# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function

import pandas as pd

# The field class_name contains the name of the class to load to execute the
# plugin.
class_name = 'OnTaskTestPlugin'


class OntaskTestPlugin(object):
    """
    Example of a class that implements the OnTask plugin interface. The
    objects of this class have to provide the following elements:

    1. Field name: Plugin name show to the users.

    2. Field description_txt: A string with the detailed description of what the
    plugin does

    3. Field input_column_names: A potentially empty list of column names 
    (strings). If the list is empty, the columns are selected by the userat 
    execution time.

    4. Field output_column_names: Non empty list of names (strings) of the 
    columns to be used for the output of the transformation.

    5. Field parameters: an optionally empty list with tuples with the following
    structure:

      ('name', type, [list of allowed values], initial value, help_text)

      These elements will be requested from the user before executing the
      plugin through a form. The conditions on these values are:

      - name must be a string
      - type must be a string equal to "integer", "double", "string", 
        "datetime" or "boolean". 
      - The list of values is to restrict the
        possible values
      - The initial value must be of the type specified by the second 
        element.
      - Help_text a string to show as help text

    6. Method "run" that receives:
       - a pandas data frame with the data to process
       - a string with the name of the key column that will be used to merge
       the result.
       - A dictionary of pairs (name, value) with the parameters described in
       the previous element.

       and returns a result Pandas data frame. This frame **must** have one
       column with the key column name provided so that it can be properly
       merged with the existing data.
    """

    def __init__(self):
        self.name = 'Plugin Template'
        self.description_txt = 'Example of plugin description'
        self.input_column_names = list()
        self.output_column_names = ['RESULT 1', 'RESULT 2']
        self.parameters = [
            ('param string', 'string', ['v1', 'v2'], 'v1', 'help param string'),
            ('param integer', 'integer', [], None, 'help param integer'),
            ('param double', 'double', [1.2, 2.2, 3.2], None,
                             'help param double'),
            ('param boolean', 'boolean', [], True, 'help param boolean'),
            ('param datetime', 'datetime', [], '2018-05-25 18:03:00+09:30',
                               'help param datetime'),
            ('param datetime2', 'datetime', 
                                [],
                                '2018-05-25 18:03:00+09:30',
                                'help param datetime'),
        ]

    def run(self, data_frame, merge_key, parameters=dict):
        """
        Method to overwrite. Receives a data frame wih a number of columns
        stipulated by the num_column_input pair, the name of a key column and a
        dictionary with parameters of the form name, value.

        Runs the algorithm and returns a pandas data frame structure that is
        merged with the existing data frame in the workflow using the merge_key.

        :param data_frame: Input data for the plugin
        :param merge_key: Name of the column key that will be used for merging
        :param parameters: Dictionary with (name, value) pairs.

        :return: a Pandas data_frame to merge with the existing one (must
        contain a column with name merge_key)
        """

        # Extract the key column from the given data frame
        result = pd.DataFrame(data_frame[merge_key])

        # Process the given data and create the result
        result[self.output_column_names[0]] = 1
        result[self.output_column_names[1]] = 2

        return result