#!/usr/bin/env python

# -*- coding: UTF-8 -*-

#

# Copyright 2017 University of Westminster. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

""" It reads and parses the variables, then it generate features, in threaded batches.

"""

from typing import List, TypeVar, Dict

import numpy as np

import statistics

from scipy.stats import itemfreq

PandasDataFrame = TypeVar('DataFrame')

NumpyNdarray = TypeVar('ndarray')

__author__ = "Mohsen Mesgarpour"

__copyright__ = "Copyright 2016, https://github.com/mesgarpour"

__credits__ = ["Mohsen Mesgarpour"]

__license__ = "GPL"

__version__ = "1.1"

__maintainer__ = "Mohsen Mesgarpour"

__email__ = "mohsen.mesgarpour@gmail.com"

__status__ = "Release"

class FeatureParserThread:

    @staticmethod

    def aggregate_cell(postfixes: str,

                       variable_type: str,

                       prevalence: Dict,

                       variable_cell: str) -> NumpyNdarray:

        """Aggregate the variable value, based on the selected aggregated functions.

        :param postfixes: the aggregated variable.

        :param variable_type: the type of the input variable.

        :param prevalence: the prevalence dictionary of values for all the variables.

        :param variable_cell: the variable value (a single row) to aggregate.

        :return: the aggregated value (a single row).

        """

        features_temp = np.zeros([len(postfixes)])

        # if null or empty

        if variable_cell is None or variable_cell == "":

            return features_temp

        # parse variables

        # Note: replace None with '0'

        variable_cell = variable_cell.split('|')

        variable_cell = [v2 for v1 in variable_cell for v2 in set(v1.split(','))]

        variable_cell = [v if v != "" else 0 for v in variable_cell]

        if variable_type == "INT":

            variable_cell = list(map(int, variable_cell))

        # generate freq. table

        freq = itemfreq(variable_cell)

        freq = np.array([tuple(row) for row in freq if row[1] != 0], dtype=[('value', 'int'), ('freq', 'int')])

        freq_sorted = np.sort(freq, order=['freq', 'value'])[::-1]['value']

        freq_dic = dict(zip(map(str, freq['value']), freq['freq']))

        # set

        for p in range(len(postfixes)):

            if len(postfixes[p]) > 11 and postfixes[p][0:11] == "prevalence_":

                index = int(postfixes[p].split('_')[1]) - 1

                if index < len(prevalence):

                    value = prevalence[index]

                    if str(value) in freq_dic.keys():

                        features_temp[p] = freq_dic[value]

            elif len(postfixes[p]) > 9 and postfixes[p][0:9] == "max_freq_":

                index = int(postfixes[p][9:]) - 1

                if len(freq_sorted) > index:

                    features_temp[p] = freq_sorted[index]

            elif postfixes[p] == "others_cnt":

                features_temp[p] = len(freq_sorted)  # np.count_nonzero(variable_cell)

            elif postfixes[p] == "max":

                features_temp[p] = max(variable_cell)

            elif postfixes[p] == "avg":

                features_temp[p] = statistics.mean(variable_cell)

            elif postfixes[p] == "min":

                features_temp[p] = min(variable_cell)

            elif postfixes[p] == "median":

                features_temp[p] = statistics.median(variable_cell)

            else:

                raise ValueError(postfixes)

        return features_temp

    @staticmethod

    def prevalence_cell(variable_cell: str) -> List:

        """Parse the inputted variable value (a single row), to a list of value.

        :param variable_cell: the variable value (a single row), to calculate the prevalence.

        :return: the list of values of the current variable value.

        """

        # if null or empty

        if variable_cell is None or variable_cell == "":

            return []

        else:

            # parse variables

            # Note: replace None with '0'

            variable_cell = variable_cell.split('|')

            variable_cell = [v2 for v1 in variable_cell for v2 in set(v1.split(','))]

            variable_cell = [v if v != "" else 0 for v in variable_cell]

            variable_cell = list(map(str, variable_cell))

            return variable_cell