Wave Period
Howard (Luhao) Wang edited this page May 30, 2019
·
3 revisions
# Reference: https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Logistic%20Regression%20balanced.ipynb
import 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)
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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 = 5000
motion_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
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2018-11-09 20:37:07.461 4834352.5
2018-11-09 20:37:07.725 4834603.5
2018-11-09 20:37:07.956 4834856.5
2018-11-09 20:37:08.220 4835107.5
2018-11-09 20:37:08.484 4835357.5
2018-11-09 20:37:08.715 4835609.5
2018-11-09 20:37:08.979 4835859.5
2018-11-09 20:37:09.210 4836101.5
2018-11-09 20:37:09.474 4836351.5
2018-11-09 20:37:09.738 4836601.5
2018-11-09 20:37:09.969 4836852.5
2018-11-09 20:37:10.233 4837102.5
2018-11-09 20:37:10.497 4837353.5
2018-11-09 20:37:10.728 4837604.5
2018-11-09 20:37:10.992 4837855.5
2018-11-09 20:37:11.256 4838106.5
2018-11-09 20:37:11.487 4838357.5
2018-11-09 20:37:11.751 4838609.5
2018-11-09 20:37:11.982 4838858.5
2018-11-09 20:37:12.246 4839110.5
2018-11-09 20:37:12.477 4839351.5
2018-11-09 20:37:12.741 4839601.5
2018-11-09 20:37:13.005 4839851.5
2018-11-09 20:37:13.236 4840102.5
2018-11-09 20:37:13.500 4840352.5
2018-11-09 20:37:13.764 4840603.5
2018-11-09 20:37:13.995 4840854.5
2018-11-09 20:37:14.259 4841105.5
2018-11-09 20:37:14.523 4841356.5
2018-11-09 20:37:14.754 4841606.5
2018-11-09 20:37:15.018 4841859.5
2018-11-09 20:37:15.282 4842110.5
2018-11-09 20:37:15.513 4842351.5
2018-11-09 20:37:15.777 4842601.5
2018-11-09 20:37:16.008 4842852.5
2018-11-09 20:37:16.272 4843103.5
2018-11-09 20:37:16.536 4843354.5
2018-11-09 20:37:16.767 4843605.5
2018-11-09 20:37:17.031 4843855.5
2018-11-09 20:37:17.262 4844105.5
2018-11-09 20:37:17.526 4844357.5
2018-11-09 20:37:17.790 4844607.5
2018-11-09 20:37:18.021 4844860.5
2018-11-09 20:37:18.285 4845109.5
2018-11-09 20:37:18.516 4845351.5
2018-11-09 20:37:18.780 4845601.5
2018-11-09 20:37:19.044 4845851.5
2018-11-09 20:37:19.275 4846101.5
2018-11-09 20:37:19.539 4846353.5
2018-11-09 20:37:19.803 4846603.5
2018-11-09 20:37:20.034 4846854.5
2018-11-09 20:37:20.298 4847105.5
2018-11-09 20:37:20.562 4847356.5
2018-11-09 20:37:20.793 4847607.5
2018-11-09 20:37:21.057 4847857.5
2018-11-09 20:37:21.288 4848107.5
2018-11-09 20:37:21.552 4848359.5
2018-11-09 20:37:21.816 4848609.5
2018-11-09 20:37:22.047 4848860.5
2018-11-09 20:37:22.311 4849110.5
2018-11-09 20:37:22.542 4849351.5
2018-11-09 20:37:22.806 4849602.5
2018-11-09 20:37:23.070 4849853.5
2018-11-09 20:37:23.301 4850103.5
2018-11-09 20:37:23.565 4850353.5
2018-11-09 20:37:23.763 4850553.5
2018-11-09 20:37:23.961 4850759.5
2018-11-09 20:37:24.159 4850961.5
2018-11-09 20:37:24.357 4851151.5
2018-11-09 20:37:24.555 4851351.5
2018-11-09 20:37:24.819 4851603.5
2018-11-09 20:37:25.083 4851853.5
2018-11-09 20:37:25.314 4852104.5
2018-11-09 20:37:25.578 4852355.5
2018-11-09 20:37:25.842 4852605.5
2018-11-09 20:37:26.073 4852856.5
2018-11-09 20:37:26.337 4853107.5
2018-11-09 20:37:26.601 4853358.5
2018-11-09 20:37:26.832 4853609.5
2018-11-09 20:37:27.096 4853860.5
2018-11-09 20:37:27.327 4854101.5
2018-11-09 20:37:27.591 4854353.5
2018-11-09 20:37:27.822 4854603.5
2018-11-09 20:37:28.086 4854854.5
2018-11-09 20:37:28.350 4855105.5
2018-11-09 20:37:28.581 4855356.5
2018-11-09 20:37:28.845 4855607.5
2018-11-09 20:37:29.109 4855857.5
2018-11-09 20:37:29.340 4856107.5
2018-11-09 20:37:29.604 4856359.5
2018-11-09 20:37:29.868 4856609.5
2018-11-09 20:37:30.099 4856851.5
2018-11-09 20:37:30.363 4857101.5
2018-11-09 20:37:30.594 4857351.5
2018-11-09 20:37:30.858 4857602.5
2018-11-09 20:37:31.122 4857852.5
2018-11-09 20:37:31.353 4858103.5
2018-11-09 20:37:31.617 4858354.5
2018-11-09 20:37:31.848 4858605.5
2018-11-09 20:37:32.112 4858856.5
2018-11-09 20:37:32.376 4859107.5
2018-11-09 20:37:32.607 4859357.5
2018-11-09 20:37:32.871 4859609.5
2018-11-09 20:37:33.135 4859859.5
2018-11-09 20:37:33.366 4860109.5
2018-11-09 20:37:33.630 4860360.5
2018-11-09 20:37:33.861 4860601.5
2018-11-09 20:37:34.125 4860851.5
2018-11-09 20:37:34.389 4861102.5
2018-11-09 20:37:34.620 4861353.5
2018-11-09 20:37:34.884 4861604.5
2018-11-09 20:37:35.148 4861855.5
2018-11-09 20:37:35.379 4862106.5
2018-11-09 20:37:35.643 4862356.5
2018-11-09 20:37:35.874 4862607.5
2018-11-09 20:37:36.138 4862858.5
2018-11-09 20:37:36.402 4863109.5
2018-11-09 20:37:36.633 4863359.5
2018-11-09 20:37:36.897 4863609.5
2018-11-09 20:37:37.128 4863852.5
2018-11-09 20:37:37.392 4864103.5
2018-11-09 20:37:37.656 4864353.5
2018-11-09 20:37:37.887 4864605.5
2018-11-09 20:37:38.151 4864855.5
2018-11-09 20:37:38.415 4865105.5
2018-11-09 20:37:38.646 4865355.5
2018-11-09 20:37:38.910 4865606.5
2018-11-09 20:37:39.174 4865856.5
2018-11-09 20:37:39.405 4866108.5
2018-11-09 20:37:39.669 4866357.5
2018-11-09 20:37:39.900 4866607.5
2018-11-09 20:37:40.164 4866857.5
2018-11-09 20:37:40.428 4867108.5
2018-11-09 20:37:40.659 4867359.5
2018-11-09 20:37:40.923 4867610.5
2018-11-09 20:37:41.187 4867860.5
2018-11-09 20:37:41.418 4868101.5
2018-11-09 20:37:41.682 4868351.5
2018-11-09 20:37:41.913 4868601.5
2018-11-09 20:37:42.177 4868851.5
2018-11-09 20:37:42.408 4869101.5
2018-11-09 20:37:42.672 4869352.5
2018-11-09 20:37:42.936 4869603.5
2018-11-09 20:37:43.167 4869853.5
2018-11-09 20:37:43.431 4870105.5
2018-11-09 20:37:43.695 4870355.5
2018-11-09 20:37:43.926 4870605.5
2018-11-09 20:37:44.190 4870855.5
2018-11-09 20:37:44.454 4871106.5
2018-11-09 20:37:44.685 4871357.5
2018-11-09 20:37:44.949 4871608.5
2018-11-09 20:37:45.213 4871859.5
2018-11-09 20:37:45.444 4872109.5
2018-11-09 20:37:45.708 4872360.5
2018-11-09 20:37:45.939 4872601.5
2018-11-09 20:37:46.203 4872851.5
2018-11-09 20:37:46.434 4873103.5
2018-11-09 20:37:46.698 4873353.5
2018-11-09 20:37:46.962 4873605.5
2018-11-09 20:37:47.193 4873855.5
2018-11-09 20:37:47.457 4874108.5
2018-11-09 20:37:47.721 4874357.5
2018-11-09 20:37:47.952 4874609.5
2018-11-09 20:37:48.216 4874859.5
2018-11-09 20:37:48.480 4875110.5
2018-11-09 20:37:48.711 4875360.5
2018-11-09 20:37:48.975 4875610.5
2018-11-09 20:37:49.206 4875852.5
2018-11-09 20:37:49.470 4876103.5
2018-11-09 20:37:49.734 4876353.5
2018-11-09 20:37:49.965 4876605.5
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
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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
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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
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2018-11-09 20:38:10.293 4896807.5
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2018-11-09 20:38:10.788 4897307.5
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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
Created by: Howard Wang, 05/08/19