openpathsampling.analysis.PathHistogram

class openpathsampling.analysis.PathHistogram(left_bin_edges, bin_widths, interpolate=True, per_traj=True)

N-dim sparse histogram for trajectories.

This allows features like interpolating between bins and normalizing the histogram to the number of trajectories.

Parameters:

• left_bin_edges (array-like) – lesser side of the bin (for each direction)

• bin_widths (array-like) – bin (voxel) size

• interpolate (bool or callable) – how to interpolate missing bin visits. Default True gives “subdivide” method, False gives no interpolation. Arbitrary callable should take old_pt and new_pt, and return the list of bins that were visited, excluding the bin for old_pt.

• per_traj (bool) – whether to normalize per trajectory (instead of per-snapshot)

__init__(left_bin_edges, bin_widths, interpolate=True, per_traj=True)

Methods

__init__(left_bin_edges, bin_widths[, ...])
add_data_to_histogram(trajectories[, weights])	Adds data to the internal histogram counter.
add_trajectory(trajectory[, weight])	Add a single trajectory to internal counter, with given weight
compare_parameters(other)	Test whether the other histogram has the same parameters.
empty_copy()	Returns a new histogram with the same bin shape, but empty
histogram([data, weights])	Build the histogram.
map_to_bins(data)	param data: input data
map_to_float_bins(trajectory)
normalized([raw_probability, bin_edge])	Callable normalized version of the sparse histogram.
single_trajectory_counter(trajectory)	Calculate the counter (local histogram) for an unweighted trajectory
sum_histograms(hists)
xvals(bin_edge_type)	Position values for the bin

Attributes

progress