def process_from_samples(self):
# populate data from files
for index, file in enumerate(self.hdf5files):
print 'Processing data from file', self.hdf5files[index]
f = h5py.File(file, "r")
# load input scalars
for scalar in self.inputscalars:
self.__dict__['in_'+scalar] = np.squeeze(f[scalar][()])
# load input data
for data in list(self.inputdata):
self.__dict__['in_'+data] = np.squeeze(f[data][()])
f.close()
# populate scalars
for scalar in self.scalars:
#self.__dict__[scalar][index] = howtocompute[scalar]()
self.__dict__[scalar][index] = self.get_t()
# populate arrays
for var, data, varindex in list(self.planes):
self.__dict__[var][index,:] = self.get_scalar(data, varindex)
# Compute mean with arsel
# Compute arsel for all variables
if self.nfiles == 1:
for var in [planes[0] for planes in self.planes]:
#print "... Computing for", var
for index in xrange(self.Ny):
self.__dict__['mean_' +var][index] = self.__dict__[var][0,index]
self.__dict__['sigma_' +var][index] = 0
self.__dict__['eff_N_' +var][index] = 0
self.__dict__['eff_var_'+var][index] = 0
self.__dict__['T0_' +var][index] = 0
else:
for var in [planes[0] for planes in self.planes]:
#print "... Computing for", var
for index in xrange(self.Ny):
t = ar.arsel(self.__dict__[var][:,index])
self.__dict__['mean_' +var][index] = t.mu[0]
self.__dict__['sigma_' +var][index] = t.mu_sigma[0]
self.__dict__['eff_N_' +var][index] = t.eff_N[0]
self.__dict__['eff_var_'+var][index] = t.eff_var[0]
self.__dict__['T0_' +var][index] = t.T0[0]
def writeResult(option, result, info='-', timeSeriesFile=None):
globalResult = [res/(nSteps-avgStart) for res in result]
resultFile = primal.resultFile
if parallel.rank == 0:
noise = 0
if option == 'perturb':
previousResult = float(open(resultFile).readline().split(' ')[3])
globalResult[0] -= previousResult
noise = [0. for res in result]
if timeSeriesFile:
import ar
timeSeries = np.loadtxt(timeSeriesFile,ndmin=1)[avgStart::sampleInterval]
if len(timeSeries.shape) == 1:
timeSeries = timeSeries.reshape(-1,1)
for index in range(0, len(result)):
noise[index] = (ar.arsel(timeSeries[:, index]).mu_sigma**2)[0]
with open(resultFile, 'a') as handle:
for index in range(0, len(result)):
handle.write('{} {} {} {} {}\n'.format(option, index, info, globalResult[index], noise[index]))
p = 3
n = len(s) + 1
Np = (2 * n - 1) * p + n
if not submean:
Np += n
s = np.array(s)
# HACK
#s = s[:,:100]
N = len(s[0])
t = np.zeros(Np)
import ar
import matplotlib.pyplot as plt
for i in range(0, n - 1):
A = ar.arsel(s[i], 0, 1, "CIC", p, p)
c = -np.array(A.AR[0][1:])
t[i:n * p:n] = c
t[n - 1:n * p:n] = c
#t[n*i+n-1] = 1.
t[(2 * n - 1) * p + i] = A.sigma2eps[0]
if not submean:
t[(2 * n - 1) * p + n + i] = ms[i]
#yp = []
#for j in range(p, N):
# yp.append(np.dot(s[i][j-p:j+1][::-1],A.AR[0]))
#plt.plot(yp)
##plt.hist(yp, bins=100)
#plt.show()
elif control=='VC':
Pin = 0.5*sum((pdata[indx:h-2,1]-pdata[indx:h-2,2])*
(pdata[indx+1:h-1,0]-pdata[indx:h-2,0]))/ \
sum(pdata[indx+1:h-1,0]-pdata[indx:h-2,0]); print(' \twdwdy\t:\t', Pin)
PinN = Pin; PinP = Pin
gamma = Pin/P0; print(' \tgamma\t:\t', gamma)
# Computing uncertainties
sbar_dudy=0.0; sbar_Ub=0.0; T0_dudy=0.0; T0_Ub=0.0
if elaborate:
t = linspace(data[indx,0],data[m-2,0],m-indx-1); dt=t[1]-t[0]
dudyI0 = zeros((1,m-indx-1)); dudyIn = zeros((1,m-indx-1));
dudyI0f = interp1d(data[indx-1:m-1,0], data[indx-1:m-1,1]); dudyI0[0,:] = dudyI0f(t)
dudyInf = interp1d(data[indx-1:m-1,0], data[indx-1:m-1,2]); dudyIn[0,:] = dudyInf(t)
ar_dudyI0 = arsel(dudyI0); ar_dudyIn = arsel(dudyIn)
zi0 = lfiltic([1], ar_dudyI0.AR[0], ar_dudyI0.autocor[0])
zin = lfiltic([1], ar_dudyIn.AR[0], ar_dudyIn.autocor[0])
rho0 = ones(m-indx-1); rho0[1:] = lfilter([1], ar_dudyI0.AR[0], \
zeros(m-indx-2), zi=zi0)[0]
rhon = ones(m-indx-1); rhon[1:] = lfilter([1], ar_dudyIn.AR[0], \
zeros(m-indx-2), zi=zin)[0]
sbar_dudy = std(concatenate((data[indx:m-1,1], data[indx:m-1,2])))/ \
sqrt(ar_dudyI0.eff_N[0]+ar_dudyIn.eff_N[0])
T0_dudy = 0.5*dt*(ar_dudyI0.T0[0]+ar_dudyIn.T0[0])
if CPI:
UbI = zeros((1,m-indx-1))
UbIf = interp1d(data[indx-1:m-1,0], data[indx-1:m-1,5]); UbI[0,:] = 0.5*UbIf(t)
ar_UbI = arsel(UbI); ziUb = lfiltic([1], ar_UbI.AR[0], ar_UbI.autocor[0])
rhoU = ones(m-indx-1); rhoU[1:] = lfilter([1], ar_UbI.AR[0], \
zeros(m-indx-2), zi=ziUb)[0]
import ar
e = [[], []]
cmap = plt.get_cmap('nipy_spectral')
colors = [cmap(i) for i in np.linspace(0, 1, len(sys.argv) - 1)]
cs = []
c = 1
start = 50000
for f in sys.argv[1:]:
y = np.loadtxt(f)[start:]
n = len(y)
x = np.arange(0, n)
m = y.mean()
y = y - m
e[0].append(m)
e[1].append(ar.arsel(y).mu_sigma[0])
print e[0][-1], e[1][-1]
token = os.path.basename(f).split('_')[0]
cs.append(c)
#plt.plot(x[:n/2], auto_corr(y)[:n/2])
#plt.xlabel('Time unit')
#plt.ylabel('Autocorrelation')
#plt.savefig(f.replace('timeSeries.txt', 'autocorr.png'))
#plt.clf()
#plt.show()
c += 1
x = np.arange(0, len(cs) + 2, 1)
for i, c in enumerate(cs):
c = (33 + 0.01 * (c - 3)) / 360.
plt.errorbar(c, e[0][i], e[1][i], ecolor='b', fmt='o')