67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
import pandas as pd
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QUESTIONNAIRE_IDS = {
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"sleep_quality": 1,
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"PANAS_positive_affect": 8,
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"PANAS_negative_affect": 9,
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"JCQ_job_demand": 10,
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"JCQ_job_control": 11,
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"JCQ_supervisor_support": 12,
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"JCQ_coworker_support": 13,
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"PFITS_supervisor": 14,
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"PFITS_coworkers": 15,
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"UWES_vigor": 16,
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"UWES_dedication": 17,
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"UWES_absorption": 18,
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"COPE_active": 19,
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"COPE_support": 20,
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"COPE_emotions": 21,
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"balance_life_work": 22,
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"balance_work_life": 23,
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"recovery_experience_detachment": 24,
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"recovery_experience_relaxation": 25,
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"symptoms": 26,
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"appraisal_stressfulness_event": 87,
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"appraisal_threat": 88,
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"appraisal_challenge": 89,
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"appraisal_event_time": 90,
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"appraisal_event_duration": 91,
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"appraisal_event_work_related": 92,
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"appraisal_stressfulness_period": 93,
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"late_work": 94,
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"work_hours": 95,
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"left_work": 96,
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"activities": 97,
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"coffee_breaks": 98,
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"at_work_yet": 99,
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}
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def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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esm_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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requested_scales = provider["SCALES"]
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base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
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"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
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#TODO Check valid questionnaire and feature names.
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not esm_data.empty:
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esm_data = filter_data_by_segment(esm_data, time_segment)
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if not esm_data.empty:
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esm_features = pd.DataFrame()
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for scale in requested_scales:
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questionnaire_id = QUESTIONNAIRE_IDS[scale]
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mask = esm_data["questionnaire_id"] == questionnaire_id
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esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
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#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
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esm_features = esm_features.reset_index()
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if 'index' in esm_features: # In calse of empty esm_features df
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esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
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return esm_features
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