nessai.utils

General utilities for nessai.

Submodules

• nessai.utils.distance
• nessai.utils.distributions
• nessai.utils.hist
• nessai.utils.indices
• nessai.utils.io
• nessai.utils.logging
• nessai.utils.multiprocessing
• nessai.utils.rescaling
• nessai.utils.sampling
• nessai.utils.stats
• nessai.utils.structures
• nessai.utils.threading

Package Contents

Classes

NessaiJSONEncoder

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

Functions

get_multivariate_normal(dims, var=1, device='cpu')	Return a Pytorch distribution that is normally distributed in n dims
get_uniform_distribution(dims, r, device='cpu')	Return a torch distribution that is uniform in the number of dims
auto_bins(x, max_bins=50)	Compute the number bins for a histogram using numpy.histogram_bin_edges
bonferroni_correction(p_values, alpha=0.05)	Apply the Bonferroni correction for multiple tests.
compute_indices_ks_test(indices, nlive, mode='D+')	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=False)	Safely dump data to a .pickle file.
save_live_points(live_points, filename)	Save live points to a file using JSON.
setup_logger(output=None, label='nessai', log_level='WARNING')	Setup the logger.
configure_edge_detection(d, detect_edges)	Configure parameters for edge detection
detect_edge(x, x_range=None, percent=0.1, cutoff=0.5, nbins='auto', allow_both=False, allow_none=False, allowed_bounds=['lower', 'upper'], test=None)	Detect edges in input distributions based on the density.
determine_rescaled_bounds(prior_min, prior_max, x_min, x_max, invert=None, inversion=False, offset=0, rescale_bounds=[-1, 1])	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, fuzz=1e-12)	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, fuzz=1e-12)	Sigmoid function that also returns log Jacobian determinant.
compute_radius(n, q=0.95)	Compute the radius that contains a fraction of the total probability in an n-dimensional unit Gaussian.
draw_gaussian(dims, r=1, N=1000, fuzz=1.0)	Wrapper for numpy.random.randn that deals with extra input parameters
draw_nsphere(dims, r=1, N=1000, fuzz=1.0)	Draw N points uniformly within an n-sphere of radius r
draw_surface_nsphere(dims, r=1, N=1000)	Draw N points uniformly from n-1 sphere of radius r using Marsaglia's
draw_truncated_gaussian(dims, r, N=1000, fuzz=1.0, var=1)	Draw N points from a truncated gaussian with a given a radius
draw_uniform(dims, r=(1, ), N=1000, fuzz=1.0)	Draw from a uniform distribution on [0, 1].
compute_minimum_distances(samples, metric='euclidean')	Compute the distance to the nearest neighbour of each sample
rolling_mean(x, N=10)	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(max_threads=None, pytorch_threads=None, n_pool=None)	Configure the number of threads available.

Attributes

rescaling_functions