pikers
/
piker
3
0
Fork 0
Code Issues 2 Pull Requests 4 Packages Projects Releases Wiki Activity
piker/piker/data/_pathops.py

433 lines
11 KiB
Python
Raw Blame History

# piker: trading gear for hackers
# Copyright (C) 2018-present Tyler Goodlet (in stewardship of piker0)
# 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/>.
"""
Super fast ``QPainterPath`` generation related operator routines.
"""
import numpy as np
from numpy.lib import recfunctions as rfn
from numba import (
# types,
njit,
float64,
int64,
# optional,
)
# TODO: for ``numba`` typing..
# from ._source import numba_ohlc_dtype
from ._m4 import ds_m4
from .._profile import (
Profiler,
pg_profile_enabled,
ms_slower_then,
)
def xy_downsample(
x,
y,
uppx,
x_spacer: float = 0.5,
) -> tuple[
np.ndarray,
np.ndarray,
float,
float,
]:
'''
Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
``uppx`` (units-per-pixel) and add space between discreet datums.
'''
# downsample whenever more then 1 pixels per datum can be shown.
# always refresh data bounds until we get diffing
# working properly, see above..
bins, x, y, ymn, ymx = ds_m4(
x,
y,
uppx,
)
# flatten output to 1d arrays suitable for path-graphics generation.
x = np.broadcast_to(x[:, None], y.shape)
x = (x + np.array(
[-x_spacer, 0, 0, x_spacer]
)).flatten()
y = y.flatten()
return x, y, ymn, ymx
@njit(
# NOTE: need to construct this manually for readonly
# arrays, see https://github.com/numba/numba/issues/4511
# (
# types.Array(
# numba_ohlc_dtype,
# 1,
# 'C',
# readonly=True,
# ),
# int64,
# types.unicode_type,
# optional(float64),
# ),
nogil=True
)
def path_arrays_from_ohlc(
data: np.ndarray,
start: int64,
bar_w: float64,
bar_gap: float64 = 0.16,
use_time_index: bool = True,
# XXX: ``numba`` issue: https://github.com/numba/numba/issues/8622
# index_field: str,
) -> tuple[
np.ndarray,
np.ndarray,
np.ndarray,
]:
'''
Generate an array of lines objects from input ohlc data.
'''
size = int(data.shape[0] * 6)
# XXX: see this for why the dtype might have to be defined outside
# the routine.
# https://github.com/numba/numba/issues/4098#issuecomment-493914533
x = np.zeros(
shape=size,
dtype=float64,
)
y, c = x.copy(), x.copy()
half_w: float = bar_w/2
# TODO: report bug for assert @
# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
for i, q in enumerate(data[start:], start):
open = q['open']
high = q['high']
low = q['low']
close = q['close']
if use_time_index:
index = float64(q['time'])
else:
index = float64(q['index'])
# XXX: ``numba`` issue: https://github.com/numba/numba/issues/8622
# index = float64(q[index_field])
# AND this (probably)
# open, high, low, close, index = q[
# ['open', 'high', 'low', 'close', 'index']]
istart = i * 6
istop = istart + 6
# x,y detail the 6 points which connect all vertexes of a ohlc bar
mid: float = index + half_w
x[istart:istop] = (
index + bar_gap,
mid,
mid,
mid,
mid,
index + bar_w - bar_gap,
)
y[istart:istop] = (
open,
open,
low,
high,
close,
close,
)
# specifies that the first edge is never connected to the
# prior bars last edge thus providing a small "gap"/"space"
# between bars determined by ``bar_gap``.
c[istart:istop] = (1, 1, 1, 1, 1, 0)
return x, y, c
def hl2mxmn(
ohlc: np.ndarray,
index_field: str = 'index',
) -> np.ndarray:
'''
Convert a OHLC struct-array containing 'high'/'low' columns
to a "joined" max/min 1-d array.
'''
index = ohlc[index_field]
hls = ohlc[[
'low',
'high',
]]
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]
return mxmn, x
@njit(
# TODO: the type annots..
# float64[:](float64[:],),
)
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 ohlc_flatten(
ohlc: np.ndarray,
use_mxmn: bool = True,
index_field: str = 'index',
) -> tuple[np.ndarray, np.ndarray]:
'''
Convert an OHLCV struct-array into a flat ready-for-line-plotting
1-d array that is 4 times the size with x-domain values distributed
evenly (by 0.5 steps) over each index.
'''
index = ohlc[index_field]
if use_mxmn:
# traces a line optimally over highs to lows
# using numba. NOTE: pretty sure this is faster
# and looks about the same as the below output.
flat, x = hl2mxmn(ohlc)
else:
flat = rfn.structured_to_unstructured(
ohlc[['open', 'high', 'low', 'close']]
).flatten()
x = np.linspace(
start=index[0] - 0.5,
stop=index[-1] + 0.5,
num=len(flat),
)
return x, flat
def slice_from_time(
arr: np.ndarray,
start_t: float,
stop_t: float,
step: int | None = None,
) -> tuple[
slice,
slice,
]:
'''
Calculate array indices mapped from a time range and return them in
a slice.
Given an input array with an epoch `'time'` series entry, calculate
the indices which span the time range and return in a slice. Presume
each `'time'` step increment is uniform and when the time stamp
series contains gaps (the uniform presumption is untrue) use
``np.searchsorted()`` binary search to look up the appropriate
index.
'''
profiler = Profiler(
msg='slice_from_time()',
disabled=not pg_profile_enabled(),
ms_threshold=ms_slower_then,
)
times = arr['time']
t_first = round(times[0])
read_i_max = arr.shape[0]
if step is None:
step = round(times[-1] - times[-2])
if step == 0:
# XXX: HOW TF is this happening?
step = 1
# compute (presumed) uniform-time-step index offsets
i_start_t = round(start_t)
read_i_start = round(((i_start_t - t_first) // step)) - 1
i_stop_t = round(stop_t)
read_i_stop = round((i_stop_t - t_first) // step) + 1
# always clip outputs to array support
# for read start:
# - never allow a start < the 0 index
# - never allow an end index > the read array len
read_i_start = min(
max(0, read_i_start),
read_i_max - 1,
)
read_i_stop = max(
0,
min(read_i_stop, read_i_max),
)
# check for larger-then-latest calculated index for given start
# time, in which case we do a binary search for the correct index.
# NOTE: this is usually the result of a time series with time gaps
# where it is expected that each index step maps to a uniform step
# in the time stamp series.
t_iv_start = times[read_i_start]
if (
t_iv_start > i_start_t
):
# do a binary search for the best index mapping to ``start_t``
# given we measured an overshoot using the uniform-time-step
# calculation from above.
# TODO: once we start caching these per source-array,
# we can just overwrite ``read_i_start`` directly.
new_read_i_start = np.searchsorted(
times,
i_start_t,
side='left',
)
# TODO: minimize binary search work as much as possible:
# - cache these remap values which compensate for gaps in the
# uniform time step basis where we calc a later start
# index for the given input ``start_t``.
# - can we shorten the input search sequence by heuristic?
# up_to_arith_start = index[:read_i_start]
if (
new_read_i_start < read_i_start
):
# t_diff = t_iv_start - start_t
# print(
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
# f'start_t:{start_t} -> 0index start_t:{t_iv_start}\n'
# f'diff: {t_diff}\n'
# f'REMAPPED START i: {read_i_start} -> {new_read_i_start}\n'
# )
read_i_start = new_read_i_start - 1
t_iv_stop = times[read_i_stop - 1]
if (
t_iv_stop > i_stop_t
):
# t_diff = stop_t - t_iv_stop
# print(
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
# f'calced iv stop:{t_iv_stop} -> stop_t:{stop_t}\n'
# f'diff: {t_diff}\n'
# # f'SHOULD REMAP STOP: {read_i_start} -> {new_read_i_start}\n'
# )
new_read_i_stop = np.searchsorted(
times[read_i_start:],
i_stop_t,
side='left',
)
if (
new_read_i_stop < read_i_stop
):
read_i_stop = read_i_start + new_read_i_stop
# sanity checks for range size
# samples = (i_stop_t - i_start_t) // step
# index_diff = read_i_stop - read_i_start + 1
# if index_diff > (samples + 3):
# breakpoint()
# read-relative indexes: gives a slice where `shm.array[read_slc]`
# will be the data spanning the input time range `start_t` ->
# `stop_t`
read_slc = slice(
int(read_i_start),
int(read_i_stop),
)
profiler(
'slicing complete'
# f'{start_t} -> {abs_slc.start} | {read_slc.start}\n'
# f'{stop_t} -> {abs_slc.stop} | {read_slc.stop}\n'
)
# NOTE: if caller needs absolute buffer indices they can
# slice the buffer abs index like so:
# index = arr['index']
# abs_indx = index[read_slc]
# abs_slc = slice(
# int(abs_indx[0]),
# int(abs_indx[-1]),
# )
return read_slc
Reference in New Issue View Git Blame Copy Permalink