import pandas as pd


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
    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
    # 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)

    esm_data = filter_data_by_segment(esm_data, time_segment)
    esm_features["mean"] = esm_data.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()
    return esm_features