Support "pin-to-target-curve" overlay method again
Yah yah, i know it's the same as before (the N > 2 curves case with
out-of-range-minor rescaling the previously scaled curves isn't fixed
yet...) but, this is a much better and optional implementation in less
code. Further we're now better leveraging various new cached properties
and methods on `Viz`.
We now handle different `overlay_technique: str` options using `match:`
syntax in the 2ndary scaling loop, stash the returns scalars per curve
in `overlay_table`, and store and iterate the curves by dispersion
measure sort order.
Further wrt "pin-to-target-curve" mode, which currently still pins to
the largest measured dispersion curve in the overlay set:
- pop major Ci overlay table entries at start for sub-calcs usage when
handling the "minor requires major rescale after pin" case.
- (finally) correctly rescale the major curve y-mxmn to whatever the
latest minor overlay curve by calcing the inverse transform from the
minor *at that point*:
- the intersect point being that which the minor has starts support on
the major's x-domain* using the new `Viz.scalars_from_index()` and,
- checking that the minor is not out of range (versus what the major's
transform calcs it to be, in which case,
- calc the inverse transform from the current out-of-range minor and
use it to project the new y-mxmn for the major/target based on the
same intersect-reference point in the x-domain used by the minor.
- always handle the target-major Ci specially by only setting the
`mx_ymn` / `mx_ymn` value when iterating that entry in the overlay
table.
- add todos around also doing the last sub-sub bullet for all previously
major-transform scaled minor overlays (this is coming next..i hope).
- add a final 3rd overlay loop which goes through a final `scaled: dict`
to apply all range values to each view; this is where we will
eventually solve that last edge case of an out-of-range minor's
scaling needing to be used to rescale already scaled minors XD
storage_cli
Tyler Goodlet
parent
55ec9ef5a0
commit
b118954bf7
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@ -62,8 +62,8 @@ class OverlayT(Struct):
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in the target co-domain.
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'''
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start_t: float | None = None
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viz: Viz | None = None
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start_t: float | None = None
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# % "range" computed from some ref value to the mn/mx
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rng: float | None = None
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@ -201,13 +201,13 @@ def overlay_viewlists(
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] | None = None,
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overlay_technique: Literal[
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'loglin_to_first',
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'loglin_to_sigma',
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'mnmx',
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'loglin_ref_to_curve',
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'loglin_ref_to_first',
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'mxmn',
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'solo',
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] = 'loglin_to_first',
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] = 'loglin_ref_to_curve',
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# internal instrumentation
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# internal debug
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debug_print: bool = False,
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) -> None:
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@ -236,8 +236,6 @@ def overlay_viewlists(
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# -> for any "group" overlay we want to dispersion normalize
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# and scale minor charts onto the major chart: the chart
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# with the most dispersion in the set.
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major_sigma_viz: Viz = None
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mx_disp: float = 0
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# collect certain flows have grapics objects **in seperate
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# plots/viewboxes** into groups and do a common calc to
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@ -245,8 +243,9 @@ def overlay_viewlists(
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# this is primarly used for our so called "log-linearized
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# multi-plot" overlay technique.
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overlay_table: dict[
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ChartView,
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float,
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tuple[
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ChartView,
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Viz,
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float, # y start
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float, # y min
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@ -254,6 +253,8 @@ def overlay_viewlists(
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float, # y median
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slice, # in-view array slice
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np.ndarray, # in-view array
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float, # returns up scalar
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float, # return down scalar
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],
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] = {}
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@ -323,11 +324,15 @@ def overlay_viewlists(
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profiler(f'{viz.name}@{chart_name} common pi sort')
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# non-overlay group case
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if not viz.is_ohlc:
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if (
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not viz.is_ohlc
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or overlay_technique == 'solo'
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):
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pi.vb._set_yrange(yrange=yrange)
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profiler(
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f'{viz.name}@{chart_name} simple std `._set_yrange()`'
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)
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continue
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# handle overlay log-linearized group scaling cases
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# TODO: a better predicate here, likely something
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@ -338,15 +343,12 @@ def overlay_viewlists(
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ymn, ymx = yrange
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# determine start datum in view
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arr = viz.shm.array
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in_view = arr[read_slc]
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in_view = viz.vs.in_view
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if not in_view.size:
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log.warning(f'{viz.name} not in view?')
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continue
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# row_start = arr[read_slc.start - 1]
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row_start = arr[read_slc.start]
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row_start = in_view[0]
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if viz.is_ohlc:
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y_ref = row_start['open']
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else:
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@ -355,9 +357,11 @@ def overlay_viewlists(
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profiler(f'{viz.name}@{chart_name} MINOR curve median')
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key = 'open' if viz.is_ohlc else viz.name
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start_t = in_view[0]['time']
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r_down = (ymn - y_ref) / y_ref
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start_t = row_start['time']
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# returns scalars
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r_up = (ymx - y_ref) / y_ref
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r_down = (ymn - y_ref) / y_ref
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msg = (
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f'### {viz.name}@{chart_name} ###\n'
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@ -431,6 +435,8 @@ def overlay_viewlists(
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if debug_print:
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print(msg)
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# is the current up `OverlayT` not yet defined or
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# the current `r_up` greater then the previous max.
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if (
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upt.rng is None
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or (
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@ -483,27 +489,37 @@ def overlay_viewlists(
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profiler(msg)
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print(msg)
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# find curve with max dispersion
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disp = abs(ymx - ymn) / y_ref
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if disp > mx_disp:
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major_sigma_viz = viz
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mx_disp = disp
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# disp = viz.disp_from_range(yref=y_ref)
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# if disp is None:
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# print(f'{viz.name}: WTF NO DISP')
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# continue
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overlay_table[viz.plot.vb] = (
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# r_up, r_dn = disp
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disp = r_up - r_down
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# register curves by a "full" dispersion metric for
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# later sort order in the overlay (technique
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# ) application loop below.
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overlay_table[disp] = (
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viz.plot.vb,
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viz,
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y_ref,
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ymn,
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ymx,
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read_slc,
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in_view,
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)
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r_up,
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r_down,
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)
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profiler(f'{viz.name}@{chart_name} yrange scan complete')
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# NOTE: if no there were no overlay charts
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# detected/collected (could be either no group detected or
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# chart with a single symbol, thus a single viz/overlay)
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# then we ONLY set the lone chart's (viz) yrange and short
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# then we ONLY set the mone chart's (viz) yrange and short
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# circuit to the next chart in the linked charts loop. IOW
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# there's no reason to go through the overlay dispersion
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# scaling in the next loop below when only one curve is
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@ -534,13 +550,20 @@ def overlay_viewlists(
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elif (
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mxmns_by_common_pi
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and not major_sigma_viz
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# and not major_sigma_viz
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and not overlay_table
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):
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# move to next chart in linked set since
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# no overlay transforming is needed.
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continue
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profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
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msg = (
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f'`Viz` curve first pass complete\n'
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f'overlay_table: {overlay_table.keys()}\n'
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)
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profiler(msg)
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if debug_print:
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print(msg)
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# if a minor curves scaling brings it "outside" the range of
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# the major curve (in major curve co-domain terms) then we
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@ -548,21 +571,39 @@ def overlay_viewlists(
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# below placeholder denotes when this occurs.
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# group_mxmn: None | tuple[float, float] = None
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# TODO: probably re-write this loop as a compiled cpython or
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# numba func.
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r_up_mx: float
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r_dn_mn: float
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mx_disp = max(overlay_table)
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mx_entry = overlay_table[mx_disp]
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(
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_, # viewbox
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mx_viz, # viz
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_, # y_ref
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mx_ymn,
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mx_ymx,
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_, # read_slc
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_, # in_view array
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r_up_mx,
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r_dn_mn,
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) = mx_entry
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scaled: dict[float, tuple[float, float, float]] = {}
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# conduct "log-linearized multi-plot" scalings for all groups
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for (
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view,
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# -> iterate all curves Ci in dispersion-measure sorted order
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# going from smallest swing to largest.
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for full_disp in sorted(overlay_table):
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(
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view,
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viz,
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y_start,
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y_min,
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y_max,
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read_slc,
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minor_in_view,
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)
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) in overlay_table.items():
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r_up,
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r_dn,
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) = overlay_table[full_disp]
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key = 'open' if viz.is_ohlc else viz.name
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@ -575,8 +616,99 @@ def overlay_viewlists(
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)
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continue
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ymn = dnt.apply_rng(y_start)
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ymx = upt.apply_rng(y_start)
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xref = minor_in_view[0]['time']
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match overlay_technique:
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# Pin this curve to the "major dispersion" (or other
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# target) curve by finding the intersect datum and
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# then scaling according to the returns log-lin transort
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# 'at that intersect reference data'. If the pinning
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# results in this (minor/pinned) curve being out of view
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# adjust the returns scalars to match this curves min
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# y-range to stay in view.
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case 'loglin_ref_to_curve':
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# TODO: technically we only need to do this here if
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#
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if viz is not mx_viz:
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(
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i_start,
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y_ref_major,
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r_major_up_here,
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r_major_down_here,
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) = mx_viz.scalars_from_index(xref)
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# transform y-range scaling to be the same as the
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# equivalent "intersect" datum on the major
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# dispersion curve (or other target "pin to"
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# equivalent).
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ymn = y_start * (1 + r_major_down_here)
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if ymn > y_min:
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ymn = y_min
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r_dn_minor = (ymn - y_start) / y_start
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mx_ymn = y_ref_major * (1 + r_dn_minor)
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# TODO: rescale all already scaled curves to
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# new increased range for this side.
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# for (
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# view,
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# (yref, ymn, ymx)
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# ) in scaled.items():
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# pass
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ymx = y_start * (1 + r_major_up_here)
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if ymx < y_max:
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ymx = y_max
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r_up_minor = (ymx - y_start) / y_start
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mx_ymx = y_ref_major * (1 + r_up_minor)
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if debug_print:
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print(
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f'Minor SCALARS {viz.name}:\n'
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f'xref: {xref}\n'
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f'dn: {r_major_down_here}\n'
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f'up: {r_major_up_here}\n'
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)
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else:
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if debug_print:
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print(
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f'MAJOR SCALARS {viz.name}:\n'
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f'dn: {r_dn_mn}\n'
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f'up: {r_up_mx}\n'
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)
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# target/major curve's mxmn may have been
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# reset by minor overlay steps above.
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ymn = mx_ymn
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ymx = mx_ymx
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# Pin all curves by their first datum in view to all
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# others such that each curve's earliest datum provides the
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# reference point for returns vs. every other curve in
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# view.
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case 'loglin_ref_to_first':
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ymn = dnt.apply_rng(y_start)
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ymx = upt.apply_rng(y_start)
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# Do not pin curves by log-linearizing their y-ranges,
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# instead allow each curve to fully scale to the
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# time-series in view's min and max y-values.
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case 'mxmn':
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ymn = y_min
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ymx = y_max
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case _:
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raise RuntimeError(
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f'overlay_technique is invalid `{overlay_technique}'
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)
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scaled[view] = (y_start, ymn, ymx)
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for (
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view,
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(yref, ymn, ymx)
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) in scaled.items():
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# NOTE XXX: we have to set each curve's range once (and
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# ONLY ONCE) here since we're doing this entire routine
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@ -594,6 +726,8 @@ def overlay_viewlists(
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f'LOGLIN SCALE CYCLE: {viz.name}@{chart_name}\n'
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f'UP MAJOR C: {upt.viz.name} with disp: {upt.rng}\n'
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f'DOWN MAJOR C: {dnt.viz.name} with disp: {dnt.rng}\n'
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f'disp: {disp}\n'
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f'xref for MINOR: {xref}\n'
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f'y_start: {y_start}\n'
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f'y min: {y_min}\n'
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f'y max: {y_max}\n'
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@ -614,7 +748,10 @@ def overlay_viewlists(
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+
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'\n'
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)
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profiler(f'<{chart_name}>.interact_graphics_cycle()')
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if not do_linked_charts:
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return
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break
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profiler.finish()
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Loading…
Reference in New Issue