This repository has been archived by the owner on Jan 27, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathdisplay_ROIs_on_surface.py
85 lines (69 loc) · 3.46 KB
/
display_ROIs_on_surface.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
# ============================================================================
#
# PUBLIC DOMAIN NOTICE
#
# National Institute on Deafness and Other Communication Disorders
#
# This software/database is a "United States Government Work" under the
# terms of the United States Copyright Act. It was written as part of
# the author's official duties as a United States Government employee and
# thus cannot be copyrighted. This software/database is freely available
# to the public for use. The NIDCD and the U.S. Government have not placed
# any restriction on its use or reproduction.
#
# Although all reasonable efforts have been taken to ensure the accuracy
# and reliability of the software and data, the NIDCD and the U.S. Government
# do not and cannot warrant the performance or results that may be obtained
# by using this software or data. The NIDCD and the U.S. Government disclaim
# all warranties, express or implied, including warranties of performance,
# merchantability or fitness for any particular purpose.
#
# Please cite the author in any work or product based on this material.
#
# ==========================================================================
# ***************************************************************************
#
# Large-Scale Neural Modeling software (LSNM)
#
# Section on Brain Imaging and Modeling
# Voice, Speech and Language Branch
# National Institute on Deafness and Other Communication Disorders
# National Institutes of Health
#
# This file (display_ROIs_on_surface.py) was created on August 31, 2015.
#
#
# Author: Antonio Ulloa. Last updated by Antonio Ulloa on June 16, 2015
#
# Based on: display_surface_parcellation.py by Stuart Knock (TVB team)
# **************************************************************************/
#
# display_ROIs_on_surface.py
#
# Displays a surface of the cerebral cortex that shows the location of
# the regions of interest (ROIs) under study, labeled by given colors.
from tvb.simulator.lab import *
from tvb.simulator.region_boundaries import RegionBoundaries
from tvb.simulator.region_colours import RegionColours
CORTEX = surfaces.Cortex.from_file("cortex_80k/surface_80k.zip")
CORTEX_BOUNDARIES = RegionBoundaries(CORTEX)
#region_colours = RegionColours(CORTEX_BOUNDARIES.region_neighbours)
#colouring = region_colours.back_track()
number_of_regions = len(CORTEX_BOUNDARIES.region_neighbours)
#for k in range(int(number_of_regions)):
# colouring[k + int(number_of_regions)] = colouring[k]
mapping_colours = list("rgbcmyRGBCMY")
colour_rgb = {"r": numpy.array([255, 0, 0], dtype=numpy.uint8),
"g": numpy.array([ 0, 255, 0], dtype=numpy.uint8),
"b": numpy.array([ 0, 0, 255], dtype=numpy.uint8),
"c": numpy.array([ 0, 255, 255], dtype=numpy.uint8),
"m": numpy.array([255, 0, 255], dtype=numpy.uint8),
"y": numpy.array([255, 255, 0], dtype=numpy.uint8),
"R": numpy.array([128, 0, 0], dtype=numpy.uint8),
"G": numpy.array([ 0, 128, 0], dtype=numpy.uint8),
"B": numpy.array([ 0, 0, 128], dtype=numpy.uint8),
"C": numpy.array([ 0, 128, 128], dtype=numpy.uint8),
"M": numpy.array([128, 0, 128], dtype=numpy.uint8),
"Y": numpy.array([128, 128, 0], dtype=numpy.uint8)}
#(surf_mesh, bpts) = surface_parcellation(CORTEX_BOUNDARIES, colouring, mapping_colours, colour_rgb, interaction=True)
##- EoF -##