Pyoos - A Python library for collecting Met/Ocean observations

Note: Pyoos is very much a work in progress and should considered experimental until a 1.0 release is made!

Pyoos attempts to fill the need for a high level data collection library for met/ocean data publically available through many different websites and webservices.

Pyoos will collect and parse the following data services into the Paegan Discrete Geometry CDM:

• IOOS SWE SOS 1.0 Services
• ex. NcSOS instance: sos.maracoos.org/stable/sos/wflow700-agg.ncml
• ex. IOOS 52N instance: ioossos.axiomalaska.com/52n-sos-ioos-stable
• NERRS Observations - SOAP
• NDBC Observations - SOS
• CO-OPS Observations - SOS
• STORET Water Quality - WqxOutbound via REST (waterqualitydata.us)
• USGS NWIS Water Quality - WqxOutbound via REST (waterqualitydata.us)
• USGS Instantaneous Values - WaterML via REST
• NWS AWC Observations - XML via REST (http://www.aviationweather.gov)
• HADS (http://www.nws.noaa.gov/oh/hads/ - limited to 7 day rolling window of data)

Common Interface

Filtering data

Geo

Filter by bbox

# (minx, miny, maxx, maxy)
collector.filter(bbox=(-74, 30, -70, 38))

Time

Filter from a datetime (the 'start' parameter)

from dateime import dateime, timedelta
collector.filter(start=datetime.utcnow() - timedelta(hours=1))

Filter until a datetime (the 'end' parameter)

from dateime import dateime
collector.filter(end=datetime.utcnow())

Filter a datetime range (both 'start' and 'end' parameters)

from dateime import dateime, timedelta
collector.filter(start=datetime.utcnow - timedelta(hours=24), end=datetime.utcnow())

Feature(s)

It is highly dependent on the data provider how they identify unique features/stations/objects. Pyoos does its best job to figure out what you are passing in. For example, you may pass WMO ID's to the NDBC collector and Pyoos will request the correct complete URN to the NDBC SOS.

Retrieve a list of unique features available to filter

collector.list_features()

Filter by unique feature

# Any iterable of strings
collector.filter(features=["21KY-BSW004"])

Variable(s)

Pyoos does its best job to format any string into the correct format for the actual request. For example, you may pass typical standard_name string from CF-1.6 to the NDBC collector and Pyoos will turn it into a complete MMI URI.

Retreive a list of unique variables available to filter

collector.list_variables()

Filter by variable name

# Any iterable of strings
collector.filter(variables=["sea_water_temperature"])

Clear active filters

collector.clear()

Filter Chaining

You may chain many filter calls together (it returns a collector object)

collection.filter(bbox=(-74, 30, -70, 38)).filter(end=datetime.utcnow())

You may also combine many filter types into one call to filter

collection.filter(bbox=(-74, 30, -70, 38), end=datetime.utcnow())

Get Data

As Paegan CDM objects

collector.collect()

As raw response from provider

collector.raw()

Specific functionality

Each collector may implement a set of functions specific to that collection. Please see the Wiki for an explanation of this type of functionality.

Setup

You are using virtualenv, right?

1. Install virtualenv-burrito
2. Create virtualenv named "pyoos-dev": mkvirtualenv -p your_python_binary pyoos-dev
3. Start using your new virtualenv: workon pyoos-dev

Installation

Pyoos requires python 2.7.x and is available on PyPI.

The best way to install Pyoos is through pip:

pip install pyoos

Pyoos requires the following python libraries which will be downloaded and installed through pip:

• Paegan>=0.9.9
• numpy>=1.7.0
• scipy
• netCDF4>=1.0.2
• Shapely>=1.2.15
• pytz
• python-dateutil>=1.5
• OWSLib (install from git with pip install git+http://github.com/geopython/OWSLib.git)
• requests
• Fiona==0.16.1
• beautifulsoup4==4.2.1
• lxml>=3.2.0

If your NetCDF4 and HDF5 libraries are in non-typical locations, you will need to pass the locations to the pip command:

NETCDF4_DIR=path HDF5_DIR=path pip install pyoos

There seems to be a problem installing numpy through pip dependency chains so you may need to install numpy before doing any of the above:

pip install numpy==1.7.0

Roadmap

• Development of a standardized Metadata concept, possibly through SensorML and/or ISO 19115-2

Use Cases

Submit a PR with your use case!

Troubleshooting

There is a Google Groups mailing list for pyoos: https://groups.google.com/forum/#!forum/pyoos

If you are having trouble getting any of the pyoos functionality to work, try running the tests:

git clone git@github.com:asascience-open/pyoos.git
cd pyoos
python setup.py test

Contributors

• Kyle Wilcox kyle@axiomdatascience.com
• Sean Cowan scowan@asascience.com
• Alex Crosby acrosby@asascience.com
• Dave Foster dave@axiomdatascience.com
• Filipe Pires Alvarenga Fernandes ocefpaf@gmail.com

Copyright and licence

Copyright (C) 2012-2016 RPS ASA

This file is part of Pyoos.

Pyoos is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Pyoos is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with Pyoos. If not, see http://www.gnu.org/licenses/.