Tolerate various "bad data" cases in `markup_gaps()`

Namely such that when the previous-df-row by our shm-abs-'index' doesn't
exist we ignore certain cases which are likely due to borked-but-benign
samples written to the tsdb or rt shm buffers prior.

Particularly we now ignore,
- any `dt`/`prev_dt` values which are UNIX-epoch timestamped (val of 0).
- any row-is-first-row in the df; there is no previous.
- any missing previous datum by 'index', in which case we lookup the
  `wdts` prior row and use that instead.
  * this would indicate a missing sample for the time-step but we can
    still detect a "gap" by looking at the prior row, by df-abs-index
    `i`, and use its timestamp to determine the period/size of missing
    samples (which need to likely still be retrieved).
  * in this case i'm leaving in a pause-point for introspecting these
    rarer cases when `--pdb` is passed via CLI.

Relatedly in the `piker store` CLI ep,
- add `--pdb` flag to `piker store`, pass it verbatim as `debug_mode`.
- when `times` has only a single row, don't calc a `period_s` median.
- only trace `null_segs` when in debug mode.
- always markup/dedupe gaps for `period_s==60`

piker/storage/cli.py

@@ -242,6 +242,7 @@ def anal(
     trio.run(main)
 
 
+# TODO, move to `.tsp._annotate`
 async def markup_gaps(
     fqme: str,
     timeframe: float,
@@ -288,18 +289,38 @@ async def markup_gaps(
         )
         # XXX: probably a gap in the (newly sorted or de-duplicated)
         # dt-df, so we might need to re-index first..
+        dt: pl.Series = row['dt']
+        dt_prev: pl.Series = row['dt_prev']
         if prev_r.is_empty():
-            await tractor.pause()
+
+            # XXX, filter out any special ignore cases,
+            # - UNIX-epoch stamped datums
+            # - first row
+            if (
+                dt_prev.dt.epoch()[0] == 0
+                or
+                dt.dt.epoch()[0] == 0
+            ):
+                log.warning('Skipping row with UNIX epoch timestamp ??')
+                continue
+
+            if wdts[0]['index'][0] == iend:  # first row
+                log.warning('Skipping first-row (has no previous obvi) !!')
+                continue
+
+            # XXX, if the previous-row by shm-index is missing,
+            # meaning there is a missing sample (set), get the prior
+            # row by df index and attempt to use it?
+            i_wdts: pl.DataFrame = wdts.with_row_index(name='i')
+            i_row: int = i_wdts.filter(pl.col('index') == iend)['i'][0]
+            prev_row_by_i = wdts[i_row]
+            prev_r: pl.DataFrame = prev_row_by_i
+
+            # debug any missing pre-row
+            if tractor._state.is_debug_mode():
+                await tractor.pause()
 
         istart: int = prev_r['index'][0]
-        # dt_start_t: float = dt_prev.timestamp()
-
-        # start_t: float = prev_r['time']
-        # assert (
-        #     dt_start_t
-        #     ==
-        #     start_t
-        # )
 
         # TODO: implement px-col width measure
         # and ensure at least as many px-cols
@@ -358,6 +379,7 @@ def ldshm(
     fqme: str,
     write_parquet: bool = True,
     reload_parquet_to_shm: bool = True,
+    pdb: bool = False,  # --pdb passed?
 
 ) -> None:
     '''
@@ -377,7 +399,7 @@ def ldshm(
             open_piker_runtime(
                 'polars_boi',
                 enable_modules=['piker.data._sharedmem'],
-                debug_mode=True,
+                debug_mode=pdb,
             ),
             open_storage_client() as (
                 mod,
@@ -397,17 +419,19 @@ def ldshm(
 
                 times: np.ndarray = shm.array['time']
                 d1: float = float(times[-1] - times[-2])
-                d2: float = float(times[-2] - times[-3])
-                med: float = np.median(np.diff(times))
-                if (
-                    d1 < 1.
-                    and d2 < 1.
-                    and med < 1.
-                ):
-                    raise ValueError(
-                        f'Something is wrong with time period for {shm}:\n{times}'
-                    )
-
+                d2: float = 0
+                # XXX, take a median sample rate if sufficient data
+                if times.size > 2:
+                    d2: float = float(times[-2] - times[-3])
+                    med: float = np.median(np.diff(times))
+                    if (
+                        d1 < 1.
+                        and d2 < 1.
+                        and med < 1.
+                    ):
+                        raise ValueError(
+                            f'Something is wrong with time period for {shm}:\n{times}'
+                        )
                 period_s: float = float(max(d1, d2, med))
 
                 null_segs: tuple = tsp.get_null_segs(
@@ -417,7 +441,9 @@ def ldshm(
 
                 # TODO: call null-seg fixer somehow?
                 if null_segs:
-                    await tractor.pause()
+
+                    if tractor._state.is_debug_mode():
+                        await tractor.pause()
                 #     async with (
                 #         trio.open_nursery() as tn,
                 #         mod.open_history_client(
@@ -498,8 +524,11 @@ def ldshm(
                 if (
                     not venue_gaps.is_empty()
                     or (
-                        period_s < 60
-                        and not step_gaps.is_empty()
+                        not step_gaps.is_empty()
+                        # XXX, i presume i put this bc i was guarding
+                        # for ib venue gaps?
+                        # and
+                        # period_s < 60
                     )
                 ):
                     # write repaired ts to parquet-file?