Move all pre-path formatting routines to `._pathops`, proto formatter type
Tyler Goodlet
parent
18857ac077
commit
0945033d38
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@ -31,7 +31,6 @@ from typing import (
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import msgspec
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import numpy as np
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from numpy.lib import recfunctions as rfn
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import pyqtgraph as pg
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from PyQt5.QtGui import QPainterPath
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from PyQt5.QtCore import QLineF
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@ -44,9 +43,21 @@ 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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xy_downsample,
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)
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from ._ohlc import (
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@ -75,55 +86,6 @@ log = get_logger(__name__)
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# flows: dict[str, np.ndarray] = {}
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def update_ohlc_to_line(
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src_shm: ShmArray,
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array_key: str,
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src_update: np.ndarray,
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slc: slice,
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ln: int,
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first: int,
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last: int,
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is_append: bool,
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) -> np.ndarray:
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fields = ['open', 'high', 'low', 'close']
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return (
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rfn.structured_to_unstructured(src_update[fields]),
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slc,
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)
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def ohlc_flat_to_xy(
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r: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.nd.array,
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str,
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]:
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# TODO: in the case of an existing ``.update_xy()``
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# should we be passing in array as an xy arrays tuple?
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# 2 more datum-indexes to capture zero at end
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x_flat = r.x_data[r._xy_first:r._xy_last]
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y_flat = r.y_data[r._xy_first:r._xy_last]
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# slice to view
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ivl, ivr = vr
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x_iv_flat = x_flat[ivl:ivr]
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y_iv_flat = y_flat[ivl:ivr]
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# reshape to 1d for graphics rendering
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y_iv = y_iv_flat.reshape(-1)
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x_iv = x_iv_flat.reshape(-1)
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return x_iv, y_iv, 'all'
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def render_baritems(
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flow: Flow,
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graphics: BarItems,
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@ -253,77 +215,6 @@ def render_baritems(
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)
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def update_step_xy(
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src_shm: ShmArray,
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array_key: str,
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y_update: np.ndarray,
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slc: slice,
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ln: int,
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first: int,
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last: int,
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is_append: bool,
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) -> np.ndarray:
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# for a step curve we slice from one datum prior
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# to the current "update slice" to get the previous
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# "level".
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if is_append:
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start = max(last - 1, 0)
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end = src_shm._last.value
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new_y = src_shm._array[start:end][array_key]
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slc = slice(start, end)
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else:
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new_y = y_update
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return (
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np.broadcast_to(
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new_y[:, None], (new_y.size, 2),
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),
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slc,
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)
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def step_to_xy(
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r: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.nd.array,
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str,
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]:
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# 2 more datum-indexes to capture zero at end
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x_step = r.x_data[r._xy_first:r._xy_last+2]
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y_step = r.y_data[r._xy_first:r._xy_last+2]
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lasts = array[['index', array_key]]
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last = lasts[array_key][-1]
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y_step[-1] = last
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# slice out in-view data
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ivl, ivr = vr
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ys_iv = y_step[ivl:ivr+1]
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xs_iv = x_step[ivl:ivr+1]
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# flatten to 1d
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y_iv = ys_iv.reshape(ys_iv.size)
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x_iv = xs_iv.reshape(xs_iv.size)
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# print(
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# f'ys_iv : {ys_iv[-s:]}\n'
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# f'y_iv: {y_iv[-s:]}\n'
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# f'xs_iv: {xs_iv[-s:]}\n'
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# f'x_iv: {x_iv[-s:]}\n'
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# )
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return x_iv, y_iv, 'all'
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class Flow(msgspec.Struct): # , frozen=True):
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'''
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(Financial Signal-)Flow compound type which wraps a real-time
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@ -786,20 +677,6 @@ class Flow(msgspec.Struct): # , frozen=True):
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g.update()
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def by_index_and_key(
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renderer: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.ndarray,
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np.ndarray,
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]:
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return array['index'], array[array_key], 'all'
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class Renderer(msgspec.Struct):
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flow: Flow
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@ -1210,7 +1087,7 @@ class Renderer(msgspec.Struct):
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and do_append
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and not should_redraw
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):
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# print(f'{array_key} append len: {append_length}')
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print(f'{array_key} append len: {append_length}')
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new_x = x_out[-append_length - 2:] # slice_to_head]
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new_y = y_out[-append_length - 2:] # slice_to_head]
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profiler('sliced append path')
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@ -1236,6 +1113,7 @@ class Renderer(msgspec.Struct):
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profiler('generated append qpath')
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if use_fpath:
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print(f'{self.flow.name}: FAST PATH')
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# an attempt at trying to make append-updates faster..
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if fast_path is None:
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fast_path = append_path
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@ -19,10 +19,12 @@ Super fast ``QPainterPath`` generation related operator routines.
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"""
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from __future__ import annotations
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from typing import (
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# Optional,
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Optional,
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Callable,
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TYPE_CHECKING,
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)
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import msgspec
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import numpy as np
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from numpy.lib import recfunctions as rfn
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from numba import njit, float64, int64 # , optional
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@ -42,6 +44,56 @@ if TYPE_CHECKING:
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from ._flows import Renderer
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def by_index_and_key(
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renderer: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.ndarray,
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np.ndarray,
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]:
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return array['index'], array[array_key], 'all'
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class IncrementalFormatter(msgspec.Struct):
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shm: ShmArray
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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_nd: 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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incr_update_xy_nd: Optional[Callable[
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[int, slice], None]
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] = None
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# default just returns index, and named array from data
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format_xy_nd_to_1d: 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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x_nd: Optional[np.ndarray] = None
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y_nd: Optional[np.ndarray] = None
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x_1d: Optional[np.ndarray] = None
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y_1d: 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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xy_nd_start: int = 0
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xy_nd_end: int = 0
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def xy_downsample(
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x,
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y,
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@ -214,6 +266,55 @@ def ohlc_to_line(
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)
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def update_ohlc_to_line(
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src_shm: ShmArray,
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array_key: str,
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src_update: np.ndarray,
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slc: slice,
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ln: int,
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first: int,
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last: int,
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is_append: bool,
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) -> np.ndarray:
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fields = ['open', 'high', 'low', 'close']
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return (
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rfn.structured_to_unstructured(src_update[fields]),
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slc,
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)
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def ohlc_flat_to_xy(
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r: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.nd.array,
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str,
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]:
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# TODO: in the case of an existing ``.update_xy()``
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# should we be passing in array as an xy arrays tuple?
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# 2 more datum-indexes to capture zero at end
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x_flat = r.x_data[r._xy_first:r._xy_last]
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y_flat = r.y_data[r._xy_first:r._xy_last]
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# slice to view
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ivl, ivr = vr
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x_iv_flat = x_flat[ivl:ivr]
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y_iv_flat = y_flat[ivl:ivr]
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# reshape to 1d for graphics rendering
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y_iv = y_iv_flat.reshape(-1)
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x_iv = x_iv_flat.reshape(-1)
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return x_iv, y_iv, 'all'
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def to_step_format(
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shm: ShmArray,
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data_field: str,
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@ -239,3 +340,74 @@ def to_step_format(
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# start y at origin level
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y_out[0, 0] = 0
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return x_out, y_out
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def update_step_xy(
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src_shm: ShmArray,
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array_key: str,
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y_update: np.ndarray,
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slc: slice,
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ln: int,
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first: int,
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last: int,
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is_append: bool,
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) -> np.ndarray:
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# for a step curve we slice from one datum prior
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# to the current "update slice" to get the previous
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# "level".
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if is_append:
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start = max(last - 1, 0)
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end = src_shm._last.value
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new_y = src_shm._array[start:end][array_key]
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slc = slice(start, end)
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else:
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new_y = y_update
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return (
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np.broadcast_to(
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new_y[:, None], (new_y.size, 2),
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),
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slc,
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)
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def step_to_xy(
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r: Renderer,
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array: np.ndarray,
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array_key: str,
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vr: tuple[int, int],
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) -> tuple[
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np.ndarray,
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np.nd.array,
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str,
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]:
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# 2 more datum-indexes to capture zero at end
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x_step = r.x_data[r._xy_first:r._xy_last+2]
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y_step = r.y_data[r._xy_first:r._xy_last+2]
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lasts = array[['index', array_key]]
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last = lasts[array_key][-1]
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y_step[-1] = last
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# slice out in-view data
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ivl, ivr = vr
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ys_iv = y_step[ivl:ivr+1]
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xs_iv = x_step[ivl:ivr+1]
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# flatten to 1d
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y_iv = ys_iv.reshape(ys_iv.size)
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x_iv = xs_iv.reshape(xs_iv.size)
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# print(
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# f'ys_iv : {ys_iv[-s:]}\n'
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# f'y_iv: {y_iv[-s:]}\n'
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# f'xs_iv: {xs_iv[-s:]}\n'
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# f'x_iv: {x_iv[-s:]}\n'
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# )
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return x_iv, y_iv, 'all'
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