74 lines
1.6 KiB
Python
74 lines
1.6 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: 1.11.4
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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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# %matplotlib inline
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import datetime
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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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# %%
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import participants.query_db
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from features import esm, helper, proximity
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# %% [markdown]
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# # 1. Get the relevant data
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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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# Consider only two participants to simplify.
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ptcp_2 = participants_inactive_usernames[0:2]
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# %% [markdown]
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# ## 1.1 Labels
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# %%
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df_esm = esm.get_esm_data(ptcp_2)
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df_esm_preprocessed = esm.preprocess_esm(df_esm)
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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 = esm.clean_up_esm(df_esm_PANAS)
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# %% [markdown]
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# ## 1.2 Sensor data
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# %%
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df_proximity = proximity.get_proximity_data(ptcp_2)
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df_proximity = helper.get_date_from_timestamp(df_proximity)
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df_proximity = proximity.recode_proximity(df_proximity)
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# %% [markdown]
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# # 2. Grouping/segmentation
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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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