- class openpathsampling.numerics.WHAM(tol=1e-10, max_iter=1000000, cutoff=0.05, interfaces=None)
Weighted Histogram Analysis Method
Several parts of the docstrings mention F&S, which is intended to refer the reader to reference , in particular pages 184-187 in the 2nd edition (section called “Self-Consistent Histogram Method”).
Other terminology: n_hists refers to the number of histograms, n_bins refers to the number of bins per histogram. Thus the input is a matrix of n_bins rows and n_hists columns.
tol (float) – tolerance for convergence or equality. default 10e-10
max_iter (int) – maximum number of iterations. Default 1000000
cutoff (float) – windowing cutoff, as fraction of maximum value. Default 0.05
frequency (in iterations) to report debug information
- __init__(tol=1e-10, max_iter=1000000, cutoff=0.05, interfaces=None)
__init__([tol, max_iter, cutoff, interfaces])
Check that all the histograms have sufficient overlaps.
generate_lnZ(lnZ, unweighting, ...[, tol])
Perform the WHAM iteration to estimate ln(Z_i) for each histogram.
get_diff(lnZ_old, lnZ_new, iteration)
Calculate the difference for this iteration.
Guess ln(Z_i) based on crossing probabilities
Load a file or files into the internal structures.
List of counts of entries per histogram.
Normalize to maximum value
output_histogram(lnZ, sum_k_Hk_Q, ...)
Recombine the data into a joined histogram
prep_reverse_cumulative(df[, cutoff, tol])
Created cleaned dataframe for further analysis.
Sum over histograms for each histogram bin.
Calculates the "unweighting" values for each histogram.
Product of unweighting and n_entries.
Perform the entire wham process.