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phenodata is a data acquisition and manipulation toolkit for open access phenology data

Project description


phenodata - phenology data acquisition for humans

About

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.

Acknowledgements

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.

Getting started

Introduction

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.

Install

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.

Usage

$ 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] [--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]
  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) loaded from ``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]

Output example

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

Invocation examples

Metadata

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

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

Forecasting

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

Humanized output examples

The option --humanize will improve textual output by resolving ID columns in the observation data to their appropriate text representions from metadata files.

Observations

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

Forecasting

Specific events

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
Event sequence for each species

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

Humanized search examples

Observations

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

Forecasting

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.

Project information

About

The “phenodata” program is released under the AGPL license. The 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.

Code license

Licensed under the AGPL license. See LICENSE file for details.

Data license

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.

Disclaimer

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.

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