"""

****This file contain utlities functions****

@author : Wish Suharitdamrong

"""

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import argparse
import math
from sklearn.metrics import accuracy_score


def save_logs(model_name,savename,**kwargs):
    """
    *********
    save_logs : Save a logs in .csv files
    *********
    @author: Wish Suharitdamrong
    ------
    inputs : 
    ------ 
        model_name : name of a model
        savename :  name of a file saving as a .csv
        **kwagrs : array containing values of metrics such as accuracy and loss 
    -------
    outputs :
    -------
    """
    
    path = "./logs/{}/".format(model_name)

    if not os.path.exists(path):
        os.mkdir(path)
    
    df = pd.DataFrame()
    for log in kwargs:

        df[log] = kwargs[log]

    savepath = os.path.join(path,savename)

    df.to_csv(savepath, index=False)


def load_logs(model_name,savename, epoch, type_model=None):
    """
    *********
    load_logs : Load a logs file from csv 
    *********
    @author: Wish Suharitdamrong
    ------
    inputs : 
    ------
        model_name : name of a model
        savename : name of a logs in .csv files
        epoch :  number of iteration continue from checkpoint
    -------
    outputs :
    -------
        train_loss : array containing training loss
        train_acc : array containing training accuracy
        vali_loss : array containing validation loss
        vali_acc : array containing validation accuracy
    """

    path = "./logs/{}/".format(model_name)
   
    savepath = os.path.join(path,savename)
    
    if type_model is None:
        
        raise ValueError("Type of model should be specified Generator or SyncNet")

    if not os.path.exists(savepath):

        print("Logs file does not exists !!!!")

        exit()
     
    df =  pd.read_csv(savepath)[:epoch+1]
    
    if type_model == "syncnet":

        train_loss = df["train_loss"]
        train_acc = df['train_acc']
        vali_loss = df['vali_loss']
        vali_acc = df['vali_acc']

        
        return train_loss, train_acc, vali_loss, vali_acc
    
    elif type_model == "generator":
        
        train_loss = df["train_loss"]
        vali_loss = df["vali_loss"]
        
        return train_loss, vali_loss
    
    else :
        
        
        raise ValueError(" Argument type of model (type_model) should be either 'generator' or 'syncnet' !!!!")
    
    
    



def get_accuracy(y_pred,y_true):
    """
    *********
    get_accuracy : calcualte accuracy of a model
    *********
    @author: Wish Suharitdamrong
    ------
    inputs : 
    ------
        y_pred : predicted label
        y_true : ground truth of a label
    -------
    outputs :
    -------
        acc : accuracy of a model
 
    """

    acc =  accuracy_score(y_pred,y_true, normalize=True) * 100

    return acc


def procrustes(fl):
    
    transformation = {}

    fl, mean = translation(fl)

    fl, scale = scaling(fl)

    #fl , rotate = rotation(fl)

    transformation['translate'] = mean 

    transformation['scale'] = scale

    #transformation['rotate'] = rotate

    return  fl , transformation


def translation(fl):

    mean =  np.mean(fl, axis=0)

    fl = fl - mean

    return fl , mean

def scaling(fl):

    scale = np.sqrt(np.mean(np.sum(fl**2, axis=1)))

    fl = fl/scale

    return  fl , scale