Move (unused) path gen routines to `.ui._pathops`
Tyler Goodlet
parent
9052ed5ddf
commit
7124a131dd
|
|
@ -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(
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
Loading…
Reference in New Issue