Expect multi-output fsps to yield a `dict` of history arrays

piker/fsp/_engine.py

@@ -20,7 +20,10 @@ core task logic for processing chains
 '''
 from dataclasses import dataclass
 from functools import partial
-from typing import AsyncIterator, Callable, Optional
+from typing import (
+    AsyncIterator, Callable, Optional,
+    Union,
+)
 
 import numpy as np
 import pyqtgraph as pg
@@ -101,28 +104,61 @@ async def fsp_compute(
 
     # Conduct a single iteration of fsp with historical bars input
     # and get historical output
+    history_output: Union[
+        dict[str, np.ndarray],  # multi-output case
+        np.ndarray,  # single output case
+    ]
     history_output = await out_stream.__anext__()
 
     func_name = func.__name__
     profiler(f'{func_name} generated history')
 
     # build struct array with an 'index' field to push as history
-    history = np.zeros(
-        len(history_output),
-        dtype=dst.array.dtype
-    )
 
     # TODO: push using a[['f0', 'f1', .., 'fn']] = .. syntax no?
     # if the output array is multi-field then push
     # each respective field.
-    fields = getattr(history.dtype, 'fields', None)
-    if fields:
+    # await tractor.breakpoint()
+    fields = getattr(dst.array.dtype, 'fields', None).copy()
+    fields.pop('index')
+    # TODO: nptyping here!
+    history: Optional[np.ndarray] = None
+    if fields and len(fields) > 1 and fields:
+        if not isinstance(history_output, dict):
+            raise ValueError(
+                f'`{func_name}` is a multi-output FSP and should yield a '
+                '`dict[str, np.ndarray]` for history'
+            )
+
         for key in fields.keys():
-            if key in history.dtype.fields:
-                history[func_name] = history_output
+            if key in history_output:
+                output = history_output[key]
+
+                if history is None:
+                    # using the first output, determine
+                    # the length of the struct-array that
+                    # will be pushed to shm.
+                    history = np.zeros(
+                        len(output),
+                        dtype=dst.array.dtype
+                    )
+
+                if output is None:
+                    continue
+
+                history[key] = output
 
     # single-key output stream
     else:
+        if not isinstance(history_output, np.ndarray):
+            raise ValueError(
+                f'`{func_name}` is a single output FSP and should yield an '
+                '`np.ndarray` for history'
+            )
+        history = np.zeros(
+            len(history_output),
+            dtype=dst.array.dtype
+        )
         history[func_name] = history_output
 
     # TODO: XXX: