251 lines
6.4 KiB
Python
251 lines
6.4 KiB
Python
# piker: trading gear for hackers
|
|
# Copyright (C) Tyler Goodlet (in stewardship for pikers)
|
|
|
|
# This program is free software: you can redistribute it and/or modify
|
|
# it under the terms of the GNU Affero General Public License as published by
|
|
# the Free Software Foundation, either version 3 of the License, or
|
|
# (at your option) any later version.
|
|
|
|
# This program is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# GNU Affero General Public License for more details.
|
|
|
|
# You should have received a copy of the GNU Affero General Public License
|
|
# along with this program. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
'''
|
|
Graphics related downsampling routines for compressing to pixel
|
|
limits on the display device.
|
|
|
|
'''
|
|
# from typing import Optional
|
|
|
|
import numpy as np
|
|
# from numpy.lib.recfunctions import structured_to_unstructured
|
|
from numba import (
|
|
jit,
|
|
float64, optional, int64,
|
|
)
|
|
|
|
from ..log import get_logger
|
|
|
|
|
|
log = get_logger(__name__)
|
|
|
|
|
|
def hl2mxmn(
|
|
ohlc: np.ndarray,
|
|
downsample_by: int = 0,
|
|
|
|
) -> np.ndarray:
|
|
'''
|
|
Convert a OHLC struct-array containing 'high'/'low' columns
|
|
to a "joined" max/min 1-d array.
|
|
|
|
'''
|
|
index = ohlc['index']
|
|
hls = ohlc[[
|
|
'low',
|
|
'high',
|
|
]]
|
|
|
|
# XXX: don't really need this any more since we implemented
|
|
# the "tracer" routine, `numba`-style..
|
|
# create a "max and min" sequence from ohlc datums
|
|
# hl2d = structured_to_unstructured(hls)
|
|
# hl1d = hl2d.flatten()
|
|
|
|
mxmn = np.empty(2*hls.size, dtype=np.float64)
|
|
x = np.empty(2*hls.size, dtype=np.float64)
|
|
trace_hl(hls, mxmn, x, index[0])
|
|
x = x + index[0]
|
|
|
|
if downsample_by < 2:
|
|
return mxmn, x
|
|
|
|
dsx, dsy = downsample(
|
|
y=mxmn,
|
|
x=x,
|
|
bins=downsample_by,
|
|
)
|
|
log.info(f'downsampling by {downsample_by}')
|
|
print(f'downsampling by {downsample_by}')
|
|
return dsy, dsx
|
|
|
|
|
|
@jit(
|
|
# TODO: the type annots..
|
|
# float64[:](float64[:],),
|
|
nopython=True,
|
|
)
|
|
def trace_hl(
|
|
hl: 'np.ndarray',
|
|
out: np.ndarray,
|
|
x: np.ndarray,
|
|
start: int,
|
|
|
|
# the "offset" values in the x-domain which
|
|
# place the 2 output points around each ``int``
|
|
# master index.
|
|
margin: float = 0.43,
|
|
|
|
) -> None:
|
|
'''
|
|
"Trace" the outline of the high-low values of an ohlc sequence
|
|
as a line such that the maximum deviation (aka disperaion) between
|
|
bars if preserved.
|
|
|
|
This routine is expected to modify input arrays in-place.
|
|
|
|
'''
|
|
last_l = hl['low'][0]
|
|
last_h = hl['high'][0]
|
|
|
|
for i in range(hl.size):
|
|
row = hl[i]
|
|
l, h = row['low'], row['high']
|
|
|
|
up_diff = h - last_l
|
|
down_diff = last_h - l
|
|
|
|
if up_diff > down_diff:
|
|
out[2*i + 1] = h
|
|
out[2*i] = last_l
|
|
else:
|
|
out[2*i + 1] = l
|
|
out[2*i] = last_h
|
|
|
|
last_l = l
|
|
last_h = h
|
|
|
|
x[2*i] = int(i) - margin
|
|
x[2*i + 1] = int(i) + margin
|
|
|
|
return out
|
|
|
|
|
|
def downsample(
|
|
x: np.ndarray,
|
|
y: np.ndarray,
|
|
bins: int = 2,
|
|
method: str = 'peak',
|
|
|
|
) -> tuple[np.ndarray, np.ndarray]:
|
|
'''
|
|
Downsample x/y data for lesser curve graphics gen.
|
|
|
|
The "peak" method is originally copied verbatim from
|
|
``pyqtgraph.PlotDataItem.getDisplayDataset()``.
|
|
|
|
'''
|
|
# py3.10 syntax
|
|
match method:
|
|
case 'peak':
|
|
# breakpoint()
|
|
if bins < 2:
|
|
log.warning('No downsampling taking place?')
|
|
|
|
ds = bins
|
|
n = len(x) // ds
|
|
x1 = np.empty((n, 2))
|
|
|
|
# start of x-values; try to select a somewhat centered point
|
|
stx = ds // 2
|
|
x1[:] = x[stx:stx+n*ds:ds, np.newaxis]
|
|
x = x1.reshape(n*2)
|
|
|
|
y1 = np.empty((n, 2))
|
|
y2 = y[:n*ds].reshape((n, ds))
|
|
|
|
y1[:, 0] = y2.max(axis=1)
|
|
y1[:, 1] = y2.min(axis=1)
|
|
y = y1.reshape(n*2)
|
|
|
|
return x, y
|
|
|
|
# TODO: this algo from infinite, see
|
|
# https://github.com/pikers/piker/issues/109
|
|
case 'infinite_4px':
|
|
|
|
# Ex. from infinite on downsampling viewable graphics.
|
|
# "one thing i remembered about the binning - if you are
|
|
# picking a range within your timeseries the start and end bin
|
|
# should be one more bin size outside the visual range, then
|
|
# you get better visual fidelity at the edges of the graph"
|
|
# "i didn't show it in the sample code, but it's accounted for
|
|
# in the start and end indices and number of bins"
|
|
|
|
def build_subchart(
|
|
self,
|
|
subchart,
|
|
width, # width of screen in pxs?
|
|
chart_type,
|
|
lower, # x start?
|
|
upper, # x end?
|
|
xvals,
|
|
yvals
|
|
):
|
|
pts_per_pixel = len(xvals) / width
|
|
if pts_per_pixel > 1:
|
|
|
|
# this is mutated in-place
|
|
data = np.zeros((width, 4), yvals.dtype)
|
|
bins = np.zeros(width, xvals.dtype)
|
|
|
|
nb = subset_by_x(
|
|
xvals,
|
|
yvals,
|
|
bins,
|
|
data,
|
|
lower,
|
|
# this is scaling the x-range by
|
|
# the width of the screen?
|
|
(upper-lower)/float(width),
|
|
)
|
|
print(f'downsampled to {nb} bins')
|
|
|
|
return x, y
|
|
|
|
|
|
@jit(nopython=True)
|
|
def subset_by_x(
|
|
|
|
xs: np.ndarray,
|
|
ys: np.ndarray,
|
|
bins: np.ndarray,
|
|
data: np.ndarray,
|
|
x_start: int,
|
|
step: float,
|
|
|
|
) -> int:
|
|
# nbins = len(bins)
|
|
count = len(xs)
|
|
bincount = 0
|
|
x_left = x_start
|
|
|
|
# Find the first bin
|
|
first = xs[0]
|
|
while first >= x_left + step:
|
|
x_left += step
|
|
|
|
bins[bincount] = x_left
|
|
data[bincount] = ys[0]
|
|
|
|
for i in range(count):
|
|
x = xs[i]
|
|
y = ys[i]
|
|
if x < x_left + step: # Interval is [bin, bin+1)
|
|
data[bincount, 1] = min(y, data[bincount, 1])
|
|
data[bincount, 2] = max(y, data[bincount, 2])
|
|
data[bincount, 3] = y
|
|
else:
|
|
# Find the next bin
|
|
while x >= x_left + step:
|
|
x_left += step
|
|
bincount += 1
|
|
bins[bincount] = x_left
|
|
data[bincount] = y
|
|
|
|
return bincount
|