Elwood

An open source dataset transformation, standardization, and normalization python library.

Usage

To use start using Elwood, simply run:

pip install elwood

Now you are able to use any of the dataset transformation, standardization, or normalization functions exposed through this library. To start, simply include from elwood import elwood in your python file.

Standardization

elwood.process(args)

#TODO STUB

Transformation

The transformation functions include geographical extent clipping (latitude/longitude), geographical regridding (gridded data such as NetCDF or GeoTIFF), temporal clipping, and temporal scaling.

Geospatial Clipping

elwood.clip_geo(dataframe, geo_columns, polygons_list)

This function takes a pandas dataframe, a list of the column names for latitude and longitude, ex: ["lat", "lng"], and a list containing lists of objects representing the polygons to clip the data to. ex:

[
     [
        {
            "lat": 11.0,
            "lng": 42.0
        },
        {
            "lat": 11.0,
            "lng": 43.0
        },
        {
            "lat": 12.0,
            "lng": 43.0
        },
        {
            "lat": 12.0,
            "lng": 42.0
        }
    ],
    ...
]

Geospatial regridding

Temporal Clipping

elwood.clip_dataframe_time(dataframe, time_column, time_ranges)

This function will produce a dataframe that only includes rows with time_column values contained within time_ranges. The time_ranges argument is a list of objects containing a start and end time. ex: [{"start": datetime, "end": datetime}, ...]

Temporal Scaling

elwood.rescale_dataframe_time(dataframe, time_column, time_bucket, aggregation_function_list)

This function will produce a dataframe who's rows are the aggregated data based on some time bucket and some aggregation function list provided. The time_column is the name of the column containing targeted time values for rescaling. The time_bucket is some DateOffset, Timedelta or str representing the desired time granularity, ex. 'M', 'A', '2H'. The aggregation_function_list is a list of aggregation functions to apply to the data. ex. ['sum'] or ['sum', 'min', 'max']

0 to 1 Normalization

elwood.normalize_features(dataframe, output_file)

This function expects a dataframe with a "feature" column and a "value" column, or long data. Each entry for a feature has its own feature/value row. This function returns a dataframe in which all numerical values under the "value" column for each "feature" have been 0 to 1 scaled. Optionally you may specify an output_file name to generate a parquet file of the dataframe.

Historys