54 lines
1.2 KiB
Python
54 lines
1.2 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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# %% [markdown]
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# # ESM data
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# %% [markdown]
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# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.
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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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df_esm_clean = clean_up_esm(df_esm_preprocessed)
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# %%
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df_esm_PANAS = df_esm_clean[
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(df_esm_clean["questionnaire_id"] == 8) | (df_esm_clean["questionnaire_id"] == 9)
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]
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df_esm_PANAS_grouped = df_esm_PANAS.groupby(["participant_id", "questionnaire_id"])
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# %%
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df_esm_PANAS.head()
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# %%
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