mpl.rc('lines', **linewidth)
linewidth = {'linewidth': 4.0}
mpl.rc('axes', **linewidth)
import matplotlib.cm as cm
import matplotlib.pyplot as plt
# *Bold Monitor*
# --------
#
#
#
# Let's start by creating an instance of the Bold monitor with its default parameters:
# In[3]:
bold = monitors.Bold()
# In general, the sampling period of a monitor is in milliseconds and must be an integral multiple of the integration-step size used in a simulation.
#
# Therefore, monitors need to know the integration time step (*dt*) because some data reduction mechanims (eg, downsampling to the monitor's sampling period) depend on it. An easy way to achieve this is:
# In[4]:
bold.dt = 2**-4 # Default value used in the scripts found at tvb/simulator/demos
# HRFs are TVB Equation datatypes, and you can explore their attributes, for instance:
# * which equation we use,
# In[5]:
def test_monitor_bold(self):
"""
This has to be verified.
"""
monitor = monitors.Bold()
assert monitor.period == 2000.0
def test_monitor_bold(self):
"""
This has to be verified.
"""
monitor = monitors.Bold()
self.assertEqual(monitor.period, 2000.0)
# sim_result = sub_sim.prepare_and_run(what_to_watch = what_to_watch, linear_coupling=linear_coupling.item(), conduction_speed=conduction_speed.item(), K11=K11, K12=K12, K21=K21, simulation_length=simulation_length_PE, period_length=period_length_bold)
sim_results.append(sim_result)
# final result of this loop will be list of dictionaries. each dictionary is a simulation with one parameter combo.
sim_results = remove_beg_of_ts(sim_results, throwaway_length_pe)
#ideal_measure = FC_Ideal_Measure(sim_results = sim_results, empirical_results = sub.empfc)
#ideal_params = ideal_measure.find_ideal_params()
ideal_params = find_ideal_params_using_fc(sim_results, sub.empfc)
# ideal_params = find_ideal_params_using_temp_subsample(sim_results, sub)
what_to_watch = monitors.Bold(period=period_length_bold,
hrf_kernel=equations.MixtureOfGammas(),
variables_of_interest=variables_of_interest)
# run the sim with these ideal params we have identified:
# sim_result_for_ideal_params = sub_sim.prepare_and_run(what_to_watch = what_to_watch, linear_coupling=linear_coupling, conduction_speed=conduction_speed, K11=ideal_params['K11'], K12=ideal_params['K12'], K21=ideal_params['K21'], simulation_length=simulation_length_sim, period_length=period_length_bold)
sim_result_for_ideal_params = sub_sim.prepare_and_run(
what_to_watch=what_to_watch,
linear_coupling=ideal_params['linear_coupling'],
conduction_speed=ideal_params['conduction_speed'],
K11=ideal_params['K11'],
K12=ideal_params['K12'],
K21=ideal_params['K21'],
simulation_length=simulation_length_sim,
period_length=period_length_bold)
##----------------------------------------------------------------------------##
LOG.info("Configuring...")
#Initialise a Model, Coupling, and Connectivity.
oscilator = models.Generic2dOscillator() #ReducedSetHindmarshRose() #
white_matter = connectivity.Connectivity()
white_matter.speed = numpy.array([4.0])
white_matter_coupling = coupling.Linear(a=0.0126)
#Initialise an Integrator
heunint = integrators.HeunDeterministic(dt=2**-4)
#Initialise some Monitors with period in physical time
momo = monitors.TemporalAverage(period=1.0) #1000Hz
mama = monitors.Bold(period=500) #defaults to one data point every 2s
#Bundle them
what_to_watch = (momo, mama)
#Define the stimulus
#Specify a weighting for regions to receive stimuli...
white_matter.configure() # Because we want access to number_of_regions
nodes = [0, 7, 13, 33, 42]
weighting = numpy.zeros((white_matter.number_of_regions, )) #1
weighting[nodes] = numpy.array([2.0**-2, 2.0**-3, 2.0**-4, 2.0**-5,
2.0**-6]) # [:, numpy.newaxis]
eqn_t = equations.Gaussian()
eqn_t.parameters["midpoint"] = 15000.0
eqn_t.parameters["sigma"] = 4.0
