add folder for benisty 2024 conversion

src/higley_lab_to_nwb/benisty_2024/__init__.py

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+from .benisty_2024_nwbconverter import Benisty2024NWBConverter

src/higley_lab_to_nwb/benisty_2024/benisty_2024_convert_all_sessions.py

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+"""Primary script to run to convert an entire session for of data using the NWBConverter."""
+
+from pathlib import Path
+from typing import Union
+import os
+from .benisty_2024_convert_session import session_to_nwb
+
+
+
+# Parameters for conversion
+root_path = Path("/media/amtra/Samsung_T5/CN_data")
+data_dir_path = root_path / "Higley-CN-data-share"
+output_dir_path = root_path / "Higley-conversion_nwb/"
+
+session_ids = os.listdir(data_dir_path)
+stub_test = True
+for session_id in session_ids:
+    session_folder = data_dir_path / Path(session_id)
+    if os.path.isdir(session_folder):
+        session_to_nwb(
+            folder_path=session_folder,
+            output_dir_path=output_dir_path,
+            session_id=session_id,
+            stub_test=stub_test,
+        )

src/higley_lab_to_nwb/benisty_2024/benisty_2024_convert_session.py

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+"""Primary script to run to convert an entire session for of data using the NWBConverter."""
+
+from pathlib import Path
+from typing import Union
+from neuroconv.utils import load_dict_from_file, dict_deep_update
+from higley_lab_to_nwb.benisty_2024 import Benisty2024NWBConverter
+import os
+import glob
+
+
+def session_to_nwb(
+    folder_path: Union[str, Path], output_dir_path: Union[str, Path], session_id: str, stub_test: bool = False
+):
+
+    output_dir_path = Path(output_dir_path)
+    if stub_test:
+        output_dir_path = output_dir_path / "nwb_stub"
+    output_dir_path.mkdir(parents=True, exist_ok=True)
+
+    nwbfile_path = output_dir_path / f"{session_id}_new.nwb"
+
+    source_data = dict()
+    conversion_options = dict()
+
+    search_pattern = "_".join(session_id.split("_")[:2])
+
+    converter = Benisty2024NWBConverter(
+        source_data=source_data,
+    )
+
+    # Add datetime to conversion
+    metadata = converter.get_metadata()
+    date = read_session_start_time(folder_path=folder_path)
+    metadata["NWBFile"]["session_start_time"] = date
+    subject_id = session_id.split("_")[1]
+    metadata["Subject"].update(subject_id=subject_id)
+    metadata["NWBFile"].update(session_id=session_id)
+
+    # Update default metadata with the editable in the corresponding yaml file
+    editable_metadata_path = Path(__file__).parent / "benisty_2024_metadata.yaml"
+    editable_metadata = load_dict_from_file(editable_metadata_path)
+    metadata = dict_deep_update(metadata, editable_metadata)
+
+    # Add ophys metadata
+    ophys_metadata_path = Path(__file__).parent / "metadata" / "benisty_2024_ophys_metadata.yaml"
+    ophys_metadata = load_dict_from_file(ophys_metadata_path)
+    metadata = dict_deep_update(metadata, ophys_metadata)
+
+    # Run conversion
+    converter.run_conversion(
+        metadata=metadata, nwbfile_path=nwbfile_path, conversion_options=conversion_options, overwrite=True
+    )
+
+
+if __name__ == "__main__":
+
+    # Parameters for conversion
+    root_path = Path("/media/amtra/Samsung_T5/CN_data")
+    data_dir_path = root_path / "Higley-CN-data-share"
+    output_dir_path = root_path / "Higley-conversion_nwb/"
+    stub_test = True
+    session_ids = os.listdir(data_dir_path)
+    session_id = "11222019_grabAM06_vis_stim"
+    folder_path = data_dir_path / Path(session_id)
+    session_to_nwb(
+        folder_path=folder_path,
+        output_dir_path=output_dir_path,
+        session_id=session_id,
+        stub_test=stub_test,
+    )

src/higley_lab_to_nwb/benisty_2024/benisty_2024_metadata.yaml

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+NWBFile:
+  session_description:
+    A rich text description of the experiment. Can also just be the abstract of the publication.
+  institution: Institution where the lab is located
+  lab: Higley
+  experimenter:
+    - Last, First Middle
+    - Last, First Middle
+Subject:
+  species: Rattus norvegicus
+  age: P1W2D  # in ISO 8601, such as "P1W2D"
+  sex: U  # One of M, F, U, or O

src/higley_lab_to_nwb/benisty_2024/benisty_2024_nwbconverter.py

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+"""Primary NWBConverter class for this dataset."""
+
+from typing import Dict, List
+from neuroconv import NWBConverter
+
+
+
+class Benisty2024NWBConverter(NWBConverter):
+    """Primary conversion class."""
+
+    data_interface_classes = dict(
+    )

src/higley_lab_to_nwb/benisty_2024/benisty_2024_requirements.txt

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+nwb-conversion-tools==0.11.1   # Example of specific pinned dependecy
+some-extra-package==1.11.3  # Example of another extra package that's necessary for the current conversion
+roiextractors
+neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@facemap

src/higley_lab_to_nwb/benisty_2024/metadata/benisty_2024_ophys_metadata.yaml

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+Ophys:
+  Device:
+  OnePhotonSeries:
+  ImagingPlane:

src/higley_lab_to_nwb/benisty_2024/notes/benisty_2024_notes.md

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+# Notes concerning the benisty_2024 conversion
+* dual mesoscopic and 2-photon imaging data, .tiff files with ScanImage format (custom microscope) --> only mesoscopic shared ([Raw Imaging](#raw-imaging))
+* output for rigid transformation, hemodynamic corrections, and filtering 
+* ROI segmentation (functional parcellation with Local Selective Spectral Clustering)
+
+## Raw Imaging 
+### Method description from [Benisty 2024](https://www.nature.com/articles/s41593-023-01498-y):
+**Mesoscopic imaging**
+Widefield mesoscopic calcium imaging was performed using a Zeiss Axiozoom with a 1×, 0.25 NA objective with a 56 mm working distance (Zeiss). Epifluorescent excitation was provided by an LED bank (Spectra X Light Engine, Lumencor) using two output wavelengths: 395/25 (isosbestic for GRABACh3.0) and 575/25 nm (jRCaMP1b). Emitted light passed through a dual camera image splitter (TwinCam, Cairn Research) then through either a 525/50 (GRABACh3.0) or 630/75 (jRCaMP1b) emission filter (Chroma) before it reached two sCMOS cameras (Orca-Flash V3, Hamamatsu). Images were acquired at 512×512 resolution after 4× pixel binning, and each channel was acquired at 10 Hz with 20 ms exposure using HCImage software (Hamamatsu).
+
+**Two-photon imaging**
+Two-photon imaging was performed using a MOM microscope (Sutter Instruments) coupled to a 16×, 0.8 NA objective (Nikon). Excitation was driven by a Titanium-Sapphire Laser (Mai-Tai eHP DeepSee, Spectra-Physics) tuned to 920 nm. Emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu). Images were acquired at 512×512 resolution at 30 Hz using a galvo-resonant scan system controlled by ScanImage software (Vidrio).
+
+**Dual mesoscopic and two-photon imaging**
+Dual imaging was carried out using a custom microscope combining a Zeiss Axiozoom (as above) and a Sutter MOM (as above), as described previously 25. To image through the implanted prism, a long-working distance objective (20×, 0.4 NA, Mitutoyo) was used. Frame acquisitions were interleaved with an overall rate of 9.15 Hz, with each cycle alternating sequentially between a 920nm two-photon acquisition (512×512 resolution), a 395/25nm widefield excitation acquisition, and a 470/20nm widefield excitation acquisition. Widefield data were collected through a 525/50nm filter into a sCMOS camera (Orca Fusion, Hamamatsu) at 576×576 resolution after 45× pixel binning with 20ms exposure.
+
+
+### Data structure:
+- **Each .tif is a trial of N frame of [512,512]**
+
+### Imaging metadata (from Benisty paper)
+- Custom miscroscope: see description in the methods
+- stimulation wavelengths for the three optical channel: Imaging was performed by strobing 575 nm (jRCaMP1b), 470 nm (ACh3.0) and 395 nm (control)
+excitation light
+- emission filters: 525/50 or 630/75 (Chroma)
+- image dimension: 512×512 
+- indicators: Here, we use dual-color mesoscopic imaging of the red-fluorescent calcium indicator jRCaMP1b36 and the green-fluorescent ACh indicator ACh3.0 across the entire dorsal neocortex of the awake mouse to quantify the relationships between behavioral state, cortical activity and cholinergic signaling.
+
+##Lab Code
+* [Benisty 2024](https://github.com/cardin-higley-lab/Benisty_Higley_2023)