Facepalm, obviously absolute array indexes are not going to necessarily
align vs. time over multiple feeds/history. Instead use
`np.searchsorted()` on whatever curve has the smallest support and find
the appropriate index of intersection in time so that alignment always
starts at a sensible reference.
Also adds a `debug_print: bool` input arg which can enable all the
prints when working on this.
We can determine the major curve (in view) in the first pass of all
`Viz`s so drop the 2nd loop and thus the `mxmn_groups: dict`. Also
simplifies logic for the case of only one (the major) curve in view.
Turns out this is a limitation of the `ViewBox.setYRange()` api: you
can't call it more then once and expect anything but the first call to
be applied without letting a render cycle run. As such, we wait until
the end of the log-linear scaling loop to finally apply the major curves
y-mx/mn after all minor curves have been evaluated.
This also drops all the debug prints (for now) to get a feel for latency
in production mode.
We ended up doing groups maxmin sorting at the interaction layer (new
the view box) and thus this method is no longer needed, though it was
the reference for the code now in `ChartView.interact_graphics_cycle()`.
Further this adds a `remove_axes: bool` arg to `.insert_plotitem()`
which can be used to drop axis entries from the inserted pi (though it
doesn't seem like we really ever need that?) and does the removal in
a separate loop to avoid removing axes before they are registered in
`ComposedGridLayout._pi2axes`.
When there are `N`-curves we need to consider the smallest
x-data-support subset when figuring out for each major-minor pair such
that the "shorter" series is always returns aligned to the longer one.
This makes the var naming more explicit with `major/minor_i_start` as
well as clarifies more stringently a bunch of other variables and
explicitly uses the `minor_y_intersect` y value in the scaling transform
calcs. Also fixes some debug prints.
In very close manner to the original (gut instinct) attempt, this
properly (y-axis-vertically) aligns and scales overlaid curves according
to what we are calling a "log-linearized y-range multi-plot" B)
The basic idea is that a simple returns measure (eg. `R = (p1 - p0)
/ p0`) applied to all curves gives a constant output `R` no matter the
price co-domain in use and thus gives a constant returns over all assets
in view styled scaling; a intuitive visual of returns correlation. The
reference point is for now the left-most point in view (or highest
common index available to all curves), though we can make this
a parameter based on user needs.
A slew of debug `print()`s are left in for now until we iron out the
remaining edge cases to do with re-scaling a major (dispersion) curve
based on a minor now requiring a larger log-linear y-range from that
previous major' range.
In the dispersion swing calcs, use the series median from the in-view
data to determine swing proportions to apply on each "minor curve"
(series with lesser dispersion the one with the greatest). Track the
major `Viz` as before by max dispersion. Apply the dispersion swing
proportions to each minor curve-series in a third loop/pass of all
overlay groups: this ensures all overlays are dispersion normalized in
their ranges but, minor curves are currently (vertically) centered (vs.
the major) via their medians.
There is a ton of commented code from attempts to try and vertically
align minor curves to the major via the "first datum" in-view/available.
This still needs work and we may want to offer it as optional.
Also adds logic to allow skipping margin adjustments in `._set_yrange()`
if you pass `range_margin=None`.
On overlaid ohlc vizs we compute the largest max/min spread and
apply that maxmimum "up and down swing" proportion to each `Viz`'s
viewbox in the group.
We obviously still need to clip to the shortest x-range so that
it doesn't look exactly the same as before XD
We were hacking this before using the whole `ChartView._maxmin()`
setting stuff since in some cases you might want similarly ranged paths
on the same view, but of course you need to max/min them together..
This adds that group sorting by using a table of `dict[PlotItem,
tuple[float, float]` and taking the abs highest/lowest value for each
plot in the viz interaction update loop.
Also removes the now commented signal registry calls and thus
`._yranger`, drops the `set_range: bool` from `._set_yrange` and adds
and extra `.maybe_downsample_graphics()` to the mouse wheel handler to
avoid a weird slow debounce where ds-ing is delayed until a further
interaction.
It's kind of hard to understand with the C++ fan-out to multiple views
(imo a cluster-f#$*&) and seems honestly just plain faster to loop (in
python) through all the linked view handlers XD
Core adjustments:
- make the panning and wheel-scroll handlers just call
`.maybe_downsample_graphics()` directly; drop all signal emissions.
- make `.maybe_downsample_graphics()` loop through all vizs per subchart
and use the new pipeline-style call sequence of:
- `Viz.update_graphics() -> <read_slc>: tuple`
- `Viz.maxmin(i_read_range=<read_slc>) -> yrange: tuple`
- `Viz.plot.vb._set_yrange(yrange=yrange)`
which inlines all the necessary calls in the most efficient way whilst
leveraging `.maxmin()` caching and ymxmn-from-m4-during-render to
boot.
- drop registering `._set_yrange()` for handling `.sigRangeChangedManually`.
Computes the maxmin values for each underlying plot's in-view range as
well as the max up/down swing (in percentage terms) from the plot with
most dispersion and returns a all these values plus a `dict` of plots to
their ranges as part of output.
This broke non-disti-mode actor tree spawn / runtime, seemingly because
the cli entrypoint for a `piker chart` also sends these values down
through the call stack independently? Pretty sure we don't need to send
the `enable_modules` from the chart actor anyway.
Needed to move the startup sequence inside the `try:` block to guarantee
we always do the (now shielded) `.cancel()` call if we get a cancel
during startup.
Also, support an optional `started_afunc` field in the config if
backends want to just provide a one-off blocking async func to sync
container startup. Add a `drop_root_perms: bool` to allow persisting
sudo perms for testing or dyanmic container spawning purposes.
Provides a more correct solution (particularly for distributed testing)
to override the `piker` configuration directory by reading the path from
a specific `tractor._state._runtime_vars` entry that can be provided by
the test harness.
Also fix some typing and comments.
Due to making ahabd supervisor init more async we need to be more
tolerant to mkts server startup: the grpc machinery needs to be up
otherwise a client which connects to early may just hang on requests..
Add a reconnect loop (which might end up getting factored into client
code too) so that we only block on requests once we know the client
connection is actually responsive.
Not really sure there's much we can do besides dump Grpc stuff when we
detect an "error" `str` for the moment..
Either way leave a buncha complaints (como siempre) and do linting
fixups..
Previously we would make the `ahabd` supervisor-actor sync to docker
container startup using pseudo-blocking log message processing.
This has issues,
- we're forced to do a hacky "yield back to `trio`" in order to be
"fake async" when reading the log stream and further,
- blocking on a message is fragile and often slow.
Instead, run the log processor in a background task and in the parent
task poll for the container to be in the client list using a similar
pseudo-async poll pattern. This allows the super to `Context.started()`
sooner (when the container is actually registered as "up") and thus
unblock its (remote) caller faster whilst still doing full log msg
proxying!
Deatz:
- adds `Container.cuid: str` a unique container id for logging.
- correctly proxy through the `loglevel: str` from `pikerd` caller task.
- shield around `Container.cancel()` in the teardown block and use
cancel level logging in that method.
Tyler Goodlet
goodboy