def test_random(self):
a = (1, 2)
sigma = .1
rho = 0
r = rarlognormal(a, sigma, rho, size=1000)
assert_array_almost_equal(np.median(r, axis=0), [1, 2], 1)
rho = .8
sigma = .1
r = rarlognormal(1, sigma, rho, size=1000)
corr = utils.autocorr(np.log(r))
assert_almost_equal(corr, rho, 1)
assert_almost_equal(r.std(), sigma / sqrt(1 - rho**2), 1)
def test_random(self):
a = (1,2)
sigma = .1
rho = 0
r = rarlognormal(a, sigma, rho, size=1000)
assert_array_almost_equal(np.median(r, axis=0), [1,2],1)
rho =.8
sigma = .1
r = rarlognormal(1, sigma, rho, size=1000)
corr = utils.autocorr(np.log(r))
assert_almost_equal(corr, rho, 1)
assert_almost_equal(r.std(), sigma/sqrt(1-rho**2),1)
def iat(x, maxlag=None):
"""Calculate the integrated autocorrelation time (IAT), given the trace from a Stochastic."""
if not maxlag:
# Calculate maximum lag to which autocorrelation is calculated
maxlag = _find_max_lag(x)
acr = [autocorr(x, lag) for lag in range(1, maxlag+1)]
# Calculate gamma values
gammas = [(acr[2*i]+acr[2*i+1]) for i in range(maxlag//2)]
cut = _cut_time(gammas)
if cut+1 == len(gammas):
print_("Not enough lag to calculate IAT")
return np.sum(2*gammas[:cut+1]) - 1.0
def iat(x, maxlag=None):
"""Calculate the integrated autocorrelation time (IAT), given the trace from a Stochastic."""
if not maxlag:
# Calculate maximum lag to which autocorrelation is calculated
maxlag = _find_max_lag(x)
acr = [autocorr(x, lag) for lag in range(1, maxlag + 1)]
# Calculate gamma values
gammas = [(acr[2 * i] + acr[2 * i + 1]) for i in range(maxlag // 2)]
cut = _cut_time(gammas)
if cut + 1 == len(gammas):
print_("Not enough lag to calculate IAT")
return np.sum(2 * gammas[:cut + 1]) - 1.0
