Factor curve-dispersion sorting into primary loop
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.log_linearized_curve_overlays
Tyler Goodlet
parent
896259d9e4
commit
776ffd2b1c
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@ -908,7 +908,6 @@ class ChartView(ViewBox):
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profiler = Profiler(
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msg=f'ChartView.interact_graphics_cycle() for {self.name}',
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# disabled=not pg_profile_enabled(),
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# ms_threshold=ms_slower_then,
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disabled=True,
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@ -941,94 +940,24 @@ class ChartView(ViewBox):
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tuple[float, float],
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] = {}
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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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mxmn_groups: dict[
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set[Viz],
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set[Viz, tuple[float, float]],
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] = {}
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for name, viz in chart._vizs.items():
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if not viz.render:
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# print(f'skipping {flow.name}')
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continue
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# pass in no array which will read and render from the last
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# passed array (normally provided by the display loop.)
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in_view, i_read_range, _ = viz.update_graphics()
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if not in_view:
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continue
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profiler(f'{viz.name}@{chart_name} `Viz.update_graphics()`')
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out = viz.maxmin(i_read_range=i_read_range)
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if out is None:
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log.warning(f'No yrange provided for {name}!?')
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return
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(
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ixrng,
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_,
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yrange
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) = out
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pi = viz.plot
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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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# TODO: a better predicate here, likely something
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# to do with overlays and their settings..
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if (
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viz.is_ohlc
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):
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# print(f'adding {viz.name} to overlay')
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mxmn_groups[viz.name] = out
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else:
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pi.vb._set_yrange(yrange=yrange)
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profiler(
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f'{viz.name}@{chart_name} `Viz.plot.vb._set_yrange()`'
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)
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profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
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# if no overlays, set lone chart's yrange and short circuit
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if (
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len(mxmn_groups) < 2
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):
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print(f'ONLY ranging major: {viz.name}')
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for viz_name, out in mxmn_groups.items():
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(
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ixrng,
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read_slc,
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yrange,
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) = out
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# determine start datum in view
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viz = chart._vizs[viz_name]
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viz.plot.vb._set_yrange(
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yrange=yrange,
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)
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return
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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_up_rng: float = 0
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mn_down_rng: float = 0
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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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start_datums: dict[
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ViewBox,
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tuple[
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@ -1042,79 +971,141 @@ class ChartView(ViewBox):
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],
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] = {}
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max_istart: float = 0
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major_viz: Viz = None
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# major_in_view: np.ndarray = None
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for viz_name, out in mxmn_groups.items():
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for name, viz in chart._vizs.items():
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if not viz.render:
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# print(f'skipping {flow.name}')
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continue
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# pass in no array which will read and render from the last
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# passed array (normally provided by the display loop.)
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in_view, i_read_range, _ = viz.update_graphics()
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if not in_view:
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continue
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profiler(f'{viz.name}@{chart_name} `Viz.update_graphics()`')
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out = viz.maxmin(i_read_range=i_read_range)
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if out is None:
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log.warning(f'No yrange provided for {name}!?')
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return
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(
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ixrng,
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read_slc,
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(ymn, ymx),
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yrange
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) = out
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# determine start datum in view
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viz = chart._vizs[viz_name]
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arr = viz.shm.array
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in_view = arr[read_slc]
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row_start = arr[read_slc.start - 1]
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pi = viz.plot
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max_istart = max(in_view[0]['index'], max_istart)
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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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if viz.is_ohlc:
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y_med = np.median(in_view['close'])
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y_start = row_start['open']
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else:
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y_med = np.median(in_view[viz.name])
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y_start = row_start[viz.name]
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mxmns_by_common_pi[pi] = yrange
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# x_start = ixrng[0]
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# print(
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# f'{viz.name} ->\n'
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# f'(x_start: {x_start}, y_start: {y_start}\n'
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# )
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start_datums[viz.plot.vb] = (
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viz,
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y_start,
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ymn,
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ymx,
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y_med,
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read_slc,
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in_view,
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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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if (
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viz.is_ohlc
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):
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ymn, ymx = yrange
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# print(f'adding {viz.name} to overlay')
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# mxmn_groups[viz.name] = out
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# viz = chart._vizs[viz_name]
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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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row_start = arr[read_slc.start - 1]
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max_istart = max(in_view[0]['index'], max_istart)
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if viz.is_ohlc:
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y_med = np.median(in_view['close'])
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y_start = row_start['open']
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else:
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y_med = np.median(in_view[viz.name])
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y_start = row_start[viz.name]
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# x_start = ixrng[0]
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# print(
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# f'{viz.name} ->\n'
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# f'(x_start: {x_start}, y_start: {y_start}\n'
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# )
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start_datums[viz.plot.vb] = (
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viz,
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y_start,
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ymn,
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ymx,
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y_med,
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read_slc,
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in_view,
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)
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# find curve with max dispersion
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disp = abs(ymx - ymn) / y_med
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# track the "major" curve as the curve with most
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# dispersion.
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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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# major_in_view = in_view
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# compute directional (up/down) y-range % swing/dispersion
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y_ref = y_med
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up_rng = (ymx - y_ref) / y_ref
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down_rng = (ymn - y_ref) / y_ref
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mx_up_rng = max(mx_up_rng, up_rng)
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mn_down_rng = min(mn_down_rng, down_rng)
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# print(
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# f'{viz.name}@{chart_name} group mxmn calc\n'
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# '--------------------\n'
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# f'y_start: {y_start}\n'
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# f'ymn: {ymn}\n'
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# f'ymx: {ymx}\n'
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# f'mx_disp: {mx_disp}\n'
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# f'up %: {up_rng * 100}\n'
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# f'down %: {down_rng * 100}\n'
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# f'mx up %: {mx_up_rng * 100}\n'
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# f'mn down %: {mn_down_rng * 100}\n'
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# )
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# non-overlay group case
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else:
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pi.vb._set_yrange(yrange=yrange)
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profiler(
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f'{viz.name}@{chart_name} `Viz.plot.vb._set_yrange()`'
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)
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profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
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if not start_datums:
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return
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# if no overlays, set lone chart's yrange and short circuit
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if (
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len(start_datums) < 2
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):
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# print(f'ONLY ranging major: {viz.name}')
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major_viz.plot.vb._set_yrange(
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yrange=yrange,
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)
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return
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# find curve with max dispersion
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disp = abs(ymx - ymn) / y_med
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# track the "major" curve as the curve with most
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# dispersion.
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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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# major_in_view = in_view
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# compute directional (up/down) y-range % swing/dispersion
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y_ref = y_med
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up_rng = (ymx - y_ref) / y_ref
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down_rng = (ymn - y_ref) / y_ref
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mx_up_rng = max(mx_up_rng, up_rng)
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mn_down_rng = min(mn_down_rng, down_rng)
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# print(
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# f'{viz.name}@{chart_name} group mxmn calc\n'
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# '--------------------\n'
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# f'y_start: {y_start}\n'
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# f'ymn: {ymn}\n'
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# f'ymx: {ymx}\n'
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# f'mx_disp: {mx_disp}\n'
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# f'up %: {up_rng * 100}\n'
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# f'down %: {down_rng * 100}\n'
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# f'mx up %: {mx_up_rng * 100}\n'
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# f'mn down %: {mn_down_rng * 100}\n'
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# )
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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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