Software Analysis Tools for Wave Sensors

Conference Paper

Title: Software Analysis Tools for Wave Sensors
Publication Date:
June 05, 2015
Conference Name: 34th International Conference on Ocean, Offshore and Arctic Engineering
Conference Location: St. John's, Canada
Pages: 4
Publisher: ASME
Technology Type:

Document Access

Website: External Link


Morrison, J.; Christie, D.; Greenwood, C.; Maciver, R.; Volger, A. (2015). Software Analysis Tools for Wave Sensors. Paper Presented at the 34th International Conference on Ocean, Offshore and Arctic Engineering, St. John's, Canada.

This paper presents a set of software tools for interrogating and processing time series data. The functionality of this toolset will be demonstrated using data from a specific deployment involving multiple sensors deployed for a specific time period. The approach was developed initially for Datawell Waverider MKII/MKII buoys [1] and expanded to include data from acoustic devices in this case Nortek AWACs. Tools of this nature are important to address a specific lack of features in the sensor manufacturers own tools. It also helps to develop standard approaches for dealing with anomalous data from sensors. These software tools build upon an effective modern interpreted programming language in this case Python which has access to high performance low level libraries. This paper demonstrates the use of these tools applied to a sensor network based on the North West coast of Scotland as described in [2,3]. Examples can be seen of computationally complex data being easily calculated for monthly averages. Analysis down to a wave by wave basis will also be demonstrated form the same source dataset. The tools make use of a flexible data structure called a DataFrame which supports mixed data types, hierarchical and time indexing and is also integrated with modern plotting libraries. This allows sub second querying and the ability for dynamic plotting of large datasets. By using modern compression techniques and file formats it is possible to process datasets which are larger than memory datasets without the need for a traditional relational database. The software library shall be of use to a wide variety of industry involved in offshore engineering along with any scientists interested in the coastal environment.

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