From 6c175f2bff60e0c2c2892ce372bc31b07282b8e5 Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Mon, 7 Oct 2024 16:16:30 +0200
Subject: [PATCH 01/10] add minian extractor

---
 .../extractors/minian/__init__.py             |  14 ++
 .../minian/miniansegmentationextractor.py     | 170 ++++++++++++++++++
 2 files changed, 184 insertions(+)
 create mode 100644 src/roiextractors/extractors/minian/__init__.py
 create mode 100644 src/roiextractors/extractors/minian/miniansegmentationextractor.py

diff --git a/src/roiextractors/extractors/minian/__init__.py b/src/roiextractors/extractors/minian/__init__.py
new file mode 100644
index 00000000..4839a679
--- /dev/null
+++ b/src/roiextractors/extractors/minian/__init__.py
@@ -0,0 +1,14 @@
+"""A Segmentation Extractor for Minian.
+
+Modules
+-------
+miniansegmentationextractor
+    A Segmentation Extractor for Minian.
+
+Classes
+-------
+MinianSegmentationExtractor
+    A class for extracting segmentation from Minian output.
+"""
+
+from .miniansegmentationextractor import MinianSegmentationExtractor
diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
new file mode 100644
index 00000000..454cf244
--- /dev/null
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -0,0 +1,170 @@
+"""A SegmentationExtractor for Minian.
+
+Classes
+-------
+MinianSegmentationExtractor
+    A class for extracting segmentation from Minian output.
+"""
+
+from pathlib import Path
+
+import zarr
+import warnings
+import numpy as np
+
+from ...extraction_tools import PathType
+from ...segmentationextractor import SegmentationExtractor
+
+
+class MinianSegmentationExtractor(SegmentationExtractor):
+    """A SegmentationExtractor for Minian.
+
+    This class inherits from the SegmentationExtractor class, having all
+    its functionality specifically applied to the dataset output from
+    the 'Minian' ROI segmentation method.
+    """
+
+    extractor_name = "MinianSegmentation"
+    is_writable = True
+    mode = "file"
+
+    def __init__(self, folder_path: PathType):
+        """Initialize a MinianSegmentationExtractor instance.
+
+        Parameters
+        ----------
+        folder_path: str
+            The location of the folder containing minian .zarr output.
+        """
+        SegmentationExtractor.__init__(self)
+        self.folder_path = folder_path
+        self._roi_response_denoised = self._trace_extractor_read(field = "C")
+        self._roi_response_baseline = self._trace_extractor_read(field = "b0")
+        self._roi_response_neuropil = self._trace_extractor_read(field = "f")
+        self._roi_response_deconvolved = self._trace_extractor_read(field = "S")
+        self._image_maximum_projection = self._file_extractor_read("/max_proj.zarr/max_proj")
+        self._image_masks = self._roi_image_mask_read()
+        self._background_image_masks = self._background_image_mask_read()
+
+
+    def _file_extractor_read(self, zarr_group = ""):
+        """Read the zarr.
+
+        Returns
+        -------
+        zarr.open
+            The zarr object specified by self.folder_path.
+        """
+        if zarr_group not in zarr.open(self.folder_path, mode="r"):
+            warnings.warn(f"Group '{zarr_group}' not found in the Zarr store.", UserWarning)
+            return None
+        else:
+            return zarr.open(self.folder_path + f"/{zarr_group}", "r")
+
+    def _roi_image_mask_read(self):
+        """Read the image masks from the zarr output.
+
+        Returns
+        -------
+        image_masks: numpy.ndarray
+            The image masks for each ROI.
+        """
+        dataset = self._file_extractor_read("/A.zarr")
+        if dataset is None or "A" not in dataset:
+            return None
+        else:
+            return np.transpose(dataset["A"], (1,2,0))
+
+    def _background_image_mask_read(self):
+        """Read the image masks from the zarr output.
+
+        Returns
+        -------
+        image_masks: numpy.ndarray
+            The image masks for each background components.
+        """
+        dataset = self._file_extractor_read("/b.zarr")
+        if dataset is None or "b" not in dataset:
+            return None
+        else:
+            return np.expand_dims(dataset["b"], axis=2)
+
+    def _trace_extractor_read(self, field):
+        """Read the traces specified by the field from the zarr object.
+
+        Parameters
+        ----------
+        field: str
+            The field to read from the zarr object.
+
+        Returns
+        -------
+        trace: numpy.ndarray
+            The traces specified by the field.
+        """
+
+        dataset = self._file_extractor_read(f"/{field}.zarr")
+
+        if dataset is None or field not in dataset:
+            return None
+        elif dataset[field].ndim == 2:
+            return np.transpose(dataset[field])
+        elif dataset[field].ndim == 1:
+            return np.expand_dims(dataset[field], axis=1)
+
+    def get_image_size(self):
+        dataset = self._file_extractor_read("/A.zarr")
+        height = dataset["height"].shape[0]
+        width = dataset["width"].shape[0]
+        return (height, width)
+
+    def get_accepted_list(self) -> list:
+        """Get a list of accepted ROI ids.
+
+        Returns
+        -------
+        accepted_list: list
+            List of accepted ROI ids.
+        """
+        return list(range(self.get_num_rois()))
+
+    def get_rejected_list(self) -> list:
+        """Get a list of rejected ROI ids.
+
+        Returns
+        -------
+        rejected_list: list
+            List of rejected ROI ids.
+        """
+        return list()
+
+    def get_roi_ids(self) -> list:
+        dataset = self._file_extractor_read("/A.zarr")
+        return list(dataset["unit_id"])
+
+    def get_traces_dict(self) -> dict:
+        """Get traces as a dictionary with key as the name of the ROiResponseSeries.
+
+        Returns
+        -------
+        _roi_response_dict: dict
+            dictionary with key, values representing different types of RoiResponseSeries:
+                Raw Fluorescence, DeltaFOverF, Denoised, Neuropil, Deconvolved, Background, etc.
+        """
+        return dict(
+            denoised=self._roi_response_denoised,
+            baseline=self._roi_response_baseline,
+            neuropil=self._roi_response_neuropil,
+            deconvolved=self._roi_response_deconvolved,
+        )
+
+    def get_images_dict(self) -> dict:
+        """Get images as a dictionary with key as the name of the ROIResponseSeries.
+
+        Returns
+        -------
+        _roi_image_dict: dict
+            dictionary with key, values representing different types of Images used in segmentation:
+                Mean, Correlation image
+        """
+        return dict(mean=self._image_mean, correlation=self._image_correlation, maximum_projection=self._image_maximum_projection)
\ No newline at end of file

From 49d377aefd9db539e29baab2402f1058f096d84d Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Mon, 7 Oct 2024 16:16:44 +0200
Subject: [PATCH 02/10] add minian extractor to extractorlist

---
 src/roiextractors/extractorlist.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/roiextractors/extractorlist.py b/src/roiextractors/extractorlist.py
index f93c6c29..c1a2e897 100644
--- a/src/roiextractors/extractorlist.py
+++ b/src/roiextractors/extractorlist.py
@@ -28,6 +28,7 @@
 from .extractors.inscopixextractors import InscopixImagingExtractor
 from .extractors.memmapextractors import NumpyMemmapImagingExtractor
 from .extractors.memmapextractors import MemmapImagingExtractor
+from .extractors.minian import MinianSegmentationExtractor
 from .extractors.miniscopeimagingextractor import MiniscopeImagingExtractor
 from .multisegmentationextractor import MultiSegmentationExtractor
 from .multiimagingextractor import MultiImagingExtractor
@@ -62,6 +63,7 @@
     ExtractSegmentationExtractor,
     SimaSegmentationExtractor,
     CaimanSegmentationExtractor,
+    MinianSegmentationExtractor,
 ]
 
 imaging_extractor_dict = {imaging_class.extractor_name: imaging_class for imaging_class in imaging_extractor_full_list}

From 7e351908485bbf9f511a68dfa98edbfede95def7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 7 Oct 2024 14:25:43 +0000
Subject: [PATCH 03/10] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../minian/miniansegmentationextractor.py     | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index 454cf244..229473e4 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -38,16 +38,15 @@ def __init__(self, folder_path: PathType):
         """
         SegmentationExtractor.__init__(self)
         self.folder_path = folder_path
-        self._roi_response_denoised = self._trace_extractor_read(field = "C")
-        self._roi_response_baseline = self._trace_extractor_read(field = "b0")
-        self._roi_response_neuropil = self._trace_extractor_read(field = "f")
-        self._roi_response_deconvolved = self._trace_extractor_read(field = "S")
+        self._roi_response_denoised = self._trace_extractor_read(field="C")
+        self._roi_response_baseline = self._trace_extractor_read(field="b0")
+        self._roi_response_neuropil = self._trace_extractor_read(field="f")
+        self._roi_response_deconvolved = self._trace_extractor_read(field="S")
         self._image_maximum_projection = self._file_extractor_read("/max_proj.zarr/max_proj")
         self._image_masks = self._roi_image_mask_read()
         self._background_image_masks = self._background_image_mask_read()
 
-
-    def _file_extractor_read(self, zarr_group = ""):
+    def _file_extractor_read(self, zarr_group=""):
         """Read the zarr.
 
         Returns
@@ -73,7 +72,7 @@ def _roi_image_mask_read(self):
         if dataset is None or "A" not in dataset:
             return None
         else:
-            return np.transpose(dataset["A"], (1,2,0))
+            return np.transpose(dataset["A"], (1, 2, 0))
 
     def _background_image_mask_read(self):
         """Read the image masks from the zarr output.
@@ -167,4 +166,8 @@ def get_images_dict(self) -> dict:
             dictionary with key, values representing different types of Images used in segmentation:
                 Mean, Correlation image
         """
-        return dict(mean=self._image_mean, correlation=self._image_correlation, maximum_projection=self._image_maximum_projection)
\ No newline at end of file
+        return dict(
+            mean=self._image_mean,
+            correlation=self._image_correlation,
+            maximum_projection=self._image_maximum_projection,
+        )

From a7d5a2e72074badedb4af61926f0c681d2a7b669 Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Mon, 7 Oct 2024 16:52:54 +0200
Subject: [PATCH 04/10] UPDATE CHANGELOG.md

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2f4bfe33..ce044cd5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,7 @@
 ### Features
 * Added a seed to dummy generators [#361](https://github.com/catalystneuro/roiextractors/pull/361)
 * Added depth_slice for VolumetricImagingExtractors [PR #363](https://github.com/catalystneuro/roiextractors/pull/363)
+* Added MinianSegmentationExtractor: [PR #368](https://github.com/catalystneuro/roiextractors/pull/368)
 
 ### Fixes
 * Added specific error message for single-frame scanimage data [PR #360](https://github.com/catalystneuro/roiextractors/pull/360)

From a96f0638ce564fe6c93c41151c8dc2b7524caca4 Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Tue, 8 Oct 2024 10:51:32 +0200
Subject: [PATCH 05/10] add extraction of timestamps

---
 .../minian/miniansegmentationextractor.py     | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index 229473e4..b7d2931a 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -11,6 +11,7 @@
 import zarr
 import warnings
 import numpy as np
+import pandas as pd
 
 from ...extraction_tools import PathType
 from ...segmentationextractor import SegmentationExtractor
@@ -43,8 +44,10 @@ def __init__(self, folder_path: PathType):
         self._roi_response_neuropil = self._trace_extractor_read(field="f")
         self._roi_response_deconvolved = self._trace_extractor_read(field="S")
         self._image_maximum_projection = self._file_extractor_read("/max_proj.zarr/max_proj")
+        # TODO add extraction of motion correction
         self._image_masks = self._roi_image_mask_read()
         self._background_image_masks = self._background_image_mask_read()
+        self._times = self._timestamps_extractor_read()
 
     def _file_extractor_read(self, zarr_group=""):
         """Read the zarr.
@@ -111,6 +114,22 @@ def _trace_extractor_read(self, field):
         elif dataset[field].ndim == 1:
             return np.expand_dims(dataset[field], axis=1)
 
+    def _timestamps_extractor_read(self):
+        """ Extract timestamps corresponding to frame numbers of the stored denoised trace
+
+            Returns
+            -------
+            list
+                The timestamps of the denoised trace.
+        """
+
+        csv_file = self.folder_path + "timeStamps.csv"
+        df = pd.read_csv(csv_file)
+        frame_numbers = self._file_extractor_read("/C.zarr/frame")
+        filtered_df = df[df['Frame Number'].isin(frame_numbers)]
+
+        return filtered_df['Time Stamp (ms)'].tolist()
+
     def get_image_size(self):
         dataset = self._file_extractor_read("/A.zarr")
         height = dataset["height"].shape[0]

From a2010bd0cfae38b53d0aa46364102d47eb6a1e1c Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Wed, 9 Oct 2024 17:22:08 +0200
Subject: [PATCH 06/10] bug fixes

---
 .../minian/miniansegmentationextractor.py         | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index b7d2931a..dd48edda 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -43,8 +43,7 @@ def __init__(self, folder_path: PathType):
         self._roi_response_baseline = self._trace_extractor_read(field="b0")
         self._roi_response_neuropil = self._trace_extractor_read(field="f")
         self._roi_response_deconvolved = self._trace_extractor_read(field="S")
-        self._image_maximum_projection = self._file_extractor_read("/max_proj.zarr/max_proj")
-        # TODO add extraction of motion correction
+        self._image_maximum_projection = np.array(self._file_extractor_read("/max_proj.zarr/max_proj"))
         self._image_masks = self._roi_image_mask_read()
         self._background_image_masks = self._background_image_mask_read()
         self._times = self._timestamps_extractor_read()
@@ -61,7 +60,7 @@ def _file_extractor_read(self, zarr_group=""):
             warnings.warn(f"Group '{zarr_group}' not found in the Zarr store.", UserWarning)
             return None
         else:
-            return zarr.open(self.folder_path + f"/{zarr_group}", "r")
+            return zarr.open(str(self.folder_path) + f"/{zarr_group}", "r")
 
     def _roi_image_mask_read(self):
         """Read the image masks from the zarr output.
@@ -104,7 +103,6 @@ def _trace_extractor_read(self, field):
         trace: numpy.ndarray
             The traces specified by the field.
         """
-
         dataset = self._file_extractor_read(f"/{field}.zarr")
 
         if dataset is None or field not in dataset:
@@ -119,16 +117,15 @@ def _timestamps_extractor_read(self):
 
             Returns
             -------
-            list
+            np.ndarray
                 The timestamps of the denoised trace.
         """
-
-        csv_file = self.folder_path + "timeStamps.csv"
+        csv_file = self.folder_path / "timeStamps.csv"
         df = pd.read_csv(csv_file)
         frame_numbers = self._file_extractor_read("/C.zarr/frame")
-        filtered_df = df[df['Frame Number'].isin(frame_numbers)]
+        filtered_df = df[df['Frame Number'].isin(frame_numbers)]*1e-3
 
-        return filtered_df['Time Stamp (ms)'].tolist()
+        return filtered_df['Time Stamp (ms)'].to_numpy()
 
     def get_image_size(self):
         dataset = self._file_extractor_read("/A.zarr")

From 9a6321c530730ae98d7fc6341fcf8f0ceddcab91 Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Wed, 9 Oct 2024 17:22:22 +0200
Subject: [PATCH 07/10] add extractor test

---
 tests/test_miniansegmentationextractor.py | 108 ++++++++++++++++++++++
 1 file changed, 108 insertions(+)
 create mode 100644 tests/test_miniansegmentationextractor.py

diff --git a/tests/test_miniansegmentationextractor.py b/tests/test_miniansegmentationextractor.py
new file mode 100644
index 00000000..b818d561
--- /dev/null
+++ b/tests/test_miniansegmentationextractor.py
@@ -0,0 +1,108 @@
+import shutil
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import zarr
+from hdmf.testing import TestCase
+from numpy.testing import assert_array_equal
+
+from roiextractors import MinianSegmentationExtractor
+from tests.setup_paths import OPHYS_DATA_PATH
+
+
+class TestMinianSegmentationExtractor(TestCase):
+    @classmethod
+    def setUpClass(cls):
+        folder_path = str(OPHYS_DATA_PATH / "segmentation_datasets" / "minian")
+
+        cls.folder_path = Path(folder_path)
+        extractor = MinianSegmentationExtractor(folder_path=cls.folder_path)
+        cls.extractor = extractor
+
+        cls.test_dir = Path(tempfile.mkdtemp())
+
+        # denoised traces
+        dataset = zarr.open(folder_path + "/C.zarr")
+        cls.denoised_traces = np.transpose(dataset['C'])
+        cls.num_frames = len(dataset['frame'][:])
+        # deconvolved traces
+        dataset = zarr.open(folder_path + "/S.zarr")
+        cls.deconvolved_traces = np.transpose(dataset['S'])
+        # baseline traces
+        dataset = zarr.open(folder_path + "/b0.zarr")
+        cls.baseline_traces = np.transpose(dataset['b0'])
+        # neuropil trace
+        dataset = zarr.open(folder_path + "/f.zarr")
+        cls.neuropil_trace = np.expand_dims(dataset["f"], axis=1)
+
+        # ROIs masks
+        dataset = zarr.open(folder_path + "/A.zarr")
+        cls.image_masks = np.transpose(dataset['A'], (1,2,0))
+        cls.image_size = (dataset["height"].shape[0], dataset["width"].shape[0])
+        cls.num_rois = dataset["unit_id"].shape[0]
+        # background mask
+        dataset = zarr.open(folder_path + "/b.zarr")
+        cls.background_image_mask = np.expand_dims(dataset["b"], axis=2)
+        # summary image: maximum projection
+        cls.maximum_projection_image = np.array(zarr.open(folder_path + "/max_proj.zarr/max_proj"))
+
+
+
+    @classmethod
+    def tearDownClass(cls):
+        # remove the temporary directory and its contents
+        shutil.rmtree(cls.test_dir)
+
+    def test_incomplete_extractor_load(self):
+        """Check extractor can be initialized when not all traces are available."""
+        # temporary directory for testing assertion when some of the files are missing
+        folders_to_copy = ["A.zarr","C.zarr","b0.zarr", "b.zarr", "f.zarr", "max_proj.zarr", ".zgroup", "timeStamps.csv"]
+        self.test_dir.mkdir(exist_ok=True)
+
+        for folder in folders_to_copy:
+            src = Path(self.folder_path) / folder
+            dst = self.test_dir / folder
+            if src.is_dir():
+                shutil.copytree(src, dst, dirs_exist_ok=True)
+            else:
+                shutil.copy(src, dst)
+
+        extractor = MinianSegmentationExtractor(folder_path=self.test_dir)
+        traces_dict = extractor.get_traces_dict()
+        self.assertEqual(traces_dict["deconvolved"], None)
+
+    def test_image_size(self):
+        self.assertEqual(self.extractor.get_image_size(), self.image_size)
+
+    def test_num_frames(self):
+        self.assertEqual(self.extractor.get_num_frames(), self.num_frames)
+
+    def test_frame_to_time(self):
+        self.assertEqual(self.extractor.frame_to_time(frames=[0]), 0.328)
+
+    def test_num_channels(self):
+        self.assertEqual(self.extractor.get_num_channels(), 1)
+
+    def test_num_rois(self):
+        self.assertEqual(self.extractor.get_num_rois(), self.num_rois)
+
+    def test_extractor_denoised_traces(self):
+        assert_array_equal(self.extractor.get_traces(name="denoised"), self.denoised_traces)
+
+    def test_extractor_neuropil_trace(self):
+        assert_array_equal(self.extractor.get_traces(name="neuropil"), self.neuropil_trace)
+
+    def test_extractor_image_masks(self):
+        """Test that the image masks are correctly extracted."""
+        assert_array_equal(self.extractor.get_roi_image_masks(), self.image_masks)
+
+    def test_extractor_background_image_masks(self):
+        """Test that the image masks are correctly extracted."""
+        assert_array_equal(self.extractor.get_background_image_masks(), self.background_image_mask)
+
+    def test_maximum_projection_image(self):
+        """Test that the mean image is correctly loaded from the extractor."""
+        images_dict = self.extractor.get_images_dict()
+        assert_array_equal(images_dict["maximum_projection"], self.maximum_projection_image)
+

From a6873d2ca0388356b9263e664f0b2ca2a6857b82 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 9 Oct 2024 15:22:36 +0000
Subject: [PATCH 08/10] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../minian/miniansegmentationextractor.py     | 14 +++++------
 tests/test_miniansegmentationextractor.py     | 24 ++++++++++++-------
 2 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index dd48edda..678bd366 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -113,19 +113,19 @@ def _trace_extractor_read(self, field):
             return np.expand_dims(dataset[field], axis=1)
 
     def _timestamps_extractor_read(self):
-        """ Extract timestamps corresponding to frame numbers of the stored denoised trace
+        """Extract timestamps corresponding to frame numbers of the stored denoised trace
 
-            Returns
-            -------
-            np.ndarray
-                The timestamps of the denoised trace.
+        Returns
+        -------
+        np.ndarray
+            The timestamps of the denoised trace.
         """
         csv_file = self.folder_path / "timeStamps.csv"
         df = pd.read_csv(csv_file)
         frame_numbers = self._file_extractor_read("/C.zarr/frame")
-        filtered_df = df[df['Frame Number'].isin(frame_numbers)]*1e-3
+        filtered_df = df[df["Frame Number"].isin(frame_numbers)] * 1e-3
 
-        return filtered_df['Time Stamp (ms)'].to_numpy()
+        return filtered_df["Time Stamp (ms)"].to_numpy()
 
     def get_image_size(self):
         dataset = self._file_extractor_read("/A.zarr")
diff --git a/tests/test_miniansegmentationextractor.py b/tests/test_miniansegmentationextractor.py
index b818d561..14e349c3 100644
--- a/tests/test_miniansegmentationextractor.py
+++ b/tests/test_miniansegmentationextractor.py
@@ -24,21 +24,21 @@ def setUpClass(cls):
 
         # denoised traces
         dataset = zarr.open(folder_path + "/C.zarr")
-        cls.denoised_traces = np.transpose(dataset['C'])
-        cls.num_frames = len(dataset['frame'][:])
+        cls.denoised_traces = np.transpose(dataset["C"])
+        cls.num_frames = len(dataset["frame"][:])
         # deconvolved traces
         dataset = zarr.open(folder_path + "/S.zarr")
-        cls.deconvolved_traces = np.transpose(dataset['S'])
+        cls.deconvolved_traces = np.transpose(dataset["S"])
         # baseline traces
         dataset = zarr.open(folder_path + "/b0.zarr")
-        cls.baseline_traces = np.transpose(dataset['b0'])
+        cls.baseline_traces = np.transpose(dataset["b0"])
         # neuropil trace
         dataset = zarr.open(folder_path + "/f.zarr")
         cls.neuropil_trace = np.expand_dims(dataset["f"], axis=1)
 
         # ROIs masks
         dataset = zarr.open(folder_path + "/A.zarr")
-        cls.image_masks = np.transpose(dataset['A'], (1,2,0))
+        cls.image_masks = np.transpose(dataset["A"], (1, 2, 0))
         cls.image_size = (dataset["height"].shape[0], dataset["width"].shape[0])
         cls.num_rois = dataset["unit_id"].shape[0]
         # background mask
@@ -47,8 +47,6 @@ def setUpClass(cls):
         # summary image: maximum projection
         cls.maximum_projection_image = np.array(zarr.open(folder_path + "/max_proj.zarr/max_proj"))
 
-
-
     @classmethod
     def tearDownClass(cls):
         # remove the temporary directory and its contents
@@ -57,7 +55,16 @@ def tearDownClass(cls):
     def test_incomplete_extractor_load(self):
         """Check extractor can be initialized when not all traces are available."""
         # temporary directory for testing assertion when some of the files are missing
-        folders_to_copy = ["A.zarr","C.zarr","b0.zarr", "b.zarr", "f.zarr", "max_proj.zarr", ".zgroup", "timeStamps.csv"]
+        folders_to_copy = [
+            "A.zarr",
+            "C.zarr",
+            "b0.zarr",
+            "b.zarr",
+            "f.zarr",
+            "max_proj.zarr",
+            ".zgroup",
+            "timeStamps.csv",
+        ]
         self.test_dir.mkdir(exist_ok=True)
 
         for folder in folders_to_copy:
@@ -105,4 +112,3 @@ def test_maximum_projection_image(self):
         """Test that the mean image is correctly loaded from the extractor."""
         images_dict = self.extractor.get_images_dict()
         assert_array_equal(images_dict["maximum_projection"], self.maximum_projection_image)
-

From f038aa0f9865311c8abf8836c71334f3b2d6ba6f Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Thu, 17 Oct 2024 14:48:11 +0200
Subject: [PATCH 09/10] add detailed doc string

---
 .../minian/miniansegmentationextractor.py         | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index 678bd366..d54a3f3c 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -23,6 +23,21 @@ class MinianSegmentationExtractor(SegmentationExtractor):
     This class inherits from the SegmentationExtractor class, having all
     its functionality specifically applied to the dataset output from
     the 'Minian' ROI segmentation method.
+
+    Users can extract key information such as ROI traces, image masks,
+    and timestamps from the output of the Minian pipeline.
+
+    Key features:
+    - Extracts fluorescence traces (denoised, baseline, neuropil, deconvolved) for each ROI.
+    - Retrieves ROI masks and background components.
+    - Provides access to timestamps corresponding to calcium traces.
+    - Retrieves maximum projection image.
+
+    Parameters
+    ----------
+    folder_path: str
+        Path to the folder containing Minian .zarr output files.
+
     """
 
     extractor_name = "MinianSegmentation"

From a56d088fc4a178e7072ba4f06908e52a70eb134b Mon Sep 17 00:00:00 2001
From: alessandratrapani <alessandramaria.trapani@gmail.com>
Date: Thu, 17 Oct 2024 14:48:45 +0200
Subject: [PATCH 10/10] make functions more verb-like

---
 .../minian/miniansegmentationextractor.py     | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/src/roiextractors/extractors/minian/miniansegmentationextractor.py b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
index d54a3f3c..6c1e59c0 100644
--- a/src/roiextractors/extractors/minian/miniansegmentationextractor.py
+++ b/src/roiextractors/extractors/minian/miniansegmentationextractor.py
@@ -54,16 +54,16 @@ def __init__(self, folder_path: PathType):
         """
         SegmentationExtractor.__init__(self)
         self.folder_path = folder_path
-        self._roi_response_denoised = self._trace_extractor_read(field="C")
-        self._roi_response_baseline = self._trace_extractor_read(field="b0")
-        self._roi_response_neuropil = self._trace_extractor_read(field="f")
-        self._roi_response_deconvolved = self._trace_extractor_read(field="S")
-        self._image_maximum_projection = np.array(self._file_extractor_read("/max_proj.zarr/max_proj"))
-        self._image_masks = self._roi_image_mask_read()
-        self._background_image_masks = self._background_image_mask_read()
-        self._times = self._timestamps_extractor_read()
-
-    def _file_extractor_read(self, zarr_group=""):
+        self._roi_response_denoised = self._read_trace_from_zarr_filed(field="C")
+        self._roi_response_baseline = self._read_trace_from_zarr_filed(field="b0")
+        self._roi_response_neuropil = self._read_trace_from_zarr_filed(field="f")
+        self._roi_response_deconvolved = self._read_trace_from_zarr_filed(field="S")
+        self._image_maximum_projection = np.array(self._read_zarr_group("/max_proj.zarr/max_proj"))
+        self._image_masks = self._read_roi_image_mask_from_zarr_filed()
+        self._background_image_masks = self._read_background_image_mask_from_zarr_filed()
+        self._times = self._read_timestamps_from_csv()
+
+    def _read_zarr_group(self, zarr_group=""):
         """Read the zarr.
 
         Returns
@@ -77,7 +77,7 @@ def _file_extractor_read(self, zarr_group=""):
         else:
             return zarr.open(str(self.folder_path) + f"/{zarr_group}", "r")
 
-    def _roi_image_mask_read(self):
+    def _read_roi_image_mask_from_zarr_filed(self):
         """Read the image masks from the zarr output.
 
         Returns
@@ -85,13 +85,13 @@ def _roi_image_mask_read(self):
         image_masks: numpy.ndarray
             The image masks for each ROI.
         """
-        dataset = self._file_extractor_read("/A.zarr")
+        dataset = self._read_zarr_group("/A.zarr")
         if dataset is None or "A" not in dataset:
             return None
         else:
             return np.transpose(dataset["A"], (1, 2, 0))
 
-    def _background_image_mask_read(self):
+    def _read_background_image_mask_from_zarr_filed(self):
         """Read the image masks from the zarr output.
 
         Returns
@@ -99,13 +99,13 @@ def _background_image_mask_read(self):
         image_masks: numpy.ndarray
             The image masks for each background components.
         """
-        dataset = self._file_extractor_read("/b.zarr")
+        dataset = self._read_zarr_group("/b.zarr")
         if dataset is None or "b" not in dataset:
             return None
         else:
             return np.expand_dims(dataset["b"], axis=2)
 
-    def _trace_extractor_read(self, field):
+    def _read_trace_from_zarr_filed(self, field):
         """Read the traces specified by the field from the zarr object.
 
         Parameters
@@ -118,7 +118,7 @@ def _trace_extractor_read(self, field):
         trace: numpy.ndarray
             The traces specified by the field.
         """
-        dataset = self._file_extractor_read(f"/{field}.zarr")
+        dataset = self._read_zarr_group(f"/{field}.zarr")
 
         if dataset is None or field not in dataset:
             return None
@@ -127,7 +127,7 @@ def _trace_extractor_read(self, field):
         elif dataset[field].ndim == 1:
             return np.expand_dims(dataset[field], axis=1)
 
-    def _timestamps_extractor_read(self):
+    def _read_timestamps_from_csv(self):
         """Extract timestamps corresponding to frame numbers of the stored denoised trace
 
         Returns
@@ -137,13 +137,13 @@ def _timestamps_extractor_read(self):
         """
         csv_file = self.folder_path / "timeStamps.csv"
         df = pd.read_csv(csv_file)
-        frame_numbers = self._file_extractor_read("/C.zarr/frame")
+        frame_numbers = self._read_zarr_group("/C.zarr/frame")
         filtered_df = df[df["Frame Number"].isin(frame_numbers)] * 1e-3
 
         return filtered_df["Time Stamp (ms)"].to_numpy()
 
     def get_image_size(self):
-        dataset = self._file_extractor_read("/A.zarr")
+        dataset = self._read_zarr_group("/A.zarr")
         height = dataset["height"].shape[0]
         width = dataset["width"].shape[0]
         return (height, width)
@@ -169,7 +169,7 @@ def get_rejected_list(self) -> list:
         return list()
 
     def get_roi_ids(self) -> list:
-        dataset = self._file_extractor_read("/A.zarr")
+        dataset = self._read_zarr_group("/A.zarr")
         return list(dataset["unit_id"])
 
     def get_traces_dict(self) -> dict: