gval 0.2.3.1

Flexible, portable, and efficient geospatial evaluations for a variety of data.

GVAL (pronounced "g-val") is a high-level Python framework to evaluate the skill of geospatial datasets by comparing candidates to benchmark maps producing agreement maps and metrics.

GVAL is intended to work on raster and vector files as xarray and geopandas objects, respectively. Abilities to prepare or homogenize maps for comparison are included. The comparisons are based on scoring philosophies for three statistical data types including categorical, continuous, and probabilistic.

See the full documentation here.

WARNING:

• Our current public API and output formats are likely to change in the future.
• Software is provided "AS-IS" without any guarantees. Please QA/QC your metrics carefully until this project matures.

Installation

General Use

To use this package:

pip install gval

Or for bleeding edge updates install from the repository:

pip install 'git+https://github.com/NOAA-OWP/gval'

Using GVAL

An example of running the entire process for two-class categorical rasters with one function using minimal arguments is demonstrated below:

import gval
import rioxarray as rxr

candidate = rxr.open_rasterio('candidate_map_two_class_categorical.tif', mask_and_scale=True)
benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_and_scale=True)

(agreement_map,
 crosstab_table,
 metric_table) = candidate.gval.categorical_compare(benchmark,
                                                   positive_categories=[2],
                                                   negative_categories=[0, 1])

Outputs

agreement_map

crosstab_table

metric_table

For more details on how to use this software, check out this notebook tutorial.

Contributing

Guidelines for contributing to this repository can be found at CONTRIBUTING.

Citation

Please cite our work if using this package. See 'cite this repository' in the about section on GitHub or refer to CITATION.cff