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stress_at_work_analysis
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Use refactored methods.

master
junos 2023-07-03 21:18:15 +02:00
parent 2aa5c5cb07
commit ca85131ed2
1 changed files with 7 additions and 5 deletions
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12
exploration/expl_esm_labels.py
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@ -26,10 +26,11 @@ from features.esm import (
get_esm_data,
increment_answers,
preprocess_esm,
reassign_question_ids,
)
from features.esm_COPE import reassign_question_ids
from features.esm_COPE import DICT_COPE_QUESTION_IDS
from features.esm_JCQ import reverse_jcq_demand_control_scoring
from features.esm_SAM import extract_stressful_events
from features.esm_SAM import DICT_SAM_QUESTION_IDS, extract_stressful_events
# import os
# import sys
@ -294,7 +295,9 @@ df_esm_SAM_clean.esm_status.value_counts()
# %%
if export_data:
df_esm_SAM_for_export = df_esm_SAM_clean[
df_esm_SAM_fixed = reassign_question_ids(df_esm_SAM_clean, DICT_SAM_QUESTION_IDS)
df_esm_SAM_fixed = increment_answers(df_esm_SAM_fixed)
df_esm_SAM_for_export = df_esm_SAM_fixed[
[
"participant_id",
"username",
@ -314,7 +317,6 @@ if export_data:
"esm_user_answer_numeric",
]
]
df_esm_SAM_for_export = increment_answers(df_esm_SAM_for_export)
df_esm_SAM_for_export.sort_values(
by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
)
@ -421,7 +423,7 @@ df_esm_COPE = df_esm_preprocessed[
# %%
df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
df_esm_COPE_clean = increment_answers(df_esm_COPE_clean)
df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean)
df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean, DICT_COPE_QUESTION_IDS)
# %%
if export_data: