row=1,
col=0,
rotate='right',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all,
clim=(1, 7),
cmap='viridis_spliced')
sc.add_to_subplot(b_obj_proj_right,
row=1,
col=1,
rotate='left',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all,
clim=(1, 7),
cmap='viridis_spliced')
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=1,
rotate='right',
use_this_cam=True)
sc.screenshot(os.path.join(fig_dir, 'freq_intersection.png'), transparent=True)
# And add connect, source and brain objects to the scene
# sc.add_to_subplot(s_obj, row=0, col=0, zoom=0.1)
b_obj = BrainObj('B2')
sc.add_to_subplot(b_obj,row=0, col=0, use_this_cam=True)
#, use_this_cam=True
# from visbrain.objects import ColorbarObj
# cb = ColorbarObj(c_default, **CBAR_STATE)
# sc.add_to_subplot(cb, width_max=200, row=0, col=1)
# clim=(4., 78.2), vmin=10.,
# vmax=72., cblabel='Colorbar title', under='gray',
# over='red', txtcolor='black', cbtxtsz=40, cbtxtsh=2.,
# txtsz=20., width=.04)
sc.screenshot('plain_brain.png', transparent=True)
sc.preview()
#sc.screenshot("test.jpg")
################Different transparency for diff strengths (one color)
# Define the connectivity object
# c_default = ConnectObj('default', nodes, edges, select=select, line_width=0.5,dynamic=(0, 1),
# cmap='viridis',custom_colors = {None: "green"})
# # Then, we define the sources
# #node size and color
# s_obj = SourceObj('sources', nodes, color='#000000', radius_min=10.)
# #title
# sc.add_to_subplot(c_default, row=0, col=0, zoom=0.1)
# # And add connect, source and brain objects to the scene
# sc.add_to_subplot(s_obj, row=0, col=0, zoom=0.1)
sc.add_to_subplot(b_obj_proj_left,
row=0,
col=1,
rotate='right',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all, clim=(0, 16), cmap=cmap)
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=2,
rotate='left',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all, clim=(0, 16), cmap=cmap)
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=3,
rotate='right',
use_this_cam=True)
# cb_proj = ColorbarObj(b_obj_proj_right, cblabel='Order', **CBAR_STATE)
# sc.add_to_subplot(cb_proj, row=0, col=4, width_max=200)
sc.screenshot(os.path.join(fig_dir, f'{c}_15.png'), transparent=True)
sc.add_to_subplot(b_obj_proj_left, row=0, col=0, rotate='left', use_this_cam=True)
b_obj_proj_left = BrainObj(template_brain, hemisphere='left', translucent=False)
b_obj_proj_left.project_sources(s_obj_1, clim=(0, 2), cmap='binary')
sc.add_to_subplot(b_obj_proj_left, row=0, col=1, rotate='right', use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain, hemisphere='right', translucent=False)
b_obj_proj_right.project_sources(s_obj_1, clim=(0, 2), cmap='binary')
sc.add_to_subplot(b_obj_proj_right, row=0, col=2, rotate='left', use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain, hemisphere='right', translucent=False)
b_obj_proj_right.project_sources(s_obj_1, clim=(0, 2), cmap='binary')
sc.add_to_subplot(b_obj_proj_right, row=0, col=3, rotate='right', use_this_cam=True)
sc.screenshot(os.path.join(fig_dir, 'intersection.png'), transparent=True)
nii_list = glob.glob(os.path.join(nii_bo_dir, '*_bestloc.nii'))
for n in nii_list:
sc = SceneObj(bgcolor='white', size=(1000, 500))
nii = nib.load(n)
fname = os.path.splitext(os.path.basename(n))[0]
b_obj_proj_ll = BrainObj(template_brain, hemisphere='left', translucent=False)
b_obj_proj_lr = BrainObj(template_brain, hemisphere='left', translucent=False)
b_obj_proj_rr = BrainObj(template_brain, hemisphere='right', translucent=False)
b_obj_proj_rl = BrainObj(template_brain, hemisphere='right', translucent=False)
def create_graph_picture(filename, xyz, hemisphere, rotation):
'''
data is a .mat file containing the location and the 3D tensor containing
N number of M*M matrices. Output path is where we will be saving the stuff
hemisphere is 'left' 'right' or 'both'
rotation is 'left', 'right', 'top', 'bottom'
'''
num_electrodes = xyz.shape[0] # Number of electrodes
# Get the connection matrix
connect = 1 * np.random.rand(num_electrodes, num_electrodes)
data = np.mean(connect, axis=1)
# need a upper triangular matrix (fine for wPLI since we have symmetry)
connect[np.tril_indices_from(connect)] = 0 # Set to zero inferior triangle
small_weight_threshold = 0.001
large_weight_threshold = 0.999
# 2 - Using masking (True: hide, 1: display):
connect = np.ma.masked_array(connect, mask=True)
# This will show (set to hide = False) only the large and small connection
connect.mask[np.where((connect >= large_weight_threshold)
| (connect <= small_weight_threshold))] = False
s_obj = SourceObj('SourceObj1',
xyz,
data,
color='whitesmoke',
alpha=.5,
edge_width=2.,
radius_min=2.,
radius_max=30.)
c_obj = ConnectObj('ConnectObj1',
xyz,
connect,
color_by='strength',
cmap='Greys',
vmin=small_weight_threshold,
vmax=large_weight_threshold,
under='blue',
over='red',
antialias=True)
CAM_STATE = dict(
azimuth=0,
elevation=90,
)
CBAR_STATE = dict(cbtxtsz=12,
txtsz=10.,
width=.1,
cbtxtsh=3.,
rect=(-.3, -2., 1., 4.))
sc = SceneObj(camera_state=CAM_STATE, size=(1400, 1000))
sc.add_to_subplot(BrainObj('B1', hemisphere=hemisphere),
row=0,
col=0,
rotate=rotation)
sc.add_to_subplot(c_obj, row=0, col=0, rotate=rotation)
sc.add_to_subplot(s_obj, row=0, col=0, rotate=rotation)
sc.screenshot(filename, print_size=(10, 20), dpi=100)
txtsz=10.,
width=.1,
cbtxtsh=3.,
rect=(-.3, -2., 1., 4.))
KW = dict(title_size=14., zoom=1)
sc_blank = SceneObj(bgcolor='white', size=(500, 500))
b_obj_proj_blank = BrainObj(template_brain,
hemisphere='left',
translucent=False)
sc_blank.add_to_subplot(b_obj_proj_blank,
row=0,
col=0,
rotate='left',
use_this_cam=True)
sc_blank.screenshot(os.path.join(fig_dir, 'blank.png'), transparent=True)
s_obj_1 = SourceObj('iEEG', xyz1, data=data1, cmap=cmap)
s_obj_1.color_sources(data=data1)
s_obj_2 = SourceObj('iEEG', xyz2, data=data2, cmap=cmap)
s_obj_2.color_sources(data=data2)
s_obj_3 = SourceObj('iEEG', xyz3, data=data3, cmap=cmap)
s_obj_3.color_sources(data=data3)
s_obj_4 = SourceObj('iEEG', xyz4, data=data4, cmap=cmap)
s_obj_4.color_sources(data=data4)
s_obj_all = s_obj_1 + s_obj_2 + s_obj_3 + s_obj_4
b_obj_proj_left = BrainObj(template_brain,
hemisphere='both',
translucent=False)
translucent=False)
b_obj_proj_left.project_sources(s_obj_all, clim=(0, 4), cmap=cmap)
sc.add_to_subplot(b_obj_proj_left,
row=0,
col=1,
rotate='right',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all, clim=(0, 4), cmap=cmap)
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=2,
rotate='left',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_all, clim=(0, 4), cmap=cmap)
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=3,
rotate='right',
use_this_cam=True)
sc.screenshot(os.path.join(fig_dir, f'{c}_largest_abs.png'),
transparent=True)
row=1,
col=1,
rotate='left',
use_this_cam=True)
b_obj_proj_right = BrainObj(template_brain,
hemisphere='right',
translucent=False)
b_obj_proj_right.project_sources(s_obj_1, clim=(1, 7), cmap=cmap)
sc.add_to_subplot(b_obj_proj_right,
row=0,
col=1,
rotate='right',
use_this_cam=True)
sc.screenshot(os.path.join(fig_dir, '7_networks.png'), transparent=True)
for f in freqs:
sc = SceneObj(bgcolor='white', size=(1000, 1000))
b_yeo = se.load(os.path.join(nii_bo_dir, f + '_network.bo'))
data1 = b_yeo.get_data().values.ravel()
xyz1 = b_yeo.locs.values
s_obj_1 = SourceObj('iEEG', xyz1, data=data1, cmap=cmap)
s_obj_1.color_sources(data=data1)
b_obj_proj_left = BrainObj(template_brain,
hemisphere='left',
"""
import numpy as np
from visbrain.objects import SourceObj, SceneObj, ColorbarObj, BrainObj, RoiObj, ConnectObj
from visbrain.io import download_file
# Create the scene
CAM_STATE = dict(
azimuth=0, # azimuth angle
elevation=90, # elevation angle
scale_factor=180 # distance to the camera
)
sc = SceneObj(bgcolor='white', size=(1600, 1000))
b_obj = BrainObj('B3', translucent=False)
sc.add_to_subplot(b_obj)
sc.screenshot('plain_brain.png')
sc.preview()
###############################################################################
# .. warning::
# To be clear with the vocabulary used, the SourceObj has a different
# meaning depending on the recording type. For scalp or intracranial EEG,
# sources reflect electrode, in MEG it could be sensors or source
# reconstruction.
###############################################################################
# Download data
###############################################################################
# To illustrate the functionalities of the source object, here, we download an
# intracranial dataset consisting of 583 deep recording sites.
def generate_img(l_file,
r_file,
activated_areas,
brain_name,
out_file,
views,
add_hemispheres,
is_estimate=False,
cmap='copper',
vmin=0.,
vmax=0.01,
save_gif=False):
"""Generate .png and .gif animation of rotating brains
"""
sc = SceneObj(size=(1000 * (len(views) // 2 +
(1 if add_hemispheres else 0)),
1000 * (len(views) > 1)),
bgcolor='black')
KW = dict(title_size=14., zoom=2.) # zoom not working
CBAR_STATE = dict(cbtxtsz=12,
txtsz=10.,
width=.1,
cbtxtsh=3.,
rect=(-.3, -2., 1., 4.))
# PLOT OBJECTS
for i, rot in enumerate(views):
# cannot use the same object
b_obj = create_brain_obj(l_file,
r_file,
activated_areas,
brain_name,
cmap=cmap,
vmin=vmin,
vmax=vmax)
sc.add_to_subplot(b_obj,
row=i // 2,
col=i % 2,
rotate=rot,
title=rot,
**KW)
# Get the colorbar of the brain object and add it to the scene
# Identical brain ==> same colorbar
if add_hemispheres:
# add left brain
b_obj = create_brain_obj(l_file,
r_file,
activated_areas,
brain_name,
hemisphere='right',
cmap=cmap,
vmin=vmin,
vmax=vmax)
sc.add_to_subplot(b_obj,
row=i // 2 + 1,
col=0,
rotate='left',
title='right half',
**KW)
b_obj = create_brain_obj(l_file,
r_file,
activated_areas,
brain_name,
hemisphere='left',
cmap=cmap,
vmin=vmin,
vmax=vmax)
sc.add_to_subplot(b_obj,
row=i // 2 + 1,
col=1,
rotate='right',
title='left half',
**KW)
if is_estimate:
# cmap needs to be set for all objects
cb_parr = ColorbarObj(b_obj,
cblabel='Data to parcellates',
**CBAR_STATE)
# not working properly and can't find a way to rotate that bar
# sc.add_to_subplot(cb_parr, row=0, col=2, row_span=i//2+1, width_max=200)
# gif and png
# sc.preview()
if save_gif:
sc.record_animation(out_file + ('_est' if is_estimate else '') +
"_areas.gif")
sc.screenshot(saveas=out_file + ('_est' if is_estimate else '') +
"_areas.png")
return sc.render()