stress_at_work_analysis/features/screen.py

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