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
# fMRI activation
###############################################################################
file = download_file('lh.sig.nii.gz', astype='example_data')
b_obj_fmri = BrainObj('inflated', translucent=False, sulcus=True)
b_obj_fmri.add_activation(file=file, clim=(5., 20.), hide_under=5,
cmap='viridis', hemisphere='left')
sc.add_to_subplot(b_obj_fmri, row=0, col=0, row_span=2,
title='fMRI activation', rotate='top')
###############################################################################
# Region Of Interest (ROI)
###############################################################################
roi_aal = RoiObj('aal')
roi_aal.select_roi(select=[29, 30], unique_color=True, smooth=11)
sc.add_to_subplot(roi_aal, row=0, col=1, title='Region Of Interest (ROI)')
sc.add_to_subplot(BrainObj('B1'), use_this_cam=True, row=0, col=1)
###############################################################################
# Sources
###############################################################################
s_obj = SourceObj('FirstSources', xyz, data=data)
s_obj.color_sources(data=data, cmap='Spectral_r')
sc.add_to_subplot(s_obj, row=1, col=1, title='Sources')
sc.add_to_subplot(BrainObj('B3'), use_this_cam=True, row=1, col=1)
###############################################################################
# 3D Time-series
###############################################################################
"""Test RoiObj."""
import numpy as np
from visbrain.objects.tests._testing_objects import _TestVolumeObject
from visbrain.objects import SourceObj, RoiObj
from visbrain.io import (download_file, path_to_visbrain_data, read_nifti,
clean_tmp)
roi_obj = RoiObj('brodmann')
roi_obj.select_roi([4, 6])
rnd = np.random.RandomState(0)
xyz = 100. * rnd.rand(50, 3)
xyz[:, 0] -= 50.
xyz[:, 1] -= 50.
s_obj = SourceObj('S1', xyz)
download_file('MIST_ROI.zip', unzip=True)
nifti_file = path_to_visbrain_data('MIST_ROI.nii.gz')
csv_file = path_to_visbrain_data('MIST_ROI.csv')
# 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]
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
###############################################################################
# Project source's activity on the surface of the DMN
###############################################################################
# Here, we first use the MIST ROI to get represent the default mode network and
# then, we project source's activity onto the surface of the DMN
# Define the source and brain objects
s_dmn = SourceObj('dmn', xyz, data=rnd_data, mask=mask, mask_color='gray')
b_mask = BrainObj('B3')
# Define the MIST roi object
roi_dmn = RoiObj('mist_7')
# print(roi_dmn.get_labels())
# Get the index of the DMN and get the mesh
dmn_idx = roi_dmn.where_is('Default mode network')
roi_dmn.select_roi(dmn_idx)
# Project source's activity on the surface of the DMN
s_dmn.project_sources(roi_dmn, cmap='viridis', radius=15.)
sc.add_to_subplot(b_mask, row=2, col=2, use_this_cam=True, row_span=2,
title="Project source's activity\non the DMN")
sc.add_to_subplot(roi_dmn, row=2, col=2, row_span=2)
###############################################################################
# Project source's repartition on the surface of the brain
###############################################################################
# Similarly to the example above, we project here the repartition of sources
# which mean the number of contributing sources per vertex
# Define the source and brain objects
s_rep = SourceObj('proj', xyz, data=rnd_data)
b_rep = BrainObj('B3', translucent=False)
###############################################################################
# Project source's activity on the surface of the DMN
###############################################################################
# Here, we first use the MIST ROI to get represent the default mode network and
# then, we project source's activity onto the surface of the DMN
# Define the source and brain objects
s_dmn = SourceObj('dmn', xyz, data=rnd_data, mask=mask, mask_color='gray')
b_mask = BrainObj('B3')
# Define the MIST roi object
roi_dmn = RoiObj('mist_7')
# print(roi_dmn.get_labels())
# Get the index of the DMN and get the mesh
dmn_idx = roi_dmn.where_is('Default mode network')
roi_dmn.select_roi(dmn_idx)
# Project source's activity on the surface of the DMN
s_dmn.project_sources(roi_dmn, cmap='viridis', radius=15.)
sc.add_to_subplot(b_mask,
row=2,
col=2,
use_this_cam=True,
row_span=2,
title="Project source's activity\non the DMN")
sc.add_to_subplot(roi_dmn, row=2, col=2, row_span=2)
###############################################################################
# Project source's repartition on the surface of the brain
###############################################################################
# Similarly to the example above, we project here the repartition of sources
# which mean the number of contributing sources per vertex
"""Test RoiObj."""
import numpy as np
from visbrain.objects.tests._testing_objects import _TestVolumeObject
from visbrain.objects import SourceObj, RoiObj
from visbrain.io import (download_file, path_to_visbrain_data, read_nifti,
clean_tmp)
roi_obj = RoiObj('brodmann')
roi_obj.select_roi([4, 6])
rnd = np.random.RandomState(0)
xyz = 100. * rnd.rand(50, 3)
xyz[:, 0] -= 50.
xyz[:, 1] -= 50.
s_obj = SourceObj('S1', xyz)
download_file('MIST_ROI.zip', unzip=True, astype='example_data')
nifti_file = path_to_visbrain_data('MIST_ROI.nii.gz', 'example_data')
csv_file = path_to_visbrain_data('MIST_ROI.csv', '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]
def engram(id):
from .gui import Engram
from visbrain.objects import RoiObj
from .objects import SourceObj, ConnectObj
from visbrain.io import download_file
# Create an empty kwargs dictionnary :
kwargs = {}
# ____________________________ DATA ____________________________
# Load the xyz coordinates and corresponding subject name :
# mat = np.load(download_file('xyz_sample.npz', astype='example_data'))
# xyz, subjects = mat['xyz'], mat['subjects']
metadata = id.durations[0].metadata
binary = id.durations[0].bins[0]
positions = metadata['stream_pattern']['positions']
assignments = metadata['stream_pattern']['hierarchy']
SPACING = 6 # In MNI coordinates
INITIAL_DISTINCTIONS = []
n_dims = np.shape(assignments)[1]
existing_levels = []
intersection_matrices = {}
intersection_matrices['indices'] = np.empty([])
intersection_matrices['streams'] = np.empty([])
intersection_matrices['positions'] = np.empty([])
intersection_matrices['hierarchy_lookup'] = []
# Derive Intersection Matrix
for k, hierarchy in enumerate(assignments):
if '' not in hierarchy:
intersection = []
for level, v in enumerate(hierarchy):
if len(intersection_matrices['hierarchy_lookup']) <= level:
intersection_matrices['hierarchy_lookup'].append([])
intersection_matrices['hierarchy_lookup'][level].extend(
[v])
if v not in intersection_matrices['hierarchy_lookup'][level]:
intersection_matrices['hierarchy_lookup'][level].extend(
[v])
distinction = np.where(
np.asarray(intersection_matrices['hierarchy_lookup']
[level]) == v)[0][0]
intersection.append(distinction)
if intersection:
intersection = np.expand_dims(np.array(intersection), axis=0)
pos = np.expand_dims(np.array(positions[k]), axis=0)
stream = np.expand_dims(np.array(metadata['all_streams'][k]),
axis=0)
source_count = np.expand_dims(np.arange(
np.array(
sum(binary.nD_labels['1D'] == metadata['all_streams']
[k]))),
axis=0)
if k is 0:
intersection_matrices['indices'] = intersection
intersection_matrices['streams'] = stream
intersection_matrices['sources'] = source_count
intersection_matrices['positions'] = pos
else:
intersection_matrices['indices'] = np.append(
intersection_matrices['indices'], intersection, axis=0)
intersection_matrices['streams'] = np.append(
intersection_matrices['streams'], stream, axis=0)
intersection_matrices['sources'] = np.append(
intersection_matrices['sources'],
source_count +
intersection_matrices['sources'][k - 1][-1] + 1,
axis=0)
intersection_matrices['positions'] = np.append(
intersection_matrices['positions'], pos, axis=0)
xyz = position_slicer(intersection_matrices,
method=INITIAL_DISTINCTIONS,
ignore_streams=True)
# Convert binary array into visualizable continuous values
print('Calculating spike durations')
TRAIL = 100
TIMEPOINTS = 100000
spikes = binary.data.T[0:TIMEPOINTS]
one_array = np.where(spikes == 1)
if not not one_array:
lb_1 = one_array[0] - TRAIL
ub_1 = one_array[0]
lb_2 = one_array[0]
ub_2 = one_array[0] + TRAIL
for ii in range(len(lb_1)):
if ub_2[ii] > len(spikes):
ub_2[ii] = len(spikes)
if lb_1[ii] < 0:
lb_1[ii] = 0
spikes[lb_1[ii]:ub_1[ii],
one_array[1][ii]] += np.linspace(0, 1, ub_1[ii] - lb_1[ii])
spikes[lb_2[ii]:ub_2[ii],
one_array[1][ii]] += np.linspace(1, 0, ub_2[ii] - lb_2[ii])
spikes = spikes.T
N = xyz.shape[0] # Number of electrodes
text = ['S' + str(k) for k in range(N)]
s_obj = SourceObj('SourceObj1',
xyz,
data=spikes,
color='crimson',
text=text,
alpha=.5,
edge_width=2.,
radius_min=1.,
radius_max=25.)
connect = np.zeros((N, N, np.shape(spikes)[1]))
valid = np.empty((N, N, np.shape(spikes)[1]))
edges = np.arange(N)
print('Calculating connectivity')
for ind, activity in enumerate(spikes):
if ind < len(spikes):
edge_activity = spikes[ind + 1:-1]
weight = edge_activity + activity
valid = ((edge_activity > 0) & (activity > 0)).astype('int')
connect[ind, ind + 1:-1] = weight * valid
umin = 0
umax = np.max(spikes)
c_obj = ConnectObj('ConnectObj1',
xyz,
connect,
color_by='strength',
dynamic=(.1, 1.),
cmap='gnuplot',
vmin=umin + .1,
vmax=umax - 1,
line_width=0.1,
clim=(umin, umax),
antialias=True)
r_obj = RoiObj('aal')
idx_rh = r_obj.where_is('Hippocampus (R)')
idx_lh = r_obj.where_is('Hippocampus (L)')
r_obj.select_roi(select=[idx_rh, idx_lh],
unique_color=False,
smooth=7,
translucent=True)
vb = Engram(source_obj=s_obj,roi_obj=r_obj,connect_obj=c_obj,metadata=metadata,\
rotation=0.1,carousel_metadata=intersection_matrices,\
carousel_display_method='text')
vb.engram_control(template='B1', alpha=.02)
vb.engram_control(visible=False)
vb.connect_control(c_obj.name, visible=False)
vb.sources_control(s_obj.name, visible=True)
vb.rotate(custom=(180 - 45.0, 0.0))
vb.show()
""")
file = download_file('lh.sig.nii.gz')
b_obj_fmri = BrainObj('inflated', translucent=False, sulcus=True)
b_obj_fmri.add_activation(file=file, clim=(5., 20.), hide_under=5,
cmap='viridis', hemisphere='left')
sc.add_to_subplot(b_obj_fmri, row=0, col=0, row_span=2,
title='fMRI activation', rotate='top')
print("""
===============================================================================
Region Of Interest (ROI)
===============================================================================
""")
roi_aal = RoiObj('aal')
roi_aal.select_roi(select=[29, 30], unique_color=True, smooth=11)
sc.add_to_subplot(roi_aal, row=0, col=1, title='Region Of Interest (ROI)')
sc.add_to_subplot(BrainObj('B1'), use_this_cam=True, row=0, col=1)
print("""
=============================================================================
Sources
=============================================================================
""")
s_obj = SourceObj('FirstSources', xyz, data=data)
s_obj.color_sources(data=data, cmap='Spectral_r')
sc.add_to_subplot(s_obj, row=1, col=1, title='Sources')
sc.add_to_subplot(BrainObj('B3'), use_this_cam=True, row=1, col=1)
print("""
# 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', '(L)'], union=False)
# =============================================================================
# BRAIN + BA6
# =============================================================================
print('\n-> Plot brodmann area 6')
b_obj = BrainObj('B1')
roi_brod = RoiObj('brodmann')
idx_ba6 = roi_brod.where_is('BA6')
roi_brod.select_roi(select=idx_ba6)
roi_brod.get_labels(save_to_path=vb_path) # print available brodmann labels
sc.add_to_subplot(roi_brod, row=0, col=0, title='Brodmann area 6')
sc.add_to_subplot(b_obj, row=0, col=0, use_this_cam=True)
# =============================================================================
# MULTIPLE ROI + UNIQUE COLORS
# =============================================================================
print('\n-> Select and plot multiple ROI with random unique colors')
roi_aal = RoiObj('aal')
roi_aal.select_roi(select=[29, 30, 77, 78], unique_color=True, smooth=11)
roi_aal.get_labels(save_to_path=vb_path) # save available AAL labels
sc.add_to_subplot(roi_aal,
row=0,
col=1,
title='Select and plot multiple ROI with unique colors')
* 'brodmann' : Brodmann areas
* 'aal' : Automatic Anatomical Labeling
* 'talairach'
You can also define your own RoiObj with your own labels.
"""
roi_obj = RoiObj('aal', cblabel="Alpha power", border=False)
"""Find index of the thalamus and get the mesh.
If you want to get all of the index of the ROI template use :
* `roi_obj.get_labels()` : return a pandas DatFrame
* `roi_obj.get_labels('/home/')` : save all supported ROI and labels in an
excel file.
"""
idx_thalamus = roi_obj.where_is('Thalamus')
roi_obj.select_roi(idx_thalamus, smooth=5)
"""Once the ROI object created, we can project source's alpha modulations
directly on the thalamus
"""
roi_obj.project_sources(s_obj,
cmap='Spectral_r',
clim=(200., 2000.),
vmin=300.,
under='gray',
vmax=1800.,
over='darkred')
"""You can also force sources to fit onto the thalamus
"""
# s_obj.fit_to_vertices(roi_obj.vertices)
"""
"""
# Load the xyz coordinates and corresponding subject name :
s_xyz = np.load(download_file('xyz_sample.npz', astype='example_data'))['xyz']
"""Create a source object with random data between [-50,50]
"""
s_data = np.random.uniform(-50, 50, s_xyz.shape[0])
s_obj = SourceObj('Sobj', s_xyz, data=s_data, color='darkred', alpha=.5,
radius_min=2., radius_max=8., edge_width=0.)
"""Create a Region of Interest Object (ROI) and display brodmann area 4 and 6
"""
roi_obj = RoiObj('brodmann')
idx_4_6 = roi_obj.where_is(['BA4', 'BA6'], exact=True)
roi_color = {idx_4_6[0]: 'red', # BA4 in red and BA6 in green
idx_4_6[1]: 'green'}
roi_obj.select_roi(idx_4_6, unique_color=True, roi_to_color=roi_color,
smooth=7)
"""Create a brain object
"""
b_obj = BrainObj('B1', hemisphere='both', translucent=True)
"""Pass the brain, source and ROI object to the GUI
"""
vb = Brain(brain_obj=b_obj, source_obj=s_obj, roi_obj=roi_obj)
"""Render the scene and save the jpg picture with a 300dpi
"""
save_as = os.path.join(save_pic_path, '0_main_brain.jpg')
vb.screenshot(save_as, dpi=300, print_size=(10, 10), autocrop=True)
"""Project source's activity onto the surface
data=s_data,
color='darkred',
alpha=.5,
radius_min=2.,
radius_max=8.,
edge_width=0.)
"""Create a Region of Interest Object (ROI) and display brodmann area 4 and 6
"""
roi_obj = RoiObj('brodmann')
idx_4_6 = roi_obj.where_is(['BA4', 'BA6'], exact=True)
roi_color = {
idx_4_6[0]: 'red', # BA4 in red and BA6 in green
idx_4_6[1]: 'green'
}
roi_obj.select_roi(idx_4_6,
unique_color=True,
roi_to_color=roi_color,
smooth=7)
"""Create a brain object
"""
b_obj = BrainObj('B1', hemisphere='both', translucent=True)
"""Pass the brain, source and ROI object to the GUI
"""
vb = Brain(brain_obj=b_obj, source_obj=s_obj, roi_obj=roi_obj)
"""Render the scene and save the jpg picture with a 300dpi
"""
save_as = os.path.join(save_pic_path, '0_main_brain.jpg')
vb.screenshot(save_as, dpi=300, print_size=(10, 10), autocrop=True)
"""Project source's activity onto the surface
"""
vb.cortical_projection(clim=(0, 50),
cmap='Spectral_r',
* 'aal' : Automatic Anatomical Labeling
* 'talairach'
You can also define your own RoiObj with your own labels.
"""
roi_obj = RoiObj('aal', cblabel="Alpha power", border=False)
"""Find index of the thalamus and get the mesh.
If you want to get all of the index of the ROI template use :
* `roi_obj.get_labels()` : return a pandas DatFrame
* `roi_obj.get_labels('/home/')` : save all supported ROI and labels in an
excel file.
"""
idx_thalamus = roi_obj.where_is('Thalamus')
roi_obj.select_roi(idx_thalamus, smooth=5)
"""Once the ROI object created, we can project source's alpha modulations
directly on the thalamus
"""
roi_obj.project_sources(s_obj, cmap='Spectral_r', clim=(200., 2000.),
vmin=300., under='gray', vmax=1800., over='darkred')
"""You can also force sources to fit onto the thalamus
"""
# s_obj.fit_to_vertices(roi_obj.vertices)
"""
"""
b_obj = BrainObj('B3')