Move overlay transform logic to new `.ui.view_mode`
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)log_linearized_curve_overlays
Tyler Goodlet
parent
753e991dae
commit
35c40e825a
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@ -1,5 +1,5 @@
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# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship for piker0)
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# Copyright (C) Tyler Goodlet (in stewardship for pikers)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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@ -14,18 +14,14 @@
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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'''
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Chart view box primitives
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"""
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'''
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from __future__ import annotations
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from contextlib import asynccontextmanager
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from math import (
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isinf,
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)
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import time
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from typing import (
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Any,
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Callable,
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TYPE_CHECKING,
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)
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@ -42,8 +38,7 @@ import trio
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from ..log import get_logger
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from .._profile import Profiler
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from .._profile import pg_profile_enabled, ms_slower_then
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from ..data.types import Struct
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from ..data._pathops import slice_from_time
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from .view_mode import overlay_viewlists
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# from ._style import _min_points_to_show
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from ._editors import SelectRect
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from . import _event
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@ -51,7 +46,6 @@ from . import _event
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if TYPE_CHECKING:
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from ._chart import ChartPlotWidget
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from ._dataviz import Viz
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# from ._overlay import PlotItemOverlay
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log = get_logger(__name__)
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@ -926,14 +920,17 @@ class ChartView(ViewBox):
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self,
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*args, # capture Qt signal (slot) inputs
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debug_print: bool = False,
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do_overlay_scaling: bool = True,
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# debug_print: bool = False,
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do_linked_charts: bool = True,
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do_overlay_scaling: bool = True,
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yrange_kwargs: dict[
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str,
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tuple[float, float],
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] | None = None,
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overlay_technique: str = 'loglin_to_first',
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):
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profiler = Profiler(
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msg=f'ChartView.interact_graphics_cycle() for {self.name}',
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@ -964,559 +961,12 @@ class ChartView(ViewBox):
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plots = {chart.name: chart}
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# TODO: a faster single-loop-iterator way of doing this?
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for chart_name, chart in plots.items():
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# Common `PlotItem` maxmin table; presumes that some path
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# graphics (and thus their backing data sets) are in the
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# same co-domain and view box (since the were added
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# a separate graphics objects to a common plot) and thus can
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# be sorted as one set per plot.
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mxmns_by_common_pi: dict[
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pg.PlotItem,
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tuple[float, float],
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] = {}
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# proportional group auto-scaling per overlay set.
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# -> loop through overlays on each multi-chart widget
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# and scale all y-ranges based on autoscale config.
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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_viz: Viz = None
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major_mx: float = 0
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major_mn: float = float('inf')
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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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# determine auto-ranging input for `._set_yrange()`.
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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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ViewBox,
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tuple[
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Viz,
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float, # y start
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float, # y min
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float, # y max
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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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],
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] = {}
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# ONLY auto-yrange the viz mapped to THIS view box
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if not do_overlay_scaling:
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viz = self._viz
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if debug_print:
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print(f'ONLY ranging THIS viz: {viz.name}')
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out = _maybe_calc_yrange(
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viz,
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yrange_kwargs,
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return overlay_viewlists(
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self._viz,
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plots,
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profiler,
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chart_name,
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)
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if out is None:
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continue
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read_slc, yrange_kwargs = out
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viz.plot.vb._set_yrange(**yrange_kwargs)
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profiler(f'{viz.name}@{chart_name} single curve yrange')
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# don't iterate overlays, just move to next chart
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continue
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# create a group overlay log-linearized y-range transform to
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# track and eventually inverse transform all overlay curves
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# to a common target max dispersion range.
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dnt = OverlayT()
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upt = OverlayT()
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if debug_print:
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print(
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f'BEGIN UX GRAPHICS CYCLE: @{chart_name}\n'
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+
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'#'*100
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+
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'\n'
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)
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for name, viz in chart._vizs.items():
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out = _maybe_calc_yrange(
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viz,
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yrange_kwargs,
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profiler,
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chart_name,
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)
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if out is None:
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continue
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read_slc, yrange_kwargs = out
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yrange = yrange_kwargs['yrange']
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pi = viz.plot
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# handle multiple graphics-objs per viewbox cases
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mxmn = mxmns_by_common_pi.get(pi)
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if mxmn:
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yrange = mxmns_by_common_pi[pi] = (
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min(yrange[0], mxmn[0]),
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max(yrange[1], mxmn[1]),
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)
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else:
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mxmns_by_common_pi[pi] = yrange
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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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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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# handle overlay log-linearized group scaling cases
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# TODO: a better predicate here, likely something
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# to do with overlays and their settings..
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# TODO: we probably eventually might want some other
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# charts besides OHLC?
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else:
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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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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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if viz.is_ohlc:
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y_ref = row_start['open']
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else:
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y_ref = row_start[viz.name]
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profiler(f'{viz.name}@{chart_name} MINOR curve median')
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overlay_table[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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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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r_up = (ymx - y_ref) / y_ref
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msg = (
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f'### {viz.name}@{chart_name} ###\n'
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f'y_ref: {y_ref}\n'
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f'down disp: {r_down}\n'
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f'up disp: {r_up}\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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# track the "major" curve as the curve with most
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# dispersion.
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if (
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dnt.rng is None
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or (
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r_down < dnt.rng
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and r_down < 0
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)
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):
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dnt.viz = viz
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dnt.rng = r_down
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dnt.in_view = in_view
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dnt.start_t = in_view[0]['time']
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major_mn = ymn
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msg = f'NEW DOWN: {viz.name}@{chart_name} r:{r_down}\n'
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profiler(msg)
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if debug_print:
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print(msg)
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else:
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# minor in the down swing range so check that if
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# we apply the current rng to the minor that it
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# doesn't go outside the current range for the major
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# otherwise we recompute the minor's range (when
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# adjusted for it's intersect point to be the new
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# major's range.
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intersect = intersect_from_longer(
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dnt.start_t,
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dnt.in_view,
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start_t,
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in_view,
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)
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profiler(f'{viz.name}@{chart_name} intersect by t')
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if intersect:
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longer_in_view, _t, i = intersect
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scaled_mn = dnt.apply_rng(y_ref)
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if scaled_mn > ymn:
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# after major curve scaling we detected
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# the minor curve is still out of range
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# so we need to adjust the major's range
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# to include the new composed range.
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y_maj_ref = longer_in_view[key]
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new_major_ymn = (
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y_maj_ref
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*
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(1 + r_down)
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)
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# rewrite the major range to the new
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# minor-pinned-to-major range and mark
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# the transform as "virtual".
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msg = (
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f'EXPAND DOWN bc {viz.name}@{chart_name}\n'
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f'y_start epoch time @ {_t}:\n'
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f'y_maj_ref @ {_t}: {y_maj_ref}\n'
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f'R: {dnt.rng} -> {r_down}\n'
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f'MN: {major_mn} -> {new_major_ymn}\n'
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)
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dnt.rng = r_down
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major_mn = dnt.y_val = new_major_ymn
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profiler(msg)
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if debug_print:
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print(msg)
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if (
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upt.rng is None
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or (
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r_up > upt.rng
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and r_up > 0
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)
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):
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upt.rng = r_up
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upt.viz = viz
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upt.in_view = in_view
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upt.start_t = in_view[0]['time']
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major_mx = ymx
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msg = f'NEW UP: {viz.name}@{chart_name} r:{r_up}\n'
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profiler(msg)
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if debug_print:
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print(msg)
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else:
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intersect = intersect_from_longer(
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upt.start_t,
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upt.in_view,
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start_t,
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in_view,
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)
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profiler(f'{viz.name}@{chart_name} intersect by t')
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if intersect:
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longer_in_view, _t, i = intersect
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scaled_mx = upt.apply_rng(y_ref)
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if scaled_mx < ymx:
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# after major curve scaling we detected
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# the minor curve is still out of range
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# so we need to adjust the major's range
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# to include the new composed range.
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y_maj_ref = longer_in_view[key]
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new_major_ymx = (
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y_maj_ref
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*
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(1 + r_up)
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)
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# rewrite the major range to the new
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# minor-pinned-to-major range and mark
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# the transform as "virtual".
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msg = (
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f'EXPAND UP bc {viz.name}@{chart_name}:\n'
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f'y_maj_ref @ {_t}: {y_maj_ref}\n'
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f'R: {upt.rng} -> {r_up}\n'
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f'MX: {major_mx} -> {new_major_ymx}\n'
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)
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upt.rng = r_up
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major_mx = upt.y_val = new_major_ymx
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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_viz = viz
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mx_disp = disp
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major_mn = ymn
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major_mx = ymx
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profiler(f'{viz.name}@{chart_name} MINOR curve scale')
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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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# 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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# detected.
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if (
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not mxmns_by_common_pi
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and len(overlay_table) < 2
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):
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if debug_print:
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print(f'ONLY ranging major: {viz.name}')
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out = _maybe_calc_yrange(
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viz,
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yrange_kwargs,
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profiler,
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chart_name,
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)
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if out is None:
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continue
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read_slc, yrange_kwargs = out
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viz.plot.vb._set_yrange(**yrange_kwargs)
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profiler(f'{viz.name}@{chart_name} single curve yrange')
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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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elif (
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mxmns_by_common_pi
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and not major_viz
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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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# 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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# need to rescale the major to also include this range. The
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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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# conduct "log-linearized multi-plot" scalings for all groups
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for (
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view,
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(
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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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key = 'open' if viz.is_ohlc else viz.name
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if (
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isinf(ymx)
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or isinf(ymn)
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):
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log.warning(
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f'BAD ymx/ymn: {(ymn, ymx)}'
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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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# 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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# inside of a single render cycle (and apparently calling
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# `ViewBox.setYRange()` multiple times within one only takes
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# the first call as serious...) XD
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view._set_yrange(
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yrange=(ymn, ymx),
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)
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profiler(f'{viz.name}@{chart_name} log-SCALE minor')
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if debug_print:
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print(
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'------------------------------\n'
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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'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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f'T scaled ymn: {ymn}\n'
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f'T scaled ymx: {ymx}\n'
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'------------------------------\n'
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)
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# profiler(f'{viz.name}@{chart_name} log-SCALE major')
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# major_mx, major_mn = group_mxmn
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# vrs = major_viz.plot.vb.viewRange()
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# if vrs[1][0] > major_mn:
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# breakpoint()
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if debug_print:
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print(
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f'END UX GRAPHICS CYCLE: @{chart_name}\n'
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+
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'#'*100
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+
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'\n'
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)
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if not do_linked_charts:
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return
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profiler.finish()
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def _maybe_calc_yrange(
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viz: Viz,
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yrange_kwargs: dict[Viz, dict[str, Any]],
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profiler: Profiler,
|
||||
chart_name: str,
|
||||
|
||||
) -> tuple[
|
||||
slice,
|
||||
dict,
|
||||
] | None:
|
||||
|
||||
if not viz.render:
|
||||
return
|
||||
|
||||
# pass in no array which will read and render from the last
|
||||
# passed array (normally provided by the display loop.)
|
||||
in_view, i_read_range, _ = viz.update_graphics()
|
||||
|
||||
if not in_view:
|
||||
return
|
||||
|
||||
profiler(f'{viz.name}@{chart_name} `Viz.update_graphics()`')
|
||||
|
||||
# check if explicit yrange (kwargs) was passed in by the caller
|
||||
yrange_kwargs = yrange_kwargs.get(viz) if yrange_kwargs else None
|
||||
if yrange_kwargs is not None:
|
||||
read_slc = slice(*i_read_range)
|
||||
|
||||
else:
|
||||
out = viz.maxmin(i_read_range=i_read_range)
|
||||
if out is None:
|
||||
log.warning(f'No yrange provided for {viz.name}!?')
|
||||
return
|
||||
(
|
||||
_, # ixrng,
|
||||
read_slc,
|
||||
yrange
|
||||
) = out
|
||||
profiler(f'{viz.name}@{chart_name} `Viz.maxmin()`')
|
||||
yrange_kwargs = {'yrange': yrange}
|
||||
|
||||
return (
|
||||
read_slc,
|
||||
yrange_kwargs,
|
||||
)
|
||||
|
||||
|
||||
class OverlayT(Struct):
|
||||
'''
|
||||
An overlay co-domain range transformer.
|
||||
|
||||
Used to translate and apply a range from one y-range
|
||||
to another based on a returns logarithm:
|
||||
|
||||
R(ymn, ymx, yref) = (ymx - yref)/yref
|
||||
|
||||
which gives the log-scale multiplier, and
|
||||
|
||||
ymx_t = yref * (1 + R)
|
||||
|
||||
which gives the inverse to translate to the same value
|
||||
in the target co-domain.
|
||||
|
||||
'''
|
||||
start_t: float | None = None
|
||||
viz: Viz = None
|
||||
|
||||
# % "range" computed from some ref value to the mn/mx
|
||||
rng: float | None = None
|
||||
in_view: np.ndarray | None = None
|
||||
|
||||
# pinned-minor curve modified mn and max for the major dispersion
|
||||
# curve due to one series being shorter and the pin + scaling from
|
||||
# that pin point causing the original range to have to increase.
|
||||
y_val: float | None = None
|
||||
|
||||
def apply_rng(
|
||||
self,
|
||||
y_start: float, # reference value for dispersion metric
|
||||
|
||||
) -> float:
|
||||
return y_start * (1 + self.rng)
|
||||
|
||||
# def loglin_from_range(
|
||||
# self,
|
||||
|
||||
# y_ref: float, # reference value for dispersion metric
|
||||
# mn: float, # min y in target log-lin range
|
||||
# mx: float, # max y in target log-lin range
|
||||
# offset: float, # y-offset to start log-scaling from
|
||||
|
||||
# ) -> tuple[float, float]:
|
||||
# r_up = (mx - y_ref) / y_ref
|
||||
# r_down = (mn - y_ref) / y_ref
|
||||
# ymn = offset * (1 + r_down)
|
||||
# ymx = offset * (1 + r_up)
|
||||
|
||||
# return ymn, ymx
|
||||
|
||||
|
||||
def intersect_from_longer(
|
||||
start_t_first: float,
|
||||
in_view_first: np.ndarray,
|
||||
|
||||
start_t_second: float,
|
||||
in_view_second: np.ndarray,
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
tdiff = start_t_first - start_t_second
|
||||
|
||||
if tdiff == 0:
|
||||
return False
|
||||
|
||||
i: int = 0
|
||||
|
||||
# first time series has an "earlier" first time stamp then the 2nd.
|
||||
# aka 1st is "shorter" then the 2nd.
|
||||
if tdiff > 0:
|
||||
longer = in_view_second
|
||||
find_t = start_t_first
|
||||
i = 1
|
||||
|
||||
# second time series has an "earlier" first time stamp then the 1st.
|
||||
# aka 2nd is "shorter" then the 1st.
|
||||
elif tdiff < 0:
|
||||
longer = in_view_first
|
||||
find_t = start_t_second
|
||||
i = 0
|
||||
|
||||
slc = slice_from_time(
|
||||
arr=longer,
|
||||
start_t=find_t,
|
||||
stop_t=find_t,
|
||||
)
|
||||
return (
|
||||
longer[slc.start],
|
||||
find_t,
|
||||
i,
|
||||
do_overlay_scaling=do_overlay_scaling,
|
||||
do_linked_charts=do_linked_charts,
|
||||
yrange_kwargs=yrange_kwargs,
|
||||
overlay_technique=overlay_technique,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,635 @@
|
|||
# piker: trading gear for hackers
|
||||
# Copyright (C) Tyler Goodlet (in stewardship for pikers)
|
||||
|
||||
# This program is free software: you can redistribute it and/or modify
|
||||
# it under the terms of the GNU Affero General Public License as published by
|
||||
# the Free Software Foundation, either version 3 of the License, or
|
||||
# (at your option) any later version.
|
||||
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU Affero General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU Affero General Public License
|
||||
# along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
'''
|
||||
Overlay (aka multi-chart) UX machinery.
|
||||
|
||||
'''
|
||||
from __future__ import annotations
|
||||
from math import (
|
||||
isinf,
|
||||
)
|
||||
from typing import (
|
||||
Any,
|
||||
Literal,
|
||||
TYPE_CHECKING,
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
import pyqtgraph as pg
|
||||
|
||||
from ..data.types import Struct
|
||||
from ..data._pathops import slice_from_time
|
||||
from ..log import get_logger
|
||||
from .._profile import Profiler
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ._chart import ChartPlotWidget
|
||||
from ._dataviz import Viz
|
||||
from ._interaction import ChartView
|
||||
|
||||
|
||||
log = get_logger(__name__)
|
||||
|
||||
|
||||
class OverlayT(Struct):
|
||||
'''
|
||||
An overlay co-domain range transformer.
|
||||
|
||||
Used to translate and apply a range from one y-range
|
||||
to another based on a returns logarithm:
|
||||
|
||||
R(ymn, ymx, yref) = (ymx - yref)/yref
|
||||
|
||||
which gives the log-scale multiplier, and
|
||||
|
||||
ymx_t = yref * (1 + R)
|
||||
|
||||
which gives the inverse to translate to the same value
|
||||
in the target co-domain.
|
||||
|
||||
'''
|
||||
start_t: float | None = None
|
||||
viz: Viz | None = None
|
||||
|
||||
# % "range" computed from some ref value to the mn/mx
|
||||
rng: float | None = None
|
||||
in_view: np.ndarray | None = None
|
||||
|
||||
# pinned-minor curve modified mn and max for the major dispersion
|
||||
# curve due to one series being shorter and the pin + scaling from
|
||||
# that pin point causing the original range to have to increase.
|
||||
y_val: float | None = None
|
||||
|
||||
def apply_rng(
|
||||
self,
|
||||
y_start: float, # reference value for dispersion metric
|
||||
|
||||
) -> float:
|
||||
return y_start * (1 + self.rng)
|
||||
|
||||
# def loglin_from_range(
|
||||
# self,
|
||||
|
||||
# y_ref: float, # reference value for dispersion metric
|
||||
# mn: float, # min y in target log-lin range
|
||||
# mx: float, # max y in target log-lin range
|
||||
# offset: float, # y-offset to start log-scaling from
|
||||
|
||||
# ) -> tuple[float, float]:
|
||||
# r_up = (mx - y_ref) / y_ref
|
||||
# r_down = (mn - y_ref) / y_ref
|
||||
# ymn = offset * (1 + r_down)
|
||||
# ymx = offset * (1 + r_up)
|
||||
|
||||
# return ymn, ymx
|
||||
|
||||
|
||||
def intersect_from_longer(
|
||||
start_t_first: float,
|
||||
in_view_first: np.ndarray,
|
||||
|
||||
start_t_second: float,
|
||||
in_view_second: np.ndarray,
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
tdiff = start_t_first - start_t_second
|
||||
|
||||
if tdiff == 0:
|
||||
return False
|
||||
|
||||
i: int = 0
|
||||
|
||||
# first time series has an "earlier" first time stamp then the 2nd.
|
||||
# aka 1st is "shorter" then the 2nd.
|
||||
if tdiff > 0:
|
||||
longer = in_view_second
|
||||
find_t = start_t_first
|
||||
i = 1
|
||||
|
||||
# second time series has an "earlier" first time stamp then the 1st.
|
||||
# aka 2nd is "shorter" then the 1st.
|
||||
elif tdiff < 0:
|
||||
longer = in_view_first
|
||||
find_t = start_t_second
|
||||
i = 0
|
||||
|
||||
slc = slice_from_time(
|
||||
arr=longer,
|
||||
start_t=find_t,
|
||||
stop_t=find_t,
|
||||
)
|
||||
return (
|
||||
longer[slc.start],
|
||||
find_t,
|
||||
i,
|
||||
)
|
||||
|
||||
|
||||
def _maybe_calc_yrange(
|
||||
viz: Viz,
|
||||
yrange_kwargs: dict[Viz, dict[str, Any]],
|
||||
profiler: Profiler,
|
||||
chart_name: str,
|
||||
|
||||
) -> tuple[
|
||||
slice,
|
||||
dict,
|
||||
] | None:
|
||||
|
||||
if not viz.render:
|
||||
return
|
||||
|
||||
# pass in no array which will read and render from the last
|
||||
# passed array (normally provided by the display loop.)
|
||||
in_view, i_read_range, _ = viz.update_graphics()
|
||||
|
||||
if not in_view:
|
||||
return
|
||||
|
||||
profiler(f'{viz.name}@{chart_name} `Viz.update_graphics()`')
|
||||
|
||||
# check if explicit yrange (kwargs) was passed in by the caller
|
||||
yrange_kwargs = yrange_kwargs.get(viz) if yrange_kwargs else None
|
||||
if yrange_kwargs is not None:
|
||||
read_slc = slice(*i_read_range)
|
||||
|
||||
else:
|
||||
out = viz.maxmin(i_read_range=i_read_range)
|
||||
if out is None:
|
||||
log.warning(f'No yrange provided for {viz.name}!?')
|
||||
return
|
||||
(
|
||||
_, # ixrng,
|
||||
read_slc,
|
||||
yrange
|
||||
) = out
|
||||
profiler(f'{viz.name}@{chart_name} `Viz.maxmin()`')
|
||||
yrange_kwargs = {'yrange': yrange}
|
||||
|
||||
return (
|
||||
read_slc,
|
||||
yrange_kwargs,
|
||||
)
|
||||
|
||||
|
||||
def overlay_viewlists(
|
||||
active_viz: Viz,
|
||||
plots: dict[str, ChartPlotWidget],
|
||||
profiler: Profiler,
|
||||
|
||||
# public config ctls
|
||||
do_linked_charts: bool = True,
|
||||
do_overlay_scaling: bool = True,
|
||||
yrange_kwargs: dict[
|
||||
str,
|
||||
tuple[float, float],
|
||||
] | None = None,
|
||||
|
||||
overlay_technique: Literal[
|
||||
'loglin_to_first',
|
||||
'loglin_to_sigma',
|
||||
'mnmx',
|
||||
'solo',
|
||||
] = 'loglin_to_first',
|
||||
|
||||
|
||||
# internal instrumentation
|
||||
debug_print: bool = False,
|
||||
|
||||
) -> None:
|
||||
'''
|
||||
Calculate and apply y-domain (axis y-range) multi-curve overlay adjustments
|
||||
a set of ``plots`` based on the requested ``overlay_technique``.
|
||||
|
||||
'''
|
||||
chart_name: str
|
||||
chart: ChartPlotWidget
|
||||
for chart_name, chart in plots.items():
|
||||
|
||||
# Common `PlotItem` maxmin table; presumes that some path
|
||||
# graphics (and thus their backing data sets) are in the
|
||||
# same co-domain and view box (since the were added
|
||||
# a separate graphics objects to a common plot) and thus can
|
||||
# be sorted as one set per plot.
|
||||
mxmns_by_common_pi: dict[
|
||||
pg.PlotItem,
|
||||
tuple[float, float],
|
||||
] = {}
|
||||
|
||||
# proportional group auto-scaling per overlay set.
|
||||
# -> loop through overlays on each multi-chart widget
|
||||
# and scale all y-ranges based on autoscale config.
|
||||
# -> for any "group" overlay we want to dispersion normalize
|
||||
# and scale minor charts onto the major chart: the chart
|
||||
# with the most dispersion in the set.
|
||||
major_sigma_viz: Viz = None
|
||||
major_mx: float = 0
|
||||
major_mn: float = float('inf')
|
||||
mx_disp: float = 0
|
||||
|
||||
# collect certain flows have grapics objects **in seperate
|
||||
# plots/viewboxes** into groups and do a common calc to
|
||||
# determine auto-ranging input for `._set_yrange()`.
|
||||
# this is primarly used for our so called "log-linearized
|
||||
# multi-plot" overlay technique.
|
||||
overlay_table: dict[
|
||||
ChartView,
|
||||
tuple[
|
||||
Viz,
|
||||
float, # y start
|
||||
float, # y min
|
||||
float, # y max
|
||||
float, # y median
|
||||
slice, # in-view array slice
|
||||
np.ndarray, # in-view array
|
||||
],
|
||||
] = {}
|
||||
|
||||
# ONLY auto-yrange the viz mapped to THIS view box
|
||||
if not do_overlay_scaling:
|
||||
viz = active_viz
|
||||
if debug_print:
|
||||
print(f'ONLY ranging THIS viz: {viz.name}')
|
||||
|
||||
out = _maybe_calc_yrange(
|
||||
viz,
|
||||
yrange_kwargs,
|
||||
profiler,
|
||||
chart_name,
|
||||
)
|
||||
if out is None:
|
||||
continue
|
||||
|
||||
read_slc, yrange_kwargs = out
|
||||
viz.plot.vb._set_yrange(**yrange_kwargs)
|
||||
profiler(f'{viz.name}@{chart_name} single curve yrange')
|
||||
|
||||
# don't iterate overlays, just move to next chart
|
||||
continue
|
||||
|
||||
# create a group overlay log-linearized y-range transform to
|
||||
# track and eventually inverse transform all overlay curves
|
||||
# to a common target max dispersion range.
|
||||
dnt = OverlayT()
|
||||
upt = OverlayT()
|
||||
|
||||
if debug_print:
|
||||
print(
|
||||
f'BEGIN UX GRAPHICS CYCLE: @{chart_name}\n'
|
||||
+
|
||||
'#'*100
|
||||
+
|
||||
'\n'
|
||||
)
|
||||
|
||||
for name, viz in chart._vizs.items():
|
||||
|
||||
out = _maybe_calc_yrange(
|
||||
viz,
|
||||
yrange_kwargs,
|
||||
profiler,
|
||||
chart_name,
|
||||
)
|
||||
if out is None:
|
||||
continue
|
||||
|
||||
read_slc, yrange_kwargs = out
|
||||
yrange = yrange_kwargs['yrange']
|
||||
pi = viz.plot
|
||||
|
||||
# handle multiple graphics-objs per viewbox cases
|
||||
mxmn = mxmns_by_common_pi.get(pi)
|
||||
if mxmn:
|
||||
yrange = mxmns_by_common_pi[pi] = (
|
||||
min(yrange[0], mxmn[0]),
|
||||
max(yrange[1], mxmn[1]),
|
||||
)
|
||||
|
||||
else:
|
||||
mxmns_by_common_pi[pi] = yrange
|
||||
|
||||
profiler(f'{viz.name}@{chart_name} common pi sort')
|
||||
|
||||
# non-overlay group case
|
||||
if not viz.is_ohlc:
|
||||
pi.vb._set_yrange(yrange=yrange)
|
||||
profiler(
|
||||
f'{viz.name}@{chart_name} simple std `._set_yrange()`'
|
||||
)
|
||||
|
||||
# handle overlay log-linearized group scaling cases
|
||||
# TODO: a better predicate here, likely something
|
||||
# to do with overlays and their settings..
|
||||
# TODO: we probably eventually might want some other
|
||||
# charts besides OHLC?
|
||||
else:
|
||||
ymn, ymx = yrange
|
||||
|
||||
# determine start datum in view
|
||||
arr = viz.shm.array
|
||||
in_view = arr[read_slc]
|
||||
if not in_view.size:
|
||||
log.warning(f'{viz.name} not in view?')
|
||||
continue
|
||||
|
||||
# row_start = arr[read_slc.start - 1]
|
||||
row_start = arr[read_slc.start]
|
||||
|
||||
if viz.is_ohlc:
|
||||
y_ref = row_start['open']
|
||||
else:
|
||||
y_ref = row_start[viz.name]
|
||||
|
||||
profiler(f'{viz.name}@{chart_name} MINOR curve median')
|
||||
|
||||
overlay_table[viz.plot.vb] = (
|
||||
viz,
|
||||
y_ref,
|
||||
ymn,
|
||||
ymx,
|
||||
read_slc,
|
||||
in_view,
|
||||
)
|
||||
|
||||
key = 'open' if viz.is_ohlc else viz.name
|
||||
start_t = in_view[0]['time']
|
||||
r_down = (ymn - y_ref) / y_ref
|
||||
r_up = (ymx - y_ref) / y_ref
|
||||
|
||||
msg = (
|
||||
f'### {viz.name}@{chart_name} ###\n'
|
||||
f'y_ref: {y_ref}\n'
|
||||
f'down disp: {r_down}\n'
|
||||
f'up disp: {r_up}\n'
|
||||
)
|
||||
profiler(msg)
|
||||
if debug_print:
|
||||
print(msg)
|
||||
|
||||
# track the "major" curve as the curve with most
|
||||
# dispersion.
|
||||
if (
|
||||
dnt.rng is None
|
||||
or (
|
||||
r_down < dnt.rng
|
||||
and r_down < 0
|
||||
)
|
||||
):
|
||||
dnt.viz = viz
|
||||
dnt.rng = r_down
|
||||
dnt.in_view = in_view
|
||||
dnt.start_t = in_view[0]['time']
|
||||
major_mn = ymn
|
||||
|
||||
msg = f'NEW DOWN: {viz.name}@{chart_name} r:{r_down}\n'
|
||||
profiler(msg)
|
||||
if debug_print:
|
||||
print(msg)
|
||||
else:
|
||||
# minor in the down swing range so check that if
|
||||
# we apply the current rng to the minor that it
|
||||
# doesn't go outside the current range for the major
|
||||
# otherwise we recompute the minor's range (when
|
||||
# adjusted for it's intersect point to be the new
|
||||
# major's range.
|
||||
intersect = intersect_from_longer(
|
||||
dnt.start_t,
|
||||
dnt.in_view,
|
||||
start_t,
|
||||
in_view,
|
||||
)
|
||||
profiler(f'{viz.name}@{chart_name} intersect by t')
|
||||
|
||||
if intersect:
|
||||
longer_in_view, _t, i = intersect
|
||||
|
||||
scaled_mn = dnt.apply_rng(y_ref)
|
||||
if scaled_mn > ymn:
|
||||
# after major curve scaling we detected
|
||||
# the minor curve is still out of range
|
||||
# so we need to adjust the major's range
|
||||
# to include the new composed range.
|
||||
y_maj_ref = longer_in_view[key]
|
||||
new_major_ymn = (
|
||||
y_maj_ref
|
||||
*
|
||||
(1 + r_down)
|
||||
)
|
||||
|
||||
# rewrite the major range to the new
|
||||
# minor-pinned-to-major range and mark
|
||||
# the transform as "virtual".
|
||||
msg = (
|
||||
f'EXPAND DOWN bc {viz.name}@{chart_name}\n'
|
||||
f'y_start epoch time @ {_t}:\n'
|
||||
f'y_maj_ref @ {_t}: {y_maj_ref}\n'
|
||||
f'R: {dnt.rng} -> {r_down}\n'
|
||||
f'MN: {major_mn} -> {new_major_ymn}\n'
|
||||
)
|
||||
dnt.rng = r_down
|
||||
major_mn = dnt.y_val = new_major_ymn
|
||||
profiler(msg)
|
||||
if debug_print:
|
||||
print(msg)
|
||||
|
||||
if (
|
||||
upt.rng is None
|
||||
or (
|
||||
r_up > upt.rng
|
||||
and r_up > 0
|
||||
)
|
||||
):
|
||||
upt.rng = r_up
|
||||
upt.viz = viz
|
||||
upt.in_view = in_view
|
||||
upt.start_t = in_view[0]['time']
|
||||
major_mx = ymx
|
||||
msg = f'NEW UP: {viz.name}@{chart_name} r:{r_up}\n'
|
||||
profiler(msg)
|
||||
if debug_print:
|
||||
print(msg)
|
||||
|
||||
else:
|
||||
intersect = intersect_from_longer(
|
||||
upt.start_t,
|
||||
upt.in_view,
|
||||
start_t,
|
||||
in_view,
|
||||
)
|
||||
profiler(f'{viz.name}@{chart_name} intersect by t')
|
||||
|
||||
if intersect:
|
||||
longer_in_view, _t, i = intersect
|
||||
|
||||
scaled_mx = upt.apply_rng(y_ref)
|
||||
if scaled_mx < ymx:
|
||||
# after major curve scaling we detected
|
||||
# the minor curve is still out of range
|
||||
# so we need to adjust the major's range
|
||||
# to include the new composed range.
|
||||
y_maj_ref = longer_in_view[key]
|
||||
new_major_ymx = (
|
||||
y_maj_ref
|
||||
*
|
||||
(1 + r_up)
|
||||
)
|
||||
|
||||
# rewrite the major range to the new
|
||||
# minor-pinned-to-major range and mark
|
||||
# the transform as "virtual".
|
||||
msg = (
|
||||
f'EXPAND UP bc {viz.name}@{chart_name}:\n'
|
||||
f'y_maj_ref @ {_t}: {y_maj_ref}\n'
|
||||
f'R: {upt.rng} -> {r_up}\n'
|
||||
f'MX: {major_mx} -> {new_major_ymx}\n'
|
||||
)
|
||||
upt.rng = r_up
|
||||
major_mx = upt.y_val = new_major_ymx
|
||||
profiler(msg)
|
||||
print(msg)
|
||||
|
||||
# find curve with max dispersion
|
||||
disp = abs(ymx - ymn) / y_ref
|
||||
if disp > mx_disp:
|
||||
major_sigma_viz = viz
|
||||
mx_disp = disp
|
||||
major_mn = ymn
|
||||
major_mx = ymx
|
||||
|
||||
profiler(f'{viz.name}@{chart_name} MINOR curve scale')
|
||||
|
||||
# NOTE: if no there were no overlay charts
|
||||
# detected/collected (could be either no group detected or
|
||||
# chart with a single symbol, thus a single viz/overlay)
|
||||
# then we ONLY set the lone chart's (viz) yrange and short
|
||||
# circuit to the next chart in the linked charts loop. IOW
|
||||
# there's no reason to go through the overlay dispersion
|
||||
# scaling in the next loop below when only one curve is
|
||||
# detected.
|
||||
if (
|
||||
not mxmns_by_common_pi
|
||||
and len(overlay_table) < 2
|
||||
):
|
||||
if debug_print:
|
||||
print(f'ONLY ranging major: {viz.name}')
|
||||
|
||||
out = _maybe_calc_yrange(
|
||||
viz,
|
||||
yrange_kwargs,
|
||||
profiler,
|
||||
chart_name,
|
||||
)
|
||||
if out is None:
|
||||
continue
|
||||
|
||||
read_slc, yrange_kwargs = out
|
||||
viz.plot.vb._set_yrange(**yrange_kwargs)
|
||||
profiler(f'{viz.name}@{chart_name} single curve yrange')
|
||||
|
||||
# move to next chart in linked set since
|
||||
# no overlay transforming is needed.
|
||||
continue
|
||||
|
||||
elif (
|
||||
mxmns_by_common_pi
|
||||
and not major_sigma_viz
|
||||
):
|
||||
# move to next chart in linked set since
|
||||
# no overlay transforming is needed.
|
||||
continue
|
||||
|
||||
profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
|
||||
|
||||
# if a minor curves scaling brings it "outside" the range of
|
||||
# the major curve (in major curve co-domain terms) then we
|
||||
# need to rescale the major to also include this range. The
|
||||
# below placeholder denotes when this occurs.
|
||||
# group_mxmn: None | tuple[float, float] = None
|
||||
|
||||
# TODO: probably re-write this loop as a compiled cpython or
|
||||
# numba func.
|
||||
|
||||
# conduct "log-linearized multi-plot" scalings for all groups
|
||||
for (
|
||||
view,
|
||||
(
|
||||
viz,
|
||||
y_start,
|
||||
y_min,
|
||||
y_max,
|
||||
read_slc,
|
||||
minor_in_view,
|
||||
)
|
||||
) in overlay_table.items():
|
||||
|
||||
key = 'open' if viz.is_ohlc else viz.name
|
||||
|
||||
if (
|
||||
isinf(ymx)
|
||||
or isinf(ymn)
|
||||
):
|
||||
log.warning(
|
||||
f'BAD ymx/ymn: {(ymn, ymx)}'
|
||||
)
|
||||
continue
|
||||
|
||||
ymn = dnt.apply_rng(y_start)
|
||||
ymx = upt.apply_rng(y_start)
|
||||
|
||||
# NOTE XXX: we have to set each curve's range once (and
|
||||
# ONLY ONCE) here since we're doing this entire routine
|
||||
# inside of a single render cycle (and apparently calling
|
||||
# `ViewBox.setYRange()` multiple times within one only takes
|
||||
# the first call as serious...) XD
|
||||
view._set_yrange(
|
||||
yrange=(ymn, ymx),
|
||||
)
|
||||
profiler(f'{viz.name}@{chart_name} log-SCALE minor')
|
||||
|
||||
if debug_print:
|
||||
print(
|
||||
'------------------------------\n'
|
||||
f'LOGLIN SCALE CYCLE: {viz.name}@{chart_name}\n'
|
||||
f'UP MAJOR C: {upt.viz.name} with disp: {upt.rng}\n'
|
||||
f'DOWN MAJOR C: {dnt.viz.name} with disp: {dnt.rng}\n'
|
||||
f'y_start: {y_start}\n'
|
||||
f'y min: {y_min}\n'
|
||||
f'y max: {y_max}\n'
|
||||
f'T scaled ymn: {ymn}\n'
|
||||
f'T scaled ymx: {ymx}\n'
|
||||
'------------------------------\n'
|
||||
)
|
||||
|
||||
# profiler(f'{viz.name}@{chart_name} log-SCALE major')
|
||||
# major_mx, major_mn = group_mxmn
|
||||
# vrs = major_sigma_viz.plot.vb.viewRange()
|
||||
# if vrs[1][0] > major_mn:
|
||||
# breakpoint()
|
||||
|
||||
if debug_print:
|
||||
print(
|
||||
f'END UX GRAPHICS CYCLE: @{chart_name}\n'
|
||||
+
|
||||
'#'*100
|
||||
+
|
||||
'\n'
|
||||
)
|
||||
if not do_linked_charts:
|
||||
return
|
||||
|
||||
profiler.finish()
|
||||
Loading…
Reference in New Issue