Specify format directly as infer_datetime_format was deprecated.
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9417a1b9f1
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@ -20,7 +20,8 @@ GROUP_QUESTIONNAIRES_BY = [
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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 within the same participant.
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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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def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
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@ -78,7 +79,8 @@ def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
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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 (two Stress Appraisal Measure subscales) means,
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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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@ -90,7 +92,8 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
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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) with threat and challenge means.
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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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@ -112,8 +115,8 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
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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 = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(
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1
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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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@ -189,10 +192,12 @@ def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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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 into a real datetime type.
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Errors during this conversion are coerced, meaning that non-datetime answers are assigned Not a Time (NaT).
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NOTE: Since the only available non-datetime answer to this question was "0 - I do not remember",
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the NaTs can be interpreted to mean this.
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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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@ -208,9 +213,10 @@ def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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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, errors="coerce", infer_datetime_format=True, exact=True
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x.esm_user_answer, errors="coerce", format="Y-m-d H:M:S %z", 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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@ -241,9 +247,12 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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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), errors="coerce"
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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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@ -251,7 +260,7 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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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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# 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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@ -264,4 +273,5 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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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 and how does this relate to events?
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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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