Move qpath-ops routines back to separate mod
Tyler Goodlet
parent
ce8279b6b1
commit
eda7b7f3c7
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@ -14,7 +14,7 @@
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# You should have received a copy of the GNU Affero General Public License
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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"""
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Super fast ``QPainterPath`` generation related operator routines.
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Pre-(path)-graphics formatted x/y nd/1d rendering subsystem.
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"""
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"""
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from __future__ import annotations
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from __future__ import annotations
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@ -26,20 +26,12 @@ from typing import (
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import msgspec
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import msgspec
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import numpy as np
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import numpy as np
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from numpy.lib import recfunctions as rfn
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from numpy.lib import recfunctions as rfn
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from numba import (
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# types,
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njit,
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float64,
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int64,
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# optional,
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)
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from ._sharedmem import (
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from ._sharedmem import (
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ShmArray,
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ShmArray,
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)
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)
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# from ._source import numba_ohlc_dtype
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from ._pathops import (
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from ._compression import (
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path_arrays_from_ohlc,
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ds_m4,
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)
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)
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if TYPE_CHECKING:
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if TYPE_CHECKING:
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@ -49,129 +41,6 @@ if TYPE_CHECKING:
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from .._profile import Profiler
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from .._profile import Profiler
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def xy_downsample(
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x,
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y,
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uppx,
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x_spacer: float = 0.5,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
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``uppx`` (units-per-pixel) and add space between discreet datums.
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'''
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# downsample whenever more then 1 pixels per datum can be shown.
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# always refresh data bounds until we get diffing
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# working properly, see above..
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bins, x, y, ymn, ymx = ds_m4(
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x,
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y,
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uppx,
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)
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# flatten output to 1d arrays suitable for path-graphics generation.
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array(
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[-x_spacer, 0, 0, x_spacer]
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)).flatten()
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y = y.flatten()
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return x, y, ymn, ymx
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@njit(
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# NOTE: need to construct this manually for readonly
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# arrays, see https://github.com/numba/numba/issues/4511
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# (
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# types.Array(
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# numba_ohlc_dtype,
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# 1,
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# 'C',
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# readonly=True,
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# ),
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# int64,
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# types.unicode_type,
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# optional(float64),
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# ),
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nogil=True
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)
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def path_arrays_from_ohlc(
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data: np.ndarray,
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start: int64,
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bar_gap: float64 = 0.43,
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# index_field: str,
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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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'''
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Generate an array of lines objects from input ohlc data.
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'''
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size = int(data.shape[0] * 6)
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# XXX: see this for why the dtype might have to be defined outside
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# the routine.
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# https://github.com/numba/numba/issues/4098#issuecomment-493914533
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x = np.zeros(
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shape=size,
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dtype=float64,
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)
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y, c = x.copy(), x.copy()
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# TODO: report bug for assert @
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# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
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for i, q in enumerate(data[start:], start):
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# TODO: ask numba why this doesn't work..
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# open, high, low, close, index = q[
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# ['open', 'high', 'low', 'close', 'index']]
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open = q['open']
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high = q['high']
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low = q['low']
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close = q['close']
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# index = float64(q[index_field])
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index = float64(q['index'])
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istart = i * 6
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istop = istart + 6
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# x,y detail the 6 points which connect all vertexes of a ohlc bar
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x[istart:istop] = (
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index - bar_gap,
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index,
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index,
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index,
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index,
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index + bar_gap,
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)
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y[istart:istop] = (
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open,
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open,
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low,
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high,
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close,
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close,
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)
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# specifies that the first edge is never connected to the
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# prior bars last edge thus providing a small "gap"/"space"
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# between bars determined by ``bar_gap``.
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c[istart:istop] = (1, 1, 1, 1, 1, 0)
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return x, y, c
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class IncrementalFormatter(msgspec.Struct):
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class IncrementalFormatter(msgspec.Struct):
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'''
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'''
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Incrementally updating, pre-path-graphics tracking, formatter.
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Incrementally updating, pre-path-graphics tracking, formatter.
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@ -652,7 +521,6 @@ class OHLCBarsFmtr(IncrementalFormatter):
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new_y_nd.shape,
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new_y_nd.shape,
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) + np.array([-0.5, 0, 0, 0.5])
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) + np.array([-0.5, 0, 0, 0.5])
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# TODO: can we drop this frame and just use the above?
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# TODO: can we drop this frame and just use the above?
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def format_xy_nd_to_1d(
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def format_xy_nd_to_1d(
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self,
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self,
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@ -0,0 +1,155 @@
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# piker: trading gear for hackers
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# Copyright (C) 2018-present Tyler Goodlet (in stewardship of piker0)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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||||||
|
"""
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||||||
|
Super fast ``QPainterPath`` generation related operator routines.
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"""
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import numpy as np
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from numba import (
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# types,
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njit,
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float64,
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int64,
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# optional,
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)
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# from ._source import numba_ohlc_dtype
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from ._compression import (
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ds_m4,
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)
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def xy_downsample(
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x,
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y,
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uppx,
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x_spacer: float = 0.5,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
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``uppx`` (units-per-pixel) and add space between discreet datums.
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'''
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# downsample whenever more then 1 pixels per datum can be shown.
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# always refresh data bounds until we get diffing
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# working properly, see above..
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bins, x, y, ymn, ymx = ds_m4(
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x,
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y,
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uppx,
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)
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# flatten output to 1d arrays suitable for path-graphics generation.
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array(
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[-x_spacer, 0, 0, x_spacer]
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)).flatten()
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y = y.flatten()
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return x, y, ymn, ymx
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@njit(
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# NOTE: need to construct this manually for readonly
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# arrays, see https://github.com/numba/numba/issues/4511
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# (
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# types.Array(
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# numba_ohlc_dtype,
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# 1,
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# 'C',
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# readonly=True,
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# ),
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# int64,
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# types.unicode_type,
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# optional(float64),
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# ),
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nogil=True
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)
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def path_arrays_from_ohlc(
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data: np.ndarray,
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start: int64,
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bar_gap: float64 = 0.43,
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# index_field: str,
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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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'''
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Generate an array of lines objects from input ohlc data.
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'''
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size = int(data.shape[0] * 6)
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# XXX: see this for why the dtype might have to be defined outside
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# the routine.
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# https://github.com/numba/numba/issues/4098#issuecomment-493914533
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x = np.zeros(
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shape=size,
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dtype=float64,
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)
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y, c = x.copy(), x.copy()
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# TODO: report bug for assert @
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# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
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for i, q in enumerate(data[start:], start):
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# TODO: ask numba why this doesn't work..
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# open, high, low, close, index = q[
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# ['open', 'high', 'low', 'close', 'index']]
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open = q['open']
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high = q['high']
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low = q['low']
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close = q['close']
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# index = float64(q[index_field])
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index = float64(q['index'])
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istart = i * 6
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istop = istart + 6
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# x,y detail the 6 points which connect all vertexes of a ohlc bar
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x[istart:istop] = (
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index - bar_gap,
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index,
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index,
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index,
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index,
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index + bar_gap,
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)
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y[istart:istop] = (
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open,
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open,
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low,
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high,
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close,
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close,
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)
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# specifies that the first edge is never connected to the
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# prior bars last edge thus providing a small "gap"/"space"
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# between bars determined by ``bar_gap``.
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c[istart:istop] = (1, 1, 1, 1, 1, 0)
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return x, y, c
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@ -42,8 +42,8 @@ from ..data._formatters import (
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OHLCBarsFmtr, # Plain OHLC renderer
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OHLCBarsFmtr, # Plain OHLC renderer
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OHLCBarsAsCurveFmtr, # OHLC converted to line
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OHLCBarsAsCurveFmtr, # OHLC converted to line
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StepCurveFmtr, # "step" curve (like for vlm)
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StepCurveFmtr, # "step" curve (like for vlm)
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xy_downsample,
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)
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)
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from ..data._pathops import xy_downsample
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from .._profile import (
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from .._profile import (
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pg_profile_enabled,
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pg_profile_enabled,
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# ms_slower_then,
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# ms_slower_then,
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Reference in New Issue