Util

Surrogate Hessian accelerated parallel line-search: utilities

class stalk.util.EffectiveVariance(samples, errorbar)

Bases: object

errorbar = None

get_errorbar(samples)

get_samples(errorbar)

samples = None

stalk.util.bipolyfit(X, Y, Z, nx, ny): Fit to a bipolynomial set of variables

stalk.util.bipolynomials(X, Y, nx, ny)

Construct a bipolynomial expansion of variables

XYp = x**0 y**0, x**0 y**1, x**0 y**2, … courtesy of Jaron Krogel

stalk.util.bipolyval(p, X, Y, nx, ny): Evaluate based on a bipolynomial set of variables

stalk.util.directorize(path): If missing, add ‘/’ to the end of path

stalk.util.get_fraction_error(data, fraction, both=False): Estimate uncertainty from a distribution based on a percentile fraction

stalk.util.get_min_params(x_n, y_n, pfn=3): Find the minimum point by fitting a curve

stalk.util.match_to_tol(val1, val2, tol=1e-08): Match the values of two vectors. True if all match, False if not.