[DOC] Fix position of plots in relation to code blocks

examples/00_tutorials/plot_python_101.py

@@ -1,11 +1,11 @@
 """
 Basic numerics and plotting with Python
 =======================================
-
-A simple example of basic Python numerics and how to
-plot it.
 """
 
+#########################################################################
+# A simple example of basic Python numerics and how to plot it.
+
 # import numpy: the module providing numerical arrays
 import numpy as np
 

examples/01_plotting/plot_haxby_masks.py

@@ -5,8 +5,9 @@
 Small script to plot the masks of the Haxby dataset.
 """
 
-
-import matplotlib.pyplot as plt
+#########################################################################
+# Load Haxby dataset
+# ------------------
 
 from nilearn import datasets
 
@@ -28,10 +29,16 @@
 
 z_slice = -14
 
-fig = plt.figure(figsize=(4, 5.4), facecolor="k")
+#########################################################################
+# Plot the masks
+# --------------
+
+import matplotlib.pyplot as plt
 
 from nilearn.plotting import plot_anat, show
 
+fig = plt.figure(figsize=(4, 5.4), facecolor="k")
+
 display = plot_anat(
     mean_img, display_mode="z", cut_coords=[z_slice], figure=fig
 )

examples/01_plotting/plot_prob_atlas.py

@@ -24,7 +24,7 @@
 # Load 4D probabilistic atlases
 from nilearn import datasets, plotting
 
-# Harvard Oxford Atlasf
+# Harvard Oxford Atlas
 harvard_oxford = datasets.fetch_atlas_harvard_oxford("cort-prob-2mm")
 harvard_oxford_sub = datasets.fetch_atlas_harvard_oxford("sub-prob-2mm")
 
@@ -67,8 +67,8 @@
     dimension=dim, resolution_mm=res, legacy_format=False
 )
 
+#########################################################################
 # Visualization
-
 atlas_types = {
     "Harvard_Oxford": harvard_oxford.maps,
     "Harvard_Oxford sub": harvard_oxford_sub.maps,

examples/02_decoding/plot_haxby_stimuli.py

@@ -6,7 +6,6 @@
 and Overlapping Representations of Faces and Objects in Ventral Temporal
 Cortex" (Science 2001)
 """
-
 import matplotlib.pyplot as plt
 
 from nilearn import datasets
@@ -15,6 +14,8 @@
 haxby_dataset = datasets.fetch_haxby(subjects=[], fetch_stimuli=True)
 stimulus_information = haxby_dataset.stimuli
 
+#########################################################################
+
 for stim_type in stimulus_information:
     # skip control images, there are too many
     if stim_type != "controls":

examples/03_connectivity/plot_simulated_connectome.py

@@ -23,6 +23,8 @@
     density=0.1,
 )
 
+#########################################################################
+# Run connectome estimations and plot the results
 from nilearn import plotting
 
 fig = plt.figure(figsize=(10, 7))

examples/04_glm_first_level/plot_design_matrix.py

@@ -113,9 +113,4 @@
 ax2.set_title("Block design matrix", fontsize=12)
 plot_design_matrix(X3, ax=ax3)
 ax3.set_title("FIR design matrix", fontsize=12)
-
-#########################################################################
-# Let's improve the layout and show the result.
-
-plt.subplots_adjust(left=0.08, top=0.9, bottom=0.21, right=0.96, wspace=0.3)
-plt.show()
+fig.show()

examples/06_manipulating_images/plot_smooth_mean_image.py

@@ -10,7 +10,6 @@
 effects that are expected.
 
 """
-
 from nilearn import datasets, image, plotting
 
 data = datasets.fetch_development_fmri(n_subjects=1)
@@ -22,14 +21,14 @@
 
 first_epi_file = data.func[0]
 
-# First the compute the mean image, from the 4D series of image
+# First compute the mean image, from the 4D series of image
 mean_func = image.mean_img(first_epi_file)
 
+#########################################################################
 # Then we smooth, with a varying amount of smoothing, from none to 20mm
 # by increments of 5mm
 for smoothing in range(0, 25, 5):
     smoothed_img = image.smooth_img(mean_func, smoothing)
     plotting.plot_epi(smoothed_img, title=f"Smoothing {int(smoothing)}mm")
 
-
 plotting.show()