Lul, actually detect gaps for 1s OHLC

Turns out we were always filtering to time gaps longer then a day smh..
Instead tweak `detect_time_gaps()` to only return venue-gaps when
a `gap_dt_unit: str` is passed and pass `'days'` (like it was by default
before) from `dedupe()` though we should really pass in an actual venue
gap duration in the future.

piker/tsp/__init__.py

@@ -440,8 +440,11 @@ async def start_backfill(
             # broker says there never was or is no more history to pull
             except DataUnavailable:
                 log.warning(
-                    f'NO-MORE-DATA: backend {mod.name} halted history:\n'
-                    f'{timeframe}@{mkt.fqme}'
+                    f'NO-MORE-DATA in range?\n'
+                    f'`{mod.name}` halted history:\n'
+                    f'tf@fqme: {timeframe}@{mkt.fqme}\n'
+                    'bf_until <- last_start_dt:\n'
+                    f'{backfill_until_dt} <- {last_start_dt}\n'
                 )
 
                 # ugh, what's a better way?

piker/tsp/_anal.py

@@ -510,10 +510,10 @@ def iter_null_segs(
         )
 
 
-# TODO: move to ._pl_anal
 def with_dts(
     df: pl.DataFrame,
     time_col: str = 'time',
+
 ) -> pl.DataFrame:
     '''
     Insert datetime (casted) columns to a (presumably) OHLC sampled
@@ -529,9 +529,7 @@ def with_dts(
             column=pl.col(f'{time_col}_prev'),
         ).alias('dt_prev'),
         pl.col('dt').diff().alias('dt_diff'),
-    ]) #.with_columns(
-        # pl.col('dt').diff().dt.days().alias('days_dt_diff'),
-    # )
+    ])
 
 
 t_unit: Literal = Literal[
@@ -546,25 +544,23 @@ t_unit: Literal = Literal[
 
 
 def detect_time_gaps(
-    df: pl.DataFrame,
+    w_dts: pl.DataFrame,
 
     time_col: str = 'time',
     # epoch sampling step diff
     expect_period: float = 60,
 
-    # datetime diff unit and gap value
-    # crypto mkts
-    # gap_dt_unit: t_unit = 'minutes',
-    # gap_thresh: int = 1,
-
     # NOTE: legacy stock mkts have venue operating hours
     # and thus gaps normally no more then 1-2 days at
     # a time.
+    gap_thresh: float = 1.,
+
+    # TODO: allow passing in a frame of operating hours?
+    # -[ ] durations/ranges for faster legit gap checks?
     # XXX -> must be valid ``polars.Expr.dt.<name>``
-    # TODO: allow passing in a frame of operating hours
-    # durations/ranges for faster legit gap checks.
-    gap_dt_unit: t_unit = 'days',
-    gap_thresh: int = 1,
+    # like 'days' which a sane default for venue closures
+    # though will detect weekend gaps which are normal :o
+    gap_dt_unit: t_unit | None = None,
 
 ) -> pl.DataFrame:
     '''
@@ -574,19 +570,24 @@ def detect_time_gaps(
     actual missing data segments.
 
     '''
-    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,
-            )().abs() > gap_thresh
-        )
+    # first select by any sample-period (in seconds unit) step size
+    # greater then expected.
+    step_gaps: pl.DataFrame = w_dts.filter(
+        pl.col('s_diff').abs() > expect_period
+    )
+
+    if gap_dt_unit is None:
+        return step_gaps
+
+    # NOTE: this flag is to indicate that on this (sampling) time
+    # scale we expect to only be filtering against larger venue
+    # closures-scale time gaps.
+    return step_gaps.filter(
+        # Second by an arbitrary dt-unit step size
+        getattr(
+            pl.col('dt_diff').dt,
+            gap_dt_unit,
+        )().abs() > gap_thresh
     )
 
 
@@ -624,6 +625,8 @@ def dedupe(
     src_df: pl.DataFrame,
     sort: bool = True,
 
+    period: float = 60,
+
 ) -> tuple[
     pl.DataFrame,  # with dts
     pl.DataFrame,  # gaps
@@ -637,33 +640,39 @@ def dedupe(
     dt-deduplicated frame.
 
     '''
-    df: pl.DataFrame = with_dts(src_df)
-
-    # TODO: enable passing existing `with_dts` df for speedup?
-    gaps: pl.DataFrame = detect_time_gaps(df)
+    wdts: pl.DataFrame = with_dts(src_df)
+    src_gaps: pl.DataFrame = detect_time_gaps(
+        wdts,
+        expect_period=period,
+        gap_dt_unit=None if period < 60 else 'days',
+    )
 
     # if no gaps detected just return carbon copies
     # and no len diff.
-    if gaps.is_empty():
+    if src_gaps.is_empty():
         return (
-            df,
-            gaps,
-            df,
+            wdts,
+            src_gaps,
+            wdts,
             0,
         )
 
     # remove duplicated datetime samples/sections
-    deduped: pl.DataFrame = df.unique(
+    deduped: pl.DataFrame = wdts.unique(
         subset=['dt'],
         maintain_order=True,
     )
     if sort:
         deduped = deduped.sort(by='time')
 
-    deduped_gaps: pl.DataFrame = detect_time_gaps(deduped)
+    deduped_gaps: pl.DataFrame = detect_time_gaps(
+        deduped,
+        expect_period=period,
+        gap_dt_unit=None if period < 60 else 'days',
+    )
 
     diff: int = (
-        df.height
+        wdts.height
         -
         deduped.height
     )
@@ -673,8 +682,8 @@ def dedupe(
         f'deduped Gaps found:\n{deduped_gaps}'
     )
     return (
-        df,
-        gaps,
+        wdts,
+        deduped_gaps,
         deduped,
         diff,
     )
@@ -708,7 +717,7 @@ def sort_diff(
 # to go from numpy struct-arrays to polars dataframes and back:
 # https://stackoverflow.com/a/72054819
 def np2pl(array: np.ndarray) -> pl.DataFrame:
-    start = time.time()
+    start: float = time.time()
 
     # XXX: thanks to this SO answer for this conversion tip:
     # https://stackoverflow.com/a/72054819