import numpy as np
import torch
import pandas as pd
import os

def adjust_learning_rate(optimizer, epoch, args):
    # lr = args.learning_rate * (0.2 ** (epoch // 2))
    if args.lradj=='type1':
        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch-1) // 1))}
    elif args.lradj=='type2':
        lr_adjust = {
            2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 
            10: 5e-7, 15: 1e-7, 20: 5e-8
        }
    if epoch in lr_adjust.keys():
        lr = lr_adjust[epoch]
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
        print('Updating learning rate to {}'.format(lr))

class EarlyStopping:
    def __init__(self, patience=50, verbose=False, delta=0):
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta

    def __call__(self, val_loss, model, path):
        score = -val_loss
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model, path)
        elif score < self.best_score + self.delta:
            self.counter += 1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss, model, path)
            self.counter = 0

    def save_checkpoint(self, val_loss, model, path):
        if self.verbose:
            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
        torch.save(model.state_dict(), path+'/'+'checkpoint.pth')
        self.val_loss_min = val_loss

class dotdict(dict):
    """dot.notation access to dictionary attributes"""
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

class StandardScaler():
    def __init__(self,root_path,data_path):
        # self.mean = 0.
        # self.std = 1.
        # print(root_path)
        # print(data_path)
        df_raw = pd.read_csv(os.path.join(root_path,
                                          data_path))
        cols_data = df_raw.columns[1:]
        df_data = df_raw[cols_data]
        self.mean = df_data[0:96145].values.mean(0)
        self.std = df_data[0:96145].values.std(0)
        # print("mean",self.mean)
        # print("std", self.std)

    
    # def fit(self, data):
    #     self.mean = data.mean(0)
    #     self.std = data.std(0)
    #     print("mean",self.mean)
    #     print("std", self.std)

    def transform(self, data):
        mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
        std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
        return (data - mean) / std

    def inverse_transform(self, data):
        mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
        std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
        return (data * std) + mean