return out
elif t < 1.75 and TARGET:
out += np.array([1.,.5])
elif t > .125 and MASK:
out += np.array([.5,1.])
return out
def stimfunc(STIM,t,nt,MASK = True,TARGET = True):
if STIM == 1:
return np.array([S1(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
elif STIM == 2:
return np.array([S2(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
elif STIM == 3:
return np.array([S3(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
nta = 80
tot = .4
ta = tot/(nta)
Fa = np.exp(-ta)*np.identity(2)
stima = np.array([10*stimfunc(s,.4,nta + 1,MASK = M[1],TARGET = M[0])[:k] for k in range(1,81,5) for s in [1,2,3] for M in [[True,False],[False,True],[True,True]]])
stimp = np.array([10*stimfunc(s,.4,nta + 1,MASK = M[1],TARGET = M[0]) for s in [1,2,3] for M in [[True,False],[False,True],[True,True]]])
cor = [.1]
resp = np.array([inference.att_gexp(0,np.array([s]),cov(c),cov(0),inference.Q_self_con(cov(c),Fa),Fa) for c in cor for s in stima])
np.savetxt("./att_2d_resp.csv",resp)
np.savetxt("./att_2d_stim.csv",np.reshape(stimp,[-1,2]))
import numpy as np
import GSM.MGSM_inference as MGSM
cov = np.array([[1, .1], [.1, 1]])
ncov = np.array([[1, 0], [0, 1]])
qcov = np.array([[.01, 0], [0, .01]])
print("cov", cov)
print("ncov", ncov)
print("qcov", qcov)
ff = np.array([[1, 0], [1, 1]])
res = MGSM.att_gexp(0, ff, ff, cov, ncov, qcov)
print(res)
0,
16,
0,
5 * 16,
int(np.max(np.linalg.norm(sites, axis=1))),
get_grat=True)
inp = np.array([get_grating_data(g, sites, RF) / fac for g in grat])
nc = CNS
f = .5 * np.identity(len(CNS))
q = inf.Q_self_con(CNS, f)
inp += np.random.multivariate_normal(np.zeros_like(inp[0]), nc, len(inp))
aI = np.tile(np.expand_dims(inp, 1), [1, 3, 1])
resp = inf.gnn(inp, CNS)
iresp = inf.att_gexp(0, aI, CNS, nc, q, f)
f = .75 * np.identity(len(CNS))
q = inf.Q_self_con(CNS, f)
aresp = inf.att_gexp(0, aI, CNS, nc, q, f)
print(oris)
np.savetxt("./random_gsm_resp.csv", resp)
np.savetxt("./a_random_gsm_resp.csv", aresp)
np.savetxt("./i_random_gsm_resp.csv", iresp)