#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 08:09:52 2019

@author: tanya
"""

import os
import sys
import pandas as pd
import numpy as np
import gc

sys.path.append(os.getcwd())

from libraries.db_migration.ParseMapping import ParseMapping
from libraries.db_migration.ParseJsonSchema import ParseJsonSchema
from libraries.utils.ClassLogging import ClassLogging
from libraries.utils.CleaningUtils import CleaningUtils


class MigrationCleaning(ClassLogging):
    '''
    Class for correcting and filtering the incorrect data.
    We keep the correcting and the filtering methods separated,
    since there might be other custom steps in between.
    '''
    def __init__(self, mapping_path: str,
                 schema_paths: (str, list),
                 inconsist_report_table: str = None,
                 filter_index_columns: (str, list) = None,
                 mapping_source: str = "internal_name",
                 mapping_target: str = "mongo_name",
                 mapping_parser: type = ParseMapping,
                 schema_parser: type = ParseJsonSchema,
                 log_name: str = "MigrationCleaning"):
        '''
        '''
        super().__init__(log_name=log_name)

        assert isinstance(inconsist_report_table, str),\
            "Inconsistent report table should be a tablename string"

        self._inconsist_report_table = inconsist_report_table

        assert isinstance(filter_index_columns, (str, list)),\
            "Filter index columns must be a str or a list"

        self._filter_index_columns = list(filter_index_columns)

        self._schema_parser = schema_parser(schema_paths)

        self._mapping_parser = mapping_parser(mapping_path,
                                              source=mapping_source,
                                              target=mapping_target)

        self._mapping_path = mapping_path
        self._schema_paths = schema_paths

    def _assert_dataframe_input(self, data: pd.DataFrame):
        '''
        '''
        assert(isinstance(data, pd.DataFrame)),\
            "Parameter 'data' must be a pandas dataframe"

    @property
    def _field_mapping(self):
        '''
        '''
        return self._mapping_parser.get_field_mapping()

    @property
    def _required_fields(self):
        '''
        '''
        source_required_fields = self._mapping_parser.get_required_fields()
        target_required_fields = self._schema_parser.get_required_fields()

        for source_field, target_field in self._field_mapping.items():

            if (target_field in target_required_fields) and\
                    (source_field not in source_required_fields):

                source_required_fields.append(source_field)

        return source_required_fields

    @property
    def _default_values(self):
        '''
        '''
        default_values = {}

        target_default_values = self._schema_parser.get_default_values()
        source_default_values = self._mapping_parser.get_default_values()

        for source_field, target_field in self._field_mapping.items():

            if source_field not in source_default_values:
                continue

            elif target_field not in target_default_values:

                target_default_values[target_field] = np.nan

            default_values[source_field] = {
                    target_default_values[target_field]:
                    source_default_values[source_field]
                    }

        return default_values

    @property
    def _python_types(self):
        '''
        '''
        target_types = self._schema_parser.get_python_types()

        result = {}

        for source_field, target_field in self._field_mapping.items():

            if target_field in target_types:
                result[source_field] = target_types[target_field]

            """
            date_type_mismatch =\
                    (target_field in target_types) and\
                    (source_field in source_types) and\
                    (target_types[target_field] == str) and\
                    (source_types[source_field] == np.dtype('<M8[ns]'))

            if date_type_mismatch:
                target_types[target_field] = np.dtype('<M8[ns]')

            if (source_field in source_types) and\
                    (target_field in target_types) and\
                    (target_types[target_field] != source_types[source_field]):

                self.log_and_raise(("Type {0} of field {1} "
                                    "in schema does not match "
                                    "type {2} of field {3} in "
                                    "migration mapping")
                                   .format(target_types[target_field],
                                           target_field,
                                           source_types[source_field],
                                           source_field))

            if target_field in target_types:
                source_types[source_field] = target_types[target_field]

            """

        return result

    @property
    def _value_mappings(self):
        '''
        '''
        return self._mapping_parser.get_value_mappings()

    @property
    def _date_formats(self):
        '''
        '''
        return self._mapping_parser.get_date_formats()

    def _get_mongo_schema_info(self, method_name: str):
        '''
        '''
        result = {}

        target_dict = getattr(self._schema_parser, method_name)()

        for source_field, target_field in self._field_mapping.items():

            if target_field in target_dict:

                result[source_field] = target_dict[target_field]

        return result

    @property
    def _allowed_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_allowed_values")

    @property
    def _minimum_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_minimum_value")

    @property
    def _maximum_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_maximum_value")

    @property
    def _patterns(self):
        '''
        '''
        return self._get_mongo_schema_info("get_patterns")

    def _filter_invalid_data(self, data: pd.DataFrame,
                             invalid_mask: pd.Series,
                             reason: (str, pd.Series)) -> pd.DataFrame:
        '''
        '''
        from libraries.db_handlers.SQLHandler import SQLHandler

        assert((self._inconsist_report_table is not None) and
               (self._filter_index_columns is not None)),\
            "Inconsistent report table or filter index is not provided"

        self._assert_dataframe_input(data)

        data = data.copy(deep=True)

        db = SQLHandler()

        if invalid_mask.sum() == 0:

            return data

        data_inconsist = data.assign(reason=reason)\
                             .loc[invalid_mask]\
                             .reset_index(drop=True)

        db.append_to_table(data=data_inconsist,
                           tablename=self._inconsist_report_table)

        n_rows_filtered = len(data_inconsist)
        n_instances_filtered = len(data_inconsist[self._filter_index_columns].drop_duplicates())

        del data_inconsist
        gc.collect()

        self._log.warning(("Filtering: {0} ."
                           "Filtered {1} rows "
                           "and {2} instances"
                           .format(reason, n_rows_filtered, n_instances_filtered)))

        nok_index_data = data.loc[invalid_mask, self._filter_index_columns]\
                             .drop_duplicates().reset_index(drop=True)

        nok_index = pd.MultiIndex.from_arrays([nok_index_data[c] for c in
                                               self._filter_index_columns])

        all_index = pd.MultiIndex.from_arrays([data[c] for c in
                                               self._filter_index_columns])

        data = data.loc[~all_index.isin(nok_index)].reset_index(drop=True)

        return data

    def _replace_values(self, data: pd.DataFrame,
                        default: bool) -> pd.DataFrame:
        '''
        '''
        if default:
            default_str = "default"
        else:
            default_str = "equal"

        self._assert_dataframe_input(data)

        data = data.copy(deep=True)

        if default:
            mapping = self._default_values
        else:
            mapping = self._value_mappings

        for column, d in mapping.items():

            try:

                if column not in data.columns:
                    continue

                dtype = data[column].dtype

                for key, values in d.items():

                    if not default:

                        mask = (data[column].astype(str).isin(values))

                    else:
                        mask = (data[column].isin(values))

                    if default:

                        mask = mask | (data[column].isnull())

                    data.loc[mask, column] = key

                data[column] = data[column].astype(dtype)

            except Exception as e:

                self.log_and_raise(("Failed to replace {0} values "
                                    "in {1}. Exit with error {2}"
                                    .format(default_str, column, e)))

        self._log.info("Replaced {} values".format(default_str))

        return data

    def replace_default_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        return self._replace_values(data=data, default=True)

    def map_equal_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        return self._replace_values(data=data, default=False)

    def convert_types(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column, python_type in self._python_types.items():

            try:
                if column not in data.columns:
                    continue

                elif column in self._date_formats:

                    data[column] = CleaningUtils.convert_dates(
                            series=data[column],
                            formats=self._date_formats[column])

                elif (python_type == int) and data[column].isnull().any():

                    self.log_and_raise(("Column {} contains missing values "
                                        "and cannot be of integer type"
                                        .format(column)))

                elif python_type == str:

                    python_type = object

                else:

                    data[column] = data[column].astype(python_type)

                if data[column].dtype != python_type:

                    self._log.warning(("After conversion type in {0} "
                                       "should be {1} "
                                       "but is still {2}"
                                       .format(column,
                                               python_type,
                                               data[column].dtype)))

            except Exception as e:

                self.log_and_raise(("Failed to convert types in {0}. "
                                    "Exit with error {1}"
                                    .format(column, e)))

        self._log.info("Converted dtypes")

        return data

    def filter_invalid_null_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column in data.columns:

            if (column in self._required_fields) and\
                    (data[column].isnull().any()):

                invalid_mask = data[column].isnull()

                reason = "Null value in the required field {}"\
                         .format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

        return data

    def filter_invalid_types(self, data: pd.DataFrame) -> pd.DataFrame():
        '''
        '''
        self._assert_dataframe_input(data)

        for column, python_type in self._python_types.items():

            if data[column].dtype != python_type:

                def mismatch_type(x):
                    return type(x) != python_type

                invalid_mask = data[column].apply(mismatch_type)

                reason = "Type mismatch if field {}".format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

        return data

    def filter_invalid_patterns(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column, pattern in self._patterns:

            invalid_mask = (~data[column].astype(str).str.match(pattern))

            reason = "Pattern mismatch in field {}".format(column)

            data = self._filter_invalid_data(data=data,
                                             invalid_mask=invalid_mask,
                                             reason=reason)

        return data

    def filter_notallowed_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        for column, value in self._minimum_values.items():

            invalid_mask = data[column] > value

            reason = "Too large values in field {}".format(column)

            data = self._filter_invalid_data(data=data,
                                             invalid_mask=invalid_mask,
                                             reason=reason)

        for column, value in self._maximum_values.items():

            invalid_mask = data[column] < value

            reason = "Too small values in field {}".format(column)

            data = self._filter_invalid_data(data=data,
                                             invalid_mask=invalid_mask,
                                             reason=reason)

        for column, allowed_values in self._allowed_values.items():

            invalid_mask = (~data[column].isin(allowed_values))

            reason = "Too small values in field {}".format(column)

            data = self._filter_invalid_data(data=data,
                                             invalid_mask=invalid_mask,
                                             reason=reason)

        return data


if __name__ == "__main__":

    # testing

    from libraries.db_handlers.SQLHandler import SQLHandler

    mapping_path = os.path.join(".", "migration_mappings", "rs1_mapping.json")

    schema_paths = [
            os.path.join(".", "mongo_schema", "schema_wheelsets.json"),
            os.path.join(".", "mongo_schema", "schema_process_instances.json")]

    inconsist_report_table = "test_inconsist_report_rs1"

    if all([os.path.isfile(p) for p in schema_paths + [mapping_path]]):

        print("Found schemas!")

        cleaner = MigrationCleaning(
                mapping_path=mapping_path,
                schema_paths=schema_paths,
                mapping_source="internal_name",
                mapping_target="mongo_name",
                filter_index_columns=["radsatznummer"],
                inconsist_report_table=inconsist_report_table)

        db = SQLHandler()

        data = db.read_sql_to_dataframe("select * from rs1 limit 100")

        data = cleaner.replace_default_values(data)

        data = cleaner.map_equal_values(data)

        data = cleaner.convert_types(data)

        non_filtered_len = len(data)

        data = cleaner.filter_invalid_types(data)

        if len(data) < non_filtered_len:

            data = cleaner.convert_types(data)

        data = cleaner.filter_invalid_null_values(data)

        data = cleaner.filter_invalid_patterns(data)

        data = cleaner.filter_notallowed_values(data)

    print("Done!")