It was getting waayy to long to be jammed in a method XD
This moves all the chart-viz iteration and transform logic into a new
`piker.ui.view_mode.overlay_viewlists()` core routine which will make it
a lot nicer for,
- AOT compilation via `numba` / `cython` / `mypyc`.
- decoupling from the `pyqtgraph.ViewBox` APIs if we ever decide to get
crazy and go without another graphics engine.
- keeping your head clear when trying to rework the code B)
As part of solving a final bullet-issue in #455, which is specifically
a case:
- with N > 2 curves, one of which is the "major" dispersion curve" and
the others are "minors",
- we can run into a scenario where some minor curve which gets pinned to
the major (due to the original "pinning technique" -> "align to
major") at some `P(t)` which is *not* the major's minimum / maximum
due to the minor having a smaller/shorter support and thus,
- requires that in order to show then max/min on the minor curve we have
to expand the range of the major curve as well but,
- that also means any previously scaled (to the major) minor curves need
to be adjusted as well or they'll not be pinned to the major the same
way!
I originally was trying to avoid doing the recursive iteration back
through all previously scaled minor curves and instead decided to try
implementing the "per side" curve dispersion detection (as was
originally attempted when first starting this work). The idea is to
decide which curve's up or down "swing in % returns" would determine the
global y-range *on that side*. Turns out I stumbled on the "align to
first" technique in the process: "for each overlay curve we align its
earliest sample (in time) to the same level of the earliest such sample
for whatever is deemed the major (directionally disperse) curve in
view".
I decided (with help) that this "pin to first" approach/style is equally
as useful and maybe often more so when wanting to view support-disjoint
time series:
- instead of compressing the y-range on "longer series which have lesser
sigma" to make whatever "shorter but larger-sigma series" pin to it at
an intersect time step, this instead will expand the price ranges
based on the earliest time step in each series.
- the output global-returns-overlay-range for any N-set of series is equal to
the same in the previous "pin to intersect time" technique.
- the only time this technique seems less useful is for overlaying
market feeds which have the same destination asset but different
source assets (eg. btceur and btcusd on the same chart since if one
of the series is shorter it will always be aligned to the earliest
datum on the longer instead of more naturally to the intersect sample
level as was in the previous approach).
As such I'm going to keep this technique as discovered and will later
add back optional support for the "align to intersect" approach from
previous (which will again require detecting the highest dispersion
curve direction-agnostic) and pin all minors to the price level at which
they start on the major.
Further details of the implementation rework in
`.interact_graphics_cycle()` include:
- add `intersect_from_longer()` to detect and deliver a common datum
from 2 series which are different in length: the first time-index
sample in the longer.
- Rewrite the drafted `OverlayT` to only compute (inversed log-returns)
transforms for a single direction and use 2 instances, one for each
direction inside the `Viz`-overlay iteration loop.
- do all dispersion-per-side major curve detection in the first pass of
all `Viz`s on a plot, instead updating the `OverlayT` instances for
each side and compensating for any length mismatch and
rescale-to-minor cases in each loop cycle.
Previously we were aligning the child's `PlotItem` to the "root" (top
most) overlays `ViewBox`..smh. This is why there was a weird gap on the
LHS next to the 'left' price axes: something weird in the implied axes
offsets was getting jammed in that rect.
Also comments out "the-skipping-of" moving axes from the overlay's
`PlotItem.layout` to the root's linear layout(s) when an overlay's axis
is read as not visible; this isn't really necessary nor useful and if we
want to remove the axes entirely we should do it explicitly and/or
provide a way through the `ComposeGridLayout` API.
Despite there being artifacts when interacting, the speedups when
cross-hair-ing are just too good to ignore. We can always play with
disabling caches when interaction takes place much like we do with feed
pausing.
When zoomed in alot, and thus a quote driven y-range resize takes place,
it makes more sense to increase the `range_margin: float` input to
`._set_yrange()` to ensure all L1 labels stay in view; generally the
more zoomed in,
- the smaller the y-range is and thus the larger the needed margin (on
that range's dispersion diff) would be,
- it's more likely to get a last datum move outside the previous range.
Also, always do overlayT style scaling on the slow chart whenever it
treads.
Since it can be desirable to dynamically adjust inputs to the y-ranging
method (such as in the display loop when a chart is very zoomed in), this
adds such support through a new `yrange_kwargs: dict[Viz, dict]` which
replaces the `yrange` tuple we were passing through prior. Also, adjusts
the y-range margin back to the original 0.09 of the diff now that we can
support dynamic control.
If there is a common `PlotItem` used for a set of `Viz`/curves (on
a given view) we don't need to do overlay scaling and thus can also
short circuit the viz iteration loop early.
Somewhat of a facepalm but, for incremental update of the auto-yrange
from quotes in the display loop obviously we only want to update the
associated `Viz`/viewbox for *that* fqsn. Further we don't need to worry
about the whole "tick margin" stuff since `._set_yrange()` already adds
margin to the yrange by default; thus we remove all of that.
When the caller passes `do_overlay_scaling=False` we skip the given
chart's `Viz` iteration loop, and set the yrange immediately, then
continue to the next chart (if `do_linked_charts` is set) instead of
a `continue` short circuit within the viz sub-loop.
Deats:
- add a `_maybe_calc_yrange()` helper which makes the `yranges`
provided-or-not case logic more terse (factored).
- add a `do_linked_charts=False` short circuit.
- drop the legacy commented swing % calcs stuff.
- use the `ChartView._viz` when `do_overlay_scaling=False` thus
presuming that we want to handle the viz mapped to *this* view box.
- add a `._yrange` "last set yrange" tracking var which keeps record of
the last ymn/ymx value set in `._set_yrange()` BEFORE doing range
margins; this will be used for incremental update in the display loop.
Since each symbol's feed is multiplexed by quote key (an fqsn), we can
avoid scaling overlay curves on any single update, presuming each quote
driven cycle will trigger **only** the specific symbol's curve.
Also disables fsp `.interact_graphics_cycle()` calls for now since it
seems they aren't really that critical to and we should be using the
same technique as above (doing incremental y-range checks/updates) for
FSPs as well.
The reason (fsp) subcharts were not linked-updating correctly was
because of the early termination of the interact update loop when only
one "overlay" (aka no other overlays then the main curve) is detected.
Obviously in this case we still need to iterate all linked charts in the
set (presuming the user doesn't disable this).
Also tweaks a few internals:
- rename `start_datums: dict` -> `overlay_table`.
- compact all "single curve" checks to one logic block.
- don't collect curve info into the `overlay_table: dict` when
`do_overlay_scaling=True`.
Such that we still y-range auto-sort inside
`ChartView.interact_graphics_cycle()` still runs on the unit vlm axis
and we always size such that the y-label stays in view.
Since we pretty much always want the 'bottom' and any side that is not
declared by the caller move the axis hides into this method. Lets us
drop the same calls in `.ui._fsp` and `._display`.
This also disables the auto-ranging back-linking for now since it
doesn't seem to be working quite yet?
In situations where clients are (dynamically) subscribing *while*
broadcasts are starting to taking place we need to handle the
`set`-modified-during-iteration case. This scenario seems to be more
common during races on concurrent startup of multiple symbols. The
solution here is to use another set to take note of subscribers which
are successfully sent-to and then skipping them on re-try.
This also contains an attempt to exception-handle throttled stream
overruns caused by higher frequency feeds (like binance) pushing more
quotes then can be handled during (UI) client startup.
This was a subtle logic error when building the `plots: dict` we weren't
adding the "main (ohlc or other source) chart" from the `LinkedSplits`
set when interacting with some sub-chart from `.subplots`..
Further this tries out bypassing `numpy.median()` altogether by just
using `median = (ymx - ymn) / 2` which should be nearly the same?
In the (incrementally updated) display loop we have range logic that is
incrementally updated in real-time by streams, as such we don't really
need to update all linked chart's (for any given, currently updated
chart) y-ranges on calls of each separate (sub-)chart's
`ChartView.interact_graphics_cycle()`. In practise there are plenty of
cases where resizing in one chart (say the vlm fsps sub-plot) requires
a y-range re-calc but not in the OHLC price chart. Therefore
we always avoid doing more resizing then necessary despite it resulting
in potentially more method call overhead (which will later be justified
by better leveraging incrementally updated `Viz.maxmin()` and
`media_from_range()` calcs).
A super snappy `numpy.median()` calculator (per input range) which we
slap an `lru_cache` on thanks to handy dunder method hacks for such
things on mutable types XD
use the new `do_overlay_scaling: bool` since we know each feed will have
its own updates (cuz multiplexed by feed..) and we can avoid
ranging/scaling overlays that will make their own calls.
Also, pass in the last datum "brighter" color for ohlc curves as it was
originally (and now that we can pass that styling bit through).
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.
Tyler Goodlet