110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
import datetime
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from collections.abc import Collection
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import numpy as np
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import pandas as pd
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from pytz import timezone
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from config.models import ESM, Participant
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from setup import db_engine, session
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TZ_LJ = timezone("Europe/Ljubljana")
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def get_esm_data(usernames: Collection) -> pd.DataFrame:
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"""
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Read the data from the esm table and return it in a dataframe.
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Parameters
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----------
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usernames: Collection
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A list of usernames to put into the WHERE condition.
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Returns
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-------
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df_esm: pd.DataFrame
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A dataframe of esm data.
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"""
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query_esm = (
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session.query(ESM, Participant.username)
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.filter(Participant.id == ESM.participant_id)
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.filter(Participant.username.in_(usernames))
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)
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with db_engine.connect() as connection:
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df_esm = pd.read_sql(query_esm.statement, connection)
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return df_esm
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def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
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"""
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Convert timestamps into human-readable datetimes and expand the JSON column into several Pandas DF columns.
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Parameters
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----------
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df_esm: pd.DataFrame
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A dataframe of esm data.
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Returns
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-------
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df_esm_preprocessed: pd.DataFrame
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A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
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"""
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df_esm["datetime_lj"] = df_esm["double_esm_user_answer_timestamp"].apply(
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lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
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)
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df_esm_json = pd.json_normalize(df_esm["esm_json"]).drop(
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columns=["esm_trigger"]
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) # The esm_trigger column is already present in the main df.
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return df_esm.join(df_esm_json)
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def classify_sessions_adherence(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
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"""
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For each distinct EMA session, determine how the participant responded to it.
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Possible outcomes are: esm_unanswered
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This is done in several steps.
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#TODO Finish the documentation.
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Parameters
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----------
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df_esm_preprocessed: pd.DataFrame
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A preprocessed dataframe of esm data, which must include the session ID (esm_session).
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Returns
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-------
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some dataframe
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"""
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sessions_grouped = df_esm_preprocessed.groupby(
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["participant_id", "device_id", "esm_session"]
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)
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df_session_counts = pd.DataFrame(sessions_grouped.count()["id"]).rename(
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columns={"id": "esm_session_count"}
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)
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df_session_counts["session_response"] = np.NaN
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esm_not_answered = sessions_grouped.apply(lambda x: (x.esm_status != 2).any())
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df_session_counts.loc[esm_not_answered, "session_response"] = "esm_unanswered"
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non_session = sessions_grouped.apply(
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lambda x: (
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(x.esm_user_answer == "DayFinished3421")
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| (x.esm_user_answer == "DayOff3421")
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).any()
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)
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df_session_counts.loc[non_session, "session_response"] = "day_finished"
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finished_sessions = sessions_grouped.apply(
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lambda x: (x.esm_trigger.str.endswith("_last")).any()
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)
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df_session_counts.loc[finished_sessions, "session_response"] = "esm_finished"
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# TODO Look at evening-evening_last sequence, if everything is caught with finished sessions
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# TODO What can be done about morning EMA, perhaps morning-morning_first (sic!) sequence?
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# TODO What can be done about workday EMA.
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return sessions_grouped.count()
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