phenodata is a data acquisition and manipulation toolkit for open access phenology data. It is written in Python.
Currently, it implements data wrappers for acquiring phenology observation data published on the DWD Climate Data Center (CDC) FTP server operated by »Deutscher Wetterdienst« (DWD).
Under the hood, it uses the fine Pandas data analysis library for data mangling, amongst others.
Thanks to the many observers, »Deutscher Wetterdienst«, the »Global Phenological Monitoring programme« and all people working behind the scenes for their commitment in recording the observations and for making the excellent datasets available to the community. You know who you are.
For most acquisition tasks, you must choose from one of two different datasets: annual-reporters and immediate-reporters.
To improve data acquisition performance, also consider applying
the --filename= parameter for file name filtering.
Example: When using --filename=Hasel,Schneegloeckchen, only file names containing
Hasel or Schneegloeckchen will be retrieved, thus minimizing the required effort
to acquire all files.
If you know your way around Python, installing this software is really easy:
pip install phenodata --upgrade
Please refer to the virtualenv page about further recommendations how to install and use this software.
$ phenodata --help
Usage:
phenodata info
phenodata list-species --source=dwd [--format=csv]
phenodata list-phases --source=dwd [--format=csv]
phenodata list-stations --source=dwd --dataset=immediate [--all] [--filter=berlin] [--sort=Stationsname] [--format=csv]
phenodata nearest-station --source=dwd --dataset=immediate --latitude=52.520007 --longitude=13.404954 [--format=csv]
phenodata nearest-stations --source=dwd --dataset=immediate [--all] --latitude=52.520007 --longitude=13.404954 [--limit=10] [--format=csv]
phenodata list-quality-levels --source=dwd [--format=csv]
phenodata list-quality-bytes --source=dwd [--format=csv]
phenodata list-filenames --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--year=2017]
phenodata list-urls --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--year=2017]
phenodata (observations|forecast) --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--station-id=164,717] [--species-id=113,127] [--phase-id=5] [--quality-level=10] [--quality-byte=1,2,3] [--station=berlin,brandenburg] [--species=hazel,snowdrop] [--species-preset=mellifera-primary] [--phase=flowering] [--quality=ROUTKLI] [--year=2017] [--humanize] [--show-ids] [--language=german] [--long-station] [--sort=Datum] [--format=csv] [--verbose]
phenodata drop-cache --source=dwd
phenodata --version
phenodata (-h | --help)
Data acquisition options:
--source=<source> Data source. Currently "dwd" only.
--dataset=<dataset> Data set. Use "immediate" or "annual" for --source=dwd.
--partition=<dataset> Partition. Use "recent" or "historical" for --source=dwd.
--filename=<file> Filter by file names (comma-separated list)
Direct filtering options:
--years=<years> Filter by years (comma-separated list)
--station-id=<station-id> Filter by station ids (comma-separated list)
--species-id=<species-id> Filter by species ids (comma-separated list)
--phase-id=<phase-id> Filter by phase ids (comma-separated list)
Humanized filtering options:
--station=<station> Filter by strings from "stations" data (comma-separated list)
--species=<species> Filter by strings from "species" data (comma-separated list)
--phase=<phase> Filter by strings from "phases" data (comma-separated list)
--species-preset=<preset> Filter by strings from "species" data (comma-separated list)
The preset will get loaded from the ``presets.json`` file.
Data output options:
--format=<format> Output data in designated format. Choose one of "tabular", "json", "csv" or "string".
With "tabular", it is also possible to specify the table format,
see https://bitbucket.org/astanin/python-tabulate. e.g. "tabular:presto".
[default: tabular:psql]
--sort=<sort> Sort by given column names (comma-separated list)
--humanize Resolve ID-based columns to real names with "observations" and "forecast" output.
--show-ids Show IDs alongside resolved text representation when using ``--humanize``.
--language=<language> Use labels in designated language when using ``--humanize`` [default: english].
--long-station Use long station name including "Naturraumgruppe" and "Naturraum".
--limit=<limit> Limit output of "nearest-stations" to designated number of entries.
[default: 10]
--verbose Turn on verbose output
| Datum | Spezies | Phase | Station |
|---|---|---|---|
| 2018-02-17 | common snowdrop | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-02-19 | common hazel | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-03-30 | goat willow | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-04-07 | dandelion | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-04-15 | cherry (late ripeness) | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-04-21 | winter oilseed rape | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-04-23 | apple (early ripeness) | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-05-03 | apple (late ripeness) | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-05-24 | black locust | beginning of flowering | Berlin-Dahlem, Berlin |
| 2018-08-20 | common heather | beginning of flowering | Berlin-Dahlem, Berlin |
List of species:
phenodata list-species --source=dwd
List of phases:
phenodata list-phases --source=dwd
List of all stations:
phenodata list-stations --source=dwd --dataset=immediate
List of filtered stations:
phenodata list-stations --source=dwd --dataset=annual --filter="Fränkische Alb"
List of file names of recent observations by the annual reporters:
phenodata list-filenames --source=dwd --dataset=annual --partition=recent
List of full URLs to observations using filename-based filtering:
phenodata list-urls --source=dwd --dataset=annual --partition=recent --filename=Hasel,Schneegloeckchen
Display nearest station for given position:
phenodata nearest-station --source=dwd --dataset=immediate --latitude=52.520007 --longitude=13.404954
Display 20 nearest stations for given position:
phenodata nearest-stations \
--source=dwd --dataset=immediate \
--latitude=52.520007 --longitude=13.404954 --limit=20
Observations of hazel and snowdrop, using filename-based filtering at data acquisition time:
phenodata observations --source=dwd --dataset=annual --partition=recent --filename=Hasel,Schneegloeckchen
Observations of hazel and snowdrop (dito), but for station ids 164 and 717 only:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen --station-id=164,717
All observations for station ids 164 and 717 in years 2016 and 2017:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station-id=164,717 --year=2016,2017
All observations for station ids 164 and 717 and species ids 113 and 127:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station-id=164,717 --species-id=113,127
All invalid observations:
phenodata list-quality-bytes --source=dwd phenodata observations --source=dwd --dataset=annual --partition=recent --quality-byte=5,6,7,8
Acquire data from observations in Berlin-Dahlem and München-Pasing and forecast to current year using grouping and by computing the "mean" value of the "Jultag" column:
phenodata forecast \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen,Apfel,Birne \
--station-id=12132,10961 --format=string
The option --humanize will improve textual output by resolving ID columns
in the observation data to their appropriate text representions from metadata files.
Observations for species "hazel", "snowdrop", "apple" and "pear" at station "Berlin-Dahlem", output texts in the German language if possible:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen,Apfel,Birne \
--station-id=12132 \
--humanize --language=german
Forecast of "beginning of flowering" events at station "Berlin-Dahlem". Use all species of the "primary group": "hazel", "snowdrop", "goat willow", "dandelion", "cherry", "apple", "winter oilseed rape", "black locust" and "common heather". Sort by date, ascending.
phenodata forecast \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen,Sal-Weide,Loewenzahn,Suesskirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \
--station-id=12132 --phase-id=5 \
--humanize \
--sort=Datum \
--format=tabular:rst
Forecast of all events at station "Berlin-Dahlem". Use all species of the "primary group" (dito). Sort by species and date, ascending.
phenodata forecast \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen,Sal-Weide,Loewenzahn,Suesskirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \
--station-id=12132 \
--humanize --language=german \
--sort=Spezies,Datum
Query observations by using textual representation of "station" information:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--filename=Hasel,Schneegloeckchen \
--station=berlin,brandenburg \
--humanize --sort=Datum
Observations near Munich for species "hazel" or "snowdrop" in 2018:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station=münchen \
--species=hazel,snowdrop \
--year=2018 \
--humanize --sort=Datum
Observations for any "flowering" events in 2017 and 2018 around Munich:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station=münchen \
--phase=flowering \
--year=2017,2018 \
--humanize --sort=Datum
Same observations but with "ROUTKLI" quality:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station=münchen \
--phase=flowering \
--quality=ROUTKLI \
--year=2017 \
--humanize --sort=Datum
Investigate some "flowering" observations near Munich which have seen corrections last year:
phenodata observations \
--source=dwd --dataset=annual --partition=recent \
--station=münchen \
--phase=flowering \
--quality=korrigiert \
--year=2017 \
--humanize --sort=Datum
Forecast based on "beginning of flowering" events of 2015-2017 in Thüringen and Bayern for the given list of species. Sort by species and date.
phenodata forecast \
--source=dwd --dataset=annual --partition=recent \
--station=thüringen,bayern \
--species=Hasel,Schneeglöckchen,Sal-Weide,Löwenzahn,Süßkirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \
--phase-id=5 \
--year=2015,2016,2017 \
--humanize --language=german \
--sort=Spezies,Datum
Forecast based on "beginning of flowering" events of 2015-2017 in Berlin for the named list of species "mellifera-de-primary". Sort by date.
phenodata forecast \
--source=dwd --dataset=annual --partition=recent \
--station=köln \
--phase="beginning of flowering" \
--year=2015,2016,2017 \
--humanize --language=german \
--sort=Datum \
--species-preset=mellifera-de-primary
Note
The species presets like mellifera-de-primary and others are currently stored in
presets.json.
The "phenodata" program is released under the GNU AGPL license. Its source code lives on GitHub and the Python package is published to PyPI. You might also want to have a look at the documentation.
The software has been tested on Python 2.7.
If you'd like to contribute you're most welcome! Spend some time taking a look around, locate a bug, design issue or spelling mistake and then send us a pull request or create an issue.
Thanks in advance for your efforts, we really appreciate any help or feedback.
Licensed under the GNU AGPL license. See LICENSE file for details.
The DWD has information about their re-use policy in German and English. Please refer to the respective Disclaimer (de, en) and Copyright (de, en) information.
The project and its authors are not affiliated with DWD, USA-NPN or any other data provider in any way. It is a sole project from the community for making data more accessible in the spirit of open data.