FIX] fixes several plotting details in glm reports

doc/changes/latest.rst

@@ -14,6 +14,8 @@ Fixes
 - :bdg-dark:`Code` Fix color bar handling with color map with only 1 level (:gh:`4255` by `Rémi Gau`_).
 - :bdg-dark:`Code` Fix positions of the markers on the images on the sphere masker reports (:gh:`4285` by `Rémi Gau`_).
 - :bdg-dark:`Code` Make sure that :class:`nilearn.maskers.NiftiSpheresMasker` reports displays properly when it contains only 1 sphere (:gh:`4269` by `Rémi Gau`_).
+- :bdg-dark:`Code` Miscellaneous fixes in GLM reports (only display FIR delay if FIR is used, display color bar "Z score" legend...) (:gh:`4266` by `Rémi Gau`_).
+
 
 Enhancements
 ------------
@@ -26,3 +28,4 @@ Changes
 - :bdg-primary:`Doc` Render the description of the templates, atlases and datasets of the :mod:`nilearn.datasets` as part of the documentation (:gh:`4232` by `Rémi Gau`_).
 - :bdg-dark:`Code` Change the colormap to ``gray`` for the background image in the :class:`nilearn.maskers.NiftiSpheresMasker` (:gh:`4269` by `Rémi Gau`_).
 - :bdg-dark:`Code` Remove unused ``**kwargs`` from :func:`nilearn.plotting.view_img` and :func:`nilearn.plotting.plot_surf` (:gh:`4270` by `Rémi Gau`_).
+- :bdg-dark:`Code` Use red to blue color map in the GLM reports (:gh:`4266` by `Rémi Gau`_).

doc/visual_testing/reporter_visual_inspection_suite.py

@@ -77,6 +77,7 @@ def report_flm_adhd_dmn():
         contrasts=contrasts,
         title="ADHD DMN Report",
         cluster_threshold=15,
+        alpha=0.0009,
         height_control="bonferroni",
         min_distance=8.0,
         plot_type="glass",
@@ -176,6 +177,7 @@ def report_flm_bids_features():
         contrasts="StopSuccess - Go",
         title=title,
         cluster_threshold=3,
+        plot_type="glass",
     )
 
     report.save_as_html(REPORTS_DIR / "flm_bids_features.html")
@@ -252,15 +254,20 @@ def report_slm_oasis():
         oasis_dataset.gray_matter_maps, design_matrix=design_matrix
     )
 
-    # TODO The following fails:
+    # TODO the following crashes
+    # contrast = [np.array([1, 0]), np.array([0, 1])]
     # contrast = [[1, 0, 0], [0, 1, 0]]
+
+    # The following are equivalent
+    # contrast = [np.array([1, 0, 0]), np.array([0, 1, 0])]
     contrast = ["age", "sex"]
 
     report = make_glm_report(
         model=second_level_model,
         contrasts=contrast,
         bg_img=datasets.fetch_icbm152_2009()["t1"],
         height_control=None,
+        plot_type="glass",
     )
 
     report.save_as_html(REPORTS_DIR / "slm_oasis.html")

nilearn/glm/first_level/first_level.py

@@ -73,7 +73,7 @@ def mean_scaling(Y, axis=0):
     mean = Y.mean(axis=axis)
     if (mean == 0).any():
         warn(
-            "Mean values of 0 observed."
+            "Mean values of 0 observed. "
             "The data have probably been centered."
             "Scaling might not work as expected",
             UserWarning,

nilearn/reporting/glm_reporter.py

@@ -14,13 +14,15 @@
 import warnings
 from collections import OrderedDict
 from collections.abc import Iterable
+from decimal import Decimal
 from html import escape
 
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 
 from nilearn.plotting import plot_glass_brain, plot_roi, plot_stat_map
+from nilearn.plotting.cm import _cmap_d as nilearn_cmaps
 from nilearn.plotting.img_plotting import MNI152TEMPLATE
 from nilearn.plotting.matrix_plotting import (
     plot_contrast_matrix,
@@ -36,6 +38,7 @@
     from nilearn.glm.thresholding import threshold_stats_img
 
 from nilearn._utils import check_niimg
+from nilearn._utils.niimg import safe_get_data
 from nilearn.maskers import NiftiMasker
 from nilearn.reporting.get_clusters_table import get_clusters_table
 from nilearn.reporting.utils import figure_to_svg_quoted
@@ -480,13 +483,15 @@
         "smoothing_fwhm",
         "target_affine",
         "slice_time_ref",
-        "fir_delays",
     ]
     attribute_units = {
         "t_r": "s",
         "high_pass": "Hz",
     }
 
+    if hasattr(model, "hrf_model") and getattr(model, "hrf_model") == "fir":
+        selected_attributes.append("fir_delays")
+
     selected_attributes.sort()
     display_attributes = OrderedDict(
         (attr_name, getattr(model, attr_name))
@@ -573,7 +578,8 @@
         dmtx_text_ = string.Template(dmtx_template_text)
         dmtx_plot = plot_design_matrix(design_matrix)
         dmtx_title = f"Run {dmtx_count}"
-        plt.title(dmtx_title, y=0.987)
+        if len(design_matrices) > 1:
+            plt.title(dmtx_title, y=1.025, x=-0.1)
         dmtx_plot = _resize_plot_inches(dmtx_plot, height_change=0.3)
         url_design_matrix_svg = _plot_to_svg(dmtx_plot)
         # prevents sphinx-gallery & jupyter from scraping & inserting plots
@@ -811,7 +817,7 @@
         )
         table_details_html = _dataframe_to_html(
             table_details,
-            precision=2,
+            precision=3,
             header=False,
             classes="cluster-details-table",
         )
@@ -877,6 +883,8 @@
         This is simpler than overloading the class using inheritance,
         especially given limited Python2 use at time of release.
         """
+        if alpha < 0.001:
+            alpha = f"{Decimal(alpha):.2E}"
         if os.sys.version_info.major == 2:
             table_details.update({"alpha": alpha})
         else:
@@ -953,12 +961,28 @@
         SVG Image Data URL representing a statistical map.
 
     """
+    data = safe_get_data(stat_img, ensure_finite=True)
+    stat_map_min = np.nanmin(data)
+    stat_map_max = np.nanmax(data)
+    symmetric_cbar = True
+    cmap = "bwr"
+    if stat_map_min >= 0.0:
+        symmetric_cbar = False
+        cmap = "red_transparent_full_alpha_range"
+    elif stat_map_max <= 0.0:
+        symmetric_cbar = False
+        cmap = "blue_transparent_full_alpha_range"
+        cmap = nilearn_cmaps[cmap].reversed()
+
     if plot_type == "slice":
         stat_map_plot = plot_stat_map(
             stat_img,
             bg_img=bg_img,
             cut_coords=cut_coords,
             display_mode=display_mode,
+            colorbar=True,
+            cmap=cmap,
+            symmetric_cbar=symmetric_cbar,
             threshold=threshold,
         )
     elif plot_type == "glass":
@@ -967,13 +991,30 @@
             display_mode=display_mode,
             colorbar=True,
             plot_abs=False,
+            symmetric_cbar=symmetric_cbar,
+            cmap=cmap,
             threshold=threshold,
         )
     else:
         raise ValueError(
             "Invalid plot type provided. "
             "Acceptable options are 'slice' or 'glass'."
         )
+
+    x_label_color = "black"
+    if plot_type == "slice":
+        x_label_color = "white"
+
+    if hasattr(stat_map_plot, "_cbar"):
+        cbar_ax = stat_map_plot._cbar.ax
+        cbar_ax.set_xlabel(
+            "Z score",
+            labelpad=5,
+            fontweight="bold",
+            loc="right",
+            color=x_label_color,
+        )
+
     with pd.option_context("display.precision", 2):
         _add_params_to_plot(table_details, stat_map_plot)
     fig = plt.gcf()