pdbufr 0.8.2

Installation

The easiest way to install pdbufr dependencies is via Conda:

$ conda install -c conda-forge python-eccodes pandas

and pdbufr itself as a Python package from PyPI with:

$ pip install pdbufr

$ sudo apt-get install libeccodes0

$ brew install eccodes

$ python -m pdbufr selfcheck
Found: ecCodes v2.19.0.
Your system is ready.

Usage

First, you need a well-formed BUFR file, if you don’t have one at hand you can download our sample file:

$ wget http://download.ecmwf.int/test-data/metview/gallery/temp.bufr

You can explore the file with ecCodes command line tools bufr_ls and bufr_dump to understand the structure and the keys/values you can use to select the observations you are interested in.

The pdbufr.read_bufr function return a pandas.DataDrame with the requested columns. It accepts query filters on the BUFR message header that are very fast and query filters on the observation keys. Filters match on an exact value or with one of the values in a list and all filters must match:

>>> import pdbufr
>>> df_all = pdbufr.read_bufr('temp.bufr', columns=('stationNumber', 'latitude', 'longitude'))
>>> df_all.head()
   stationNumber  latitude  longitude
0            907     58.47     -78.08
1            823     53.75     -73.67
2              9    -90.00       0.00
3            486     18.43     -69.88
4            165     21.98    -159.33

>>> df_one = pdbufr.read_bufr(
...     'temp.bufr',
...     columns=('stationNumber', 'latitude', 'longitude'),
...     filters={'stationNumber': 907},
... )
>>> df_one.head()
   stationNumber  latitude  longitude
0            907     58.47     -78.08

>>> df_two = pdbufr.read_bufr(
...     'temp.bufr',
...     columns=('stationNumber', 'data_datetime', 'pressure', 'airTemperature'),
...     filters={'stationNumber': [823, 9]},
... )

>>> df_two.head()
   stationNumber  pressure  airTemperature       data_datetime
0            823  100000.0             NaN 2008-12-08 12:00:00
1            823   97400.0           256.7 2008-12-08 12:00:00
2            823   93700.0           255.1 2008-12-08 12:00:00
3            823   92500.0           255.3 2008-12-08 12:00:00
4            823   90600.0           256.7 2008-12-08 12:00:00

>>> df_two.tail()
     stationNumber  pressure  airTemperature       data_datetime
190              9    2990.0             NaN 2008-12-08 12:00:00
191              9    2790.0           206.3 2008-12-08 12:00:00
192              9    2170.0             NaN 2008-12-08 12:00:00
193              9    2000.0           203.1 2008-12-08 12:00:00
194              9    1390.0           197.9 2008-12-08 12:00:00

Contributing

The main repository is hosted on GitHub, testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/ecmwf/pdbufr

Please see the CONTRIBUTING.rst document for the best way to help.

Lead developer:

• Alessandro Amici - B-Open

Main contributors:

• Sandor Kertesz - ECMWF

See also the list of contributors who participated in this project.

License

Copyright 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.