Info
$info
diff --git a/tedana/reporting/dynamic_figures.py b/tedana/reporting/dynamic_figures.py
index 99c1a6fae..e6cc19cc6 100644
--- a/tedana/reporting/dynamic_figures.py
+++ b/tedana/reporting/dynamic_figures.py
@@ -421,3 +421,120 @@ def _link_figures(fig, comptable_ds, div_content, io_generator):
"""
fig.js_on_event(events.Tap, _tap_callback(comptable_ds, div_content, io_generator))
return fig
+
+
+def _create_clustering_tsne_plt(cluster_labels, similarity_t_sne):
+ """Plot the clustering results of robustica using Bokeh.
+
+ Parameters
+ ----------
+ cluster_labels : (n_pca_components x n_robust_runs,) : numpy.ndarray
+ A one dimensional array that has the cluster label of each run.
+ similarity_t_sne : (n_pca_components x n_robust_runs,2) : numpy.ndarray
+ An array containing the coordinates of projected data.
+ """
+ title = "2D projection of clustered ICA runs using TSNE"
+ marker_size = 8
+ alpha = 0.8
+ line_width = 2
+
+ # First create the figure without the hover tool
+ p = plotting.figure(
+ title=title,
+ width=800,
+ height=600,
+ tools=["pan", "box_zoom", "wheel_zoom", "reset", "save"], # No hover tool here
+ )
+
+ point_renderers = [] # List to store point renderers
+
+ # Plot regular clusters
+ for cluster_id in range(np.max(cluster_labels) + 1):
+ cluster_mask = cluster_labels == cluster_id
+ if not np.any(cluster_mask):
+ continue
+
+ # Get points for this cluster
+ cluster_points = similarity_t_sne[cluster_mask]
+
+ # Add scatter plot for cluster points with hover info
+ circle_renderer = p.circle(
+ x="x",
+ y="y",
+ source=models.ColumnDataSource(
+ {
+ "x": cluster_points[:, 0],
+ "y": cluster_points[:, 1],
+ "cluster": [f"Cluster {cluster_id}"] * len(cluster_points),
+ }
+ ),
+ size=marker_size,
+ alpha=alpha,
+ line_color="black",
+ fill_color=None,
+ line_width=line_width,
+ legend_label="Clustered runs",
+ name="points",
+ )
+ point_renderers.append(circle_renderer)
+
+ # Add hull if enough points
+ if cluster_points.shape[0] > 2:
+ from scipy.spatial import ConvexHull
+
+ hull = ConvexHull(cluster_points)
+ centroid = np.mean(cluster_points[hull.vertices], axis=0)
+ scaled_points = centroid + 1.5 * (cluster_points - centroid)
+
+ # Create hull line segments
+ xs = []
+ ys = []
+ for simplex in hull.simplices:
+ xs.extend([scaled_points[simplex[0], 0], scaled_points[simplex[1], 0], None])
+ ys.extend([scaled_points[simplex[0], 1], scaled_points[simplex[1], 1], None])
+
+ # Add line without hover tooltips
+ p.line(
+ x=xs,
+ y=ys,
+ line_color="blue",
+ line_dash="dashed",
+ line_width=line_width,
+ legend_label="Cluster's boundary",
+ )
+
+ # Plot noise clusters if they exist
+ if np.min(cluster_labels) == -1:
+ noise_mask = cluster_labels == -1
+ noise_points = similarity_t_sne[noise_mask]
+
+ # Add noise points with hover tooltips
+ x_renderer = p.x(
+ x="x",
+ y="y",
+ size=marker_size * 2,
+ alpha=0.6,
+ color="red",
+ legend_label="Unclustered runs",
+ source=models.ColumnDataSource(
+ {
+ "x": noise_points[:, 0],
+ "y": noise_points[:, 1],
+ "cluster": ["Unclustered"] * len(noise_points),
+ }
+ ),
+ )
+ point_renderers.append(x_renderer)
+
+ # Add hover tool after creating all renderers, specifically for points
+ hover_tool = models.HoverTool(
+ tooltips=[("Cluster", "@cluster")],
+ renderers=point_renderers, # Only apply to stored point renderers
+ )
+ p.add_tools(hover_tool)
+
+ # Configure legend
+ p.legend.click_policy = "hide"
+ p.legend.location = "top_right"
+
+ return p
diff --git a/tedana/reporting/html_report.py b/tedana/reporting/html_report.py
index b07cd8fbe..0ed20681d 100644
--- a/tedana/reporting/html_report.py
+++ b/tedana/reporting/html_report.py
@@ -113,7 +113,9 @@ def _generate_buttons(out_dir, io_generator):
return buttons_html
-def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, bokeh_js, buttons):
+def _update_template_bokeh(
+ bokeh_id, info_table, about, prefix, references, bokeh_js, buttons, tsne
+):
"""
Populate a report with content.
@@ -133,6 +135,8 @@ def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, boke
Javascript created by bokeh.embed.components
buttons : str
HTML div created by _generate_buttons()
+ tsne : str
+ HTML div created by _create_clustering_tsne_plt()
Returns
-------
@@ -181,6 +185,7 @@ def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, boke
references=references,
javascript=bokeh_js,
buttons=buttons,
+ tsne=tsne,
)
return body
@@ -231,7 +236,7 @@ def _generate_info_table(info_dict):
return info_html
-def generate_report(io_generator: OutputGenerator) -> None:
+def generate_report(io_generator: OutputGenerator, cluster_labels, similarity_t_sne) -> None:
"""Generate an HTML report.
Parameters
@@ -320,6 +325,15 @@ def get_elbow_val(elbow_prefix):
)
varexp_pie_plot = df._create_varexp_pie_plt(comptable_cds)
+ # Create clustering plot
+ if cluster_labels is not None:
+ clustering_tsne_plot = df._create_clustering_tsne_plt(cluster_labels, similarity_t_sne)
+ tsne_script, tsne_div = embed.components(clustering_tsne_plot)
+ tsne_html = f"{tsne_script}"
+ tsne_html += (
+ f"
Robust ICA component clustering
{tsne_div}"
+ )
+
# link all dynamic figures
figs = [kappa_rho_plot, kappa_sorted_plot, rho_sorted_plot, varexp_pie_plot]
@@ -371,6 +385,7 @@ def get_elbow_val(elbow_prefix):
prefix=io_generator.prefix,
bokeh_js=kr_script,
buttons=buttons_html,
+ tsne=tsne_html,
)
html = _save_as_html(body)
with open(opj(io_generator.out_dir, f"{io_generator.prefix}tedana_report.html"), "wb") as f:
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index 1f54361e6..2f6efe52e 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -789,7 +789,7 @@ def tedana_workflow(
n_restarts = 0
seed = fixed_seed
while keep_restarting:
- mixing, seed = decomposition.tedica(
+ mixing, seed, cluster_labels, similarity_t_sne = decomposition.tedica(
data_reduced,
n_components,
seed,
@@ -1070,7 +1070,7 @@ def tedana_workflow(
)
LGR.info("Generating dynamic report")
- reporting.generate_report(io_generator)
+ reporting.generate_report(io_generator, cluster_labels, similarity_t_sne)
LGR.info("Workflow completed")
utils.teardown_loggers()