# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/5/29 16:00
@Usage : 风机状态监测
@Desc :  从Kafka中读取dwm_wind_wide数据,通过CNN-LSTM模型进行状态监测
         暂时存在问题:如果来一条的话，有维度的数据比如LSTM,GRU等就没办法了，只能跑AE类型的模型
'''

import tensorflow as tf
import numpy as np
import pandas as pd
from utils.KafkaUtils import *
from keras.callbacks import EarlyStopping
import json

# 超参数定义
timestamp = 100
feature_num = 10
learning_rate = 0.01
EPOCH = 100
batch_size = 32
model_name = "AE"
save_name = "./model/{0}_timestamp{0}_feature_num{2}_batchSize{4}_epoch{4}.h5".format(model_name, timestamp,
                                                                                      feature_num, batch_size, EPOCH)


def condition_monitoring_model():
    input = tf.keras.Input(shape=[timestamp, feature_num])
    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
    d1 = tf.keras.layers.Dense(300)(GRU1)
    output = tf.keras.layers.Dense(10)(d1)
    model = tf.keras.Model(inputs=input, outputs=output)

    return model

def condition_monitoring_model_AE():
    input = tf.keras.Input(shape=[feature_num])
    conv1 = tf.keras.layers.Dense(feature_num/2)(input)
    GRU1 = tf.keras.layers.Dense(3)(conv1)
    d1 = tf.keras.layers.Dense(feature_num/2)(GRU1)
    output = tf.keras.layers.Dense(feature_num)(d1)
    model = tf.keras.Model(inputs=input, outputs=output)

    return model
# 读取数据
def read_data():
    data = pd.read_csv("G:\data\SCADA数据/jb4q_8_delete_all_zero_simple.csv")
    data = data.iloc[:10000, 3:13]
    data = np.array(data)
    return data
    pass


# 重叠采样
def get_training_data_overlapping(data, time_stamp=timestamp):
    rows, cols = data.shape
    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
    for i in range(rows):
        if i + time_stamp >= rows:
            break
        if i + time_stamp < rows - 1:
            train_data[i] = data[i:i + time_stamp]
            train_label[i] = data[i + time_stamp]
    print("重叠采样以后：")
    print("data:", train_data)
    print("label:", train_label)

    return train_data, train_label


# 归一化
def normalization(data):
    rows, cols = data.shape
    print("归一化之前:", data)
    print(data.shape)
    print("======================")

    # 归一化
    max = np.max(data, axis=0)
    max = np.broadcast_to(max, [rows, cols])
    min = np.min(data, axis=0)
    min = np.broadcast_to(min, [rows, cols])

    data = (data - min) / (max - min)
    print("归一化之后:", data)
    print(data.shape)

    return data


def kafkaSource():
    pass


if __name__ == '__main__':

    # ##  TODO training
    # data = read_data()
    # data = normalization(data=data)
    # # train_data, train_label = get_training_data_overlapping(data)
    # model = condition_monitoring_model_AE()
    # checkpoint = tf.keras.callbacks.ModelCheckpoint(
    #     filepath=save_name,
    #     monitor='val_loss',
    #     verbose=1,
    #     save_best_only=True,
    #     mode='min',
    #     period=1)
    # lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001)
    # early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=30, mode='min', verbose=1)
    # model.compile(optimizer=tf.optimizers.Adam(learning_rate=learning_rate), loss=tf.losses.mse)
    # model.summary()
    # model.fit(data, data, batch_size=batch_size, epochs=EPOCH, validation_split=0.1,
    #           callbacks=[checkpoint, lr_scheduler, early_stop])

    ###  TODO testing

    consumer = getKafkaConsumer("dwm_wind_wide", group_id="wind_condition_monitoring_group")
    model = tf.keras.models.load_model(save_name)
    for message in consumer:
        record = str(message.value, encoding="utf-8")
        recordDict = eval(record)
        print("转换后的json:",record)
        data = np.array([recordDict['num_gearbox_sumptemp'], recordDict['num_gearbox_inletoiltemp'], recordDict['num_gearbox_inletpress'],
                         recordDict['num_gearbox_pumpoutletpress'],
                         recordDict['num_gearbox_coolingwatertemp'], recordDict['num_envtemp'],
                         recordDict['num_gearbox_highspeedshaft_front_temp'], recordDict['num_gearbox_highspeedshaft_rear_temp'],
                         recordDict['num_gen_max_windingtemp'], recordDict['num_engineroom_temp']])
        data = np.expand_dims(data,axis=0)
        # data = normalization(data)
        # data = np.expand_dims(data,axis=0)
        predicted_data = model.predict(data)
        print("实际值:",data)
        print("预测出来的值:",predicted_data)
        # model.predict(message)