import numpy as np
import pywt
import wfdb
import torch

from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH


def denoise(date):
    # Wavelet db5 level9
    coeffs = pywt.wavedec(data=date, wavelet='db5', level=9)
    cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs
    # noice del
    threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
    # take cD 1,2 turn 0 to denoise
    cD1.fill(0)
    cD2.fill(0)
    for i in range(1, len(coeffs) - 2):
        coeffs[i] = pywt.threshold(coeffs[i], threshold)
    # get signal that before wavelet
    rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
    return rdata


# get ECG data,date type and denoise
# this function without return
# use append take return data to pass data
def getDataSet(number, X_data, Y_data):
    ecgClassSet = ['N', 'A', 'V', 'l', 'R']
    print('get' + number + ' ECG data')
    record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII'])
    data = record.p_signal.flatten()
    # use def denoise
    rdata = denoise(date=data)

    # get type of ECG data
    annotation = wfdb.rdann(ECGDATAPATH + number, 'atr')
    Rlocation = annotation.sample  # annotation.sample:get R
    Rclass = annotation.symbol

    # delete stable date near R
    start = 10
    end = 5
    i = start
    j = len(annotation.symbol) - end

    # get NAVLR type data
    while i < j:
        try:
            lable = ecgClassSet.index(Rclass[i])
            x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201]
            X_data.append(x_train)
            Y_data.append(lable)
            i += 1
        except ValueError:
            i += 1
        return


# RATIO=The ratio of the training set to the test set，usually is 0.3
def loadData(RATIO):
    numberSet = [
        '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119',
        '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217',
        '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234'
    ]
    dataSet = []
    labelSet = []
    for n in numberSet:
        getDataSet(n, dataSet, labelSet)

    dataSet = np.array(dataSet).reshape(-1, 300)
    labelSet = np.array(labelSet).reshape(-1, 1)
    train_ds = np.hstack((dataSet, labelSet))
    np.random.shuffle(train_ds)

    X = train_ds[:, :300].reshape(-1, 300, 1)
    Y = train_ds[:, 300]
    # cut test and train data from ECG data
    shuffle_index = np.random.permutation(len(X))
    test_length = int(RATIO * len(shuffle_index))
    test_index = shuffle_index[:test_length]
    train_index = shuffle_index[test_index:]
    X_test, Y_test = X[test_index:], Y[test_index]
    X_train, Y_train = X[train_index], Y[train_index]
    return X_train, Y_test, X_test, Y_train


class CNN_model_level_one(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv1d(in_channels=1,out_channels=1,)