import csv
import os
import xlrd
import numpy as np
from sklearn.impute import SimpleImputer

import element
import clear_data
import pandas as pd

'''
The purpose of this file is to read the contents of the dataset, 
normalize the elements, in the file of "parser_category".

Output three different null values in three folders "drop_null", "fill_null", "interpolate".
'''

category = ["compressive_strength","elongation","hardness","plasticity","tensile_strength","yield_strength"]

def read_data(category):
    csv_reader = csv.reader(open(category+".csv"))
    total_row = sum(1 for line in open(category+".csv"))

    ## Build a new array whose elements are all 0.
    result = np.zeros(((total_row, len(element.elements_list))), dtype=float)
    count = 0
    for alloy in csv_reader:
        ## interate every line(alloy) in the csv file.
        alloy_ratio = clear_data.normalize_molar_ratios(clear_data.clean_row(str(alloy[0]))[1])
        alloy_dic = dict(zip(clear_data.clean_row(str(alloy[0]))[0], alloy_ratio))

        ## Add the corresponding ratios at the proper location.
        for key in alloy_dic.keys():
            result[count, element.elements_list.index(key)] = float(alloy_dic.get(key))
        count += 1

    ## Save the result(array) as the 'Parser.csv'
    err_csv = os.path.join(os.path.expanduser('.'), 'deploy', 'error.csv')

    with open("parser_result/parser_category/"+"Parser_element.csv", 'w') as f:
        writer = csv.writer(f)
        writer.writerow(element.elements_list)
        count = 0
        for row in result:
            writer.writerow(row)
            count += 1

def get_mechnical(path,category):
    ## For Mechnical Targets.csv
    m_target = xlrd.open_workbook(path)
    m_sheet = m_target.sheets()[0]

    # Get the target data of the machine learning model
    hardness = m_sheet.col_values(4)[2:]
    hardness.insert(0,"hardness")
    yield_strength = m_sheet.col_values(5)[2:]
    yield_strength.insert(0, "yield_strength")
    tensile_strength = m_sheet.col_values(6)[2:]
    tensile_strength.insert(0,"tensile_strength")
    elongation = m_sheet.col_values(7)[2:]
    elongation.insert(0,"elongation")
    compressive_strength = m_sheet.col_values(8)[2:]
    compressive_strength.insert(0,"compressive_strength")
    plasticity = m_sheet.col_values(9)[2:]
    plasticity.insert(0,"plasticity")

    # Save the mechanical properties of alloys.
    with open("parser_result/Parser_element.csv") as csvFile:
        rows = csv.reader(csvFile)
        with open(("parser_result/parser_category/Parser_"+category+".csv"), 'w') as f:
            writer = csv.writer(f)
            index = 0
            for row in rows:
                if category=="hardness":
                    row.append(hardness[index])
                elif category=="yield_strength":
                    row.append(yield_strength[index])
                elif category == "tensile_strength":
                    row.append(tensile_strength[index])
                elif category == "elongation":
                    row.append(elongation[index])
                elif category == "compressive_strength":
                    row.append(compressive_strength[index])
                elif category == "plasticity":
                    row.append(plasticity[index])
                writer.writerow(row)
                index += 1
        data = pd.read_csv('parser_result/parser_category/Parser_'+category+'.csv')

        last_column = data.iloc[:, -1]
        null_ratio = last_column.isnull().mean()
        print("Null ratio in " + category +"dataset is: ", round(null_ratio,2))

        # Replace null with 0s.
        data_fillna = data.fillna(0)
        df1 = pd.DataFrame(data=data_fillna)
        df1.to_csv('parser_result/fill_null/'+category+'_fill_null.csv', index=False)

        # Delete null.
        data_dropna = data.dropna(axis=0, how='any')
        df1 = pd.DataFrame(data=data_dropna)
        df1.to_csv('parser_result/drop_null/'+category+'_drop_null.csv', index=False)

        # # Split dataset to knn&rf model.
        # data = data.fillna(0)
        # df_test = data.drop(index=data.index)
        # idx = 0
        # idx_exit = int(data.shape[0] * 0.07)
        # for index, row in data.iterrows():
        #     if row.astype(int)[-1] != 0 and idx <= idx_exit:
        #         df_test = df_test.append(row, ignore_index=True)
        #         data = data.drop([index])
        #         idx += 1
        # df_test.to_csv('parser_result/RF_test/'+category+'_RF_test.csv', index=False)
        #
        # # Dealing with rfr_train, split it into knn_train and knn_test.
        # df_train = pd.DataFrame(data=data)
        # # Calculate the average number X of data(not 0).
        # sum_num = 0
        # num = 0
        # for index, row in df_train.iterrows():
        #     if row.astype(int)[-1] != 0:
        #         num += 1
        #         sum_num += row.astype(int)[-1]
        # mean_num = sum_num / num
        # # df_0: which need to be imputed by KNN.
        # df_0 = data.drop(index=data.index)
        # df_pure = data.drop(index=data.index)
        # for index, row in df_train.iterrows():
        #     if row.astype(int)[-1] == 0:
        #         df_0 = df_0.append(row, ignore_index=True)
        #     else:
        #         df_pure = df_pure.append(row, ignore_index=True)
        # df_0.to_csv('parser_result/KNN_test/'+category+'_KNN_test.csv', index=False)
        # df_pure.to_csv('parser_result/KNN_train/' + category + '_KNN_train.csv', index=False)


if __name__ =="__main__":
    read_data("mechanical_composition")
    for c in category:
        get_mechnical('mechanical.xls', c)