Fix channel_conversion use in TimeSeries.get_data_in_units (#1923)

* fix channel_conversion in TimeSeries get_data_in_units * update CHANGELOG * simply loop range
NeurodataWithoutBorders · Jun 26, 2024 · 9a46106 · 9a46106
1 parent 90e6048
commit 9a46106
Show file tree

Hide file tree

Showing 3 changed files with 15 additions and 8 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,6 +6,9 @@
 - Simplified the introduction to NWB tutorial. @rly [#1914](https://github.com/NeurodataWithoutBorders/pynwb/pull/1914)
 - Simplified the ecephys and ophys tutorials. [#1915](https://github.com/NeurodataWithoutBorders/pynwb/pull/1915)
 
+### Bug fixes
+- Fixed use of `channel_conversion` in `TimeSeries` `get_data_in_units`. @rohanshah [1923](https://github.com/NeurodataWithoutBorders/pynwb/pull/1923)
+
 
 ## PyNWB 2.8.0 (May 28, 2024)
 

diff --git a/src/pynwb/base.py b/src/pynwb/base.py
@@ -357,7 +357,7 @@ def get_data_in_units(self):
 
         """
         if "channel_conversion" in self.fields:
-            scale_factor = self.conversion * self.channel_conversion[:, np.newaxis]
+            scale_factor = self.conversion * self.channel_conversion
         else:
             scale_factor = self.conversion
         return np.asarray(self.data) * scale_factor + self.offset

diff --git a/tests/unit/test_ecephys.py b/tests/unit/test_ecephys.py
@@ -117,22 +117,26 @@ def test_dimensions_warning(self):
                    ) in str(w[-1].message)
 
     def test_get_data_in_units(self):
-
-        data = np.asarray([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]])
-        conversion = 1.0
+        samples = 100
+        channels = 2
+        conversion = 10.0
         offset = 3.0
-        channel_conversion = np.asarray([2.0, 2.0])
+        channel_conversion = np.random.rand(channels)
+
         electrical_series = mock_ElectricalSeries(
-            data=data,
+            data=np.ones((samples, channels)),
             conversion=conversion,
             offset=offset,
             channel_conversion=channel_conversion,
         )
 
         data_in_units = electrical_series.get_data_in_units()
-        expected_data = data * conversion * channel_conversion[:, np.newaxis] + offset
 
-        np.testing.assert_almost_equal(data_in_units, expected_data)
+        for channel_index in range(channels):
+            np.testing.assert_almost_equal(
+                data_in_units[:, channel_index],
+                np.ones(samples) * conversion * channel_conversion[channel_index] + offset
+            )
 
 
 class SpikeEventSeriesConstructor(TestCase):