130 lines
3.9 KiB
Python
130 lines
3.9 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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import numpy as np
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from numba import (
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jit, float64, optional, int64,
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)
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def downsample(
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x: np.ndarray,
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y: np.ndarray,
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bins: int,
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method: str = 'peak',
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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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The "peak" method is originally copied verbatim from
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``pyqtgraph.PlotDataItem.getDisplayDataset()``.
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'''
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# py3.10 syntax
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match method:
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case 'peak':
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ds = bins
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n = len(x) // ds
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x1 = np.empty((n, 2))
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# start of x-values; try to select a somewhat centered point
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stx = ds//2
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x1[:] = x[stx:stx+n*ds:ds, np.newaxis]
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x = x1.reshape(n*2)
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y1 = np.empty((n, 2))
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y2 = y[:n*ds].reshape((n, ds))
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y1[:, 0] = y2.max(axis=1)
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y1[:, 1] = y2.min(axis=1)
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y = y1.reshape(n*2)
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case '4px':
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# Ex. 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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def build_subchart(
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self,
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subchart,
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width, # width of screen?
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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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# 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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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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)
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return x, y
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@jit(nopython=True)
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def subset_by_x(
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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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x_start: int,
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step: float,
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) -> int:
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count = len(xs)
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# nbins = len(bins)
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bincount = 0
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x_left = start
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# Find the first bin
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while xs[0] >= 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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for i in range(count):
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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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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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return bincount
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