Gautoedit Documentation — UHDAS+CODAS 2015.05.01-python documentation

Python “gautoedit” is a program for extracting and displaying velocity and other ancillary data from a codas database in panel plots, selecting data to flag as ‘bad’, and applying those flags to the CODAS database. This is the third generation of graphical editing tools for CODAS data. In all cases, ascii files are generated, which contain information to flag data as ‘bad’. Information from these files is then applied to the database so that further data extraction will not include those flagged values. This html documentation is meant to give you an introduction to the gautoedit package and provide some guidance as to its use. Figures may not look exactly as they do on your computer: some screenshots are taken under Mac OSX or Linux interfaces, and some figures illustrating editing concepts are retained from the earlier Matlab gautoedit tutorial. The original “Gautoedit” was written in Matlab. The present version is written in Python. It has the same basic components: When gautoedit is invoked, three windows appear. Here is an example of the window layout for editing (click thumbnail for a larger image): wire interference - set the number of bins to evaluate - set the ship speed (only looks when ship is slower) - enable/disable: set Error Velocity cutoff (best signature for wire interference) – this value is smaller than the general error velocity criterion below These are all disabled be default. None of them is good enough to really do the job, but they can be used together or as indicators of a problem This is a way to require a minimum number of high Percent Good bins in a reference layer in order to accept the profile. It can be useful in situations with intermittant bad weather and periods with few, isolated profiles After you have reached a stopping point, eg. “the whole dataset”, run this command to ensure a clean sweep of any remaining ascii files, rerunning the navigation steps and updating calibration: (This is done from the processing directory) Now look at the dataset again dataviewer.py to be sure you edited out all the bad parts. There’s often something left, so it’s worth checking. Source. Looking for vector maps of Germany (Deutschland Vektorkarten) for Adobe Illustrator?."


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Last Modified: April 24, 2016 @ 12:26 am