Move (unused) path gen routines to `.ui._pathops`

multichartz
Tyler Goodlet 2022-11-30 19:14:36 -05:00
parent 9c69636388
commit 4a9896a29d
2 changed files with 110 additions and 112 deletions

View File

@ -23,9 +23,8 @@ import math
from typing import Optional
import numpy as np
from numpy.lib import recfunctions as rfn
from numba import (
jit,
njit,
# float64, optional, int64,
)
@ -35,114 +34,6 @@ from ..log import get_logger
log = get_logger(__name__)
def hl2mxmn(
ohlc: np.ndarray,
index_field: str = 'index',
) -> np.ndarray:
'''
Convert a OHLC struct-array containing 'high'/'low' columns
to a "joined" max/min 1-d array.
'''
index = ohlc[index_field]
hls = ohlc[[
'low',
'high',
]]
mxmn = np.empty(2*hls.size, dtype=np.float64)
x = np.empty(2*hls.size, dtype=np.float64)
trace_hl(hls, mxmn, x, index[0])
x = x + index[0]
return mxmn, x
@jit(
# TODO: the type annots..
# float64[:](float64[:],),
nopython=True,
)
def trace_hl(
hl: 'np.ndarray',
out: np.ndarray,
x: np.ndarray,
start: int,
# the "offset" values in the x-domain which
# place the 2 output points around each ``int``
# master index.
margin: float = 0.43,
) -> None:
'''
"Trace" the outline of the high-low values of an ohlc sequence
as a line such that the maximum deviation (aka disperaion) between
bars if preserved.
This routine is expected to modify input arrays in-place.
'''
last_l = hl['low'][0]
last_h = hl['high'][0]
for i in range(hl.size):
row = hl[i]
l, h = row['low'], row['high']
up_diff = h - last_l
down_diff = last_h - l
if up_diff > down_diff:
out[2*i + 1] = h
out[2*i] = last_l
else:
out[2*i + 1] = l
out[2*i] = last_h
last_l = l
last_h = h
x[2*i] = int(i) - margin
x[2*i + 1] = int(i) + margin
return out
def ohlc_flatten(
ohlc: np.ndarray,
use_mxmn: bool = True,
index_field: str = 'index',
) -> tuple[np.ndarray, np.ndarray]:
'''
Convert an OHLCV struct-array into a flat ready-for-line-plotting
1-d array that is 4 times the size with x-domain values distributed
evenly (by 0.5 steps) over each index.
'''
index = ohlc[index_field]
if use_mxmn:
# traces a line optimally over highs to lows
# using numba. NOTE: pretty sure this is faster
# and looks about the same as the below output.
flat, x = hl2mxmn(ohlc)
else:
flat = rfn.structured_to_unstructured(
ohlc[['open', 'high', 'low', 'close']]
).flatten()
x = np.linspace(
start=index[0] - 0.5,
stop=index[-1] + 0.5,
num=len(flat),
)
return x, flat
def ds_m4(
x: np.ndarray,
y: np.ndarray,
@ -263,8 +154,7 @@ def ds_m4(
return nb, x_out, y_out, ymn, ymx
@jit(
nopython=True,
@njit(
nogil=True,
)
def _m4(

View File

@ -18,6 +18,7 @@ Super fast ``QPainterPath`` generation related operator routines.
"""
import numpy as np
from numpy.lib import recfunctions as rfn
from numba import (
# types,
njit,
@ -153,3 +154,110 @@ def path_arrays_from_ohlc(
c[istart:istop] = (1, 1, 1, 1, 1, 0)
return x, y, c
def hl2mxmn(
ohlc: np.ndarray,
index_field: str = 'index',
) -> np.ndarray:
'''
Convert a OHLC struct-array containing 'high'/'low' columns
to a "joined" max/min 1-d array.
'''
index = ohlc[index_field]
hls = ohlc[[
'low',
'high',
]]
mxmn = np.empty(2*hls.size, dtype=np.float64)
x = np.empty(2*hls.size, dtype=np.float64)
trace_hl(hls, mxmn, x, index[0])
x = x + index[0]
return mxmn, x
@njit(
# TODO: the type annots..
# float64[:](float64[:],),
)
def trace_hl(
hl: 'np.ndarray',
out: np.ndarray,
x: np.ndarray,
start: int,
# the "offset" values in the x-domain which
# place the 2 output points around each ``int``
# master index.
margin: float = 0.43,
) -> None:
'''
"Trace" the outline of the high-low values of an ohlc sequence
as a line such that the maximum deviation (aka disperaion) between
bars if preserved.
This routine is expected to modify input arrays in-place.
'''
last_l = hl['low'][0]
last_h = hl['high'][0]
for i in range(hl.size):
row = hl[i]
l, h = row['low'], row['high']
up_diff = h - last_l
down_diff = last_h - l
if up_diff > down_diff:
out[2*i + 1] = h
out[2*i] = last_l
else:
out[2*i + 1] = l
out[2*i] = last_h
last_l = l
last_h = h
x[2*i] = int(i) - margin
x[2*i + 1] = int(i) + margin
return out
def ohlc_flatten(
ohlc: np.ndarray,
use_mxmn: bool = True,
index_field: str = 'index',
) -> tuple[np.ndarray, np.ndarray]:
'''
Convert an OHLCV struct-array into a flat ready-for-line-plotting
1-d array that is 4 times the size with x-domain values distributed
evenly (by 0.5 steps) over each index.
'''
index = ohlc[index_field]
if use_mxmn:
# traces a line optimally over highs to lows
# using numba. NOTE: pretty sure this is faster
# and looks about the same as the below output.
flat, x = hl2mxmn(ohlc)
else:
flat = rfn.structured_to_unstructured(
ohlc[['open', 'high', 'low', 'close']]
).flatten()
x = np.linspace(
start=index[0] - 0.5,
stop=index[-1] + 0.5,
num=len(flat),
)
return x, flat