| Commit message (Collapse) | Author | Age |
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Also, not setting pen=None seems to draw horizontal lines for the dots,
which is fine too.
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pyqtgraph's intColor() is less than ideal, especially on white
background. Can't see anything.
Unfortunately the rendering of the legend can't be fixed, because
pyqtgraph is terrible and hardcodes its rendering, including colors.
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matplotlib is pathetically slow, which makes using stats-conv.py to view
dumps longer than a few seconds a huge pain.
pyqtgraph is slightly faster than matplotlib. Other than that, it seems
to be worse in every aspect (at least for plotting), but such is life.
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...instead of milliseconds.
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Add an explicit "signal" event type, because the implicit one was
confusing.
Don't rescale the Y axis of the second graph, it was nonsense.
Make the legend for the second graph separate (and cleanup the code
creating the graphs).
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Sort the legend by the used y value of binary events/signals, add a way
to filter branches (although that requires editing the script), and use
the full screen if the second subplot is not used.
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This is still pretty useful for debugging timing-dependent things.
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Use for all AO thread events y=0.5, while playloop events remain at y=1.
This makes the graph easier to read.
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This collects statistics and other things. The option dumps raw data
into a file. A script to visualize this data is included too.
Litter some of the player code with calls that generate these
statistics.
In general, this will be helpful to debug timing dependent issues, such
as A/V sync problems. Normally, one could argue that this is the task of
a real profiler, but then we'd have a hard time to include extra
information like audio/video PTS differences. We could also just
hardcode all statistics collection and processing in the player code,
but then we'd end up with something like mplayer's status line, which
was cluttered and required a centralized approach (i.e. getting the data
to the status line; so it was all in mplayer.c). Some players can
visualize such statistics on OSD, but that sounds even more complicated.
So the approach added with this commit sounds sensible.
The stats-conv.py script is rather primitive at the moment and its
output is semi-ugly. It uses matplotlib, so it could probably be
extended to do a lot, so it's not a dead-end.
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wm4