lst-toolssetuptracking
# auto-fill parameters from the meanflowlst-toolssetuptracking--auto-fill
# auto-fill and overwrite existing valueslst-toolssetuptracking--auto-fill--force
# set a fixed initialization frequencylst-toolssetuptracking--finit120000.0
setup spectra
Generate input decks for spectral analysis at multiple streamwise locations:
# use default tracking volume file and defaultslst-toolsvisualizetracking
# explicit input/output pathslst-toolsvisualizetracking\--inputlst_vol.dat\--outviz_tracking
# custom field and variable mappinglst-toolsvisualizetracking\--field"-im(alpha)"\--xvars\--yvar"freq,freq."\--kvarbeta
Tracking fallback behavior:
If lst_vol.dat exists, lst-tools visualize tracking renders directly from it.
If lst_vol.dat is missing, it discovers kc_* directories and reads
growth_rate_with_nfact_amps.dat from each completed case.
In fallback mode, output PNGs are written to alpi_contours_tracking/
in the current working directory with a shared contour scale across all
discovered cases.
Note
12
The `visualize` wrappers call `cfd-viz` internally. Install `cfd-viz`
in the same Python environment as `lst-tools`.
# clean all kc_* directories in current directorylst-toolscleantracking--force
# clean selected directories onlylst-toolscleantracking--dirkc_10pt00--dirkc_20pt00--force
# 1. initialize configlst-toolsinit--geometrycone
# 2. convert base flow to LASTRAC formatlst-toolslastrac
# 3. run parsing sweeplst-toolssetupparsing--auto-fill
# 4. set up and run trackinglst-toolssetuptracking--auto-fill
# 5. post-process tracking resultslst-toolsprocesstracking--interpolate
# 6. set up and run spectralst-toolssetupspectra
# 7. post-process spectra resultslst-toolsprocessspectra