The skinny WMS is a small WMS server that will help you to visualise your NetCDF and Grib Data. The principle is simple: skinny will browse the directory, or the single file passed as argument, and try to interpret each NetCDF or GRIB files. From the metadata, it will be built the getCapabilities document, and find a relevant style to plot the data.
SkinnyWMS implements 3 of the WMS endpoints:
- getCapabilities: Discover the data, build an XML Document presenting each identified parameter in the file(s) as a layer with the list of their predefined styles. (There is always a default style)
- getMap : Return the selected layer suing the selected style.
- getLegendGraphic: Return the legend.
There are 2 ways to start using it, they both will start a small Flask server. Once running, a small leaflet client is accessible [http://127.0.0.1:5000/]
- The demo:
python demo.py --path /path/to/mydata- The command line:
skinny-wms --path /path/to/mydata- Or with uwsgi:
uwsgi --http localhost:5000 --master --process 20 --mount /=skinnywms.wmssvr:application --env SKINNYWMS_DATA_PATH=/path/to/mydataBy default the docker image will start the application using uwsgi and will load and display some demo data.
- Run the demo:
docker run --rm -p 5000:5000 -it ecmwf/skinnywms Now you can try the leaflet demo at http://localhost:5000/
- Run using data on your machine:
docker run --rm -p 5000:5000 -it \
--volume=/path/to/my/data:/path/inside/the/container \
--env SKINNYWMS_DATA_PATH=/path/inside/the/container \
ecmwf/skinnywmsNow you can access the leaflet demo with your data at http://localhost:5000/
- Configure different options by setting environment variables accordingly:
docker run --rm -p 5000:5000 -it \
--volume=/path/to/my/data:/path/inside/the/container \
--env SKINNYWMS_DATA_PATH=/path/inside/the/container \
--env SKINNYWMS_HOST=0.0.0.0 \
--env SKINNYWMS_PORT=5000 \
--env SKINNYWMS_MOUNT=/mymodel/ \
--env SKINNYWMS_UWSGI_WORKERS=4 \
--env SKINNYWMS_ENABLE_DIMENSION_GROUPING=1 \
ecmwf/skinnywmsNow you can access the ```GetCapabilities`` document for your data at http://localhost:5000/mymodel/wms?request=GetCapabilities
SkinnyWMS depends on the ECMWF Magics library.
If you do not have Magics installed on your platform, skinnywms is available on conda forge https://conda-forge.org/
conda config --add channels conda-forge
conda install skinnywmsIf you have Magics already installed you can use pip:
pip install skinnywms-
SkinnyWMS will perform better on well formatted and documented NetCDF and GRIB.
-
grib fields containing corresponding wind components u,v need to be placed together in a single grib file in order to be displayed as vectors/wind barbs in SkinnyWMS. You can combine multiple grib files into a single file using ecCodes
grib_copy(included in the docker image), e.g.:
grib_copy input_wind_u_component.grb2 input_wind_v_component.grib2 output_wind_u_v_combined.grb2-
The time and elevation dimension implementations follow OGC Met Ocean DWG WMS 1.3 Best Practice for using Web Map Services (WMS) with Time-Dependent or Elevation-Dependent Data. To enable dimension grouping (disabled by default) set the environment variable
SKINNYWMS_ENABLE_DIMENSION_GROUPING=1 -
development stage: Alpha,
that must be installed on the system and accessible as a shared library. Some Linux distributions ship a binary version that may be installed with the standard package manager.
As an alternative you may install the official source distribution by following the instructions at https://software.ecmwf.int/magics/Installation+Guide Magics is available on github https://github.com/ecmwf/magics
Note that Magics support for the Windows operating system is experimental.
Alternatively you can use the ecmwflibs package (currently in Alpha development stage) to install magics and eccodes libraries:
pip install ecmwflibs
Make sure you have Docker and docker-compose installed. Then run:
docker-compose upThis will build a dev image and start up a local flask development server (with automatic reload on code changes) at http://localhost:5000 based on the configuration stored in docker-compose.yml and .env and by default try to load all GRIB and NetCDF data stored in skinnywms/testdata.
The main repository, as well as related projects are hosted on GitHub. Testing, bug reports and contributions to all our projects are highly welcomed and appreciated:
- https://github.com/ecmwf/skinnywms
- https://github.com/ecmwf/magics-python
- https://github.com/ecmwf/magics
- https://github.com/ecmwf/eccodes
- https://github.com/ecmwf/ecmwflibs
Lead developers:
Sylvie Lamy-Thepaut <https://github.com/sylvielamythepaut>_ - ECMWFBaudouin Raoult <https://github.com/b8raoult>- ECMWFEduard Rosert <https://github.com/EduardRosert>- ECMWF
Main contributors:
Stephan Siemen <https://github.com/stephansiemen>_ - ECMWFMilana Vuckovic <https://github.com/milanavuckovic>- ECMWF
Copyright 2017-2019 European Centre for Medium-Range Weather Forecasts (ECMWF).
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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