From 8587ac28f45a2bf3870909ad2ea99dff5cd0c72e Mon Sep 17 00:00:00 2001 From: Alessandra Trapani Date: Thu, 23 Apr 2026 16:33:38 +0200 Subject: [PATCH] Add example notebook for 001772 (Embargoed) --- .../IBL-Fiberphotometry/public_demo/README.md | 14 + .../public_demo/environment.yml | 16 + .../public_demo/fiber_photometry.ipynb | 1073 +++++++++++++++++ 3 files changed, 1103 insertions(+) create mode 100644 001772/IBL-Fiberphotometry/public_demo/README.md create mode 100644 001772/IBL-Fiberphotometry/public_demo/environment.yml create mode 100644 001772/IBL-Fiberphotometry/public_demo/fiber_photometry.ipynb diff --git a/001772/IBL-Fiberphotometry/public_demo/README.md b/001772/IBL-Fiberphotometry/public_demo/README.md new file mode 100644 index 0000000..09e7e86 --- /dev/null +++ b/001772/IBL-Fiberphotometry/public_demo/README.md @@ -0,0 +1,14 @@ +# IBL - Fiber Photometry Data + +This example notebook demonstrates how to access the dataset published at [DANDI:001772](https://dandiarchive.org/dandiset/001772/draft). + +This experiment investigates the distinct roles of key neuromodulators—dopamine, serotonin, norepinephrine, and acetylcholine—in shaping decision-making and adaptive behavior. Using the International Brain Laboratory's standardized decision-making task, which has been validated for reproducibility across behavior and neural recordings, we systematically examine neuromodulatory activity across multiple brain regions and task events. This approach allows us to link neuromodulatory dynamics to learning, behavioral strategy shifts, and theoretical models of decision making. To support this scientific effort, the project includes the development of integrated hardware for high-throughput fiber photometry acquisition, fully compatible with the IBL behavioral platform. + +## Installing the dependencies + +```bash +git clone https://github.com/dandi/example-notebooks +cd example-notebooks/001772/IBL-Fiberphotometry +conda env create --file environment.yml +conda activate 001772_demo +``` diff --git a/001772/IBL-Fiberphotometry/public_demo/environment.yml b/001772/IBL-Fiberphotometry/public_demo/environment.yml new file mode 100644 index 0000000..3c01117 --- /dev/null +++ b/001772/IBL-Fiberphotometry/public_demo/environment.yml @@ -0,0 +1,16 @@ +name: 001772_demo +channels: + - conda-forge +dependencies: + - python==3.13 + - ipywidgets + - pip + - pip: + - dandi + - jupyter + - matplotlib + - pynwb + - remfile + - plotly + - ndx_anatomical_localization + - ndx-fiber-photometry diff --git a/001772/IBL-Fiberphotometry/public_demo/fiber_photometry.ipynb b/001772/IBL-Fiberphotometry/public_demo/fiber_photometry.ipynb new file mode 100644 index 0000000..d7c417d --- /dev/null +++ b/001772/IBL-Fiberphotometry/public_demo/fiber_photometry.ipynb @@ -0,0 +1,1073 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **IBL - Fiber Photometry Data**\n", + "\n", + "This tutorial shows how to access data from [DANDI:001772](https://dandiarchive.org/dandiset/001772/draft) for the IBL fiber photometry dataset.\n", + "\n", + "## Study Overview\n", + "\n", + "This dataset contains fiber photometry recordings from mice performing the IBL decision-making task. The experiment investigates the roles of key neuromodulators — dopamine, serotonin, norepinephrine, and acetylcholine — in shaping decision-making and adaptive behavior. Neuromodulatory activity was measured via GCaMP fluorescence across multiple brain regions using chronically implanted optical fibers, with simultaneous isosbestic control recordings to correct for motion artifacts and bleaching.\n", + "\n", + "## Contents\n", + "\n", + "1. [Setup and Data Access](#setup)\n", + "2. [Session and Subject Metadata](#metadata)\n", + "3. [Fiber Photometry Metadata](#fp_metadata)\n", + "4. [Fiber Photometry Data](#data)\n", + "5. [Optical Fiber Localization](#loc)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Setup and Data Access \n", + "\n", + "## Import Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Core data manipulation and analysis\n", + "from pathlib import Path\n", + "\n", + "import h5py\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import remfile\n", + "from dandi.dandiapi import DandiAPIClient\n", + "\n", + "# NWB and DANDI access\n", + "from pynwb import NWBHDF5IO\n", + "\n", + "# Configure matplotlib\n", + "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", + "plt.rcParams[\"font.size\"] = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Access Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_nwb_from_dandi(dandiset_id, subject_id, session_id):\n", + " \"\"\"\n", + " Load NWB file from DANDI Archive via streaming.\n", + " \"\"\"\n", + " pattern = f\"sub-{subject_id}/sub-{subject_id}_ses-{session_id}*.nwb\"\n", + "\n", + " with DandiAPIClient() as client:\n", + " client.dandi_authenticate()\n", + " assets = client.get_dandiset(dandiset_id, \"draft\").get_assets_by_glob(pattern=pattern, order=\"path\")\n", + "\n", + " s3_urls = []\n", + " for asset in assets:\n", + " s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n", + " s3_urls.append(s3_url)\n", + "\n", + " if len(s3_urls) != 1:\n", + " raise ValueError(f\"Expected 1 file, found {len(s3_urls)} for pattern {pattern}\")\n", + "\n", + " s3_url = s3_urls[0]\n", + "\n", + " file = remfile.File(s3_url)\n", + " h5_file = h5py.File(file, \"r\")\n", + " io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n", + " nwbfile = io.read()\n", + "\n", + " return nwbfile, io\n", + "\n", + "\n", + "def load_nwb_local(directory_path, subject_id, session_id):\n", + " \"\"\"\n", + " Load NWB file from local directory.\n", + " \"\"\"\n", + " directory_path = Path(directory_path)\n", + " nwbfile_path = directory_path / f\"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_behavior+image.nwb\"\n", + "\n", + " if not nwbfile_path.exists():\n", + " raise FileNotFoundError(f\"NWB file not found: {nwbfile_path}\")\n", + "\n", + " io = NWBHDF5IO(path=nwbfile_path, load_namespaces=True)\n", + " nwbfile = io.read()\n", + "\n", + " return nwbfile, io" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 2. Session and Subject Metadata " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SESSION INFORMATION ===\n", + "Experiment description:\n", + " This experiment investigates the distinct roles of key neuromodulators—dopamine, serotonin, norepinephrine, and acetylcholine—in shaping decision-making and adaptive behavior. Using the International Brain Laboratory's standardized decision-making task, which has been validated for reproducibility across behavior and neural recordings, we systematically examine neuromodulatory activity across multiple brain regions and task events. This approach allows us to link neuromodulatory dynamics to learning, behavioral strategy shifts, and theoretical models of decision making. To support this scientific effort, the project includes the development of integrated hardware for high-throughput fiber photometry acquisition, fully compatible with the IBL behavioral platform.\n", + "Session description:\n", + " The task protocol(s) performed in this experimental session:\n", + "1. Training choice world — the standard IBL training task. Mice learn to turn a wheel to move a Gabor patch to the center of the screen. Uses adaptive contrast levels: training starts with only high-contrast stimuli (100%) and progressively introduces harder contrasts (25%, 12.5%, 6.25%, 0%) as performance improves. No probability blocks — stimulus appears 50/50 left/right throughout. The mouse progresses through training stages until meeting criteria for the biased task.\n", + "\n", + "Session start time:\n", + " 2025-05-15 13:01:52.429899+01:00\n" + ] + } + ], + "source": [ + "# Load session data\n", + "dandiset_id = \"001772\" # TODO Replace with actual DANDI dandiset ID\n", + "subject_id = \"ZFM-08652\" # Example subject\n", + "session_id = \"fd688232-0dd8-400b-aa66-dc23460d9f98\" # EID for the session\n", + "\n", + "\n", + "# Choose data source (DANDI streaming or local)\n", + "USE_DANDI = True # Set to False to use local files\n", + "\n", + "if USE_DANDI:\n", + " nwbfile, io = load_nwb_from_dandi(dandiset_id, subject_id, session_id)\n", + "else:\n", + " # Specify your local directory path\n", + " local_directory = \"E:/IBL-fiberphotometry-nwbfiles/full\" # Replace with actual path\n", + " nwbfile, io = load_nwb_local(local_directory, subject_id, session_id)\n", + "\n", + "print(\"=== SESSION INFORMATION ===\")\n", + "print(f\"Experiment description:\\n {nwbfile.experiment_description}\")\n", + "print(f\"Session description:\\n {nwbfile.session_description}\")\n", + "print(f\"Session start time:\\n {nwbfile.session_start_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SUBJECT INFORMATION ===\n", + "ID: ZFM-08652\n", + "Age: None\n", + "Strain: Mus musculus\n", + "Genotype: None\n", + "Sex: F\n" + ] + } + ], + "source": [ + "print(\"=== SUBJECT INFORMATION ===\")\n", + "print(f\"ID: {nwbfile.subject.subject_id}\")\n", + "print(f\"Age: {nwbfile.subject.age}\")\n", + "print(f\"Strain: {nwbfile.subject.species}\")\n", + "print(f\"Genotype: {nwbfile.subject.genotype}\")\n", + "print(f\"Sex: {nwbfile.subject.sex}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 3. Fiber Photometry Metadata " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fiber Photometry Metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FIBER PHOTOMETRY METADATA ===\n", + "All fiber photometry metadata are stored in the fiber_photometry module in lab_meta_data:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

fiber_photometry (FiberPhotometry)

fiber_photometry_table (FiberPhotometryTable)
description: For monitoring calcium dynamics in .
columns
location
Location of fiber.
excitation_wavelength_in_nm
Wavelength of excitation light in nanometers.
emission_wavelength_in_nm
Wavelength of emission light in nanometers.
indicator
Link to the indicator object.
optical_fiber
Link to the optical fiber device.
excitation_source
Link to the excitation source device.
photodetector
Link to the photodetector device.
dichroic_mirror
Link to the dichroic mirror device.
emission_filter
Link to the emission filter device.
excitation_filter
Link to the excitation filter device.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationexcitation_wavelength_in_nmemission_wavelength_in_nmindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirroremission_filterexcitation_filter
id
0LC470.0515.0GCaMP abc.Indicator at 0x2108886224912\\nFields:\\n description: GCaMP calcium sensor in <location>.\\n label: GCaMP\\n manufacturer: <manufacturer of the GCaMP>\\noptical_fiber_LC abc.OpticalFiber at 0x2108886230960\\nFields:\\n description: Chronically implanted optic fiber in LC.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2108886230624\\nFields:\\n hemisphere: <hemisphere>\\n insertion_position_ap_in_mm: 0.0\\n insertion_position_dv_in_mm: 0.0\\n insertion_position_ml_in_mm: 0.0\\n position_reference: <reference point>\\n\\n model: optical_fiber_model abc.OpticalFiberModel at 0x2108886230288\\nFields:\\n active_length_in_mm: 0.0\\n core_diameter_in_um: 0.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 0.0\\n ferrule_name: <ferrule name of the optical fiber>\\n manufacturer: <manufacturer of the optical fiber>\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.0\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_gcamp_signal abc.ExcitationSource at 0x2108886229952\\nFields:\\n description: excitation source for the sensor's fluorescence signal (470nm).\\n model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2108886229616\\nFields:\\n description: excitation source for the sensor's fluorescence signal.\\n excitation_mode: one-photon\\n manufacturer: <manufacturer of the excitation source>\\n model_number: <model number of the excitation source>\\n source_type: LED\\n wavelength_range_in_nm: [460. 490.]\\n\\nphotodetector abc.Photodetector at 0x2108886231632\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2108886231296\\nFields:\\n description: <description of the photodetector model>\\n detector_type: <type of the photodetector>\\n gain: 0.0\\n gain_unit: n.a\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2108886228608\\nFields:\\n description: <description of the dichroic mirror>\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2108886228272\\nFields:\\n description: <description of the dichroic mirror model>\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2108886229280\\nFields:\\n description: <description of emission filter>\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2108886228944\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2108872581008\\nFields:\\n description: <description of excitation filter>\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2108872580688\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
1LC415.0515.0GCaMP abc.Indicator at 0x2108886224912\\nFields:\\n description: GCaMP calcium sensor in <location>.\\n label: GCaMP\\n manufacturer: <manufacturer of the GCaMP>\\noptical_fiber_LC abc.OpticalFiber at 0x2108886230960\\nFields:\\n description: Chronically implanted optic fiber in LC.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2108886230624\\nFields:\\n hemisphere: <hemisphere>\\n insertion_position_ap_in_mm: 0.0\\n insertion_position_dv_in_mm: 0.0\\n insertion_position_ml_in_mm: 0.0\\n position_reference: <reference point>\\n\\n model: optical_fiber_model abc.OpticalFiberModel at 0x2108886230288\\nFields:\\n active_length_in_mm: 0.0\\n core_diameter_in_um: 0.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 0.0\\n ferrule_name: <ferrule name of the optical fiber>\\n manufacturer: <manufacturer of the optical fiber>\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.0\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x2108872581968\\nFields:\\n description: excitation source for the sensor's isosbestic control (415nm).\\n model: excitation_source_model_isosbestic abc.ExcitationSourceModel at 0x2108872581648\\nFields:\\n description: excitation source for the sensor's isosbestic control.\\n excitation_mode: one-photon\\n manufacturer: <manufacturer of the excitation source>\\n model_number: <model number of the excitation source>\\n source_type: LED\\n wavelength_range_in_nm: [400. 410.]\\n\\nphotodetector abc.Photodetector at 0x2108886231632\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2108886231296\\nFields:\\n description: <description of the photodetector model>\\n detector_type: <type of the photodetector>\\n gain: 0.0\\n gain_unit: n.a\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2108886228608\\nFields:\\n description: <description of the dichroic mirror>\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2108886228272\\nFields:\\n description: <description of the dichroic mirror model>\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2108886229280\\nFields:\\n description: <description of emission filter>\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2108886228944\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2108872581008\\nFields:\\n description: <description of excitation filter>\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2108872580688\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
fiber_photometry_indicators (FiberPhotometryIndicators)
GCaMP (Indicator)
label: GCaMP
description: GCaMP calcium sensor in .
manufacturer:
" + ], + "text/plain": [ + "fiber_photometry abc.FiberPhotometry at 0x2108886225584\n", + "Fields:\n", + " fiber_photometry_indicators: fiber_photometry_indicators abc.FiberPhotometryIndicators at 0x2108886225248\n", + "Fields:\n", + " indicators: {\n", + " GCaMP \n", + " }\n", + "\n", + " fiber_photometry_table: fiber_photometry_table " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== FIBER PHOTOMETRY METADATA ===\")\n", + "print(\"All fiber photometry metadata are stored in the fiber_photometry module in lab_meta_data:\")\n", + "nwbfile.lab_meta_data[\"fiber_photometry\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optical Setup Devices\n", + "\n", + "The fiber photometry hardware is stored as NWB `Device` objects. The setup consists of:\n", + "\n", + "| Device | Role |\n", + "|--------|------|\n", + "| `excitation_source_gcamp_signal` | 470 nm LED — excites Ca²⁺-bound GCaMP |\n", + "| `excitation_source_isosbestic_control` | 415 nm LED — excites GCaMP at its Ca²⁺-independent (isosbestic) point |\n", + "| `excitation_filter` | Bandpass filter on the excitation path |\n", + "| `emission_filter` | Bandpass filter (~520 nm) selecting GCaMP emission |\n", + "| `dichroic_mirror` | Separates excitation and emission light paths |\n", + "| `photodetector` | Collects the emission signal |\n", + "| `optical_fiber_LC` | Chronically implanted fiber targeting locus coeruleus (LC) |" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Excitation source: GCaMP signal (470 nm) ---\n", + "excitation_source_gcamp_signal abc.ExcitationSource at 0x2108886229952\n", + "Fields:\n", + " description: excitation source for the sensor's fluorescence signal (470nm).\n", + " model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2108886229616\n", + "Fields:\n", + " description: excitation source for the sensor's fluorescence signal.\n", + " excitation_mode: one-photon\n", + " manufacturer: \n", + " model_number: \n", + " source_type: LED\n", + " wavelength_range_in_nm: [460. 490.]\n", + "\n", + "\n", + "\n", + "--- Excitation source: Isosbestic control (415 nm) ---\n", + "excitation_source_isosbestic_control abc.ExcitationSource at 0x2108872581968\n", + "Fields:\n", + " description: excitation source for the sensor's isosbestic control (415nm).\n", + " model: excitation_source_model_isosbestic abc.ExcitationSourceModel at 0x2108872581648\n", + "Fields:\n", + " description: excitation source for the sensor's isosbestic control.\n", + " excitation_mode: one-photon\n", + " manufacturer: \n", + " model_number: \n", + " source_type: LED\n", + " wavelength_range_in_nm: [400. 410.]\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Excitation sources — GCaMP signal (470 nm) and isosbestic control (415 nm)\n", + "print(\"--- Excitation source: GCaMP signal (470 nm) ---\")\n", + "print(nwbfile.devices[\"excitation_source_gcamp_signal\"])\n", + "\n", + "print(\"\\n--- Excitation source: Isosbestic control (415 nm) ---\")\n", + "print(nwbfile.devices[\"excitation_source_isosbestic_control\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Excitation filter ---\n", + "excitation_filter abc.BandOpticalFilter at 0x2108872581008\n", + "Fields:\n", + " description: \n", + " model: excitation_filter_model abc.BandOpticalFilterModel at 0x2108872580688\n", + "Fields:\n", + " bandwidth_in_nm: 90.0\n", + " center_wavelength_in_nm: 445.0\n", + " description: excitation filter model for GCaMP fluorescence signal.\n", + " filter_type: Bandpass\n", + " manufacturer: \n", + " model_number: \n", + "\n", + "\n", + "\n", + "--- Emission filter ---\n", + "emission_filter abc.BandOpticalFilter at 0x2108886229280\n", + "Fields:\n", + " description: \n", + " model: emission_filter_model abc.BandOpticalFilterModel at 0x2108886228944\n", + "Fields:\n", + " bandwidth_in_nm: 40.0\n", + " center_wavelength_in_nm: 520.0\n", + " description: emission filter model for GCaMP fluorescence signal.\n", + " filter_type: Bandpass\n", + " manufacturer: \n", + " model_number: \n", + "\n", + "\n", + "\n", + "--- Dichroic mirror ---\n", + "dichroic_mirror abc.DichroicMirror at 0x2108886228608\n", + "Fields:\n", + " description: \n", + " model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2108886228272\n", + "Fields:\n", + " description: \n", + " manufacturer: \n", + " model_number: \n", + "\n", + " serial_number: \n", + "\n" + ] + } + ], + "source": [ + "# Optical filters and dichroic mirror\n", + "print(\"--- Excitation filter ---\")\n", + "print(nwbfile.devices[\"excitation_filter\"])\n", + "\n", + "print(\"\\n--- Emission filter ---\")\n", + "print(nwbfile.devices[\"emission_filter\"])\n", + "\n", + "print(\"\\n--- Dichroic mirror ---\")\n", + "print(nwbfile.devices[\"dichroic_mirror\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Photodetector ---\n", + "photodetector abc.Photodetector at 0x2108886231632\n", + "Fields:\n", + " description: \n", + " model: photodetector_model abc.PhotodetectorModel at 0x2108886231296\n", + "Fields:\n", + " description: \n", + " detector_type: \n", + " gain: 0.0\n", + " gain_unit: n.a\n", + " manufacturer: \n", + " model_number: \n", + " wavelength_range_in_nm: [500. 540.]\n", + "\n", + " serial_number: \n", + "\n", + "\n", + "--- Implanted optical fiber ---\n", + "optical_fiber_LC abc.OpticalFiber at 0x2108886230960\n", + "Fields:\n", + " description: Chronically implanted optic fiber in LC.\n", + " fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2108886230624\n", + "Fields:\n", + " hemisphere: \n", + " insertion_position_ap_in_mm: 0.0\n", + " insertion_position_dv_in_mm: 0.0\n", + " insertion_position_ml_in_mm: 0.0\n", + " position_reference: \n", + "\n", + " model: optical_fiber_model abc.OpticalFiberModel at 0x2108886230288\n", + "Fields:\n", + " active_length_in_mm: 0.0\n", + " core_diameter_in_um: 0.0\n", + " description: Chronically implantable optic fiber.\n", + " ferrule_diameter_in_mm: 0.0\n", + " ferrule_name: \n", + " manufacturer: \n", + " model_number: \n", + " numerical_aperture: 0.0\n", + "\n", + " serial_number: \n", + "\n" + ] + } + ], + "source": [ + "# Photodetector and implanted optical fiber\n", + "print(\"--- Photodetector ---\")\n", + "print(nwbfile.devices[\"photodetector\"])\n", + "\n", + "print(\"\\n--- Implanted optical fiber ---\")\n", + "optical_fiber = next(v for k, v in nwbfile.devices.items() if \"optical_fiber\" in k)\n", + "print(optical_fiber)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FLUORESCENT CALCIUM SENSOR ===\n", + "Fluorescent calcium sensor used in this experiment:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

fiber_photometry_indicators (FiberPhotometryIndicators)

GCaMP (Indicator)
label: GCaMP
description: GCaMP calcium sensor in .
manufacturer:
" + ], + "text/plain": [ + "fiber_photometry_indicators abc.FiberPhotometryIndicators at 0x2108886225248\n", + "Fields:\n", + " indicators: {\n", + " GCaMP \n", + " }" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== FLUORESCENT CALCIUM SENSOR ===\")\n", + "print(\"Fluorescent calcium sensor used in this experiment:\")\n", + "nwbfile.lab_meta_data[\"fiber_photometry\"].fiber_photometry_indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 4. Fiber Photometry Data \n", + "\n", + "## FiberPhotometryTable\n", + "\n", + "The `FiberPhotometryTable` links each data series to its experimental context — which optical fiber, indicator, excitation source, and emission filter were used. Each row corresponds to one recording channel." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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locationexcitation_wavelength_in_nmemission_wavelength_in_nmindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirroremission_filterexcitation_filter
id
0LC470.0515.0GCaMP abc.Indicator at 0x2108886224912\\nFields...optical_fiber_LC abc.OpticalFiber at 0x2108886...excitation_source_gcamp_signal abc.ExcitationS...photodetector abc.Photodetector at 0x210888623...dichroic_mirror abc.DichroicMirror at 0x210888...emission_filter abc.BandOpticalFilter at 0x210...excitation_filter abc.BandOpticalFilter at 0x2...
1LC415.0515.0GCaMP abc.Indicator at 0x2108886224912\\nFields...optical_fiber_LC abc.OpticalFiber at 0x2108886...excitation_source_isosbestic_control abc.Excit...photodetector abc.Photodetector at 0x210888623...dichroic_mirror abc.DichroicMirror at 0x210888...emission_filter abc.BandOpticalFilter at 0x210...excitation_filter abc.BandOpticalFilter at 0x2...
\n", + "
" + ], + "text/plain": [ + " location excitation_wavelength_in_nm emission_wavelength_in_nm \\\n", + "id \n", + "0 LC 470.0 515.0 \n", + "1 LC 415.0 515.0 \n", + "\n", + " indicator \\\n", + "id \n", + "0 GCaMP abc.Indicator at 0x2108886224912\\nFields... \n", + "1 GCaMP abc.Indicator at 0x2108886224912\\nFields... \n", + "\n", + " optical_fiber \\\n", + "id \n", + "0 optical_fiber_LC abc.OpticalFiber at 0x2108886... \n", + "1 optical_fiber_LC abc.OpticalFiber at 0x2108886... \n", + "\n", + " excitation_source \\\n", + "id \n", + "0 excitation_source_gcamp_signal abc.ExcitationS... \n", + "1 excitation_source_isosbestic_control abc.Excit... \n", + "\n", + " photodetector \\\n", + "id \n", + "0 photodetector abc.Photodetector at 0x210888623... \n", + "1 photodetector abc.Photodetector at 0x210888623... \n", + "\n", + " dichroic_mirror \\\n", + "id \n", + "0 dichroic_mirror abc.DichroicMirror at 0x210888... \n", + "1 dichroic_mirror abc.DichroicMirror at 0x210888... \n", + "\n", + " emission_filter \\\n", + "id \n", + "0 emission_filter abc.BandOpticalFilter at 0x210... \n", + "1 emission_filter abc.BandOpticalFilter at 0x210... \n", + "\n", + " excitation_filter \n", + "id \n", + "0 excitation_filter abc.BandOpticalFilter at 0x2... \n", + "1 excitation_filter abc.BandOpticalFilter at 0x2... " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the FiberPhotometryTable as a DataFrame\n", + "fp_table = nwbfile.lab_meta_data[\"fiber_photometry\"].fiber_photometry_table\n", + "fp_df = fp_table.to_dataframe()\n", + "fp_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FiberPhotometrySeries\n", + "\n", + "The raw fluorescence time series are stored in `nwbfile.acquisition`. Two channels are recorded simultaneously using frequency-division multiplexing:\n", + "\n", + "- **GCaMP signal** (470 nm excitation): reflects Ca²⁺-dependent fluorescence changes, including neural activity and motion artifacts.\n", + "- **Isosbestic control** (415 nm excitation): reflects Ca²⁺-independent changes (e.g., motion, bleaching) and is used to correct the GCaMP signal." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ACQUISITION MODULE ===\n", + "\n", + "--------------------\n", + "Name: gcamp_signal\n", + "Description: The fluorescence signal from GCaMP Ca2+ bound emission.\n", + "Metadata:\n", + " Location: LC\n", + " Excitation wavelength: 470.0 nm\n", + "--------------------\n", + "Name: isosbestic_signal\n", + "Description: The fluorescence signal from GCaMP Ca2+ independent emission (isosbestic signal).\n", + "Metadata:\n", + " Location: LC\n", + " Excitation wavelength: 415.0 nm\n" + ] + } + ], + "source": [ + "print(\"=== ACQUISITION MODULE ===\\n\")\n", + "for name, acq in nwbfile.acquisition.items():\n", + " if \"signal\" in name:\n", + " print(\"-\"*20)\n", + " print(f\"Name: {acq.name}\")\n", + " print(f\"Description: {acq.description}\")\n", + " print(f\"Metadata:\")\n", + " fp_metadata = acq.fiber_photometry_table_region[:]\n", + " print(f\" Location: {fp_metadata['location'].values[0]}\")\n", + " print(f\" Excitation wavelength: {fp_metadata['excitation_wavelength_in_nm'].values[0]} nm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling rate: 30.0 Hz\n", + "Total duration: 5407.1 s\n" + ] + } + ], + "source": [ + "gcamp_signal = nwbfile.acquisition[\"gcamp_signal\"]\n", + "isosbestic_signal = nwbfile.acquisition[\"isosbestic_signal\"]\n", + "timestamps = gcamp_signal.timestamps[:]\n", + "\n", + "# Limit to first 30 seconds for readability\n", + "mask = timestamps <= timestamps[0] + 30\n", + "t = timestamps[mask]\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(15, 4), sharex=True)\n", + "\n", + "axes[0].plot(t, gcamp_signal.data[:len(t)], linewidth=0.7, color=\"green\", label=\"GCaMP (470 nm)\")\n", + "axes[0].set_ylabel(\"Fluorescence (a.u.)\")\n", + "axes[0].set_title(\"Raw Fiber Photometry Signal — First 30 Seconds\")\n", + "axes[0].legend(loc=\"upper right\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "axes[1].plot(t, isosbestic_signal.data[:len(t)], linewidth=0.7, color=\"purple\", label=\"Isosbestic (415 nm)\")\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Fluorescence (a.u.)\")\n", + "axes[1].legend(loc=\"upper right\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Sampling rate: {1 / np.median(np.diff(timestamps)):.1f} Hz\")\n", + "print(f\"Total duration: {timestamps[-1] - timestamps[0]:.1f} s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 5. Optical Fiber Localization \n", + "\n", + "The anatomical location of each implanted fiber tip is stored in `nwbfile.lab_meta_data[\"localization\"]` using the `ndx-anatomical-localization` extension.\n", + "\n", + "Two coordinate tables are provided per fiber:\n", + "- **`AnatomicalCoordinatesIBLBregmaOpticalFibers`** — coordinates in IBL-Bregma space (origin at bregma, RAS orientation, units: µm)\n", + "- **`AnatomicalCoordinatesCCFv3OpticalFibers`** — coordinates registered to the Allen Common Coordinate Framework v3 (units: µm)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coordinate spaces available:\n", + " - AllenCCFv3\n", + " - IBLBregma\n", + "\n", + "Anatomical coordinate tables:\n", + "\n", + " AnatomicalCoordinatesCCFv3OpticalFibers\n", + " Description: Fiber tip positions in the Allen CCF v3 coordinate system (units: um). One row per unique OpticalFiber. The localized_entity column references the first FiberPhotometryTable row index associated with each fiber, since multiple rows (e.g., different excitation wavelengths) can share the same physical fiber.\n", + " Method: TODO: IBL histology / atlas registration pipeline for fiber photometry\n" + ] + }, + { + "data": { + "text/html": [ + "
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xyzlocalized_entitybrain_region
id
0NaNNaNNaNlocation excitation_wavelength_in_nm emis...TODO
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xyzlocalized_entitybrain_region
id
0NaNNaNNaNlocation excitation_wavelength_in_nm emis...TODO
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" + ], + "text/plain": [ + " x y z localized_entity brain_region\n", + "id \n", + "0 NaN NaN NaN location excitation_wavelength_in_nm emis... TODO" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "localization = nwbfile.lab_meta_data[\"localization\"]\n", + "\n", + "print(\"Coordinate spaces available:\")\n", + "for space_name in localization.spaces:\n", + " print(f\" - {space_name}\")\n", + "\n", + "print(\"\\nAnatomical coordinate tables:\")\n", + "for table_name, table in localization.anatomical_coordinates_tables.items():\n", + " print(f\"\\n {table_name}\")\n", + " print(f\" Description: {table.description}\")\n", + " print(f\" Method: {table.method}\")\n", + " df = table.to_dataframe()\n", + " display(df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ibl_fiberphotometry_to_nwb_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}