Import `np2pl()` from `.data.tsp`
Also toss in todo for a timeseries search CLI cmd which can be handy when doing offine store mgmt.distribute_dis
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b95932ea09
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f274c3db3b
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@ -99,6 +99,18 @@ def ls(
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trio.run(query_all)
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trio.run(query_all)
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# TODO: like ls but takes in a pattern and matches
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# @store.command()
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# def search(
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# patt: str,
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# backends: list[str] = typer.Argument(
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# default=None,
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# help='Storage backends to query, default is all.'
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# ),
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# ):
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# ...
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@store.command()
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@store.command()
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def delete(
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def delete(
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symbols: list[str],
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symbols: list[str],
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@ -189,10 +201,10 @@ def anal(
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df,
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df,
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gaps,
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gaps,
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deduped,
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deduped,
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shortened,
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diff,
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) = tsp.dedupe(shm_df)
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) = tsp.dedupe(shm_df)
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if shortened:
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if diff:
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await client.write_ohlcv(
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await client.write_ohlcv(
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fqme,
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fqme,
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ohlcv=deduped,
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ohlcv=deduped,
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@ -260,8 +272,8 @@ def iter_dfs_from_shms(fqme: str) -> Generator[
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assert not opened
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assert not opened
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ohlcv = shm.array
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ohlcv = shm.array
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from .nativedb import np2pl
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from ..data import tsp
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df: pl.DataFrame = np2pl(ohlcv)
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df: pl.DataFrame = tsp.np2pl(ohlcv)
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yield (
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yield (
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shmfile,
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shmfile,
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@ -273,7 +285,6 @@ def iter_dfs_from_shms(fqme: str) -> Generator[
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@store.command()
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@store.command()
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def ldshm(
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def ldshm(
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fqme: str,
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fqme: str,
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write_parquet: bool = False,
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write_parquet: bool = False,
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) -> None:
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) -> None:
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@ -309,7 +320,7 @@ def ldshm(
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df,
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df,
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gaps,
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gaps,
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deduped,
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deduped,
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was_dded,
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diff,
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) = tsp.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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# TODO: maybe only optionally enter this depending
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