Move `dedupe()` to `.data.tsp` (so it has pals)
Includes a rename of `.data._timeseries` -> `.data.tsp` for "time series processing", making it a public sub-mod; it contains a highly useful set of data-frame and `numpy.ndarray` ops routines in various subsystems Bodistribute_dis
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7311000846
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b94582cb35
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@ -56,6 +56,7 @@ __all__: list[str] = [
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'ShmArray',
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'iterticks',
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'maybe_open_shm_array',
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'match_from_pairs',
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'attach_shm_array',
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'open_shm_array',
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'get_shm_token',
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@ -406,7 +406,7 @@ async def start_backfill(
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# TODO: ideally these never exist but somehow it seems
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# sometimes we're writing zero-ed segments on certain
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# (teardown) cases?
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from ._timeseries import detect_null_time_gap
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from .tsp import detect_null_time_gap
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gap_indices: tuple | None = detect_null_time_gap(shm)
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while gap_indices:
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@ -23,11 +23,12 @@ Routines are generally implemented in either ``numpy`` or
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'''
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from __future__ import annotations
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from typing import Literal
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from functools import partial
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from math import (
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ceil,
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floor,
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)
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from typing import Literal
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import numpy as np
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import polars as pl
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@ -38,6 +39,18 @@ from ..toolz.profile import (
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pg_profile_enabled,
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ms_slower_then,
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)
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from ..log import (
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get_logger,
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get_console_log,
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)
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# for "time series processing"
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subsys: str = 'piker.tsp'
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log = get_logger(subsys)
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get_console_log = partial(
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get_console_log,
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name=subsys,
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)
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def slice_from_time(
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@ -349,3 +362,49 @@ def detect_price_gaps(
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# (pl.col(time_col) - pl.col(f'{time_col}_previous')).alias('diff'),
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# ])
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...
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def dedupe(src_df: pl.DataFrame) -> tuple[
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pl.DataFrame, # with dts
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pl.DataFrame, # gaps
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pl.DataFrame, # with deduplicated dts (aka gap/repeat removal)
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bool,
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]:
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'''
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Check for time series gaps and if found
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de-duplicate any datetime entries, check for
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a frame height diff and return the newly
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dt-deduplicated frame.
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'''
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df: pl.DataFrame = with_dts(src_df)
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gaps: pl.DataFrame = detect_time_gaps(df)
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if not gaps.is_empty():
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# remove duplicated datetime samples/sections
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deduped: pl.DataFrame = dedup_dt(df)
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deduped_gaps = detect_time_gaps(deduped)
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log.warning(
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f'Gaps found:\n{gaps}\n'
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f'deduped Gaps found:\n{deduped_gaps}'
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)
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# TODO: rewrite this in polars and/or convert to
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# ndarray to detect and remove?
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# null_gaps = detect_null_time_gap()
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diff: int = (
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df.height
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-
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deduped.height
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)
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was_deduped: bool = False
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if diff:
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was_deduped: bool = True
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return (
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df,
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gaps,
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deduped,
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was_deduped,
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)
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@ -40,6 +40,7 @@ from piker.data import (
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maybe_open_shm_array,
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def_iohlcv_fields,
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ShmArray,
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tsp,
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)
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from piker.data.history import (
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_default_hist_size,
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@ -136,53 +137,6 @@ def delete(
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trio.run(main, symbols)
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def dedupe(src_df: pl.DataFrame) -> tuple[
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pl.DataFrame, # with dts
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pl.DataFrame, # gaps
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pl.DataFrame, # with deduplicated dts (aka gap/repeat removal)
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bool,
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]:
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'''
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Check for time series gaps and if found
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de-duplicate any datetime entries, check for
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a frame height diff and return the newly
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dt-deduplicated frame.
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'''
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from piker.data import _timeseries as tsp
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df: pl.DataFrame = tsp.with_dts(src_df)
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gaps: pl.DataFrame = tsp.detect_time_gaps(df)
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if not gaps.is_empty():
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# remove duplicated datetime samples/sections
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deduped: pl.DataFrame = tsp.dedup_dt(df)
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deduped_gaps = tsp.detect_time_gaps(deduped)
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log.warning(
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f'Gaps found:\n{gaps}\n'
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f'deduped Gaps found:\n{deduped_gaps}'
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)
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# TODO: rewrite this in polars and/or convert to
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# ndarray to detect and remove?
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# null_gaps = tsp.detect_null_time_gap()
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diff: int = (
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df.height
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-
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deduped.height
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)
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was_deduped: bool = False
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if diff:
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was_deduped: bool = True
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return (
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df,
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gaps,
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deduped,
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was_deduped,
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)
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@store.command()
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def anal(
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fqme: str,
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@ -236,7 +190,7 @@ def anal(
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gaps,
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deduped,
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shortened,
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) = dedupe(shm_df)
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) = tsp.dedupe(shm_df)
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if shortened:
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await client.write_ohlcv(
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@ -371,7 +325,7 @@ def ldshm(
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gaps,
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deduped,
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was_dded,
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) = dedupe(shm_df)
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) = tsp.dedupe(shm_df)
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# TODO: maybe only optionally enter this depending
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# on some CLI flags and/or gap detection?
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@ -49,7 +49,7 @@ from ..data._formatters import (
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OHLCBarsAsCurveFmtr, # OHLC converted to line
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StepCurveFmtr, # "step" curve (like for vlm)
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)
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from ..data._timeseries import (
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from ..data.tsp import (
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slice_from_time,
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)
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from ._ohlc import (
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@ -31,7 +31,7 @@ import pendulum
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import pyqtgraph as pg
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from piker.types import Struct
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from ..data._timeseries import slice_from_time
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from ..data.tsp import slice_from_time
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from ..log import get_logger
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from ..toolz import Profiler
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