Add OHLC to m4 line converters
Helpers to quickly convert ohlc struct-array sequences into lines for consumption by the m4 downsampler. Strip trailing zero entries from the `ds_m4()` output if found (avoids lines back to origin).big_data_lines
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@ -22,7 +22,7 @@ limits on the display device.
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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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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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@ -174,6 +174,51 @@ def downsample(
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return ds_m4(x, y, kwargs['px_width'])
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def ohlc_flatten(
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ohlc: np.ndarray,
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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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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=4*len(ohlc),
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)
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return x, flat
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def ohlc_to_m4_line(
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ohlc: np.ndarray,
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px_width: int,
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Convert an OHLC struct-array to a m4 downsampled 1-d array.
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'''
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xpts, flat = ohlc_flatten(ohlc)
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bins, x, y = ds_m4(
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xpts,
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flat,
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px_width=px_width * 16,
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)
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array([-0.43, 0, 0, 0.43])).flatten()
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y = y.flatten()
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return x, y
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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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@ -233,7 +278,7 @@ def ds_m4(
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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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pts_per_pixel, r = divmod(len(x), frames)
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if r:
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frames += 1
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@ -257,6 +302,12 @@ def ds_m4(
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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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i_win = i_win[i_win != 0]
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y_out = y_out[:i_win.size]
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return nb, i_win, y_out
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