Release notes

Release notes#

Below, the release notes for all minor versions and our roadmap to a first major version.

0.2.0#

GeoUtils version 0.2 is the second minor release since the creation of the project. It is the result of months of work to consolidate the point cloud features towards a stable API that interfaces well with other objects. Parallel work on scalability with Dask and Multiprocessing is ongoing, and should soon be released in a 0.3.

GeoUtils 0.2 adds:

  • A point cloud object with its own specific methods (e.g, gridding), that can be used as match-reference for other operations (e.g., interpolating at points), and supports arithmetic (e.g., indexing, NumPy stats) and geometric (e.g., masking) functionalities with the same API as for Raster objects,

  • Preliminary statistics features common to rasters and point clouds, which will be expanded with binning and spatial statistics.

A few changes might be required to adapt from previous versions:

  • Specify Raster.interp_points(as_array=True) to mirror the previous behaviour of returning a 1D array of interpolated values, otherwise now returns a point cloud by default.

  • The Mask class is deprecated in favor of Raster(is_mask=True) to declare a boolean-type Raster, but should keep working until 0.3.

0.1.0#

GeoUtils version 0.1 is the first minor release since the creation of the project in 2020. It is the result of years of work to consolidate and re-structure features into a mature and stable API to minimize future breaking changes.

All the core features drafted at the start of the project are now supported, and there is a clear roadmap towards a first major release 1.0. This minor release also adds many tests and improves significantly the documentation from the early-development state of the package.

The re-structuring created some breaking changes, though minor.

See details below, including a guide to help migrate code from early-development versions.

Features#

GeoUtils now gathers the following core features:

  • Geospatial data objects core to quantatiative analysis, which are rasters, vectors and point cloud (preliminary) functionalities,

  • Referencing and transformations using a consistent API with match-reference functionalities,

  • Raster–vector–point interface to interface between the core objects, including rasterize and polygonize, interpolate and grid, and conversions,

  • Distance operations for all objects.

Migrate from early versions#

The following changes might be required to solve breaking changes, depending on your early-development version:

  • Rename .show() to .plot() for all data objects,

  • Rename .dtypes to dtype for Raster objects,

  • Operations .crop(), shift() and to_vcrs() are not done in-place by default anymore, replace by rst = rst.crop() or rst.crop(..., inplace=True) to mirror the old default behaviour,

  • Rename .shift() to .translate() for Raster objects,

  • Several function arguments are renamed, in particular dst_xxx arguments of .reproject() are all renamed to xxx e.g. dst_crs to crs,

  • New user warnings are sometimes raised, in particular if some metadata is not properly defined such as .nodata. Those should give an indication as how to silence them.

Roadmap to 1.0#

Based on recent and ongoing progress, we envision the following roadmap.

Releases of 0.2, 0.3, 0.4, etc, for the following planned (ongoing) additions:

  • The addition of a point cloud PointCloud data object, inherited from the Vector object alongside many features at the interface of point and raster,

  • The addition of a Xarray accessor rst mirroring the Raster object, to work natively with Xarray objects and add support on out-of-memory Dask operations for most of GeoUtils’ features,

  • The addition of a GeoPandas accessor pc mirroring the PointCloud object, to work natively with GeoPandas objects,

  • The addition of statistical features including zonal statistics (e.g., statistics per vector geometry), grouped statistics (e.g., binning with other variables) and spatial statistics (variogram and kriging) through optional dependencies.

  • The addition of filtering and gap-filling features natively robust to nodata and working similarly for all type of geospatial objects.

Release of 1.0 once all these additions are fully implemented, and after feedback from the community.