Add (ostensibly) working first attempt at M4 algo
All the refs are in the comments and original sample code from infinite has been reworked to expect the input x/y arrays to already be sliced (though we can later support passing in the start-end indexes if desired). The new routines are `ds_m4()` the python top level API and `_m4()` the fast `numba` implementation.marketstore_backup
Tyler Goodlet
parent
638caada4e
commit
ae36d2cecf
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@ -19,13 +19,13 @@ 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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# from typing import Optional
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from typing import Optional
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import numpy as np
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# from numpy.lib.recfunctions import structured_to_unstructured
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from numba import (
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jit,
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float64, optional, int64,
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# float64, optional, int64,
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)
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from ..log import get_logger
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@ -36,7 +36,7 @@ log = get_logger(__name__)
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def hl2mxmn(
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ohlc: np.ndarray,
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downsample_by: int = 0,
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# downsample_by: int = 0,
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) -> np.ndarray:
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'''
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@ -61,17 +61,19 @@ def hl2mxmn(
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trace_hl(hls, mxmn, x, index[0])
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x = x + index[0]
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if downsample_by < 2:
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return mxmn, x
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dsx, dsy = downsample(
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y=mxmn,
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x=x,
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bins=downsample_by,
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)
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log.info(f'downsampling by {downsample_by}')
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print(f'downsampling by {downsample_by}')
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return dsy, dsx
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# if downsample_by < 2:
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# return mxmn, x
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# dsx, dsy = downsample(
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# y=mxmn,
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# x=x,
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# bins=downsample_by,
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# )
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# log.info(f'downsampling by {downsample_by}')
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# print(f'downsampling by {downsample_by}')
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# return dsy, dsx
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@jit(
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@ -129,8 +131,11 @@ def downsample(
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x: np.ndarray,
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y: np.ndarray,
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bins: int = 2,
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method: str = 'peak',
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**kwargs,
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Downsample x/y data for lesser curve graphics gen.
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@ -142,7 +147,6 @@ def downsample(
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# py3.10 syntax
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match method:
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case 'peak':
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# breakpoint()
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if bins < 2:
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log.warning('No downsampling taking place?')
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@ -164,11 +168,37 @@ def downsample(
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return x, y
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# TODO: this algo from infinite, see
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# https://github.com/pikers/piker/issues/109
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case 'infinite_4px':
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case 'm4':
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return ds_m4(x, y, kwargs['px_width'])
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# Ex. from infinite on downsampling viewable graphics.
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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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# this is the width of the data in view
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# in display-device-local pixel units.
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px_width: int,
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factor: Optional[int] = None,
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Downsample using the M4 algorithm.
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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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@ -176,75 +206,104 @@ def downsample(
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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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def build_subchart(
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self,
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subchart,
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width, # width of screen in pxs?
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chart_type,
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lower, # x start?
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upper, # x end?
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xvals,
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yvals
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):
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pts_per_pixel = len(xvals) / width
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if pts_per_pixel > 1:
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assert px_width > 1 # width of screen in pxs?
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# this is mutated in-place
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data = np.zeros((width, 4), yvals.dtype)
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bins = np.zeros(width, xvals.dtype)
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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 = 0 # x index start
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x_end = len(x) # x index end
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nb = subset_by_x(
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xvals,
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yvals,
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bins,
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data,
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lower,
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# this is scaling the x-range by
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# the width of the screen?
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(upper-lower)/float(width),
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# uppx: units-per-pixel
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pts_per_pixel = len(x) / px_width
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print(f'UPPX: {pts_per_pixel}')
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# ratio of indexed x-value to width of raster in pixels.
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if factor is None:
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w = (x_end-x_start) / float(px_width)
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print(f' pts/pxs = {w}')
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else:
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w = factor
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# these are pre-allocated and mutated by ``numba``
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# code in-place.
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ds = np.zeros((px_width, 4), y.dtype)
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i_win = np.zeros(px_width, x.dtype)
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# call into ``numba``
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nb = _m4(
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x,
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y,
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i_win,
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ds,
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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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print(f'downsampled to {nb} bins')
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return x, y
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return i_win, ds.flatten()
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@jit(nopython=True)
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def subset_by_x(
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@jit(
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nopython=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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bins: np.ndarray,
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data: np.ndarray,
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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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ds: np.ndarray,
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x_start: int,
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step: float,
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) -> int:
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# nbins = len(bins)
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count = len(xs)
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# nbins = len(i_win)
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# count = len(xs)
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bincount = 0
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x_left = x_start
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# Find the first bin
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# Find the first window's starting index which *includes* the
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# first value in the x-domain array.
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# (this allows passing in an array which is indexed (and thus smaller then)
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# the ``x_start`` value normally passed in - say if you normally
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# want to start 0-indexed.
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first = xs[0]
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while first >= x_left + step:
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x_left += step
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bins[bincount] = x_left
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data[bincount] = ys[0]
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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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i_win[bincount] = x_left
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# set all y-values to the first value passed in.
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ds[bincount] = ys[0]
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for i in range(count):
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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: # Interval is [bin, bin+1)
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data[bincount, 1] = min(y, data[bincount, 1])
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data[bincount, 2] = max(y, data[bincount, 2])
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data[bincount, 3] = y
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if x < x_left + step: # the current window "step" is [bin, bin+1)
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ds[bincount, 1] = min(y, ds[bincount, 1])
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ds[bincount, 2] = max(y, ds[bincount, 2])
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ds[bincount, 3] = y
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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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bins[bincount] = x_left
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data[bincount] = y
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i_win[bincount] = x_left
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ds[bincount] = y
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return bincount
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Reference in New Issue