import numpy as np
from keras.layers.core import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.models import Sequential, load_model
from keras.callbacks import Callback
import keras.backend.tensorflow_backend as KTF
import tensorflow as tf
import pandas as pd
import os
import keras.callbacks
import matplotlib.pyplot as plt
import copy

# 设定为自增长
config=tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session=tf.compat.v1.Session(config=config)
KTF.set_session(session)


def rmse(predictions, targets):
    return np.sqrt(((predictions - targets) ** 2).mean())


def mse(predictions, targets):
    return ((predictions - targets) ** 2).mean()


def reshape_y_hat(y_hat, dim):
    re_y = []
    i = 0
    while i < len(y_hat):
        tmp = []
        for j in range(dim):
            tmp.append(y_hat[i + j])
        i = i + dim
        re_y.append(tmp)
    re_y = np.array(re_y, dtype='float64')
    return re_y


# 多维反归一化
def FNormalizeMult(data, normalize):
    data = np.array(data, dtype='float64')
    # 列
    for i in range(0, data.shape[1]):
        listlow = normalize[i, 0]
        listhigh = normalize[i, 1]
        delta = listhigh - listlow
        print("listlow, listhigh, delta", listlow, listhigh, delta)
        # 行
        if delta != 0:
            for j in range(0, data.shape[0]):
                data[j, i] = data[j, i] * delta + listlow

    return data


# 使用训练数据的归一化
def NormalizeMultUseData(data, normalize):
    for i in range(0, data.shape[1]):

        listlow = normalize[i, 0]
        listhigh = normalize[i, 1]
        delta = listhigh - listlow

        if delta != 0:
            for j in range(0, data.shape[0]):
                data[j, i] = (data[j, i] - listlow) / delta

    return data


from math import sin, asin, cos, radians, fabs, sqrt

EARTH_RADIUS = 6371  # 地球平均半径，6371km


# 计算两个经纬度之间的直线距离
def hav(theta):
    s = sin(theta / 2)
    return s * s


def get_distance_hav(lat0, lng0, lat1, lng1):
    # "用haversine公式计算球面两点间的距离。"
    # 经纬度转换成弧度
    lat0 = radians(lat0)
    lat1 = radians(lat1)
    lng0 = radians(lng0)
    lng1 = radians(lng1)

    dlng = fabs(lng0 - lng1)
    dlat = fabs(lat0 - lat1)
    h = hav(dlat) + cos(lat0) * cos(lat1) * hav(dlng)
    distance = 2 * EARTH_RADIUS * asin(sqrt(h))
    return distance


if __name__ == '__main__':
    test_num = 6
    per_num = 1
    data_all = pd.read_csv('Data/test2.csv', sep=',').iloc[-2 * (test_num + per_num):-1 * (test_num + per_num),
               0:2].values
    data_all.dtype = 'float64'

    data = copy.deepcopy(data_all[:-per_num, :])
    y = data_all[-per_num:, :]

    # #归一化
    normalize = np.load("./traj_model_trueNorm.npy")
    data = NormalizeMultUseData(data, normalize)

    model = load_model("./traj_model_120.h5")
    test_X = data.reshape(1, data.shape[0], data.shape[1])
    y_hat = model.predict(test_X)
    y_hat = y_hat.reshape(y_hat.shape[1])
    y_hat = reshape_y_hat(y_hat, 2)

    # 反归一化
    y_hat = FNormalizeMult(y_hat, normalize)
    print("predict: {0}\ntrue：{1}".format(y_hat, y))
    print('预测均方误差：', mse(y_hat, y))
    print('预测直线距离：{:.4f} KM'.format(get_distance_hav(y_hat[0, 0], y_hat[0, 1], y[0, 0], y[0, 1])))

    # 画测试样本数据库
    p1 = plt.scatter(data_all[:-per_num, 1], data_all[:-per_num, 0], c='b', marker='o', label='traj_A')
    p2 = plt.scatter(y_hat[:, 1], y_hat[:, 0], c='r', marker='o', label='pre')
    p3 = plt.scatter(y[:, 1], y[:, 0], c='g', marker='o', label='pre_true')
    plt.legend(loc='upper left')
    plt.grid()
    plt.show()