"""
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)