Skip to main content

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

Project description

https://img.shields.io/badge/Python-2.7-green.svg https://img.shields.io/pypi/v/phenodata.svg https://img.shields.io/github/tag/hiveeyes/phenodata.svg

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 [--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 | --forecast]
  phenodata list-urls --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--year=2017 | --forecast]
  phenodata observations --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] [--year=2017 | --forecast] [--format=csv]
  phenodata observations --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--station=berlin,brandenburg] [--species=hazel,snowdrop] [--phase=flowering] [--year=2017 | --forecast] [--format=csv]
  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)

Data formatting options:
  --format=<format>         Output data in designated format. Choose one of "tabular", "json" or "csv".
                            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]

Examples

Metadata

List of species:

phenodata list-species --source=dwd

List of phases:

phenodata list-phases --source=dwd

List of stations:

phenodata list-stations --source=dwd --dataset=immediate

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

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 examples

Todo

Display regular flowering events for hazel and snowdrop around Berlin and Brandenburg (Germany) in 2017:

phenodata calendar --source=dwd --dataset=immediate --partition=recent --regions=berlin,brandenburg --species=hazel,snowdrop --phases=flowering --years=2017

phenodata calendar --source=dwd --dataset=immediate --partition=historical --regions=berlin,brandenburg --species=hazel,snowdrop --phases=flowering --years=1958

Display forecast for “beginning of flowering” events for canola and sweet cherry around Thüringen and Bayern (Germany), deduced from annual/recent data:

phenodata calendar --source=dwd --dataset=annual --partition=recent --regions=thüringen,bayern --species=raps,süßkirsche --phases-bbch=60 --forecast

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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

phenodata-0.4.0.tar.gz (26.6 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page