Getting StartedΒΆ

Note

As of April 2013, this version of the documentation is no longer maintained; however, it is consistent with the last (now retired) version of CODAS processing that used Matlab. We no longer update or fix the Matlab processing code, but we will maintain the Matlab code that reads CODAS Matlab output. Although the notes refer to both Python and Matlab processing, none of the code here will be maintained. This (now retired) documentation and code will remain available for awhile longer.

Python processing code is actively maintained and developed, and CODAS Python processing is documented here.

FIRST: become familiar with CODAS post-processing (i.e. the steps that are run after the averaged data have been loaded into the CODAS database.) These steps include

  • checking calibration
  • checking health of accurate heading device (if relevant)
  • editing
  • applying scale factor or rotation

SECOND: If you need to process data from scratch, there are various examples provided.

The examples you follow should reflect the processing engine (Matlab or Python) and what kind of data you have (VmDAS [LTA, ENX, ENS], or UHDAS). This table shows what kinds of data are supported by Matlab or Python processing. Once the data are loaded into the CODAS database, any kind of data can be post-processed with either Matlab or Python.

Each example has three components:

  1. a readme
  2. the processed data directory
  3. the original data

Pick your example, read the readme, and follow along in the detailed guide to CODAS processing, as you work your way through the steps in the examples. Look at the example of a final dataset to see what it should look like at the end.