Add gap detection into the `store ldshm` cmd

piker/storage/cli.py

@@ -150,6 +150,7 @@ def anal(
             open_piker_runtime(
                 'tsdb_polars_anal',
                 # enable_modules=['piker.service._ahab']
+                debug_mode=True,
             ),
             open_storage_client() as (mod, client),
         ):
@@ -168,10 +169,10 @@ def anal(
 
             src_df = await client.as_df(fqme, period)
             from piker.data import _timeseries as tsmod
-            df = tsmod.with_dts(src_df)
+            df: pl.DataFrame = tsmod.with_dts(src_df)
             gaps: pl.DataFrame = tsmod.detect_time_gaps(df)
 
-            if gaps:
+            if not gaps.is_empty():
                 print(f'Gaps found:\n{gaps}')
 
             # TODO: something better with tab completion..
@@ -216,7 +217,13 @@ def iter_dfs_from_shms(fqme: str) -> Generator[
 
         # lookup array buffer size based on file suffix
         # being either .rt or .hist
-        size: int = sizes[shmfile.name.rsplit('.')[-1]]
+        key: str = shmfile.name.rsplit('.')[-1]
+
+        # skip FSP buffers for now..
+        if key not in sizes:
+            continue
+
+        size: int = sizes[key]
 
         # attach to any shm buffer, load array into polars df,
         # write to local parquet file.
@@ -271,23 +278,30 @@ def ldshm(
             open_piker_runtime(
                 'polars_boi',
                 enable_modules=['piker.data._sharedmem'],
+                debug_mode=True,
             ),
         ):
-
             df: pl.DataFrame | None = None
-            for shmfile, 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']
                 secs: float = times[-1] - times[-2]
                 if secs < 1.:
-                    breakpoint()
                     raise ValueError(
                         f'Something is wrong with time period for {shm}:\n{times}'
                     )
 
+                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?
+                if (
+                    not gaps.is_empty()
+                    or secs > 2
+                ):
                     await tractor.pause()
 
                 # write to parquet file?