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Wave Period
# Reference: https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Logistic%20Regression%20balanced.ipynbimport pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
#from mpl_toolkits.basemap import Basemap
import os
import datetime
import pytz
import re
import peakutils
import statsmodels.api as sm
import requests
#Read data from a local csv file:
##Will change this to scrape files from the Smartfin.org website later.
#data = pd.read_csv('Motion_13735.CSV', header=0)
#data = data.dropna()
#Print out the column headings:
#print(data.shape)
#print(list(data.columns))#Make sure the ride_id and the footage file match up:
ride_ids = ['15692']
footage_file = 'Footage3.txt'
#ride_ids = ['14827']
# 14743 - Motion Control July 10th
# 14750 - Magnetometer Control July 11th
# 14814 - Pool Displacement Control July 17th
# 14815 - Compass Orientation (Lying on Charger Side) July 19th
# 14816 - Orientation w Higher Sampling (Lying on Charger Side) July 20th
# 14827 - Pool Displacement Control w Higher Sampling (Jul 23)
# 14888 - First Buoy Calibration Experiment (July 30)
# 15218 - Jasmine's Second Ride Sesh filmed with GoPro (Aug 29) //no footage
# 15629 - Jasmine's First Ride Sesh filmed with VIRB (Oct. 24) //first labelled footage!
# 15669 - Jasmine's Second Ride Sesh filmed with VIRB (Nov. 7) //second labelled footage!
# 15692 - Jasmine's 3rd Ride Sesh filmed with VIRB (Nov. 9) //third labelled footage!
# 15686 - Jasmine's 4th Ride Sesh filmed with VIRB (Nov. 11) //fourth labelled footage!#%% Fin ID scraper
# Input fin ID, get all ride IDs
# base URL to which we'll append given fin IDs
fin_url_base = 'http://surf.smartfin.org/fin/'
# Look for the following text in the HTML contents in fcn below
str_id_ride = 'rideId = \'' # backslash allows us to look for single quote
str_id_date = 'var date = \'' # backslash allows us to look for single quote
#%% Ride ID scraper
# Input ride ID, get ocean and motion CSVs
# Base URL to which we'll append given ride IDs
ride_url_base = 'https://surf.smartfin.org/ride/'
# Look for the following text in the HTML contents in fcn below
str_id_csv = 'img id="temperatureChart" class="chart" src="'
def get_csv_from_ride_id(rid):
# Build URL for each individual ride
ride_url = ride_url_base+str(rid)
print(ride_url)
# Get contents of ride_url
html_contents = requests.get(ride_url).text
# Find CSV identifier
loc_csv_id = html_contents.find(str_id_csv)
# Different based on whether user logged in with FB or Google
offset_googleOAuth = [46, 114]
offset_facebkOAuth = [46, 112]
if html_contents[loc_csv_id+59] == 'f': # Facebook login
off0 = offset_facebkOAuth[0]
off1 = offset_facebkOAuth[1]
else: # Google login
off0 = offset_googleOAuth[0]
off1 = offset_googleOAuth[1]
csv_id_longstr = html_contents[loc_csv_id+off0:loc_csv_id+off1]
# print(csv_id_longstr)
# Stitch together full URL for CSV
if ("media" in csv_id_longstr) & ("Calibration" not in html_contents): # other junk URLs can exist and break everything
ocean_csv_url = 'https://surf.smartfin.org/'+csv_id_longstr+'Ocean.CSV'
motion_csv_url = 'https://surf.smartfin.org/'+csv_id_longstr+'Motion.CSV'
print(ocean_csv_url)
# Go to ocean_csv_url and grab contents (theoretically, a CSV)
ocean_df_small = pd.read_csv(ocean_csv_url, parse_dates = [0])
elapsed_timedelta = (ocean_df_small['UTC']-ocean_df_small['UTC'][0])
ocean_df_small['elapsed'] = elapsed_timedelta/np.timedelta64(1, 's')
motion_df_small = pd.read_csv(motion_csv_url, parse_dates = [0])
# Reindex on timestamp if there are at least a few rows
if len(ocean_df_small) > 1:
ocean_df_small.set_index('UTC', drop = True, append = False, inplace = True)
motion_df_small.set_index('UTC', drop = True, append = False, inplace = True)
#print(ocean_df_small)
#print(motion_df_small)
#May need to change this sampling interval:
sample_interval = '33ms'
ocean_df_small_resample = ocean_df_small.resample(sample_interval).mean()
motion_df_small_resample = motion_df_small.resample(sample_interval).mean()
# No need to save many extra rows with no fix
motion_df_small = motion_df_small[~np.isnan(motion_df_small.Latitude)]
return ocean_df_small_resample, motion_df_small_resample
else:
ocean_df_small_resample = pd.DataFrame() # empty DF just so something is returned
motion_df_small_resample = pd.DataFrame()
return ocean_df_small_resample, motion_df_small_resample
appended_ocean_list = [] # list of DataFrames from original CSVs
appended_motion_list = []
appended_multiIndex = [] # fin_id & ride_id used to identify each DataFrame
## Nested loops (for each fin ID, find all ride IDs, then build a DataFrame from all ride CSVs)
## (Here, ride IDS are either ocean or motion dataframes)
count_good_fins = 0
# Loop over ride_ids and find CSVs
for rid in ride_ids:
try:
new_ocean_df, new_motion_df = get_csv_from_ride_id(rid) # get given ride's CSV from its ride ID using function above
#print(len(new_ocean_df))
#print(len(new_motion_df))
if not new_ocean_df.empty: # Calibration rides, for example
# Append only if DF isn't empty. There may be a better way to control empty DFs which are created above
appended_multiIndex.append(str(rid)) # build list to be multiIndex of future DataFrame
appended_ocean_list.append(new_ocean_df)
appended_motion_list.append(new_motion_df)
print("Ride data has been uploaded.")
#print("Ride: ", rid, "data has been uploaded.")
count_good_fins += 1
except:
print("Ride threw an exception!")
#print("Ride ", rid, "threw an exception!")
#%% Build the "Master" DataFrame
# appended_ocean_df.summary()
df_keys = tuple(appended_multiIndex) # keys gotta be a tuple, a list which data in it cannot be changed
ocean_df = pd.concat(appended_ocean_list, keys = df_keys, names=['ride_id'])
motion_df = pd.concat(appended_motion_list, keys = df_keys, names = ['ride_id'])
##Here, maybe just use info from the motion_df and don't worry about ocean_df data for now.
##If you do want ocean_df data, look at how Phil was getting it from "July 10th and 11th Calibration" jupyter notebook file.
#We can also check to see if the surfboard was recording "in-water-freq" or
#"out-of-water-freq" based on how many NaN values we see.
print(motion_df)https://surf.smartfin.org/ride/15692
https://surf.smartfin.org/media/201811/google_105349665704999793400_0006667E229D_181109191556_Ocean.CSV
Ride data has been uploaded.
Time IMU A1 IMU A2 IMU A3 IMU G1 \
ride_id UTC
15692 2018-11-09 19:16:03.789 1.414743e+09 493.0 48.0 110.0 75.0
2018-11-09 19:16:03.822 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.855 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.888 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.921 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.954 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.987 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.020 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.053 1.414743e+09 513.0 89.0 62.0 34.0
2018-11-09 19:16:04.086 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.119 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.152 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.185 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.218 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.251 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.284 1.414743e+09 494.0 92.0 80.0 69.0
2018-11-09 19:16:04.317 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.350 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.383 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.416 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.449 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.482 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.515 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.548 1.414744e+09 421.0 205.0 -104.0 192.0
2018-11-09 19:16:04.581 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.614 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.647 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.680 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.713 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.746 NaN NaN NaN NaN NaN
... ... ... ... ... ...
2018-11-09 20:38:14.121 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.154 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.187 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.220 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.253 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.286 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.319 1.419644e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.352 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.385 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.418 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.451 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.484 1.419644e+09 NaN NaN NaN NaN
2018-11-09 20:38:14.517 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.550 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.583 1.419644e+09 502.0 -11.0 99.0 10.0
2018-11-09 20:38:14.616 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.649 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.682 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.715 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.748 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.781 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.814 1.419644e+09 501.0 -13.0 99.0 10.0
2018-11-09 20:38:14.847 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.880 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.913 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.946 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.979 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.012 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.045 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.078 1.419644e+09 501.0 -11.0 98.0 10.0
IMU G2 IMU G3 IMU M1 IMU M2 IMU M3 \
ride_id UTC
15692 2018-11-09 19:16:03.789 -124.0 -86.0 -309.0 209.0 39.0
2018-11-09 19:16:03.822 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.855 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.888 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.921 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.954 NaN NaN NaN NaN NaN
2018-11-09 19:16:03.987 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.020 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.053 -36.0 -92.0 -320.0 194.0 38.0
2018-11-09 19:16:04.086 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.119 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.152 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.185 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.218 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.251 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.284 -63.0 -42.0 -329.0 189.0 49.0
2018-11-09 19:16:04.317 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.350 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.383 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.416 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.449 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.482 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.515 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.548 -92.0 -37.0 -330.0 180.0 64.0
2018-11-09 19:16:04.581 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.614 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.647 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.680 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.713 NaN NaN NaN NaN NaN
2018-11-09 19:16:04.746 NaN NaN NaN NaN NaN
... ... ... ... ... ...
2018-11-09 20:38:14.121 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.154 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.187 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.220 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.253 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.286 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.319 20.0 2.0 -303.0 267.0 37.0
2018-11-09 20:38:14.352 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.385 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.418 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.451 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.484 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.517 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.550 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.583 21.0 1.0 -308.0 262.0 32.0
2018-11-09 20:38:14.616 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.649 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.682 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.715 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.748 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.781 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.814 21.0 1.0 -310.0 260.0 38.0
2018-11-09 20:38:14.847 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.880 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.913 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.946 NaN NaN NaN NaN NaN
2018-11-09 20:38:14.979 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.012 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.045 NaN NaN NaN NaN NaN
2018-11-09 20:38:15.078 20.0 2.0 -309.0 265.0 35.0
Latitude Longitude
ride_id UTC
15692 2018-11-09 19:16:03.789 3285871.0 -11725690.0
2018-11-09 19:16:03.822 NaN NaN
2018-11-09 19:16:03.855 NaN NaN
2018-11-09 19:16:03.888 NaN NaN
2018-11-09 19:16:03.921 NaN NaN
2018-11-09 19:16:03.954 NaN NaN
2018-11-09 19:16:03.987 NaN NaN
2018-11-09 19:16:04.020 NaN NaN
2018-11-09 19:16:04.053 NaN NaN
2018-11-09 19:16:04.086 NaN NaN
2018-11-09 19:16:04.119 NaN NaN
2018-11-09 19:16:04.152 NaN NaN
2018-11-09 19:16:04.185 NaN NaN
2018-11-09 19:16:04.218 NaN NaN
2018-11-09 19:16:04.251 NaN NaN
2018-11-09 19:16:04.284 NaN NaN
2018-11-09 19:16:04.317 NaN NaN
2018-11-09 19:16:04.350 NaN NaN
2018-11-09 19:16:04.383 NaN NaN
2018-11-09 19:16:04.416 NaN NaN
2018-11-09 19:16:04.449 NaN NaN
2018-11-09 19:16:04.482 NaN NaN
2018-11-09 19:16:04.515 NaN NaN
2018-11-09 19:16:04.548 NaN NaN
2018-11-09 19:16:04.581 NaN NaN
2018-11-09 19:16:04.614 NaN NaN
2018-11-09 19:16:04.647 NaN NaN
2018-11-09 19:16:04.680 NaN NaN
2018-11-09 19:16:04.713 NaN NaN
2018-11-09 19:16:04.746 NaN NaN
... ... ...
2018-11-09 20:38:14.121 NaN NaN
2018-11-09 20:38:14.154 NaN NaN
2018-11-09 20:38:14.187 NaN NaN
2018-11-09 20:38:14.220 NaN NaN
2018-11-09 20:38:14.253 NaN NaN
2018-11-09 20:38:14.286 NaN NaN
2018-11-09 20:38:14.319 NaN NaN
2018-11-09 20:38:14.352 NaN NaN
2018-11-09 20:38:14.385 NaN NaN
2018-11-09 20:38:14.418 NaN NaN
2018-11-09 20:38:14.451 NaN NaN
2018-11-09 20:38:14.484 3287082.0 -11722116.0
2018-11-09 20:38:14.517 NaN NaN
2018-11-09 20:38:14.550 NaN NaN
2018-11-09 20:38:14.583 NaN NaN
2018-11-09 20:38:14.616 NaN NaN
2018-11-09 20:38:14.649 NaN NaN
2018-11-09 20:38:14.682 NaN NaN
2018-11-09 20:38:14.715 NaN NaN
2018-11-09 20:38:14.748 NaN NaN
2018-11-09 20:38:14.781 NaN NaN
2018-11-09 20:38:14.814 NaN NaN
2018-11-09 20:38:14.847 NaN NaN
2018-11-09 20:38:14.880 NaN NaN
2018-11-09 20:38:14.913 NaN NaN
2018-11-09 20:38:14.946 NaN NaN
2018-11-09 20:38:14.979 NaN NaN
2018-11-09 20:38:15.012 NaN NaN
2018-11-09 20:38:15.045 NaN NaN
2018-11-09 20:38:15.078 NaN NaN
[149434 rows x 12 columns]
#Drop the latitude and longitude values since most of them are Nan:
motion_df_dropped = motion_df.drop(columns=['Latitude', 'Longitude'])
#Drop the NAN values from the motion data:
motion_df_dropped = motion_df_dropped.dropna(axis=0, how='any')
print(motion_df_dropped) Time IMU A1 IMU A2 IMU A3 IMU G1 \
ride_id UTC
15692 2018-11-09 19:16:03.789 1.414743e+09 493.0 48.0 110.0 75.0
2018-11-09 19:16:04.053 1.414743e+09 513.0 89.0 62.0 34.0
2018-11-09 19:16:04.284 1.414743e+09 494.0 92.0 80.0 69.0
2018-11-09 19:16:04.548 1.414744e+09 421.0 205.0 -104.0 192.0
2018-11-09 19:16:04.812 1.414744e+09 534.0 306.0 -32.0 -421.0
2018-11-09 19:16:05.043 1.414744e+09 455.0 149.0 -102.0 -355.0
2018-11-09 19:16:05.307 1.414744e+09 474.0 342.0 -219.0 -234.0
2018-11-09 19:16:05.571 1.414745e+09 363.0 323.0 -131.0 60.0
2018-11-09 19:16:05.802 1.414745e+09 -21.0 510.0 -447.0 78.0
2018-11-09 19:16:06.066 1.414745e+09 35.0 283.0 -132.0 -114.0
2018-11-09 19:16:06.330 1.414745e+09 130.0 323.0 -255.0 160.0
2018-11-09 19:16:06.561 1.414746e+09 -55.0 504.0 -173.0 -152.0
2018-11-09 19:16:06.825 1.414746e+09 58.0 420.0 -128.0 -77.0
2018-11-09 19:16:07.056 1.414746e+09 -179.0 454.0 -389.0 58.0
2018-11-09 19:16:07.320 1.414746e+09 13.0 181.0 -229.0 128.0
2018-11-09 19:16:07.551 1.414747e+09 -23.0 815.0 -258.0 -323.0
2018-11-09 19:16:07.815 1.414747e+09 5.0 254.0 -120.0 190.0
2018-11-09 19:16:08.079 1.414747e+09 -158.0 487.0 -291.0 -8.0
2018-11-09 19:16:08.310 1.414747e+09 37.0 207.0 -236.0 100.0
2018-11-09 19:16:08.574 1.414748e+09 -123.0 659.0 -206.0 -222.0
2018-11-09 19:16:08.838 1.414748e+09 -24.0 368.0 -187.0 106.0
2018-11-09 19:16:09.069 1.414748e+09 -200.0 516.0 -402.0 7.0
2018-11-09 19:16:09.333 1.414748e+09 -40.0 300.0 -298.0 126.0
2018-11-09 19:16:09.564 1.414749e+09 -62.0 556.0 -242.0 -249.0
2018-11-09 19:16:09.828 1.414749e+09 13.0 482.0 -212.0 92.0
2018-11-09 19:16:10.092 1.414749e+09 -158.0 583.0 -277.0 -73.0
2018-11-09 19:16:10.323 1.414749e+09 -102.0 315.0 -379.0 18.0
2018-11-09 19:16:10.587 1.414750e+09 -81.0 421.0 -70.0 -90.0
2018-11-09 19:16:10.851 1.414750e+09 -36.0 489.0 -226.0 111.0
2018-11-09 19:16:11.082 1.414750e+09 53.0 195.0 -107.0 20.0
... ... ... ... ... ...
2018-11-09 20:38:07.785 1.419637e+09 443.0 -13.0 84.0 -70.0
2018-11-09 20:38:08.016 1.419637e+09 509.0 -29.0 87.0 -58.0
2018-11-09 20:38:08.280 1.419638e+09 502.0 27.0 97.0 4.0
2018-11-09 20:38:08.544 1.419638e+09 501.0 -23.0 103.0 13.0
2018-11-09 20:38:08.775 1.419638e+09 499.0 -5.0 105.0 -9.0
2018-11-09 20:38:09.039 1.419638e+09 501.0 -10.0 104.0 6.0
2018-11-09 20:38:09.303 1.419639e+09 501.0 -8.0 104.0 9.0
2018-11-09 20:38:09.534 1.419639e+09 500.0 -9.0 104.0 8.0
2018-11-09 20:38:09.798 1.419639e+09 501.0 -8.0 104.0 8.0
2018-11-09 20:38:10.062 1.419639e+09 500.0 -8.0 103.0 8.0
2018-11-09 20:38:10.293 1.419640e+09 500.0 -9.0 105.0 9.0
2018-11-09 20:38:10.557 1.419640e+09 500.0 -8.0 105.0 8.0
2018-11-09 20:38:10.788 1.419640e+09 501.0 -20.0 102.0 7.0
2018-11-09 20:38:11.052 1.419640e+09 497.0 2.0 107.0 -4.0
2018-11-09 20:38:11.316 1.419641e+09 500.0 -7.0 106.0 -78.0
2018-11-09 20:38:11.547 1.419641e+09 503.0 -19.0 98.0 77.0
2018-11-09 20:38:11.811 1.419641e+09 497.0 17.0 110.0 21.0
2018-11-09 20:38:12.042 1.419641e+09 502.0 -13.0 103.0 18.0
2018-11-09 20:38:12.306 1.419642e+09 498.0 13.0 107.0 -26.0
2018-11-09 20:38:12.570 1.419642e+09 502.0 -23.0 96.0 -157.0
2018-11-09 20:38:12.801 1.419642e+09 502.0 -11.0 99.0 17.0
2018-11-09 20:38:13.065 1.419642e+09 501.0 -9.0 100.0 12.0
2018-11-09 20:38:13.296 1.419643e+09 501.0 -12.0 99.0 11.0
2018-11-09 20:38:13.560 1.419643e+09 502.0 -13.0 99.0 9.0
2018-11-09 20:38:13.824 1.419643e+09 502.0 -11.0 98.0 9.0
2018-11-09 20:38:14.055 1.419643e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.319 1.419644e+09 501.0 -11.0 99.0 9.0
2018-11-09 20:38:14.583 1.419644e+09 502.0 -11.0 99.0 10.0
2018-11-09 20:38:14.814 1.419644e+09 501.0 -13.0 99.0 10.0
2018-11-09 20:38:15.078 1.419644e+09 501.0 -11.0 98.0 10.0
IMU G2 IMU G3 IMU M1 IMU M2 IMU M3
ride_id UTC
15692 2018-11-09 19:16:03.789 -124.0 -86.0 -309.0 209.0 39.0
2018-11-09 19:16:04.053 -36.0 -92.0 -320.0 194.0 38.0
2018-11-09 19:16:04.284 -63.0 -42.0 -329.0 189.0 49.0
2018-11-09 19:16:04.548 -92.0 -37.0 -330.0 180.0 64.0
2018-11-09 19:16:04.812 -233.0 -229.0 -325.0 161.0 97.0
2018-11-09 19:16:05.043 -376.0 -397.0 -337.0 117.0 151.0
2018-11-09 19:16:05.307 -527.0 -465.0 -311.0 25.0 217.0
2018-11-09 19:16:05.571 -662.0 -305.0 -238.0 -8.0 272.0
2018-11-09 19:16:05.802 -643.0 -153.0 -159.0 -21.0 321.0
2018-11-09 19:16:06.066 -430.0 132.0 -86.0 -38.0 326.0
2018-11-09 19:16:06.330 -243.0 349.0 -62.0 -24.0 326.0
2018-11-09 19:16:06.561 -297.0 191.0 -39.0 -55.0 311.0
2018-11-09 19:16:06.825 -228.0 138.0 -14.0 -80.0 294.0
2018-11-09 19:16:07.056 -23.0 12.0 -3.0 -79.0 297.0
2018-11-09 19:16:07.320 19.0 10.0 -12.0 -76.0 296.0
2018-11-09 19:16:07.551 -117.0 46.0 6.0 -86.0 292.0
2018-11-09 19:16:07.815 -49.0 11.0 16.0 -90.0 266.0
2018-11-09 19:16:08.079 114.0 -28.0 15.0 -75.0 285.0
2018-11-09 19:16:08.310 71.0 -17.0 10.0 -82.0 282.0
2018-11-09 19:16:08.574 -103.0 -77.0 28.0 -88.0 268.0
2018-11-09 19:16:08.838 -26.0 -13.0 40.0 -80.0 268.0
2018-11-09 19:16:09.069 84.0 -68.0 36.0 -70.0 276.0
2018-11-09 19:16:09.333 77.0 38.0 33.0 -67.0 285.0
2018-11-09 19:16:09.564 -100.0 -22.0 37.0 -69.0 265.0
2018-11-09 19:16:09.828 -13.0 26.0 44.0 -80.0 270.0
2018-11-09 19:16:10.092 42.0 -45.0 53.0 -83.0 263.0
2018-11-09 19:16:10.323 7.0 39.0 38.0 -78.0 260.0
2018-11-09 19:16:10.587 -50.0 -95.0 52.0 -92.0 244.0
2018-11-09 19:16:10.851 101.0 -17.0 50.0 -88.0 250.0
2018-11-09 19:16:11.082 109.0 -51.0 54.0 -76.0 256.0
... ... ... ... ... ...
2018-11-09 20:38:07.785 57.0 15.0 -295.0 219.0 7.0
2018-11-09 20:38:08.016 55.0 -10.0 -294.0 226.0 0.0
2018-11-09 20:38:08.280 17.0 4.0 -290.0 232.0 -6.0
2018-11-09 20:38:08.544 20.0 0.0 -290.0 228.0 -10.0
2018-11-09 20:38:08.775 19.0 -3.0 -285.0 225.0 7.0
2018-11-09 20:38:09.039 21.0 0.0 -291.0 221.0 -3.0
2018-11-09 20:38:09.303 21.0 1.0 -297.0 221.0 -3.0
2018-11-09 20:38:09.534 21.0 1.0 -288.0 222.0 -6.0
2018-11-09 20:38:09.798 21.0 1.0 -286.0 222.0 -4.0
2018-11-09 20:38:10.062 21.0 0.0 -290.0 220.0 2.0
2018-11-09 20:38:10.293 21.0 1.0 -286.0 224.0 8.0
2018-11-09 20:38:10.557 21.0 1.0 -289.0 217.0 1.0
2018-11-09 20:38:10.788 21.0 1.0 -297.0 225.0 3.0
2018-11-09 20:38:11.052 20.0 -1.0 -294.0 222.0 4.0
2018-11-09 20:38:11.316 14.0 -9.0 -282.0 224.0 0.0
2018-11-09 20:38:11.547 25.0 6.0 -291.0 229.0 3.0
2018-11-09 20:38:11.811 21.0 1.0 -289.0 243.0 7.0
2018-11-09 20:38:12.042 22.0 0.0 -291.0 267.0 19.0
2018-11-09 20:38:12.306 19.0 -3.0 -302.0 256.0 14.0
2018-11-09 20:38:12.570 9.0 -20.0 -313.0 263.0 27.0
2018-11-09 20:38:12.801 22.0 2.0 -307.0 263.0 39.0
2018-11-09 20:38:13.065 21.0 1.0 -305.0 265.0 37.0
2018-11-09 20:38:13.296 20.0 1.0 -296.0 266.0 32.0
2018-11-09 20:38:13.560 21.0 1.0 -298.0 262.0 30.0
2018-11-09 20:38:13.824 21.0 1.0 -303.0 257.0 35.0
2018-11-09 20:38:14.055 21.0 2.0 -293.0 259.0 41.0
2018-11-09 20:38:14.319 20.0 2.0 -303.0 267.0 37.0
2018-11-09 20:38:14.583 21.0 1.0 -308.0 262.0 32.0
2018-11-09 20:38:14.814 21.0 1.0 -310.0 260.0 38.0
2018-11-09 20:38:15.078 20.0 2.0 -309.0 265.0 35.0
[21645 rows x 10 columns]
#Create an elapsed_timedelta field:
#timedelta_values = (motion_df_dropped['Time']-motion_df_dropped['Time'][0])
#motion_df_dropped.insert(loc=1, column='TimeDelta', value=timedelta_values, drop=True)
motion_df_dropped['TimeDelta'] = (motion_df_dropped['Time']-motion_df_dropped['Time'][0])
#print(elapsed_timedelta)
#motion_df_dropped.head()
motion_df_dropped.head(10)
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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 = 5000motion_df_simple = label_data(footage_file)
motion_df_simple.head(10)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | 493.0 | 48.0 | 110.0 | 75.0 | -124.0 | -86.0 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | 513.0 | 89.0 | 62.0 | 34.0 | -36.0 | -92.0 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | 494.0 | 92.0 | 80.0 | 69.0 | -63.0 | -42.0 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | 421.0 | 205.0 | -104.0 | 192.0 | -92.0 | -37.0 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | 534.0 | 306.0 | -32.0 | -421.0 | -233.0 | -229.0 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | 455.0 | 149.0 | -102.0 | -355.0 | -376.0 | -397.0 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | 474.0 | 342.0 | -219.0 | -234.0 | -527.0 | -465.0 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | 363.0 | 323.0 | -131.0 | 60.0 | -662.0 | -305.0 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | -21.0 | 510.0 | -447.0 | 78.0 | -643.0 | -153.0 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | 35.0 | 283.0 | -132.0 | -114.0 | -430.0 | 132.0 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
#motion_df_complex = label_data('Footage3.txt', 'complex')
#motion_df_complex.head(10)#df1_complex = label_data('Footage.txt', 'complex')
#df2_complex = label_data('Footage2.txt', 'complex')
#df3_complex = label_data('Footage3.txt', 'complex')
#df4_complex = label_data('Footage4.txt', 'complex')
#df_concatenated = pd.concat([df1_complex, df2_complex, df3_complex, df4_complex])
#print("Shape of first dataframe:", df1_complex.shape)
#print("Shape of all combined dataframes:", df_concatenated.shape)
#print("Printing dataframe...")
##print(df1_complex.head(10))
#print(df_concatenated.head(10))##correct IMU data
##make a deep copy of motion_df_labelled
#df_converted = motion_df_complex.copy(deep = 'true')
df_converted = motion_df_simple.copy(deep = 'true')
##for rows in df_corrected
for row in range(0, df_converted.shape[0]):
#convert acceleromters (new: m/s^2)
df_converted.iloc[row, df_converted.columns.get_loc('IMU A1')] *= -0.019141 #forwards/backwards
df_converted.iloc[row, df_converted.columns.get_loc('IMU A2')] *= 0.019141 #upside down/right side up
df_converted.iloc[row, df_converted.columns.get_loc('IMU A3')] *= 0.019141 #sideways: negative = left, positive = right
#convert gyroscopes (new: deg/s)
df_converted.iloc[row, df_converted.columns.get_loc('IMU G1')] /= 8.2 #roll
df_converted.iloc[row, df_converted.columns.get_loc('IMU G2')] /= 8.2 #yaw
df_converted.iloc[row, df_converted.columns.get_loc('IMU G3')] /= 8.2 #pitch (flipping forwards/backwards)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | 493.0 | 48.0 | 110.0 | 75.0 | -124.0 | -86.0 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | 513.0 | 89.0 | 62.0 | 34.0 | -36.0 | -92.0 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | 494.0 | 92.0 | 80.0 | 69.0 | -63.0 | -42.0 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | 421.0 | 205.0 | -104.0 | 192.0 | -92.0 | -37.0 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | 534.0 | 306.0 | -32.0 | -421.0 | -233.0 | -229.0 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | 455.0 | 149.0 | -102.0 | -355.0 | -376.0 | -397.0 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | 474.0 | 342.0 | -219.0 | -234.0 | -527.0 | -465.0 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | 363.0 | 323.0 | -131.0 | 60.0 | -662.0 | -305.0 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | -21.0 | 510.0 | -447.0 | 78.0 | -643.0 | -153.0 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | 35.0 | 283.0 | -132.0 | -114.0 | -430.0 | 132.0 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
df_converted.head(10)
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| Time | IMU A1 | IMU A2 | IMU A3 | IMU G1 | IMU G2 | IMU G3 | IMU M1 | IMU M2 | IMU M3 | TimeDelta | PADDLING | FLOATING | WALKING | SURFING | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ride_id | UTC | |||||||||||||||
| 15692 | 2018-11-09 19:16:03.789 | 1.414743e+09 | -9.436513 | 0.918768 | 2.105510 | 9.146341 | -15.121951 | -10.487805 | -309.0 | 209.0 | 39.0 | 0.0 | 0 | 0 | 0 | 0 |
| 2018-11-09 19:16:04.053 | 1.414743e+09 | -9.819333 | 1.703549 | 1.186742 | 4.146341 | -4.390244 | -11.219512 | -320.0 | 194.0 | 38.0 | 252.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.284 | 1.414743e+09 | -9.455654 | 1.760972 | 1.531280 | 8.414634 | -7.682927 | -5.121951 | -329.0 | 189.0 | 49.0 | 501.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.548 | 1.414744e+09 | -8.058361 | 3.923905 | -1.990664 | 23.414634 | -11.219512 | -4.512195 | -330.0 | 180.0 | 64.0 | 753.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:04.812 | 1.414744e+09 | -10.221294 | 5.857146 | -0.612512 | -51.341463 | -28.414634 | -27.926829 | -325.0 | 161.0 | 97.0 | 1003.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.043 | 1.414744e+09 | -8.709155 | 2.852009 | -1.952382 | -43.292683 | -45.853659 | -48.414634 | -337.0 | 117.0 | 151.0 | 1253.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.307 | 1.414744e+09 | -9.072834 | 6.546222 | -4.191879 | -28.536585 | -64.268293 | -56.707317 | -311.0 | 25.0 | 217.0 | 1504.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.571 | 1.414745e+09 | -6.948183 | 6.182543 | -2.507471 | 7.317073 | -80.731707 | -37.195122 | -238.0 | -8.0 | 272.0 | 1755.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:05.802 | 1.414745e+09 | 0.401961 | 9.761910 | -8.556027 | 9.512195 | -78.414634 | -18.658537 | -159.0 | -21.0 | 321.0 | 2006.5 | 0 | 0 | 0 | 0 | |
| 2018-11-09 19:16:06.066 | 1.414745e+09 | -0.669935 | 5.416903 | -2.526612 | -13.902439 | -52.439024 | 16.097561 | -86.0 | -38.0 | 326.0 | 2258.5 | 0 | 0 | 0 | 0 |
## Drop data columns that we don't care about predicting/visualizing:
drop_columns = ["FLIP BOARD RIGHT SIDE UP", "NEW", "DONE, OUT OF WATER"]
for dc in drop_columns:
if dc in df_converted.columns:
df_converted = df_converted.drop(dc)
##df_converted = df_converted.drop(columns!=["SURFING, FLOATING, PADDLING INTO WAVES, PADDLING FOR A WAVE, PADDLING FOR POSITION, PADDLING"])import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = [50, 10]
#define a function that plots a column of dataf in relation to time. color coded to match labels in dataf
#requires that:
#dataf has a 'TimeDelta' column
#labels: walking, surfing, floating, paddling
def createPlot (dataf, column):
#create new data frame to be plotted
#Only consider columns after Velocity
dfPlot = pd.DataFrame(columns = ['TIME'] + list(dataf)[list(dataf).index('TimeDelta') + 1:], dtype = float)
#add timedelta column from dataf to dfPlot
dfPlot['TIME'] = dataf['TimeDelta']
#get the index of the column to be graphed
columnInd = dataf.columns.get_loc(column)
#for each row in dfPlot (number of IMU readings)
for row in range(0, dfPlot.shape[0]):
#for the indexes of the label columns in dfPlot
for col in range(1, dfPlot.shape[1]):
#if a label in the row is 1 in dataf
if dataf.iloc[row, dataf.columns.get_loc(dfPlot.columns[col])] == 1:
#add the sensors value to the corresponding column in dfPlot
dfPlot.iloc[row, dfPlot.columns.get_loc(dfPlot.columns[col])] = dataf.iloc[row, columnInd]
#dfPlot.iloc[row, dfPlot.columns.get]
#Set up colormap so that we don't see a repeat in color when graphing
#plt.gca().set_prop_cycle('color',plt.cm.plasma(np.linspace(0,1,dfPlot.shape[1])))
plt.gca().set_prop_cycle('color',plt.cm.tab20(np.linspace(0,1,dfPlot.shape[1])))
for col in range (1, dfPlot.shape[1]):
plt.plot(dfPlot['TIME'], dfPlot[list(dfPlot)[col]])
plt.gca().legend(loc = 'lower left')
plt.title(column)
plt.xlabel("Time")
plt.ylabel("IMU Data")
#file_name = column
#pdf_string = '.jpg'
#file_name += pdf_string
#plt.savefig(file_name)
plt.show()
return #For plotting, just comment out the "concatenation" lines.
#Need to clear kernel and then only run all above so that it plots on axes directly below, rather than on another plot
print("Creating Plots...")
createPlot(df_converted,'IMU A1')
createPlot(df_converted,'IMU A2')
createPlot(df_converted,'IMU A3')
#createPlot(df_converted,'IMU G1')
#createPlot(df_converted,'IMU G2')
#createPlot(df_converted,'IMU G3')
createPlot(df_converted,'IMU M1')
createPlot(df_converted,'IMU M2')
createPlot(df_converted,'IMU M3')
print("Done")Creating Plots...
Done
print(list(df_converted.columns))
drop_list = ['SYNC', 'WALKING IN WATER', 'PUSH-OFF', 'PADDLING INTO WAVES', 'SIT-UP', "TURNING TO SURFER'S LEFT", 'LAY-DOWN', 'PADDLING FOR A WAVE', 'POP-UP', 'SURFING', 'STEP-OFF', "TURNING TO SURFER'S RIGHT", 'SIT-BACK', 'OFF-BOARD', 'PADDLING', 'WIPE-OUT', 'PULL-BACK LEASH', 'PADDLING FOR POSITION', 'DISCARD', 'WALKING OUT OF WATER', 'PADDLING', 'WALKING']
for x in drop_list:
if x in df_converted.columns:
df_converted = df_converted.drop(columns=[x])
['Time', 'IMU A1', 'IMU A2', 'IMU A3', 'IMU G1', 'IMU G2', 'IMU G3', 'IMU M1', 'IMU M2', 'IMU M3', 'TimeDelta', 'FLOATING', 'WALKING']
print("Creating Plots...")
createPlot(df_converted,'IMU A1')
createPlot(df_converted,'IMU A2')
createPlot(df_converted,'IMU A3')Creating Plots...
#df_converted["IMU A2"]
df_converted["TimeDelta"]ride_id UTC
15692 2018-11-09 19:16:03.789 0.0
2018-11-09 19:16:04.053 252.5
2018-11-09 19:16:04.284 501.5
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2018-11-09 19:17:36.552 92201.5
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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
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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
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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
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2018-11-09 20:37:35.379 4862106.5
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2018-11-09 20:37:36.402 4863109.5
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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
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2018-11-09 20:37:40.428 4867108.5
2018-11-09 20:37:40.659 4867359.5
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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
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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
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2018-11-09 20:38:06.795 4893309.5
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2018-11-09 20:38:09.039 4895555.5
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2018-11-09 20:38:13.560 4900051.5
2018-11-09 20:38:13.824 4900301.5
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2018-11-09 20:38:14.319 4900803.5
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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
