Updated API of module that makes sparse arrays dense.

src/ezmsg/event/sparse.py

@@ -1,32 +1,23 @@
 from dataclasses import replace
-import typing
 
-import numpy as np
 import ezmsg.core as ez
-from ezmsg.sigproc.base import GenAxisArray
-from ezmsg.util.generator import consumer
 from ezmsg.util.messages.axisarray import AxisArray
-
-
-@consumer
-def to_dense() -> typing.Generator[AxisArray, AxisArray, None]:
-    msg_out = AxisArray(np.array([]), dims=[""])
-    while True:
-        msg_in: AxisArray = yield msg_out
-        if hasattr(msg_in.data, "todense"):
-            msg_out = replace(msg_in, data=msg_in.data.todense())
-        else:
-            msg_out = msg_in
+from ezmsg.sigproc.base import BaseTransformer, BaseTransformerUnit
 
 
 class DensifySettings(ez.Settings):
     pass
 
 
-class Densify(GenAxisArray):
-    """:obj:`Unit` for :obj:`bandpower`."""
+class DensifyTransformer(BaseTransformer[DensifySettings, AxisArray, AxisArray]):
+    def _process(self, message: AxisArray) -> AxisArray:
+        if hasattr(message.data, "todense"):
+            return replace(message, data=message.data.todense())
+        else:
+            return message
 
-    SETTINGS = DensifySettings
 
-    def construct_generator(self):
-        self.STATE.gen = to_dense()
+class DensifyUnit(
+    BaseTransformerUnit[DensifySettings, AxisArray, AxisArray, DensifyTransformer]
+):
+    SETTINGS = DensifySettings

tests/test_sparse.py

@@ -3,21 +3,21 @@
 import sparse
 from ezmsg.util.messages.axisarray import AxisArray
 
-from ezmsg.event.sparse import to_dense
+from ezmsg.event.sparse import DensifyTransformer
 
 
 @pytest.mark.parametrize("sparse_input", [True, False])
-def test_to_dense(sparse_input: bool):
+def test_densify(sparse_input: bool):
     arr_shape = (100, 50, 30)
     if sparse_input:
         rng = np.random.default_rng()
         data = sparse.random(arr_shape, density=0.1, random_state=rng)
     else:
         data = np.random.rand(*arr_shape)
-    in_msg = AxisArray(data=data, dims=["time", "ch", "freq"], key="test_to_dense")
+    in_msg = AxisArray(data=data, dims=["time", "ch", "freq"], key="test_densify")
 
-    proc = to_dense()
-    out_msg = proc.send(in_msg)
+    transformer = DensifyTransformer()
+    out_msg = transformer(in_msg)
     assert out_msg.data.shape == in_msg.data.shape
     assert isinstance(out_msg.data, np.ndarray)
     if sparse_input: