Initial Orientation
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
2019-05-21 21:12:29.658000+00:00 NaN NaN NaN NaN
2019-05-21 21:12:29.691000+00:00 12.0 11.0 18.0 -535.0
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 12.0 10.0 18.0 -543.0
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 13.0 11.0 17.0 -535.0
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 72.0 95.0 -97.0 -291.0
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 19.0 -38.0 26.0 -286.0
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 74.0 80.0 22.0 -295.0
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 -7.0 18.0 47.0 -285.0
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 2.0 66.0 21.0 -318.0
IMU M2 IMU M3 Latitude Longitude
ride_id UTC
16090 2019-05-21 21:12:29.229000+00:00 162.0 -76.0 NaN NaN
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+00:00 648.0 -302.0 NaN NaN
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+00:00 649.0 -303.0 NaN NaN
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+00:00 655.0 -311.0 NaN NaN
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+00:00 655.0 -303.0 NaN NaN
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+00:00 323.0 7.0 NaN NaN
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+00:00 316.0 6.0 NaN NaN
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+00:00 329.0 9.0 NaN NaN
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+00:00 309.0 19.0 NaN NaN
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+00:00 326.0 -12.0 NaN NaN
[34735 rows x 12 columns]
#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', 'IMU G1', 'IMU G2', 'IMU G3', 'IMU M1',
'IMU M2', 'IMU M3'])
#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 = []
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))
print("Done.")Done.
## 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
gyro_const = 8.2 # Raw gyrscope constant 8.2 bits per degree
gravity = 9.80665 # Approximate measurement for gravity
# 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. (??) --> Not yet, we'll do it later on
new_a = a / g_const * gravity# - gravity
ret_array.append(new_a)
return ret_array
imuA1_array = convert_acc_units(imu_array_A1) #new units in m/s^2
imuA2_array = convert_acc_units(imu_array_A2) #new units in m/s^2
imuA3_array = convert_acc_units(imu_array_A3) #new units in m/s^2
##To check:
#for x,y in zip(imuA2_array, imu_array_A2):
#print(x,y)
def convert_gyr_units(gyr_array):
ret_array = []
for g in gyr_array:
# Gyroscopic Rotation converts to deg/s
new_g = g / gyro_const
ret_array.append(new_g)
return ret_array
imuG1_array_not_cal = convert_gyr_units(imu_array_G1) #new units in deg/s (will need to be rads for some later functions)
imuG2_array_not_cal = convert_gyr_units(imu_array_G2)
imuG3_array_not_cal = convert_gyr_units(imu_array_G3)
#To check:
#for x,y in zip(imuG2_array, imu_array_G2):
#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(len(time_e_array))
print(len(time_e_list))
print(len(imuA2_array))
#print(imuA1_array)
#print(rotmat.R(imuA1_array, ___))
plt.plot(time_e_array, imuA1_array)
plt.show()
plt.plot(time_e_array, imuA2_array)
plt.show()
plt.plot(time_e_array, imuA3_array)
plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG2_array)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG1_array_not_cal)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG1_array)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG2_array_not_cal)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG2_array)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG3_array_not_cal)
#plt.show()
#print("Gyro_plot:")
#plt.plot(time_e_array, imuG3_array)
#plt.show()
#print("Why are the y-axis values so small?")5648
5648
5648
def adaptive_gyroscope_calibration(gyr, thresh):
# Takes in gyroscope array [1xN] and a threshold value in deg/s
print("The current threshold value is:",thresh) # Prints the threshold
ret_array = gyr.copy() # Initialize the calibrated return array (which will be calibrated and returned)
calibration_constant = 0 # Initialize loop variables
last_touched_value = 0
calibration_was_attempted = 0
current = 0
#calibrations_completed = 0
calarray_pos = np.zeros(shape=(500,1)) #Initialize an array to store calibration constant positions in (made excessively large for longer sessions)
calarray_constants = np.zeros(shape=(500,1)) #Initialize an array to store calibration constant values
print("Total rows of data:", len(gyr))
calibration_was_attempted = 0 # Reset all state variables
last_touched_value = 0
calibrations_completed = 0
current = 0 # Reset iterator
while current < len(gyr) - 100:
#for current in range(len(gyr)):
calibration_was_attempted = 0 #Reset the state of calibration attempted
curr_pos = gyr[current]
next_pos = gyr[current+1]
difference = next_pos - curr_pos #Find the delta between current and next value
if (difference < thresh) and (difference > -1*thresh): #If the delta is within the threshold:
calibration_was_attempted = 1 #Change state: calibration_was_attempted to true
start_average = current #Mark the start of a potential averaging window
end_average = current+1 #Initialize the end of a potential averaging window (size 1 to be safe)
next_seven_fail = 0 #Initialize variable to tell if next 7 deltas were within threshold
end_of_deltas = 0 #Initialize variable to tell when delta is no longer within threshold
final_sev_check_pos = 0 #Initialize variable to hold marker at the last seven check position
for seven_check in range(7): # Check the next 7 deltas (1/4 second @ 30Hz) to get min window
sev_check_curr_pos = gyr[current+seven_check+1]
sev_check_next_pos = gyr[current+seven_check+2]
sev_check_difference = sev_check_next_pos - sev_check_curr_pos
if (sev_check_difference > thresh) or (sev_check_difference < -1*thresh): # If at any point a delta is not within threshold, then stop and start over
next_seven_fail = 1 #Change failure status of seven_check to TRUE
final_sev_check_pos = current+seven_check+1
last_touched_value = current+seven_check+1 #Update the last_touched_value by the loop
break #Break out of the loop and restart
final_sev_check_pos = current+seven_check+1 #Update the last position in seven_check
end_average = final_sev_check_pos #Update the end of the averaging window
last_touched_value = end_average #Update the last touched value
if next_seven_fail == 0: #If the next seven deltas are within threshold, continue looking for last valid delta (if seven_check failed, then this loop shouldn't run)
iterator = 0
while (end_of_deltas == 0) and (not (gyr[final_sev_check_pos+iterator] is None)):
#while we haven't found the last valid delta, AND the next value isn't empty/DNE
if final_sev_check_pos+iterator+1 >= len(gyr):
end_of_deltas = 1
end_average = final_sev_check_pos+iterator
last_touched_value = end_average
else:
finding_end_curr_pos = gyr[final_sev_check_pos+iterator]
finding_end_next_pos = gyr[final_sev_check_pos+iterator+1]
finding_end_difference = finding_end_next_pos - finding_end_curr_pos
if (finding_end_difference >= thresh) or (finding_end_difference <= -1*thresh): #if difference is outside threshold, enter "end found" state
end_of_deltas = 1 #Set "end found" state to true
end_average = final_sev_check_pos+iterator #Mark ending of window with the last valid delta position
last_touched_value = end_average #Update the last touched value
break
iterator += 1
averaging_pos = start_average
total_sum = 0
num_of_nums = 0
while (averaging_pos < end_average) and (averaging_pos < len(gyr)-1): #Start averaging the values within the averaging window
total_sum += gyr[averaging_pos]
num_of_nums += 1
averaging_pos += 1
calibration_constant = total_sum / num_of_nums
calarray_pos[calibrations_completed] = current #Store calibration start location into an array
calarray_constants[calibrations_completed] = calibration_constant #Store calibrations into an array
calibrations_completed += 1
#print("Start of the average was",start_average)
#print("Position of the last touched value was",last_touched_value)
#print("Newest calibration calculated:",calibration_constant)
if calibration_was_attempted == 1:
current = last_touched_value #Assign current to the last value looked at by the loop, to avoid redundant searches
else:
current += 1
cal_pos_iterator = 0 # Iterator to point to next upcoming calibration position, so we know when to change calibration value
for i in range(len(ret_array)):
if cal_pos_iterator == 0: # Unique case for setting the first calibration constant
enforcer = calarray_constants[0]
cal_pos_iterator = 1
#cal_value_iterator = 1
ret_array[i] -= enforcer
else:
if i < calarray_pos[cal_pos_iterator] and i < len(ret_array)-1: # if the current position hasn't reached the start of the next calibration window
ret_array[i] -= enforcer
elif i == calarray_pos[cal_pos_iterator] and i < len(ret_array)-1: # if the current position is at the start of the next calibration window
enforcer = calarray_constants[cal_pos_iterator]
cal_pos_iterator += 1
ret_array[i] -= enforcer
elif i > calarray_pos[cal_pos_iterator] and i < len(ret_array)-1 and calarray_pos[cal_pos_iterator] == 0:
ret_array[i] -= enforcer
else:
print("Current position skipped unexpectedly past next calibration window.")
print(i)
break
return ret_array
threshold = 5 / 8.2 # Set the threshold that passes to the calibration function
imuG1_array = adaptive_gyroscope_calibration(imuG1_array_not_cal, threshold)
imuG2_array = adaptive_gyroscope_calibration(imuG2_array_not_cal, threshold)
imuG3_array = adaptive_gyroscope_calibration(imuG3_array_not_cal, threshold)
G1_comparison = np.stack((imuG1_array_not_cal, imuG1_array), axis=-1)
print(G1_comparison)
print("Done calibrating.")
print("Gyro_plot:")
plt.plot(time_e_array, imuG1_array)
plt.show()
print("Gyro_plot:")
plt.plot(time_e_array, imuG2_array)
plt.show()
print("Gyro_plot:")
plt.plot(time_e_array, imuG3_array)
plt.show()The current threshold value is: 0.6097560975609757
Total rows of data: 5648
Current position skipped unexpectedly past next calibration window.
5647
The current threshold value is: 0.6097560975609757
Total rows of data: 5648
Current position skipped unexpectedly past next calibration window.
5647
The current threshold value is: 0.6097560975609757
Total rows of data: 5648
Current position skipped unexpectedly past next calibration window.
5647
[[1.4634146341463417 array([-0.05712001])]
[1.4634146341463417 array([-0.05712001])]
[1.4634146341463417 array([-0.05712001])]
...
[2.3170731707317076 array([0.73378922])]
[9.02439024390244 array([7.44110629])]
[-0.853658536585366 -0.853658536585366]]
Done calibrating.
Gyro_plot:
Gyro_plot:
Gyro_plot:
# Offset variables help in recentering the magnetic data in order to define direction and use trig functions
# for some calculations. These values were estimated from data collected from multiple tests and hold fairly well.
# Magnetic data is relative, so as long as all of the axis share similar magnitude in value, then data should be fine
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)
#print(imu_array_M1)
#print(imuM1_array)
print("Done.")Done.
# Create N x 3 arrays for functions that need them later on, such as Scikit Kinematics
acc_height = len(imuA1_array)
print(acc_height)
gyr_height = len(imuG1_array)
magn_height = len(imuM1_array)
acc_array = np.zeros(shape=(acc_height,3))
gyr_array = np.zeros(shape=(gyr_height,3))
magn_array = np.zeros(shape=(magn_height,3))
for x in range(len(acc_array)):
acc_array[x,0] = imuA1_array[x]
acc_array[x,1] = imuA2_array[x]
acc_array[x,2] = imuA3_array[x]
#print(acc_array)
for x in range(len(gyr_array)):
gyr_array[x,0] = imuG1_array[x]
gyr_array[x,1] = imuG2_array[x]
gyr_array[x,2] = imuG3_array[x]
#print(gyr_array)
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.")5648
Done.
# The new array for board reference frame will have the IMUs in columns according to X,Y,Z directions
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[:,2] * (-1)
temp_z_acc = board_acc[:,1] * (-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('X ', 'Y ', 'Z')
#print(acc_array[:4,:])
#print(board_acc[:4,:]) # Display check to see if they are properly assigned
board_gyr = gyr_array.copy()
temp_x_gyr = board_gyr[:,0] * (-1)
temp_y_gyr = board_gyr[:,2] * (-1)
temp_z_gyr = board_gyr[:,1] * (-1)
board_gyr[:,0] = temp_x_gyr
board_gyr[:,1] = temp_y_gyr
board_gyr[:,2] = temp_z_gyr
board_magn = magn_array.copy()
temp_x_magn = board_magn[:,0] * (-1)
temp_y_magn = board_magn[:,2] * (-1)
temp_z_magn = board_magn[:,1] * (-1)
board_magn[:,0] = temp_x_magn
board_magn[:,1] = temp_y_magn
board_magn[:,2] = temp_z_magn
print("Done.")Done.
print(board_acc)
#print(board_gyr)
#print(board_magn)[[ 0.55545479 -10.03649336 0.01915361]
[ 0.55545479 -10.03649336 0.03830723]
[ 0.5746084 -10.03649336 -0.07661445]
...
[ 0.19153613 -8.31266816 5.82269844]
[ -0.03830723 -7.39329473 6.03338818]
[ -0.13407529 -8.04451758 5.68862314]]
import numpy as np
from sklearn import preprocessing
import math
board_acc_norms = preprocessing.normalize(board_acc, norm='l2')
board_magn_norms = preprocessing.normalize(board_magn, norm='l2')
#print(board_acc[0])
#print(board_acc_norms[0])
gravity_vec = board_acc_norms[0]
entry_1_1 = (math.pow(gravity_vec[1], 2) - (math.pow(gravity_vec[0], 2) * gravity_vec[2]))
entry_1_1 /= (math.pow(gravity_vec[0], 2) + math.pow(gravity_vec[1], 2))
entry_2_1 = (-(gravity_vec[0] * gravity_vec[1]) - (gravity_vec[0] * gravity_vec[1] * gravity_vec[2]))
entry_2_1 /= (math.pow(gravity_vec[0], 2) + math.pow(gravity_vec[1], 2))
entry_3_1 = -gravity_vec[0]
first_column = np.array([entry_1_1, entry_2_1, entry_3_1])
entry_1_2 = entry_2_1
entry_2_2 = (math.pow(gravity_vec[0], 2) - (math.pow(gravity_vec[1], 2) * gravity_vec[2]))
entry_2_2 /= (math.pow(gravity_vec[0], 2) + math.pow(gravity_vec[1], 2))
entry_3_2 = -gravity_vec[1]
second_column = np.array([entry_2_1, entry_2_2, entry_3_2])
third_column = np.array([gravity_vec[0], gravity_vec[1], -gravity_vec[2]])
rotation_matrix = np.column_stack((first_column, second_column, third_column))
gravity_as_np = np.array(gravity_vec)
gravity_after_rotation = np.matmul(rotation_matrix, np.transpose(gravity_as_np))
print(gravity_after_rotation)
ent_1_1 = 1 / (math.sqrt(1 + (math.pow(gravity_vec[1], 2) / math.pow(gravity_vec[0], 2))))
ent_2_1 = gravity_vec[1] / (gravity_vec[0] * (math.sqrt(1 + (math.pow(gravity_vec[1], 2) / math.pow(gravity_vec[0], 2)))))
first_col = np.array([ent_1_1, ent_2_1, 0])
ent_1_2 = -ent_2_1
ent_2_2 = ent_1_1
second_col = np.array([ent_1_2, ent_2_2, 0])
third_col = np.array([0, 0, 1])
rot_matrix = np.column_stack((first_col, second_col, third_col))
board_acc_rotation = board_acc.copy()
for x in range(len(board_acc_rotation)):
board_acc_rotation[x] = np.matmul(rotation_matrix, np.transpose(board_acc_rotation[x]))
board_magn_rotation = board_magn.copy()
for x in range(len(board_magn_rotation)):
board_magn_rotation[x] = np.matmul(rot_matrix, np.transpose(board_magn_rotation[x]))
#print(board_acc_norms)
#print(board_magn_norms)
#north_vec = board_magn_norms[0]
#down_vec = board_acc_norms[0]
#east_vec = np.cross(down_vec, north_vec)
#rotation_matrix = np.column_stack((north_vec, east_vec, down_vec))
#print(rotation_matrix)
#rotation_matrix = np.transpose(rotation_matrix)
#print(rotation_matrix)[-6.77626358e-18 0.00000000e+00 -1.00000000e+00]
# 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
# Altitude is the angle between the ground and the vector
# Azimuth is the angle going clockwise from 0 deg North
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):
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])
#printAltAzi(heading_altitude[i],heading_azimuth[i])
i += 1
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]")
azi_plot.set_ylim([-50,400])
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()
# Enter the EXACT number that is shown for declination (since yaw and azimuth opposite directions/signs)
# Enter the EXACT number that is shown for inclination
declination = 11.5
inclination = 57.87
LFF_fick_yaw = (-1)*declination
LFF_fick_pitch = inclination
LFF_fick_roll = 0
# Convert angles entered to radians for trig functions
Y = np.deg2rad(LFF_fick_yaw)
P = np.deg2rad(LFF_fick_pitch)
R = np.deg2rad(LFF_fick_roll)
# Set up the Fick angle matrix
c11 = np.cos(Y)*np.cos(P)
c12 = np.cos(Y)*np.sin(P)*np.sin(R) - np.sin(Y)*np.sin(R)
c13 = np.cos(Y)*np.sin(P)*np.cos(R) + np.sin(Y)*np.sin(R)
c21 = np.sin(Y)*np.cos(P)
c22 = np.sin(Y)*np.sin(P)*np.sin(R) + np.cos(Y)*np.cos(R)
c23 = np.sin(Y)*np.sin(P)*np.cos(R) - np.cos(Y)*np.sin(R)
c31 = (-1)*np.sin(P)
c32 = np.cos(P)*np.sin(R)
c33 = np.cos(P)*np.cos(R)
rotmat_lff2mag = np.array([[c11, c12, c13],
[c21, c22, c23],
[c31, c32, c33]])
print("rotmat_lff2mag: \n", rotmat_lff2mag, "\n")
lff2mag_rad = rotmat.sequence(rotmat_lff2mag, 'Fick') # Check if the matrix accurately represents the desired Fick angles
lff2mag_deg = lff2mag_rad * 180/np.pi
print("lff2mag_deg Fick Angles: \n", lff2mag_deg,"\n")
# Now find the inverse of the matrix, since we want to go from magnetic field to LFF
#rotmat_mag2lff = np.matrix.transpose(rotmat_lff2mag)
rotmat_mag2lff = np.linalg.inv(rotmat_lff2mag)
print("rotmat_mag2lff: \n", rotmat_mag2lff, "\n")
mag2lff_rad = rotmat.sequence(rotmat_mag2lff, 'Fick')
mag2lff_deg = mag2lff_rad * 180/np.pi
print("mag2lff_deg Fick Angles: \n", mag2lff_deg, "\n")
print("Is it identity matrix?: \n", np.matmul(rotmat_lff2mag,rotmat_mag2lff))
mag2lff_helm_rad = rotmat.sequence(rotmat_mag2lff, 'Helmholtz')
mag2lff_helm_deg = mag2lff_helm_rad * 180/np.pi
print("mag2lff_deg Helmholtz Angles: \n", mag2lff_helm_deg, "\n")
Q_mag2lff = rotmat.seq2quat(mag2lff_deg, 'Fick')
print("Transformation Quaternion for mag2lff: \n", Q_mag2lff) #Print out quaternion describing the transformationrotmat_lff2mag:
[[ 0.52116517 0. 0.82984293]
[-0.10603225 0.9799247 -0.16883345]
[-0.84684357 0. 0.53184206]]
lff2mag_deg Fick Angles:
[-11.5 57.87 0. ]
rotmat_mag2lff:
[[ 5.42737678e-01 -0.00000000e+00 -8.46843566e-01]
[ 2.07620339e-01 1.02048657e+00 3.65362550e-17]
[ 8.64192485e-01 0.00000000e+00 5.31842059e-01]]
mag2lff_deg Fick Angles:
[ 20.933987 -59.79062512 0. ]
Is it identity matrix?:
[[ 1.00000000e+00 0.00000000e+00 7.09307209e-17]
[-1.13267885e-17 1.00000000e+00 1.91919225e-17]
[ 1.27816841e-17 0.00000000e+00 1.00000000e+00]]
mag2lff_deg Helmholtz Angles:
[ 1.19829344e+01 -6.20605416e+01 -2.14000502e-15]
Transformation Quaternion for mag2lff:
[[ 0.85251145 0.09054692 -0.49012302 0.15749573]]
# Yaw rotation - convert azimuth to yaw, since they are opposites of each other in direction
print("Initial Azimuth and Altitude: \n", heading_azimuth[0], heading_altitude[0], "\n")
init_yaw = heading_azimuth[0] * (-1)
init_pos_first_rotation = rotmat.R('z', init_yaw) #print(init_pos_first_rotation, "\n")
rotmat_first_init_rot_rad = rotmat.sequence(init_pos_first_rotation, to='Fick')
rotmat_first_init_rot_deg = rotmat_first_init_rot_rad * 180/np.pi #print(rotmat_first_init_rot_deg)
# Pitch rotation - convert alitude to pitch, since they are opposites of each other in direction
init_pitch = heading_altitude[0] * (-1)
init_pos_second_rotation = rotmat.R('y', init_pitch) #print(init_pos_second_rotation, "\n")
rotmat_second_init_rot_rad = rotmat.sequence(init_pos_second_rotation, to='Fick')
rotmat_second_init_rot_deg = rotmat_second_init_rot_rad * 180/np.pi #print(rotmat_second_init_rot_deg)
rotmat_init_pos = np.matmul(init_pos_first_rotation, init_pos_second_rotation) #print(rotmat_init_pos, "\n")
# Combine rotations into a single sequence of rotations
Initial_Position_offset_rad = rotmat.sequence(rotmat_init_pos, to='Fick')
Initial_Position_offset_deg = Initial_Position_offset_rad * 180/np.pi
Q_init_pos = rotmat.seq2quat(Initial_Position_offset_deg, 'Fick')
print("Initial Orientation Rotation Matrix: \n", rotmat_init_pos, "\n")
print("Initial Orientation Fick Angles: \n", Initial_Position_offset_deg, "\n")
print("Initial Orientation Quaternion: \n", Q_init_pos)Initial Azimuth and Altitude:
85.73392596704763 -53.484897944244686
Initial Orientation Rotation Matrix:
[[ 0.04426359 0.99722936 0.05978585]
[-0.59338602 0.07438826 -0.80147328]
[-0.80370005 0. 0.59503465]]
Initial Orientation Fick Angles:
[-85.73392597 53.48489794 0. ]
Initial Orientation Quaternion:
[[ 0.65453925 0.30612117 0.32980677 -0.60753247]]
# Calculate the delta quaternions relative to the Earth's magnetic reference frame from the rate, accelerometer, gyro,
# and magnetic data. All functions automatically iterate through arrays unless specified otherwise
rate = 33 # Frequency of data polling in Hz
board_gyr_rad = board_gyr * np.pi/180 # Converting from degrees to radians for the kalman function to create quaternions
delta_quats = imus.kalman(rate,board_acc_rotation,board_gyr_rad,board_magn_rotation)
#print(len(delta_quats))
#print(delta_quats)
print("Kalman quaternions finished.")
init_rotated_quats = quat.q_mult(Q_init_pos, delta_quats) # Using delta_quats instead of vector quats to avoid unit length
#print(init_rotated_quats, "\n")
#init_rotated_quats2 = quat.q_mult(delta_vectors, Q_init_pos)
#print(init_rotated_quats2)
print("Initial orientation quaternion applied to Kalman quats.")
final_orient_quats = quat.q_mult(Q_mag2lff, init_rotated_quats)
#print(final_orient_quats) # Still delta quaternions, not absolute position/angles
print("Magnetic to LFF quaternion applied to other quats.")Kalman quaternions finished.
Initial orientation quaternion applied to Kalman quats.
Magnetic to LFF quaternion applied to other quats.
#vector_quats = quat.q_vector(delta_quats)
print(delta_quats)
print(init_rotated_quats)[[ 1. 0. 0. 0. ]
[ 0.99909679 0.02915118 -0.03062097 0.00426274]
[ 0.9971206 0.05062205 -0.05599647 0.00723215]
...
[ 0.82611918 0.40650447 -0.39012711 0.00905862]
[ 0.82549396 0.40825509 -0.38947518 0.01402075]
[ 0.82198732 0.40656892 -0.3987823 0.00335126]]
[[ 0.65453925 0.30612117 0.32980677 -0.60753247]
[ 0.65771302 0.30772791 0.29045106 -0.62318159]
[ 0.66001987 0.30673938 0.25923678 -0.63488661]
...
[ 0.55045777 0.28493841 -0.23263151 -0.74945909]
[ 0.55231228 0.28792548 -0.23499357 -0.74620914]
[ 0.54712071 0.27657511 -0.23795139 -0.75335534]]
# Enter 2 to view geographic animation, 1 to view raw animation, otherwise set to 0 for none
view_animation = 2
print(len(delta_quats))
print(np.floor(1000/60))
print("Done.")5648
16.0
Done.
pygame.init()
if view_animation == 1:
#view.orientation(quat_array, out_file=None, title_text='Ride 13735 Filtered', deltaT = 1000)
viewer = view.Orientation_OGL(delta_quats, rate = 30) #Make the simulation run at 250ms intervals, so show "real time" movement
viewer.run(looping=False, rate=120) #Force the simulation to run at 4Hz to match the 250ms intervals
# Close the window to prevent kernel crash at the end
pygame.display.quit()
#view.ts(quat_array)
elif view_animation == 2:
viewer = view.Orientation_OGL(final_orient_quats, rate == 30)
viewer.run(looping=False, rate = 120) # Run it at 4 times speed to speed things up
pygame.display.quit()
print("All Done.")All Done.