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Tools to visualize OTN data products in Python

A collection of all the examples and presentation files given by Christoffer Moesgaard Albertsen (cmoe@aqua.dtu.dk) at OTN's TMB Workshop in Halifax NS on July 28-30th 2014.

Modified version of https://github.com/SECOORA/GUTILS/commit/087967888cb037bd7166c51af65b464052aba8b5 (version 2.7.0)

A collection of all the examples and presentation files given by Christoffer Moesgaard Albertsen (cmoe@aqua.dtu.dk) at OTN's TMB Workshop in Halifax NS on July 28-30th 2014.

Tools for using TMB to fit models to ecological data, with a focus on fitting movement models on marine animals. Tools will range from general functions to look at parameter estimates (likelihood profile) to data-specific state-space models.

Information for TMB lunch and learn organise for the ICFT conference in July 2015

Tools for using TMB to fit models to ecological data, with a focus on fitting movement models on marine animals. Tools will range from general functions to look at parameter estimates (likelihood profile) to data-specific state-space models.

A bundle of tools and examples for working with slocum glider data

testing a vtrack workflow using glatos and FACT data

A few examples of how to use R packages to pull environmental data and match it to your geospatial data

The quick solution to your Flask problems

Information for the OTN workshop in June 2015. This workshop will focuss on demonstrating how to visualise and analyse OTN data in R. It will include tutorials on how to use basic GIS tools in R and a section on advance methods with TMB.

A collection of all the examples and presentation files given by Christoffer Moesgaard Albertsen (cmoe@aqua.dtu.dk) at OTN's TMB Workshop in Halifax NS on July 28-30th 2014.

Some demo scripts showing how to access data in an ERDDAP instance.

This gitlab project contains the package swim, which fits Switching Movement Models.

A tool for parsing Slocum Glider surface logs and a schema for storing the data

Code associated with the manuscript: Auger-Methe, et al. State-space models' dirty little secrets: even simple linear Gaussian models can have parameter and state estimation problems.