Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

Abstract

The affiliation for Evgeni Burovski was given as Higher School of Economics; the correct affiliation is National Research University, Higher School of Economics. In Box 1, "SciPy is an open-source package that builds on the strengths of Python and Numeric, providing a wide range of fast scientific and numeric functionality" was used as the box title; this has been moved to the beginning of the box text and a new title has been provided: "Excerpt from the SciPy 0.1 release announcement (typos corrected), posted 20 August 2001 on the Python-list mailing list. " From the original first sentence of this box, "(text following the % symbol indicates that a typo in the original text has been corrected in the version reproduced here)" has been deleted, and "% hanker to Hankel" and "% Netwon to Newton" have been deleted from the ends of the special functions row and the optimization row, respectively. In the first sentence of the ndimage section of Box 2, "nonlinear filter" has been changed to plural. At the end of the first paragraph of the section "SciPy matures, " "The library was expanded carefully, with the patience affordable in open-source projects and via best practices common in industry" has been changed to "The library was expanded carefully, with the patience affordable in open-source projects and via best practices, which are increasingly common in the scientific Python ecosystem and industry. " In Table 2, "Inequality constraint" has been changed to plural. In the "Nonlinear optimization: global minimization" section, "scipy.optimize.differentialevolution" had been changed to "scipy.optimize.differential_evolution. " In the first sentence of the section "Maintainers and contributors, " "SciPy developer guide" has been changed to "SciPy contributor guide" and the URL has been changed from

High-throughput laboratory evolution and evolutionary constraints inEscherichia coli

“…To obtain the IC50 values, the OD620 values for the dose-response series were fitted to the following sigmoidal model: = / 1 + exp " log − log &' () * + , where x and f(x) represent the concentration of antibiotics and observed OD620 values, respectively, and a, b, c, and IC50 are fitting parameters. The fitting was performed using optimize.curve_fit in the SciPy package (Virtanen et al, 2019). The relative IC50s were computed by comparing the IC50 of each evolved strain to the mean of 13 independent replicas of the parent strain.…”

Preparation of ordered states in ultra-cold gases using Bayesian optimization

“…For comparison with gradient based method, the spontaneous perturbation stochastic approximation (SPSA) algorithm was chosen [74] as it was shown to be competitive in the number of iterations for convergence and robust to noise [51]. For comparison with non-gradient based methods, the differential evolution(DE) and Nelder-Mead(NM) optimizers were implemented using Scipy [75]. Finally random search (RANDOM) was also included in the list of optimizers.…”

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

“…• SciPy (Virtanen et al, 2019) • NumPy (Oliphant, 2006) • pandas (McKinney, 2010) • Matplotlib (Hunter, 2007) • OR-Tools (Perron and Furnon, 2019) • tqdm (da Costa-Luis, 2019) • NetworkX (Hagberg et al, 2008)…”