344 lines
9.0 KiB
Python
344 lines
9.0 KiB
Python
# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship for pikers)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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'''
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Graphics related downsampling routines for compressing to pixel
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limits on the display device.
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'''
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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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# float64, optional, int64,
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)
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from ..log import get_logger
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log = get_logger(__name__)
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def hl2mxmn(ohlc: np.ndarray) -> 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']
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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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) -> 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']
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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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# units-per-pixel-x(dimension)
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uppx: float,
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# XXX: troll zone / easter egg..
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# want to mess with ur pal, pass in the actual
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# pixel width here instead of uppx-proper (i.e. pass
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# in our ``pg.GraphicsObject`` derivative's ``.px_width()``
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# gto mega-trip-out ur bud). Hint, it used to be implemented
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# (wrongly) using "pixel width", so check the git history ;)
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xrange: Optional[float] = None,
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) -> tuple[int, np.ndarray, np.ndarray]:
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'''
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Downsample using the M4 algorithm.
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This is more or less an OHLC style sampling of a line-style series.
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'''
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# NOTE: this method is a so called "visualization driven data
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# aggregation" approach. It gives error-free line chart
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# downsampling, see
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# further scientific paper resources:
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# - http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
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# - http://www.vldb.org/2014/program/papers/demo/p997-jugel.pdf
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# Details on implementation of this algo are based in,
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# https://github.com/pikers/piker/issues/109
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# XXX: from infinite on downsampling viewable graphics:
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# "one thing i remembered about the binning - if you are
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# picking a range within your timeseries the start and end bin
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# should be one more bin size outside the visual range, then
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# you get better visual fidelity at the edges of the graph"
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# "i didn't show it in the sample code, but it's accounted for
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# in the start and end indices and number of bins"
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# should never get called unless actually needed
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assert uppx > 1
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# NOTE: if we didn't pre-slice the data to downsample
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# you could in theory pass these as the slicing params,
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# do we care though since we can always just pre-slice the
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# input?
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x_start = x[0] # x value start/lowest in domain
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if xrange is None:
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x_end = x[-1] # x end value/highest in domain
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xrange = (x_end - x_start)
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# XXX: always round up on the input pixels
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# lnx = len(x)
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# uppx *= max(4 / (1 + math.log(uppx, 2)), 1)
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pxw = math.ceil(xrange / uppx)
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# scale up the frame "width" directly with uppx
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w = uppx
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# ensure we make more then enough
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# frames (windows) for the output pixel
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frames = pxw
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# if we have more and then exact integer's
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# (uniform quotient output) worth of datum-domain-points
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# per windows-frame, add one more window to ensure
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# we have room for all output down-samples.
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pts_per_pixel, r = divmod(xrange, frames)
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if r:
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# while r:
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frames += 1
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pts_per_pixel, r = divmod(xrange, frames)
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# print(
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# f'uppx: {uppx}\n'
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# f'xrange: {xrange}\n'
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# f'pxw: {pxw}\n'
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# f'frames: {frames}\n'
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# )
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assert frames >= (xrange / uppx)
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# call into ``numba``
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(
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nb,
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x_out,
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y_out,
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ymn,
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ymx,
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) = _m4(
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x,
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y,
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frames,
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# TODO: see func below..
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# x_out,
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# y_out,
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# first index in x data to start at
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x_start,
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# window size for each "frame" of data to downsample (normally
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# scaled by the ratio of pixels on screen to data in x-range).
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w,
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)
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# filter out any overshoot in the input allocation arrays by
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# removing zero-ed tail entries which should start at a certain
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# index.
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x_out = x_out[x_out != 0]
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y_out = y_out[:x_out.size]
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# print(f'M4 output ymn, ymx: {ymn},{ymx}')
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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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nogil=True,
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)
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def _m4(
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xs: np.ndarray,
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ys: np.ndarray,
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frames: int,
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# TODO: using this approach, having the ``.zeros()`` alloc lines
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# below in pure python, there were segs faults and alloc crashes..
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# we might need to see how it behaves with shm arrays and consider
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# allocating them once at startup?
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# pre-alloc array of x indices mapping to the start
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# of each window used for downsampling in y.
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# i_win: np.ndarray,
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# pre-alloc array of output downsampled y values
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# y_out: np.ndarray,
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x_start: int,
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step: float,
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) -> tuple[
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int,
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Implementation of the m4 algorithm in ``numba``:
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http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
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'''
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# these are pre-allocated and mutated by ``numba``
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# code in-place.
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y_out = np.zeros((frames, 4), ys.dtype)
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x_out = np.zeros(frames, xs.dtype)
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bincount = 0
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x_left = x_start
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# Find the first window's starting value which *includes* the
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# first value in the x-domain array, i.e. the first
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# "left-side-of-window" **plus** the downsampling step,
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# creates a window which includes the first x **value**.
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while xs[0] >= x_left + step:
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x_left += step
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# set all bins in the left-most entry to the starting left-most x value
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# (aka a row broadcast).
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x_out[bincount] = x_left
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# set all y-values to the first value passed in.
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y_out[bincount] = ys[0]
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# full input y-data mx and mn
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mx: float = -np.inf
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mn: float = np.inf
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# compute OHLC style max / min values per window sized x-frame.
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for i in range(len(xs)):
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x = xs[i]
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y = ys[i]
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if x < x_left + step: # the current window "step" is [bin, bin+1)
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ymn = y_out[bincount, 1] = min(y, y_out[bincount, 1])
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ymx = y_out[bincount, 2] = max(y, y_out[bincount, 2])
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y_out[bincount, 3] = y
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mx = max(mx, ymx)
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mn = min(mn, ymn)
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else:
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# Find the next bin
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while x >= x_left + step:
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x_left += step
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bincount += 1
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x_out[bincount] = x_left
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y_out[bincount] = y
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return bincount, x_out, y_out, mn, mx
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