from scipy.io import loadmat
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D
from tensorflow.keras import Sequential
import math
import pathlib
from sklearn import preprocessing
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import confusion_matrix
import seaborn as sns
import onnx
import onnxruntime
import onnxmltools

# 定义训练集路径
TRAIN_DIR = "./ecg_1000/TRAIN/"
# 定义测试集路径
TEST_DIR = "./ecg_1000/TEST/"
# 定义文本文件路径
TXT_DIR = "./ecg_1000/reference.txt"

MANIFEST = "./ecg_1000/reference.csv"

# 定义TFRecord文件路径
TFRECODE = "./ecgdataset.tfrecords"

# 定义预测结果路径
PRE_DIR = "./ecg_1000/test.txt"
PRE_CSV_DIR = "./ecg_1000/test.csv"
PRE_RESULT_DIR = "./ecg_1000/test_result.csv"

# 定义测试TFRecord文件路径
TEST_TFRECODE = "./ecgdataset_test.tfrecords"

batch_size = 20
epochs = 50
sample_rate = 500
sample_time = 10
sample_count = sample_rate*sample_time
lead_count = 12

train_label_file = pd.read_csv(TXT_DIR, sep="\t", header=None)
train_label_pd = pd.DataFrame(train_label_file)
train_label_pd.columns = ['name','label']
#显示dataframe的前三行数据，python代码
print(train_label_pd.head(3))

pre_label_file = pd.read_csv(PRE_DIR, sep=" ", header=None)
pre_label_pd = pd.DataFrame(pre_label_file)

# pre_label_pd = pre_label_pd.sort_values(by=[0],ascending=True)
# pre_label_pd.to_csv(PRE_RESULT_DIR,index=None)

# pre_label_file = pd.read_csv(PRE_RESULT_DIR, sep=",", header=None)
# pre_label_pd = pd.DataFrame(pre_label_file)
# pre_label_pd.drop(0, inplace=True)


pre_label_pd.columns = ['name', 'label']

print(pre_label_pd.head(3),len(pre_label_pd))