Move qpath-ops routines back to separate mod

epoch_index_backup
Tyler Goodlet 2022-11-30 18:54:42 -05:00
parent 4ca8e23b5b
commit a2d23244e7
3 changed files with 159 additions and 136 deletions

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@ -14,7 +14,7 @@
# You should have received a copy of the GNU Affero General Public License # You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>. # along with this program. If not, see <https://www.gnu.org/licenses/>.
""" """
Super fast ``QPainterPath`` generation related operator routines. Pre-(path)-graphics formatted x/y nd/1d rendering subsystem.
""" """
from __future__ import annotations from __future__ import annotations
@ -26,20 +26,12 @@ from typing import (
import msgspec import msgspec
import numpy as np import numpy as np
from numpy.lib import recfunctions as rfn from numpy.lib import recfunctions as rfn
from numba import (
# types,
njit,
float64,
int64,
# optional,
)
from ._sharedmem import ( from ._sharedmem import (
ShmArray, ShmArray,
) )
# from ._source import numba_ohlc_dtype from ._pathops import (
from ._compression import ( path_arrays_from_ohlc,
ds_m4,
) )
if TYPE_CHECKING: if TYPE_CHECKING:
@ -49,129 +41,6 @@ if TYPE_CHECKING:
from .._profile import Profiler from .._profile import Profiler
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
@njit(
# NOTE: need to construct this manually for readonly
# arrays, see https://github.com/numba/numba/issues/4511
# (
# types.Array(
# numba_ohlc_dtype,
# 1,
# 'C',
# readonly=True,
# ),
# int64,
# types.unicode_type,
# optional(float64),
# ),
nogil=True
)
def path_arrays_from_ohlc(
data: np.ndarray,
start: int64,
bar_gap: float64 = 0.43,
# index_field: str,
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
'''
Generate an array of lines objects from input ohlc data.
'''
size = int(data.shape[0] * 6)
# XXX: see this for why the dtype might have to be defined outside
# the routine.
# https://github.com/numba/numba/issues/4098#issuecomment-493914533
x = np.zeros(
shape=size,
dtype=float64,
)
y, c = x.copy(), x.copy()
# TODO: report bug for assert @
# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
for i, q in enumerate(data[start:], start):
# TODO: ask numba why this doesn't work..
# open, high, low, close, index = q[
# ['open', 'high', 'low', 'close', 'index']]
open = q['open']
high = q['high']
low = q['low']
close = q['close']
# index = float64(q[index_field])
index = float64(q['index'])
istart = i * 6
istop = istart + 6
# x,y detail the 6 points which connect all vertexes of a ohlc bar
x[istart:istop] = (
index - bar_gap,
index,
index,
index,
index,
index + bar_gap,
)
y[istart:istop] = (
open,
open,
low,
high,
close,
close,
)
# specifies that the first edge is never connected to the
# prior bars last edge thus providing a small "gap"/"space"
# between bars determined by ``bar_gap``.
c[istart:istop] = (1, 1, 1, 1, 1, 0)
return x, y, c
class IncrementalFormatter(msgspec.Struct): class IncrementalFormatter(msgspec.Struct):
''' '''
Incrementally updating, pre-path-graphics tracking, formatter. Incrementally updating, pre-path-graphics tracking, formatter.
@ -652,7 +521,6 @@ class OHLCBarsFmtr(IncrementalFormatter):
new_y_nd.shape, new_y_nd.shape,
) + np.array([-0.5, 0, 0, 0.5]) ) + np.array([-0.5, 0, 0, 0.5])
# TODO: can we drop this frame and just use the above? # TODO: can we drop this frame and just use the above?
def format_xy_nd_to_1d( def format_xy_nd_to_1d(
self, self,

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@ -0,0 +1,155 @@
# piker: trading gear for hackers
# Copyright (C) 2018-present Tyler Goodlet (in stewardship of piker0)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
Super fast ``QPainterPath`` generation related operator routines.
"""
import numpy as np
from numba import (
# types,
njit,
float64,
int64,
# optional,
)
# from ._source import numba_ohlc_dtype
from ._compression import (
ds_m4,
)
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
@njit(
# NOTE: need to construct this manually for readonly
# arrays, see https://github.com/numba/numba/issues/4511
# (
# types.Array(
# numba_ohlc_dtype,
# 1,
# 'C',
# readonly=True,
# ),
# int64,
# types.unicode_type,
# optional(float64),
# ),
nogil=True
)
def path_arrays_from_ohlc(
data: np.ndarray,
start: int64,
bar_gap: float64 = 0.43,
# index_field: str,
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
'''
Generate an array of lines objects from input ohlc data.
'''
size = int(data.shape[0] * 6)
# XXX: see this for why the dtype might have to be defined outside
# the routine.
# https://github.com/numba/numba/issues/4098#issuecomment-493914533
x = np.zeros(
shape=size,
dtype=float64,
)
y, c = x.copy(), x.copy()
# TODO: report bug for assert @
# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
for i, q in enumerate(data[start:], start):
# TODO: ask numba why this doesn't work..
# open, high, low, close, index = q[
# ['open', 'high', 'low', 'close', 'index']]
open = q['open']
high = q['high']
low = q['low']
close = q['close']
# index = float64(q[index_field])
index = float64(q['index'])
istart = i * 6
istop = istart + 6
# x,y detail the 6 points which connect all vertexes of a ohlc bar
x[istart:istop] = (
index - bar_gap,
index,
index,
index,
index,
index + bar_gap,
)
y[istart:istop] = (
open,
open,
low,
high,
close,
close,
)
# specifies that the first edge is never connected to the
# prior bars last edge thus providing a small "gap"/"space"
# between bars determined by ``bar_gap``.
c[istart:istop] = (1, 1, 1, 1, 1, 0)
return x, y, c

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@ -42,8 +42,8 @@ from ..data._formatters import (
OHLCBarsFmtr, # Plain OHLC renderer OHLCBarsFmtr, # Plain OHLC renderer
OHLCBarsAsCurveFmtr, # OHLC converted to line OHLCBarsAsCurveFmtr, # OHLC converted to line
StepCurveFmtr, # "step" curve (like for vlm) StepCurveFmtr, # "step" curve (like for vlm)
xy_downsample,
) )
from ..data._pathops import xy_downsample
from .._profile import ( from .._profile import (
pg_profile_enabled, pg_profile_enabled,
# ms_slower_then, # ms_slower_then,