in addition to the binary files for visualization, SNaC generates a log file that contains information about the saved data; this information is then used to generate a single Xdmf
metadata file for visualizing field data as a time series.
the steps are as follows:
- after the simulation has run, copy the contents of
utils/visualize_fields/write_xdmf_all.py
to the simulationdata
folder; - run the file with
python write_xdmf_all.py
. - load the generated Xdmf (
*.xmf
) file using paraview/visit or other visualization software.
when running the script write_xdmf_all.py
we get the following prompts:
$ python write_xdmf_all.py
Name of the log file written by SNaC (excluding the block-specific suffix) [log_visu_3d]:
Name to be appended to the grid files to prevent overwriting []:
Block # 001 log files parsed and grid files generated.
Block # 002 log files parsed and grid files generated.
Block # 003 log files parsed and grid files generated.
Name of the output file [viewfld_DNS.xmf]:
- the first input is the name of the file that logged the saved data;
- the second is a name to append to the grid files that are generated, which should change for different log files to prevent conflicts;
- the last is the name of the visualization file.
by pressing enter after each prompt, the default values in the square brackets are assumed by the script; these correspond to the default steps required for visualizing 3D field data; the data can then be visualized with, e.g., $ paraview viewfld_DNS.xmf
.
a similar script also located in utils/visualize_fields/
, named write_xdmf_restart.py
, can be used to generate metadata that allows to visualize the field data contained in saved checkpoint files.
under utils/read_bindary_data
are MATLAB and python scripts which may be used to load binary field data for post-processing; see tests/lid_driven_cavity/test.py
for an example use of the python script.