Add example notebook for 001713 (Embargoed)

001713/IBL-Widefield/public_demo/README.md

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+#  IBL Widefield - Brain-wide representations of prior information in mouse decision-making
+
+This example notebook demonstrates how to access the dataset published at [DANDI:001713](https://dandiarchive.org/dandiset/001713/draft).
+
+This study provides a brain-wide survey of representations of prior information during mouse decision-making. Using a standardized task, the International Brain Laboratory (IBL) recorded activity across multiple brain regions while mice performed a perceptual decision-making task where the probability of a stimulus appearing on the left or right changed across blocks. This data contains the raw widefield imaging data used to analyze how prior expectations about the environment are represented across the cortex.
+
+## Installing the dependencies
+
+```bash
+git clone https://github.com/dandi/example-notebooks
+cd example-notebooks/001713/IBL-Widefield
+conda env create --file environment.yml
+conda activate 001713_demo
+```
+

001713/IBL-Widefield/public_demo/environment.yml

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+name: 001713_demo
+channels:
+    - conda-forge
+dependencies:
+    - python==3.13
+    - ipywidgets
+    - pip
+    - pip:
+          - dandi
+          - jupyter
+          - matplotlib
+          - pynwb
+          - remfile
+          - plotly
+          - ibllib

001713/IBL-Widefield/public_demo/load_nwb_utils.py

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+# Core data manipulation and analysis
+from pathlib import Path
+
+# NWB access
+import h5py
+
+# DANDI access
+import remfile
+from dandi.dandiapi import DandiAPIClient
+from pynwb import NWBHDF5IO
+
+
+def load_nwb_from_dandi(dandiset_id, subject_id, session_id, description):
+    """
+    Load NWB file from DANDI Archive via streaming.
+    """
+    pattern = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_desc-{description}*.nwb"
+
+    with DandiAPIClient() as client:
+        client.dandi_authenticate()
+        assets = client.get_dandiset(dandiset_id, "draft").get_assets_by_glob(pattern=pattern, order="path")
+
+        s3_urls = []
+        for asset in assets:
+            s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
+            s3_urls.append(s3_url)
+
+        if len(s3_urls) != 1:
+            raise ValueError(f"Expected 1 file, found {len(s3_urls)} for pattern {pattern}")
+
+        s3_url = s3_urls[0]
+
+    file = remfile.File(s3_url)
+    h5_file = h5py.File(file, "r")
+    io = NWBHDF5IO(file=h5_file, load_namespaces=True)
+    nwbfile = io.read()
+
+    return nwbfile, io
+
+
+def load_nwb_local(directory_path, subject_id, session_id, description):
+    """
+    Load NWB file from local directory.
+    """
+    directory_path = Path(directory_path)
+    pattern = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_desc-{description}*.nwb"
+    matches = list(directory_path.glob(pattern))
+    if len(matches) != 1:
+        raise ValueError(f"Expected 1 file, found {len(matches)} for pattern {pattern}")
+    nwbfile_path = matches[0]
+
+    io = NWBHDF5IO(path=nwbfile_path, load_namespaces=True)
+    nwbfile = io.read()
+
+    return nwbfile, io