def load(_, transp=False, slices=slice(None), mmap_mode=None, sprint=True, **kwa):
'''Load files to a dictionnary.
mmap_mode='r' for memory access.
'''
items = _.find(subk=['adress'], **kwa)
mats, titles = [], []
for k in sorted(items.keys()):
if slices != None:
tmp = array(data2array(items[k]['adress'], mmap_mode='r')[slices])
else:
tmp = data2array(items[k]['adress'], mmap_mode=mmap_mode)
try: _[k]['shape']
except: _[k]['shape'] = tmp.shape
if transp: mats.append(tmp.T)
else: mats.append(tmp)
titles.append( k )
nruter = {}
for k in range(len(mats)):
nruter[titles[k]] = mats[k]
if sprint: print _.size(items)
return nruter
def funcNbofAttr(P, G):
endDir = 'G_%.3f_P_%.5f.npy' %(G,P)
try:
pattsx = data2array(dir_prix + '/patterns_' + endDir, mmap_mode="r+")
pattsA = data2array(dir_priA + '/patterns_' + endDir, mmap_mode="r+")
return len(pattsx)
except:
try:
pattsx = data2array(dir_prix + '/allPatt_' + endDir)
pattsA = data2array(dir_priA + '/allPatt_' + endDir)
except:
N = data2array(dco).shape[0]
pattsx = zeros((mmax,N))
pattsA = zeros((mmax,N))
conn = {'connAd': dco,
'normType': '1'}
noise = {'colors': None}
model = {'model': 'HopfieldBasedStatic',
'threshold': 'local',
'tauT': 0,
'P': P,
'G': G}
out = []
other = {'init': 'rand',
'dens': rand(),} #p2,!!!!!!!!!!!!!! RAND
for d in range(mmax):
eva = main.evaCure(evaCon=conn, evaNoi=noise, evaMod=model, out=out, **other)
eva.toEquilibrium()
pattsx[d] = eva.evaMod.x.copy()
pattsA[d] = eva.evaMod.A.copy()
array2data(pattsx, dir_prix + '/allPatt_' + endDir)
array2data(pattsA, dir_priA + '/allPatt_' + endDir)
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], dir_prix + '/patterns_' + endDir)
array2data(pattsA[S][C1][C2][SC], dir_priA + '/patterns_' + endDir)
array2data(freq, dir_priT + '/tendances_' + endDir)
os.system('rm ' + dir_prix + '/allPatt_' + endDir)
os.system('rm ' + dir_priA + '/allPatt_' + endDir)
return len(pattsx[S][C1][C2][SC])
def display(_, titles=None, dispOpt={}, transp=False, sGM=False, **kwa):
'''Display from finding function using display tools.
Profites of the loading function to add the files shape in the database.
'''
from Tools.display import mapMatrices
items = _.find(**kwa)
mats = []
if titles == None:
titles = []
upti = True
else:
upti = False
for k in sorted(items.keys()):
tmp = data2array(items[k]['adress'])
try: _[k]['shape']
except: _[k]['shape'] = tmp.shape
if sGM:
tmp -= tmp.mean()
if transp: mats.append(tmp.T)
else: mats.append(tmp)
if upti:
titles.append( items[k]['name'] )
if upti:
titles = dimTitles(titles)
mapMatrices(mats, lTitl= titles, **dispOpt)
def __init__(_, new=False, size=False, dir=defaultDir):
_.dir = dir
if not new:
from Tools.ext import data2array
_.update(data2array(_.dir, dic=True))
#_.delete(adress=['CFSS','Norm'])
if size:
print _.size()
dir1S = main + '/Cand_S/'
dir1X = main + '/Cand_X/'
dir2S = main + typ2 + '/Cand_S/'
dir2X = main + typ2 + '/Cand_X/'
mkdir(dir2S)
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)
# **Parameters**
while len(sys.argv) > 1:
option = sys.argv[1]; del sys.argv[1]
if option == '-G': G = float(sys.argv[1].replace(',','.')); del sys.argv[1]
elif option == '-sx': sx = float(sys.argv[1].replace(',','.')); del sys.argv[1]
elif option == '-sT': sT = float(sys.argv[1].replace(',','.')); del sys.argv[1]
elif option == '-dir': dir_= str(sys.argv[1]); del sys.argv[1]
else:
print 'Options invalides :',option,'->',sys.argv[0]
mdir = '/home/mathieuG/TCS/%s' %dir_
name = 'G_%.2f_sx_%.3f_sT_%.3f.npy' %(G, sx, sT)
dir_DFC = mdir + '/DFC_998/'
dir_TC = mdir + '/TC_998/'
try:
mkdir(dir_DFC)
except:
pass
############################################################################################'
# **Main**
if not adressExists(dir_DFC + name):
TC = data2array(dir_TC + name)
FCs = windowedFCs(TC, window=6000, jump=100)
DFC = windowedCorrelations(FCs)
array2data(DFC, dir_DFC + name)
'tauT': 80,
'P': 0.86,
'G': 900.
}
out = ['x', 'A']
other = {'init': 'ext', 'rperiod': 100}
dir_ = '../SearchForPatt/SG_900_pC/Cand_S'
pars = findParameters(dir_)
ksix = zeros((0, 998))
ksiA = zeros((0, 998))
for f0 in pars['f0']:
pattAd = dir_ + '/patterns_P_0.86_f0_%.2f.npy' % f0
kx = data2array(pattAd.replace('Cand_S', 'Cand_X'))
kA = data2array(pattAd.replace('Cand_X', 'Cand_S'))
ksix = concatenate((ksix, kx))
ksiA = concatenate((ksiA, kA))
T0 = 20000 # 0.1 ms
TF = 80000 # 0.1 ms
sx = 0.2
sT = 0.2
x, A = [], []
############################################################################################'
# **Loop**
tic()
for iii in range(100):
# Init
m = randint(ksix.shape[0])
'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)
del sys.argv[1]
else:
print 'Options invalides :', option, '->', sys.argv[0]
dir_priA = './%s/Cand_S' % di
dir_prix = './%s/Cand_X' % di
mkdir(dir_priA)
mkdir(dir_prix)
dir_sub = '/patterns_P_%.2f_f0_%.2f.npy' % (p1, p2)
#dir_sub = '/patterns_P_%.2f_G_%.2f.npy' %(p1,p2)
############################################################################################'
# **Main**
if not os.path.exists(dir_prix + dir_sub):
multi_dens = arange(0.02, 0.981, 0.03) # (33)
N = data2array(co).shape[0]
all_pattx = zeros((3300, N))
all_pattA = zeros((3300, N))
conn = {
'connAd': co, #Pdir('Connectomes/p05-pg-epi_1.txt'),
'normType': '1'
}
noise = {'colors': None}
model = {
'model': 'HopfieldBasedStatic',
'threshold': 'local',
'tauT': 0,
'P': p1,