6450 2019-05-15T16:35:43.0170+00:00 763591009 431.0 -12.0
6451 2019-05-15T16:35:43.2190+00:00 763591209 440.0 -29.0
6452 2019-05-15T16:35:43.4200+00:00 763591409 477.0 5.0
6453 2019-05-15T16:35:43.6220+00:00 763591610 465.0 -35.0
6454 2019-05-15T16:35:43.8240+00:00 763591811 465.0 -38.0
6455 2019-05-15T16:35:44.0260+00:00 763592011 474.0 -77.0
6456 2019-05-15T16:35:44.2270+00:00 763592211 475.0 -15.0
6457 2019-05-15T16:35:44.4290+00:00 763592412 509.0 -13.0
6458 2019-05-15T16:35:44.6310+00:00 763592612 520.0 -3.0
6459 2019-05-15T16:35:44.8320+00:00 763592812 500.0 -308.0
6460 2019-05-15T16:35:45.0000+00:00 763592979 NaN NaN
6461 2019-05-15T16:35:45.0340+00:00 763593013 533.0 -5.0
6462 2019-05-15T16:35:45.2370+00:00 763593214 544.0 -18.0
6463 2019-05-15T16:35:45.4290+00:00 763593405 529.0 -21.0
6464 2019-05-15T16:35:45.6300+00:00 763593605 542.0 -20.0
6465 2019-05-15T16:35:45.8310+00:00 763593805 549.0 -93.0
6466 2019-05-15T16:35:46.0330+00:00 763594005 521.0 -29.0
6467 2019-05-15T16:35:46.2350+00:00 763594206 522.0 -22.0
6468 2019-05-15T16:35:46.4370+00:00 763594407 499.0 -17.0
6469 2019-05-15T16:35:46.6390+00:00 763594608 421.0 371.0
IMU A3 IMU G1 IMU G2 IMU G3 IMU M1 IMU M2 IMU M3 \
ride_id
16083 0 63.0 3.0 15.0 56.0 29.0 -145.0 197.0
1 62.0 -68.0 -14.0 65.0 12.0 -148.0 194.0
2 87.0 -27.0 -12.0 23.0 16.0 -150.0 188.0
3 97.0 66.0 22.0 -84.0 15.0 -161.0 177.0
4 91.0 6.0 30.0 -103.0 29.0 -139.0 187.0
5 82.0 -36.0 34.0 -31.0 37.0 -141.0 185.0
6 78.0 28.0 36.0 74.0 36.0 -148.0 192.0
7 63.0 27.0 25.0 107.0 28.0 -138.0 200.0
8 76.0 -103.0 3.0 0.0 23.0 -155.0 195.0
9 89.0 -1.0 26.0 -36.0 26.0 -154.0 184.0
10 88.0 77.0 48.0 -38.0 28.0 -144.0 184.0
11 77.0 -4.0 36.0 -31.0 26.0 -142.0 194.0
12 63.0 -59.0 11.0 32.0 37.0 -145.0 187.0
13 75.0 -42.0 -5.0 104.0 23.0 -155.0 181.0
14 87.0 29.0 2.0 90.0 18.0 -158.0 178.0
15 99.0 14.0 22.0 -77.0 18.0 -156.0 180.0
16 87.0 3.0 22.0 -32.0 17.0 -153.0 191.0
17 93.0 2.0 28.0 -30.0 29.0 -151.0 179.0
18 97.0 9.0 30.0 16.0 26.0 -150.0 186.0
19 81.0 52.0 35.0 13.0 26.0 -148.0 188.0
20 71.0 -3.0 12.0 22.0 26.0 -146.0 190.0
21 81.0 -6.0 0.0 46.0 19.0 -145.0 195.0
22 91.0 36.0 18.0 28.0 26.0 -152.0 190.0
23 70.0 -18.0 22.0 -22.0 18.0 -144.0 200.0
24 67.0 -55.0 13.0 -25.0 24.0 -150.0 194.0
25 72.0 -8.0 3.0 27.0 25.0 -145.0 187.0
26 90.0 -12.0 6.0 24.0 23.0 -147.0 175.0
27 81.0 -19.0 19.0 -23.0 23.0 -147.0 181.0
28 77.0 -33.0 22.0 -63.0 33.0 -153.0 181.0
29 73.0 -24.0 21.0 -57.0 33.0 -141.0 177.0
... ... ... ... ... ... ... ...
6440 230.0 38.0 24.0 -28.0 -219.0 43.0 -133.0
6441 213.0 -3.0 18.0 0.0 -221.0 39.0 -123.0
6442 209.0 -12.0 10.0 75.0 -220.0 44.0 -120.0
6443 194.0 -56.0 -12.0 151.0 -225.0 49.0 -123.0
6444 167.0 -55.0 -26.0 163.0 -230.0 70.0 -126.0
6445 147.0 -34.0 -23.0 88.0 -234.0 80.0 -124.0
6446 137.0 49.0 25.0 3.0 -238.0 94.0 -112.0
6447 127.0 -106.0 -8.0 -62.0 -241.0 89.0 -123.0
6448 118.0 -117.0 -4.0 -61.0 -250.0 88.0 -118.0
6449 127.0 30.0 -15.0 -10.0 -245.0 93.0 -111.0
6450 127.0 -78.0 -20.0 7.0 -246.0 92.0 -110.0
6451 125.0 21.0 -27.0 65.0 -255.0 91.0 -105.0
6452 139.0 -59.0 3.0 49.0 -256.0 100.0 -96.0
6453 142.0 -157.0 5.0 -2.0 -250.0 112.0 -100.0
6454 123.0 -291.0 26.0 -31.0 -250.0 130.0 -100.0
6455 130.0 -31.0 10.0 -61.0 -255.0 131.0 -103.0
6456 142.0 11.0 23.0 -57.0 -255.0 125.0 -105.0
6457 136.0 25.0 17.0 -16.0 -259.0 125.0 -97.0
6458 137.0 -32.0 0.0 24.0 -249.0 113.0 -93.0
6459 156.0 39.0 4.0 26.0 -259.0 121.0 -89.0
6460 NaN NaN NaN NaN NaN NaN NaN
6461 122.0 -19.0 -8.0 57.0 -262.0 120.0 -92.0
6462 101.0 -26.0 4.0 36.0 -261.0 129.0 -93.0
6463 124.0 2.0 21.0 -3.0 -261.0 129.0 -93.0
6464 107.0 -3.0 26.0 -24.0 -267.0 125.0 -99.0
6465 82.0 -29.0 30.0 -44.0 -257.0 119.0 -93.0
6466 97.0 21.0 29.0 -38.0 -269.0 119.0 -99.0
6467 108.0 0.0 12.0 -22.0 -266.0 120.0 -100.0
6468 97.0 -17.0 0.0 5.0 -255.0 121.0 -93.0
6469 45.0 88.0 4.0 46.0 -272.0 112.0 -90.0
Latitude Longitude
ride_id
16083 0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN
13 NaN NaN
14 NaN NaN
15 NaN NaN
16 NaN NaN
17 NaN NaN
18 NaN NaN
19 NaN NaN
20 NaN NaN
21 NaN NaN
22 NaN NaN
23 NaN NaN
24 NaN NaN
25 NaN NaN
26 NaN NaN
27 NaN NaN
28 NaN NaN
29 NaN NaN
... ... ...
6440 NaN NaN
6441 NaN NaN
6442 NaN NaN
6443 NaN NaN
6444 NaN NaN
6445 NaN NaN
6446 NaN NaN
6447 NaN NaN
6448 NaN NaN
6449 NaN NaN
6450 NaN NaN
6451 NaN NaN
6452 NaN NaN
6453 NaN NaN
6454 NaN NaN
6455 NaN NaN
6456 NaN NaN
6457 NaN NaN
6458 NaN NaN
6459 NaN NaN
6460 3286983.0 -11726667.0
6461 NaN NaN
6462 NaN NaN
6463 NaN NaN
6464 NaN NaN
6465 NaN NaN
6466 NaN NaN
6467 NaN NaN
6468 NaN NaN
6469 NaN NaN
[6470 rows x 13 columns]
UTC Time Temperature 1 \
ride_id
16083 0 2019-05-15T16:11:50.7700+00:00 762167903 303
1 2019-05-15T16:11:56.8280+00:00 762173923 303
2 2019-05-15T16:12:02.8860+00:00 762179942 303
3 2019-05-15T16:12:08.9450+00:00 762185962 303
4 2019-05-15T16:12:15.0030+00:00 762191982 303
5 2019-05-15T16:12:21.0620+00:00 762198002 303
6 2019-05-15T16:12:27.1200+00:00 762204021 303
7 2019-05-15T16:12:33.1780+00:00 762210041 303
8 2019-05-15T16:12:39.2360+00:00 762216060 303
9 2019-05-15T16:12:45.2950+00:00 762222080 303
10 2019-05-15T16:12:51.3540+00:00 762228101 303
11 2019-05-15T16:12:57.4130+00:00 762234121 303
12 2019-05-15T16:13:03.4720+00:00 762240141 303
13 2019-05-15T16:13:09.5310+00:00 762246162 303
14 2019-05-15T16:13:15.5910+00:00 762252183 303
15 2019-05-15T16:13:21.6500+00:00 762258203 303
16 2019-05-15T16:13:27.7080+00:00 762264223 303
17 2019-05-15T16:13:33.7660+00:00 762270242 303
18 2019-05-15T16:13:39.8260+00:00 762276263 303
19 2019-05-15T16:13:45.8830+00:00 762282282 303
20 2019-05-15T16:13:51.9420+00:00 762288302 303
21 2019-05-15T16:13:58.0010+00:00 762294322 302
22 2019-05-15T16:14:04.0590+00:00 762300342 302
23 2019-05-15T16:14:10.1170+00:00 762306361 303
24 2019-05-15T16:14:16.1770+00:00 762312382 303
25 2019-05-15T16:14:22.2350+00:00 762318402 303
26 2019-05-15T16:14:28.2940+00:00 762324422 303
27 2019-05-15T16:14:34.3530+00:00 762330442 302
28 2019-05-15T16:14:40.4100+00:00 762336461 303
29 2019-05-15T16:14:46.4680+00:00 762342480 303
... ... ... ...
208 2019-05-15T16:32:50.9970+00:00 763420087 303
209 2019-05-15T16:32:57.0570+00:00 763426108 303
210 2019-05-15T16:33:03.1170+00:00 763432129 304
211 2019-05-15T16:33:09.1750+00:00 763438149 304
212 2019-05-15T16:33:15.2340+00:00 763444169 305
213 2019-05-15T16:33:21.2920+00:00 763450188 306
214 2019-05-15T16:33:27.3500+00:00 763456208 306
215 2019-05-15T16:33:33.4080+00:00 763462227 306
216 2019-05-15T16:33:39.4680+00:00 763468248 307
217 2019-05-15T16:33:45.5270+00:00 763474269 307
218 2019-05-15T16:33:51.5860+00:00 763480289 308
219 2019-05-15T16:33:57.6440+00:00 763486308 308
220 2019-05-15T16:34:03.7020+00:00 763492328 309
221 2019-05-15T16:34:09.7610+00:00 763498348 309
222 2019-05-15T16:34:15.8210+00:00 763504369 309
223 2019-05-15T16:34:21.8790+00:00 763510389 310
224 2019-05-15T16:34:27.9380+00:00 763516409 310
225 2019-05-15T16:34:33.9990+00:00 763522431 310
226 2019-05-15T16:34:40.0570+00:00 763528451 311
227 2019-05-15T16:34:46.1160+00:00 763534471 311
228 2019-05-15T16:34:52.1740+00:00 763540490 311
229 2019-05-15T16:34:58.2320+00:00 763546510 311
230 2019-05-15T16:35:04.2910+00:00 763552530 312
231 2019-05-15T16:35:10.3510+00:00 763558551 312
232 2019-05-15T16:35:16.4100+00:00 763564571 312
233 2019-05-15T16:35:22.4680+00:00 763570591 312
234 2019-05-15T16:35:28.5270+00:00 763576611 312
235 2019-05-15T16:35:34.5870+00:00 763582632 312
236 2019-05-15T16:35:40.6450+00:00 763588652 312
237 2019-05-15T16:35:46.7050+00:00 763594673 313
Calibrated Temperature 1 Temperature 1 Stable Temperature 2 \
ride_id
16083 0 18.938 False 4734
1 18.938 False 4744
2 18.938 False 4743
3 18.938 False 4745
4 18.938 False 4745
5 18.938 True 4751
6 18.938 True 4747
7 18.938 True 4748
8 18.938 True 4741
9 18.938 True 4746
10 18.938 True 4748
11 18.938 True 4749
12 18.938 True 4743
13 18.938 True 4747
14 18.938 True 4750
15 18.938 True 4747
16 18.938 True 4756
17 18.938 True 4744
18 18.938 True 4744
19 18.938 True 4743
20 18.938 True 4745
21 18.875 True 4739
22 18.875 True 4743
23 18.938 True 4740
24 18.938 False 4744
25 18.938 False 4741
26 18.938 True 4746
27 18.875 True 4746
28 18.938 True 4748
29 18.938 True 4748
... ... ... ...
208 18.938 False 4703
209 18.938 False 4711
210 19.000 False 4714
211 19.000 False 4719
212 19.062 True 4717
213 19.125 True 4725
214 19.125 True 4731
215 19.125 True 4732
216 19.188 True 4731
217 19.188 False 4730
218 19.250 False 4736
219 19.250 False 4734
220 19.312 False 4742
221 19.312 False 4746
222 19.312 False 4746
223 19.375 False 4755
224 19.375 False 4753
225 19.375 False 4756
226 19.438 False 4773
227 19.438 False 4765
228 19.438 False 4788
229 19.438 False 4783
230 19.500 False 4780
231 19.500 False 4776
232 19.500 False 4771
233 19.500 False 4770
234 19.500 False 4766
235 19.500 False 4761
236 19.500 False 4763
237 19.562 False 4771
Calibrated Temperature 2 Temperature 2 Stable salinity \
ride_id
16083 0 18.657 False NaN
1 18.696 False NaN
2 18.692 False NaN
3 18.700 False NaN
4 18.700 False NaN
5 18.724 False NaN
6 18.708 True NaN
7 18.712 True NaN
8 18.684 True NaN
9 18.704 True NaN
10 18.712 True NaN
11 18.716 True NaN
12 18.692 True NaN
13 18.708 True NaN
14 18.720 True NaN
15 18.708 True NaN
16 18.743 True NaN
17 18.696 True NaN
18 18.696 True NaN
19 18.692 True NaN
20 18.700 True NaN
21 18.676 True NaN
22 18.692 True NaN
23 18.680 True NaN
24 18.696 False NaN
25 18.684 False NaN
26 18.704 True NaN
27 18.704 True NaN
28 18.712 True NaN
29 18.712 True NaN
... ... ... ...
208 18.535 False NaN
209 18.567 False NaN
210 18.578 False NaN
211 18.598 False NaN
212 18.590 True NaN
213 18.622 True NaN
214 18.645 False NaN
215 18.649 False NaN
216 18.645 False NaN
217 18.641 False NaN
218 18.665 False NaN
219 18.657 False NaN
220 18.688 False NaN
221 18.704 False NaN
222 18.704 False NaN
223 18.739 False NaN
224 18.731 False NaN
225 18.743 False NaN
226 18.810 False NaN
227 18.778 False NaN
228 18.869 False NaN
229 18.849 False NaN
230 18.837 False NaN
231 18.822 False NaN
232 18.802 False NaN
233 18.798 False NaN
234 18.782 False NaN
235 18.763 False NaN
236 18.771 False NaN
237 18.802 False NaN
Calibrated Salinity Salinity Stable pH Calibrated pH \
ride_id
16083 0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 NaN NaN NaN NaN
8 NaN NaN NaN NaN
9 NaN NaN NaN NaN
10 NaN NaN NaN NaN
11 NaN NaN NaN NaN
12 NaN NaN NaN NaN
13 NaN NaN NaN NaN
14 NaN NaN NaN NaN
15 NaN NaN NaN NaN
16 NaN NaN NaN NaN
17 NaN NaN NaN NaN
18 NaN NaN NaN NaN
19 NaN NaN NaN NaN
20 NaN NaN NaN NaN
21 NaN NaN NaN NaN
22 NaN NaN NaN NaN
23 NaN NaN NaN NaN
24 NaN NaN NaN NaN
25 NaN NaN NaN NaN
26 NaN NaN NaN NaN
27 NaN NaN NaN NaN
28 NaN NaN NaN NaN
29 NaN NaN NaN NaN
... ... ... .. ...
208 NaN NaN NaN NaN
209 NaN NaN NaN NaN
210 NaN NaN NaN NaN
211 NaN NaN NaN NaN
212 NaN NaN NaN NaN
213 NaN NaN NaN NaN
214 NaN NaN NaN NaN
215 NaN NaN NaN NaN
216 NaN NaN NaN NaN
217 NaN NaN NaN NaN
218 NaN NaN NaN NaN
219 NaN NaN NaN NaN
220 NaN NaN NaN NaN
221 NaN NaN NaN NaN
222 NaN NaN NaN NaN
223 NaN NaN NaN NaN
224 NaN NaN NaN NaN
225 NaN NaN NaN NaN
226 NaN NaN NaN NaN
227 NaN NaN NaN NaN
228 NaN NaN NaN NaN
229 NaN NaN NaN NaN
230 NaN NaN NaN NaN
231 NaN NaN NaN NaN
232 NaN NaN NaN NaN
233 NaN NaN NaN NaN
234 NaN NaN NaN NaN
235 NaN NaN NaN NaN
236 NaN NaN NaN NaN
237 NaN NaN NaN NaN
pH Stable
ride_id
16083 0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
17 NaN
18 NaN
19 NaN
20 NaN
21 NaN
22 NaN
23 NaN
24 NaN
25 NaN
26 NaN
27 NaN
28 NaN
29 NaN
... ...
208 NaN
209 NaN
210 NaN
211 NaN
212 NaN
213 NaN
214 NaN
215 NaN
216 NaN
217 NaN
218 NaN
219 NaN
220 NaN
221 NaN
222 NaN
223 NaN
224 NaN
225 NaN
226 NaN
227 NaN
228 NaN
229 NaN
230 NaN
231 NaN
232 NaN
233 NaN
234 NaN
235 NaN
236 NaN
237 NaN
[238 rows x 14 columns]
#print(motion_df)
saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data
print(saved_copy_motion_df)
#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', 'IMU G1', 'IMU G2', 'IMU G3', 'IMU M1',
'IMU M2', 'IMU M3'])
print(dropped_motion_df) UTC Time IMU A1 IMU A2 \
ride_id
16083 0 2019-05-15T16:11:51.2410+00:00 762168371 -59.0 505.0
1 2019-05-15T16:11:51.4940+00:00 762168623 -56.0 493.0
2 2019-05-15T16:11:51.7460+00:00 762168873 -60.0 524.0
3 2019-05-15T16:11:51.9990+00:00 762169125 -58.0 551.0
4 2019-05-15T16:11:52.2510+00:00 762169375 -57.0 519.0
5 2019-05-15T16:11:52.5030+00:00 762169625 -56.0 450.0
6 2019-05-15T16:11:52.7540+00:00 762169875 -57.0 439.0
7 2019-05-15T16:11:53.0070+00:00 762170126 -56.0 497.0
8 2019-05-15T16:11:53.2580+00:00 762170376 -59.0 506.0
9 2019-05-15T16:11:53.5110+00:00 762170627 -57.0 501.0
10 2019-05-15T16:11:53.7630+00:00 762170877 -59.0 499.0
11 2019-05-15T16:11:54.0140+00:00 762171127 -60.0 502.0
12 2019-05-15T16:11:54.2680+00:00 762171379 -59.0 446.0
13 2019-05-15T16:11:54.5190+00:00 762171629 -56.0 478.0
14 2019-05-15T16:11:54.7710+00:00 762171879 -59.0 535.0
15 2019-05-15T16:11:55.0240+00:00 762172130 -59.0 553.0
16 2019-05-15T16:11:55.2670+00:00 762172372 -60.0 520.0
17 2019-05-15T16:11:55.5200+00:00 762172623 -61.0 567.0
18 2019-05-15T16:11:55.7710+00:00 762172873 -63.0 589.0
19 2019-05-15T16:11:56.0250+00:00 762173125 -64.0 601.0
20 2019-05-15T16:11:56.2770+00:00 762173375 -62.0 510.0
21 2019-05-15T16:11:56.5290+00:00 762173626 -59.0 470.0
22 2019-05-15T16:11:56.7810+00:00 762173876 -60.0 537.0
23 2019-05-15T16:11:57.0330+00:00 762174127 -56.0 457.0
24 2019-05-15T16:11:57.2850+00:00 762174377 -56.0 421.0
25 2019-05-15T16:11:57.5370+00:00 762174627 -58.0 456.0
26 2019-05-15T16:11:57.7880+00:00 762174877 -63.0 527.0
27 2019-05-15T16:11:58.0410+00:00 762175128 -59.0 492.0
28 2019-05-15T16:11:58.2940+00:00 762175379 -57.0 480.0
29 2019-05-15T16:11:58.5460+00:00 762175630 -56.0 440.0
... ... ... ... ...
6440 2019-05-15T16:35:41.0010+00:00 763589006 465.0 42.0
6441 2019-05-15T16:35:41.2040+00:00 763589207 495.0 48.0
6442 2019-05-15T16:35:41.4050+00:00 763589407 482.0 72.0
6443 2019-05-15T16:35:41.6070+00:00 763589608 511.0 47.0
6444 2019-05-15T16:35:41.8090+00:00 763589808 540.0 22.0
6445 2019-05-15T16:35:42.0110+00:00 763590009 546.0 -31.0
6446 2019-05-15T16:35:42.2120+00:00 763590209 547.0 -69.0
6447 2019-05-15T16:35:42.4130+00:00 763590409 486.0 -21.0
6448 2019-05-15T16:35:42.6160+00:00 763590610 453.0 -31.0
6449 2019-05-15T16:35:42.8160+00:00 763590809 445.0 -22.0
6450 2019-05-15T16:35:43.0170+00:00 763591009 431.0 -12.0
6451 2019-05-15T16:35:43.2190+00:00 763591209 440.0 -29.0
6452 2019-05-15T16:35:43.4200+00:00 763591409 477.0 5.0
6453 2019-05-15T16:35:43.6220+00:00 763591610 465.0 -35.0
6454 2019-05-15T16:35:43.8240+00:00 763591811 465.0 -38.0
6455 2019-05-15T16:35:44.0260+00:00 763592011 474.0 -77.0
6456 2019-05-15T16:35:44.2270+00:00 763592211 475.0 -15.0
6457 2019-05-15T16:35:44.4290+00:00 763592412 509.0 -13.0
6458 2019-05-15T16:35:44.6310+00:00 763592612 520.0 -3.0
6459 2019-05-15T16:35:44.8320+00:00 763592812 500.0 -308.0
6460 2019-05-15T16:35:45.0000+00:00 763592979 NaN NaN
6461 2019-05-15T16:35:45.0340+00:00 763593013 533.0 -5.0
6462 2019-05-15T16:35:45.2370+00:00 763593214 544.0 -18.0
6463 2019-05-15T16:35:45.4290+00:00 763593405 529.0 -21.0
6464 2019-05-15T16:35:45.6300+00:00 763593605 542.0 -20.0
6465 2019-05-15T16:35:45.8310+00:00 763593805 549.0 -93.0
6466 2019-05-15T16:35:46.0330+00:00 763594005 521.0 -29.0
6467 2019-05-15T16:35:46.2350+00:00 763594206 522.0 -22.0
6468 2019-05-15T16:35:46.4370+00:00 763594407 499.0 -17.0
6469 2019-05-15T16:35:46.6390+00:00 763594608 421.0 371.0
IMU A3 IMU G1 IMU G2 IMU G3 IMU M1 IMU M2 IMU M3 \
ride_id
16083 0 63.0 3.0 15.0 56.0 29.0 -145.0 197.0
1 62.0 -68.0 -14.0 65.0 12.0 -148.0 194.0
2 87.0 -27.0 -12.0 23.0 16.0 -150.0 188.0
3 97.0 66.0 22.0 -84.0 15.0 -161.0 177.0
4 91.0 6.0 30.0 -103.0 29.0 -139.0 187.0
5 82.0 -36.0 34.0 -31.0 37.0 -141.0 185.0
6 78.0 28.0 36.0 74.0 36.0 -148.0 192.0
7 63.0 27.0 25.0 107.0 28.0 -138.0 200.0
8 76.0 -103.0 3.0 0.0 23.0 -155.0 195.0
9 89.0 -1.0 26.0 -36.0 26.0 -154.0 184.0
10 88.0 77.0 48.0 -38.0 28.0 -144.0 184.0
11 77.0 -4.0 36.0 -31.0 26.0 -142.0 194.0
12 63.0 -59.0 11.0 32.0 37.0 -145.0 187.0
13 75.0 -42.0 -5.0 104.0 23.0 -155.0 181.0
14 87.0 29.0 2.0 90.0 18.0 -158.0 178.0
15 99.0 14.0 22.0 -77.0 18.0 -156.0 180.0
16 87.0 3.0 22.0 -32.0 17.0 -153.0 191.0
17 93.0 2.0 28.0 -30.0 29.0 -151.0 179.0
18 97.0 9.0 30.0 16.0 26.0 -150.0 186.0
19 81.0 52.0 35.0 13.0 26.0 -148.0 188.0
20 71.0 -3.0 12.0 22.0 26.0 -146.0 190.0
21 81.0 -6.0 0.0 46.0 19.0 -145.0 195.0
22 91.0 36.0 18.0 28.0 26.0 -152.0 190.0
23 70.0 -18.0 22.0 -22.0 18.0 -144.0 200.0
24 67.0 -55.0 13.0 -25.0 24.0 -150.0 194.0
25 72.0 -8.0 3.0 27.0 25.0 -145.0 187.0
26 90.0 -12.0 6.0 24.0 23.0 -147.0 175.0
27 81.0 -19.0 19.0 -23.0 23.0 -147.0 181.0
28 77.0 -33.0 22.0 -63.0 33.0 -153.0 181.0
29 73.0 -24.0 21.0 -57.0 33.0 -141.0 177.0
... ... ... ... ... ... ... ...
6440 230.0 38.0 24.0 -28.0 -219.0 43.0 -133.0
6441 213.0 -3.0 18.0 0.0 -221.0 39.0 -123.0
6442 209.0 -12.0 10.0 75.0 -220.0 44.0 -120.0
6443 194.0 -56.0 -12.0 151.0 -225.0 49.0 -123.0
6444 167.0 -55.0 -26.0 163.0 -230.0 70.0 -126.0
6445 147.0 -34.0 -23.0 88.0 -234.0 80.0 -124.0
6446 137.0 49.0 25.0 3.0 -238.0 94.0 -112.0
6447 127.0 -106.0 -8.0 -62.0 -241.0 89.0 -123.0
6448 118.0 -117.0 -4.0 -61.0 -250.0 88.0 -118.0
6449 127.0 30.0 -15.0 -10.0 -245.0 93.0 -111.0
6450 127.0 -78.0 -20.0 7.0 -246.0 92.0 -110.0
6451 125.0 21.0 -27.0 65.0 -255.0 91.0 -105.0
6452 139.0 -59.0 3.0 49.0 -256.0 100.0 -96.0
6453 142.0 -157.0 5.0 -2.0 -250.0 112.0 -100.0
6454 123.0 -291.0 26.0 -31.0 -250.0 130.0 -100.0
6455 130.0 -31.0 10.0 -61.0 -255.0 131.0 -103.0
6456 142.0 11.0 23.0 -57.0 -255.0 125.0 -105.0
6457 136.0 25.0 17.0 -16.0 -259.0 125.0 -97.0
6458 137.0 -32.0 0.0 24.0 -249.0 113.0 -93.0
6459 156.0 39.0 4.0 26.0 -259.0 121.0 -89.0
6460 NaN NaN NaN NaN NaN NaN NaN
6461 122.0 -19.0 -8.0 57.0 -262.0 120.0 -92.0
6462 101.0 -26.0 4.0 36.0 -261.0 129.0 -93.0
6463 124.0 2.0 21.0 -3.0 -261.0 129.0 -93.0
6464 107.0 -3.0 26.0 -24.0 -267.0 125.0 -99.0
6465 82.0 -29.0 30.0 -44.0 -257.0 119.0 -93.0
6466 97.0 21.0 29.0 -38.0 -269.0 119.0 -99.0
6467 108.0 0.0 12.0 -22.0 -266.0 120.0 -100.0
6468 97.0 -17.0 0.0 5.0 -255.0 121.0 -93.0
6469 45.0 88.0 4.0 46.0 -272.0 112.0 -90.0
Latitude Longitude
ride_id
16083 0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN
13 NaN NaN
14 NaN NaN
15 NaN NaN
16 NaN NaN
17 NaN NaN
18 NaN NaN
19 NaN NaN
20 NaN NaN
21 NaN NaN
22 NaN NaN
23 NaN NaN
24 NaN NaN
25 NaN NaN
26 NaN NaN
27 NaN NaN
28 NaN NaN
29 NaN NaN
... ... ...
6440 NaN NaN
6441 NaN NaN
6442 NaN NaN
6443 NaN NaN
6444 NaN NaN
6445 NaN NaN
6446 NaN NaN
6447 NaN NaN
6448 NaN NaN
6449 NaN NaN
6450 NaN NaN
6451 NaN NaN
6452 NaN NaN
6453 NaN NaN
6454 NaN NaN
6455 NaN NaN
6456 NaN NaN
6457 NaN NaN
6458 NaN NaN
6459 NaN NaN
6460 3286983.0 -11726667.0
6461 NaN NaN
6462 NaN NaN
6463 NaN NaN
6464 NaN NaN
6465 NaN NaN
6466 NaN NaN
6467 NaN NaN
6468 NaN NaN
6469 NaN NaN
[6470 rows x 13 columns]
UTC Time IMU A1 IMU A2 \
ride_id
16083 0 2019-05-15T16:11:51.2410+00:00 762168371 -59.0 505.0
1 2019-05-15T16:11:51.4940+00:00 762168623 -56.0 493.0
2 2019-05-15T16:11:51.7460+00:00 762168873 -60.0 524.0
3 2019-05-15T16:11:51.9990+00:00 762169125 -58.0 551.0
4 2019-05-15T16:11:52.2510+00:00 762169375 -57.0 519.0
5 2019-05-15T16:11:52.5030+00:00 762169625 -56.0 450.0
6 2019-05-15T16:11:52.7540+00:00 762169875 -57.0 439.0
7 2019-05-15T16:11:53.0070+00:00 762170126 -56.0 497.0
8 2019-05-15T16:11:53.2580+00:00 762170376 -59.0 506.0
9 2019-05-15T16:11:53.5110+00:00 762170627 -57.0 501.0
10 2019-05-15T16:11:53.7630+00:00 762170877 -59.0 499.0
11 2019-05-15T16:11:54.0140+00:00 762171127 -60.0 502.0
12 2019-05-15T16:11:54.2680+00:00 762171379 -59.0 446.0
13 2019-05-15T16:11:54.5190+00:00 762171629 -56.0 478.0
14 2019-05-15T16:11:54.7710+00:00 762171879 -59.0 535.0
15 2019-05-15T16:11:55.0240+00:00 762172130 -59.0 553.0
16 2019-05-15T16:11:55.2670+00:00 762172372 -60.0 520.0
17 2019-05-15T16:11:55.5200+00:00 762172623 -61.0 567.0
18 2019-05-15T16:11:55.7710+00:00 762172873 -63.0 589.0
19 2019-05-15T16:11:56.0250+00:00 762173125 -64.0 601.0
20 2019-05-15T16:11:56.2770+00:00 762173375 -62.0 510.0
21 2019-05-15T16:11:56.5290+00:00 762173626 -59.0 470.0
22 2019-05-15T16:11:56.7810+00:00 762173876 -60.0 537.0
23 2019-05-15T16:11:57.0330+00:00 762174127 -56.0 457.0
24 2019-05-15T16:11:57.2850+00:00 762174377 -56.0 421.0
25 2019-05-15T16:11:57.5370+00:00 762174627 -58.0 456.0
26 2019-05-15T16:11:57.7880+00:00 762174877 -63.0 527.0
27 2019-05-15T16:11:58.0410+00:00 762175128 -59.0 492.0
28 2019-05-15T16:11:58.2940+00:00 762175379 -57.0 480.0
29 2019-05-15T16:11:58.5460+00:00 762175630 -56.0 440.0
... ... ... ... ...
6439 2019-05-15T16:35:40.8000+00:00 763588806 449.0 31.0
6440 2019-05-15T16:35:41.0010+00:00 763589006 465.0 42.0
6441 2019-05-15T16:35:41.2040+00:00 763589207 495.0 48.0
6442 2019-05-15T16:35:41.4050+00:00 763589407 482.0 72.0
6443 2019-05-15T16:35:41.6070+00:00 763589608 511.0 47.0
6444 2019-05-15T16:35:41.8090+00:00 763589808 540.0 22.0
6445 2019-05-15T16:35:42.0110+00:00 763590009 546.0 -31.0
6446 2019-05-15T16:35:42.2120+00:00 763590209 547.0 -69.0
6447 2019-05-15T16:35:42.4130+00:00 763590409 486.0 -21.0
6448 2019-05-15T16:35:42.6160+00:00 763590610 453.0 -31.0
6449 2019-05-15T16:35:42.8160+00:00 763590809 445.0 -22.0
6450 2019-05-15T16:35:43.0170+00:00 763591009 431.0 -12.0
6451 2019-05-15T16:35:43.2190+00:00 763591209 440.0 -29.0
6452 2019-05-15T16:35:43.4200+00:00 763591409 477.0 5.0
6453 2019-05-15T16:35:43.6220+00:00 763591610 465.0 -35.0
6454 2019-05-15T16:35:43.8240+00:00 763591811 465.0 -38.0
6455 2019-05-15T16:35:44.0260+00:00 763592011 474.0 -77.0
6456 2019-05-15T16:35:44.2270+00:00 763592211 475.0 -15.0
6457 2019-05-15T16:35:44.4290+00:00 763592412 509.0 -13.0
6458 2019-05-15T16:35:44.6310+00:00 763592612 520.0 -3.0
6459 2019-05-15T16:35:44.8320+00:00 763592812 500.0 -308.0
6461 2019-05-15T16:35:45.0340+00:00 763593013 533.0 -5.0
6462 2019-05-15T16:35:45.2370+00:00 763593214 544.0 -18.0
6463 2019-05-15T16:35:45.4290+00:00 763593405 529.0 -21.0
6464 2019-05-15T16:35:45.6300+00:00 763593605 542.0 -20.0
6465 2019-05-15T16:35:45.8310+00:00 763593805 549.0 -93.0
6466 2019-05-15T16:35:46.0330+00:00 763594005 521.0 -29.0
6467 2019-05-15T16:35:46.2350+00:00 763594206 522.0 -22.0
6468 2019-05-15T16:35:46.4370+00:00 763594407 499.0 -17.0
6469 2019-05-15T16:35:46.6390+00:00 763594608 421.0 371.0
IMU A3 IMU G1 IMU G2 IMU G3 IMU M1 IMU M2 IMU M3 \
ride_id
16083 0 63.0 3.0 15.0 56.0 29.0 -145.0 197.0
1 62.0 -68.0 -14.0 65.0 12.0 -148.0 194.0
2 87.0 -27.0 -12.0 23.0 16.0 -150.0 188.0
3 97.0 66.0 22.0 -84.0 15.0 -161.0 177.0
4 91.0 6.0 30.0 -103.0 29.0 -139.0 187.0
5 82.0 -36.0 34.0 -31.0 37.0 -141.0 185.0
6 78.0 28.0 36.0 74.0 36.0 -148.0 192.0
7 63.0 27.0 25.0 107.0 28.0 -138.0 200.0
8 76.0 -103.0 3.0 0.0 23.0 -155.0 195.0
9 89.0 -1.0 26.0 -36.0 26.0 -154.0 184.0
10 88.0 77.0 48.0 -38.0 28.0 -144.0 184.0
11 77.0 -4.0 36.0 -31.0 26.0 -142.0 194.0
12 63.0 -59.0 11.0 32.0 37.0 -145.0 187.0
13 75.0 -42.0 -5.0 104.0 23.0 -155.0 181.0
14 87.0 29.0 2.0 90.0 18.0 -158.0 178.0
15 99.0 14.0 22.0 -77.0 18.0 -156.0 180.0
16 87.0 3.0 22.0 -32.0 17.0 -153.0 191.0
17 93.0 2.0 28.0 -30.0 29.0 -151.0 179.0
18 97.0 9.0 30.0 16.0 26.0 -150.0 186.0
19 81.0 52.0 35.0 13.0 26.0 -148.0 188.0
20 71.0 -3.0 12.0 22.0 26.0 -146.0 190.0
21 81.0 -6.0 0.0 46.0 19.0 -145.0 195.0
22 91.0 36.0 18.0 28.0 26.0 -152.0 190.0
23 70.0 -18.0 22.0 -22.0 18.0 -144.0 200.0
24 67.0 -55.0 13.0 -25.0 24.0 -150.0 194.0
25 72.0 -8.0 3.0 27.0 25.0 -145.0 187.0
26 90.0 -12.0 6.0 24.0 23.0 -147.0 175.0
27 81.0 -19.0 19.0 -23.0 23.0 -147.0 181.0
28 77.0 -33.0 22.0 -63.0 33.0 -153.0 181.0
29 73.0 -24.0 21.0 -57.0 33.0 -141.0 177.0
... ... ... ... ... ... ... ...
6439 226.0 52.0 24.0 -26.0 -225.0 51.0 -131.0
6440 230.0 38.0 24.0 -28.0 -219.0 43.0 -133.0
6441 213.0 -3.0 18.0 0.0 -221.0 39.0 -123.0
6442 209.0 -12.0 10.0 75.0 -220.0 44.0 -120.0
6443 194.0 -56.0 -12.0 151.0 -225.0 49.0 -123.0
6444 167.0 -55.0 -26.0 163.0 -230.0 70.0 -126.0
6445 147.0 -34.0 -23.0 88.0 -234.0 80.0 -124.0
6446 137.0 49.0 25.0 3.0 -238.0 94.0 -112.0
6447 127.0 -106.0 -8.0 -62.0 -241.0 89.0 -123.0
6448 118.0 -117.0 -4.0 -61.0 -250.0 88.0 -118.0
6449 127.0 30.0 -15.0 -10.0 -245.0 93.0 -111.0
6450 127.0 -78.0 -20.0 7.0 -246.0 92.0 -110.0
6451 125.0 21.0 -27.0 65.0 -255.0 91.0 -105.0
6452 139.0 -59.0 3.0 49.0 -256.0 100.0 -96.0
6453 142.0 -157.0 5.0 -2.0 -250.0 112.0 -100.0
6454 123.0 -291.0 26.0 -31.0 -250.0 130.0 -100.0
6455 130.0 -31.0 10.0 -61.0 -255.0 131.0 -103.0
6456 142.0 11.0 23.0 -57.0 -255.0 125.0 -105.0
6457 136.0 25.0 17.0 -16.0 -259.0 125.0 -97.0
6458 137.0 -32.0 0.0 24.0 -249.0 113.0 -93.0
6459 156.0 39.0 4.0 26.0 -259.0 121.0 -89.0
6461 122.0 -19.0 -8.0 57.0 -262.0 120.0 -92.0
6462 101.0 -26.0 4.0 36.0 -261.0 129.0 -93.0
6463 124.0 2.0 21.0 -3.0 -261.0 129.0 -93.0
6464 107.0 -3.0 26.0 -24.0 -267.0 125.0 -99.0
6465 82.0 -29.0 30.0 -44.0 -257.0 119.0 -93.0
6466 97.0 21.0 29.0 -38.0 -269.0 119.0 -99.0
6467 108.0 0.0 12.0 -22.0 -266.0 120.0 -100.0
6468 97.0 -17.0 0.0 5.0 -255.0 121.0 -93.0
6469 45.0 88.0 4.0 46.0 -272.0 112.0 -90.0
Latitude Longitude
ride_id
16083 0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN
13 NaN NaN
14 NaN NaN
15 NaN NaN
16 NaN NaN
17 NaN NaN
18 NaN NaN
19 NaN NaN
20 NaN NaN
21 NaN NaN
22 NaN NaN
23 NaN NaN
24 NaN NaN
25 NaN NaN
26 NaN NaN
27 NaN NaN
28 NaN NaN
29 NaN NaN
... ... ...
6439 NaN NaN
6440 NaN NaN
6441 NaN NaN
6442 NaN NaN
6443 NaN NaN
6444 NaN NaN
6445 NaN NaN
6446 NaN NaN
6447 NaN NaN
6448 NaN NaN
6449 NaN NaN
6450 NaN NaN
6451 NaN NaN
6452 NaN NaN
6453 NaN NaN
6454 NaN NaN
6455 NaN NaN
6456 NaN NaN
6457 NaN NaN
6458 NaN NaN
6459 NaN NaN
6461 NaN NaN
6462 NaN NaN
6463 NaN NaN
6464 NaN NaN
6465 NaN NaN
6466 NaN NaN
6467 NaN NaN
6468 NaN NaN
6469 NaN NaN
[6203 rows x 13 columns]
# To store time elapsed between each measurement, and time offset from 0s
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 = []
imuA1_array_nans = np.array(dropped_motion_df.loc[:,"IMU A1"], dtype=float)
imu_array_A1 = []
imuA2_array_nans = np.array(dropped_motion_df.loc[:,"IMU A2"], dtype=float)
imu_array_A2 = []
imuA3_array_nans = np.array(dropped_motion_df.loc[:,"IMU A3"], dtype=float)
imu_array_A3 = []
imuG1_array_nans = np.array(dropped_motion_df.loc[:,"IMU G1"], dtype=float)
imu_array_G1 = []
imuG2_array_nans = np.array(dropped_motion_df.loc[:,"IMU G2"], dtype=float)
imu_array_G2 = []
imuG3_array_nans = np.array(dropped_motion_df.loc[:,"IMU G3"], dtype=float)
imu_array_G3 = []
imuM1_array_nans = np.array(dropped_motion_df.loc[:,"IMU M1"], dtype=float)
imu_array_M1 = []
imuM2_array_nans = np.array(dropped_motion_df.loc[:,"IMU M2"], dtype=float)
imu_array_M2 = []
imuM3_array_nans = np.array(dropped_motion_df.loc[:,"IMU M3"], dtype=float)
imu_array_M3 = []
#Get all the times and imus where time, imu a1, imu a2, and imu a3 are NOT nan values:
for t,x,y,z,a,b,c,d,e,f in zip(time_array_nans, imuA1_array_nans, imuA2_array_nans, imuA3_array_nans, imuG1_array_nans,
imuG2_array_nans, imuG3_array_nans, imuM1_array_nans, imuM2_array_nans, imuM3_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_array_A1.append(x)
imu_array_A2.append(y)
imu_array_A3.append(z)
imu_array_G1.append(a)
imu_array_G2.append(b)
imu_array_G3.append(c)
imu_array_M1.append(d)
imu_array_M2.append(e)
imu_array_M3.append(f)
#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]
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_array_A1[-1])
del(imu_array_A2[-1])
del(imu_array_A3[-1])
del(imu_array_G1[-1])
del(imu_array_G2[-1])
del(imu_array_G3[-1])
del(imu_array_M1[-1])
del(imu_array_M2[-1])
del(imu_array_M3[-1])
print(len(time_e_list))
print(len(time_o_list))
print(len(imu_array_A1))
print(len(imu_array_A2))
print(len(imu_array_A3))
print(len(imu_array_G1))
print(len(imu_array_G2))
print(len(imu_array_G3))
print(len(imu_array_M1))
print(len(imu_array_M2))
print(len(imu_array_M3))
CheckLength = len(time_e_list) + len(time_o_list) + len(imu_array_A1) + len(imu_array_A2) + len(imu_array_A3) + len(imu_array_G1) + len(imu_array_G2) + len(imu_array_G3) + len(imu_array_M1)+ len(imu_array_M2) + len(imu_array_M3)
if CheckLength//11 == len(time_e_list):
print("All columns are matching!")6202
6202
6202
6202
6202
6202
6202
6202
6202
6202
6202
All columns are matching!
#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
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
ret_array.append(new_a)
return ret_array
imu1_array = convert_acc_units(imu_array_A1) #new units in m/s^2
imu2_array = convert_acc_units(imu_array_A2) #new units in m/s^2
imu3_array = convert_acc_units(imu_array_A3) #new units in m/s^2
# To check:
#for x,y in zip(imu2_array, imu_array_A2):
# 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
# To check:
# for t in time_e_array:
# print(t)
print("Done!")Done!
print("Graph of X Acceleration vs. Time")
plt.plot(time_e_array, imu1_array)
plt.xlabel("Time (s)")
plt.ylabel("Acceleration-X (m/s^2)")
plt.show()Graph of X Acceleration vs. Time
# Offset variables help in recentering the magnetic data in order to define direction and use trig functions
M1_offset_var = 219.786
M2_offset_var = 180
M3_offset_var = 280
def calibrate_magn_data(magn_array, offset_value):
ret_array = []
for m in magn_array:
new_m = m - offset_value
ret_array.append(new_m)
return ret_array
imuM1_array = calibrate_magn_data(imu_array_M1, M1_offset_var)
imuM2_array = calibrate_magn_data(imu_array_M2, M2_offset_var)
imuM3_array = calibrate_magn_data(imu_array_M3, M3_offset_var)
# Check
# print(imuM1_array)
print("Done.")Done.
# Create N x 3 arrays for functions that need them later on, such as Scikit Kinematics
magn_height = len(imuM1_array)
acc_height = len(imu1_array)
acc_array = np.zeros(shape=(acc_height,3))
magn_array = np.zeros(shape=(magn_height,3))
print("For Accelerometer: ")
for x in range(len(acc_array)):
acc_array[x,0] = imu1_array[x]
acc_array[x,1] = imu2_array[x]
acc_array[x,2] = imu3_array[x]
print(acc_array)
print("\nFor Magnetometer: ")
for x in range(len(magn_array)):
magn_array[x,0] = imuM1_array[x]
magn_array[x,1] = imuM2_array[x]
magn_array[x,2] = imuM3_array[x]
print(magn_array)
print("Done.")For Accelerometer:
[[-1.13006318 9.67257471 1.20667764]
[-1.07260234 9.44273135 1.18752402]
[-1.1492168 10.03649336 1.66636436]
...
[ 9.97903252 -0.55545479 1.85790049]
[ 9.99818613 -0.42137949 2.06859023]
[ 9.55765303 -0.32561143 1.85790049]]
For Magnetometer:
[[-190.786 -325. -83. ]
[-207.786 -328. -86. ]
[-203.786 -330. -92. ]
...
[-488.786 -61. -379. ]
[-485.786 -60. -380. ]
[-474.786 -59. -373. ]]
Done.
Orientation from here onwards will be from the board/surfers reference frame (yaw left = turning left)
x = -IMU1, y = -IMU3, z = -IMU2
# The new array for board reference frame will have the IMUs in columns according to X,Y,Z directions
print("For Accelerometer:")
board_acc = acc_array.copy() # Reassign to the correct axes as stated above
temp_x_acc = board_acc[:,0] * (-1)
temp_y_acc = board_acc[:,1] * (-1)
temp_z_acc = board_acc[:,2] * (-1)
board_acc[:,0] = temp_x_acc # X acceleration
board_acc[:,1] = temp_y_acc # Y acceleration
board_acc[:,2] = temp_z_acc # Z acceleration
print(board_acc)
print("\nFor Magnetometer:")
board_magn = magn_array.copy()
temp_x_magn = board_magn[:,0] * (-1)
temp_y_magn = board_magn[:,1] * (-1)
temp_z_magn = board_magn[:,2] * (-1)
board_magn[:,0] = temp_x_magn
board_magn[:,1] = temp_y_magn
board_magn[:,2] = temp_z_magn
print(board_magn)
print("Done.")For Accelerometer:
[[ 1.13006318 -9.67257471 -1.20667764]
[ 1.07260234 -9.44273135 -1.18752402]
[ 1.1492168 -10.03649336 -1.66636436]
...
[ -9.97903252 0.55545479 -1.85790049]
[ -9.99818613 0.42137949 -2.06859023]
[ -9.55765303 0.32561143 -1.85790049]]
For Magnetometer:
[[190.786 325. 83. ]
[207.786 328. 86. ]
[203.786 330. 92. ]
...
[488.786 61. 379. ]
[485.786 60. 380. ]
[474.786 59. 373. ]]
Done.
# Azimuth and Altitude LEGEND:
# Altitude is the angle between the ground and the vector
# Azimuth is the angle going clockwise from 0 deg North:
# N - 0/360deg, E - 90deg, S - 180deg, W - 270deg
# This will get complicated (ie make cases or lots of if statements) when rotations about the heading become more prevalent
def azimuth(x,y,z):
real_y = y * (-1) # This is to account for y
return (180/math.pi * math.atan2(real_y,x)) % 360
def altitude(x,y,z):
h = math.hypot(y, x)
return 180/math.pi * math.atan2(z,h)
def printAltAzi(alt, azi):
print ("Alt:", alt, "\n", "Azi:",azi,"\n")
# These values are uncorrected values: still need to add or subtract 'declination'
# (for AziMuth) and 'inclination' (for Altitude) correction values for geographical location
heading_altitude = board_magn[:,0].copy()
heading_azimuth = board_magn[:,0].copy()
i = 0 #iterator
#for i in range(len(M1_no_out)):
while i < len(heading_altitude):
factor = 0;
# Use acceleration values to calibrate magnetometer
if (board_acc[i, 0] >= 0):
factor = 1
else:
factor = -1
heading_altitude[i] = altitude(board_magn[i,0], board_magn[i,1], board_magn[i,2])
heading_azimuth[i] = azimuth(board_magn[i,0], board_magn[i,1], board_magn[i,2])
heading_azimuth[i] = heading_azimuth[i] * factor
#printAltAzi(heading_altitude[i],heading_azimuth[i])
i += 1
#for t in range(len(time_e_array)):
#printAltAzi(heading_altitude[t], heading_azimuth[t])# Fixing random state for reproducibility
heading_alt_plot = plt.figure(figsize=(10,5))
alt_plot = heading_alt_plot.add_subplot(111)
alt_plot.plot(time_e_array, heading_altitude)
alt_plot.set_title("Altitude vs. Time")
alt_plot.set_xlabel("Time Elapsed [sec]")
alt_plot.set_ylabel("Altitude [deg]")
plt.show()
np.random.seed(19680801)
# Compute areas and colors
r = [i for i in range(0, len(board_magn))]
theta = heading_azimuth/360 * 2 * np.pi
area = 1
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111, projection='polar')
c = ax.scatter(theta, r, c='red', s=area, cmap='hsv', alpha=1)
# Compute areas and colors
alt_r = [i for i in range(0, len(board_magn))]
alt_theta = heading_altitude/360 * 2 * np.pi
alt_area = 1
alt_colors = theta
fig2 = plt.figure(figsize=(10,10))
ax2 = fig2.add_subplot(111, projection='polar')
c2 = ax2.scatter(alt_theta, alt_r, c='blue', s=alt_area, cmap='hsv', alpha=1)
# Plot first 10 directional changes
magnfig = plt.figure(figsize=(20,20))
magnaxi = magnfig.add_subplot(111, projection='3d')
magnaxi.scatter(board_magn[0,0], board_magn[0,1], board_magn[0,2], c='black', s=300, marker = "<")
magnaxi.scatter(board_magn[1:,0], board_magn[1:,1], board_magn[1:,2], c='black', s=10, marker = "o")
magnaxi.plot(board_magn[:,0], board_magn[:,1], board_magn[:,2], color='green')
magnaxi.set_xlabel("X Axis ->", fontsize=30)
magnaxi.set_ylabel("Y Axis ->", fontsize=30)
magnaxi.set_zlabel("Z Axis ->", fontsize=30)
plt.show()
# Plot Smartfin data
heading_azi_plot = plt.figure(figsize=(10,5))
azi_plot = heading_azi_plot.add_subplot(111)
azi_plot.plot(time_e_array, heading_azimuth)
azi_plot.set_title("Azimuth vs. Time")
azi_plot.set_xlabel("Time Elapsed[sec]")
azi_plot.set_ylabel("Azimuth [deg]")
print()
# Get buoy data from CDIP website
# Link: https://cdip.ucsd.edu/themes/?d2=p70:s:201&zoom=auto&pub_set=public®ions=california_south&tz=UTC&units=standard
# Calculate average heading using my own graph
total_sum = 0
for i in heading_azimuth:
total_sum += i
my_average = total_sum/len(heading_azimuth)
expected_average = 282.41
print("My calculated average heading (deg) is:")
print(my_average)
print("\nThe expected heading (deg) is:")
print(expected_average)
# Calculate error in findings
error_percent = (my_average - expected_average) / expected_average
print("\nThe percentage error in my model is:")
print(error_percent)My calculated average heading (deg) is:
286.95832871711616
The expected heading (deg) is:
282.41
The percentage error in my model is:
0.016105409571602048
As displayed in the diagram, the method for determining peak wave direction is to pool all the data from each of these Smartfins so that we can conduct a cumulative spectral analysis. This means instead of generating a frequency graph for a single ride, we will generate multiple graphs that represents the data for all the Smartfins, and then analyze the frequency transitions between close-by Smartfins.
As the graph suggests, the only difference between this frequency graph and the one used to determine wave height is the parameter wave density, which is essentially the average acceleration per unit of frequency. It represents the energy density per wave unit. Overall, we can compare each frequency graph side-by-side to see how the wave behaves when it starts from one Smartfin and reaches the next. Using this data, we can generalize the directional propagation and behaviour of the waves in the region covered by all the Smartfins. The projected result of my method can be visualized below:
-
Currently, the most popular methods to determine wave direction involve using Buoys, which don't give sufficient data in terms of determining wave direction in a small region. Since buoys are spaced out hundred of meters apart and way offshore, it is almost impossible to determine proximal wave direction without the help of Smartfins.
-
One way to generate a directional spectrum is to measure the same parameter - such as pressure - at a series of nearby locations using pressor sensors. I considered this, but realized that we didn't have the resources to conduct such an experiment.
-
The other way to produce a directional spectrum is by measuring different parameters at the same point. This is the approach used in directional buoys, which measure pitch and roll in addition to vertical heave. However, even this could easily be achieved using an array of Smartfins. It would essentially accomplish the same task as the directional buoys, and it would be much easier to gather wave data for smaller regions.
-
Using spectral analysis and frequency graphs, this method reuses the one for determining wave height, and is much simpler to execute.
-
The only disadvantage I can forsee is the level of noise and inaccuracy presented by Smartfins and the potential for human error, for example when a surfer decides to paddle upstream, or push their surfboard deeper underwater into a wave.
# Reference: https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Logistic%20Regression%20balanced.ipynbimport pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
#from mpl_toolkits.basemap import Basemap
import os
import datetime
import pytz
import re
import peakutils
import statsmodels.api as sm
import requests
#Read data from a local csv file:
##Will change this to scrape files from the Smartfin.org website later.
#data = pd.read_csv('Motion_13735.CSV', header=0)
#data = data.dropna()
#Print out the column headings:
#print(data.shape)
#print(list(data.columns))#Make sure the ride_id and the footage file match up:
ride_ids = ['15692']
footage_file = 'Footage3.txt'
#ride_ids = ['14827']
# 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) //no footage
# 15629 - Jasmine's First Ride Sesh filmed with VIRB (Oct. 24) //first labelled footage!
# 15669 - Jasmine's Second Ride Sesh filmed with VIRB (Nov. 7) //second labelled footage!
# 15692 - Jasmine's 3rd Ride Sesh filmed with VIRB (Nov. 9) //third labelled footage!
# 15686 - Jasmine's 4th Ride Sesh filmed with VIRB (Nov. 11) //fourth labelled footage!#%% 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)
#print(motion_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.
#We can also check to see if the surfboard was recording "in-water-freq" or
#"out-of-water-freq" based on how many NaN values we see.
print(motion_df)https://surf.smartfin.org/ride/15692
https://surf.smartfin.org/media/201811/google_105349665704999793400_0006667E229D_181109191556_Ocean.CSV
Ride data has been uploaded.
Time IMU A1 IMU A2 IMU A3 IMU G1 \
ride_id UTC
15692 2018-11-09 19:16:03.789 1.414743e+09 493.0 48.0 110.0 75.0
2018-11-09 19:16:03.822 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.855 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.888 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.921 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.954 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.987 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.020 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.053 1.414743e+09 513.0 89.0 62.0 34.0
2018-11-09 19:16:04.086 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.119 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.152 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.185 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.218 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.251 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.284 1.414743e+09 494.0 92.0 80.0 69.0
2018-11-09 19:16:04.317 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.350 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.383 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.416 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.449 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.482 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.515 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.548 1.414744e+09 421.0 205.0 -104.0 192.0
2018-11-09 19:16:04.581 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.614 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.647 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.680 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.713 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.746 NaN NaN NaN NaN NaN
... ... ... ... ... ...
2018-11-09 20:38:14.121 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.154 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.187 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.220 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.253 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.286 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.319 1.419644e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.352 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.385 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.418 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.451 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.484 1.419644e+09 NaN NaN NaN NaN
2018-11-09 20:38:14.517 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.550 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.583 1.419644e+09 502.0 -11.0 99.0 10.0
2018-11-09 20:38:14.616 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.649 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.682 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.715 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.748 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.781 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.814 1.419644e+09 501.0 -13.0 99.0 10.0
2018-11-09 20:38:14.847 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.880 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.913 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.946 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.979 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.012 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.045 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.078 1.419644e+09 501.0 -11.0 98.0 10.0
IMU G2 IMU G3 IMU M1 IMU M2 IMU M3 \
ride_id UTC
15692 2018-11-09 19:16:03.789 -124.0 -86.0 -309.0 209.0 39.0
2018-11-09 19:16:03.822 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.855 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.888 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.921 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.954 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.987 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.020 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.053 -36.0 -92.0 -320.0 194.0 38.0
2018-11-09 19:16:04.086 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.119 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.152 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.185 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.218 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.251 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.284 -63.0 -42.0 -329.0 189.0 49.0
2018-11-09 19:16:04.317 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.350 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.383 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.416 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.449 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.482 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.515 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.548 -92.0 -37.0 -330.0 180.0 64.0
2018-11-09 19:16:04.581 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.614 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.647 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.680 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.713 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.746 NaN NaN NaN NaN NaN
... ... ... ... ... ...
2018-11-09 20:38:14.121 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.154 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.187 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.220 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.253 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.286 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.319 20.0 2.0 -303.0 267.0 37.0
2018-11-09 20:38:14.352 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.385 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.418 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.451 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.484 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.517 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.550 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.583 21.0 1.0 -308.0 262.0 32.0
2018-11-09 20:38:14.616 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.649 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.682 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.715 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.748 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.781 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.814 21.0 1.0 -310.0 260.0 38.0
2018-11-09 20:38:14.847 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.880 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.913 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.946 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.979 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.012 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.045 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.078 20.0 2.0 -309.0 265.0 35.0
Latitude Longitude
ride_id UTC
15692 2018-11-09 19:16:03.789 3285871.0 -11725690.0
2018-11-09 19:16:03.822 NaN NaN
2018-11-09 19:16:03.855 NaN NaN
2018-11-09 19:16:03.888 NaN NaN
2018-11-09 19:16:03.921 NaN NaN
2018-11-09 19:16:03.954 NaN NaN
2018-11-09 19:16:03.987 NaN NaN
2018-11-09 19:16:04.020 NaN NaN
2018-11-09 19:16:04.053 NaN NaN
2018-11-09 19:16:04.086 NaN NaN
2018-11-09 19:16:04.119 NaN NaN
2018-11-09 19:16:04.152 NaN NaN
2018-11-09 19:16:04.185 NaN NaN
2018-11-09 19:16:04.218 NaN NaN
2018-11-09 19:16:04.251 NaN NaN
2018-11-09 19:16:04.284 NaN NaN
2018-11-09 19:16:04.317 NaN NaN
2018-11-09 19:16:04.350 NaN NaN
2018-11-09 19:16:04.383 NaN NaN
2018-11-09 19:16:04.416 NaN NaN
2018-11-09 19:16:04.449 NaN NaN
2018-11-09 19:16:04.482 NaN NaN
2018-11-09 19:16:04.515 NaN NaN
2018-11-09 19:16:04.548 NaN NaN
2018-11-09 19:16:04.581 NaN NaN
2018-11-09 19:16:04.614 NaN NaN
2018-11-09 19:16:04.647 NaN NaN
2018-11-09 19:16:04.680 NaN NaN
2018-11-09 19:16:04.713 NaN NaN
2018-11-09 19:16:04.746 NaN NaN
... ... ...
2018-11-09 20:38:14.121 NaN NaN
2018-11-09 20:38:14.154 NaN NaN
2018-11-09 20:38:14.187 NaN NaN
2018-11-09 20:38:14.220 NaN NaN
2018-11-09 20:38:14.253 NaN NaN
2018-11-09 20:38:14.286 NaN NaN
2018-11-09 20:38:14.319 NaN NaN
2018-11-09 20:38:14.352 NaN NaN
2018-11-09 20:38:14.385 NaN NaN
2018-11-09 20:38:14.418 NaN NaN
2018-11-09 20:38:14.451 NaN NaN
2018-11-09 20:38:14.484 3287082.0 -11722116.0
2018-11-09 20:38:14.517 NaN NaN
2018-11-09 20:38:14.550 NaN NaN
2018-11-09 20:38:14.583 NaN NaN
2018-11-09 20:38:14.616 NaN NaN
2018-11-09 20:38:14.649 NaN NaN
2018-11-09 20:38:14.682 NaN NaN
2018-11-09 20:38:14.715 NaN NaN
2018-11-09 20:38:14.748 NaN NaN
2018-11-09 20:38:14.781 NaN NaN
2018-11-09 20:38:14.814 NaN NaN
2018-11-09 20:38:14.847 NaN NaN
2018-11-09 20:38:14.880 NaN NaN
2018-11-09 20:38:14.913 NaN NaN
2018-11-09 20:38:14.946 NaN NaN
2018-11-09 20:38:14.979 NaN NaN
2018-11-09 20:38:15.012 NaN NaN
2018-11-09 20:38:15.045 NaN NaN
2018-11-09 20:38:15.078 NaN NaN
[149434 rows x 12 columns]
#Drop the latitude and longitude values since most of them are Nan:
motion_df_dropped = motion_df.drop(columns=['Latitude', 'Longitude'])
#Drop the NAN values from the motion data:
motion_df_dropped = motion_df_dropped.dropna(axis=0, how='any')
print(motion_df_dropped) Time IMU A1 IMU A2 IMU A3 IMU G1 \
ride_id UTC
15692 2018-11-09 19:16:03.789 1.414743e+09 493.0 48.0 110.0 75.0
2018-11-09 19:16:04.053 1.414743e+09 513.0 89.0 62.0 34.0
2018-11-09 19:16:04.284 1.414743e+09 494.0 92.0 80.0 69.0
2018-11-09 19:16:04.548 1.414744e+09 421.0 205.0 -104.0 192.0
2018-11-09 19:16:04.812 1.414744e+09 534.0 306.0 -32.0 -421.0
2018-11-09 19:16:05.043 1.414744e+09 455.0 149.0 -102.0 -355.0
2018-11-09 19:16:05.307 1.414744e+09 474.0 342.0 -219.0 -234.0
2018-11-09 19:16:05.571 1.414745e+09 363.0 323.0 -131.0 60.0
2018-11-09 19:16:05.802 1.414745e+09 -21.0 510.0 -447.0 78.0
2018-11-09 19:16:06.066 1.414745e+09 35.0 283.0 -132.0 -114.0
2018-11-09 19:16:06.330 1.414745e+09 130.0 323.0 -255.0 160.0
2018-11-09 19:16:06.561 1.414746e+09 -55.0 504.0 -173.0 -152.0
2018-11-09 19:16:06.825 1.414746e+09 58.0 420.0 -128.0 -77.0
2018-11-09 19:16:07.056 1.414746e+09 -179.0 454.0 -389.0 58.0
2018-11-09 19:16:07.320 1.414746e+09 13.0 181.0 -229.0 128.0
2018-11-09 19:16:07.551 1.414747e+09 -23.0 815.0 -258.0 -323.0
2018-11-09 19:16:07.815 1.414747e+09 5.0 254.0 -120.0 190.0
2018-11-09 19:16:08.079 1.414747e+09 -158.0 487.0 -291.0 -8.0
2018-11-09 19:16:08.310 1.414747e+09 37.0 207.0 -236.0 100.0
2018-11-09 19:16:08.574 1.414748e+09 -123.0 659.0 -206.0 -222.0
2018-11-09 19:16:08.838 1.414748e+09 -24.0 368.0 -187.0 106.0
2018-11-09 19:16:09.069 1.414748e+09 -200.0 516.0 -402.0 7.0
2018-11-09 19:16:09.333 1.414748e+09 -40.0 300.0 -298.0 126.0
2018-11-09 19:16:09.564 1.414749e+09 -62.0 556.0 -242.0 -249.0
2018-11-09 19:16:09.828 1.414749e+09 13.0 482.0 -212.0 92.0
2018-11-09 19:16:10.092 1.414749e+09 -158.0 583.0 -277.0 -73.0
2018-11-09 19:16:10.323 1.414749e+09 -102.0 315.0 -379.0 18.0
2018-11-09 19:16:10.587 1.414750e+09 -81.0 421.0 -70.0 -90.0
2018-11-09 19:16:10.851 1.414750e+09 -36.0 489.0 -226.0 111.0
2018-11-09 19:16:11.082 1.414750e+09 53.0 195.0 -107.0 20.0
... ... ... ... ... ...
2018-11-09 20:38:07.785 1.419637e+09 443.0 -13.0 84.0 -70.0
2018-11-09 20:38:08.016 1.419637e+09 509.0 -29.0 87.0 -58.0
2018-11-09 20:38:08.280 1.419638e+09 502.0 27.0 97.0 4.0
2018-11-09 20:38:08.544 1.419638e+09 501.0 -23.0 103.0 13.0
2018-11-09 20:38:08.775 1.419638e+09 499.0 -5.0 105.0 -9.0
2018-11-09 20:38:09.039 1.419638e+09 501.0 -10.0 104.0 6.0
2018-11-09 20:38:09.303 1.419639e+09 501.0 -8.0 104.0 9.0
2018-11-09 20:38:09.534 1.419639e+09 500.0 -9.0 104.0 8.0
2018-11-09 20:38:09.798 1.419639e+09 501.0 -8.0 104.0 8.0
2018-11-09 20:38:10.062 1.419639e+09 500.0 -8.0 103.0 8.0
2018-11-09 20:38:10.293 1.419640e+09 500.0 -9.0 105.0 9.0
2018-11-09 20:38:10.557 1.419640e+09 500.0 -8.0 105.0 8.0
2018-11-09 20:38:10.788 1.419640e+09 501.0 -20.0 102.0 7.0
2018-11-09 20:38:11.052 1.419640e+09 497.0 2.0 107.0 -4.0
2018-11-09 20:38:11.316 1.419641e+09 500.0 -7.0 106.0 -78.0
2018-11-09 20:38:11.547 1.419641e+09 503.0 -19.0 98.0 77.0
2018-11-09 20:38:11.811 1.419641e+09 497.0 17.0 110.0 21.0
2018-11-09 20:38:12.042 1.419641e+09 502.0 -13.0 103.0 18.0
2018-11-09 20:38:12.306 1.419642e+09 498.0 13.0 107.0 -26.0
2018-11-09 20:38:12.570 1.419642e+09 502.0 -23.0 96.0 -157.0
2018-11-09 20:38:12.801 1.419642e+09 502.0 -11.0 99.0 17.0
2018-11-09 20:38:13.065 1.419642e+09 501.0 -9.0 100.0 12.0
2018-11-09 20:38:13.296 1.419643e+09 501.0 -12.0 99.0 11.0
2018-11-09 20:38:13.560 1.419643e+09 502.0 -13.0 99.0 9.0
2018-11-09 20:38:13.824 1.419643e+09 502.0 -11.0 98.0 9.0
2018-11-09 20:38:14.055 1.419643e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.319 1.419644e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.583 1.419644e+09 502.0 -11.0 99.0 10.0
2018-11-09 20:38:14.814 1.419644e+09 501.0 -13.0 99.0 10.0
2018-11-09 20:38:15.078 1.419644e+09 501.0 -11.0 98.0 10.0
IMU G2 IMU G3 IMU M1 IMU M2 IMU M3
ride_id UTC
15692 2018-11-09 19:16:03.789 -124.0 -86.0 -309.0 209.0 39.0
2018-11-09 19:16:04.053 -36.0 -92.0 -320.0 194.0 38.0
2018-11-09 19:16:04.284 -63.0 -42.0 -329.0 189.0 49.0
2018-11-09 19:16:04.548 -92.0 -37.0 -330.0 180.0 64.0
2018-11-09 19:16:04.812 -233.0 -229.0 -325.0 161.0 97.0
2018-11-09 19:16:05.043 -376.0 -397.0 -337.0 117.0 151.0
2018-11-09 19:16:05.307 -527.0 -465.0 -311.0 25.0 217.0
2018-11-09 19:16:05.571 -662.0 -305.0 -238.0 -8.0 272.0
2018-11-09 19:16:05.802 -643.0 -153.0 -159.0 -21.0 321.0
2018-11-09 19:16:06.066 -430.0 132.0 -86.0 -38.0 326.0
2018-11-09 19:16:06.330 -243.0 349.0 -62.0 -24.0 326.0
2018-11-09 19:16:06.561 -297.0 191.0 -39.0 -55.0 311.0
2018-11-09 19:16:06.825 -228.0 138.0 -14.0 -80.0 294.0
2018-11-09 19:16:07.056 -23.0 12.0 -3.0 -79.0 297.0
2018-11-09 19:16:07.320 19.0 10.0 -12.0 -76.0 296.0
2018-11-09 19:16:07.551 -117.0 46.0 6.0 -86.0 292.0
2018-11-09 19:16:07.815 -49.0 11.0 16.0 -90.0 266.0
2018-11-09 19:16:08.079 114.0 -28.0 15.0 -75.0 285.0
2018-11-09 19:16:08.310 71.0 -17.0 10.0 -82.0 282.0
2018-11-09 19:16:08.574 -103.0 -77.0 28.0 -88.0 268.0
2018-11-09 19:16:08.838 -26.0 -13.0 40.0 -80.0 268.0
2018-11-09 19:16:09.069 84.0 -68.0 36.0 -70.0 276.0
2018-11-09 19:16:09.333 77.0 38.0 33.0 -67.0 285.0
2018-11-09 19:16:09.564 -100.0 -22.0 37.0 -69.0 265.0
2018-11-09 19:16:09.828 -13.0 26.0 44.0 -80.0 270.0
2018-11-09 19:16:10.092 42.0 -45.0 53.0 -83.0 263.0
2018-11-09 19:16:10.323 7.0 39.0 38.0 -78.0 260.0
2018-11-09 19:16:10.587 -50.0 -95.0 52.0 -92.0 244.0
2018-11-09 19:16:10.851 101.0 -17.0 50.0 -88.0 250.0
2018-11-09 19:16:11.082 109.0 -51.0 54.0 -76.0 256.0
... ... ... ... ... ...
2018-11-09 20:38:07.785 57.0 15.0 -295.0 219.0 7.0
2018-11-09 20:38:08.016 55.0 -10.0 -294.0 226.0 0.0
2018-11-09 20:38:08.280 17.0 4.0 -290.0 232.0 -6.0
2018-11-09 20:38:08.544 20.0 0.0 -290.0 228.0 -10.0
2018-11-09 20:38:08.775 19.0 -3.0 -285.0 225.0 7.0
2018-11-09 20:38:09.039 21.0 0.0 -291.0 221.0 -3.0
2018-11-09 20:38:09.303 21.0 1.0 -297.0 221.0 -3.0
2018-11-09 20:38:09.534 21.0 1.0 -288.0 222.0 -6.0
2018-11-09 20:38:09.798 21.0 1.0 -286.0 222.0 -4.0
2018-11-09 20:38:10.062 21.0 0.0 -290.0 220.0 2.0
2018-11-09 20:38:10.293 21.0 1.0 -286.0 224.0 8.0
2018-11-09 20:38:10.557 21.0 1.0 -289.0 217.0 1.0
2018-11-09 20:38:10.788 21.0 1.0 -297.0 225.0 3.0
2018-11-09 20:38:11.052 20.0 -1.0 -294.0 222.0 4.0
2018-11-09 20:38:11.316 14.0 -9.0 -282.0 224.0 0.0
2018-11-09 20:38:11.547 25.0 6.0 -291.0 229.0 3.0
2018-11-09 20:38:11.811 21.0 1.0 -289.0 243.0 7.0
2018-11-09 20:38:12.042 22.0 0.0 -291.0 267.0 19.0
2018-11-09 20:38:12.306 19.0 -3.0 -302.0 256.0 14.0
2018-11-09 20:38:12.570 9.0 -20.0 -313.0 263.0 27.0
2018-11-09 20:38:12.801 22.0 2.0 -307.0 263.0 39.0
2018-11-09 20:38:13.065 21.0 1.0 -305.0 265.0 37.0
2018-11-09 20:38:13.296 20.0 1.0 -296.0 266.0 32.0
2018-11-09 20:38:13.560 21.0 1.0 -298.0 262.0 30.0
2018-11-09 20:38:13.824 21.0 1.0 -303.0 257.0 35.0
2018-11-09 20:38:14.055 21.0 2.0 -293.0 259.0 41.0
2018-11-09 20:38:14.319 20.0 2.0 -303.0 267.0 37.0
2018-11-09 20:38:14.583 21.0 1.0 -308.0 262.0 32.0
2018-11-09 20:38:14.814 21.0 1.0 -310.0 260.0 38.0
2018-11-09 20:38:15.078 20.0 2.0 -309.0 265.0 35.0
[21645 rows x 10 columns]
#Create an elapsed_timedelta field:
#timedelta_values = (motion_df_dropped['Time']-motion_df_dropped['Time'][0])
#motion_df_dropped.insert(loc=1, column='TimeDelta', value=timedelta_values, drop=True)
motion_df_dropped['TimeDelta'] = (motion_df_dropped['Time']-motion_df_dropped['Time'][0])
#print(elapsed_timedelta)
#motion_df_dropped.head()
motion_df_dropped.head(10)
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | 493.0 | 48.0 | 110.0 | 75.0 | -124.0 | -86.0 | -309.0 | 209.0 | 39.0 | 0.0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | 513.0 | 89.0 | 62.0 | 34.0 | -36.0 | -92.0 | -320.0 | 194.0 | 38.0 | 252.5 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | 494.0 | 92.0 | 80.0 | 69.0 | -63.0 | -42.0 | -329.0 | 189.0 | 49.0 | 501.5 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | 421.0 | 205.0 | -104.0 | 192.0 | -92.0 | -37.0 | -330.0 | 180.0 | 64.0 | 753.5 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | 534.0 | 306.0 | -32.0 | -421.0 | -233.0 | -229.0 | -325.0 | 161.0 | 97.0 | 1003.5 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | 455.0 | 149.0 | -102.0 | -355.0 | -376.0 | -397.0 | -337.0 | 117.0 | 151.0 | 1253.5 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | 474.0 | 342.0 | -219.0 | -234.0 | -527.0 | -465.0 | -311.0 | 25.0 | 217.0 | 1504.5 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | 363.0 | 323.0 | -131.0 | 60.0 | -662.0 | -305.0 | -238.0 | -8.0 | 272.0 | 1755.5 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | -21.0 | 510.0 | -447.0 | 78.0 | -643.0 | -153.0 | -159.0 | -21.0 | 321.0 | 2006.5 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | 35.0 | 283.0 | -132.0 | -114.0 | -430.0 | 132.0 | -86.0 | -38.0 | 326.0 | 2258.5 |
#Footage sync code written by Alina: (Miulti-Column)
import time
#simple method: only walking, paddling, floating, surfing
#complex method: columns created based on footage file labels
def label_data( footage_file = 'Footage.txt', labelling_method = 'simple', sync_threshold = 20000 ):
#First, perform sync
sync_buf = 0
with open(footage_file) as file:
for line in file:
labelled_time = line.split(None, 2)
try:
cur_time = time.strptime(labelled_time[0], '%M:%S')
except:
continue
labelled_time[1] = labelled_time[1].rstrip()
if labelled_time[1].lower() == 'sync': #Assumption that first word in sync line is "sync"
sync_time = cur_time.tm_min * 60 * 1000 + cur_time.tm_sec * 1000
index = 0
start = 0
end = 0
#Syncing occurs when IMU A2 data is negative for a longer period than the provided threshold
#Default is 20 seconds
for data in motion_df_dropped['IMU A2']:
if data < 0 and start == 0:
start = motion_df_dropped['TimeDelta'][index]
elif data > 0 and start != 0:
end = motion_df_dropped['TimeDelta'][index]
if end - start > sync_threshold:
sync_buf = start - sync_time
break
start = 0
index += 1
accepted_labels = set()
if labelling_method == 'simple':
accepted_labels = {'WALKING', 'PADDLING', 'FLOATING', 'SURFING'}
#Create new DataFrame containing label info
label_frame = pd.DataFrame(0, index = motion_df_dropped.index, columns = accepted_labels)
for label in accepted_labels:
label_frame[label] = [0] * len(motion_df_dropped['Time'])
#Convention of labelled footage text: "MINUTE:SECOND LABEL"
elapsed_time = 0
cur_label = ''
buffer = 0
with open(footage_file) as file:
for line in file:
if labelling_method == 'simple':
labelled_time = line.split(None, 2) #simple categorizes on a one-word basis
else:
labelled_time = line.split(None, 1) #complex requires the entire label
#If the first word is not a properly formatted time, the line cannot be read
try:
cur_time = time.strptime(labelled_time[0], '%M:%S')
cur_timeMS = cur_time.tm_min * 60 * 1000 + cur_time.tm_sec * 1000 + sync_buf
except:
continue
labelled_time[1] = labelled_time[1].rstrip() #Remove potential newline
#Check for end of video and modify buffer accordingly
if labelled_time[1].lower() == 'end of video': #Assumption that label end video with "end of video"
buffer += cur_timeMS
#----Complex "mode" below: --------
#Modify accepted labels list if reading a new label and in complex mode
elif labelling_method == 'complex' and (labelled_time[1].upper() not in accepted_labels):
accepted_labels.add(labelled_time[1].upper())
if not cur_label:
label_frame = pd.DataFrame(0, index = motion_df_dropped.index, columns = accepted_labels)
label_frame[labelled_time[1].upper()] = [0] * len(motion_df_dropped['Time'])
if labelled_time[1].upper() in accepted_labels:
while (elapsed_time < len(motion_df_dropped['Time']) and
(np.isnan(motion_df_dropped['TimeDelta'][elapsed_time]) or
motion_df_dropped['TimeDelta'][elapsed_time] < cur_timeMS + buffer)):
if cur_label != '':
label_frame[cur_label][elapsed_time] = 1
elapsed_time += 1
if labelled_time[1].upper() != 'end of video':
cur_label = labelled_time[1].upper()
labelled = pd.concat([motion_df_dropped, label_frame], axis = 1)
return labelled
pd.options.display.max_rows = 5000
pd.options.display.max_columns = 5000motion_df_simple = label_data(footage_file)
motion_df_simple.head(10)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | 493.0 | 48.0 | 110.0 | 75.0 | -124.0 | -86.0 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | 513.0 | 89.0 | 62.0 | 34.0 | -36.0 | -92.0 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | 494.0 | 92.0 | 80.0 | 69.0 | -63.0 | -42.0 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | 421.0 | 205.0 | -104.0 | 192.0 | -92.0 | -37.0 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | 534.0 | 306.0 | -32.0 | -421.0 | -233.0 | -229.0 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | 455.0 | 149.0 | -102.0 | -355.0 | -376.0 | -397.0 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | 474.0 | 342.0 | -219.0 | -234.0 | -527.0 | -465.0 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | 363.0 | 323.0 | -131.0 | 60.0 | -662.0 | -305.0 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | -21.0 | 510.0 | -447.0 | 78.0 | -643.0 | -153.0 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | 35.0 | 283.0 | -132.0 | -114.0 | -430.0 | 132.0 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
#motion_df_complex = label_data('Footage3.txt', 'complex')
#motion_df_complex.head(10)#df1_complex = label_data('Footage.txt', 'complex')
#df2_complex = label_data('Footage2.txt', 'complex')
#df3_complex = label_data('Footage3.txt', 'complex')
#df4_complex = label_data('Footage4.txt', 'complex')
#df_concatenated = pd.concat([df1_complex, df2_complex, df3_complex, df4_complex])
#print("Shape of first dataframe:", df1_complex.shape)
#print("Shape of all combined dataframes:", df_concatenated.shape)
#print("Printing dataframe...")
##print(df1_complex.head(10))
#print(df_concatenated.head(10))##correct IMU data
##make a deep copy of motion_df_labelled
#df_converted = motion_df_complex.copy(deep = 'true')
df_converted = motion_df_simple.copy(deep = 'true')
##for rows in df_corrected
for row in range(0, df_converted.shape[0]):
#convert acceleromters (new: m/s^2)
df_converted.iloc[row, df_converted.columns.get_loc('IMU A1')] *= -0.019141 #forwards/backwards
df_converted.iloc[row, df_converted.columns.get_loc('IMU A2')] *= 0.019141 #upside down/right side up
df_converted.iloc[row, df_converted.columns.get_loc('IMU A3')] *= 0.019141 #sideways: negative = left, positive = right
#convert gyroscopes (new: deg/s)
df_converted.iloc[row, df_converted.columns.get_loc('IMU G1')] /= 8.2 #roll
df_converted.iloc[row, df_converted.columns.get_loc('IMU G2')] /= 8.2 #yaw
df_converted.iloc[row, df_converted.columns.get_loc('IMU G3')] /= 8.2 #pitch (flipping forwards/backwards)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | 493.0 | 48.0 | 110.0 | 75.0 | -124.0 | -86.0 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | 513.0 | 89.0 | 62.0 | 34.0 | -36.0 | -92.0 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | 494.0 | 92.0 | 80.0 | 69.0 | -63.0 | -42.0 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | 421.0 | 205.0 | -104.0 | 192.0 | -92.0 | -37.0 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | 534.0 | 306.0 | -32.0 | -421.0 | -233.0 | -229.0 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | 455.0 | 149.0 | -102.0 | -355.0 | -376.0 | -397.0 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | 474.0 | 342.0 | -219.0 | -234.0 | -527.0 | -465.0 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | 363.0 | 323.0 | -131.0 | 60.0 | -662.0 | -305.0 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | -21.0 | 510.0 | -447.0 | 78.0 | -643.0 | -153.0 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | 35.0 | 283.0 | -132.0 | -114.0 | -430.0 | 132.0 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
df_converted.head(10)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | -9.436513 | 0.918768 | 2.105510 | 9.146341 | -15.121951 | -10.487805 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | -9.819333 | 1.703549 | 1.186742 | 4.146341 | -4.390244 | -11.219512 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | -9.455654 | 1.760972 | 1.531280 | 8.414634 | -7.682927 | -5.121951 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | -8.058361 | 3.923905 | -1.990664 | 23.414634 | -11.219512 | -4.512195 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | -10.221294 | 5.857146 | -0.612512 | -51.341463 | -28.414634 | -27.926829 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | -8.709155 | 2.852009 | -1.952382 | -43.292683 | -45.853659 | -48.414634 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | -9.072834 | 6.546222 | -4.191879 | -28.536585 | -64.268293 | -56.707317 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | -6.948183 | 6.182543 | -2.507471 | 7.317073 | -80.731707 | -37.195122 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | 0.401961 | 9.761910 | -8.556027 | 9.512195 | -78.414634 | -18.658537 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | -0.669935 | 5.416903 | -2.526612 | -13.902439 | -52.439024 | 16.097561 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
## Drop data columns that we don't care about predicting/visualizing:
drop_columns = ["FLIP BOARD RIGHT SIDE UP", "NEW", "DONE, OUT OF WATER"]
for dc in drop_columns:
if dc in df_converted.columns:
df_converted = df_converted.drop(dc)
##df_converted = df_converted.drop(columns!=["SURFING, FLOATING, PADDLING INTO WAVES, PADDLING FOR A WAVE, PADDLING FOR POSITION, PADDLING"])import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = [50, 10]
#define a function that plots a column of dataf in relation to time. color coded to match labels in dataf
#requires that:
#dataf has a 'TimeDelta' column
#labels: walking, surfing, floating, paddling
def createPlot (dataf, column):
#create new data frame to be plotted
#Only consider columns after Velocity
dfPlot = pd.DataFrame(columns = ['TIME'] + list(dataf)[list(dataf).index('TimeDelta') + 1:], dtype = float)
#add timedelta column from dataf to dfPlot
dfPlot['TIME'] = dataf['TimeDelta']
#get the index of the column to be graphed
columnInd = dataf.columns.get_loc(column)
#for each row in dfPlot (number of IMU readings)
for row in range(0, dfPlot.shape[0]):
#for the indexes of the label columns in dfPlot
for col in range(1, dfPlot.shape[1]):
#if a label in the row is 1 in dataf
if dataf.iloc[row, dataf.columns.get_loc(dfPlot.columns[col])] == 1:
#add the sensors value to the corresponding column in dfPlot
dfPlot.iloc[row, dfPlot.columns.get_loc(dfPlot.columns[col])] = dataf.iloc[row, columnInd]
#dfPlot.iloc[row, dfPlot.columns.get]
#Set up colormap so that we don't see a repeat in color when graphing
#plt.gca().set_prop_cycle('color',plt.cm.plasma(np.linspace(0,1,dfPlot.shape[1])))
plt.gca().set_prop_cycle('color',plt.cm.tab20(np.linspace(0,1,dfPlot.shape[1])))
for col in range (1, dfPlot.shape[1]):
plt.plot(dfPlot['TIME'], dfPlot[list(dfPlot)[col]])
plt.gca().legend(loc = 'lower left')
plt.title(column)
plt.xlabel("Time")
plt.ylabel("IMU Data")
#file_name = column
#pdf_string = '.jpg'
#file_name += pdf_string
#plt.savefig(file_name)
plt.show()
return #For plotting, just comment out the "concatenation" lines.
#Need to clear kernel and then only run all above so that it plots on axes directly below, rather than on another plot
print("Creating Plots...")
createPlot(df_converted,'IMU A1')
createPlot(df_converted,'IMU A2')
createPlot(df_converted,'IMU A3')
#createPlot(df_converted,'IMU G1')
#createPlot(df_converted,'IMU G2')
#createPlot(df_converted,'IMU G3')
createPlot(df_converted,'IMU M1')
createPlot(df_converted,'IMU M2')
createPlot(df_converted,'IMU M3')
print("Done")Creating Plots...
Done
print(list(df_converted.columns))
drop_list = ['SYNC', 'WALKING IN WATER', 'PUSH-OFF', 'PADDLING INTO WAVES', 'SIT-UP', "TURNING TO SURFER'S LEFT", 'LAY-DOWN', 'PADDLING FOR A WAVE', 'POP-UP', 'SURFING', 'STEP-OFF', "TURNING TO SURFER'S RIGHT", 'SIT-BACK', 'OFF-BOARD', 'PADDLING', 'WIPE-OUT', 'PULL-BACK LEASH', 'PADDLING FOR POSITION', 'DISCARD', 'WALKING OUT OF WATER', 'PADDLING', 'WALKING']
for x in drop_list:
if x in df_converted.columns:
df_converted = df_converted.drop(columns=[x])
['Time', 'IMU A1', 'IMU A2', 'IMU A3', 'IMU G1', 'IMU G2', 'IMU G3', 'IMU M1', 'IMU M2', 'IMU M3', 'TimeDelta', 'FLOATING', 'WALKING']
print("Creating Plots...")
createPlot(df_converted,'IMU A1')
createPlot(df_converted,'IMU A2')
createPlot(df_converted,'IMU A3')Creating Plots...
#df_converted["IMU A2"]
df_converted["TimeDelta"]ride_id UTC
15692 2018-11-09 19:16:03.789 0.0
2018-11-09 19:16:04.053 252.5
2018-11-09 19:16:04.284 501.5
2018-11-09 19:16:04.548 753.5
2018-11-09 19:16:04.812 1003.5
2018-11-09 19:16:05.043 1253.5
2018-11-09 19:16:05.307 1504.5
2018-11-09 19:16:05.571 1755.5
2018-11-09 19:16:05.802 2006.5
2018-11-09 19:16:06.066 2258.5
2018-11-09 19:16:06.330 2507.5
2018-11-09 19:16:06.561 2758.5
2018-11-09 19:16:06.825 3009.5
2018-11-09 19:16:07.056 3260.5
2018-11-09 19:16:07.320 3501.5
2018-11-09 19:16:07.551 3752.5
2018-11-09 19:16:07.815 4003.5
2018-11-09 19:16:08.079 4253.5
2018-11-09 19:16:08.310 4503.5
2018-11-09 19:16:08.574 4758.5
2018-11-09 19:16:08.838 5009.5
2018-11-09 19:16:09.069 5259.5
2018-11-09 19:16:09.333 5509.5
2018-11-09 19:16:09.564 5751.5
2018-11-09 19:16:09.828 6001.5
2018-11-09 19:16:10.092 6251.5
2018-11-09 19:16:10.323 6503.5
2018-11-09 19:16:10.587 6753.5
2018-11-09 19:16:10.851 7003.5
2018-11-09 19:16:11.082 7255.5
2018-11-09 19:16:11.346 7507.5
2018-11-09 19:16:11.610 7757.5
2018-11-09 19:16:11.841 8007.5
2018-11-09 19:16:12.105 8257.5
2018-11-09 19:16:12.336 8508.5
2018-11-09 19:16:12.600 8760.5
2018-11-09 19:16:12.831 9001.5
2018-11-09 19:16:13.095 9252.5
2018-11-09 19:16:13.359 9503.5
2018-11-09 19:16:13.590 9753.5
2018-11-09 19:16:13.854 10003.5
2018-11-09 19:16:14.118 10254.5
2018-11-09 19:16:14.349 10504.5
2018-11-09 19:16:14.613 10758.5
2018-11-09 19:16:14.877 11009.5
2018-11-09 19:16:15.108 11260.5
2018-11-09 19:16:15.372 11501.5
2018-11-09 19:16:15.603 11752.5
2018-11-09 19:16:15.867 12003.5
2018-11-09 19:16:16.131 12253.5
2018-11-09 19:16:16.362 12503.5
2018-11-09 19:16:16.626 12755.5
2018-11-09 19:16:16.890 13005.5
2018-11-09 19:16:17.121 13257.5
2018-11-09 19:16:17.385 13509.5
2018-11-09 19:16:17.649 13759.5
2018-11-09 19:16:17.880 14009.5
2018-11-09 19:16:18.144 14260.5
2018-11-09 19:16:18.375 14502.5
2018-11-09 19:16:18.639 14751.5
2018-11-09 19:16:18.870 15001.5
2018-11-09 19:16:19.134 15251.5
2018-11-09 19:16:19.398 15503.5
2018-11-09 19:16:19.629 15753.5
2018-11-09 19:16:19.893 16005.5
2018-11-09 19:16:20.157 16255.5
2018-11-09 19:16:20.388 16508.5
2018-11-09 19:16:20.652 16759.5
2018-11-09 19:16:20.916 17010.5
2018-11-09 19:16:21.147 17260.5
2018-11-09 19:16:21.411 17501.5
2018-11-09 19:16:21.642 17753.5
2018-11-09 19:16:21.906 18003.5
2018-11-09 19:16:22.170 18254.5
2018-11-09 19:16:22.401 18504.5
2018-11-09 19:16:22.665 18755.5
2018-11-09 19:16:22.929 19006.5
2018-11-09 19:16:23.160 19257.5
2018-11-09 19:16:23.424 19508.5
2018-11-09 19:16:23.655 19759.5
2018-11-09 19:16:23.919 20010.5
2018-11-09 19:16:24.150 20251.5
2018-11-09 19:16:24.414 20501.5
2018-11-09 19:16:24.678 20751.5
2018-11-09 19:16:24.909 21001.5
2018-11-09 19:16:25.173 21252.5
2018-11-09 19:16:25.437 21502.5
2018-11-09 19:16:25.668 21754.5
2018-11-09 19:16:25.932 22003.5
2018-11-09 19:16:26.196 22253.5
2018-11-09 19:16:26.427 22504.5
2018-11-09 19:16:26.691 22759.5
2018-11-09 19:16:26.955 23009.5
2018-11-09 19:16:27.186 23259.5
2018-11-09 19:16:27.450 23509.5
2018-11-09 19:16:27.681 23754.5
2018-11-09 19:16:27.945 24006.5
2018-11-09 19:16:28.209 24257.5
2018-11-09 19:16:28.440 24508.5
2018-11-09 19:16:28.704 24758.5
2018-11-09 19:16:28.968 25009.5
2018-11-09 19:16:29.199 25259.5
2018-11-09 19:16:29.463 25510.5
2018-11-09 19:16:29.694 25759.5
2018-11-09 19:16:29.958 26010.5
2018-11-09 19:16:30.189 26251.5
2018-11-09 19:16:30.453 26502.5
2018-11-09 19:16:30.717 26753.5
2018-11-09 19:16:30.948 27004.5
2018-11-09 19:16:31.212 27255.5
2018-11-09 19:16:31.476 27506.5
2018-11-09 19:16:31.707 27756.5
2018-11-09 19:16:31.971 28007.5
2018-11-09 19:16:32.235 28257.5
2018-11-09 19:16:32.466 28507.5
2018-11-09 19:16:32.730 28751.5
2018-11-09 19:16:32.961 29001.5
2018-11-09 19:16:33.225 29254.5
2018-11-09 19:16:33.489 29505.5
2018-11-09 19:16:33.720 29756.5
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2018-11-09 20:37:50.229 4876855.5
2018-11-09 20:37:50.460 4877105.5
2018-11-09 20:37:50.724 4877355.5
2018-11-09 20:37:50.988 4877605.5
2018-11-09 20:37:51.219 4877855.5
2018-11-09 20:37:51.483 4878106.5
2018-11-09 20:37:51.747 4878357.5
2018-11-09 20:37:51.978 4878609.5
2018-11-09 20:37:52.242 4878859.5
2018-11-09 20:37:52.506 4879109.5
2018-11-09 20:37:52.737 4879359.5
2018-11-09 20:37:53.001 4879609.5
2018-11-09 20:37:53.232 4879860.5
2018-11-09 20:37:53.496 4880097.0
2018-11-09 20:37:53.727 4880352.5
2018-11-09 20:37:53.991 4880603.5
2018-11-09 20:37:54.255 4880858.5
2018-11-09 20:37:54.519 4881109.5
2018-11-09 20:37:54.750 4881361.5
2018-11-09 20:37:55.014 4881601.5
2018-11-09 20:37:55.245 4881851.5
2018-11-09 20:37:55.509 4882101.5
2018-11-09 20:37:55.740 4882351.5
2018-11-09 20:37:56.004 4882602.5
2018-11-09 20:37:56.268 4882852.5
2018-11-09 20:37:56.499 4883103.5
2018-11-09 20:37:56.763 4883354.5
2018-11-09 20:37:57.027 4883605.5
2018-11-09 20:37:57.258 4883857.5
2018-11-09 20:37:57.522 4884107.5
2018-11-09 20:37:57.786 4884357.5
2018-11-09 20:37:58.017 4884607.5
2018-11-09 20:37:58.281 4884857.5
2018-11-09 20:37:58.512 4885108.5
2018-11-09 20:37:58.776 4885358.5
2018-11-09 20:37:59.040 4885609.5
2018-11-09 20:37:59.271 4885860.5
2018-11-09 20:37:59.535 4886101.5
2018-11-09 20:37:59.766 4886351.5
2018-11-09 20:38:00.030 4886601.5
2018-11-09 20:38:00.294 4886857.5
2018-11-09 20:38:00.525 4887109.5
2018-11-09 20:38:00.789 4887359.5
2018-11-09 20:38:01.020 4887601.5
2018-11-09 20:38:01.284 4887851.5
2018-11-09 20:38:01.548 4888103.5
2018-11-09 20:38:01.779 4888353.5
2018-11-09 20:38:02.043 4888605.5
2018-11-09 20:38:02.307 4888855.5
2018-11-09 20:38:02.505 4889056.5
2018-11-09 20:38:02.703 4889257.5
2018-11-09 20:38:02.901 4889458.5
2018-11-09 20:38:03.099 4889660.5
2018-11-09 20:38:03.297 4889859.5
2018-11-09 20:38:03.495 4890060.5
2018-11-09 20:38:03.759 4890310.5
2018-11-09 20:38:03.990 4890539.0
2018-11-09 20:38:04.254 4890802.5
2018-11-09 20:38:04.518 4891054.5
2018-11-09 20:38:04.749 4891304.5
2018-11-09 20:38:05.013 4891555.5
2018-11-09 20:38:05.277 4891805.5
2018-11-09 20:38:05.508 4892055.5
2018-11-09 20:38:05.772 4892305.5
2018-11-09 20:38:06.036 4892555.5
2018-11-09 20:38:06.267 4892806.5
2018-11-09 20:38:06.531 4893058.5
2018-11-09 20:38:06.795 4893309.5
2018-11-09 20:38:07.026 4893560.5
2018-11-09 20:38:07.290 4893810.5
2018-11-09 20:38:07.521 4894051.5
2018-11-09 20:38:07.785 4894301.5
2018-11-09 20:38:08.016 4894551.5
2018-11-09 20:38:08.280 4894803.5
2018-11-09 20:38:08.544 4895053.5
2018-11-09 20:38:08.775 4895303.5
2018-11-09 20:38:09.039 4895555.5
2018-11-09 20:38:09.303 4895805.5
2018-11-09 20:38:09.534 4896056.5
2018-11-09 20:38:09.798 4896307.5
2018-11-09 20:38:10.062 4896557.5
2018-11-09 20:38:10.293 4896807.5
2018-11-09 20:38:10.557 4897057.5
2018-11-09 20:38:10.788 4897307.5
2018-11-09 20:38:11.052 4897558.5
2018-11-09 20:38:11.316 4897809.5
2018-11-09 20:38:11.547 4898059.5
2018-11-09 20:38:11.811 4898310.5
2018-11-09 20:38:12.042 4898551.5
2018-11-09 20:38:12.306 4898802.5
2018-11-09 20:38:12.570 4899058.5
2018-11-09 20:38:12.801 4899309.5
2018-11-09 20:38:13.065 4899560.5
2018-11-09 20:38:13.296 4899801.5
2018-11-09 20:38:13.560 4900051.5
2018-11-09 20:38:13.824 4900301.5
2018-11-09 20:38:14.055 4900552.5
2018-11-09 20:38:14.319 4900803.5
2018-11-09 20:38:14.583 4901054.5
2018-11-09 20:38:14.814 4901305.5
2018-11-09 20:38:15.078 4901557.5
Name: TimeDelta, Length: 21645, dtype: float64
#Look at IMU A2 time-slice from 1,050,000 to 1,300,000:
keys = df_converted["TimeDelta"].values
values = df_converted["IMU A2"].values
imu_a2_time_dict = dict(zip(keys, values))
print(type(keys))
print(type(values))
#Now need to slice the dictionary into 2 new lists:
time_slice = []
imu_a2_slice = []
for time in imu_a2_time_dict:
if time > 1050000.0 and time < 1300000.0:
time_slice.append(float(time))
#print(imu_a2_time_dict[time] - 9.80665)
#Need to also subtract gravity from IMU A2 value:
imu_a2_slice.append(float(imu_a2_time_dict[time]) - 9.80665)
#print(type(time_slice))
#print(imu_a2_slice)<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
#import matplotlib.pyplot as plt
#import pylab
#%matplotlib inline
print("Plot not showing up for some reason - axis tick issue?")
plt.plot(time_slice, imu_a2_slice)
#plt.xticks=(np.arange(1050000, 1300000, step=50000))
plt.xlabel('Time Elapsed [ms]')
plt.ylabel('IMU A2')
plt.show()Plot not showing up for some reason - axis tick issue?
#Step 1. Detrend data
from scipy import signal
dacc_array = signal.detrend(imu_a2_slice)
time_array = time_slice
f_s = 4.0 #sampling frequency#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
## Butter Filters for Bandpass:
def butter_bandpass(lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = signal.butter(order, [low, high], btype='band')
return b, a
def butter_bandpass_lfilter(data, lowcut, highcut, fs, order=5):
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
y = signal.lfilter(b, a, data)
return y
def butter_bandpass_filtfilt(data, lowcut, highcut, fs, order=5):
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
y = signal.filtfilt(b, a, data)
return y
##Butter Filters for Highpass:
def butter_highpass(highcut, fs, order=5):
nyq = 0.5 * fs
high = highcut / nyq
b, a = signal.butter(order, high, btype='high')
return b, a
def butter_highpass_lfilter(data, highcut, fs, order=5):
b, a = butter_lowpass(highcut, fs, order=order)
y = signal.lfilter(b, a, data)
return y
##Butter Filters for Lowpass:
def butter_lowpass(lowcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
b, a = signal.butter(order, low, btype='low')
return b, a
def butter_lowpass_lfilter(data, lowcut, fs, order=5):
b, a = butter_lowpass(lowcut, fs, order=order)
y = signal.lfilter(b, a, data)
return y#Removes outliers from the array (outliers = values larger than 3x the standard deviation) and replaces them with the mean value.
def func_remove_outliers(acc_array):
updated_acc_array = []
std = np.std(acc_array)
#print("Std: ", std)
mean = np.median(acc_array)
#print("Mean: ", mean)
for value in acc_array:
if value > mean + 3*std:
#print("Value is:", value)
value = mean
elif value < -mean - 3*std:
value = -mean
updated_acc_array.append(value)
return updated_acc_array# Define the sample rate and the Low and High Cutoff frequencies
fs = 4.0 #redefine the sampling frequency
order= 6
lowcut = 0.09
highcut = 1.0
def double_integral_bandpass(time_array, acc_array, dacc_array, lowcut, highcut, fs, order):
butter_lfilter = butter_bandpass_lfilter(dacc_array, lowcut, highcut, fs, order=5)
#First integral is the velocity:
v_integral = integrate.cumtrapz(x=time_array, y=butter_lfilter, initial=0)
detrend_v_integral = signal.detrend(v_integral)
v_butter_filter_integral = butter_bandpass_lfilter(detrend_v_integral, lowcut, highcut, fs, order=5)
detrend_v_integral = signal.detrend(v_butter_filter_integral)
#Second integral is the displacment:
disp_integral = integrate.cumtrapz(x=time_array, y=v_butter_filter_integral, initial=0)
detrend_disp_integral = signal.detrend(disp_integral)
disp_butter_filter_integral = butter_bandpass_lfilter(detrend_disp_integral, lowcut, highcut, fs, order=5)
detrend_disp_butter_integral = signal.detrend(disp_butter_filter_integral)
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)
f3 = plt.figure(figsize=(12,5))
ax5 = f3.add_subplot(121)
ax6 = f3.add_subplot(122)
#Acceleration graphs:
ax1.plot(time_array, dacc_array)
ax1.set_title('Detrended Acceleration vs. Time')
ax1.set_xlabel('Time [s]')
ax1.set_ylabel('Acceleration [m/s^2]')
ax1.axhline(0, color="orange", ls='--')
ax2.plot(time_array, butter_lfilter)
ax2.set_title('Butter Filtered Detrended Acceleration vs. Time')
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Acceleration [m/s^2]')
ax2.axhline(0, color="orange", ls='--')
#Velocity graphs:
ax3.plot(time_array, v_butter_filter_integral)
ax3.set_title('Butter Filtered Velocity vs. Time')
ax3.set_xlabel('Time [s]')
ax3.set_ylabel('Velocity [m/s]')
ax3.axhline(0, color="orange", ls='--')
ax4.plot(time_array, detrend_v_integral)
ax4.set_title('Butter Filtered Detrended Velocity vs. Time')
ax4.set_xlabel('Time [s]')
ax4.set_ylabel('Velocity [m/s]')
ax4.axhline(0, color="orange",ls='--')
#Displacement graphs:
ax5.plot(time_array, disp_butter_filter_integral)
ax5.set_title('Butter Filtered Displacement vs. Time')
ax5.set_xlabel('Time [s]')
ax5.set_ylabel('Displacement [m]')
ax6.plot(time_array, detrend_disp_butter_integral)
ax6.set_title('Detrended Butter Filtered Displacement vs. Time')
ax6.set_xlabel('Time [s]')
ax6.set_ylabel('Displacement [m]')
plt.show()
return detrend_disp_butter_integral
print(dacc_array)
##For subexperiment1:
print("Starting subexperiment 1 analysis:")
#Get as close to just the signal (don't want noise)
new_t1, new_acc1 = calculate_new_range(df_converted["TimeDelta"].values, df_converted["IMU A2"].values, 1050000.0, 1300000.0)
#new_t1, new_dacc1 = calculate_new_range(time_slice, dacc_array1, 0, 800)
new_acc1 = func_remove_outliers(new_acc1)
new_dacc1 = signal.detrend(new_acc1)
for i in range(0, len(new_t1)):
t = new_t1[i] - 1050000.0
new_t1[i] = t*.001
disp_butter_integral1 = double_integral_bandpass(new_t1, new_acc1, new_dacc1, lowcut, highcut, fs, order)
print("Done.")
print(new_acc1[0:10])
print(new_dacc1[0:10])
print(new_t1[0:10])
## Using butterworth filter bc it is designed to have a flat frequency response, i.e. most accurately reproducing input through output.
print("Using butterworth filter bc it is designed to have a flat frequency response, i.e. most accurately reproducing input through output.")[-0.00220229 -0.01093901 -0.0252306 ... 0.0647823 0.09156903
0.11876466]
Starting subexperiment 1 analysis:
Done.
[8.919706, 9.015410999999999, 9.417371999999999, 9.819332999999999, 9.800192, 9.187679999999999, 9.398231, 9.723628, 9.685346, 9.647063999999999]
[-0.58069961 -0.48498812 -0.08302062 0.31894687 0.29981236 -0.31269314
-0.10213565 0.22326785 0.18499234 0.14671683]
[0.1525, 0.3535, 0.5535, 0.7545000000000001, 0.9555, 1.1555, 1.3555, 1.5565, 1.7575, 1.9575]
Using butterworth filter bc it is designed to have a flat frequency response, i.e. most accurately reproducing input through output.
new_dacc1 = disp_butter_integral1#First, find peaks and valleys of the waveforms:
#(Found that this works better when the data has been detrended.)
#Need to convert to numpy array types:
new_dacc1 = np.array(new_dacc1)
new_t1 = np.array(new_t1)
indexes0 = peakutils.indexes(new_dacc1, thres=0.02/max(new_dacc1), min_dist=100)
col_0t = new_t1 # First column data
col_0a = new_dacc1 # 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)
##Plotting:
f1 = plt.figure(figsize=(12,5))
ax1 = f1.add_subplot(111)
ax1.plot(col_0t,col_0a, lw=0.4, alpha=1.0, color='black' )
#ax1.plot(col_0t[index_max0],col_0a[index_max0], marker="o", ls="", ms=3, color="red" )
#ax1.plot(col_0t[index_min0],col_0a[index_min0], color ="orange", marker="o", ls="", ms=3 )
ax1.set_title("Vertical Displacement vs. Time")
ax1.set_xlabel("Time [s]")
ax1.set_ylabel('Displacement [m]')
#ax1.axhline(0, color="orange", ls='--')
#ax1.axhline(0.3, color="green", ls='-')
#ax1.axhline(-0.3, color="green", ls='-')
#ax1.axhline(0.6, color="red", ls='-')
#ax1.axhline(-0.6, color="red", ls='-')
plt.show()
#According to historic CDIP data:
#http://cdip.ucsd.edu/offline/wavecdf/wnc_browse.php?ARCHIVE/201p1/201p1_historic+waveHs+201811
print("Expecting significant wave height between 0.5 and 0.7m")
print("Expecting wave period between 7s and 12s")Expecting significant wave height between 0.5 and 0.7m
Expecting wave period between 7s and 12s
### Choose the tallest 1/3 of waves to observe, then take the mean of that subset: #Calculating significant wave height by doing analysis over entire wave (not just max peaks picked):
print("Number of datapoints: ", len(new_dacc1))
n = int(len(new_dacc1)/3)
print("Number of datapoints divided by 3: ", n)
print("\n")
x0 = new_dacc1
#print (x0[np.argsort(x0)[-n:]])
largest_third = (x0[np.argsort(x0)[-n:]])
#print(largest_third)
print("Mean of largest third of waves: ", np.mean(largest_third))
wave_height = 2*np.mean(largest_third)
expected_SWH = 0.6
print("Expecting Significant wave height between 0.5 and 0.7m.")
print("Significant wave height calculated as: ", wave_height)
print("SWH Percentage Error: ", abs(expected_SWH-wave_height)/expected_SWH)Number of datapoints: 1193
Number of datapoints divided by 3: 397
Mean of largest third of waves: 0.111178257707184
Expecting Significant wave height between 0.5 and 0.7m.
Significant wave height calculated as: 0.222356515414368
SWH Percentage Error: 0.62940580764272
from scipy import signal
f_s = 4.0
##PSD Step 2: Detrend the data
#dacc_array = signal.detrend(new_dacc1)
dacc_array = new_dacc1
#Taking the FFT of acceleration:
fft_acc = np.fft.fft(dacc_array)
len_fft_acc = len(fft_acc)
freq_acc = np.fft.fftfreq(len_fft_acc, 1/f_s)
#Shifting the FFT of acceleration:
fft_acc_shifted = np.fft.fftshift(fft_acc)
freq_acc_shifted = np.fft.fftshift(freq_acc)
#Normalize:
fft_acc_normal = np.abs(fft_acc_shifted/len_fft_acc)*2
#Now, multiply the accelerations by 1/w^2 to get the displacement in the frequency domain:
f = abs(freq_acc_shifted[np.argmax(fft_acc_normal)])
#print(f)
w = 2*np.pi*f
omega = 1/w**2
disp_normal = []
for a in fft_acc_normal:
new_a = a*omega
disp_normal.append(new_a)
#So now disp_normal should have the new values,
#representing displacement in the frequency domain.
print("\n")
#Calculating wave statistics
peak_freq = np.abs(freq_acc_shifted[np.argmax(fft_acc_normal)])
print("Smartfin Peak Frequnecy: ", peak_freq)
CDIP_peak_freq = 1.0/9.5
print("CDIP Peak Frequnecy: ", CDIP_peak_freq)
print("Frequency Standard Error: ", abs(CDIP_peak_freq-peak_freq)/CDIP_peak_freq)
peak_period = float(1/peak_freq)
CDIP_peak_period = 9.5
print("\n")
print("Smartfin Peak Period: ", peak_period)
print("CDIP Peak Period: ", CDIP_peak_period)
print("Period Standard Error: ", abs(CDIP_peak_period-peak_period)/CDIP_peak_period)
#representing displacement in the frequency domain.
fig = plt.figure(num=None, figsize=(6,3), dpi=80, facecolor='w', edgecolor='k')
# Returns the Axes instance
ax = fig.add_subplot(111)
ax.plot(freq_acc_shifted, fft_acc_normal)
ax.set_title("Displacement in the Frequency Domain")
ax.set_xlabel('Frequency [Hz]')
plt.xlim(0,1)
plt.ylim(0,)
plt.xticks(np.arange(0,1, step=.1))
plt.show()Smartfin Peak Frequnecy: 0.10058675607711651
CDIP Peak Frequnecy: 0.10526315789473684
Frequency Standard Error: 0.04442581726739313
Smartfin Peak Period: 9.941666666666666
CDIP Peak Period: 9.5
Period Standard Error: 0.04649122807017542
import os
os.environ['PROJ_LIB'] = r'C:\Users\QM\Anaconda3\pkgs\proj4-5.2.0-ha925a31_1\Library\share'
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d import Axes3D
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
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 qt
#%matplotlib notebook
%matplotlib inline
#import cmocean
print("Done!")Done!
ride_ids = ['16090']
# 13735 - One of Phil's Smartfin Surf Session Rides
# 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 (4Hz data at MESOM Lab)
# 14816 - Compass Orientation (Lying with LED down) July 20th (30Hz data at CSE UCSD)
# 14827 - Pool Displacement Control July 23rd (30Hz at CSE UCSD)
# 14840 - MESOM Parking Lot Static Rotation Combined Tracking (30Hz)
# 14865 - Martz Lab Rotation Vertical Component Manual Test (30Hz)
# 14888 - CDIP Buoy Calibration Run #1 July 30 (Fin in vertical position, pointing upwards)
# 14893 and 14894 - Junk files to drain battery (stationary tests an hour long at constant 30Hz)
# 14902 - Junk data when fin accidentally turn on while walking to beach for magneto calibration
# 14903 - Beach magnetometer calibration (y-x plane (horizontal or yaw) rotation of surfboard frame) August 3rd
# 14904 - Beach magnetometer calibration (x-z plane (lengthwise or pitch) rotation of surfboard frame) August 3rd
# 14905 - Beach magnetometer calibration (y-z plane (crosswise or roll) rotation of surfboard frame) August 3rd
# 15138 - First Ocean Data Experiment "Sitting" (August 20)
# 16083 - Floating Next to CDIP Buoy (May 15)
# 16090 - Orientation Calibration Sitting Outside CSE Building (May 21)
print("Ride",ride_ids,"loaded up.")Ride ['16090'] loaded up.
#%% 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
print("Done.")Done.
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)https://surf.smartfin.org/ride/16090
https://surf.smartfin.org/media/201905/google_102620292919180005796_0006667E228E_190521211219_Ocean.CSV
Time Temperature 1 \
UTC
2019-05-21 21:12:28.806000+00:00 28897 441
2019-05-21 21:12:34.826000+00:00 34917 439
2019-05-21 21:12:40.846000+00:00 40937 437
2019-05-21 21:12:46.867000+00:00 46957 434
2019-05-21 21:12:52.887000+00:00 52977 432
2019-05-21 21:12:58.912000+00:00 59001 429
2019-05-21 21:13:04.932000+00:00 65021 426
2019-05-21 21:13:10.952000+00:00 71041 424
2019-05-21 21:13:16.973000+00:00 77061 420
2019-05-21 21:13:22.992000+00:00 83080 418
2019-05-21 21:13:29.013000+00:00 89100 415
2019-05-21 21:13:35.033000+00:00 95120 413
2019-05-21 21:13:41.054000+00:00 101140 412
2019-05-21 21:13:47.074000+00:00 107160 409
2019-05-21 21:13:53.094000+00:00 113180 407
2019-05-21 21:13:59.123000+00:00 119208 404
2019-05-21 21:14:05.142000+00:00 125227 401
2019-05-21 21:14:11.164000+00:00 131248 399
2019-05-21 21:14:17.184000+00:00 137268 396
2019-05-21 21:14:23.205000+00:00 143288 393
2019-05-21 21:14:29.225000+00:00 149308 391
2019-05-21 21:14:35.246000+00:00 155329 388
2019-05-21 21:14:41.266000+00:00 161348 387
2019-05-21 21:14:47.286000+00:00 167368 385
2019-05-21 21:14:53.307000+00:00 173388 383
2019-05-21 21:14:59.327000+00:00 179408 381
2019-05-21 21:15:05.346000+00:00 185427 380
2019-05-21 21:15:11.367000+00:00 191447 378
2019-05-21 21:15:17.387000+00:00 197467 375
2019-05-21 21:15:23.408000+00:00 203487 373
... ... ...
2019-05-21 21:28:38.148000+00:00 998172 302
2019-05-21 21:28:44.169000+00:00 1004193 301
2019-05-21 21:28:50.190000+00:00 1010213 301
2019-05-21 21:28:56.209000+00:00 1016232 301
2019-05-21 21:29:02.231000+00:00 1022253 301
2019-05-21 21:29:08.250000+00:00 1028272 301
2019-05-21 21:29:14.270000+00:00 1034292 301
2019-05-21 21:29:20.291000+00:00 1040312 301
2019-05-21 21:29:26.310000+00:00 1046331 301
2019-05-21 21:29:32.338000+00:00 1052358 302
2019-05-21 21:29:38.358000+00:00 1058378 302
2019-05-21 21:29:44.379000+00:00 1064398 302
2019-05-21 21:29:50.398000+00:00 1070417 302
2019-05-21 21:29:56.419000+00:00 1076438 302
2019-05-21 21:30:02.441000+00:00 1082459 302
2019-05-21 21:30:08.461000+00:00 1088479 302
2019-05-21 21:30:14.482000+00:00 1094499 303
2019-05-21 21:30:20.501000+00:00 1100518 304
2019-05-21 21:30:26.521000+00:00 1106538 304
2019-05-21 21:30:32.543000+00:00 1112559 305
2019-05-21 21:30:38.563000+00:00 1118579 304
2019-05-21 21:30:44.586000+00:00 1124601 303
2019-05-21 21:30:50.606000+00:00 1130621 303
2019-05-21 21:30:56.627000+00:00 1136641 304
2019-05-21 21:31:02.648000+00:00 1142662 305
2019-05-21 21:31:08.668000+00:00 1148682 306
2019-05-21 21:31:14.689000+00:00 1154702 307
2019-05-21 21:31:20.708000+00:00 1160721 308
2019-05-21 21:31:26.730000+00:00 1166742 310
2019-05-21 21:31:32.750000+00:00 1172762 311
Calibrated Temperature 1 \
UTC
2019-05-21 21:12:28.806000+00:00 27.562
2019-05-21 21:12:34.826000+00:00 27.438
2019-05-21 21:12:40.846000+00:00 27.312
2019-05-21 21:12:46.867000+00:00 27.125
2019-05-21 21:12:52.887000+00:00 27.000
2019-05-21 21:12:58.912000+00:00 26.812
2019-05-21 21:13:04.932000+00:00 26.625
2019-05-21 21:13:10.952000+00:00 26.500
2019-05-21 21:13:16.973000+00:00 26.250
2019-05-21 21:13:22.992000+00:00 26.125
2019-05-21 21:13:29.013000+00:00 25.938
2019-05-21 21:13:35.033000+00:00 25.812
2019-05-21 21:13:41.054000+00:00 25.750
2019-05-21 21:13:47.074000+00:00 25.562
2019-05-21 21:13:53.094000+00:00 25.438
2019-05-21 21:13:59.123000+00:00 25.250
2019-05-21 21:14:05.142000+00:00 25.062
2019-05-21 21:14:11.164000+00:00 24.938
2019-05-21 21:14:17.184000+00:00 24.750
2019-05-21 21:14:23.205000+00:00 24.562
2019-05-21 21:14:29.225000+00:00 24.438
2019-05-21 21:14:35.246000+00:00 24.250
2019-05-21 21:14:41.266000+00:00 24.188
2019-05-21 21:14:47.286000+00:00 24.062
2019-05-21 21:14:53.307000+00:00 23.938
2019-05-21 21:14:59.327000+00:00 23.812
2019-05-21 21:15:05.346000+00:00 23.750
2019-05-21 21:15:11.367000+00:00 23.625
2019-05-21 21:15:17.387000+00:00 23.438
2019-05-21 21:15:23.408000+00:00 23.312
... ...
2019-05-21 21:28:38.148000+00:00 18.875
2019-05-21 21:28:44.169000+00:00 18.812
2019-05-21 21:28:50.190000+00:00 18.812
2019-05-21 21:28:56.209000+00:00 18.812
2019-05-21 21:29:02.231000+00:00 18.812
2019-05-21 21:29:08.250000+00:00 18.812
2019-05-21 21:29:14.270000+00:00 18.812
2019-05-21 21:29:20.291000+00:00 18.812
2019-05-21 21:29:26.310000+00:00 18.812
2019-05-21 21:29:32.338000+00:00 18.875
2019-05-21 21:29:38.358000+00:00 18.875
2019-05-21 21:29:44.379000+00:00 18.875
2019-05-21 21:29:50.398000+00:00 18.875
2019-05-21 21:29:56.419000+00:00 18.875
2019-05-21 21:30:02.441000+00:00 18.875
2019-05-21 21:30:08.461000+00:00 18.875
2019-05-21 21:30:14.482000+00:00 18.938
2019-05-21 21:30:20.501000+00:00 19.000
2019-05-21 21:30:26.521000+00:00 19.000
2019-05-21 21:30:32.543000+00:00 19.062
2019-05-21 21:30:38.563000+00:00 19.000
2019-05-21 21:30:44.586000+00:00 18.938
2019-05-21 21:30:50.606000+00:00 18.938
2019-05-21 21:30:56.627000+00:00 19.000
2019-05-21 21:31:02.648000+00:00 19.062
2019-05-21 21:31:08.668000+00:00 19.125
2019-05-21 21:31:14.689000+00:00 19.188
2019-05-21 21:31:20.708000+00:00 19.250
2019-05-21 21:31:26.730000+00:00 19.375
2019-05-21 21:31:32.750000+00:00 19.438
Temperature 1 Stable Temperature 2 \
UTC
2019-05-21 21:12:28.806000+00:00 False 4882
2019-05-21 21:12:34.826000+00:00 False 4879
2019-05-21 21:12:40.846000+00:00 False 4877
2019-05-21 21:12:46.867000+00:00 False 4862
2019-05-21 21:12:52.887000+00:00 False 4857
2019-05-21 21:12:58.912000+00:00 False 4838
2019-05-21 21:13:04.932000+00:00 False 4821
2019-05-21 21:13:10.952000+00:00 False 4805
2019-05-21 21:13:16.973000+00:00 False 4787
2019-05-21 21:13:22.992000+00:00 False 4787
2019-05-21 21:13:29.013000+00:00 False 4783
2019-05-21 21:13:35.033000+00:00 False 4781
2019-05-21 21:13:41.054000+00:00 False 4780
2019-05-21 21:13:47.074000+00:00 False 4760
2019-05-21 21:13:53.094000+00:00 False 4749
2019-05-21 21:13:59.123000+00:00 False 4728
2019-05-21 21:14:05.142000+00:00 False 4723
2019-05-21 21:14:11.164000+00:00 False 4711
2019-05-21 21:14:17.184000+00:00 False 4695
2019-05-21 21:14:23.205000+00:00 False 4688
2019-05-21 21:14:29.225000+00:00 False 4677
2019-05-21 21:14:35.246000+00:00 False 4690
2019-05-21 21:14:41.266000+00:00 False 4684
2019-05-21 21:14:47.286000+00:00 False 4696
2019-05-21 21:14:53.307000+00:00 False 4712
2019-05-21 21:14:59.327000+00:00 False 4704
2019-05-21 21:15:05.346000+00:00 False 4702
2019-05-21 21:15:11.367000+00:00 False 4685
2019-05-21 21:15:17.387000+00:00 False 4679
2019-05-21 21:15:23.408000+00:00 False 4684
... ... ...
2019-05-21 21:28:38.148000+00:00 False 4612
2019-05-21 21:28:44.169000+00:00 False 4625
2019-05-21 21:28:50.190000+00:00 False 4621
2019-05-21 21:28:56.209000+00:00 False 4629
2019-05-21 21:29:02.231000+00:00 False 4633
2019-05-21 21:29:08.250000+00:00 False 4646
2019-05-21 21:29:14.270000+00:00 False 4664
2019-05-21 21:29:20.291000+00:00 False 4673
2019-05-21 21:29:26.310000+00:00 False 4698
2019-05-21 21:29:32.338000+00:00 False 4704
2019-05-21 21:29:38.358000+00:00 False 4705
2019-05-21 21:29:44.379000+00:00 False 4711
2019-05-21 21:29:50.398000+00:00 False 4717
2019-05-21 21:29:56.419000+00:00 False 4730
2019-05-21 21:30:02.441000+00:00 False 4731
2019-05-21 21:30:08.461000+00:00 False 4728
2019-05-21 21:30:14.482000+00:00 False 4724
2019-05-21 21:30:20.501000+00:00 False 4692
2019-05-21 21:30:26.521000+00:00 False 4661
2019-05-21 21:30:32.543000+00:00 False 4635
2019-05-21 21:30:38.563000+00:00 False 4611
2019-05-21 21:30:44.586000+00:00 False 4612
2019-05-21 21:30:50.606000+00:00 False 4652
2019-05-21 21:30:56.627000+00:00 False 4677
2019-05-21 21:31:02.648000+00:00 False 4723
2019-05-21 21:31:08.668000+00:00 False 4742
2019-05-21 21:31:14.689000+00:00 False 4770
2019-05-21 21:31:20.708000+00:00 False 4797
2019-05-21 21:31:26.730000+00:00 False 4804
2019-05-21 21:31:32.750000+00:00 False 4831
Calibrated Temperature 2 \
UTC
2019-05-21 21:12:28.806000+00:00 19.269
2019-05-21 21:12:34.826000+00:00 19.257
2019-05-21 21:12:40.846000+00:00 19.249
2019-05-21 21:12:46.867000+00:00 19.190
2019-05-21 21:12:52.887000+00:00 19.171
2019-05-21 21:12:58.912000+00:00 19.096
2019-05-21 21:13:04.932000+00:00 19.030
2019-05-21 21:13:10.952000+00:00 18.967
2019-05-21 21:13:16.973000+00:00 18.896
2019-05-21 21:13:22.992000+00:00 18.896
2019-05-21 21:13:29.013000+00:00 18.881
2019-05-21 21:13:35.033000+00:00 18.873
2019-05-21 21:13:41.054000+00:00 18.869
2019-05-21 21:13:47.074000+00:00 18.791
2019-05-21 21:13:53.094000+00:00 18.748
2019-05-21 21:13:59.123000+00:00 18.665
2019-05-21 21:14:05.142000+00:00 18.646
2019-05-21 21:14:11.164000+00:00 18.599
2019-05-21 21:14:17.184000+00:00 18.536
2019-05-21 21:14:23.205000+00:00 18.509
2019-05-21 21:14:29.225000+00:00 18.466
2019-05-21 21:14:35.246000+00:00 18.517
2019-05-21 21:14:41.266000+00:00 18.493
2019-05-21 21:14:47.286000+00:00 18.540
2019-05-21 21:14:53.307000+00:00 18.603
2019-05-21 21:14:59.327000+00:00 18.571
2019-05-21 21:15:05.346000+00:00 18.564
2019-05-21 21:15:11.367000+00:00 18.497
2019-05-21 21:15:17.387000+00:00 18.473
2019-05-21 21:15:23.408000+00:00 18.493
... ...
2019-05-21 21:28:38.148000+00:00 18.211
2019-05-21 21:28:44.169000+00:00 18.262
2019-05-21 21:28:50.190000+00:00 18.246
2019-05-21 21:28:56.209000+00:00 18.278
2019-05-21 21:29:02.231000+00:00 18.293
2019-05-21 21:29:08.250000+00:00 18.344
2019-05-21 21:29:14.270000+00:00 18.415
2019-05-21 21:29:20.291000+00:00 18.450
2019-05-21 21:29:26.310000+00:00 18.548
2019-05-21 21:29:32.338000+00:00 18.571
2019-05-21 21:29:38.358000+00:00 18.575
2019-05-21 21:29:44.379000+00:00 18.599
2019-05-21 21:29:50.398000+00:00 18.622
2019-05-21 21:29:56.419000+00:00 18.673
2019-05-21 21:30:02.441000+00:00 18.677
2019-05-21 21:30:08.461000+00:00 18.665
2019-05-21 21:30:14.482000+00:00 18.650
2019-05-21 21:30:20.501000+00:00 18.524
2019-05-21 21:30:26.521000+00:00 18.403
2019-05-21 21:30:32.543000+00:00 18.301
2019-05-21 21:30:38.563000+00:00 18.207
2019-05-21 21:30:44.586000+00:00 18.211
2019-05-21 21:30:50.606000+00:00 18.368
2019-05-21 21:30:56.627000+00:00 18.466
2019-05-21 21:31:02.648000+00:00 18.646
2019-05-21 21:31:08.668000+00:00 18.720
2019-05-21 21:31:14.689000+00:00 18.830
2019-05-21 21:31:20.708000+00:00 18.936
2019-05-21 21:31:26.730000+00:00 18.963
2019-05-21 21:31:32.750000+00:00 19.069
Temperature 2 Stable salinity \
UTC
2019-05-21 21:12:28.806000+00:00 False NaN
2019-05-21 21:12:34.826000+00:00 False NaN
2019-05-21 21:12:40.846000+00:00 False NaN
2019-05-21 21:12:46.867000+00:00 False NaN
2019-05-21 21:12:52.887000+00:00 False NaN
2019-05-21 21:12:58.912000+00:00 False NaN
2019-05-21 21:13:04.932000+00:00 False NaN
2019-05-21 21:13:10.952000+00:00 False NaN
2019-05-21 21:13:16.973000+00:00 False NaN
2019-05-21 21:13:22.992000+00:00 False NaN
2019-05-21 21:13:29.013000+00:00 False NaN
2019-05-21 21:13:35.033000+00:00 False NaN
2019-05-21 21:13:41.054000+00:00 False NaN
2019-05-21 21:13:47.074000+00:00 False NaN
2019-05-21 21:13:53.094000+00:00 False NaN
2019-05-21 21:13:59.123000+00:00 False NaN
2019-05-21 21:14:05.142000+00:00 False NaN
2019-05-21 21:14:11.164000+00:00 False NaN
2019-05-21 21:14:17.184000+00:00 False NaN
2019-05-21 21:14:23.205000+00:00 False NaN
2019-05-21 21:14:29.225000+00:00 False NaN
2019-05-21 21:14:35.246000+00:00 False NaN
2019-05-21 21:14:41.266000+00:00 False NaN
2019-05-21 21:14:47.286000+00:00 False NaN
2019-05-21 21:14:53.307000+00:00 False NaN
2019-05-21 21:14:59.327000+00:00 False NaN
2019-05-21 21:15:05.346000+00:00 False NaN
2019-05-21 21:15:11.367000+00:00 False NaN
2019-05-21 21:15:17.387000+00:00 False NaN
2019-05-21 21:15:23.408000+00:00 False NaN
... ... ...
2019-05-21 21:28:38.148000+00:00 False NaN
2019-05-21 21:28:44.169000+00:00 False NaN
2019-05-21 21:28:50.190000+00:00 False NaN
2019-05-21 21:28:56.209000+00:00 False NaN
2019-05-21 21:29:02.231000+00:00 False NaN
2019-05-21 21:29:08.250000+00:00 False NaN
2019-05-21 21:29:14.270000+00:00 True NaN
2019-05-21 21:29:20.291000+00:00 False NaN
2019-05-21 21:29:26.310000+00:00 False NaN
2019-05-21 21:29:32.338000+00:00 False NaN
2019-05-21 21:29:38.358000+00:00 False NaN
2019-05-21 21:29:44.379000+00:00 False NaN
2019-05-21 21:29:50.398000+00:00 False NaN
2019-05-21 21:29:56.419000+00:00 False NaN
2019-05-21 21:30:02.441000+00:00 False NaN
2019-05-21 21:30:08.461000+00:00 False NaN
2019-05-21 21:30:14.482000+00:00 False NaN
2019-05-21 21:30:20.501000+00:00 False NaN
2019-05-21 21:30:26.521000+00:00 False NaN
2019-05-21 21:30:32.543000+00:00 False NaN
2019-05-21 21:30:38.563000+00:00 False NaN
2019-05-21 21:30:44.586000+00:00 False NaN
2019-05-21 21:30:50.606000+00:00 False NaN
2019-05-21 21:30:56.627000+00:00 False NaN
2019-05-21 21:31:02.648000+00:00 False NaN
2019-05-21 21:31:08.668000+00:00 False NaN
2019-05-21 21:31:14.689000+00:00 False NaN
2019-05-21 21:31:20.708000+00:00 False NaN
2019-05-21 21:31:26.730000+00:00 False NaN
2019-05-21 21:31:32.750000+00:00 False NaN
Calibrated Salinity Salinity Stable pH \
UTC
2019-05-21 21:12:28.806000+00:00 NaN NaN NaN
2019-05-21 21:12:34.826000+00:00 NaN NaN NaN
2019-05-21 21:12:40.846000+00:00 NaN NaN NaN
2019-05-21 21:12:46.867000+00:00 NaN NaN NaN
2019-05-21 21:12:52.887000+00:00 NaN NaN NaN
2019-05-21 21:12:58.912000+00:00 NaN NaN NaN
2019-05-21 21:13:04.932000+00:00 NaN NaN NaN
2019-05-21 21:13:10.952000+00:00 NaN NaN NaN
2019-05-21 21:13:16.973000+00:00 NaN NaN NaN
2019-05-21 21:13:22.992000+00:00 NaN NaN NaN
2019-05-21 21:13:29.013000+00:00 NaN NaN NaN
2019-05-21 21:13:35.033000+00:00 NaN NaN NaN
2019-05-21 21:13:41.054000+00:00 NaN NaN NaN
2019-05-21 21:13:47.074000+00:00 NaN NaN NaN
2019-05-21 21:13:53.094000+00:00 NaN NaN NaN
2019-05-21 21:13:59.123000+00:00 NaN NaN NaN
2019-05-21 21:14:05.142000+00:00 NaN NaN NaN
2019-05-21 21:14:11.164000+00:00 NaN NaN NaN
2019-05-21 21:14:17.184000+00:00 NaN NaN NaN
2019-05-21 21:14:23.205000+00:00 NaN NaN NaN
2019-05-21 21:14:29.225000+00:00 NaN NaN NaN
2019-05-21 21:14:35.246000+00:00 NaN NaN NaN
2019-05-21 21:14:41.266000+00:00 NaN NaN NaN
2019-05-21 21:14:47.286000+00:00 NaN NaN NaN
2019-05-21 21:14:53.307000+00:00 NaN NaN NaN
2019-05-21 21:14:59.327000+00:00 NaN NaN NaN
2019-05-21 21:15:05.346000+00:00 NaN NaN NaN
2019-05-21 21:15:11.367000+00:00 NaN NaN NaN
2019-05-21 21:15:17.387000+00:00 NaN NaN NaN
2019-05-21 21:15:23.408000+00:00 NaN NaN NaN
... ... ... ..
2019-05-21 21:28:38.148000+00:00 NaN NaN NaN
2019-05-21 21:28:44.169000+00:00 NaN NaN NaN
2019-05-21 21:28:50.190000+00:00 NaN NaN NaN
2019-05-21 21:28:56.209000+00:00 NaN NaN NaN
2019-05-21 21:29:02.231000+00:00 NaN NaN NaN
2019-05-21 21:29:08.250000+00:00 NaN NaN NaN
2019-05-21 21:29:14.270000+00:00 NaN NaN NaN
2019-05-21 21:29:20.291000+00:00 NaN NaN NaN
2019-05-21 21:29:26.310000+00:00 NaN NaN NaN
2019-05-21 21:29:32.338000+00:00 NaN NaN NaN
2019-05-21 21:29:38.358000+00:00 NaN NaN NaN
2019-05-21 21:29:44.379000+00:00 NaN NaN NaN
2019-05-21 21:29:50.398000+00:00 NaN NaN NaN
2019-05-21 21:29:56.419000+00:00 NaN NaN NaN
2019-05-21 21:30:02.441000+00:00 NaN NaN NaN
2019-05-21 21:30:08.461000+00:00 NaN NaN NaN
2019-05-21 21:30:14.482000+00:00 NaN NaN NaN
2019-05-21 21:30:20.501000+00:00 NaN NaN NaN
2019-05-21 21:30:26.521000+00:00 NaN NaN NaN
2019-05-21 21:30:32.543000+00:00 NaN NaN NaN
2019-05-21 21:30:38.563000+00:00 NaN NaN NaN
2019-05-21 21:30:44.586000+00:00 NaN NaN NaN
2019-05-21 21:30:50.606000+00:00 NaN NaN NaN
2019-05-21 21:30:56.627000+00:00 NaN NaN NaN
2019-05-21 21:31:02.648000+00:00 NaN NaN NaN
2019-05-21 21:31:08.668000+00:00 NaN NaN NaN
2019-05-21 21:31:14.689000+00:00 NaN NaN NaN
2019-05-21 21:31:20.708000+00:00 NaN NaN NaN
2019-05-21 21:31:26.730000+00:00 NaN NaN NaN
2019-05-21 21:31:32.750000+00:00 NaN NaN NaN
Calibrated pH pH Stable elapsed
UTC
2019-05-21 21:12:28.806000+00:00 NaN NaN 0.000
2019-05-21 21:12:34.826000+00:00 NaN NaN 6.020
2019-05-21 21:12:40.846000+00:00 NaN NaN 12.040
2019-05-21 21:12:46.867000+00:00 NaN NaN 18.061
2019-05-21 21:12:52.887000+00:00 NaN NaN 24.081
2019-05-21 21:12:58.912000+00:00 NaN NaN 30.106
2019-05-21 21:13:04.932000+00:00 NaN NaN 36.126
2019-05-21 21:13:10.952000+00:00 NaN NaN 42.146
2019-05-21 21:13:16.973000+00:00 NaN NaN 48.167
2019-05-21 21:13:22.992000+00:00 NaN NaN 54.186
2019-05-21 21:13:29.013000+00:00 NaN NaN 60.207
2019-05-21 21:13:35.033000+00:00 NaN NaN 66.227
2019-05-21 21:13:41.054000+00:00 NaN NaN 72.248
2019-05-21 21:13:47.074000+00:00 NaN NaN 78.268
2019-05-21 21:13:53.094000+00:00 NaN NaN 84.288
2019-05-21 21:13:59.123000+00:00 NaN NaN 90.317
2019-05-21 21:14:05.142000+00:00 NaN NaN 96.336
2019-05-21 21:14:11.164000+00:00 NaN NaN 102.358
2019-05-21 21:14:17.184000+00:00 NaN NaN 108.378
2019-05-21 21:14:23.205000+00:00 NaN NaN 114.399
2019-05-21 21:14:29.225000+00:00 NaN NaN 120.419
2019-05-21 21:14:35.246000+00:00 NaN NaN 126.440
2019-05-21 21:14:41.266000+00:00 NaN NaN 132.460
2019-05-21 21:14:47.286000+00:00 NaN NaN 138.480
2019-05-21 21:14:53.307000+00:00 NaN NaN 144.501
2019-05-21 21:14:59.327000+00:00 NaN NaN 150.521
2019-05-21 21:15:05.346000+00:00 NaN NaN 156.540
2019-05-21 21:15:11.367000+00:00 NaN NaN 162.561
2019-05-21 21:15:17.387000+00:00 NaN NaN 168.581
2019-05-21 21:15:23.408000+00:00 NaN NaN 174.602
... ... ... ...
2019-05-21 21:28:38.148000+00:00 NaN NaN 969.342
2019-05-21 21:28:44.169000+00:00 NaN NaN 975.363
2019-05-21 21:28:50.190000+00:00 NaN NaN 981.384
2019-05-21 21:28:56.209000+00:00 NaN NaN 987.403
2019-05-21 21:29:02.231000+00:00 NaN NaN 993.425
2019-05-21 21:29:08.250000+00:00 NaN NaN 999.444
2019-05-21 21:29:14.270000+00:00 NaN NaN 1005.464
2019-05-21 21:29:20.291000+00:00 NaN NaN 1011.485
2019-05-21 21:29:26.310000+00:00 NaN NaN 1017.504
2019-05-21 21:29:32.338000+00:00 NaN NaN 1023.532
2019-05-21 21:29:38.358000+00:00 NaN NaN 1029.552
2019-05-21 21:29:44.379000+00:00 NaN NaN 1035.573
2019-05-21 21:29:50.398000+00:00 NaN NaN 1041.592
2019-05-21 21:29:56.419000+00:00 NaN NaN 1047.613
2019-05-21 21:30:02.441000+00:00 NaN NaN 1053.635
2019-05-21 21:30:08.461000+00:00 NaN NaN 1059.655
2019-05-21 21:30:14.482000+00:00 NaN NaN 1065.676
2019-05-21 21:30:20.501000+00:00 NaN NaN 1071.695
2019-05-21 21:30:26.521000+00:00 NaN NaN 1077.715
2019-05-21 21:30:32.543000+00:00 NaN NaN 1083.737
2019-05-21 21:30:38.563000+00:00 NaN NaN 1089.757
2019-05-21 21:30:44.586000+00:00 NaN NaN 1095.780
2019-05-21 21:30:50.606000+00:00 NaN NaN 1101.800
2019-05-21 21:30:56.627000+00:00 NaN NaN 1107.821
2019-05-21 21:31:02.648000+00:00 NaN NaN 1113.842
2019-05-21 21:31:08.668000+00:00 NaN NaN 1119.862
2019-05-21 21:31:14.689000+00:00 NaN NaN 1125.883
2019-05-21 21:31:20.708000+00:00 NaN NaN 1131.902
2019-05-21 21:31:26.730000+00:00 NaN NaN 1137.924
2019-05-21 21:31:32.750000+00:00 NaN NaN 1143.944
[191 rows x 14 columns]
Ride data has been uploaded.
#print(motion_df)
saved_copy_motion_df = motion_df.copy(deep=True) #make a copy of the dataframe with raw data
print(saved_copy_motion_df) Time IMU A1 IMU A2 IMU A3 \
ride_id UTC
16090 2019-05-21 21:12:29.229000+00:00 29334.0 -29.0 -1.0 524.0
2019-05-21 21:12:29.262000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.295000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.328000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.361000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.394000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.427000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.460000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.493000+00:00 29590.0 -29.0 -2.0 524.0
2019-05-21 21:12:29.526000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.559000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.592000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.625000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.658000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.691000+00:00 29790.0 -30.0 4.0 524.0
2019-05-21 21:12:29.724000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.757000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.790000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.823000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.856000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.889000+00:00 29989.0 -28.0 0.0 525.0
2019-05-21 21:12:29.922000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.955000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.988000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:30.021000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:30.054000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:30.087000+00:00 30190.0 -29.0 -1.0 524.0
2019-05-21 21:12:30.120000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:30.153000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:30.186000+00:00 NaN NaN NaN NaN
... ... ... ... ...
2019-05-21 21:31:34.494000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.527000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.560000+00:00 1174589.0 -5.0 -304.0 419.0
2019-05-21 21:31:34.593000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.626000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.659000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.692000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.725000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.758000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.791000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.824000+00:00 1174840.0 -10.0 -304.0 434.0
2019-05-21 21:31:34.857000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.890000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.923000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.956000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:34.989000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.022000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.055000+00:00 1175090.0 2.0 -315.0 386.0
2019-05-21 21:31:35.088000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.121000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.154000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.187000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.220000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.253000+00:00 1175290.0 7.0 -297.0 420.0
2019-05-21 21:31:35.286000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.319000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.352000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.385000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.418000+00:00 NaN NaN NaN NaN
2019-05-21 21:31:35.451000+00:00 1175490.0 8.0 -310.0 428.0
IMU G1 IMU G2 IMU G3 IMU M1 \
ride_id UTC
16090 2019-05-21 21:12:29.229000+00:00 12.0 11.0 17.0 -137.0
2019-05-21 21:12:29.262000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.295000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.328000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.361000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.394000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.427000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.460000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.493000+00:00 12.0 11.0 16.0 -542.0
2019-05-21 21:12:29.526000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.559000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.592000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.625000+00:00 NaN NaN NaN NaN