Generalize time-gap detector to accept unit and threshold

piker/storage/nativedb.py

@@ -54,6 +54,9 @@ from contextlib import asynccontextmanager as acm
 from datetime import datetime
 from pathlib import Path
 import time
+from typing import (
+    Literal,
+)
 
 # from bidict import bidict
 # import tractor
@@ -388,15 +391,38 @@ def with_dts(
         pl.from_epoch(pl.col(time_col)).alias('dt'),
     ]).with_columns([
         pl.from_epoch(pl.col(f'{time_col}_prev')).alias('dt_prev'),
-    ]).with_columns(
-        (pl.col('dt') - pl.col('dt_prev')).alias('dt_diff'),
-    )
+        pl.col('dt').diff().alias('dt_diff'),
+    ]) #.with_columns(
+        # pl.col('dt').diff().dt.days().alias('days_dt_diff'),
+    # )
+
+
+t_unit: Literal[
+    'days',
+    'hours',
+    'minutes',
+    'seconds',
+    'miliseconds',
+    'microseconds',
+    'nanoseconds',
+]
 
 
 def detect_time_gaps(
     df: pl.DataFrame,
-    expect_period: float = 60,
+
     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,
+
+    # legacy stock mkts
+    gap_dt_unit: t_unit = 'days',
+    gap_thresh: int = 2,
 
 ) -> pl.DataFrame:
     '''
@@ -406,7 +432,19 @@ def detect_time_gaps(
     actual missing data segments.
 
     '''
-    return with_dts(df).filter(pl.col('s_diff') > expect_period)
+    dt_gap_col: str = f'{gap_dt_unit}_diff'
+    return with_dts(
+        df
+    ).filter(
+        pl.col('s_diff').abs() > expect_period
+    ).with_columns(
+        getattr(
+            pl.col('dt_diff').dt,
+            gap_dt_unit,  # NOTE: must be valid ``Expr.dt.<name>``
+        )().alias(dt_gap_col)
+    ).filter(
+        pl.col(dt_gap_col).abs() > gap_thresh
+    )
 
 
 def detect_price_gaps(