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For documentation: https://ocgis.readthedocs.io/en/latest/

For additional project information: http://www.earthsystemcog.org/projects/openclimategis/

For questions or to file a bug report, please create a GitHub issue.

Overview

OpenClimateGIS (OCGIS) is a Python package designed for geospatial manipulation, subsetting, computation, and translation of spatiotemporal datasets stored in local NetCDF files or files served through THREDDS data servers. OpenClimateGIS has a straightforward, request-based API that is simple to use yet complex enough to perform a variety of computational tasks. The software is built entirely from open source packages.

OpenClimateGIS supports many file formats and NetCDF metadata conventions:

  • Climate & Forecast (CF) Grid
  • Unstructured Grid (UGRID)
  • SCRIP
  • ESMF Unstructured
  • GDAL-supported vector formats (ESRI Shapefiles, File Geodatabases, GeoPackage, GeoJSON, etc.)
  • Comma-Separated Value (CSV)

GIS Capabilities

  • Subsetting (intersects and intersection) of climate datasets by bounding box, Shapely geometries, or shapefiles (city centroid, river reach, a single county or watershed, state boundaries).
  • Time and level range subsetting. Also allows for arbitrary label-based slicing.
  • Single or multi-dataset requests (concatenation).
  • Area-weighted aggregation (spatial averaging) to selection geometries.
  • Handles CF-based coordinate systems with full support for coordinate transformations (including the rotated pole coordinate system)
  • Geometry wrapping and unwrapping to maintain logically consistent longitudinal domains.
  • Polygon, line, and point geometric abstractions.
  • ESMPy regridding

Data Conversion

  • Access to local NetCDF data or data hosted remotely on a THREDDS (OPeNDAP protocol) data server. Only the piece of data selected by an area-of-interest is transferred from the remote server.
  • Stream climate data to multiple formats.
  • Extensible converter framework to add custom formats.
  • Automatic generation of request metadata.
  • Push data to a familiar format to perform analysis or keep the data as NumPy arrays, perform analysis, and dump to a supported format.

Computation

  • Extensible computational framework for arbitrary inclusion of NumPy-based calculations.
  • Apply computations to entire data arrays or temporal groups.
  • Computed data may be streamed to any supported formats.