def compute_fcd(con, bold_data,parameter_monitor,wind_len=180e3,wind_sp=4e3):
bold_period = parameter_monitor['parameter_Bold']['period']
# Build the time series object
tsr = TimeSeriesRegion(connectivity=con,
data=bold_data,
sample_period=bold_period)
tsr.configure()
# Create and evaluate the analysis
fcd_analyser = fcd.FcdCalculator(time_series=tsr, sw=wind_len, sp=wind_sp)
fcd_data = fcd_analyser.evaluate()
# Store the results
FCD=fcd_data[0][:,:,0,0]
return FCD
def compute_fc(con, bold_data, parameter_monitor):
import tvb.analyzers.correlation_coefficient as corr_coeff
from tvb.datatypes.time_series import TimeSeriesRegion
bold_period = parameter_monitor['parameter_Bold']['period']
# Remove transient
bold_data = bold_data[10:, :, :]
tsr = TimeSeriesRegion(connectivity=con,
data=bold_data,
sample_period=bold_period)
tsr.configure()
# Compute FC
corrcoeff_analyser = corr_coeff.CorrelationCoefficient(time_series=tsr)
corrcoeff_data = corrcoeff_analyser.evaluate()
corrcoeff_data.configure()
FC = corrcoeff_data.array_data[..., 0, 0]
return FC
LOG.info("Finished simulation.")
##----------------------------------------------------------------------------##
##- Plot pretty pictures of what we just did -##
##----------------------------------------------------------------------------##
#Make the lists numpy.arrays for easier use.
LOG.info("Converting result to array...")
TAVG_TIME = numpy.array(tavg_time)
BOLD_TIME = numpy.array(bold_time)
BOLD = numpy.array(bold_data)
TAVG = numpy.array(tavg_data)
#Create TimeSeries instance
tsr = TimeSeriesRegion(data=TAVG, time=TAVG_TIME, sample_period=2.)
tsr.configure()
#Create and run the monitor/analyser
bold_model = bold.BalloonModel(time_series=tsr)
bold_data = bold_model.evaluate()
bold_tsr = TimeSeriesRegion(connectivity=white_matter,
data=bold_data.data,
time=bold_data.time)
#Prutty puctures...
tsi = timeseries_interactive.TimeSeriesInteractive(time_series=bold_tsr)
tsi.configure()
tsi.show()
###EoF###
#Load the demo region timeseries dataset
try:
data = numpy.load("demo_data_region_16s_2048Hz.npy")
except IOError:
LOG.error("Can't load demo data. Run demos/generate_region_demo_data.py")
raise
period = 0.00048828125 #s
#Put the data into a TimeSeriesRegion datatype
white_matter = connectivity.Connectivity()
tsr = TimeSeriesRegion(connectivity=white_matter,
data=data,
sample_period=period)
tsr.configure()
#Create and run the analyser
pca_analyser = pca.PCA(time_series=tsr)
pca_data = pca_analyser.evaluate()
#Generate derived data, such as, compnent time series, etc.
pca_data.configure()
#Put the data into a TimeSeriesSurface datatype
component_tsr = TimeSeriesRegion(connectivity=white_matter,
data=pca_data.component_time_series,
sample_period=period)
component_tsr.configure()
#Prutty puctures...
#Load the demo region timeseries dataset
try:
data = numpy.load("demo_data_region_16s_2048Hz.npy")
except IOError:
LOG.error("Can't load demo data. Run demos/generate_region_demo_data.py")
raise
period = 0.00048828125 #s
#Put the data into a TimeSeriesRegion datatype
white_matter = connectivity.Connectivity()
tsr = TimeSeriesRegion(connectivity = white_matter,
data = data,
sample_period = period)
tsr.configure()
#Create and run the analyser
pca_analyser = pca.PCA(time_series = tsr)
pca_data = pca_analyser.evaluate()
#Generate derived data, such as, compnent time series, etc.
pca_data.configure()
#Put the data into a TimeSeriesSurface datatype
component_tsr = TimeSeriesRegion(connectivity = white_matter,
data = pca_data.component_time_series,
sample_period = period)
component_tsr.configure()
#Prutty puctures...
#Load the demo region timeseries dataset
try:
data = numpy.load("demo_data_region_16s_2048Hz.npy")
except IOError:
LOG.error("Can't load demo data. Run demos/generate_region_demo_data.py")
raise
period = 0.00048828125 # s
#Put the data into a TimeSeriesRegion datatype
white_matter = connectivity.Connectivity()
tsr = TimeSeriesRegion(connectivity=white_matter,
data=data,
sample_period=period)
tsr.configure()
#Create and run the analyser
corrcoeff_analyser = corr_coeff.CorrelationCoefficient(time_series=tsr)
corrcoeff_data = corrcoeff_analyser.evaluate()
#Generate derived data, if any...
corrcoeff_data.configure()
# Plot matrix with numbers
# For visualization purposes, the diagonal is set to zero.
FC = corrcoeff_data.array_data[:, :, 0, 0]
numpy.fill_diagonal(FC, 0.)
pyplot.matshow(FC, cmap='RdBu', vmin=-0.5, vmax=0.5, interpolation='nearest')
pyplot.colorbar()
pyplot.show()