# The region of interest object (RoiObj) is basically a volume where each voxel
# is known to be part of an anatomical region. Hence, you can define the RoiObj
# and use it to get the anatomical informations of each source
# First, create a basic source object
s_obj_ba = SourceObj('S4', xyz)
# Then, we define a region of interest object (RoiObj). We use brodmann areas
# but you should take a look to the complete tutorial on ROIs because visbrain
# povides several templates (Brodmann, AAL, Talairach and MIST)
roi_obj = RoiObj('brodmann')
# If you want to see labels associated with the brodmann areas, uncomment the
# following line
# print(roi_obj.get_labels())
# Now, analyse sources using the RoiObj. The argument returned by the
# `SourceObj.analyse_sources` method is a Pandas dataframe
df_brod = s_obj_ba.analyse_sources(roi_obj=roi_obj)
# The dataframe contains a column `brodmann` which is the name of the
# associated brodmann area. Hence, we use it to color sources according to the
# name of brodmann area
s_obj_ba.color_sources(analysis=df_brod, color_by='brodmann')
# Finally, add the object to the scene
sc.add_to_subplot(s_obj_ba, row=1, col=0,
title='Color sources according to\n Brodmann area', **S_KW)
###############################################################################
# Color sources, using predefined colors, according to the AAL location
###############################################################################
# Similarly to the example above, here, we color sources according to the
# Automated Anatomical Labeling (AAL)
"""Analyse where sources are located using the AAL ROI template and color
###############################################################################
# Get anatomical informations of sources
###############################################################################
# If you defined sources (like intracranial recording sites, MEG source
# reconstruction...) you can use the SourceObj to defined those sources and
# then, the RoiObj to identify where are those sources located using the ROI
# volume. Here, we use the MIST at the `ROI` resolution to identify where are
# located those sources
# Define the MIST object at the ROI level
roi_mist = RoiObj('mist_ROI')
# roi_mist.get_labels(save_to_path=vb_path) # save the labels
# Define the source object and analyse those sources using the MIST
s_obj = SourceObj('anat', xyz, data=data)
analysis = s_obj.analyse_sources(roi_mist)
# print(analysis) # anatomical informations are included in a dataframe
# Color those sources according to the anatomical informations
s_obj.color_sources(analysis=analysis, color_by='name_ROI')
# Add the source object to the scene
sc.add_to_subplot(s_obj, row=1, col=0, rotate='top', zoom=.6,
title='Get anatomical informations of sources')
###############################################################################
# .. note::
# In the example above, we analyse sources using only one ROI object. But
# you can also combine anatomical informations that come from several
# ROI. For example, if you want to analyse your sources using brodmann
# areas, AAL and MIST at level 7 :
#
# brod_roi = RoiObj('brodmann')
###############################################################################
# Get anatomical informations of sources
###############################################################################
# If you defined sources (like intracranial recording sites, MEG source
# reconstruction...) you can use the SourceObj to defined those sources and
# then, the RoiObj to identify where are those sources located using the ROI
# volume. Here, we use the MIST at the `ROI` resolution to identify where are
# located those sources
# Define the MIST object at the ROI level
roi_mist = RoiObj('mist_ROI')
# roi_mist.get_labels(save_to_path=vb_path) # save the labels
# Define the source object and analyse those sources using the MIST
s_obj = SourceObj('anat', xyz, data=data)
analysis = s_obj.analyse_sources(roi_mist)
# print(analysis) # anatomical informations are included in a dataframe
# Color those sources according to the anatomical informations
s_obj.color_sources(analysis=analysis, color_by='name_ROI')
# Add the source object to the scene
sc.add_to_subplot(s_obj,
row=1,
col=0,
rotate='top',
zoom=.6,
title='Get anatomical informations of sources')
###############################################################################
# .. note::
# In the example above, we analyse sources using only one ROI object. But
# you can also combine anatomical informations that come from several
# The region of interest object (RoiObj) is basically a volume where each voxel
# is known to be part of an anatomical region. Hence, you can define the RoiObj
# and use it to get the anatomical informations of each source
# First, create a basic source object
s_obj_ba = SourceObj('S4', xyz)
# Then, we define a region of interest object (RoiObj). We use brodmann areas
# but you should take a look to the complete tutorial on ROIs because visbrain
# povides several templates (Brodmann, AAL, Talairach and MIST)
roi_obj = RoiObj('brodmann')
# If you want to see labels associated with the brodmann areas, uncomment the
# following line
# print(roi_obj.get_labels())
# Now, analyse sources using the RoiObj. The argument returned by the
# `SourceObj.analyse_sources` method is a Pandas dataframe
df_brod = s_obj_ba.analyse_sources(roi_obj=roi_obj)
# The dataframe contains a column `brodmann` which is the name of the
# associated brodmann area. Hence, we use it to color sources according to the
# name of brodmann area
s_obj_ba.color_sources(analysis=df_brod, color_by='brodmann')
# Finally, add the object to the scene
sc.add_to_subplot(s_obj_ba,
row=1,
col=0,
title='Color sources according to\n Brodmann area',
**S_KW)
###############################################################################
# Color sources, using predefined colors, according to the AAL location
###############################################################################
# Similarly to the example above, here, we color sources according to the
radius_min=10., radius_max=20., alpha=.4)
"""Color the sources according to data
"""
s_obj_l.color_sources(data=data_l, cmap='plasma')
"""Create a second source object. Then, we analyse where are located the
sources using the AAL region of interest and used color according to gyrus
"""
s_obj_rb = SourceObj('S_right_back', s_xyz_rb, symbol='arrow', radius_min=20.,
edge_width=0., text_color='white', text_size=1.,
text_bold=True)
"""Analyse where the source are located using the Brodmann area (BA) atlas.
This method returns a pandas.DataFrame
"""
df = s_obj_rb.analyse_sources('brodmann')
# print(df.keys())
"""Then, color the sources according to the BA. Without further arguments,
this function use random colors for each BA :
"""
s_obj_rb.color_sources(df, 'brodmann')
"""Finally, set the name of the BA for the text :
"""
s_obj_rb.text = df['brodmann']
"""Create a third source object
"""
s_obj_rf = SourceObj('S_right_front', s_xyz_rf, symbol='ring', radius_min=10.,
edge_width=0., alpha=.8)
"""Analyse source's locations using the Talairach atlas
"""
row=0,
col=2,
title='Plot dorsal and ventral thalamus with fixed colors')
# =============================================================================
# ANATOMICAL LOCATION OF SOURCES
# =============================================================================
print('\n-> Anatomical location of sources using an ROI object')
# Define the ROI object :
roi_tal = RoiObj('talairach')
roi_tal.select_roi(select=[681, 682, 808, 809])
roi_tal.translucent = True
roi_tal.get_labels(save_to_path=vb_path) # save available Talairach labels
# Define a source object :
s_obj = SourceObj('FirstSources', xyz, data=data)
analysis = s_obj.analyse_sources(roi_tal)
s_obj.color_sources(analysis=analysis, color_by='gyrus')
sc.add_to_subplot(s_obj, row=1, col=0, title='Anatomical location of sources')
sc.add_to_subplot(roi_tal, row=1, col=0, use_this_cam=True)
# =============================================================================
# SELECT SOURCES INSIDE ROI'S
# =============================================================================
print('\n-> Select only sources inside BA 4, 6 and 8')
# Define the ROI object :
roi_brod_2 = RoiObj('brodmann')
roi_brod_2.select_roi(select=[4, 6, 8])
roi_brod_2.translucent = True
# Define a source object :
s_obj_2 = SourceObj('SecondSources', xyz, data=data)
analysis = s_obj_2.analyse_sources(roi_brod_2,
s_obj_l.color_sources(data=data_l, cmap='plasma')
"""Create a second source object. Then, we analyse where are located the
sources using the AAL region of interest and used color according to gyrus
"""
s_obj_rb = SourceObj('S_right_back',
s_xyz_rb,
symbol='arrow',
radius_min=20.,
edge_width=0.,
text_color='white',
text_size=1.,
text_bold=True)
"""Analyse where the source are located using the Brodmann area (BA) atlas.
This method returns a pandas.DataFrame
"""
df = s_obj_rb.analyse_sources('brodmann')
# print(df.keys())
"""Then, color the sources according to the BA. Without further arguments,
this function use random colors for each BA :
"""
s_obj_rb.color_sources(df, 'brodmann')
"""Finally, set the name of the BA for the text :
"""
s_obj_rb.text = df['brodmann']
"""Create a third source object
"""
s_obj_rf = SourceObj('S_right_front',
s_xyz_rf,
symbol='ring',
radius_min=10.,
edge_width=0.,