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‎.buildinfo‎

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# Sphinx build info version 1
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# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: e9bc9ff6c1eaad865c8b68e5313d0c5d
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config: dbd5131509cb776dd31f2553c9b414ea
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎_downloads/01eacccaa3110e4f8317ee6d3f576cce/example_kdtree.ipynb‎

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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": "3.12.9"
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"version": "3.12.10"
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}
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},
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"nbformat": 4,

‎_downloads/05fae4bb5ea585ab61e284fb627d1f1c/example_hinge_gyri.ipynb‎

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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": "3.12.9"
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"version": "3.12.10"
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}
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},
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"nbformat": 4,

‎_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎

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‎_downloads/0a4fa3820d162b9242a9817a75b75206/example_kdtree.zip‎

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‎_downloads/0c93bd4a631cfcba923eeacbecf6f593/example_mapping.zip‎

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‎_downloads/2446d4fd91e7eff0e7ff771de386f590/example_sulcal_graph.zip‎

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‎_downloads/2ae5a38ce465a74ea10a3d6153edfa78/example_dpf_Boucher.ipynb‎

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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": "3.12.9"
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"version": "3.12.10"
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}
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},
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"nbformat": 4,

‎_downloads/2f90affbab0fda990c33357048dad393/example_watershed.zip‎

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‎_downloads/3504b037f8e71415b67db962ee4718a2/example_watershed.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n\n# Example of watershed in slam\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Authors:\n# Lucile Hashimoto lucile-hashimoto\n# Guillaume Auzias <[email protected]>\n\n# License: MIT\n# sphinx_gallery_thumbnail_number = 2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NOTE: there is no visualization tool in slam, but we provide at the\n end of this script exemplare code to do the visualization with\n an external solution\n##############################################################################\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"importation of slam modules\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import slam.io as sio\nimport slam.watershed as swat\nimport slam.sulcal_graph as ssg"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"loading an examplar mesh and corresponding texture\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"path_to_mesh = \"../examples/data/example_mesh.gii\"\npath_to_mask = \"\"\npath_to_output = \"\"\n\nmesh = sio.load_mesh(path_to_mesh)\nside = \"left\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"compute curvature, dpf and voronoi\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"_, dpf, voronoi = swat.compute_mesh_features(mesh, save=False, outdir=path_to_output, check_if_exist=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"normalize watershed thresholds\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"thresh_dist, thresh_ridge, thresh_area = swat.normalize_thresholds(mesh, voronoi,\n thresh_dist=20.0,\n thresh_ridge=1.5,\n thresh_area=50.0,\n side=side)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"define the exclusion mask (cingular pole)\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"if path_to_mask:\n mask = sio.load_texture(path_to_mask).darray[0]\nelse:\n mask = None"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"extract sulcal pits and associated basins\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"basins, ridges, adjacency = swat.watershed(mesh, voronoi, dpf, thresh_dist, thresh_ridge, thresh_area, mask)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"generate the textures from watershed outputs\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"tex_labels, tex_pits, tex_ridges = swat.get_textures_from_dict(mesh, basins, ridges, save=True, outdir=path_to_output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"generate the sulcal graph\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"g = ssg.get_sulcal_graph(adjacency, basins, ridges, save=True, outdir=path_to_output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"generate the textures from graph\n\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"tex_labels, tex_pits, tex_ridges = ssg.get_textures_from_graph(g, mesh, save=True, outdir=path_to_output)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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