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aalto.py
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aalto.py
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# Aalto-specific niimpy functions
from .database import ALL
from . import survey
import pandas as pd
PHQ9_ANSWER_MAP = {
"Ei lainkaan": 0,
"Useina päivinä": 1,
"Useina päivinä": 1,
"Useammin kuin puolet ajasta": 2,
"Useammin kuin puolet ajasta": 2,
"Lähes joka päivä": 3,
"Lähes joka päivä": 3,
}
def phq9_raw(db, user=ALL):
"""Given a database, extract all the relevant PHQ9 from pilot study.
This extracts PHQ9 survey responses from a database and normalizes
them properly.
"""
tables = db.tables()
scores = [ ]
for survey_table in ['HyksSurveyAllAnswers', 'HyksPHQ9Answers', # pilot
'MMMBaseline', 'MMMPostActive',
]:
if survey_table in tables:
initial = db.raw(survey_table, user=user)
initial = initial[initial['id'].str.startswith('PHQ9_')]
initial['source'] = survey_table
scores.append(initial[["user", "id", "answer", "source"]])
del initial
if 'AwareHyksConverter' in tables:
apphyks = db.raw('AwareHyksConverter', user=user)
apphyks = apphyks[apphyks['id'].str.startswith('PHQ9_')]
apphyks['answer'] = apphyks['answer'].replace(PHQ9_ANSWER_MAP)
apphyks['source'] = 'AwarePilot'
scores.append(apphyks[["user", "id", "answer", "source"]])
del apphyks
scores = pd.concat(scores)
scores['answer'] = pd.to_numeric(scores['answer'])
return scores
def phq9_scores(db, user=ALL):
raw = phq9_raw(db, user=user)
scores = survey.sum_survey_scores(raw, "PHQ9")
return scores