def get_HvM(self, ):
# Read Meta
Meta = ReadMeta(neuralfeaturesdir)
DF_img = Meta.get_DF_img()
DF_neu = Meta.get_DF_neu()
times = Meta.get_times()
# Read Neural data
Data = ReadData(datadir, DF_neu)
IT, V4 = Data.get_data()
D = Mapping.get_Neu_trial_V36(IT[1:], [70, 170], times)
image_indices = np.random.randint(low=0, high=D.shape[1], size=ni)
D = D[:, image_indices, :]
D = np.swapaxes(D, 0, 1)
nf = D.shape[1]
nt = D.shape[2]
mu = np.zeros((self.nf, self.ni))
sd = np.zeros((self.nf, self.ni))
for f in range(self.nf):
for i in range(self.ni):
mu[f, i] = D[i, f, :].mean()
sd[f, i] = D[i, f, :].std()
hf = h5py.File(resultdir + 'HvM_stats.h5', 'w')
hf.create_dataset('mu', data=mu)
hf.create_dataset('sd', data=sd)
hf.close()
# #test synthetic as HvM
# nf = 168
# nt = 46
# noise_dist = 'poisson'
# sds = np.logspace(-1, 1, num=int(nf))
# D = np.zeros((ni, nf, nt))
# D_mean = np.random.rand(ni, nf)
# for tr in range(nt):
# D[:, :, tr] = D_mean
#
# noise1 = np.zeros((ni, nf, int(nt * splitfract)))
# noise2 = np.zeros((ni, nf, int(nt * splitfract)))
# for i in range(ni):
# if noise_dist == 'normal':
# n = np.random.rand()
# noise1[i] = np.array([np.random.normal(0, sd + n, size=int(nt * splitfract)) for sd in sds])
# noise2[i] = np.array([np.random.normal(0, sd + n, size=int(nt * splitfract)) for sd in sds])
# elif noise_dist == 'poisson':
# n = np.random.rand()
# noise1[i] = np.array([np.random.poisson(sd + n, size=int(nt * splitfract)) for sd in sds])
# noise2[i] = np.array([np.random.poisson(sd + n, size=int(nt * splitfract)) for sd in sds])
#
# D[:, :, :int(nt * splitfract)] = D[:, :, :int(nt * splitfract)] + noise1
# D[:, :, int(nt * splitfract):] = D[:, :, int(nt * splitfract):] + noise2
# to test HvM as syntheic
# hf = h5py.File(resultdir+'D.h5', 'w')
# hf.create_dataset('D', data=D)
# hf.close()
sds = []
Collinearity = 'HvM'
noise_dist = 'HvM'
return D
