The RSK format that all Logger2 and Logger3 instruments (RBRsolo, RBRvirtuoso, RBRduo, RBRconcerto, RBRmaestro) generate is not just another proprietary file format. We use a widely-used single file database called SQLite that allows us to have very large files with high-speed access to any part of the dataset. As a result, you can read RSKs from any programming language that supports SQLite. All you need to know is the schema of our table structure.
RSKtools for MATLAB
A lot has changed since our last update; therefore, we highly encourage you to read the release notes prior to download. If you’re not ready to make the change, you can always download the previous version (v2.3.1). However, the latest version (v3.5.3) offers more functionality and the previous version will no longer be supported.Download the latest RSKtools for MATLAB
To download the previous version of RSKtools (v2.3.1) that is no longer supported, please click here.
As an example to get you started, we are happy to provide a set of M files for MATLAB that handle opening, extracting subsets of data, and plotting the results from RSK files. Download the package, unzip and type “help RSKtools” in MATLAB for more. Now with support for both 32 bit and 64 bit Windows, OS X, and Linux. Please note, RSKtools requires MATLAB R2013b or later.
To get started, do something like:
RSK = RSKopen('sample.rsk'); RSK = RSKreaddata(RSK); RSKplotdata(RSK);
This is a work in progress, but we hope it will be useful. Please send us feedback with things you’d like to see added – or contribute to it yourself! Development of the latest version of RSKtools happens on our BitBucket page – please download, clone, or fork the repo! For a quick introduction, check out these documents:
Getting started with RSKtools
Post-processing RBR data with RSKtools
For detailed RSKtools function documentation, please check the online user manual.
RSK files in R
The oce package in the R language can also handle reading RSK files. The latest release of oce can be installed from directly within R by typing:
at the command prompt. Reading RSK files also requires the
DBI packages, which can be installed the same way:
More advanced users who wish to follow the development version can see the Installation page for more details.
To get started reading RSK files, use either the
read.rsk() functions, like:
library(oce) RSK <- read.oce('sample.rsk')
rsk-class object can then be plotted by doing:
or for “CTD”-type loggers, converted to a
ctd <- as.ctd(RSK)
For any issues or requests to the oce package, see the Github Issues page.