From d6f09b0abe62fdf69991a32326ce02f7d3113b36 Mon Sep 17 00:00:00 2001
From: jps6 <jps6@cornell.edu>
Date: Thu, 12 May 2022 13:30:26 +0000
Subject: Update on Overleaf.

---
 ising_scaling.tex | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'ising_scaling.tex')

diff --git a/ising_scaling.tex b/ising_scaling.tex
index 210f63c..58cd767 100644
--- a/ising_scaling.tex
+++ b/ising_scaling.tex
@@ -47,7 +47,7 @@ linkcolor=purple
   coordinate transformation. For the two-dimensional Ising model, we show that
   this procedure converges exponentially with the order to which the series are
   matched, up to seven digits of accuracy.
-  To facilitate use, we provide Python and Mathematica implementations of the code at both lowest order (three digit) and high accuracy.
+  To facilitate use, we provide a Mathematica implementation of the code at both lowest order (three digit) and high accuracy.
   %We speculate that with appropriately modified parametric coordinates, the method may converge even deep into the metastable phase.
 \end{abstract}
 
@@ -100,7 +100,7 @@ With six derivatives, it is accurate to about $10^{-7}$. We hope that with some
 refinement, this idea might be used to establish accurate scaling functions for
 critical behavior in other universality classes, doing for scaling functions
 what advances in conformal bootstrap did for critical exponents
-\cite{Gliozzi_2014_Critical}. Mathematica and Python implementations will be provided in the supplemental material.
+\cite{Gliozzi_2014_Critical}. A Mathematica implementation will be provided in the supplemental material.
 
 \section{Universal scaling functions}
 \label{sec:UniversalScalingFunctions}
@@ -1204,7 +1204,7 @@ the ratio.
 We have introduced explicit approximate functions forms for the two-dimensional
 Ising universal scaling function in the relevant variables. These functions are
 smooth to all orders, include the correct singularities, and appear to converge
-exponentially to the function as they are fixed to larger polynomial order. The universal scaling function will be available in both Mathematica and Python in the supplemental material. It is implicitly defined by $\mathcal{F}_0$ and $\mathcal{F}_\pm$ in Eq.~\eqref{eq:scaling.function.equivalences.2d}, where $g(\theta)$ is defined in Eq.~\eqref{eq:schofield.funcs}, $\mathcal{F}$ in Eqs.~\eqref{eq:FfromFoFYLG}--\eqref{eq:FYL}, and the fit constants at various levels of approximation are given in Table~\ref{tab:fits}.
+exponentially to the function as they are fixed to larger polynomial order. The universal scaling function will be available in Mathematica in the supplemental material. It is implicitly defined by $\mathcal{F}_0$ and $\mathcal{F}_\pm$ in Eq.~\eqref{eq:scaling.function.equivalences.2d}, where $g(\theta)$ is defined in Eq.~\eqref{eq:schofield.funcs}, $\mathcal{F}$ in Eqs.~\eqref{eq:FfromFoFYLG}--\eqref{eq:FYL}, and the fit constants at various levels of approximation are given in Table~\ref{tab:fits}.
 
 This method, although spectacularly successful, could be improved. It becomes difficult to fit the
 unknown functions at progressively higher order due to the complexity of the
-- 
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