# ============================================================================
#
# 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
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# and reliability of the software and data, the NIDCD and the U.S. Government
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# ***************************************************************************
#
# 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 (megVisual.py) was created on June 7, 2015.
#
# Based on Sanz-Leon et al (2015) and Sarvas (1987) and on TVB's monitors.py
#
# Author: Antonio Ulloa
#
# Last updated by Antonio Ulloa on December 14 2015
# **************************************************************************/
# megVisual.py
#
# Calculate and plot MEG signal at source locations based on data from visual
# delay-match-to-sample simulation
import numpy as np
import matplotlib.pyplot as plt
#mu_0 = 1.25663706e-6 # mH/mm
# hypothesized Talairach coordinates of LSNM brain regions
v1_loc = [18, -88, 8]
v4_loc = [30, -72, -12]
it_loc = [28, -36, -8]
pf_loc = [42, 26, 20]
# define the hypothetical Talairach locations of each LSNM auditory module
#a1_lsnm = [48,-26,10]
#a2_lsnm = [62,-32,10]
#st_lsnm = [59,-17,4]
#pf_lsnm= [56,21,5]
# initialize source positions
r_0 = [v1_loc, v4_loc, it_loc, pf_loc]
#initialize vector from sources to sensor
#Q = simulator.connectivity.orientations
#centre = numpy.mean(r_0, axis=0)[numpy.newaxis, :]
#radius = 1.01 * max(numpy.sqrt(numpy.sum((r_0 - centre)**2, axis=1)))
# Load V1 synaptic activity data files into a numpy array
ev1h = np.loadtxt('ev1h_signed_syn.out')
ev1v = np.loadtxt('ev1v_signed_syn.out')
# Load V4 synaptic activity data files into a numpy array
ev4h = np.loadtxt('ev4h_signed_syn.out')
ev4c = np.loadtxt('ev4c_signed_syn.out')
ev4v = np.loadtxt('ev4v_signed_syn.out')
# Load IT synaptic activity data files into a numpy array
exss = np.loadtxt('exss_signed_syn.out')
# Load PFC synaptic activity data files into a numpy array
efd1 = np.loadtxt('efd1_signed_syn.out')
efd2 = np.loadtxt('efd2_signed_syn.out')
exfs = np.loadtxt('exfs_signed_syn.out')
exfr = np.loadtxt('exfr_signed_syn.out')
# Extract number of timesteps from one of the synaptic activity arrays
synaptic_timesteps = ev1h.shape[0]
# add all units within each region (V1, V4, IT, D1, D2, FS, R) together across space to calculate
# MEG source dynamics in each brain region
v1 = np.sum(ev1h + ev1v, axis = 1)
v4 = np.sum(ev4h + ev4c + ev4v, axis=1)
it = np.sum(exss, axis = 1)
d1 = np.sum(efd1, axis = 1)
d2 = np.sum(efd2, axis = 1)
fs = np.sum(exfs, axis = 1)
fr = np.sum(exfr, axis = 1)
# Set up figure to plot MEG source dynamics
plt.figure(1)
plt.suptitle('SIMULATED MEG SOURCE DYNAMICS')
# Plot MEG signal
v1_plot=plt.plot(v1, label='V1')
v4_plot=plt.plot(v4, label='V4')
it_plot=plt.plot(it, label='IT')
d1_plot=plt.plot(d1, label='D1')
d2_plot=plt.plot(d2, label='D2')
fs_plot=plt.plot(fs, label='FS')
fr_plot=plt.plot(fr, label='FR')
plt.legend()
# Show the plot on the screen
plt.show()