Move all pre-path formatting routines to `._pathops`, proto formatter type

pre_viz_calls
Tyler Goodlet 2022-11-22 13:20:44 -05:00
parent 18857ac077
commit 0945033d38
2 changed files with 187 additions and 137 deletions

View File

@ -31,7 +31,6 @@ from typing import (
import msgspec import msgspec
import numpy as np import numpy as np
from numpy.lib import recfunctions as rfn
import pyqtgraph as pg import pyqtgraph as pg
from PyQt5.QtGui import QPainterPath from PyQt5.QtGui import QPainterPath
from PyQt5.QtCore import QLineF from PyQt5.QtCore import QLineF
@ -44,9 +43,21 @@ from .._profile import (
# ms_slower_then, # ms_slower_then,
) )
from ._pathops import ( from ._pathops import (
by_index_and_key,
# Plain OHLC renderer
gen_ohlc_qpath, gen_ohlc_qpath,
# OHLC -> line renderer
ohlc_to_line, ohlc_to_line,
update_ohlc_to_line,
ohlc_flat_to_xy,
# step curve renderer
to_step_format, to_step_format,
update_step_xy,
step_to_xy,
xy_downsample, xy_downsample,
) )
from ._ohlc import ( from ._ohlc import (
@ -75,55 +86,6 @@ log = get_logger(__name__)
# flows: dict[str, np.ndarray] = {} # flows: dict[str, np.ndarray] = {}
def update_ohlc_to_line(
src_shm: ShmArray,
array_key: str,
src_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
fields = ['open', 'high', 'low', 'close']
return (
rfn.structured_to_unstructured(src_update[fields]),
slc,
)
def ohlc_flat_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# TODO: in the case of an existing ``.update_xy()``
# should we be passing in array as an xy arrays tuple?
# 2 more datum-indexes to capture zero at end
x_flat = r.x_data[r._xy_first:r._xy_last]
y_flat = r.y_data[r._xy_first:r._xy_last]
# slice to view
ivl, ivr = vr
x_iv_flat = x_flat[ivl:ivr]
y_iv_flat = y_flat[ivl:ivr]
# reshape to 1d for graphics rendering
y_iv = y_iv_flat.reshape(-1)
x_iv = x_iv_flat.reshape(-1)
return x_iv, y_iv, 'all'
def render_baritems( def render_baritems(
flow: Flow, flow: Flow,
graphics: BarItems, graphics: BarItems,
@ -253,77 +215,6 @@ def render_baritems(
) )
def update_step_xy(
src_shm: ShmArray,
array_key: str,
y_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
# for a step curve we slice from one datum prior
# to the current "update slice" to get the previous
# "level".
if is_append:
start = max(last - 1, 0)
end = src_shm._last.value
new_y = src_shm._array[start:end][array_key]
slc = slice(start, end)
else:
new_y = y_update
return (
np.broadcast_to(
new_y[:, None], (new_y.size, 2),
),
slc,
)
def step_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# 2 more datum-indexes to capture zero at end
x_step = r.x_data[r._xy_first:r._xy_last+2]
y_step = r.y_data[r._xy_first:r._xy_last+2]
lasts = array[['index', array_key]]
last = lasts[array_key][-1]
y_step[-1] = last
# slice out in-view data
ivl, ivr = vr
ys_iv = y_step[ivl:ivr+1]
xs_iv = x_step[ivl:ivr+1]
# flatten to 1d
y_iv = ys_iv.reshape(ys_iv.size)
x_iv = xs_iv.reshape(xs_iv.size)
# print(
# f'ys_iv : {ys_iv[-s:]}\n'
# f'y_iv: {y_iv[-s:]}\n'
# f'xs_iv: {xs_iv[-s:]}\n'
# f'x_iv: {x_iv[-s:]}\n'
# )
return x_iv, y_iv, 'all'
class Flow(msgspec.Struct): # , frozen=True): class Flow(msgspec.Struct): # , frozen=True):
''' '''
(Financial Signal-)Flow compound type which wraps a real-time (Financial Signal-)Flow compound type which wraps a real-time
@ -786,20 +677,6 @@ class Flow(msgspec.Struct): # , frozen=True):
g.update() g.update()
def by_index_and_key(
renderer: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
return array['index'], array[array_key], 'all'
class Renderer(msgspec.Struct): class Renderer(msgspec.Struct):
flow: Flow flow: Flow
@ -1210,7 +1087,7 @@ class Renderer(msgspec.Struct):
and do_append and do_append
and not should_redraw and not should_redraw
): ):
# print(f'{array_key} append len: {append_length}') print(f'{array_key} append len: {append_length}')
new_x = x_out[-append_length - 2:] # slice_to_head] new_x = x_out[-append_length - 2:] # slice_to_head]
new_y = y_out[-append_length - 2:] # slice_to_head] new_y = y_out[-append_length - 2:] # slice_to_head]
profiler('sliced append path') profiler('sliced append path')
@ -1236,6 +1113,7 @@ class Renderer(msgspec.Struct):
profiler('generated append qpath') profiler('generated append qpath')
if use_fpath: if use_fpath:
print(f'{self.flow.name}: FAST PATH')
# an attempt at trying to make append-updates faster.. # an attempt at trying to make append-updates faster..
if fast_path is None: if fast_path is None:
fast_path = append_path fast_path = append_path

View File

@ -19,10 +19,12 @@ Super fast ``QPainterPath`` generation related operator routines.
""" """
from __future__ import annotations from __future__ import annotations
from typing import ( from typing import (
# Optional, Optional,
Callable,
TYPE_CHECKING, TYPE_CHECKING,
) )
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 njit, float64, int64 # , optional from numba import njit, float64, int64 # , optional
@ -42,6 +44,56 @@ if TYPE_CHECKING:
from ._flows import Renderer from ._flows import Renderer
def by_index_and_key(
renderer: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
return array['index'], array[array_key], 'all'
class IncrementalFormatter(msgspec.Struct):
shm: ShmArray
# 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
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
x_nd: Optional[np.ndarray] = None
y_nd: Optional[np.ndarray] = None
x_1d: Optional[np.ndarray] = None
y_1d: Optional[np.ndarray] = None
# indexes which slice into the above arrays (which are allocated
# based on source data shm input size) and allow retrieving
# incrementally updated data.
# _xy_first: int = 0
# _xy_last: int = 0
xy_nd_start: int = 0
xy_nd_end: int = 0
def xy_downsample( def xy_downsample(
x, x,
y, y,
@ -214,6 +266,55 @@ def ohlc_to_line(
) )
def update_ohlc_to_line(
src_shm: ShmArray,
array_key: str,
src_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
fields = ['open', 'high', 'low', 'close']
return (
rfn.structured_to_unstructured(src_update[fields]),
slc,
)
def ohlc_flat_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# TODO: in the case of an existing ``.update_xy()``
# should we be passing in array as an xy arrays tuple?
# 2 more datum-indexes to capture zero at end
x_flat = r.x_data[r._xy_first:r._xy_last]
y_flat = r.y_data[r._xy_first:r._xy_last]
# slice to view
ivl, ivr = vr
x_iv_flat = x_flat[ivl:ivr]
y_iv_flat = y_flat[ivl:ivr]
# reshape to 1d for graphics rendering
y_iv = y_iv_flat.reshape(-1)
x_iv = x_iv_flat.reshape(-1)
return x_iv, y_iv, 'all'
def to_step_format( def to_step_format(
shm: ShmArray, shm: ShmArray,
data_field: str, data_field: str,
@ -239,3 +340,74 @@ def to_step_format(
# start y at origin level # start y at origin level
y_out[0, 0] = 0 y_out[0, 0] = 0
return x_out, y_out return x_out, y_out
def update_step_xy(
src_shm: ShmArray,
array_key: str,
y_update: np.ndarray,
slc: slice,
ln: int,
first: int,
last: int,
is_append: bool,
) -> np.ndarray:
# for a step curve we slice from one datum prior
# to the current "update slice" to get the previous
# "level".
if is_append:
start = max(last - 1, 0)
end = src_shm._last.value
new_y = src_shm._array[start:end][array_key]
slc = slice(start, end)
else:
new_y = y_update
return (
np.broadcast_to(
new_y[:, None], (new_y.size, 2),
),
slc,
)
def step_to_xy(
r: Renderer,
array: np.ndarray,
array_key: str,
vr: tuple[int, int],
) -> tuple[
np.ndarray,
np.nd.array,
str,
]:
# 2 more datum-indexes to capture zero at end
x_step = r.x_data[r._xy_first:r._xy_last+2]
y_step = r.y_data[r._xy_first:r._xy_last+2]
lasts = array[['index', array_key]]
last = lasts[array_key][-1]
y_step[-1] = last
# slice out in-view data
ivl, ivr = vr
ys_iv = y_step[ivl:ivr+1]
xs_iv = x_step[ivl:ivr+1]
# flatten to 1d
y_iv = ys_iv.reshape(ys_iv.size)
x_iv = xs_iv.reshape(xs_iv.size)
# print(
# f'ys_iv : {ys_iv[-s:]}\n'
# f'y_iv: {y_iv[-s:]}\n'
# f'xs_iv: {xs_iv[-s:]}\n'
# f'x_iv: {x_iv[-s:]}\n'
# )
return x_iv, y_iv, 'all'