"""This file contains the ecg processing pipelines."""
import pandas as pd

import neurokit2 as nk

from src.deprecated.ecg_feature_extraction import get_hrv_features, normalize_features
from src.deprecated.utils import cut_out_window, create_windows

pd.set_option('display.float_format', '{:.6f}'.format)

from src.logger import setup_logger
logger = setup_logger(__name__)


def process_window(window, window_id, frequency):
    features = get_hrv_features(window['ecg'].values, frequency)
    tmp = pd.DataFrame(features, index=[0])
    tmp['subject_id'] = window['subject_id'].unique()[0]
    tmp['sample_id'] = str(window['sample_id'].unique()[0])
    tmp['window_id'] = window_id
    tmp['w_start_time'] = window['timestamp_idx'].min().strftime('%Y-%m-%d %H:%M:%S')
    tmp['w_end_time'] = window['timestamp_idx'].max().strftime('%Y-%m-%d %H:%M:%S')
    tmp['baseline'] = window_id == 0
    tmp['frequency'] = frequency
    return tmp


def process_batch(samples, configs):
    features_list = []
    for i, sample in enumerate(samples):
        logger.info(f"Processing sample ({i + 1}/{len(samples)})...")
        sample_df = pd.DataFrame.from_dict(sample.dict())
        # Preprocess the ecg signal
        logger.info("Preprocess ECG signals...")
        sample_df['ecg'] = nk.ecg_clean(sample_df['ecg'], sampling_rate=sample.frequency, method="pantompkins1985")
        # Cut out the windows and process them
        if configs.baseline_start:
            logger.info("Cut out baseline window...")
            baseline_window = cut_out_window(sample_df, 'timestamp_idx', start=configs.baseline_start,
                                             end=configs.baseline_end)
            sample_df = sample_df[sample_df['timestamp_idx'] > baseline_window['timestamp_idx'].max()]
            logger.info("Processing baseline window...")
            features_list.append(process_window(baseline_window, 0, sample.frequency))
        logger.info("Cut out windows...")
        windows = create_windows(df=sample_df, time_column='timestamp_idx', window_size=configs.window_size,
                                 window_slicing_method=configs.window_slicing_method)
        logger.info(f"Processing windows (Total: {len(windows)})...")
        features_list.extend(process_window(window, i, sample.frequency) for i, window in enumerate(windows, start=1))
        features_df = pd.concat(features_list, ignore_index=True)
        # Normalize the features via baseline subtraction
        if configs.baseline_start:
            features_df = normalize_features(features_df, configs.normalization_method)

    return features_df