26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
from esm_preprocess import *
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from esm_JCQ import reverse_jcq_demand_control_scoring
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requested_scales = snakemake.params["scales"]
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df_esm = pd.read_csv(snakemake.input[0])
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df_esm_preprocessed = preprocess_esm(df_esm)
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if not all([scale in QUESTIONNAIRE_IDS for scale in requested_scales]):
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unknown_scales = set(requested_scales) - set(QUESTIONNAIRE_IDS.keys())
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print("The requested questionnaire name should be one of the following:")
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print(QUESTIONNAIRE_IDS.keys())
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raise ValueError("You requested scales not collected: ", unknown_scales)
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df_esm_clean = clean_up_esm(df_esm_preprocessed)
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df_esm_clean["esm_user_score"] = df_esm_clean["esm_user_answer_numeric"]
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for scale in requested_scales:
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questionnaire_id = QUESTIONNAIRE_IDS[scale]
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mask = df_esm_clean["questionnaire_id"] == questionnaire_id
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if scale.startswith("JCQ"):
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df_esm_clean.loc[mask] = reverse_jcq_demand_control_scoring(df_esm_clean.loc[mask])
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#TODO Reverse other questionnaires if needed and/or adapt esm_user_score to original scoring.
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df_esm_clean.to_csv(snakemake.output[0], index=False)
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