Call `open_ledger_dfs()` for `disect` sub-cmd

Drop all the old `polars` (groupby + agg related) mangling to get a df
per fqme by delegating to the new routine and add in the `.cumsum()`ing
(per frame) as a first start on computing pps using dfs instead of
python dicts + loops as in `ppu()`.

piker/accounting/cli.py

@@ -40,10 +40,8 @@ from ._ledger import (
     # open_trade_ledger,
     # TransactionLedger,
 )
-from ._pos import (
-    PpTable,
-    load_pps_from_ledger,
-    # load_account,
+from .calc import (
+    open_ledger_dfs,
 )
 
 
@@ -241,8 +239,10 @@ def disect(
     # "fully_qualified_account_name"
     fqan: str,
     fqme: str,  # for ib
-    pdb: bool = False,
 
+    # TODO: in tractor we should really have
+    # a debug_mode ctx for wrapping any kind of code no?
+    pdb: bool = False,
     bs_mktid: str = typer.Option(
         None,
         "-bid",
@@ -252,34 +252,32 @@ def disect(
         "-l",
     ),
 ):
+    from piker.log import get_console_log
+    get_console_log(loglevel)
+
     pair: tuple[str, str]
     if not (pair := unpack_fqan(fqan)):
         raise ValueError('{fqan} malformed!?')
 
     brokername, account = pair
 
-    # ledger: TransactionLedger
-    # records: dict[str, dict]
-    table: PpTable
-    df: pl.DataFrame  # legder df
-    ppt: pl.DataFrame  # piker position table
-    df, ppt, table = load_pps_from_ledger(
+    # ledger dfs groupby-partitioned by fqme
+    dfs: dict[str, pl.DataFrame]
+    with open_ledger_dfs(
         brokername,
         account,
-        filter_by_ids={'fqme': [fqme]},
-    )
-    # sers = [
-    #     pl.Series(e['fqme'], e['cumsum'])
-    #     for e in ppt.to_dicts()
-    # ]
-    # ppt_by_id: pl.DataFrame = ppt.filter(
-    #     pl.col('fqme') == fqme,
-    # )
-    assert not df.is_empty()
-    breakpoint()
-    # with open_trade_ledger(
-    #     brokername,
-    #     account,
-    # ) as ledger:
-    #     for tid, rec in ledger.items():
-    #         bs_mktid: str = rec['bs_mktid']
+    ) as dfs:
+
+        for key in dfs:
+            df = dfs[key]
+            dfs[key] = df.with_columns([
+                pl.cumsum('size').alias('cumsum'),
+            ])
+
+        ppt = dfs[fqme]
+        assert not df.is_empty()
+        assert not ppt.is_empty()
+
+        # TODO: we REALLY need a better console REPL for this
+        # kinda thing..
+        breakpoint()