import pandas as pd
from scipy.stats import entropy

def statsFeatures(heartrate_data, features, features_type, heartrate_features):

    if features_type == "hr":
        col_name = "heartrate"
    elif features_type == "restinghr":
        col_name = "heartrate_daily_restinghr"
    elif features_type == "caloriesoutofrange":
        col_name = "heartrate_daily_caloriesoutofrange"
    elif features_type == "caloriesfatburn":
        col_name = "heartrate_daily_caloriesfatburn"
    elif features_type == "caloriescardio":
        col_name = "heartrate_daily_caloriescardio"
    elif features_type == "caloriespeak":
        col_name = "heartrate_daily_caloriespeak"
    else:
        raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].")

    if "sum" + features_type in features:
        heartrate_features["sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum()
    if "max" + features_type in features:
        heartrate_features["max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max()
    if "min" + features_type in features:
        heartrate_features["min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
    if "avg" + features_type in features:
        heartrate_features["avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean()
    if "median" + features_type in features:
        heartrate_features["median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median()
    if "mode" + features_type in features:
        heartrate_features["mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0])
    if "std" + features_type in features:
        heartrate_features["std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std()
    if "diffmaxmode" + features_type in features:
        heartrate_features["diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0])
    if "diffminmode" + features_type in features:
        heartrate_features["diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
    if "entropy" + features_type in features:
        heartrate_features["entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy)
    
    return heartrate_features

def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features):
    heartrate_summary_features = pd.DataFrame()

    # get stats of resting heartrate
    heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "restinghr", heartrate_summary_features)

    # get stats of calories features
    # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.)
    heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesoutofrange", heartrate_summary_features)
    heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesfatburn", heartrate_summary_features)
    heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriescardio", heartrate_summary_features)
    heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriespeak", heartrate_summary_features)

    heartrate_summary_features.reset_index(inplace=True)
    
    return heartrate_summary_features


def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):

    heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"])

    requested_summary_features = provider["FEATURES"]
    # name of the features this function can compute
    base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
    # the subset of requested features this function can compute
    summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))

    # extract features from summary data
    heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute)
    if not heartrate_summary_data.empty:
        heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, time_segment)

        if not heartrate_summary_data.empty:
            # only keep the segments start at 00:00:00 and end at 23:59:59
            datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
            datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"

            segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex)
            heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)]

            if not heartrate_summary_data.empty:
                heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute)
    
    return heartrate_summary_features