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PLOTPROFILZEIT

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Basic Information

Name of Program

plotProfilZeit

Version-Date

Juli 2018

Description-Date

Juli 2018

Catchwords

Profile
Aggregation
Perzentile
Hovmoeller
ProfilZeit
MATLAB Kategorie:MATLAB stand-alone application

Short Description of Functionality

The application plotProfilZeit can be used to display data on profiles and time. Data can also be based on profiles in terms of adjascent control volumes.

plotProfilZeit is the predecessor of DISPLAY_PERCENTILES und "DISPLAY_HOVMOLLER". Both had previously been developed to display data along a profile. However, both used the same data types and were different only in the way they presented the data. Therefore, to simplify application maintenance, both functionalities were incorporated into one application, plotProfilZeit. DISPLAY_PERCENTILES is still available, but will not be further developed. "DISPLAY_HOVMOLLER" not available anymore.

Possible ways to display data:

  • Type = Hovmoeller: Physical quantities, which are based on a profile and are dependant on time, can be displayed with a hovmoeller diagram. The spatial dimension is displayed along the x-axis and time is displayed along the y-axis. The physical value at each point is color-coded.
  • Type = Percentiles: The temporal variation of the values can also be displayed in this way. The spatial dimension is again displayed along the x-axis, but the physical value itself is displayed along the y-axis. The temporal average at each point is displayed as a solid line over the x-dimension. The temporal variation is displayed as a grey area in the background, which is the area between to chosen percentile values, default is 5 % and 95 %.
  • Type = Allevents: The temporal evolution is displayed by showing all available synoptic values as single lines along the profile. A color spectrum is used to show the temporal evolution, as long as not too many events are overlapping.
  • Type = Multidata: This allows no display of the temporal variation. Instead, values from different runs can be compared. Single values from different data sets are displayed in one diagram. The variables must not contain a time dimension, thus can either be averages of a tidal characteristic analysis or results of a long-term analysis.

Beispiel Hovmoeller Beispiel Percentiles Beispiel Allevents Beispiel Multidata

Input formats:

  • Input_Format = Xtrdata: Output of XTRDATA, available as single events along a profile, format being excel.dat
  • Input_Format = CVMesh2 for Netcdf analysis results fuer Netcdf as well as aggregated but not analysed simulation results
  • Input_Format = Mesh1 for Netcdf simulation output on profiles (DATACONVERT result of BDF profile file)
  • Input_Format = Mesh0 for Netcdf results on profiles (Profile output of UnTRIM2009)

Error handling: In contrast to the prior version, this program does not yet include a check of the steering file by Fortran-Modul dictionary_ui. Therefore, wrong keywords can lead to uncontrolled crashes withou understandable error messages.

Input-Files

  1. Steering file (example in $PROGHOME/examples/plotProfilZeit, type plotProfilZeit_steer.dat)
  2. UNTRIM results in above mentioned formats.

Output-Files

  1. Figures (*.pdf, *.png, *.eps or *.fig)
  2. Excel sheet (optional)

Methodology

Visualization with MATLAB.
Further, some statistic values are calculated by the program in case of Type = Percentiles. This used the following Matlab functions:

  • percentiles: prctile
  • mean: nanmean (nan meaning that NaNs are not taken into account)
  • median: nanmedian
  • maximum: nanmax
  • minimum: nanmin

Program(s) to run before this Program

XTRDATA, DATACONVERT, NCAGGREGATE, NCANALYSE, UNTRIM

Program(s) to run after this Program

MATLABFIGVIEWER

Additional Information

Language

MATLAB (erstellt mit Version R2016b)

Additional software

Needs installation of MATLAB Compiler Runtime (under linux: /usr/local/MATLAB/MATLAB_Runtime/v85)

Original Version

A. Zorndt

Maintenance

A. Zorndt

Documentation/Literature

Examples can be found unter $PROGHOME/examples/plotProfilZeit/.
Descriptions of the method can be found here: http://de.mathworks.com/help/stats/descriptive-statistics.html .


Thanks to Tabea Brodhagen, Marissa Albers und Jens Jürges.


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