import pandas as pd

QUESTIONNAIRE_IDS = {
    "sleep_quality": 1,
    "PANAS_positive_affect": 8,
    "PANAS_negative_affect": 9,
    "JCQ_job_demand": 10,
    "JCQ_job_control": 11,
    "JCQ_supervisor_support": 12,
    "JCQ_coworker_support": 13,
    "PFITS_supervisor": 14,
    "PFITS_coworkers": 15,
    "UWES_vigor": 16,
    "UWES_dedication": 17,
    "UWES_absorption": 18,
    "COPE_active": 19,
    "COPE_support": 20,
    "COPE_emotions": 21,
    "balance_life_work": 22,
    "balance_work_life": 23,
    "recovery_experience_detachment": 24,
    "recovery_experience_relaxation": 25,
    "symptoms": 26,
    "appraisal_stressfulness_event": 87,
    "appraisal_threat": 88,
    "appraisal_challenge": 89,
    "appraisal_event_time": 90,
    "appraisal_event_duration": 91,
    "appraisal_event_work_related": 92,
    "appraisal_stressfulness_period": 93,
    "late_work": 94,
    "work_hours": 95,
    "left_work": 96,
    "activities": 97,
    "coffee_breaks": 98,
    "at_work_yet": 99,
}


def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
    esm_data = pd.read_csv(sensor_data_files["sensor_data"])
    requested_features = provider["FEATURES"]
    # name of the features this function can compute
    requested_scales = provider["SCALES"]
    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
    #TODO Check valid questionnaire and feature names.
    # the subset of requested features this function can compute
    features_to_compute = list(set(requested_features) & set(base_features_names))
    esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
    if not esm_data.empty:
        esm_data = filter_data_by_segment(esm_data, time_segment)

        if not esm_data.empty:
            esm_features = pd.DataFrame()
            for scale in requested_scales:
                questionnaire_id = QUESTIONNAIRE_IDS[scale]
                mask = esm_data["questionnaire_id"] == questionnaire_id
                esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
                #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.

            esm_features = esm_features.reset_index()
            if 'index' in esm_features: # In calse of empty esm_features df 
                esm_features.rename(columns={'index': 'local_segment'}, inplace=True)

    return esm_features