Timeseries can be tricked by a couple bad first inputs

If you have one or two wildly off datum points for timeseries.detectEquilibration from the start of the timeseries, then the true equilibration time is woefully underpredicted. @jchodera requested I post sample data and script to process and regenerate the plot and output from below.

The output below is from Top plot is the full timeseries with bars showing where “Equilibration” stops, bottom is the same set from the 2nd sample onwards (series[1:]). Below the image is the code used to create. The dataset is in the attached zip file. @jchodera If you only want the series to look at, you only need the imports and the first three lines of the code below.

Full Trajectory               -- Equilibration 0,  Subsample Rate 1.75, Num Effective 1710
Trajectory w/o initial sample -- Equilibration 20, Subsample Rate 3.75, Num Effective 794

import numpy as np
import matplotlib.pyplot as plt
from pymbar import timeseries

y = np.load('explicit0_timeseries.npy')
[n_equilibration, g_t, n_effective_max] = timeseries.detectEquilibration(y)
[n_short, g_t_short, n_eff_max_short] = timeseries.detectEquilibration(y[1:])

print("Full Trajectory -- Equilibration {0:d}, Subsample Rate {1:3.2f}, Num Effective {2:d}".format(
    n_equilibration, g_t, int(np.floor(n_effective_max))
))
print("Trajectory w/o initial sample -- Equilibration {0:d}, Subsample Rate {1:3.2f}, Num Effective {2:d}".format(
    n_short, g_t_short, int(np.floor(n_eff_max_short))
))

f, (a,b) = plt.subplots(2, 1)
x = np.arange(y.size)
a.plot(x, y, 'k-', label='Timeseries')
b.plot(x[1:], y[1:], '-k')
for p in [a, b]:
    ylim = p.get_ylim()
    xlim = p.get_xlim()
    p.set_xlabel('Iteration')
    p.set_ylabel(r'$\sum_k u_{k,k,n}$')
    p.vlines(n_equilibration, *ylim,
             colors='b', linewidth=1,
             label='Full Timeseries: Num Samples={}'.format(int(np.floor(n_effective_max))))
    p.vlines(n_short, *ylim,
             colors='r', linewidth=1,
             label='Timeseries[1:]: Num Samples={}'.format(int(np.floor(n_eff_max_short))))
    p.set_ylim(ylim)
    p.set_xlim(xlim)
a.legend()
f.savefig('bad_series.png', bbox_inches='tight')

explicit0_timeseries.npy.zip

[mrshirts]

Can you post a sample data set that is causing problems?

[jchodera]

Thanks! This is super helpful @ocmadin!