def test_compare_detectEquil(show_hist=False):
"""
compare detectEquilibration implementations (with and without binary search + fft)
"""
t_res = []
N = 100
for _ in xrange(100):
A_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 2.0
B_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 1.0
C_t = testsystems.correlated_timeseries_example(N=N * 2, tau=5.0)
D_t = np.concatenate([A_t, B_t, C_t])
bs_de = timeseries.detectEquilibration_binary_search(D_t, bs_nodes=10)
std_de = timeseries.detectEquilibration(D_t, fast=False, nskip=1)
t_res.append(bs_de[0] - std_de[0])
t_res_mode = float(stats.mode(t_res)[0][0])
eq(t_res_mode, 0., decimal=1)
if show_hist:
import matplotlib.pyplot as plt
plt.hist(t_res)
plt.show()
def test_compare_detectEquil(show_hist=False):
"""
compare detectEquilibration implementations (with and without binary search + fft)
"""
t_res = []
N=100
for _ in xrange(100):
A_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 2.0
B_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 1.0
C_t = testsystems.correlated_timeseries_example(N=N*2, tau=5.0)
D_t = np.concatenate([A_t, B_t, C_t, np.zeros(20)]) #concatenate and add flat region to one end (common in MC data)
bs_de = timeseries.detectEquilibration_binary_search(D_t, bs_nodes=10)
std_de = timeseries.detectEquilibration(D_t, fast=False, nskip=1)
t_res.append(bs_de[0]-std_de[0])
t_res_mode = float(stats.mode(t_res)[0][0])
eq(t_res_mode,0.,decimal=1)
if show_hist:
import matplotlib.pyplot as plt
plt.hist(t_res)
plt.show()
def test_detectEquil_binary():
x = np.random.normal(size=10000)
(t, g, Neff_max) = timeseries.detectEquilibration_binary_search(x)
def test_detectEquil_binary():
x = np.random.normal(size=10000)
(t, g, Neff_max) = timeseries.detectEquilibration_binary_search(x)
