seed.lib.mappings package

Submodules

seed.lib.mappings.mapper module

:copyright (c) 2014 - 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved. :author Dan Gunter <dkgunter@lbl.gov>

seed.lib.mappings.mapper.create_column_regexes(raw_columns)

Take the columns in the format below and sanitize the keys and add in the regex.

Parameters

raw_data – list of strings (columns names from imported file)

Returns

list of dict

seed.lib.mappings.mapper.get_pm_mapping(raw_columns, mapping_data=None, resolve_duplicates=True)

Create and return Portfolio Manager (PM) mapping for a given version of PM and the given list of column names.

The method will take the raw_columns (from the CSV/XLSX file) and attempt to normalize the column names so that they can be mapped to the data in the pm-mapping.json[‘from_field’].

seed.lib.mappings.mapping_columns module

:copyright (c) 2014 - 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved. :author Nicholas Long <nicholas.long@nrel.gov>

class seed.lib.mappings.mapping_columns.MappingColumns(raw_columns, dest_columns, previous_mapping=None, map_args=None, default_mappings=None, threshold=0)

Bases: object

This class handles the probabilistic mapping of unknown columns to defined fields. This is mainly used in the build_column_mapping API endpoint.

add_mappings(raw_column, mappings, previous_mapping=False)

Add mappings to the data structure for later processing.

Parameters

• raw_column – list of strings

• mappings – list of tuples of potential mappings and confidences

• previous_mapping – boolean, if true these these mappings will take precedence

Returns

Bool, whether or not the mapping was added

apply_threshold(threshold)

Remove mapping suggestions that do not meet the defined threshold

This method is forced as part of the workflow for now, but could easily be made as a separate call.

Parameters

threshold – int, min value to be greater than or equal to.

Returns

None

property duplicates

Check for duplicate initial mapping results. Duplicates exist if the first suggested mapping for two different raw_columns are the same. The example below would be one of those cases.

Returns

List of raw col

property final_mappings

Return the final mappings in a format that can be used downstream from this method {

“raw_column_1”: (‘table’, ‘db_column_1’, confidence), “raw_column_2”: (‘table’, ‘db_column_1’, confidence),

}

first_suggested_mapping(raw_column)

Grab the first suggested mapping for a raw column

Parameters

raw_column – String

Returns

tuple of the mapping (‘table’, ‘field’, confidence), or ()

resolve_duplicate(dup_map_field, raw_columns)

If there are duplicates, that is two raw_columns are trying to map to the same suggested column, then select the next available one on the duplicate column. The one with the highest confidence will ‘win’ the duplicate battle.

Parameters

• dup_map_field – String, name of the field that is a duplicate

• raw_columns – list, raw columns that mapped to the same result

Returns

None

set_initial_mapping_cmp(raw_column)

Set the initial_mapping_cmp helper item in the self.data hash. This is used to detect if there are any duplicates. The initial mapping cmp will be the first match in the list (i.e. the one with the highest confidence).

Parameters

raw_column – String, name of the raw column to set the initial_mapping_cmp

Returns

None

seed.lib.mappings.mapping_columns.sort_duplicates(a, b)

Custom sort for the duplicate hash to decide which raw column will get the mapping suggestion based on the confidence.

seed.lib.mappings.mapping_data module

seed.lib.mappings.test_mapper module

seed.lib.mappings.test_mapping_columns module

seed.lib.mappings.test_mapping_data module

Module contents

:copyright (c) 2014 - 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved. :author