pyoos 0.5

# 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](https://github.com/asascience-open/paegan#paegan—the-python-cdm-for-metocean-data) Discrete Geometry CDM:

• NERRS Observations - SOAP

• NDBC Observations - IOOS SWE SOS 1.0

• CO-OPS Observations - IOOS SWE SOS 1.0

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

##### Filter from a datetime (the ‘start’ parameter) `python from dateime import dateime, timedelta collector.filter(start=datetime.utcnow() - timedelta(hours=1)) ` ##### Filter until a datetime (the ‘end’ parameter) `python from dateime import dateime collector.filter(end=datetime.utcnow()) `

##### Filter a datetime range (both ‘start’ and ‘end’ parameters) `python 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 `python collector.list_features() ` ##### Filter by unique feature `python # 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 `python collector.list_variables() `

##### Filter by variable name `python # Any iterable of strings collector.filter(variables=["sea_water_temperature"]) `

#### Clear active filters `python collector.clear() `

## Filter Chaining You may chain many filter calls together (it returns a collector object) `python collection.filter(bbox=(-74, 30, -70, 38)).filter(end=datetime.utcnow()) ` You may also combine many filter types into one call to filter `python collection.filter(bbox=(-74, 30, -70, 38), end=datetime.utcnow()) `

## Get Data

### As Paegan CDM objects `python collector.collect() `

### As raw response from provider `python 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](https://github.com/brainsik/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:

`bash 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: `bash 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:

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

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

## Contributors * Kyle Wilcox <kwilcox@asascience.com> * Sean Cowan <scowan@asascience.com> * Alex Crosby <acrosby@asascience.com> * Dave Foster <dfoster@asascience.com>