TKApp in Detail

using Timekeepers

run_tkapp()                                          # start empty, load from the toolbar
run_tkapp("data/LEMI090.txt")                        # one file
run_tkapp("data/RK137")                              # a whole site directory
app = run_tkapp(load_lemi424("data/LEMI090.txt"))    # an in-memory TimeArray

app = TKApp("data/LEMI090.txt"); display(app)        # don't wait for the window to close

Or from a clone: julia --project=. examples/tkapp.jl.

Needs a display

GLMakie needs OpenGL 3.3 or newer (check it). Everything outside the windows works without one.

Toolbar

Control	What it does
Rate (left)	Sampling rate on screen; a Metronix site with several rates lists them all
Load Run…	One .txt, .dat, .lem or .xyz file, or a Metronix run (pick its .xml or an .ats inside meas_*)
Load Site…	A directory: all runs joined in time order, NaN in the gaps. A Metronix site loads one rate at a time
Mask / Unmask	Mark the selection bad / good
Clear	Remove every mask
Write	Save clean data and the mask (below)
View	Time, Spectra or Time | Spectra
Window	Visible span: a number (press Enter) and a unit, or All

Mouse

left-drag	select an interval
right-click	mask the selection
right-drag	pan
scroll	zoom the y axis

Tips:

• Cut a whole bad day: select the full window, press Mask. Cut a spike: set Window to one minute first.

• The checkbox next to a channel only hides it; masks and Write still apply. Empty channels (all NaN or constant) start hidden.

• Each Metronix rate keeps its own masks, and Write cuts every rate you masked.

What Write saves

Source	Files
LEMI-424, GEOMAG, .xyz	<name>_clean.<ext> (masked rows NaN) and <name>_mask.csv, next to the source
A site directory	<site>_combined_clean.<ext> and <site>_combined_mask.csv in it
Metronix	cuts in <site>.<rate> copies via write_metronix_site_masked; the site is never changed. See Metronix Sites

Continue in code

app = run_tkapp("data/LEMI090.txt")   # mask a few intervals, then close the window

app.data                                    # the loaded TimeArray
app.mask                                    # the TimekeeperMask
masked_samples(app.mask)

cleaned  = cleaned_timearray(app)           # NaN in masked rows
segments = good_segments(app; min_samples = 256)
weights  = sample_weights(app)

write_cleaned("out/clean.csv", app)
write_mask("out/mask.csv", app)