Visbrain Setup

1.
Setup

OpenGL driver and git, plus some support libraries.

apt-get -qq update
apt-get install libgl1-mesa-dev git \
  libxi6 libnss3 libxtst6 libnspr4 \
  libxrender1 libxcomposite1 libxcursor1 libxrandr2 libasound2
apt-get clean
rm -r /var/lib/apt/lists/* # Clear package list so it isn't stale

Required, optional, and other deps for packages or examples. nibabel and mne require conda-forge. Vispy requires a pre-2.2 version of matplotlib.

conda install -c anaconda -c conda-forge \
  vispy pyqt pyopengl matplotlib'<2.2' \
  xlrd pillow nibabel mne \
  joblib openpyxl
conda clean -tipsy

Two remaining optionals and visbrain itself.

pip install \
  tensorpac \
  git+https://github.com/hbldh/lspopt.git#egg=lspopt \
  visbrain

2.
Test

From the Visbrain Examples. Final line changed, to use screenshot() for output.

48.2s
import numpy as np

from visbrain.objects import RoiObj, ColorbarObj, SceneObj, SourceObj, BrainObj
from visbrain.io import download_file, path_to_visbrain_data, read_nifti

###############################################################################
# Download data
###############################################################################
# In order to work, this example need to download some data i.e coordinates of
# intracranial sources and a parcellates atlas (MIST) to illustrate how to
# define your own RoiObj

# Get the path to the ~/visbrain_data/example_data folder
vb_path = path_to_visbrain_data(folder='example_data')
# Download (x, y, z) coordinates of intracranial sources
mat = np.load(download_file('xyz_sample.npz', astype='example_data'))
xyz, subjects = mat['xyz'], mat['subjects']
data = np.random.uniform(low=-1., high=1., size=(xyz.shape[0],))
# Download the MIST parcellates
download_file('MIST_ROI.zip', unzip=True, astype='example_data')

###############################################################################
# Scene creation
###############################################################################
# First, we need to create the scene that will host objects

# Scene creation with a dark background and a custom size
sc = SceneObj(size=(1400, 1000))
# In this example, we also illustrate the use of the colorbar object. Hence, we
# centralize colorbar properties inside a dictionary
CBAR_STATE = dict(cbtxtsz=12, txtsz=10., width=.1, cbtxtsh=3.,
                  rect=(-.3, -2., 1., 4.))

###############################################################################
# Find the index of a region of interest
###############################################################################
# ROIs are defined with two variables : 1) a volume which contains integers
# and 2) a vector of labels which link every integer inside the volume with a
# label (for example, with the brodmann atlas, the index 4 refers to the label
# brodmann 4). Here, we illustrate how to find the index of a region of
# interest

#####################################
# **Method 1 :** export all ROI labels and indices in an excel file
#
# This first method load a ROI atlas then, we use the
# :class:`visbrain.objects.RoiObj.get_labels` method to save every related ROI
# informations in an excel file. This first method implies that you manually
# inspect in this file the index of the ROI that you're looking for.

roi_to_find1 = RoiObj('brodmann')             # Use Brodmann areas
ref_brod = roi_to_find1.get_labels(vb_path)   # Save Brodmann
roi_to_find1('aal')                           # Switch to AAL
ref_aal = roi_to_find1.get_labels(vb_path)    # Save AAL
roi_to_find1('talairach')                     # Switch to Talairach
ref_tal = roi_to_find1.get_labels(vb_path)    # Save Talairach

#####################################
# **Method 2 :** explicitly search where is the ROI that you're looking for
#
# Here, we use the :class:`visbrain.objects.RoiObj.where_is` method of the ROI
# object to explicitly search string patterns

# Method 2 : use the `where_is` method
roi_to_find1('brodmann')                      # Switch to Brodmann
idx_ba6 = roi_to_find1.where_is('BA6')        # Find only BA6
print(ref_brod.loc[idx_ba6])
roi_to_find1('aal')                           # Switch to AAL
idx_sma = roi_to_find1.where_is('Supp Motor Area')

###############################################################################
# Extract the mesh of an ROI
###############################################################################
# Once you have the index of the ROI that you want to plot, use the
# :class:`visbrain.objects.RoiObj.select_roi` method to extract the mesh (i.e
# vertices and faces) of the ROI. Here, we illustrate this question with the
# brodmann 6 ROI

# Load the brodmann 6 atlas, get the index of BA6 and extract the mesh
roi_brod = RoiObj('brodmann')
idx_ba6 = roi_brod.where_is('BA6')
roi_brod.select_roi(select=idx_ba6)
# Define a brain object and add this brain and ROI objects to the scene
b_obj = BrainObj('B1')
sc.add_to_subplot(b_obj, row=0, col=0, use_this_cam=True,
                  title='Brodmann area 6 mesh')
sc.add_to_subplot(roi_brod, row=0, col=0)

###############################################################################
# Set a unique color per ROI mesh
###############################################################################
# If you need, you can set a unique color per plotted ROI mesh. Here, we plot
# the left and right insula and thalamus and set a unique color to each

# Load the AAL atlas
roi_aal = RoiObj('aal')
# Select indicies 29, 30, 77 and 78 (respectively insula left, right and
# thalamus left and right)
roi_aal.select_roi(select=[29, 30, 77, 78], unique_color=True, smooth=11)
# Add the ROI to the scene
sc.add_to_subplot(roi_aal, row=0, col=1, rotate='top', zoom=.4,
                  title='Select and plot multiple ROI with unique colors')

###############################################################################
# Project source's data onto the surface of ROI mesh
###############################################################################
# Once you've extract the mesh of the ROI, you can explicitly specify to the
# :class:`visbrain.object.SourceObj.project_sources` to project the activity
# onto the surface of the ROI. Here, we extract the mesh of the default mode
# network (DMN) and project source's activity on it

# Define the roi object using the MIST at resolution 7
roi_dmn = RoiObj('mist_7')
roi_dmn.get_labels(save_to_path=vb_path)  # save the labels
dmn_idx = roi_dmn.where_is('Default mode network')
roi_dmn.select_roi(select=dmn_idx)
# Define the source object and project source's data on the DMN
s_dmn = SourceObj('SecondSources', xyz, data=data)
s_dmn.project_sources(roi_dmn, cmap='plasma', clim=(-1., 1.), vmin=-.5,
                      vmax=.7, under='gray', over='red')
# Get the colorbar of the projection
cb_dmn = ColorbarObj(s_dmn, cblabel='Source activity', **CBAR_STATE)
# Add those objects to the scene
sc.add_to_subplot(roi_dmn, row=0, col=2, rotate='top', zoom=.4,
                  title="Project source's activity onto the DMN")
sc.add_to_subplot(cb_dmn, row=0, col=3, width_max=200)


###############################################################################
# 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')
#
#         brod_aal = RoiObj('aal')
#
#         brod_mist = RoiObj('mist_7')
#
#         s_obj.analyse_sources([brod_roi, brod_aal, brod_mist])

###############################################################################
# Select sources that are inside an ROI
###############################################################################
# Here, we illustrate how to only select sources that are inside the
# somatomotor network.

# Define the roi MIST object at level 7
somato_str = 'Somatomotor network'
roi_somato = RoiObj('mist_7')
idx_somato = roi_somato.where_is(somato_str)
roi_somato.select_roi(idx_somato, translucent=True)
# Define the source object and analyse anatomical informations
s_somato = SourceObj('somato', xyz, data=data)
analysis = s_somato.analyse_sources(roi_somato, keep_only=somato_str)
s_somato.color_sources(data=data, cmap='bwr')
# Add those objects to the scene
sc.add_to_subplot(roi_somato, row=1, col=1, use_this_cam=True, rotate='top',
                  title='Display only sources inside the\nsomatomotor network',
                  zoom=.6)
sc.add_to_subplot(s_somato, row=1, col=1)

###############################################################################
# Define and use your own region of interest
###############################################################################
# Visbrain comes with several ROI volumes, but you can define your own ROI
# object. To do this, you need a volume (i.e an array with three dimensions)
# and an array of labels. Here, for the sake of illustration, we explain how
# to rebuild the MIST at the ROI resolution.

# Download the MIST_ROI.zip archive. See the README inside the archive
nifti_file = path_to_visbrain_data(file='MIST_ROI.nii.gz',
                                   folder='example_data')
csv_file = path_to_visbrain_data(file='MIST_ROI.csv', folder='example_data')
# Read the .csv file :
arr = np.genfromtxt(csv_file, delimiter=';', dtype=str)
# Get column names, labels and index :
column_names = arr[0, :]
arr = np.delete(arr, 0, 0)
n_roi = arr.shape[0]
roi_index = arr[:, 0].astype(int)
roi_labels = arr[:, [1, 2]].astype(object)
# Build the struct array :
label = np.zeros(n_roi, dtype=[('label', object), ('name', object)])
label['label'] = roi_labels[:, 0]
label['name'] = roi_labels[:, 1]
# Get the volume and the hdr transformation :
vol, _, hdr = read_nifti(nifti_file, hdr_as_array=True)
# Define the ROI object and save it :
roi_custom = RoiObj('custom_roi', vol=vol, labels=label, index=roi_index,
                    hdr=hdr)
# Find thalamus entries :
idx_thalamus = roi_custom.where_is('THALAMUS')
colors = {55: 'slateblue', 56: 'olive', 63: 'darkred', 64: '#ab4642'}
roi_custom.select_roi(idx_thalamus, roi_to_color=colors)
sc.add_to_subplot(roi_custom, row=1, col=2, zoom=.5,
                  title='Plot dorsal and ventral thalamus with fixed colors')

###############################################################################
# .. note::
#     Once your RoiObj is defined, you can save it using
#     :class:`visbrain.objects.RoiObj.save`. Once the object is saved, you can
#     reload it using the name you've used (here we've used the `custom_roi`
#     name which means that you can reload it later using RoiObj('custom_roi'))

# Finally, display the scene
#sc.preview()
sc.screenshot("/results/test.png")
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