Move `Flume` to a new `.data._flumes` module
Tyler Goodlet
parent
8793b76ee2
commit
13e86fbe30
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@ -0,0 +1,304 @@
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# 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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"""
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Real-time data flow abstractions.
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<writeup on "buffers, streams, flumes and flows..">
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"""
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from contextlib import asynccontextmanager as acm
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from functools import partial
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from typing import (
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AsyncIterator,
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TYPE_CHECKING,
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)
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import tractor
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from tractor.trionics import (
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maybe_open_context,
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)
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import pendulum
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import numpy as np
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from .types import Struct
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from ._source import (
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Symbol,
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)
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from ._sharedmem import (
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attach_shm_array,
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ShmArray,
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_Token,
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)
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from ._sampling import (
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iter_ohlc_periods,
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)
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if TYPE_CHECKING:
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from pyqtgraph import PlotItem
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from .feed import Feed
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class Flume(Struct):
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'''
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Composite reference type which points to all the addressing handles
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and other meta-data necessary for the read, measure and management
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of a set of real-time updated data flows.
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Can be thought of as a "flow descriptor" or "flow frame" which
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describes the high level properties of a set of data flows that can
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be used seamlessly across process-memory boundaries.
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Each instance's sub-components normally includes:
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- a msg oriented quote stream provided via an IPC transport
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- history and real-time shm buffers which are both real-time
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updated and backfilled.
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- associated startup indexing information related to both buffer
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real-time-append and historical prepend addresses.
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- low level APIs to read and measure the updated data and manage
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queuing properties.
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'''
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symbol: Symbol
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first_quote: dict
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_hist_shm_token: _Token
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_rt_shm_token: _Token
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# private shm refs loaded dynamically from tokens
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_hist_shm: ShmArray | None = None
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_rt_shm: ShmArray | None = None
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stream: tractor.MsgStream | None = None
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izero_hist: int = 0
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izero_rt: int = 0
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throttle_rate: int | None = None
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# TODO: do we need this really if we can pull the `Portal` from
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# ``tractor``'s internals?
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feed: Feed | None = None
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@property
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def rt_shm(self) -> ShmArray:
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if self._rt_shm is None:
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self._rt_shm = attach_shm_array(
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token=self._rt_shm_token,
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readonly=True,
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)
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return self._rt_shm
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@property
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def hist_shm(self) -> ShmArray:
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if self._hist_shm is None:
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self._hist_shm = attach_shm_array(
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token=self._hist_shm_token,
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readonly=True,
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)
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return self._hist_shm
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async def receive(self) -> dict:
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return await self.stream.receive()
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@acm
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async def index_stream(
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self,
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delay_s: int = 1,
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) -> AsyncIterator[int]:
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if not self.feed:
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raise RuntimeError('This flume is not part of any ``Feed``?')
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# TODO: maybe a public (property) API for this in ``tractor``?
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portal = self.stream._ctx._portal
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assert portal
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# XXX: this should be singleton on a host,
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# a lone broker-daemon per provider should be
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# created for all practical purposes
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async with maybe_open_context(
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acm_func=partial(
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portal.open_context,
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iter_ohlc_periods,
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),
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kwargs={'delay_s': delay_s},
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) as (cache_hit, (ctx, first)):
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async with ctx.open_stream() as istream:
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if cache_hit:
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# add a new broadcast subscription for the quote stream
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# if this feed is likely already in use
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async with istream.subscribe() as bistream:
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yield bistream
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else:
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yield istream
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def get_ds_info(
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self,
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) -> tuple[float, float, float]:
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'''
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Compute the "downsampling" ratio info between the historical shm
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buffer and the real-time (HFT) one.
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Return a tuple of the fast sample period, historical sample
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period and ratio between them.
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'''
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times = self.hist_shm.array['time']
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end = pendulum.from_timestamp(times[-1])
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start = pendulum.from_timestamp(times[times != times[-1]][-1])
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hist_step_size_s = (end - start).seconds
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times = self.rt_shm.array['time']
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end = pendulum.from_timestamp(times[-1])
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start = pendulum.from_timestamp(times[times != times[-1]][-1])
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rt_step_size_s = (end - start).seconds
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ratio = hist_step_size_s / rt_step_size_s
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return (
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rt_step_size_s,
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hist_step_size_s,
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ratio,
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)
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# TODO: get native msgspec decoding for these workinn
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def to_msg(self) -> dict:
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msg = self.to_dict()
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msg['symbol'] = msg['symbol'].to_dict()
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# can't serialize the stream or feed objects, it's expected
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# you'll have a ref to it since this msg should be rxed on
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# a stream on whatever far end IPC..
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msg.pop('stream')
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msg.pop('feed')
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return msg
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@classmethod
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def from_msg(cls, msg: dict) -> dict:
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symbol = Symbol(**msg.pop('symbol'))
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return cls(
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symbol=symbol,
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**msg,
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)
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def get_index(
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self,
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time_s: float,
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) -> int:
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'''
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Return array shm-buffer index for for epoch time.
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'''
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array = self.rt_shm.array
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times = array['time']
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mask = (times >= time_s)
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if any(mask):
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return array['index'][mask][0]
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# just the latest index
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array['index'][-1]
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def slice_from_time(
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self,
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array: np.ndarray,
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start_t: float,
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stop_t: float,
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timeframe_s: int = 1,
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return_data: bool = False,
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) -> np.ndarray:
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'''
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Slice an input struct array providing only datums
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"in view" of this chart.
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'''
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arr = {
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1: self.rt_shm.array,
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60: self.hist_shm.arry,
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}[timeframe_s]
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times = arr['time']
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index = array['index']
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# use advanced indexing to map the
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# time range to the index range.
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mask = (
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(times >= start_t)
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&
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(times < stop_t)
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)
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# TODO: if we can ensure each time field has a uniform
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# step we can instead do some arithmetic to determine
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# the equivalent index like we used to?
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# return array[
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# lbar - ifirst:
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# (rbar - ifirst) + 1
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# ]
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i_by_t = index[mask]
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i_0 = i_by_t[0]
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abs_slc = slice(
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i_0,
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i_by_t[-1],
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)
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# slice data by offset from the first index
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# available in the passed datum set.
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read_slc = slice(
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0,
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i_by_t[-1] - i_0,
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)
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if not return_data:
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return (
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abs_slc,
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read_slc,
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)
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# also return the readable data from the timerange
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return (
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abs_slc,
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read_slc,
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arr[mask],
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)
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def view_data(
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self,
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plot: PlotItem,
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timeframe_s: int = 1,
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) -> np.ndarray:
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# get far-side x-indices plot view
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vr = plot.viewRect()
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l = vr.left()
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r = vr.right()
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(
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abs_slc,
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buf_slc,
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iv_arr,
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) = self.slice_from_time(
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start_t=l,
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stop_t=r,
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timeframe_s=timeframe_s,
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return_data=True,
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)
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return iv_arr
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@ -29,7 +29,6 @@ import time
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from types import ModuleType
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from typing import (
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Any,
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AsyncIterator,
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AsyncContextManager,
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Callable,
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Optional,
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@ -57,11 +56,10 @@ from .._daemon import (
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maybe_spawn_brokerd,
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check_for_service,
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)
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from ._flumes import Flume
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from ._sharedmem import (
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maybe_open_shm_array,
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attach_shm_array,
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ShmArray,
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_Token,
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_secs_in_day,
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)
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from .ingest import get_ingestormod
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@ -76,7 +74,6 @@ from ._sampling import (
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sampler,
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broadcast,
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increment_ohlc_buffer,
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iter_ohlc_periods,
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sample_and_broadcast,
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uniform_rate_send,
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_default_delay_s,
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@ -87,7 +84,6 @@ from ..brokers._util import (
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if TYPE_CHECKING:
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from .marketstore import Storage
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from pyqtgraph import PlotItem
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log = get_logger(__name__)
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@ -894,258 +890,6 @@ async def manage_history(
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await trio.sleep_forever()
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class Flume(Struct):
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'''
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Composite reference type which points to all the addressing handles
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and other meta-data necessary for the read, measure and management
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of a set of real-time updated data flows.
|
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|
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Can be thought of as a "flow descriptor" or "flow frame" which
|
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describes the high level properties of a set of data flows that can
|
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be used seamlessly across process-memory boundaries.
|
||||
|
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Each instance's sub-components normally includes:
|
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- a msg oriented quote stream provided via an IPC transport
|
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- history and real-time shm buffers which are both real-time
|
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updated and backfilled.
|
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- associated startup indexing information related to both buffer
|
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real-time-append and historical prepend addresses.
|
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- low level APIs to read and measure the updated data and manage
|
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queuing properties.
|
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|
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'''
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symbol: Symbol
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first_quote: dict
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_hist_shm_token: _Token
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_rt_shm_token: _Token
|
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|
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# private shm refs loaded dynamically from tokens
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_hist_shm: ShmArray | None = None
|
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_rt_shm: ShmArray | None = None
|
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|
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stream: tractor.MsgStream | None = None
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izero_hist: int = 0
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izero_rt: int = 0
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throttle_rate: int | None = None
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|
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# TODO: do we need this really if we can pull the `Portal` from
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# ``tractor``'s internals?
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feed: Feed | None = None
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@property
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def rt_shm(self) -> ShmArray:
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|
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if self._rt_shm is None:
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self._rt_shm = attach_shm_array(
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token=self._rt_shm_token,
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readonly=True,
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)
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return self._rt_shm
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@property
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def hist_shm(self) -> ShmArray:
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if self._hist_shm is None:
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self._hist_shm = attach_shm_array(
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token=self._hist_shm_token,
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readonly=True,
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)
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return self._hist_shm
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async def receive(self) -> dict:
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return await self.stream.receive()
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|
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@acm
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async def index_stream(
|
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self,
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delay_s: int = 1,
|
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|
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) -> AsyncIterator[int]:
|
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|
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if not self.feed:
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raise RuntimeError('This flume is not part of any ``Feed``?')
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# TODO: maybe a public (property) API for this in ``tractor``?
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portal = self.stream._ctx._portal
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assert portal
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|
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# XXX: this should be singleton on a host,
|
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# a lone broker-daemon per provider should be
|
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# created for all practical purposes
|
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async with maybe_open_context(
|
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acm_func=partial(
|
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portal.open_context,
|
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iter_ohlc_periods,
|
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),
|
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kwargs={'delay_s': delay_s},
|
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) as (cache_hit, (ctx, first)):
|
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async with ctx.open_stream() as istream:
|
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if cache_hit:
|
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# add a new broadcast subscription for the quote stream
|
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# if this feed is likely already in use
|
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async with istream.subscribe() as bistream:
|
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yield bistream
|
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else:
|
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yield istream
|
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|
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def get_ds_info(
|
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self,
|
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) -> tuple[float, float, float]:
|
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'''
|
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Compute the "downsampling" ratio info between the historical shm
|
||||
buffer and the real-time (HFT) one.
|
||||
|
||||
Return a tuple of the fast sample period, historical sample
|
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period and ratio between them.
|
||||
|
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'''
|
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times = self.hist_shm.array['time']
|
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end = pendulum.from_timestamp(times[-1])
|
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start = pendulum.from_timestamp(times[times != times[-1]][-1])
|
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hist_step_size_s = (end - start).seconds
|
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|
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times = self.rt_shm.array['time']
|
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end = pendulum.from_timestamp(times[-1])
|
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start = pendulum.from_timestamp(times[times != times[-1]][-1])
|
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rt_step_size_s = (end - start).seconds
|
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|
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ratio = hist_step_size_s / rt_step_size_s
|
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return (
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rt_step_size_s,
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hist_step_size_s,
|
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ratio,
|
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)
|
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|
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# TODO: get native msgspec decoding for these workinn
|
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def to_msg(self) -> dict:
|
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msg = self.to_dict()
|
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msg['symbol'] = msg['symbol'].to_dict()
|
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|
||||
# can't serialize the stream or feed objects, it's expected
|
||||
# you'll have a ref to it since this msg should be rxed on
|
||||
# a stream on whatever far end IPC..
|
||||
msg.pop('stream')
|
||||
msg.pop('feed')
|
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return msg
|
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|
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@classmethod
|
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def from_msg(cls, msg: dict) -> dict:
|
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symbol = Symbol(**msg.pop('symbol'))
|
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return cls(
|
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symbol=symbol,
|
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**msg,
|
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)
|
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|
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def get_index(
|
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self,
|
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time_s: float,
|
||||
|
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) -> int:
|
||||
'''
|
||||
Return array shm-buffer index for for epoch time.
|
||||
|
||||
'''
|
||||
array = self.rt_shm.array
|
||||
times = array['time']
|
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mask = (times >= time_s)
|
||||
|
||||
if any(mask):
|
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return array['index'][mask][0]
|
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|
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# just the latest index
|
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array['index'][-1]
|
||||
|
||||
def slice_from_time(
|
||||
self,
|
||||
array: np.ndarray,
|
||||
start_t: float,
|
||||
stop_t: float,
|
||||
timeframe_s: int = 1,
|
||||
return_data: bool = False,
|
||||
|
||||
) -> np.ndarray:
|
||||
'''
|
||||
Slice an input struct array providing only datums
|
||||
"in view" of this chart.
|
||||
|
||||
'''
|
||||
arr = {
|
||||
1: self.rt_shm.array,
|
||||
60: self.hist_shm.arry,
|
||||
}[timeframe_s]
|
||||
|
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times = arr['time']
|
||||
index = array['index']
|
||||
|
||||
# use advanced indexing to map the
|
||||
# time range to the index range.
|
||||
mask = (
|
||||
(times >= start_t)
|
||||
&
|
||||
(times < stop_t)
|
||||
)
|
||||
|
||||
# TODO: if we can ensure each time field has a uniform
|
||||
# step we can instead do some arithmetic to determine
|
||||
# the equivalent index like we used to?
|
||||
# return array[
|
||||
# lbar - ifirst:
|
||||
# (rbar - ifirst) + 1
|
||||
# ]
|
||||
|
||||
i_by_t = index[mask]
|
||||
i_0 = i_by_t[0]
|
||||
|
||||
abs_slc = slice(
|
||||
i_0,
|
||||
i_by_t[-1],
|
||||
)
|
||||
# slice data by offset from the first index
|
||||
# available in the passed datum set.
|
||||
read_slc = slice(
|
||||
0,
|
||||
i_by_t[-1] - i_0,
|
||||
)
|
||||
if not return_data:
|
||||
return (
|
||||
abs_slc,
|
||||
read_slc,
|
||||
)
|
||||
|
||||
# also return the readable data from the timerange
|
||||
return (
|
||||
abs_slc,
|
||||
read_slc,
|
||||
arr[mask],
|
||||
)
|
||||
|
||||
def view_data(
|
||||
self,
|
||||
plot: PlotItem,
|
||||
timeframe_s: int = 1,
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
# get far-side x-indices plot view
|
||||
vr = plot.viewRect()
|
||||
l = vr.left()
|
||||
r = vr.right()
|
||||
|
||||
(
|
||||
abs_slc,
|
||||
buf_slc,
|
||||
iv_arr,
|
||||
) = self.slice_from_time(
|
||||
start_t=l,
|
||||
stop_t=r,
|
||||
timeframe_s=timeframe_s,
|
||||
return_data=True,
|
||||
)
|
||||
return iv_arr
|
||||
|
||||
|
||||
async def allocate_persistent_feed(
|
||||
bus: _FeedsBus,
|
||||
sub_registered: trio.Event,
|
||||
|
|
|
|||
Loading…
Reference in New Issue