"kneePain", "foreheadAndEyesPain", "handsAndFingerPain", "aroundEyesPain",
"shoulderNeckPain", "sick_tired", "painInOtherRegion"
]].max(axis=1)
# Filling the "generalmood" column for time before Participant1 started recording it
data.loc[data.index < "2016-11-07", "generalmood"] = np.mean(
data.loc[data.index >= "2016-11-07"]["generalmood"].tolist())
# Adjusting WhatPulse for missing data (0 values)
period1 = ("2015-12-26", "2020-12-29") #("2015-12-26", "2020-02-01")
data["whatPulseT_corrected"] = data["whatPulseT"]
[data, reg] = check_if_zero_then_adjust_var_and_place_in_data(
period1, data, "manicTimeDelta", "whatPulseT", "whatPulseT_corrected")
# Selecting the time interval to look at the data
data = subset_period(data, "2016-01-05", "2020-12-29")
# Plotting results
fig, axes = plt.subplots(nrows=6, ncols=1)
# Number of clicks made on computer (both clicks and key presses)
scaler = MinMaxScaler()
column_list = ["whatPulseT", "whatPulseT_corrected"]
data[column_list] = scaler.fit_transform(data[column_list])
data[column_list].plot(ax=axes[0])
axes[0].legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Physical activities related to forearm and Elbow pain
scaler = MinMaxScaler()
column_list = ["surfing", "climbing", "viaFerrata", "swimming"]
data[column_list] = scaler.fit_transform(data[column_list])
data[column_list].plot(ax=axes[1])
axes[1].legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Transforming pain scale
data["kneePain"] = transformPain(data["kneePain"])
data["handsAndFingerPain"] = transformPain(data["handsAndFingerPain"])
data["foreheadAndEyesPain"] = transformPain(data["foreheadAndEyesPain"])
data["forearmElbowPain"] = transformPain(data["forearmElbowPain"])
data["aroundEyesPain"] = transformPain(data["aroundEyesPain"])
data["shoulderNeckPain"] = transformPain(data["shoulderNeckPain"])
data["sick_tired"] = transformPain(data["sick_tired"])
data["painInOtherRegion"] = transformPain(data["painInOtherRegion"])
data["maxPainOtherThanKnee"] = data[[
"handsAndFingerPain", "foreheadAndEyesPain", "forearmElbowPain",
"aroundEyesPain", "shoulderNeckPain", "sick_tired", "painInOtherRegion"
]].max(axis=1)
# Selecting the time interval to look at the data
data = subset_period(data, "2016-01-05", "2022-03-26")
# Plotting results
fig, axes = plt.subplots(nrows=6, ncols=1)
# Steps
scaler = MinMaxScaler()
column_list = [
"fitbitDistance", "movesSteps", "googlefitSteps", "basisPeakSteps",
"tracker_mean_distance"
]
data[column_list] = scaler.fit_transform(data[column_list])
data[column_list].plot(ax=axes[0])
axes[0].legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Cumulated elevation gain
scaler = MinMaxScaler()
column_list = [
import sys
sys.path.insert(1, '../src/MyAIGuide/utilities')
import pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from dataFrameUtilities import insert_rolling_mean_columns, subset_period
input = open("../data/preprocessed/preprocessedDataParticipant1.txt", "rb")
data = pickle.load(input)
input.close()
timeSelected = subset_period(data, "2016-09-01", "2019-10-20")
# pain = selectColumns(timeSelected, ["kneePain"])
pain = insert_rolling_mean_columns(timeSelected, ["kneePain"], 21)
env = insert_rolling_mean_columns(timeSelected, ["steps", "denivelation"], 21)
# envRollingMean = selectColumns(env, ["stepsRollingMean", "denivelationRollingMean"])
# envBrut = selectColumns(env, ["steps", "denivelation"])
fig, axes = plt.subplots(nrows=3, ncols=1)
pain["kneePain"].plot(ax=axes[0])
env[["steps", "denivelation"]].plot(ax=axes[1])
# envRollingMean.plot(ax=axes[2])
leg = plt.legend(loc="best")