Port data apis to not touch primary index

piker/data/__init__.py

@@ -42,7 +42,7 @@ from ._sharedmem import (
     ShmArray,
     get_shm_token,
 )
-from ._source import base_ohlc_dtype
+from ._source import base_iohlc_dtype
 from ._buffer import (
     increment_ohlc_buffer,
     subscribe_ohlc_for_increment
@@ -139,6 +139,7 @@ class Feed:
     name: str
     stream: AsyncIterator[Dict[str, Any]]
     shm: ShmArray
+    # ticks: ShmArray
     _broker_portal: tractor._portal.Portal
     _index_stream: Optional[AsyncIterator[Dict[str, Any]]] = None
 
@@ -188,7 +189,7 @@ async def open_feed(
         key=sym_to_shm_key(name, symbols[0]),
 
         # use any broker defined ohlc dtype:
-        dtype=getattr(mod, '_ohlc_dtype', base_ohlc_dtype),
+        dtype=getattr(mod, '_ohlc_dtype', base_iohlc_dtype),
 
         # we expect the sub-actor to write
         readonly=True,

piker/data/_buffer.py

@@ -91,19 +91,20 @@ async def increment_ohlc_buffer(
 
                 # append new entry to buffer thus "incrementing" the bar
                 array = shm.array
-                last = array[-1:].copy()
-                (index, t, close) = last[0][['index', 'time', 'close']]
+                last = array[-1:][shm._write_fields].copy()
+                # (index, t, close) = last[0][['index', 'time', 'close']]
+                (t, close) = last[0][['time', 'close']]
 
                 # this copies non-std fields (eg. vwap) from the last datum
                 last[
-                    ['index', 'time', 'volume', 'open', 'high', 'low', 'close']
-                ][0] = (index + 1, t + delay_s, 0, close, close, close, close)
+                    ['time', 'volume', 'open', 'high', 'low', 'close']
+                ][0] = (t + delay_s, 0, close, close, close, close)
 
                 # write to the buffer
                 shm.push(last)
 
         # broadcast the buffer index step
-        yield {'index': shm._i.value}
+        yield {'index': shm._last.value}
 
 
 def subscribe_ohlc_for_increment(

piker/data/_normalize.py

@@ -33,6 +33,6 @@ def iterticks(
     ticks = quote.get('ticks', ())
     if ticks:
         for tick in ticks:
-            print(f"{quote['symbol']}: {tick}")
+            # print(f"{quote['symbol']}: {tick}")
             if tick.get('type') in types:
                 yield tick

piker/data/_source.py

@@ -15,27 +15,36 @@
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 """
-Numpy data source machinery.
+numpy data source coversion helpers.
 """
 import decimal
 from dataclasses import dataclass
 
 import numpy as np
 import pandas as pd
+# from numba import from_dtype
 
 
-# our minimum structured array layout for ohlc data
-base_ohlc_dtype = np.dtype(
-    [
-        ('index', int),
+ohlc_fields = [
     ('time', float),
     ('open', float),
     ('high', float),
     ('low', float),
     ('close', float),
     ('volume', int),
+    ('bar_wap', float),
 ]
-)
+
+ohlc_with_index = ohlc_fields.copy()
+ohlc_with_index.insert(0, ('index', int))
+
+# our minimum structured array layout for ohlc data
+base_iohlc_dtype = np.dtype(ohlc_with_index)
+base_ohlc_dtype = np.dtype(ohlc_fields)
+
+# TODO: for now need to construct this manually for readonly arrays, see
+# https://github.com/numba/numba/issues/4511
+# numba_ohlc_dtype = from_dtype(base_ohlc_dtype)
 
 # map time frame "keys" to minutes values
 tf_in_1m = {
@@ -110,18 +119,27 @@ def from_df(
         'Low': 'low',
         'Close': 'close',
         'Volume': 'volume',
+
+        # most feeds are providing this over sesssion anchored
+        'vwap': 'bar_wap',
+
+        # XXX: ib_insync calls this the "wap of the bar"
+        # but no clue what is actually is...
+        # https://github.com/pikers/piker/issues/119#issuecomment-729120988
+        'average': 'bar_wap',
     }
 
     df = df.rename(columns=columns)
 
     for name in df.columns:
-        if name not in base_ohlc_dtype.names[1:]:
+        # if name not in base_ohlc_dtype.names[1:]:
+        if name not in base_ohlc_dtype.names:
             del df[name]
 
     # TODO: it turns out column access on recarrays is actually slower:
     # https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
     # it might make sense to make these structured arrays?
-    array = df.to_records()
+    array = df.to_records(index=False)
     _nan_to_closest_num(array)
 
     return array