import pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from dataFrameUtilities import addRollingMeanColumns, selectColumns, selectTime
from sklearn.preprocessing import MinMaxScaler
input = open("data.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, "2016-01-15", "2019-03-06")
pain = selectColumns(timeSelected, ["forearmElbowPain"])
pain = addRollingMeanColumns(pain, ["forearmElbowPain"], 21)
timeSelected[
"swimAndSurf"] = timeSelected["swimming"] + timeSelected["surfing"]
timeSelected["climbs"] = timeSelected["climbing"] + timeSelected["viaFerrata"]
env = addRollingMeanColumns(timeSelected,
["whatPulseT", "swimAndSurf", "climbs"], 21)
envOrdi = env[["whatPulseT"]]
envSport = env[["swimAndSurf", "climbs"]]
envSportMean = env[[
"whatPulseTRollingMean", "swimAndSurfRollingMean", "climbsRollingMean"
]]
# Creating the trackerMeanSteps variable in the dataframe and puts harmonized steps value inside
data["trackerMeanSteps"] = data["steps"]
data = adjustVarAndPlaceInData(period1, data, "googlefitSteps",
"basisPeakSteps", "2015-12-26", "2016-06-21")
data = addDataToTrackerMeanSteps(period2, data, "basisPeakSteps", "2016-06-22",
"2016-09-01")
data = adjustVarAndPlaceInData(period3, data, "basisPeakSteps", "steps",
"2016-09-02", "2017-01-01")
data = addDataToTrackerMeanSteps(period4, data, "steps", "2017-01-02",
"2017-10-16")
data = adjustVarAndPlaceInData(period5, data, "movesSteps", "steps",
"2017-10-17", "2018-07-02")
data = adjustVarAndPlaceInData(period6, data, "movesSteps", "steps",
"2018-07-03", "2018-07-30")
data = adjustVarAndPlaceInData(period7, data, "googlefitSteps", "steps",
"2018-07-31", "2020-02-01")
# Plotting results
steps = selectColumns(data, [
"steps", "movesSteps", "googlefitSteps", "basisPeakSteps",
"trackerMeanSteps"
])
fig, axes = plt.subplots(nrows=2, ncols=1)
steps.plot(ax=axes[0])
selectColumns(data, ["trackerMeanSteps"]).plot(ax=axes[1])
leg = plt.legend(loc="best")
leg.set_draggable(True)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from dataFrameUtilities import addRollingMeanColumns, selectColumns, selectTime
from sklearn.preprocessing import MinMaxScaler
input = open("../data/preprocessed/preprocessedDataParticipant1.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, "2016-03-01", "2019-03-06")
timeSelected["foreheadAndEyesPain"] = timeSelected[
"foreheadAndEyesPain"] + timeSelected["aroundEyesPain"]
pain = selectColumns(timeSelected, ["foreheadAndEyesPain"])
pain = addRollingMeanColumns(pain, ["foreheadAndEyesPain"], 21)
env = addRollingMeanColumns(timeSelected,
["manicTimeT", "eyeRelatedActivities"], 21)
envRollingMean = selectColumns(
env, ["manicTimeTRollingMean", "eyeRelatedActivitiesRollingMean"])
envBrut = selectColumns(env, ["manicTimeT", "eyeRelatedActivities"])
fig, axes = plt.subplots(nrows=3, ncols=1)
pain.plot(ax=axes[0])
envBrut.plot(ax=axes[1])
envRollingMean.plot(ax=axes[2])
leg = plt.legend(loc="best")
# This scripts assumes that the dataframe has been created and saved in data.txt
import pickle
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from dataFrameUtilities import selectTime, selectColumns, addRollingMeanColumns
input = open('data.txt', 'rb')
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, '2016-01-15', '2019-03-06')
pain = selectColumns(timeSelected, ['forearmElbowPain'])
pain = addRollingMeanColumns(pain, ['forearmElbowPain'], 21)
timeSelected[
'swimAndSurf'] = timeSelected['swimming'] + timeSelected['surfing']
timeSelected['climbs'] = timeSelected['climbing'] + timeSelected['viaFerrata']
env = addRollingMeanColumns(timeSelected,
['whatPulseT', 'swimAndSurf', 'climbs'], 21)
envOrdi = env[['whatPulseT']]
envSport = env[['swimAndSurf', 'climbs']]
envSportMean = env[[
'whatPulseTRollingMean', 'swimAndSurfRollingMean', 'climbsRollingMean'
]]
# This scripts assumes that the dataframe has been created and saved in data.txt
import pickle
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from dataFrameUtilities import selectTime, selectColumns, addRollingMeanColumns
input = open('data.txt', 'rb')
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, '2016-01-15', '2019-03-06')
pain = selectColumns(timeSelected, ['handsAndFingerPain'])
pain = addRollingMeanColumns(pain, ['handsAndFingerPain'], 21)
timeSelected[
'swimAndSurf'] = timeSelected['swimming'] + timeSelected['surfing']
timeSelected['climbs'] = timeSelected['climbing'] + timeSelected['viaFerrata']
env = addRollingMeanColumns(timeSelected,
['whatPulseT', 'swimAndSurf', 'climbs'], 21)
envOrdi = env[['whatPulseT']]
envSport = env[['swimAndSurf', 'climbs']]
envSportMean = env[[
'whatPulseTRollingMean', 'swimAndSurfRollingMean', 'climbsRollingMean'
]]
# This scripts assumes that the dataframe has been created and saved in data.txt
import pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from dataFrameUtilities import addRollingMeanColumns, selectColumns, selectTime
input = open("data.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, "2016-09-01", "2019-10-20")
pain = selectColumns(timeSelected, ["kneePain"])
pain = addRollingMeanColumns(pain, ["kneePain"], 21)
env = addRollingMeanColumns(timeSelected, ["steps", "denivelation"], 21)
envRollingMean = selectColumns(env,
["stepsRollingMean", "denivelationRollingMean"])
envBrut = selectColumns(env, ["steps", "denivelation"])
fig, axes = plt.subplots(nrows=3, ncols=1)
pain.plot(ax=axes[0])
envBrut.plot(ax=axes[1])
envRollingMean.plot(ax=axes[2])
plt.legend(loc="best")
plt.show()
import pickle
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from dataFrameUtilities import selectTime, selectColumns, addRollingMeanColumns
input = open('data.txt', 'rb')
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, '2016-03-01', '2019-03-06')
timeSelected['foreheadAndEyesPain'] = timeSelected[
'foreheadAndEyesPain'] + timeSelected['aroundEyesPain']
pain = selectColumns(timeSelected, ['foreheadAndEyesPain'])
pain = addRollingMeanColumns(pain, ['foreheadAndEyesPain'], 21)
env = addRollingMeanColumns(timeSelected,
['manicTimeT', 'eyeRelatedActivities'], 21)
envRollingMean = selectColumns(
env, ['manicTimeTRollingMean', 'eyeRelatedActivitiesRollingMean'])
envBrut = selectColumns(env, ['manicTimeT', 'eyeRelatedActivities'])
fig, axes = plt.subplots(nrows=3, ncols=1)
pain.plot(ax=axes[0])
envBrut.plot(ax=axes[1])
envRollingMean.plot(ax=axes[2])
plt.legend(loc='best')
input = open("data.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, "2016-09-01", "2019-10-20")
# Removing "steps" caused by scooter riding
timeSelected["steps"] = timeSelected["steps"] - 37 * timeSelected["scooterRiding"]
timeSelected["steps"][timeSelected["steps"] < 0] = 0
# Getting knee pain information
kneePain = selectColumns(timeSelected, ["kneePain"])
thres = kneePain.copy()
thres[:] = 3.3
# Calculating knee stress over time
env = addInsultIntensityColumns(timeSelected, ["steps", "kneePain"], 21, 30)
envRollingMean = selectColumns(env, ["stepsInsultIntensity"])
envMaxInsultDiff = selectColumns(env, ["stepsMaxInsultDiff"])
kneePainRollingMean = selectColumns(env, ["kneePainInsultIntensity"])
kneePainRollingMean = kneePainRollingMean.replace(0, 0.4)
scaler = MinMaxScaler()
kneePainRollingMeanArray = scaler.fit_transform(kneePainRollingMean)
import pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from dataFrameUtilities import addRollingMeanColumns, selectColumns, selectTime
from sklearn.preprocessing import MinMaxScaler
input = open("data.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = selectTime(data, "2016-01-15", "2019-03-06")
pain = selectColumns(timeSelected, ["handsAndFingerPain"])
pain = addRollingMeanColumns(pain, ["handsAndFingerPain"], 21)
timeSelected["swimAndSurf"] = timeSelected["swimming"] + timeSelected["surfing"]
timeSelected["climbs"] = timeSelected["climbing"] + timeSelected["viaFerrata"]
env = addRollingMeanColumns(timeSelected, ["whatPulseT", "swimAndSurf", "climbs"], 21)
envOrdi = env[["whatPulseT"]]
envSport = env[["swimAndSurf", "climbs"]]
envSportMean = env[
["whatPulseTRollingMean", "swimAndSurfRollingMean", "climbsRollingMean"]
]
fig, axes = plt.subplots(nrows=4, ncols=1)
