Increment answers separately if needed.
parent
8c0b66eddc
commit
c688580fe8
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@ -20,7 +20,13 @@ import datetime
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import seaborn as sns
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import seaborn as sns
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import participants.query_db
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import participants.query_db
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from features.esm import QUESTIONNAIRE_IDS, clean_up_esm, get_esm_data, preprocess_esm
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from features.esm import (
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QUESTIONNAIRE_IDS,
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clean_up_esm,
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get_esm_data,
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increment_answers,
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preprocess_esm,
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)
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from features.esm_COPE import reassign_question_ids
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from features.esm_COPE import reassign_question_ids
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from features.esm_JCQ import reverse_jcq_demand_control_scoring
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from features.esm_JCQ import reverse_jcq_demand_control_scoring
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from features.esm_SAM import extract_stressful_events
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from features.esm_SAM import extract_stressful_events
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@ -308,6 +314,7 @@ if export_data:
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"esm_user_answer_numeric",
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"esm_user_answer_numeric",
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]
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]
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]
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]
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df_esm_SAM_for_export = increment_answers(df_esm_SAM_for_export)
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df_esm_SAM_for_export.sort_values(
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df_esm_SAM_for_export.sort_values(
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by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
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by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
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)
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)
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@ -413,6 +420,7 @@ df_esm_COPE = df_esm_preprocessed[
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# %%
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# %%
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df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
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df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
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df_esm_COPE_clean = increment_answers(df_esm_COPE_clean)
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df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean)
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df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean)
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# %%
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# %%
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@ -1,7 +1,5 @@
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import pandas as pd
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import pandas as pd
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from features.esm import increment_answers
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COPE_ORIGINAL_MAX = 4
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COPE_ORIGINAL_MAX = 4
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COPE_ORIGINAL_MIN = 1
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COPE_ORIGINAL_MIN = 1
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@ -188,7 +186,4 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
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)
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)
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)
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)
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# Finally, increment numeric answers.
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df_cope_fixed = increment_answers(df_cope_fixed)
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return df_cope_fixed
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return df_cope_fixed
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@ -2,7 +2,6 @@ import numpy as np
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import pandas as pd
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import pandas as pd
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import features.esm
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import features.esm
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from features.esm import increment_answers
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SAM_ORIGINAL_MAX = 5
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SAM_ORIGINAL_MAX = 5
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SAM_ORIGINAL_MIN = 1
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SAM_ORIGINAL_MIN = 1
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@ -506,7 +505,4 @@ def reassign_question_ids(df_sam_cleaned: pd.DataFrame) -> pd.DataFrame:
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)
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)
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)
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)
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# Finally, increment numeric answers.
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df_sam_fixed = increment_answers(df_sam_fixed)
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return df_sam_fixed
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return df_sam_fixed
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