def test_statistical_inefficiency_fft_gaussian():
# Run multiple times to get things with and without negative "spikes" at C(1)
for i in range(5):
x = np.random.normal(size=100000)
g0 = timeseries.statisticalInefficiency(x, fast=False)
g1 = timeseries.statisticalInefficiency(x, x, fast=False)
g2 = timeseries.statisticalInefficiency_fft(x)
g3 = timeseries.statisticalInefficiency(x, fft=True)
eq(g0, g1, decimal=5)
eq(g0, g2, decimal=5)
eq(g0, g3, decimal=5)
eq(np.log(g0), np.log(1.0), decimal=1)
for i in range(5):
x = np.random.normal(size=100000)
x = np.repeat(
x, 3
) # e.g. Construct correlated gaussian e.g. [a, b, c] -> [a, a, a, b, b, b, c, c, c]
g0 = timeseries.statisticalInefficiency(x, fast=False)
g1 = timeseries.statisticalInefficiency(x, x, fast=False)
g2 = timeseries.statisticalInefficiency_fft(x)
g3 = timeseries.statisticalInefficiency(x, fft=True)
eq(g0, g1, decimal=5)
eq(g0, g2, decimal=5)
eq(g0, g3, decimal=5)
eq(np.log(g0), np.log(3.0), decimal=1)
def test_statistical_inefficiency_fft_gaussian():
# Run multiple times to get things with and without negative "spikes" at C(1)
for i in range(5):
x = np.random.normal(size=100000)
g0 = timeseries.statisticalInefficiency(x, fast=False)
g1 = timeseries.statisticalInefficiency(x, x, fast=False)
g2 = timeseries.statisticalInefficiency_fft(x)
g3 = timeseries.statisticalInefficiency(x, fft=True)
eq(g0, g1, decimal=5)
eq(g0, g2, decimal=5)
eq(g0, g3, decimal=5)
eq(np.log(g0), np.log(1.0), decimal=1)
for i in range(5):
x = np.random.normal(size=100000)
x = np.repeat(x, 3) # e.g. Construct correlated gaussian e.g. [a, b, c] -> [a, a, a, b, b, b, c, c, c]
g0 = timeseries.statisticalInefficiency(x, fast=False)
g1 = timeseries.statisticalInefficiency(x, x, fast=False)
g2 = timeseries.statisticalInefficiency_fft(x)
g3 = timeseries.statisticalInefficiency(x, fft=True)
eq(g0, g1, decimal=5)
eq(g0, g2, decimal=5)
eq(g0, g3, decimal=5)
eq(np.log(g0), np.log(3.0), decimal=1)
def pymbarDetectEquilibration_fft(self, fast=True, nskip=1):
"""
More documentation can be found in pymbar timeseries detectEquilibration function
"""
#results = np.zeros((self.ncols, 3))
results = self.ncols * [4 * [None]]
for coli in range(self.ncols):
T = (self.cols[coli]).size
# Special case if timeseries is constant.
if (self.cols[coli]).std() == 0.0:
results[coli] = (
0, 1, 1, np.ones([T - 1], np.float32)
) # Changed from Neff=N to Neff=1 after issue #122
g_t = np.ones([T - 1], np.float32)
Neff_t = np.ones([T - 1], np.float32)
for t in range(0, T - 1, nskip):
try:
g_t[t] = ts.statisticalInefficiency_fft(
self.cols[coli][t:T])
except ParameterError: # Fix for issue https://github.com/choderalab/pymbar/issues/122
g_t[t] = (T - t + 1)
Neff_t[t] = (T - t + 1) / g_t[t]
Neff_max = Neff_t.max()
t = Neff_t.argmax()
g = g_t[t]
results[coli] = (t, g, Neff_max, Neff_t)
return results
def test_statistical_inefficiency_fft():
X, Y, energy = generate_data()
timeseries.statisticalInefficiency_fft(X[0])
timeseries.statisticalInefficiency_fft(X[0]**2)
timeseries.statisticalInefficiency_fft(energy[0])
g0 = timeseries.statisticalInefficiency_fft(X[0])
g1 = timeseries.statisticalInefficiency(X[0])
g2 = timeseries.statisticalInefficiency(X[0], X[0])
g3 = timeseries.statisticalInefficiency(X[0], fft=True)
eq(g0, g1)
eq(g0, g2)
eq(g0, g3)
def test_statistical_inefficiency_fft():
X, Y, energy = generate_data()
timeseries.statisticalInefficiency_fft(X[0])
timeseries.statisticalInefficiency_fft(X[0] ** 2)
timeseries.statisticalInefficiency_fft(energy[0])
g0 = timeseries.statisticalInefficiency_fft(X[0])
g1 = timeseries.statisticalInefficiency(X[0])
g2 = timeseries.statisticalInefficiency(X[0], X[0])
g3 = timeseries.statisticalInefficiency(X[0], fft=True)
eq(g0, g1)
eq(g0, g2)
eq(g0, g3)