mkdir(dir2X)
sim_coef = 0.9
#endFName = '_P_%.2f_f0_%.2f.npy' %(p1,p2)
endFName = '_P_%.2f_G_%.2f.npy' % (p1, p2)
fileName = 'patterns' + endFName
############################################################################################'
# **Main**
if not adressExists(dir2X + fileName):
tic()
pattsX = data2array(dir1X + fileName,
mmap_mode="r+") #.reshape((3300,998))
pattsS = data2array(dir1S + fileName,
mmap_mode="r+") #.reshape((3300,998))
patts = pattsX #!!!
S = sortBy(patts.mean(1) - patts.mean(), inverse=1)[0]
C1, freq = preClustering(patts[S],
sim_coef=sim_coef,
sim_func=similarity_Euclidean)
C2, freq = preClustering(patts[S][C1],
freq=freq,
sim_coef=sim_coef,
sim_func=fPearsonCorrelation)
SC, freq = sortBy(freq, inverse=1)
array2data(pattsX[S][C1][C2][SC], dir2X + fileName)
array2data(pattsS[S][C1][C2][SC], dir2S + fileName)
array2data(freq, dir2X + '/tendances' + endFName)
tac()
'tauT': 80,
'P': P,
'G': 900.,
}
noise = {'stdD_x': sx, 'stdD_T': sT, 'colors': ['white', 'white']}
out = ['x']
other = {'init': 'rand', 'dens': 0.5, 'rperiod': 100, 'dur': '20m'}
eva = main.evaCure(evaCon=conn,
evaNoi=noise,
evaMod=model,
out=out,
**other)
eva.updateTillEnd()
TC = eva.out['x']
array2data(TC, dir_TC + '/TC_998_' + name)
if not adressExists(dir_FC + '/FC_998_' + name):
try:
TC = array(TC)
except:
TC = data2array(dir_TC + '/TC_998_' + name)
tic()
FC = fPearsonCorrelation(TC)
tac('h')
array2data(FC, dir_FC + '/FC_998_' + name)