124 lines
2.8 KiB
Python
124 lines
2.8 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.11.2
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %%
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import os
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import sys
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import seaborn as sns
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import participants.query_db
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from features.esm import *
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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df_esm_inactive = get_esm_data(participants_inactive_usernames)
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# %%
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df_esm_preprocessed = preprocess_esm(df_esm_inactive)
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# %% [markdown]
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# # PANAS
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# %%
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df_esm_PANAS = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] == 8)
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| (df_esm_preprocessed["questionnaire_id"] == 9)
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]
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df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
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# %% [markdown]
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# Group by participants, date, and subscale and calculate daily means.
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# %%
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df_esm_PANAS_daily_means = (
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df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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# %% [markdown]
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# Next, calculate mean, median, and standard deviation across all days for each participant.
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# %%
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df_esm_PANAS_summary_participant = (
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df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
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.agg(["mean", "median", "std"])
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.reset_index(col_level=1)
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)
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df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
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1
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)
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df_esm_PANAS_summary_participant[
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"PANAS_subscale"
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] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
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{8.0: "PA", 9.0: "NA"}
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant,
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x="median",
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hue="PANAS_subscale",
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binwidth=0.2,
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05
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)
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# %%
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df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
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# %% [markdown]
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# # Stress appraisal measure
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# %%
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df_esm_SAM = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] >= 87)
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& (df_esm_preprocessed["questionnaire_id"] <= 93)
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]
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# %%
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clean_up_esm(df_esm_SAM)[["esm_user_answer", "esm_user_answer_numeric"]].head(9)
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# %%
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df_esm_PANAS_clean[["esm_user_answer", "esm_user_answer_numeric"]].head(n=10)
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# %%
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df_esm_SAM[
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[
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"esm_instructions",
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"question_id",
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"questionnaire_id",
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"esm_user_answer",
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"esm_type",
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]
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].head(n=10)
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# %%
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