Move (unused) path gen routines to `.ui._pathops`
Tyler Goodlet
parent
eda7b7f3c7
commit
87e47f9eed
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@ -23,9 +23,8 @@ import math
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from typing import Optional
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import numpy as np
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from numpy.lib import recfunctions as rfn
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from numba import (
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jit,
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njit,
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# float64, optional, int64,
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)
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@ -35,114 +34,6 @@ from ..log import get_logger
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log = get_logger(__name__)
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def hl2mxmn(
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ohlc: np.ndarray,
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index_field: str = 'index',
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) -> np.ndarray:
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'''
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Convert a OHLC struct-array containing 'high'/'low' columns
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to a "joined" max/min 1-d array.
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'''
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index = ohlc[index_field]
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hls = ohlc[[
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'low',
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'high',
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]]
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mxmn = np.empty(2*hls.size, dtype=np.float64)
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x = np.empty(2*hls.size, dtype=np.float64)
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trace_hl(hls, mxmn, x, index[0])
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x = x + index[0]
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return mxmn, x
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@jit(
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# TODO: the type annots..
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# float64[:](float64[:],),
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nopython=True,
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)
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def trace_hl(
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hl: 'np.ndarray',
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out: np.ndarray,
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x: np.ndarray,
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start: int,
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# the "offset" values in the x-domain which
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# place the 2 output points around each ``int``
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# master index.
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margin: float = 0.43,
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) -> None:
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'''
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"Trace" the outline of the high-low values of an ohlc sequence
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as a line such that the maximum deviation (aka disperaion) between
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bars if preserved.
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This routine is expected to modify input arrays in-place.
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'''
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last_l = hl['low'][0]
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last_h = hl['high'][0]
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for i in range(hl.size):
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row = hl[i]
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l, h = row['low'], row['high']
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up_diff = h - last_l
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down_diff = last_h - l
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if up_diff > down_diff:
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out[2*i + 1] = h
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out[2*i] = last_l
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else:
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out[2*i + 1] = l
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out[2*i] = last_h
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last_l = l
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last_h = h
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x[2*i] = int(i) - margin
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x[2*i + 1] = int(i) + margin
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return out
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def ohlc_flatten(
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ohlc: np.ndarray,
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use_mxmn: bool = True,
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index_field: str = 'index',
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Convert an OHLCV struct-array into a flat ready-for-line-plotting
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1-d array that is 4 times the size with x-domain values distributed
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evenly (by 0.5 steps) over each index.
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'''
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index = ohlc[index_field]
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if use_mxmn:
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# traces a line optimally over highs to lows
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# using numba. NOTE: pretty sure this is faster
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# and looks about the same as the below output.
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flat, x = hl2mxmn(ohlc)
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else:
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flat = rfn.structured_to_unstructured(
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ohlc[['open', 'high', 'low', 'close']]
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).flatten()
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x = np.linspace(
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start=index[0] - 0.5,
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stop=index[-1] + 0.5,
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num=len(flat),
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)
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return x, flat
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def ds_m4(
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x: np.ndarray,
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y: np.ndarray,
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@ -263,8 +154,7 @@ def ds_m4(
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return nb, x_out, y_out, ymn, ymx
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@jit(
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nopython=True,
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@njit(
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nogil=True,
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)
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def _m4(
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@ -18,6 +18,7 @@ Super fast ``QPainterPath`` generation related operator routines.
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"""
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import numpy as np
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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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@ -153,3 +154,110 @@ def path_arrays_from_ohlc(
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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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def hl2mxmn(
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ohlc: np.ndarray,
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index_field: str = 'index',
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) -> np.ndarray:
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'''
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Convert a OHLC struct-array containing 'high'/'low' columns
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to a "joined" max/min 1-d array.
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'''
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index = ohlc[index_field]
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hls = ohlc[[
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'low',
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'high',
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]]
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mxmn = np.empty(2*hls.size, dtype=np.float64)
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x = np.empty(2*hls.size, dtype=np.float64)
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trace_hl(hls, mxmn, x, index[0])
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x = x + index[0]
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return mxmn, x
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@njit(
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# TODO: the type annots..
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# float64[:](float64[:],),
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)
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def trace_hl(
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hl: 'np.ndarray',
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out: np.ndarray,
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x: np.ndarray,
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start: int,
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# the "offset" values in the x-domain which
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# place the 2 output points around each ``int``
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# master index.
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margin: float = 0.43,
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) -> None:
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'''
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"Trace" the outline of the high-low values of an ohlc sequence
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as a line such that the maximum deviation (aka disperaion) between
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bars if preserved.
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This routine is expected to modify input arrays in-place.
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'''
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last_l = hl['low'][0]
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last_h = hl['high'][0]
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for i in range(hl.size):
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row = hl[i]
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l, h = row['low'], row['high']
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up_diff = h - last_l
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down_diff = last_h - l
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if up_diff > down_diff:
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out[2*i + 1] = h
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out[2*i] = last_l
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else:
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out[2*i + 1] = l
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out[2*i] = last_h
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last_l = l
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last_h = h
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x[2*i] = int(i) - margin
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x[2*i + 1] = int(i) + margin
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return out
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def ohlc_flatten(
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ohlc: np.ndarray,
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use_mxmn: bool = True,
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index_field: str = 'index',
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Convert an OHLCV struct-array into a flat ready-for-line-plotting
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1-d array that is 4 times the size with x-domain values distributed
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evenly (by 0.5 steps) over each index.
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'''
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index = ohlc[index_field]
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if use_mxmn:
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# traces a line optimally over highs to lows
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# using numba. NOTE: pretty sure this is faster
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# and looks about the same as the below output.
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flat, x = hl2mxmn(ohlc)
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else:
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flat = rfn.structured_to_unstructured(
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ohlc[['open', 'high', 'low', 'close']]
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).flatten()
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x = np.linspace(
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start=index[0] - 0.5,
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stop=index[-1] + 0.5,
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num=len(flat),
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)
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return x, flat
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