`polars.cumsum()` is now `.cum_sum()`

Since I was trying out the neat lookin `polars-fuzzy-match` (also added
for now as a core dep here) which requires the new plugin sys, plus it's
about time we synced with upstream!

Adjust some column syntax to the new `.name` sub-field-space and the
`uv` lock-file to match.

Other,
- add back `trio-typing` bc i guess something else needs it (debug
  tooling stuff in new `tractor`?)
- flip back to the `tractor` pre-main pin since the new `main`-branch
  requires new `trio` stuff we haven't ported yet..

piker/accounting/calc.py

@@ -465,7 +465,7 @@ def ledger_to_dfs(
 
         df = dfs[key] = ldf.with_columns([
 
-            pl.cumsum('size').alias('cumsize'),
+            pl.cum_sum('size').alias('cumsize'),
 
             # amount of source asset "sent" (via buy txns in
             # the market) to acquire the dst asset, PER txn.
@@ -480,7 +480,7 @@ def ledger_to_dfs(
         ]).with_columns([
 
             # rolling balance in src asset units
-            (pl.col('dst_bot').cumsum() * -1).alias('src_balance'),
+            (pl.col('dst_bot').cum_sum() * -1).alias('src_balance'),
 
             # "position operation type" in terms of increasing the
             # amount in the dst asset (entering) or decreasing the
@@ -622,7 +622,7 @@ def ledger_to_dfs(
                 # cost that was included in the least-recently
                 # entered txn that is still part of the current CSi
                 # set.
-                # => we look up the cost-per-unit cumsum and apply
+                # => we look up the cost-per-unit cum_sum and apply
                 # if over the current txn size (by multiplication)
                 # and then reverse that previusly applied cost on
                 # the txn_cost for this record.

piker/tsp/_anal.py

@@ -517,7 +517,7 @@ def with_dts(
 
     '''
     return df.with_columns([
-        pl.col(time_col).shift(1).suffix('_prev'),
+        pl.col(time_col).shift(1).name.suffix('_prev'),
         pl.col(time_col).diff().alias('s_diff'),
         pl.from_epoch(pl.col(time_col)).alias('dt'),
     ]).with_columns([
@@ -623,7 +623,7 @@ def detect_vlm_gaps(
 
 ) -> pl.DataFrame:
 
-    vnull: pl.DataFrame = w_dts.filter(
+    vnull: pl.DataFrame = df.filter(
         pl.col(col) == 0
     )
     return vnull