def test_norm(self):
'''Test normalization.'''
# Simple array
a = np.arange(10)
c_cpp = asignal.acorr(a, mode='twosided', norm=True)
c_np = np.correlate(a, a, mode='full')[::-1]
np.testing.assert_allclose(c_cpp, c_np / np.max(c_np), rtol=RTOL)
# A zero array will return zero
zero_array = np.zeros(13)
c_cpp = asignal.acorr(zero_array, mode='twosided', norm=True)
assert np.all(c_cpp == 0.)
def test_norm(self):
'''Test normalization.'''
# Simple array
a = np.arange(10)
c_cpp = asignal.acorr(a, mode='twosided', norm=True)
c_np = np.correlate(a, a, mode='full')[::-1]
np.testing.assert_allclose(c_cpp, c_np / np.max(c_np), rtol=RTOL)
# A zero array will return zero
zero_array = np.zeros(13)
c_cpp = asignal.acorr(zero_array, mode='twosided', norm=True)
assert np.all(c_cpp == 0.)
def extractACStat(self, mon):
'''
Extract autocorrelation statistics from a monitor.
For each monitored neuron, extract the (highest) frequency, value of
the autocorrelation at the frequency and the autocorrelation function
itself.
Parameters
----------
mon : list of dicts
A list of (NEST) state monitors' status dictionaries
output : tuple
A tuple (freq, acval, acVec), containing the arrays of frequencies
for the monitored neurons, autocorrelation values at the
corresponding frequencies, and autocorrelation functions of all the
neurons.
'''
freq = [] # Frequency of input signal
acval = [] # Auto-correlation at the corresponding frequency
acVec = []
for n_id in range(len(mon)):
#for n_id in range(5):
#print "n_id: ", n_id
sig, dt = simei.sumAllVariables(mon, n_id, self.stateList)
startIdx = 0
endIdx = len(sig)
if (self.tStart is not None):
startIdx = int(self.tStart / dt)
if (self.tEnd is not None):
endIdx = int(self.tEnd / dt)
sig = sig[startIdx:endIdx]
sig = butterBandPass(sig, dt * self.dtMult, self.bandStart,
self.bandEnd)
ac = acorr(sig - np.mean(sig),
max_lag=self.maxLag / dt,
norm=self.norm)
ext_idx, ext_t = localExtrema(ac)
acVec.append(ac)
f, a = findFreq(ac, dt * self.dtMult, ext_idx, ext_t)
freq.append(f)
acval.append(a)
return freq, acval, acVec, dt
def extractACStat(self, mon):
'''
Extract autocorrelation statistics from a monitor.
For each monitored neuron, extract the (highest) frequency, value of
the autocorrelation at the frequency and the autocorrelation function
itself.
Parameters
----------
mon : list of dicts
A list of (NEST) state monitors' status dictionaries
output : tuple
A tuple (freq, acval, acVec), containing the arrays of frequencies
for the monitored neurons, autocorrelation values at the
corresponding frequencies, and autocorrelation functions of all the
neurons.
'''
freq = [] # Frequency of input signal
acval = [] # Auto-correlation at the corresponding frequency
acVec = []
for n_id in range(len(mon)):
#for n_id in range(5):
#print "n_id: ", n_id
sig, dt = simei.sumAllVariables(mon, n_id, self.stateList)
startIdx = 0
endIdx = len(sig)
if (self.tStart is not None):
startIdx = int(self.tStart / dt)
if (self.tEnd is not None):
endIdx = int(self.tEnd / dt)
sig = sig[startIdx:endIdx]
sig = butterBandPass(sig, dt*self.dtMult, self.bandStart,
self.bandEnd)
ac = acorr(sig - np.mean(sig), max_lag=self.maxLag/dt,
norm=self.norm)
ext_idx, ext_t = localExtrema(ac)
acVec.append(ac)
f, a = findFreq(ac, dt*self.dtMult, ext_idx, ext_t)
freq.append(f)
acval.append(a)
return freq, acval, acVec, dt
def test_default_params(self):
'''Test default parameters.'''
a = np.arange(10)
c_cpp = asignal.acorr(a)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 1:]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def test_twosided(self):
'''Test the two-sided version of ``corr``.'''
a = np.arange(10)
c_cpp = asignal.acorr(a, mode='twosided', max_lag=5)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 6:a.size + 5]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def test_default_params(self):
'''Test default parameters.'''
a = np.arange(10)
c_cpp = asignal.acorr(a)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 1:]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def test_twosided(self):
'''Test the two-sided version of ``corr``.'''
a = np.arange(10)
c_cpp = asignal.acorr(a, mode='twosided', max_lag=5)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 6:a.size + 5]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
