piker/data/_timeseries.py

@@ -263,19 +263,6 @@ def with_dts(
     # )
 
 
-def dedup_dt(
-    df: pl.DataFrame,
-) -> pl.DataFrame:
-    '''
-    Drop duplicate date-time rows (normally from an OHLC frame).
-
-    '''
-    return df.unique(
-        subset=['dt'],
-        maintain_order=True,
-    )
-
-
 def detect_time_gaps(
     df: pl.DataFrame,
 
@@ -307,12 +294,10 @@ def detect_time_gaps(
     '''
     return (
         with_dts(df)
-        # First by a seconds unit step size
         .filter(
             pl.col('s_diff').abs() > expect_period
         )
         .filter(
-            # Second by an arbitrary dt-unit step size
             getattr(
                 pl.col('dt_diff').dt,
                 gap_dt_unit,

piker/data/history.py

@@ -716,14 +716,9 @@ async def tsdb_backfill(
             backfill_diff: Duration = mr_start_dt - last_tsdb_dt
             offset_s: float = backfill_diff.in_seconds()
 
-            # XXX EDGE CASEs: the most recent frame overlaps with
-            # prior tsdb history!!
-            # - so the latest frame's start time is earlier then
-            # the tsdb's latest sample.
-            # - alternatively this may also more generally occur
-            # when the venue was closed (say over the weeknd)
-            # causing a timeseries gap, AND the query frames size
-            # (eg. for ib's 1s we rx 2k datums ~= 33.33m) IS
+            # XXX EDGE CASE: when the venue was closed (say over
+            # the weeknd) causing a timeseries gap, AND the query
+            # frames size (eg. for 1s we rx 2k datums ~= 33.33m) IS
             # GREATER THAN the current venue-market's operating
             # session (time) we will receive datums from BEFORE THE
             # CLOSURE GAP and thus the `offset_s` value will be
@@ -732,35 +727,20 @@ async def tsdb_backfill(
             # tsdb. In this case we instead only retreive and push
             # the series portion missing from the db's data set.
             if offset_s < 0:
-                non_overlap_diff: Duration = mr_end_dt - last_tsdb_dt
-                non_overlap_offset_s: float = backfill_diff.in_seconds()
+                backfill_diff: Duration = mr_end_dt - last_tsdb_dt
+                offset_s: float = backfill_diff.in_seconds()
 
             offset_samples: int = round(offset_s / timeframe)
 
             # TODO: see if there's faster multi-field reads:
             # https://numpy.org/doc/stable/user/basics.rec.html#accessing-multiple-fields
             # re-index  with a `time` and index field
-            if offset_s > 0:
-                # NOTE XXX: ONLY when there is an actual gap
-                # between the earliest sample in the latest history
-                # frame do we want to NOT stick the latest tsdb
-                # history adjacent to that latest frame!
             prepend_start = shm._first.value - offset_samples + 1
-                to_push = tsdb_history[-prepend_start:]
-            else: 
-                # when there is overlap we want to remove the
-                # overlapping samples from the tsdb portion (taking
-                # instead the latest frame's values since THEY
-                # SHOULD BE THE SAME) and prepend DIRECTLY adjacent
-                # to the latest frame!
-                # TODO: assert the overlap segment array contains
-                # the same values!?!
-                prepend_start = shm._first.value
-                to_push = tsdb_history[-(shm._first.value):offset_samples - 1]
 
             # tsdb history is so far in the past we can't fit it in
             # shm buffer space so simply don't load it!
             if prepend_start > 0:
+                to_push = tsdb_history[-prepend_start:]
                 shm.push(
                     to_push,
 

piker/service/_actor_runtime.py

@@ -282,21 +282,16 @@ async def maybe_open_pikerd(
             loglevel=loglevel,
             **kwargs,
         ) as (actor, addrs),
-    ):
-        if _root_dname in actor.uid:
-            yield None
-            return
 
-        # NOTE: IFF running in disti mode, try to attach to any
-        # existing (host-local) `pikerd`.
-        else:
-            async with tractor.find_actor(
+        # try to attach to any existing (host-local) `pikerd`
+        tractor.find_actor(
             _root_dname,
             registry_addrs=registry_addrs,
             only_first=True,
             # raise_on_none=True,
-            ) as pikerd_portal:
+        ) as pikerd_portal,
 
+    ):
         # connect to any existing remote daemon presuming its
         # registry socket was selected.
         if pikerd_portal is not None:

piker/storage/cli.py

@@ -136,53 +136,6 @@ def delete(
     trio.run(main, symbols)
 
 
-def dedupe(src_df: pl.DataFrame) -> tuple[
-    pl.DataFrame,  # with dts
-    pl.DataFrame,  # gaps
-    pl.DataFrame,  # with deduplicated dts (aka gap/repeat removal)
-    bool,
-]:
-    '''
-    Check for time series gaps and if found
-    de-duplicate any datetime entries, check for
-    a frame height diff and return the newly
-    dt-deduplicated frame.
-
-    '''
-    from piker.data import _timeseries as tsp
-    df: pl.DataFrame = tsp.with_dts(src_df)
-    gaps: pl.DataFrame = tsp.detect_time_gaps(df)
-    if not gaps.is_empty():
-
-        # remove duplicated datetime samples/sections
-        deduped: pl.DataFrame = tsp.dedup_dt(df)
-        deduped_gaps = tsp.detect_time_gaps(deduped)
-
-        log.warning(
-            f'Gaps found:\n{gaps}\n'
-            f'deduped Gaps found:\n{deduped_gaps}'
-        )
-        # TODO: rewrite this in polars and/or convert to
-        # ndarray to detect and remove?
-        # null_gaps = tsp.detect_null_time_gap()
-
-        diff: int = (
-            df.height
-            -
-            deduped.height
-        )
-        was_deduped: bool = False
-        if diff:
-            deduped: bool = True
-
-        return (
-            df,
-            gaps,
-            deduped,
-            was_deduped,
-        )
-
-
 @store.command()
 def anal(
     fqme: str,
@@ -191,11 +144,7 @@ def anal(
 
 ) -> np.ndarray:
     '''
-    Anal-ysis is when you take the data do stuff to it.
-
-    NOTE: This ONLY loads the offline timeseries data (by default
-    from a parquet file) NOT the in-shm version you might be seeing
-    in a chart.
+    Anal-ysis is when you take the data do stuff to it, i think.
 
     '''
     async def main():
@@ -213,7 +162,6 @@ def anal(
             syms: list[str] = await client.list_keys()
             log.info(f'{len(syms)} FOUND for {mod.name}')
 
-            history: ShmArray  # np buffer format
             (
                 history,
                 first_dt,
@@ -224,26 +172,13 @@ def anal(
             )
             assert first_dt < last_dt
 
-            shm_df: pl.DataFrame = await client.as_df(
-                fqme,
-                period,
-            )
+            src_df = await client.as_df(fqme, period)
+            from piker.data import _timeseries as tsmod
+            df: pl.DataFrame = tsmod.with_dts(src_df)
+            gaps: pl.DataFrame = tsmod.detect_time_gaps(df)
 
-            df: pl.DataFrame  # with dts
-            deduped: pl.DataFrame  # deduplicated dts
-            (
-                df,
-                gaps,
-                deduped,
-                shortened,
-            ) = dedupe(shm_df)
-
-            if shortened:
-                await client.write_ohlcv(
-                    fqme,
-                    ohlcv=deduped,
-                    timeframe=period,
-                )
+            if not gaps.is_empty():
+                print(f'Gaps found:\n{gaps}')
 
             # TODO: something better with tab completion..
             # is there something more minimal but nearly as
@@ -340,7 +275,7 @@ def ldshm(
     '''
     Linux ONLY: load any fqme file name matching shm buffer from
     /dev/shm/ into an OHLCV numpy array and polars DataFrame,
-    optionally write to offline storage via `.parquet` file.
+    optionally write to .parquet file.
 
     '''
     async def main():
@@ -352,7 +287,7 @@ def ldshm(
             ),
         ):
             df: pl.DataFrame | None = None
-            for shmfile, shm, shm_df in iter_dfs_from_shms(fqme):
+            for shmfile, shm, src_df in iter_dfs_from_shms(fqme):
 
                 # compute ohlc properties for naming
                 times: np.ndarray = shm.array['time']
@@ -362,16 +297,9 @@ def ldshm(
                         f'Something is wrong with time period for {shm}:\n{times}'
                     )
 
-
-                # over-write back to shm?
-                df: pl.DataFrame  # with dts
-                deduped: pl.DataFrame  # deduplicated dts
-                (
-                    df,
-                    gaps,
-                    deduped,
-                    was_dded,
-                ) = dedupe(shm_df)
+                from piker.data import _timeseries as tsmod
+                df: pl.DataFrame = tsmod.with_dts(src_df)
+                gaps: pl.DataFrame = tsmod.detect_time_gaps(df)
 
                 # TODO: maybe only optionally enter this depending
                 # on some CLI flags and/or gap detection?

piker/storage/nativedb.py

@@ -272,21 +272,9 @@ class NativeStorageClient:
         # limit: int = int(200e3),
 
     ) -> np.ndarray:
-        path: Path = self.mk_path(
-            fqme,
-            period=int(timeframe),
-        )
+        path: Path = self.mk_path(fqme, period=int(timeframe))
         df: pl.DataFrame = pl.read_parquet(path)
-
-        # cache df for later usage since we (currently) need to
-        # convert to np.ndarrays to push to our `ShmArray` rt
-        # buffers subsys but later we may operate entirely on
-        # pyarrow arrays/buffers so keeping the dfs around for
-        # a variety of purposes is handy.
-        self._dfs.setdefault(
-            timeframe,
-            {},
-        )[fqme] = df
+        self._dfs.setdefault(timeframe, {})[fqme] = df
 
         # TODO: filter by end and limit inputs
         # times: pl.Series = df['time']
@@ -341,7 +329,7 @@ class NativeStorageClient:
             time.time() - start,
             ndigits=6,
         )
-        log.info(
+        print(
             f'parquet write took {delay} secs\n'
             f'file path: {path}'
         )
@@ -351,7 +339,7 @@ class NativeStorageClient:
     async def write_ohlcv(
         self,
         fqme: str,
-        ohlcv: np.ndarray | pl.DataFrame,
+        ohlcv: np.ndarray,
         timeframe: int,
 
     ) -> Path: