380 lines
13 KiB
Python
380 lines
13 KiB
Python
import numpy as np
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import pandas as pd
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import features.esm
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SAM_ORIGINAL_MAX = 5
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SAM_ORIGINAL_MIN = 1
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QUESTIONNAIRE_ID_SAM = {
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"event_stress": 87,
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"event_threat": 88,
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"event_challenge": 89,
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"event_time": 90,
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"event_duration": 91,
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"event_work_related": 92,
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"period_stress": 93,
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}
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QUESTIONNAIRE_ID_SAM_LOW = min(QUESTIONNAIRE_ID_SAM.values())
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QUESTIONNAIRE_ID_SAM_HIGH = max(QUESTIONNAIRE_ID_SAM.values())
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GROUP_QUESTIONNAIRES_BY = [
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"participant_id",
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"device_id",
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"esm_session",
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]
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# Each questionnaire occurs only once within each esm_session on the same device
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# within the same participant.
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DICT_SAM_QUESTION_IDS = {
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87: (
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"Was there a particular event that created tension in you?",
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"Was er een bepaalde gebeurtenis die spanning veroorzaakte?",
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"Je prišlo do kakega dogodka, ki je v vas ustvaril napetost?",
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),
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88: (
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"Did this event make you feel anxious?",
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"Voelde je je angstig door deze gebeurtenis?",
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"Ste se zaradi tega dogodka počutili tesnob",
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),
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89: (
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"Will the outcome of this event be negative?",
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"Zal de uitkomst van deze gebeurtenis negatief zijn?",
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"Bo izid tega dogodka negativen?",
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),
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90: (
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"How threatening was this event?",
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"Hoe bedreigend was deze gebeurtenis?",
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"Kako grozeč je bil ta dogodek?",
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),
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91: (
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"Is this going to have a negative impact on you?",
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"Zal dit een negatieve impact op je hebben?",
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"Ali bo to negativno vplivalo na vas?",
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),
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92: (
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"Is this going to have a positive impact on you?",
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"Zal dit een positief effect op je hebben?",
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"Ali bo to pozitivno vplivalo na vas?",
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),
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93: (
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"How eager are you to tackle this event?",
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"Hoe graag wil je deze gebeurtenis aanpakken?",
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"Kako zagnani ste bili",
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),
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94: (
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"To what extent can you become a stronger person because of this event?",
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"In welke mate kan je een sterkere persoon worden door deze gebeurtenis?",
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"V kolikšni meri lahko zaradi tega dogodka postanete močnejša oseba?",
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),
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95: (
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"To what extent are you excited thinking about the outcome of this event?",
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"In welke mate ben je enthousiast bij de gedachte",
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"V kolikšni meri vas misel na izid tega dogodka navdušuje?",
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),
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96: (
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"At what time did this event occur?",
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"Hoe laat vond deze gebeurtenis plaats?",
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"Kdaj se je ta dogodek zgodil?",
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),
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97: (
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"How long did this event last?",
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"Hoe lang duurde deze gebeurtenis?",
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"Kako dolgo je trajal ta dogodek?",
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),
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98: (
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"Was/is this event work-related?",
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"Was/is deze gebeurtenis werkgerelateerd?",
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"Je (bil) ta dogodek povezan s službo?",
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"Je bil ali je ta dogodek povezan s službo?",
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),
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99: (
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"Did this overall period create tension in you?",
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"Heeft deze globale periode spanning veroorzaakt?",
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"Je to obdobje kot celota v vas ustvarilo napetost?",
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"Je to celo obdobje v vas ustvarilo napetost?",
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),
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100: (
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"To what extent do you perceive this overall period as stressful?",
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"In welke mate ervaar je deze globale periode als stressvol?",
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"V kolikšni meri ste to obdobje dojemali kot stresno?",
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"V kolikšni meri ste celo to obdobje dojemali kot stresno?",
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),
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}
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def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
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"""
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Extract information about stressful events.
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Participants were asked: "Was there a particular event that created tension in you?"
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Then a subset of questions related to this event followed.
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This function goes through the follow-up questions one by one
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and preprocesses them, so that it adds new columns to the dataframe.
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Parameters
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----------
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df_esm: pd.DataFrame
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A raw dataframe of all ESM data.
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Returns
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-------
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df_esm_events: pd.DataFrame
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A cleaned up df of Stress Appraisal Measure items with additional columns.
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"""
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# 0. Select only questions from Stress Appraisal Measure.
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df_esm_preprocessed = features.esm.preprocess_esm(df_esm)
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df_esm_sam = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_ID_SAM_LOW)
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& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_ID_SAM_HIGH)
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]
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df_esm_sam_clean = features.esm.clean_up_esm(df_esm_sam)
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# 1.
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df_esm_event_threat_challenge_mean_wide = calculate_threat_challenge_means(
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df_esm_sam_clean
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)
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# 2.
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df_esm_event_stress = detect_stressful_event(df_esm_sam_clean)
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# Join to the previously calculated features related to the events.
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df_esm_events = df_esm_event_threat_challenge_mean_wide.join(
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df_esm_event_stress[
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GROUP_QUESTIONNAIRES_BY + ["event_present", "event_stressfulness"]
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].set_index(GROUP_QUESTIONNAIRES_BY)
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)
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# 3.
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df_esm_event_work_related = detect_event_work_related(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_work_related[
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GROUP_QUESTIONNAIRES_BY + ["event_work_related"]
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].set_index(GROUP_QUESTIONNAIRES_BY)
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)
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# 4.
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df_esm_event_time = convert_event_time(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_time[GROUP_QUESTIONNAIRES_BY + ["event_time"]].set_index(
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GROUP_QUESTIONNAIRES_BY
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)
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)
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# 5.
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df_esm_event_duration = extract_event_duration(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_duration[
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GROUP_QUESTIONNAIRES_BY + ["event_duration", "event_duration_info"]
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].set_index(GROUP_QUESTIONNAIRES_BY)
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)
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return df_esm_events
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def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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This function calculates challenge and threat
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(two Stress Appraisal Measure subscales) means,
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for each ESM session (within participants and devices).
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It creates a grouped dataframe with means in two columns.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_threat_challenge_mean_wide: pd.DataFrame
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A dataframe of unique ESM sessions (by participants and devices)
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with threat and challenge means.
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"""
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# Select only threat and challenge assessments for events
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df_esm_event_threat_challenge = df_esm_sam_clean[
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(
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_threat")
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)
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_challenge")
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)
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]
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# Calculate mean of threat and challenge subscales for each ESM session.
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df_esm_event_threat_challenge_mean_wide = pd.pivot_table(
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df_esm_event_threat_challenge,
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index=["participant_id", "device_id", "esm_session"],
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columns=["questionnaire_id"],
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values=["esm_user_answer_numeric"],
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aggfunc="mean",
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)
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# Drop unnecessary column values.
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df_esm_event_threat_challenge_mean_wide.columns = (
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df_esm_event_threat_challenge_mean_wide.columns.get_level_values(1)
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)
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df_esm_event_threat_challenge_mean_wide.columns.name = None
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df_esm_event_threat_challenge_mean_wide.rename(
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columns={
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QUESTIONNAIRE_ID_SAM.get("event_threat"): "threat_mean",
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QUESTIONNAIRE_ID_SAM.get("event_challenge"): "challenge_mean",
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},
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inplace=True,
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)
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return df_esm_event_threat_challenge_mean_wide
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def detect_stressful_event(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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Participants were asked: "Was there a particular event that created tension in you?"
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The following options were available:
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0 - No,
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1 - Yes, slightly,
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2 - Yes, moderately,
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3 - Yes, considerably,
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4 - Yes, extremely.
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This function indicates whether there was a stressful event (True/False)
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and how stressful it was on a scale of 1 to 4.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_stress: pd.DataFrame
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The same dataframe with two new columns:
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- event_present, indicating whether there was a stressful event at all,
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- event_stressfulness, a numeric answer (1-4) to the single item question.
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"""
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df_esm_event_stress = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_stress")
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]
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df_esm_event_stress = df_esm_event_stress.assign(
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event_present=lambda x: x.esm_user_answer_numeric > 0,
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event_stressfulness=lambda x: x.esm_user_answer_numeric,
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)
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return df_esm_event_stress
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def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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This function simply adds a column indicating the answer to the question:
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"Was/is this event work-related?"
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_stress: pd.DataFrame
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The same dataframe with a new column event_work_related (True/False).
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"""
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df_esm_event_stress = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_work_related")
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]
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df_esm_event_stress = df_esm_event_stress.assign(
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event_work_related=lambda x: x.esm_user_answer_numeric > 0
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)
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return df_esm_event_stress
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def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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This function only serves to convert the string datetime answer
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into a real datetime type.
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Errors during this conversion are coerced, meaning that non-datetime answers
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are assigned Not a Time (NaT).
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NOTE: Since the only available non-datetime answer to this question was
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"0 - I do not remember", the NaTs can be interpreted to mean this.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_time: pd.DataFrame
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The same dataframe with a new column event_time of datetime type.
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"""
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df_esm_event_time = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_time")
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].assign(
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event_time=lambda x: pd.to_datetime(
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x.esm_user_answer,
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errors="coerce",
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format="%Y-%m-%d %H:%M:%S %z",
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exact=True,
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)
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)
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# Example answer: 2020-09-29 00:05:00 +0200
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return df_esm_event_time
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def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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If participants indicated a stressful events, they were asked:
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"How long did this event last? (Answer in hours and minutes)"
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This function extracts this duration time and saves additional answers:
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0 - I do not remember,
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1 - It is still going on.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_duration: pd.DataFrame
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The same dataframe with two new columns:
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- event_duration, a time part of a datetime,
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- event_duration_info, giving other options to this question:
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0 - I do not remember,
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1 - It is still going on
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"""
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df_esm_event_duration = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_duration")
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].assign(
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event_duration=lambda x: pd.to_datetime(
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x.esm_user_answer.str.slice(start=0, stop=-6),
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errors="coerce",
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format="%Y-%m-%d %H:%M:%S",
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).dt.time
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)
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# Example answer: 2020-09-29 00:05:00 +0200
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# TODO Explore the values recorded in event_duration and possibly fix mistakes.
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# For example, participants reported setting 23:50:00 instead of 00:50:00.
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# For the events that no duration was found (i.e. event_duration = NaT),
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# we can determine whether:
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# - this event is still going on ("1 - It is still going on")
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# - the participant couldn't remember it's duration ("0 - I do not remember")
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# Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm
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# but only the numeric types of questions and answers.
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# Since this was of "datetime" type, convert these specific answers here again.
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df_esm_event_duration["event_duration_info"] = np.nan
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df_esm_event_duration[
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df_esm_event_duration.event_duration.isna()
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] = df_esm_event_duration[df_esm_event_duration.event_duration.isna()].assign(
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event_duration_info=lambda x: x.esm_user_answer.str.slice(stop=1).astype(int)
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)
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return df_esm_event_duration
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# TODO: How many questions about the stressfulness of the period were asked
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# and how does this relate to events?
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