From ddfc16a90bbadf6e0fef32d8c3e77091112e175d Mon Sep 17 00:00:00 2001 From: Abhishek Yenpure Date: Fri, 27 Jun 2025 11:12:02 -0700 Subject: [PATCH] fix(janelia example): Adding Janelia COSEM example notebook --- examples/jupyter/janelia_cosem_dataset.ipynb | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 examples/jupyter/janelia_cosem_dataset.ipynb diff --git a/examples/jupyter/janelia_cosem_dataset.ipynb b/examples/jupyter/janelia_cosem_dataset.ipynb new file mode 100644 index 0000000..be2496c --- /dev/null +++ b/examples/jupyter/janelia_cosem_dataset.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4cb17f8e-d21c-44fe-91f1-91edb1440023", + "metadata": {}, + "source": [ + "### 1. Load a Janelia COSEM Dataset into Xarray\n", + "\n", + "This function, `open_cosem_dataset`, helps load a 3D image volume from the [Janelia COSEM](https://www.janelia.org/project-team/cosem) dataset into an `xarray.Dataset`. It does the following:\n", + "\n", + "- Constructs the full URL from the dataset root and group path.\n", + "- Opens a Zarr array using anonymous access via `fsspec`.\n", + "- Extracts voxel spacing metadata (in nanometers) and uses it to create physical coordinates (in meters).\n", + "- Wraps the data as an `xarray.DataArray` and then into a `Dataset` for ease of use.\n", + "\n", + "The result is a spatially aware dataset, with proper coordinates, ready for visualization or analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "362a7c99-9b51-40d5-82c0-c9e786e2dbc0", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import fsspec\n", + "import zarr\n", + "import numpy as np\n", + "\n", + "\n", + "def open_cosem_dataset(dataset_url, group_path, var_name=\"values\"):\n", + " \"\"\"\n", + " Open a COSEM Zarr volume as an xarray.Dataset with physical coordinates.\n", + "\n", + " Parameters\n", + " ----------\n", + " dataset_url : str\n", + " e.g. \"s3://janelia-cosem-datasets/jrc_hela-1/jrc_hela-1.zarr\"\n", + " group_path : str\n", + " e.g. \"recon-1/em/fibsem-uint8/s4\"\n", + " var_name : str\n", + " Name to assign to the variable in the Dataset\n", + "\n", + " Returns\n", + " -------\n", + " xr.Dataset\n", + " \"\"\"\n", + " # Open remote store\n", + " full_url = f\"{dataset_url}/{group_path}\"\n", + " z = zarr.open_array(full_url, mode=\"r\", storage_options={\"anon\": True})\n", + "\n", + " # Default dims (Z, Y, X in COSEM, we'll reverse to X, Y, Z for xarray consistency)\n", + " shape = z.shape\n", + " dims = [\"z\", \"y\", \"x\"]\n", + " if len(shape) != 3:\n", + " raise ValueError(f\"Expected 3D data, got shape: {shape}\")\n", + "\n", + " # Try to get voxel spacing in nm\n", + " voxel_size_nm = z.attrs.get(\"pixelResolution\", {}).get(\n", + " \"dimensions\", [4.0, 4.0, 4.0]\n", + " ) # [Z, Y, X]\n", + "\n", + " # Build coordinate arrays in physical units (meters)\n", + " coords = {}\n", + " for dim, size, spacing_nm in zip(dims, shape, voxel_size_nm):\n", + " coords[dim] = np.arange(size) * spacing_nm * 1e-9 # convert nm → meters\n", + "\n", + " # Construct DataArray with coords\n", + " da = xr.DataArray(z, dims=dims, coords=coords, attrs=dict(z.attrs))\n", + "\n", + " # Wrap into a Dataset\n", + " ds = xr.Dataset({var_name: da})\n", + " return ds" + ] + }, + { + "cell_type": "markdown", + "id": "251c71cd-7db9-4ab9-8d02-92dfe0f693c6", + "metadata": {}, + "source": [ + "### 2. Retrieve the COSEM Dataset\n", + "\n", + "Here, we use the `open_cosem_dataset` function to load a specific FIB-SEM volume from the COSEM dataset collection:\n", + "\n", + "- `url` points to the dataset root in the S3 bucket.\n", + "- `group` identifies the subvolume (at a particular resolution level).\n", + "\n", + "The result is assigned to `ds`, an `xarray.Dataset` that includes:\n", + "\n", + "- A single data variable called `\"values\"`,\n", + "- Dimensions named `z`, `y`, and `x`,\n", + "- Coordinates in physical space (meters), based on metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f23562c-bc1b-4e60-b372-d660be6c6bb7", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"s3://janelia-cosem-datasets/jrc_hela-1/jrc_hela-1.zarr\"\n", + "group = \"recon-1/em/fibsem-uint8/s4\"\n", + "\n", + "ds = open_cosem_dataset(url, group)" + ] + }, + { + "cell_type": "markdown", + "id": "234cd32a-2675-439e-9202-58ea492557e7", + "metadata": {}, + "source": [ + "### 3. Visualize with Pan3D Viewer\n", + "\n", + "We now create a `Pan3D` viewer instance to visualize the dataset.\n", + "\n", + "- The dataset `ds` is passed to `XArrayViewer`, which sets up a Trame-based UI for interactive volume exploration.\n", + "- Once `viewer.ui.ready` resolves, the viewer is fully initialized.\n", + "- The `viewer.ui` object can be rendered directly in the notebook (if supported), or externally in a browser window.\n", + "\n", + "This viewer makes it easy to inspect and explore 3D microscopy datasets with physical scale preserved.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "264f7e80-578f-4b65-b247-dd5f87c2ba17", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the instance of the viewer, and pass the filter to the pipeline argument\n", + "\n", + "from pan3d.viewers.preview import XArrayViewer\n", + "\n", + "viewer = XArrayViewer(xarray=ds, server=\"preview\")\n", + "await viewer.ui.ready\n", + "\n", + "viewer.ui" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}