M = np.outer(m, n) / N
J = R + M
### Simulate
Z = sim.SimulateActivity(t, sim.GetGaussianVector(0, 1, N), J, I=0)
# Store
fac.Store(Z[:, 0:Nsample], 'Z.p', path_here)
else:
# Retrieve
Z = fac.Retrieve('Z.p', path_here)
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### Plot
fac.SetPlotParams()
fg = plt.figure()
ax0 = plt.axes(frameon=True)
for i in range(Nsample):
plt.plot(t, (Z[:, i]), color='0.6')
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.yaxis.set_ticks_position('left')
fac.Store(mu_s, 'mu_s.p', path_here)
fac.Store(delta0_s, 'delta0_s.p', path_here)
fac.Store(delta0I_s, 'delta0I_s.p', path_here)
fac.Store(mu_c, 'mu_c.p', path_here)
fac.Store(delta0_c, 'delta0_c.p', path_here)
fac.Store(deltainf_c, 'deltainf_c.p', path_here)
fac.Store(delta0I_c, 'delta0I_c.p', path_here)
fac.Store(deltainfI_c, 'deltainfI_c.p', path_here)
fac.Store(radius, 'radius.p', path_here)
fac.Store(outlier, 'outlier.p', path_here)
else:
# Retrieve
mu_s = fac.Retrieve('mu_s.p', path_here)
delta0_s = fac.Retrieve('delta0_s.p', path_here)
delta0I_s = fac.Retrieve('delta0I_s.p', path_here)
mu_c = fac.Retrieve('mu_c.p', path_here)
delta0_c = fac.Retrieve('delta0_c.p', path_here)
deltainf_c = fac.Retrieve('deltainf_c.p', path_here)
delta0I_c = fac.Retrieve('delta0I_c.p', path_here)
deltainfI_c = fac.Retrieve('deltainfI_c.p', path_here)
radius = fac.Retrieve('radius.p', path_here)
outlier = fac.Retrieve('outlier.p', path_here)
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### Plot
# We plot K = Mn*phi as first-order statistics and individual variances as second-order statistics
K_s = mu_s / Mm
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### Import functions
import matplotlib.pyplot as plt
import numpy as np
import fct_simulations as sim
import fct_facilities as fac
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### Load data
path_data = 'Data/'
structure = fac.Retrieve('structure.p', path_data)
activation = fac.Retrieve('activation.p', path_data)
readout = fac.Retrieve('readout.p', path_data)
t = fac.Retrieve('t.p', path_data)
t1 = fac.Retrieve('t1.p', path_data)
t2 = fac.Retrieve('t2.p', path_data)
t3 = fac.Retrieve('t3.p', path_data)
Sii, Siw = fac.Retrieve('ParVec.p', path_data)
m = structure[0]
IA = structure[1]
IB = structure[2]
N = activation.shape[2]
### Compute CENTRAL solution
mu_s[2,i], delta0_s[2,i], K_s[2,i] = mf.SolveStatic ( ics_2, g, ParVec, backwards = -1. )
# Store
fac.Store( K_s, 'K_s.p', path_here)
fac.Store( mu_s, 'mu_s.p', path_here)
fac.Store( delta0_s, 'delta0_s.p', path_here)
else:
# Retrieve
K_s = fac.Retrieve('K_s.p', path_here)
mu_s = fac.Retrieve('mu_s.p', path_here)
delta0_s = fac.Retrieve('delta0_s.p', path_here)
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### Plot
fac.SetPlotParams()
dashes = [3, 3]
color_s = '#4872A1'
# Kappa
# Plot the negative and central branch of the solution only when they are not equal to the positive one
import matplotlib.pyplot as plt
import numpy as np
import fct_simulations as sim
import fct_facilities as fac
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### Load data
doCompute = 1
path_data = 'Data/'
structure = fac.Retrieve('structure.p', path_data)
activation = fac.Retrieve('activation.p', path_data)
connectivity = fac.Retrieve('average_connectivity.p', path_data)
N = activation.shape[2]
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### Compute PC axis
# We compute the PC axis separately from the Go and the Nogo trials
XA = activation[0,:,:]
XB = activation[1,:,:]
# Z-score
eig = mf.EigStationary(g, ParVec, [readout[2, i], delta0_cl], p,
pm, pI)
outlier[2, i] = eig[0]
radius[2, i] = g * np.sqrt(integ.PrimeSq(0, delta0_cl))
# Store
fac.Store(readout, 'readout.p', 'VaryA/')
fac.Store(outlier, 'outlier.p', 'VaryA/')
fac.Store(radius, 'radius.p', 'VaryA/')
else:
# Retrieve
readout = fac.Retrieve('readout.p', 'VaryA/')
outlier = fac.Retrieve('outlier.p', 'VaryA/')
radius = fac.Retrieve('radius.p', 'VaryA/')
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### Plot
fac.SetPlotParams()
dashes = [3, 3]
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### Readout
fg = plt.figure()
ax0 = plt.axes(frameon=True)