{"id":26163845,"url":"https://github.com/xarray-contrib/xvec","last_synced_at":"2025-05-16T14:04:48.338Z","repository":{"id":63344306,"uuid":"566062095","full_name":"xarray-contrib/xvec","owner":"xarray-contrib","description":"Vector data cubes for Xarray","archived":false,"fork":false,"pushed_at":"2025-05-12T10:05:50.000Z","size":2035,"stargazers_count":115,"open_issues_count":12,"forks_count":11,"subscribers_count":8,"default_branch":"main","last_synced_at":"2025-05-12T10:15:27.918Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://xvec.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/xarray-contrib.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-11-14T22:18:52.000Z","updated_at":"2025-05-12T10:01:49.000Z","dependencies_parsed_at":"2023-02-19T06:15:40.416Z","dependency_job_id":"b6e2e265-909d-4f19-b4b9-9723078973de","html_url":"https://github.com/xarray-contrib/xvec","commit_stats":{"total_commits":50,"total_committers":4,"mean_commits":12.5,"dds":"0.42000000000000004","last_synced_commit":"cd33d6de0c2ff34c428032ddc784917a4c0c5eae"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xarray-contrib%2Fxvec","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xarray-contrib%2Fxvec/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xarray-contrib%2Fxvec/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xarray-contrib%2Fxvec/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xarray-contrib","download_url":"https://codeload.github.com/xarray-contrib/xvec/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254544146,"owners_count":22088807,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-11T14:53:17.945Z","updated_at":"2025-05-16T14:04:48.320Z","avatar_url":"https://github.com/xarray-contrib.png","language":"Python","readme":"# Vector data cubes for Xarray\n\nIn geospatial analysis, data cubes can be of two sorts. The first is a raster data cube, typically represented by an [Xarray](https://docs.xarray.dev/en/stable/) DataArray indexed either by `x` or `y` dimensions or `latitude` and `longitude`. The second is a vector data cube, which is an n-D array that has either at least one dimension indexed by a 2-D array of vector geometries ([Pebesma, 2022](https://r-spatial.org/r/2022/09/12/vdc.html)) or contains geometries as variables (e.g. moving features or time-evolving shapes), possibly both.\n\nWe can distinguish between two types of geometries in a DataArray or Dataset:\n\n- **coordinate geometry** - an array (typically one dimensional) is used as coordinates along one or more dimensions. A typical example would be an outcome of zonal statistics of a multi-dimensional raster, avoiding the need for _flattenning_ of the array to a data frame.\n- **variable geometry** - an array (typicially multi-dimensional) is used as a variable within a DataArray. This may encode evolving shapes of lava flows in time, trajectories, or growth of city limits.\n\nThe Xvec package brings support for both of these to the Xarray ecosystem. It uses [Shapely](https://shapely.readthedocs.io/en/stable/) package, allowing a seamless interface between Xvec and [GeoPandas](https://geopandas.org/). See [this post](https://r-spatial.org/r/2022/09/12/vdc.html) by Edzer Pebesma on an introduction of the concept of coordinate geometry or [introduction](https://xvec.readthedocs.io/en/latest/intro.html) page in Xvec documentation.\n\n## Project status\n\nThe project is in the early stage of development and its API may still change.\n\n## Installing\n\nYou can install Xvec from PyPI using `pip` or from conda-forge using `mamba` or `conda`:\n\n```sh\npip install xvec\n```\n\nOr (recommended):\n\n```sh\nmamba install xvec -c conda-forge\n```\n\n### Development version\n\nThe development version can be installed from GitHub.\n\n```sh\npip install git+https://github.com/xarray-contrib/xvec.git\n```\n\nWe recommend installing its dependencies using `mamba` or `conda` before.\n\n```sh\nmamba install xarray shapely pyproj -c conda-forge\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxarray-contrib%2Fxvec","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxarray-contrib%2Fxvec","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxarray-contrib%2Fxvec/lists"}