openpathsampling.numerics.Histogram

class openpathsampling.numerics.Histogram(n_bins=None, bin_width=None, bin_range=None)

Wrapper for numpy.histogram with additional conveniences.

In addition to the behavior in numpy.histogram, this provides a few additional calculations, as well as behavior that allows for better interactive use (tricks to assist caching by libraries using it, etc.)

__init__(n_bins=None, bin_width=None, bin_range=None)

Creates the parameters for the histogram.

Either n_bins or bin_width must be given. If bin_width is used, then bin_range is required. If n_bins is used, then bin_range is optional. n_bins overrides bin_width.

If no options are given, the default is to use 20 bins and the range generated by np.histogram.

Methods

__init__([n_bins, bin_width, bin_range])	Creates the parameters for the histogram.
add_data_to_histogram(data[, weights])	Adds data to the internal histogram counter.
compare_parameters(other)	Return true if other has the same bin parameters as self.
cumulative([maximum, bin_edge])	Cumulative from the left: number of values less than bin value.
empty_copy()	Returns a new histogram with the same bin shape, but empty
histogram([data, weights])	Build the histogram based on data.
map_to_bins(data)	param data: input data
map_to_float_bins(trajectory)
normalized([raw_probability, bin_edge])	Return normalized version of histogram.
plot_bins([scaling])	Bins used in plotting.
rebinned(scaling)	Redistributes histogram bins of width binwidth*scaling
reverse_cumulative([maximum, bin_edge])	Cumulative from the right: number of values greater than bin value.
sum_histograms(hists)
xvals([bin_edge_type])	Position values for the bin