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WMS plugin for xpublish

Project description

xpublish-wms

PyPI Conda Version

Tests pre-commit.ci status

Xpublish routers for the OGC WMS API.

Installation

For conda users you can

conda install --channel conda-forge xpublish_wms

or, if you are a pip users

pip install xpublish_wms

Once it's installed, the plugin will register itself with Xpublish and WMS endpoints will be included for each dataset on the server.

Dataset Requirements

At this time, only a subset of xarray datasets will work out of the box with this plugin. To be compatible, a dataset must contain CF compliant coordinate variables for lat, lon, time, and vertical. time and vertical are optional.

Currently the following grid/model types are supported:

  • Regularly spaced lat/lon grids (Tested with GFS, GFS Wave models)
  • Curvilinear grids (Tested with ROMS models CBOFS, DBOFS, TBOFS, WCOFS, GOMOFS, and CIOFS models)
  • FVCOM grids (Tested with LOOFS, LSOFS, LMHOFS, and NGOFS2 models)
  • SELFE grids (Tested with CREOFS model)
  • 2d Non Dimensional grids (Tested with RTOFS, HRRR-Conus models)

Supporting new grid/model types

If you have a dataset that is not supported, you can add support by creating a new xpublish_wms.Grid subclass and registering it with the xpublish_wms.register_grid_impl function. See the xpublish_wms.grids module for examples.

Get in touch

Report bugs, suggest features or view the source code on GitHub.

License and copyright

xpublish-wms is licensed under BSD 3-Clause "New" or "Revised" License (BSD-3-Clause).

Development occurs on GitHub at https://github.com/xpublish-community/xpublish-wms.

Support

Work on this plugin is sponsored by:

IOOS

IOOS (github) funds work on this plugin via the "Reaching for the Cloud: Architecting a Cloud-Native Service-Based Ecosystem for DMAC" project being led by RPS Ocean Science.

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  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
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The following attestation bundles were made for xpublish_wms-0.8.0-py3-none-any.whl:

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