Add _roi_response_denoised to store denoised traces

docs/source/build_re.rst

@@ -25,6 +25,7 @@ To build a custom SegmentationExtractor that interfaces with the output of a cus
             self._roi_response_raw = self._load_traces()# define a method to extract Flourescence traces
             self._roi_response_dff = self._load_traces()# define a method to extract dF/F traces if any else None
             self._roi_response_neuropil = self._load_traces()# define a method to extract neuropil info if any else None
+            self._roi_response_denoised = self._load_traces() # define a method to extract denoised traces if any else None
             self._roi_response_deconvolved = self._load_traces() # define a method to extract deconvolved traces if any else None
             self._image_correlation = self._load_summary_images()# define method to extract a correlation image else None
             self._image_mean = self._load_summary_images() # define method to extract a mean image else None

src/roiextractors/extractors/caiman/caimansegmentationextractor.py

@@ -56,7 +56,8 @@ def __init__(self, file_path: PathType):
         self.file_path = file_path
         self._dataset_file = self._file_extractor_read()
         self._roi_response_dff = self._trace_extractor_read("F_dff")
-        self._roi_response_neuropil = self._trace_extractor_read("C")
+        self._roi_response_denoised = self._trace_extractor_read("C")
+        self._roi_response_neuropil = self._trace_extractor_read("b")
         self._roi_response_deconvolved = self._trace_extractor_read("S")
         self._image_correlation = self._summary_image_read()
         self._sampling_frequency = self._dataset_file["params"]["data"]["fr"][()]
@@ -177,8 +178,10 @@ def write_segmentation(segmentation_object, save_path, overwrite=True):
             estimates = f.create_group("estimates")
             params = f.create_group("params")
             # adding to estimates:
+            if segmentation_object.get_traces(name="denoised") is not None:
+                estimates.create_dataset("C", data=segmentation_object.get_traces(name="denoised"))
             if segmentation_object.get_traces(name="neuropil") is not None:
-                estimates.create_dataset("C", data=segmentation_object.get_traces(name="neuropil"))
+                estimates.create_dataset("b", data=segmentation_object.get_traces(name="neuropil"))
             if segmentation_object.get_traces(name="dff") is not None:
                 estimates.create_dataset("F_dff", data=segmentation_object.get_traces(name="dff"))
             if segmentation_object.get_traces(name="deconvolved") is not None:

src/roiextractors/segmentationextractor.py

@@ -40,6 +40,7 @@ def __init__(self):
         self._roi_response_raw = None
         self._roi_response_dff = None
         self._roi_response_neuropil = None
+        self._roi_response_denoised = None
         self._roi_response_deconvolved = None
         self._image_correlation = None
         self._image_mean = None
@@ -223,13 +224,14 @@ def get_traces_dict(self):
         -------
         _roi_response_dict: dict
             dictionary with key, values representing different types of RoiResponseSeries:
-                Fluorescence, Neuropil, Deconvolved, Background, etc.
+                Raw Fluorescence, DeltaFOverF, Denoised, Neuropil, Deconvolved, Background, etc.
         """
         return dict(
             raw=self._roi_response_raw,
             dff=self._roi_response_dff,
             neuropil=self._roi_response_neuropil,
             deconvolved=self._roi_response_deconvolved,
+            denoised=self._roi_response_denoised,
         )
 
     def get_images_dict(self):

src/roiextractors/testing.py

@@ -356,7 +356,7 @@ def check_segmentation_return_types(seg: SegmentationExtractor):
     )
     assert isinstance(seg.get_traces_dict(), dict)
     assert isinstance(seg.get_images_dict(), dict)
-    assert {"raw", "dff", "neuropil", "deconvolved"} == set(seg.get_traces_dict().keys())
+    assert {"raw", "dff", "neuropil", "deconvolved", "denoised"} == set(seg.get_traces_dict().keys())
 
 
 def check_imaging_equal(