import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np

from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
from cr_features_helper_methods import extract_second_order_features

import math
import sys

# pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', None)


def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
    ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)

    if not ibi_intraday_data.empty:   

        ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)

        if not ibi_intraday_data.empty:

            ibi_intraday_features = pd.DataFrame()

            # apply methods from calculate features module
            if window_length is None:
                ibi_intraday_features = \
                    ibi_intraday_data.groupby('local_segment').apply(\
                    lambda x: 
                        extract_hrv_features_2d_wrapper(
                            signal_2D = \
                                convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0], 
                            ibi_timings = \
                                convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1], 
                            sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 
            else:
                ibi_intraday_features = \
                    ibi_intraday_data.groupby('local_segment').apply(\
                    lambda x: 
                        extract_hrv_features_2d_wrapper(
                            signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
                            ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
                            sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 

            ibi_intraday_features.reset_index(inplace=True)

    return ibi_intraday_features


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

    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'inter_beat_interval': 'float64', 'timings': 'float64', 'local_date_time': 'str', 
                  'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}

    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
    ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])

    requested_intraday_features = provider["FEATURES"]
    
    calc_windows = kwargs.get('calc_windows', False)

    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
    else:
        requested_window_length = None

    # name of the features this function can compute
    base_intraday_features_names = hrv_features
    # the subset of requested features this function can compute
    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))

    # extract features from intraday data
    ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, 
                                                                requested_window_length, time_segment, filter_data_by_segment)

    if calc_windows:        
        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
        ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)

        return ibi_intraday_features, ibi_second_order_features

    return ibi_intraday_features