nessai.utils

General utilities for nessai.

Submodules

• nessai.utils.bilbyutils
• nessai.utils.distance
• nessai.utils.distributions
• nessai.utils.hist
• nessai.utils.indices
• nessai.utils.information
• nessai.utils.io
• nessai.utils.logging
• nessai.utils.multiprocessing
• nessai.utils.optimise
• nessai.utils.rescaling
• nessai.utils.sampling
• nessai.utils.settings
• nessai.utils.sorting
• nessai.utils.stats
• nessai.utils.structures
• nessai.utils.testing
• nessai.utils.threading
• nessai.utils.torchutils

Package Contents

Classes

NessaiJSONEncoder

Class to encode numpy arrays and other non-serialisable objects.

Functions

get_multivariate_normal(dims[, var, device])	Return a Pytorch distribution that is normally distributed in n dims
get_uniform_distribution(dims, r[, device])	Return a torch distribution that is uniform in the number of dims
auto_bins(x[, max_bins])	Compute the number bins for a histogram using numpy.histogram_bin_edges
bonferroni_correction(p_values[, alpha])	Apply the Bonferroni correction for multiple tests.
compute_indices_ks_test(indices, nlive[, mode])	Compute the two-sided KS test for discrete insertion indices for a given
is_jsonable(x)	Check if an object is JSON serialisable.
safe_file_dump(data, filename, module[, save_existing])	Safely dump data to a .pickle file.
save_dict_to_hdf5(d, filename)	Save a dictionary to a HDF5 file.
save_live_points(live_points, filename)	Save live points to a file using JSON.
save_to_json(d, filename, **kwargs)	Save a dictionary to a JSON file.
setup_logger([output, label, log_level, ...])	Setup the logger.
configure_edge_detection(d, detect_edges)	Configure parameters for edge detection
detect_edge(x[, x_range, percent, cutoff, nbins, ...])	Detect edges in input distributions based on the density.
determine_rescaled_bounds(prior_min, prior_max, x_min, ...)	Determine the values of the prior min and max in the rescaled
inverse_rescale_minus_one_to_one(x, xmin, xmax)	Rescale from -1 to 1 to xmin to xmax
inverse_rescale_zero_to_one(x, xmin, xmax)	Rescale from 0 to 1 to xmin to xmax
logit(x[, eps])	Logit function that also returns log Jacobian determinant.
rescale_minus_one_to_one(x, xmin, xmax)	Rescale a value to -1 to 1
rescale_zero_to_one(x, xmin, xmax)	Rescale a value to 0 to 1
sigmoid(x)	Sigmoid function that also returns log Jacobian determinant.
compute_radius(n[, q])	Compute the radius that contains a fraction of the total probability in an n-dimensional unit Gaussian.
draw_gaussian(dims[, r, N, fuzz])	Wrapper for numpy.random.randn that deals with extra input parameters
draw_nsphere(dims[, r, N, fuzz])	Draw N points uniformly within an n-sphere of radius r
draw_surface_nsphere(dims[, r, N])	Draw N points uniformly from n-1 sphere of radius r using Marsaglia's
draw_truncated_gaussian(dims, r[, N, fuzz, var])	Draw N points from a truncated gaussian with a given a radius
draw_uniform(dims[, r, N, fuzz])	Draw from a uniform distribution on [0, 1].
compute_minimum_distances(samples[, metric])	Compute the distance to the nearest neighbour of each sample
rolling_mean(x[, N])	Compute the rolling mean with a given window size.
replace_in_list(target_list, targets, replacements)	Replace (in place) an entry in a list with a given element.
configure_threads([pytorch_threads])	Configure the number of threads available.

Attributes

rescaling_functions