Fix x-range -> # of frames calculation
Obviously determining the x-range from indices was wrong and was the reason for the incorrect (downsampled) output size XD. Instead correctly determine the x range and start value from the *values of* the input x-array. Pretty sure this makes the implementation nearly production ready. Relates to #109big_data_lines
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4d4f745918
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d02b1a17ad
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@ -19,7 +19,7 @@ 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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import math
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import numpy as np
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# from numpy.lib.recfunctions import structured_to_unstructured
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@ -141,7 +141,9 @@ def downsample(
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Downsample x/y data for lesser curve graphics gen.
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The "peak" method is originally copied verbatim from
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``pyqtgraph.PlotDataItem.getDisplayDataset()``.
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``pyqtgraph.PlotDataItem.getDisplayDataset()`` which gets
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all credit, though we will likely drop this in favor of the M4
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algo below.
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'''
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# py3.10 syntax
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@ -180,14 +182,13 @@ def ds_m4(
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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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) -> tuple[int, 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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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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@ -212,24 +213,34 @@ def ds_m4(
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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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x_start = x[0] # x value start/lowest in domain
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x_end = x[-1] # x end value/highest in domain
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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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# XXX: always round up on the input pixels
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px_width = math.ceil(px_width)
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x_range = x_end - x_start
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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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# this is more or less, uppx: units-per-pixel.
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w = x_range / float(px_width)
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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 = px_width
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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(len(x), px_width)
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if r:
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frames += 1
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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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y_out = np.zeros((frames, 4), y.dtype)
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i_win = np.zeros(frames, x.dtype)
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# call into ``numba``
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nb = _m4(
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@ -237,7 +248,7 @@ def ds_m4(
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y,
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i_win,
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ds,
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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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@ -245,9 +256,8 @@ def ds_m4(
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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 i_win, ds.flatten()
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return nb, i_win, y_out
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@jit(
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@ -275,13 +285,11 @@ def _m4(
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bincount = 0
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x_left = x_start
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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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# 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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