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2nd try: dispersion normalize y-ranges around median 

In the dispersion swing calcs, use the series median from the in-view
data to determine swing proportions to apply on each "minor curve"
(series with lesser dispersion the one with the greatest). Track the
major `Viz` as before by max dispersion. Apply the dispersion swing
proportions to each minor curve-series in a third loop/pass of all
overlay groups: this ensures all overlays are dispersion normalized in
their ranges but, minor curves are currently (vertically) centered (vs.
the major) via their medians.

There is a ton of commented code from attempts to try and vertically
align minor curves to the major via the "first datum" in-view/available.
This still needs work and we may want to offer it as optional.

Also adds logic to allow skipping margin adjustments in `._set_yrange()`
if you pass `range_margin=None`.
log_linearized_curve_overlays
Tyler Goodlet 2023-01-20 14:06:36 -05:00
parent dfc35253ea
commit 52ac1053aa
1 changed files with 201 additions and 79 deletions
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256
piker/ui/_interaction.py
View File

@ -737,7 +737,7 @@ class ChartView(ViewBox):
# NOTE: this value pairs (more or less) with L1 label text
# height offset from from the bid/ask lines.
range_margin: float = 0.09,
range_margin: float | None = 0.09,
bars_range: Optional[tuple[int, int, int, int]] = None,
@ -811,6 +811,7 @@ class ChartView(ViewBox):
ylow, yhigh = yrange
# view margins: stay within a % of the "true range"
if range_margin is not None:
diff = yhigh - ylow
ylow = max(
ylow - (diff * range_margin),
@ -979,12 +980,194 @@ class ChartView(ViewBox):
# print(f'adding {viz.name} to overlay')
mxmn_groups[viz.name] = out
else:
pi.vb._set_yrange(yrange=yrange)
profiler(
f'{viz.name}@{chart_name} `Viz.plot.vb._set_yrange()`'
)
# if 'dolla_vlm' in viz.name:
profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
# 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_mx: float = 0
major_mn: float = float('inf')
mx_up_rng: float = 0
mn_down_rng: float = 0
mx_disp: float = 0
start_datums: dict[
ViewBox,
tuple[
Viz,
float, # y start
float, # y min
float, # y max
float, # y median
slice, # in-view array slice
],
] = {}
max_start: float = 0
major_viz: Viz = None
for viz_name, out in mxmn_groups.items():
(
ixrng,
read_slc,
(ymn, ymx),
) = out
x_start = ixrng[0]
max_start = max(x_start, max_start)
# determine start datum in view
viz = chart._vizs[viz_name]
arr = viz.shm.array
in_view = arr[read_slc]
row_start = arr[read_slc.start - 1]
# row_stop = arr[read_slc.stop - 1]
if viz.is_ohlc:
y_median = np.median(in_view['close'])
y_start = row_start['open']
else:
y_median = np.median(in_view[viz.name])
y_start = row_start[viz.name]
# y_stop = row_stop[viz.name]
start_datums[viz.plot.vb] = (
viz,
y_start,
ymn,
ymx,
y_median,
read_slc,
)
# compute directional (up/down) y-range % swing/dispersion
y_ref = y_median
up_rng = (ymx - y_ref) / y_ref
down_rng = (ymn - y_ref) / y_ref
disp = abs(ymx - ymn) / y_ref
# track the "major" curve as the curve with most
# dispersion.
if disp > mx_disp:
major_viz = viz
mx_disp = disp
major_mn = ymn
major_mx = ymx
mx_up_rng = max(mx_up_rng, up_rng)
mn_down_rng = min(mn_down_rng, down_rng)
print(
f'{viz.name}@{chart_name} group mxmn calc\n'
f'y_start: {y_start}\n'
f'ymn: {ymn}\n'
f'ymx: {ymx}\n'
f'mx_disp: {mx_disp}\n'
f'up %: {up_rng * 100}\n'
f'down %: {down_rng * 100}\n'
f'mx up %: {mx_up_rng * 100}\n'
f'mn down %: {mn_down_rng * 100}\n'
)
for (
view,
(
viz,
y_start,
y_min,
y_max,
y_median,
read_slc,
)
) in start_datums.items():
# TODO: just use y_min / y_max directly for the major
# `Viz` instead of the below calc since it should be the
# same output..
symn = y_median * (1 + mn_down_rng)
symx = y_median * (1 + mx_up_rng)
if not (viz is major_viz):
# compute dispersion normed offsets at the start
# index of the smaller dispersion curve.
maj_viz_arr = major_viz.shm.array
key = 'open' if viz.is_ohlc else viz.name
# handle case where major (dispersion) curve has
# a smaller domain then minor one(s).
istart = read_slc.start
if read_slc.start > maj_viz_arr.size:
istart = 0
maj_start_y = maj_viz_arr[istart][key]
maj_start_offset = maj_start_y / major_mn
maj_max_offset = major_mx / major_mn
# XXX: or this?
# maj_start_offset = (maj_start_y - major_mn) / major_mn
# maj_max_offset = (major_mx - maj_start_y) / major_mn
# XXX: or this?
# major_disp_offset = (
# (maj_viz_arr[istart][key] - major_mn)
# /
# major_mn
# )
# minor_disp_offset_mn = (
# (y_start - y_min)
# /
# y_min
# )
# minor_disp_offset_mx = (
# (ymx - y_start)
# /
# y_min
# normed_disp_ratio = minor_disp_offset - major_disp_offset
# adjust mxmn range to align curve start point in
# the minor overlay with the major one.
# symn = symn * (1 + normed_disp_ratio)
# symx = symx * (1 + normed_disp_ratio)
# symn = symn - (symn * normed_disp_ratio)
# symx = symx - (symn * normed_disp_ratio)
# symn = y_min * maj_start_offset
# symx = y_min * maj_max_offset
print(
f'{view.name} APPLY group mxmn\n'
# f'disp offset ratio diff %: {normed_disp_ratio}\n'
# f'major disp offset %: {major_disp_offset}\n'
# f'minor disp offset %: {minor_disp_offset}\n'
f'y_start: {y_start}\n'
f'mn_down_rng: {mn_down_rng * 100}\n'
f'mx_up_rng: {mx_up_rng * 100}\n'
f'scaled ymn: {symn}\n'
f'scaled ymx: {symx}\n'
f'scaled mx_disp: {mx_disp}\n'
)
view._set_yrange(
yrange=(symn, symx),
# range_margin=None,
)
# if 'mnq' in viz.name:
# print(
# f'AUTO-Y-RANGING: {viz.name}\n'
# f'i_read_range: {i_read_range}\n'
@ -995,76 +1178,15 @@ class ChartView(ViewBox):
# view_xrange,
# view_yrange,
# ) = viz.plot.vb.viewRange()
# view_ymx = view_yrange[1]
# print(
# f'{viz.name}@{chart_name}\n'
# f' xRange -> {view_xrange}\n'
# f' yRange -> {view_yrange}\n'
# f' view y-max -> {view_ymx}\n'
# )
profiler(f'autoscaled overlays {chart_name}')
profiler(f'<{chart_name}>.interact_graphics_cycle({name})')
# proportional group auto-scaling per overlay set.
# -> loop through overlays on each multi-chart widget
# and scale all y-ranges based on autoscale config.
group_mx: float = 0
group_mn: float = 0
mx_up_rng: float = 0
mn_down_rng: float = 0
start_datums: dict[ViewBox, float] = {}
for viz_name, out in mxmn_groups.items():
(
ixrng,
read_slc,
(ymn, ymx),
) = out
# determine start datum in view
viz = chart._vizs[viz_name]
arr = viz.shm.array
row_start = arr[read_slc.start - 1]
# row_stop = arr[read_slc.stop - 1]
if viz.is_ohlc:
y_start = row_start['open']
# y_stop = row_stop['close']
else:
y_start = row_start[viz.name]
# y_stop = row_stop[viz.name]
start_datums[viz.plot.vb] = (viz, y_start)
# update max for group
up_rng = (ymx - y_start) / y_start
down_rng = (ymn - y_start) / y_start
# compute directional (up/down) y-range % swing/dispersion
mx_up_rng = max(mx_up_rng, up_rng)
mn_down_rng = min(mn_down_rng, down_rng)
# pis2ranges[pi] = (ymn, ymx)
group_mx = max(group_mx, ymx)
group_mn = min(group_mn, ymn)
print(
f'{viz.name}@{chart_name} group mxmn calc\n'
f'ymn: {ymn}\n'
f'ymx: {ymx}\n'
f'down %: {mx_up_rng * 100}\n'
f'up %: {mn_down_rng * 100}\n'
)
for view, (viz, ystart) in start_datums.items():
ymn = ystart * (1 + mn_down_rng)
ymx = ystart * (1 + mx_up_rng)
print(
f'{view.name} APPLY group mxmn\n'
f'ystart: {ystart}\n'
f'ymn: {ymn}\n'
f'ymx: {ymx}\n'
)
view._set_yrange(yrange=(ymn, ymx))
# if view_ymx != symx:
# breakpoint()
profiler.finish()