Export writes the visible range of every plotted trace to a file: NumPy
Pick
A file chooser asks where; the format follows the path's extension.
The table is collected at the moment you click, not when the chooser returns. A live view keeps scrolling while a dialog is up, so a late collection would write a window you never saw.
The rows are the
The stored rate is not always the wire rate. A window long enough to need decimation is stored averaged at a lower rate, and the export carries that — read the status bar's ⓘ popover for the figure before trusting a sample count.
Two time columns. time is the display timeline — negative ages
ending at 0 while streaming, offsets from the trigger edge while an event is held — so it
is the x you read a value at on screen. abs_seconds is the source's own
clock for the same sample, which is what lets two exports taken in different modes line
up. Both are strictly increasing and evenly spaced; duplicate or backwards stamps would
mean the file held two clocks.
An export taken mid-capture writes the partly filled window that is on screen, which is shorter than the event span. Export outside a capture if you want the whole event.
abs_seconds, named time — the clock that is
the same in every display mode — and the display axis goes out beside it as an ordinary
channel, time_relative. Both are seconds; each signal carries the source's
own unit. That is also what
A 2 s window at 10 kS/s over 3 traces writes 20001 rows and 5 columns
(time, abs_seconds, three signals). The 64·⌈(10+len(header)+1)/64⌉ + rows·(16 + 4·cols) bytes. Read it back with
pd.DataFrame(np.load(path)). The 272 + comment + rows·record + 28 + channels·274 + 30
bytes, with record = 16 + 4·cols and channels = cols + 2 for the
master and the relative axis. Read that one back with asammdf.MDF(path) or in
ScryLab.