plot styling¶

yabplot provides several styling presets and customization options to make your figures publication-ready.

in this tutorial, we will explore:

1. lighting styles.
2. customizing the background context brain.
3. handling missing data (nan_color, nan_alpha).
4. tract-specific visualizations.

In [ ]:

import yabplot as yab
import numpy as np

import yabplot as yab import numpy as np

1. lighting styles¶

different styles can highlight shape (sculpted) or data values (flat/matte). available styles include: 'default', 'matte', 'glossy', 'sculpted', 'flat'.

In [23]:

# generate random data for demonstration
regions = yab.get_atlas_regions('aseg', 'subcortical')
data = np.random.rand(len(regions))

# compare 'flat' vs 'glossy' vs 'matte' styles
# glossy enhances the 3d curvature giving it a shiny look
yab.plot_subcortical(
    data=data,
    cmap='viridis',
    atlas='aseg',
    views=['left_lateral', 'superior', 'anterior'],
    figsize=(1200, 400),
    style='glossy'
)

# flat is great for seeing exact data colors without shadows
yab.plot_subcortical(
    data=data,
    cmap='viridis',
    atlas='aseg',
    views=['left_lateral', 'superior', 'anterior'],
    figsize=(1200, 400),
    style='flat'
)

# matte is a balance between both
yab.plot_subcortical(
    data=data,
    cmap='viridis',
    atlas='aseg',
    views=['left_lateral', 'superior', 'anterior'],
    figsize=(1200, 400),
    style='matte'
)

# generate random data for demonstration regions = yab.get_atlas_regions('aseg', 'subcortical') data = np.random.rand(len(regions)) # compare 'flat' vs 'glossy' vs 'matte' styles # glossy enhances the 3d curvature giving it a shiny look yab.plot_subcortical( data=data, cmap='viridis', atlas='aseg', views=['left_lateral', 'superior', 'anterior'], figsize=(1200, 400), style='glossy' ) # flat is great for seeing exact data colors without shadows yab.plot_subcortical( data=data, cmap='viridis', atlas='aseg', views=['left_lateral', 'superior', 'anterior'], figsize=(1200, 400), style='flat' ) # matte is a balance between both yab.plot_subcortical( data=data, cmap='viridis', atlas='aseg', views=['left_lateral', 'superior', 'anterior'], figsize=(1200, 400), style='matte' )

Out[23]:

<pyvista.plotting.plotter.Plotter at 0x335103510>

2. context brain (bmesh)¶

for subcortical and tract plots, a context brain mesh is rendered to show anatomical location. you can change its style or hide it.

In [24]:

# styling the context brain
yab.plot_subcortical(
    atlas='brainnetome_sc',
    views=['left_lateral', 'superior', 'anterior'],
    figsize=(1200, 400),
    bmesh_type='conte69',  # standard surface
    bmesh_alpha=0.05,      # very transparent (ghost-like)
    bmesh_color='black',   # dark background mesh
    style='default',
    display_type='static'
)

# hiding the context brain
yab.plot_subcortical(
    atlas='brainnetome_sc',
    views=['left_lateral', 'superior', 'anterior'],
    figsize=(1200, 400),
    bmesh_type=None,  # no context surface
    style='default',
    display_type='static'
)

# styling the context brain yab.plot_subcortical( atlas='brainnetome_sc', views=['left_lateral', 'superior', 'anterior'], figsize=(1200, 400), bmesh_type='conte69', # standard surface bmesh_alpha=0.05, # very transparent (ghost-like) bmesh_color='black', # dark background mesh style='default', display_type='static' ) # hiding the context brain yab.plot_subcortical( atlas='brainnetome_sc', views=['left_lateral', 'superior', 'anterior'], figsize=(1200, 400), bmesh_type=None, # no context surface style='default', display_type='static' )

Out[24]:

<pyvista.plotting.plotter.Plotter at 0x3569a8250>

3. handling missing data (nan)¶

when plotting dictionaries or incomplete arrays, you can control how missing regions appear using nan_color and nan_alpha.

In [25]:

# define data for only one region
sparse_data = {'Left_Putamen': 1.0}

yab.plot_subcortical(
    data=sparse_data,
    atlas='aseg',
    # make missing regions white and fully opaque (solid)
    nan_color='white',
    nan_alpha=0.5,
    style='matte',
    display_type='static'
)

# define data for only one region sparse_data = {'Left_Putamen': 1.0} yab.plot_subcortical( data=sparse_data, atlas='aseg', # make missing regions white and fully opaque (solid) nan_color='white', nan_alpha=0.5, style='matte', display_type='static' )

Out[25]:

<pyvista.plotting.plotter.Plotter at 0x388326bd0>

4. tract customization¶

for white matter tracts, you can pass specific parameters to pyvista to change the rendering of lines/tubes via tract_kwargs.

In [26]:

# render tracts as thin lines instead of 3d tubes (faster, different look)
yab.plot_tracts(
    atlas='xtract_tiny',
    orientation_coloring=True,
    tract_kwargs={
        'render_lines_as_tubes': False,  # simple lines
        'line_width': 2.0                # slightly thicker lines
    },
    display_type='static'
)

# render as thick tubes
yab.plot_tracts(
    atlas='xtract_tiny',
    orientation_coloring=True,
    tract_kwargs={
        'render_lines_as_tubes': True,
        'line_width': 4.0  # very thick tubes
    },
    display_type='static'
)

# render tracts as thin lines instead of 3d tubes (faster, different look) yab.plot_tracts( atlas='xtract_tiny', orientation_coloring=True, tract_kwargs={ 'render_lines_as_tubes': False, # simple lines 'line_width': 2.0 # slightly thicker lines }, display_type='static' ) # render as thick tubes yab.plot_tracts( atlas='xtract_tiny', orientation_coloring=True, tract_kwargs={ 'render_lines_as_tubes': True, 'line_width': 4.0 # very thick tubes }, display_type='static' )

Out[26]:

<pyvista.plotting.plotter.Plotter at 0x105db4c50>