Add graphics incr-updated "formatter" subsys
After trying to hack epoch indexed time series and failing miserably, decided to properly factor out all formatting routines into a common subsystem API: ``IncrementalFormatter`` which provides the interface for incrementally updating and tracking pre-path-graphics formatted data. Previously this functionality was mangled into our `Renderer` (which also does the work of `QPath` generation and update) but splitting it out also preps for being able to do graphics-buffer downsampling and caching on a remote host B) The ``IncrementalFormatter`` (parent type) has the default behaviour of tracking a single field-array on some source `ShmArray`, updating a flattened `numpy.ndarray` in-mem allocation, and providing a default 1d conversion for pre-downsampling and path generation. Changed out of `Renderer`, - `.allocate_xy()`, `update_xy()` and `format_xy()` all are moved to more explicitly named formatter methods. - all `.x/y_data` nd array management and update - "last view range" tracking - `.last_read`, `.diff()` - now calls `IncrementalFormatter.format_to_1d()` inside `.render()` The new API gets, - `.diff()`, `.last_read` - all view range diff tracking through `.track_inview_range()`. - better nd format array names: `.x/y_nd`, `xy_nd_start/stop`. - `.format_to_1d()` which renders pre-path formatted arrays ready for both m4 sampling and path gen. - better explicit overloadable formatting method names: * `.allocate_xy()` -> `.allocate_xy_nd()` * `.update_xy()` -> `.incr_update_xy_nd()` * `.format_xy()` -> `.format_xy_nd_to_1d()` Finally this implements per-graphics-type formatters which define each set up related formatting routines: - `OHLCBarsFmtr`: std multi-line style bars - `OHLCBarsAsCurveFmtr`: draws an interpolated line for ohlc sampled data - `StepCurveFmtr`: handles vlm style curvesepoch_index
Tyler Goodlet
parent
0db5451e47
commit
ab1f15506d
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@ -25,8 +25,6 @@ incremental update.
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from __future__ import annotations
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from typing import (
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Optional,
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Callable,
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Union,
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)
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import msgspec
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@ -43,21 +41,10 @@ from .._profile import (
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# ms_slower_then,
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)
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from ._pathops import (
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by_index_and_key,
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# Plain OHLC renderer
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gen_ohlc_qpath,
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# OHLC -> line renderer
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ohlc_to_line,
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update_ohlc_to_line,
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ohlc_flat_to_xy,
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# step curve renderer
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to_step_format,
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update_step_xy,
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step_to_xy,
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IncrementalFormatter,
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OHLCBarsFmtr, # Plain OHLC renderer
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OHLCBarsAsCurveFmtr, # OHLC converted to line
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StepCurveFmtr, # "step" curve (like for vlm)
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xy_downsample,
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)
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from ._ohlc import (
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@ -76,16 +63,6 @@ from .._profile import Profiler
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log = get_logger(__name__)
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# class FlowsTable(msgspec.Struct):
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# '''
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# Data-AGGRegate: high level API onto multiple (categorized)
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# ``Flow``s with high level processing routines for
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# multi-graphics computations and display.
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# '''
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# flows: dict[str, np.ndarray] = {}
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def render_baritems(
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flow: Flow,
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graphics: BarItems,
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@ -117,21 +94,24 @@ def render_baritems(
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r = self._src_r
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if not r:
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show_bars = True
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# OHLC bars path renderer
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r = self._src_r = Renderer(
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flow=self,
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format_xy=gen_ohlc_qpath,
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last_read=read,
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fmtr=OHLCBarsFmtr(
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shm=flow.shm,
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flow=flow,
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_last_read=read,
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),
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)
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ds_curve_r = Renderer(
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flow=self,
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last_read=read,
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# incr update routines
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allocate_xy=ohlc_to_line,
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update_xy=update_ohlc_to_line,
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format_xy=ohlc_flat_to_xy,
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fmtr=OHLCBarsAsCurveFmtr(
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shm=flow.shm,
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flow=flow,
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_last_read=read,
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),
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)
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curve = FlattenedOHLC(
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@ -228,7 +208,7 @@ class Flow(msgspec.Struct): # , frozen=True):
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'''
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name: str
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plot: pg.PlotItem
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graphics: Union[Curve, BarItems]
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graphics: Curve | BarItems
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_shm: ShmArray
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yrange: tuple[float, float] = None
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@ -237,7 +217,6 @@ class Flow(msgspec.Struct): # , frozen=True):
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# normally this is just a plain line.
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ds_graphics: Optional[Curve] = None
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is_ohlc: bool = False
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render: bool = True # toggle for display loop
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@ -445,9 +424,14 @@ class Flow(msgspec.Struct): # , frozen=True):
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slice_to_head: int = -1
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should_redraw: bool = False
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should_line: bool = False
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rkwargs = {}
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should_line = False
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# TODO: probably specialize ``Renderer`` types instead of
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# these logic checks?
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# - put these blocks into a `.load_renderer()` meth?
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# - consider a OHLCRenderer, StepCurveRenderer, Renderer?
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r = self._src_r
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if isinstance(graphics, BarItems):
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# XXX: special case where we change out graphics
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# to a line after a certain uppx threshold.
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@ -467,14 +451,34 @@ class Flow(msgspec.Struct): # , frozen=True):
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should_redraw = changed_to_line or not should_line
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self._in_ds = should_line
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elif not r:
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if isinstance(graphics, StepCurve):
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r = self._src_r = Renderer(
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flow=self,
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fmtr=StepCurveFmtr(
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shm=self.shm,
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flow=self,
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_last_read=read,
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),
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)
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# TODO: append logic inside ``.render()`` isn't
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# correct yet for step curves.. remove this to see it.
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should_redraw = True
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slice_to_head = -2
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else:
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r = self._src_r
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if not r:
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# just using for ``.diff()`` atm..
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r = self._src_r = Renderer(
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flow=self,
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# TODO: rename this to something with ohlc
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last_read=read,
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fmtr=IncrementalFormatter(
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shm=self.shm,
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flow=self,
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_last_read=read,
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),
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)
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# ``Curve`` derivative case(s):
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@ -486,19 +490,6 @@ class Flow(msgspec.Struct): # , frozen=True):
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should_ds: bool = r._in_ds
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showing_src_data: bool = not r._in_ds
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# step_mode = getattr(graphics, '_step_mode', False)
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step_mode = isinstance(graphics, StepCurve)
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if step_mode:
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r.allocate_xy = to_step_format
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r.update_xy = update_step_xy
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r.format_xy = step_to_xy
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# TODO: append logic inside ``.render()`` isn't
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# correct yet for step curves.. remove this to see it.
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should_redraw = True
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slice_to_head = -2
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# downsampling incremental state checking
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# check for and set std m4 downsample conditions
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uppx = graphics.x_uppx()
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@ -680,34 +671,7 @@ class Flow(msgspec.Struct): # , frozen=True):
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class Renderer(msgspec.Struct):
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flow: Flow
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# last array view read
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last_read: Optional[tuple] = None
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# default just returns index, and named array from data
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format_xy: Callable[
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[np.ndarray, str],
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tuple[np.ndarray]
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] = by_index_and_key
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# optional pre-graphics xy formatted data which
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# is incrementally updated in sync with the source data.
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allocate_xy: Optional[Callable[
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[int, slice],
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tuple[np.ndarray, np.nd.array]
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]] = None
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update_xy: Optional[Callable[
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[int, slice], None]
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] = None
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x_data: Optional[np.ndarray] = None
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y_data: Optional[np.ndarray] = None
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# indexes which slice into the above arrays (which are allocated
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# based on source data shm input size) and allow retrieving
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# incrementally updated data.
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_xy_first: int = 0
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_xy_last: int = 0
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fmtr: IncrementalFormatter
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# output graphics rendering, the main object
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# processed in ``QGraphicsObject.paint()``
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@ -729,58 +693,11 @@ class Renderer(msgspec.Struct):
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_last_uppx: float = 0
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_in_ds: bool = False
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# incremental update state(s)
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_last_vr: Optional[tuple[float, float]] = None
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_last_ivr: Optional[tuple[float, float]] = None
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def diff(
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self,
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new_read: tuple[np.ndarray],
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) -> tuple[
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np.ndarray,
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np.ndarray,
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]:
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(
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last_xfirst,
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last_xlast,
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last_array,
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last_ivl,
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last_ivr,
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last_in_view,
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) = self.last_read
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# TODO: can the renderer just call ``Flow.read()`` directly?
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# unpack latest source data read
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(
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xfirst,
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xlast,
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array,
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ivl,
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ivr,
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in_view,
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) = new_read
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# compute the length diffs between the first/last index entry in
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# the input data and the last indexes we have on record from the
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# last time we updated the curve index.
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prepend_length = int(last_xfirst - xfirst)
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append_length = int(xlast - last_xlast)
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# blah blah blah
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# do diffing for prepend, append and last entry
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return (
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slice(xfirst, last_xfirst),
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prepend_length,
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append_length,
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slice(last_xlast, xlast),
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)
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def draw_path(
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self,
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x: np.ndarray,
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y: np.ndarray,
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connect: Union[str, np.ndarray] = 'all',
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connect: str | np.ndarray = 'all',
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path: Optional[QPainterPath] = None,
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redraw: bool = False,
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@ -858,166 +775,50 @@ class Renderer(msgspec.Struct):
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'''
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# TODO: can the renderer just call ``Flow.read()`` directly?
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# unpack latest source data read
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fmtr = self.fmtr
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(
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xfirst,
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xlast,
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_,
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_,
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array,
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ivl,
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ivr,
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in_view,
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) = new_read
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(
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pre_slice,
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prepend_length,
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append_length,
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post_slice,
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) = self.diff(new_read)
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if self.update_xy:
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shm = self.flow.shm
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if self.y_data is None:
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# we first need to allocate xy data arrays
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# from the source data.
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assert self.allocate_xy
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self.x_data, self.y_data = self.allocate_xy(
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shm,
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array_key,
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)
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self._xy_first = shm._first.value
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self._xy_last = shm._last.value
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profiler('allocated xy history')
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if prepend_length:
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y_prepend = shm._array[pre_slice]
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if read_from_key:
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y_prepend = y_prepend[array_key]
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xy_data, xy_slice = self.update_xy(
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shm,
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array_key,
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# this is the pre-sliced, "normally expected"
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# new data that an updater would normally be
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# expected to process, however in some cases (like
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# step curves) the updater routine may want to do
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# the source history-data reading itself, so we pass
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# both here.
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y_prepend,
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pre_slice,
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prepend_length,
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self._xy_first,
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self._xy_last,
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is_append=False,
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)
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self.y_data[xy_slice] = xy_data
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self._xy_first = shm._first.value
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profiler('prepended xy history: {prepend_length}')
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if append_length:
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y_append = shm._array[post_slice]
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if read_from_key:
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y_append = y_append[array_key]
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xy_data, xy_slice = self.update_xy(
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shm,
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array_key,
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y_append,
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post_slice,
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append_length,
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self._xy_first,
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self._xy_last,
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is_append=True,
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)
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# self.y_data[post_slice] = xy_data
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# self.y_data[xy_slice or post_slice] = xy_data
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self.y_data[xy_slice] = xy_data
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self._xy_last = shm._last.value
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profiler('appened xy history: {append_length}')
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if use_vr:
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array = in_view
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# else:
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# ivl, ivr = xfirst, xlast
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hist = array[:slice_to_head]
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# xy-path data transform: convert source data to a format
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# able to be passed to a `QPainterPath` rendering routine.
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if not len(hist):
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fmt_out = fmtr.format_to_1d(
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new_read,
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array_key,
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profiler,
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slice_to_head=slice_to_head,
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read_src_from_key=read_from_key,
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slice_to_inview=use_vr,
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)
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# no history in view case
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if not fmt_out:
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# XXX: this might be why the profiler only has exits?
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return
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x_out, y_out, connect = self.format_xy(
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self,
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# TODO: hist here should be the pre-sliced
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# x/y_data in the case where allocate_xy is
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# defined?
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hist,
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array_key,
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(ivl, ivr),
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)
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(
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x_1d,
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y_1d,
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connect,
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prepend_length,
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append_length,
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view_changed,
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profiler('sliced input arrays')
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if (
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use_vr
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):
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# if a view range is passed, plan to draw the
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# source ouput that's "in view" of the chart.
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view_range = (ivl, ivr)
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# print(f'{self._name} vr: {view_range}')
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profiler(f'view range slice {view_range}')
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vl, vr = view_range
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zoom_or_append = False
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last_vr = self._last_vr
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last_ivr = self._last_ivr or vl, vr
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# incremental in-view data update.
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if last_vr:
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# relative slice indices
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lvl, lvr = last_vr
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# abs slice indices
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al, ar = last_ivr
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# left_change = abs(x_iv[0] - al) >= 1
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# right_change = abs(x_iv[-1] - ar) >= 1
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if (
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# likely a zoom view change
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(vr - lvr) > 2 or vl < lvl
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# append / prepend update
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# we had an append update where the view range
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# didn't change but the data-viewed (shifted)
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# underneath, so we need to redraw.
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# or left_change and right_change and last_vr == view_range
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# not (left_change and right_change) and ivr
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# (
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# or abs(x_iv[ivr] - livr) > 1
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):
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zoom_or_append = True
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self._last_vr = view_range
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if len(x_out):
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self._last_ivr = x_out[0], x_out[slice_to_head]
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) = fmt_out
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# redraw conditions
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if (
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prepend_length > 0
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or new_sample_rate
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or append_length > 0
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or zoom_or_append
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or view_changed
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):
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should_redraw = True
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@ -1039,9 +840,9 @@ class Renderer(msgspec.Struct):
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elif should_ds and uppx > 1:
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x_out, y_out, ymn, ymx = xy_downsample(
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x_out,
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y_out,
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x_1d, y_1d, ymn, ymx = xy_downsample(
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x_1d,
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y_1d,
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uppx,
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)
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self.flow.yrange = ymn, ymx
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@ -1052,8 +853,8 @@ class Renderer(msgspec.Struct):
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self._in_ds = True
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path = self.draw_path(
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x=x_out,
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y=y_out,
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x=x_1d,
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y=y_1d,
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connect=connect,
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path=path,
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redraw=True,
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@ -1088,8 +889,8 @@ class Renderer(msgspec.Struct):
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and not should_redraw
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):
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||||
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]
|
||||
new_x = x_1d[-append_length - 2:] # slice_to_head]
|
||||
new_y = y_1d[-append_length - 2:] # slice_to_head]
|
||||
profiler('sliced append path')
|
||||
|
||||
profiler(
|
||||
|
|
@ -1137,10 +938,4 @@ class Renderer(msgspec.Struct):
|
|||
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
|
||||
|
|
|
|||
|
|
@ -20,7 +20,6 @@ Super fast ``QPainterPath`` generation related operator routines.
|
|||
from __future__ import annotations
|
||||
from typing import (
|
||||
Optional,
|
||||
Callable,
|
||||
TYPE_CHECKING,
|
||||
)
|
||||
|
||||
|
|
@ -29,7 +28,7 @@ import numpy as np
|
|||
from numpy.lib import recfunctions as rfn
|
||||
from numba import njit, float64, int64 # , optional
|
||||
# import pyqtgraph as pg
|
||||
from PyQt5 import QtGui
|
||||
# from PyQt5 import QtGui
|
||||
# from PyQt5.QtCore import QLineF, QPointF
|
||||
|
||||
from ..data._sharedmem import (
|
||||
|
|
@ -41,7 +40,11 @@ from ._compression import (
|
|||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ._flows import Renderer
|
||||
from ._flows import (
|
||||
Renderer,
|
||||
Flow,
|
||||
)
|
||||
from .._profile import Profiler
|
||||
|
||||
|
||||
def by_index_and_key(
|
||||
|
|
@ -59,77 +62,455 @@ def by_index_and_key(
|
|||
|
||||
|
||||
class IncrementalFormatter(msgspec.Struct):
|
||||
'''
|
||||
Incrementally updating, pre-path-graphics tracking, formatter.
|
||||
|
||||
Allows tracking source data state in an updateable pre-graphics
|
||||
``np.ndarray`` format (in local process memory) as well as
|
||||
incrementally rendering from that format **to** 1d x/y for path
|
||||
generation using ``pg.functions.arrayToQPath()``.
|
||||
|
||||
'''
|
||||
shm: ShmArray
|
||||
flow: Flow
|
||||
|
||||
# optional pre-graphics xy formatted data which
|
||||
# is incrementally updated in sync with the source data.
|
||||
allocate_xy_nd: Optional[Callable[
|
||||
[int, slice],
|
||||
tuple[np.ndarray, np.nd.array]
|
||||
]] = None
|
||||
# last read from shm (usually due to an update call)
|
||||
_last_read: tuple[
|
||||
int,
|
||||
int,
|
||||
np.ndarray
|
||||
|
||||
incr_update_xy_nd: Optional[Callable[
|
||||
[int, slice], None]
|
||||
] = None
|
||||
]
|
||||
|
||||
# default just returns index, and named array from data
|
||||
format_xy_nd_to_1d: Callable[
|
||||
[np.ndarray, str],
|
||||
tuple[np.ndarray]
|
||||
] = by_index_and_key
|
||||
@property
|
||||
def last_read(self) -> tuple | None:
|
||||
return self._last_read
|
||||
|
||||
x_nd: Optional[np.ndarray] = None
|
||||
y_nd: Optional[np.ndarray] = None
|
||||
def __repr__(self) -> str:
|
||||
msg = (
|
||||
f'{type(self)}: ->\n\n'
|
||||
f'fqsn={self.flow.name}\n'
|
||||
f'shm_name={self.shm.token["shm_name"]}\n\n'
|
||||
|
||||
x_1d: Optional[np.ndarray] = None
|
||||
y_1d: Optional[np.ndarray] = None
|
||||
f'last_vr={self._last_vr}\n'
|
||||
f'last_ivdr={self._last_ivdr}\n\n'
|
||||
|
||||
# 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
|
||||
xy_nd_start: int = 0
|
||||
xy_nd_end: int = 0
|
||||
f'xy_nd_start={self.xy_nd_start}\n'
|
||||
f'xy_nd_stop={self.xy_nd_stop}\n\n'
|
||||
)
|
||||
|
||||
x_nd_len = 0
|
||||
y_nd_len = 0
|
||||
if self.x_nd is not None:
|
||||
x_nd_len = len(self.x_nd)
|
||||
y_nd_len = len(self.y_nd)
|
||||
|
||||
def xy_downsample(
|
||||
x,
|
||||
y,
|
||||
uppx,
|
||||
msg += (
|
||||
f'x_nd_len={x_nd_len}\n'
|
||||
f'y_nd_len={y_nd_len}\n'
|
||||
)
|
||||
|
||||
x_spacer: float = 0.5,
|
||||
return msg
|
||||
|
||||
def diff(
|
||||
self,
|
||||
new_read: tuple[np.ndarray],
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
float,
|
||||
float,
|
||||
]:
|
||||
(
|
||||
last_xfirst,
|
||||
last_xlast,
|
||||
last_array,
|
||||
last_ivl,
|
||||
last_ivr,
|
||||
last_in_view,
|
||||
) = self.last_read
|
||||
|
||||
# downsample whenever more then 1 pixels per datum can be shown.
|
||||
# always refresh data bounds until we get diffing
|
||||
# working properly, see above..
|
||||
bins, x, y, ymn, ymx = ds_m4(
|
||||
x,
|
||||
y,
|
||||
uppx,
|
||||
# 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),
|
||||
)
|
||||
|
||||
# flatten output to 1d arrays suitable for path-graphics generation.
|
||||
x = np.broadcast_to(x[:, None], y.shape)
|
||||
x = (x + np.array(
|
||||
[-x_spacer, 0, 0, x_spacer]
|
||||
)).flatten()
|
||||
y = y.flatten()
|
||||
# Incrementally updated xy ndarray formatted data, a pre-1d
|
||||
# format which is updated and cached independently of the final
|
||||
# pre-graphics-path 1d format.
|
||||
x_nd: Optional[np.ndarray] = None
|
||||
y_nd: Optional[np.ndarray] = None
|
||||
|
||||
return x, y, ymn, ymx
|
||||
# indexes which slice into the above arrays (which are allocated
|
||||
# based on source data shm input size) and allow retrieving
|
||||
# incrementally updated data.
|
||||
xy_nd_start: int = 0
|
||||
xy_nd_stop: int = 0
|
||||
|
||||
# TODO: eventually incrementally update 1d-pre-graphics path data?
|
||||
# x_1d: Optional[np.ndarray] = None
|
||||
# y_1d: Optional[np.ndarray] = None
|
||||
|
||||
# incremental view-change state(s) tracking
|
||||
_last_vr: tuple[float, float] | None = None
|
||||
_last_ivdr: tuple[float, float] | None = None
|
||||
|
||||
def _track_inview_range(
|
||||
self,
|
||||
view_range: tuple[int, int],
|
||||
|
||||
) -> bool:
|
||||
# if a view range is passed, plan to draw the
|
||||
# source ouput that's "in view" of the chart.
|
||||
vl, vr = view_range
|
||||
zoom_or_append = False
|
||||
last_vr = self._last_vr
|
||||
|
||||
# incremental in-view data update.
|
||||
if last_vr:
|
||||
lvl, lvr = last_vr # relative slice indices
|
||||
|
||||
# TODO: detecting more specifically the interaction changes
|
||||
# last_ivr = self._last_ivdr or (vl, vr)
|
||||
# al, ar = last_ivr # abs slice indices
|
||||
# left_change = abs(x_iv[0] - al) >= 1
|
||||
# right_change = abs(x_iv[-1] - ar) >= 1
|
||||
|
||||
# likely a zoom/pan view change or data append update
|
||||
if (
|
||||
(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
|
||||
|
||||
return zoom_or_append
|
||||
|
||||
def format_to_1d(
|
||||
self,
|
||||
new_read: tuple,
|
||||
array_key: str,
|
||||
profiler: Profiler,
|
||||
|
||||
slice_to_head: int = -1,
|
||||
read_src_from_key: bool = True,
|
||||
slice_to_inview: bool = True,
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
]:
|
||||
shm = self.shm
|
||||
|
||||
(
|
||||
_,
|
||||
_,
|
||||
array,
|
||||
ivl,
|
||||
ivr,
|
||||
in_view,
|
||||
|
||||
) = new_read
|
||||
|
||||
(
|
||||
pre_slice,
|
||||
prepend_len,
|
||||
append_len,
|
||||
post_slice,
|
||||
) = self.diff(new_read)
|
||||
|
||||
if self.y_nd is None:
|
||||
# we first need to allocate xy data arrays
|
||||
# from the source data.
|
||||
self.x_nd, self.y_nd = self.allocate_xy_nd(
|
||||
shm,
|
||||
array_key,
|
||||
)
|
||||
self.xy_nd_start = shm._first.value
|
||||
self.xy_nd_stop = shm._last.value
|
||||
profiler('allocated xy history')
|
||||
|
||||
if prepend_len:
|
||||
y_prepend = shm._array[pre_slice]
|
||||
if read_src_from_key:
|
||||
y_prepend = y_prepend[array_key]
|
||||
|
||||
(
|
||||
new_y_nd,
|
||||
y_nd_slc,
|
||||
|
||||
) = self.incr_update_xy_nd(
|
||||
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_len,
|
||||
|
||||
self.xy_nd_start,
|
||||
self.xy_nd_stop,
|
||||
is_append=False,
|
||||
)
|
||||
|
||||
# y_nd_view = self.y_nd[y_nd_slc]
|
||||
self.y_nd[y_nd_slc] = new_y_nd
|
||||
# if read_src_from_key:
|
||||
# y_nd_view[:][array_key] = new_y_nd
|
||||
# else:
|
||||
# y_nd_view[:] = new_y_nd
|
||||
|
||||
self.xy_nd_start = shm._first.value
|
||||
profiler('prepended xy history: {prepend_length}')
|
||||
|
||||
if append_len:
|
||||
y_append = shm._array[post_slice]
|
||||
if read_src_from_key:
|
||||
y_append = y_append[array_key]
|
||||
|
||||
(
|
||||
new_y_nd,
|
||||
y_nd_slc,
|
||||
|
||||
) = self.incr_update_xy_nd(
|
||||
shm,
|
||||
array_key,
|
||||
|
||||
y_append,
|
||||
post_slice,
|
||||
append_len,
|
||||
|
||||
self.xy_nd_start,
|
||||
self.xy_nd_stop,
|
||||
is_append=True,
|
||||
)
|
||||
# self.y_nd[post_slice] = new_y_nd
|
||||
# self.y_nd[xy_slice or post_slice] = xy_data
|
||||
self.y_nd[y_nd_slc] = new_y_nd
|
||||
# if read_src_from_key:
|
||||
# y_nd_view[:][array_key] = new_y_nd
|
||||
# else:
|
||||
# y_nd_view[:] = new_y_nd
|
||||
|
||||
self.xy_nd_stop = shm._last.value
|
||||
profiler('appened xy history: {append_length}')
|
||||
|
||||
view_changed: bool = False
|
||||
view_range: tuple[int, int] = (ivl, ivr)
|
||||
if slice_to_inview:
|
||||
view_changed = self._track_inview_range(view_range)
|
||||
array = in_view
|
||||
profiler(f'{self.flow.name} view range slice {view_range}')
|
||||
|
||||
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
|
||||
|
||||
# TODO: hist here should be the pre-sliced
|
||||
# x/y_data in the case where allocate_xy is
|
||||
# defined?
|
||||
x_1d, y_1d, connect = self.format_xy_nd_to_1d(
|
||||
hist,
|
||||
array_key,
|
||||
view_range,
|
||||
)
|
||||
|
||||
# update the last "in view data range"
|
||||
if len(x_1d):
|
||||
self._last_ivdr = x_1d[0], x_1d[slice_to_head]
|
||||
|
||||
# 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
|
||||
|
||||
profiler('.format_to_1d()')
|
||||
return (
|
||||
x_1d,
|
||||
y_1d,
|
||||
connect,
|
||||
prepend_len,
|
||||
append_len,
|
||||
view_changed,
|
||||
)
|
||||
|
||||
###############################
|
||||
# Sub-type override interface #
|
||||
###############################
|
||||
|
||||
# optional pre-graphics xy formatted data which
|
||||
# is incrementally updated in sync with the source data.
|
||||
# XXX: was ``.allocate_xy()``
|
||||
def allocate_xy_nd(
|
||||
self,
|
||||
src_shm: ShmArray,
|
||||
data_field: str,
|
||||
index_field: str = 'index',
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray, # x
|
||||
np.nd.array # y
|
||||
]:
|
||||
'''
|
||||
Convert the structured-array ``src_shm`` format to
|
||||
a equivalently shaped (and field-less) ``np.ndarray``.
|
||||
|
||||
Eg. a 4 field x N struct-array => (N, 4)
|
||||
|
||||
'''
|
||||
y_nd = src_shm._array[data_field].copy()
|
||||
x_nd = src_shm._array[index_field].copy()
|
||||
return x_nd, y_nd
|
||||
|
||||
# XXX: was ``.update_xy()``
|
||||
def incr_update_xy_nd(
|
||||
self,
|
||||
|
||||
src_shm: ShmArray,
|
||||
data_field: str,
|
||||
|
||||
new_from_src: np.ndarray, # portion of source that was updated
|
||||
|
||||
read_slc: slice,
|
||||
ln: int, # len of updated
|
||||
|
||||
nd_start: int,
|
||||
nd_stop: int,
|
||||
|
||||
is_append: bool,
|
||||
index_field: str = 'index',
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
slice,
|
||||
]:
|
||||
# write pushed data to flattened copy
|
||||
new_y_nd = new_from_src
|
||||
|
||||
# XXX
|
||||
# TODO: this should be returned and written by caller!
|
||||
# XXX
|
||||
# generate same-valued-per-row x support based on y shape
|
||||
if index_field != 'index':
|
||||
self.x_nd[read_slc, :] = new_from_src[index_field]
|
||||
|
||||
return new_y_nd, read_slc
|
||||
|
||||
# XXX: was ``.format_xy()``
|
||||
def format_xy_nd_to_1d(
|
||||
self,
|
||||
|
||||
array: np.ndarray,
|
||||
array_key: str,
|
||||
vr: tuple[int, int],
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray, # 1d x
|
||||
np.ndarray, # 1d y
|
||||
np.ndarray | str, # connection array/style
|
||||
]:
|
||||
'''
|
||||
Default xy-nd array to 1d pre-graphics-path render routine.
|
||||
|
||||
Return single field column data verbatim
|
||||
|
||||
'''
|
||||
return (
|
||||
array['index'],
|
||||
array[array_key],
|
||||
|
||||
# 1d connection array or style-key to
|
||||
# ``pg.functions.arrayToQPath()``
|
||||
'all',
|
||||
)
|
||||
|
||||
|
||||
class OHLCBarsFmtr(IncrementalFormatter):
|
||||
|
||||
fields: list[str] = ['open', 'high', 'low', 'close']
|
||||
|
||||
def allocate_xy_nd(
|
||||
self,
|
||||
|
||||
ohlc_shm: ShmArray,
|
||||
data_field: str,
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray, # x
|
||||
np.nd.array # y
|
||||
]:
|
||||
'''
|
||||
Convert an input struct-array holding OHLC samples into a pair of
|
||||
flattened x, y arrays with the same size (datums wise) as the source
|
||||
data.
|
||||
|
||||
'''
|
||||
y_nd = ohlc_shm.ustruct(self.fields)
|
||||
|
||||
# generate an flat-interpolated x-domain
|
||||
x_nd = (
|
||||
np.broadcast_to(
|
||||
ohlc_shm._array['index'][:, None],
|
||||
(
|
||||
ohlc_shm._array.size,
|
||||
# 4, # only ohlc
|
||||
y_nd.shape[1],
|
||||
),
|
||||
) + np.array([-0.5, 0, 0, 0.5])
|
||||
)
|
||||
assert y_nd.any()
|
||||
|
||||
# write pushed data to flattened copy
|
||||
return (
|
||||
x_nd,
|
||||
y_nd,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
@njit(
|
||||
# TODO: for now need to construct this manually for readonly arrays, see
|
||||
# https://github.com/numba/numba/issues/4511
|
||||
# TODO: for now need to construct this manually for readonly
|
||||
# arrays, see https://github.com/numba/numba/issues/4511
|
||||
# ntypes.tuple((float64[:], float64[:], float64[:]))(
|
||||
# numba_ohlc_dtype[::1], # contiguous
|
||||
# int64,
|
||||
|
|
@ -142,7 +523,11 @@ def path_arrays_from_ohlc(
|
|||
start: int64,
|
||||
bar_gap: float64 = 0.43,
|
||||
|
||||
) -> np.ndarray:
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
]:
|
||||
'''
|
||||
Generate an array of lines objects from input ohlc data.
|
||||
|
||||
|
|
@ -198,110 +583,93 @@ def path_arrays_from_ohlc(
|
|||
|
||||
return x, y, c
|
||||
|
||||
# TODO: can we drop this frame and just use the above?
|
||||
def format_xy_nd_to_1d(
|
||||
self,
|
||||
|
||||
def gen_ohlc_qpath(
|
||||
r: Renderer,
|
||||
data: np.ndarray,
|
||||
array_key: str, # we ignore this
|
||||
array: np.ndarray,
|
||||
array_key: str,
|
||||
vr: tuple[int, int],
|
||||
|
||||
start: int = 0, # XXX: do we need this?
|
||||
# 0.5 is no overlap between arms, 1.0 is full overlap
|
||||
w: float = 0.43,
|
||||
|
||||
) -> QtGui.QPainterPath:
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
]:
|
||||
'''
|
||||
More or less direct proxy to ``path_arrays_from_ohlc()``
|
||||
but with closed in kwargs for line spacing.
|
||||
More or less direct proxy to the ``numba``-fied
|
||||
``path_arrays_from_ohlc()`` (above) but with closed in kwargs
|
||||
for line spacing.
|
||||
|
||||
'''
|
||||
x, y, c = path_arrays_from_ohlc(
|
||||
data,
|
||||
x, y, c = self.path_arrays_from_ohlc(
|
||||
array,
|
||||
start,
|
||||
bar_gap=w,
|
||||
)
|
||||
return x, y, c
|
||||
|
||||
def incr_update_xy_nd(
|
||||
self,
|
||||
|
||||
def ohlc_to_line(
|
||||
ohlc_shm: ShmArray,
|
||||
src_shm: ShmArray,
|
||||
data_field: str,
|
||||
fields: list[str] = ['open', 'high', 'low', 'close']
|
||||
|
||||
new_from_src: np.ndarray, # portion of source that was updated
|
||||
|
||||
read_slc: slice,
|
||||
ln: int, # len of updated
|
||||
|
||||
nd_start: int,
|
||||
nd_stop: int,
|
||||
|
||||
is_append: bool,
|
||||
index_field: str = 'index',
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
slice,
|
||||
]:
|
||||
'''
|
||||
Convert an input struct-array holding OHLC samples into a pair of
|
||||
flattened x, y arrays with the same size (datums wise) as the source
|
||||
data.
|
||||
|
||||
'''
|
||||
y_out = ohlc_shm.ustruct(fields)
|
||||
first = ohlc_shm._first.value
|
||||
last = ohlc_shm._last.value
|
||||
|
||||
# write pushed data to flattened copy
|
||||
y_out[first:last] = rfn.structured_to_unstructured(
|
||||
ohlc_shm.array[fields]
|
||||
# write newly pushed data to flattened copy
|
||||
# a struct-arr is always passed in.
|
||||
new_y_nd = rfn.structured_to_unstructured(
|
||||
new_from_src[self.fields]
|
||||
)
|
||||
|
||||
# generate an flat-interpolated x-domain
|
||||
x_out = (
|
||||
np.broadcast_to(
|
||||
ohlc_shm._array['index'][:, None],
|
||||
(
|
||||
ohlc_shm._array.size,
|
||||
# 4, # only ohlc
|
||||
y_out.shape[1],
|
||||
),
|
||||
) + np.array([-0.5, 0, 0, 0.5])
|
||||
)
|
||||
assert y_out.any()
|
||||
# XXX
|
||||
# TODO: this should be returned and written by caller!
|
||||
# XXX
|
||||
# generate same-valued-per-row x support based on y shape
|
||||
if index_field != 'index':
|
||||
self.x_nd[read_slc, :] = new_from_src[index_field]
|
||||
|
||||
return (
|
||||
x_out,
|
||||
y_out,
|
||||
)
|
||||
return new_y_nd, read_slc
|
||||
|
||||
|
||||
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,
|
||||
class OHLCBarsAsCurveFmtr(OHLCBarsFmtr):
|
||||
|
||||
) -> np.ndarray:
|
||||
def format_xy_nd_to_1d(
|
||||
self,
|
||||
|
||||
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,
|
||||
np.ndarray,
|
||||
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]
|
||||
x_flat = self.x_nd[self.xy_nd_start:self.xy_nd_stop]
|
||||
y_flat = self.y_nd[self.xy_nd_start:self.xy_nd_stop]
|
||||
|
||||
# slice to view
|
||||
ivl, ivr = vr
|
||||
|
|
@ -315,12 +683,20 @@ def ohlc_flat_to_xy(
|
|||
return x_iv, y_iv, 'all'
|
||||
|
||||
|
||||
def to_step_format(
|
||||
class StepCurveFmtr(IncrementalFormatter):
|
||||
|
||||
def allocate_xy_nd(
|
||||
self,
|
||||
|
||||
shm: ShmArray,
|
||||
data_field: str,
|
||||
|
||||
index_field: str = 'index',
|
||||
|
||||
) -> tuple[int, np.ndarray, np.ndarray]:
|
||||
) -> tuple[
|
||||
np.ndarray, # x
|
||||
np.nd.array # y
|
||||
]:
|
||||
'''
|
||||
Convert an input 1d shm array to a "step array" format
|
||||
for use by path graphics generation.
|
||||
|
|
@ -341,19 +717,25 @@ def to_step_format(
|
|||
y_out[0, 0] = 0
|
||||
return x_out, y_out
|
||||
|
||||
def incr_update_xy_nd(
|
||||
self,
|
||||
|
||||
def update_step_xy(
|
||||
src_shm: ShmArray,
|
||||
array_key: str,
|
||||
y_update: np.ndarray,
|
||||
|
||||
src_update: np.ndarray, # portion of source that was updated
|
||||
slc: slice,
|
||||
ln: int,
|
||||
ln: int, # len of updated
|
||||
|
||||
first: int,
|
||||
last: int,
|
||||
|
||||
is_append: bool,
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
slice,
|
||||
]:
|
||||
# for a step curve we slice from one datum prior
|
||||
# to the current "update slice" to get the previous
|
||||
# "level".
|
||||
|
|
@ -364,7 +746,7 @@ def update_step_xy(
|
|||
slc = slice(start, end)
|
||||
|
||||
else:
|
||||
new_y = y_update
|
||||
new_y = src_update
|
||||
|
||||
return (
|
||||
np.broadcast_to(
|
||||
|
|
@ -373,22 +755,21 @@ def update_step_xy(
|
|||
slc,
|
||||
)
|
||||
|
||||
def format_xy_nd_to_1d(
|
||||
self,
|
||||
|
||||
def step_to_xy(
|
||||
r: Renderer,
|
||||
array: np.ndarray,
|
||||
array_key: str,
|
||||
vr: tuple[int, int],
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.nd.array,
|
||||
np.ndarray,
|
||||
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]
|
||||
x_step = self.x_nd[self.xy_nd_start:self.xy_nd_stop+2]
|
||||
y_step = self.y_nd[self.xy_nd_start:self.xy_nd_stop+2]
|
||||
|
||||
lasts = array[['index', array_key]]
|
||||
last = lasts[array_key][-1]
|
||||
|
|
@ -411,3 +792,40 @@ def step_to_xy(
|
|||
# )
|
||||
|
||||
return x_iv, y_iv, 'all'
|
||||
|
||||
|
||||
def xy_downsample(
|
||||
x,
|
||||
y,
|
||||
uppx,
|
||||
|
||||
x_spacer: float = 0.5,
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray,
|
||||
np.ndarray,
|
||||
float,
|
||||
float,
|
||||
]:
|
||||
'''
|
||||
Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
|
||||
``uppx`` (units-per-pixel) and add space between discreet datums.
|
||||
|
||||
'''
|
||||
# downsample whenever more then 1 pixels per datum can be shown.
|
||||
# always refresh data bounds until we get diffing
|
||||
# working properly, see above..
|
||||
bins, x, y, ymn, ymx = ds_m4(
|
||||
x,
|
||||
y,
|
||||
uppx,
|
||||
)
|
||||
|
||||
# flatten output to 1d arrays suitable for path-graphics generation.
|
||||
x = np.broadcast_to(x[:, None], y.shape)
|
||||
x = (x + np.array(
|
||||
[-x_spacer, 0, 0, x_spacer]
|
||||
)).flatten()
|
||||
y = y.flatten()
|
||||
|
||||
return x, y, ymn, ymx
|
||||
|
|
|
|||
Loading…
Reference in New Issue