55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from collections.abc import Collection
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import pandas as pd
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from config.models import Participant, Screen
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from setup import db_engine, session
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screen_status = {0: "off", 1: "on", 2: "locked", 3: "unlocked"}
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def get_screen_data(usernames: Collection) -> pd.DataFrame:
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"""
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Read the data from the screen 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_screen: pd.DataFrame
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A dataframe of screen data.
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"""
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query_screen = (
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session.query(Screen, Participant.username)
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.filter(Participant.id == Screen.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_screen = pd.read_sql(query_screen.statement, connection)
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return df_screen
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def identify_screen_sequence(df_screen: pd.DataFrame) -> pd.DataFrame:
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# TODO Implement a method that identifies "interesting" sequences of screen statuses.
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# The main one are:
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# - OFF -> ON -> unlocked (a true phone unlock)
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# - OFF -> ON -> OFF/locked (no unlocking, i.e. a screen status check)
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# Consider that screen data is sometimes unreliable as shown in expl_screen.ipynb:
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# "I have also seen
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# off -> on -> unlocked (with 2 - locked missing)
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# and
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# off -> locked -> on -> off -> locked (*again*)."
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# Either clean the data beforehand or deal with these inconsistencies in this function.
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pass
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def time_screen_sequence(df_screen: pd.DataFrame) -> pd.DataFrame:
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# TODO Use the results of indentify_screen_sequence to calculate time statistics related to transitions.
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# For example, from the two main sequences outlined above, the time of "real" phone usage can be calculated,
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# i.e. how long the screen was unlocked.
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# Another example might be the average time between screen unlocks and/or screen status checks.
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pass
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