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)