openpathsampling.numerics.WHAM

class openpathsampling.numerics.WHAM(tol=1e-10, max_iter=1000000, cutoff=0.05, interfaces=None)[source]

Weighted Histogram Analysis Method

Notes

Several parts of the docstrings mention F&S, which is intended to refer the reader to reference 1, 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.

References

1

Daan Frenkel and Berend Smit. Understanding Molecular Simulation: From Algorithms to Applications. 2nd Edition. 2002.

Parameters
  • 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

sample_every

frequency (in iterations) to report debug information

Type

int

__init__(tol=1e-10, max_iter=1000000, cutoff=0.05, interfaces=None)[source]

Methods

__init__([tol, max_iter, cutoff, interfaces])

check_cleaned_overlaps(cleaned_df)

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_lnZ_crossing_probability(cleaned_df)

Guess ln(Z_i) based on crossing probabilities

load_files(fnames)

Load a file or files into the internal structures.

n_entries(cleaned_df)

List of counts of entries per histogram.

normalize_cumulative(series)

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_k_Hk_Q(cleaned_df)

Sum over histograms for each histogram bin.

unweighting_tis(cleaned_df)

Calculates the "unweighting" values for each histogram.

weighted_counts_tis(unweighting, n_entries)

Product of unweighting and n_entries.

wham_bam_histogram(input_df)

Perform the entire wham process.

Attributes

float_format

10.8 (default)