data mapping¶

one of the main features of yabplot is flexible data mapping. you can pass data as:

1. dictionaries (for partial data or specific regions).
2. arrays/lists (for mapping entire atlas vectors).

this tutorial demonstrates how to visualize statistical maps and roi analysis results.

In [ ]:

import yabplot as yab
import numpy as np
import pandas as pd

import yabplot as yab import numpy as np import pandas as pd

1. mapping dictionaries (partial data)¶

this is the safest way to plot data. you only need to provide values for the regions you care about. regions not in the dictionary will be hidden (colored with nan_color).

In [6]:

# define some specific values for subcortical regions
# notice how we can mix left and right hemisphere regions
roi_data = {
    'Left_Amygdala': 0.8,
    'Right_Amygdala': 0.75,
    'Left_Thalamus': 0.4,
    'Right_Thalamus': 0.45,
    'Left_Hippocampus': -0.5
}

yab.plot_subcortical(
    data=roi_data,
    atlas='aseg',
    cmap='coolwarm',
    vminmax=[-1, 1],   # center the colormap at 0
    nan_alpha=0.1,     # make other regions almost invisible
    display_type='static'
)

# define some specific values for subcortical regions # notice how we can mix left and right hemisphere regions roi_data = { 'Left_Amygdala': 0.8, 'Right_Amygdala': 0.75, 'Left_Thalamus': 0.4, 'Right_Thalamus': 0.45, 'Left_Hippocampus': -0.5 } yab.plot_subcortical( data=roi_data, atlas='aseg', cmap='coolwarm', vminmax=[-1, 1], # center the colormap at 0 nan_alpha=0.1, # make other regions almost invisible display_type='static' )

Out[6]:

<pyvista.plotting.plotter.Plotter at 0x13fa325d0>

2. mapping arrays (full atlas)¶

if you have a vector of numbers (e.g., effect sizes for each region), you can pass it directly.

important: when using arrays, the order must match the atlas exactly. use get_atlas_regions to verify.

In [7]:

# 1. get the list of regions for the schaefer 100 atlas
atlas_name = 'schaefer_100'
regions = yab.get_atlas_regions(atlas_name, 'cortical')

print(f"atlas '{atlas_name}' has {len(regions)} regions.")
print(f"first 3: {regions[:3]}")

# 2. simulate some "functional connectivity" data
# let's create a gradient from posterior to anterior
data_array = np.linspace(0, 1, len(regions)) 
# add some noise
data_array += np.random.normal(0, 0.1, len(regions))

# 3. plot using the 'plasma' colormap
yab.plot_cortical(
    data=data_array,
    atlas=atlas_name,
    cmap='plasma',
    views=['left_lateral', 'right_medial'],
    display_type='static'
)

# 1. get the list of regions for the schaefer 100 atlas atlas_name = 'schaefer_100' regions = yab.get_atlas_regions(atlas_name, 'cortical') print(f"atlas '{atlas_name}' has {len(regions)} regions.") print(f"first 3: {regions[:3]}") # 2. simulate some "functional connectivity" data # let's create a gradient from posterior to anterior data_array = np.linspace(0, 1, len(regions)) # add some noise data_array += np.random.normal(0, 0.1, len(regions)) # 3. plot using the 'plasma' colormap yab.plot_cortical( data=data_array, atlas=atlas_name, cmap='plasma', views=['left_lateral', 'right_medial'], display_type='static' )

atlas 'schaefer_100' has 100 regions.
first 3: ['7Networks_LH_Vis_1', '7Networks_LH_Vis_2', '7Networks_LH_Vis_3']

Out[7]:

<pyvista.plotting.plotter.Plotter at 0x13fa17f50>

3. using pandas series¶

yabplot integrates well with pandas. if you have a dataframe of results, you can pass a series directly if the index matches region names.

In [9]:

# simulate a dataframe of tract statistics (e.g. fractional anisotropy)
tract_names = yab.get_atlas_regions('xtract_tiny', 'tracts')

# create a pandas series with names as index
df = pd.DataFrame({
    'fa_value': np.random.uniform(0.2, 0.8, len(tract_names))
}, index=tract_names)

print(df.head())

# pass the series directly
yab.plot_tracts(
    data=df['fa_value'],
    atlas='xtract_tiny',
    cmap='magma',
    style='matte',
    bmesh_type='fsaverage',
    display_type='static'
)

# simulate a dataframe of tract statistics (e.g. fractional anisotropy) tract_names = yab.get_atlas_regions('xtract_tiny', 'tracts') # create a pandas series with names as index df = pd.DataFrame({ 'fa_value': np.random.uniform(0.2, 0.8, len(tract_names)) }, index=tract_names) print(df.head()) # pass the series directly yab.plot_tracts( data=df['fa_value'], atlas='xtract_tiny', cmap='magma', style='matte', bmesh_type='fsaverage', display_type='static' )

       fa_value
AC     0.473477
AF_L   0.430353
AF_R   0.766017
AR_R   0.556351
ATR_L  0.577860

Out[9]:

<pyvista.plotting.plotter.Plotter at 0x30d480a10>

In [ ]: