diff --git a/piker/data/__init__.py b/piker/data/__init__.py
index 25c2912a..207eeaa1 100644
--- a/piker/data/__init__.py
+++ b/piker/data/__init__.py
@@ -56,6 +56,7 @@ __all__: list[str] = [
     'ShmArray',
     'iterticks',
     'maybe_open_shm_array',
+    'match_from_pairs',
     'attach_shm_array',
     'open_shm_array',
     'get_shm_token',
diff --git a/piker/data/history.py b/piker/data/history.py
index 0598e08c..048769fa 100644
--- a/piker/data/history.py
+++ b/piker/data/history.py
@@ -406,7 +406,7 @@ async def start_backfill(
             # TODO: ideally these never exist but somehow it seems
             # sometimes we're writing zero-ed segments on certain
             # (teardown) cases?
-            from ._timeseries import detect_null_time_gap
+            from .tsp import detect_null_time_gap
 
             gap_indices: tuple | None = detect_null_time_gap(shm)
             while gap_indices:
diff --git a/piker/data/_timeseries.py b/piker/data/tsp.py
similarity index 87%
rename from piker/data/_timeseries.py
rename to piker/data/tsp.py
index 6da534b4..3f293d83 100644
--- a/piker/data/_timeseries.py
+++ b/piker/data/tsp.py
@@ -23,11 +23,12 @@ Routines are generally implemented in either ``numpy`` or
 
 '''
 from __future__ import annotations
-from typing import Literal
+from functools import partial
 from math import (
     ceil,
     floor,
 )
+from typing import Literal
 
 import numpy as np
 import polars as pl
@@ -38,6 +39,18 @@ from ..toolz.profile import (
     pg_profile_enabled,
     ms_slower_then,
 )
+from ..log import (
+    get_logger,
+    get_console_log,
+)
+# for "time series processing"
+subsys: str = 'piker.tsp'
+
+log = get_logger(subsys)
+get_console_log = partial(
+    get_console_log,
+    name=subsys,
+)
 
 
 def slice_from_time(
@@ -349,3 +362,49 @@ def detect_price_gaps(
     #     (pl.col(time_col) - pl.col(f'{time_col}_previous')).alias('diff'),
     # ])
     ...
+
+
+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.
+
+    '''
+    df: pl.DataFrame = with_dts(src_df)
+    gaps: pl.DataFrame = detect_time_gaps(df)
+    if not gaps.is_empty():
+
+        # remove duplicated datetime samples/sections
+        deduped: pl.DataFrame = dedup_dt(df)
+        deduped_gaps = 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 = detect_null_time_gap()
+
+        diff: int = (
+            df.height
+            -
+            deduped.height
+        )
+        was_deduped: bool = False
+        if diff:
+            was_deduped: bool = True
+
+        return (
+            df,
+            gaps,
+            deduped,
+            was_deduped,
+        )
diff --git a/piker/storage/cli.py b/piker/storage/cli.py
index c9f5bb74..34d46046 100644
--- a/piker/storage/cli.py
+++ b/piker/storage/cli.py
@@ -40,6 +40,7 @@ from piker.data import (
     maybe_open_shm_array,
     def_iohlcv_fields,
     ShmArray,
+    tsp,
 )
 from piker.data.history import (
     _default_hist_size,
@@ -136,53 +137,6 @@ 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:
-            was_deduped: bool = True
-
-        return (
-            df,
-            gaps,
-            deduped,
-            was_deduped,
-        )
-
-
 @store.command()
 def anal(
     fqme: str,
@@ -236,7 +190,7 @@ def anal(
                 gaps,
                 deduped,
                 shortened,
-            ) = dedupe(shm_df)
+            ) = tsp.dedupe(shm_df)
 
             if shortened:
                 await client.write_ohlcv(
@@ -371,7 +325,7 @@ def ldshm(
                     gaps,
                     deduped,
                     was_dded,
-                ) = dedupe(shm_df)
+                ) = tsp.dedupe(shm_df)
 
                 # TODO: maybe only optionally enter this depending
                 # on some CLI flags and/or gap detection?
diff --git a/piker/ui/_dataviz.py b/piker/ui/_dataviz.py
index c011bff0..d5a7195e 100644
--- a/piker/ui/_dataviz.py
+++ b/piker/ui/_dataviz.py
@@ -49,7 +49,7 @@ from ..data._formatters import (
     OHLCBarsAsCurveFmtr,  # OHLC converted to line
     StepCurveFmtr,  # "step" curve (like for vlm)
 )
-from ..data._timeseries import (
+from ..data.tsp import (
     slice_from_time,
 )
 from ._ohlc import (
diff --git a/piker/ui/view_mode.py b/piker/ui/view_mode.py
index a9a093d0..31a06645 100644
--- a/piker/ui/view_mode.py
+++ b/piker/ui/view_mode.py
@@ -31,7 +31,7 @@ import pendulum
 import pyqtgraph as pg
 
 from piker.types import Struct
-from ..data._timeseries import slice_from_time
+from ..data.tsp import slice_from_time
 from ..log import get_logger
 from ..toolz import Profiler