"""

The CINC data is provided by https://physionet.org/challenge/2017/ 

"""

from __future__ import division, print_function

import numpy as np

from config import get_config

from utils import *

from graph import *

import os 

def cincData(config):

    if config.cinc_download:

      cmd = "curl -O https://archive.physionet.org/challenge/2017/training2017.zip"

      os.system(cmd)

      os.system("unzip training2017.zip")

    num = config.num

    import csv

    testlabel = []

    with open('training2017/REFERENCE.csv') as csv_file:

      csv_reader = csv.reader(csv_file, delimiter=',')

      line_count = 0

      for row in csv_reader:

        testlabel.append([row[0],row[1]])

        #print(row[0], row[1])

        line_count += 1

      print(f'Processed {line_count} lines.')

    if num == None:

      high = len(testlabel)-1

      num = np.random.randint(1,high)

    filename , label = testlabel[num-1]

    filename = 'training2017/'+ filename + '.mat'

    from scipy.io import loadmat

    data = loadmat(filename)

    print("The record of "+ filename)

    if not config.upload:

        data = data['val']

        _, size = data.shape

        data = data.reshape(size,)

    else:

        data = np.array(data)

    return data, label

def predict(data, label, peaks, config):

    classesM = ['N','Ventricular','Paced','A','F','Noise']

    predicted, result  = predictByPart(data, peaks)

    print("The predicted", predicted)

    sumPredict = sum(predicted[x][1] for x in range(len(predicted)))

    avgPredict = sumPredict/len(predicted)

    print("The average of the predict is:", avgPredict)

    print("The most predicted label is {} with {:3.1f}% certainty".format(classesM[avgPredict.argmax()], 100*max(avgPredict)))

    print("avgPredict", avgPredict)

    sec_idx = avgPredict.argsort()[-2]

    print("The second predicted label is {} with {:3.1f}% certainty".format(classesM[sec_idx], 100*avgPredict[sec_idx]))

    print("The original label of the record is " + label)

    if config.upload:

      return predicted, classesM[avgPredict.argmax()], 100*max(avgPredict)

def predictByPart(data, peaks):

    classesM = ['N','Ventricular','Paced','A','F','Noise']#,'L','R','f','j','E','a','J','Q','e','S']

    predicted = list()

    result = ""

    counter = [0]* len(classesM)

    from keras.models import load_model

    model = load_model(

    'models/MLII-latest.keras', 

    custom_objects={

        'zeropad': zeropad,

        'zeropad_output_shape': zeropad_output_shape

      }

    )

    config = get_config() 

    for i, peak in enumerate(peaks[3:-1]):

      total_n =len(peaks)

      start, end =  peak-config.input_size//2 , peak+config.input_size//2

      prob = model.predict(data[:, start:end])

      prob = prob[0]

      ann = np.argmax(prob)

      counter[ann]+=1

      if classesM[ann] != "N":

        print("The {}/{}-record classified as {} with {:3.1f}% certainty".format(i,total_n,classesM[ann],100*prob[ann]))

      result += "("+ classesM[ann] +":" + str(round(100*prob[ann],1)) + "%)"

      predicted.append([classesM[ann],prob])

      if classesM[ann] != 'N' and prob[ann] > 0.95:

        import matplotlib.pyplot as plt

        plt.plot(data[:, start:end][0,:,0],)

        mkdir_recursive('results')

        plt.savefig('results/hazard-'+classesM[ann]+'.png', format="png", dpi = 300)

        plt.close()

    result += "{}-N, {}-Venticular, {}-Paced, {}-A, {}-F, {}-Noise".format(counter[0], counter[1], counter[2], counter[3], counter[4], counter[5])

    return predicted, result

def main(config):

  classesM= ['N','Ventricular','Paced','A','F', 'Noise']#,'L','R','f','j','E','a','J','Q','e','S']

  if config.upload:

    data = uploadedData(file)

  else:

    data, label = cincData(config)

  data, peaks = preprocess(data, config)

  return predict(data, label, peaks, config)

if __name__=='__main__':

  config = get_config()

  main(config)