114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
|
import json
|
||
|
import numpy as np
|
||
|
import pandas as pd
|
||
|
|
||
|
|
||
|
ESM_TYPE = {
|
||
|
"text": 1,
|
||
|
"radio": 2,
|
||
|
"checkbox": 3,
|
||
|
"likert": 4,
|
||
|
"quick_answers": 5,
|
||
|
"scale": 6,
|
||
|
"datetime": 7,
|
||
|
"pam": 8,
|
||
|
"number": 9,
|
||
|
"web": 10,
|
||
|
"date": 11,
|
||
|
}
|
||
|
|
||
|
ESM_STATUS_ANSWERED = 2
|
||
|
|
||
|
GROUP_SESSIONS_BY = ["participant_id", "device_id", "esm_session"]
|
||
|
|
||
|
SESSION_STATUS_UNANSWERED = "ema_unanswered"
|
||
|
SESSION_STATUS_DAY_FINISHED = "day_finished"
|
||
|
SESSION_STATUS_COMPLETE = "ema_completed"
|
||
|
|
||
|
ANSWER_DAY_FINISHED = "DayFinished3421"
|
||
|
ANSWER_DAY_OFF = "DayOff3421"
|
||
|
ANSWER_SET_EVENING = "DayFinishedSetEvening"
|
||
|
|
||
|
MAX_MORNING_LENGTH = 3
|
||
|
# When the participants was not yet at work at the time of the first (morning) EMA,
|
||
|
# only three items were answered.
|
||
|
# Two sleep related items and one indicating NOT starting work yet.
|
||
|
# Daytime EMAs are all longer, in fact they always consist of at least 6 items.
|
||
|
|
||
|
|
||
|
def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
|
||
|
"""
|
||
|
Convert timestamps into human-readable datetimes and dates
|
||
|
and expand the JSON column into several Pandas DF columns.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
df_esm: pd.DataFrame
|
||
|
A dataframe of esm data.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
df_esm_preprocessed: pd.DataFrame
|
||
|
A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
|
||
|
"""
|
||
|
df_esm_json = df_esm["esm_json"].apply(json.loads)
|
||
|
df_esm_json = pd.json_normalize(df_esm_json).drop(
|
||
|
columns=["esm_trigger"]
|
||
|
) # The esm_trigger column is already present in the main df.
|
||
|
return df_esm.join(df_esm_json)
|
||
|
|
||
|
|
||
|
def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
|
||
|
"""
|
||
|
This function eliminates invalid ESM responses.
|
||
|
It removes unanswered ESMs and those that indicate end of work and similar.
|
||
|
It also extracts a numeric answer from strings such as "4 - I strongly agree".
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
df_esm_preprocessed: pd.DataFrame
|
||
|
A preprocessed dataframe of esm data.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
df_esm_clean: pd.DataFrame
|
||
|
A subset of the original dataframe.
|
||
|
|
||
|
"""
|
||
|
df_esm_clean = df_esm_preprocessed[
|
||
|
df_esm_preprocessed["esm_status"] == ESM_STATUS_ANSWERED
|
||
|
]
|
||
|
df_esm_clean = df_esm_clean[
|
||
|
~df_esm_clean["esm_user_answer"].isin(
|
||
|
[ANSWER_DAY_FINISHED, ANSWER_DAY_OFF, ANSWER_SET_EVENING]
|
||
|
)
|
||
|
]
|
||
|
df_esm_clean["esm_user_answer_numeric"] = np.nan
|
||
|
esm_type_numeric = [
|
||
|
ESM_TYPE.get("radio"),
|
||
|
ESM_TYPE.get("scale"),
|
||
|
ESM_TYPE.get("number"),
|
||
|
]
|
||
|
df_esm_clean.loc[
|
||
|
df_esm_clean["esm_type"].isin(esm_type_numeric)
|
||
|
] = df_esm_clean.loc[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
|
||
|
esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
|
||
|
int
|
||
|
)
|
||
|
)
|
||
|
return df_esm_clean
|
||
|
|
||
|
|
||
|
df_esm = pd.read_csv(snakemake.input[0])
|
||
|
df_esm_preprocessed = preprocess_esm(df_esm)
|
||
|
#TODO Enable getting the right questionnaire here.
|
||
|
df_esm_PANAS = df_esm_preprocessed[
|
||
|
(df_esm_preprocessed["questionnaire_id"] == 8)
|
||
|
| (df_esm_preprocessed["questionnaire_id"] == 9)
|
||
|
]
|
||
|
df_esm_clean = clean_up_esm(df_esm_PANAS)
|
||
|
|
||
|
df_esm_clean.to_csv(snakemake.output[0])
|
||
|
|
||
|
|