piker/piker/ui/_flows.py

1269 lines
35 KiB
Python

# 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/>.
'''
High level streaming graphics primitives.
This is an intermediate layer which associates real-time low latency
graphics primitives with underlying FSP related data structures for fast
incremental update.
'''
from __future__ import annotations
from typing import (
Optional,
Callable,
Union,
)
import msgspec
import numpy as np
from numpy.lib import recfunctions as rfn
import pyqtgraph as pg
from PyQt5.QtGui import QPainterPath
from PyQt5.QtCore import QLineF
from ..data._sharedmem import (
ShmArray,
)
from .._profile import (
pg_profile_enabled,
# ms_slower_then,
)
from ._pathops import (
gen_ohlc_qpath,
ohlc_to_line,
to_step_format,
xy_downsample,
)
from ._ohlc import (
BarItems,
# bar_from_ohlc_row,
)
from ._curve import (
Curve,
StepCurve,
FlattenedOHLC,
)
from ..log import get_logger
from .._profile import Profiler
log = get_logger(__name__)
# class FlowsTable(msgspec.Struct):
# '''
# Data-AGGRegate: high level API onto multiple (categorized)
# ``Flow``s with high level processing routines for
# multi-graphics computations and display.
# '''
# flows: dict[str, np.ndarray] = {}
def update_ohlc_to_line(
src_shm: ShmArray,
array_key: str,
src_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
fields = ['open', 'high', 'low', 'close']
return (
rfn.structured_to_unstructured(src_update[fields]),
slc,
)
def ohlc_flat_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# TODO: in the case of an existing ``.update_xy()``
# should we be passing in array as an xy arrays tuple?
# 2 more datum-indexes to capture zero at end
x_flat = r.x_data[r._xy_first:r._xy_last]
y_flat = r.y_data[r._xy_first:r._xy_last]
# slice to view
ivl, ivr = vr
x_iv_flat = x_flat[ivl:ivr]
y_iv_flat = y_flat[ivl:ivr]
# reshape to 1d for graphics rendering
y_iv = y_iv_flat.reshape(-1)
x_iv = x_iv_flat.reshape(-1)
return x_iv, y_iv, 'all'
def render_baritems(
flow: Flow,
graphics: BarItems,
read: tuple[
int, int, np.ndarray,
int, int, np.ndarray,
],
profiler: Profiler,
**kwargs,
) -> None:
'''
Graphics management logic for a ``BarItems`` object.
Mostly just logic to determine when and how to downsample an OHLC
lines curve into a flattened line graphic and when to display one
graphic or the other.
TODO: this should likely be moved into some kind of better abstraction
layer, if not a `Renderer` then something just above it?
'''
bars = graphics
# if no source data renderer exists create one.
self = flow
show_bars: bool = False
r = self._src_r
if not r:
show_bars = True
# OHLC bars path renderer
r = self._src_r = Renderer(
flow=self,
format_xy=gen_ohlc_qpath,
last_read=read,
)
ds_curve_r = Renderer(
flow=self,
last_read=read,
# incr update routines
allocate_xy=ohlc_to_line,
update_xy=update_ohlc_to_line,
format_xy=ohlc_flat_to_xy,
)
curve = FlattenedOHLC(
name=f'{flow.name}_ds_ohlc',
color=bars._color,
)
flow.ds_graphics = curve
curve.hide()
self.plot.addItem(curve)
# baseline "line" downsampled OHLC curve that should
# kick on only when we reach a certain uppx threshold.
self._render_table = (ds_curve_r, curve)
ds_r, curve = self._render_table
# do checks for whether or not we require downsampling:
# - if we're **not** downsampling then we simply want to
# render the bars graphics curve and update..
# - if instead we are in a downsamplig state then we to
x_gt = 6
uppx = curve.x_uppx()
in_line = should_line = curve.isVisible()
if (
in_line
and uppx < x_gt
):
# print('FLIPPING TO BARS')
should_line = False
flow._in_ds = False
elif (
not in_line
and uppx >= x_gt
):
# print('FLIPPING TO LINE')
should_line = True
flow._in_ds = True
profiler(f'ds logic complete line={should_line}')
# do graphics updates
if should_line:
r = ds_r
graphics = curve
profiler('updated ds curve')
else:
graphics = bars
if show_bars:
bars.show()
changed_to_line = False
if (
not in_line
and should_line
):
# change to line graphic
log.info(
f'downsampling to line graphic {self.name}'
)
bars.hide()
curve.show()
curve.update()
changed_to_line = True
elif in_line and not should_line:
# change to bars graphic
log.info(f'showing bars graphic {self.name}')
curve.hide()
bars.show()
bars.update()
return (
graphics,
r,
{'read_from_key': False},
should_line,
changed_to_line,
)
def update_step_xy(
src_shm: ShmArray,
array_key: str,
y_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
# for a step curve we slice from one datum prior
# to the current "update slice" to get the previous
# "level".
if is_append:
start = max(last - 1, 0)
end = src_shm._last.value
new_y = src_shm._array[start:end][array_key]
slc = slice(start, end)
else:
new_y = y_update
return (
np.broadcast_to(
new_y[:, None], (new_y.size, 2),
),
slc,
)
def step_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# 2 more datum-indexes to capture zero at end
x_step = r.x_data[r._xy_first:r._xy_last+2]
y_step = r.y_data[r._xy_first:r._xy_last+2]
lasts = array[['index', array_key]]
last = lasts[array_key][-1]
y_step[-1] = last
# slice out in-view data
ivl, ivr = vr
ys_iv = y_step[ivl:ivr+1]
xs_iv = x_step[ivl:ivr+1]
# flatten to 1d
y_iv = ys_iv.reshape(ys_iv.size)
x_iv = xs_iv.reshape(xs_iv.size)
# print(
# f'ys_iv : {ys_iv[-s:]}\n'
# f'y_iv: {y_iv[-s:]}\n'
# f'xs_iv: {xs_iv[-s:]}\n'
# f'x_iv: {x_iv[-s:]}\n'
# )
return x_iv, y_iv, 'all'
class Flow(msgspec.Struct): # , frozen=True):
'''
(Financial Signal-)Flow compound type which wraps a real-time
shm array stream with displayed graphics (curves, charts)
for high level access and control as well as efficient incremental
update.
The intention is for this type to eventually be capable of shm-passing
of incrementally updated graphics stream data between actors.
'''
name: str
plot: pg.PlotItem
graphics: Union[Curve, BarItems]
yrange: tuple[float, float] = None
# in some cases a flow may want to change its
# graphical "type" or, "form" when downsampling,
# normally this is just a plain line.
ds_graphics: Optional[Curve] = None
_shm: ShmArray
is_ohlc: bool = False
render: bool = True # toggle for display loop
# downsampling state
_last_uppx: float = 0
_in_ds: bool = False
# map from uppx -> (downsampled data, incremental graphics)
_src_r: Optional[Renderer] = None
_render_table: dict[
Optional[int],
tuple[Renderer, pg.GraphicsItem],
] = (None, None)
# TODO: hackery to be able to set a shm later
# but whilst also allowing this type to hashable,
# likely will require serializable token that is used to attach
# to the underlying shm ref after startup?
# _shm: Optional[ShmArray] = None # currently, may be filled in "later"
# last read from shm (usually due to an update call)
_last_read: Optional[np.ndarray] = None
# cache of y-range values per x-range input.
_mxmns: dict[tuple[int, int], tuple[float, float]] = {}
@property
def shm(self) -> ShmArray:
return self._shm
# TODO: remove this and only allow setting through
# private ``._shm`` attr?
@shm.setter
def shm(self, shm: ShmArray) -> ShmArray:
self._shm = shm
def maxmin(
self,
lbar: int,
rbar: int,
) -> Optional[tuple[float, float]]:
'''
Compute the cached max and min y-range values for a given
x-range determined by ``lbar`` and ``rbar`` or ``None``
if no range can be determined (yet).
'''
rkey = (lbar, rbar)
cached_result = self._mxmns.get(rkey)
if cached_result:
return cached_result
shm = self.shm
if shm is None:
return None
arr = shm.array
# build relative indexes into shm array
# TODO: should we just add/use a method
# on the shm to do this?
ifirst = arr[0]['index']
slice_view = arr[
lbar - ifirst:
(rbar - ifirst) + 1
]
if not slice_view.size:
return None
elif self.yrange:
mxmn = self.yrange
# print(f'{self.name} M4 maxmin: {mxmn}')
else:
if self.is_ohlc:
ylow = np.min(slice_view['low'])
yhigh = np.max(slice_view['high'])
else:
view = slice_view[self.name]
ylow = np.min(view)
yhigh = np.max(view)
mxmn = ylow, yhigh
# print(f'{self.name} MANUAL maxmin: {mxmin}')
# cache result for input range
assert mxmn
self._mxmns[rkey] = mxmn
return mxmn
def view_range(self) -> tuple[int, int]:
'''
Return the indexes in view for the associated
plot displaying this flow's data.
'''
vr = self.plot.viewRect()
return int(vr.left()), int(vr.right())
def datums_range(self) -> tuple[
int, int, int, int, int, int
]:
'''
Return a range tuple for the datums present in view.
'''
l, r = self.view_range()
# TODO: avoid this and have shm passed
# in earlier.
if self.shm is None:
# haven't initialized the flow yet
return (0, l, 0, 0, r, 0)
array = self.shm.array
index = array['index']
start = index[0]
end = index[-1]
lbar = max(l, start)
rbar = min(r, end)
return (
start, l, lbar, rbar, r, end,
)
def read(
self,
array_field: Optional[str] = None,
) -> tuple[
int, int, np.ndarray,
int, int, np.ndarray,
]:
# read call
array = self.shm.array
indexes = array['index']
ifirst = indexes[0]
ilast = indexes[-1]
ifirst, l, lbar, rbar, r, ilast = self.datums_range()
# get read-relative indices adjusting
# for master shm index.
lbar_i = max(l, ifirst) - ifirst
rbar_i = min(r, ilast) - ifirst
if array_field:
array = array[array_field]
# TODO: we could do it this way as well no?
# to_draw = array[lbar - ifirst:(rbar - ifirst) + 1]
in_view = array[lbar_i: rbar_i + 1]
return (
# abs indices + full data set
ifirst, ilast, array,
# relative indices + in view datums
lbar_i, rbar_i, in_view,
)
def update_graphics(
self,
use_vr: bool = True,
render: bool = True,
array_key: Optional[str] = None,
profiler: Optional[Profiler] = None,
do_append: bool = True,
**kwargs,
) -> pg.GraphicsObject:
'''
Read latest datums from shm and render to (incrementally)
render to graphics.
'''
profiler = Profiler(
msg=f'Flow.update_graphics() for {self.name}',
disabled=not pg_profile_enabled(),
ms_threshold=4,
# ms_threshold=ms_slower_then,
)
# shm read and slice to view
read = (
xfirst, xlast, src_array,
ivl, ivr, in_view,
) = self.read()
profiler('read src shm data')
graphics = self.graphics
if (
not in_view.size
or not render
):
# print('exiting early')
return graphics
slice_to_head: int = -1
should_redraw: bool = False
rkwargs = {}
should_line = False
if isinstance(graphics, BarItems):
# XXX: special case where we change out graphics
# to a line after a certain uppx threshold.
(
graphics,
r,
rkwargs,
should_line,
changed_to_line,
) = render_baritems(
self,
graphics,
read,
profiler,
**kwargs,
)
should_redraw = changed_to_line or not should_line
self._in_ds = should_line
else:
r = self._src_r
if not r:
# just using for ``.diff()`` atm..
r = self._src_r = Renderer(
flow=self,
# TODO: rename this to something with ohlc
last_read=read,
)
# ``Curve`` derivative case(s):
array_key = array_key or self.name
# print(array_key)
# ds update config
new_sample_rate: bool = False
should_ds: bool = r._in_ds
showing_src_data: bool = not r._in_ds
# step_mode = getattr(graphics, '_step_mode', False)
step_mode = isinstance(graphics, StepCurve)
if step_mode:
r.allocate_xy = to_step_format
r.update_xy = update_step_xy
r.format_xy = step_to_xy
# TODO: append logic inside ``.render()`` isn't
# correct yet for step curves.. remove this to see it.
should_redraw = True
slice_to_head = -2
# downsampling incremental state checking
# check for and set std m4 downsample conditions
uppx = graphics.x_uppx()
uppx_diff = (uppx - self._last_uppx)
profiler(f'diffed uppx {uppx}')
if (
uppx > 1
and abs(uppx_diff) >= 1
):
log.debug(
f'{array_key} sampler change: {self._last_uppx} -> {uppx}'
)
self._last_uppx = uppx
new_sample_rate = True
showing_src_data = False
should_ds = True
should_redraw = True
elif (
uppx <= 2
and self._in_ds
):
# we should de-downsample back to our original
# source data so we clear our path data in prep
# to generate a new one from original source data.
new_sample_rate = True
should_ds = False
should_redraw = True
showing_src_data = True
# reset yrange to be computed from source data
self.yrange = None
# MAIN RENDER LOGIC:
# - determine in view data and redraw on range change
# - determine downsampling ops if needed
# - (incrementally) update ``QPainterPath``
out = r.render(
read,
array_key,
profiler,
uppx=uppx,
# use_vr=True,
# TODO: better way to detect and pass this?
# if we want to eventually cache renderers for a given uppx
# we should probably use this as a key + state?
should_redraw=should_redraw,
new_sample_rate=new_sample_rate,
should_ds=should_ds,
showing_src_data=showing_src_data,
slice_to_head=slice_to_head,
do_append=do_append,
**rkwargs,
)
if showing_src_data:
# print(f"{self.name} SHOWING SOURCE")
# reset yrange to be computed from source data
self.yrange = None
if not out:
log.warning(f'{self.name} failed to render!?')
return graphics
path, data, reset = out
# if self.yrange:
# print(f'flow {self.name} yrange from m4: {self.yrange}')
# XXX: SUPER UGGGHHH... without this we get stale cache
# graphics that don't update until you downsampler again..
if reset:
with graphics.reset_cache():
# assign output paths to graphicis obj
graphics.path = r.path
graphics.fast_path = r.fast_path
# XXX: we don't need this right?
# graphics.draw_last_datum(
# path,
# src_array,
# data,
# reset,
# array_key,
# )
# graphics.update()
# profiler('.update()')
else:
# assign output paths to graphicis obj
graphics.path = r.path
graphics.fast_path = r.fast_path
graphics.draw_last_datum(
path,
src_array,
data,
reset,
array_key,
)
graphics.update()
profiler('.update()')
# TODO: does this actuallly help us in any way (prolly should
# look at the source / ask ogi). I think it avoid artifacts on
# wheel-scroll downsampling curve updates?
# TODO: is this ever better?
# graphics.prepareGeometryChange()
# profiler('.prepareGeometryChange()')
# track downsampled state
self._in_ds = r._in_ds
return graphics
def draw_last(
self,
array_key: Optional[str] = None,
only_last_uppx: bool = False,
) -> None:
# shm read and slice to view
(
xfirst, xlast, src_array,
ivl, ivr, in_view,
) = self.read()
g = self.graphics
array_key = array_key or self.name
x, y = g.draw_last_datum(
g.path,
src_array,
src_array,
False, # never reset path
array_key,
)
# the renderer is downsampling we choose
# to always try and updadte a single (interpolating)
# line segment that spans and tries to display
# the las uppx's worth of datums.
# we only care about the last pixel's
# worth of data since that's all the screen
# can represent on the last column where
# the most recent datum is being drawn.
if self._in_ds or only_last_uppx:
dsg = self.ds_graphics or self.graphics
# XXX: pretty sure we don't need this?
# if isinstance(g, Curve):
# with dsg.reset_cache():
uppx = self._last_uppx
y = y[-uppx:]
ymn, ymx = y.min(), y.max()
# print(f'drawing uppx={uppx} mxmn line: {ymn}, {ymx}')
try:
iuppx = x[-uppx]
except IndexError:
# we're less then an x-px wide so just grab the start
# datum index.
iuppx = x[0]
dsg._last_line = QLineF(
iuppx, ymn,
x[-1], ymx,
)
# print(f'updating DS curve {self.name}')
dsg.update()
else:
# print(f'updating NOT DS curve {self.name}')
g.update()
def by_index_and_key(
renderer: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
return array['index'], array[array_key], 'all'
class Renderer(msgspec.Struct):
flow: Flow
# last array view read
last_read: Optional[tuple] = None
# default just returns index, and named array from data
format_xy: Callable[
[np.ndarray, str],
tuple[np.ndarray]
] = by_index_and_key
# optional pre-graphics xy formatted data which
# is incrementally updated in sync with the source data.
allocate_xy: Optional[Callable[
[int, slice],
tuple[np.ndarray, np.nd.array]
]] = None
update_xy: Optional[Callable[
[int, slice], None]
] = None
x_data: Optional[np.ndarray] = None
y_data: Optional[np.ndarray] = None
# indexes which slice into the above arrays (which are allocated
# based on source data shm input size) and allow retrieving
# incrementally updated data.
_xy_first: int = 0
_xy_last: int = 0
# output graphics rendering, the main object
# processed in ``QGraphicsObject.paint()``
path: Optional[QPainterPath] = None
fast_path: Optional[QPainterPath] = None
# XXX: just ideas..
# called on the final data (transform) output to convert
# to "graphical data form" a format that can be passed to
# the ``.draw()`` implementation.
# graphics_t: Optional[Callable[ShmArray, np.ndarray]] = None
# graphics_t_shm: Optional[ShmArray] = None
# path graphics update implementation methods
# prepend_fn: Optional[Callable[QPainterPath, QPainterPath]] = None
# append_fn: Optional[Callable[QPainterPath, QPainterPath]] = None
# downsampling state
_last_uppx: float = 0
_in_ds: bool = False
# incremental update state(s)
_last_vr: Optional[tuple[float, float]] = None
_last_ivr: Optional[tuple[float, float]] = None
def diff(
self,
new_read: tuple[np.ndarray],
) -> tuple[
np.ndarray,
np.ndarray,
]:
(
last_xfirst,
last_xlast,
last_array,
last_ivl,
last_ivr,
last_in_view,
) = self.last_read
# TODO: can the renderer just call ``Flow.read()`` directly?
# unpack latest source data read
(
xfirst,
xlast,
array,
ivl,
ivr,
in_view,
) = new_read
# compute the length diffs between the first/last index entry in
# the input data and the last indexes we have on record from the
# last time we updated the curve index.
prepend_length = int(last_xfirst - xfirst)
append_length = int(xlast - last_xlast)
# blah blah blah
# do diffing for prepend, append and last entry
return (
slice(xfirst, last_xfirst),
prepend_length,
append_length,
slice(last_xlast, xlast),
)
def draw_path(
self,
x: np.ndarray,
y: np.ndarray,
connect: Union[str, np.ndarray] = 'all',
path: Optional[QPainterPath] = None,
redraw: bool = False,
) -> QPainterPath:
path_was_none = path is None
if redraw and path:
path.clear()
# TODO: avoid this?
if self.fast_path:
self.fast_path.clear()
# profiler('cleared paths due to `should_redraw=True`')
path = pg.functions.arrayToQPath(
x,
y,
connect=connect,
finiteCheck=False,
# reserve mem allocs see:
# - https://doc.qt.io/qt-5/qpainterpath.html#reserve
# - https://doc.qt.io/qt-5/qpainterpath.html#capacity
# - https://doc.qt.io/qt-5/qpainterpath.html#clear
# XXX: right now this is based on had hoc checks on a
# hidpi 3840x2160 4k monitor but we should optimize for
# the target display(s) on the sys.
# if no_path_yet:
# graphics.path.reserve(int(500e3))
# path=path, # path re-use / reserving
)
# avoid mem allocs if possible
if path_was_none:
path.reserve(path.capacity())
return path
def render(
self,
new_read,
array_key: str,
profiler: Profiler,
uppx: float = 1,
# redraw and ds flags
should_redraw: bool = False,
new_sample_rate: bool = False,
should_ds: bool = False,
showing_src_data: bool = True,
do_append: bool = True,
slice_to_head: int = -1,
use_fpath: bool = True,
# only render datums "in view" of the ``ChartView``
use_vr: bool = True,
read_from_key: bool = True,
) -> list[QPainterPath]:
'''
Render the current graphics path(s)
There are (at least) 3 stages from source data to graphics data:
- a data transform (which can be stored in additional shm)
- a graphics transform which converts discrete basis data to
a `float`-basis view-coords graphics basis. (eg. ``ohlc_flatten()``,
``step_path_arrays_from_1d()``, etc.)
- blah blah blah (from notes)
'''
# TODO: can the renderer just call ``Flow.read()`` directly?
# unpack latest source data read
(
xfirst,
xlast,
array,
ivl,
ivr,
in_view,
) = new_read
(
pre_slice,
prepend_length,
append_length,
post_slice,
) = self.diff(new_read)
if self.update_xy:
shm = self.flow.shm
if self.y_data is None:
# we first need to allocate xy data arrays
# from the source data.
assert self.allocate_xy
self.x_data, self.y_data = self.allocate_xy(
shm,
array_key,
)
self._xy_first = shm._first.value
self._xy_last = shm._last.value
profiler('allocated xy history')
if prepend_length:
y_prepend = shm._array[pre_slice]
if read_from_key:
y_prepend = y_prepend[array_key]
xy_data, xy_slice = self.update_xy(
shm,
array_key,
# this is the pre-sliced, "normally expected"
# new data that an updater would normally be
# expected to process, however in some cases (like
# step curves) the updater routine may want to do
# the source history-data reading itself, so we pass
# both here.
y_prepend,
pre_slice,
prepend_length,
self._xy_first,
self._xy_last,
is_append=False,
)
self.y_data[xy_slice] = xy_data
self._xy_first = shm._first.value
profiler('prepended xy history: {prepend_length}')
if append_length:
y_append = shm._array[post_slice]
if read_from_key:
y_append = y_append[array_key]
xy_data, xy_slice = self.update_xy(
shm,
array_key,
y_append,
post_slice,
append_length,
self._xy_first,
self._xy_last,
is_append=True,
)
# self.y_data[post_slice] = xy_data
# self.y_data[xy_slice or post_slice] = xy_data
self.y_data[xy_slice] = xy_data
self._xy_last = shm._last.value
profiler('appened xy history: {append_length}')
if use_vr:
array = in_view
# else:
# ivl, ivr = xfirst, xlast
hist = array[:slice_to_head]
# xy-path data transform: convert source data to a format
# able to be passed to a `QPainterPath` rendering routine.
if not len(hist):
# XXX: this might be why the profiler only has exits?
return
x_out, y_out, connect = self.format_xy(
self,
# TODO: hist here should be the pre-sliced
# x/y_data in the case where allocate_xy is
# defined?
hist,
array_key,
(ivl, ivr),
)
profiler('sliced input arrays')
if (
use_vr
):
# if a view range is passed, plan to draw the
# source ouput that's "in view" of the chart.
view_range = (ivl, ivr)
# print(f'{self._name} vr: {view_range}')
profiler(f'view range slice {view_range}')
vl, vr = view_range
zoom_or_append = False
last_vr = self._last_vr
last_ivr = self._last_ivr or vl, vr
# incremental in-view data update.
if last_vr:
# relative slice indices
lvl, lvr = last_vr
# abs slice indices
al, ar = last_ivr
# left_change = abs(x_iv[0] - al) >= 1
# right_change = abs(x_iv[-1] - ar) >= 1
if (
# likely a zoom view change
(vr - lvr) > 2 or vl < lvl
# append / prepend update
# we had an append update where the view range
# didn't change but the data-viewed (shifted)
# underneath, so we need to redraw.
# or left_change and right_change and last_vr == view_range
# not (left_change and right_change) and ivr
# (
# or abs(x_iv[ivr] - livr) > 1
):
zoom_or_append = True
self._last_vr = view_range
if len(x_out):
self._last_ivr = x_out[0], x_out[slice_to_head]
# redraw conditions
if (
prepend_length > 0
or new_sample_rate
or append_length > 0
or zoom_or_append
):
should_redraw = True
path = self.path
fast_path = self.fast_path
reset = False
# redraw the entire source data if we have either of:
# - no prior path graphic rendered or,
# - we always intend to re-render the data only in view
if (
path is None
or should_redraw
):
# print(f"{self.flow.name} -> REDRAWING BRUH")
if new_sample_rate and showing_src_data:
log.info(f'DEDOWN -> {array_key}')
self._in_ds = False
elif should_ds and uppx > 1:
x_out, y_out, ymn, ymx = xy_downsample(
x_out,
y_out,
uppx,
)
self.flow.yrange = ymn, ymx
# print(f'{self.flow.name} post ds: ymn, ymx: {ymn},{ymx}')
reset = True
profiler(f'FULL PATH downsample redraw={should_ds}')
self._in_ds = True
path = self.draw_path(
x=x_out,
y=y_out,
connect=connect,
path=path,
redraw=True,
)
profiler(
'generated fresh path. '
f'(should_redraw: {should_redraw} '
f'should_ds: {should_ds} new_sample_rate: {new_sample_rate})'
)
# TODO: get this piecewise prepend working - right now it's
# giving heck on vwap...
# elif prepend_length:
# breakpoint()
# prepend_path = pg.functions.arrayToQPath(
# x[0:prepend_length],
# y[0:prepend_length],
# connect='all'
# )
# # swap prepend path in "front"
# old_path = graphics.path
# graphics.path = prepend_path
# # graphics.path.moveTo(new_x[0], new_y[0])
# graphics.path.connectPath(old_path)
elif (
append_length > 0
and do_append
and not should_redraw
):
# print(f'{array_key} append len: {append_length}')
new_x = x_out[-append_length - 2:] # slice_to_head]
new_y = y_out[-append_length - 2:] # slice_to_head]
profiler('sliced append path')
profiler(
f'diffed array input, append_length={append_length}'
)
# if should_ds:
# new_x, new_y = xy_downsample(
# new_x,
# new_y,
# uppx,
# )
# profiler(f'fast path downsample redraw={should_ds}')
append_path = self.draw_path(
x=new_x,
y=new_y,
connect=connect,
path=fast_path,
)
profiler('generated append qpath')
if use_fpath:
# an attempt at trying to make append-updates faster..
if fast_path is None:
fast_path = append_path
# fast_path.reserve(int(6e3))
else:
fast_path.connectPath(append_path)
size = fast_path.capacity()
profiler(f'connected fast path w size: {size}')
# print(f"append_path br: {append_path.boundingRect()}")
# graphics.path.moveTo(new_x[0], new_y[0])
# path.connectPath(append_path)
# XXX: lol this causes a hang..
# graphics.path = graphics.path.simplified()
else:
size = path.capacity()
profiler(f'connected history path w size: {size}')
path.connectPath(append_path)
self.path = path
self.fast_path = fast_path
# TODO: eventually maybe we can implement some kind of
# transform on the ``QPainterPath`` that will more or less
# detect the diff in "elements" terms?
# update diff state since we've now rendered paths.
self.last_read = new_read
return self.path, array, reset