"""MIT License

Copyright (c) 2019 David Luevano Alvarado

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from colorama import init, Fore, Style
import pandas as pd

init()


def printc(text, color):
    """
    Prints texts normaly, but in color. Using colorama.
    text: text to print.
    color: color to be used, same as available in colorama.
    """
    color = color.upper()
    colors = {'BLACK': Fore.BLACK,
              'RED': Fore.RED,
              'GREEN': Fore.GREEN,
              'YELLOW': Fore.YELLOW,
              'BLUE': Fore.BLUE,
              'MAGENTA': Fore.MAGENTA,
              'CYAN': Fore.CYAN,
              'WHITE': Fore.WHITE,
              'RESET': Fore.RESET}

    av_colors = colors.keys()
    if color not in av_colors:
        raise KeyError(f'{color} not found.')

    print(colors[color] + text + Style.RESET_ALL)


def plot_benchmarks():
    """
    For plotting the benchmarks.
    """
    # Original columns.
    or_cols = ['ml_type',
               'tr_size',
               'te_size',
               'kernel_s',
               'mae',
               'time',
               'lj_s',
               'lj_e',
               'date_ran']
    # Drop some original columns.
    dor_cols = ['te_size',
                'kernel_s',
                'time',
                'date_ran']

    # Read benchmarks data and drop some columns.
    data_temp = pd.read_csv('data\\benchmarks.csv',)
    data = pd.DataFrame(data_temp, columns=or_cols)
    data = data.drop(columns=dor_cols)

    # Get the data of the first benchmarks and drop unnecesary columns.
    first_data = pd.DataFrame(data, index=range(0, 22))
    first_data = first_data.drop(columns=['lj_s', 'lj_e'])

    # Columns to keep temporarily.
    fd_columns = ['ml_type',
                  'tr_size',
                  'mae']

    # Create new dataframes for each matrix descriptor and fill them.
    first_data_cm = pd.DataFrame(columns=fd_columns)
    first_data_ljm = pd.DataFrame(columns=fd_columns)
    for i in range(first_data.shape[0]):
        temp_df = first_data.iloc[[i]]
        if first_data.at[i, 'ml_type'] == 'CM':
            first_data_cm = first_data_cm.append(temp_df)
        else:
            first_data_ljm = first_data_ljm.append(temp_df)

    # Drop unnecesary column and rename 'mae' for later use.
    first_data_cm = first_data_cm.drop(columns=['ml_type'])\
        .rename(columns={'mae': 'cm_mae'})
    first_data_ljm = first_data_ljm.drop(columns=['ml_type'])\
        .rename(columns={'mae': 'ljm_mae'})
    # print(first_data_cm)
    # print(first_data_ljm)

    # Get the cm data axis so it can be joined with the ljm data axis.
    cm_axis = first_data_cm.plot(x='tr_size',
                                 y='cm_mae',
                                 kind='line')
    # Get the ljm data axis and join it with the cm one.
    plot_axis = first_data_ljm.plot(ax=cm_axis,
                                    x='tr_size',
                                    y='ljm_mae',
                                    kind='line')
    plot_axis.set_xlabel('tr_size')
    plot_axis.set_ylabel('mae')
    plot_axis.set_title('mae for different tr_sizes')
    # Get the figure and save it.
    # plot_axis.get_figure().savefig('.figs\\mae_diff_tr_sizes.pdf')

    # Get the rest of the benchmark data and drop unnecesary column.
    new_data = data.drop(index=range(0, 22))
    new_data = new_data.drop(columns=['ml_type'])

    # Get the first set and rename it.
    nd_first = first_data_ljm.rename(columns={'ljm_mae': '1, 1'})
    ndf_axis = nd_first.plot(x='tr_size',
                             y='1, 1',
                             kind='line')
    last_axis = ndf_axis
    for i in range(22, 99, 11):
        lj_s = new_data['lj_s'][i]
        lj_e = new_data['lj_e'][i]
        new_mae = '{}, {}'.format(lj_s, lj_e)
        nd_temp = pd.DataFrame(new_data, index=range(i, i + 11))\
            .drop(columns=['lj_s', 'lj_e'])\
            .rename(columns={'mae': new_mae})
        last_axis = nd_temp.plot(ax=last_axis,
                                 x='tr_size',
                                 y=new_mae,
                                 kind='line')
        print(nd_temp)

    last_axis.set_xlabel('tr_size')
    last_axis.set_ylabel('mae')
    last_axis.set_title('mae for different parameters of lj(s)')

    last_axis.get_figure().savefig('.figs\\mae_diff_param_lj_s.pdf')

    ndf_axis = nd_first.plot(x='tr_size',
                             y='1, 1',
                             kind='line')
    last_axis = ndf_axis
    for i in range(99, data.shape[0], 11):
        lj_s = new_data['lj_s'][i]
        lj_e = new_data['lj_e'][i]
        new_mae = '{}, {}'.format(lj_s, lj_e)
        nd_temp = pd.DataFrame(new_data, index=range(i, i + 11))\
            .drop(columns=['lj_s', 'lj_e'])\
            .rename(columns={'mae': new_mae})
        last_axis = nd_temp.plot(ax=last_axis,
                                 x='tr_size',
                                 y=new_mae,
                                 kind='line')
        print(nd_temp)

    last_axis.set_xlabel('tr_size')
    last_axis.set_ylabel('mae')
    last_axis.set_title('mae for different parameters of lj(e)')

    last_axis.get_figure().savefig('.figs\\mae_diff_param_lj_e.pdf')