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Merge pull request #43 from nipreps/codex/update-book.yml-and-tutorial-markdown-files
ENH: Factor tutorial dataset access into helper
2 parents 35919c9 + c1ea7d1 commit c428c58

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‎.github/workflows/book.yml‎

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- name: Install project environment
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run: pixi install --locked
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- name: Prepare tutorial data
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run: |
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set -xeuo pipefail
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data_dir="${{ github.workspace }}/data"
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data_file="$data_dir/dwi_full_brainmask.h5"
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mkdir -p "$data_dir"
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curl -L \
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https://files.osf.io/v1/resources/8k95s/providers/osfstorage/68e5464a451cf9cf1fc51a53 \
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--output "$data_file"
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test -s "$data_file"
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echo "NIPREPS_TUTORIAL_DATA=$data_file" >> "$GITHUB_ENV"
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# Build the page
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- name: Build the book
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run: pixi run build-book

‎.gitignore‎

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.pixi/*
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!.pixi/config.toml
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report.svg
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data/dwi_full_brainmask.h5
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__pycache__

‎docs/notebook/01-intro.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "07c0e96c",
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"id": "41f638b5",
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"metadata": {},
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"source": [
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"# The problem of head-motion in dMRI"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "045bf720",
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"id": "c48d22ad",
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"metadata": {
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"tags": [
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"remove-cell"
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},
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{
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"cell_type": "markdown",
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"id": "1e062d91",
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"id": "0b4fe167",
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"metadata": {},
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"source": [
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"A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "53de77d7",
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"id": "607ba45b",
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"metadata": {
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"tags": [
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"remove-input"
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]
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},
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"outputs": [],
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"source": [
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"HTML(\"\"\"<video width=\"640\" height=\"680\" loop=\"yes\" muted=\"yes\" autoplay=\"yes\" controls=\"yes\"><source src=\"../assets/videos/hm-sagittal.mp4\" type=\"video/mp4\"/></video>\"\"\")"
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"HTML(\"\"\"<video width=\"640\" height=\"680\" loop=\"yes\" muted=\"yes\" autoplay=\"yes\" controls=\"yes\"><source src=\"../videos/hm-sagittal.mp4\" type=\"video/mp4\"/></video>\"\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e5c553c",
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"id": "28cc163f",
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"metadata": {},
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"source": [
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"## Dimensions of the head-motion problem\n",
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"* A reliable registration framework to estimate the misalignment.\n",
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"* The integration of the three elements above in an iterable loop."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fd4f6138-709b-4368-b1f6-69123edcaf13",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"jupytext": {
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"default_lexer": "python"
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"default_lexer": "ipython3"
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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}
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},
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"nbformat": 4,

‎docs/notebook/02-data.ipynb‎

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"cells": [
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"id": "a41f16fb",
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"metadata": {},
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"source": [
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"# Introduction to dMRI data"
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{
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"execution_count": null,
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"id": "95dc8c25",
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"metadata": {
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"tags": [
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"remove-cell"
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},
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{
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"cell_type": "markdown",
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"id": "da052ddd",
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"metadata": {},
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"source": [
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"Diffusion imaging probes the random, microscopic movement of water molecules by using MRI sequences that are sensitive to the geometry and environmental organization surrounding these protons.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "946abc6d",
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"metadata": {
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"tags": [
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"remove-input"
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]
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},
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"outputs": [],
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"HTML(\"\"\"<video loop=\"yes\" muted=\"yes\" autoplay=\"yes\" controls=\"yes\"><source src=\"../assets/videos/dMRI-signal-movie.mp4\" type=\"video/mp4\"/></video>\"\"\")"
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"HTML(\"\"\"<video loop=\"yes\" muted=\"yes\" autoplay=\"yes\" controls=\"yes\"><source src=\"../videos/dMRI-signal-movie.mp4\" type=\"video/mp4\"/></video>\"\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3abc4df2",
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"metadata": {},
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"source": [
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"*Sourced from Dr. A. Rokem, DIPY Workshop 2021*\n",
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"execution_count": null,
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"id": "c5a0b738",
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"cell_type": "markdown",
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"id": "fe4c7e01",
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"metadata": {},
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"source": [
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"This code implements several *attributes* as well as a *behavior* - the `__len__` *method*.\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "fb24834b",
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{
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"cell_type": "markdown",
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"id": "17fe71bd",
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"metadata": {},
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"source": [
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"The output of this `print()` statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.\n",
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"id": "e18a2488",
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},
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"cell_type": "markdown",
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"id": "ed3a0dc1",
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"In this case, the dataset is reporting to have 102 diffusion-weighted samples.\n",
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"cell_type": "markdown",
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"Perhaps, the most interesting aspect of our DWI data structure is that it allows indexed access.\n",
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"## Visualizing the data\n",
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},
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"cell_type": "markdown",
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"When calling `plot_dwi()`, we will be required a 3D data object, and optionally, an `affine` orientation matrix and a `gradient` or b-vector.\n",
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"cell_type": "code",
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"cell_type": "code",
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"We get a $4\\times102$ -- three spatial coordinates ($b_x$, $b_y$, $b_z$) of the unit-norm \"*b-vector*\", plus the gradient sensitization magnitude (the \"*b-value*\"), with a total of 102 different orientations for the case at hand.\n",
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"default_lexer": "python"
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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"language_info": {
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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"nbformat": 4,

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